Mastitis-causing bacteria can establish persistent infections in the mammary gland of commercially important dairy animals despite the presence of strong specific humoral and cellular immune mechanisms. The aim of this study was to encapsulate vitamin D3 by complex coacervation with sulfur-saturated bovine lactoferrin-alginate using microbial transglutaminase-catalyzed crosslinking and investigate the oral administration of encapsulated vitamin D3 in blood serum biomarkers of the immune response in late-lactating dairy goats. Dairy goats (n = 18) in late lactation were randomly assigned to three experimental groups (n = 6), after balancing weight. Dairy goats were orally administered with 0.35 mg vitamin D3/day in the unencapsulated form and 0.35 mg vitamin D3/day in the encapsulated powder form. Another group received the basal diet. The experimental period lasted 6 weeks. The blood serum concentrations of 25-hydroxyvitamin D3 [25-(OH)-D3], lactoferrin, immunoglobulin A (IgA), and interferon-gamma (INF-γ) were measured. The delivery of vitamin D3 to dairy goats resulted in a marked increase of 25-(OH)-D3 concentration in serum while the serum level of lactoferrin also increased. Alternatively, the serum levels of IgA and the immunomodulatory cytokine INF-γ were elevated following supplementation with the encapsulated vitamin D3. Overall, the capsules in the powder form used in this study kept vitamin D3 highly bioavailable.

Rhododendron chrysanthum Pall., live in Changbai Mountain being exposed to chilling temperature, high light intensities and water scarcity condition. To adapt to the harsh environment, the cold resistance mechanisms of R. chrysanthum have been successfully evolved in the long-term adaptive process. In our present work, the methods of proteomics combined with physiological and biochemical analyses were used to investigate the effects of cold stress on the photosynthesis and antioxidant system of Rhododendron chrysanthum Pall. and the molecular mechanisms involved in cold resistance of plants. A total of 153 photosynthesis related proteins were identified in present work, of which 7 proteins including Rubisco large subunit (rbcL) were up-regulated in experiment group (EG) compared with control group (CG). Simultaneously, four chlorophyll fluorescence parameters were measured in present study. The results showed that the maximum photochemical efficiency of photosystem II (Fv/Fm), actual quantum yield of PSII (Y(II)) and photochemical quenching (qP) were significantly higher in EG, whereas the non-photochemical quenching (NPQ) was notably decreased. Cold stress could lead to a significant reduction in electron transport rate (ETR) accompanied with an increase in excitation pressure (1-qP). The abundance of PetE which involved in the plants photosynthetic electron transfer was also significantly influenced by cold stress. Moreover, the up-regulated expressions and higher levels of enzymatic activities of Glutathione peroxidase (GPX) and Ascorbate peroxidases (APXs) were detected in EG. All these changes which can help plants to survive in low temperature are considered as the crucial parts of cold tolerance mechanisms. These results revealed that photosynthesis and redox adjustment play significant roles in the defense of cold-induced damage.

The study addresses the urgent environmental issue of climate change, focusing specifically on the role of local food, which is produced and consumed locally, helps reduce carbon emissions by eliminating the need for long-distance transportation. Through quantitative analysis, this study elucidates the potential benefits in environmental impact achieved through the consumption of local food: the significant reduction in carbon emissions. Specifically, the consumption of local food is found to yield an annual decrease of 2,421,296 tCO2 emissions, representing 2.5% of South Korea’s total greenhouse gas emissions from the transportation sector. This reduction further translates into an estimated economic value of USD 54.23 million (KRW 70.5 billion). This study underscores the potential of local food consumption as a tangible strategy to overcome environmental problems. Moreover, its academic contribution lies in its comprehensive analysis of the economic and empirical impacts of local food consumption on environment. Moving forward, we propose policies such as supporting local food distribution networks, providing public education on local food, fostering the local food industry, and providing incentives to revitalize local food consumption.

This study presents the synthesis and electrochemical evaluation of nitrogen-doped vanadium oxide (N-V2O3/C) as a cathode material for aqueous zinc-ion batteries (AZIBs), using a hydrothermal method. Compared to undoped V2O3/C, N-V2O3/C exhibits enhanced electrical conductivity, capacity, and electrochemical kinetics, attributed to the incorporation of pyridinic and pyrrolic nitrogen. Initial charge-discharge cycles indicate phase transitions to amorphous vanadium oxides, enhancing conductivity. N-V2O3/C shows a high specific capacity of 168.4 mAh g−1 at 10 A g−1 and remarkable reversibility, highlighted by the transient existence of intermediate species during cycling. Optimal electrochemical performance is achieved with a vanadium to nitrogen molar ratio of 2:3, indicating the significant impact of nitrogen doping concentration on the material’s efficiency. This work underscores the potential of N-V2O3/C as a superior cathode material for AZIBs.

Using density functional theory (DFT), the density of states of NH4V3O8 (NVO) was analyzed pre and post-oxygen defects (Od) formation. The findings revealed a reduced bandgap in NVO after Od introduction, emphasizing the role of Od in enhancing conductivity of the material, thus improving its electrochemical attributes. Through the water bath method, both NVO and its oxygen-deficient counterpart, (NH4)2V10O25⸱8H2O (NVOd), were synthesized as potential cathode materials for aqueous zinc-ion batteries (AZIBs). Experimental outcomes resonated with DFT predictions, highlighting the beneficial role of oxygen defects in boosting electrical conductivity. Notably, the refined material displayed a remarkable capacity of 479.3 mAh g-1 at 0.1 A g-1, underscoring its promise for advanced energy storage solutions.

This paper presents a review on numerical and experimental methods to predict fracture of ad-vanced high strength steels (AHSS). AHSS are widely processed through sheet metal forming processes. Although being an excellent choice when lightweight, high strength and ductility are critical factors, their multi-phase microstructure accentuates forming challenges. To accurately model forming behavior, necking based failure criteria as well as direct fracture models require improvements. As a necking based failure model, the conventional forming limit diagram/curve (FLD/FLC) presents limitations such as in estimating direct fracture (surface cracks, edge cracks, shear cracks), as well as deformation histories under nonlinear strain paths. Thus, significant re-search efforts are being made towards the needs of advanced fracture constitutive models capa-ble of predicting fracture scenarios without necking, more frequently observed in the realm of AHSS. Scientific community research is divided into several directions aiming at improving the forming and fracture behavior accuracy of parts subjected to sheet metal forming operations. In this review paper, a comprehensive overview on ductile fracture modeling is presented. Firstly, the FLD/FLC limitations modeling fracture behavior in sheet metal forming operations are stud-ied. Followed by the recent trends in constitutive material modeling. Afterwards, material char-acterization methods advancements to cover a broad range of stress states are comprised. Final-ly, damage and fracture models predicting failure in AHSS are investigated. This review paper supplies relevant information on the current issues the sheet metal forming community is chal-lenged with due to the trend towards AHSS employment in the automotive industry.

For the last two decades, an increasing demand for low-cost adsorbents for drinking water treatment has been challenged in the rural areas of developing countries that have problems with arsenic (As) contamination. In this work, spent bleaching earth (SBE), a residual material that is formed during the process of refining vegetable oil, has been utilized to prepare the new magnetic adsorbent Fe-SBE/C by modifying its surface area through calcination and co-precipitation with iron. The experimental results demonstrate that Fe-SBE/C exhibits a substantial adsorption capacity towards both arsenite (As(III)) and arsenate (As(V)). Optimal pH conditions were found to be pH 10 for As(III) and pH 3 for As(V), resulting in adsorption percentages of 76.7% and 94.1%, respectively. Other experimental parameters such as calcination conditions, adsorbent dosage, iron-loading ratio, and the presence of co-existing ions were thoroughly investigated. The kinetic study revealed that the adsorption processes follow the Elovich and intraparticle diffusion models, suggesting a combined adsorption mechanism of boundary layer control and chemisorption. Furthermore, the isotherm followed the Langmuir model with a maximum adsorption capacity of 202.61 μg g-1 for As(III) and 187.61 μg g-1 for As(V). Through thermodynamic analysis and reinforcement by activation energy data, this research validates that the observed adsorption process is indicative through chemisorption. The regeneration study revealed the potential for multiple cycles of arsenic removal, highlighting the practical applicability of Fe-SBE/C in environmental contexts. In conclusion, the findings of this study underscore the efficacy of Fe-SBE/C in arsenic removal and present it as a promising and environmentally friendly solution for mitigating arsenic pollution.

The genetic causes of the differentiated, highly treatable and mostly non-fatal papillary thyroid cancer (PTC) are not yet fully known. The widely accepted PTC etiology blames altered sequence or/and expression level of certain biomarker genes. However, our previous studies found that the PTC biomarkers played significantly lower roles than the personalized PTC Gene Master Regulators. Our publicly accessible gene expression data from the cancer nodule and the surrounding normal tissue of surgically removed PTC tumor were re-analyzed to determine the transcriptomic effects on the genomic fabrics responsible for the DNA replication, repair and transcription. Tumor results were compared to the gene expression profiles of the papillary (BCPAP) and the anaplastic (8505C) human thyroid cancer cell lines. The analyses were carried out from the Genomic Fabric Paradigm (GFP) perspective that provides the most theoretically possible comprehensive characterization of the transcriptome and its alterations in disease. The study indicated that, in addition to regulating numerous genes, changes in the homeostatic control of transcript abundances and remodeling of the gene networks had major contributions to the PTC occurrence, resilience and proliferation. Therefore, the molecular mechanisms responsible for gene expression control and inter-coordination should also be considered when designing personalized anti-cancer gene therapies.

The Circular Bioeconomy (CBE) combines the concepts of bioeconomy and circular economy. As an alternative concept to the current fossil-based, linear economy, it de-scribes an economy based on the efficient valorization of biomass. It is regional in nature and aims to improve sustainability. An analysis of the transition process, by identifying its success criteria and assessing its impacts through the modelling of technology-specific scenarios is necessary to ensure that CBE concepts are sustainable. However, a compre-hensive consideration of regional influences on both is lacking. Based on extensive literature research and an expert survey, we (i) present a compre-hensive catalog of CBE success criteria and discuss their region-specific character and (ii) develop a methodology based on evaluation matrices that enable to match CBE technolo-gies with regions. The matrices support the evaluation of technological and regional characteristics influencing the successful CBE implementation. The results show that the success criteria "biomass resources", "technological", and "social" are perceived as highly important, and that most of the success criteria are both region- and technology-specific, highlighting the relevance of developing matrices to match them. We describe such matrices indicatively for the two broadest and most im-portant success criteria clusters “social acceptance” and “biomass supply chain”. With this, we substantiate the regional nature of CBE and raise the awareness on the importance of considering regional conditions in CBE transition processes. Furthermore, we provide practical guidance on how regional conditions can be reflected in the selection of technologies, e.g. in regional CBE technology scenarios.

This paper aims to propose a new approach of territorial competitiveness assessment revisited from the resource-based view, as the combination of location-specific resources and capabilities can improve the territorial socio-economic development. A territorial competitiveness index is calculated in order to assess the potential of renewable energy sources to improve the sustainable development in islands. Different sources of information and methodologies have been employed to measure the variables included in the model, thus ensuring a rigorous process in the index calculation. In order to quantify the basic resources, for example, a methodology based on a multicriteria analysis (MCA) with geographic information system (GIS) is suggested, with the objective of obtaining an indicator called index of available territorial resources. This index synthesizes the map information through a numerical value that allows integrating the territorial resource with other indicators of the model. The results of the study show that capability development is a key factor to better exploit the territorial resource endowment in order to achieve a competitive advantage.

As the natural resources are getting exhausted, the concept of sustainable development of region has received increasing attentions, especially for resource-dependent cities. In this paper an innovative method that emergy analysis and IPAT (Human Impact Population Affluence Technology) model were combined in order to analyze the quantitative relationship of economic growth and energy consumption and further to evaluate its overall sustainability level. Taiyuan, a traditional, resource-dependent city in China, is selected as the case study region. The main results show that total emergy of Taiyuan increased from 9.023× 1023 sej in 2007 to 9.116× 1023sej in 2014, with 38% reduction on non-renewable emergy and 125% growth on imported emergy. Regional emergy money ratio (RMB) was reduced by 48% from 5.31× 1013sej/$ in 2007 to 2.74× 1013sej/$ in 2014, indicating that the increasing speed of consuming resources and energy was faster than the increase of GDP, and that Taiyuan’s money purchasing power declined. The lower emergy sustainability index (ESI) indicates that Taiyuan was explored and produced large quantities of mineral resources, which puts more stress on the environment as a consequence, and that this is not sustainable in the long run. The IPAT analysis demonstrates that Taiyuan sticks to the efforts of energy conservation and environmental protection, in order to promote regional sustainable development, it is necessary to take an integrated effort. Policy insights suggest that resourceful regions should improve include energy and resource efficiency, optimizing energy and resourceful structure.

