Search Results (4,059)

As a Chinese national key protected medicinal fern naturally occurring in forest understories, Cibotium barometz faces severe threats of wild population degradation, while standardized large-scale artificial breeding technology for conservation purposes remains immature. To establish an efficient spore-based conservation propagation system for this endangered forest fern, this study quantified the independent and interactive effects of spore storage temperature, storage duration and sowing density on spore germination, gametophyte growth and sporophyte seedling establishment. Spores were preserved under four gradient temperature treatments with sequential sampling at multiple storage durations, followed by sowing trials with a series of density gradients; germination rate, seedling establishment rate and gametophyte–sporophyte conversion rate were dynamically recorded and statistically analyzed. The results demonstrated that appropriately extended storage significantly shortened the germination phase and simultaneously elevated both spore germination and sporophyte seedling formation rates. Among all temperature treatments, storage at −4 °C achieved the maximum germination and seedling establishment capacity, whereas ultra-low-temperature cryopreservation at −196 °C greatly promoted gametophyte–sporophyte conversion rate. The optimal sowing density balancing growth space and survival rate was determined to be 30 spores per cm². The complete dynamic developmental traits covering the full spore propagation life cycle of C. barometz were systematically summarized in this work. Our findings supply reliable technical parameters to standardize spore breeding protocols, and offer critical support for ex situ conservation, wild forest population restoration and sustainable resource utilization of C. barometz. Full article

In resource-constrained UAV swarm systems, multimodal sensory data are often affected by complex environmental factors, resulting in modality missing, signal degradation, and asynchrony, which significantly reduce the reliability of multimodal learning and incremental model updates. To address this issue, we propose a Compensatory Collaboration Modality-Balanced Sample Selection framework (CC-MBS), which improves robustness through modality quality modeling and cross-UAV collaborative compensation. Specifically, a modality confidence vector is introduced to quantify modality reliability from missing rate, degradation, and asynchrony. A lightweight collaboration mechanism is designed to exchange low-dimensional confidence information instead of high-dimensional features or model parameters. Based on the compensated confidence, a modality-aware sample selection strategy is further developed to prioritize high-value samples under limited memory. Experimental results in simulated UAV-swarm-inspired benchmark settings show that CC-MBS outperforms representation-based methods such as ShaSpec and its parameter aggregation variants (AVG, PFM, POW) in both modality compensation accuracy and communication–computation efficiency under missing conditions. In addition, it achieves stronger robustness than MBS and training-dynamics-based methods such as EL2N and GraNd in sample selection. These results demonstrate that CC-MBS effectively improves robustness and data efficiency for multimodal incremental learning under incomplete modalities. Full article

Water scarcity in Peru is increasingly shaped by competing sectoral demands, particularly between large-scale mining and agriculture. Both sectors rely heavily on limited freshwater resources in arid coastal and Andean basins, generating complex trade-offs between economic productivity, environmental sustainability, and social equity. This review synthesizes and critically evaluates current knowledge on water footprint (WF) dynamics within mining–agriculture systems, integrating hydrosocial theory, water–energy–food nexus thinking, and sustainability transition frameworks. Mining activities in Peru are characterized by high blue and grey water footprints, associated with intensive extraction processes and contamination risks, while agriculture exhibits diverse water footprints depending on crop type, irrigation efficiency, and climatic conditions. The interaction of these sectors creates hydrosocial conflicts driven by unequal water allocation, environmental degradation, and institutional fragmentation. This paper identifies key drivers of conflict and evaluates emerging pathways for sustainability transitions, including technological innovation, nature-based solutions, and participatory governance mechanisms. An integrative conceptual framework derived from a thematic synthesis of the reviewed literature is proposed. The findings provide actionable insights for policymakers and researchers seeking to reconcile economic development with water sustainability in resource-constrained environments. Full article

