Biodiversity plays an important role in maintaining the ecological balance of the earth. The study of traditional knowledge related to biological resources is a hot issue in the field of international biodiversity conservation. Xishuangbanna is a key area of biodiversity and a cultural hotspot of international significance. According to the Technical Regulation for Classification, Investigation, and Inventory of Traditional Knowledge Relating to Biological Diversity issued by the Ministry of Ecology and Environment, we investigated and catalogued the traditional knowledge related to biodiversity of the Jino people who have lived in Xishuangbanna for generations, and collected 490 entries of traditional knowledge related to biodiversity of the Jino people. Drawing on the traditional knowledge diversity index calculation method proposed by Wang Guoping, the overall traditional knowledge α-diversity index of the Jino people is 0.63, indicating that the richness of the traditional knowledge of the Jinuo people is relatively high. The traditional culture related to biodiversity, the traditional knowledge related to agricultural genetic resources, and the traditional technology related to the sustainable utilization of biological resources are relatively rich and diverse. The diversity index is 0.86, 0.82 and 0.79 respectively. In addition, Xishuangbanna has invested a lot of energy in biodiversity protection, including the establishment of nature reserves, botanical gardens, zoos, ecological tea gardens and other species reserves, the promulgation of laws and policies related to biodiversity protection, and has achieved remarkable results in in-situ protection and ex-situ protection. On the basis of analyzing the progress and achievements of biodiversity conservation in Xishuangbanna, this study points out that Xishuangbanna faces challenges such as the loss of traditional knowledge, insufficient conservation efforts, and great changes in land use, and puts forward corresponding suggestions.

The persistence of Traditional Ecological Knowledge (TEK) among Indigenous and local communities is often associated with socio-economic constraints, poverty, and lack of choices. Such a perspective argues against underpinning profound environmental consciousness and sustainable practices that underlie TEK. The paper challenges reductionism—viewing TEK solely as a creation of necessity—and tries to argue that it entails a sophisticated understanding of ecological balance and conservation ethics. Through various examples, the paper exemplifies how TEK contributes to environmental sustainability for biodiversity conservation, management and use of natural resources, and the enhancement of resilience to climatic change—the same elements crucial for culture, tradition, and community living.

Molecular hybridization is a widely used strategy in drug discovery and development processes that consists in the combination of two bioactive compounds toward a novel entity. In the current study, two libraries of hybrid derivatives coming from the linkage of sesquiterpene counterparts dihydroartemisinin and artesunic acid with a series of monoterpenes were synthesized and evaluated by cell viability assay on primary and metastatic melanoma cell lines. Almost all the obtained compounds showed micromolar antimelanoma activity and selectivity toward the metastatic form of this cancer. Four hybrid derivatives containing perillyl alcohol, citronellol, and nerol as monoterpene counterpart, emerged as the best compounds of the series, with nerol being active in combination with both sesquiterpenes, dihydroartemisinin and artesunic acid. Preliminary studies on the mechanism of action have shown the dependence of the pharmacological activity of newly synthesized hybrids on the formation of carbon- and oxygen-centered radical species. This study demonstrated the positive modulation of the pharmacodynamic effect of artemisinin semisynthetic derivatives dihydroartemisinin and artesunic acid due to the hybridization with monoterpene counterparts.

Ulcerative colitis (UC) is a chronic inflammatory disease, the incidence of which is increasing worldwide. However, the etiology and pathogenesis of UC remains unclear. The n-butanol extract of pulsatilla decoction (BEPD) of the traditional Chinese medicine herbal formula has been shown to be effective in treating UC. We aimed to explore the molecular mechanism underlying the effects of BEPD on UC, in particular its effects on neutrophil extracellular trap (NET) formation by neutrophils. High-performance liquid chromatography was used to determine the principal compounds of BEPD. UC was induced in mice using dextran sodium sulfate, and mice were treated with 20, 40, or 80 mg/kg BEPD daily for seven days. Colonic inflammation was determined by assessing the disease activity index, histopathology, colonic mucosal damage index, colonic mucosal permeability, and pro- and anti-inflammatory cytokine levels. The infiltration and activation status of neutrophils in the colon were determined by analyzing the levels of chemokine (C-X-C motif) ligand (CXCL)1 and CXCL2, reactive oxygen species, Ly6G, and numerous NET proteins. Our results showed that BEPD improved the disease activity index, histopathology, and colonic mucosal damage index scores of mice with UC, and restored colonic mucosal permeability compared with untreated mice. The expression levels of the pro-inflammatory cytokines interleukin-1β, interleukin-6, and tumor necrosis factor-α in colon tissues were significantly decreased, while the expression levels of anti-inflammatory cytokines in colon tissues were significantly increased, exceeding those of control mice. In addition, BEPD reduce the expression of the neutrophil chemokines CXCL1 and CXCL2 in the colon tissue of mice with UC, reduced neutrophil infiltration, reduced reactive oxygen species levels, and significantly reduced the expression of NET proteins. BEPD also significantly reduced NET formation. The results of this study suggest that BEPD exerts therapeutic effects in a murine model of UC by inhibiting neutrophil infiltration and activation in the colon, as well as by inhibiting the expression of key proteins involved in NET formation and reducing NET formation, thereby alleviating local tissue damage and disease manifestations.

Cortex Periplocae, as a traditional Chinese herbal medicine, has been widely used for autoimmune diseases, especially rheumatoid arthritis. Due to its potential pharmaceutical values, more studies about the biological activities of Cortex Periplocae have been conducted recently. Meanwhile, the adverse reaction of Cortex Periplocae is not a negligible problem in clinic. In this article, we reviewed a series of articles and summarized the recent studies of Cortex Periplocae in the areas of phytochemistry and pharmacology. More than 100 constituents have been isolated and identified from Cortex Periplocae, including steroids, cardiac glycosides, terpenoids, and fatty acid compounds. The crude extracts of Cortex Periplocae and its active compounds exhibit various biological activities, such as cardiotonic effect, anticancer action, and anti-inflammatory effect. This paper aims to provide an overall review on the bioactive ingredients, pharmacological effect, and toxicity of this plant. Furthermore, this review suggests investigating and developing new clinical usages according to the above pharmacological effects.

Understanding the interrelationships between land use, climate change, and regional water yield is critical for effective water resource management and ecosystem protection. However, comprehensive insights into how water yield evolves under different land use scenarios and climate change remain elusive. This study employs the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) models, Patch-generating Land Use Simulation (PLUS) model, and Geodetector within a unified framework to evaluate the dynamics of land use, water yield, and their relationships with various factors (meteorological, social, economic, etc.). To forecast the Land Use/Cover Change (LUCC) pattern of the Yellow River Basin by 2030, three scenarios were considered: economic development priority (Scenario1), ecological development priority(Scenario2), and cropland development priority(Scenario3). Climate change scenarios were constructed using CMIP6 data, representing low stress (SSP119), medium stress (SSP245), and high stress (SSP585) conditions. The results show the following: (1) From 2000 to 2020, cropland was predominant in the Yellow River Basin, Henan Province, with significant land conversion to impervious land (construction land) and forest land; (2) Water yield changes during this period were primarily influenced by meteorological factors, with land use changes having negligible impact; (3) By 2030, the water yield of Scenario1 is highest among different land use scenarios, marginally surpassing ecological development priority by 0.5%; (4) Climate scenarios reveal significant disparities, with SSP126 yielding 67% higher water production than SSP245, driven predominantly by precipitation; (5) Geodetector analysis identifies precipitation as the most influential single factor, with significant interactions among meteorological and socio-economic factors. These findings offer valuable insights for policymakers and researchers in formulating land use and water resource management strategies.

Related studies have shown that mitophagy plays a role in the progression of Atherosclerosis (AS). Utilizing GSE43292, GSE100927, and GSE159677 datasets, WGCNA and differential expression analysis identified mitophagy-related module genes and DEGs1, while single-cell analysis screened DEGs2. MR analysis revealed causal AS-linked genes in DEGs1. LASSO and expression validation identified biomarkers, evaluated via ROC curves. Further analyses included GSEA, immune infiltration, ANN, regulatory networks, and drug prediction. Four candidate genes were identified through WGCNA, differential expression analysis and MR analysis. The LASSO analysis identified CD36, IL1RN, and MME as biomarkers, while the MR analysis showed that IL1RN and MME are protective factors and CD36 is a risk factor in AS. The ROC curves disclosed that the ANN model had greater diagnostic power for AS than any other biomarker. The GSEA revealed that the biomarkers were enriched in lysosomes and hematopoietic cell lineage and were significantly positively correlated with monocytes. The regulatory network construct showed that small nucleolar RNA host gene 16 regulates MME via the micro RNAs. This study identified CD36, IL1RN, and MME as biomarkers of mitophagy in AS and indicated that they could be applied in the diagnosis and treatment of this condition.

As a vital component of the terrestrial ecosystem, grassland accounts for one-third of the global vegetation system. Grassland degradation has been exacerbated due to extreme overgrazing in China's Inner Mongolia Autonomous Region (IMAR). While conservation was carried out as the Ecological Subsidy and Award Program (ESAP) to mitigate grassland degradation, little is known about its effectiveness in improving the biophysical conditions of grassland. This paper integrates the conceptual frameworks of total socio-environmental systems to assess how ecological systems respond to ESAP, investigate spatial heterogeneity of ESAP, and explore the meddling effects of socio-environmental interactions on ESAP. We integrate ecological, climate, and socioeconomic data and developed several hierarchical linear mixed models (HLMM) to investigate how these factors interact with ESAP in IMAR. Our findings prove that the above-ground biomass between 2011 and 2015 responds significantly to variations in socioeconomic conditions and ecological communities. Available land resources, hospital and medical facilities, and net farmer and herdsman income are the most critical factors contributing to grassland productivity. Primary industries like mining, total consumer retail value, farming, forestry, animal husbandry, fishery productions, and GDP are the most damaging factors affecting biomass. Our study recommends a regionally or locally tailored ecological recovery policy, instead of a generalized one, in the future effort to conserve grassland.

Autonomous driving vehicles rely on sensors for the robust perception of surroundings. Such vehicles are equipped with multiple perceptive sensors with a high level of redundancy to ensure safety and reliability in any driving condition. However, multi-sensor, such as camera, LiDAR and radar, systems bring up the requirements related to sensor calibration and synchronization, which are the fundamental blocks of any autonomous system. On the other hand, sensor fusion and integration have become important aspects of autonomous driving research and directly determine the efficiency and accuracy of advanced functions such as object detection and path planning. Classical model-based estimation and data-driven models are two mainstream approaches to achieving such integration. Most recent research is shifting to the latter, showing high robustness in real-world applications but requiring large quantities of data to be collected, synchronized, and properly categorized. To generalize the implementation of the multi-sensor perceptive system, we introduce an end-to-end generic sensor dataset collection framework that includes both hardware deploying solutions and sensor fusion algorithms. The framework prototype integrates a diverse set of sensors, such as cameras, LiDAR, and radar. Furthermore, we present a universal toolbox to calibrate and synchronize three types of sensors based on their characteristics. The framework also includes the fusion algorithms, which utilize the merits of three sensors , namely, camera, LiDAR and radar, and fuse their sensory information in a manner that is helpful for object detection and tracking research. The generality of this framework makes it applicable in any robotic or autonomous applications, also suitable for quick and large-scale practical deployment.

Motion related human activity recognition using wearable sensors can potentially enable various useful daily applications. So far, most studies view it as a stand-alone mathematical classification problem without considering the physical nature of human motions. Consequently, they suffer from data dependencies and encounter the dimension disaster problem and the over-fitting issue, and their models are never human-readable. In this study, we start from a deep analysis on natural physical properties of human motions, and then propose a useful feature selection method to quantify each feature's classification contribution capability. On one hand, the "dimension disaster" problem can be avoid to some extent, due to the affined dimension of key features; On the other hand, over-fitting issue can be depressed since the knowledge implied in human motions are nearly invariant, which compensates the possible data inadequacy. The experiment results indicate that the proposed method performs superior to those adopted in related works, such as decision tree, k-NN, SVM, neural networks.

In this study, a novel and efficient method was developed for the extraction of total flavonoids from Flos Sophorae Immaturus (FSI) using ultrasound-assisted deep eutectic solvent (UAE-DES). Different DES formulations were first investigated and choline chloride/1,2-propanediol (ChCl/PD) was identified as the optimal solvent due to its highest flavonoid yield. The extraction process was optimized through single factor experiments and response surface methodology (RSM). An extraction temperature of 68 °C, a sonication time of 41 minutes, a water content of 21% and a liquid-solid ratio of 21 mL/g were identified as the optimal conditions. Under these conditions, a maximum flavonoid yield of 378.77 mg RE/g DW was achieved. The crude extract was further purified using a macroporous D-101 resin, resulting in a flavonoid extract with a purity of 83.11 ± 3.54%. The antioxidant activity of the purified extract (FSPE) was evaluated using DPPH and ABTS radical scavenging assays, showing significant antioxidant properties with IC50 values of 52.50 μg/mL and 49.47 μg/mL, respectively. The UAE-DES method developed in this study not only significantly improves the extraction yield of flavonoids from FSI, but is also in line with sustainable and environmentally friendly practices. This method offers an environmentally friendly, efficient and scalable approach for the extraction of bioactive compounds from medicinal plants and has potential applications in the food, pharmaceutical and dietary supplement industries.

Pre-Western contact Hawaiʻi stands as a quintessential sustainability example of a large human population that practiced intensive agriculture, yet minimally displaced native habitats that comprised the foundation of its vitality. An explicit geospatial footprint of human-transformed areas across the pre-contact Hawaiian archipelago comprised less than 15% of total land area, yet provided 100% of human needs, supporting a thriving Polynesian society. A post-contact history of disruption of traditional Hawaiian land-use and its supplanting by Western land tenure and agriculture based on ranching, sugarcane, and pineapple, culminated in a landscape, in which over 50% of native habitats have been lost, while self-sufficiency has plummeted to 15% or less. Recapturing the ʻāina momona (productive lands) of ancient times can be accomplished through study of pre-contact agriculture, assessment of biological and ecological changes imposed on Hawaiian social-ecological systems, and conscious planned efforts to increase self-sufficiency and reduce importation. Impediments include the current tourism-based economy, competition from habitat-modifying introduced species, a suite of agricultural pests severely limiting traditional agriculture, and changes in climate rendering some pre-contact agricultural centers suboptimal. Modified agricultural methods will be required to counteract these limitations, and diversified agriculture to broaden the production base, without contributing to further degradation of native habitats.

