Search Results (39,498)

With the intensification of global climate change, frequent extreme temperature events pose increasing challenges to agricultural production. The aim of this study is to characterize the spatiotemporal evolution of county-level agricultural total factor productivity (ATFP) under extreme temperature events, reveal key driving factors and crop-specific heterogeneity, and predict potential high-risk areas, which is crucial for providing scientific basis for risk management and adaptive policy formulation in globally climate-sensitive agricultural regions. This paper selects Jiangsu Province as a typical case study, uses the DEA-Malmquist model to measure agricultural total factor productivity (ATFP), systematically analyzes the spatiotemporal dynamic evolution characteristics of ATFP at the county scale, and selects the random forest and XGBoost ensemble models with optimal accuracy through model comparison for prediction, assessing the evolution trends of ATFP under different climate scenarios. The results showed that: (1) From 1993 to 2022, the average ATFP increased from 0.7460 to 1.1063 in the province, though development showed uneven distribution across counties, exhibiting a “high in the south, low in the north” gradient pattern. (2) Mechanization, agricultural film and land inputs are the core elements driving the overall ATFP increase but there are obvious crop differences: mechanization has a more prominent role in promoting the productivity of wheat and maize, while labor inputs have a greater impact on the ATFP of rice. (3) The negative impacts of extreme climate events on agricultural production will be significantly amplified under high-emission scenarios, while moderate climate change may have a promotional effect on certain crops in some regions. Full article

Wildfires, which encompass all fires that occur outside urban areas, represent one of the most frequent forms of natural disaster worldwide. This study presents the wildfire occurrence across the territory of Southeastern Europe, covering an area of 800,000 km² (Greece, Romania, Serbia, Slovenia, Croatia, Bosnia and Herzegovina, Montenegro, Albania, North Macedonia, Bulgaria, and Moldova). The research applies geospatial artificial intelligence techniques, based on the integration of machine learning (Random Forest (RF), XGBoost), deep learning (Deep Neural Network (DNN), Kolmogorov–Arnold Networks (KAN)), remote sensing (Sentinel-2, VIIRS), and Geographic Information Systems (GIS). From the geospatial database, 11 natural and anthropogenic criteria were analyzed, along with a wildfire inventory comprising 28,952 historical fire events. The results revealed that areas of very high susceptibility were most prevalent in Greece (10.5%), while the smallest susceptibility percentage was recorded in Slovenia (0.2%). Among the applied models, RF demonstrated the highest predictive performance (AUC = 90.7%), whereas XGBoost, DNN, and KAN achieved AUC values ranging from 86.7% to 90.5%. Through a SHAP analysis, it was determined that the most influential factors were global horizontal irradiation, elevation, and distance from settlements. The obtained results hold international significance for the implementation of preventive wildfire protection measures. Full article

Mangrove ecosystems are extremely sensitive to compounded stress, as evidenced by the widespread degradation and mortality of the pioneer mangrove species Avicennia marina along the Guangxi coast in recent years. However, research on how mangrove ecosystems respond to compound biotic stressors remains limited. Therefore, the present study aimed to systematically examine the ecological response mechanisms of A. marina under dual threats from the burrowing isopod Sphaeroma terebrans and the defoliating moth Hyblaea puera. Two contrasting sites were selected: Guchengling (subject to chronic stem-boring and sudden defoliator outbreaks) and Tieshangang (free from compounded stress). Photosynthetic capacity, metabolic function, and root structural integrity were all compromised considerably by chronic boring stress. During insect outbreaks, 15.33 ha of mangroves were destroyed due to impairments that breached the ecological threshold. In contrast, the healthier Tieshangang community exhibited strong ecological resilience, with rapid green canopy regeneration following defoliation and notable recovery in the normalized difference vegetation index. To enable early identification and precise intervention in mangrove decline, a comprehensive health index model was developed that includes root–canopy coordination, root length, and boring density. Field validation results, showing 100% agreement with expert evaluations across 19 validation sites (Cohen’s κ = 1.0), confirmed the high accuracy of the model. This study highlights the importance of identifying sensitive zones and undertaking timely ecological restoration, thereby providing a scientific basis and a practical tool that could facilitate early warning and timely management of mangrove degradation events. Full article

