Search Results (1,444)

In the service-oriented modern society, logistics enterprises focusing solely on cost minimization can no longer meet market demands, as customers place greater emphasis on timely delivery and service satisfaction. Therefore, this paper constructs a multi-objective optimization model that simultaneously minimizes distribution costs and hierarchical customer delivery duration. From the perspective of symmetry, the two objectives form a symmetric complementary system, which reflects the mutually restrictive and trade-off relationship between the two objectives, thereby facilitating the achievement of a balance between enterprise benefits and customer satisfaction. An improved multi-objective grey wolf optimizer (IMOGWO) is proposed to solve the model, incorporating a chaotic mapping initialization mechanism, a cosine nonlinear convergence factor, and a learning factor-based hunting mechanism to enhance global optimization capability. The algorithm’s effectiveness is validated through comparisons on benchmark cases. Applied to a Zhengzhou food company, the solution improved distribution efficiency while prioritizing key clients, thereby enhancing service levels and stabilizing important customer relationships, providing a practical reference for logistics enterprises to increase revenue and undergo digital transformation. Full article

Understanding the services provided by coastal ecosystems is vital for their study, preservation, and restoration. Mangrove forests, in particular, provide key ecosystem services: they sequester carbon, support fisheries and biodiversity, and facilitate sustainable tourism. In the Caribbean and the Gulf of Mexico, mangrove-related services have been studied extensively, but often via fragmented approaches. This meta-analysis combines a literature review, bibliometric tools, and thematic mapping to identify emerging trends and long-standing gaps. We analyzed 61 peer-reviewed studies across 21 sovereign states and U.S. states, which highlighted shifting research priorities and a lack of convergence—defined herein as the failure of individual studies to examine multiple ecosystem service categories (regulating, cultural, supporting, and provisioning) simultaneously to assess potential trade-offs. While early research emphasized supporting services such as fishery nurseries, recent studies focus on regulating services, especially carbon sequestration. Stakeholder engagement remains limited, with only 18% of studies incorporating local perspectives. We argue for greater integration of stakeholder input and convergence across service categories to enhance the scientific basis for mangrove management and policy design. Full article

The impact of a quarter-circle corner crack on an adjacent parallel semi-elliptical surface crack (SESC) located in a semi-infinite solid subjected to in-plane bending is studied using a 3-D finite element analysis. The stress intensity factor (SIF) distributions along the front of the SESC are evaluated to determine said impact. The SESC’s semi-major axis ranged from a₁ = 10 mm to 30 mm with ellipticities of b₁/a₁ varying from 0.1 to 1.0 for a constant quarter-circle corner crack length of a₂ = 15 mm. Furthermore, several crack configurations are considered where the normalized vertical and horizontal gaps between the two cracks are taken to be H/a₂ = 0.4 and 1.2 and S/a₂ = −0.5 and 1.0, respectively. The results show that the effect of the quarter-circle corner crack on the SESC can be considerable both in amplifying and in attenuating the SIFs along the semi-elliptical surface crack front. Moreover, these opposite effects can occur simultaneously, but in different sections of the SESC’s crack front. The magnitude and pattern of these effects depend on the length and ellipticity of the SESC. It is further concluded that when considering the fitness-for-service of a critical real mechanical component, a complete 3-D analysis is needed to provide a reliable solution for such crack configurations. Full article

Steel bridge deck pavements (SBDPs) are susceptible to complex mechanical and service environmental conditions, yet current design methods often struggle to simultaneously capture global bridge system behavior and local pavement responses. To address this issue, this study develops a multi-scale finite element modeling framework that integrates a full-bridge model, a refined girder-segment model, and a detailed pavement submodel. The framework is applied to an extra-long suspension bridge to evaluate the mechanical responses of five typical pavement structural configurations—including double-layer SMA, double-layer Epoxy Asphalt (EA), EA-SMA combinations, and a composite scheme with a thin epoxy resin aggregate overlay. By coupling global deformations from a full-bridge model to the local pavement submodel, the proposed method enables a consistent assessment of both bridge-level effects and pavement-level stress concentrations. The analysis reveals that pavement structures significantly alter the stress and strain distributions within the deck system. The results indicate that while the composite configuration with a thin overlay effectively reduces shear stress at the pavement–deck interface, it results in excessive tensile strain, posing a high risk of fatigue cracking. Conversely, the double-layer EA configuration exhibits the lowest fatigue-related strain, demonstrating superior deformation coordination, while the optimized EA-SMA combination offers a robust balance between fatigue control and interfacial stress distribution. These findings validate the effectiveness of the multi-scale approach for SBDP analysis and highlight that rational structural configuration selection—specifically balancing layer stiffness and thickness—is critical for enhancing the durability and long-term performance of steel bridge deck pavements. Full article

