Search Results (51,478)

In this article, we explored how open-source software serves as a strategic enabler for safety and security in the digital transformation of logistics systems. Open source is examined across multiple dimensions, including transparency, community collaboration, digital sovereignty, and long-term infrastructure resilience. The analysis focuses on the logistics domain, where interoperability, critical infrastructure protection, and supply chain continuity are essential. Key elements of open-source development—such as modular architectures, legal and licensing frameworks, and peer-reviewed codebases—support rapid vulnerability management, increased transparency, and the creation of sustainable digital ecosystems. Emphasis is placed on the role of open-source models in strengthening institutional trust, reducing dependency on proprietary vendors, and enhancing responsiveness to cyber threats. Our findings indicate that open source is not merely a technical alternative, but a strategic decision with legal, economic, and political implications, shaping secure, sovereign, and adaptive digital environments—particularly in mission-critical sectors. Full article

This paper employs a Time-Varying Parameter Vector Autoregression Directional–Spillover (TVP-VAR-DY) model to investigate the dynamic spillover effects among China’s energy, real estate, and stock markets from 2013 to 2023, with a focus on the impact of extreme events. The findings show that the total conditional spillover index (TCI) typically remains below 40% in the absence of extreme events, but significantly increases during such events, reaching 51.09% during the 2015 stock market crisis and nearing 60% during the COVID-19 pandemic in 2020. Specifically, the oil and gas market exhibited a net spillover index of 4.61%, emerging as a major source of risk transmission. In contrast, the real estate market, which had a net spillover index of −9.38%, became a net risk absorber. The net spillover index indicates that the risk transmission role of different markets towards other markets is dynamically changing over time and is closely related to significant global or domestic economic events. These results indicate that extreme events not only directly impact specific markets but also rapidly propagate risks through complex inter-market linkages, exacerbating systemic risks. Therefore, it is recommended to enhance market monitoring, improve transparency, and optimize risk management strategies to cope with uncertainties in the global economy and financial markets. Full article

This study presents a non-invasive approach to monitoring post-harvest fruit quality by applying CO₂ laser photoacoustic spectroscopy (CO₂LPAS) to study the respiration of “Conference” pears from local and commercially stored (supermarket) sources. Concentrations of ethylene (C₂H₄), ethanol (C₂H₆O), and ammonia (NH₃) were continuously monitored under shelf-life conditions. Our results reveal that ethylene emission peaks earlier in supermarket pears, likely due to post-harvest treatments, while ethanol accumulates over time, indicating fermentation-related deterioration. Significantly, ammonia levels increased during the late stages of senescence, suggesting its potential role as a novel biomarker for fruit degradation. The application of CO₂LPAS enabled highly sensitive, real-time detection of trace gases without damaging the fruit, offering a powerful alternative to traditional monitoring methods. Additionally, artificial intelligence (AI) models, particularly convolutional neural networks (CNNs), were explored to enhance data interpretation, enabling early detection of ripening and spoilage patterns through volatile compound profiling. This study advances our understanding of post-harvest physiological processes and proposes new strategies for improving storage and distribution practices for climacteric fruits. Full article

The spreading and rebound of impacting droplets on superhydrophobic interfaces is a complex phenomenon governed by the interconnected contributions of surface, fluid and environmental factors. In this work, we employed a collection of 1498 water–glycerin droplet impact experiments on monolayer-functionalized laser-structured aluminum samples to train, validate and optimize a machine learning regression model. To elucidate the role of each influential parameter, we analyzed the model-predicted individual parameter contributions on key descriptors of the phenomenon, such as contact time, maximum spreading coefficient and rebound efficiency. Our results confirm the dominant contribution of droplet impact velocity while highlighting that the droplet spreading phase appears to be independent of surface microtopography, i.e., the depth and width of laser-made features. Interestingly, once the rebound transitions to the retraction stage, the importance of the unwetted area fraction is heightened, manifesting in higher rebound efficiency on samples with smaller distances between laser-fabricated microchannels. Finally, we exploited the trained models to develop empirical correlations for predicting the maximum spreading coefficient and rebound efficiency, both of which strongly outperform the currently published models. This work can aid future studies that aim to bridge the gap between the observed macroscale surface-droplet interactions and the microscale properties of the interface or the thermophysical properties of the fluid. Full article