The Indian meal moth, Plodia interpunctella, poses a significant threat to global agricultural products. Although pheromone-based technologies show promise for P. interpunctella management, limitations like single-sex targeting necessitate exploring complementary strategies. Vegetable-based oils represent a potential alternative, but their efficacy, sex-specificity, and interaction with sex pheromones remain unclear. To answer these questions, we first examined attraction of P. interpunctella female and male adults to ten different vegetable oils, i.e., camellia, corn, olive, peanut, rapeseed, sesame, soybean, sunflower, walnut, and a blended oil (mixture of sunflower seed, rapeseed, soybean, peanut, and sesame oils), in the semi-field conditions. Sesame, olive, and blended oils demonstrated most attractive, capturing significantly more adults compared to other oils. We then evaluated effectiveness of these three attractive oils and their combinations with P. interpunctella sex pheromone in a grain warehouse. Traps baited with these oils captured significantly more females and males compared to control traps without attractants. However, sex pheromone addition did not improve male capture and significantly reduced female capture, suggesting an inhibitory effect. Our findings highlight the potential of blended, olive, or sesame oil trap alone as alternative monitoring and trapping tools for P. interpunctella, delivering novel insights into the development of efficient integrated pest management strategies for stored products.

The use of privileged scaffolds as starting point for the construction of libraries of bioactive compounds is a widely used strategy in drug discovery and development. Scaffold decoration, morphing and hopping are additional techniques that allow to modify the privileged framework chosen and better explore the chemical space around it. In this study, two series of highly functionalized pyrimidine and pyridine derivatives were synthesized by scaffold morphing approach of triazine compounds obtained previously as antiviral agents. Newly synthesized azines were evaluated against lymphoma, hepatocarcinoma, and colon epithelial carcinoma cells showing in five cases acceptable to good anticancer activity associated to low cytotoxicity on healthy fibroblasts. Finally, ADME in vitro studies were conducted on the best derivatives of the two series showing good passive permeability and resistance to metabolic degradation.

While traditional AC mechanical circuit breakers have been competent for protecting AC circuits, high penetration of DC power distribution technologies like DC microgrids (MGs) obligate better disruption performance features such as quick and reliable switch-ing speeds. However, a DC circuit breaker (DCCB) novel design is challenging due to the need to quickly break high currents within milliseconds, caused by the high fault current rise in DC grids compared to AC grids. In DC grids, the circuit breaker must provide zero current crossing and be designed for absorbing surges since the arc is not naturally extinguished by the system. Additionally, the DC breaker must mitigate the magnetic energy stored in the system inductance and withstand residual over-voltages after current interruption. These challenges require a fundamentally different topology for DCCBs, which are typically made using solid-state semiconductor technology, metal oxide varistors (MOV), and ultra-fast switches. This study aims to provide a comprehensive review on development, design, and performance description of DCCBs in parallel with a specific concentration on analysis of internal topology, energy absorption path and sub-circuits in Solid-State (SS)-based DCCBs. The research explores various novel designs that introduce different structures for energy dissipation solution. The classification of these designs is based on the fundamental principles of surge mitigation and a detailed analysis of the techniques employed in DCCBs. In addition, our framework offers an advantageous reference point for the future evolution of SS-circuit breakers in numerous developing power delivery systems.

Nanling Mountain region is a typical southern hilly region, which plays an important ecological and environmental protection role in China's overall land protection pattern. Based on the remote sensing image data of Longnan City in Nanling Mountain in 2013, 2018 and 2023, this paper interpreted the land use type and analyzed the land use transfer situation by using Land use transfer flow, and Land use transfer matrix. At the same time, based on the remote sensing Ecological index (RSEI) model, the ecological environment quality of Longnan City from 2013 to 2023 was retrieved. The temporal and spatial response model of ecological environment quality to land use transfer in Longnan City from 2013 to 2023 was discussed based on Spatial autocorrelation and Geographic detector. The results show that from 2013 to 2023, the decrease of forest land (16.23km²) and the increase of construction land (13.25km²) were the main land use transfer in Longnan City. The ecological environment index of Longnan City in 2013, 2018 and 2023 was 0.789, 0.917 and 0.872, respectively, showing a trend of "first rising and then decreasing". The ecological environment quality in the north of Longnan City was significantly lower than that in the south, and the poor ecological quality area appeared in and around the northern main urban area, showing a trend of "inward contraction". Forest land, garden land, grassland, cultivated land and water area have a positive impact on ecological environment quality, while traffic land, construction land and other land have a negative impact on ecological environment quality. The response of ecological environment quality to different land use transfer modes is related to the change of the overall ecological environment quality. The interaction between LUCC and other factors had a great impact on the evolution of ecological environment quality in Longnan City.

Despite the demonstrated benefits of data monetization initiatives in achieving competitive advantages, many of these efforts struggle to realize their potential. Companies often find it challenging to sustain even initially successful data monetization initiatives due to significant data quality issues. This reflects a disconnect between advancements in data monetization research—spanning digitization, digitalization, and digital transformation—and practical implementation within companies. Consequently, misguided approaches to data monetization are relatively common. A critical prerequisite for successful data monetization is the establishment and maintenance of clean, high-quality data. This study underscores the importance of data quality by conducting an in-depth analysis of Medical Inc., a company engaged in preparing pristine customer master data for advanced customer analytics. The investigation aims to elucidate Medical Inc.’s approach to addressing data cleanliness challenges and to distill a general framework for the customer master data cleansing process. This framework illuminates a relatively unexplored aspect of data monetization supplementing existing literature on digitization, digitalization, and digital transformation.

Some aporphine alkaloids, such as crebanine, were found to present arrhythmic activity and also higher toxicity. A series of derivatives were synthesized by using three kinds of aporphine alkaloids (crebanine, isocorydine, and stephanine) as lead compounds. Chemical methods, including ring-opening reaction, bromination, methylation, acetylation, quaternization, and dehydrogenation, were adopted. Nineteen target derivatives were evaluated for their antiarrhythmic potential in the mouse model of ventricular fibrillation (VF), induced by CHCl₃, and five of the derivatives were investigated further in the rat model of arrhythmia, induced by BaCl₂. Meanwhile, preliminary structure-activity/toxicity relationship analyses were carried out. Significantly, N-acetamidesecocrebanine (1d), three bromo-substituted products of crebanine (2a, 2b, 2c), N-methylcrebanine (2d), and dehydrostephanine (4a) displayedantiarrhythmic effects in the CHCl₃-induced model. Among them, 7.5 mg/kg of 2b was able to significantly reduce the incidence of VF induced by CHCl₃ (p<0.05), increase the number of rats that resumed sinus rhythm from arrhythmia, induced by BaCl₂ (p<0.01), and the number of rats that maintained sinus rhythm for more than 20 minutes (p<0.01). Therefore, 2b showed remarkably higher antiarrhythmic activity and a lower toxicity (LD₅₀=59.62 mg/kg, mice), simultaneously, indicating that 2b could be considered as a promising candidate in the treatment of arrhythmia. Structural-activity analysis suggested that variationsin antiarrhythmic efficacy and toxicity of aporphines were related to the C-1,C-2-methylenedioxy group on ring A, restricted ring B structural conformation, N-quaternization of ring B, levoduction of 6a in ring C, and the 8-, 9-, 10-methoxy groups on ring D on the skeleton.

Sustainable Development Goals (SDGs) envisaged under Agenda 2030 are a set of seventeen goals which envisage a holistic approach towards attaining certain targets keeping humankind and the planet at center. There are total 169 targets spread across seventeen goals covering wide ranging issues and challenges the world is facing in the twenty-first century. And they are to be achieved by 2030. Concerted efforts of all the stakeholders ranging from indigenous communities, common citizens, scientists, policy makers, world leaders are needed to achieve all the goals and targets Of the seventeen goals, at least seven goals are of interest to the ethnobotanists and are associated with traditional ethnobotanical knowledge. Therefore to achieve those set of goals, a thorough understanding is required to disentangle the intricacies involving traditional ethnobotanical knowledge, indigenous people as traditional knowledge holders and their future role. Understanding relationships between traditional ethnobotanical knowledge and indigenous communities, seeking cooperation from and establishing partnerships with them would help us design policies to achieve intended outcomes of SDGs. In this paper, particular attention is attracted towards the potential role of traditional ethnobotanical knowledge in achieving select sustainable development goals and targets.

Sudden cardiac arrest (SAC) is a leading cause of mortality. In recent years, though, many lives have been saved through the use of defibrillators by random bystanders. As a result, defibrillators have been popping up in various locations. In this work we propose a state mandate demanding the presence of a defibrillator at every pharmacy store and argue that it could greatly enhance and augment the impact of the defibrillator network. We support our claim via detailed calculations of the immediate (a defibrillator readily available) and partial (a defibrillator available at a reasonable distance) population coverage achievable by the implementation of our proposal, taking the Argolis area in Peloponnese Greece as a case study. Our findings indicate that, in addition to other benefits, the use of pharmacies to establish a defibrillator network could bring an eight-fold increase in the immediate population coverage of defibrillators and would provide immediate / partial access to a defibrillator to 18 / 24 incidents of SCA per year that would otherwise lead to death, potentially leading to the saving of numerous lives. Alternative scenaria are also discussed.

Alternative fertilizations are essential to minimize the deteriorating effects of chemical fertilizers on soil and water quality/health. Accordingly, the present work investigated the effects of combined organic-inorganic fertilization (COIF) on wheat and rice yields, soil nutrients, and soil Cd accumulation. Hence, seven different treatments were set up: control (CK); conventional fertilization (CF); optimized fertilization (OF); organic fertilizer replacing 25% (T1) and 50% (T2) of OF; and organic nitrogen (N) replacing 25% (M1) and 50% (M2) of OF-N. Overall, significant increases occurred in the yields of COIF crops. Compared with the CF, the highest wheat and rice yields happened in M1 treatment (approximately by18.5%) (P &lt; 0.05). COIF slightly alleviated soil acidification, and improved the cation exchange capacity (CEC) of the study soils. Besides, COIF treatments significantly increased the contents of total phosphorus, total potassium, available phosphorus, and available potassium by 6.35 to 16.9 %, 3.17 to 10.9 %, 5.53 to 28.7 %, and 2.6 to 12 %, respectively (P &lt;0.05). Nevertheless, negligible increases took place in Cd content of COIF soils compared with the CK. Altogether, our results concluded on 25% replacement of OF-N by organic N (M1) to effectively improve the fertility/ecological sustainability of the study soils.

The catastrophic twin earthquakes that struck southern Turkey and northern Syria on February 6 2023, caused massive casualties and extensive damage to infrastructure, with the province of Hatay bearing the brunt of the impact. To investigate the potential of nighttime light (NTL) data for earthquake disaster assessment, we performed an analysis of pre- and post-earthquake NPP-VIIRS and SDGSAT-1 NTL data. The NTL change ratio (NCR) was employed to identify the extent of damage in Hatay Province following the earthquake to assess the impact on affected buildings and populations. NPP-VIIRS daily NTL Time-series data was used to establish an economic recovery framework for earthquake disasters in Hatay Province, assessing regional economic recovery after earthquake. The results revealed a strong correlation between the total reduced NTLs of NPP-VIIRS and SDGSAT-1 and the presence of severely and moderately damaged buildings. Conversely, the correlation with slightly damaged buildings was relatively weaker. Furthermore, the reduced NTLs of NPP-VIIRS provide a more precise identification of all earthquake damage compared to the SDGSAT-1 data, achieving an overall accuracy of 86.21% in earthquake damage identification and a Kappa coefficient of 0.72. In accordance with the earthquake economic recovery framework, as of the end of March 2024, neither the provincial economy nor any of its districts have fully restored to their pre-earthquake levels. The findings offered new and valuable insights into earthquake disaster assessment, assisting decision-makers in accurately assessing the level of disaster risk following an earthquake and efficiently allocating post-earthquake relief funds.

The rapid advancement of artificial intelligence (AI) and robotics has led to significant progress in various medical fields, including interventional radiology (IR). This review focuses on the research progress and applications of AI and robotics in IR, including deep learning (DL), machine learning (ML), and convolutional neural networks (CNNs) across specialties such as oncology, neurology, and cardiology. To ensure the breadth and depth of this review, we implemented a systematic literature search strategy, selecting research published within the last five years. We conducted searches in databases such as PubMed and Google Scholar to find relevant literature. Special emphasis was placed on selecting large-scale studies to ensure comprehensiveness and reliability of the results. We discuss the role of AI techniques used before, during, and after intervention treatments and the advantages of robotics in achieving precise operations and reducing intra-operative risks. Challenges faced in the implementation of these techniques are also addressed. Finally, we explore the future trends and potential applications of AI and robotics in interventional therapy, highlighting their potential to improve patient treatment outcomes and quality of life.

Ecological footprint calculators are digital tools that help individuals calculate their environmental or climate impact, with the aim of stimulating pro-environmental behaviour change. These footprint calculators typically take an information-provision approach, but this strategy assumes that increased levels of knowledge result in increased levels of pro-environmental behaviour (i.e., a reduced footprint). This is not a given – existing literature on the relationship between environmental knowledge and pro-environmental behaviour is inconclusive, and this relationship may be different from that of environmental knowledge and ecological footprint. As such, we investigated the relationship between environmental knowledge and ecological footprint as estimated by a footprint calculator. 448 Dutch participants completed an online survey, including an ecological footprint calculator. We found no evidence for a relationship between environmental knowledge and ecological footprint calculator outcome. Rather, an exploratory analysis of our data showed that environmental values were more important predictors of ecological footprint. The finding that increased levels of knowledge are not related to a reduced ecological footprint suggests that calculators would do well to move beyond information provision, and employ additional behaviour change strategies. Based on our exploratory analysis, we provide several concrete examples of potential strategies.

"Public legal information from all countries and international institutions is part of the common heritage of humanity. Maximizing access to this information promotes justice and the rule of law". In accordance with the aforementioned declaration on Free Access to Law by Legal information institutes of the world, a plethora of legal information is available through the Internet, while the provision of legal information has never before been easier. Given that law is accessed by a much wider group of people, the majority of whom are not legally trained or qualified, diversification techniques, should be employed in the context of legal information retrieval, as to increase user satisfaction. We address diversification of results in legal search by adopting several state of the art methods from the web search, network analysis and text summarization domains. We provide an exhaustive evaluation of the methods, using a standard data set from the Common Law domain that we subjectively annotated with relevance judgments for this purpose. Our results i) reveal that users receive broader insights across the results they get from a legal information retrieval system, ii) demonstrate that web search diversification techniques outperform other approaches (e.g., summarization-based, graph-based methods) in the context of legal diversification and iii) offer balance boundaries between reinforcing relevant documents or sampling the information space around the legal query.