The structural vulnerability of RC structures during major seismic events raises several concerns regarding structural design and behaviour. Additionally, corrosion’s impact on steel and concrete, including a reduction in ductility, confinement and strength, can compromise structural performance, especially for reversal loading. This work investigates the combined effect of corrosion and seismic actions on the structural performance of RC structures. Numerical models of RC structures with 0%, 5%, 10%, 15% and 20% corrosion were proposed. The effect of corrosion in the numerical models was calibrated based on experimental studies carried out on corroded RC elements. Afterwards, we considered the scenario of corrosion in all peripheral structural elements of 5- and 10-storey MRF structures in three distinct conditions. To enforce vertical irregularity, we have imposed vertical irregularity at the ground level in each structure. An adaptive pushover analysis was performed to assess the effect of corrosion and vertical irregularity on the seismic response. The results demonstrate that, for the levels of 5% and 10% corrosion, uniform corrosion produces a deleterious impact on structural responses in 10- and 5-storey MRF structures, respectively, regardless of the level of irregularity of the elevation. However, the irregularity generates a higher impact in the seismic response than the uniformly distributed corrosion in height. The combined effect of those parameters must be considered in seismic codes for new and existing buildings in order to maintain safe performance levels. Full article

The avalanche speed is an important indicator for measuring the intensity of avalanches, and its measurement method is relatively complex. In practical engineering, empirical formulas based on statistics are usually adopted. However, research on the applicability of existing calculation methods in different regions is still insufficient, and further verification and improvement are urgently needed. Based on the integrated space–air–ground field survey data, this study uses RAMMS::AVALANCHE to conduct dynamic numerical simulations of 78 avalanche events in the Qiet’ akesu Gully of the Wenquan to Horgos transportation corridor in the Western Tianshan Mountains during the winter of 2023–2024, analyses the avalanche movement process, and compares the calculation results of the numerical tests of avalanche movement speed with empirical formulas. The results indicate that the velocities calculated using Formula A and Formula B are generally overestimated, approaching approximately 1.5 times the reference value. The mean absolute percentage error of Formula A (19.46%) is lower than that of Formula B (48.27%). In contrast, Formula C exhibits a significantly lower mean absolute percentage error (8.42%) compared with the other two methods, and its results remain stably around one-half of the reference value. Based on these findings, a comprehensive estimation strategy is proposed: twice the value calculated by Formula C is adopted as the primary reference, while two-thirds of the value from Formula A is taken into consideration, and the larger of the two is selected as the final estimated velocity. This strategy ensures the robustness of the results while effectively avoiding the potential overestimation or underestimation associated with reliance on a single empirical formula. This study provides a scientific basis for highway route selection and the placement of avalanche mitigation measures in high-altitude mountainous areas, and offers technical support for the construction and operational safety of infrastructure along the G219 Wenquan–Horgos transportation corridor. Full article

Large density fluctuations near the QCD critical point can be probed via intermittency analysis, which involves measuring scaled factorial moments (SFMs) of multiplicity distributions in relativistic heavy-ion collisions. Intermittency reflects the emergence of scale invariance and self-similar structures, which are closely related to symmetry principles and their breaking near a second-order phase transition. We present a systematic model study of intermittency for charged hadrons in Au+Au collisions at $\sqrt{s_{\mathrm{NN}}}$ = 7.7, 11.5, 19.6, 27, 39, 62.4, and 200 $\mathrm{GeV}$. Using the cascade UrQMD model, we demonstrate that non-critical background effects can produce sizable SFMs and a large scaling exponent if they are not properly removed using the mixed-event subtraction method. To estimate the possible critical intermittency signal in experimental data, we employ a hybrid UrQMD+CMC model, in which fractal critical fluctuations are embedded into the UrQMD background. A direct comparison of the second-order SFM between the model and STAR experimental data suggests that a critical intermittency signal on the order of approximately $1.8\%$ could be present in the most central Au+Au collisions at RHIC energies. This study provides practical guidance for evaluating background contributions in intermittency measurements and offers a quantitative estimate for the critical signal fraction present in the STAR data. Full article

In this study, an adaptive event-driven coordination paradigm is proposed for achieving consensus in nonlinear multi-agent systems (MASs) over directed networks. First, a newly dynamic event-triggered mechanism with single-point historical information is introduced to minimize unnecessary network communication. And a more general form of an event triggering mechanism with moving window historical information is designed for further saving network resources. Considering that the use of historical information over a long period of time may cause deviations, an event-triggered mechanism that can adjust the maximum memory length is proposed in this work to minimize unnecessary network communication. Secondly, the unknown nonlinearities in the MAS model are addressed using the universal approximation capability of neural networks. Then, a methodology for distributed adaptive control under event-triggered mechanisms is introduced leveraging the memory-based command-filtered backstepping methodology, and the proposed scheme resolves the complexity explosion problem. Finally, a case study is conducted to validate the feasibility of the proposed method. Full article

