Search Results (1,348)

Ten years after the Paris Agreement the escalating global geopolitical turmoil and waning interest in climate change’s effects, posit cities again as critical arenas for addressing the global energy transition. Drawing on the concept of the city as a living entity, the role of energy at the urban scale is considered not only as a technical infrastructure but as a complex system embedded in the spatial, political, and social fabric. The energy transition is situated within the broader context of urban governance and spatial planning, arguing that energy should be considered a foundational urban good essential to everyday life and ensuring equitable development. The study adopts a conceptual and literature-based approach, synthesizing insights from urban studies, energy geography, and climate governance literature. Special attention is given to the Italian context, where a lack of coordination across European, national, and regional political levels hinders energy transition efforts. Key references include theoretical frameworks on urban metabolism, socio-technical systems, and planning innovation, focusing on the intersection of infrastructure, policy, and local agency. The findings highlight the need to reframe energy planning as an integral part of urban and territorial governance. While grounded in Italy, the study’s insights reveal how governance fragmentation and multi-level coordination barriers resonate with European urban energy challenges, offering transferable lessons for territories with complex political and spatial systems. This would help integrate energy concerns into urban design, reduce consumption through spatial organization, and foster civic and institutional cooperation for rapid, often unplanned local energy actions to respond more swiftly to crises than traditional planning mechanisms. As a result, embedding energy within urban policy and spatial design fosters co-evolution between energy production, behavioral change, and infrastructural transformation. Recognizing this is vital for global urban policy and planning to drive resilient, equitable transitions in a rapidly changing energy landscape. Full article

Signed networks, which incorporate both cooperative and antagonistic interactions, naturally give rise to symmetric behaviors in multi-agent systems. One such behavior is bipartite containment tracking, where follower agents converge to a symmetric configuration determined by multiple groups of leaders with opposing influence. Moreover, a timely response is critical to ensuring high performance in containment tracking tasks, particularly for high-order multi-agent systems operating in dynamic and uncertain environments. To this end, this paper investigates the predefined-time bipartite containment tracking problem for high-order multi-agent systems affected by external disturbances. A robust tracking control scheme is developed based on the backstepping method to ensure that the tracking errors converge to a predefined residual set within a user-specified time. The convergence time is explicitly adjustable through a design parameter, and the proposed scheme effectively avoids the singularities often encountered in conventional predefined-time control approaches. The stability and robustness of the proposed scheme are rigorously established through Lyapunov-based analysis, and extensive simulation results are provided to validate our theoretical findings. Full article

As a key connection technology in prefabricated buildings, offshore wind power, and bridge engineering, the performance and environmental sustainability of grouted sleeve connections are essential for the long-term development of civil infrastructure. To address the environmental burden of conventional high-strength cement-based grouts, an eco-friendly sleeve grouting material incorporating industrial solid waste was developed. In this study, silica fume (15%) and fly ash (5%) were employed as supplementary cementitious materials, while nanosilica (NS) was introduced to enhance the material properties. Mechanical testing, microstructural characterization, and half-grouted sleeve uniaxial tensile tests were conducted to systematically evaluate the effect of NS content on grout performance. Results indicate that the incorporation of NS significantly accelerates the hydration of silica fume and fly ash. At an optimal dosage of 0.4%, the 28-day compressive strength reached 105.5 MPa, representing a 37.9% increase compared with the control group without NS. In sleeve tensile tests, specimens with NS exhibited reinforcement necking failure, and the load–displacement response closely aligned with the stress–strain behavior of the reinforcement. A linear relationship was observed between sleeve wall strain and reinforcement stress, confirming the cooperative load-bearing behavior between the grout and the sleeve. These findings provide theoretical guidance and technical support for developing high-strength, low-impact grouting materials suitable for sustainable engineering applications. Full article

