Search Results (538)

Non-orthogonal multiple access (NOMA) has been recognized as a promising technique to alleviate the bandwidth limitation in visible light communication (VLC) downlinks. Nevertheless, the corresponding power allocation problem is typically non-convex and computationally challenging under practical system constraints, which limits the effectiveness of conventional optimization approaches. To address this issue, this paper proposes an improved particle swarm optimization (IPSO)-based strategy that aims at maximizing the system sum rate and employs adaptive mechanisms including an adaptive dynamic inertia weight, cooperative evolutionary learning factors, and enhanced elite opposition-based learning (EEOBL) to strengthen both global search capability and convergence performance. Simulation results indicate that the proposed scheme significantly improves the overall system capacity across diverse interference scenarios, while achieving accelerated convergence and enhanced robustness. Full article

Agriculture in Xinjiang, a region in arid northwest China, is almost entirely dependent on irrigation, leading to significant supply–demand contradictions. This study addresses the spatial and temporal mismatches between water supply and demand, and the resulting conflicts in crop water supply. Using the primary irrigation cycle of Wutai branch canal as a case study, we developed a two-level optimal water allocation model based on large-scale system optimization. For the lateral canal water distribution, a model minimizing the sum of squares of the water shortage rate was solved using the Sequential Quadratic Programming (SQP) algorithm. For the drip irrigation systems, water distribution time was incorporated as a second objective, and the resulting bi-objective model was solved using the Non-dominated Sorting Genetic Algorithm II (NSGA-II). Compared to actual distribution processes, our results show that (1) 74% of the distribution canals and pipelines achieved over 90% of their design flow rate, fully utilizing flow capacity and reducing the overall distribution time of the branch canal by 4.68 h. (2) The overall water shortage rate was reduced by 1.59% compared to the actual rate, with a more balanced water allocation among users. These results demonstrate that the model can effectively coordinate water distribution in a multi-level canal system, enhance the fairness of water use, and provide a valuable reference for single-event water distribution in water-scarce areas. Full article

As the demand for digital storage capacity continues to grow, bit-patterned magnetic recording (BPMR) has emerged as a promising technology to overcome the superparamagnetic limit of conventional recording methods. Nevertheless, the extremely close spacing of magnetic islands in BPMR can result in significant signal corruption, particularly due to inter-track interference. This paper presents robust signal-processing schemes for a two-reader, three-track detection system in a staggered BPMR configuration to address these challenges. The first proposed method employs a sum-soft-information technique, which combines log-likelihood ratios from two detectors to maximize mutual information. This approach significantly improves the reliability of middle-track detection. We also propose the inter-track interference subtraction technique, in which the highly reliable data recovered from the middle track are used to reconstruct the interference signal, which is then subtracted from the upper and lower tracks using an optimized weighting factor. Simulation results at an areal density of 3.0 Tb/in² demonstrate that an optimized weighting factor of 1.78 effectively cancels interference. Moreover, the results indicate that our proposed scheme achieves a bit-error rate (BER) comparable to that of the three-reader, one-track detection BPMR systems. Furthermore, our method also demonstrates a lower BER for both adjacent tracks when compared to the conventional single-reader, two-track reading system, even in the presence of 10% media noise. Full article

The large-scale grid integration of distributed renewable energy enhances the flexible regulation capacity of the power system. However, the inherent randomness and volatility of its output, coupled with weak coupling access characteristics, pose severe challenges to the safe and stable operation of the power system. To address these issues, this paper proposes a power system planning method suitable for urban power grids. To accurately characterize the uncertainty of renewable energy output, the method incorporates the concept of multi-scenario stochastic optimization and introduces a dynamic scenario generation method for wind and solar power based on nonparametric kernel density estimation and standard multivariate normal distribution sequence sampling. This method generates a set of typical daily dynamic output scenarios for wind and solar power that closely match actual output characteristics. Considering the spatiotemporal response characteristics of flexible resources, the Soft Open Point (SOP) DC link enables flexible cross-node power transmission and spatiotemporal coupling regulation of flexible resources. Therefore, this paper constructs a mathematical model for the grid integration of flexible resources based on the SOP DC link. By integrating operational constraints such as power flow constraints in the power grid and source-load uncertainty constraints, a power system planning model is established. However, traditional convex optimization methods require approximate simplifications of the model, which can easily lead to a loss of accuracy. Although the Particle Swarm Optimization (PSO) algorithm is suitable for nonlinear optimization, it is prone to getting trapped in local optima. Therefore, this paper introduces an improved PSO algorithm based on refraction opposite learning, which enhances the algorithm’s global optimization capability by expanding the particle search space and increasing population diversity. Finally, simulation verification is conducted based on an improved IEEE-39 bus test system, and the results show that the proposed scenario generation method achieves a sum of squared errors of only 4.82% and a silhouette coefficient of 0.94, significantly improving accuracy compared to traditional methods such as Monte Carlo sampling. Full article