Floods remain among the most devastating natural disasters globally, disproportionately impacting low-resource regions where real-time flood forecasting is constrained by limited computational infrastructure and the scarcity of fine-resolution predictive models. Although state-of-the-art global climate models achieve high predictive accuracy, their scale and computational complexity restrict their applicability in localized and resource-constrained settings. This study proposes a deep climate model distillation framework that transfers knowledge from a high-capacity Fourier Neural Operator (FNO)-based global climate model inspired by FourCastNet into lightweight, regionally adaptive student networks suitable for edge deployment. The framework combines climate variables, satellite observations, and hydrological measurements to improve localized flood prediction. Knowledge transfer is achieved through a multi-objective distillation strategy that combines supervised learning, soft-target alignment, and intermediate feature matching. Experimental evaluation across multiple flood-prone regions in Sub-Saharan Africa and South Asia shows that the distilled student model achieves an average classification accuracy of 0.89, an AUC of 0.91, and an F1-score of 0.88, retaining approximately 96.7% of the teacher model’s predictive performance. In continuous discharge estimation, the model attains a mean absolute error of 0.17, RMSE of 0.24, and an R² score of 0.85. The proposed distillation approach yields an 8× reduction in inference latency and over a 20× reduction in model size, enabling real-time execution on low-power edge devices such as the Raspberry Pi 4 and NVIDIA Jetson Nano. The student model further demonstrates robust regional and temporal generalization, with limited performance degradation in unseen geographic areas and during extreme flood years. Full article

Small-scale island communities whose livelihoods depend on aquaculture are increasingly vulnerable under interacting climatic and non-climatic stressors. Conventional indicator-based assessments are useful for describing the level of vulnerability, but many empirical assessments remain less able to explain how multiple stressors are mediated through local social–ecological structures and feedback processes to produce different vulnerability patterns. This study aims to explain how vulnerability is formed in island aquaculture communities by linking social–ecological system structures with vulnerability processes and by examining empirically informed feedback pathways. Drawing on evidence from three island aquaculture communities in southeastern China, household survey data were first used to classify community types through hierarchical clustering. Semi-structured interviews, field observations, and documentary materials were then qualitatively coded to develop empirically informed conceptual causal loop diagrams (CLDs) for each type. Key variables and recurring feedback pathways were identified through loop-based structural analysis and cross-case comparison. The analysis indicates that vulnerability formation in island aquaculture communities is associated with recurring reinforcing feedbacks within local social–ecological system structures, through which multiple climatic, ecological and socio-economic stressors are translated into differentiated vulnerability outcomes. Across the case communities, resource overexploitation and marine pollution reinforce an ecology–livelihood degradation loop, while labor outmigration erodes social capital, disrupts intergenerational knowledge transmission, and weakens collective action and adaptive capacity, exacerbating socio-ecological vulnerability. At the same time, dominant stressors, key drivers, and feedback configurations vary across community types, generating divergent vulnerability trajectories and highlighting the context-dependent nature of vulnerability dynamics. These results suggest that governance interventions targeting isolated stressors or relying on static vulnerability analyses are insufficient where reinforcing feedbacks dominate. Effective adaptation strategies should explicitly target critical feedback pathways and strengthen stabilizing processes. By integrating social–ecological systems thinking with vulnerability analysis, this study provides a feedback-oriented approach for diagnosing vulnerability formation and supports more feedback and context-sensitive governance in small-scale island aquaculture communities. Full article

The increasing penetration of renewable energy sources and converter-interfaced loads has intensified the need for fast and reliable grid-support services. Although electric vehicle (EV) battery chargers have emerged as promising resources for Vehicle-to-Grid (V2G) applications, existing solutions typically focus on individual services such as virtual inertia or frequency regulation, while limited attention has been given to the coordinated provision of multiple ancillary services within a unified framework. Furthermore, the use of batteries alone for fast frequency support may accelerate battery degradation due to frequent high-power transients. To address these challenges, this paper proposes a hybrid energy storage-based EV battery charger architecture and a coordinated multi-timescale control strategy capable of simultaneously providing virtual inertia support, long-term frequency regulation, reactive power compensation, and harmonic mitigation. The proposed approach utilizes a DC-link capacitor to deliver fast inertial response while the battery supplies sustained frequency support, thereby reducing battery stress and improving energy management efficiency. An enhanced frequency estimation method based on a phase-locked loop combined with a low-pass filter is also introduced to improve dynamic performance. Simulation results demonstrate the effectiveness of the proposed strategy under various grid disturbances. The system achieves an equivalent virtual inertia constant of approximately 1.85 s and delivers up to 786 W of transient inertial support within 80 ms during frequency events. The enhanced frequency estimation method significantly reduces transient overshoot, while harmonic compensation limits the grid current and voltage total harmonic distortion to 1.50% and 3.23%, respectively. In addition, the controller provides up to 400 VAR of reactive power support during voltage disturbances while maintaining stable battery operation. These results demonstrate that the proposed EV battery charger can function as a multifunctional grid-support resource, enhancing frequency stability, voltage regulation, power quality, and overall V2G capability in future smart grids. Full article