Abstract: Global ecological concern regarding the transfer of fouling organisms to ship hulls is increasing. This study investigated the species composition, dominant species, distribution patterns, community structure, and life-cycle differences of hull-fouling macroinvertebrates on five research vessels (R/Vs: Isabu, Onnuri, Eardo, Jangmok 1, and Jangmok 2) operated by the Korea Institute of Ocean Science and Technology (KIOST). Hull-fouling macroinvertebrates were collected three to five times on quadrats from the upper and middle sectors of the hull sides, bottom, and niche areas (the propellers and shafts and the thrusters). A total of 47 macroinvertebrate species were identified, represented by 8,519 individuals(ind.)/m2 and a biomass of 1,967 gWWt/m2 on the five vessels. The number of species, density, and biomass were greater on the coastal vessels the Eardo, Jangmok 1, and Jangmok 2 than on the ocean-going vessels the Isabu and Onnuri. Among the coastal vessels, barnacles were the most abundant and had the greatest density, while mollusks had the highest biomass. Differences between hull sectors showed that the highest species abundance and density appeared on all hulls in ports and bays where the Jangmok 1 operated, while the highest species abundance, density, and biomass were identified in the niche areas of the Eardo, which operated farther from the coast. The hull-fouling macroinvertebrates that exceeded 1% of all organisms were the barnacles Amphibalanus amphitrite, Balanus trigonus, and Amphibalanus improvisus; the polychaete Hydroides ezoensis; the bivalves Magallana gigas and Mytilus galloprovincialis; and the amphipod Jassa slatteryi. The dominant species were cosmopolitan and globally distributed, and many of them were cryptogenic. Six native species were identified: M. gigas, H. ezoensis, the amphipod Melita koreana, the isopod Cirolana ko-reana, and the barnacle B. trigonus and F. kondakovi. Eight non-indigenous species (NIS) were de-tected: barnacles A. amphitrite and A. improvisus, the bivalve M. galloprovincialis, the polychaete Perinereis nuntia, amphipods J. slatteryi and Caprella californica, and bryozoans Bugulina californica and Bugula neritina. Of the fouling macroinvertebrates found on the vessel hulls, 13% were native and 17% were NIS. The nMDS ordination analysis of the five vessels showed four clusters of fouling macroinvertebrate communities: two ocean clusters (the Isabu and Onnuri), one coastal cluster (the Eardo and Jangmok 2), and one local cluster (the Jangmok 1). More diverse communities developed on the hulls of vessels that operated locally rather than globally or in deep oceans. Spearman rank correlation showed that biological indices such as number of species, density, and biomass correlated highly and positively with the numbers of days on coastal operations and negatively with the number days on ocean operations. The species diversity index correlated positively with the total number of anchoring days and coastal operation days and negatively with the total number of operation days and ocean operation days. The macroinvertebrates differed by the area of operation, the port of anchorage, the number of days in operation and at anchor, and the hull sectors. There is no previous research data on hull-fouling macroinvertebrates in the Republic of Korea, and this study provides a basis for future studies to identify introduced species and their differences based on operation area.

Culture signifies the customs, language, ceremonies, Indigenous Medical Knowledge (IMK) and other material objects of a tribe. These and many other features are a representation of identity for many Zambian communities. IMK practices are skills inherent by certain individuals in a tribal grouping who are chosen by the spirits (gods) or trained by those who practice. This intrinsic skill is in danger of diminishing as a result of modern medication. This article attempts to understand the role of traditional healers (also known as asing’anga in local language) in preservation of IMK in Chibombo District of Zambia. In so doing, the paper examined methods of preservation used by asing’anga and the challenges faced. Using hermeneutic phenomenology and linear snowball sampling, findings revealed that preservation was done through training. The study found unwillingness, expiration, and spirituality as challenges in the process of preserving IMK. The study recommended that the Lenje Cultural Association consider documenting and disseminating information to the local people by showcasing what is available in the Mukuni Culture Village Museum and Library. This was seen as a way through which many youths would be encouraged to appreciate the indigenous values in their culture.

We leverage the explainability feature of KAN network and build an explainable language model where certain neurons encode individual words and neuron activation is fully interpretable in terms of the basis of a word. To do that, we propose a continuous word2vec model where a meaning of a word is expressed by a continuous profile of distances of this word from the words in the basis of words which is interpolated. As a result, the whole KAN network can be interpreted as a sequential procedure with word expressions. We then proceed from words to logic programs and develop a clause learning technique based on KAN. A logic program construction process from facts now become fully interpretable. We follow the differentiable inductive logic programming technique, representing a logic program as a matrix learned by KAN. Hence, we obtain an efficient and fully interpretable rule learning approach.

Today, LLMs are not good at personalization providing recommendation. They advise physicians and financial advisors to ask professionals in respective fields for help, even having user information available. Answering questions of software professionals, LLM needs to deliver in-depth answers with codes or algorithms, whereas for professionals in other fields would need definitions and main concepts. The intent of this chapter is to make LLM answer tailored to the needs of users, taking into account available information about them. To do that, we need to generalize available information about a person like her health record, maintaining the privacy of this person. We rely on meta-learning techniques to design a LLM prompt to produce a personalization prompt to obtain a suitable relevant information. Such “meta-prompt” is produced by generalization operation applied to available documents for the user. These documents need to be de-identified so that they are sufficient for personalization on one hand and will maintain user privacy on the other hand. The second neuro-symbolic technique to support personalization is abductive reasoning, acting in parallel to LLM fine-tuning. Traditional recommendation and personalization techniques as well as modern, deep learning – based are presented, and the comparison is drawn to the proposed approach. We also share the evaluation and comparative analyses of these approaches. We consider an example for how to build personalization LLM systems coming from Langchain platform. We will explore how to construct chains to form a personalization profile for a user and apply it to user search and recommendation requests.

In the process of sewage coagulation treatment, quartz sand is often used as a heavy medium to accelerate the settling rate of flocs, but it will adversely affect the removal efficiency of pollutants. The factors that affect the coagulation effect and can be controlled manually include the quartz sand dosage, coagulant dosage, sewage pH, stirring time, settling time, etc, their reasonable setting is critical to. This paper was to study the optimal conditions of quartz sand enhanced coagulation(QSEC), first, single-factor tests were conducted to preliminarily explore the optimal range of influencing factors, and then response surface methodology（RSM）tests were performed to accurately determine the optimums of significant factors. The results showed that the addition of quartz sand can’t improve the water quality of coagulation treatment, but it can significantly accelerate the coagulation settlement process, the quartz sand dosage, the coagulant dosage and sewage pH all impacted significantly on its coagulation effect, and existed inflection points. A model that could guide QSEC was obtained by RSM tests. The model optimization and experimental validation showed that the optimal QSEC conditions for treating domestic sewage were as follows: the polyaluminum chloride(PAC) dosage, cationic polyacrylamide(CPAM) dosage, the sewage pH, quartz sand dosage, stirring time and settling time were 0.97 g·L-1, 2.25 mg·L-1, 7.22, 2 g·L-1, 5 minutes and 30 minutes, respectively, and the turbidity of treated sewage was reduced to 1.15 NTU.

Recent interest in preventing the development of osteoporosis has focused on the regulation of redox homeostasis. However, the action of lycopene (LYC), a strong natural antioxidant com-pound, on osteoporotic bone loss remains largely unknown. Here, we show that the administra-tion of LYC to OVX rats reduces body weight gain, improves lipid metabolism and preserves bone quality. In addition, LYC treatment inhibits ROS overgeneration in serum and bone marrow in OVX rats, and in BMSCs upon H2O2 stimulation, leading to inhibiting adipogenesis and pro-moting osteogenesis during bone remodeling. Mechanically, LYC may improve bone quality via an increase in the expressions of FoxO1 and Runx2, and a decrease in the expressions of PPARγ and C/EBPα in OVX rats and BMSCs. Collectively, these findings suggest that LYC attenuates osteoporotic bone loss through promoting osteogenesis and inhibiting adipogenesis via regula-tion of the FoxO1/PPARγ pathway driven by oxidative stress, presenting a novel strategy for os-teoporosis management.

Increased anthropogenic pressure on forest resources leads to deforestation and forest degradation, significantly limiting the regeneration capacity of native woody species and consequently the restoration of miombo woodlands in anthropized habitats within the rural area of Lubumbashi. This study assessed the diversity and natural regeneration capacity of miombo species through floristic inventories in three different habitats (unexploited forests, degraded forests, and post-cultivation fallows). The results reveal that for the adult’s stratum unexploited and degraded forests exhibit higher dendrometric (density, mean square diameter, basal area) and floristic parameter (taxa, genera, families) values compared to post-cultivation fallows. Furthermore, the regeneration of miombo woody species is higher in degraded forests (21 taxa; 105 juveniles/plot). However, regarding the sapling’s stratum (1 cm ≤ dbh &lt; 10 cm), the three habitats display nearly similar situations. Additionally, the floristic composition and diversity of unexploited and degraded forests show a significantly higher similarity (76.50%) among them compared to these habitats and the post-cultivation fallows (56.00%). These findings indicate that miombo woodlands have the potential to regenerate and maintain floristic diversity. To sustain this natural regeneration capacity of miombo woody species and promote the restoration of forest mass and its floristic diversity, it is imperative to determine the rotation period after habitat exploitation and regulate anthropogenic activities and late bush fires, particularly in anthropized habitats at the village level.

Personal protective equipment (P PE) rules must be respected and followed to the letter of healthcare professionals in Cameroon to avoid the transmission of infectious diseases. This research proposes solutions to the problems of non-adherence and non-compliance among healthcare workers in Cameroon towards PPE rules. Inadequate access to suitable PPE, discomfort when wearing PPE during poor quality, and a lack of training on correct PPE use are just a few of these obstacles amongst HCWs in Cameroon. Hospitals and healthcare facilities should prioritize offering thorough instruction on PPE use and pinpoint the need to adhere consistently to PPE rules to remove these obstacles. They should also ensure that all healthcare personnel have access to and a sufficient supply of the proper PPE. Through the organization of impromptu visits by surveillance teams to identify areas of support and training, there is a need for assistance from government bodies and public health groups in creating awareness of the security value of PPE use among healthcare professionals. There is a lack of PPE in health care settings in rural areas in Cameroon, thus a need for an adequate supply. Then can we put appropriate PPE policies and practices for HCWs in Cameroon, as they will keep to it.Healthcare professionals in Cameroon must comprehend that following PPE standards protects not just their selves from diseases but also their patients, coworkers, and communities. We are accountable for ensuring everyone's safety and well-being, and adhering to PPE regulations is an essential first step.

Vaccine hesitancy has emerged as a significant barrier to achieving herd immunity and protecting populations from preventable diseases. Despite the proven effectiveness and safety of vaccines, many individuals remain hesitant or refuse to vaccinate themselves or their children. This review aims to identify the factors contributing to vaccine hesitancy, including misinformation, lack of trust in authorities, cultural beliefs, and access barriers. We also discuss the impact of vaccine hesitancy on public health, including outbreaks of vaccine-preventable diseases and erosion of trust in public health authorities. Furthermore, we present evidence-based communication strategies for addressing vaccine hesitancy, including building trust, providing accurate information, addressing myths and misconceptions, engaging with local influencers, and utilizing social media and digital platforms. By understanding the factors contributing to vaccine hesitancy and implementing effective communication strategies, healthcare providers, public health officials, and community leaders can work towards promoting widespread vaccine acceptance and protecting public health.

It is concerning that Cameroon's urban centers are experiencing an alarming rise in air pollution as a result of urbanization, population growth, industrial activity, and transportation emissions. We examined the causes, health impacts, research methodology, case studies, and government measures to address the urgent issue of air pollution in Cameroon's urban regions. We have emphasized how Cameroon's air pollution levels have significantly increased as a result of causes like population expansion, urbanization, industrial emissions, and automobile exhaust A thorough discussion of the negative health impacts of extended exposure to air pollution is included, with special attention to the sensitivity of the old and young. These effects include respiratory disorders, cardiovascular ailments, and early mortality. We emphasized the pressing need for all-encompassing strategies to address air pollution, such as increased public awareness campaigns, stronger emissions regulations, the development of renewable energy sources, and better waste management. The techniques employed in studies on air pollution, like as epidemiological research and air quality monitoring, are also explained to provide light on the unique health dangers that Cameroonian urban dwellers must contend with. We emphasized in our conclusion the significance of joint efforts by legislators, medical professionals, and the general people to reduce air pollution and protect public health in Cameroon in order to create a cleaner and healthier future. In order to protect public health in the future, it is imperative that Cameroon's air pollution be addressed.

Background: Schistosomiasis is the third most important Neglected Tropical Disease, with more than 4 million South Africans being infected. Cases of re-infection with schistosomiasis after being treated using modern medication has opened ways to seeking alternative treatment modalities using medicinal plants. This study aims to explore and understand the way traditional healers diagnose schistosomiasis, the types of traditional medicines they use to treat it in communities with a high prevalence of infection, and to establish whether the unavailability of prescribed anti-schistosomal medicines leads to the use of traditional medicine. Methods: A qualitative exploratory study was conducted using a questionnaire from December 2022 to March 2023 in the iLembe District, KwaZulu-Natal Province, South Africa. In-depth individual interviews were carried out among traditional health practitioners (THPs) involved in managing female urogenital schistosomiasis (FUS). Recorded narrative data was transcribed verbatim, read through thoroughly and coded, and analysed using thematic or content analysis Results: Traditional Health Practitioners provide services to many women who did not want to be treated in the mainstream health care system for their FUS. The disease was perceived and understood the same way by most of the respondents, that being caused by use of fresh water. Various plants species, either alone or in combination, were used to manage bilharzia in women. Senecio serratuloides (Asteraceae) and Hypoxis hemerocallidea (Hypoxidaceae) were prominently used by THPs in FUS management. Conclusion: Traditional Health Practitioners are playing a key role in managing bilharzia in women in rural areas of the iLembe District, this study reported that THPs can diagnose schistosomiasis symptoms based on their knowledge and experience. Respondents indicated that modern conventional treatment was readily available and free in public sector healthcare facilities; however, they consulted patients suffering from FUS. Various traditional herbal medicines are reportedly used by THPs to manage bilharzia in women. Further studies are needed to investigate the views and experiences of clients regarding the success of the treatments used and why they seek care from THPs.

Sub-Saharan social-ecological systems are undergoing changes in environmental conditions, including modifications in rainfall pattern and biodiversity loss. Consequences of such changes depend on complex causal chains which call for integrated management strategies whose efficiency could benefit from ecosystem dynamic modelling. However, ecosystem models often require lots of quantitative information for estimating parameters, which is often unavailable. Alternatively, qualitative modelling frameworks have proved useful for explaining ecosystem response to perturbations, while requiring fewer information and providing more general predictions. However, current qualitative methods have some shortcomings which may limit their utility for specific issues. In this paper, we propose the Ecological Discrete-Event Network (EDEN), an innovative qualitative dynamic modelling framework based on "if-then" rules which generates many alternative event sequences (trajectories). Based on expert knowledge, observations and literature, we use this framework to assess the effect of permanent changes in surface water and herbivores diversity on vegetation and socio-economic transitions in an East African savanna. Results show that water availability drives changes in vegetation and socio-economic transitions, while herbivore functional groups had highly contrasted effects depending on the group. This first use of EDEN in a savanna context is promising for bridging expert knowledge and ecosystem modelling.

