Search Results (17,487)

Using data collected from 294 farm households across Egypt, Morocco, and Tunisia, this study quantifies crop losses due to soil salinity and analyzes the key factors associated with it. Further, it analyzes the factors driving the farmers’ choice of adaptation measures against salinity. Almost 54% of households surveyed reported yield losses due to salinity, with a sizable portion experiencing losses above 20%. In response to salinization, farmers adopted five adaptation practices, including crop rotation, salt stress-tolerant varieties, drainage management, soil amendments, and improved irrigation practices. A generalized linear model is applied to examine the factors explaining crop loss due to salinity. Results show that a higher share of irrigated land correlates with greater salinity-related crop loss, particularly in areas with poor drainage and low water quality. Conversely, farms with good soil quality reported significantly lower losses. Crop losses due to salinity were much lower in quinoa compared to wheat. Farmers who received agricultural training or belonged to cooperatives reported lower losses. A multivariate probit model was employed to understand drivers of adaptive behaviors. The analysis shows credit access, cooperative membership, training, and resource endowments as significant predictors of adaptation choices. The results underscore the importance of expanding credit availability, strengthening farmer organizations, and investing in training for effective salinity management. Full article

Background/Objectives: Ecballium elaterium is a widely distributed species and is one of the earliest recorded in traditional medicine. With global temperatures rising, this study aimed to investigate the changes in E. elaterium plantlets subjected to thermal stress. The goal was to understand how thermal stress affects morphology, physiology, and bioactive metabolite production, both for ecological adaptation and potential therapeutic applications. Methods: Seedlings were cultivated under controlled conditions and subjected to either the control temperature (22 °C) or the heat stress temperature (35 °C) for one week. Morphological and anatomical traits were assessed, along with physiological parameters such as chlorophyll content, malondialdehyde (MDA), hydrogen peroxide (H₂O₂), L-proline, soluble sugars, and total phenolic content. Methanolic leaf extracts from both groups were analyzed via LC-HRMS/MS and examined in vitro for cytotoxic activity against three human cancer cell lines: MCF-7 (breast), DU-145 (prostate), and SH-SY5Y (neuroblastoma). Results: Heat stress reduced dry mass and stomatal density but increased the diameter of the root transition zone, indicating anatomical adaptation. Leaves exhibited elevated oxidative stress markers and altered metabolite accumulation, while the roots showed a more integrated stress response. LC-HRMS/MS profiling revealed significant shifts in Cucurbitacin composition. Extracts from heat-stressed plants displayed stronger cytotoxicity, particularly toward DU-145 and SH-SY5Y cells, correlating with higher levels of glycosylated Cucurbitacins. Conclusions: E. elaterium demonstrates organ-specific thermotolerance mechanisms, with heat stress enhancing the production of bioactive metabolites. These stress-induced phytochemicals, especially Cucurbitacins, hold promise for future cancer research and therapeutic applications. Full article

Surrogate models are widely recognized as effective, data-driven predictive tools for storm surge risk assessment. For such applications, surrogate models (referenced also as emulators or metamodels) are typically developed using existing databases of synthetic storm simulations, and once calibrated can provide fast-to-compute approximations of the storm surge for a variety of downstream analyses. The storm surge predictions need to be established for different geographic locations of interest, typically corresponding to the computational nodes of the original numerical model. A number of inland nodes will remain dry for some of the database storm scenarios, requiring an imputation for them to estimate the so-called pseudo-surge in support of the surrogate model development. Past work has examined the adoption of kNN (k-nearest neighbor) spatial interpolation for this imputation. The enhancement of kNN with hydraulic connectivity information, using the grid or mesh of the original numerical model, was also previously considered. In this enhancement, neighboring nodes are considered connected only if they are connected within the grid. This work revisits the imputation of peak storm surge within a surrogate modeling context and examines three distinct advancements. First, a response-based correlation concept is considered for the hydraulic connectivity, replacing the previous notion of connectivity using the numerical model grid. Second, a Gaussian Process interpolation (GPI) is examined as alternative spatial imputation strategy, integrating a recently established adaptive covariance tapering scheme for accommodating an efficient implementation for large datasets (large number of nodes). Third, a data completion approach is examined for imputation, treating dry instances as missing data and establishing imputation using probabilistic principal component analysis (PPCA). The combination of spatial imputation with PPCA is also examined. In this instance, spatial imputation is first deployed, followed by PPCA for the nodes that were misclassified in the first stage. Misclassification corresponds to the instances for which imputation provides surge estimates higher than ground elevation, creating the illusion that the node is inundated even though the original predictions correspond to the node being dry. In the illustrative case study, different imputation variants established based on the aforementioned advancements are compared, with comparison metrics corresponding to the predictive accuracy of the surrogate models developed using the imputed databases. Results show that incorporating hydraulic connectivity based on response similarity into kNN enhances the predictive performance, that GPI provides a competitive (to kNN) spatial interpolation approach, and that the combination of data completion and spatial interpolation emerges as the recommended approach. Full article

