Search Results (2,268)

The increasing vulnerability of global food systems—exacerbated by the pandemic, climate change, and disruptions to international supply chains—has highlighted the importance of local food production for sustainability, food security, and rural resilience. At the same time, the LOHAS (Lifestyles of Health and Sustainability) value system is gaining prominence, shaping consumer demand for locally produced, environmentally responsible, and health-oriented products. While the existing literature predominantly addresses LOHAS consumers and local food systems as separate research domains, limited empirical attention has been paid to the value-based alignment between LOHAS principles and local food producers, particularly from a territorial and place-based perspective. This study seeks to address this gap by examining how LOHAS value dimensions are reflected in the self-identification and operational practices of local food producers, and by analyzing how such value alignment may be interpreted as contributing to the sustainability and resilience of territorially embedded rural production systems. From a systems perspective, LOHAS-related value alignment may be interpreted as a potential coordination mechanism that may contribute to strengthening feedback loops between producers and consumers and may enhance the adaptive capacity of short food supply chains as socio-ecological systems. The empirical analysis draws on an online survey conducted in the second quarter of 2024 among 73 local producers operating in Zala and Vas counties in Western Hungary. Factor analysis and cluster analysis were applied to identify underlying value structures and producer typologies. The results reveal two distinct producer clusters, one of which exhibits a strong alignment with LOHAS values. Producers within this cluster place particular emphasis on sustainability, environmental responsibility, health consciousness, and authenticity, alongside a pronounced commitment to local embeddedness and community-oriented practices. Overall, the findings demonstrate that LOHAS-related values are not confined to the consumer side but are increasingly embedded in territorially grounded local production models. This value alignment may contribute to strengthening short food supply chains rooted in specific geographical contexts, thereby contributing to the long-term socio-economic and environmental sustainability of rural regions. Full article

Developing effective catalysts for the epoxidation of propylene with H₂ and O₂ holds significant scientific and industrial significance. This study synthesized a series of Au/TS-1 catalysts modified with alkali metals (Na⁺, Cs⁺) and carefully examined their impact on gas-phase propylene epoxidation, with particular attention to the role of anions. The optimal Au–CsC(1:10)/TS-1 catalyst (Cs₂CO₃ modified, Au/Cs molar ratio = 1:10) achieves a propylene conversion of 16.8%, a PO selectivity of 88.5%, an H₂ efficiency of 40.8%, a record PO formation rate of 383.9 g_po·kg_cat⁻¹·h⁻¹, and unprecedented long term stability (>380 h without deactivation). To the best of our knowledge, no previous study has simultaneously achieved such balanced and outstanding performance across all these key indicators. Comprehensive characterization reveals that Cs⁺ modification suppresses side reactions and coke formation, increases microporosity, tunes surface acid–base properties and hydrophobicity, restricts Au particle size, and stabilizes both Au⁰ and tetra coordinated Ti sites, thereby inhibiting H₂O₂ decomposition and PO isomerization while greatly enhancing reaction efficiency. This holistic advancement represents a significant leap forward for Au based catalysts in gas phase propylene epoxidation, offering both a theoretical foundation and practical guidance for the development of high performance epoxidation catalysts. Full article

Accurate rice mapping is critical for grain yield estimation and food security, yet traditional methods often struggle with asynchronous data quality and the inherent statistical gap between SAR and optical signals. To bridge this gap, we propose MMDFRNet, a novel multi-modal deep learning framework that synergistically integrates Sentinel-1 SAR and Sentinel-2 optical imagery. Unlike conventional static fusion approaches, MMDFRNet features a dual-stream modality-specific encoder architecture designed to decouple structural backscattering signals from spectral reflectance. Central to this framework is the multi-modal feature fusion (MMF) module, which employs an adaptive attention mechanism to dynamically align and recalibrate features based on their reliability, effectively mitigating noise from compromised modalities. Additionally, a multi-scale feature fusion (MSF) module is incorporated to coordinate hierarchical semantic information, enhancing boundary delineation in fragmented landscapes. Extensive experiments conducted across multiple study areas in China demonstrate the superiority of MMDFRNet. The model achieves a Precision of 0.9234, an IoU of 0.8612, and an F1-score of 0.9252. Notably, it consistently outperforms state-of-the-art benchmarks (e.g., UNetFormer, STMA, and CCRNet) by margins of up to 11.72% (Precision) and 7.39% (IoU) compared to classic baselines. Furthermore, rigorous ablation studies and degradation analyses confirm the model’s robustness, verifying its ability to transform the degradation paradox into a performance booster through pixel-wise adaptive alignment. Consequently, MMDFRNet offers a promising solution for precise rice area statistics and long-term monitoring in complex agricultural landscapes. Full article