We monitored the water quality and hydrological conditions of a horizontal subsurface constructed wetland (HSSF-CW) in Beijing, China, for 2 years. We simulated the area rate constant and the temperature coefficient with the first-order kinetic model. We examined the relationships between the nitrogen (N) removal rate, N load, seasonal variations in the N removal rate, and environmental factors, such as the area rate constant, temperature, and dissolved oxygen (DO). The effluent ammonia (NH₄⁺-N) and nitrate (NO₃⁻-N) concentrations were significantly lower than the influent concentrations (p<0.01, n=38). The NO₃⁻-N load was significantly correlated with the removal rate (R²=0.9566, p<0.01), but the NH^₄+-N load was not correlated with the removal rate (R²=0.0187, p>0.01). The area rate constants of NO₃⁻-N and NH₄⁺-N at 20 °C were 27.01±26.49 and 16.63±10.58 m∙yr⁻¹, respectively. The temperature coefficients for NO₃⁻-N and NH₄⁺-N were estimated at 1.0042 and 0.9604, respectively. The area rate constants for NO₃⁻-N and NH₄⁺-N were not correlated with temperature (p>0.01). The NO₃⁻-N area rate constant was correlated with the corresponding load (R²=0.9625, p<0.01). The NH₄⁺-N area rate was correlated with DO (R²=0.6922, p<0.01), suggesting that the factors that influenced the N removal rate in this wetland met Liebig's law of the minimum.

This paper numerically investigates a 3D mesh independency study of a straight blade horizontal axis tidal turbine modelled using Computational Fluid Dynamics (CFD). The solution was produced by employing two turbulence models, the standard k-ε model and Shear Stress Transport (SST) in ANSYS CFX. Three parameters were investigated: mesh resolution, turbulence model, and power coefficient in the initial CFD, analysis. It was found that the mesh resolution and the turbulence model affect the power coefficient results. The power coefficients obtained from the standard k-ε model are 15% to 20% lower than the accuracy of the SST model. It can also be demonstrated that the torque coefficient increases with the increasing Tip Speed Ratio (TSR), but drops drastically after TSR = 5 and k-ε model failing to capture the non-linearity in the torque coefficient with the increasing TSR.

Molecularly imprinted polymers (MIPs) were synthesized and applied for the selective extraction of oblongifolin C (OC) from fruit extracts of Garcinia yunnanensis Hu. A series of experiments and computational approaches were employed to improve the efficiency of screening for optimal MIP systems in the study. The molar ratio (1:4) was chosen at last based on the comparison of the binding energy of the complexes between the template (OC) and functional monomers using density functional theory (DFT) at the RI-PBE-D3-gCP/def2-TZVP level of theory. The binding characterization and the molecular recognition mechanism of MIPs were further explained using the molecular modeling method and NMR and IR spectra data. The reusability of this approach was demonstrated in over 20 batch rebinding experiments. 140.5 mg of OC (>95% purity) was obtained from the 5 g extracts with 2 g of MIPs with the best binding properties through gradient elution program from 35% to 70% methanol-water solution. At the same time, another structural analog, 46.5 mg of guttiferone K (GK) (>88% purity), was also obtained by the gradient elution procedure. Our results showed that the structural analogs could be separated from the crude extracts by the molecularly imprinted solid-phase extraction (MISPE) using a gradient elution procedure for the first time.

This study evaluates the efficiency of biodegradable polyurethane foams as thermal insulation materials compared to conventional materials like expanded polystyrene (EPS). Key parameters analyzed include thermal conductivity, energy savings, CO2 emission reductions, and investment payback periods. Biodegradable polyurethane foams demonstrated a thermal conductivity of 0.022 W/(m·K), significantly lower than EPS's 0.035 W/(m·K), leading to higher energy savings. Calculations reveal an annual energy saving of 443,820 kWh for polyurethane foams, compared to 441,330 kWh for EPS. Financially, polyurethane foams save approximately €53,258.4 annually, while EPS saves €52,959.6. In terms of environmental impact, polyurethane foams reduce CO2 emissions by 88,764 kg annually, versus 88,266 kg for EPS. Despite the higher initial cost of polyurethane foams (€50/m² compared to €30/m² for EPS), the payback period is remarkably short at 0.094 years (1.13 months) compared to EPS's 0.057 years (0.68 months). These findings highlight biodegradable polyurethane foams as a superior option for thermal insulation, offering significant energy, financial, and environmental benefits.

Background: in this paper, a new physical penetration technology for transdermal administration with traditional Chinese medicine (TCM) characteristics - Fu’s cupping therapy (FCT) - was established and studied by in-vitro and in-vivo experiments; the penetration effect and mechanism of FCT physical penetration technology (FCT-PPT) was preliminarily discussed. Method: Indomethacin (IM) as a model drug，by transdermal in vitro tests the establishment of the high，medium and low reference were finished as the chemical permeation system；chemical penetration enhancers and iontophoresis as a reference，the percutaneous penetration effect of FCT for IM patch was evaluated with 7 species diffusion kinetics model and in vitro drug distribution；naproxen as an internal standard，using UPLC-MS/MS technology，the IM quantitative analysis method in vivo was established，and pharmacokinetic parameters (AUC0-t，AUC0-∞，AUMC0-t，AUMC0-∞, Cmax and MRT) as indicators were used evaluate to FCT penetration role in vivo；in the same time，the group used 3K factorial design to study joint synergistic penetration effect on FCT and chemical penetration enhancers (CPEs)；by SEM and TEM，the skin micro and ultrastructural changes of the stratum corneum (SC) surface were observed, to explore pay tank penetration mechanism. Results: In vitro and in-vivo skin permeation experiments revealed that both the total cumulative percutaneous amount and in-vivo percutaneous absorption amount (AUC and AUMC) of indomethacin that permeated SD mouse skin using FCT techniques were greater than the amount observed using CPE and iontophoresis: Firstly, in contrast to the control group, the indomethacin percutaneous rate (PR) of the FCT lower group (FCTL) was 35.52%, and the enhancement ratio (ER) at 9h was 1.76X, which was roughly equivalent to the penetration enhancing effect of the CPEs and iontophoresis; secondly, the indomethacin PR of the FCT middle (FCTM) group and the FCT high intensity group (FCTH) were respectively 47.36% and 54.58%, ER at 9h were separately 3.58X and 8.39X; thirdly, pharmacokinetic studies showed that in-vivo indomethacin percutaneous absorption of the FCTs was higher than that of the control group, that of the FCTM group was slightly higher than that of the CPEs group, and that of the FCTM group was significantly higher than that of the others. Meanwhile, the variance analysis indicated that the combination of the FCT penetration enhancement method and the CPE method had beneficial effects in penetration enhancing of the skin: the significance level of the CPE method was 0.0004, which was apparently lower than the 0.001, meaning the difference was markedly significant; the significance level of the FCT was under 0.0001, its difference markedly significant; and the significance level of factor interaction A×B was lower than 0.0001, indicating that its difference of the synergism was markedly significant. Moreover, SEM and TEM images showed that the SC surfaces of SD rats treaded with FCT-PPT was damaged, and hard to observe the complete surface structure with its SC pores growing bigger and its special “brick structure” becoming looser, indicating that it broke the barrier function of skin, which revealed potentially a major route of skin penetration. Conclusion: FCT, as percutaneous penetration new technologies, has penetration effects significantly, with Chinese characteristics and highly clinical value, worth promoting development.

Abstract: Dispersal strategies of a species can affect its invasion success. Investigation into the dispersal strategies of invasive species in relation to different factors helps our understanding of the invasion mechanisms and provides knowledge for population management and invasion evaluation. Tetranychus ludeni Zacher (Acari: Tetranychidae) is an invasive species, which is native to Europe but now cosmopolitan. Here, we examined the effects of age and density on dispersal in mated females. Our results show that older females with more eggs were more likely to disperse and moved longer distances than younger ones with fewer eggs. Older females spread most of their eggs out of the natal habitats and over longer distances, which reduced competition and increased offspring fitness. Females significantly increased dispersal probability and distance with the increase of population density to avoid crowding. The synchronization of dispersal and reproduction, along with the positive density-dependent dispersal strategy, may facilitate habitat colonization and invasion speed of T. ludeni.

Adult neurogenesis involves generation of functional neurons from neural progenitor cells, which have the potential to complement and restore damaged neurons and neural circuits. Therefore, the development of drugs that stimulate neurogenesis represents a promising strategy in stem cell therapy and neural regeneration, greatly facilitating the reconstruction of neural circuits in cases of neurodegeneration and brain injury. Our study reveals that compound A5, previously designed and synthesized by our team, exhibits remarkable neuritogenic activities, effectively inducing neurogenesis in neural stem/progenitor cells (NSPCs). Subsequently, transcriptome analysis using high-throughput Illumina RNA-seq technology was performed to further elucidate the underlying molecular mechanisms by which compound A5 promotes neurogenesis. Notably, comparative transcriptome analysis showed that the up-regulated genes were mainly associated with neurogenesis and the down-regulated genes were mainly concerned with cell cycle progression. Furthermore, we confirmed that compound A5 significantly affected the expression of transcription factors related to neurogenesis and cell cycle regulatory proteins. Collectively, these findings identify a new compound with neurogenic activity and may provide insights into drug discovery for neural repair and regeneration.

The exponential growth of user-contributed data provides a comprehensive basis for assessing collective perceptions of landscape change. A variety of possible public data sources exist, such as geospatial data from social media or Volunteered Geographic Information (VGI). Key challenges with such ‘opportunistic’ data sampling are variability in platform popularity and bias due to changing user groups and contribution rules. In this study, we use five case studies to demonstrate how intra- and inter-dataset comparisons can help to assess the temporality of landscape scenic resources, such as identifying seasonal characteristics for a given area, or testing hypotheses about shifting popularity trends observed in the field. By focusing on the consistency and reproducibility of temporal patterns for selected scenic resources and comparisons across different dimensions of data, we aim to contribute to the development of systematic methods for disentangling the perceived impact of events and trends from other technological and social phenomena included in the data. The proposed techniques may help to draw attention to overlooked or underestimated patterns of landscape change, fill in missing data between periodic surveys, or corroborate and support field observations. Despite limitations, the results already provide a comprehensive basis for developing indicators with a high degree of timeliness for monitoring perceived landscape change over time.

Despite the partial disclosure of driving scenario knowledge graphs, they still fail to meet the comprehensive needs of intelligent connected vehicles for driving knowledge. Current issues include the high complexity of pattern layer construction, insufficient accuracy of information extraction and fusion, and limited performance of knowledge reasoning models. To address these challenges, a hybrid knowledge graph method was adopted in the construction of the Driving Scenario Knowledge Graph (DSKG). Firstly, core concepts in the field were systematically sorted and classified, laying the foundation for the construction of a multi-level classified knowledge graph top-level ontology. Subsequently, by deeply exploring and analyzing the Traffic Genome data, 34 entities and 51 relations were extracted and integrated with the ontology layer, achieving the expansion and updating of the knowledge graph. Then, in terms of knowledge reasoning models, an analysis of the training results of the TransE, Complex, Distmult, and Rotate models in the entity linking prediction task of DSKG revealed that the Distmult model performed the best in metrics such as hit rate, making it more suitable for inference in DSKG. Finally, a standardized and widely applicable Driving Scenario Knowledge Graph was proposed. The DSKG and related materials have been publicly released for use by industry and academia.

Background. The Stanford Health Assessment Questionnaire-Disability Index (HAQ) is widely used to measure functional ability in persons with Rheumatoid Arthritis (RA). The instrument was developed with limited involvement from persons with RA, and their perception of the instrument has not been studied in depth. The aim of this study was to explore how persons with RA experience the use of the HAQ in care. Methods. Forty persons with RA were purposefully recruited to participate in semi-structured interviews. The interviews were then analyzed qualitatively using thematic analysis. Results. The participants questioned the relevance of the HAQ but nevertheless experienced that the instrument had a profound effect on their understanding of health and how care is delivered. The analysis resulted in three themes: Problems with individual items, meaning of the summative score, and effects on care and health perceptions. Conclusions. To make the HAQ relevant to persons with RA, it needs to be revised or to include an option to select items most meaningful to the respondent. To ensure relevance, the HAQ update should preferably be co-created by researchers, clinicians and persons with RA.

This paper explores innovative approaches to determining eigenvalues and eigenvectors through the combination of neural networks and traditional numerical methods. Traditional methods like the Newton-Raphson and Durand-Kerner algorithms are proven effective but often require good initial approximations and can be sensitive to the choice of starting points. In this study, we propose a hybrid approach that uses neural networks to generate better initial approximations, which are then iteratively refined using traditional methods. By combining the pattern recognition power of neural networks with the adaptability of traditional numerical methods, we demonstrate faster convergence and higher accuracy in determining eigenvalues and eigenvectors. Experimental results on various polynomials of different degrees and characteristics show that our hybrid approach outperforms individual methods, providing a more robust and efficient solution.

The textile sector is responsible for a number of environmental impacts. Due to the in-creasing quantities of textiles, their share is rising and a trend reversal from a linear to a circular textile chain is needed. This article presents the background, methodological approach and results of a participatory textile development model. In the commercial B2B sector, three textile prototypes were developed together with users and trialled over several months in three application areas. Textile development took into account the requirements of fibre regeneration in the product design and focused on innova-tive chemical recycling solutions. The three sustainably aligned textiles were subject-ed to spectroscopic and textile-technological tests. Screening life cycle assessments an-alysed their environmental profile and compared it with reference textiles that are used as standard. Overall, it is clear that the three textiles can match conventional ref-erence textiles in terms of quality and have considerable environmental benefits com-pared to the reference textiles. The user survey did identify concerns about a high arti-ficial fibre content, although a general rejection of recycled fibres was not observed. The results show that a transformation is possible, but must start with the fibre com-position; recycling, on the other hand, is of minor importance.

Iturbide is located in the Northeast of Mexico, it has a rich native and exotic flora, however, there are no ethnobotanical records, therefore, it requires attention in the documentation of traditional practices and uses of its botanical resources. In 2021, twelve field trips were carried out, applying 110 semi-structured interviews. Plant samples were collected, identified and deposited in an herbarium. We used the Chi-square test to compare ethnobotanical uses with respect to others reported in Mexico. To determine the cultural importance, three ethnobotanical indices were applied (UVI, ICF and FL). We recorded 250 species with ethnobotanical uses associated with 121 genera and 83 families, including 140 native and 110 exotic species. The most common plant families were Asteraceae, Lamiaceae, and Fabaceae. The main uses were ornamental, medicinal and food. The species with the highest UVI values ​​were Lepidium peruvianum, Ocimum basilicum and Rosmarinus officinale. The multifunctionality of the native and exotic flora demonstrates the extensive knowledge associated with botanical resources. Examples, the role of ornamental plants, with a direct impact on human well-being, the resilience of healers and traditional inhabitants by using different species for the treatment of various ailments. or indigenous edible plants in the daily diet.

