Search Results (19,889)

Battery energy storage systems (BESSs) are increasingly required to provide grid-support services under weak-grid conditions, where the stability of virtual synchronous generator (VSG) control largely depends on the health status and dynamic characteristics of the battery unit. However, existing VSG strategies typically assume fixed parameters and neglect the intrinsic coupling between battery aging, DC-link energy variations, and converter dynamic performance, resulting in reduced damping, degraded transient regulation, and accelerated lifetime degradation. This paper proposes a health-responsive VSG control strategy enabled by real-time energy-dynamics sensing. By reconstructing the DC-link energy state from voltage and current measurements, an intrinsic indicator of battery health and instantaneous power capability is established. This energy-dynamics indicator is then embedded into the VSG inertia and damping loops, allowing the control parameters to adapt to battery health evolution and operating conditions. The proposed method achieves coordinated enhancement of transient stability, weak-grid robustness, and lifetime management. Simulation studies on a multi-unit BESS demonstrate that the proposed strategy effectively suppresses low-frequency oscillations, accelerates transient convergence, and maintains stability across different aging stages. Full article

Load balancing is a widely adopted strategy in modern distributed systems because it distributes workloads across servers, mitigating overload and improving overall performance. However, the rapid growth of such systems has created a need for more adaptive strategies to ensure optimal utilization and responsiveness of resources. Traditional algorithms such as Round Robin (RR) and Weighted Round Robin (WRR) assign requests without considering server states or request characteristics. We implement a machine learning (ML)–based predictive load balancer, forecasting the latency of a request based on the request itself and container parameters, specifically the average latency of the last 50 requests and the count of active requests, and evaluate it against RR and WRR. For the experiment, synthetic data were generated to replicate real-world requests by creating random URL and method combinations, attaching a task size in Million Instructions (MI), and distributing them among three containers with varying resources according to the load balancing strategies described above. Under the conditions tested, the ML approach achieved the worst performance, trailing both RR and WRR in terms of throughput and average latency, although the model accuracy was sufficiently high (R² = 0.8+). Post hoc analysis indicates that limited and occasionally stale runtime features caused the load balancer to direct all requests to a single container until the next statistics update, since that container was considered the ‘best’ during that interval. Full article

Sustainability in the agri-food sector has become a cornerstone of global efforts to combat climate change, ensure food security through climate-smart agriculture, and strengthen economic resilience. Sustainability reporting within agri-food systems has gained increasing regulatory significance with the introduction of mandatory frameworks such as the Turkish Sustainability Reporting Standards (TSRSs). This article searches for the sustainability reports of agri-business firms listed in BIST in Turkey. Although TSRS reporting is not yet mandatory for the agribusiness sector, this study examines the first TSRS-aligned sustainability reports published by eight agri-food companies, excluding the retail sector. The analysis assesses how effectively these reports address sector-specific environmental and social challenges defined in the GRI 13 Agriculture, Aquaculture and Fishing Sector Standard and their alignment with the United Nations Sustainable Development Goals (SDGs). Using a structured content analysis approach, disclosure patterns were examined at both thematic and company levels. The findings indicate that TSRS-aligned reports place strong emphasis on environmental and climate-related disclosures, particularly emissions, climate adaptation and resilience, water management, and waste. In contrast, agro-ecological and land-based impacts—such as soil health, pesticide use, and ecosystem conversion—are weakly addressed. Economic disclosures are predominantly framed around climate-related financial risks and supply chain traceability, while social reporting focuses mainly on occupational health and safety, employment practices, and food safety, with limited attention to labor and equity issues across the broader value chain. Company-level results reveal marked heterogeneity, with internationally active firms demonstrating deeper alignment with GRI 13 requirements. From an SDG alignment perspective, high levels of coverage are observed across all companies for SDG 13 (Climate Action), SDG 12 (Responsible Consumption and Production), and SDG 6 (Clean Water and Sanitation). By contrast, SDGs critical to agro-ecological integrity and social equity—namely SDG 1 (No Poverty), SDG 2 (Zero Hunger), SDG 10 (Reduced Inequalities), and SDG 15 (Life on Land)—are weakly represented or entirely absent. Overall, the results suggest that while TSRS-aligned reporting enhances transparency in climate-related domains, it achieves only selective alignment with the SDG agenda. This underscores the need for a stronger integration of sector-specific sustainability priorities into mandatory sustainability reporting frameworks. Full article