The pursuit of high-energy-density cathode materials has positioned LiNiO₂ as a promising candidate due to its high theoretical capacity. However, its practical application is hindered by structural instability, cation mixing, and sluggish Li-ion mobility. This study presents a strategic co-doping approach to enhance the electrochemical performance of R3m-structured LiNiO₂ by introducing Na at the Li site and Nb/Al/W at the Ni site. First-principles calculations based on density functional theory (DFT), combined with the bond valence sum energy (BVSE) method, were employed to evaluate the structural, electronic, and transport properties of the doped systems. The optimized lattice parameters reveal that co-doping induces lattice expansion and suppresses cation disorder, thereby improving structural integrity. Formation energy validates the thermodynamics of the modified structures. Furthermore, BVSE-based ion migration mapping shows that Na/Nb and Na/Al co-doping significantly broadens Li-ion diffusion pathways and lowers migration barriers compared to pristine LiNiO₂. These results demonstrate that dual-site doping is an effective strategy to overcome intrinsic limitations of Ni-rich layered oxides, offering a rational design route cathode for next-generation Li-ion battery. Full article

To effectively enhance the seismic performance of traditional Chinese timber structures, this study proposes a reinforcement method utilizing friction dampers. Based on the working mechanism of friction dampers and the extended discrete element theory, an analytical model for timber structures equipped with these dampers was developed and validated through shake table tests. Subsequently, dynamic analyses were conducted to systematically evaluate the enhanced seismic energy dissipation capacity of the ancient timber structures by the reinforcement of friction dampers. The friction coefficient (μ), bolt pre-tension strain (ε), and action distance (l) were selected as key parameters. A multi-objective optimization function was constructed using the weighted sum method, enabling a multi-objective parameter optimization analysis for the friction dampers to identify the optimal parameter combination under specific conditions. The results indicate that the established extended discrete element model effectively simulates the dynamic characteristics of the structure. The installation of friction dampers significantly enhanced the structure’s energy dissipation capacity and substantially reduced the peak displacement. However, due to the initial stiffness introduced by the dampers, the lateral stiffness of the column frame increased markedly, leading to a significant amplification of the acceleration response, with a maximum increase in peak acceleration reaching 77%. The multi-objective optimization analysis revealed that with weighting coefficients λ_a = λ_b = 0.5, the optimal damper parameter combination is μ = 0.36, ε = 102 με, and l = 268 mm. Under these conditions, the structural displacement response decreased by 38.5%, while the acceleration response increased by 93.7%. It is noted that the derived optimal design solutions are pertinent to the specific structural typology and ground motions considered. Full article

Reconfigurable intelligent surfaces (RISs) hold promising technical prospects for 6G wireless communications to enhance system capacity, coverage and sum-rate. Unlike existing studies deploying only passive or active RISs, this paper adopts a novel hybrid RIS architecture that optimally allocates the number of active and passive elements. Under fixed quantities of both RIS element types in the fixed hybrid RIS, it simultaneously increases the number of base station antennas and served users, focusing on solving rate optimization for hybrid RIS-assisted MISO systems deployed in various scenarios. This paper establishes a fundamental model for hybrid RIS reflection signals. To better characterize the performance of the proposed hybrid RIS architecture, an optimization problem is formulated to maximize the sum-rate of the hybrid RIS-assisted multi-user, multiple-input, single-output (MU-MISO) system. An efficient algorithm is proposed combining fractional programming (FP), alternating optimization, and Lagrange duality transformation. Simulation results demonstrate that with hybrid RIS assistance, the system’s sum-rate gain increases by 49.1% and 40%, respectively, compared to systems with only active RIS deployment. This achieves higher sum-rate gains at lower power consumption. Full article

Configuring a battery energy storage system (BESS) is an effective approach to alleviating the peak shaving and valley filling burden on conventional thermal power units. However, excessive capacity increases investment cost, whereas insufficient capacity limits operational effectiveness. To address this trade-off, a multi-objective optimization framework is proposed to simultaneously maximize annual economic revenue and minimize load variance. The model comprehensively incorporates investment, operation and maintenance, decommissioning, environmental benefits, and deferred grid investment revenue, together with practical operational constraints on power limits, state of charge (SOC), charge/discharge states, and daily energy balance. A multi-objective particle swarm optimization (MOPSO) algorithm is employed to obtain the Pareto frontier, and the technique for order preference by similarity to ideal solution (TOPSIS) is applied to select the final optimal configuration. Simulation results based on a typical 24 h load profile indicate that the optimal BESS configuration is 27.7 MW/78.3 MWh, which reduces load variance by 32.15% and peak demand by 13.5%, while achieving an average annual revenue of 5.73 million CNY. Comparative analysis shows that the proposed method outperforms the traditional weighted-sum approach in both economic and technical indicators. Furthermore, the framework is extended to a WSCC nine-bus system with photovoltaic (PV) integration by introducing node voltage fluctuation as an additional objective. The results verify that the optimized BESS configuration can effectively mitigate voltage fluctuations under high PV penetration, demonstrating the scalability and applicability of the proposed method in renewable-energy integrated power systems. Full article

