Search Results (5,415)

Islands often suffer from relatively limited freshwater resources, and the effective utilization and distribution of water resources are a key issues for the sustainable development of island-based economies and societies. While island water security has been widely discussed, few studies trace the socio-technical construction of island water-supply systems across the stages of planning, construction, and operation. Integrating Actor-Network Theory with political ecology, this study investigates the water-supply infrastructure of Kinmen. Drawing on official archives, participant observation, and in-depth interviews, this research analyzes the collective actions mobilized to address Kinmen’s water scarcity following the lifting of martial law in 1992. These efforts jointly reshaped both water-supply practices and the infrastructural network. Over the past three decades, Kinmen’s water-supply system has transformed into a sophisticated technological network, integrating reservoirs, desalination plants, and advanced sewage infrastructure. The introduction of these technologies, which function as critical non-human actors within the system, marks a clear shift in how water is managed and distributed. However, the rapid expansion of water-intensive industries, especially tourism, liquor distilling, and cattle farming, has outpaced local ecological limits, precipitating the current water crisis. The study concludes that this shortage has been mitigated through the strategic integration of water sources, most notably the cross-strait pipeline from mainland China, which now provides more than 80 percent of the island’s water. This transition marks a profound shift in the island’s socio-technical and geopolitical network. Full article

Mountain Social–Ecological Systems (MtSES) are global assets, providing essential ecosystem services to nearly half of humanity, yet they are disproportionately vulnerable to global change, experiencing “polytraps” of depopulation, poverty, and environmental degradation. Despite the inherent human dimension in sustainability, the social pillar remains conceptually chaotic, forming a highly fragmented “publication labyrinth”, and is often neglected in favor of more easily quantifiable environmental and economic metrics. These oversights leave mountain communities in a precarious state, underscoring an urgent need for robust, context-specific assessment tools. This paper addresses this critical gap by employing a two-step methodology: first, a literature review identifies prevailing social sustainability issues in mountain contexts; second, a comparative analysis evaluates prominent frameworks and indicator-based tools against these themes, using Ostrom’s multi-tier Social–Ecological Systems (SES) framework as the theoretical lens. Our findings reveal a persistent environmental bias in MtSES research and highlight the necessity for frameworks that integrate local knowledge, address power imbalances, and support bottom-up governance. A tool is proposed with indicators specifically for mountainous contexts. This study contributes to theory by offering a structured approach to unpack the elusive “social” in SES and to practice by providing a model and tool for developing actionable, context-sensitive social sustainability assessments, thereby fostering resilience and equitable development in vulnerable mountain regions. Ultimately, by operationalizing these social dimensions, this research provides a direct roadmap for achieving key United Nations Sustainable Development Goals in marginalized high-altitude contexts, particularly focusing on No Poverty (SDG 1), Good Health and Well-being (SDG 3), Reduced Inequalities (SDG 10), Sustainable Communities (SDG 11), and Peace, Justice, and Strong Institutions (SDG 16). Full article

The proliferation of Internet of Things (IoT) devices has introduced significant security challenges. Resource-constrained devices face sophisticated threats but lack the computational capacity for advanced security analysis. This study investigates optimal security task allocation in Cognitive IoT (CIoT) networks. It specifically examines when IoT devices should process security tasks locally or offload them to Mobile Edge Computing (MEC) servers. The problem is formulated as a Continuous-Time Markov Decision Process (CTMDP). The study demonstrates that the optimal offloading policy has a threshold structure. Security tasks are offloaded to MEC servers when the offloading queue length is below a critical threshold, $k^{\ast }$. Otherwise, tasks are processed locally. This structural property is robust to changes in MEC server configurations and threat arrival patterns. It ensures an optimal and easily implementable security policy under the exponential model. Theoretical analysis establishes upper bounds on the performance of AI-based security controllers using the same models. The results also show that standard model-free Q-learning algorithms can recover optimal thresholds without any prior knowledge of the system parameters. Simulations across multiple reinforcement learning architectures, including Q-learning, State–Action–Reward–State–Action (SARSA), and Deep Q-networks (DQN), confirm that all methods converge to the predicted threshold. This empirically validates the analytical findings. The threshold structure remains effective under practical imperfections such as imperfect sensing and parameter estimation errors. Systems maintain 85% to 93% of their optimal performance. This work extends threshold Markov Decision Process (MDP) analysis from classical queuing theory to the context of CIoT security offloading. It provides optimal and practical policies and model-free algorithms for use by resource-constrained devices. Full article

