In this study a method for predicting the preferred pleasantness induced by different forest environments, represented by virtual photographs, was proposed and evaluated using a novel Anti-Environmental Forest Experience Scale psychometric test. The evaluation questionnaire contained twenty-one items divided into four different subscales. The factor structure was assessed in two separate samples collected online (sample 1: N = 254, sample 2: N = 280). The internal validity of the four subscales was confirmed using an exploratory factor analysis. Discriminant validity was tested and confirmed using the Amoebic Self Scale (Spatial-Symbolic domain). Concurrent validity was confirmed using the Connectedness to Nature Scale. Predictive validity was based on assessment of pleasantness induced by nine different photographs (control – urban landscapes, forest landscapes, dense forest landscapes), with subscales differently correlated with the level of pleasantness assessed for each photograph. This evaluation instrument is appropriate for predicting preferred pleasantness induced by different forest environments.

This paper aims to quantify the influence of hydrogen embrittlement on Wierzbicki and Xue (W-X) model parameters. This model has 4 parameters which have been obtained by tensile tests on smooth specimens, tensile notch specimens, single notch tensile specimens (SENT) and pure shear specimens. Hydrogen embrittlement has been obtained by electrolytic method. Application of the (W-X) model has been done to get the local resistance used to predict failure by the Volumetric Method (VM). An example is given for the case of an embedded pipe submitted to service pressure and lateral seismic displacement. The effect of this displacement is then assimilated to the shearing of the straight section of the pipe. The pipe shows a corrosion defect considered a semi-elliptical crack. The influence of hydrogen embrittlement on local strain resistance and safety factors is discussed.

Medicinal plants have antibacterial, antifungal and antiviral activities. More or less all plants have medicinal properties. In this research article, we have selected four economically important plants (three fruit plants and an economically important plant), Malus domestica Borkh., Prunus persica L., Ricinus communis L., and Carica papaya L. found in several areas of Indian state Uttarakhand. Using the methanolic extract of leaves, we have screened those four plants against four human pathogenic bacteria, Escherichia coli, Streptococcus pneumoniae, Staphylococcus aureus and Micrococcus luteus. For our experiment we have screened the methanolic leaf extracts of four plants against the above-mentioned bacteria. Statistical analysis was also performed for validation. Result revealed the said bacteria have potential antibacterial activities. So, these leaves can be used for clinical trial. These plants can also be used for making herbal medicines.

In this study, we reported that novel single-chain fusion-proteins linked thromboxane A2 (TXA2) receptor (TP) to a selected G-protein α-subunit q (SC-TP-Gαq), or s (SC-TP-Gαs) could be stably expressed in megakaryocytes (MK). We tested the MK-released platelet-linked particles (PLPs) to be used as a vehicle to deliver the over-expressed SC-TP-Gαq), or s (SC-TP-Gαs) to regulate human platelet function. To understand how the single-chain TP-Gα fusion proteins could regulate opposite platelet activities by an identical ligand TXA2, we used dual-binding domains linked to different signaling pathways within a single polypeptide chain using a 3D structural model. The immature MKs were cultured and transfected with cDNAs constructed from structural models of the individual SC-TP-Gαq and SC-TP-Gαs, respectively. After transient expression was identified, the immature MKs stably expressing SC-TP-Gαq or SC-TP-Gαs (stable cell lines) were selected. The stable cell lines were induced into mature MKs which released PLPs. Western blot analysis confirmed that the released PLPs were carrying the recombinant SC-TP-Gαq or SC-TP-Gαs. Flow cytometry analysis showed the PLPs carrying SC-TP-Gαq were able to perform the activity by promoting platelet aggregation. In contrast, PLPs carrying SC-TP-Gαs reversed Gq to Gs signaling to inhibit platelet aggregation. This is the first time demonstrating that SC-TP-Gαq and SC-TP-Gαs were successfully overexpressed in MK cells and released as PLPs with proper folding. This bio-engineering led to the formation of two sets of biologically active PLP forms, which mediate calcium and cAMP signaling, respectively. As a result, these PLPs are able to bind to identical endogenous TXA2 with opposite activities, inhibiting and promoting platelet aggregation as required for therapeutic process.The results also demonstrated the nucleus-free PLPs could be used to deliver recombinant membrane-bound GPCRs to regulate cellular activity in general.

Two interesting compounds such as quaternary ammonium lignin(QAL) and 2-ethylhexyl phosphoric acid mono-2-ethylhexyl ester (P507) were synthesized as ionic liquids(QP-ILs) for the extraction of metal ions. The extraction behavior of Co(II) and Ni(II) from chloride solution was compared between QP-ILs and P507. The results showed that the extraction rates of Co(II) and Ni(II) in the two solutions varied with the extraction conditions, but QP-ILs has stronger extractability for cobalt than P507, while Ni is on the contrary. Under the same conditions, the Co/Ni separation performance(βCo/Ni) in QP-ILs is superior to that in p507. At pH4.0, 55℃, concentration of 20%, extraction time of 10 minutes, the βCo/Ni of QP-ILs reached to 2703, while that of P507 was only 164. In conclusion, QP-ILs was more efficient than P507 for separating Co from mixed solutions.

Parents play an important role in shaping the eating habits of their children through: appropriate selection of food products, snacking between meals and appropriate exposure to information from television advertisements, the press or the Internet. The level of parents' knowledge about proper nutrition and substances added to food is therefore crucial in promoting appropriate dietary choices in children and adolescents in order to prevent diet-related diseases. Objectives: Assessment of the consumption of snack foods by children and adolescents according to the surveyed parents and analysis of parents' knowledge about additional substances in food. Methods: The study was conducted among 129 parents of students from selected primary schools. The research tool was an original survey questionnaire consisting of 24 questions. Survey interviews were conducted with parents during meetings at schools. Statistical analysis was performed using the Pearson ch2 test, the significance level was set to p<0.05. Results: Parents most often indicated sweets, confectionery products and sweetened drinks (25%) and vegetables or fruit (21%) as the form of snack for their children. 46% of children and adolescents drank sweetened drinks at least once a day. Parents had an average level of knowledge about food additives. Significant correlations were found between the level of knowledge of parents of school-age children about food additives and their level of education. Parents' knowledge and education did not significantly influence the level of snack consumption by their children. Children's eating behavior should be considered unsatisfactory. Conclusions: Research has shown the need for parents to be educated about food additives and to pay more attention to reading food labels so that they can reliably prepare their children to make the right food choices. Measures should be implemented to improve the eating habits of children and adolescents.

In today's highly digitized and globalized financial environment, the need to protect payment systems from risk is more urgent than ever. Artificial intelligence (AI) technology is rapidly becoming a key tool in this space, with machine learning algorithms, big data analytics, and real-time monitoring enabling AI to effectively detect and prevent fraudulent activity, optimize risk management processes, and deliver intelligent services. AI not only improves the security and efficiency of payment systems, but also significantly improves customer satisfaction and loyalty. With the continuous development of AI technology and financial payment technology, the financial payment industry will usher in more innovations and changes in the future, further optimizing the payment process, improving payment efficiency, and ensuring payment security.

This study investigates the spatial and temporal dynamics of Paomo restaurants in Xi'an's central urban area from 2012 to 2023, leveraging Point of Interest (POI) data through a series of sophisticated analytical techniques. Methods including nearest neighbor distance, kernel density estimation, and band set statistical analysis were employed to thoroughly examine the evolutionary patterns and key drivers influencing the spatial distribution of these eateries. Our results highlight a notable trend of expansion from the central city to the periphery, characterized by significant spatial agglomeration. Initially concentrated in the city center, the distribution of Paomo restaurants has progressively shifted towards multiple distinct cores, reflecting the dual influence of urban policy initiatives and the dynamics of market competition. Furthermore, the research identifies several pivotal factors shaping this spatial distribution, particularly transportation access, residential quality of life, and the availability of commercial services. These findings demonstrate the profound effects of urban spatial reconfiguration and socioeconomic development on the specialized catering industry. The insights gleaned from this analysis offer valuable guidance for urban planners and policymakers, providing strategic recommendations for the spatial planning of specialty catering services to optimize urban layouts and foster sustainable economic progress.

Amid escalating urbanization, devising rational commercial space layouts is a critical challenge. Leveraging machine learning, this study uses a Back-propagation (BP) neural network to optimize commercial spaces in Weinan City's central urban area. The results indicate an increased number of commercial facilities with a trend of multi-centered agglomeration and outward expansion. Based on these findings, we propose a strategic framework for rational commercial space development emphasizing aggregation centers, development axes, and spatial guidelines. This strategy provides valuable insights for urban planners in small and medium-sized cities in the Yellow River Basin and metropolitan areas, ultimately showcasing the power of machine learning in enhancing urban planning.

The escalation of mining activities in the karst regions of Guizhou Province has heightened the occurrence of water-inrush incidents in deep coal mines. This study focused on water-inrush phenomena within the Xinhua mining area of Jinsha County, Guizhou Province, aiming to investigate the sources of these incidents. The findings indicated that the overlying limestone of the Changxing Formation in the coal seam served as a vulnerable aquifer under certain conditions, leading to water inrushes. The analysis of the spatiotemporal distribution patterns of water-inrush incidents at the working face indicated that previous mining operations damaged the shallow Changxing Formation limestone, resulting in the accumulation of goaf water and the formation of numerous mining-induced fractures. These fractures served as rapid conduits for water inrushes from both atmospheric precipitation and underground sources at the deep working face. The examination of surface water and mine water quality demonstrated that both exhibited similar characteristics, predominantly featuring bicarbonate, sulfate, and sodium compositions. Investigation into the relationship between mine water inflow and atmospheric precipitation established that atmospheric precipitation influenced the mine water supply cycle, with a replenishment period of ~10 months during the operational phase of the Jinyuan Coal Mine and about one month post-closure. The fractures induced by mining activities within the Changxing Formation limestone facilitated water flow, with atmospheric precipitation serving as the primary water source for the mine. This study offered a valuable scientific foundation for addressing water-related damage resulting from atmospheric precipitation in mines susceptible to water inrushes under analogous hydrogeological conditions.

“Blue carbon”, apart from marine humus, includes the carbon (C) stock of coastal ecosystems such as mangroves, marshes, and seagrass meadows, which have been overlooked until recently. Information about the role of coastal wetlands in C sequestration and providing other ecosystem services is still insufficient. In the present study, we assessed the C reserves of soils and vegetation biomass in two complex coastal landscapes (tombolos) located on the coasts of the White and Baltic seas. The soil and plant C stocks were slightly higher at the plot at the Baltic Sea (93.4 ± 46.7 Mg C·ha-1 and 5.22 ± 2.51 Mg C·ha-1, respectively) than at the plot on the White Sea (71.4 ± 38.2 Mg C·ha-1 and 3.95 ± 2.42 Mg C·ha-1, respectively). We attributed the higher values of the C reserved to a warmer climate and less saline water at the plot on the Baltic Sea. Both soil and plant C showed high heterogeneity due to geomorphological complexity and differences in vegetative communities. The Phragmites australis community showed the highest plant biomass and, in some places, high soil C reserves. Allochthonous C contributed to the soil C stock at the site on the White Sea. Though P. australis sequestered more C than other communities, its effect on ecosystem services was mostly negative because the invasion of reeds reduced the biological diversity of the marshes.

Researchers are exploring solutions to meet the growing demand for protein due to the expected increase in global population by 2050. Interest in alternative protein sources like insects has risen, driven by concerns about environmental impact and the need for sustainable food production. This study aimed to develop and evaluate the physicochemical properties of soy protein-based burger enriched with insect protein from Alphitobius diaperinus. Three formulations were developed: a control (B0), and burgers with 5% (B5) and 10% (B10) insect protein - Whole Buffalo Powder (WBP). Results showed that adding insect protein decreased the burger analogue's pH. There was observed a clear trend of increasing total lipids and SFA, and decreasing MUFA and PUFA, as the WBP concentration increases from 0% to 10%. No significant differences with increasing WBP concentration in the protein content of the burger analogue, as well as the cooking yield, were noted. The WBP addition had a notable effect on the color change, especially decrease in brightness (L*). It was shown that as the WBP concentration increased, there were no significant differences in the texture profile of the burger analogues. The formulation with 5% WBP concentration was the most acceptable in sensory analysis.

Landslides, resulting from disturbances in slope equilibrium, pose a significant threat to landscapes, infrastructure, and human life. Triggered by factors such as intense precipitation, seismic activities, or volcanic eruptions, these events can cause extensive damage and endanger nearby communities. A comprehensive understanding of landslide characteristics, including spatio-temporal patterns, dimensions, and morphology, is vital for effective landslide-disaster management. Existing Remote Sensing approaches mostly use either optical or Synthetic Aperture Radar (SAR) sensors. Integrating information from both these types of sensors promises greater accuracy for identifying and locating landslides. This study proposes a novel approach, the ML-LaDeCORsat (Machine Learning-based coseismic Landslide Detection using Combined Optical and Radar Satellite Imagery), that integrates freely available Sentinel-1, Palsar-2, and Sentinel-2 imagery data, along with relevant spectral indices and suitable bands using machine-learning-based classification for coseismic landslide detection implemented in Google Earth Engine (GEE). The approach includes a robust and reproducible training and validation strategy. Using landslides from four different earthquake case studies, we demonstrate the superiority of our approach over existing solutions in coseismic landslide iden-tification and localization, providing a detection accuracy of 87-92%. ML-LaDeCORsat can be adapted to other landslide events (GEE script is provided). Transfer learning experiments proofed that our model can be applied to other coseismic landslide events without the need for additional training data. Our novel approach therefore facilitates quick and reliable identification of co-seismic landslides, highlighting its potential to contribute towards more effective disaster management.

