Search Results (14,666)

Wildfire regimes are undergoing rapid transformation under anthropogenic climate change, with major implications for biodiversity, carbon cycling, and ecosystem resilience. This systematic review synthesizes findings from 42 studies across global, continental, and regional scales to assess emerging patterns in fire frequency, intensity, and seasonality, and to identify climatic, ecological, and anthropogenic drivers shaping these changes. Across biomes, evidence shows increasingly fire-conducive conditions driven by rising temperatures, vapor-pressure deficit, and intensifying drought, with climate model projections indicating amplification of extreme fire weather this century. Boreal ecosystems show heightened fire danger and carbon-cycle vulnerability; Mediterranean and Iberian regions face extended fire seasons and faster spread rates; tropical forests, particularly the Amazon, are shifting toward more flammable states due to drought–fragmentation interactions; and savannas display divergent moisture- and fuel-limited dynamics influenced by climate and land use. These results highlight the emergence of biome-specific fire–climate–fuel feedback that may push certain ecosystems toward alternative stable states. The review underscores the need for improved attribution frameworks, integration of fire–vegetation–carbon feedback into Earth system models, and development of adaptive, regionally tailored fire-management strategies. Full article

For the Simeulue community, trauma does not remain a source of fear or psychological burden. Instead, it becomes a guideline for their survival. This study explores how societies reconstruct themselves through memory by examining smong, the local knowledge of the Simeulue community in Indonesia, as a cultural mechanism that transforms disaster experience into social resilience. Using a qualitative ethnographic approach, the research utilizes interviews, nandong and song lyrics, field notes, and historical documentation. The findings indicate that smong operates through interconnected layers of communicative and cultural memory: it is preserved in family stories, bedtime stories, artistic expressions, commemorative practices, and symbolic markers such as monuments and grave inscriptions. Through these processes, traumatic experiences are reframed as moral instructions and actionable knowledge that guide rapid evacuation, mutual aid, and collective vigilance during earthquakes and tsunamis. This study demonstrates that the reconstruction of the Simeulue community is driven not by a formal disaster management system but by practices rooted in culture. Past disaster experiences are continuously reinterpreted and integrated into everyday life. This highlights the importance of memory-based strategies for strengthening community resilience and offers directions for future research on intergenerational knowledge transmission, cultural adaptation, and disaster preparedness in oral societies. Full article

The rapid mobilization of sediment stored behind dams, in amounts that are large relative to mean annual sediment loads, can jumpstart river restoration but can also adversely impact habitat, infrastructure, land, and water use upstream of, within, and downstream of the former impoundment. A wide range of geomorphic and engineering assessment tools were applied to help manage sediment-related risks associated with the removal of two dams from the Elwha River in Washington State and the release of roughly 21 million m³ of sediment. Each of these tools had its strengths and weaknesses, which are explored here. The processes of sediment erosion, transport and deposition were complex. No one model was able to fully simulate all these with the accuracy necessary for predicting the magnitude and timing of coarse and fine sediment release from the reservoir. Collectively, however, the model outputs provided enough information to guide the adaptive sediment management process during dam removal. When the complexity of the morphodynamic responses to dam removal and the associated risks exceeded the capacity of any one tool to adequately assess, synoptic forecasting proved useful. The lessons learned on the Elwha have provided insights into how to use a variety of modeling techniques to address sediment management issues as dam removal scale, complexity and risk increase. Full article

Short-term traffic prediction is vital for intelligent transportation systems, enabling adaptive congestion control, real-time signal management, and dynamic route planning for autonomous vehicles (AVs). This study introduces a custom Transformer-based deep learning framework for joint forecasting of traffic flow and vehicle speed, leveraging handcrafted positional encoding and stacked multi-head attention layers to model multivariate traffic patterns. Evaluated against baselines including Long Short-Term Memory (LSTM), Support Vector Machine (SVM), Random Tree, and Random Forest on the Next-Generation Simulation (NGSIM) dataset, the model achieves 94.2% accuracy (Root Mean Squared Error (RMSE) 0.16) for flow and 92.1% accuracy for speed, outperforming traditional and deep learning approaches. A hybrid evaluation metric, integrating RMSE and threshold-based accuracy tailored to AV operational needs, enhances its practical relevance. With its parallel processing capability, this framework offers a scalable, real-time solution, advancing AV ecosystems and smart mobility infrastructure. Full article