This study reports on the development of bio-based hydrophobic coatings for packaging papers through deposition of polyhydroxybutyrate (PHB) particles in combination with nanofibrillated cellulose (NFC) and plant wax. In a first approach, PHB particles in micrometer range (PHB-MP) were prepared through a phase-separation technique providing internally nanosized structures. The particles were transferred as a coating by dip-coating filter papers in the particle suspension, followed by sizing with a carnauba wax solution. This approach allowed partial to almost full surface coverage of PHB-MP over the paper surface resulting in static water contact angles of 105° to 122° and 129° to 144° after additional wax coating. In a second approach, PHB particles with submicron sizes (PHB-SP) were synthesized by an oil-in-emulsion (o/w) solvent evaporation method, and mixed in aqueous suspensions with 0 to 7 wt% NFC. After dip-coating filter papers in PHB-SP/NFC suspensions and sizing with a carnauba wax solution, static water contact angles of 112° to 152° were obtained. The intrinsic properties of the particles were analyzed by scanning electron microscopy, thermal analysis and infrared spectroscopy, indicating higher crystallinity for PHB-SP than PHB-MP. The chemical interactions between the more amorphous PHB-MP particles and paper fibers were identified as an esterification reaction, while the morphology of the NFC fibrillar network was playing a key role as binding agent in the retention of more crystalline PHB-SP at the paper surface, hence contributing to higher hydrophobicity.

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.

Gene regulation is vital for cellular function and homeostasis, involving diverse mechanisms controlling specific gene products and contributing to tissue-specific gene expression. Dysregulation leads to diseases, emphasizing the need for understanding these mechanisms. Non-coding regions, notably enhancers and super-enhancers (SEs), act as crucial gene regulators, orchestrating transcriptional activity via transcription factors (TFs). SEs, with high activity, are pivotal in cell identity and disease progression. Current computational approaches focus on individual regulators like TFs and miRNAs, neglecting SE interactions. We emphasize incorporating SEs into models to enhance understanding. Experimental studies have recently linked miRNAs and TFs to SEs. Chromatin immunoprecipitation followed by sequencing (ChIP-Seq) provides valuable SE information. In this review, we categorize computational methods leveraging TF and miRNA data into three areas and explore challenges integrating enhancers/SEs. These include unraveling indirect regulatory networks, identifying network motifs, and enriching pathway identification by dissecting gene regulators, including SEs. We hypothesize that addressing these challenges will enhance our understanding of gene regulation, aiding in identifying therapeutic targets and disease biomarkers. We believe that understanding the role of ChIP-Seq data in predicting the effect of miRNA on gene regulation is crucial to construct statistical/computational model that would tackle these challenges.

Many types of banana (Musa spp.)—one of the most economically important horticultural crops—exist, and their ploidy (diploid, triploid, and tetraploid, usually) and genome types (containing A or/and B genome in most cases) differ. At present, observation and genome type detection are commonly used to identify banana germplasm resources. However, the former is tedious, while the latter cannot distinguish categories below genome types. It is therefore urgent that a simple and effective method for identifying banana germplasm resources is established. We sequenced and analyzed the ribosomal DNA internal transcribed spacer (ITS) sequences of 62 banana germplasm and found that the sequencing peaks, especially the 20 bp region near the 420 bp position (referred to as the “420 bp region”), exhibited relatively recognizable and repeatable polymorphism characteristics. Using the “420 bp region” as a marker, we were able to quickly distinguish bananas belonging to different genome type groups (comprising the AA, AB, AAA, ABB, AAB, ABBB, AAAB, and AAAA groups) or different subgroups in the same genome type group (for example, the Cavendish, Gros Michel, Red, Lakatan, and Ibota Bota subgroups in the AAA group; and the Pisang Raja, Plantain, Pome and Silk subgroups in the AAB group). Moreover, it appeared that Sanger sequencing of ITS could be used for identifying banana hybrid offspring. These results demonstrated that the banana ITS region contains rich genetic information that is useful for genotyping. In general, ITS sequencing simplifies the classification of banana germplasm resources and has potential application in several areas of Musa improvement.

This work presents a novel approach for the study of the movement of droplets on inclined surfaces under the influence of gravity and chemical heterogeneities. The developed numerical methodology uses data-driven modeling to extend the applicability limits of an analytically derived reduced-order model for the contact line velocity. More specifically, while the reduced-order model is able to capture the effects of the chemical heterogeneities to a satisfactory degree, it does not account for gravity. To alleviate this shortcoming, datasets generated from direct numerical simulations are used to train a data-driven model for the contact line velocity, which is based on the Fourier neural operator and corrects the reduced-order model predictions to match the reference solutions. This hybrid surrogate model, which comprises of both analytical and data-driven components, is then integrated in time to simulate the droplet movement, offering a speedup of five orders of magnitude compared to direct numerical simulations. The performance of this hybrid model is quantified and assessed in different wetting scenarios, by considering various inclination angles and values for the Bond number, demonstrating the accuracy of the predictions as long as the adopted parameters lie within the ranges considered in the training dataset.

The question of the relevance of human and natural capital, as well as the potential adverse effect of natural capital on economic growth, has gained increased attention in development economics. The aim of this paper is to theoretically and empirically assess the relevance of several forms of capital on economic growth in small economies that are dependent upon tourism or natural resources. The empirical framework is based on Impulse Response Functions obtained from Vector Autoregressive models in which we focus on the model where economic growth is the dependent variable for ten small economies that are dependent upon either tourism or natural resources. We find that there is evidence of the ‘’natural resource curse’’, especially in the economies that have a strong dependence on resources that are easily substitutable and whose prices constantly fluctuate. We further find that in the majority of observed cases the type of capital these small economies are most dependent on for their economic growth causes negative impulses in the majority of the observed periods. The main policy recommendation should be to assure that even these small economies should strive towards further diversification and avoid dependence on only one segment of their economy.

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.

In this paper, carbon nanotubes modified screen-printed electrode (CNTs/SPE) was prepared and the CNTs/SPE was employed for the electrochemical determination of antioxidant substance Chlorogenic acids (CGAs). A pair of well-defined redox peak of CGA was observed at the CNTs/SPE in 0.10 mol∙L^-1 acetic acid-sodium acetate buffer (pH 6.2) and electrode process is adsorption-controlled. Cyclic voltammetry (CV) and differential pulse voltammetry (DPV) method for the determination of CGA were proposed based on the CNTs/SPE. Under the optimal conditions, the proposed method exhibited linear ranges from 4.73×10^-7 to 4.45×10^-5 mol∙L^-1, the linear regression equation was Ipa(µA) = 4.1993 C (mol∙L^-1)+1.1039 (r = 0.9976) and the detection limit for CGA could reach 3.25×10^-6 mol∙L^-1. The recovery of matrine was 94.74~106.65% (RSD = 2.92%) in coffe beans. The proposed method is quick, sensitive, reliable, and can be used for the determination of CGA.

Intrahepatic cholangiocarcinoma (ICCA) is a malignant epithelial neoplasm characterized by biliary differentiation within the liver. ICCA is molecularly heterogeneous and exhibits a broad spectrum of histopathological features. It is a highly aggressive carcinoma with high mortality and poor survival rates. ICCAs are classified into two main subtypes: small duct type and large duct types. These two tumor types have different cell origins and clinicopathological features. ICCAs are characterized by numerous molecular alterations, including mutations in KRAS, TP53, IDH1/2, ARID1A, BAP1, BRAF, SAMD4, and EGFR, and FGFR2 fusion. Two main molecular subtypes–inflammation and proliferation–have been proposed. Recent advances in high-throughput assays using next-generation sequencing have improved our understanding of ICCA pathogenesis and molecular genetics. The diagnosis of ICCA poses a significant challenge for pathologists because of its varied morphologies and phenotypes. Accurate diagnosis of ICCA is essential for effective patient management and prognostic determination. This article provides an updated overview of ICCA pathology, focusing particularly on molecular features, histological subtypes, and diagnostic approaches.

A method based on ultra performance liquid chromatography with diode array detector (UPLC-DAD) was developed for quantitative analysis of five active compounds and chemical fingerprint analysis of Rosa rugosa. Ten batches of Rosa rugosa collected from different plantations in the Xinjiang region of China were used to establish the fingerprint. The feasibility and advantages of the used UPLC fingerprint were verified for its similarity evaluation by systematically comparing chromatograms with professional analytical software recommended by State Food and Drug Administration (SFDA) of China. In quantitative analysis, the five compounds showed good regression (R²=0.999 5) within the test ranges and the recovery of the method was in the range of 94.2–103.8%. The similarities of the fingerprints of 10 batches of the samples were more than 0.981. The developed UPLC fingerprint method is simple, reliable and validated for the quality control and identification of Rosa rugosa. Additionally, simultaneous quantification of five major bioactive ingredients in the Rosa rugosa samples was conducted to interpret the consistency of the quality test. The results indicated that the UPLC fingerprint as a characteristic distinguishing method combining similarity evaluation and quantification analysis, can be successfully used to assess the quality and to identify the authenticity of Rosa rugosa.

The Himalaya is well known for high diversity and ethnobotanical uses of medicinal plants. However, not all areas of the Himalayas are well studied. In particular, studies on ethnobotanical uses of plants from the Eastern Himalayas are rare and lacking for many tribes. Past studies primarily focused on listing plants name and their traditional medicinal uses. However, studies on traditional ethnopharmacological practices on medicine preparation had not yet been reported in published literature from the Eastern Himalaya. In this study, we are reporting the first time ethnopharmacological used 24 medicines, their procedures of preparation and listed 53 plant species used for those medicines for Monpa tribe. Such documentations had not yet been done for other tribes in India. Our research demonstrates the urgent need to documents traditional medicine preparation procedures from the local healers before rapid cultural modernization forgets them in transforming country like India. This study should motivate national and international researchers to do more works on ethnopharmacology and bioprospecting.

Abstract: Due to sever land degradation throughout Ethiopia, the government has been implementing massive soil and water conservation works at the watershed scale since 2010 to conserve soil and water resources. However, most of the rivers in Ethiopia carry huge amount of sediment in rainy season and streams dried up in the dry period. Planned research was conducted on two paired micro-catchments Mt.Yewel with the main objective of evaluating the impact of soil and water conservation measures on base flow modification. Stream flow and rainfall data were collected from 22/11/2020 to 8/11/2022 on both micro-catchments at their outlets and center respectively. Base flow separation was done using Sp. Hydro base flow separation tool and SPSS Ver.21 statistical software was employed for data analysis. Flow duration curve (FDC) for both watersheds were developed. The FDC indicated that the base flow from treated micro-catchment ishigher and sustained in the dry season while it is low and un-sustained in untreated micro-catchment. From statistical analysis, there is a significant difference in base flow enhancement between the treated and untreated micro-catchment (P<0.001). Hence, catchment treatment contributed to improve groundwater recharge and sustain base flow in the dry season by reduce surface runoff during rainy season.

Abstract: Severe land degradation is the principal environmental problem in Ethiopian highlands in the form of soil erosion, and soil fertility loss, which is caused by human intervention for food, shelter, and energy demands. Soil erosion contributes to the loss of precious soil and water resources which are the basis of agricultural production and provide numerous other eco-system services. The Ethiopian highlands are one of the most degraded regions in the world. To combat such problems, the government of Ethiopia has been implementing massive soil and water conservation works at the watershed scale since 2010 to conserve soil and water resources. Mount Yewel is one of the highlands in Northeastern Ethiopia where intensive soil and water conserva-tion has been implemented. Despite the massive investment in soil and water conservation measures, most of the rivers in the highlands of Ethiopia carry huge amounts of sediment with high flood risks in the wet season and streams are dried up in the dry period. Nonetheless, the impact of soil and water conservation measures on flood reduction and dry season flow en-hancement has not yet been quantified and evaluated. This research was then conducted on paired micro-catchments of Mount Yewel with the main objective of evaluating the impact of soil and water conservation measures on base flow modification. Stream flow and rainfall data were col-lected from 22/11/2020 to 8/11/2022 on paired micro-catchments of Mount Yewel at their outlets and centers respectively. Base flow separation was done from the stream hydrograph using Sp. Hydro base flow separation tool and SPSS Ver.21 statistical software were employed for data analysis. Flow duration curves (FDC) for both watersheds were developed to compare the stream flow patterns between treated and untreated micro-catchments of Mount Yewel. The FDC indi-cated that the base flow from treated micro-catchment is higher and sustained in the dry season while it is low and unsustained in untreated micro-catchment. From statistical analysis, there is a significant (P

Background: Circulating rare cells participate in breast cancer evolution as systemic components of the disease and thus, are a source of theranostic information. Exploration of cancer-associated rare cells is in its infancy. We determined the validity of a rare-cell detection platform and demonstrated the usefulness of a certain rare cell subset as a novel approach to characterize the breast cancer system. Methods: Linearity of the Rarmax® platform was established using a spike-in approach. The platform includes red blood cell lysis, leukocyte depletion and high-resolution fluorescence image recording. Rare cell analysis was conducted on 28 samples (before and after surgery) from 14 patients with breast cancer, 20 healthy volunteers and 9 non-cancer control volunteers. In-depth identification of rare cells, including circulating tumor cells, endothelial-like cells, erythroblasts and inflammation-associated cells, was performed using a phenotype and morphology-based classification system. Results: The platform performed linearly over a range of 5-950 spiked cells, with an average recovery of 84.6%. Circulating epithelial and endothelial-like cell subsets have been demonstrated to be associated with or independent of cancer with tumor presence. Furthermore, certain cell patterns may be associated with treatment-related adverse effects. The sensitivity in detecting tumor-presence and cancer-associated abnormality before surgery was 50% and 85.7%, respectively and the specificity was 100% and 96.6%, respectively. Conclusion: The present study supports the idea of a cancer-associated rare cell abnormality to represent tumor entities as well as systemic cancer. The latter is independent of the apparent clinical cancer.