This study used Dutch potatoes at the end of dormancy as the material to explore the impact of menthol (0.2 and 0.5 g/kg based on potato mass) treatment on sprouting inhibition and potato quality. The findings revealed that a menthol concentration of 0.5 g/kg effectively inhibited potato tuber sprouting and significantly reduced glucoside alkaloids production. After a storage period of 15 days, the sprouting percentage and glucoside alkaloids content of potatoes treated with 0.5 g/kg menthol were observed to be significantly lower at 4.17% and 68.63 mg/kg, respectively, compared to the control group which exhibited values of 100% and 282.01 mg/kg, respectively (P < 0.05). Throughout the storage period, 0.5 g/kg of menthol promoted respiration, reduced malondialdehyde production in potatoes, inhibited polyphenol oxidase activity, and slowed down tissue browning. Additionally, it mitigated the decline in starch and soluble protein content, inhibiting the accumulation of reducing sugars.

Liposome-mediated delivery is a possible means to overcome several shortcomings with C. elegans as a model for identifying and testing drugs that retard aging. These include interactions between drugs and the nematodes’ bacterial food source, and failure of drugs to be taken up into nematode tissues. To explore this, we have tested liposome-mediated delivery of a range of fluorescent dyes and drugs in C. elegans. Liposome encapsulation led to enhanced effects on lifespan, using smaller quantities of compound, and enhanced uptake of three dyes into the gut lumen. However, one dye (Texas red) did not cross into nematode tissues, indicating that liposomes cannot ensure uptake of any compound. Of six compounds previously reported to extend lifespan (vitamin C, N-acetylcysteine, glutathione (GSH), trimethadione, thioflavin T (ThT) and rapamycin), this effect was reproduced for the latter four in a condition-dependent manner. For GSH and ThT, antibiotics abrogated life extension, implying a bacterially-mediated effect. With GSH, this was attributable to reduced early death from pharyngeal infection, and associated with alterations of mitochondrial morphology in a manner suggesting a possible innate immune training effect. By contrast, ThT exhibited antibiotic effects. For rapamycin, significant increases in lifespan were only seen when bacterial proliferation was prevented. These results document the utility and limitations of liposome-mediated drug delivery for C. elegans. They also show how nematode-bacteria interactions can determine the effects of compounds on C. elegans lifespan in a variety of ways.

Existing methods for simulating missile dynamics and training often rely on three degrees of freedom (3DOF) motions, limiting their ability to represent the complex maneuvers and targeting strategies of modern missiles. This paper proposes integrating virtual reality (VR) technology with six degrees of freedom (6DOF) dynamics to enhance the accuracy and efficiency of missile simulation and training. By incorporating 6DOF dynamics, simulations can capture the full range of missile motion, providing a comprehensive understanding of their capabilities and limitations. Through VR, users can immerse themselves in realistic missile scenarios, interact with dynamic behaviors in real-time, and refine their targeting and interception skills. The integration of VR with 6DOF dynamics also facilitates real-time feedback and evaluation, enhancing training efficiency and proficiency in missile operations. This approach aims to revolutionize missile training by bridging the gap between traditional simulation methods and real-world dynamics.

Civic participation is of great significance to urban management decision-making. In order to facilitate citizens to participate in city management decision-making, this paper proposes a large-scale group decision-making (LSGDM) method based on multi-granular probabilistic linguistic preference relations (MG-PLPRs). First, each decision maker selects a language terms set from the multi-granularity language terms set to represent individual preference relations and the MG-PLPRs are obtained by statistical calculation to represent sub-group’s preferences information. Then, an optimization model based on the expected consistency of PLPR and consensus measure of groups is established to achieving consensus reaching processes, which can ensure satisfactory individual consistency and group consensus. Finally, the validity and applicability of the proposed method is verified by a case of a city "shared garden" site selection with the participation of citizens.

This paper proposed an optimal tracking control scheme through adaptive dynamic programming(ADP) for a class of partially unknown discrete-time nonlinear systems based on radial basis function neural network(RBF-NN). In order to acquire the unknown system dynamics, we use two RBF-NNs, the one is used to construct the identifier, and the another is used to directly approximate the steady-state control input, where a novel adaptive law is proposed to update neural network weights. While the optimal feedback control and the cost function are derived via feedforward neural networks approximating, it is proposed to regulate the tracking error, the critic network and the actor network are then trained online to obtain the solution of the associated Hamilton–Jacobi–Bellman (HJB) equation being built under the ADP framework. Simulations verify the effectiveness of the optimal tracking control technique using the neural networks.

In order to achieve global climate goals, it is necessary to estimate greenhouse gases (GHG) fluxes from ecosystems. To obtain a comprehensive assessment of CO2, СН4, N2O natural fluxes for the Russian Federation we used the "bottom-up" method according to [1]. We updated estimates for forest ecosystems based on State Forest Inventory data and satellite monitoring of forest disturbances. For grassland ecosystems, it was based on the correct distribution of areas between steppe and non-steppe zones. The estimated net uptake of natural ecosystems of Russia was 1.1 ± 1.8 billion tons of CO2-eq./year. The study shows that if only CO2 is taken into account, the net absorption of terrestrial ecosystems in Russia corresponds to more than -2.5 billion tons of CO2 (35% of forests’ contribution). However, given the emissions of non-CO2 GHGs, total net absorption in Russia’s natural ecosystems is reduced to about -1 billion tons of CO2-eq (with the forests’ contribution increasing to 80%). With regard to anthropogenic fluxes, the overall balance of GHGs in Russia corresponds to net-emissions of 1 billion tons of CO2-eq/year into the atmosphere. To improve reporting under the Paris Agreement, countries should aim to include only anthropogenic ("manageable") GHG fluxes on managed land.

Carbon dots are carbon based nanoparticles, which have the characteristics of simple preparation process, photoluminescence, biocompatibility, adjustable surface function, water solubility, and low toxicity. They are widely used in biological applications such as imaging, biosensing, photocatalysis, and molecular transfer. It has also aroused great interest among researchers in agriculture and has made significant progress in improving crop growth and production. This review presents the physiological functions of carbon dots for crop growth and development, photosynthesis, water and nutrient absorption, and abiotic stress resistance and their applications in improving ecological environment and agriculture as biosensors, and the future application prospects and research directions of carbon dots in agriculture were prospected.

Saikosaponin (SS) a is a compound with various pharmacological properties and easily degraded into SSb1 and SSg in acid condition. In present work another two new degradation products of SSa formed under acid hydrolytic condition were detected and isolated by analytical and semi-preparative liquid chromatography technology, furthermore their structures were characterized as hydroxyl-saikosaponin a and SSb2 by spectral analysis, which is not only essential in stability-indicating method development and validation but also could be used as a worst case to assess the analytical method performance of SSa. Moreover their structural transformation pathways were also proposed.

Real-time object detection remains a pivotal topic in the realm of computer vision. Balancing the accuracy and speed of object detectors poses a formidable challenge for both academic researchers and industry practitioners. While recent transformer-based models have showncase the prowess of the attention mechanism, delivering remarkable performance improvements over CNNs, their computational demands can hinder their effectiveness in real-time detection tasks. In this paper, we elect to use YOLOX as our robust starting point and introduce a series of effective enhancements, culminating in a new high-performance detector named YOLOAX. To further exploit the power of the attention mechanism, we devise multi-dimensional attention-based modules that activate CNNs, emphasizing the most pertinent regions and bolstering the capacity to learn the most informative image representations from feature maps. Moreover, we introduce a novel leading label assignment strategy called STA, along with a groundbreaking loss function named GEIOU Loss, to further refine our detector's performance. We provide extensive ablation studies on the COCO and PASCAL VOC 2012 detection datasets to validate our proposed methods. Remarkably, our YOLOAX is trained solely on the MS-COCO dataset from scratch, without leveraging any prior knowledge, which achieves an impressive 54.2% AP on the COCO 2017 test set while maintaining a real-time speed of 72.3 fps, surpassing YOLOX by a significant margin of 3.0% AP. Our source code and pre-trained models are openly accessible at https://github.com/KejianXu/yoloax.

Despite Low Power Wide Area Networks’ ability to offer an extended range, it might still encounter challenges with coverage blind spots in the network. This article proposes an innovative energy-efficient nature-inspired relay selection algorithm for LoRa-based LPWAN networks, serving as a solution for challenges related to poor signal range in areas with limited coverage. A behavior swarm-inspired approach has been utilized to select the relays’ localization in the network, providing network energy efficiency and radio signal extension. These relays help bridge communication gaps, significantly reducing the impact of coverage blind spots by forwarding signals from devices with poor direct connectivity with the gateway. The proposed algorithm considers critical factors for the LoRa standard, such as the Spreading Factor or device energy budget analysis. The simulation experiments validate the proposed scheme’s effectiveness in terms of energy efficiency under diverse multi-gateway topology scenarios involving thousands of devices. Specifically, it has been verified that the proposed approach outperforms the reference method in preventing the battery depletion of relays, which is vital for battery-powered IoT devices. Furthermore, for some large-scale problems, the proposed heuristic method achieves over twice the speed of the exact method with a negligible accuracy loss of less than 2%.

The development of virtual models has recently been generalized in the field of complex mechatronic product design. Virtual models have various advantages such as: the systemic design approach, most often parametric, the implementation of extreme, expensive, and perhaps risky scenarios for the operation of the designed product, the testing of a large number of variants, etc. The main contribution of the paper consists of the development of an interface family called DeSeRoI (Dedicated Serial Robot Interface). Each member of the interface family integrates Simulink libraries and is created by the programmer in accordance with user requests. It follows that the interfaces composing the DeSeRoI family can have different levels of complexity depending on user needs and constraints. We are implementing a MATLAB-based application with user-provided configuration for serial robots in order to automatically visualize the kinematics data and simulation. Within MATLAB, two scripts are created responsible for generating Simulink models that compute the kinematics problem. One of the scripts (Main) saves information about the configuration of the robot and the other one (Generates_Simulink_Models) computes the kinematics problem. Different types of functions (software module) have been developed. Using these functions, the Simulink models result as modular entities. The automated approach eases user’s interaction and understanding of serial robots’ kinematics by performing the simulation procedure in a shorter amount of time and with higher efficiency, assuring a precise outcome despite the chosen parameters.

Mathematical models of cognition measure individual differences in cognitive processes, such as processing speed, working memory capacity, and executive functions, that may underlie general intelligence. As such, cognitive models allow identifying associations between specific cognitive processes and tracking the effect of experimental interventions aimed at the enhancement of intelligence on mediating process parameters. Moreover, cognitive models provide an explicit theoretical formalization of theories regarding specific cognitive process that may help overcoming ambiguities in the interpretation of fuzzy verbal theories. In this paper, we give an overview of the advantages of cognitive modeling in intelligence research and present models in the domains of processing speed, working memory, and selective attention that may be of particular interest for intelligence research. Moreover, we provide guidelines for the application of cognitive models in intelligence research, including data collection, the evaluation of model fit, and statistical analyses.

The rapid growth of the sharing economy has propelled shared mobility to the forefront of attention. The continuous advancement of autonomous driving technology also brings new opportunities and challenges to the shared mobility market. This study comprehensively analyzes the market potential concerning autonomous vehicles (AVs) to provide shared mobility services, utilizing SWOT analysis, PESTLE analysis, and Porter’s Five Forces. The findings reveal that AVs can provide improved shared mobility services by increasing transportation safety, reducing emissions, reducing costs, enhancing traffic efficiency, and increasing customer satisfaction and the profitability of shared mobility services. However, challenges such as technological and policy uncertainties, safety concerns, high initial costs, inadequate public communication infrastructure, and the absence of standardized regulations can hinder the widespread adoption of AVs. The benefits are also restricted with a low market penetration rate of AVs. To promote AVs in the shared mobility market, this study also provides implications for AV stakeholders tailored to the evolving shared mobility market dynamics.

High dimensional statistical analysis for the yield of large-scale circuits is quite difficult due to expensive simulations, especially for the memory circuits with high sigma requirement (e.g., SRAM). In this paper, we developed an efficient sparse additive model to substitute simulations. To fit high sigma region accurately, the modeling center is moved to near failure boundary searched by scaling the shape of sampling function. To solve the model efficiently, the process variables are grouped by standard cells so that the model can be solved by our developed blockwise greedy algorithm. The experiments on the 28nm memory circuits validate that our method achieves high accuracy and efficiency compared with other state-of-art works.