Drought events intensified by climate change severely compromise the physiological stability and productivity of Coffea arabica, particularly in rainfed systems, underscoring the need to identify cultivars with greater functional resilience. This study evaluated the physiological, nutritional and biochemical responses of seedlings from ten cultivars subjected to adequate irrigation (AW), severe water deficit (SWD) and rehydration (RI). Water potential, gas exchange, oxidative stress markers, stomatal traits and foliar macro- and micronutrients were quantified. Most cultivars exhibited pronounced reductions in the pre-dawn leaf water potential (Ψ_pd), photosynthesis (A), stomatal conductance (g_s) and transpiration (E), together with increases in oxidative stress indicators under SWD. In contrast, Obatá amarillo, Castillo, and Arará maintained greater hydraulic stability, more efficient stomatal regulation, higher water-use efficiency, and lower oxidative stress, accompanied by a more effective post-stress recovery after RI. Regarding nutrient dynamics, Geisha, Castillo, and Arará showed higher K⁺ accumulation, while Catimor bolo presented elevated Ca²⁺, P, and Fe²⁺ contents, elements associated with metabolic reactivation and structural recovery after stress. Geisha and Marsellesa displayed an adaptive, recovery-driven resilience strategy following drought stress. Overall, the findings identify Obatá amarillo, Castillo, and Arará as the most drought-tolerant cultivars, highlighting their potential relevance for breeding programs aimed at improving drought resilience in coffee. Full article

Precision agriculture has increasingly adopted controlled agricultural microclimates, particularly smart greenhouses, as a strategy to enhance crop yields while maintaining environmental conditions within suitable ranges for each crop. Among the variables that govern the water balance in these systems, air humidity plays a critical role; therefore, accurate humidity forecasting is essential for implementing timely control actions that support productivity levels. However, greenhouse conditions are frequently perturbed by extreme weather events, which lead to nonlinear and non-stationary humidity dynamics. In this context, the aim of this study was to design an optimized evolving fuzzy inference system for humidity forecasting that can adapt to changing and unforeseen situations in agricultural microclimates. A prototyping-based methodology was followed, including phases of communication, quick planning, modeling and quick design, construction of the prototype, and deployment. A hybrid genetic algorithm was used to optimize the parameters of an evolving Mamdani-type fuzzy inference system, extended to handle missing values in online data streams. Thirty independent optimization runs were performed, and the best configuration achieved a mean squared error of 1.20 × 10⁻² in humidity forecasting using one minute of data for three months. The resulting model showed high interpretability, with an average number of 1.35 rules, tolerance for missing values, imputing 2% of the data, and robustness to sudden changes in the data stream with a p-value of 0.01 for the Augmented Dickey–Fuller test at alpha = 0.05. In general, the optimized evolving fuzzy inference system obtained an effectiveness rate greater than 90% and demonstrated adaptability to extreme weather conditions, suggesting its applicability to other phenomena with similar characteristics. Full article

The crude oil futures market is highly susceptible to policy changes and international relations, which often trigger abrupt jumps in prices. The existing literature rarely considers jump volatility and the underlying impact mechanisms. This study proposes a hybrid forecasting model integrating a convolutional neural network (CNN) and self-attention (Transformer) for high-frequency financial data, based on the complex network characteristics between trading information and multi-market financialization indicators. Empirical results demonstrate that incorporating complex network indicators enhances model performance, with the CNN–Transformer model with a complex network achieving the highest predictive accuracy. Furthermore, we verify the model’s effectiveness and robustness in the WTI crude oil market via Diebold–Mariano tests and external event shock. Notably, this study also extends the analytical framework to jump intensity, thereby providing a more accurate and robust jump forecasting model for risk management and trading strategies in the crude oil futures market. Full article

Background and Objectives: This study compared the effects of two posterior-chain strength training strategies on eccentric hamstring strength, jump and sprint performance, and hamstring injury incidence in elite youth soccer players. Materials and Methods: Twenty-three players were randomly allocated to either a Nordic Hamstring Exercise Group (NHEG; n = 11) or a Deadlift + Leg Curl Slides Group (D + LCSG; n = 12). Both groups completed a 9-week in-season resistance training program consisting of one strength-oriented session (MD-4) and one power-oriented session (MD-2) per week, in addition to regular soccer training. Pre- and post-intervention assessments included eccentric hamstring strength (NordBord), countermovement jump (CMJ), and 10 m and 30 m linear sprint performance. Results: Eccentric hamstring strength increased significantly only in the NHEG (p ≤ 0.05), though this improvement did not transfer to enhancements in jump or sprint performance (p > 0.05). No significant changes were observed in the D + LCSG for any variable (p > 0.05), and no between-group differences were found across all performance outcomes. During the 12-week monitoring period, one hamstring injury was recorded, occurring in the NHEG. Conclusions: These findings suggest that, while the NHE elicited greater exercise-specific eccentric strength gains, neither posterior-chain strategy produced improvements in sprint or jump performance. However, given the small sample size and low number of injury events, these trends cannot be attributed with certainty to the implemented protocols, and both programs reported a low incidence of hamstring injuries per 1000 h of exposure with no statistically protective effect associated with the use of the NHE. Full article