Background: Cold chain distribution in Agri-Biotech supply chains faces serious challenges due to strict time windows, high temperature sensitivity, and conflict between different operational objectives, and conventional static approaches are unable to address these complexities. Methods: In this study, an integrated decision support framework is presented that combines multi-objective fuzzy modeling and an adaptive digital twin to simultaneously manage logistics costs, product quality degradation, and service time compliance under operational uncertainty. Key uncertain parameters are modeled using triangular fuzzy numbers, and the digital twin dynamically updates the decision parameters based on operational information. The proposed framework is evaluated using real industrial data and comprehensive computational experiments. Results: The results show that the proposed approach is able to produce stable and balanced solutions, provides near-optimal performance in benchmark cases, and is highly robust to demand fluctuations and temperature deviations. Digital twin activation significantly improves the convergence behavior and stability of the solutions. Conclusions: The proposed framework provides a reliable and practical tool for adaptive planning of cold chain distribution in Agri-Biotech industries and effectively reduces the gap between advanced optimization models and real-world operational requirements. Full article

Semantic Communication (SC), driven by a deep learning (DL)-based “understand-before-transmit” paradigm, transmits lightweight semantic information (SI) instead of raw data. This approach significantly reduces data volume and communication overhead while maintaining performance, making it particularly suitable for UAV communications where the platform is constrained by size, weight, and power (SWAP) limitations. To alleviate the computational burden of semantic extraction (SE) on the UAV, this paper introduces federated learning (FL) as a distributed training framework. By establishing a collaborative architecture with edge users, computationally intensive tasks are offloaded to the edge devices, while the UAV serves as a central coordinator. We first demonstrate the feasibility of integrating FL into SC systems and then propose a novel solution based on Proximal Policy Optimization (PPO) to address the critical challenge of ensuring service fairness in UAV-assisted semantic communications. Specifically, we formulate a joint optimization problem that simultaneously designs the UAV’s flight trajectory and bandwidth allocation strategy. Experimental results validate that our FL-based training framework significantly reduces computational resource consumption, while the PPO-based algorithm approach effectively minimizes both energy consumption and task completion time while ensuring equitable quality-of-service (QoS) across all edge users. Full article

To address the diverse requirements of satellite communication applications involving medium-/low-rate reliable links and high-rate high-capacity services, an integrated S/Ka dual-band dual-reflector antenna is proposed as an effective solution. Owing to the stringent spatial constraints of satellite platforms, the longer operating wavelengths in the S-band lead to oversized feed horns in the integrated antenna design, which induces severe secondary aperture blockage, thus degrading aperture efficiency and impeding practical mechanical layout implementation. To alleviate this critical drawback, the proposed antenna achieves multi-band aperture reuse by deploying an array with four miniaturized S-band radiating elements around a broadband Ka-band feed horn. A frequency-selective surface (FSS)-based sub-reflector is further designed to effectively enhance the effective aperture size for the S-band operation, while ensuring unobstructed electromagnetic propagation in the Ka-band, thus enabling simultaneous dual-band high-gain radiation. After comprehensive electromagnetic simulation and parametric optimization for the antenna feed and the FSS sub-reflector, experimental measurements verify that the S-band left-hand and right-hand circularly polarized (LHCP/RHCP) channels achieve more than 20.2 dBic gains with more than 6° half-power beamwidths (HPBWs), and the Ka-band channel yields gains exceeding 41.2 dBic, with HPBWs greater than 0.8°. Full article