Biotechnological potential of Thermoanaerobacter strain AK15 to produce ethanol and valuable alcohols from carbohydrates and amino acids were evaluated in present study. The strain is highly ethanologenic, producing a maximum of 1.57 mol ethanol per mol of glucose degraded at high liquid-gas phase ratios. The strain degrades most of the sugars tested as well as starch and pretreated hydrolysates of biopolymers (Whatman paper, newspaper, Timothy grass, Rhubarb leaves, and several macroalgae species) to ethanol mainly. The strains degraded serine and threonine when used as a single substrate, producing mainly acetate and ethanol as end products, and the branched-chain amino acids (leucine, isoleucine, valine) when cultivated in the presence of thiosulfate. The main end products from branched-chain amino acids were a mixture of their corresponding branched-chain fatty acids and alcohols. Finally, the strain was also shown to use butyrate as an electron sink during glucose degradation resulting in the reduced product butanol in addition to end products produced from glucose. Thus, strain AK15 is a promising candidate for ethanol and fine chemical production from a range of lignocellulosic and algal biomass.

Cloud computing refers to applications delivered as services over the Internet. Cloud systems employ policies that are inherently dynamic in nature and that depend on temporal conditions defined in terms of external events, such as the measurement of bandwidth, use of hosts, intrusion detection or specific time events. In this paper, we investigate an optimized resource management scheme named v-Mapper. The basic premise of v-Mapper is to exploit application-awareness concepts using software-defined networking (SDN) features. This paper makes three key contributions to the field: (1) We propose a virtual machine (VM) placement scheme that can effectively mitigate the VM placement issues for data-intensive applications; (2) We propose a validation scheme that will ensure that a service is entertained only if there are sufficient resources available for its execution and (3) We present a scheduling policy that aims to eliminate network load constraints. An evaluation was carried out with various benchmarks and demonstrated that v-Mapper shows improved performance over other state-of-the-art approaches in terms of average task completion time, service delay time and bandwidth utilization. Given the growing importance of supporting large-scale data processing and analysis in datacentres, the v-Mapper system has the potential to make a positive impact in improving datacentre performance in the future.

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.

Most empirical work on sources of economic growth for different countries lack country-specific empirical evidence to guide policy choices in individual developing countries and previous studies of factor productivity tend to focus on the entire economy or a single sector. In this study, we use the recently developed growth accounting tools to explicitly determine the sources of economic growth at both national and sectoral levels in Zambia between 1970 and 2013. We use data from World Development Indicators and Zambia’s Central Statistical Office. On average, total factor productivity (TFP) contributes about 5.7% to economic growth. Sectoral analysis shows that agriculture contributes the least to GDP and that within each sector; factors that contribute to growth differ. Structural transformation has been slow and contributed to the observed inefficiency. We outline the implications of the observed growth and provide recommendations.

The dynamic factors in the environment violate the static environment assumption of the SLAM algorithm, leading to a decrease in the accuracy of camera positioning. In recent years, many studies have attempted to address dynamic objects by combining semantic constraints and geometric constraints, but issues such as poor real-time performance, the potential for mistakenly treating humans as rigid objects, and subpar performance in high-dynamic scenes still persist. This paper proposes a dynamic scene visual SLAM algorithm called Dynamic SLAM based on target tracking and multi-view geometry (TKG-SLAM), based on object detection, Kalman filters, and multi-view geometry. This algorithm takes into consideration both real-time performance and algorithm accuracy. It combines semantic constraints and multi-view geometry constraints, selectively running the algorithm in different scenarios, filtering and preserving static points for camera pose estimation. Experimental results demonstrate that, compared to current state-of-the-art dynamic SLAM methods, our approach achieves optimal performance in some scenarios and exhibits stronger real-time capabilities.

Human activities (e.g., exploitation, utilization, and conservation) exert substantial impact on forest ecological functions (FEF). The human influence on FEF significantly varies across developmental stages, which are attributed to the temporal and spatial in nature. This study evaluates some notable FEF indices (FEFI) in the 448 counties within the Yellow River basin (YRB) using panel data from the Seventh to Ninth National Forest Inventory. Data analysis involves employing the residual trends, geographical and temporal weighted regression. Results indicate that, firstly, the overall forest ecological function in the YRB is moderate to inferior, with superior FEF in counties endowed with more natural forest resources. Afforestation demonstrates a short-term improvement, but its effectiveness diminishes over time. Secondly, the positive spatial correlation is stronger among counties, characterized by both high-high and low-low agglomeration effects. Scattered planting, simple stand structure, and concentrated harvesting of forests at the identical stand ages hinder the formation of complex and large-scale FEF agglomerations. Thirdly, significant spatial-temporal differences exist in the impact of human activities on FEF. In the upper reaches of the basin, increased vegetation coverage through agricultural and forestry production benefited FEF, however, some over-exploitation of forests and grasslands is observed there. In the middle reaches, the economic development, expanding population and the greening activities help to improve FEF effectively, but excessive water using in agricultural production brought more difficulties to FEF improvement. In the lower reaches of the basin, improved wastelands and forest protection positively influence FEF. This study recommends that county governments should prioritize forest management with multi-species and multi-layered complex forests. Each county should define its position in regional development and ecological protection, considering the potential impacts on neighboring regions. This holistic approach promotes more effective integrated regional management.

Convolution forms one of the most essential operations for the FPGA-based hardware accelerator. However, the existing designs often neglect the inherent architecture of FPGA, which puts forward an austere challenge on hardware resource requirements. Even though some previous works have proposed approximate multipliers or convolution acceleration algorithms to deal with this issue, the inevitable accuracy loss and resource occupation easily lead to performance degradation. Toward this, we first propose two kinds of resource-efficient optimized accurate multipliers based on LUTs or carry chains. Then targeting FPGA-based platforms, a generic multiply-accumulate structure is constructed by directly accumulating the partial products produced by our proposed optimized radix-4 Booth multipliers without intermediate multiplication and addition results. Experimental results demonstrate that our proposed multiplier achieves a maximum 51% look-up-table (LUT) reduction compared to the Vivado area optimized multiplier IP. Furthermore, the convolutional process unit using the proposed structure achieves a 36% LUT reduction compared to existing methods. As case studies, the proposed method is applied to DCT transformer and LeNet to achieves hardware resource saving without loss of accuracy.

Plants can utilize the different forms of nitrogen (N), including inorganic and organic N nutrients, and show the great difference in the utilizing efficiency of N-form among species and genotypes within a species. In our previous study, we found that Tibetan wild barley (XZ16) was better in utilization of organic nitrogen in comparison with cultivated barley (Hua30). In this study, metabolite profiles of the two barley genotypes were comprehensively compared in their response to four N forms, including nitrate (NO3-), ammonium (NH4+), urea and glycine. Macro-and micro-nutrient concentrations in shoots were mostly found higher in nitrate and urea treatments than in ammonium and glycine in both the genotypes. XZ16 had higher concentrations of nutrient ions in glycine treatment, but Hua30 accumulated more nutrients in ammonium treatment. Among the total 163 differentially expressed metabolites, highest up-regulation and highest down-regulation was found in XZ16 in glycine and urea treatments respectively. Some important metabolites such as proline, glutamine, serine, asparagine, L-homoserine, aspartic acid, putrescine, ornithine, 4-aminobutyrate were up-regulated in glycine treatment in both the genotypes with higher fold change in XZ16 than in Hua30. Similarly, fructose-6-PO4, aconitic acid and isocitrate were only up-regulated in XZ16 in glycine treatment. Here, we concluded that the genotype XZ16 exhibited higher response to glycine treatment, while Hua30 showed higher response to NH4+ treatment which is attributed to their better utilization of glycine-N and NH4+-N, respectively. .

The rapid evolution of technology has reshaped the global economy, intensifying competition and prompting industry players to embrace innovation for sustained competitiveness. In Indonesia, Business Incubators, overseen by Universities and Provincial/City Governments, are pivotal. However, understanding the determinants of Business Incubator Performance remains limited. This study investigates 24 statement items aiming to uncover the factors influencing Technology Business Incubators in Indonesia, with a focus on knowledge management as a mediating variable derived from a knowledge-based perspective. Employing a quantitative approach, Structural Equation Modeling (SEM) is utilized to scrutinize these factors' impact on Business Incubator Performance. The findings reveal that knowledge management fully and partially mediates between funding support, government assistance, incubator governance, and Business Incubator Technology Performance. This research offers valuable insights for entrepreneurs and stakeholders, emphasizing the significance of funding, governmental backing, incubator governance, and knowledge management in enhancing Incubator Business Technology Performance in Indonesia.

The current study was designed to investigate the anti-inflammatory effect of salidroside (SDS) and the underlying mechanism by using lipopolysaccharide (LPS)-stimulated RAW 264.7 macrophages in vitro and a mouse model of binge drinking induced liver injury in vivo. SDS downregulated protein expression of toll-like receptor 4 (TLR4) and CD14. SDS inhibited LPS-triggered phosphorylation of LPS-activated kinase 1 (TAK1), p38, c-Jun terminal kinase (JNK), and extracellular signal-regulated kinase (ERK). Degradation of IκB-α and nuclear translocation of nuclear factor (NF)-κB were effectively blocked by SDS. SDS concentration-dependently suppressed LPS mediated inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2) protein levels, as well as their downstream products, NO. SDS significantly inhibited protein secretion of interleukin (IL)-1β. Additionally C57BL/6 mice were orally administrated SDS for continuous 5 days, followed by three gavages of ethanol every 30 min. Alcohol binge drinking caused the increasing of hepatic lipid accumulation and serum transaminases levels. SDS pretreatment significantly alleviated liver inflammatory changes and serum transaminases levels. Further investigation indicated that SDS markedly decreased protein level of IL-1β in serum. Taken together, these data implied that SDS inhibits liver inflammation both in vitro and in vivo, and may be a promising candidate for the treatment of inflammatory liver injury.

Given the opportunities offered by foreign investment in energy infrastructure mostly by Chinese firms, the Government of Cambodia is giving high priority to developing hydropower resources for reducing energy poverty and powering economic growth. Using a “Political ecology of the Asian drivers” framework, this paper assesses China’s involvement in the development of large dams’ in Cambodia and its impacts on the access of natural resources such as water and energy by dam builders, local communities and the government. This analysis is based on 61 interviews and 10 focus group discussions with affected communities, institutional actors, Chinese dam builders and financiers in relation to the first large Chinese dam built in Cambodia, the Kamchay dam. Based on the results of the analysis this paper makes recommendations on how to improve the planning, implementation and governance of large dams to ensure that the dams’ benefits are shared more equally.

The primary attraction of IaaS is providing elastic resources on demand. It becomes imperative that IaaS-users have an effective methodology for learning what resources they require, how many resources and for how long they need. However, the heterogeneity of resources, the diversity resource demands of different cloud applications and the variation of application-user behaviors pose IaaS-users big challenge. In this paper, we purpose a unified resource demand forecasting model suiting for different applications, various resources and diverse time-varying workload patterns. With the model, taking input from parameterized applications, resources and workload scenarios, the corresponding resources demands during any time interval can be deduced as output. The experiments configure concrete functions and parameters to help understanding the above model.

Poultry farming in Slovakia has a tradition and an irreplaceable position to ensure rational nutrition for the population. In terms of a nutritional value, poultrymeat (Galliformes) contains a high amount of proteins, essential amino acids, essential unsaturated fatty acids, minerals, vitamins and a low fat content. Poultrymeat production has a relatively short reproductive cycle and thus allows regulating the demand and supply. The poultry market also influences the development of domestic consumption. According to the Statistical Office of the Slovak Republic, the consumption of meat has witnessed a persistent increase over the last 10 years. [1] According to the 2023 AVEC (The Voice of European Meat Sector) annual report, the consumption of poultrymeat in Slovakia was 20.0 kg per person per year. The average consumption in the EU-27 (2021) was 23.4 kg, with the countries with the highest consumption being Portugal (37.8 kg), Ireland (36.0 kg), Hungary (33.5 kg) and Spain (29.9 kg). On the other hand, the countries with the lowest consumption of poultrymeat, apart from Slovakia, also include Austria and Greece with a consumption of 21.5, resp. 21.0 kg [2]. The breeding of broiler chickens, as well as the production and placing on the market of poultrymeat must comply with the requirements laid down by legislation, while the compliance is regularly checked by the competent authorities.