Long-term safety of a nutraceutical combination based on agnus castus and magnolia extracts combined with soy isoflavones (SI) and lactobacilli, and effectiveness on vasomotor symptoms and sleep disorders in postmenopausal women, were assessed. A controlled study was carried-out in menopausal women comparing this nutraceutical combination (ESP group) with formulation containing isoflavones alone (C group), at the dosage recommended. Kuppermann index, PSQI, and SF-36 were determined at baseline, 3, 6 and 12 months. Endometrial thickness, mammary density and liver function were evaluated at baseline and after 12 months. 180 women (100 in ESP group and 80 in C group, mean age 55.5 years, in menopause for about 36 months) were enrolled in the study. At the treatment end, mammary density, endometrial thickness, and hepatic function did not show substantial differences between groups. Kuppermann index, and particularly hot flushes, were progressively and significantly decreased in frequency and severity during ESP versus C treatment. No adverse events were observed. Agnus castus and magnolia, combined with SI + lactobacilli, can effectively and safely be used in symptomatic postmenopausal women, mainly when quality of sleep is the most disturbing complaint. Endometrium, mammary glands and liver function, were unaffected after 12 months treatment.

We study a two-echelon supply chain made up of a supplier and a manufacturer, both of which can reduce their component/product carbon emissions. With the vertical technology spillovers, we explore the optimal decisions of centralized and decentralized supply chains with price dependent demand and propose coordination strategy for the decentralized supply chain. Considering the cost contraction effectiveness of the technology spillovers, the centralized and decentralized game theoretic models of a two-echelon supply chain are developed to investigate optimal decisions of pricing and carbon emissions reduction. Through a systematic comparison and numerical analysis, we show that the profits of both players and the entire supply chain improve with the effect of technology spillovers increasing. Carbon emissions reduction will be taken by various protective measures so that the supplier and the manufacturer who do not innovate can hardly share the results of innovating via the “free-riding” methods when the technology spillover is relatively small. We also propose a revenue-cost sharing contract through bargaining to enhance the performance of the decentralized supply chain.

Telemonitoring and telerehabilitation have shown promise in delivering individualized healthcare remotely. We introduce STASISM, a Sensor-Based Telerehabilitation and Telemonitoring System, in this work. This platform has been created to facilitate individualized telerehabilitation and telemonitoring for those who need rehabilitation or ongoing monitoring. To gather and analyze pertinent and validated physiological, kinematic, and environmental data, the system combines a variety of sensors and data analytic methodologies. The platform facilitates customized rehabilitation activities based on individual needs, allows for remote monitoring of patients' progress, and offers real-time feedback. To protect the security of patient data and to safeguard patient privacy, STASISM also provides secure data transmission and storage. The platform has the potential to significantly improve the accessibility and efficacy of telerehabilitation and telemonitoring programs, enhancing patients' quality of life and allowing healthcare professionals to provide individualized care outside of traditional clinical settings/

The use of chimeric antigen receptor (CAR) in natural killer (NK) cells for cancer therapy is gaining momentum, marking a significant shift in cancer treatment. This review aims to explore the potential of CAR-NK cell therapy in cancer immunotherapy, providing a fresh perspective. It discusses the innovative approaches in CAR-NK cell design and engineering, particularly targeting refractory or recurrent cancers. By comparing CAR-NK cells with traditional therapies, the review highlights their unique ability to tackle tumor heterogeneity and immune system suppression. Additionally, it explains how novel cytokines and receptors can enhance CAR-NK cell efficacy, specificity, and functionality. The review underscores the advantages of CAR-NK cells, including reduced toxicity, lower cost, and broader accessibility compared to CAR-T cells, along with their potential in treating both blood cancers and solid tumors.

Tomato (Solanum lycopersicum L.) has almost 190 million tonnes produced in 2023. Tomato fruit suffer losses of up to 50% during harvest and transport, which causes financial hardship, reduces the amount of food available and causes environmental harm. Calcium plays an important role in cell wall strength. This work assessed the use of a calcium transport stimulants (MCAS) to increase the firmness of tomato fruit in the laboratory and the foliar application to tomato plants. Then handling damage was simulated. In the laboratory, the calcium with MCAS significantly increased the fruit firmness compared to the untreated and calcium without MCAS, which were not significantly different. When calcium with MCAS was applied to tomato plants foliarly before harvest, the calcium with MCAS treated fruit were significantly firmer than the untreated or calcium without MCAS treated fruit for up to 10 weeks after harvest, and this was achieved by applying only 0.91 kg ha-1 calcium. Finally, when fruit were exposed to a simulated drop, the calcium with MCAS treated fruit remained firmer than the undamaged fruit or dropped fruit with calcium without MCAS. Calcium with MCAS could significantly increase the fruit firmness and increase the shelf-life of tomatoes while applying less calcium.

Developing and utilising of underground space is an vital direction for urban growth. Underground commercial streets, as a significant component of underground space accommodating extensive human social activities, and consequently necessitate the creation of human-scale spaces. In the evolution of urban design development towards greater refinement, applying architectural theories and excessively subjective designs has resulted in a deficiency of human-centered design and a disordered spatial environment. This study merges environmental psychology and architectural theory to determine the appropriate length of spatial scale. Two experiments focusing on spatial perception evaluation were conducted using a virtual experimental platform that featured varying dimensions of spatial scale combinations. These quantified combinations were correlated with perception evaluation, and regression analysis was employed to identify appropriate scale ranges, which were superimposed on the range of length selection. Finally, the optimal length and scale combination for underground commercial street spaces were established, providing a reference for the human-centered design of these environments.

As a precious natural resource, forests are being destroyed. In previous studies, there is a lack of interactive assessment of its distribution that comprehensively considers multiple external disturbances. This paper takes the Barcelona Metropolitan Region as an example. Based on remote sensing, it analyzes the development process of the forest from 2006 to 2018 through multiple landscape indicators, and OLS models were established to analyze variables that have direct and indirect effects on forest distribution. In addition, the ecological structure of the forest was analyzed based on NDVI. It was found that the forest area is the largest but has been decreasing, becoming more complex in distribution structure. Much of the forest was converted to agricultural land and grassland. The green quality of forests was increasing, and the broad-leaved forest, the second largest area, contributes the most. NDVI is the most important positively correlated variable, and daytime surface temperature is an important inverse factor related to NDVI. In addition, NDBI is also a negative condition that inhibits forest development. In conclusion: The BMR forest area is decreasing and becoming more fragmented. NDVI and daytime LST are the two most significant factors. Climate warming may lead to worse forest development.

Few-shot object detection (FSOD) aims to address the challenge of requiring a substantial amount of annotations for training in conventional object detection, which is very labor-intensive. However, existing few-shot methods achieve high precision with the sacrifice of time-consuming for exhaustive fine-tuning, or take poor performance in novel-class adaptation. We presume the major reason is that the valuable correlation feature among different categories is insufficiently exploited, hindering the generalization of knowledge from base to novel categories for object detection. In this paper, we propose Few-Shot object detection via Correlation-RPN and Transformer Encoder-Decoder (CRTED), a novel training network to learn object-relevant features of inter-class correlation and intra-class compactness while suppressing object-agnostic features in the background with limited annotated samples. And we also introduce a 4-way tuple-contrast training strategy to positively activate the training progress of our object detector. Experiments over two few-shot benchmarks (Pascal VOC, MS-COCO) demonstrate that, our proposed CRTED without further fine-tuning can achieve comparable performance with current state-of-the-art fine-tuned works. The codes and pre-trained models will be released.

The development of computational acoustics allows simulation of sound generation and propagation in complex environment. In particular, meshfree methods are widely used to solve acoustics problems through arbitrarily distributed field points and approximation smoothness flexibility. As a Lagrangian meshfree method, smoothed particle hydrodynamics (SPH) method reduce the difficulty in solving problems with deformable boundaries, complex topologies, or multiphase medium. The traditional SPH method has been applied in acoustic simulation. This study presents the corrective smoothed particle method (CSPM), which is a combination of SPH kernel estimate and Taylor series expansion. The CSPM is introduced as a Lagrangian approach to improve accuracy in solving acoustic wave equations in the time domain. Moreover, a boundary treatment technique based on the hybrid meshfree and finite difference time domain (FDTD) method is proposed to represent different acoustic boundaries with particles. To model sound propagation in pipes with different boundaries, soft, rigid, and absorbing boundary conditions are built with this technique. Numerical results show that the CSPM algorithm is consistent and demonstrates convergence with exact solutions. Main computational parameters are discussed, and different boundary conditions are validated to be effective for benchmark problems in computational acoustics.

The eddy current loss should be optimized to be as less as possible for the stability of permanent magnet in high speed permanent magnet synchronous motor (HSPMSM) rotor and ensure the high efficiency and low temperature of the motor. This paper analyzes the eddy current distribution in rotor, with consideration of the conflict of the thickness of sleeve and diameter of the rotor, calculating the eddy current loss (ECL) and the thermal distribution via Separation of variables method for solving Maxwell's equations with analytical hieratical model of ECL constructed. The optimization result of ECL of the HSPMSM whose power and rated speed is 30kw 48000r/min can be got by multi-objective optimization method, combined weighting coefficient method and traversal algorithm based on chaotic local search particle swarm optimization (CLSPSO), utilizing ECL analytical model and other analytical constraints. Related experiment and measurement has been implemented with new approach of loss separation.

Continuous technological progress places significant demands on the materials used in increasingly modern devices. An important parameter is often the long-term thermal resistance of the material. The use of heat-resistant polymer materials worked well in technologically advanced products. An economically justified direction in searching for new materials is the area of polymer nanocomposite materials. It is necessary to appropriately select both the polymer matrix and the nanofillers best able to demonstrate the synergistic effect. A promising area of exploration for such nanocomposites is the use of organosilicon polymers, which results from the unique properties of these polymers related to their structure. This review presents the results of the analysis of the most important literature reports regarding organosilicon polymer nanocomposites with increased thermal resistance. Particular attention was paid to modification methods of silicone nanocomposites focused on the increase of their thermal resistance related to the modification of siloxane molecular structure and by making nanocomposites using inorganic additives, carbon nanomaterials. Attention was also paid to such important issues as the influence of the dispersion of additives in the polymer matrix on the thermal resistance of silicone nanocomposites and the possibility of modifying the polymer matrix and permanently introducing nanofillers thanks to the presence of various reactive groups. The thermal stability mechanism of these nanocomposites was also analysed.

Although several carbon sequestration methods have been proposed according to theories from various disciplines, it is not known which method is the best. This study evaluated and summarized the advantage and disadvantages of several major carbon sequestration methods, including carbon capture and sequestration, ocean alkalinization, algal cultivation, and wood burial, based on the first principles, namely, sequestration scale and time, elemental stoichiometry, and energy use efficiency. This study suggested that, at present, wood burial may be a leading candidate for carbon sequestration because it can be implemented immediately on a large scale, is cost-effective and efficient, has a long sequestration time, has low technical requirements, and has a relatively low impact on agriculture. This study also discussed the implementation of wood burial from the perspective of nutrient requirements. To achieve carbon neutrality by 2050, the present study proposed a 25-year project starting in 2025 with an average sequestration rate of 3 Pg C per year, which includes burying 1.5 Pg of dead wood carbon and the creation of 9.7 × 10⁶ ha woodland.

This paper aims at tackling with the task of fusion feature from images and its corresponding point clouds for 3D object detection in autonomous driving scenarios basing on AVOD, an Aggregate View Object Detection network. The proposed fusion algorithms fuse features targeted from Bird’s Eye View (BEV) LIDAR point clouds and its corresponding RGB images. Differs in existing fusion methods, which are simply the adoptions of concatenation module, element-wise sum module or element-wise mean module, our proposed fusion algorithms enhance the interaction between BEV feature maps and its corresponding images feature maps by designing a novel structure, where single level feature maps and another utilizes multilevel feature maps. Experiments show that our proposed fusion algorithm produces better results on 3D mAP and AHS with less speed loss comparing to existing fusion method used on the KITTI 3D object detection benchmark.

In offshore drilling, accidents such as gas invasion, overflow, and kicks are inevitable, and they can escalate into blowouts and other catastrophic events that result in casualties and substantial economic losses. Therefore, maintaining drilling safety requires accurate monitoring of gas invasion and overflow. The majority of overflow monitoring methods currently utilized at drilling sites are threshold-based. However, monitoring parameters acquired during actual drilling operations frequently contain noise signals, making it difficult for threshold-based methods to strike a balance between improving accuracy and minimizing false positives. In this paper, Pattern Recognition-based Kick Detection (PRKD) is proposed as a novel method for diagnosing overflow in offshore drilling. This method utilizes the overflow evolution process by integrating multi-phase flow calculations, data filtering theory, pattern recognition theory, the Bayesian framework, and other theoretical models. The PRKD effectively detects and monitors gas intrusion and overflow based on single parameters by analyzing the shape and wave characteristics of the curves. The case analysis demonstrates that the proposed method for monitoring drilling overflow achieves high precision while maintaining a low false positive rate. By combining advanced computational techniques with pattern recognition algorithms, the PRKD improves the accuracy and reliability of kick detection, enabling proactive responses to potential risks, protecting the environment and human lives, and optimizing drilling operations.