Glaciers, often regarded as “frozen reservoirs”, play a crucial role in replenishing numerous rivers in arid regions, contributing to ecological balance and managing river flow. Recently, the rapid shrinkage of the glaciers in the East Tianshan Mountains has affected the water quantity in the Karez system. However, studies on glacier changes in this region are limited, and recent data are scarce. This study utilizes annual Landsat composite images from 1990 to 2022 obtained via the Google Earth Engine (GEE). It utilizes a ratio threshold approach in conjunction with visual analysis to gather the glacier dataset specific to the Turpan–Hami region. The Open Global Glacier Model (OGGM) is used to model the flowlines and mass balance of around 300 glaciers. The study analyzes the glacier change trends, distribution characteristics, and responses to climate factors in the Turpan–Hami region over the past 30 years. Additionally, future glacier changes through the end of the century are projected using CMIP6 climate data. The findings indicate that the following: (1) From 1990 to 2022, glaciers in the research area underwent considerable retreat. The total glacier area decreased from 204.04 ± 0.887 km² to 133.52 ± 0.742 km², a reduction of 70.52 km², representing a retreat rate of 34.56%. The number of glaciers also decreased from 304 in 1990 to 236 in 2022. The glacier length decreased by an average of 7.54 m·a⁻¹, with the average mass balance at −0.34 m w.e.·a⁻¹, indicating a long-term loss of glacier mass. (2) Future projections to 2100 indicate that under three climate scenarios, the area covered by glaciers could diminish by 89%, or 99%, or even vanish entirely. In the SSP585 scenario, glaciers are projected to nearly disappear by 2057. (3) Rising temperatures and solar radiation are the primary factors driving glacier retreat in the Turpan–Hami area. Especially under high emission scenarios, climate warming will accelerate the glacier retreat process. Full article

In recent years, frequent vehicle–bridge pier collision accidents have posed a serious threat to people’s economic and life security. In order to avert the impairment of reinforced concrete bridge piers (RCBPs) under the impact of vehicles, three kinds of Mg–Al alloy AlSi10Mg anti-collision structures designed by selective laser melting (SLM) printing were tested by the numerical simulation method in this study: an ultra-high performance concrete (UHPC) anti-collision structure, a bio-inspired honeycomb column thin-walled structure (BHTS) buffer interlayer, and a UHPC–BHTS composite structure were used to reduce the damage degree of RCBPs caused by vehicle impact. In accordance with the prototype configuration of the pier, a scaled model with a scale ratio of 1:10 was fabricated. Three anti-collision structures were installed on the reinforced concrete (RC) column specimens for the steel ball impact test. The impact simulation under low-energy and high-energy input was carried out successively, and the protective effect of the three anti-collision devices on the RC column was comprehensively evaluated. The outcomes demonstrate that the BHTS buffer interlayer and the UHPC–BHTS composite structure are capable of converting the shear failure of RC columns into bending failure, thereby exerting an efficacious role in safeguarding RC columns. The damage was evaluated under all impact conditions of BHTS and UHPC–BHTS composite structures, and the RC column only suffered slight damage, while the RC column without protective measures and the RC column with the UHPC anti-collision structure alone showed serious damage and collapse behavior. This approach can offer a valuable reference for anti-collision design within analogous projects. Full article