The growing global demand for energy, driven by population growth and industrial development, has increased the importance of renewable sources such as wind energy. In this context, Türkiye has made remarkable progress in expanding its wind energy capacity, particularly in the Aegean Region. The Bergama district, located in the northern part of İzmir, stands out as a promising area for sustainable wind power plant investments due to its favorable average wind speeds of 8–9 m/s measured at a hub height of 100 m. This study proposes an intelligent fuzzy multi criteria decision framework to determine the most suitable sites for wind power plant installation in the Bergama region. The evaluation process is structured around four main criteria, economic, technical, environmental, and social, each comprising five sub-criteria. Six alternative locations are comparatively assessed using an integrated Fuzzy Analytic Hierarchy Process and Fuzzy Weighted Sum Model approach. The combined model enabled effective handling of uncertainty in decision parameters and provided a consistent ranking of alternatives. Based on the results, Site 6 emerged as the most suitable location due to its superior wind resource characteristics, technical feasibility, and accessibility advantages, and the proposed approach offers a decision support framework for regional planners to guide strategic wind energy development. Full article

The COVID-19 pandemic has revealed profound social–psychological vulnerabilities and strengths across societies worldwide. Beyond its immediate health implications, the pandemic has triggered a wave of mental health issues, disrupted social cohesion, and challenged community resilience. This paper synthesizes the current literature, critically discusses five recent studies as part of the Special Issue “Mental Health Consequences of COVID-19: The Role of Social Determinants”, and articulates an agenda for future research within a social–psychological framework. Moving beyond mere negative effects such as anxiety, this review highlights the role of resilience, prosocial behavior, (digital) mental health interventions, and community social capital. Correspondingly, I advocate for interdisciplinary efforts to enhance awareness, preparedness, and adaptive capacity during health crises, emphasizing the need for a clearer focus on vulnerable social groups. In sum, recognizing the evolving global landscape, this work underscores the urgency of integrating psychological insights into public health policies to build resilient societies capable of confronting future pandemics and health emergencies. Full article

In recent years, the installed capacity of renewable energy sources, such as wind power and photovoltaic generation, has been steadily increasing in power systems. However, the inherent randomness and volatility of renewable energy generation pose greater challenges to grid frequency stability. To address this issue, this paper first introduces the Minkowski sum algorithm to map the feasible regions of dispersed individual units into a high-dimensional hypercube space, achieving efficient aggregation of large-scale schedulable capacity. Compared with conventional geometric or convex-hull aggregation methods, the proposed approach better captures spatio-temporal coupling characteristics and reduces computational complexity while preserving accuracy. Subsequently, aiming at the coordination challenge between day-ahead planning and real-time dispatch, a “hierarchical coordination and dynamic optimization” control framework is proposed. This three-layer architecture, comprising “day-ahead pre-dispatch, intraday rolling optimization, and terminal execution,” combined with PID feedback correction technology, stabilizes the output deviation within ±15%. This performance is significantly superior to the market assessment threshold. The research results provide theoretical support and practical reference for the engineering promotion of vehicle–grid interaction technology and the construction of new power systems. Full article

This paper studies a novel movable antenna (MA)-aided Cell-Free Massive MIMO system to leverage the corresponding spatial degrees of freedom (DoFs) for improving the performance of distributed wireless networks. We aim to maximize the ergodic sum capacity by jointly optimizing the MA positions and the transmit covariance matrix based on statistical channel state information (CSI). To address the non-convex stochastic optimization problem, we propose a novel Constrained Stochastic Successive Convex Approximation (CSSCA) framework, enhanced with a robust slack-variable mechanism to handle non-convex antenna spacing constraints and ensure iterative feasibility. Numerical results show that the considered MA-enhanced system can significantly improve the ergodic capacity compared to fixed-antenna cell-free systems and that the proposed algorithm exhibits robust convergence behavior. Full article