Accurately identifying electric vehicle (EV) users with high demand response (DR) potential is critical for grid stability but remains challenging due to behavioral heterogeneity, data sparsity, and the subjectivity of expert-dependent methods. In particular, the absence of behavior labels and the low temporal frequency of EV charging events limit the effectiveness of conventional rule-based and clustering approaches. To address these issues, we propose a novel unsupervised framework that integrates deep behavioral modeling with multi-source indicator fusion. Our approach begins by developing a behavior recognition model robust to sparse data, effectively characterizing user charging patterns. Subsequently, a multi-dimensional potential feature system is established. A key innovation lies in our unsupervised weighting mechanism, which automatically learns the importance of each indicator by assessing inter-indicator correlations, thereby eliminating subjective bias. Finally, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is employed to rank users comprehensively based on their fused potential scores. Case studies on a large-scale real-world EV charging dataset demonstrate that the proposed method can effectively distinguish high-potential users from low-potential ones. The results indicate clear separability across multiple behavioral and willingness-related dimensions. This provides a practical and data-driven basis for targeted DR incentive design and user-side flexible resource planning. Full article

Musculoskeletal disorders (MSDs) remain one of the most persistent occupational health challenges in the U.S. construction industry, where physically demanding tasks such as heavy lifting, kneeling, and working in awkward postures contribute to elevated injury rates. This study aims to identify significant job-related determinants of MSDs in construction-sector occupations. By integrating publicly available datasets from the Survey of Occupational Injuries and Illnesses (SOII) and the Occupational Information Network (O*NET) datasets, a stepwise multiple regression analysis was conducted on 344 occupation-condition observations representing 86 construction occupations, yielding a final model that explained 49% of the variance. Ten significant predictors of MSD events were identified and classified as either risk amplifiers or mitigators. Amplifiers included factors such as exposure to noise, disease, hazardous conditions, and time pressure, all of which heightened MSD risk, while mitigators—such as reduced cramped-space exposure and regulated work environments—were associated with lower risk. MSDs resulting from sprains, strains, or tears accounted for 62.8% of all cases, frequently leading to days away from work (36.3%) or job restrictions (26.5%). The findings underscore that ergonomic risk in construction extends beyond physical strain to include scheduling, equipment design, and work organization. These results provide actionable insights for employers and safety professionals to redesign tools, optimize task rotation, and implement realistic work pacing strategies, ultimately reducing MSD incidence and improving productivity in this high-risk sector. Full article

This meta-review systematically examines 88 review papers from the scientific literature, focusing on the diverse ways scholars define and conceptualise the resilience of complex sociotechnical systems (STS). Among the 484 different conceptualisations identified in the reviews, we observe recurring patterns based on their semantics. In particular, four constructs are predominant: some positive elements, some negative events, specific actions, and some constraints on these actions. Our analysis involves a meticulous categorisation and synthesis of these findings, revealing underlying convergences in the academic discourse on STS resilience. Despite what seemed to be apparent disagreements among scholars in the last decade, our study shows that many differing viewpoints are actually complementary, representing varied expressions of similar underlying principles converging towards a large consensus. This comprehensive synthesis offers a unique perspective on the field of STS resilience, demonstrating the feasibility of moving from diverse meta-theoretical paradigms towards a more unified paradigmatic approach. Full article

Background: Respiratory Syncytial Virus is a predominant source of morbidity and mortality, particularly among babies, the elderly, and immunocompromised patients. Recent developments in RSV vaccines, approved by the FDA for high-risk groups, have highlighted the necessity for post-marketing surveillance to evaluate their real-world safety and efficacy. Method: This study utilized data from the Vaccine Adverse Event Reporting System (VAERS) covering RSV vaccine administration between 2023 and May 2025. The VAERS database reported data on vaccine types, including Arexvy^®, Abrysvo^®, and mRESVIA^® was analyzed for adverse events and vaccination errors. The demographic information, vaccination trends, and hospitalizations post-vaccination among the vaccinated individuals were accessed. Results: The analysis revealed that the most common adverse events were mild, such as injection site pain, erythema, fatigue, and extremity pain. The data also showed a gradual increase in hospitalization rates from 4.8% in 2023 to 7.5% in 2025. Vaccination errors, including inappropriate administration during pregnancy and excess doses, were also observed. A notable trend was the growing proportion of patients who experienced no adverse events, with the highest rate of symptom-free reports seen in 2025 (25.9%). Conclusions: RSV vaccines demonstrate a generally acceptable safety profile based on post-marketing surveillance data. However, the observed increase in hospitalization rates, vaccination errors, and pregnancy-related outcomes warrants continued active surveillance and cautious interpretation. Full article