This paper addresses the input coupling problem for the shape memory alloy (SMA) actuated parallel platform with completely unknown nonlinear dynamics. The problem means that in a parallel platform with multiple SMA actuators acting together, the displacement of one of the actuators will simultaneously affect the remaining actuators to change. Due to the coupling problem and nonlinear characteristics of the SMA actuators, it is not easy to control the platform to achieve bi-directional swing with completely unknown nonlinear dynamics. Therefore, a model-free adaptive sliding mode control method is proposed to improve the control accuracy of the complex motion of the platform, avoiding the complex modeling process of the SMA actuated parallel platform. Based on the model-free adaptive control (MFAC) theory, a multiple input and multiple output (MIMO) dynamic linearized mathematical model of the parallel platform is established using the online input and output data of the system. Then, a sliding mode controller is designed based on the dynamic model. A rigorous theoretical analysis proves that the proposed model-free adaptive sliding mode controller is able to stably control the SMA actuated parallel platform, and its tracking error is uniformly ultimately bounded (UUB). Finally, experiments are conducted on the established SMA actuated parallel platform. The results show that the proposed model-free controller is feasible and effective, and can control the system well to achieve the complex motion of bi-directional swing.

In this paper, inspired by Jleli and Samet [journal of inequalities and applications 38 (2014) 2 1–8] we introduce two new classes of auxiliary functions and utilize the same to define (q, y)R-weak 3 contractions. Utilizing (q, y)R-weak contractions, we prove some fixed point theorems in the setting 4 of relational metric spaces. We employ some examples to substantiate the utility of our newly proved 5 results. Finally, we apply one of our newly proved results to ensure the existence and uniqueness of 6 solution of a Volterra-type integral equation.

The sustainable development of resource-based cities has long been a focal point for both governmental policies and academic research. A wealth of academic literature identifies the 'resource curse' as a critical barrier to sustainable development in these urban areas. Despite extensive studies, a consensus on effective strategies to circumvent or resolve this challenge remains elusive. Recent empirical data reveals a noticeable pattern of economic growth differentiation in China's resource-based cities. Why do some resource-based cities experience economic decline while others achieve sustainable economic growth? Therefore, this paper analyzes the reasons for the divergence of economic growth in resource cities from the perspective of heterogeneity. The results of the study show that the phenomenon of the resource curse is conditional, and the heterogeneity of demo-graphic changes, geographic location and institutional environment has a significant impact on the economic growth of resource cities. These factors are investigated to elucidate the observed diversification and heterogeneity in the economic growth of resource-based cities. Building upon this analysis, the paper proposes a framework for differentiated sustainable development pathways tailored to the unique contexts of these cities. This approach not only contributes to the current understanding of the 'resource curse' but also offers novel insights for future research aiming to adopt a more nuanced and context-specific perspective in examining sustainable urban development.

Bridges enable communications and transportation of goods nationally and internationally, underpinning economic and social activities, thus they are pylons of our prosperity and mobility. Bridges worldwide are constantly subjected to physical wear, ageing, deterioration, hazards, environmental influences, and increased loading. Loss of performance and functionality of bridge structures would have a crucial impact on overall infrastructural resilience and would cause significant negative economic and social consequences. Monitoring their behaviour for different loading conditions relies on accurate estimations of the stress-strain state of various critical components and remaining capacities. These activities are of high importance for better planning lifespan prolongation, that is the extension of their service life and prevention of unforeseen collapses, in line with sustainability principles of well-informed maintenance. In many cases, access to the structure is limited or even impossible, which causes the need for the deployment of remote and contactless methods. One such innovative technique, which has recently attracted attention in scientific and practical applications is the digital image correlation (DIC). DIC is a contactless approach applicable for long-term measurements, which enables obtaining the full field of strains and deformations. Although the DIC approach has been widely used in world engineering practice for monitoring bridges and has proved to be a reliable and accurate method, there is a lack of systematic integral review on previous practical applications, revealing limitations and perspectives. This is the main motivation and novelty of this study, which will describe selected case studies, in which DIC was used on real full-scale bridge structures and propose improvements for the method.

Outdoor lighting is becoming increasingly widespread, and residents are suffering from serious light pollution as a result. Residents’ awareness of their rights to protection has gradually increased. However, due to the sometimes-inaccessible nature of residential vertical light incidence intensity data and the high cost of obtaining specific measurements, there is no appropriate hierarchic compensation for residents suffering from different degrees of light pollution. It is therefore important to measure light pollution levels and their damage at the neighborhood scale to provide residents with basic materials for proper protection and to create more politically suitable solutions. This article presents a light pollution assessment method that is easy to perform, is low-cost, and has a short data-processing cycle. This method can be used to monitor residential zone light pollution in other cities. We chose three open areas to test the spatial variation pattern of light intensity. The results are in accordance with spatial interpolation patterns and can be fit, with high precision, using the IDW method. This approach can also be used in 3 dimensions to quantitatively evaluate the distribution of light intensity distribution. We use a mixed-use zone in Beijing known as The Place as our case study area. The vertical illumination at the windows of residential buildings ranges from 2 lux to 23 lux; the illumination in some areas is far higher than the value recommended by CIE. Such severe light pollution can seriously interfere with people's daily lives and has a serious influence on their rest and health. The results of this survey will serve as an important database to assess whether the planning of night-time lighting is scientific and whether it provides residents with a basis for the protection of their rights.

The discovery of first ATP-binding cassette (ABC) transporter, whose overexpression in cancer cells is responsible for exporting anticancer drugs out of tumor cells, initiated enormous efforts to overcome tumor cell multidrug resistance (MDR) by inhibition of ABC-transporter. Because of its many physiological functions, diverse studies have been conducted on the mechanism, function and regulation of this important group of transmembrane transport proteins. In this review, we will focus on the structural aspects of this transporter superfamily. Since the resolution revolution of electron microscope, experimentally solved structures increased rapidly. A summary of the structures available and an overview of recent structure-based studies are provided. More specifically, the artificial intelligence (AI)-based predictions from AlphaFold 2 will be discussed.

Bio-molecular reagents like antibodies required in experimental biology are expensive and their effectiveness, among other things, is critical to the success of the experiment. Although such resources are sometimes donated by one investigator to another through personal communication between the two, there is no previous study to our knowledge on the extent of such donations, nor a central platform that directs resource seekers to donors. In this paper, we describe, to our knowledge, a first attempt at building a web-portal titled Bio-Resource Exchange that attempts to bridge this gap between resource seekers and donors in the domain of experimental biology. Users on this portal can request for or donate antibodies, cell-lines and DNA Constructs. This resource could also serve as a crowd-sourced database of resources for experimental biology. Further, in order to index donations outside of our portal, we mined scientific articles to find instances of donations of antibodies and attempted to extract information about these donations at the finest granularity. Specifically, we extracted the name of the donor, his/her affiliation and the name of the antibody for every donation by parsing the acknowledgements sections of articles. To extract annotations at this level, we propose two approaches – a rule based algorithm and a bootstrapped relation learning algorithm. The algorithms extracted donor names, affiliations and antibody names with average accuracies of 57% and 62% respectively. We also created a dataset of 50 expert-annotated acknowledgements sections that will serve as a gold standard dataset to evaluate extraction algorithms in the future. Contact: [email protected], [email protected] Database URL: http://tonks.dbmi.pitt.edu/brx Supplementary information: Supplementary data are available at Database online.

The domain of construction engineering and management is evolving rapidly. Optimizing resource positioning is crucial for meeting the challenges of efficiency and productivity. This research presents a novel methodology that incorporates Agent-Based Modeling (ABM), Building Information Modeling (BIM), and Bluetooth Low Energy (BLE) technologies, augmented with a Client Early Warning System (CEWS) and a Decision Support System (DSS). What makes this study unique is that it provides a real-time methodology for resource allocation and project management, tailored to address the complexities of modern construction projects. The application of this integrated technological framework marks a significant leap in construction management, empowering data-driven decision-making and significantly improving project efficiency. Empirically validated through a case study at Helwan University's College of Engineering and substantiated by expert surveys, this research showcases a transformative model for resource management. The findings not only underscore the practical applicability of this approach but also open new horizons for its adoption in global construction practices, offering insights for future research endeavors focused on expanding these methodologies to a wider range of resources.

With the proliferating of video surveillance system deployment and related applications, real-time video analysis is very critical to achieve intelligent monitoring, autonomous driving, etc. It is non-trivial to achieve high accuracy and low latency video stream analysis through the traditional cloud computing. In this paper, we propose a non-orthogonal multiple access (NOMA) based edge real-time video analysis framework with one edge server (ES) and multiple user equipments (UEs). A cost minimization problem composed of delay, energy and accuracy is formulated to improve the QoE of UEs. In order to efficiently solve this problem, we propose the joint video frame resolution scaling, task offloading, and resource allocation algorithm based on the Deep Q-Learning Network (JVFRS-TO-RA-DQN), which effectively overcomes the sparsity of the single-layer reward function and accelerates the training convergence speed. JVFRS-TO-RA-DQN consists of two DQN networks to reduce the curse of dimension, which respectively select the offloading and resource allocation action, the resolution scaling action. Experimental results show that JVFRS-TO-RA-DQN can effectively reduce the cost of transmission and computation, and have better performance in convergence compared to other baseline.

Remote sensing datasets are increasingly being used to provide spatially explicit large scale evapotranspiration (ET) estimates. The focus of this study was to estimate and thematically map on a pixel-by-pixel basis, the actual evapotranspiration (ET_a) of the Wonji Shoa Sugarcane Estate using the Surface Energy Balance Algorithm for Land (SEBAL), Simplified Surface Energy Balance (SSEB) and Operational Simplified Surface Energy Balance (SSEBop) algorithms. The results obtained revealed that the ranges of the daily ETa estimated on January 25, February 26, September 06 and October 08, 2002 using SEBAL were 0.0 - 6.85, 0.0 – 9.36, 0.0 – 3.61, 0.0 – 6.83 mm/day; using SSEB 0.0 - 6.78, 0.0 – 7.81, 0.0 – 3.65, 0.0 – 6.46 mm/day, and SSEBop were 0.05 - 8.25, 0.0 – 8.82, 0.2 – 4.0, 0.0 – 7.40 mm/day, respectively. The Root Mean Square Error (RMSE) values between SSEB and SEBAL, SSEBop and SEBAL, and SSEB and SSEBop were 0.548, 0.548, and 0.99 for January 25, 2002; 0.739, 0.753, and 0.994 for February 26, 2002;0.847, 0.846, and 0.999 for September 06, 2002; 0.573, 0.573, and 1.00 for October 08, 2002, respectively. The standard deviation of ET_a over the sugarcane estate showed high spatio-temporal variability perhaps due to soil moisture variability and surface cover. The three algorithm results showed that well watered sugarcane fields in the mid-season growing stage of the crop had higher ET_a values compared with the other dry agricultural fields confirming that they consumptively use more water. Generally during the dry season, ET_a is limited to water surplus areas only and in wet season, ET_a was high throughout the entire sugarcane estate. The evaporation fraction (ETrF) results also followed the same pattern as the daily ET_a over the sugarcane estate. The total crop and irrigation water requirement and effective rainfall estimated using the Cropwat model were 2468.8, 2061.6 and 423.8 mm/yr for January 2001 planted and 2281.9, 1851.0 and 437.8 mm/yr for March 2001 planted sugarcanes, respectively. The mean annual ET_a estimated for the whole estate were 107 Mm³, 140 Mm³, and 178 Mm³ using SEBAL, SSEB, and SSEBop, respectively. Even though the algorithms should be validated through field observation, they have potential to be used for effective estimation of ET in the sugarcane estate.

Biocultural heritage-based products, including regional specialty foods, are increasingly part of sustainable rural development strategies. While export-oriented biocultural products are often the most visible, we examine the role of campesino gastronomic heritage in the Central Valley of Tarija, Bolivia, as a case study of a local market-centered biocultural resource-based development strategy reflected in an alternative agri-food network. We develop a biocultural sustainability framework to examine this network from ecological, economic and sociocultural perspectives. Data are drawn from interviews (n=77), surveys (n=89) and participant observation, with primary and secondary producers of traditional and new products, as well as restaurant owners, market vendors and local consumers. We find that campesino biocultural heritage and the alternative agri-food network surrounding it represent an influential territorial project that underpins many household economies, particularly for women. We conclude that the relatively small investments by local governments to promote campesino gastronomic heritage are having positive ripple effects on small-scale producer livelihoods and on biocultural sustainability. We suggest that further support to increase market access and reduce other barriers to participation in alternative food networks will likely increase the options and benefits available to small-scale producers mobilising campesino gastronomic heritage within the local economy.

This paper examines the transformation of the Ukrainian stock market by analyzing daily returns from the PFTS stock index spanning 1995-2022. Segmented into sub-periods, it tests the hypothesis that the market's efficiency has grown over time. Various data properties, including persistence, volatility, normality, and resistance to anomalies, are explored using different statistical techniques and methods. Volatility, indicated by standard deviation, exhibits a general decreasing trend, suggesting a shift towards a more efficient market. Returns show no significant differences across sub-periods, except for 2005-2007. R/S analysis indicates the decrease of persistence in the market: a trend towards increased efficiency. Normality tests support a normal distribution of daily returns throughout sub-periods. An analysis of anomalies reveals the market's resilience to day-of-the-week effects, with no specific trends observed. The results suggest the absence of serious changes in data properties in the Ukrainian stock market over the time, means evolutionary processes over 25 years can’t be clearly observed. Reforms and changes have had limited impact, justifying continued reform for the market's evolution.