Fault-induced delayed voltage recovery (FIDVR) poses a serious threat to modern power grid operation, where stalled induction motors following a fault can sustain dangerously low bus voltages and potentially trigger cascading failures. While deep reinforcement learning (DRL) has shown promise for emergency load shedding control, existing centralized DRL approaches require extensive communication infrastructure and large neural network models that are computationally prohibitive to train at scale. Fully decentralized approaches, on the other hand, lack inter-agent information sharing and coordination, often resulting in inadequate voltage recovery across area boundaries. To address these limitations, we propose a Cloud–Edge Collaborative DRL framework that combines lightweight, area-specific edge agents for local load shedding control with a supervisory cloud agent that coordinates their actions globally, achieving scalable training and system-wide voltage recovery simultaneously. Training is further accelerated through a modified Guided Surrogate-gradient-based Evolutionary Random Search (GSERS) algorithm. Validation on the IEEE 300-bus system demonstrates that the proposed framework reduces training time by approximately 90% compared to the fully centralized approach, while achieving comparable voltage recovery performance to the centralized method and approximately 80% better reward performance than the fully decentralized approach, confirming the critical benefit of the cloud-level coordination mechanism. Full article

Arch-suspended structures represent a distinctive form of hybrid suspension system. By combining an arch with a suspended floor system, this structural typology leverages the inherent advantages of both components while mitigating the limitations of each when used independently. This synergy effectively reduces peak internal forces and flexural deformations in structural members. Although widely applied in bridge engineering, research on arch-suspended building structures remains scarce. This paper investigates the construction techniques employed for a large-scale arch-suspended building. The stability of temporary support systems during construction is verified, and the mechanical behavior of critical joints—including the composite slab hanging pillar, arch support, and arch roof—is examined through both experimental testing and numerical simulation. The results demonstrate that a partitioned and segmented construction method is feasible for such complex structures. Structural internal forces and deformations can be effectively controlled by installing tubular temporary supports on both sides of the hanging pillars and lattice temporary supports at the base. Step-by-step unloading of these temporary supports ensures their stability throughout the construction process. Furthermore, the welds in the composite slab hanging pillar effectively transfer tensile forces from the middle plate to the side plates, enabling composite action and collaborative load-bearing among the steel plates. When subjected to loads of 2 times and 4.3 times the design load, localized plasticity was observed in the arch support and arch roof, respectively, while the overall structural integrity remained secure. This study provides a valuable reference for the design and construction of innovative long-span building structures, offering insights that can inform the development and practical application of arch-suspended systems in future architectural projects. Full article