Using data collected from 294 farm households across Egypt, Morocco, and Tunisia, this study quantifies crop losses due to soil salinity and analyzes the key factors associated with it. Further, it analyzes the factors driving the farmers’ choice of adaptation measures against salinity. Almost 54% of households surveyed reported yield losses due to salinity, with a sizable portion experiencing losses above 20%. In response to salinization, farmers adopted five adaptation practices, including crop rotation, salt stress-tolerant varieties, drainage management, soil amendments, and improved irrigation practices. A generalized linear model is applied to examine the factors explaining crop loss due to salinity. Results show that a higher share of irrigated land correlates with greater salinity-related crop loss, particularly in areas with poor drainage and low water quality. Conversely, farms with good soil quality reported significantly lower losses. Crop losses due to salinity were much lower in quinoa compared to wheat. Farmers who received agricultural training or belonged to cooperatives reported lower losses. A multivariate probit model was employed to understand drivers of adaptive behaviors. The analysis shows credit access, cooperative membership, training, and resource endowments as significant predictors of adaptation choices. The results underscore the importance of expanding credit availability, strengthening farmer organizations, and investing in training for effective salinity management. Full article

Background/Objectives: There is growing interest in non-diet approaches to support health, well-being, and performance in different populations. The aim of this study was to evaluate the effects of a 12-week intuitive eating (IE) intervention on participants’ body composition, body image, eating behaviors, and athletic performance. Methods: The study included both an intervention group and a control group. It was conducted between September and December 2021. Participants were recruited from a sports center in Istanbul, where they had applied for nutrition and exercise counseling. Inclusion criteria included being 18–65 years old, not having engaged in regular physical activity in the past month, having no chronic disease, and not using any regular medications. Participants were not randomly assigned to groups; allocation was based on availability and willingness to attend the intervention sessions. The study involved 57 participants who were healthy adults between 18 and 65 years old and followed a structured exercise program. At the beginning of the study, a demographic questionnaire was administered. The anthropometric measurements were taken at the beginning and at the end of the intervention. In addition, validated performance and psychometric assessment tools were used, including the Cooper test for cardiovascular endurance, the Davies test for upper-body agility, and the 1-RM bench press for muscular strength, alongside standardized self-report questionnaires for eating attitudes (EAT-26), IE (IES-2), and body image (BCS). Results: The intervention group did not show any statistically significant changes in body composition (p > 0.05). The post-intervention means of the intervention and control groups were not statistically different (p > 0.05). The intervention group showed significant improvements in cardiovascular endurance, agility, and strength performance scores compared to the control group after the intervention (p < 0.05). The intervention group showed significant improvements in body image scores (p < 0.05) and eating attitude scores (p < 0.05). The post-intervention eating attitude and body image scores of the intervention group were significantly different from those of the control group (p < 0.05). The results of the correlation analysis showed a significant positive correlation between intuitive eating and body image (r = 0.455; p < 0.05) and a significant negative correlation between IE and disordered eating attitudes (r = −0.449; p < 0.05). Conclusions: These findings suggest that longer-term interventions may be beneficial and warrant further investigation. IE may serve as a promising strategy to enhance psychological well-being and performance outcomes without focusing on weight control. Full article

This theoretical article explores the contrasting ontologies, axiologies, and political economies of transhumanism and posthumanism. Transhumanism envisions the human as an enhanced, autonomous agent shaped by neoliberal and Enlightenment ideals. Posthumanism challenges this by emphasizing relationality, ecological entanglement, and critiques of commodification. Both engage with technology’s role in reshaping humanity. Drawing on Braidotti’s posthumanism, Haraway’s cyborg figuration, Ahmed’s politics of emotion, Berlant’s cruel optimism, Massumi’s affective modulation, Seigworth and Gregg’s affective intensities, Zuboff’s surveillance capitalism, Fisher’s capitalist realism, Cooper’s surplus life, Sadowski’s digital capitalism, Lupton’s quantified self, Schafheitle et al.’s datafied subject, Pasquale’s black box society, Terranova’s network culture, Bratton’s platform sovereignty, Dean’s communicative capitalism, and Morozov’s technological solutionism, the article elucidates how subjectivity, data, and infrastructure are reorganized by corporate systems. Introducing technogensis as the co-creation of human and technological subjectivities, it links corporate-platform practices to future trajectories governed by Apple, Meta, and Google. These branded technologies function not only as enhancements but as infrastructures of governance that commodify subjectivity, regulate affect and behavior, and reproduce socio-economic stratification. A future is extrapolated where humans are not liberated by technology but incubated, intubated, and ventilated by techno-conglomerate governments. These attention-monopolizing, affective-capturing, behavior-modulating, and profit-extracting platforms do more than enhance; they brand subjectivity, rendering existence subscription-based under the guise of personal optimization and freedom. This reframes transhumanism as a cybernetic intensification of liberal subjectivity, offering tools to interrogate governance, equity, agency, and democratic participation, and resist techno-utopian narratives. Building on this, a posthumanist alternative emphasizes relational, multispecies subjectivities, collective agency, and ecological accountability, outlining pathways for ethical design and participatory governance to resist neoliberal commodification and foster emergent, open-ended techno-social futures. Full article