This study investigates the relationship between Corporate Social Responsibility (CSR) dimensions and employees' satisfaction and retention for sustainability in banks. Carroll (1991) proposed a theoretical approach that consists of four components (economic, legal, ethical, and philanthropic) to analyze CSR activities and their effects on employee’s satisfaction and retention in the company. Simple random sampling was used to get the information from 221 participants. The entire form of the dataset is utilized to execute regression and correlation analysis using SPSS. In order to find out the relationship between economic, legal, ethical, and philanthropic factors and employee's satisfaction and retention, regression beta coefficient and correlation coefficients were used to analyze. This study also examines the relationship between job satisfaction and intentions to retain with an organization. The findings demonstrate that the CSR aspects of ethical and philanthropic have a considerable and favorable influence on employee's satisfaction. The outcome also demonstrates a good and prominent influence of legal CSR on the satisfaction of employee's to retain with the firm. Moreover, this study demonstrates that economic aspect of CSR has no significant impact on employee's retention and satisfaction. Correlation analysis depicts that economic CSR is positively and significantly connected with employee's retention and satisfaction. This research came to the conclusion that enhancing employees view regarding CSR activities such as economic, legal, ethical, and philanthropic will increase employee's satisfaction. Therefore, executives and managers in the banks should take steps to influence how employees see CSR areas in order to raise employee's satisfaction and retention in the banks for sustainability.

Forest fire identification is important for forest resource protection. Effective monitoring of forest fires requires the deployment of multiple monitors with different viewpoints, while most traditional recognition models can only recognize images from a single source. By ignoring the information from images with different viewpoints, these models produce high rates of missed and false alarms. In this paper, we propose a graph neural network model based on the similarity of dynamic features of multi-view images to improve the accuracy of forest fire recognition. The input features of the nodes on the graph are converted into relational features of different gallery pairs by establishing pairs (nodes) representing different viewpoint images and gallery images. The new feature library relationship is used to update the image gallery with dynamic features in order to achieve the estimation of similarity between images and improve the image recognition rate of the model. In addition, to reduce the complexity of image pre-processing process and extract key features in images effectively, this paper also proposes a dynamic feature extraction method for fire regions based on image segment ability. By setting the threshold value of HSV color space, the fire region is segmented from the image, and the dynamic features of successive frames of the fire region are extracted. The experimental results show that, compared with the baseline method Resnet, this paper's method is more effective in identifying forest fires, and its recognition accuracy is improved by 2%. And the scheme of this paper can adapt to different forest fire scenes, with better generalization ability and anti-interference ability.

For the task planning problem of the command and control architecture, the existing algorithms have problems such as low efficiency and poor re-planning quality under abnormal conditions. Based on the requirements of the current accusation architecture, this paper constructs a distributed command and control architecture model based on multi-agents, which makes use of the superiority of multi-agents in dealing with complex tasks. The concept of MultiAgents-HTN is proposed under the framework. The original hierarchical task network planning algorithm is optimized, the multi-agent collaboration framework is redefined, and the coordination mechanism of local conflict is designed. Taking the classical resource scheduling problem as the experimental background, the comparison between the proposed algorithm and the classical HTN algorithm is carried out. The experimental results show that the proposed algorithm has higher quality and higher efficiency than the existing algorithm, and the space anomaly is heavy during processing. The planning is more efficient, and the time is more complicated and superior in dealing with the same problem, with good convergence and adaptability. The conclusion proves that the distributed command and control architecture proposed in this paper has high practicability in related fields and can solve the problem of distributed command and control architecture in multi-agent environment.

The Greenland ice sheet is a major contributor to sea level rise, adding an estimated 0.47 +/− 0.23 mm/yr to global mean sea level between 1991 and 2015 (van den Broeke et al., 2016). Making sea level rise projections for the future and understanding the processes controlling current observed rates of sea level rise are crucially dependent on understanding the present-day state of the ice sheet. Here, we provide an overview of the current state of the mass budget of Greenland based on satellite gravimetry and remote sensing observations of surface elevation change, ice sheet velocity and calving front positions. We also combine these essential climate variables with a regional climate model (RCM) output from an ice sheet model (ISM) to gain insight into poorly understood ice sheet dynamical and surface mass processes. On average from 1992 to 2017 the ice sheet in some locations has lost up −2.65 m/yr in elevation based on ESA Radar altimetry analysis. Calving fronts have retreated all around Greenland since the 1990s and in only two out of 28 study locations have they remained stable. The locations of grounding lines at 5 key glaciers with floating ice tongues have remained stable over the observation period. However a detailed case study at Petermann glacier with an ice fracture model shows the sensitivity of these floating ice shelves to future climate change. GRACE gravimetric mass balance (GMB) data allows us to tie together disparate lines of evidence showing that Greenland has lost about 265 +/− 25 Gt/yr of ice over the period 2002 to 2015. RCM and ISM simulations show that surface mass processes dominate the overall Greenland ice sheet mass budget except for areas of fast ice sheet flow but marked differences between models and between models and observations indicate that not all processes are captured accurately, indicating areas of greater uncertainty and directions of future research for future sea level rise projections.

The concept of a "circular economy" needs a more cogent theoretical anchor which will allow for transference of its goals and methods of attainment across cases. Tensions between ecological goals of this concept and the social implications of its implementation need to be addressed. This paper attempts to provide a theoretical framework for harnessing the strengths of a circular economy. Building on theories of social ecology which are predicated in Murray Bookchin's notion of "dialectical naturalism", the analysis presented here addresses some of the criticism of circular economic paradigms, such as their potential for stifling innovation or a neglect of human development challenges. A model for managing human "need" and "greed" within a circular economy framework is presented that also incorporates consumer choice and innovation. Planned obsolescence as a means of livelihood generation is also problematized with a view towards balancing durability of products on the one hand and ensuring throughput for manufacturing employment and innovation incentives on the other. Finally, the need for governance systems is considered to ensure that a planetary vision for a circular economy can be realized that efficiently harnesses local initiatives rather than an atomized and insular view of circularity.

Background: Capsaicinoid are a group of compounds and widely used in the food, medical, and pharmaceutical industries. Capsaicinoid are unique synthesized and accumulated in the pepper fruits. MeJA can enhance the capsaicinoid production. Temporal and spatial expression of capsaicinoid biosynthetic genes are helpful to understand the molecular mechnism of capsaicinoid biosynthesis in the fruits of pepper. Although some of the capsaicinoid biosynthetic genes in pepper have been identified, the expression of these genes at different developing stages of fruit has not been systemically investigated, and little is known about the molecular basis of MeJA inducing capsaicinoid biosynthesis. Results: HPLC study revealed that the capsaicinoid accumulation in the developing fruit of pepper initially appeared at 24 DAP (day after pollination), was actively development at 36 DAP and peaked at 48 DAP. 11 genes that encoded enzymes involved in capsaicinoid biosynthesis were isolated and characterized. Gene expression with quantitative reverse-transcription polymerase chain reaction analysis demonstrated that the CaCS was unique expressed in placenta and the other 10 genes were expressed in all selected tissues and 9 of 11 genes (except CaCa4H and CaCa3H) were strongly expressed in placenta tissue. Spatial expression analysis demostrated that the 11 gene could be collectively grouped into four categories based on the patterns of relative expression of the genes during fruit development. Category I has 2 and they displayed a bell-shaped expression pattern with the peak expression at 24 DAP, Category II contains 5 genes and expression of the 5 genes was constantly increased from 0 to 36 DAP and peaked at 36 DAP. Category III comprises of 2 genes and both genes reached the peak at 48 DAP. Category IV consists of 2 genes and they showed a high expression at 36 and 48 DAF, but unexpressed from 0 to 12 DAP. The gene expressions of the 11 genes were up-regulated by MeJA. 3 genes showed a high expression at 24 h; 4 genes reached the peak at 12 h; the top expression were observed at 18 h; The last one, CaACYase, achieved the highest level at 8 h. Conclusion: The biosynthesis of capsaicinoid in pepper fruit is developmentally regulated. The expression of the majority of capsaicinoid biosynthetic genes is highly consistent with the development accumulation of capsaicinoid in pepper fruit. These results not only provide the initial information on spatial and temporal expression of capsaicinoid biosynthetic genes in pepper developing fruit, but are also valuable to identify the MeJA-induced genes for capsaicinoid biosynthesis and accumulation during pepper fruit development.

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.

Significant challenges exist globally regarding literacy teaching and learning, particularly in poor socio-economic settings in countries of the Global South. In this paper we argue that to address these challenges, major features of how the brain works that are currently ignored in the educational literature should be taken into account. First, perception is an active process based in detection of errors in hierarchical predictions of sensory data and action outcomes. Reading is a particular case. Second, emotions play a key role in underlying cognitive functioning. Innate affective systems underlie and shape all brain functioning, including oral and written forms of language and sign. Third, there is not the fundamental difference between listening/speaking and reading/writing often alleged on the basis of evolutionary arguments. Both are socio-cultural practices driven and learnt by the communication imperative of the social brain. Fourth, like listening, reading is not a linear, bottom-up process. Both are non-linear contextually shaped psycho-social processes of understanding, shaped by current knowledge and cultural contexts and practices. Reductionist neuroscience studies which focus on decontextualized parts of reading cannot access all the relevant processes. An integrated view of brain function reflecting this non-linear nature implies that an ongoing focus on personal meaning and understanding provides positive conditions for all aspects of literacy learning. Assessment of literacy teaching at all its stages should include indicators that take into account these foundational features relating reading and writing to neuroscience.

To effectively estimate airport terminal area capacity and assess the maximum throughput the sector can achieve when the capacity is given, this research proposes an approach to assess the terminal area capacity from the viewpoint of service provision resources. Terminal area capacity is optimized based on the equilibrium of air traffic service resource supply and demand. The supply-demand nexus is examined in consideration of terminal area route structure, traffic flow characteristics, and safety regulations. A flight service probability matrix and a terminal area demand and supply service time model are constructed to quantify resource expenditure at varied capacity levels. A optimization model is then developed to apportion maximal capacity under resource limitations. Model computation and computation results demonstrate the deviation between estimated and amended capacities is under 0.3 flight sorties per hour. The outcomes are congruent with historical statistics, thereby validating the accuracy and reliability of the model proposed in this study. Given capacity parameters, the model can deduce the maximal aircraft quantity served concurrently in terminal areas during peak periods. These revelations indicate the submitted model furnishes theoretical foundation and reference for terminal area sector partition and traffic alerting.

Solar Insolation is the major contributor of earth’s radiation and energy budget. The insolation reaching the surface is a prime input for eco-physiological processes such as evapotranspiration and photosynthesis. Therefore, it is as critical component to assess bio-energy and bio-fuel resources. It is also a crucial input to crop simulation model for yield forecasting and its further applications in solar energy solutions. Although ground observations are better for accuracy purpose, they have challenges of maintenance, regular calibration and upkeeping etc. This call for the continuous spatio-temporal satellite based observations barring the acceptable accuracy. In case of INSAT3D/3DR, Bhattacharya et al. (2015) have derived the surface insolation product which is being used widely. We propose a method of improvement in this product. It is envisaged that a correction applied with the help of ground truth estimates may enhance the utilization of insolation products derived from INSAT3D/3DR datasets. In the present study, surface insolation product derived from INSAT 3D/3DR data at an interval of 30 minutes each (collectively 15 minutes interval) with 4 km spatial resolution was used for duration from May-2017 to Apr-2019 over Nagpur. Ground truth observations for same duration were carried out with CNR4, which were used to correct the INSAT 3D/3DR surface insolation product using the statistical best-fit method. Corrected INSAT 3D/3DR products are found correlating with the ground values well with differences of approximately < 1 W/m2. Best-fit parameters evolved in the present study uses only 2 years of simultaneous ground and satellite data which can be further improved by multi-year data base. We propose better utilization of INSAT 3D/3DR based surface insolation products in the assessment of solar resource mapping over Nagpur (and possibly other regions, such as Bhandara) with the help of best-fit parameters as assessed in the present study.