For monitoring animal adaptation when facing environmental challenges, and more specifically when addressing the impacts of global warming—particularly responses to heat stress and short-term fluctuations in osmotic regulations in the different organs influencing animal physiology—there is an increasing demand for digital tools to understand and monitor a range of biomarkers. Microneedle arrays (MNAs) have recently emerged as promising devices minimally invasively penetrating human skin to access dermal interstitial fluid (ISF) to monitor deviations in physiology and consequences on health. The ISF is a blood filtrate where the concentrations of ions, low molecular weight metabolites (<70 kDa), hormones, and drugs, often closely correlate with those in blood. However, anatomical skin differences between human and farm animals, especially large animals, as well as divergent tolerances of such devices among species with behavior specificities, motivate new MNA designs. We addressed technological challenges to design higher microneedles for farm animal (pigs and cattle) measurements. We designed microneedle arrays composed of 37 microneedles, each 2.8 mm in height, using dextran-methacrylate, a photo-crosslinked biocompatible biopolymer-based hydrogel. The arrays were characterized geometrically and mechanically. Their abilities to perforate pig and cow skin were demonstrated through histological analysis. The MNAs successfully absorbed approximately 10 µL of fluid within 3 h of application. Full article

The low quality of mechanized tea harvesting in China’s hilly plantations, often caused by irregular canopy morphology, necessitates improved technology. This study addresses this issue by proposing a contact-based profiling mechanism and a corresponding control method for tea cutting platforms. This cutting platform mainly consists of a canopy profiling mechanism, a tea harvesting unit, a lifting actuator, and a control system, containing a mathematical model correlating the tea canopy pose with sensor signals. Following a theoretical analysis of key components of the profiling device, we determined their structural parameters. Subsequently, a profiling control strategy was formulated, and an automatic control system for the profiling cutting platform was developed. Finally, a prototype was constructed and subjected to experimental validation to assess the dynamic characteristics of its pose adjustment and its profiling-based harvesting performance. The results of this experiment illustrate that after implementing the profiling system, the proportion of time the cutting blade remained in an optimal cutting position increased from 26.5% to 95.0%, an improvement of 68.5%, demonstrating that the system successfully achieves its design objective of the adaptive profiling apparatus in response to variation in canopy morphology. In addition, the integrity rate of harvested tea leaves increased from 50.7% without profiling to 74.6% with profiling, an improvement of 47.1%, which indicates the good performance of this profiling cutting platform. Therefore, this research provides a valuable reference for the design of intelligent tea harvesting machinery for the hilly tea plantations in China. Full article