Gastric cancer is an aggressive disease with increasing global incidence in recent years. Human epidermal growth receptor 2 (HER2) is overexpressed in approximately 10-20% of gastric cancers. The implementation of targeted therapy against HER2 as part of the standard of care treatment in metastatic disease has improved the prognosis of this subset of patients. However, gastric cancer still has high mortality rates and urgently requires new treatment strategies. The combination of immunotherapy with HER2-targeted therapies has shown synergistic effects in preclinical models, being the rationale behind exploring this combination in clinical trials in locally advanced and metastatic settings. Additionally, the irruption of antibody-drug conjugates and other novel HER2-targeted agents has led to the development of numerous clinical trials showing promising results. This review presents the molecular mechanisms supporting the use of HER2-targeted drugs in combination with immunotherapy and provides an overview of the therapeutic scenario of HER2-positive disease. We focus on the role of immunotherapy but also summarize emerging therapies and combinations under clinical research that may change the standard treatment in HER-2 positive disease in the future.

The massive use of electronic equipment has raised concerns about electromagnetic (EM) pollution, leading to increased attention to EM shielding in both civil and military applications. EM absorption materials play a crucial role in reducing the energy of microwaves projected onto their surface. In this work, we propose a novel OTA (over-the-air) antenna test method to evaluate and analyze the functionality of EM absorption materials. This method offers a new and practical way to assess the effectiveness of these materials. Measured results indicate that the presence of the EM absorption material led to a decrease in antenna gain of approximately 2-3 dB, a reduction in efficiency of more than 10-15%, and a smaller radiation pattern during the actual testing process. These findings highlight the impact of EM absorption materials on antenna performance. Compared to the traditional S-parameter testing method, the gain comparison testing method exhibits the advantages of higher accuracy and simpler operation in the lower frequency experiment.

: This study focuses on optimizing chlorophyll extraction techniques and strategically placing leaf disks to enhance chlorophyll concentration in sesame (Sesamum indicum L.) leaves. Thirty sesame genotypes, categorized into light green (LG), middle green (MG), and deep green (DG) pigment groups based on leaf coloration, were selected from a larger pool of field-grown accessions. The investigation involved determining optimal SPAD value index measurements, quantifying pigment concentrations, exploring extraction solvents, and selecting suitable leaf disk positions. Significant variations in chlorophyll content were observed across genotypes, greenness categories, and leaf disk positions. The categorization of genotypes into DG, MG, and LG groups revealed a correlation between leaf appearance and chlorophyll content. The study highlighted a consistent relationship between carotenoids and chlorophyll, indicating their role in adaptation to warm environments. Examination of leaf disk positions revealed a significant chlorophyll gradient along the leaf blade, emphasizing the need for standardized protocols. Chlorophyll extraction experiments identified DMSO and 96% ethanol, particularly the later incubated for 10 minutes at 85°C, as effective choices. This recommendation considers factors like cost-effectiveness, time efficiency, safety, and environmental regulations, ensuring consistent and simplified extraction processes. For higher chlorophyll extraction, focusing on leaf tips and the 75% position along the sesame leaf blade is suggested, consistently yielding higher chlorophyll content. Our findings therefore, underscores insightful knowledge on chlorophyll gradients and encouraging for standardized protocols, that enabling researchers to refine experimental designs for precise and comparable chlorophyll measurements. Recommended solvent choices ensure reliable outcomes in plant physiology, ecology, and environmental studies.

L-gulonolactone oxidase enzyme (GULO) catalyzes the last step of L-ascorbic acid (vitamin C) biosynthesis. This enzymatic activity is lost in primates. The full-length rat GULO has been previously produced in plants and demonstrated to be active. In this study, we compare activity of two variants of GULO produced in Escheriachia coli cells, full-length rat GULO (fGULO) and its C-terminal catalytic domain (cGULO). The expression and purification of the recombinant proteins were optimised and their biological activity was confirmed by two methods, the GULO activity assay in the protein extracts and the ‘in-gel’ staining for GULO activity. Both variants of recombinant GULO were biologically active in both assays. However, cGULO is more promising than fGULO for ascorbic acid production because it is more efficiently produced by bacteria.

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.

In order to study the effects of irradiation on the physicochemical properties and digestion behavior of chickpea protein, chickpea protein concentrate (CPC) was treated with electron beam irradiation (EBI) at doses of 5, 10, 15 and 20 kGy, respectively. After irradiation, protein solubility first increased at 10 kGy and 15 kGy, and then decreased at higher dose of 20 kGy. This was supported by SDS-PAGE where the intensity of major protein bands first increased and then decreased. Increased does of EBI generally led to greater oxidative modification of proteins in CPC, indicated by reduced sulfhydryls and increased carbonyls. In addition, protein structure was modified by EBI shown by Fourier transform infrared spectroscopy analysis where α-helix generally decreased and β-sheet increased. Although the protein digestibility was not significantly affected by EBI, the peptidomic analysis of the digests revealed significant difference among CPC irradiated with varying doses. A total of 337 peptides were identified from CPC irradiated with 0 kGy, 10 kGy and 20 kGy, respectively, with 18 overlapping peptides and 60, 29 and 40 peptides specific to the groups of 0, 10 and 20 kGy respectively. Theoretical calculation showed that the distribution of peptide length, hydrophobicity, net charge, and C-terminal residues were affected by irradiation. The 2, 2'-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) radical scavenging activity showed a marginal decrease with increasing dose of irradiation. In conclusion, EBI led to oxidative modification and structural changes of the chickpea protein, which subsequently affected the physicochemical properties of peptides obtained from in-vitro digestion of CPC despite of similar digestibility.

The structure and physicochemical properties of polyvinyl alcohol (PVA) and PVA/glycerol films have been investigated by Fourier-transform infrared spectroscopy (FT-IR), X-ray Diffraction (XRD), thermogravimetry/differential thermal analysis (TG/DTA), and advanced scanning probe microscopy (SPM). In the pure PVA films, SPM allowed us to observe ribbon-shaped domains with a different frictional and elastic contrast, which apparently originated from a correlated growth or assembly of PVA crystalline nuclei located within individual PVA clusters. The incorporation of 1.0 wt.% glycerol led to modification in shape of those domains from ribbon-like in pure PVA to rounded in PVA/glycerol 1.0 wt.% films; changes in the relative intensities of the XRD peaks and a decrease of the amorphous halo in the XRD pattern were also detected, while the DTA peak corresponding to the melting point remained at almost the same temperature. For higher glycerol content, FT-IR revealed additional glycerol-characteristic peaks presumably related to the formation of glycerol aggregates, and XRD, FT-IR, and DTA all indicated a reduction in crystallinity. For more than 2.0 wt.% glycerol, the plasticization of the films complicated the acquisition of SPM images without tip-induced surface modification. Our study contributes to the understanding of crystallinity in PVA and how it is altered by a plasticizer such as glycerol.

(S)-Atenolol ((S)-2-(4-(2-Hydroxy-3-(isopropylamino)propoxy)phenyl)acetamide) has been synthesized in >99% ee with use of Candida antarctica lipase B from Syncozymes (Shanghai, China), in a kinetic resolution of the corresponding racemic chlorohydrin. Deprotonation of the starting phenol building block was base catalyzed. The enantiopurity of the chlorohydrin building block remained unchanged upon subsequent amination. All four steps in the synthesis protocol have been optimized compared to previously reported methods, which makes this new protocol more sustainable and in accordance with green chemistry principles compared to previously reported protocols.

This paper presents a piezoresistive pressure sensor with a shield layer for improved stability. Compared with the conventional piezoresistive pressure sensors, p-type piezoresistors are covered by an n-type shield layer, which is formed by ion implantation. The proposed pressure sensors have been successfully fabricated by bulk micromachining techniques. The impact of electrical field on piezoresistors is studied by simulation. The temperature drift of the pressure sensor has been investigated by both simulation and experimental measurement. Characteristics of developed pressure sensors are tested from -40 C to 125 C. A sensitivity of 0.022 mV/V/KPa and a maximum non-linearity of 0.085% FS are measured for the fabricated sensor in a pressure range of 1 MPa. The temperature coefficients of resistance of shielded piezoresistors are found to be smaller than those of un-shielded ones. It is demonstrated that the shield layer is able to reduce the drift caused by electrical field and ambient temperature variation.

Proactivity is a particularly important attribute of knowledge-intensive organizations, where the work required to increase the potential of knowledge-intensive employees is crucial. A priority challenge for these firms is also to understand the increasing importance of the functioning of cognitive mechanisms leading to increased knowledge workers' well-being following the implementation of the psychological contract. The aim of this article is to identify the relationship between inclusive leadership, the fulfillment of the psychological contract, two dimensions of well-being: workplace and life well-being and knowledge workers' proactivity. Based on survey data collected through the CAWI method from 1.000 knowledge workers employed in Polish companies in the business services sector, the research hypotheses were tested using a stepwise equation modelling (SEM) technique, leading to a model containing all the main constructs. The results obtained indicate that inclusive leadership positively impacts the fulfillment of the psychological contract. Furthermore, the fulfillment of the psychological contract positively affects proactive working behavior with and the wellbeing of knowledge workers. Along with proactive work behavior, two dimensions of well-being, were examined as outcome variables. The analysis also shows that knowledge-intensive organizations, intending to build the proactivity of their employees and nurture a high level of well-being in their lives and in the workplace, should take care to fulfil the expectations and obligations of the psychological contract. However, one way to achieve this is for managers to use an inclusive management style, which supports an atmosphere of a safe working environment in a diverse setting and leads to employees feeling comfortable sharing their opinions and ideas. The study of inclusive leadership in the context of knowledge-intensive organizations provides human resource professionals and employee man-agers with important insights into how inclusive leadership can effectively contribute to the psychological contract, which will consequently lead to proactive work behavior and also improve employees' workplace and life well-being.

A low cost, scalable and robust process has been proposed as innovative method for coating solar mirrors with a self-cleaning, transparent in the full solar range and versatile material based on auxetic aluminium nitrides, previously obtained on the laboratory scale. This work presents the scaling-up of fabrication process from laboratory to prototypal scale and pre-liminary results of outdoor self-cleaning solar mirror prototypes testing in the demonstrative CSP plant ENEASHIP located in Casaccia (Rome) ENEA research centre. Prototypes 50x40cm have shown stability in external conditions: no degradation occurs during the test campaign. Their washing restores the initial reflectance affected by soiling and the self-cleaning performance allows the utilization of a reduced quantity of water for cleaning operations with respect to uncoated glass of back surface mirrors. A similar self-cleaning AlN coating could be utilized on other solar components affected by soiling, such as glass envelopes of heat collecting elements, PV panels and everywhere are required self-cleaning performance combined to optical ones.

In this paper, the distributed formation tracking control problem of quadrotor unmanned aerial vehicles (UAVs) is considered. Adaptive backstepping technology is used to design flight control schemes for quadrotor UAVs. For the position subsystem, a distributed robust formation tracking control scheme is developed to achieve formation flight of quadrotor UAVs and track the desired flight trajectory. For the attitude subsystem, an adaptive disturbance rejection control scheme is proposed to achieve attitude stabilization during UAV flight under uncertain disturbances. Compared with the existing results, the novelty of this paper is that the proposed formation control scheme does not require the use of the quadrotor model parameters. Finally, a quadrotor UAV swarm system is used to verify the effectiveness of the proposed control scheme.

The increasing popularity of Robo-Advisors highlights the need for sophisticated systems combining big data analytics and deep learning for portfolio optimization. Despite their potential, these AI-driven platforms face notable challenges such as financial data inaccuracies and the risk of overfitting in deep learning models. Overcoming these hurdles requires a concerted effort to enhance reliability and performance. To tackle these challenges effectively, it is imperative to prioritize real-time data integration while minimizing reliance on historical data. By adopting this approach, Robo-Advisors can better adapt to dynamic market conditions, enabling them to provide informed and timely investment recommendations. This proactive stance not only ensures computational efficiency but also fosters investor confidence in the platform's ability to navigate evolving market dynamics. In conclusion, by continually refining data integration methods and reducing dependencies on historical data, Robo-Advisors can offer more accurate and timely recommendations. This not only enhances the overall investor experience but also upholds ethical standards in automated investment platforms.

In this paper, we present a novel approach for managing the file system in Linux using a voice assistant. Our system allows users to perform file system operations such as creating directories, renaming files, and deleting files by issuing voice commands. We develop a voice assistant using Python libraries and integrate it with the file system in Linux. The voice assistant is capable of understanding natural language and executing commands based on the user's voice inputs. We conduct experiments to evaluate the performance of the system and demonstrate that our approach is effective and efficient in managing the file system using voice commands. Our system can enhance the accessibility and usability of the file system in Linux for individuals with disabilities or those who prefer a hands-free approach to file management.

There is a need to search for new antifungals, especially for the treatment of the invasive Candida infections, caused mainly by C. albicans. These infections are steadily increasing at an alarming rate, mostly among immunocompromised patients. The newly synthesized compounds (3a-3k) were characterized by physico-chemical parameters and investigated for antimicrobial activity using the microdilution broth method to estimate minimal inhibitory concentration (MIC). Additionally, their antibiofilm activity and mode of action together with the effect on the membrane permeability in C. albicans were investigated. Biofilm biomass and its metabolic activity were quantitatively measured using crystal violet (CV) staining and tetrazolium salt (XTT) reduction assay. The cytotoxic effect on normal human lung fibroblasts and hemolytic effect were also evaluated. The results showed differential activity of the compounds against yeasts (MIC = 0.24-500 µg/mL) and bacteria (MIC = 125-1000 µg/mL). Most compounds possessed strong antifungal activity (MIC = 0.24-7.81 µg/mL). The compounds 3b, 3c, and 3e, showed no inhibitory (at 1/2 MIC) and eradication (at 8 x MIC) effect on C. albicans biofilm. Only slight decrease in the biofilm metabolic activity was observed for compound 3b. Moreover, the studied compounds increased the permeability of the membrane/cell wall of C. albicans and their mode of action may be related to action within the fungal cell wall structure and/or within the cell membrane. It is worth noting that the compounds had no cytotoxicity effect on pulmonary fibroblasts and erythrocytes at concentrations showing anticandidal activity. The present studies in vitro confirm that these derivatives appear to be a very promising group of antifungals for further preclinical studies.

