Search Results (1,166)

The rapid growth of electric vehicles (EVs) has significantly increased the demand for charging infrastructure, posing a challenge in balancing charging demand and infrastructure supply. The development of battery swapping and charging stations (BSCSs) is crucial for addressing these challenges and serves as a fundamental pillar for the sustainable advancement of EVs. This study develops a bi-level optimization model for the location and route planning of BSCSs. The upper-level model optimizes station locations to minimize total cost and service delay, while the lower-level model optimizes driver travel routes to minimize total time. An updated Non-Dominated Sorting Genetic Algorithm (UNSGA) is applied to enhance solution efficiency. The experimental results show that the bi-level model outperforms the single-level model, reducing total cost by 1.5% and travel time by 6.6%. Compared to other algorithms, the UNSGA achieves 9.43% and 8.23% lower costs than MOPSO and MOSA, respectively. Furthermore, BSCSs, despite 15.42% higher construction costs, reduce driver travel time by 22.43% and waiting time by 71.19%, highlighting their operational advantages. The bi-level optimization method provides more cost-effective decision support for EV infrastructure investors, enabling them to adapt to dynamic drivers’ needs and optimize resource allocation. Full article

Background: Chimeric Antigen Receptor (CAR) T-cell therapy has transformed the treatment of hematological malignancies, yet its application in solid tumors remains constrained by unique biological and logistical barriers. Objective: This review critically examines the evolving landscape of CAR T-cell therapy in solid malignancies, with a focus on antigen heterogeneity, the immunosuppressive tumor microenvironment, and risks of on-target, off-tumor toxicity. Methods: We outline recent advances in CAR engineering, including co-stimulatory optimization, dual- and multi-antigen targeting, armored CARs, and gene-edited constructs designed to enhance persistence and anti-tumor activity. Clinical progress is highlighted by recent FDA approvals of genetically modified T-cell therapies in synovial sarcoma and melanoma, underscoring the potential for broader solid tumor application. Additionally, we synthesize early-phase clinical trial findings across multiple solid tumor types (e.g., lung, colorectal, ovarian, glioblastoma), and discuss innovative approaches such as regional delivery, checkpoint blockade combinations, and incorporation of chemokine receptors for improved tumor infiltration. The review also considers future strategies, including artificial intelligence-guided target discovery and rational trial design to overcome translational bottlenecks. Conclusions: With expanding clinical experience and continued technological innovation, CAR T-cell therapy is steadily transitioning from an experimental strategy to a therapeutic reality in solid tumors, poised to reshape the future of cancer immunotherapy. Full article

County economies are essential drivers of national economic development, acting as critical engines for growth and regional equilibrium. This study uses the National Rural E-commerce Comprehensive Demonstration policy, initiated by the Ministry of Finance and the Ministry of Commerce, China to empirically investigate the impact of rural e-commerce on county economic development and inequality, based on economic and night light data from 2000 to 2021. By applying a staggered difference-in-differences (DID) model, we find that rural e-commerce significantly boosts county economic development. This result remains robust after a series of robustness tests. The impact is stronger on the primary sector compared to the secondary and tertiary sectors, and the effect is more pronounced in the central and western regions than in the eastern regions. Furthermore, rural e-commerce effectively reduces economic inequality, contributing to inclusive development. Mechanistically, e-commerce into rural demonstration policy fosters county economic development by enhancing human capital mobility, accelerating logistics development, and promoting the growth of local enterprises. Full article

This paper presents a comprehensive analysis of artificial intelligence-powered automated grading systems (AI AGSs) in STEM education, systematically examining their algorithmic foundations, mathematical modeling approaches, and quantitative evaluation methodologies. AI AGSs enhance grading efficiency by providing large-scale, instant feedback and reducing educators’ workloads. Compared to traditional manual grading, these systems improve consistency and scalability, supporting a wide range of assessment types, from programming assignments to open-ended responses. This paper provides a structured taxonomy of AI techniques including logistic regression, decision trees, support vector machines, convolutional neural networks, transformers, and generative models, analyzing their mathematical formulations and performance characteristics. It further examines critical challenges, such as user trust issues, potential biases, and students’ over-reliance on automated feedback, alongside quantitative evaluation using precision, recall, F1-score, and Cohen’s Kappa metrics. The analysis includes feature engineering strategies for diverse educational data types and prompt engineering methodologies for large language models. Lastly, we highlight emerging trends, including explainable AI and multimodal assessment systems, offering educators and researchers a mathematical foundation for understanding and implementing AI AGSs into educational practices. Full article