Heat waves have emerged as an escalating climate threat, triggering cascading disruptions across food, energy, and water systems, thereby undermining resilience and sustainability. However, reviews addressing heat wave impacts on the food–energy–water (FEW) nexus remain scarce, resulting in a fragmented understanding of cross-system interactions and limiting the ability to assess cascading risks under extreme heat. This critical issue is examined through bibliometric analysis, scoping review, and policy analysis. A total of 103 publications from 2015 to 2024 were retrieved from Web of Science and Scopus, and 63 policy documents from the United States, the European Union, Japan, China, and India were collected for policy analysis. Bibliometric analysis was conducted to identify the most influential articles, journals, countries, and research themes in this field. The scoping review indicates that agricultural losses are most frequently reported (32), followed by multiple impacts (19) and cross-sectoral disruptions (18). The use of spatial datasets and high-frequency temporal data remains limited, and community-scale studies and cross-regional comparisons are uncommon. Mechanism synthesis reveals key pathways, including direct system-specific stress on food production, water availability, and energy supply; indirect pressures arising from rising demand and constrained supply across interconnected systems; cascading disruptions mediated by infrastructure and system dependencies; and maladaptation risks associated with uncoordinated sectoral responses. Policy analysis reveals that most countries adopt sector-based adaptation approaches with limited across-system integration, and insufficient data and monitoring infrastructures. Overall, this study proposes an integrated analytical framework for understanding heat wave impacts on the FEW nexus, identifies critical research and governance gaps, and provides conceptual and practical guidance for advancing future research and strengthening coordinated adaptation across food, energy, and water sectors. Full article

Climate change introduces a critical threat to global viticulture, compromising grape yield, quality, and the long-term sustainability of Vitis vinifera cultivation. Addressing these challenges requires innovative strategies to enhance grapevine resilience. The integration of multi-omics data, predictive breeding, and physiological insights into ripening and stress responses is refining our understanding of grapevine adaptation mechanisms. In parallel, recent advances in plant biotechnology have accelerated progress from marker-assisted and genomic selection to targeted genome editing, with CRISPR/Cas systems and other New Genomic Techniques (NGTs) offering advanced precision tools for sustainable improvement. This review synthesizes the major achievements in grapevine genetic improvement over time, tracing the evolution of strategies from traditional breeding to modern genome editing technologies. Overall, we highlight how combining genetics, biotechnology, and physiology is reshaping grapevine breeding towards more sustainable viticulture. The convergence of these disciplines establishes a new integrated framework for developing resilient, climate-adapted grapevines that maintain yield and quality while preserving varietal identity in the face of environmental change. Full article

Accurate egocentric distance estimation in vista space depends on the interaction between perceptual encoding and cognitive recalibration. This study examined how iterative, feedback-based learning modulates spatial accuracy, perceptual bias, and task efficiency in large-scale environments. A total of 133 participants (mean age = 26.3 ± 7.44 years) performed distance estimations on three outdoor targets (134 m, 575 m, 1463 m) using a mobile web application providing immediate corrective feedback (too short/too long). Six variables were analyzed: first estimate (FE), error of first estimate (EFE), mean estimate (ME), error of mean estimate (EME), number of attempts (NAs), and trial duration (TD). Given the non-normal data distribution, nonparametric tests were applied (Friedman and Wilcoxon signed-rank tests with Bonferroni correction). All variables showed significant within-subject effects across distances (p < 0.001). Post hoc analyses indicated that EFE and EME differed significantly between all target pairs (p < 0.0167), revealing a shift from slight overestimation at 134 m to increasing underestimation at 575 m and 1463 m. NA was significantly higher for the farthest target (p < 0.0167), indicating greater cognitive load and iterative correction effort. TD differed significantly only between consecutive distances (p < 0.0167), suggesting non-linear temporal adaptation. These results demonstrate that iterative feedback improves perceptual stability and efficiency but does not remove distance compression. The consistent bias and adaptive response patterns support a feedback-driven, binary search-like recalibration mechanism. The proposed mobile paradigm offers a scalable and valid approach for assessing perceptual–cognitive calibration in both natural and virtual spatial contexts. Full article