The rapid development of the Industrial Internet of Things (IIoT) has brought enormous opportunities to the industrial environment but has also posed significant challenges to network security, especially the physical layer security challenges,which is crucial for the reliable transmission of messages interconnected devices and sensors. Focus on the enhancing the physical layer security in the IIoT communication, this paper proposed a mechanism of Spatial Secrecy Outage Probability (SSOP) to ensure the secure transmission of data. The SSOP algorithm assumes that the location of legitimate device is known, while eavesdropper on the random distribution of device locations, forming the SSOP model related to device locations from the perspective of insecure regions (ISR) and secure regions (SR), and deduce the closed-form expression for its upper bound. Subsequently under the constraint of SSOP conditions, establish an optimization model with the objective of system throughput maximization based on Spatial Secrecy Outage Probability(SRM-SSOP). By optimizing the transmission rate of legitimate channels or controlling the transmission power, the maximization of system throughput can be achieved, thus reducing the consumption of communication resources. Through sophisticated modeling and simulation in the MATLAB, we verified the accuracy of the definition and derived upper bound of SSOP. The experimental results also show that SSOP algorithm gradually converges to a specific value as the number of antenna elements increases. In addition, we observed that when the receiver device is aligned with the normal direction of the transmit antenna array, the SSOP reaches a minimum value. These performance are crucial for understanding and optimizing the security performance of IIoT communication systems.

Urbanization is considered as a main indicator of regional economic development due to its positive effect on promoting industrial development; however, many regions, especially developing countries, are troubled by its negative effect — the aggravating environmental pollution. Many researchers have indicated that rapid urbanization stimulated the expansion of industrial production scale and increased industrial pollutant emissions. However, this judgement contains a grave deficiency in that urbanization not only expands industrial production scales but can also increase industrial labour productivity and change the industrial structure. To modify this deficiency, we first decompose the influence which urbanization impacts on industrial pollutant emissions into the scale effect, the intensive effect and the structure effect by using the Kaya Identity and the LMDI Method; second, we perform an empirical study of the three effects’ impacts by applying the spatial panel model with data from 282 Chinese cities between 2003 and 2013. Our results indicate that (1) there are significant reverse U-shapes between Chinese urbanization rate and its industrial pollutant emissions; (2) the scale effect and the structure effect have aggravated Chinese industrial waste water discharge, sulphur dioxide emissions and soot (dust) emissions, while the intensive effect has generated a decreasing and ameliorative impact on that aggravated trend. The definite relationship between urbanization and industrial pollutant emissions depends on the combined influence of the scale effect, the intensive effect and the structure effect; (3) there are significant spatial autocorrelations of industrial pollutant emissions between Chinese cities, but the spatial spillover effect from other cities does not aggravate local urban industrial pollutant emissions, we offer an explanation to this contradiction that the vast rural areas surrounding Chinese cities have served as sponge belts and have absorbed the spatial spillover of cities’ industrial pollutant emissions. According to the results, we argue that this type of decomposition of the influence into three effects is necessary and meaningful, it establishes a solid foundation for understanding the relationship between urbanization and industrial pollutant emissions, and effectively helps to meet relative policy making.

In this study, we evaluated how gut microbes associated with hepatopancreatic and gonadal expression genes impact on growth traits in different sex of M. rosenbergii. By comparing the relative abundance of gut microbes between the male (high weight gain, HiWG) and female (low weight gain, LoWG) animals, we have shown that at the phyla, family and genera level, the abundance of microbes existed significantly difference between both group. At the genera level, the abundance of Candidatus Hepatoplasma and unidentified Chloroplast lower in the LoWG animals than in the HiWG animals, and Lactobacillus and Ligilactobacillus have opposed trend. The abundance of Candidatus Hepatoplasma and unclassfied Chloroplast was associated with the growth traits in the HiWG group. The Lolium perenne microbial species belong to unclassfied Chloroplast genera have tight association with Ralstonia pickettii, both might association on Geobacter anodirecteducens abundance, which correlation with the expression of glutathione peroxidase and collagen, which were highly expressed in the hepatopancreas and gonads of HiWG group animals of M. rosenbergii and might promote growth and development. These associations were not detected in the female LoWG group.

Aim: To investigate the effects of intensive physical exercise on retinal microvascular regulation in healthy volunteers by the adaptive optics retinal camera measurement. Methods: We included healthy volunteers (11 men and 14 women) aged 20.6± 0.9. Heart rate (HR) and systolic and diastolic blood pressure (SBP, DBP) were recorded before and after a submaximal physical exertion of continuously riding a training ergometer. The superior temporal retinal artery measurements were captured using the adaptive optics (AO) camera rtx1TM (Imagine Eyes, Orsay, France) without pupil dilatation. We compared measures of vessel diameter (VD), lumen diameter (LD), two walls (Wall 1, 2), wall-to-lumen ratio (WLR), and wall cross-sectional analysis (WCSA) before and immediately after the cessation of exercise. Results: Cardiovascular parameters: After exercise, SBP, DBP and HR changed significantly from 130.2±13.2 to 159.7±15.6 mm Hg, 81.2±6.3 to 77.1±8.2 mm Hg and 80.8±16.1 to 175.0±6.2 bpm, respectively (p < 0.002). Retinal microcirculation analysis: Before the exercise, the mean LD was 96.0±6.8, VD 118.5±8.3, WLR 0.234±0.02, WCSA 3802.8±577.6. The mean LD and Wall 1 did not change significantly after exercise but slightly decreased. The VD, Wall 2, WLR and WCSA decreased significantly (p

Lily tepals have a short lifespan. Once the tepals senesce, the ornamental value of the flower is lost. Some cultivars have attractive purple ovaries and fruits which greatly enhance the ornamental value of Asiatic hybrid lilies. However, little is known about the molecular mechanisms of anthocyanin biosynthesis in Asiatic hybrid lily ovaries. To investigate the transcriptional network that governs purple ovary coloration in Asiatic hybrid lilies, we obtained transcriptome data from green ovaries (S1) and purple ovaries (S2) of Asiatic ‘Tiny Padhye’. Comparative transcriptome analysis revealed 4228 differentially expressed genes. Differential expression analysis revealed that nine unigenes including four CHS genes, one CHI gene, one F3H gene, one F3′H gene, one DFR gene, and one UFGT gene were significantly up-regulated in purple ovaries. One MYB gene, LhMYB12-Lat, was identified as a key transcription factor determining the distribution of anthocyanins in Asiatic hybrid lily ovaries. Further qPCR results showed unigenes related to anthocyanin biosynthesis were highly expressed in purple ovaries of Asiatic ‘Tiny Padhye’ at stages 2 and 3, while they showed an extremely low level of expression in ovaries of Asiatic ‘Yellow Pixels’ during all developmental stages. In addition, shading treatment significantly decreased pigment accumulation by suppressing the expression of several unigenes related to anthocyanin biosynthesis in ovaries of Asiatic ‘Tiny Padhye’. These results could further our understanding of the molecular mechanisms of anthocyanin biosynthesis in Asiatic hybrid lily ovaries.

This paper analyzed the main diseases of the friction high-strength bolt (FHSB) joints in order to study the characteristics of the corrosion products of the joints in steel bridge and the influence factors. Seven representative FHSB connection pairs and eight steel plate samples were selected from the Dongying Shengli Yellow River Bridge, which has been in operation for 33 years. Then Scanning Electron Microscope (SEM), Energy Dispersive Spectroscopy (EDS) and X-Ray Diffraction (XRD) were used to analyze the microscopic morphology and chemical composition of the corroded surface of the samples. Finally, this paper discussed the relationship between the corroded sample surface and the environmental corrosion. The research results showed that construction quality problems, such as non-standard hole expansion, segmental slab joint dislocation and contact surface pollution, accelerated corrosion of the contact surface. With the increase of corrosion degree of joints, the contact surface of aluminum spraying layer gradually changed from a rugged and dense state to a smooth and porous powder state. With the corrosion of the steel plate substrate, the contact surface gradually changed from a fluffy stratified state to a surface bonding state. It was inferred that the friction coefficient of the FHSB connection node on the corroded aluminum sprayed contact surface first decreased and then increased. The corroded node samples detected not only a large number of S and Cl elements, but also the oxides of Mn, Si and other elements as well as FeS. It was speculated that node corrosion was also related to atmospheric (acid rain) and industrial dust and other corrosive environments. Almost all samples detected the component SiO2 of the Yellow River soil, as well as Mg, Ca, K, Na and other elements at the same time, which is basically consistent with the component elements of the Yellow River soil. Therefore, it was inferred that it was related to the inclusion of the Yellow River soil.

In recent years, the evaluation of many plant-derived compounds as potential new drugs or functional foods has become an active research topic. The characteristics of quinces of the genera Cydonia, Chaenomeles and Pseuocydonia are largely similar, which is why these fruits are often confused. Although they have been appreciated in Asia for centuries as a valuable component of local ethnomedicine, they are less known in Western countries and scientific knowledge about their health benefits remains fragmentary. This literature review presents the bioactive compounds of these fruits with special emphasis on antioxidants. Most space has been devoted to the presentation of the biological activities and potential medical applications of the fruits due to their antioxidant, anti-inflammatory, anticancer, cardioprotective and antidiabetic effects.

Quaking aspen (Populus tremuloides Michx.) landscapes are valued for their biodiversity, water retention, fire mitigation, aesthetics, and recreation opportunities. Across North America, some aspen populations are experiencing population declines as they face uninhibited ungulate browsing, drought, fire suppression, insects, disease, and inappropriate management. Increased human development and recreational use within aspen landscapes can serve as additive stressors, though there is a dearth of literature examining these elements. At a popular recreational area in Utah, USA, identifying the cause of apparent decline within a larger aspen community is complicated by development upstream and recreation-related activities. We sought to: 1) assess the overall condition of the aspen at the site, 2) understand key variables that influence aspen conditions, and 3) elucidate how aspen fitness varies across the site. We collected data from forty-five plots using established aspen sampling methods, including ungulate presence, tree characteristics, soil chemistry, and environmental descriptors. Results suggest that a combination of higher levels of browsing and elevated soil sodium may be causing premature mortality and limiting aspen recruitment in a portion of the study area. These findings will inform future management at this site, as well as similar recreational forest settings experiencing compound stressors.

COVID-19 is the most severe health crisis of the 21st century. COVID-19 presents a threat to almost all countries world-wide. The restriction of human mobility is one of the strategies used to control the transmission of COVID-19. However, it has yet to be determined how effective this restriction is in controlling the rise in COVID-19 cases, particularly in major capital cities such as Jakarta, Indonesia. Using Facebook's mobility data, our study explores the impact of restricting human mobility on COVID-19 case control in Jakarta. Our main contribution is showing how the restriction of human mobility data can give important information about how COVID-19 spreads in different places. We proposed modifying a global regression model into a local regression model by accounting for the spatial and temporal interdependence of COVID-19 transmission across space and time. We applied Bayesian hierarchical Poisson spatiotemporal models with spatially varying regression coefficients. We estimated the regression parameters using an Integrated Nested Laplace Approximation. We found that the local regression model with spatially varying regression coefficients outperforms the global regression model based on DIC, WAIC, MPL, and R2 criteria for model selection. In Jakarta's 44 districts, the impact of human mobility varies significantly. The impacts of human mobility on the log relative risk of COVID-19 range from –4.445 to 2.353. The prevention strategy involving the restriction of human mobility may be beneficial in some districts but ineffective in others. Therefore, a cost-effective strategy had to be adopted.

In the conservation of monumental heritage, the collection and utilization of information are of primary importance. The Heritage Building Information Modeling (HBIM) procedure harnesses the potential of three-dimensional models, offering significant advantages in accessing documentation, interoperability, multidimensionality of intervention design, cost evaluation and maintenance management. Attention here is focused on the Certosa di Pisa, a large historical complex built in the 14th century as a monastery of the Carthusian order, currently in a state of deterioration and in need of restoration and re-functionalization. The multifaceted nature of this monumental complex, with its intricate interplay of architectural elements spanning different historical periods and featuring diverse techniques, poses a significant challenge for structural safety assessment. This case study presents an opportunity to explore an HBIM approach to streamline the diagnostic process and facilitate the intervention design phase. The goal is achieved by utilizing an accurate 3D model enriched with data from multiple sources and automating certain operations for structural assessment. The usefulness of the HBIM methodology is highlighted as a valuable tool in the realm of Cultural Heritage structures for both technicians and scholars alike.