In this study, we aimed to develop a high-performance, anti-fouling ultrafiltration membrane by integrating photocatalytic and Fenton-like functions into a polymer matrix, in order to address the critical challenge of membrane fouling and achieve simultaneous separation and degradation of organic pollutants. To this end, a novel Fe-V_O-TiO₂-embedded polyethersulfone (PES) composite membrane was designed and fabricated using a facile phase inversion method. The key innovation lies in the incorporation of Fe-V_O-TiO₂ nanoparticles containing abundant bulk-phase single-electron-trapped oxygen vacancies, which not only modulate membrane morphology and hydrophilicity but also enable sustained generation of reactive oxygen species for the pollutant degradation under light irradiation and H₂O₂. The optimized Fe-V_O-TiO₂-PES-0.04 membrane exhibited a significantly enhanced pure water flux of 222.6 L·m⁻²·h⁻¹ (2.2 times higher than the pure PES membrane) while maintaining a high bovine serum albumin (BSA) retention of 93% and an improved hydrophilic surface. More importantly, the membrane demonstrated efficient and stable synergistic Photocatalytic-Fenton activity, achieving 82% degradation of norfloxacin (NOR) and retaining 75% efficiency after eight consecutive cycles. A key finding is the membrane’s Photocatalytic-Fenton-assisted self-cleaning capability, with an 80% flux recovery after methylene blue (MB) fouling, which was attributed to in situ reactive oxygen species (·OH) generation (verified by ESR). This work provides a feasible strategy for designing multifunctional membranes with enhanced antifouling performance and extended service life through built-in catalytic self-cleaning. Full article

Wood flooring manufacturers face complex challenges in dynamically allocating resources across multi-channel markets, characterized by channel conflicts, demand uncertainty, and long-term cumulative effects of decisions. Traditional static optimization or myopic approaches struggle to address these intertwined factors, particularly when critical market states like brand reputation and customer base cannot be precisely observed. This paper establishes a systematic and theoretically grounded online decision framework to tackle this problem. We first model the problem as a Partially Observable Stochastic Dynamic Game. The core innovation lies in introducing an unobservable market position vector as the central system state, whose evolution is jointly influenced by firm investments, inter-channel competition, and macroeconomic randomness. The model further captures production lead times, physical inventory dynamics, and saturation/cross-channel effects of marketing investments, constructing a high-fidelity dynamic system. To solve this complex model, we propose a hierarchical online learning and control algorithm named L-BAP (Lyapunov-based Bayesian Approximate Planning), which innovatively integrates three core modules. It employs particle filters for Bayesian inference to nonparametrically estimate latent market states online. Simultaneously, the algorithm constructs a Lyapunov optimization framework that transforms long-term discounted reward objectives into tractable single-period optimization problems through virtual debt queues, while ensuring stability of physical systems like inventory. Finally, the algorithm embeds a game-theoretic module to predict and respond to rational strategic reactions from each channel. We provide theoretical performance analysis, rigorously proving the mean-square boundedness of system queues and deriving the performance gap between long-term rewards and optimal policies under complete information. This bound clearly quantifies the trade-off between estimation accuracy (determined by particle count) and optimization parameters. Extensive simulations demonstrate that our L-BAP algorithm significantly outperforms several strong baselines—including myopic learning and decentralized reinforcement learning methods—across multiple dimensions: long-term profitability, inventory risk control, and customer service levels. Full article

This study investigates the factors influencing Chinese K-12 teachers’ adoption of generative artificial intelligence (GenAI) for instructional purposes by extending the Technology Acceptance Model (TAM) with pedagogical beliefs, perceived intelligence, perceived ethical risks, GenAI anxiety, and demographic moderators. Drawing on a theory-driven framework, survey data were collected from 218 in-service teachers across K-12 schools in China. The respondents were predominantly from urban schools and most had prior GenAI use experience. Eight latent constructs and fourteen hypotheses were tested using structural equation modeling and multi-group analysis. Results show that perceived usefulness and perceived ease of use are the strongest predictors of teachers’ intention to adopt GenAI. Constructivist pedagogical beliefs positively predict both perceived usefulness and intention, whereas transmissive beliefs negatively predict intention. Perceived intelligence exerts strong positive effects on perceived usefulness and ease of use but has no direct effect on intention. Perceived ethical risks significantly heighten GenAI anxiety, yet neither directly reduces adoption intention. Gender, teaching stage, and educational background further moderate key relationships, revealing heterogeneous adoption mechanisms across teacher subgroups. The study extends TAM for the GenAI era and highlights the need for professional development and policy initiatives that simultaneously strengthen perceived usefulness and ease of use, engage with pedagogical beliefs, and address ethical and emotional concerns in context-sensitive ways. Full article