Under complex operating conditions, the single-loop control structure of permanent magnet synchronous motors (PMSMs) suffers from various uncertain disturbances. Although extended state observers with high-gain designs have been widely adopted for disturbance rejection control due to their rapid convergence characteristics, they typically induce significant noise amplification and increased sensitivity to disturbances. To address this issue, this paper proposes a hybrid extended state observer-based control with adaptive switching strategy (AS-HyESO) for suppressing uncertain disturbances. In the AS-HyESO framework, matched disturbances from the control channel and unmatched disturbances from non-control channels are separately estimated using the HyESO, which are subsequently eliminated through the designed control law to ensure precise tracking of the speed reference input. Furthermore, the proposed observer incorporates an adaptive bandwidth switching mechanism that employs larger bandwidth during steady-state operation and reduced bandwidth during dynamic transients. This innovative approach achieves overshoot-free speed regulation while maintaining enhanced disturbance rejection capability, thereby effectively resolving the inherent conflict between dynamic response performance and anti-disturbance robustness. Experimental validation conducted on a 64 W PMSM dual-motor test platform demonstrates the superior effectiveness of the AS-HyESO, control strategy in practical applications. Full article

Lithium-ion batteries (LIBs) have garnered extensive application across various domains. However, frequent safety incidents associated with these LIBs have emerged as a significant impediment to their further advancement. Consequently, there is an urgent necessity to develop a novel fire extinguishing agent that possesses both rapid fire suppression and efficient cooling capabilities, thereby effectively mitigating the occurrence and propagation of fires in LIBs. This study pioneers the development of an adaptive thermosensitive microcapsule (TM) fire extinguishing agent synthesized via in situ polymerization. The TM encapsulates a ternary composite core—perfluorohexanone (C₆F₁₂O), heptafluorocyclopentane (C₅H₃F₇), and 2-bromo-3,3,3-trifluoropropene (2-BTP)—within a melamine–urea–formaldehyde (MUF) resin shell. The TM was prepared via in situ polymerization, combined with FE-SEM, FTIR, TG–DSC, and laser particle size analysis to verify that the TM had a uniform particle size and complete coating structure. The results demonstrate that the TM can effectively suppress the thermal runaway (TR) of LIBs through the synergistic effects of physical cooling, chemical suppression, and gas isolation. Specifically, the peak TR temperature of a single-cell LIB is reduced by 14.0 °C, and the heating rate is decreased by 0.17 °C/s. Additionally, TM successfully blocked the propagation of TR thereby preventing its spread in the dual-LIB module test. Limitations of single-component agents are overcome by this innovative system by leveraging the ternary core’s complementary functionalities, enabling autonomous TR suppression without external systems. Furthermore, the TM design integrates precise thermal responsiveness, environmental friendliness, and cost-effectiveness, offering a transformative safety solution for next-generation LIBs. Full article

The increasing complexity and scale of service-oriented architectures in cloud computing have heightened the demand for intelligent, decentralized, and adaptive service composition techniques. This study proposes an advanced framework that integrates a Multi-Agent System (MAS) with a novel hybrid metaheuristic optimization method, the Integrated Particle-Ant Algorithm (IPAA), to achieve efficient, scalable, and Quality of Service (QoS)-aware service composition. The IPAA dynamically combines the global search capabilities of Particle Swarm Optimization (PSO) with the local exploitation strength of Ant Colony Optimization (ACO), thereby enhancing convergence speed and solution quality. The proposed system is structured into three logical layers—agent, optimization, and infrastructure—facilitating autonomous decision-making, distributed coordination, and runtime adaptability. Extensive simulations using a synthetic cloud service dataset demonstrate that the proposed approach significantly outperforms traditional optimization methods, including standalone PSO, ACO, and random composition strategies, across key metrics such as utility score, execution time, and scalability. Moreover, the framework enables real-time monitoring and automatic re-optimization in response to QoS degradation or Service-Level Agreement (SLA) violations. Through decentralized negotiation and minimal communication overhead, agents exhibit high resilience and flexibility under dynamic service availability. These results collectively suggest that the proposed IPAA-based framework provides a robust, intelligent, and scalable solution for service composition in complex cloud computing environments. Full article