This paper focuses on enhancing the object detection and decision-making capabilities of Autonomous Emergency Medical Response Systems (AEMRS). Using advanced sensors, Convolutional Neural Networks (CNN), and reinforcement learning, we propose a model that processes environmental data to identify obstacles and make optimal navigation decisions. The integration of these technologies aims to minimize response time and improve the efficiency of emergency medical services. The perception system utilizes radar, LiDAR, and camera inputs to create a comprehensive understanding of the environment. These data are processed through a series of CNN layers to detect and classify objects such as vehicles, pedestrians, and road signs. On the decision-making front, a Q-learning algorithm is employed to enable the ambulance to learn from its interactions with the environment, continuously improving its route planning and collision avoidance strategies. By combining these advanced AI techniques, the proposed AEMRS can significantly enhance the speed and reliability of emergency responses, ultimately saving more lives. This paper presents the design, implementation, and simulation results of the proposed system, demonstrating its potential to revolutionize emergency medical services.

As the fast growth of information technology (IT), the safety of image transmission and the storing of images are increasingly concerned. Traditional image encryption algorithms have certain limitations in transmission and security, so there is an urgent need for a secure and reliable image encryption algorithm. A universal compression sensing (CS) image encryption (IE) algorithm based on DNA-triploid mutation (DTM) is presented in this paper. Firstly, by using CS algorithm an image is compressed while obtaining a range of chaotic sequences by iteration of chaotic map. Then DNA sequences are generated by encoding the image, and based on the DTM, new mutant DNA sequences are generated according to specific rules. Next, the chaotic sequences are operated at the DNA level to do confusion and diffusion operations on the image to ensure the security of the data. At last, DNA decoding is carried out to obtain the compressed-encrypted image. Both simulation experiments and performance tests fully show that a high level of security and reliability of the proposed algorithm in protecting image privacy is achieved.

Attribute reduction is a core technique in the rough set domain and an important step in data preprocessing. Researchers have proposed numerous innovative methods to enhance the capability of attribute reduction, such as the emergence of multi-granularity rough set models, which can effectively process distributed and multi-granularity data. However, these innovative methods still have many shortcomings, such as how to deal with complex constraints and how to perform multi-angle effectiveness evaluations. Based on the multi-granularity model, this study proposes a new method of attribute reduction, namely using multi-granularity neighborhood information gain ratio as the measurement criterion. This method combines both supervised and unsupervised perspectives, and by integrating multi-granularity technology with neighborhood rough set theory, constructs a model that can adapt to multi-level data features. This model can select the optimal granularity level or attribute set according to the requirements. Finally, the proposed method is experimented with on 15 UCI datasets against six other attribute reduction algorithms. The experimental outcomes, upon analysis and comparative review, substantiate the reliability and consistency of the attribute reduction approach introduced in this study.

Snow cover products are primarily derived from the Moderate-resolution Imaging Spectrometer (MODIS) and Advanced Very High-Resolution Radiometer (AVHRR) datasets. MODIS achieves both snow/non-snow discrimination and snow cover fractional retrieval, while early AVHRR-based snow cover products only focused on snow/non-snow discrimination. The AVHRR Climate Data Record (AVHRR -CDR) provides a nearly 40-year global dataset that has a potential to fill a gap in long term snow cover fractional monitoring. Our study selects the Qinghai-Tibet Plateau as the experimental area, utilizing AVHRR-CDR surface reflectance data (AVH09C1) and calibrating with MODIS snow product MOD10A1. The snow cover percentage retrieval from the AVHRR dataset is performed using Surface reflectance at 0.64μm (SR1) and Surface reflectance at 0.86μm (SR2), along with a simulated Normalized Difference Snow Index (NDSI) model. Also, in order to detect the effects of land cover type and topography on snow inversion, we tested the accuracy of the algorithm with and without these influences, respectively (vanilla algorithm & improved algorithm). The accuracy of the AVHRR snow cover percentage data product is evaluated using MOD10A1、 ground snow-depth measurements and ERA5. Results indicate that the logic model based on NDSI has the best fitting effect, with R-square and RMSE values of 0.83 and 0.10, respectively. Meanwhile, the accuracy was improved after taking into account the effects of land cover type and topography. The model is validated using MOD10A1 snow-covered areas, showing snow cover area differences of less than 4% across 6 temporal phases. Improved algorithm results in better consistency with MOD10A1 than vanilla algorithm. Moreover, the RMSE reaches greater levels when the elevation is below 2,000 meters or above 6,000 meters, and is lower when the slope is between 16° and 20°. Using ground snow-depth measurements as ground truth, the multi-year recall rates are mostly above 0.7, with an average recall rate of 0.81. Results also show a high degree of consistency with ERA5. The validation results demonstrate that the AVHRR snow cover percentage remote sensing product proposed in this study exhibits high accuracy in the Tibetan Plateau region, also demonstrating that land cover type and topographic factors are important to the algorithm. Our study lays the foundation for the global snow cover percentage product based on AVHRR-CDR，furthermore lays a basic work for generating a long-term AVHRR-MODIS fractional snow cover dataset.

This work investigated the isothermal holding time dependence of the densification, microstructure, weight loss and tensile properties of Fe-Mn-Si powder compacts. Elemental Fe, Mn and Si powder mixtures with a nominal composition of Fe-28Mn-3Si (in weight percent) were ball milled for 5h and subsequently pressed under a uniaxial pressure of 400 MPa. The compacted Fe-Mn-Si powder mixtures were sintered at 1200 ℃ for 0, 1, 2 and 3 h, respectively. In general, the density, weight loss and tensile properties increased with the increase of isothermal holding time. A significant increase in density, weight loss and tensile properties occurred in the compacts isothermally holding for 1 h, as compared to those with no isothermal holding. However, further extension of isothermal holding time (2 and 3 h) only played a limited role in promoting the density and tensile properties. The weight loss of the sintered compacts was mianly caused by the sublimation of Mn in Mn depletion region on the surface layer of the sintered Fe-Mn-Si compacts. The length of the Mn depletion region increased as isothermal holding time increased. A single α-Fe phase was detected on the surface of all the sintered compacts, and the locations beyond the Mn depletion region were comprised of a dual dominant γ-austenite and minor ε-martensite.

An approach of implementing X-bar and R control charts as a statistical control tool to monitor changes in the melting profile of fresh and stored flaxseed oils by differential scanning calorimetry (DSC) was used. Phase transition melting profiles were collected after 0, 2, 4, 6 months of storing flaxseed oils, originated from five different cultivars. Four peaks at around -36, -30, -25, -12 °C were identified using the deconvolution analysis procedure, which enabled data to be collected on peak temperature (T), peak height (h) and the peak area (A), as well as the ratio calculated from these parameters. Control charts of DSC parameters, linked to the second peak (h2, A2) and calculated ratios of those parameters showed an increasing or decreasing trend within the storage time, thus were considered to be indicators of oil deterioration. Since DSC parameters related to the first peak (h1, A1) and third peak (h3, A3) remained unchanged within storage, they were established as the markers of flaxseed oil authenticity.

This article comprehensively explores multiple aspects of cryptocurrencies and their price forecasting. Firstly, the article introduces the definition of cryptocurrency and its development process on a global scale, especially focusing on the launch of Facebook Libra and China's central bank digital currency, highlighting the importance and influence of digital currency in the global financial market. Subsequently, the article analyzes the advantages of digital currencies over traditional currencies, including improving economic transaction efficiency, reducing transaction costs and enhancing transaction transparency. Meanwhile, the article also explores the challenges and risks potentially brought by the development of digital currencies, such as regulatory uncertainty and market volatility. In this context, the article raises the importance of cryptocurrency price forecasting and introduces the forecasting models and techniques commonly used today. Finally, through specific experimental analysis, the effectiveness of using the deep learning model CNN-LSTM to predict the price of Bitcoin is demonstrated, and the directions of future research and optimization strategy are proposed. In summary, this paper comprehensively presents the research status and prospects of cryptocurrency and its price prediction field through systematic introduction and analysis.

In this study, long-term mortality effects associated with exposure to PM10, PM2.5, BC (black carbon), and NOx were analyzed in a cohort in southern Sweden during the period from 1991‒2016. Participants (those residing in Malmö, Sweden, born between 1923‒1950) were randomly recruited from 1991‒1996. At enrollment, 30,438 participants underwent a health screening, which consisted of questionnaires about lifestyle and diet, a clinical examination, and blood sampling. Mortality data were retrieved from the Swedish national cause of death register. The modeled concentrations of PM10 (particles with an aerodynamic diameter smaller than or equal to 10 µm), PM2.5 (particles with an aerodynamic diameter smaller than or equal to 2.5 µm), BC (black carbon), and NOx (nitrogen oxides) at the cohort participants' home addresses were used to assess air pollution exposure. Cox proportional hazard models were used to estimate the associations between long-term exposure to PM10, PM2.5, BC, and NOx and the time until death among the participants during the period from 1991‒2016. The hazard ratios (HRs) associated with an interquartile range (IQR) increase in each air pollutant were calculated based on the exposure lag windows of the same year (lag0), 1‒5 years (lag1‒5), and 6‒10 years (lag6‒10). Three models were used with varying adjustments for possible confounders including both single-pollutant estimates and two-pollutant estimates. With adjustments for all covariates, the HRs for PM10, PM2.5, BC, and NOx in the single-pollutant models at lag1‒5 were 1.06 (95% CI: 1.02‒1.11), 1.01 (95% CI: 0.95‒1.08), 1.07 (95% CI: 1.04‒1.11), and 1.11 (95% CI: 1.07‒1.16) per IQR increase, respectively. The HRs were in most cases decreased by the inclusion of a larger number of covariates in the models. The most robust associations were shown for NOx, with statistically significant positive HRs in all models. An overall conclusion is that road traffic-related pollutants had a significant association with mortality in the cohort.

Lirianthe delavayi has valuable ornamental and medicinal value, is an important economic tree, and has a long history of cultivation. As important regulatory molecules, miRNAs are involved in important physiological processes, such as the plant stress response, organ morphogenesis, hormone secretion, and signaling. In this study, based on miRNA sequencing analysis of wild and cultivated Lirianthe delavayi, we explored the differences in posttranscriptional regulation during the cultivation of Lirianthe delavayi from the aspect of miRNA molecular regulation. The results revealed the following: 1. A total of 365 miRNAs were sequenced and identified, and their first bases had obvious uracil (U) base preferences. Among them, 242 miRNAs were classified into 32 miRNA families; 2. Analysis of miRNA expression differences revealed that 39 miRNAs had significant expression differences between wild and cultivated populations; 3. Bioinformatics target gene prediction and target gene enrichment analysis of significantly differentially expressed miRNAs revealed that the target genes were significantly enriched in plant‒pathogen interactions, sphingolipid metabolism, defense responses, and ADP binding. 4. The plant‒pathogen interaction pathway involves seven differentially expressed miRNAs and 30 miRNA‒target gene regulatory pairs, which are involved mainly in pathogen signature molecule recognition and binding, signal transduction, and the regulation of disease resistance molecules or gene expression; Ath-MIR169g-p3_2ss7AG19GA, ath-MIR169g-p5_2ss7AG19GA, and ath-miR408-3p_L-3 are expressed only in wild population, and it is hypothesized that the domestication process from wild to cultivation species may be accompanied by the loss of disease resistance-related miRNAs. This study provides a reference for exploring the posttranscriptional-level regulatory mechanisms of adaptive changes in Lirianthe delavayi from wild to cultivated.

Within the fields of English for Academic Purposes (EAP) and English for Research Publication Purposes (ERPP), there is an unquestioned orthodoxy that scholars with English as an Additional Language (EAL) are particularly disadvantaged by the pressure to publish in English (though see Kuteeva 2015 and debate between Hyland 2016a, 2016b and Politzer-Ahlesa et al. 2016). In this paper, I challenge this orthodoxy, raising questions about the evidence upon which it is based. Within a framework of ‘verbal hygiene’ (Cameron 1995, 2012), I will argue that the attention accorded to ‘non-nativeness’ may be disproportionate to its significance for publication success. I conclude by proposing some reorientations for researchers and practitioners in the field that centre on broadening the scope to encompass non-linguistic structures of inequity.

Menin inhibitors are new and promising agents currently in clinical development that target the HOXA/MEIS1 transcriptional program which is critical for leukemogenesis in histone-lysine N-methyltransferase 2A rearranged (KMT2Ar) and in NPM1-mutated (NPM1mut) acute leukemia. The mechanism of action of this new class of agents is based on the disruption of menin-KMT2A complex (constitute of chromatin remodelling proteins), leading to the differentiation and apoptosis of AML cells expressing KMT2A or mutated NPM1. This new class of drugs has been tested alone, or in combination with synergistic drugs, in phase I and II clinical trials (AUGMENT-01 and 02, KOMET-01 and 07, SAVE, COVALENT trial and others), showing promising results in terms of response rates and safety in heavily pre-treated acute leukemia patients. In this review, we summarise the key findings on menin inhibitors, focusing on the mechanism of action and preliminary clinical data of this emerging and promising class of agents, in particular revumenib and ziftomenib.