Membrane curvature is a fundamental biophysical property of cellular membranes that underlies essential processes such as vesicle formation, organelle shaping, intracellular trafficking, and membrane scission. While traditionally studied in the context of cell biology and membrane dynamics, membrane curvature is now emerging as a critical, albeit underrecognized, regulator of oncogenic transformation and tumor progression. Curvature not only governs the mechanical properties of the membrane but also influences the spatial localization and activation of key signaling proteins, including Ras family GTPases, whose oncogenic functions are closely dependent on membrane topology. Cancer is frequently associated with disruptions in the regulation of membrane curvature as a result of aberrant lipid metabolism, overexpression of curvature-modulating proteins, and cytoskeletal remodeling. These changes facilitate the hallmarks of malignancy such as uncontrolled proliferation, enhanced motility, immune evasion, metabolic rewiring, and therapy resistance. Notably, recent evidence reveals that curvature acts as a spatial cue for Ras activation, particularly during epithelial-to-mesenchymal transition (EMT), where curvature-driven Ras relocalization amplifies growth factor signaling and promotes metastasis. This review provides a comprehensive overview of the molecular determinants that generate and sense membrane curvature from lipid shape and membrane asymmetry, BAR domain proteins, and actin dynamics, and explores how these mechanisms are hijacked in cancer. We describe the feedback between membrane architecture and oncogenic pathways such as Ras/MAPK and PI3K/AKT, emphasizing the role of curvature in shaping signal transduction platforms. It should be noted that “curvature-driven signaling” is defined as signaling regulation that arises from membrane-geometry-dependent localization, clustering, or activation of signaling proteins, while “curvature-sensitive platforms” refer to membrane subdomains whose specific curvature selectively recruits and stabilizes signaling complexes. Furthermore, we examine how these biophysical alterations impact vesicular trafficking, organelle morphology, and secretion, all of which are co-opted to support tumor development. From a translational standpoint, we assess emerging therapeutic strategies designed to target curvature-regulating factors and leverage membrane topology for precision drug delivery. Innovations in nanomedicine, super-resolution imaging, and curvature-sensing biosensors are also discussed as tools for both diagnostics and therapeutic monitoring. By integrating advances in membrane biophysics, cancer signaling, and bioengineering, this review highlights membrane curvature as a central and actionable dimension of cancer biology. Full article

This study uses Shenyang as a case to integrate multi-source dynamic data with spatial modeling. A comprehensive resistance surface was planned using 12 indicators across the natural, built, and socio-economic dimensions, with objective weighting via the CRITIC method. A hierarchical corridor network was generated based on the MCR model and circuit theory, validated by chi-square goodness-of-fit tests and network structural analysis. The results indicate that socio-economic factors, particularly path activity frequency, dominate the spatial patterns of the corridors, confirming that the network captures connectivity rooted in human activity rather than simply replicating transportation infrastructure. The distribution of national, provincial, and municipal heritage sites across the three higher-importance tiers (L1–L3) shows no significant deviation from the regional baseline, validating the network’s inherent de-hierarchization capacity. Network structure analysis further confirms that this equitable network simultaneously exhibits robust connectivity. The resultant network displays a distinct core–periphery structure with a monocentric-multinuclear radial pattern, forming a four-tier corridor system (core, primary, secondary, and local) that provides an actionable framework for graded protection and targeted interventions. This study advances cultural heritage conservation from passive isolation towards proactive systemic network governance, offering a transferable pathway for the sustainable preservation of heritage in high-density urban environments. Full article

Osteocalcin (OCN) is increasingly recognized as a multifunctional hormone whose actions extend far beyond its traditional role as a marker of bone turnover. This review provides an integrated examination of the molecular, endocrine, and translational dimensions of osteocalcin biology, with emphasis on its bioactive undercarboxylated form (ucOCN), which links skeletal remodeling to systemic physiological processes. The structural determinants, biosynthetic pathways, and vitamin K-dependent carboxylation mechanisms underlying OCN isoform diversity are summarized, together with analytical limitations arising from assay variability and differences between N-MID and ucOCN-specific measurements. Mechanistic evidence demonstrates that ucOCN signals through GPRC6A and GPR158 to modulate insulin secretion, muscle glucose uptake, adipokine production, testosterone synthesis, neurocognitive function, hepatic lipid metabolism, and acute stress response. These receptor-level pathways position osteocalcin as a central regulator at the intersection of bone metabolism and whole-body homeostasis. The review synthesizes data across major clinical contexts, including metabolic syndrome, type 2 diabetes (T2DM), non-alcoholic fatty liver disease (NAFLD), chronic kidney disease–mineral and bone disorder (CKD-MBD), cardiovascular dysfunction, and neurodegeneration, highlighting the modifying influence of vitamin K status, circadian rhythms, renal clearance, and local tissue microenvironments. The need for biomarker standardization, methodological harmonization, and receptor-targeted translational strategies is emphasized, alongside emerging therapeutic concepts involving vitamin K supplementation and exercise-induced activation of OCN. Collectively, the evidence reframes osteocalcin as a versatile endocrine mediator at the interface of bone physiology, systemic metabolic regulation, and disease mechanisms. Full article