With the increasing integration of renewable energy, virtual power plants (VPPs) have emerged as key market participants by aggregating distributed energy resources. However, their involvement in electricity markets is increasingly challenged by two major uncertainties: price volatility and the intermittency of renewable generation. This study presents the first application of Information Gap Decision Theory (IGDT) within a two-stage cooperative scheduling framework for VPPs. A novel bidding strategy model is proposed, incorporating both robust and opportunistic optimization methods to explicitly account for decision-making behaviors under different risk preferences. In the day-ahead stage, a risk-responsive bidding mechanism is designed to address price uncertainty. In the real-time stage, the coordinated dispatch of micro gas turbines, energy storage systems, and flexible loads is employed to minimize adjustment costs arising from wind and solar forecast deviations. A case study using spot market data from Shandong Province, China, shows that the proposed model not only achieves an effective balance between risk and return but also significantly improves renewable energy integration and system flexibility. This work introduces a new modeling paradigm and a practical optimization tool for precision trading under uncertainty, offering both theoretical and methodological contributions to the coordinated operation of flexible resources and the design of electricity market mechanisms. Full article

The use of commercial off-the-shelf smart devices in digital signage for sentient spaces is emerging as a promising solution within smart city environments. In such scenarios, these devices are often required to execute resource-intensive applications despite limited local computational capacity. Although cloud and fog infrastructures have been proposed to offload demanding workloads, they are not always suitable due to privacy and security concerns. As a result, executing sentient space applications directly on smart devices may exceed their processing capabilities. To address this limitation, state-of-the-art solutions have introduced load balancing techniques for smart devices. However, these approaches typically rely on centralized coordination or require extensive system profiling, making them unsuitable for sentient spaces, where device availability is intermittent and cooperative behavior must remain lightweight, adaptive, and decentralized. This paper proposes a distributed load balancing strategy tailored for sentient spaces that operate without reliance on cloud or fog infrastructures. The approach is based on reactive cooperation among neighboring devices and employs a local feasibility-check mechanism to determine when to offload computation and which neighboring devices are available to process it. The proposed solution is evaluated in a laboratory setting that emulates a real-world sentient space scenario within a commercial mall. Experimental results show the effectiveness of the proposed approach in maintaining real-time performance and mitigating local computational overload without relying on centralized infrastructure. Even under dynamic operating conditions, the system achieves a load balancing execution time of 5 ms on an ARM Cortex-A53 processor integrated in an AMD Zynq UltraScale+ platform. Full article

Predictive maintenance (PdM) is a cornerstone of smart manufacturing, enabling the early detection of equipment degradation and reducing unplanned downtimes. This study proposes an advanced machine learning framework that integrates the Extreme Learning Machine (ELM) with a novel hybrid metaheuristic optimization algorithm, the Polar Lights Salp Cooperative Optimizer (PLSCO), to enhance predictive modeling in manufacturing processes. PLSCO combines the strengths of the Polar Light Optimizer (PLO), Competitive Swarm Optimization (CSO), and Salp Swarm Algorithm (SSA), utilizing a cooperative strategy that adaptively balances exploration and exploitation. In this mechanism, particles engage in a competitive division process, where winners intensify search via PLO and losers diversify using SSA, effectively avoiding local optima and premature convergence. The performance of PLSCO was validated on CEC2015 and CEC2020 benchmark functions, demonstrating superior convergence behavior and global search capabilities. When applied to a real-world predictive maintenance dataset, the ELM-PLSCO model achieved a high prediction accuracy of 95.4%, outperforming baseline and other optimization-assisted models. Feature importance analysis revealed that torque and tool wear are dominant indicators of machine failure, offering interpretable insights for condition monitoring. The proposed approach presents a robust, interpretable, and computationally efficient solution for predictive maintenance in intelligent manufacturing environments. Full article