The combination of e-commerce and the green economy allows enterprises to distribute their green products directly to consumers through Internet channels. This paper investigates the impact of service spillovers on emission reduction and channel decisions in a two-echelon supply chain,in which the supplier is a Stackelberg leader and the retailer is a follower.By comparing the equilibrium outcomes of the single-channel and dual-channel supply chain in a setting with and without retail services, we observe that the supplier prefers to encroach on the market when services that retail locations provide largely spillover to and benefit the direct sales channel. Contrary to popular belief, a higher degree of service spillovers is beneficial for the retailer to get more returns under the dual-channel structure, whereas supplier encroachment will lead to a decline in the service level if the spillover degree is relatively low. In addition, the emission reduction level of products under supplier encroachment is always higher than that employed in the single-channel structure if consumers have both low-carbon preference and high degree of service sensitivity. Furthermore, we extend the equilibrium decisions under a carbon cap-and-trade (CCT) mechanism; the results fully verify the above conclusions. Interestingly, the implementation of a carbon price not only motivates carbon emission abatement and supplier encroachment, but also helps achieve a win-win outcome for supply chain members when service spillovers happen.

Ti-6Al-4V (TC4) titanium alloy parts were successfully fabricated by laser melting deposition (LMD)technology in this study. Proper normalizing temperatures were presented in detailed for bulk LMD specimens. Optical microscope, scanning electron microscopy, X-ray diffraction and electronic universal testing machine were used to characterize the microstructures, phase compositions, the tensile properties and hardness of the TC4 alloy parts treated using different normalizing temperature. The experimental results showed that the as-fabricated LMD speceimens microstructures mainly consisted of α-Ti phase with a small amount of β-Ti phase. After normalizing treatment, in the area of α-Ti phase, the recrystallized length and width of α-Ti phase both increased. When normalizing in the (α+β) phase field, the elongated primary α-Ti phase in the as-deposited state was truncated due to the precipitation of β-Ti phase and became a short rod-like primary α-Ti phase. In as-fabricated microstructure, the β-Ti phase was precipitated between different short rod-shaped α-Ti phases distributed as basketweave. After normalizing treatment at 990 for two hours with subsequent air cooling, the TC4 titanium alloy had significant different microstructures from original sample produced by LMD. Moreover, the mismatch of tensile and hardness property was mitigated in this heat treatment. So the normalizing treatment methods and temperature can be qualified as a prospective heat treatment of titanium alloy fabricating by laser melting deposition.

This study presents a novel approach for quantifying the dynamics of monthly hydraulic geometry relationships in the Jingjiang reach of the Yangtze River by integrating leaf area index (LAI) data as an indicator of riparian vegetation. A comprehensive system of ordinary differential equations (ODEs) couples traditional power-law hydraulic geometry equations with LAI, capturing the influence of erosion control on channel morphology. The periodic delayed response theory and automatic differentiation techniques are employed to enhance the modeling framework's capabilities. Calibration and verification demonstrate the models' effectiveness in capturing overall trends in discharge, width, and depth dynamics, outperforming traditional constant-parameter approaches. However, model predictive abilities are limited under extreme climate conditions due to training on normal climate data. Key assumptions, including the periodic delayed response theory's neglect of peak discharges, simplified representation of artificial cut-offs, and reliance on remotely sensed data with limited local validation, introduce uncertainties. The proposed modeling framework shows potential for broader application to other river systems with appropriate adjustments. Future research could incorporate additional environmental factors, integrate models across scales, couple with advanced simulation techniques, and expand stability assessments beyond the riverbed index. By addressing related challenges, this integrated approach lays a foundation for enhancing understanding of river morphodynamics and channel stability.

Considering the spectrum shortage problem of IoT devices, we introduce a collaborative spectrum sensing (CSS) framework in this paper to identify available spectrum resources, so that IoT devices can access it and meanwhile avoid causing harmful interference to the normal communication of the primary user (PU). However, in the process of the PU’s signal detection in IoT devices, the issue about the stopping time and decision cost arises. To this end, we propose a distributed cognitive IoT model, which includes two IoT devices independently using sequential decision rules to detect the PU. On this basis, we define the stopping time and cost function for IoT devices, and formulate an average cost optimization problem in CSS. To solve this problem, we further regard the optimal stopping time problem as a finite horizon problem, and solve the threshold of the optimal decision rule by dynamic programming. At last, numerical simulation results demonstrate the correctness of our proposal in terms of the global false alarm and miss detection probability, and it always achieves minimal average cost under various cost of each observation taken and thresholds.

Cows produce saliva in very large quantities to lubricate and facilitate food processing. Estimates indicate an amount of 50-150 liter per day. Human saliva has previously been found to contain numerous antibacterial components, such as lysozyme, histatins, members of the S-100 family and lactoferrin to limit pathogen colonization. Cows are dependent of a complex microbial community in their digestive system for food digestion. We wondered how this would influence the content of their saliva. We therefore sampled saliva from five humans and both nose secretions and saliva from six cows and separated the saliva on SDS-PAGE gradient gels and analyzed the major protein bands by LC-MS/MS. The cow saliva was found to be dominated by a few major proteins only, the carbonic anhydrase 6, a pH stabilizing enzyme, and the short palate, lung and nasal epithelium carcinoma-associated protein 2A (SPLUNC2A), also named bovine salivary protein 30kDa (BSP30). This latter protein has been proposed to play a role in local antibacterial response by binding bacterial lipopolysaccharide (LPS) and inhibit bacterial growth, but may instead according to more recent data instead have primarily surfactant activity. Numerous peptide fragments of mucin-5B were also detected in the MS analysis. However, mucins stain poorly by the gel staining solution and they are large and have difficult to enter regular gels why their presence easily are overseen. Interestingly, no major band on gel was detected representing any of the antibacterial proteins indicating that cows may produce them at very low levels not to harm the microbial flora of their digestive system. The nose secretions of the cows primarily contained the odorant protein, a protein thought to be involved in enhancing the sensing of smell by the olfactory receptors to enhance the possibility to quickly sense potential poisonous food components. High levels of secretory IgA was also found in one sample of cow mouth drippings indicating a strong upregulation during an infection. The human saliva we more complex containing both secretory IgA, amylase, carbonic anhydrase 6, lysozyme, histatins and a number of other less abundant proteins.

The flowering of Amorphophallus bulbifer plays an important role in its reproduction. The flowers and leaves of A. bulbifer cannot grow at the same time. However, the physiological and molecular mechanisms involved in flower bud and leaf bud formation are still unclear. In this study, the flower buds and leaf buds of A. bulbifer in the early stage of growth were used as research materials, transcriptome and metabolome analyses were carried out, and the soluble sugar and starch contents of A. bulbifer corms were determined. Transcriptome analysis revealed 5 542 differentially expressed genes (DEG) between flower buds and leaf buds, 3 107 of which were upregulated and 2 435 of which were downregulated. Enrichment analysis of the KEGG pathway showed that these differential genes were enriched mainly in the plant hormone signal transduction, DNA replication and fatty acid elongation pathways. Plant hormone signal transduction, DNA replication and fatty acid elongation pathways. A total of 5 296 significant differentially abundant metabolites were screened out by nontargeted metabolomics analysis. The differentially abundant metabolites were functionally classified in the HMDB, and 118 were successfully matched, including 17 that were highly expressed in flower buds. The differentially abundant metabolites in the flower buds were mainly enriched in pathways such as amino acid metabolism, isoquinoline alkaloid biosynthesis and pyrimidine metabolism. Targeted metabolomics analysis revealed that the contents of ABA, ZT and iPA in flower buds were significantly greater than those in leaf buds, while the opposite trend was observed for IAA. The analysis of soluble sugar and starch contents showed that the starch and soluble sugar contents in flower buds were significantly greater than those in leaf buds. The results of this study showed that flower bud development in A. bulbifer was regulated by amino acids, starch, ABA, ZT, iPA, IAA and other hormones. These findings could lead to valuable genetic resources for further study of A. bulbifer flowering and provide a deeper understanding of the molecular basis of A. bulbifer flowering.

The altimeter-derived ocean currents have been sparsely evaluated against in situ measurements in the Arctic, particularly for extended time periods. This study uses the extensive mooring array situated in the Fram Strait, representing one of the longest ocean current records, to investigate the relationship between in situ and satellite-derived currents in the region. The primary objective of this research is to analyze the vertical structure of the West Spitsbergen Current (WSC), its spatio-temporal coherence, and its association with surface geostrophic currents during the period of 1997-2012. To assess the vertical structure of the WSC, we utilized the Empirical Orthogonal Functions (EOF) and cross-correlation analysis between neighboring moorings. Remarkably, both velocity components exhibited robust vertical coherence, particularly within the waters of the North Atlantic Inflow, at depths exceeding 800 m. These findings underscore the potential for surface measurements to effectively complement data gaps and facilitate the reconstruction of deeper currents with a monthly temporal resolution. Notably, the surface measurements explain approximately 67% of the variance in the top 800 m of the water column.This comprehensive investigation advances our understanding of the West Spitsbergen Current and its vertical characteristics in the Arctic. Moreover, our findings highlight the valuable synergy between in situ and satellite-derived data, providing critical insights for future ocean current studies in this ecologically sensitive region.

The way people in many countries eat today is disconnected to the resources and land locally available. In Europe, for instance, too much meat is eaten, but often cannot be fed by local resources. The percentage of non-local and non-seasonal food is tremendous, exploiting other regions and their water reservoirs. Current diets harm eco systems and people’s health. (Re-)regionalising food systems and aligning diets to planetary boundaries could be one way to reconnect people to the food they eat. Before demanding the (re-)regionalisation of food, it should be analysed whether current consumption patterns can be met at all with the regionally available agricultural land. We looked at the region Hesse in Central Germany, calculated and compared land consumption of current diets with the consumption as recommended by the Planetary Health Diet. Our focus is on livestock because land consumption to produce meat, dairy and eggs is relatively high. Our results show that the region is far from being able to feed the current livestock population, that it does not have the land to support the livestock needed to meet current consumption patterns, but that it could support a smaller livestock population according to the Planetary Health Diet, especially if farmers adopt crop rotation systems and extensive husbandry.

With the continuous advancement of digital transformation, digital payments are playing an increasingly important role in the financial industry. This study aims to utilize machine learning models to predict and analyze digital payment behavior. Initially, the background and significance of digital payments in the financial sector are introduced. Subsequently, the current status and trends of traditional digital payment distribution are reviewed, alongside related work on digital payment behavior prediction. Methodologically, principles and applications of machine learning models such as logistic regression, decision trees, and random forests are elaborated, along with experimental design and data preprocessing methods. The experimental results and discussion section illustrates the performance of each model in digital payment prediction and explores their impact on credit decisions. This exploration equips financial institutions with more effective user behavior analysis and risk management tools, thereby fostering future development and application of digital payment technologies.

With the continuous advancement of digital transformation, digital payments are playing an increasingly important role in the financial industry. This study aims to utilize machine learning models to predict and analyze digital payment behavior. Initially, the background and significance of digital payments in the financial sector are introduced. Subsequently, the current status and trends of traditional digital payment distribution are reviewed, alongside related work on digital payment behavior prediction. Methodologically, principles and applications of machine learning models such as logistic regression, decision trees, and random forests are elaborated, along with experimental design and data preprocessing methods. The experimental results and discussion section illustrates the performance of each model in digital payment prediction and explores their impact on credit decisions. This exploration equips financial institutions with more effective user behavior analysis and risk management tools, thereby fostering future development and application of digital payment technologies.

Water resources provided by alpine glaciers are an important pillar for people living in the arid regions in the west of China. In this study, the HBV (Hydrologiska Byrans Vattenavdelning) light model was applied to simulate glacier mass balance and runoff in the Koxkar River Basin (KRB) on the south slope of Mt. Tumur, western Tianshan Mts.. Daily temperature and precipitation were calculated by multiple linear regressions and gradient-inverse distance weighting, respectively, based on in-situ observed data by automatic weather stations (AWSs) in the basin (2007–2009) and at four meteorological stations neighbering the basin (1959–2009). Observed daily air temperature and precipitation were taken as input data for the HBV model, which was calibrated using runoff in 2007/08 and 2009/10, and validated in 2008/09 and 2010/11. Generally, the model could simulate runoff very well. The annual glacier mass balance and runoff were calculated using the HBV model driven by interpolated meteorological data for the period of 1959–2009. The simulated glacier mass balance were reasonable when compared with those observed values at nearby glaciers, indicating a decrease trend of mass balance in the basin with an average value of –370.4 mm a^-1 since 1959. The annual runoff showed a slight increase trend (5.51 mm a^-1). Futher analysis indicated that the runoff is more sensitive to temperature than precipitation amuont in the Koxkar river basin.

Multi-rotor drones, a kind of unmanned equipment which is widely used in military, commercial consumption and other fields, has been developed very rapidly in recent years. However, their short flight time has hindered the expansion of their application range. This can be addressed by utilizing hydrogen fuel cells, which exhibit high energy density, strong adaptability to ambient temperature, and no pollution emissions, as the power source. Accordingly, the application of hydrogen fuel cells as the power source in multi-rotor drones is a promising technology that has attracted significant research attention. This paper summarizes the development process of hydrogen fuel cell mul-ti-rotor drones and analyzes the key obstacles that need to be addressed for the further development of hydrogen fuel cell multi-rotor drones, including structural lightweight, hydrogen storage methods, energy management strategies. Additionally, prospects for the future development of hydrogen fuel cell multi-rotor drones are presented.