Cryptocurrencies have obtained a crucial position in the international financial landscape. The cryptocurrency market has been perceived as a highly volatile market since the inception of Bitcoin. This study investigates the relevant performance of extreme value models (EVM) in estimating the Value-at-Risk (VaR) of Bitcoin and Ethereum returns. The extreme value mixture models, GPD-Normal-GPD (GNG) and GPD-KDE-GPD models are fitted to the returns of Bitcoin and Ethereum and the Kupiec likelihood backtesting procedure is performed on the VaR estimates to assess the fits. Both models’ results showed that the fits were a much more decent representation of the observed data when compared to the Normal distribution. The backtesting results showed that the GPD-KDE-GPD model’s fit was superior to that of the GPD-Normal-GPD for both sets of returns at all VaR risk levels except at the 99% level. The results of this study may assist with understanding the dynamics and risks associated with cryptocurrencies and can serve as a beneficial tool for decision-making and risk management to investors, traders, financial institutions and many other participants in the cryptocurrency ecosystem.

This study evaluated eight herbal and fruit teas to produce kombucha beverages. Besides, the effect of initial sucrose concentration (10, 40, and 70% w/v) on kombucha fermentation from tea extracts was investigated. Kinetic characterization and modeling of kombucha fermentation were also performed. Results showed that kombucha beverages' characteristics and chemical content changed according to tea type and initial sucrose concentration. It was determined that as initial sucrose concentration increased, the consumed substrate concentration increased. The highest total biomass values were determined when green tea and pomegranate tea were supplemented with 70 g/L sucrose. The highest total phenolic content and antioxidant activity were achieved with green tea samples. The total acidity and phenolic substance concentration increased as fermentation progressed in general. The kinetic parameters including maximum consumption rate, sugar utilization, yield, biomass yield, maximum biomass, total acidity, phenolic production rate, and the phenolic result were computed depending on changing substrate concentration. Concerning kinetic modeling, proposed models (Logistic and Luedeking-Piret models) estimated satisfactorily experimental substrate consumption and total acidity values. Besides, total acidity was associated with the substrate consumption.

In southern South America, zonal flora, defined by macroclimatic conditions, is strongly influenced by an aridity filter near 29°S. Cushion bogs, high-altitude wetlands along the Andes, display homogeneous flora with cushion species dominance, contrasting with zonal vegetation. Despite being influenced by microclimatic conditions, these ecosystems experience varied environmental effects. This study identifies environmental filters affecting bog communities along a broad-scale latitudinal gradient from 15°S to 42°S. We analyzed 420 bogs and 284 species across three macroclimatic regions with distinct summer, winter, and transitional arid rainfall regimes. Using variance partitioning and membership-based regionalization models, we examined the impacts of climatic, edaphic, and spatial variables on beta diversity. We also assessed species' niche overlap and the influence of environmental filters on the communities' phylogenetic diversity. Results show that species turnover and niche overlap vary with macroclimatic differences, delineating three distinct regions. Notably, phylogenetic clustering in the driest part of the gradient (23°S —24°S) highlights the impact of the environmental filter. Aridity and temperature variations at a broad scale serve as environmental filters shaping the composition of bog communities across southern South America.

In this study, we analyzed the microbial community of traditional fermented foods in Jeju Island to identify the distribution of useful microorganisms and confirm their anti-inflammatory and anti-melanogenic effects to determine their potential use as cosmetic ingredients. Firstly, we examined the microbial communities of Omphalius rusticus Jeotgal (OR), Spratelloides gracilis Jeotgal (SG), Chromis notata Jeotgal (CN), Turbo cornutus Jeotgal (TC), Trichiurus lepturus intestine Jeotgal (TL), Branchiostegus japonicus Sweet Rice Punch (BJ), Salted Anchovy Sauce (SA), Jeju Soy Sauce (JSS), and Jeju Soybean Paste (JSP). We found that Latilactobacillus sakei (87.2%), Tetragenococcus halophilus (37.7%), T. halophilus (96.8%), Bacillus subtilis (23.4%), T. halophilus (71.3%), L. sakei (53.7%), Lentibacillus sp. (42.9%), Enterococcus durans (14.6%), and E. durans (32.8%) were the dominant species. Secondly, to study the nine Jeju fermented foods’ anti-inflammatory and anti-melanogenic effects, we employed RAW 264.7 and B16F10 cells, classic cell models for inflammation and melanogenesis studies. Ethyl acetate extracts of the nine Jeju fermented foods all inhibited nitric oxide (NO) and melanin production in a concentration-dependent manner. Thirdly, to test the applicability of the nine Jeju fermented foods to human skin, we used the MTT assay to assess their cytotoxic effects on human keratinocytes (HaCaT cells). Finally, the topical applicability of the nine Jeju fermented foods was tested through primary skin irritation, and it was found that they did not cause any adverse effects. Therefore, extracts from the nine Jeju fermented foods have potential applications as ingredients in anti-inflammatory and anti-melanogenic products and can be used in the cosmetic industry.

Phytopathogenic fungi are living organisms that cause plant diseases and great damage to agri-cultural products. Despite the wide range of commercial fungicide products in use, there is a clear need for new and environmentally friendly fungicides. Here we propose a new ecological fun-gicide, copper alginate microspheres prepared by ionic gelation. The microspheres were charac-terized (morphology and topography, encapsulation efficiency, load capacity, swelling behavior, rheology, kinetics and mechanism of copper ions release) and their in vitro antifungal potential against selected genera of phytopathogenic fungi was evaluated. Novel copper alginate micro-spheres inhibited spore germination of Botrytis cinerea and mycelial growth of Cercospora beticola and Phytophthora ramorum. They offer effective disease control while minimizing environmental impact and promoting sustainable agriculture practices.

Biodiversity loss and nature degradation are pressing issues with huge impacts on our society and economy. Businesses, investors, and regulators focus on corporate efforts to support biodiversity and nature positive actions. This review provides a comprehensive overview about the importance of biodiversity in businesses, the materiality of biodiversity and the role of mandatory and non-mandatory regulations on corporate environmental reporting and sustainability disclosure frameworks. The review also discusses descriptive information on the evolution of sustainability frameworks with a comparison of most prominent sustainability frameworks with a key focus on the materiality approach and biodiversity-related disclosure recommendations. Further, we provide suggestions for more holistic approaches to improve the future sustainability frameworks focusing on the biodiversity impact while showing the necessity of more focus on the decision-making paradigm. Further research on measuring biodiversity impact and innovative trends in sustainability reporting are required to better reflect nature-positive outcomes in corporate sector businesses.

Identifying and anticipating potential failures in the cloud is an effective method for increasing cloud reliability and proactive failure management. Many studies have been conducted to predict potential failure, but none have combined SMART (Self-Monitoring, Analysis, and Reporting Technology) hard drive metrics with other system metrics such as CPU utilisation. Therefore, we propose a combined metrics approach for failure prediction based on Artificial Intelligence to improve reliability. We tested over 100 cloud servers’ data and four AI algorithms: Random Forest, Gradient Boosting, Long-Short-Term Memory, and Gated Recurrent Unit. Our experimental result shows the benefits of combining metrics, outperforming state-of-the-art.

This review article focuses on the role of Artificial Intelligence (AI) in transforming healthcare in Africa, specifically in combatting infectious diseases, neglected tropical diseases (NTDs), and antimicrobial resistance. We provide a comprehensive overview of the significance of AI in the healthcare industry, highlighting its urgency and importance in addressing these specific health challenges in Africa. We begin by discussing the role of AI in infectious disease surveillance and outbreak detection. We explore how AI technology can be employed for real-time tracking and prediction of outbreaks, providing examples of successful AI applications in infectious disease surveillance within the African context.Next, we examine the potential of AI-enabled diagnosis and treatment for faster and more accurate diagnoses of infectious diseases and NTDs. We highlight specific examples of AI applications in diagnosing and treating these diseases in Africa, showcasing the potential of AI to improve clinical outcomes and save lives.Furthermore, we focus on how AI-driven drug discovery and development can expedite the search for new treatments for infectious diseases and combat antimicrobial resistance. We present examples of AI applications in drug discovery within the African context, illustrating the potential for AI to revolutionize the development of effective therapeutics.In addition, we delve into how AI-powered public health interventions can enhance the design and implementation of targeted interventions. We explore how AI can optimize resource allocation and facilitate data-driven decision-making processes, providing examples of AI applications in public health in Africa.Finally, we address the challenges and limitations of implementing AI in combatting infectious diseases, NTDs, and antimicrobial resistance in Africa. We discuss potential barriers and ethical concerns surrounding AI applications in healthcare, aiming to encourage informed and responsible utilization of AI technologies.Overall, this review emphasizes the importance and potential of AI in combatting infectious diseases, NTDs, and antimicrobial resistance in Africa. It positions AI as a catalyst for revolutionizing healthcare in the region, leading to more effective disease surveillance, diagnosis, treatment, drug discovery, and public health interventions.

Soil extracellular enzyme activity (EEAs) and enzymatic stoichiometry (ES) can provide a crucial indication of changes in soil ecosystem's nutrient availability and the microbial resource limitations. However, the changing characteristics of soil EEAs and ES at different stages of the native succession process and their key drivers are unclear. In order to investigate the soil EEAs, ES and driving factors of soil under vegetation at different succession stages, we adopted the "spatio-temporal substitution" method to collect the surface soil of bryophyte community, herbaceous community, shrub community and tree community in the new volcanic lava platform of Wudalianchi Volcanic Nature Reserve. We measured seven soil EEA, including carbon(C)-acquiring enzyme (β-1,4-glucosidase (BG)), N-acquiring enzymes (β-N-acetyl-glucosaminidase (NAG) and leucine aminopeptidase (LAP)) and phosphorus (P)-acquiring enzyme (acid phosphatase (AP)) activities. The length and angle of vectors defined by ratios of enzyme activities (BG/(NAG + LAP) vs. BG/AP) were used to indicate relative microbial investments in C- (length), and N- and P- (angle) acquiring enzymes. Our results showed that the contents of TC, TN, TP, MBC, DOC and NO3-N in shrub community soil were significantly higher than those in bryophyte, herb and tree communities, and increased by 441%, 246%, 137%, 5570%, 12% and 484%, respectively. The highest soil EEA of C-, N- and P-acquiring were found in shrub community, and the soil EEAs/MBC of C-, N- and P-acquiring were the highest in bryophyte community. Enzyme C:N, C:P and N:P ratios increased progressively in the order of bryophyte, herb and shrub community, but the enzyme C:N, C:P and N:P ratios of tree community were both far less than shrub community. Vector lengths increased progressively in the order of bryophyte (1.16), herb (1.27), tree (1.29) and shrub (1.40), and Vector angles decreased progressively in the order of bryophyte (49.15°), herb (45.65°), Tree (45.31°) and shurb (44.54°), suggested that as succession progresses, soil microbial nutrients transforms from P limitation (angle＞45°) to N limitation (angle＜45°). Redundancy analysis showed that TC, TN, EC and C:N were important drivers of variation in soil EEAs and ES in vegetation at different succession stages. Our findings highlight that the primary succession process cause nutrient limitation transformation. Soil ES might be a sensitive indicator mediated by soil microorganisms to the relative resource limitation at different stages of the primary succession process.

In recent years, the rapid development of computer-aided planning and design technology has provided a new perspective for the study of complex problems such as the generation of architectural complex forms. This study focuses on how to apply parameterization technology to analyze and articulate traditional spatial form composition rules, aiming to minimize reliance on subjective human judgment in the protection and renewal design of the historical style of traditional villages. It aims to establish digital generative design tools to address the challenges of accurately inheriting and innovatively utilizing historical and cultural information in traditional settlements. It introduces how to rely on parameterization technology to analyze the spatial form composition rules, parameter extraction rules, and spatial reconstruction rules of traditional villages, facilitating the complete process from spatial features to parameterization rules, and then to the application of computational methods to deduce spatial features. It also includes case studies demonstrating the application of parameterization technology tools for village protection and explores the role of generative design tools in preserving the spatial style of these settlements.

Plain lakes play a crucial role in the hydrological cycle of a watershed, but their interactions with adjacent rivers and downstream water bodies can create complex river-lake relationships, often leading to frequent flooding disasters. Taking Poyang Lake as an example, this paper delves into its interaction with the Yangtze River, revealing the spatiotemporal patterns of flood propagation within the lake and its impact on surrounding flood control measures. The aim is to provide insights for flood management in similar environments worldwide. This study employs a comprehensive approach combining hydrological statistical analysis and two-dimensional hydrodynamic modeling, based on extensive hydrological, topographic, and socio-economic data. The results indicate that the annual maximum outflow from Poyang Lake is primarily controlled by floods within the watershed, while the highest annual lake water level is predominantly influenced by floods from the Yangtze River. The peak discharge typically reaches the lake outlet within 48 hours, with the peak water level taking slightly longer at 54 hours. However, water storage in the lake can shorten the time for the peak discharge to arrive. When converging with floods from the Yangtze River, the peak water level may be delayed by up to 10 days due to the top-supporting interaction. Furthermore, floods from the "Five Rivers" propagate differently within the lake, affecting various lake regions to differing degrees. Notably, floods from the Fu River cause the most significant rise in lake water level under the same flow rate. The top-supporting effect from the Yangtze River also significantly impacts the water surface slope of Poyang Lake. When the Yangtze River flood discharge far exceeds that of the "Five Rivers" (i.e., when the top-supporting intensity f value exceeds 4), the lake surface becomes as flat as a reservoir. During major floods in the watershed, the water level difference in the lake can increase dramatically, potentially creating a "dynamic storage capacity" of up to 840 million cubic meters.

In order to improve contrast and restore color for underwater image captured by camera sensors without suffering from insufficient details and color cast, a fusion algorithm for image enhancement in different color spaces based on contrast limited adaptive histogram equalization (CLAHE) is proposed in this article. The original color image is first converted from RGB color space to two different special color spaces: YIQ and HSI. The color space conversion from RGB to YIQ is a linear transformation, while the RGB to HSI conversion is nonlinear. Then, the algorithm separately operates CLAHE in YIQ and HSI color spaces to obtain two different enhancement images. The luminance component (Y) in the YIQ color space and the intensity component (I) in the HSI color space are enhanced with CLAHE algorithm. The CLAHE has two key parameters: Block Size and Clip Limit, which mainly control the quality of CLAHE enhancement image. After that, the YIQ and HSI enhancement images are respectively converted backward to RGB color. When the three components of red, green, and blue are not coherent in the YIQ-RGB or HSI-RGB images, the three components will have to be harmonized with the CLAHE algorithm in RGB space. Finally, with 4 direction Sobel edge detector in the bounded general logarithm ratio operation, a self-adaptive weight selection nonlinear image enhancement is carried out to fuse YIQ-RGB and HSI-RGB images together to achieve the final fused image. The enhancement fusion algorithm has two key factors: average of Sobel edge detector and fusion coefficient, and these two factors determine the effects of enhancement fusion algorithm. A series of evaluate metrics such as mean, contrast, entropy, colorfulness metric (CM), mean square error (MSE) and peak signal to noise ratio (PSNR) are used to assess the proposed enhancement algorithm. The experiments results showed that the proposed algorithm provides more detail enhancement and higher values of colorfulness restoration as compared to other existing image enhancement algorithms. The proposed algorithm can suppress effectively noise interference, improve the image quality for underwater image availably.