Recent advances in artificial intelligence (AI) have transformed ophthalmic diagnostics, particularly for retinal diseases. In this prospective, non-randomized study, we evaluated the performance of an AI-based software system against conventional clinical assessment—both quantitative and qualitative—of optical coherence tomography (OCT) images for diagnosing diabetic macular edema (DME). A total of 700 OCT exams were analyzed across 26 features, including demographic data (age, sex), eye laterality, visual acuity, and 21 quantitative OCT parameters (Macula Map A X-Y). We tested two classification scenarios: binary (DME presence vs. absence) and multiclass (six distinct DME phenotypes). To streamline feature selection, we applied paraconsistent feature engineering (PFE), isolating the most diagnostically relevant variables. We then compared the diagnostic accuracies of logistic regression, support vector machines (SVM), K-nearest neighbors (KNN), and decision tree models. In the binary classification using all features, SVM and KNN achieved 92% accuracy, while logistic regression reached 91%. When restricted to the four PFE-selected features, accuracy modestly declined to 84% for both logistic regression and SVM. These findings underscore the potential of AI—and particularly PFE—as an efficient, accurate aid for DME screening and diagnosis. Full article

This paper presents a hybrid computational architecture for efficient and robust digital transmission inspired by helical genetic structures. The proposed system integrates advanced modulation schemes, such as multi-pulse-position modulation (MPPM), high-order quadrature amplitude modulation (QAM), and chirp spread spectrum (CSS), along with Reed–Solomon error correction and quantum-assisted search, to optimize performance in noisy and non-line-of-sight (NLOS) optical environments, including VLC channels modeled with log-normal fading. Through mathematical modeling and simulation, we demonstrate that the number of helical transmissions required for genome-scale data can be drastically reduced—up to 95% when using parallel strands and high-order modulation. The trade-off between redundancy, spectral efficiency, and error resilience is quantified across several configurations. Furthermore, we compare classical genetic algorithms and Grover’s quantum search algorithm, highlighting the potential of quantum computing in accelerating decision-making and data encoding. These results contribute to the field of operations research and supply chain communication by offering a scalable, energy-efficient framework for data transmission in distributed systems, such as logistics networks, smart sensing platforms, and industrial monitoring systems. The proposed architecture aligns with the goals of advanced computational modeling and optimization in engineering and operations management. Full article

Amid globalization, tourism has increasingly emphasized health and well-being through sustainable, wellness-oriented services. Thailand has been recognized as having strong potential to become a regional hub for wellness tourism, supported by its efficient healthcare system and diverse attractions. This study aims to identify key indicators of wellness tourism based on holistic health principles and to examine their relationships with tourists’ intentions to use wellness services. Data were collected from 1200 wellness tourists in Thailand through stratified random sampling and analyzed using Exploratory Factor Analysis (EFA) and Structural Equation Modeling (SEM). The results revealed six significant wellness factors, with Environmental Wellness being the most influential. In addition, gender, income, and occupation were found to positively affect wellness tourism behavior. Attitude and subjective norms also significantly influenced tourists’ intentions to engage in wellness services. This study provides policy recommendations to assist tourism and public health agencies in promoting wellness tourism and enhancing health-focused travel experiences. Full article

A lunar landing pad (LLP) represents essential initial infrastructure for establishing sustainable lunar settlements. This study investigates the feasibility of constructing LLPs through in situ resource utilization (ISRU), focusing on an innovative composite material comprising lunar regolith and the high-performance thermoplastic Polyether Ether Ketone (PEEK). The proposed manufacturing approach involves mechanically blending regolith with PEEK granules, compacting the mixture in a mold, and thermally processing it to induce polymer melting and binding. Experimental analysis indicates that a modest binder fraction (15 wt. % PEEK) yields a robust composite with a flexural strength of 14.6 MPa, although exhibiting inherently brittle characteristics. Compaction pressure emerges as a crucial factor influencing material performance. Utilizing these findings, hexagonal modular tiles were designed as the fundamental LLP elements, specifically engineered to optimize manufacturing simplicity, mechanical robustness, stackability for redundancy, and ease of replacement or repair. The tile geometry strategically mitigates brittleness-induced vulnerabilities by avoiding stress concentrations. Explicit finite element analyses validated tile performance under simulated lunar landing conditions corresponding to the European Large Logistic Lander specifications. Results demonstrated safe landing velocities between 0.1 and 0.7 m/s, governed by the binder content and compaction pressure. A clearly identified linear correlation between the binder fraction and permissible impact velocity enables predictive tailoring of the material composition, confirming the suitability and scalability of thermoplastic–regolith composites for future lunar infrastructure development. Full article