Developments in agriculture is reshaping the agribusiness landscape, altering farms’ bargaining power and strategic positioning within supply chains. These dynamics raise important questions about how financial strength influences farmers’ preferences for different components of business relationships with input suppliers. The primary objective of this study is to examine the relationship between a farm’s financial power and the importance it assigns to the behavioral dimension in such relationships. To address this objective, we employ a two-stage research design. In the first stage, qualitative interviews with farmers were conducted to identify the key attributes contributing to relationship value, encompassing economic, strategic, and behavioral dimensions. In the second stage, a quantitative survey was administered to 249 farmers, supplemented with financial data from the Farm Accountancy Data Network (FADN). The Maximum Difference Scaling (MaxDiff) method was applied to assess the relative importance of these attributes, followed by statistical analysis linking the observed preferences to a composite indicator of financial power. The results indicate that financially stronger farms place greater emphasis on economic factors while attaching less importance to behavioral aspects. Among less financially powerful farms, two distinct patterns emerge: one characterized by opportunistic, price-oriented behavior, and another reflecting a relational orientation that values trust, communication, and long-term cooperation alongside economic conditions. These findings contribute to a better understanding of business relationships in agribusiness by explaining how financial power shapes the trade-off between economic and behavioral components. Full article

This paper introduces a novel four-party evolutionary game model to analyze cooperation dynamics in High-Value Mixed Cropping (HMC) systems integrating non-pesticide cacao, cashew nut, and free-range chicken farming within circular and sharing economy frameworks. The model uniquely examines strategic interactions among local government and three farming family types (cacao, cashew, and chicken), incorporating both regulatory mechanisms and cooperative behaviors. Through rigorous stability analysis and MATLAB simulations based on empirical data from Southeast Vietnam, we identify precise conditions for Evolutionarily Stable Strategies (ESSs) that sustain long-term cooperation. Our results demonstrate that government incentives (subsidies, technical support) and reputational sanctions critically shape farmers’ and consumers’ payoffs, thereby steering the system toward collective action equilibria. In particular, increasing the strength of positive incentives or reputational benefits enlarges the basin of attraction for full-cooperation ESSs, regardless of initial strategy distributions. Conversely, overly punitive sanctions can destabilize collaborative outcomes. These findings underscore the pivotal role of well-balanced policy instruments in fostering resilience, innovation, and resource circulation within rural agroecosystems. Finally, we propose targeted policy recommendations, such as graduated subsidy schemes, participatory monitoring platforms, and cooperative branding initiatives, to reinforce circular economy practices and accelerate progress toward the United Nations Sustainable Development Goals. Full article

As a crucial vehicle for advancing the transition to a green low-carbon economy, the green supply chain plays a pivotal role in alleviating pollution pressures and facilitating the green transformation of products. Existing studies mainly focus on static optimization and cost coordination in green supply chains, with limited attention to the dynamic impact of consumer behavior on green production and channel coordination. Based on consumer green preferences and the evolution of reference prices, we developed a differential game model for a two-tier green supply chain composed of a manufacturer and a retailer. The model incorporates green goodwill and consumer memory variables to capture the dynamic interaction among product greenness, sales effort, and consumer perception. By comparing the dynamic optimal response paths under integrated and non-integrated strategies, the study analyzes how reference price effects and goodwill accumulation influence decision-making and system performance. The results show that the stable reference price of green products is significantly higher than the actual selling price. When consumer environmental awareness is strong, cooperative strategies can markedly improve both green performance and supply chain profits, offering potential for Pareto improvement. This research enhances behavior-oriented modeling in green supply chains and provides theoretical and empirical support for designing collaboration mechanisms in green product promotion. Full article