Ecological changes evoke many felt losses and types of grief. These affect sustainability efforts in profound ways. Scholarship on the topic is growing, but the relationship between general grief research and ecological grief has received surprisingly little attention. This interdisciplinary article applies theories of grief, loss, and bereavement to ecological grief. Special attention is given to research about “non-death loss” and other broad frameworks about grief. Dynamics related to both local and global ecological grief are discussed. Kinds of potential losses arising out of ecological issues are clarified with the frameworks of tangible/intangible loss, ambiguous loss, nonfinite loss, and shattered assumptions. Various possible types of ecological grief are illuminated by discussing the frameworks of chronic sorrow and anticipatory grief/mourning. Earlier scholarship about disenfranchised ecological grief is augmented by further distinctions about various forms it may take. The difficulties in defining complicated or prolonged grief in ecological context are discussed, and four types of “complicated ecological grief” are explored. Based on the findings, three special forms of ecological loss and grief are named and discussed: transitional loss and grief, lifeworld loss, and shattered dreams. The implications of the results for ecological grief scholarship, counselling and coping are briefly discussed. The results can be used by psychological and healthcare professionals and researchers, but also by members of the public who wish to reflect on their eco-emotions.

The Folla Redonda grape is indigenous to the region of Rias Bajas in Galicia, Spain. This grape is distinguished by its characteristic acid flavor and pulp of remarkable coloration. Despite the wine obtained from this grape is highly valued by consumers of this region for its surprising acidic notes and thickness, it is important to note that it is not a variety of Vitis vinifera and its production is not legally regulated. In the present study, an evaluation of the physicochemical properties of the Folla Redonda grape (pH, °Brix, °Bé, density, acidity, sugar content, etc.) as well as its content in bioactive compounds (total polyphenols and anthocyanins) was carried out. Additionally, the antioxidant potential of the extracts obtained from this grape was demonstrated. This study proved the interesting organoleptic characteristics of the grape, along with a high content of compounds of interest and an important antioxidant activity comparable to that of Vitis vinifera grapes. Therefore, the grape's natural consumption and its application in different industries such as nutraceutical or pharmacological would result in an important revaluation of the grape and significant health benefits. In case that the production of wine from this variety is legalized, consumers will not only be able to enjoy a wine with a characteristic dense reddish color and important acid tones, but also with interesting concentrations of bioactive compounds.

Some wrong decisions and faulty practices applied during the repair and restoration of traditional buildings cause more damage to the structures due to the materials used in the repair. In this study, brick, mortar, plaster, and wood samples were taken from one registered building in the Konuralp neighborhood of Düzce Province, which has survived to the present day by preserving its original structural features and reflecting the characteristics of traditional housing. Texture, chemical and petrographic analyses were carried out on the samples. In line with these analyses, a scientific basis was created for selecting material properties in the repair and reuse processes of traditional buildings, and suggestions are made for the analysis of materials specific to traditional buildings in Konuralp.

The healthcare situation in Africa is characterized by limited resources and a high burden of disease, presenting significant challenges to the healthcare systems in the region. The integration of Artificial Intelligence (AI) technologies in African healthcare holds immense potential for addressing these challenges and improving health outcomes. This paper aims to provide a comprehensive understanding of the current healthcare landscape in Africa and the urgent need for AI-driven solutions.In addition to discussing the potential applications of AI in African healthcare, such as disease diagnosis, treatment planning, and medical imaging analysis, this manuscript seeks to shed light on the specific challenges hindering the implementation of AI in the region. By providing detailed examples and case studies, we aim to offer insights into the obstacles that need to be overcome and potential solutions that can be pursued.By leveraging AI technologies, Africa has the opportunity to revolutionize its health systems, bridging the gap in access to quality healthcare across the continent. The objective of this review article is to not only highlight the potential benefits that AI can bring to African healthcare but also to emphasize the urgent need for action and collaboration in implementing and scaling up AI innovations in the region. By doing so, African countries can pave the way for improved health outcomes and ensure that their populations receive the healthcare services they deserve.

Artificial intelligence (AI) has emerged as a powerful tool for addressing the challenges posed by climate change and their impact on public health. By leveraging its capacity to analyze and predict climatic patterns, AI offers opportunities to enhance resource management and develop effective strategies for climate change mitigation. Moreover, AI can contribute to generating sustainable solutions that address the complex and interconnected nature of climate change. For example, AI can enable the optimization of energy consumption and facilitate the integration of renewable energy sources into existing systems. It can also support the development of climate models that provide timely and accurate predictions, enabling policymakers to implement proactive measures for disaster preparedness and response. Furthermore, AI-powered disease surveillance and mitigation techniques can improve public health outcomes by identifying patterns and trends in the spread of diseases in relation to climatic factors.However, the widespread adoption of AI-based solutions is not without challenges. Ethical concerns surrounding privacy and data ownership must be addressed, as the use of AI requires access to large datasets, raising potential privacy risks. Technical constraints, such as limited computational power and the need for sophisticated algorithms, also pose obstacles to the implementation of AI in climate change mitigation strategies. Furthermore, issues related to the accessibility and affordability of AI technologies must be resolved to ensure equitable distribution and maximize its potential impact on public health.To fully harness the power of AI in addressing climate change and improving public health outcomes, it is crucial to promote innovation, multidisciplinary collaboration, and open data science. Innovation can drive the development of new AI algorithms and technologies specifically tailored to address climate change challenges. Multidisciplinary approaches that bring together experts from diverse fields, including climate science, public health, and computer science, can foster a holistic understanding of complex systems and enable the design of comprehensive solutions. Finally, open data science practices, such as sharing data and algorithms, can facilitate collaboration and accelerate progress in mitigating climate change and its public health impacts.In conclusion, AI offers promising opportunities for effectively addressing climate change and its impact on public health. However, to fully realize its potential, it is essential to tackle ethical and privacy concerns, overcome technical constraints, and ensure accessibility and affordability. By promoting innovation, multidisciplinary collaboration, and open data science, we can unlock the transformative power of AI in mitigating climate change and improving public health outcomes.

As an important part of coastal wetlands, tidal flats provide unique ecosystem services and functions. China's tidal flats are significantly threatened by industrialization, urbanization, aquaculture expansion and coastline reconstruction, and there is an urgent need for sustainable strategies to balance the protection and utilization of tidal flats. Remote sensing technology can realize large-scale spatiotemporal research of tidal flats, comprehensively improve the ecological environment of coastal zones, and help achieve the overall planning goals of major projects for the protection and restoration of important ecosystems in China. In this study, based on GEE (Google Earth Engine) platform, Sentinel-2 (MSI), Landsat 8 (OLI) and Landsat 9 (OLI-2) remote sensing images were used to construct multi-source intensive time series remote sensing image collection, combined with MSIC-OA algorithm. The tidal flat data of China in 2018 and 2023 are extracted and analyzed. The results show as follows: 1) The overall classification accuracy of the tidal flat in 2023 is 95.19%, and the Kappa coefficient is 0.89; In 2018, they were 92.77% and 0.83 respectively. 2) The total tidal flat area in 2018 and 2023 is 8300.34km2 and 8151.54km2, respectively, a decrease of 148.80km2; 3) In 2023, estuarine and bay tidal flats will account for 54.88% of the total area, and most of the tidal flats will be distributed near river inlets and bays; 4) In 2023, the total length of the coastline adjacent to the tidal flat is 10196.17km, of which the artificial shoreline accounts for 67.06%, and the development degree of the tidal flat is 2.04, indicating that most of the tidal flat has been developed and utilized. The research results provide valuable reference for the scientific planning and rational utilization of tidal flat resources in China.

Promoting the sustainable development of resource-based cities is of great significance to the ecological protection and high-quality development of the Yellow River Basin. Combining the environmental status and economic development, we objectively evaluate the ecological safety level of resource-based cities in the Yellow River Basin, compare and analysis the main factors affecting the ecological safety of different resource-based cities, and formulate precise and differentiated ecological safety protection measures in river basins to achieve high-quality development. Taking the Yellow River Basin as an example, this paper analyses the ecological level of the Yellow River Basin based on the theory of sustainable development, calculates the ecological safety level of 30 resource-based cities in the Yellow River Basin from 2006 to 2020 by TOPSIS model, and makes convergence analysis by classification. Through the DID empirical test of sustainable development planning policy on ecological safety factors, distinguish the development characteristics of different resource-based cities. The results show that: (1) the ecological safety level of resource-based cities in the Yellow River Basin is generally developing well, and there are differences among different types of resource-based cities; (2) the kinetic energy of sustainable development of resource-based cities in the Yellow River Basin is mainly industrial transformation, and the power of science and technology is insufficient; (3) local governments pay limited attention to environmental protection, and the sustainable development planning of resource-based cities in 2013 mainly promotes the adjustment of industrial structure of resource-based cities.

In the intricate dance between viruses and host cells, RNA-binding proteins (RBPs) serve as crucial orchestrators of gene expression and cellular processes. We will delve into the riveting realm of viral hijacking, where viruses deftly exploit host RBPs to manipulate cellular machinery for their replication and pathogenesis. Through a masterstroke of molecular subterfuge, viruses co-opt RBPs involved in various facets of RNA metabolism - from transcription to degradation - to promote viral gene expression and evade host immune defenses. This manipulation leads to global alterations in cellular RNA metabolism, impacting essential host genes vital for immune responses and homeostasis.Unveiling this clandestine alliance between viruses and RBPs is not just a scientific pursuit, but a critical imperative for devising innovative antiviral strategies to disrupt these interactions. By unraveling the intricate interplay between viral proteins and host RBPs throughout the viral life cycle - from entry to assembly - researchers aim to identify vulnerabilities that can be targeted for therapeutic intervention. Strategies such as disrupting viral protein-RBP interactions hold promise for inhibiting viral replication and curbing pathogenesis, offering a beacon of hope in the battle against viral infections.Our manuscript elucidates the indispensable roles played by RBPs in viral replication, translation, and pathogenesis, shedding light on the molecular mechanisms driving viral success. Delving deeper, it explores how viruses intricately entwine with host RBPs, manipulating cellular signaling pathways to create a hospitable environment for viral spread. By dissecting these manipulative tactics, researchers uncover new targets for antiviral therapy, envisioning a future where tailored interventions disrupt viral-host RBP interactions with precision and efficacy.As the narrative unfolds, the therapeutic implications of targeting RBPs or their interactions with viral proteins emerge as a promising frontier in the fight against viral infections. From small molecule inhibitors to RNA-based therapeutics, innovative approaches are on the horizon, offering new avenues for combatting viral diseases. We set the stage for future research, beckoning researchers to delve deeper into the molecular intricacies of viral hijacking of RBPs and charting a course towards novel therapeutic interventions that promise to reshape the landscape of antiviral therapy.\Ultimately, it beckons the scientific community to embark on a voyage of discovery, unraveling the secrets of viral hijacking of RBPs and paving the way for transformative advances in antiviral therapeutics. The stage is set for a new chapter in the battle against viral infections, where knowledge becomes the sword and innovation the shield against the pernicious machinations of viral pathogens.

Artificial intelligence (AI) is increasingly being used to improve the quality control of point-of-care diagnostics. This is caused by a number of factors, including the following: 1. AI can accelerate and improve testing accuracy. In comparison to humans, AI technology can review data more quickly and precisely, reducing errors and improving overall quality assurance. 2. When it comes to improving POC test findings on healthcare issues such as infectious diseases or medical crises such as heart attacks, AI can be used for sophisticated predictive analytics and modeling that aid in better decision-making. 3. Artificial intelligence facilitates process automation, increasing productivity and lowering labor costs in labor-intensive tasks such as testing and analyzing samples collected at point-of-care facilities. 4. The use of AI enables organizations implementing these solutions to gain insights from large volumes of raw diagnostic data generated faster and more accurately, allowing them to build solid frameworks around preventive care initiatives and significantly influence public health outcomes.5. Artificial intelligence (AI) has been demonstrated to be a useful tool for real-time monitoring systems that identify any problems with test results early so that they can be corrected before affected patients receive inaccurate diagnoses or treatment plans based on false information provided by diagnostic tests performed at points of care such as clinics or hospitals.Using these technologies would allow healthcare organizations to spend less on labor while still receiving exact diagnoses and rapid treatment delivery at a fraction of the cost that manual approaches required earlier.

The environment significantly impacts the life of bees and their feeding. This study aimed to investigate bee foraging using melissopalynological analysis and DNA metabarcoding in ​​intensive farming, reserved, and urbanized areas. The highest alpha diversity was observed in the reserved and in the intensive farming areas. The urbanized area has less diversity. In the intensive farming area predominated: Sinapis, Helianthus, and Fagopyrum; in the reserved area: Melilotus, Helianthus, and Brassica. In the urbanized area garden plants: radish (Raphanus sativus) and cucumber (Cucumis hystrix) and agricultural plants: soybean (Glycine max) and melon (Cucumis melo) were often found. The most significant agreement was between the rbcL and the melissopalynological analysis. The ITS2 revealed equal matches with both rbcL and melissopalynology but this marker missed or underestimated some genera. Trifolium pretense and Brassica nigra, were identified simultaneously by three analyses. Species Convolvulus arvensis, Melilotus officinalis, Echium vulgare, Brassica rapa, Helianthus divaricatus, and Onobrychis viciifolia were found in all ecological areas. Imperfect databases put some limits for identification of some taxa using metabarcoding. Further research and expansion of plant databases is needed. Studying the food preferences of bees in different environmental conditions and landscapes is necessary to develop measures to preserve their population.

This paper introduces a novel framework for zero-shot learning (ZSL), i.e., to recognize new categories that are unseen during training, by distilling knowledge from foundation models. Specifically, we first employ ChatGPT and DALL-E to synthesize reference images of unseen categories from text prompts. Then, the test image is aligned with text and reference images using CLIP and DINO. Finally, the predicted logits are aggregated according to their confidence to produce the final prediction.Experiments are conducted on multiple datasets, including CIFAR-10, CIFAR-100, and TinyImageNet. The results demonstrate that our model can significantly improve classification accuracy compared to previous approaches, achieving AUROC scores above 96\% across all test datasets. Our code is available at https://github.com/1134112149/MICW-ZIC.