Understanding how plants respond to water limitation is increasingly important under accelerating climate change. Lycoris aurea, a widely distributed ornamental and medicinal bulbous plant, frequently inhabits environments with fluctuating soil moisture, yet its molecular drought-response mechanisms remain largely unexplored. In this study, we investigated L. aurea growing under field-based, in situ soil moisture regimes, comparing low (~20% soil water content) and high (~40% soil water content) conditions. We combined soil property assessments with high-resolution transcriptomic and untargeted metabolomic profiling to characterize the adaptive responses of bulb tissues under contrasting soil water conditions. Although total nitrogen, phosphorus, and potassium levels were comparable across treatments, soil moisture, representing the primary contrasting field condition, and soil pH, a correlated environmental factor, were significantly associated with variation in gene expression and metabolite accumulation (p < 0.05, n = 3). Transcriptome analyses identified a total of 1034 differentially expressed genes enriched in pathways related to amino acid metabolism, cuticle formation, cell wall modification, and osmotic adjustment. Metabolomic analysis identified a total of 1867 differentially expressed metabolites belonging to carboxylic acids and prenol lipids, showing alterations involved in amino acids, lipids, phenolic acids, and alkaloids associated with osmoprotection, membrane stabilization, and structural reinforcement under low soil moisture. Pathway-based integration analysis highlighted four core pathways, including “alanine, aspartate and glutamate metabolism” (p = 0.00371) and “cutin, suberine and wax biosynthesis” (p = 0.00873), as central hubs linking transcriptional regulation with metabolic reconfiguration. Gene-metabolite-soil correlation networks further demonstrated that drought adaptation arises from tightly coordinated biochemical and structural adjustments rather than shifts in nutrient acquisition. Together, this species-specific study provides a comprehensive multi-omics framework for understanding drought tolerance in L. aurea, reveals key molecular targets associated with plant resilience, and offers potential targets and insights for the conservation of drought-resilient Lycoris cultivars. Full article

Salinity stress represents a critical environmental constraint that significantly limits the development of tilapia aquaculture in brackish water environments. Its substantial impacts on fundamental physiological processes in fish, particularly osmotic balance, energy metabolism, and antioxidant defense mechanisms, have become a major scientific concern in aquaculture research. To systematically elucidate the molecular mechanisms underlying the response of genetically improved farmed tilapia (Oreochromis niloticus) to salinity stress and to test the hypothesis that it adapts through metabolic reprogramming for energy reallocation under such conditions, this study employed an integrated transcriptomic and metabolomic approach. Through a rigorously controlled experimental design with freshwater (0‰) as the control group and brackish water (24‰) as the experimental group, we conducted a comprehensive analysis of dynamic changes in gene expression profiles and metabolite spectra in the liver tissues of experimental fish. The study yielded the following key findings: First, salinity stress significantly suppressed growth performance indicators, including body weight and length, while simultaneously inducing extensive transcriptomic restructuring and profound metabolic remodeling in liver tissue. A total of 1529 differentially expressed genes (including 399 up-regulated and 1130 down-regulated genes) and 127 significantly differential metabolites were identified. Second, the organism achieved strategic reallocation of energy resources through coordinated suppression of multiple energy-consuming anabolic pathways, particularly steroid biosynthesis and fatty acid metabolism, with the remarkable down-regulation of Fasn, a key gene in the fatty acid synthesis pathway, being especially prominent. Energy-sensing and metabolic homeostasis regulatory networks played a central coordinating role in this process, guiding the organism through metabolic reprogramming by regulating downstream metabolic nodes. From a multi-omics integrative perspective, this study provides in-depth insights into the sophisticated metabolic remodeling and energy allocation strategies employed by GIFT to cope with salinity stress. These findings, particularly the suppression of fatty acid biosynthesis and the reprogramming of glycolysis/gluconeogenesis pathways, not only elucidate the molecular mechanisms by which teleosts achieve environmental adaptation through energy reallocation, but also provide actionable molecular targets for the selective breeding of salinity-resilient tilapia strains. Full article

The purpose of this study is to examine how employees’ artificial intelligence awareness (AIA) influences adaptive performance in the workplace through the mediating roles of eustress and task crafting within the Job Demands–Resources (JD-R) Theory. Data were collected from 372 full-time employees working in five-star hotels and analyzed using PLS-SEM with WarpPLS. The findings reveal that employees’ AI awareness significantly enhances adaptive performance both directly and indirectly. AI awareness also positively predicts eustress and task crafting, suggesting that informed employees experience motivating stress and actively reshape their tasks to optimize work processes. Moreover, both eustress and task crafting serve as significant mediators, amplifying the effect of AI awareness on adaptive performance. These results underscore the value of cultivating AI knowledge among employees to foster proactive behaviors and positive stress responses, ultimately supporting adaptability in dynamic work environments. The study contributes to JD-R Theory by integrating AI-related awareness as a personal resource driving employee adaptation. Full article