Efficient surface water allocation in reservoir-equipped basins is essential for balancing competing demands within the Water–Energy–Food (WEF) nexus. This study investigated the applicability of Particle Swarm Optimization (PSO) for optimizing water distribution in the Machángara River Basin, Ecuador; a complex, constraint-rich hydrological system. Implemented via the Pymoo package in Python, the PSO model was evaluated across calibration, validation, and execution phases, and benchmarked against exact methods, including Linear Programming (LP) and Mixed Integer Linear Programming (MILP). The results revealed that standard PSO struggled to satisfy equality constraints and yielded suboptimal solutions, with elevated penalty costs. Despite incorporating MILP-inspired encoding and repair functions, the algorithm failed to identify feasible solutions that met operational requirements. The execution phase, which includes reservoir construction decisions, resulted in a total penalty exceeding EUR 164.95 billion, with no improvement observed from adding reservoirs. Comparative analysis confirmed that LP and MILP outperformed PSO in constraint compliance and penalty minimization. Nonetheless, the study contributes a reproducible implementation framework and a comprehensive benchmarking strategy, including synthetic test functions, performance metrics, and diagnostic visualizations. These tools can facilitate systematic evaluation of PSO’s behavior in high-dimensional, nonlinear environments and provide a foundation for future hybrid or adaptive heuristic models. The findings underscore the limitations of standard PSO in hydrological optimization but also highlight its potential when enhanced through hybridization. Future research should explore PSO variants that integrate exact solvers, adaptive control mechanisms, or cooperative search strategies to improve feasibility and convergence. This work advances the methodological understanding of metaheuristics in environmental resource management and supports the development of robust optimization tools under the WEF-nexus paradigm. Full article

Coordinating Multi-Unmanned Surface Vehicle (USV) swarms in complex, adversarial maritime environments is a significant challenge, as existing multi-agent reinforcement learning (MARL) methods often fail to capture intricate spatiotemporal dependencies, leading to suboptimal policies. To address this, we propose Adv-TransAC, a novel Spatio-Temporal Meta-Reinforcement Learning framework. Its core innovation is a hybrid GAT-transformer architecture that decouples spatial and temporal reasoning: a Graph Attention Network (GAT) models instantaneous tactical formations, while a transformer analyzes their temporal evolution to infer intent. This is combined with an adversarial meta-learning mechanism to enable rapid adaptation to opponent tactics. In high-fidelity escort and defense simulations, Adv-TransAC significantly outperforms state-of-the-art MARL baselines in task success rate and policy robustness. The learned policies demonstrate the emergence of complex cooperative behaviors, such as intelligent risk-aware coordination and proactive interception maneuvers. The framework’s practicality is further validated by a communication-efficient federated optimization architecture. By effectively modeling spatiotemporal dynamics and enabling rapid adaptation, Adv-TransAC provides a powerful solution that moves beyond reactive decision-making, establishing a strong foundation for next-generation, intelligent maritime platforms. Full article

With the growing adoption of connected and autonomous vehicles (CAVs), their market penetration is expected to rise. This study investigates the mixed traffic flow dynamics of human-driven vehicles (HDVs) and CAVs at airport terminal curbsides. A two-lane parking simulation model is developed, integrating the intelligent driver model, PATH-calibrated cooperative adaptive cruise control, and a degraded adaptive cruise control model to capture different driving behaviors. The model accounts for varying time headways among HDV drivers based on their information acceptance levels and imposes departure constraints to enhance safety. Simulation results show that the addition of CAVs can significantly increase the average speed of vehicles and reduce the average delay time. Two metrics are inversely proportional. Specifically, as illustrated by a curbside length of 400 m and a parking demand of 1300 pcph, when the CAV penetration rate p is 10%, 30%, 50%, 70%, and 100%, respectively, compared to p = 0, the average traffic flow speed increases by 1.7%, 6.4%, 15.0%, 27.2%, and 48.7%, respectively. The average delay time decreases by 2.8%, 6.4%, 10.5%, 13.5%, and 20.0%, respectively. Meanwhile, CAVs and HDVs exhibit consistent patterns in terms of parking space utilization: the first stage (0–30% of parking spaces) showed a stable and concentrated trend; the second stage (30–70% of parking spaces) showed a slow downward trend but remained at a high level; the third stage (70–100% of parking spaces) showed a rapid decline at a steady rate. Full article