Gelatinization of starch content in pet foods can be impacted by several factors including moisture, retention time, and ingredients used. Starch gelatinization has been associated with digestibility but isn’t well studied using ingredients common in non-traditional canine diets. The objective of this research was to examine the impacts of dietary ingredient profile (traditional vs non-traditional) and assess impacts to total starch content and starch gelatinization. Traditional diets (n = 10) utilizing meat-based ingredients including chicken, chicken by-product meal, meat and bone meal and plant-based ingredients including rice, barley, oats, and corn were examined in comparison with non-traditional diets (n = 10) utilizing meat-based ingredients including alligator, buffalo, venison, kangaroo, squid, quail, rabbit, rabbit and salmon along with plant-based ingredients including tapioca, peas, chickpeas, lentils, potato, and pumpkin. Representative samples were collected via grab sample technique (5 samples/diet) and were assessed for total starch content as well as percent starch gelatinization. Difference between ingredient type was assessed using a Students t-test in SAS 9.4. Significance was set at P < 0.05. Distribution of total starch content based on ingredient type (traditional vs non-traditional) revealed that mean total starch content was higher in traditional diets as compared to non-traditional diets (P <0.0001). Conversely, starch gelatinization was found to be higher in non-traditional diets (P < 0.0001). Total starch content and total gelatinized starch had a strong negative correlation (P < 0.01) in traditional diets, though no correlation was observed in non-traditional diets. This negative correlation indicates a decrease in total gelatinized starch associated with increased total starch content. These novel data reveal important differences between starch content and gelatinization and could impact manufacturing processes for ingredient types as well as feeding recommendations. Unpredicted variation between ingredient formulations could potentially lead to decreased digestibility and absorption and may result in nutrient deficiencies.

The thesis proposes a argument that a mass point moving in a horizontal circle on the Earth's surface can generate a radial centrifugal force of the Earth's center of mass, thus realizing an unconventional anti-gravity path. And through the theoretical analysis of the difference in the distribution of gravitational acceleration at different latitudes caused by the rotation of the earth, and the centrifugal force of a mass point moving in a horizontal circle on the earth's surface is analyzed, the validity of the argument is demonstrated. At the same time, the paper puts forward two possible basic principles, and makes theoretical analysis and demonstration. Conceive of the concrete experimental verification of principle two is proposed. Four technical schemes for antigravity ascent are proposed and basic analysis is made. On the premise of not fully considering the practical technical ability, it is theoretically proved that three of them have realistic operability and development potential. In theory, this paper opens up a different way of anti-gravity for human beings, which will bring new ideas and prospects for human beings to get rid of the bondage of gravity.

The rapid development of China’s textile industry leads to consumption and pollution of large volumes of water. Therefore, the textile industry has been the focus of water conservation and waste reduction in China’s 13th Five-Year Plan (2016–2020). The premise of sustainable development is to achieve decoupling of economic growth from water consumption and wastewater discharge. In this work, changes in blue water (water consumption), grey water (water pollutants), and water footprints of the textile industry from 2001 to 2014 were calculated. The relationship between water footprint and economic growth was then examined using the Tapio decoupling model. Furthermore, factors influencing water footprint were determined through logarithmic mean Divisia index (LMDI) method. Results show that the water footprint of China’s textile industry has strongly decoupled for five years (2002, 2006, 2008, 2011, and 2013) and weakly decoupled for four years (2002, 2007, 2009, and 2010). A decoupling trend occurred during 2001–2014, but a steady stage of decoupling has not been achieved yet. Based on the decomposition analysis, the total water footprint is mainly increased by production scale and inhibited by the technology. In addition, the effect of industrial structure adjustment is relatively weak.

The flowering period of rice significantly impacts variety adaptability and yield formation. Properly shortening the reproductive period of rice varieties can expand their ecological range without significant yield reduction. Targeted genome editing, like CRISPR/Cas9, is an ideal tool to fine-tune rice growth stages and boost yield synergistically. In this study, we developed a CRISPR/Cas9-mediated multiplex genome editing vector containing five genes related to three traits: Hd2, Ghd7, and DTH8 (flowering stage genes), along with the recessive rice blast resistance gene Pi21 and the aromatic gene BADH2. This vector was introduced into the high-quality Geng rice variety in Zhejiang province, Jiahe212 (JH212), resulting in 34 T0 plants with various effective mutations. Among the 17 mutant T1 lines, several displayed diverse flowering dates, but most exhibited undesirable agronomic traits. Notably, three homozygous mutant lines (JH-C15, JH-C18, and JH-C31) showed slightly earlier flowering dates without significant differences in yield-related traits compared to JH212. Through special Hyg and Cas marker selection of T2 plants, we identified 7, 6, and 2 fragrant glutinous plants devoid of transgenic components, respectively. These single plants will serve as sib lines of JH212 and potential resources for breeding applications, including maintenance lines for Xian-Geng interspecific three-line hybrid rice. In summary, our research offers practical insights and potential resources for targeted improvement of Geng rice varieties, and would also promote the Geng rice varieties expending from the southward to the northward.

Bat feces were reported in ancient Chinese medicine books to have the effect of reducing heat and improving eyesight, but the mechanism of vision improvement still needs further research. To this end, we used 2,2-diphenyl-1-picrylhydrazyl (DPPH) and liquid chromatography/tandem mass spectrometry (LC/MS/MS) to analyze the antioxidant capacity of and the types of vitamins in bat feces. We hoped to screen the pharmacological components of bat feces, and to explain the role that these components may play in the treatment of visual deterioration. Our results found that bat feces had a good antioxidant capacity and mainly contain vitamins B1 (thiamine), B2 (riboflavin), B3 (nicotinamide), B3 (nicotinic acid), and B5 (pantothenic acid). Although these vitamins may help maintain the health of the optic nerve and cornea, the vitamin content of bat feces is low, but the heavy metal content is high, as shown using inductively coupled plasma mass spectrometry (ICP-MS) analysis. Therefore, we suggest that the use of bat feces as traditional Chinese medicine to improve vision should be strictly restricted.

The optimization of photovoltaic (PV) systems is vital for enhancing efficiency and economic viability, especially under Partial Shading Conditions (PSCs). This study focuses on the development and comparison of traditional and advanced algorithms, including Perturb and Observe (P&O), Incremental Conductance (IC), Fuzzy Logic Control (FLC), Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and Artificial Neural Networks (ANN), for efficient Maximum Power Point Tracking (MPPT). Simulations conducted in MATLAB/SIMULINK evaluated these algorithms’ performance under various shading scenarios. The results indicate that while traditional methods like P&O and IC are effective under uniform conditions, advanced techniques, particularly ANN-based MPPT, exhibit superior efficiency and faster convergence under PSCs. The study concludes that integrating AI and ML into MPPT algorithms significantly enhances the reliability and efficiency of PV systems, paving the way for broader adoption of solar energy technologies in diverse environmental conditions. These findings contribute to advancing renewable energy technology and support the green energy transition.

Solar pasteurization, as a means of treating milk in remote rural areas without electrical power, is based on using solar energy to thermally inactivate pathogenic microorganisms at temperatures below the boiling point to maintain or improve milk quality. A solar milk pasteurizer of flat‐plate water‐heating glass acting as the solar collector, connected to a stainless-steel cylindrical milk vat, was constructed and evaluated in Navrongo, in the tropical savanna zone of Ghana, West Africa. The novel approach taken in this project is to integrate the solar collection and heat exchanger into a single unit using materials and fabrication techniques readily available in the developing world. The vat comprises a 1.5 mm thick stainless steel cylindrical tank, a 2.2 cm wide hot water jacket, and an outer layer of 5.0 mm thick aluminium foil insulation. Hot water produced by the collector was used for pasteurizing milk. The optimum quantity of milk that this device could pasteurize under the study conditions was 8 L. The device could pasteurize raw milk to a maximum temperature of 74 °C at 14:00 h GMT. The ambient temperature during the pasteurization ranged from 30 °C to 43 °C. The microbial analysis before and after the pasteurization was done, and the result shows that the total bacteria count before pasteurization was 6.6x10⁶ log CFU/mL per unit volume, and the total bacteria count after pasteurization was 1.0x10² log CFU/mL. By acceptable standards, the total bacteria count after pasteurization is within a safe range for consumption, and coliform counts were negative. The solar milk pasteurization system is cost-effective. Hence, it is appropriate for milk producers in arid pastoral areas without electricity to adopt the solar pasteurizer as an alternative to firewood as a fuel source for milk pasteurization. This approach can potentially create a cottage industry that may also play a crucial role as a water treatment technology that will improve the health of rural populations.

Knowledge is not merely the characteristic of certain pieces of information being deemed true and justifiable, but rather a process. Within an axiomatic system, the set of information that reaches the status of knowledge forms a universe that provides an interpretation of the reality one aims to describe. Language exchanges information through communication and the process of justification (exchange of information), if it consistently justifies a particular piece of information, it elevates it to knowledge. In this paper, we provide a framework for knowledge within an axiomatic model where we define the fundamental elements of communication and grammar. As a result, the structure dictates that knowledge, to be considered as such, must be investigateable by anyone who utilizes it in its entirety, both in form and content. Ultimately, the development of knowledge processing, referred to as \emph{knowmatics}, represents a clear evolution of the computer science that processes information (subset of knowledge).

The Eastern Kunlun Fault Zone (EKFZ) is located in a seismic blank area. This study investigates the hydrogeochemical characteristics of varied-temperature springs in the EKFZ to understand the relationship between fractures, fluids and earthquakes. It aids in establishing their induction mechanisms and assists in monitoring the earthquake precursors of the EKFZ. In this study, the main elements, trace elements, hydrogen and oxygen isotopes and strontium isotopes of 23 varied-temperature springs in the EKFZ were analyzed. The results reveal the source of groundwater recharge, hydrochemical types, mineral saturation, and for a select few springs indicative of geothermal activity, collected geothermal reservoir temperatures. The study also explores the relationship between earthquakes and springs, identifying sensitive chemical indicators. Based on continuous monitoring at Maqu Station, we found that before the occurrence of the Maerkang Ms 6.0 earthquake, the concentration of Cl− rose by 2.9 times, and 46 days before the Gande earthquake (Ms = 4.7), the concentrations of Na+ and SO42− significantly increased. Finally, a conceptual model of the hydrogeochemistry of springs is presented to describe the groundwater circulation in the study area. This research is of great significance for the analysis of fluid geochemistry in this area and for assessing the seismic hazards of the EKFZ.

Ecological city is currently a hot topic in urban planning research. How to balance urban development and ecological environment protection has become a focus and difficulty of research. Traditional urban planning is mainly based on experience, but its disadvantage is that it is easy to overlook key geographical factors such as terrain and vegetation, and cannot fully reflect the actual spatial characteristics of the city. Therefore, this article applied the combination of urban and rural planning and environmental remote sensing technology to urban planning and design, obtaining a massive amount of data. Through the actual analysis of Langfang, this paper monitored the vegetation, water, land and landscape data of Langfang through environmental remote sensing technology. By using these data to identify existing problems, this article proposed reasonable planning for the problems, in order to achieve the effective goal of ecological city planning and design. It provided a more accurate analysis of urban structure, including urban planning and urban ecological landscape planning. Among them, ecological landscape planning includes renovating ecological embankments, protecting ecological green belts around the city, and creating characteristic landscape areas, thereby providing more scientific basis for urban planning and design, making urban planning and design more reasonable and scientific. This paper investigated the planned Langfang City, and the average errors of vegetation, soil, water quality, and landscape data obtained from traditional field surveys were 0.35, 0.33, 0.34, and 0.31, respectively. The average errors of remote sensing technology in obtaining vegetation, soil, water quality, and landscape data were 0.16, 0.15, 0.17, and 0.15, respectively. The total suspended solids content of waterscape 1-10 before planning was 5.51mg/L and 7.41mg/L, respectively. After planning, the total suspended solids content of waterscape 1-10 was 3.60mg/L and 4.47mg/L, respectively. From this, it can be seen that the error in obtaining data after planning has decreased, and the total suspended solids content has also decreased.

The aim of present study was to assess the microbiological quality of eight traditional meat products (black pudding, white brown, baked pate, roasted pork, raw smoked ham, smoked steamed sausage, raw smoked bacon, raw smoked loin) manufactured by polish farmers in the Lodz region. A total of 37 samples were collected and analyzed twice: within 24 h after purchase from manufactures and after 7 days of storage. Mesophiles, lactic acid bacteria (LAB), Enterobacteriaceae, presence of Listeria monocytogenes and Salmonella spp. were determined. The counts of isolated microorganisms varied depending on the product type and manufacturer. Mesophiles and lactic acid bacteria constituted the main microflora in the tested meat products. Enterobacteriace value was below detection limit in the most samples. The highest mean bacteria counts (mesophiles, LAB and Enterobacteriace) during storage were estimated in black pudding, followed by smoked steamed sausage and white brown. In opposite, the lowest mean bacteria levels were in baked pate and roasted pork. The obtained results showed significant influence of storage time on the mean number of mesophiles and LAB in the meat samples. Negative results for Salmonella spp. and Listeria monocytogenes were observed during 7 days of storage, indicating the safety of the studied meat products.

The fish found in the Tibetan Plateau and the Himalayas are referred to as schizothoracids, and they have evolved unique biological adaptations to high elevations. These adaptations include changes in the shape of the mouth, size, and length of gill rakers according to the prey size, sucking attachment organs to cope with fast currents, and genetic differences due to hypoxia and UV light. Nevertheless, there are recent threats like destruction of natural habitats, pollution, overfishing, and global warming that have posed serious conservation issues for numerous species. Conservation measures have been initiated even though well-articulated management techniques or other protective measures are absent from several protected areas for freshwater fisheries located in India. To resolve these issues, Schizothoracid population rescue, rehabilitation and restocking in safe water bodies should be implemented. For achieving their sustainability, scientists, conservationists, and legislators need to coordinate with each other.

Teaching, learning, research, and development are all supported in a favorable atmosphere at the University of Lagos (UNILAG). The wetland systems made up of freshwater that are extremely biodiverse makeup about 10% of the total land area. Flooding, deforestation, excessive sedimentation, blockages to streams, abuse, overuse, and invasion of wetlands are some of the recent environmental challenges that the university faced. Urban heat island impacts, contaminants in the air, wetland degradation, social issues including stress, and a detachment from nature are all major concerns. With the green corridor behind the faculties of law and management science serving as a model supported by sustainability as a true foundation for UNILAG's campus landscapes, this article investigates the sustainable restoration of wetlands within the boundaries of the university. The use of qualitative techniques was employed, utilizing the Strengths-Weaknesses-Opportunities-Threats (SWOT) analysis. Additionally, a geospatial mapping of the wetlands and tributaries that connect the Lagos Lagoon was conducted to evaluate the intrinsic ecological goods and services through a series of analytical steps, beginning with an analysis of the land area, current biodiversity, hydrology, and its potential through landscape mapping and analysis. The biophysical setup, landscape features, climatic data analysis, the ecological goods and services derived from the wetlands, and the records of flooding and rainfall are all included in the geospatial evaluation and analysis. The educational institution's site planning for the remediation of its wetlands takes into account the actual evolution of the area's drainage network. Throughout the wetlands, low carbon emissions are generated by off-road bike lanes, trails for walking, and raised hardwood platforms. Combining native plant species and park furniture, the academic landscape atmosphere will be improved in each of the wetlands and aesthetic values. the country's "University of First Choice" and its biophysically restored wetlands will help illuminate the landscape while the green corridors have an innate ability to attract human affinity within the academic community. All of these elements are implemented following an environmentally friendly landscape planning, management, design model, and enforcement process.