Breast cancer remains a major global health challenge, with treatment access further constrained during the COVID-19 pandemic, particularly in resource-limited settings. This study evaluates the in vitro effects of hypofractionated versus conventionally fractionated radiotherapy on three breast cell lines: MCF-7 (oestrogen receptor-positive, ER⁺/PR⁺), MDA-MB-231 (triple-negative: ER⁻/PR⁻/HER2⁻), and MCF-10A (non-tumorigenic mammary epithelial). Cells were exposed to cobalt-60 γ-rays, and radiobiological endpoints assessed included clonogenic survival, α/β ratios, adaptive responses, migration, invasion, and cytotoxicity through lactate dehydrogenase assays. The α/β ratios ranged from 2.5 to 5.4 Gy across breast cancer subtypes. Hypofractionation reduced survival in hormone receptor-positive cells, whereas triple-negative cells exhibited increased survival. Adaptive radiation responses enhanced viability across all cell lines, while non-cancerous MCF-10A cells demonstrated reduced migration following treatment. These findings suggest that hypofractionated radiotherapy may be beneficial in hormone receptor-positive breast cancer, while triple-negative disease may show a trend toward different responses, although this was not statistically significant (MDA-MB-231, p = 0.290). The results underscore the importance of tailoring fractionation strategies to breast cancer subtype and highlight the translational potential of preclinical radiobiology in guiding personalised radiation oncology approaches. Full article

Chemical contaminants are increasingly detected in freshwater environments, yet the physiological and molecular responses of many non-model freshwater invertebrates to acute chemical stress remain poorly understood. In this study, we investigated the physiological and transcriptomic responses of the freshwater hydrozoan Craspedacusta sowerbii to two widespread aquatic pollutants: the antibiotic sulfamethoxazole (20 μM) and the heavy metal salt CdSO₄ (10 μM). Morphological and behavioral observations showed that sulfamethoxazole exposure led to reduced motility and body shrinkage, whereas cadmium exposure caused rapid loss of movement and complete mortality within 24 h. RNA sequencing revealed distinct transcriptional response patterns to the two stressors. Sulfamethoxazole exposure primarily induced the up-regulation of genes associated with oxidative stress, apoptosis, immune responses, and signaling pathways, suggesting an active but limited stress-adaptation response. In contrast, cadmium exposure resulted in extensive down-regulation of genes involved in metabolic pathways, cell cycle regulation, fatty acid metabolism, and anti-aging processes, suggesting severe disruption of core metabolic processes. Comparative pathway analyses identified both shared stress-related responses and pollutant-specific transcriptional signatures, with cadmium exerting markedly stronger inhibitory effects at both physiological and molecular levels. These results reveal clear thresholds of stress tolerance and response failure in C. sowerbii under chemical pollution, and highlight its ecological sensitivity to water quality deterioration. Together, these findings provide mechanistic insight into acute pollutant-induced stress responses in a freshwater Cnidarian and offer a useful reference for understanding how freshwater invertebrates respond to short-term chemical disturbances. Full article

Listeria monocytogenes is a persistent foodborne pathogen capable of surviving in food processing environments, often in association with diverse environmental microflora. This study examines genomic determinants of persistence, specifically stress adaptation and biofilm-associated traits, in environmental Listeria species and other environmental microflora from a dairy processing facility by analyzing whole-genome sequences of 6 environmental Listeria isolates, 4 ATCC reference strains, and 22 air and floor swab cultures, annotated using the RAST platform. Subsystem analysis revealed that Listeria isolates carried a defined set of genes linked to biofilm formation, antimicrobial resistance, and stress response, though in lower abundance than environmental cultures. Listeria exhibited fewer flagellar genes but greater consistency in core stress-related genes, including those for disinfectant and osmotic stress resistance, with SigB operon and RpoN genes highlighting strong stress tolerance. In contrast, environmental cultures exhibited broader transcriptional regulators (RpoE, RpoH) and greater diversity in acid and heat shock response genes, indicating distinct survival strategies. All examined Listeria species harbor biofilm and stress-resistance genes enabling independent survival, while environmental microbiota show greater genetic diversity that may promote persistence and multispecies biofilm formation. This study underscores the complex genetic landscape that may contribute to the persistence of Listeria and environmental microbiota in dairy processing environments, providing foundational insights for environmental cross contamination control strategies. Full article