This follow-up study investigates the long-term environmental sustainability and remediation outcomes of the UPPER (‘Urban Productive Parks for Sustainable Urban Regeneration’-UIA04-252) project in Latina, Italy, focusing on Nature-Based Solutions (NbS) applied to urban green infrastructure. By integrating proximal and satellite-based remote sensing methodologies, the research evaluates persistent improvements in vegetation health, soil moisture dynamics, and overall environmental quality over multiple years. Building upon the initial monitoring framework, this case study incorporates updated data and refined techniques to quantify temporal changes and assess the ecological performance of NbS interventions. In more detail, ground-based data from meteo-climatic, air quality stations and remote satellite data from the Sentinel-2 mission are adopted. Ground-based measurements such as temperature, humidity, radiation, rainfall intensity, PM10 and PM2.5 are carried out to monitor the overall environmental quality. Updated satellite imagery from Sentinel-2 is analyzed using advanced band ratio indices, including the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Water Index (NDWI) and the Normalized Difference Moisture Index (NDMI). Comparative temporal analysis revealed consistent enhancements in vegetation health, with NDVI values significantly exceeding baseline levels (NDVI 2022–2024: +0.096, p = 0.024), demonstrating successful vegetation establishment with larger gains in green areas (+27.0%) than parking retrofits (+11.4%, p = 0.041). However, concurrent NDWI decline (−0.066, p = 0.063) indicates increased vegetation water stress despite irrigation infrastructure. NDMI improvements (+0.098, p = 0.016) suggest physiological adaptation through stomatal regulation. Principal Component Analysis (PCA) of meteo-climatic variables reveals temperature as the dominant environmental driver (PC2 loadings > 0.8), with municipality-wide NDVI-temperature correlations of r = −0.87. These multi-scale findings validate sustained NbS effectiveness in enhancing vegetation density and ecosystem services, yet simultaneously expose critical water-limitation trade-offs in Mediterranean semi-arid contexts, necessitating adaptive irrigation management and continued monitoring for long-term urban climate resilience. The integrated monitoring approach underscores the critical role of continuous, multi-scale assessment in ensuring long-term success and adaptive management of NbS-based interventions. Full article

Accurate nearshore bathymetry is essential for various marine applications, including navigation, resource management, and the protection of coastal ecosystems and the services they provide. This study presents an approach to enhance the accuracy of bathymetric estimates derived from high-spatial- and high-temporal-resolution optical satellite imagery. The proposed technique is particularly suited for multispectral sensors that acquire spectral bands sequentially rather than simultaneously. PlanetScope SuperDove imagery was employed and validated against bathymetric data collected using a multibeam echosounder. The study area is the Gulf of Sciacca, located along the southwestern coast of Sicily in the Mediterranean Sea. Here, multibeam data were acquired along transects that are subparallel to the shoreline, covering depths ranging from approximately 7 m to 50 m. Satellite imagery was radiometrically and atmospherically corrected and then processed using a simplified radiative transfer transformation to generate a continuous bathymetric map extending over the entire gulf. The resulting satellite-derived bathymetry achieved reliable accuracy between approximately 5 m and 25 m depth. Beyond these limits, excessive signal attenuation for higher depths and increased water turbidity close to shore introduced significant uncertainties. The innovative aspect of this approach lies in the combined use of spectral averaging among the most water-penetrating bands, temporal averaging across multiple acquisitions, and a liquid-facets noise reduction technique. The integration of these multi-layer inputs led to improved accuracy compared to using single-date or single-band imagery alone. Results show a strong correlation between the satellite-derived bathymetry and multibeam measurements over sandy substrates, with an estimated error of ±6% at a 95% confidence interval. Some discrepancies, however, were observed in the presence of mixed pixels (e.g., submerged vegetation or rocky substrates) or surface artifacts. Full article