This study examines how Green Human Resource Management (GHRM) is linked to sustainable organizational performance, encompassing environmental, economic, and social outcomes through the capability-building mechanisms of green innovation (GI) and green organizational culture (GOCL) in emerging manufacturing systems. Drawing on the Resource-Based View and capability-based sustainability perspectives, GHRM is conceptualized as a strategic organizational capability that enables firms in developing economies to beyond short-term regulatory compliance toward measurable and integrated sustainability performance outcomes. Survey data were collected from 446 managerial and technical respondents in Ethiopia’s garment and textile industrial parks, one of Africa’s fastest-growing industrial sectors facing significant sustainability challenges. Using Partial Least Squares Structural Equation Modeling (PLS-SEM) with bootstrapping-based mediation analysis, the results show that GHRM is positively associated with sustainable organizational performance, with GI and GOCL operating as key mediating mechanisms that translate HR-related practices into measurable sustainability outcomes. The findings highlight the role of GHRM in strengthening firms’ adaptive and developmental sustainability capabilities by fostering pro-sustainability mindsets and innovation-oriented behaviors, which are particularly critical in resource-constrained and weak-institutional contexts. The study contributes to sustainability and management literature by explicitly linking Green HRM to triple-bottom-line performance through a capability-building framework and by providing rare firm-level empirical evidence from a low-income emerging economy. Practically, the results provide guidance for managers and policy makers to design, monitor, and evaluate HRM systems that intentionally cultivate human, cultural, and innovative capabilities to support long-term organizational sustainability transitions. Full article

In an increasingly digitalized work environment, the expectation of perpetual work availability—constant connectivity (CC)—has become central to employees’ daily experiences, influencing productivity, well-being, and work–life balance. This study validates the Constant Connectivity Scale in the Italian organizational context, assessing its psychometric properties through exploratory and confirmatory factor analyses with 300 employees from three organizations. Reliability and validity assessments revealed the scale’s unidimensional structure, strong internal consistency, and high construct validity, demonstrating its effectiveness in measuring perceived hyperconnectivity at work. Findings reveal important relationships between constant connectivity and employee outcomes: significant associations with increased anxiety and a paradoxical moderate positive correlation with job performance, suggesting complex mechanisms whereby connectivity simultaneously activates engagement and strain processes. The weak correlation with smart working perception indicates that organizational flexibility policies have not substantially reduced connectivity expectations in Italian organizations. This study contributes to the digital work literature by providing a validated, culturally adapted instrument for as sessing constant connectivity in the Italian workforce. The validated CCS offers organizations evidence-based measurement for understanding hyperconnectivity intensity and implementing targeted strategies for building workforce resilience and promoting mental health through better management of digital connectivity demands. Full article

Purpose: Periprosthetic joint infection (PJI) is a devastating complication following limb salvage surgery with tumor megaprosthetic reconstruction, leading to high morbidity and complex management. Despite advancements in prosthesis design and materials, infection rates are notably higher than in conventional arthroplasty. This narrative review synthesizes current evidence on the etiology, diagnosis, and management of PJIs in this unique setting. Methods: We conducted narrative review of literature from PubMed and Embase using keywords related to PJIs and tumor megaprostheses, aiming to summarize risk factors, diagnostic criteria, pathogen profiles, and treatment outcomes. Results: Key findings indicate that the risk of PJI is multifactorial, involving patient-related, disease-related, and treatment-related factors. Diagnosis relies on a combination of clinical presentation, serological markers, imaging, and microbiological studies, though established criteria for conventional PJI may require adaptation for tumor cases. Treatment strategies include irrigation and debridement (I&D), debridement, antibiotics, implant retention with modular component exchange (DAIR), one-stage or two-stage revision, and amputation. Success rates vary, and optimal management requires a multidisciplinary, individualized approach. However, two-stage revision is considered the gold standard for chronic PJIs. Conclusions: PJIs after tumor megaprosthetic reconstruction presents distinct challenges. Management requires a multidisciplinary, individualized approach. Future research should focus on validated diagnostic criteria for this population, novel anti-biofilm strategies, and standardized treatment protocols. Full article