Existing studies on frequency diverse array (FDA) radar sensing systems have primarily focused on radio-frequency (RF) stealth characteristics with limited attention to the balance between RF stealth and active detection performance. To address this issue, this paper proposes a joint transmit–receive weight optimization scheme for FDA radar systems to achieve an effective balance between active detection and RF stealth. The resulting optimization problem is non-convex, and a block coordinate descent (BCD)-based alternating optimization method with a carefully designed initialization strategy is developed to solve it efficiently. Simulation results demonstrate that the proposed method achieves improved RF stealth performance while maintaining comparable active detection capability, compared with conventional FDA radar and representative existing optimization-based benchmark methods. These results demonstrate the effectiveness of the proposed method for balancing active detection and RF stealth performance in FDA radar sensing systems. Full article

Conventional cooperative control methods for multi-AUV systems typically rely on quasi-steady hydrodynamic assumptions and do not explicitly account for time-varying uncertainties in ocean dynamics. In addition, controller parameters are often tuned empirically. As a result, under complex disturbed flow fields and communication constraints, AUV swarms are prone to group fragmentation and reduced polarization, which undermines stable cooperative navigation. To address these limitations, we propose a double-gyre-flow-optimized autonomous underwater vehicle swarm (DGF-OAS) model for coordinated operations in time-varying flow fields. The proposed model incorporates a heading-aware graph attention mechanism to adaptively adjust adjacency weights among agents with different roles. It further integrates the Lennard–Jones potential to preserve safe inter-vehicle spacing and embeds a periodically varying double-gyre flow field to characterize ocean disturbances. Bayesian optimization is then employed to automatically identify suitable weights for the alignment and attraction–repulsion terms, thereby improving swarm cohesion and environmental adaptability. Simulation results demonstrate that, under flow-field disturbances, DGF-OAS achieves group polarization of up to 96%, reduces the average task completion time by 15.84% compared with the baseline model, and attains a task completion rate of 97%, significantly outperforming the compared methods. These findings indicate that the proposed approach exhibits strong adaptability and stability in complex environments and offers an effective solution for AUV swarm control. Full article

Background: The integration of active breaks during the school day has been widely demonstrated to be effective in counteracting sedentary behaviors. The present study assessed the efficacy of a structured active breaks (ABs) intervention implemented during recess on multiple domains of Physical Literacy (PL) in primary-school children. Methods: A single-blind randomized controlled trial was conducted with 139 children (aged 9–10 years). Classes were randomized into an Experimental Group (EG, n = 66) and a Control Group (CG, n = 73). The EG participated in an 8-week intervention (six sessions/week, ~10 min) consisting of coordinative and interdisciplinary motor tasks during recess. Pre- and post-intervention assessments included physical fitness (SLJ, 4 × 10 m SR, 6MWT, MBT), gross motor skills (TGMD-2), selective attention (Bell Test), physical activity levels (PAQ-C), physical self-perception (PSP), and enjoyment (PACES). Results: A mixed-design MANOVA revealed a significant multivariate Time × Group interaction (p < 0.001). Univariate analyses showed significant improvements in the EG compared to the CG for explosive strength (p < 0.001), agility (p < 0.001), Gross Motor Quotient (p = 0.003), and selective attention (p < 0.001). Furthermore, the EG demonstrated significant increases in physical activity levels, self-perception, and enjoyment (p < 0.05). No significant gender interaction was found, indicating equal effectiveness for boys and girls. Conclusions: Transforming recess into a structured opportunity for movement through ABs effectively enhances physical, cognitive, and affective domains. This intervention represents a sustainable strategy for Health-Promoting Schools to foster PL and psychophysical well-being without reducing curricular instruction time. Full article