(1) Background: Most of the existing studies focus on the evaluation of technology finance; the relationship between technology finance and technology innovation. But there are few studies on the development of technology finance and the quality of economic development in our country; (2) Methods: Based on the panel data of 30 provinces in China, this paper constructs an index system to measure the development of technology finance through the improved entropy method, and tests the spatial correlation of the development of technology finance in China by Moran'I index. According to the test results, this paper constructs a spatial econometric model to empirically analyze the promoting effect of scientific, technological and financial development on high-quality economic development, and analyzes its promoting effect in different regions and different time periods; (3) Results: The results show that the quality of China's economic growth is spatially dependent, and the development of science, technology and finance can significantly promote the quality economic development in China. And the promotion coefficient of the central region is the largest, as well as the coefficient of the eastern region is the smallest. The promotion coefficient was small and not significant before 2015, and was significantly positive after 2015; (4) Conclusions: this paper puts forward the corresponding policy recommendations according to the research results.

Introduction: Cleft lip with or without cleft palate (CL/P) is the most common congenital face anomaly in the world of multifactorial etiology. Objective: To determine the prevalence and characteristics of cleft palate. To identify associated risk factors to guide prevention and/or prenatal detection for early intervention and better health outcomes. To assess the rehabilitation protocols after operations in cleft palate. Design: Retrospective descriptive and clinical study. Patients: One hundred and three children with cleft palate treated in the Department of Head and Neck Surgery Clinic for Children and Young Adults, Department of Clinical Pediatrics, University of Warmia and Mazury, were included in the study. Methods: Records evaluation was conducted regarding sex, cleft type, mother occupation, parents education, histories of cleft in any of parents. The data were analyzed using R (R core team 2021) and the ordinal logistic regression analyses. Results: Children of stressed mothers during pregnancy had 9.4 times higher odds of having bilateral cleft. While there was no evidence that children sex, order of birth, and body mass as well as risk of maternal toxicity at work or reporting infections or drugs toxicity had a significant effect on the dependent variable. Conclusions: Children of parents with cleft history and having low educated mothers had higher probability of bilateral cleft palate. Children of stressed mothers during pregnancy had higher odds of having bilateral cleft. In literature, there is a lack of reports about rehabilitation in different operations methods in children with cleft palates.

The main purpose of this paper is to present a comprehensive view of the application mathematical models in the designing and implementing SSCM beside to solving problems and making decision. The research questions are: what mathematical models are used for designing and implementing sustainable supply chain management, how to use them, which industries implemented in, what modules of SSCM depth in and finally finding the gaps of researches. The methodology of research is Systematic Literature review through peer review papers which are published in high ranking journals. In this paper, First, we search all papers through scientific data bases like Scopus, science direct, MDPI, Springer, Google Scholar, then, screening papers based on the criteria such as subject of paper, journals impact factor which is published in-should be peer review journal- and relative content of the papers. Finally, we selected 245 papers with three steps screening through 2806 papers that they have enough quality and relative to our research goals for context analysis. Through context analysis, first we categorized the information of the papers and drew the current situation of researches in the framework of our topic. Then, we evaluate and compare the goals of sustainability and current situation and found the gapes, then, offered new suggestions like implementing SSCMs models in pollutant industries like casting industry, Heavy industry, coal Industry and so on. On the other hand, there are gaps in researches in some modules of SSCM such as packaging, designing products, etc.

Acute and chronic graft versus host disease (a/cGVHD) are major complications of allogeneic stem cell transplantation and a frequent cause of non relapse-mortality. The treatment of a/cGVHD consists of steroid administration at full dose. In the case of aGVHD, around 60 % of patients with grade III/IV fails to respond (steroid-resistant, SR), requiring a second line therapy. Extracorporeal photopheresis (ECP) represents a good option for second line treatment, leading to multiorgan response in several patients. Here, we have evaluated the performances of a cytofluorimetric quality control of the first ECP procedure in 29 patients with severe SR aGVHD who underwent ECP. The quality control represents a check control of in vitro cell apoptosis/death induction in collected and irradiated CD3+ cells by ECP. Our analysis shows that in more than 70 % quality controls a relevant apoptotosis/death of CD3+ cells was observed after cell irradiation, as compared to non-irradiated controls. Albeit observed in a small patients’ series, the occurrence of relevant in vitro effect after ECP relates to less procedures and/or to a better survival on ECP. Age, disease, gender, aGVHD grade and time from transplant to the first ECP had not impact on ECP efficacy in this patients’ series.

This study investigates the transformative impact of ChatGPT, a cutting-edge AI language model developed by OpenAI, on the financial sector. We explore ChatGPT's applications in financial analysis, focusing on its ability to perform tasks traditionally handled by human analysts. By creating a set of multi-step and advanced reasoning financial tasks, and establishing several task-specific evaluation metrics, our experimental results demonstrate that ChatGPT-4o can effectively perform both basic and some complex financial analysis tasks comparable to those executed by humans. However, our empirical evaluation reveals limitations in its performance on complex tasks that require comprehensive understanding and critical thinking. The study highlights the necessity for meticulous task formulation and robust evaluation to achieve consistent functionality. While ChatGPT enhances efficiency in financial analysis, its integration with human expertise remains crucial for effective decision-making. This study contributes to the understanding of AI's role in finance and provides insights into its potential and constraints, guiding future integration of AI technologies in financial practices.

Obesity results in hepatic fat accumulation, i.e., steatosis. Besides the fat overload, impaired fatty acid β-oxidation also promotes steatosis. Fatty acid β-oxidation takes place in mitochondria and peroxisomes. Usually very long chain and branched-chain fatty acids are first oxidized in peroxisomes, and the resultant short chain fatty acids are further oxidized in mitochondria. Peroxisome biogenesis is regulated by peroxin 16 (PEX16). In liver-specific PEX16 knockout (Pex16Alb-Cre) mice, hepatocyte peroxisomes were absent, but hepatocytes proliferated, and liver mass was enlarged. These results suggest that normal liver peroxisomes restrain hepatocyte proliferation and liver sizes. After high fat diet (HFD) feeding, body weights were increased in PEX16 floxed (Pex16fl/fl) mice and adipose-specific PEX16 knockout (Pex16AdipoQ-Cre) mice but not in the Pex16Alb-Cre mice, suggesting that the development of obesity is regulated by liver PEX16 but not by adipose PEX16. However, it was previously reported that HFD-induced obesity in the Pex16AdipoQ-Cre mice was more severe than in the wild type mice, which was not observed until 27 weeks of HFD feeding. Thus, liver initiates the development of obesity, which is enhanced by abnormal adipocytes. HFD increased liver mass in the Pex16fl/fl mice but somehow reduced the already enlarged liver mass in the Pex16Alb-Cre mice. Basal levels of serum triglyceride, free fatty acids and cholesterol were decreased whereas serum bile acids were increased in the Pex16Alb-Cre mice, and HFD-induced steatosis was not observed in the Pex16Alb-Cre mice. These results suggest that normal liver peroxisomes contribute to the development of liver steatosis and obesity.

Blood has certain significant physical properties, which the authors investigated. This paper provides values of surface tension and viscosity of blood at 37 °C, as well as relationship between them plotted on a cross-plot. By applying Hagen- Poiseuille equation and Jurin's law to the experimentally obtained data, the authors calculated with great accuracy viscosity and surface tension of blood. Evaluating both of the properties using described methods required from the authors to know the density of investigated liquid, which was found to be on average $\rho=1063,56 \frac{kg}{m^3}$. The authors took blood samples from 30 healthy subjects and determined aforementioned physical properties. There has been made a distinction in regard of a sex of the blood donor. Results indicate that both surface tension and viscosity are independent from sex of a subject, as well as indicate that there is no correlation between viscosity and surface tension of blood at 37\degree C. Average values of surface tension and viscosity were found to be $\sigma=5,241\pm 0,262 \cdot 10^{-2}\frac{J}{m^2}$ and $\eta=3,352\pm0,360 \cdot 10^{-3} Pa \cdot s$, respectively.

The vegetation types on the Qinghai-Tibet Plateau are complex and diverse, and the ecological environment is fragile and sensitive. It is very important to study the dynamic changes in vegetation and the main factors related to these changes to grasp the present state of the regional ecosystem, maintain the balance of the ecosystem and promote the sustainable development of the ecosystem. Therefore, this paper is based on SPOT/VEG NDVI (normalized difference vegetation index) data, land use data, topographic data and meteorological data from 1999 to 2019. The spatiotemporal variation in the NDVI over the Tibetan Plateau in the last 21 years and its response to different driving factors were investigated by using the Sen slope method, Mann–Kendall mutation test, partial correlation analysis and geographical detector method. The results showed that (1) the vegetation coverage on the Qinghai-Tibet Plateau showed an increasing trend from 1999 to 2019, with increases in approximately 67.00% of the plateau area. (2) The spatial differences in vegetation coverage were large; notably, low-density vegetation areas decreased obviously, moderate-density vegetation areas accounted for approximately 50% of the total area, high-density vegetation areas were the least common, and the overall growth rate was significant. (3) The NDVI was positively correlated with temperature and precipitation, and a positive correlation was observed in more than 66% of the region. (4) The order of the influence of single driving factors on the NDVI was as follows: precipitation > soil type > altitude > temperature > gradient > slope > population density > GDP. (5) The effect of multiple factors was significantly higher than that of single driving factors, with a notable nonlinear influence. The interactions between meteorological factors, such as precipitation, and topographic factors, such as altitude, were important, with a q value over 0.79.

The International tax regime (ITR) has been transformed after the Great Recession of 2008–2009. The G20/Organization for Economic Cooperation and Development (OECD)’s Base Erosion and Profit Shifting (BEPS) project (2013–2015) has fundamentally changed the ITR, giving new life to the single tax principle (income should be taxed once, i.e., no double taxation and no double non taxation). Reaction to BEPS has varied dramatically between the EU and the US, the two largest markets in the world. In the EU BEPS is taken very seriously, as shown for example by the new Anti-Tax Avoidance Directives that implement the single tax principle. In the US BEPS is almost invisible; while the US model tax treaty has been amended to incorporate it the US has refused to sign the Multilateral Instrument to implement BEPS in its treaties and the only other BEPS action that the US has taken is country by country reporting. It thus appears that the future of BEPS and the ITR depends on whether the EU or the US view prevails, i.e., whether multinationals can be forced to pay significant tax on the 160–240 billion that are currently not taxed annually because of BEPS. While US multinationals as well as EU multinationals are exposed to the EU ATAD and related measures while operating in Europe, they are less subject to EU anti BEPS measures elsewhere in the world. It therefore is crucial to assess the reaction to BEPS in the other large economy that was involved in its development, namely China. This article attempts to assess China’s reaction to BEPS based on Chinese sources. It shows that China takes BEPS seriously. Therefore, given the reactions of China (as well as India, which is even more aggressive than China for example in taxing the digital economy) it seems likely that eventually the EU view of BEPS will prevail and US based multinationals will eventually be forced to pay tax on the over 100 billion they shift offshore each year.

Ruminant animals naturally emit methane gas owing to anaerobic microbial fermentation in the rumen, and these gases are considered major contributors to global warming. Scientists worldwide are attempting to minimize methane emissions from ruminant animals. Some of these attempts include the manipulation of rumen microbes using antibiotics, synthetic chemicals, dietary interventions, probiotics, propionate enhancers, stimulation of acetogens, manipulation of rumination time, vaccination, and genetic selection of animals that produce low methane (CH4). The majority of synthetic additives are harmful to both beneficial rumen microbes and the host or only temporarily affect methanogenesis. Phytogenic feed additives (PFAs) have recently emerged as the best alternatives to antibiotics and synthetic chemicals because of growing public concerns regarding drug resistance and the negative impacts of antibiotics and synthetic chemicals on humans, livestock, and the environment. These additives reduce methane production and improve the volatile fatty acid profile. In this review, we provide an overview of PFA sources and how their bioactive components affect the rumen microbiome to reduce methane emissions. Additionally, we highlight the mechanisms of action of PFAs as a whole as well as some of their bioactive components.

Studying models of healthy aging and exceptional longevity is crucial to understand a possible longevity signature as most of them show resistance to age-related diseases. In particular, semi- and supercentenarians are a highly selected group, having survived significant adversities, including the Spanish Flu and COVID-19 pandemics, indicating distinctive immune system characteristics. This paper analyse the inflammatory scores (INFLA-score, Systemic Inflammation Response Index (SIRI)) and Aging-Related Immune Phenotype (ARIP) indicators calculated from the dataset of the DESIGN project, including 249 participants aged 19-111 years, trying to understand the immune-inflammatory (IMFLAM) role to achieve longevity. Statistical analyses, including linear regression and one-way ANOVA, were performed to explore the correlations between these parameters and age. Both INFLA-score and SIRI showed a significant increase with age. However, no statistical difference is observed when analysing the values for semi- and supercentenarians with those for other age groups, whose values are similar to that of adults and less than younger centenariansRegarding ARIP values, it is noteworthy that when comparing the CD8 Naive/Effector scores between groups, no significant differences were observed between the semi- and supercentenarian group and the other groups. These results support the idea that the control of IMFLAM response can promote extreme longevity.