Obesity has reached epidemic proportions, driven by energy imbalance and limited capacity for adaptive thermogenesis. Brown (BAT) and beige adipose tissues dissipate energy through non-shivering thermogenesis (NST), primarily via uncoupling protein-1 (UCP1), making them attractive targets for increasing energy expenditure (EE). The canonical β-adrenergic pathway robustly activates NST in rodents through β3 adrenoceptors; however, translational success in humans has been limited by low β3 expression, off-target cardiovascular effects, and the emerging dominance of β2-mediated signaling in human BAT. Consequently, attention has shifted to non-adrenergic and UCP1-independent mechanisms that offer greater tissue distribution and improved safety profiles. This review examines a broad spectrum of alternative receptors and pathways—including GPRs, TRP channels, TGR5, GLP-1R, thyroid hormone receptors, estrogen receptors, growth hormone, BMPs, sirtuins, PPARs, and interleukin signaling—as well as futile substrate cycles (Ca²⁺, creatine, and glycerol-3-phosphate) that sustain thermogenesis in beige adipocytes and skeletal muscle. Pharmacological agents (natural compounds, peptides, and small molecules) and non-pharmacological interventions (cold exposure, exercise, diet, and time shift) targeting these pathways are critically evaluated. We highlight the translational gaps between rodent and human studies, the promise of multimodal therapies combining low-dose adrenergic agents with non-adrenergic activators, and emerging strategies such as sarco/endoplasmic reticulum calcium ATPase protein (SERCA) modulators and tissue-specific delivery. Ultimately, integrating adrenergic and non-adrenergic approaches holds the greatest potential for safe, effective, and sustainable obesity management. Full article

Background: Dysglycemia remains a persistent challenge in hospital care. Despite advances in outpatient diabetes technology, inpatient insulin management largely depends on intermittent point-of-care glucose testing, static insulin dosing protocols and rule-based decision support systems. Artificial intelligence (AI) offers potential to transform this care through predictive modeling and adaptive insulin control. Methods: Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines, a scoping review was conducted to characterize AI algorithms for insulin dosing and glycemic management in hospitalized patients. An interdisciplinary team of clinicians and engineers reached consensus on AI definitions to ensure inclusion of machine learning, deep learning, and reinforcement learning approaches. A librarian-assisted search of five databases identified 13,768 citations. After screening and consensus review, 26 studies (2006–2025) met the inclusion criteria. Data were extracted on study design, population, AI methods, data inputs, outcomes, and implementation findings. Results: Studies included ICU (N = 13) and general ward (N = 9) patients, including patients with diabetes and stress hyperglycemia. Early randomized trials of model predictive control demonstrated improved mean glucose (5.7–6.2 mmol/L) and time in target range compared with standard care. Later machine learning models achieved strong predictive accuracy (AUROC 0.80–0.96) for glucose forecasting or hypoglycemia risk. Most algorithms used data from Medical Information Mart for Intensive Care (MIMIC) databases; few incorporated continuous glucose monitoring (CGM). Implementation and usability outcomes were seldom reported. Conclusions: Hospital AI-driven models showed strong algorithmic performance but limited clinical validation. Future co-designed, interpretable systems integrating CGM and real-time workflow testing are essential to advance safe, adaptive insulin management in hospital settings. Full article

The article presents an analysis of the mechanisms used by a vocational high school in Bulgaria to develop dual training and implement it sustainably. It focuses on the school management’s leadership role in the network of different stakeholders, demonstrating the importance of this aspect in the entire process of developing dual education. Apart from the case analysis of the Bulgarian vocational high school’s successful implementation of dual learning, the research strategy includes examining regulatory documents, evaluation reports and publications in media and by companies, as well as analyzing the attitudes among key stakeholders. An overview of the challenges facing dual education in Bulgaria is also provided. Based on the case study findings, the factors supporting the implementation and sustainability of the dual system have been identified. We conclude that the long-term development of the dual education model depends on the understanding that the formation of professional skills is a dynamic process, requiring attention to the needs of the local environment, adaptability to current changes and active participation by all stakeholders. The role of school leadership—with regard to both its motivation and activity—has proven to be essential, and therefore it should not be overlooked when creating state incentives to support dual training. Full article