Background: This scoping review examines how schedule-related load affects athletic and team performance in professional sport, an issue that has received less systematic attention than training and competition load despite its clear implications for recovery, injury risk, and performance. Methods: Following PRISMA-ScR guidelines, Web of Science, SPORTDiscus, Scopus, and MEDLINE were searched for relevant publications (1993–2025) examining schedule-related load in professional sport. Five theoretical frameworks (Fitness-Fatigue, Circadian Disruption, Allostatic Load, Training-Injury Prevention, and Conservation of Resources) were used to interpret underlying mechanisms. Results: Seventy-two sources were included. At the athlete level, schedule-related load degrades physical performance, impairs sleep and recovery, increases injury risk, and disrupts circadian function. At the team level, it deteriorates game outcomes, alters offensive and defensive strength, and constrains lineup management. Six research gaps were identified involving measurement, interaction effects, advanced metrics, player heterogeneity, integration with training load, and longitudinal analysis. Conclusions: The findings position schedule design as a measurable performance variable and highlight the need for more rigorous sport analytics research to support evidence-based optimization of competition calendars and workload management. Full article

Back-mixing has been widely applied during practical composting to initiate the process and improve compost product quality. However, for antibiotic mycelial residue (AMR), a fermentation by-product containing residual antibiotics, the ecological safety of this treatment remains unclear. In this study, pleuromutilin mycelial residue (PMR) was subjected to a 35-day aerobic composting experiment with a back-mixing treatment (T group) and the conventional composting group (CK group) to evaluate composting performance and antibiotic resistance risk. The results demonstrated that the T group exhibited more rapid heating and a higher degree of humification. Additionally, the T group not only exhibited faster pleuromutilin degradation, reaching below the detection limit 3 days earlier than in the CK group, but also achieved up to a 3.1-fold reduction in antibiotic resistance genes (ARGs) and a 93.2% overall reduction in mobile genetic elements (MGEs). Redundancy analysis (RDA), variance partitioning analysis (VPA), and co-occurrence network analysis indicated that microbial community structure appeared to be more strongly associated with ARG variation than MGEs under the tested conditions. Overall, back-mixing accelerated pleuromutilin degradation and enhanced PMR composting performance, while no substantial enrichment of the detected ARGs was observed under the tested composting conditions. This study provides a scientific basis for the safe resource utilization of AMR. Full article

Across the European Union, deposit–refund systems (DRSs) are increasingly seen as a crucial response to mounting waste management challenges, including low recycling rates and rising plastic pollution. By attaching a refundable deposit to beverage containers, these systems incentivise civilians to return packaging, thereby significantly reducing litter and improving the quality of recyclable materials. In the context of ambitious EU circular economy and waste reduction targets, DRSs play a key role in addressing resource inefficiency and environmental degradation. The empirical results obtained from a comprehensive panel regression analysis, which accounts for heterogeneity in policy effectiveness, reveal that the effectiveness of a DRS is not uniform across the packaging spectrum in the EU. The results indicate that the presence of a DRS and higher deposit values have a statistically significant and positive effect on packaging waste recycling rates, especially for plastic packaging waste, and that DRSs play a major role in improving and increasing the separate collection of packaging waste, ensuring good quality of the aforementioned fractions. In addition, some structural factors, such as population density and tourism intensity, have a strong impact on recycling efficiency. The findings highlight the importance of implementing deposit systems to achieve circular economy objectives and provide an empirical basis for improving waste management policies in the EU, thereby strengthening evidence-based decision-making. Full article

The rapid localisation of survivors in disaster-affected areas remains a critical challenge due to complex terrains and limited communication infrastructure. Existing multi-UAV cooperative search methods suffer from insufficient perception of target distribution, inefficient use of scarce communication resources, and redundant coverage that degrades exploration efficiency. To address these issues, this paper proposes a multi-agent deep reinforcement learning framework based on the Multi-Agent Proximal Policy Optimisation (MAPPO) algorithm under centralised training with the decentralised execution (CTDE) paradigm. A detection-augmented observation mechanism is designed to encode target distribution information without introducing additional trainable parameters. A lightweight mean-pooling communication strategy is developed to optimise communication resource utilisation, reducing per-agent bandwidth to 64 bytes per step while preserving effective inter-agent coordination. Furthermore, a composite reward function is constructed to balance target detection, exploration, and redundancy suppression. Simulation results demonstrate that the proposed method achieves a recall of 0.70 and improves flight efficiency by 28% compared with the Grid Search baseline, while consuming over two orders of magnitude less communication bandwidth than representative feature-sharing approaches. The results support the use of the framework as a communication-resource-efficient solution for cooperative UAV swarm perception in bandwidth-constrained emergency rescue scenarios. Full article