Multi-hop relaying can solve the problems of limited single-hop wireless communication distance, poor signal quality, or the inability to communicate directly by “relaying” data transmission through multiple intermediate nodes. It serves as the cornerstone for building large-scale, highly reliable, and self-adapting wireless networks, especially for the Internet of Things (IoT) and future 6G. This paper focuses on a decode-and-forward (DF) multi-hop relay network that employs simultaneous wireless information and power transfer (SWIPT) technology, with relays operating in a passive state. We first investigate the optimization of the power splitting (PS) ratio at each relay, given the source node transmit power, to maximize end-to-end network throughput. Subsequently, we jointly optimized the PS ratios and the source transmit power to minimize the source transmit power while satisfying the system’s minimum quality of service (QoS) requirement. Although both problems are non-convex, they can be reformulated as convex optimization problems. Closed-form optimal solutions are then derived based on the Karush–Kuhn–Tucker (KKT) conditions and a recursive method, respectively. Moreover, we find that the closed-form optimal solutions for the PS ratios corresponding to the two problems are identical. Through simulations, we validate that the performance of the two proposed schemes based on the closed-form solutions is optimal, while also demonstrating their extremely fast algorithm execution speeds, thereby proving the deployment value of the two proposed schemes in practical communication scenarios. Full article

This study presents an integrated optimization framework for fleet assignment, flight scheduling, and aircraft routing under uncertainty, addressing a core challenge in airline operational planning. A three-stage stochastic mixed-integer nonlinear programming model is developed that, for the first time, simultaneously incorporates station purity constraints, codeshare agreements, and overbooking decisions. The formulation also includes realistic operational factors such as stochastic passenger demand and non-cruise times (NCT), along with adjustable cruise speeds and flexible departure time windows. To handle the computational complexity of this large-scale stochastic problem, a Sample Average Approximation (SAA) scheme is combined with two tailored metaheuristic algorithms: Simulated Annealing and Cuckoo Search. Extensive experiments on real-world flight data demonstrate that the proposed hybrid approach achieves tight optimality gaps below 0.5%, with narrow confidence intervals across all instances. Moreover, the SA-enhanced method consistently yields superior solutions compared with the CS-based variant. The results highlight the significant operational and economic benefits of jointly optimizing codeshare decisions, station purity restrictions, and overbooking policies. The proposed framework provides a scalable and robust decision-support tool for airlines seeking to enhance resource utilization, reduce operational costs, and improve service quality under uncertainty. Full article

The prevailing challenges of fading characteristics and identity crises in historical and cultural districts of small and medium-sized cities have been identified. Traditional analytical methods have been found to be deficient in systematically capturing the unique forms and urban memory of these districts. The present study thus adopts the Shangcheng Historical and Cultural District of Huangling County as a case study, proposing a comprehensive analytical framework that integrates urban memory and multi-dimensional methods such as space syntax, grounded-theory-inspired coding, and urban image analysis. The district is subject to a systematic assessment of its spatial form, structural design, and the mechanisms by which urban memory is conveyed. The proposal sets out targeted renewal strategies for four aspects: paths, edges, nodes and landmarks, and districts. The research findings are as follows: (1) Paths with high integration and connection degrees simultaneously serve as both sacrificial axes and carriers of folk narratives. (2) Edges are composed of the city wall ruins, Loess Plateau landform, and street spaces. The fishbone-like street structure leads to significant differences in the connection degrees of main and secondary roads. (3) Nodes such as Guanyv Temple-Confucian Temple, the South Gate, and the North City Wall Ruins Square have high visual control, while the visual integration and visual control of the Qiaoshan Middle School and Gongsun Road historical nodes are relatively low, and their spatial accessibility is insufficient. (4) Based on the “memory–space” coupling relationship, the district is divided into the Academy Life Area, the Historical and Cultural Core Experience Area, and the Comprehensive Service Area, providing an effective path to alleviate the problem of functional homogenization. The present study proffers a novel perspective on the revitalization mechanisms of historical districts in small and medium-sized cities, encompassing both theoretical integration and practical strategy levels. It further contributes methodological inspirations and localized planning experiences for addressing the cultural disconnection and spatial inactivity problems of historical urban areas on a global scale. Full article