South Italy is characterised by a semi-arid climate with scarce rain and high evaporative demand, therefore the need to optimise water resources in this area is crucial, and climate change could worsen this condition. In citrus cultivation, which is one of the most important crops bred in Southern Italy, and more generally in Mediterranean regions, deficit irrigation strategies are im-plemented in order to cope with limited resource availability. On this basis, knowledge on how the territorial distribution of citrus would change in relation to these strategies constitutes valuable information for the stakeholders. Therefore, the objective of this study was to determine the prob-ability of presence of citrus in Sicily at changing of the percentage of water deficit application, in order to analyse change in the surface area and localisation of the crop. The methodology was based on the application of Species Distribution Models and Geographic Information Systems to the case study of the Province of Syracuse in Sicily. Different geostatistical and machine learning models were applied, based on 3-decades bioclimatic variables, DTM and irrigation; assessment of the outcomes was carried by using classification evaluation metrics. The analysis of the outcomes showed that uncorrelated predictor layers mainly included water input that affected most the probability of presence. Moreover, GIS analyses showed that deficit irrigation strategies would generate an overall reduction of cultivation surfaces in the territory and a decrease of citrus pres-ence in southern areas of the considered territory, where climate conditions are less favourable in terms of temperature and precipitations, thus providing useful information for decision support tools in agriculture and land use policy.

Aluminum is a strategic mineral resource, and China’s aluminum production and consumption is fairly large. However, its supply guarantee is uncertain because of a high dependency on external raw materials. This uncertainty may expand, so finding a way to reduce the uncertainty of aluminum resource supply is especially important. This paper applies the SFA method to analyze the aluminum flows in mainland China from 1996 to 2014, and establishes a supply structure model to measure its supply guarantee degree. The results claim that: (1) China’s aluminum production can satisfy demand and even create a surplus; (2) Domestic self-productive primary and secondary aluminum increased at an annual rate of 12% and 24%; (3) The proportion of self-productive secondary aluminum in the supply structure increased from 7.7% in 1996 to 12.8% in 2014, while that of primary aluminum decreased from 79.6% to 42.8%; (4) The total supply guarantee degree decreased from 87.3% to 55.6% in this period. These results provide a feasible way to solve this plight: the proportion of secondary aluminum in the supply structure should be enhanced, and an efficient aluminum resource recycling system needs to be established as soon as possible to ensure its sustainable supply.

The enormous growth in the penetration of electric vehicles (EVs), has laid the path to advancements in the charging infrastructure. Connectivity between charging stations is an essential prerequisite for future EV adoption to alleviate users’ “range anxiety”. The existing charging stations fail to adopt power provision allocation and scheduling management. To improve the existing charging infrastructure data based on real-time information and availability of reserves at charging stations could be uploaded to the users to help them locate the nearest charging station for an EV. This research article focuses on an a interactive user application developed through SQL and PHP platform to allocate the charging slots based on estimated battery parameters, which uses data communication with charging stations to receive the slot availability information. The proposed server-based real-time forecast charging infrastructure avoids waiting times and its scheduling management efficiently prevents the EV from halting on road due to battery drain out. The proposed model is implemented using a low-cost microcontroller and the system etiquette tested.

Owing to the advantages of cost-effectiveness, compactness, and the avoidance of complicated camera synchronization, single-camera three-dimensional (3D) digital image correlation (DIC) techniques have gained increasing attention for deformation measurement of materials and structures. In the traditional single-camera 3D-DIC system, the left and right view images can be recorded by a single camera using a diffraction grating, a bi-prism or a set of planar mirrors. To further improve the measurement accuracy of single-camera 3D-DIC, this paper introduces a single-camera four-view imaging technique by installing a pyramidal prism in front of the camera. The 3D reconstruction of the measured points before and after deformation is realized with eight governing equations induced by four views, and the strong geometric constraints of four views can help to improve the measurement accuracy. A static experiment, a rigid body translation experiment and a four-point bending experiment show that the proposed single-camera 3D-DIC method can achieve higher measurement accuracy than the dual-view single-camera 3D-DIC techniques and has advantages in reducing both random error and systematic error.

For an enterprise, the most critical aspect of development is resource management, which has a significant impact on all aspects of the enterprise. Therefore, enterprises must pay attention to resource management allocation, which can better promote the sustainable development of the enterprise and obtain optimal benefits. In the production and development of modern enterprises, the acquisition of benefits also involves the allocation of resources in enterprise management. This paper proposed a benefit optimization scheduling algorithm based on a macroeconomic prediction model under auction mechanism and a grid resource scheduling algorithm driven by a benefit function to allocate resources reasonably. This article used macroeconomic forecasting models to fully understand the resource needs of various departments and the resources held by enterprises, and rationally allocate various resources in various departments. This can improve the work efficiency of various departments, thereby reducing the cost of the enterprise, and achieving optimal benefits for the enterprise. The experimental results in this paper showed that the cost of resource management allocation for the benefit optimization scheduling algorithm and the grid resource scheduling algorithm based on the benefit function driven under the auction mechanism was 105.6 yuan and 46.8 yuan respectively when the task volume was 125 under the multi user environment. The time allocated for resource management was 36.6s and 18.9s respectively. It can be seen that the efficiency function driven grid resource scheduling algorithm had a lower cost and time for resource management allocation, so the efficiency function driven grid resource scheduling algorithm can achieve enterprise efficiency optimization.

Project managers should balance a variety of resource elements in building projects while taking into account many major concerns, including time, cost, quality, risk, and the environment. This study presents a framework for resource trade-offs in project scheduling based on the Building Information Modeling (BIM) methodology and metaheuristic algorithms. First, a new metaheuristic algorithm called Fire Hawk Optimizer (FHO) is used. Using project management software and the BIM process, a 3D model of the construction is created. In order to maximize quality while minimizing time, cost, risk, and CO2 in the project under consideration, an optimization problem is created, and the FHO's capability for solving it is assessed. A predefined stopping condition is taken into account while doing 30 independent optimization runs to obtain the statistical metrics, such as the mean, standard deviation, and the required number of objective function evaluations. The results show that the FHO algorithm is capable of producing competitive and exceptional outcomes when it comes to trade-off various resource options in projects.

Gastrointestinal cancers are a specific group of oncological processes in which the location and nature of growth are of key importance for clinical symptoms and prognosis. At the same time, as research shows, they can pose a very serious threat to the patient's life, especially at an advanced stage of development. The type of therapy used depends on the anatomical location of the cancer, its type and degree of progression. One of the modern forms of therapy used to treat gastrointestinal cancers is photodynamic therapy, which, as already mentioned, has been approved for the treatment of esophageal cancer in the United States. Despite the increasingly rapidly developing clinical use of this treatment method, the exact immunological mechanism it induces on cancer cells has not yet been known. This article presents the current findings on the mode of action of dynamic phototherapy on cells of various gastrointestinal cancers. There are most of them about the impact on colorectal cancer. The types of cell death induced by PDT include apoptosis, necrosis, and pyroptosis. The anticancer effect also results from the induction of destruction of tumor vessels and activation of the immune system. Most reports exist on the mechanism of apoptosis induction, in which the mitochondrial pathway is most often emphasized. Photodynamic therapy may also have a beneficial effect on such aspects of cancer as the ability to develop metastases, or contribute to reducing resistance to known pharmacological agents.

Background: The enduring psychological effects of the COVID-19 pandemic continue to affect individuals long after the immediate health concerns have subsided. This research aims to identify specific groups within the Romanian population who are at a higher risk of experiencing mental health challenges that can interfere with everyday life and may lead to more serious mental health disorders. Methods: Conducted as a cross-sectional survey, this study evaluated the prevalence and intensity of psychological distress using the DASS-21 questionnaire. An online survey distributed via Google Forms also gathered socio-demographic data and COVID-19-specific information. Statistical analyses included the Shapiro-Wilk test, Fisher's exact test, and Z-tests with Bonferroni correction. Quantitative independent variables that lacked a normal distribution were compared between groups using either the Mann-Whitney U test or the Kruskal-Wallis H test. Results: Analysis of 521 questionnaires, filled out by a predominantly middle-aged cohort of 320 women and 201 men, revealed that 63.72% of participants worked outside the healthcare field. Those unemployed or retired reported significantly higher anxiety levels than individuals in other sectors. Moreover, living alone, experiencing the loss of close relatives (6.14%) or friends (33.59%), and undergoing hospitalization or reinfection due to COVID-19 were linked to significantly elevated distress scores. Conclusion. By identifying the segments of the population most vulnerable to psychological distress, as evidenced by higher scores among the unemployed, retirees, individuals living alone, and those directly affected by COVID-19 through personal health or loss, targeted initiatives for psychological screening and therapy can be established. Such measures are essential for enhancing the post-pandemic mental well-being of Romanians, addressing the specific needs uncovered in this study.

Nanoporous materials, such as Metal-Organic Frameworks (MOFs), are renowned for their high selectivity as gas adsorbents due to their specific surface area, nanoporosity, and active surface chemistry. A significant challenge for their widespread application is their behavior and reduced gas uptake in wet conditions, attributed to competitive adsorption between gas and water. The current understanding of gas adsorption in wet materials is constrained by studies that utilize only small amounts of powdered porous materials (in the milligram range) within very small reactors (1-5 mL). This leaves a significant gap in knowledge about gas adsorption behaviors in larger reactors and when MOF sample sizes are increased (to the gram scale) for scaling up the process. Additionally, there is a notable dearth of experimental research in situations where MOFs are heavily saturated with water. To address these gaps, this study measures CH4 adsorption in MOFs under conditions of high moisture content, using larger samples of MOFs (in grams) and a large volume reactor. We selected commercially available MOFs, including HKUST-1, ZIF-8, MOF-303, and activated carbon. A high-pressure isotherms (at T = 274.15 K) using volumetric approach were measured to qualitatively compare the moles of gas adsorbed under both dry and wet conditions across different MOFs and weights. Experimental results show that the presence of water led to a decrease in total CH4 mmol adsorption in MOFs, with a more significant decrease observed in hydrophilic MOFs compared to hydrophobic ones. For hydrophilic MOFs, presence of crossover isotherms at crossover pressure provided evidence of water's conversion to hydrate and its positive role in enhancing total gas uptake, which varied among different hydrophilic MOFs. Oversaturated MOFs showed a larger deviation between dry and wet cases at higher pressures. The high-pressure isotherm data confirm the positive role of hydrophilic MOFs in the conversion of water to hydrate in oversaturated MOFs when MOFs sample size is large.

The application of smartphone-based color evaluation of passive sampling devices for gases has only been sparsely reported. The present review aims to compile available publications with respect to the configuration of the passive samplers, conditions of smartphone photographing, analytical procedure for color detection and quantification (including calibration processes), and their application to different target gases. The performance of the methods - whenever available - is presented regarding the analytical specifications selectivity, sensitivity, and limit of detection in comparison with other color evaluation methods of passive samplers. Practical aspects like requirements of instrumentation and ease of use will be outlined in view of the potential employment in education and citizen science projects. In one section of the review the inconsistent terminology of passive and diffusive sampling is discussed in order to clarify the distinction of information obtained from the uptake of the passive samplers between gas-phase concentration and the accumulated deposition flux of gaseous analytes. Colorimetric gas sensors are included in the review when applied in passive sampling configurations and evaluation is performed with smartphone-based color evaluation. Differences in the analytical procedures employed after the passive sampling step and prior to the detection of the colored compounds are also presented.

Recently, deep learning-based image quality enhancement models have been proposed to improve the perceptual quality of distorted synthesized views impaired by compression and Depth Image Based Rendering (DIBR) process in multiview video systems. However, due to the lack of multi-view video plus depth data, the training data for quality enhancement models is small, which limits the performance and progress of these models. Augmenting the training data to enhance the Synthesized View Quality Enhancement (SVQE) models is a feasible solution. In this paper, we suggest a deep learning-based SVQE model using more synthetic Synthesized View Images (SVIs). To simulate the irregular geometric displacement of DIBR distortion, a random irregular polygon-based SVI synthesis method is proposed based on existing massive RGB/RGBD data, and a synthetic synthesized view database is constructed, which includes synthetic SVIs and DIBR distortion masks. Moreover, to further guide the SVQE models to focus more precisely on DIBR distortion, the DIBR distortion mask prediction network which could predict the position and variance of DIBR distortion is embedded into the SVQE models. The experimental results demonstrate that by pretraining on the synthetic SVI database, the performance of the existing SVQE models could be greatly promoted. In addition, by introducing the DIBR distortion mask prediction network, the SVI quality could be further enhanced.

The Earth's fresh water resources predominantly is groundwater that is pumped out due to rapid urbanization and aggravated by climate change. Groundwater modeling is a crucial tool for understanding aquifer systems, employing 1D, 2D, and 3D numerical models with distinct applications. One-dimensional models focus on vertical dynamics, examining aquifer properties and simulating vertical contaminant transport. Two-dimensional models extend to regional scales, considering horizontal variations and assessing groundwater-surface water interactions, making them valuable for watershed-scale studies. Three-dimensional models provide a comprehensive representation of hydrogeological systems, capturing intricate flow patterns and aiding site-specific assessments. Comparative analysis reveals model strengths and limitations, emphasizing the importance of calibration for reliable results. Case studies showcase practical applications, such as 1D models in flooding analysis and 2D models for simulating debris flows. Three-dimensional modeling proves essential for understanding complex river-aquifer exchange fluxes. Future directions call for a global groundwater platform to address data variability and technical challenges. Despite substantial investments required, the anticipated returns in scientific advancements, societal benefits, and economic gains are expected to outweigh the initial costs. This review aims in highlighting the significance of advancing all three-dimension groundwater modeling for sustainable water resource management and environmental protection.