Modern photovoltaic technologies are increasingly evaluated not only in terms of power conversion efficiency and cost, but also with respect to resource origin, toxicity, recyclability, and overall life-cycle impacts. Within this broader sustainability framework, bio-based and bio-inspired materials derived from biomass or mimicking biological structures have emerged as promising candidates for a wide range of photovoltaic components, including active layers, interfacial modifiers, substrates, encapsulants, and natural dyes. This review provides a layer-by-layer overview of such materials implemented or proposed in dye-sensitized, organic, perovskite, biohybrid, and silicon solar cells, linking their molecular structures and optoelectronic properties to representative device performances and key degradation pathways. Cross-cutting challenges related to moisture and thermal stability, barrier performance, feedstock variability, and the risk of “greenwashing” are highlighted, emphasizing that sustainability claims must be supported by quantitative metrics such as life-cycle assessment, circularity indicators, and durability studies. Finally, we outline promising research directions in molecular engineering, hybrid biosynthetic architectures, and advanced encapsulation concepts that could enable bio-based materials to make a meaningful contribution to low-impact photovoltaic technologies. Full article

Introduction: The upper Arlanza River (Duero Basin, Burgos, Spain) hosts a genetically distinct local lineage of brown trout (Salmo trutta fario), the “Arlanza strain”, largely free from hatchery-derived introgression, alongside other native cyprinids of conservation concern, including the Iberian chub (Achondrostoma arcasii, Vulnerable—IUCN). The river also supports the Iberian desman (Galemys pyrenaicus, Endangered—IUCN) and Eurasian otter (Lutra lutra). Despite these values, the study reach presents multiple transverse obstacles limiting longitudinal connectivity and degraded riparian cover in critical sections due to livestock erosion, compromising habitat quality for all species. Objective: This study aimed to design engineering interventions to improve fluvial and riparian habitat in a 4 km reach of the upper Arlanza River, restoring longitudinal connectivity and thermal refuge availability while strictly preserving the genetic integrity of the native Arlanza trout strain. Methodology: The reach was characterised through electrofishing surveys, riparian quality assessment (modified RQI index), hydraulic refuge evaluation (IR index), and hydrological analysis based on a 30-year flow record. Brown trout population dynamics were modelled using dimP 1.0 software, with a comparative analysis between upstream (Quintanar de la Sierra village) and downstream (Vilviestre del Pinar village) sampling points to identify connectivity bottlenecks. Engineering works were scheduled to avoid reproductive periods of all target species. Results: The upstream population showed a rejuvenated age structure (density: ~1.40 ind/m; mean length: 12.0 cm), consistent with good spawning conditions but limited growth capacity due to cold temperatures and low summer flows. The downstream point exhibited a severely reduced population (~0.10 ind/m), indicating marked loss of connectivity and habitat degradation. Priority intervention zones were identified in the Camping and lower Prado Mayor sub-reaches. Proposed measures included weir notching to restore fish passage, livestock watering points to reduce bank erosion, and riparian restoration by planting native species (Populus tremula, Betula alba, Salix spp.) protected with fences. Conclusions: Restoring longitudinal connectivity and riparian cover in the upper Arlanza River are essential to protect the genetically valuable Arlanza trout strain, the endangered G. pyrenaicus, and other native fish species, providing a transferable framework for headwater fluvial restoration that jointly addresses biodiversity conservation and genetic resource protection. Full article

Socio-spatial inequality remains a defining feature of climate vulnerability in South Africa, where historically formed patterns of segregation continue to shape uneven access to infrastructure, services, and environmental resources. This study presents a narrative review of how historical spatial planning has structured persistent disparities in exposure, sensitivity, and adaptive capacity across urban and rural landscapes. Evidence from the literature demonstrates that apartheid-era spatial planning established durable inequalities in water and sanitation provision, green infrastructure distribution, and proximity to environmental hazards, which continue to influence contemporary climate risk profiles. These inequalities are further reinforced through socio-economic stratification, particularly in the context of energy transitions, where access to private renewable energy systems is concentrated among wealthier households, while poorer communities remain dependent on unstable public electricity infrastructure. The review also incorporates land and soil systems as critical but often minimized dimensions of vulnerability, showing how soil degradation and unequal access to productive land contribute to livelihood insecurity and reinforce rural and peri-urban marginalization. In addition, emerging responses such as just transition frameworks, grassroots environmental justice movements, and energy democracy initiatives are examined with regard to the structural constraints that limit their effectiveness in addressing entrenched inequalities. Overall, the analysis highlights that climate vulnerability in South Africa is deeply embedded in historical and ongoing socio-spatial and socio-economic inequalities that continue to shape differentiated environmental outcomes. Full article