Agricultural land represents a fundamental production resource and one of the key factors of ecological and economic stability in rural and peri-urban areas. In the municipality of Grocka, the impacts of urbanization, demographic decline, and changes in the agrarian production structure have led to spatial degradation and reduced economic sustainability. To assess the current state and potential of agriculture at the settlement level, a multi-criteria analysis (MCA) integrated with Geographic Information Systems (GIS) was applied. The analysis encompassed demographic, production, environmental, and spatial indicators, normalized using the min–max scaling method and aggregated through a weighted sum. Criteria weights were defined based on a combination of literature review and expert judgment. The results reveal spatial variations in the level of sustainability and enable the identification of priority zones for agro-economic improvement, areas of moderate stability, and spaces suitable for developing sustainable agricultural models. Sensitivity testing (±20% variation in weights) confirmed the robustness of the results. The identified zones and proposed measures aim to revitalize degraded areas, preserve permanent crops, and strengthen production and institutional capacities. The applied methodological framework can serve as a tool for planning and policymaking in sustainable agricultural development, particularly in peri-urban contexts. Full article

In this study, using global liner shipping schedules, UNCTAD’s Port Liner Shipping Connectivity Index and Liner Shipping Bilateral Connectivity Index, together with bilateral trade-value data for 2022–2024, we construct a multilayer weighted port-to-port network that explicitly embeds port-level cargo-handling and service organisation capabilities, as well as demand-side routing pressure, into node and edge weights. Building on this network, we apply CONCOR-based structural-equivalence analysis to delineate functionally homogeneous port clusters, and adopt a structural role identification framework that combines multi-indicator connectivity metrics with Rank-Sum Ratio–entropy weighting and Probit-based binning to classify ports into high-efficiency core, bridge-control, and free-form bridge roles, thereby tracing the reconfiguration of cluster-level functional structures before and after the Red Sea crisis. Empirically, the clustering identifies four persistent communities—the Intertropical Maritime Hub Corridor (IMHC), Pacific Rim Mega-Port Agglomeration (PRMPA), Southern Commodity Export Gateway (SCEG), and Euro-Asian Intermodal Chokepoints (EAIC)—and reveals a marked spatial and functional reorganisation between 2022 and 2024. IMHC expands from 96 to 113 ports and SCEG from 33 to 56, whereas EAIC contracts from 27 to 10 nodes as gateway functions are reallocated across clusters, and the combined share of bridge-control and free-form bridge ports increases from 9.6% to 15.5% of all nodes, demonstrating a thicker functional backbone under rerouting pressures. Spatially, IMHC extends from a Mediterranean-centred configuration into tropical, trans-equatorial routes; PRMPA consolidates its role as the densest trans-Pacific belt; SCEG evolves from a commodity-based export gateway into a cross-regional Southern Hemisphere hub; and EAIC reorients from an Atlantic-dominated structure towards Eurasian corridors and emerging bypass routes. Functionally, Singapore, Rotterdam, and Shanghai remain dominant high-efficiency cores, while several Mediterranean and Red Sea ports (e.g., Jeddah, Alexandria) lose centrality as East and Southeast Asian nodes gain prominence; bridge-control functions are increasingly taken up by European and East Asian hubs (e.g., Antwerp, Hamburg, Busan, Kobe), acting as secondary transshipment buffers; and free-form bridge ports such as Manila, Haiphong, and Genoa strengthen their roles as elastic connectors that enhance intra-cluster cohesion and provide redundancy for inter-cluster rerouting. Overall, these patterns show that resilience under the Red Sea crisis is expressed through the cluster-level rebalancing of core–control–bridge roles, suggesting that port managers should prioritise parallel gateways, short-sea and coastal buffers, and sea–land intermodality within clusters when designing capacity expansion, hinterland access, and rerouting strategies. Full article

The integration of terrestrial and aerial components in future wireless networks is a key enabler for achieving wide-area coverage and providing ubiquitous services. In this context, and with the goal of enhancing spectral efficiency through opportunistic spectrum reuse, this paper investigates a cooperative spectrum sensing approach in which cognitive UAVs equipped with full-duplex (FD) MIMO technology operate as aerial base stations (ABS). Each UAV performs local detection using the sphericity test, then a push–sum consensus protocol is employed to fuse local test statistics without relying on a fusion center. Unlike conventional unweighted consensus or centralized hard-decision fusion, the proposed approach accounts for the heterogeneity introduced by residual self-interference in FD transceivers. Specifically, multipath in the self-interference channel induces temporal correlation, increasing the variance of the local test statistic and, consequently, the false-alarm probability. To mitigate this effect, we design variance-aware consensus weights proportional to the inverse of the sphericity test variance enhancing robustness to RSI-induced variability. Numerical results demonstrate that the proposed scheme outperforms both unweighted consensus and centralized OR-rule fusion in user capacity, while maintaining negligible communication overhead. Moreover, the operational altitude of the UAVs is evaluated to balance the coverage provided to users and the primary signal detection capability. Full article