Climate change is radically altering the Earth’s natural ecosystems, with temperature/precipitation alterations resulting in mismatches between specific ecosystems and their ‘new’ climatic profiles. Without political action to curb greenhouse gas emissions, most plant/animal species will need to move to higher latitudes to ensure survival. Many are incapable of migrating rapidly and will thus be reliant on human intervention to relocate to new regions (assisted migration). The first hypothetical steps of assisted migration are explored here, using the UK as a model. Urban parks/gardens have a history of hosting non-native plant species and could be used to test the validity of moving non-native plants and animals to regions of higher latitude. In this perspective paper, we added a small experimental component to examine public attitudes to species introductions into urban parks/gardens. Results showed support for using parks and gardens to protect both UK native and non-native wildlife. Indeed, >50% of respondents favoured utilising urban landscapes to conserve small non-native animals (e.g., tortoises and bee-eaters). These results imply there may be some public acceptance of assisted migration. Thus, the paper explores the potential to develop novel, but more sustainable ecosystems in new localities. Full article

Wearable biosensors increasingly stream multi-channel physiological and behavioral data outside the laboratory, yet most deployments still end in dashboards or threshold alarms that leave interpretation open to the user. In high-stakes domains, such as military, emergency response, aviation, industry, and elite sport, the constraint is rarely data availability but the cognitive effort required to convert noisy signals into timely, actionable decisions. We argue for on-person cognitive co-pilots: systems that integrate multimodal sensing, compute probabilistic state estimates on devices, synthesize those states with task and environmental context using locally hosted large language models (LLMs), and deliver recommendations through attention-appropriate cues that preserve autonomy. Enabling conditions include mature wearable sensing, edge artificial intelligence (AI) accelerators, tiny machine learning (TinyML) pipelines, privacy-preserving learning, and open-weight LLMs capable of local deployment with retrieval and guardrails. However, critical research gaps remain across layers: sensor validity under real-world conditions, uncertainty calibration and fusion under distribution shift, verification of LLM-mediated reasoning, interaction design that avoids alarm fatigue and automation bias, and governance models that protect privacy and consent in constrained settings. We propose a layered technical framework and research agenda grounded in cognitive engineering and human–automation interaction. Our core claim is that local, uncertainty-aware reasoning is an architectural prerequisite for trustworthy, low-latency augmentation in isolated, confined, and extreme environments. Full article

Symmetry plays a crucial role in balancing production efficiency and energy consumption within distributed manufacturing systems. This study leverages symmetric decision-making structures in resource allocation and maintenance scheduling to achieve an equilibrium between productivity and sustainability. To address the multi-factory collaboration requirements for large-scale orders, a distributed dual-resource flexible job shop scheduling model considering multiple speeds and preventive maintenance on energy consumption is constructed. It aims to minimize the maximum completion time and total machine energy consumption. An artificial bee colony algorithm with adaptive scout bees is proposed to solve the model. An improved decoding method is designed according to the model characteristics to enhance convergence speed. Neighborhood structures based on preventive maintenance and machine speeds are designed, and a dynamic neighborhood search strategy is proposed to improve the local search capability. Three food source generation methods are defined as actions, and Q-learning is employed to dynamically select actions, ensuring population diversity while improving population quality. Extensive experiments are conducted to validate the effectiveness of the improved strategies, and the superiority of the proposed algorithm is verified through performance comparisons with state-of-the-art algorithms. Full article

Video action recognition faces persistent challenges in balancing accuracy with computational efficiency. While state space models, such as Mamba, have emerged with linear complexity advantages, they exhibit inefficiency in capturing critical spatiotemporal dependencies within video data. To address this core limitation, this paper proposes ETMamba, an enhanced architecture built upon the Mamba baseline. The ETMamba achieve performance breakthroughs via three core innovation modules: (1) the Spatiotemporal Feature Preservation module retains complete original spatiotemporal correlations before data flattening, solving the problem of spatiotemporal feature loss; (2) the Efficient Bidirectional Sharing strategy accurately models bidirectional temporal dependencies, enhancing key temporal dynamic information; and (3) the Spatiotemporal Collaborative Modulation mechanism combines global temporal and local spatial information to achieve collaborative capture of long-short term dependencies and fine-grained features. We conduct experiments on multiple benchmark datasets, achieving recognition accuracies of 88.3%, 74.6%, 75.7%, and 98.1% on Kinetics-400, Something-Something V2, HMDB-51, and Breakfast datasets, respectively, while maintaining low to medium computational complexity. Full article