Boron (B) is increasingly recognized as more than a trace dietary element, emerging as a context-dependent organizer of molecular interactions at the host–microbiome interface. B exhibits reversible covalent chemistry driven by Lewis’ acidity and selective affinity for cis-diol-rich biomolecules, enabling dynamic complexation with polyols, glycans, and phenolic ligands that dominate the intestinal mucus environment and shape microbial ecology. We synthesize evidence supporting an architecture-based framework in which B modulates biological function by conditioning the physicochemical context of microbial communication rather than acting as a single-pathway effector. Central to this model is spatial bioavailability, distinguishing plasma-accessible boron from microbiota-accessible boron (MAB), species that persist in the lumen and mucus layer long enough to influence interface-level processes. We propose that insufficient or altered MAB availability may contribute to dysbiosis (DYS) by destabilizing quorum-associated coordination, signal persistence, and mucosal microstructure, thereby promoting barrier dysfunction and inflammaging. Particular attention is given to B-mediated symbiotaxis, a hypothesis-driven concept describing how B-containing molecular assemblies may bias microbial communities toward cooperative, barrier-supportive configurations and reduce ecological volatility. We identify key knowledge gaps and experimental priorities (speciation-aware measurements, signal-centric readouts) necessary to determine when, where, and how B-mediated molecular architecture may counteract DYS and support healthspan. Full article

Facial expression recognition (FER) is crucial for endowing service robots with emotional perception capabilities. Achieving high-performance facial expression recognition hinges on effectively balancing the capture of subtle local textures with the understanding of overall facial configurations. However, coordinating local feature variations with global semantic dependencies in unconstrained environments while maintaining semantic alignment remains a challenge. To address this issue, we propose FER-SDAM, a network architecture based on hierarchical attention collaboration. Through a dual-attention hierarchical collaboration mechanism, this architecture introduces an Attention Consistency Loss (ACL) to explicitly align shallow structural awareness with deep global dependencies. It simultaneously captures structural sensitivity and cross-regional correlations, facilitating the effective fusion of local structural information with global semantics, thereby balancing accuracy, robustness, and computational efficiency. We conducted extensive experiments on AffectNet, RAF-DB, and their subsets containing occlusion and pose variations, achieving accuracy rates of 68.12%, 66.68%, and 88.87% on the AffectNet-7, AffectNet-8, and RAF-DB datasets, respectively. The experimental results demonstrate that FER-SDAM achieves a critical balance between accuracy and efficiency, delivering highly competitive recognition performance while maintaining low computational overhead, making it an ideal solution for real-time deployment in service robots. Full article

With the rapid intensification of industrial and agricultural activities, water contamination by heavy metal ions has emerged as a critical global challenge, gravely imperiling ecosystem stability and public health. Among the various remediation technologies, adsorption has been widely adopted due to its high efficiency, low-cost water treatment, and simplicity of operation. However, conventional inorganic or synthetic adsorbents often exhibit poor degradability and pose a risk of secondary contamination, substantially limiting their sustainable application. Consequently, the development of environmentally benign and renewable adsorbent materials has become a central research focus in this field. Recently, cellulose-based composite hydrogels, derived from renewable resources and characterized by excellent eco-friendliness and highly tunable three-dimensional porous structures, have attracted considerable attention as promising green adsorption materials. These hydrogels demonstrate outstanding performance in the efficient sequestration of heavy metal contaminants from aqueous environments. This review systematically summarizes recent advances in cellulose-based composite hydrogels for heavy metal removal, to elucidate the structure–performance relationships linking material fabrication strategies, structural modulation, and adsorption efficiency. First, we outline the principal construction approaches, including physical crosslinking, chemical modification, and supramolecular self-assembly, and comprehensively analyze how different synthesis routes regulate pore architecture, mechanical properties, and the distribution of surface functional groups. Second, the underlying adsorption mechanisms, primarily coordination complexation, electrostatic interactions, and ion exchange, are discussed in detail. Finally, recent studies on the adsorption of cationic heavy metals (e.g., Pb(II), Cu(II), and Cd(II)) and anionic oxyanions (e.g., As(III) and Cr(VI)) are critically reviewed, with particular emphasis on the relationships between selective adsorption performance, material design principles, and specific recognition mechanisms. Overall, this review provides a theoretical foundation and practical guidance for the design and development of next-generation water treatment materials with high adsorption capacity, excellent selectivity, non-toxicity, and strong environmental compatibility, followed by future research recommendations. Full article