The coexistence of heterogeneous congestion control algorithms causes network unfairness and performance degradation. However, existing solutions suffer from the following issues: poor isolation reduces the overall performance, while sensitivity to tuning complicates deployment. In this work, we propose Warbler, a machine learning-driven active queue management (AQM) framework. Warbler classifies flows based on traffic characteristics and utilizes machine learning to adaptively control the bandwidth allocation to improve fairness. We implemented and evaluated the Warbler prototype on a programmable switch. The experimental results show that Warbler significantly improves the network performance, achieving a near-optimal Jain’s fairness index of 0.99, while reducing the delay to 60% of the baseline, cutting jitter by half, and saving 43% of buffer usage. In terms of scalability, it supports 10,000 concurrent long flows with latency below 0.7 s. The Warbler has a low cost and strong adaptability with no need for precise tuning, demonstrating its potential in dealing with heterogeneous CCAs. Full article

As a core node of the air transportation network, airports rely on aircraft model identification as a key link to support the development of smart aviation. Synthetic Aperture Radar (SAR), with its strong-penetration imaging capabilities, provides high-quality data support for this task. However, the field of SAR image interpretation faces numerous challenges. To address the core challenges in SAR image-based aircraft recognition, including insufficient dataset samples, single-dimensional target features, significant variations in target sizes, and high missed-detection rates for small targets, this study proposed an improved network architecture, SAR-YOLOv8l-ADE. Four modules achieve collaborative optimization: SAR-ACGAN integrates a self-attention mechanism to expand the dataset; SAR-DFE, a parameter-learnable dual-residual module, extracts multidimensional, detailed features; SAR-C2f, a residual module with multi-receptive field fusion, adapts to multi-scale targets; and 4SDC, a four-branch module with adaptive weights, enhances small-target recognition. Experimental results on the fused dataset SAR-Aircraft-EXT show that the mAP₅₀ of the SAR-YOLOv8l-ADE network is 6.1% higher than that of the baseline network YOLOv8l, reaching 96.5%. Notably, its recognition accuracy for small aircraft targets shows a greater improvement, reaching 95.2%. The proposed network outperforms existing methods in terms of recognition accuracy and generalization under complex scenarios, providing technical support for airport management and control, as well as for emergency rescue in smart aviation. Full article

The North China Plain, a major grain production base in China, is facing the chronic threat of climate-change-induced delays in winter wheat sowing, with late sowing constraining yields by shortening the pre-winter growth period, and soil moisture at sowing potentially serving as a key factor to alleviate late-sowing losses. However, previous studies have mostly independently analyzed the effects of sowing time or water stress, and there is still a lack of systematic quantitative evaluation on how the interaction effects between the two determine long-term yield potential and risk. To fill this gap, this study aims to quantify, in the context of long-term climate change, the independent and interactive effects of different sowing dates and baseline soil moisture on the growth, yield, and production risk of winter wheat in the North China Plain, and to propose regionally adaptive management strategies. We selected three representative stations—Beijing (BJ), Wuqiao (WQ), and Zhengzhou (ZZ)—and, using long-term meteorological data (1981–2025) and field trial data, undertook local calibration and validation of the APSIM-Wheat model. Based on the validated model, we simulated 20 management scenarios comprising four sowing dates and five baseline soil moisture levels to examine the responses of phenology, aboveground dry matter, and yield, and further defined yield-reduction risk probability and expected yield loss indicators to assess long-term production risk. The results show that the APSIM-Wheat model can reliably simulate the winter wheat growing period (RMSE 4.6 days), yield (RMSE 727.1 kg ha⁻¹), and soil moisture dynamics for the North China Plain. Long-term trend analysis indicates that cumulative rainfall and the number of rainy days within the conventional sowing window have risen at all three sites. Delayed sowing leads to substantial yield reductions; specifically, compared with S1, the S4 treatment yields about 6.9%, 16.2%, and 16.0% less at BJ, WQ, and ZZ, respectively. Moreover, increasing the baseline soil moisture can effectively compensate for the losses caused by late sowing, although the effect is regionally heterogeneous. In BJ and WQ, raising the baseline moisture to a high level (P85) continues to promote biomass accumulation, whereas in ZZ this promotion diminishes as growth progresses. The risk assessment indicates that increasing baseline moisture can notably reduce the probability of yield loss; for example, in BJ under S4, elevating the baseline moisture from P45 to P85 can reduce risk from 93.2% to 0%. However, in ZZ, even the optimal management (S1P85) still carries a 22.7% risk of yield reduction, and under late sowing (S4P85) the risk reaches 68.2%, suggesting that moisture management alone cannot fully overcome late-sowing constraints in this region. Optimizing baseline soil moisture management is an effective adaptive strategy to mitigate late-sowing losses in winter wheat across the North China Plain, but the optimal approach must be region-specific: for BJ and WQ, irrigation should raise baseline moisture to high levels (P75-P85); for ZZ, the key lies in ensuring baseline moisture crosses a critical threshold (P65) and should be coupled with cultivar selection and fertilizer management to stabilize yields. The study thus provides a scientific basis for regionally differentiated adaptation of winter wheat in the North China Plain to address climate change and achieve stable production gains. Full article