Deep space exploration missions face technical challenges such as long-distance communication delays and high-precision autonomous positioning. Traditional ground-based telemetry and control as well as inertial navigation schemes struggle to meet mission requirements in the complex environment of deep space. As a vision-based autonomous navigation technology, image-based navigation enables spacecraft to obtain real-time images of the target celestial body surface through a variety of onboard remote sensing devices, and it achieves high-precision positioning using stable terrain features, demonstrating good autonomy and adaptability. Craters, due to their stable geometry and wide distribution, serve as one of the most important terrain features in deep space image-based navigation and have been widely adopted in practical missions. This paper systematically reviews the research progress of deep space image-based navigation technology, with a focus on the main sources of remote sensing data and a comprehensive summary of its typical applications in lunar, Martian, and asteroid exploration missions. Focusing on key technologies in image-based navigation, this paper analyzes core methods such as surface feature detection, including the accurate identification and localization of craters as critical terrain features in deep space exploration. On this basis, the paper further discusses possible future directions of image-based navigation technology in response to key challenges such as the scarcity of remote sensing data, limited computing resources, and environmental noise in deep space, including the intelligent evolution of image navigation systems, enhanced perception robustness in complex environments, hardware evolution of autonomous navigation systems, and cross-mission adaptability and multi-body generalization, providing a reference for subsequent research and engineering practice. Full article

Background: Regulating sensations of fatigue and discomfort while performing maximal endurance exercise becomes essential for making informed decisions about persistence and/or failure during intense exercise. Athletes with a higher effort capacity have competitive advantages over those with a lower one. The microbiota–brain axis is a considered the sixth sense and a modulator of the host’s emotional stability and physical well-being. Objectives: This narrative review aims to explore and evaluate the potential mechanisms involved in regulating perceptions during endurance exercise, with a focus on the possible relationship between the gut microbiota balance and the neural system as an adaptive response to high fatigue chronic exposure. Methods: Electronic databases (PubMed, Web of Science, Google Scholar, and Scopus) were used to identify studies and hypotheses that had documented predefined search terms related to endurance exercise, gut microbiota, the central nervous system, pain, discomfort, fatigue, and tolerance to effort. Results: This narrative review shifts the focus concerning the symbiotic relationship between the gut microbiota, the vagus nerve, the central/enteric nervous system, and the regulation of afferences from different organs and systems to manage discomfort and fatigue perceptions during maximal physical effort. Consequently, the chronicity supporting fatigued exercise and nutritional stimuli could specifically adapt the microbiota–brain connection through chronic efferences and afferences. The present hypothesis could represent a new focus to be considered, analysing individual differences in tolerating fatigue and discomfort in athletes supporting conditions of intense endurance exercise. Conclusions: A growing body of evidence suggests that the gut microbiota has rapid adaptations to afferences from the brain axis, with a possible relationship to the management of fatigue, pain, and discomfort. Therefore, the host–microbiota relationship could determine predisposition to endurance performance by increasing thresholds of sensitive afferences perceived and tolerated. A richer and more diverse GM of athletes in comparison with sedentary subjects can improve the bacteria-producing metabolites connected to brain activity related with fatigue. The increase in fatigue thresholds directly improves exercise performance, and the gut–brain axis may contribute through the equilibrium of metabolites produced for the microbiota. Full article

The growing deployment of electric vehicles (EVs) offers a unique opportunity to utilize them as mobile energy resources during large-scale emergencies. However, existing emergency dispatch strategies often neglect the compounded uncertainties of hazard disruptions, infrastructure fragility, and user behavior. To address this gap, we propose the Emergency-Responsive Aggregation Framework (ERAF)—a behaviorally informed, spatially aware, and probabilistic optimization model for resilient EV energy dispatch. ERAF integrates a Bayesian inference engine to estimate plug-in availability based on hazard exposure, behavioral willingness, and charger operability. This is dynamically coupled with a GIS-based spatial filter that captures road inaccessibility and corridor degradation in real time. The resulting probabilistic availability is fed into a multi-objective dispatch optimizer that jointly considers power support, response time, and delivery reliability. We validate ERAF using a high-resolution case study in Southern California, simulating 122,487 EVs and 937 charging stations across three compound hazard scenarios: earthquake, wildfire, and cyberattack. The results show that conventional deterministic models overestimate dispatchable energy by up to 35%, while ERAF improves deployment reliability by over 28% and reduces average delays by 42%. Behavioral priors reveal significant willingness variation across regions, with up to 47% overestimation in isolated zones. These findings underscore the importance of integrating behavioral uncertainty and spatial fragility into emergency energy planning. ERAF demonstrates that EVs can serve not only as grid assets but also as intelligent mobile agents for adaptive, decentralized resilience. Full article