In the past years, consumers have increased their interest to buy healthier food products, rejecting those products with more additives and giving preference to the fresh ones. Moreover, the current environmental situation has made society more aware of the importance of reducing the production of plastic and food waste. In this way and considering the Food Industry need of reducing the food spoilage along the food chain, edible coating has been considered as an eco-friendly food packaging that can replace traditional plastic packaging providing an improvement in the product’s shelf-life. Edible coating are thin layers applied straightaway on the food materials’ surface, that are made of biopolymers that usually incorporate other elements, such as nanoparticles or essential oils, to improve their physicochemical properties. These materials must provide a barrier that can prevent the pass of water vapor and other gasses, the microbial growth, the moisture loss, and the oxidation, so the shelf-life could be extended. The aim of this review was to compile the current data available to give a global vision of the formulation process and the different ways to improve the characteristics of the coats applied in both fruits and vegetables. In this way, the suitability of compounds in by-products produced during the Food Industry chain were also considered for the edible coating production.

Background: Microscopic examination of acid-fast stained sputum smears for detecting mycobacterial acid-fast bacilli (AFB) remains the most economical and readily available method for laboratory diagnosis of tuberculosis (TB). However, this conventional approach has limitations, including low sensitivity and labor-intensive procedures. Methods: An automated microscopy system incorporating artificial intelligence (AI) and machine learning for AFB identification was evaluated. The study was conducted at an Infectious Disease Hospital in Jiangsu Province, China, utilizing an intelligent microscope system (TB-Scan, Wellgen Medical, Kaohsiung). A total of 1,000 sputum smears were included in the analysis, with the system capturing digital microscopic images and employing an image recognition model to automatically identify and classify AFB. Referee technicians provided the gold standard for discrepant results. Results: The automated system demonstrated an overall accuracy of 95.00% (950/1,000), sensitivity of 91.24% (177/194), and specificity of 95.91% (773/806). Notably, the system identified 21 smears as positive that were previously reported as negative, with referee technicians confirming 17 of these as true positive and retracting the test results accordingly. Recalculating the performance, the accuracy increased to 96.70% (967/1,000), sensitivity to 91.94% (194/211), and specificity to 97.97% (773/789), with a false negative rate of 8.06% (17/211) and a false positive rate of 2.03% (16/789). Conclusions: The incorporation of AI and machine learning into an automated microscopy system demonstrated the potential to enhance the sensitivity and efficiency of AFB detection in sputum smears compared to conventional manual microscopy. This approach holds promise for widespread application in TB diagnostics and potentially other fields requiring labor-intensive microscopic examination.

Thyroid surgery rates have tripled over the past 3 decades making it one of the most frequently performed procedures within general surgery. They are associated with the possibility of serious postoperative complications having a significant impact on the patient's quality of life. The recurrent laryngeal nerve (RLN) palsy and the external branch of the superior laryngeal nerve (EBSLN) palsy are, next to hypoparathyroidism and bleeding after operation, one of the most common complications. The introduction of neuromonitoring into thyroid surgery, enabling both confirmation of anatomical integrity and assessment of laryngeal nerve function, was a milestone that has begun a new era in thyroid surgery. The International Neural Monitoring Study Group made standardization of the technique of RLN and EBSLN monitoring during thyroid and parathyroid surgery which increased prevalence of neural monitoring during thyroidectomy. Current status of IONM and benefits of its use have been presented in this publication.

Microcystis-dominated cyanobacterial blooms (MCBs) frequently occur in freshwaters worldwide due to massive Microcystis colonies formation and severely threaten human and ecosystems health. Quorum sensing (QS) is a direct cause of Microcystis colonies formation, possibly regulating MCBs occur and Microcystis population behaviors [1, 2]. Many novel findings in fundamental knowledge of Microcystis QS phenomenon and the signaling molecules have been documented. However, little effort has devoted to comprehensively summarize and discuss research progresses and exploration direction on QS signaling molecules-mediated QS system in Microcystis. This article summarizes action process of N-acyl homoserine lactones (AHLs) as major signaling molecules in Microcystis and discussed the detailed roles of AHLs-mediated QS system in cells aggregation, colonies formation, ecological adaptability and competitive advantage. And the progress was summarized in research on the QS mechanism in Microcystis. Compared to other QS system, the LuxI/LuxR-type QS system is more likely to found in Microcystis. Also, we introduced Quorum quenching (QQ) — QS blocking process in Microcystis, with emphasizing on the potential of QS inhibitors in MCBs control. Finally, in response to the research deficiencies and gaps in Microcystis QS and QQ, we proposed several future research directions in these fields. This review may deepen the understanding of Microcystis QS knowledge and provide theoretical guidance for the development of strategies to monitor, control and harness MCBs.

Keywords: contamination; bioaccumulation; fish and mollusks tissue; ecosystem components; heavy metals; organochlorine pesticides; polychlorinated biphenyls; polycyclic aromatic hydrocarbons.

Capulus danieli, a distinct member of Capulidae, exhibits a unique ecological behaviour by attaching and drilling onto the pecten, distinguishing itself from other gastropods. Herein, we present the first characterisation of the mitochondrial genome of Capulus danieli. The results showed that the genome spans 15600 base pairs, with an A + T content of 71.12% and a G + C content of 28.88%. Within this genetic makeup, we identified 13 protein-coding genes, 22 transfer RNA genes, and 2 ribosomal RNA genes. Phylogenetic analysis revealed a close evolutionary relationship between Capulus danieli and members of the Ficoidea superfamily. The divergence time estimation suggested that Capulus danieli diverged approximately 52.29 million years ago. These findings significantly contribute to our understanding of the evolutionary history and genetic architecture of Capulus danieli, shedding light on its unique ecological adaptations to the marine environment.

Responsible, efficient, and environmentally conscious energy consumption practices are increasingly essential for ensuring the reliability of the modern electricity grid. This study focuses on leveraging time series analysis to improve forecasting accuracy, crucial for various application domains where real-world time series data often exhibit complex, non-linear patterns. Our approach advocates for utilizing Long Short Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) models for precise time series forecasting. To ensure a fair evaluation, we compare the performance of our proposed approach with traditional neural networks, time-series forecasting methods, and conventional decline curves. Additionally, individual models based on LSTM, Bi-LSTM, and other machine learning methods are implemented for a comprehensive assessment. Experimental results consistently demonstrate that our proposed model outperforms all benchmarking methods in terms of mean absolute error (MAE) across most datasets. Addressing the imbalance between activations by consumer and prosumer groups, our predictions show superior performance compared to several traditional forecasting methods, such as the Autoregressive Integrated Moving Average model (ARIMA) and Seasonal Autoregressive Integrated Moving Average model (SARIMA). Specifically, the root mean square error (RMSE) of Bi-LSTM is 5.35%, 46.08%, and 50.6% lower than LSTM, ARIMA, and SARIMA, respectively, on the May test data.

Traditional lime-based materials have considerable potential for use as components of green materials for plastering and finishing building surfaces in both new construction and historic heritage conservation. The paper presents the preliminary results of a research activity aimed at developing pre-packaged products based on Traditional Lime Putty (TLP) by partially replacing natural aggregates with Stone Waste Sludge (SWS), with a low rate of recovery from Apricena limestone production district in Apulia. The mineralogical and chemical analysis carried out by means XRD (X-Ray Diffraction), TG-DTA (Thermo Gravimetry-Differential Thermal Analysis) and hydrochloric acid attack test showed that the SWS consisted of 98.4 % CaCO3 by mass. The particle sizes measured by laser diffraction technique are below 22.5 μm for the 92% by mass of the sample. The high fineness of the stone waste was confirmed by the Blaine specific surface method equal to 9273.79 cm2/gr. The behavior of three fresh mixtures for prepacked coarse plaster, fine plaster, and finishing plaster with 12.90%, 17.94%, and 18.90 by mass of SWS, respectively, was evaluated by spreading test and applicability tests on a perforated ceramic slab. The finishing plaster has the highest consistency value of 235 mm, while the fine plaster and the coarse plaster have values of 205 mm and 155 mm, respectively. The coarse plaster is suitable for the use as base plaster (arriccio) or second layer rendering (tonachino) up to a thickness of approximately 1 cm. Both the fine plaster and finishing plasters can be used for the surfaces finishing with the application of layers of a few millimeters thick.

From grape cultivation to ripening and harvest timing to processing, each step can be a critical point when it comes to wine quality and phenolic composition. In this study, the influence of winemaking technology on resveratrol and quercetin content, as well as other polyphenolic compounds, was investigated. Resveratrol is a non-flavonoid polyphenolic stilbene synthesized by grape skin when damaged by infectious diseases or ionizing radiation. Quercetin is a phenol found in grape skins and stems and is produced to protect grapes from UV light damage. Trans-resveratrol and quercetin are known to act as antioxidants, reduce the risk of atherosclerosis and type 2 diabetes, inhibit the growth of cancer cells, and prevent the release of allergic and inflammatory molecules. However, the question was whether red wine could be enriched with these phenols using an innovative winemaking technology. The innovative idea was to completely replace the cold maceration process with maceration with the addition of wild yeast (Torulaspora delbrueckii, Td). Maceration with the addition of wild yeast (Td) offers the following advantages over traditional cold maceration: (1) higher concentrations of trans-resveratrol (>35-40%) and quercetin (>35-40%) in the final wine, (2) the new wine has a higher potential for human health, (3) the wine has a better aroma and stability due to the higher mannoprotein content, and (4) the better energy efficiency in the production process. The study of stability during storage and aging also included derivatives of benzoic acid and hydroxycinnamic acid, piceid, catechin, naringenin, rutin, kaempherol, hesperetin, and anthocyanins. The study found that younger wines had higher phenolic content, while storage of the wine resulted in a decrease in total phenolic content, especially monomeric stilbenes and quercetin.

Leishmaniasis is the third most common vector-borne disease and a very important protozoan infection. Cutaneous leishmaniasis is one of the most common types of leishmaniasis infectious diseases with up to 2 million occurrences of new cases each year worldwide. A dynamic transmission multivariate time series model was applied to the data to account for overdispersion and evaluate the effects of three environmental layers as well as seasonality in the data. Furthermore, ecological niche modeling was used to investigate the geographical suitable conditions for cutaneous leishmaniasis using temperature, precipitation and altitude as environmental layers, together with the leishmaniasis presence data. A retrospective analysis of the cutaneous leishmaniasis spatial data in Afghanistan between 2003 and 2009 indicates a steady increase from 2003 to 2007, a small decrease in 2008, then another increase in 2009. An upward trend and regularly repeating patterns of highs and lows was observed related to the months of the year which suggests seasonality effect in the data. Two peaks were observed in the disease occurrence-- January to March and September to December -- which coincide with the cold period. Ecological niche modelling indicates that precipitation has the greatest contribution to the potential distribution of leishmaniasis.

Knowledge graphs (KGs) play a crucial role in many applications, such as question answering, but incompleteness is an urgent issue for their broad application. Much research in knowledge graph completion (KGC) has been performed to resolve this issue. The methods of KGC can be classified into two major categories: rule-based reasoning and embedding-based reasoning. The former has high accuracy and good interpretability, but a major challenge is to obtain effective rules on large-scale KGs. The latter has good efficiency and scalability, but it relies heavily on data richness and cannot fully use domain knowledge in the form of logical rules. We propose a novel method that injects rules and learns representations iteratively to take full advantage of rules and embeddings. Specifically, we model the conclusions of rule groundings as 0-1 variables and use a rule confidence regularizer to remove the uncertainty of the conclusions. The proposed approach has the following advantages: 1) It combines the benefits of both rules and knowledge graph embeddings (KGEs) and achieves a good balance between efficiency and scalability. 2) It uses an iterative method to continuously improve KGEs and remove incorrect rule conclusions. Evaluations on two public datasets show that our method outperforms the current state-of-the-art methods, improving performance by 2.7% and 4.3% in mean reciprocal rank (MRR).

The silver fir (Abies alba Mill.) is among the most valuable conifers in Europe for ecological and economic reasons. In the course of history, primarily in the 20th century, its share in stands has been declining due to ill-suited management practices, especially clear-cut management, air pollution, and wildlife-induced damage. Based on recent knowledge of fir ecology and population dynamics, small-scale shelterwood and selection management have been introduced in fir stands, which have also stabilized them. Fir is an essential species for maintaining high stability and biodiversity, especially on planosols and in waterlogged habitats. Due to its shade tolerance and environmental flexibility, particularly at higher altitudes, it can coexist very well with many tree species in mixtures, which can increase the productive potential of stands. It can form stands of heterogeneous structure, ranging from single to multi-layer to selection. For its successful natural regeneration, it is essential to reduce cloven-hoofed game and thus prevent bud browsing damage. On the other hand, fir is a species relatively resistant to bark stripping and the spread of secondary rot compared to Norway spruce (Picea abies [L.] Karst.). During global climate change, fir is expected to shift to higher elevations with sufficient precipitation, while in the southern part of its natural range or at lower elevations, outside water-influenced habitats, it is likely to decline. This paper essentially reviews the description and distribution of the species, its ecological requirements, threats and diseases, habitat and stand conditions, and close-to-nature forest management practices with emphasis on ongoing climate change.

The assessment of existing bridges constitutes a serious challenge for the researchers since their safety evaluation is a complex task due to several sources of uncertainty affecting the parameters identified in the knowledge process. In particular, a high level of uncertainties may arise in the case of prestressing systems requiring special investigation strategies and experimental technologies. The paper aims to investigate how the knowledge uncertainties influence the safety assessment of existing post-tensioned bridges. The semi-probabilistic approach is used, and uncertainties related to the knowledge process are described considering the number of tests, the confidence level assumed for the estimation of the problem parameters, and the measurement errors of in-situ tests. Results concerning a typical post-tensioned bridge are reported and the variability of the outcomes is discussed and compared with a reference case of “perfect knowledge”. The main results of the study confirm the general robustness of the semi-probabilistic Eurocodes safety format and provide an overview of dispersion due to different choices about the number of tests and confidence level. The measurement errors may lead to significant under-estimation of the computed structural capacity with respect to the reference one.

Driven by the importance of early detection for improving patient outcomes, this study investigates the effectiveness of various machine learning algorithms in predicting lung cancer based on clinical data. Lung cancer remains a significant global health concern, and this research seeks to contribute to advancements in early diagnosis through the application of machine learning techniques. The dataset consists of 385 training and 97 test samples, with a k-fold cross-validation approach (k=5) utilized to mitigate overfitting. Additionally, the ADASYN oversampling technique is employed to address class imbalance. Our analysis includes traditional algorithms such as Logistic Regression and Decision Trees, as well as ensemble methods like XGBoost, LGBM, and AdaBoost. Furthermore, we explore the effectiveness of deep learning models, particularly Deep Neural Networks (DNN). Results demonstrate promising accuracy rates across all models, with some outperforming others in terms of precision, recall, and F1-score. Our findings reveal that LGBM achieves the highest accuracy among all models, with an accuracy rate of 96.91%. It also exhibits superior performance in terms of precision, recall, and F1-score, particularly in classifying both positive and negative instances of lung cancer.