Sensor-based human activity recognition (HAR) plays a fundamental role in healthcare monitoring, sports analytics, and ambient-assisted living. Although deep learning has substantially advanced HAR performance, two practical issues still limit its real-world deployment: (i) the distribution shift caused by changes in users or sensor placements can degrade generalization, and (ii) the quadratic $\mathcal{O}\left(L^2\right)$ complexity of standard self-attention hinders efficient long-sequence modeling on resource-constrained wearable devices. To address these issues, we propose DSHformer, which is an accuracy-oriented HAR framework that combines compact channel–temporal encoding with locality-sensitive hashing (LSH)-based attention. Specifically, DSHformer (i) employs a low-parameter patch-based graph-attention encoder to jointly model latent relationships among sensor channel–temporal dynamics; (ii) introduces a trainable prototype pool together with a multi-layer decomposition network to improve intra-class compactness and inter-class separability via prototype alignment; and (iii) introduces a decomposition-stable LSH-based attention mechanism tailored for HAR, whose core design couples prototype-guided feature decomposition with locality-sensitive hashing to ensure that semantically related tokens remain consistently grouped in the same hash bucket even after decomposition-induced attenuation. The mechanism thereby operates at $\mathcal{O}(LlogL)$ attention complexity on longer sensor sequences. Extensive experiments on five public benchmarks (WISDM, UCI-HAR, PAMAP2, Opportunity, and UniMiB-SHAR) show that DSHformer achieves accuracies of 98.6%, 93.7%, 98.4%, 88.5%, and 96.6%, respectively, achieving competitive or superior performance compared with both Transformer variants and HAR-specific baselines under the adopted benchmark protocols. Ablation studies further confirm the complementary contribution of each component. Full article

The shift to circular agrosystems necessitates using new ideas like sustainable biochar, which provides many eco-beneficial attributes like enhancing soil fertility, storing atmospheric carbon dioxide, and retaining soil moisture. However, there is still a small number of farmers worldwide (particularly those located in low-income countries) adopting biochar. Accordingly, this research is focused primarily on determining how factors affecting behavior will influence the decision of wheat producers in Marvdasht County, in Iran’s Fars Province, to use biochar as a circular technology for farming. The study will focus on addressing issues related to environmental challenges (e.g., degradation of soil and drought) through the implementation of resource-efficient, sustainable agricultural technologies. The intent of this paper was to research the behavioral characteristics associated with wheat farmers who choose to use biochar in the city of Marvdasht, Fars Region, Iran, using a new Theory of Planned Behavior (TPB). The model is theoretically enriched through the inclusion of personal norms and connectedness to the land, allowing for a more comprehensive understanding of pro-environmental decision-making. Data was collected from a total of 386 wheat farmers through the use of a structured survey. The data was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with the software Smart-PLS 3.0. The results reveal that attitude (β = 0.342, p < 0.001) and personal norms (β = 0.278, p < 0.001) are the strongest predictors of behavioral intention, while perceived behavioral control showed a weaker but significant effect (β = 0.178, p = 0.049). Subjective norms do not have a significant direct effect (β = 0.115, p = 0.199) but significantly influence intention indirectly through personal norms (β = 0.100, p < 0.001). Furthermore, connectedness to the land strongly affects personal norms (β = 0.420, p < 0.001) and exerts a significant indirect effect on intention (β = 0.117, p < 0.001), highlighting the importance of emotional attachment to land. The findings are significant because they demonstrated that farmers’ biochar adoption decisions are shaped not only by rational evaluations but also by moral obligations and emotional relationships with land. This study makes significant theoretical contributions by extending TPB with moral and relational constructs and empirically demonstrating their mediating roles in agricultural innovation adoption. The novelty of this study lies in integrating personal norms and connectedness to the land into the TPB framework to explain biochar adoption behavior within the context of circular agriculture in a developing country. Practically, the findings provide evidence-based insights for designing policies that integrate cognitive, ethical, and emotional drivers to promote biochar adoption and advance circular agriculture. Specifically, policymakers and extension agencies should prioritize behavioral-level strategies such as awareness campaigns, farmer training programs, and community-based initiatives that strengthen positive attitudes, environmental responsibility, and farmers’ emotional connection to land in order to enhance biochar adoption. Full article