A processual texture lies at the core of phenomena that undergo change. Philosophers have observed the incessant becoming of our world from antiquity to the present day, but an explanation must still be found in terms of contemporary metaphysics. This article strives to bring the focus on the domain of possibles, an under-theorised philosophical issue that dwells in the ontological realm between the possibility and its actual realization. Sketching a more comprehensive concept of the possible unravelling the thread of its main meanings, the process of change will be not simply clarified from inside, but also understood as an activator of reality. Finally, the discussion will argue for a principle of prudence and responsibility derived from an ontological basis.

Based on the panel data of 114 Chinese resource-based cities from 2013 to 2022, this paper uses principal component analysis, Super-SBM and Malmquist index to measure the development level of digital economy and green total factor productivity of resource-based cities, and empirically studies the impact of digital economy on The impact effect and mechanism of green total factor productivity in resource-based cities. The research found that: (1)From 2013 to 2022, the overall green total factor productivity of resource-based cities in my country showed a trend of first decline and then an upward trend; (2) Digital economy can promote the improvement of green total factor productivity of resource-based cities, and digital economy has more long-term effects than short-term effects. (3)Mechanism analysis shows that energy efficiency and industrial struc-ture optimization are important paths for the digital economy to promote green total factor productivity in resource-based cities. (4)Heterogeneity research shows that digital economy has the largest effect on green total factor productivity in mature cities and the least effect on growth cities; From a regional perspective, the digital economy has the greatest effect on promoting re-source-based cities in the eastern region, followed by the central region, and the western region is weaker.

Gene expression data is key for the functional annotation of single nucleotide polymorphisms (SNPs) identified in genome-wide association studies (GWAS). Expression and splicing quantitative trait loci (e/sQTLs) in normal colon tissue, such as those from the University of Barcelona and University of Virginia RNA sequencing project (BarcUVa-Seq) and the Genotype-Tissue Expression project (GTEx), are required to gain biological insight of colon-related diseases risk loci. Moreover, transcriptome-wide association studies (TWAS) rely on reference gene expression imputation panels in the tissue of interest to nominate susceptibility genes. Also, it is of high interest to study the relationships between genes in a network framework. For facilitating these analyses, we have updated and expanded the scope of the Colon Transcriptome Explorer (CoTrEx) to the version 2.0. This web-based resource provides exhaustive visualization and analysis of transcriptome-wide gene expression profiles of normal colon tissue from BarcUVa-Seq and GTEx. In addition to the integration of new datasets, CoTrEx 2.0 provides additional e/sQTLs sets, as well as gene expression prediction models and regulatory and co-expression networks. It is freely available at https://barcuvaseq.org/cotrex/. Overall, it is of high interest for researchers aiming to investigate the genetic susceptibility to colon-related complex traits and diseases.

This study addresses the challenge of objectively quantifying the value of specific views, known to provide psychological and physical benefits, from city center apartments. Unlike subjective evaluations, objective measurement is hindered by limited research and methodology. The research focuses on quantifying visibility by projecting rays from measurement points and determining whether they intersect with unobstructed views, such as the sky and natural elements. Two metrics are proposed to evaluate visibility: the number of view rays and the ratio of view rays to windows. The study analyzed 17 types of residential units in 12 complexes in northern Seoul, South Korea, with the aim of isolating the effect of views on housing prices from other factors. Correlation coefficients were used to assess the relationship between view and housing prices. The results indicated that valuations based on average visibility are more favorable than those based on maximum visibility. The amount of visibility showed a stronger correlation with apartment prices than the number of floors, suggesting the need to measure views across the entire space. The study's methodology can be applied to analyze views in different buildings and urban areas.

We introduce the Concept-Model-Graph-View Cycle (CMGVC). The CMGVC facilitates coherent architecture analysis, reasoning, insight, and decision-making based on conceptual models that are transformed into a generic, robust graph data structure (GDS). The GDS is then transformed into multiple views of the model, which inform stakeholders in various ways. This GDS-based approach decouples the view from the model and constitutes a powerful enhancement of model-based systems engineering (MBSE). The CMGVC applies the rigorous foundations of Category Theory, a mathematical framework of representations and transformations. We show that modeling languages are categories, drawing an analogy to programming languages. The CMGVC architecture is superior to direct transformations and language-coupled common representations. We demonstrate the CMGVC to transform a conceptual system architecture model built with the Object Process Modeling Language (OPM) into dual graphs and a stakeholder-informing matrix that stimulates system architecture insight.

In this study, the resource blocks (RB) are allocated to user equipment (UE) according to the evolutional algorithms for long term evolution (LTE) systems. Particle Swarm Optimization (PSO) algorithm is one of the evolutionary algorithms, based on the imitation of a flock of birds foraging behavior through learning and grouping the best experience. In previous work, the Simple Particle Swarm Optimization (SPSO) algorithm was proposed for RB allocation to enhance the throughput of Device-to-Device (D2D) communications and improve the system capacity performance. In simulation results, with less population size of M = 10, the SPSO can perform quickly convergence to sub-optimal solution in the 100^th generation and obtained sub-optimum performance with more 2 UEs than the Rand method. Genetic algorithm (GA) is one of the evolutionary algorithms, based on Darwinian models of natural selection and evolution. Therefore, we further proposed a Refined PSO (RPSO) and a novel GA to enhance the throughput of UEs and to improve the system capacity performance. Simulation results show that the proposed GA with 100 populations, in 200 generations can converge to suboptimal solutions. Therefore, with comparing with the SPSO algorithm the proposed GA and RPSO can improve system capacity performance with 1.8 and 0.4 UEs, respectively.

A free-fall baited camera lander was launched for the first time on the Cosmonaut Sea shelf of East Antarctica at the depth of 694 m during the 38th Chinese National Antarctic Research Expedition (CHINARE) in 2022. We identified 31 unique taxa (23 of invertebrates and 8 of fishes) belonging to 8 phyla from 2403 pictures and 40 videos. The Antarctic jonasfish (Notolepis coatsi) was the most frequently observed fish taxa. Ten species of vulnerable marine ecosystem (VME) taxa were observed, accounting for 32% of all species. The maximum number (MaxN) of Natatolana meridionalis individuals per image frame was ten, and they were attracted to the bait. The macrobenthic community type was sessile suspension feeders with associated fauna (SSFA), which was shaped by the muddy substrata with scattered rocks. Rocks served as the best habitats for sessile fauna. The study reveals the megafauna community and their habitat by image survey in the Cosmonaut Sea for the first time. It helped us obtain Antarctic biodiversity baselines and monitoring data for future’s ecosystem health assessment and better protection.

This article presents a critical review of the current and future state of “business architecture” within the enterprise architecture frameworks. The review is conducted in the following four stages. In the first stage of analysis, the research and publication questions are established. In the second stage, the search for information is designed and implemented using the Prisma methodology, and once the selection criteria were applied, 75 article-type documents were selected from Scopus, WoS, and Dimensions databases. In the third stage, the information analysis is conducted, and then it is synthesized. Finally, in fourth stage, the discussion is elaborated. This research contributes in understanding the current and future trends of business architecture in Industry 4.0. Considering the challenges of the changing market, the results show the need to implement architectures and dynamic business models, including their artifacts, in a globalized context immersed in new technologies.

First came the digital banks without offices, and then the “fintech” companies that combined technology and finance to achieve greater efficiency and new business models. Later was the turn of transport and logistics, understood as the sector whose economic activities derived from the transport of people and goods, while on the one hand, it has had a new spring thanks to factors such as online sales. Then, perhaps catalyzed by the pandemic scenario, the turn has reached education, where years ago was only a complement of traditional education, now is a central part with multiple modern methodologies. Numerous studies exist about using or implementing these e-learning procedures, but technical analysis is barely found in references. This work aims to analyze the digital transformation of the main sectors of our society, including finances, transport, and education, together with several examples of how technology was helping to fight the pandemic scenario.

The discovery by the Large Area Telescope on board Fermi of variable gamma-ray emission from radio-loud narrow-line Seyfert 1 (NLSy1) galaxies revealed the presence of a possible third class of AGN with relativistic jets in addition to blazars and radio galaxies. Considering that NLSy1 are usually hosted in spiral galaxies, this finding poses intriguing questions about the nature of these objects and the formation of relativistic jets. We report on a systematic investigation of the gamma-ray properties of a sample of radio-loud NLSy1, including the detection of new objects, using 7 years of Fermi-LAT data with the new Pass 8 event-level analysis. In addition we discuss the radio-to-very-high-energy properties of the gamma-ray emitting NLSy1, their host galaxy, and black hole mass in the context of the blazar scenario and the unification of relativistic jets at different scales.

The electroencephalogram (EEG) is a record of brain activity. Brain Computer Interface (BCI) technology formed by the EEG signal has become one of the hotspots at present. How to extract the feature signal of EEG is the most basic research of BCI technology. In this paper, A new method of recognizing fatigue, conscious, concentrated state of human brain is proposed by the combination of discrete wavelet transform and the neural network based on EEG signal. First of all, the law signal is preprocessed by the wavelet denoising method because the law EEG signal contains a large number of high frequency noise, which is decomposed into multi-layer high frequency signal and low frequency signal. thus, δ wave, θ wave, α wave, β wave are obtained by the wavelet transform. And then, frequency band energy of the different wave is regards as the feature signal of EEG. In the experiment, the feature signal is classified by radial basic function (RBF) and annealed chaotic competitive learning network (ACCLN). RBF and ACCLN networks are trained with 500 sets of sample data and are tested by 100 sets of samples in different mental states. The experimental results show that the average accuracy of RBF network under three conditions are 88.75%, 88.25%, 88.5%, respectively, and the correct rate of ACCLN network is 97%, 98%, 98%, respectively.

Lymphedema is a chronic and debilitating disease that affects up to 250 million patients worldwide. Recent advances in understanding its pathophysiology, along with improved diagnosis and microsurgical techniques, have enhanced our ability to cope with the challenging task of treating this disease. This review provides an overview of the disease from a surgeon's point of view, including existing imaging modalities used for preoperative assessment, as well as surgical procedures used in its treatment. Advantages and drawbacks of various existing modalities used for pre or intraoperative assessment of lymphatic vessels are discussed. Lymphedema treatment has shifted from palliative debulking procedures (liposuction and direct excision) to those aimed at restoring lymphatic flow and countering the pathophysiology of the disease (lymphaticovenous anastomosis and vascularized lymph node transfer). A combination of both approaches can result in a synergistic benefit for patients and are discussed in this review. Despite recent advances, some controversies persist, and further studies are needed to better define surgical treatment algorithms.

In this article a simplified view is given on Verlinde’s entropic gravity. It reveals several shortcomings in the theory. This holds in particular for the inclusion of the dark matter effect in the derived modified expression for the Newtonian gravitational acceleration. Although the merit of the entropic view on gravity is recognized, the proposed explicit axiomatic hypothesis as a basis is criticized. There is no reason why it should be preferred above Einstein’s Field Equation.

In this paper, in order to reduce the energy consumption and delay of data transmission, the non-orthogonal multiple access (NOMA) and edge caching technologies are jointly considered. As for the cache-assisted vehicular NOMA-MEC networks, a problem of minimizing the energy consumed by vehicles (mobile devices, MDs) is formulated under the latency and resource constraints, which jointly optimizes the computing resource allocation, subchannel selection, device association, offloading and caching decisions. To solve the formulated problem, we develop an effective joint computation offloading and task caching algorithm based on the twin delayed deep deterministic policy gradient (TD3) algorithm. Such a TD3-based offloading (TD3O) algorithm includes a designed action transformation (AT) algorithm used for transforming continuous action space into a discrete one. In addition, to solve the formulated problem in a non-iterative manner, an effective heuristic algorithm (HA) is also designed. As for the designed algorithms, we provide some detailed analyses of computation complexity and convergence, and give some meaningful insights through simulation. Simulation results show that the TD3O algorithm may achieve lower local energy consumption than several benchmark algorithms, and HA may achieve a lower one than the completely offloading algorithm and local execution algorithm.

Background: Online survey-based observational cross-sectional study aimed at elucidating experience and attitudes of unstructured population regarding diagnostic imaging. Methods: Invitations to participate were distributed using mixed-mode design to deidentified residents aged 18 years and older. Main outcome measures included morbidity structure and incidence of diagnostic imaging administrations. Results: Respondents (n = 1069) aged 44.3 ± 14.4 years; 32.8% suffered from cardiovascular diseases (CVD); 9.5% had chronic respiratory pathology; 28.9% considered themselves healthy. Respondents with COVID-19 history (49.7%) reported higher rates of computed tomography (CT) (p &lt; .0001), magnetic resonance imaging (MRI) (p &lt; .001), and ultrasound (p &lt; .05). COVID-19 history in CVD respondents shifted imaging administrations towards CT and MRI (p &lt; .05). Every tenth respondent received MRI, CT, and ultrasound on paid basis; 29.0% could not pay for diagnostic procedures; 13.1% reported unavailable MRI. Professional status significantly affected the pattern of diagnostic modalities (p &lt; .05). MRI and CT availability differed between respondents in urban and rural areas (p &lt; .0001). History of technogenic events predisposed responders to overestimate diagnostic value of fluorography (p &lt; .05). Conclusions: Preparedness to future pandemics requires development of community-based outreach programs focusing on people's awareness regarding medical imaging safety and diagnostic value.