Joint action involves more than coordinated activity; it is cooperation grounded in shared intentionality, whereby partners represent an activity as something “we” are doing together. This “we-mode” stance should shape attention and memory, making partner-relevant information psychologically significant because it supports a collective goal. Using a joint-search paradigm, we tested whether people automatically attend to and remember partner goals. Pairs of participants searched for targets from different item categories, and trials were successful only when both responded correctly. A surprise recognition test followed the joint-search task assessing memory for the items. Across Experiments 1 (animate stimuli) and 2 (inanimate stimuli), participants showed better recognition of partner-goal items compared to distractors. Participants also showed enhanced attention to partner-goal items in Experiment 2. In Experiment 3, participants completed the same task, and returned three days later for a recognition test first followed by a second joint-search task with switched targets. Participants continued to show superior recognition for partner-goal items, and search efficiency improved after targets switched, indicating that partner-goal was retained over time and supported later cooperation. Together, these findings demonstrate that human cognition supports joint actions over time by organizing attention and memory around what “we” are doing together. Full article

Tourism and hospitality organisations in remote, highly seasonal Arctic and sub-Arctic destinations face persistent workforce instability, multicultural team dynamics, and well-being risks that threaten service reliability and organisational continuity. Previous research has focused mainly on destination- and community-level resilience, while giving less attention to how resilience is built within tourism organisations through everyday workforce-related practices. This study examines people-centred organisational resilience through a qualitative comparative design in two northern contexts: Iceland and Finnish Lapland. The empirical material comprised semi-structured interviews in Iceland and interviews, organisational documents, and field observations in Finnish Lapland, collected in autumn 2025. The data were analysed using thematic analysis. The findings identify four recurring resilience mechanisms: leadership under seasonal and environmental pressure; employee experience across employment phases; living conditions and belonging; and ethical governance. Here, “mechanisms” refers not simply to broad topics but to organisational processes through which recurring practices support resilience in remote, highly seasonal tourism settings. Together, these mechanisms show that resilience in remote tourism is built not only through operational flexibility or crisis response, but through people-centred organisational practices that support continuity, coordination, safety, and trust across seasons. The study contributes a workforce-centred extension of resilience theory in tourism and offers a comparative account of how these mechanisms operate across two northern tourism settings. Full article

Background: Food security systems are central to nutritional health and Sustainable Development Goal 2 (SDG 2), yet existing assessments have paid limited attention to cross-dimensional coordination within food security systems. This study assessed both system performance and coordination in low- and middle-income countries (LMICs) during 2019–2021. Methods: Based on a multidimensional 25-indicator framework, the entropy-weighted Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) approach was used to evaluate system performance. Spearman’s rank correlation and Bland–Altman agreement analyses against the SDG 2 Index and the Under-Five Mortality Rate (U5MR) were used to examine the validity. The coupling coordination degree (CCD) model was used to assess coordination across the four dimensions of food security: availability, access, utilization, and stability. Results: Among all included LMICs, composite scores ranged from 0.103 to 0.698. Regionally, Europe and Central Asia showed the strongest overall performance (mean = 0.54), whereas Sub-Saharan Africa exhibited the lowest levels (mean = 0.27). The dimensions of access and stability were identified as the principal global bottlenecks of overall food security system development. The proposed index correlated positively with the SDG 2 Index (R = 0.662, p < 0.001) and inversely with the U5MR (R = −0.769, p < 0.001). The coupling degrees were consistently high but exceeded coordination levels across regions, indicating that strong interdependence among dimensions did not necessarily translate into balanced or synergistic system development. Conclusions: Food security systems in LMICs are constrained by weaknesses in the access and stability dimensions, as well as by insufficient cross-dimensional coordination. Strengthening them requires integrated, cross-sectoral strategies that enhance both system performance and interdimensional coordination. Full article