Norovirus is a leading cause of viral gastroenteritis worldwide and has been studied extensively from the perspective of life and biomedical sciences. However, no biothermodynamic analysis of Norovirus has been reported in the literature. Such an analysis would provide insights into the role of energetic constraints in the interactions between Norovirus and its host cells and other viruses. In this research, Norovirus was characterized from the aspect of chemistry and chemical thermodynamics, with the determination of its molecular formula, empirical formula, molar mass and thermodynamic properties (enthalpy, entropy, Gibbs energy) of formation. Based on these properties, biosynthesis reactions were formulated that show how Norovirus particles are synthetized inside host cells, and the thermodynamic properties of biosynthesis were determined. Moreover, the thermodynamic properties of the binding of Norovirus to its host cell receptor were determined. These were then used to develop a model of virus–host interactions at the cell membrane (antigen-receptor binding) and inside the cytoplasm (virus multiplication), with the phenomenological equations of nonequilibrium thermodynamics. Based on the model, an analysis of the virus–virus competition between Norovirus and Rotavirus was conducted. Full article

Glucagon-like peptide-1 receptor agonists, originally developed for the treatment of metabolic disorders, have recently emerged as promising candidates for the management of substance use disorders. This review synthesizes preclinical, clinical, and translational evidence on the effects of glucagon-like peptide-1 receptor agonists across addiction models involving alcohol, nicotine, psychostimulants, and opioids. In animal studies, glucagon-like peptide-1 receptor agonists consistently reduce drug intake, attenuate dopamine release in reward circuits, and decrease relapse-like behavior. Clinical and observational studies provide preliminary support for these findings, particularly among individuals with comorbid obesity or insulin resistance. However, several translational barriers remain, including limited blood–brain barrier penetration, species differences in pharmacokinetics, and variability in treatment response due to genetic and metabolic factors. Ethical considerations and methodological heterogeneity further complicate clinical translation. Future directions include the development of central nervous system penetrant analogues, personalized medicine approaches incorporating pharmacogenomics, and rigorously designed trials in diverse populations. Glucagon-like peptide-1 receptor agonists may offer a novel therapeutic strategy that addresses both metabolic and neuropsychiatric dimensions of addiction, warranting further investigation to define their role in the evolving landscape of substance use disorder treatment. Full article

As the largest carbon emitter globally, China has formally adopted dual-carbon targets of achieving a carbon peak by 2030 and carbon neutrality by 2060. Urban renewal, as an essential approach to promoting sustainable urban development, plays a critical role in realizing dual-carbon targets. However, carbon emission reduction in urban renewal involves multiple stakeholders with divergent interests, significantly hindering the effective achievement of emission reduction goals. In this context, this paper innovatively selects the government, developers, and construction enterprises as game subjects and constructs an evolutionary game model of the three parties’ participation in carbon emission reduction from the perspective of carbon trading. Through simulation analysis, it explores the impacts of government subsidies, penalty mechanisms, additional benefits, and carbon trading on stakeholder decision-making. The findings indicate the following: (1) The emission reduction process in urban renewal follows an evolutionary pattern of the initial, growth, and mature stages. (2) Sensitivity analysis demonstrates that government subsidies and penalty mechanisms play important roles. (3) Additional benefits serve as intrinsic motivation for developers and construction enterprises to reduce emissions, while a well-developed carbon trading market provides additional incentives and benefit pathways for stakeholders. By integrating urban renewal with carbon trading for the first time, this study aims to enhance stakeholders’ engagement in emission reduction and provide practical reference suggestions, thereby contributing to sustainable urban development. Full article