The aromaticity of small boron clusters remains under scrutiny due to persistent inconsistencies between magnetic and electronic descriptors. Here, we reexamine B₃⁻, B₃⁺, B₄, B₄²⁺, and B₄²⁻ using a multidimensional approach that integrates Adaptive Natural Density Partitioning, Electron Density of Delocalized Bonds, magnetically induced current density, and the z-component of the induced magnetic field. We introduce a model in which σ-aromaticity arises from two distinct delocalization topologies: a radial 2e⁻ σ-pathway and a tangential multicenter circuit formed by alternating filled and vacant sp² orbitals. This framework accounts for the evolution of aromaticity upon oxidation or reduction, preserving coherence between electronic structure and magnetic response. B₃⁻ features cooperative radial and tangential σ-delocalization, together with a delocalized 2e⁻ π-bond, yielding robust double aromaticity. B₃⁺ retains σ- and π-aromaticity, but only via a tangential 6e⁻ σ-framework, leading to a more compact delocalization and slightly attenuated ring currents. In B₄, the presence of a radial 2e⁻ σ-bond and a 4c–2e π-bond confers partial aromatic character, while the tangential 8e⁻ σ-framework satisfies the 4n rule and induces a paratropic current. In contrast, B₄²⁺ lacks the radial σ-component but retains a tangential 8e⁻ σ-circuit and a 2e⁻ 4c–2e π-bond, leading to a σ-antiaromatic and π-aromatic configuration. Finally, B₄²⁻, exhibits delocalized π- and σ-circuits, yielding consistent diatropic ring currents, which confirms its fully doubly aromatic nature. Altogether, this analysis underscores the importance of resolving σ-framework topology and demonstrates that, when radial and tangential contributions are correctly distinguished, Hückel’s rule remains a powerful tool for interpreting aromaticity in small boron rings. Full article

Background/Objectives: Following the recent publication of reassuring outcomes from the ARA MED 330 protocol regarding long-term insulin use in pilots, combined with continuous advancements in diabetes technology, European aeromedical examiners are increasingly optimistic about establishing more flexible medical requirements for insulin-treated aviation professionals. These professionals have historically been considered unfit for duty due to hypoglycemic risks. According to current research, hypoglycemia, the primary incapacitation risk for flight crew, is considered virtually non-existent among air traffic controllers (ATCOs). Additionally, stress-induced hyperglycemia also represents a low-frequency risk in these professionals, who are experienced in managing highly stressful operational environments. This study presents a narrative review examining stress and its metabolic effects in healthy individuals, ATCOs, and people with diabetes (PwD). Methods: This narrative review was conducted based on a comprehensive PubMed search performed by two independent reviewers (GAR and AM) spanning January 2023 to January 2025. The search strategy focused on English-language, peer-reviewed studies involving human participants and addressed stress, glucose regulation, and occupational factors in ATCOs and people with diabetes. Additional relevant articles were identified through reference screening. A total of 33 studies met the inclusion criteria. Studies focusing solely on oxidative or molecular mechanisms were excluded from the analysis. Results: Stressful events consistently triggered the expected hyperglycemic reaction in both healthy individuals and PwD. However, the literature indicates ATCOs demonstrate remarkable stress resilience and adaptation to the demanding conditions of their work environment, suggesting a unique occupational profile regarding metabolic stress responses. Conclusions: These findings contribute valuable insights to ongoing discussions regarding aeromedical fitness standards. The evidence suggests that ATCOs may not face the same metabolic risks as flight crews, indicating that current medical certification processes for insulin-treated aviation professionals warrant reconsideration in light of this emerging evidence. This research supports the potential for more individualized, occupation-specific aeromedical standards that better reflect the actual risk profiles of different aviation roles. Full article