Background/Objectives: Neonatal Intensive Care Units (NICUs) constitute a highly complex clinical environment characterized by patient fragility and frequent ethically sensitive decisions. To date, systematic studies investigating how Italian NICUs address these challenges and what forms of ethics support are effectively available are lacking. The aim of this study is therefore to assess how ethical issues are managed in Italian NICUs, with particular attention to the availability, use, and perceived usefulness of clinical ethics support in everyday practice. Methods: A 25-item questionnaire was developed by adapting an existing tool for investigating clinical ethics activities to the neonatal context. Following expert review by the GIBCE (Gruppo Interdisciplinare di Bioetica Clinica e Consulenza Etica in ambito sanitario), the final instrument covered four areas (general data, experience with ethical dilemmas, tools and procedures, opinions and training needs). A manual web search identified all Italian NICUs and their clinical directors, who were asked to disseminate the survey among staff. Participation was voluntary and anonymous. Data collection was conducted via Google Forms and analyzed through qualitative thematic analysis. Results: A total of 217 questionnaires were collected. The most frequent ethical dilemmas concern quality of life with anticipated multiple or severe disabilities (72.4%) and decisions to withdraw or withhold life-sustaining treatments (64.5%). Major challenges include fear of medico-legal repercussions (57.6%) and communication divergences between physicians and nurses (49,8%). More than half of respondents (52.1%) reported no formal training in clinical ethics, and 68.7% had never developed a Shared Care Plan (Shared Document for healthcare ethics planning) as defined by the Italian Law 219/2017. Conclusions: Findings highlight marked fragmentation in ethical practices across Italian NICUs. On this basis, establishing structured and accessible CEC services could help promote consistency, reinforce shared ethical standards, and support transparent and equitable decision-making in critical neonatal care. Full article

Traditional methods of asset management in electric power systems rely upon fixed schedules and reactive measurements, leading to challenges in the transparent prioritization of maintenance under evolving operating conditions and incomplete data. In this paper, we introduce a new, fully integrated artificial intelligence (AI)-driven approach for enhancing the resilience and reliability of open-source asset management tools to support improved performance and decisions in electric power system operations. This methodology addresses and overcomes several significant challenges, including data heterogeneity, algorithmic limitations, and inflexible decision-making, through a three-module workflow. The data fidelity module provides a domain-aware pipeline for identifying structural (missing) values from explicit missingness using sophisticated imputation methods, including Multiple Imputation Chain Equations (MICE) and Generative Adversarial Network (GAN)-based hybrids. The characterization module employs seven complementary weighting strategies, including PCA, Autoencoder, GA-based optimization, SHAP, Decision-Tree Importance, and Entropy Weighting, to achieve objective feature weight assignment, thereby eliminating the need for subjective manual rules. The optimization module enhanced the action space through multi-objective optimization, balancing reliability maximization and cost minimization. A synthetic dataset of 100 power transformers was used to validate that the MICE achieved better imputation than other methods. The optimized weighting framework successfully categorizes Health Index values into five condition levels, while the multi-objective maintenance policy optimization generates decisions that align with real-world asset management practices. The proposed framework provides the Transmission and Distribution System Operators (TSOs/DSOs) with an adaptable, industry-oriented decision-support workflow system for enhancing reliability, optimizing maintenance expenses, and improving asset management policies for critical power infrastructure. Full article