Background: The toxicity of pesticides for fish in general and Rainbow Trout (Oncorhynchus mykiss) in particular is an important ecological indicator required by regulations, and it implies the use of a large number of fish. The number of animals needed would be even higher to evaluate metabolites and pesticide impurities. Considering ethical issues, the costs, and the necessary resources, the use of in silico models is often proposed. Aim of the study: We explore the use of advanced Monte Carlo methods to obtain improved results for models testing Rainbow Trout (Oncorhynchus mykiss) acute toxicity. Several versions of the stochastic Monte Carlo simulation of pesticide toxicity for Rainbow Trout, carried out using CORAL software, were studied. The set of substances was split into four subsets: active training, passive training, calibration, and validation. Modeling was repeated five times to enable better statistical evaluation. To improve the predictive potential of models, the index of ideality of correlation (IIC), correlation intensity index (CII), and coefficient of conformism of correlation prediction (CCCP) were applied. Main results and novelty: The most suitable results were observed in the case of the CCCP-based optimization for SMILES-based descriptors, achieving an R² of 0.88 on the validation set, in all five random splits, demonstrating consistent and robust modeling performance. The relationship of information systems related to QSAR simulation and new ideas is discussed, assigning a key role to fundamental concepts like mass and energy. The study of the mentioned criteria of predictive potential during the conducted computer experiments showed that even though they are all aimed at improving the predictive potential, their values do not correlate, except for the CII and the CCCP. This means that, in general, the information impact of the considered criteria has a different nature, at least in the case of the simulation of toxicity for Rainbow Trout (Oncorhynchus mykiss). The applicability domain of the model is specific for pesticides; the software identifies potential outliers by looking at rare molecular fragments. Full article

This study investigates the annual carbon emission disparities between privately-owned electric vehicles (EVs) and internal combustion engine vehicles (ICEVs) by developing a usage-phase life cycle assessment (LCA) model, with a focus on the synergistic impacts of grid carbon intensity, driving intensity (e.g., annual mileage), and vehicle energy efficiency. Through scenario analyses and empirical case studies in four Chinese megacities, three key findings are obtained: (1) Grid carbon intensity is the primary factor affecting the emission advantages of EVs. EVs demonstrate significant carbon reduction benefits in regions with low-carbon power grids, even when the annual mileage is doubled. However, in coal-dependent grids under intensive usage scenarios, high-energy-consuming EVs may experience emission reversals, where their emissions exceed those of ICEVs. (2) Higher annual mileage among EV owners (1.5–2 times that of ICEV owners) accelerates carbon accumulation, particularly diminishing per-kilometer emission advantages in regions where electricity grids are heavily reliant on fossil fuels. (3) Vehicle energy efficiency heterogeneity plays a critical role: compact, low-energy EVs (e.g., A0-class sedans/SUVs) maintain emission advantages across all scenarios, while high-energy models (e.g., C-class sedans/SUVs) may exceed ICEV emissions even in regions with low-carbon power grids under specific conditions. The study proposes a differentiated policy framework that emphasizes the synergistic optimization of grid decarbonization, vehicle-class-specific management, and user behavior guidance to maximize the carbon reduction potential of EVs. These insights provide a scientific foundation for refining EV adoption strategies and achieving sustainable transportation transitions. Full article

With the expansion of the scale of high-proportion wind and solar power grid connections, the problems of abandoned wind and solar power and insufficient peak shaving have become increasingly prominent. The electric–hydrogen coupling system has greater potential in flexible regulation, providing a new technological approach for the consumption of new energy. This paper proposes a two-layer optimization model for an electricity–hydrogen coupled distributed power generation system. The model is based on the collaborative regulation of flexible loads by electrolytic cells and fuel cells. Through the collaborative optimization of capacity configuration and operation scheduling, it breaks through the strong dependence of traditional systems on the distribution network and enhances the autonomous consumption capacity of new energy. The upper-level optimization model aims to minimize the total life-cycle cost of the system, and the lower-level optimization model aims to minimize the system’s operating cost. The capacity configuration of each module before and after the integration of flexible loads is compared. The simulation results show that the integration of flexible loads can not only effectively reduce the level of wind and solar power consumption in distributed power generation systems, but also play a role in load peak shaving and valley filling. At the same time, it can effectively reduce the system’s peak electricity purchase and sale cost and reduce the system’s dependence on the distribution network. Based on this, with the premise of meeting the load demand, the capacity configuration results of each module were compared when connecting electrolytic cells of different capacities. The results show that the simulated area has the best economic benefits when connected to a 4 MW electrolytic cell. This optimization model can increase the high wind and solar power consumption rate by 23%, reduce the peak purchase and sale cost of electricity by 40%, and achieve an economic benefit coefficient of up to 0.097. Full article