Cells execute biological functions to support phenotypes such as growth, migration, and secretion. Complementarily, each function of a cell has resource costs that constrain phenotype. Resource allocation by a cell allows it to manage these costs and optimize their phenotypes. In fact, the management of resource constraints (e.g., nutrient availability, bioenergetic capacity, and macromolecular machinery production) shape activity and ultimately impact phenotype. In mammalian systems, quantification of resource allocation provides important insights into higher-order multicellular functions; it shapes intercellular interactions and relays environmental cues for tissues to coordinate individual cells to overcome resource constraints and achieve population-level behavior. Furthermore, these constraints, objectives, and phenotypes are context-dependent, with cells adapting their behavior according to their microenvironment, resulting in distinct steady-states. This review will highlight the biological insights gained from probing resource allocation in mammalian cells and tissues.

(1) Background: Considering highly selected patients with ductal carcinoma in situ (DCIS), active surveillance is a valid alternative to surgery. Our study is aimed at showing the reliability of post-biopsy complete lesion removal, documented by mammogram, as additional criterion to select these patients. (2) Methods: 2173 Vacuum Assisted Breast Biopsies (VABB) documented as DCIS have been reviewed. Surgery has been performed in all cases. We retrospectively collected the reports of post-VABB complete lesion removal and the histological results of the biopsy and surgery. We calculated the rate of upgrade of DCIS identified on VABB upon excision for patients with post-biopsy complete lesion removal and for those showing residual lesion. (3) Results: We observed 2173 cases of DCIS: 408 classified as low grade; 1262 as intermediate grade; 503 as high grade. The overall upgrading rate to invasive carcinoma was 15.2% (330/2173). The upgrade rate was significantly lower (8.2%) when considering patients showing mammographically documented complete removal of the lesion. (4) Conclusion: The absence of mammographically documented residual lesion following VABB is associated to a lower upgrading rate of DCIS to invasive carcinoma on surgical excision and should be considered when deciding the proper management DCIS diagnosis.

Dolichandrone spathacea is a rare and endangered semi-mangrove plant species with an extremely small population, naturally distributed only in Zhanjiang City and the east coast of Hainan Island, China. Regrettably, the present situation of the D. spathacea population in China has not yet been carefully investigated. In this study, we aim to evaluate the current resource status and population dynamic characteristics of D. spathacea on Hainan Island, China, in order to propose sustainable conservation and management strategies for future ecological restoration of its natural population. Our results show that D. spathacea on Hainan Island can be divided into four populations. All the D. spathacea populations present a state of overall dispersion, local concentration, and occasionally sporadic existence, and they exist in geographical isolation. The young, middle, and old D. spathacea plants account for 20.42%, 66.20%, and 13.38%, respectively, indicating that the overall age class of the D. spathacea population on Hainan Island belongs to a declining type. Furthermore, instead of temporal structure, we chose diameter at breast height (DBH) to establish a static life table, draw a population survival curve, and quantify the future development trend through population dynamic analysis and time-series prediction. These results directly point out that the D. spathacea population on Hainan Island is sensitive to external disturbances and possesses two main increases in mortality rate—namely, in its juvenile and mature stages—due to weak vitality, competition, and anthropogenic interferences, which are the most important reasons for its endangered status. Depending on the current conditions of the D. spathacea population, we should conserve and expand mature trees in situ, preserve their germplasm resources, rehabilitate their habitats to promote provenance restoration, and conduct artificial cultivation and spreading planting in order to realize the sustainable conservation and management of D. spathacea.

Molecular markers, including Simple Sequence Repeat (SSR), Single Nucleotide Polymorphism (SNP), and Intron Length Polymorphism (ILP), are widely utilized in crop improvement and population genetics studies. However, these marker resources remain insufficient for Musa species. In this study, we developed genome-wide SSR, SNP, and ILP markers from Musa and its sister species, creating a comprehensive molecular marker repository for the improvement of Musa species. This database contains 2115474 SSR, 63588 SNP, and 91547 ILP markers developed from thirteen Musa species and two of its relative species. We found that 77% of the SSR loci are suitable for marker development; 38% of SNP markers originated from the genic region, and transition mutations (C↔T; A↔G) were more frequent than transversion. The database is freely accessible and follows a ‘three-tier architecture,’ organizing marker information in MySQL tables. It has a user-friendly interface, written in JavaScript, PHP, and HTML code. Users can employ flexible search parameters, including marker location in the chromosome, transferability, polymorphism, and functional annotation, among others. These distinctive features distinguish the Musa Marker Database (MMdb) from existing marker databases by offering a novel approach that is tailored to the precise needs of the Musa research community. Despite being an in silico method, searching for markers based on various attributes holds promise for Musa research. These markers serve various purposes, including germplasm characterization, gene discovery, population structure analysis, and QTL mapping.

Glycosidic linkage analysis was conducted on the unfractionated polysaccharides in the alcohol-insoluble residues (AIRs) prepared from six red seaweeds (Gracilariopsis sp., Prionitis sp., Mastocarpus papillatus, Callophyllis sp., Mazzaella splendens, and Palmaria palmata) using GC-MS/FID analysis of partially methylated alditol acetates (PMAAs). The cell walls of P. palmata primarily contained mixed-linkage xylans and small amounts of sulfated galactans and cellulose. In contrast, the unfractionated polysaccharides of the other five species were rich in galactans displaying diverse 3,6-anhydro-galactose and galactose linkages with varied sulfation patterns. Different levels of cellulose were also observed. This glycosidic linkage method offers advantages for cellulose analysis over traditional monosaccharide analysis that is known for underrepresenting glucose in crystalline cellulose. Relative linkage compositions calculated from GC-MS and GC-FID measurements showed that anhydro sugar linkages generated more response in the latter detection method. This improved linkage workflow presents a useful tool for studying polysaccharide structural variations across red seaweed species. Furthermore, for the first time, relative linkage compositions from GC-MS and GC-FID measurements, along with normalized FID and total ion current (TIC) chromatograms without peak assignments, were analyzed using principal component analysis (PCA) as a proof-of-concept demonstration of the technique's potential to differentiate various red seaweed species.

Glioblastoma (GBM) is the most common malignant brain tumor in adults. Despite advancements in treatment, the prognosis for patients with GBM remains poor due to its aggressive nature and resistance to therapy. CRISPR-based genetic screening has emerged as a powerful tool for identifying genes crucial for tumor progression and treatment resistance, offering promising targets for tumor therapy. In this review, we provide an overview of the recent advancements in CRISPR-based genetic screening approaches and their applications in GBM. We highlight how these approaches have been used to uncover genetic determinants of GBM progression and susceptibility to various therapies. Furthermore, we discuss ongoing challenges and future directions of CRISPR-based screening methods in advancing GBM research.

Leukemia cutis (LC) is defined as leukemic infiltration of the epidermis, the dermis, and the subcutaneous tissue. Leukemia cutis may follow or occur simultaneously with the diagnosis of systemic leukemia. However, cutaneous lesions are occasionally diagnosed as the primary manifestation of leukemia. Leukemic skin infiltrations demonstrate considerable variation regarding number of changes, distribution, and morphology. The highest incidence of LC is observed in chronic lymphocytic leukemia, monocytic and myelomonocytic acute myeloid leukemia, and in T-cell lineage leukemia. Although the pathogenic mechanism of the invasion of leukemic cells into the skin is not well understood, chemokine receptors and adhesion molecules as well as genetic characteristics of leukemia are thought to play a role. Leukemic skin lesions may be localized or disseminated and may occur alone or in combination on any site of the skin, most frequently in the trunk and extremities. The most common clinical presentations of leukemia cutis are papules, nodules, macules, plaques, and ulcers. In most patients, complete or partial resolution of cutaneous infiltrations occurs simultaneously with hematologic remission. However, in patients with resistant disease or recurrent skin infiltration, local radiotherapy can be used. This review presents recent data on the pathogenesis, diagnosis and treatment of leukemic skin involvement in different types of leukemia

The growing need for water has pressured society and governments to focus more on preservation, planning and management of this natural resource, in which is fundamental to ecosystems and especially to humans. In this sense, the goal of this study was to analyze the national policy of water resources in Brazil and Italy, figuring out aspects that could promote its improvement, aiming at the preservation of water sources, guaranteeing satisfactory quantity and quality. They were carried out in 2019 by the environmental agencies of both countries, listing the main disciplinary regulations. The results show that although they are countries with different realities, they resemble similarities in managerial aspects of water resources, with legislation addressing qualitative and quantitative aspects of water, with guiding principles, instruments and actions aimed at the defence of this natural resource.

Carotid atherosclerotic lesions are a major cause of cerebrovascular disease (CVD). Identification and quantification of carotid plaques are important for categorizing the vulnerability of plaques for rupture and assessing the impact of treatments. The irregularity of plaque surface is associated with previous plaque rupture and plays an important role in the risk of stroke. Thus, the aim of this study is to develop and validate novel vulnerability biomarkers from three-dimensional ultrasound (3DUS) images by analyzing the surface morphological characterization of carotid plaque using fractal geometry features. 3D box-counting and 3D blanket are the two types of 3D fractal dimension that were employed to describe the smoothness of plaques. This fractal dimension analysis tool was used to evaluate the effect of atorvastatin using 3DUS carotid images, which were acquired from 6 patients treated with atorvastatin with 80 mg daily and 5 patients with placebo. The Student's T Test results showed that those two fractal features were effective for detecting the statin-related changes in carotid atherosclerosis with p<0.0068 and p<0.015 respectively, suggesting that 3D fractal dimension measurements can be used effectively to analyze the surface characteristics of carotid plaques, especially for evaluating the impact of the treatment.

In view of the diminishing contribution of natural products from plants to the discovery of new chemical structures, the aim of this study was to compile the articles from 2021 to 2022, assessing: (1) which classes of compounds these new structures belong to; (2) which plants and families these compounds belong to and (3) assessing the degree of novelty of the compound with an already known structure. The review was elaborate following the Preferred Reporting Items for System-atics Reviews and Meta-analyses (PRISMA) statement checklist for guide reporting of systematic reviews. A total of 464 articles were selected for the new compounds of natural origin survey. We included 117 complete articles in this review and reported approximately 109 new structures elucidated during the years 2021 and 2022. Many of the compounds showed small structural var-iations in relation to already known molecules. For some, however, this small modification was decisive for the biological activity reported, demonstrating the importance of descriptive phytochemical studies.

Background/Objectives: The purpose of the proposed study is disclosure of correlations between the dynamics of the Breathe Rate (BR) and Heart Rate (HR) when performing the intermittent physical operation at maximum power on a cycle ergometer.Methods: The stage on the study of the General functional athlete readiness (GFAR) performed using the sports standard “Red Engine” and the cycle ergometer in 16 volunteers (10 men, 6 women) whose average age was 21±1.17 years. To determine the sports potential of the volunteers in this study, we used own Coefficient of Anaerobic Capacity (CANAC Q, beats). Continuous recording of the heart rate and respiratory rate of volunteers in the maximum power sports test was carried out by the “RheoCardioMonitor” device with an athlete functional readiness module based on the method of Transthoracic electrical impedance rheography (TEIRG).Results: The degree of correlation of functional indicators (M, HR_M, GFAR) with CANAC Q for the group in full (n=80) occurred at a very high level, which confirmed the effectiveness of using the Coefficient of Anaerobic Capacity (CANAC Q) in assessing the general functional athlete readiness of the volunteers. Conclusions: CANAC Q measured in “beats” of the heart and recorded very accurately using the method of transthoracic electrical impedance rheography (TEIRG). For this reason as promising sports PSM-system, CANAC Q can replace the methods for determining the functional athlete readiness by blood lactate concentration and maximum oxygen consumption.

In this article it is shown how Milgrom’s acceleration constant is related with the Ω_Λ, Ω_m and Ω_B values of the matter distribution within observable universe as defined in the Standard Model of Cosmology, established by the Planck Collaboration Group. The result is a two-parameter model for the observable universe. The two parameters are the age of the universe and Milgrom’s acceleration constant. It is shown that these are sufficient to calculate the amounts of matter and dark energy in the universe, as well as the contributions of dark matter and baryonic matter in the matter part. The numerical results are in agreement with those of the Lamda-CDM model.

Cloud infrastructure provides computing services where computing resources can be adjusted on-demand. However, the adoption of cloud infrastructures brings concerns like reliance on the service provider network, reliability, compliance for service level agreements (SLAs), etc. Software-defined networking (SDN) is a networking concept that suggests the segregation of a network’s data plane from the control plane. This concept improves networking behavior. In this paper, we present an SDN-enabled resource-aware topology framework. The proposed framework employs SLA compliance, Path Computation Element (PCE) and shares fair loading to achieve better topology features. We also present an evaluation, showcasing the potential of our framework.