This study presents a standardized method for multimodal monitoring of exercise execution in isolated, confined, and extreme (ICE) environments, addressing the need for reproducible assessment of neuromuscular and cardiovascular responses under space- and equipment-limited conditions. The method integrates wearable surface electromyography (sEMG), inertial measurement units (IMU), and electrocardiography (ECG) to capture muscle activation, movement, and cardiac dynamics during space-efficient exercise. Ten exercises suitable for confined habitats were implemented during analog missions conducted in the DeepLabH03 facility, with feasibility evaluated in a seven-day campaign involving three adult participants. Signals were synchronized using video-verified repetition boundaries, sEMG was normalized to maximum voluntary contraction, and sEMG amplitude- and frequency-domain features were extracted alongside heart rate variability indices. The protocol enabled stable real-time data acquisition, reliable repetition-level segmentation, and consistent detection of muscle-specific activation patterns across exercises. While amplitude-based sEMG indices showed no uniform main effect of exercise, robust exercise-by-muscle interactions were observed, and sEMG mean frequency demonstrated sensitivity to differences in movement strategy. Cardiac measures showed limited condition-specific modulation, consistent with short exercise bouts and small sample size. As a proof-of-concept feasibility study, the proposed protocol provides a practical and reproducible framework for multimodal physiological monitoring of exercise in ICE analogs and other constrained environments, supporting future studies on exercise quality, training load, and adaptive feedback systems. The protocol is designed to support near-real-time monitoring and forms a technical basis for future exercise-quality feedback in confined habitats. Full article

Carotenoids are a class of C40 isoprenoid-derived fat-soluble pigments that play vital roles in plant physiology and human health and serve as precursors for several biologically critical regulatory molecules. Carotenoid cleavage dioxygenases (CCDs) are key enzymes that catalyze the selective oxidative cleavage of carotenoids into apocarotenoids, thereby significantly influencing plant development and responses to abiotic stress. Although extensive research has been conducted on many model species, comprehensive studies on the StCCD gene family in potato remain limited. In this study, we conducted a genome-wide analysis to identify and characterize the CCD gene family in potato. Phylogenetic and structural analyses classified the 17 StCCD genes into six distinct subfamilies, which are distributed across five chromosomes of the genome. Analysis of cis-acting regulatory elements revealed that the promoters of most StCCD genes contain various elements associated with light responsiveness, stress signaling, and phytohormone regulation. Molecular docking analysis indicated that CCD proteins exhibit distinct substrate specificity in their binding to carotenoids and intermediate products. The expression profiling of StCCD genes uncovered pronounced specificity in their expression, which was evident across tissues, throughout tuber maturation, and following exposure to abiotic stresses and hormonal applications. This specificity strongly implicates these genes in the regulation of developmental processes and stress adaptation mechanisms. This study provides a comprehensive genomic and transcriptomic overview of the CCD gene family in potato, establishing a foundation for functional characterization of individual genes in the future. Full article

In GPS-denied environments, the spatio-temporal coupling of errors caused by dynamic network topologies poses a fundamental challenge to cooperative localization, presenting existing methods with a dilemma: approaches pursuing global optimization lack dynamic adaptability, while those focusing on local adaptation struggle to guarantee global convergence. To address this challenge, this paper proposes ST-DCL, a cooperative localization framework based on a novel principle of closed-loop spatio-temporal decoupling. The core of ST-DCL comprises two modules: a Dynamic Weighted Multidimensional Scaling (DW-MDS) optimizer, responsible for providing a globally consistent coarse estimate with provable convergence, and a specially designed Spatio-Temporal Graph Neural Network (ST-GNN) corrector, tasked with compensating for local nonlinear errors. The DW-MDS effectively suppresses interference from historical errors via an adaptive sliding window and confidence weights derived from our error propagation model. The key innovation of the ST-GNN lies in its two newly designed components: a Dynamic Topological Attention Module for actively modulating neighbor aggregation to inhibit spatial error diffusion, and a Dilated Causal Convolution Module for modeling long-term temporal dependencies to curb error accumulation. These two modules form a closed loop via a confidence feedback mechanism, working in synergy to achieve continuous error suppression. Theoretical analysis indicates that the framework exhibits bounded-error convergence under dynamic topologies. In simulations involving 200 nodes, velocities up to 50 m/s, and 15% NLOS links, the ST-DCL achieves a normalized root mean square error (NRMSE) of 0.0068, representing a 21% performance improvement over state-of-the-art methods. The practical efficacy and real-time capability are further validated through real-world flight experiments with a 10-UAV swarm in complex, GPS-denied scenarios. Full article