Demand response is shifting towards continuous coordination of flexible demand, storage, and distributed generation across buildings and prosumer communities. Multi-agent reinforcement learning has gained attention because it can support decentralized execution under partial observability while still learning coordinated behavior through centralized training. This systematic review follows PRISMA 2020 guidance and synthesizes $n=70$ peer-reviewed studies published in the 2021 to 2025 window, covering building clusters, grid-aware district coordination, program-level aggregation, industrial demand response, and transactive energy mechanisms. The results show that the dominant evaluation context is grid-responsive building clusters, with growing reliance on benchmark environments that standardize interfaces and encourage reproducible multi-KPI reporting. Across the methods, centralized training with decentralized execution is the prevailing pattern, often combined with attention-based critics or value factorization to handle heterogeneity and global rewards. Reward design and constraint handling emerge as primary determinants of stability, since objectives mix cost, peak, ramp, comfort, and emissions, while rebound and synchronized behavior are recurring risks. A descriptive and cross-variable quantitative synthesis is also provided, showing that publication activity increased from three studies (4.3%) in 2021 to 28 studies (40.0%) in 2025, with the strongest concentration in 2024–2025. Quantitatively, grid-responsive building clusters accounted for 26 of 70 studies (37.1%), actor–critic methods for 24 studies (34.3%), CityLearn for 16 studies (22.9%), and cost-based evaluation was reported in 64 studies (91.4%), whereas robustness testing appeared in only 16 studies (22.9%). Across the reviewed studies, peak reduction was reported in 55 (78.6%) studies, whereas robustness testing appeared in only 16 studies (22.9%) and transferability or deployment realism in 11 (15.7%), indicating that evaluation remains much stronger for operational performance than for real-world generalization. Full article

The increasing penetration of distributed energy resources (DERs), electric vehicles (EVs), dynamic line ratings (DLRs), and flexible loads is reshaping modern power systems while introducing significant operational uncertainties. Reinforcement learning (RL) has gained attention as a data-driven solution for optimal coordination and control under uncertainty. However, existing studies that used RL for optimal coordination reviewed in this research primarily address uncertainties from DERs and load variability, largely neglecting DLRs and EVs as a time-varying network constraint. Moreover, long training times and limited interpretability hinder the practical deployment of RL-based controllers. This paper presents a comprehensive review of RL applications in power system operational control, categorizing approaches based on uncertainty sources, control objectives, and learning architectures. The review highlights the operational advantages of incorporating DLR uncertainty, including improved line utilization, congestion mitigation, enhanced renewable hosting capacity, and increased system flexibility. A critical research gap is identified in the absence of integrated RL frameworks that jointly consider DLRs and learning efficiency. To address this gap, a future research direction integrating a Belief–Desire–Intention (BDI) framework within RL is proposed, enabling faster convergence, constraint-aware decision-making, improved transparency, and enhanced resilience in modern power system coordination and control. Full article

This study aims to enhance the precision of humanoid robots in imitating complex human “walking–grasping” coordinated movements. Addressing limitations in sample efficiency and reward function design in Generative Adversarial Imitation Learning (GAIL), we propose the Similarity Reward-Augmented Generative Adversarial Imitation Learning (SRA-GAIL) framework. The method integrates plantar thin-film resistive pressure sensors to measure the real-time pressure distribution at four key points on both feet, combined with roll/pitch angle data acquired from JY901S inertial measurement units (IMUs). A Lagrangian constraint optimization strategy is employed to achieve gait stability control based on the zero moment point (ZMP). Simultaneously, a visual similarity evaluation module is established using human demonstration trajectories captured by a Logitech C920E camera, augmented by grip force feedback from flexible thin-film pressure sensors on the hands. This enables the design of a multimodal sensor-fused similarity reward function. By incorporating Lagrangian constraint optimization and a maximum entropy reinforcement learning framework, Similarity Reward-Augmented Generative Adversarial Imitation Learning synchronously optimizes gait stability control—guided by zero moment point (ZMP) and roll/pitch data—and vision-based trajectory similarity evaluation. These components address motion stability constraints and trajectory similarity metrics, respectively, generating biomechanically plausible gait strategies. A spatiotemporal attention mechanism parses human motion trajectory features to drive the end-effector for high-precision trajectory tracking. To validate the proposed method, an imitation learning experimental system was constructed on a physical XIAOLI humanoid robot platform, integrating inertial measurement units (IMUs), plantar pressure sensors, and a vision system. Quantitative evaluations were conducted across multiple dimensions, including robot platform analysis, walking stability, object grasping success rates, and end-effector trajectory similarity. The results demonstrate that, compared to Generative Adversarial Imitation Learning (GAIL) and behavioral cloning, Similarity Reward-Augmented Generative Adversarial Imitation Learning achieves a stable object grasping success rate of 93.7% in complex environments, with a 23.8% improvement in sample efficiency. The method maintains a 96.5% compliance rate for zero moment point (ZMP) trajectories within the support polygon, significantly outperforming baseline approaches. This effectively addresses the bottleneck in robot policies adapting to dynamic changes in real-world environments. Full article