Harnessing state-of-the-art technologies to improve disease resistance is a critical objective in modern plant breeding. Artificial intelligence (AI), particularly deep learning and big model (large language model and large multi-modal model), has emerged as a transformative tool to enhance disease detection and omics prediction in plant science. This paper provides a comprehensive review of AI-driven advancements in plant disease detection, highlighting convolutional neural networks and their linked methods and technologies through bibliometric analysis from recent research. We further discuss the groundbreaking potential of large language models and multi-modal models in interpreting complex disease patterns via heterogeneous data. Additionally, we summarize how AI accelerates genomic and phenomic selection by enabling high-throughput analysis of resistance-associated traits, and explore AI’s role in harmonizing multi-omics data to predict plant disease-resistant phenotypes. Finally, we propose some challenges and future directions in terms of data, model, and privacy facets. We also provide our perspectives on integrating federated learning with a large language model for plant disease detection and resistance prediction. This review provides a comprehensive guide for integrating AI into plant breeding programs, facilitating the translation of computational advances into disease-resistant crop breeding. Full article

Small-scale inshore fisheries significantly contribute to the total landing volumes and have an important role in Vietnamese socioeconomic development, food security, livelihoods, and social well-being. The setnet fishery has been used throughout coastal communities of Vietnam for many decades. Being a passive fishing gear, the catch efficiency of setnet depends on various conditions such as fish density, season, oceanography, environment, and others. However, very little information exists about the relationship between catch rates and national conditions. Recognizing this research gap, this study examined the effect of temporal and environmental conditions on the catch rates of the narrow-barred Spanish mackerel (Scomberomorus commerson) setnet fishery using long-term data from 2005 to 2016. Overall, the catch of narrow-barred Spanish mackerel decreased over the course of the study. The generalized additive model (GAM) showed that catch rates were significantly affected by sea surface temperature (SST), which peaked at 27 °C. After this temperature point, the catch rates significantly decreased. Temporal variables also contributed to the catch variation. The setnet caught the highest yield in April and May, and more fish were caught during periods of low nightlight intensity than during high illuminated periods. Our study contributes to the understanding of critical factors affecting the catch rates of valuable species, which helps to determine the optimal fishing process of the setnet fishery within the shifting of marine heatwaves. Full article

Background/Objectives: The study was aimed to determine the value of the modified Brixia score (MBS) in predicting in-hospital mortality and acute respiratory distress syndrome (ARDS) in hospitalized COVID-19 patients. Methods: We conducted an observational retrospective study including 292 COVID-19 patients (61% males, median age 74 years, interquartile range 63–82) admitted to our institution from 2 February 2020 to 31 December 2021. Patients with ARDS were diagnosed according to the Berlin criteria. To determine MBS, each lung on initial chest X-ray images was divided into three zones, and for each zone, a numerical value between 0 and 3 was assigned (maximum value 18). Binary logistic regression was used to identify the best-predicting models for ARDS development and fatal outcomes. Results: MBS was higher in patients with ARDS than in patients without ARDS (median MBS 12 (interquartile range (IQR) 9–18) vs. 8 (IQR 6–11), respectively). Patients with fatal outcomes had significantly higher MBSs than surviving patients (median MBS 12 (IQR 9–16) vs. 6 (IQR 5–9), respectively). The best model that classified ARDS patients incorporated MBS, lactate dehydrogenase levels on admission, and obesity (accuracy 74.7%, sensitivity 73.1%, specificity 75.9%, area under the curve (AUC) 0.74 (95% confidence interval (CI) 0.68–0.79)). The best model that classified patients with fatal outcomes incorporated MBS, obesity, oxygen saturation, and percentage of lymphocytes on admission (accuracy 80.5%, sensitivity 78.4%, specificity 82.6%, AUC 0.86 (95% CI 0.81–0.91)). Conclusions: MBS could have an important role in predicting ARDS and mortality and stratifying patients with COVID-19 pneumonia, aiding in clinical decision-making. Full article