This study proposes TNAP-DDQN, a deep reinforcement learning method for urban low-altitude UAV path planning under residential noise threshold constraints. With time cost and safety risk as the optimization objectives, operational constraints such as collision risk and maximum AGL altitude are incorporated to achieve coordinated optimization of noise compliance, operational safety, and efficiency. To mitigate action space contraction and training instability induced by multiple constraints, a Noise-Degradation-Mask-based Action Bias Network (NDM-ABN) is introduced at the action selection layer. A three-tier degradation scheme prevents empty candidate sets, while bias-based decision making is applied to approximately tied actions to stabilize the policy. Moreover, multi-step prioritized experience replay (PER) improves sample efficiency and long-horizon return modeling, and potential-based reward shaping (PBRS) transforms sparse constraint signals into auxiliary rewards. Simulation results indicate that: (1) NDM-ABN is the key module for stabilizing the noise-exposure process by suppressing high-noise actions; (2) the required AGL is related to the UAV source noise level and local noise limits, implying the need for differentiated AGL altitude classes; and (3) the maximum admissible UAV source noise level increases as the threshold is relaxed. The proposed method provides quantitative guidance for noise-entry and AGL altitude regulation, while future work will incorporate additional metrics (e.g., A-weighted equivalent sound level) to better capture noise fluctuations and short-term peaks. Full article

Approximately 2–4% of all breast cancer cases are inflammatory breast cancer (IBC), an extremely rare and severe subtype of the disease. Current therapies, including chemotherapy, surgery, and radiotherapy, remain insufficient, underscoring the need for novel therapeutic approaches. IBC exhibits elevated basal endoplasmic reticulum (ER) stress, suggesting a potential vulnerability. We recently developed ERX-41, a small molecule that exacerbates ER stress in cancer cells by inhibiting the endoplasmic reticulum-localized function of Lysosomal acid lipase A (LIPA). Here, we evaluated the therapeutic potential of ERX-41 in IBC models. ERX-41 markedly reduced the viability of IBC cells and significantly impaired clonogenic survival while promoting apoptosis. The specificity of ERX-41 was confirmed using LIPA-knockdown and LIPA-knockout cells. RT-PCR-based assays revealed rapid induction of XBP1 splicing within 6 h of treatment, and Western blot analyses demonstrated activation of ER stress markers including CHOP, PERK, and ATF4. In KPL4 xenografts, ERX-41 treatment significantly decreased tumor volume, accompanied by reduced proliferation and increased ER stress marker expression by IHC. Collectively, these findings identify LIPA as a therapeutically actionable vulnerability in IBC and establish ERX-41 as a potential drug for IBC. Full article

Illegal artisanal and small-scale mining (galamsey) and climate stress jointly degrade ecosystems and livelihoods in Ghana. This paper demonstrates how community-driven governance can realign incentives toward environmental stewardship and inclusive livelihoods. Using an explanatory sequential mixed-methods design—quantitative difference-in-differences followed by qualitative case analysis and Participatory Action Research—we evaluate a structured program combining vocational training, financial literacy, environmental stewardship, and governance alignment. We operationalize Environmental, Social, and Governance (ESG) outcomes via transparent composite indices and triangulate survey, administrative, and focus group evidence. The study identifies conditions under which alternative livelihoods reduce participation in illegal mining, strengthen women’s economic agency, and improve adoption of climate-smart practices. Implications include practical guidance for program design (community delivery, matched incentives, oversight), policy (local climate finance and accountability mechanisms), and research (scalable indicators and rigorous impact evaluation in resource-dependent communities). Full article