Monkeypox virus (MPXV) experienced an unprecedented global outbreak in 2022, characterized by a significant departure from historical patterns: a rapid spread of the epidemic to more than 110 non-traditional endemic countries, with more than 90,000 confirmed cases; a fundamental shift in the mode of transmission, with human-to-human transmission (especially among men who have sex with men (MSM)) becoming the dominant route (95.2%); and genetic sequencing revealing a key adaptive mutation in a novel evolutionary branch (Clade IIb) that triggered the outbreak. These features highlight the significant evolution of MPXV in terms of host adaptation, transmission efficiency, and immune escape ability. The aim of this paper is to provide insights into the viral adaptive evolutionary mechanisms driving this global outbreak, with a particular focus on the role of immune escape (e.g., novel mechanisms of M2 proteins targeting the T cell co-stimulatory pathway) in enhancing viral transmission and pathogenicity. At the same time, we systematically evaluate the cross-protective efficacy and limitations of existing vaccines (ACAM2000, JYNNEOS, and LC16), as well as recent advances in novel vaccine platforms, especially mRNA vaccines, in inducing superior immune responses. The study further reveals the constraints to outbreak control posed by grossly unequal global vaccine distribution (e.g., less than 10% coverage in high-burden regions such as Africa) and explores the urgency of optimizing stratified vaccination strategies and facilitating technology transfer to promote equitable access. The core of this paper is to elucidate the dynamic game between viral evolution and prevention and control strategies (especially vaccines). The key to addressing the long-term epidemiological challenges of MPXV in the future lies in continuously strengthening global surveillance of viral evolution (early warning of highly transmissible/pathogenic variants), accelerating the development of next-generation vaccines based on new mechanisms and platforms (e.g., multivalent mRNAs), and resolving the vaccine accessibility gap through global collaboration to build an integrated defense system of “Surveillance, Research and Development, and Equitable Vaccination,” through global collaboration to address the vaccine accessibility gap. Full article

The phosphoinositide 3-kinase (PI3K)/AKT signaling pathway is a crucial regulator of cellular metabolism, proliferation, and survival. It is frequently dysregulated in metabolic, cardiovascular, and neoplastic disorders. Despite the advancements in multi-omics technology, existing methods often fail to provide real-time, pathway-specific insights for precision medicine and drug repurposing. We offer Agentic RAG-Driven Multi-Omics Analysis (ARMOA), an autonomous, hypothesis-driven system that integrates retrieval-augmented generation (RAG), large language models (LLMs), and agentic AI to thoroughly analyze genomic, transcriptomic, proteomic, and metabolomic data. Through the use of graph neural networks (GNNs) to model complex interactions within the PI3K/AKT pathway, ARMOA enables the discovery of novel biomarkers, probable candidates for drug repurposing, and customized therapy responses to address the complexities of PI3K/AKT dysregulation in disease states. ARMOA dynamically gathers and synthesizes knowledge from multiple sources, including KEGG, TCGA, and DrugBank, to guarantee context-aware insights. Through adaptive reasoning, it gradually enhances predictions, achieving 91% accuracy in external testing and 92% accuracy in cross-validation. Case studies in breast cancer and type 2 diabetes demonstrate that ARMOA can identify synergistic drug combinations with high clinical relevance and predict therapeutic outcomes specific to each patient. The framework’s interpretability and scalability are greatly enhanced by its use of multi-omics data fusion and real-time hypothesis creation. ARMOA provides a cutting-edge example for precision medicine by integrating multi-omics data, clinical judgment, and AI agents. Its ability to provide valuable insights on its own makes it a powerful tool for advancing biomedical research and treatment development. Full article