Mass gatherings such as the annual Hajj pilgrimage in Makkah, Saudi Arabia, generate extreme, short-term anthropogenic emission loads with significant air quality and public health implications. This study assesses the spatiotemporal dynamics of key atmospheric pollutants—including nitrogen dioxide (NO₂), carbon monoxide (CO), sulfur dioxide (SO₂), formaldehyde (HCHO), and aerosols—across Makkah and its holy sites before and during the Hajj seasons of 2023 and 2024. Using high-resolution Sentinel-5P TROPOMI satellite data, pollutant fields were reconstructed at 100 m spatial resolution via cloud-based geospatial analysis on the Google Earth Engine. During Hajj 2023, spatially resolved NO₂ concentrations ranged from 15.4 μg/m³ to 38.3 μg/m³ with an average of 24.7 μg/m³, while SO₂ during the 2024 event peaked at 51.2 μg/m³ in key hotspots, occasionally exceeding World Health Organization (WHO) guideline values. Aerosol index values showed episodic surges (up to 1.43), particularly over transportation corridors, parking areas, and logistics facilities. CO concentrations reached values as high as 1069.8 μg/m³ in crowded zones, and HCHO concentrations surged up to 9.99 μg/m³ during peak periods. Quantitative correlation analysis revealed that during Hajj, atmospheric chemistry diverged from urban baseline: the NO₂–SO₂ relationship shifted from strongly negative pre-Hajj (r = −0.74) to moderately positive during the event (r = 0.35), while aerosol–HCHO correlations intensified negatively from r = −0.23 pre-Hajj to r = −0.50 during Hajj. Meteorological analysis indicated significant positive correlations between wind speed and NO₂ (r = 0.35) and wind speed and CO (r = 0.35) during 2024, demonstrating that extreme emission rates overwhelmed typical dispersive processes. Relative humidity was positively correlated with aerosol loading (r = 0.37), pointing to hygroscopic growth patterns. These results quantitatively demonstrate that Hajj drives a distinct, event-specific pollution regime, characterized by sharp increases in key pollutant concentrations, altered inter-pollutant and pollutant–meteorology relationships, and spatially explicit hotspots driven by human activity and infrastructure. The integrated satellite–meteorology workflow enabled near-real-time monitoring in a data-sparse environment and establishes a scalable framework for evidence-based air quality management and health risk reduction in mass gatherings. Full article

The betel palm is a critical economic crop on Hainan Island. Accurate and timely maps of betel palms are fundamental for the industry’s management and ecological environment evaluation. To date, mapping the spatial distribution of betel palms across a large regional scale remains a significant challenge. In this study, we propose an integrated framework that combines knowledge-based and machine learning approaches to produce a map of betel palms at 10 m spatial resolution based on Sentinel-1/2 data and Google Earth Engine (GEE) for 2023 on Hainan Island, which accounts for 95% of betel nut acreage in China. The forest map was initially delineated based on signature information and the Green Normalized Difference Vegetation Index (GNDVI) acquired from Sentinel-1 and Sentinel-2 data, respectively. Subsequently, patches of betel palms were extracted from the forest map using a random forest classifier and feature selection method via logistic regression (LR). The resultant 10 m betel palm map achieved user’s, producer’s, and overall accuracy of 86.89%, 88.81%, and 97.51%, respectively. According to the betel palm map in 2023, the total planted area was 189,805 hectares (ha), exhibiting high consistency with statistical data (R² = 0.74). The spatial distribution was primarily concentrated in eastern Hainan, reflecting favorable climatic and topographic conditions. The results demonstrate the significant potential of Sentinel-1/2 data for identifying betel palms in complex tropical regions characterized by diverse land cover types, fragmented cultivated land, and frequent cloud and rain interference. This study provides a reference framework for mapping tropical crops, and the findings are crucial for tropical agricultural management and optimization. Full article

In the context of China’s ‘dual carbon’ strategy, sustainable port–city integration has become critical for regional transformation. Based on the green development perspective, this study constructed a “port–industry–city” (PIC) coordinated development indicator system, conceptualizing ports, industries, and cities as distinct but interrelated subsystems. An improved coupling coordination degree model and an obstacle degree model were employed to analyze the coordinated development between Shanghai Port and its associated industries and urban areas during the green transformation process from 2014 to 2023. Three key findings were found: (1) The comprehensive development index of Shanghai Port exhibited a W-shaped fluctuation followed by rapid growth, while the overall PIC system showed a continuous upward trajectory, with the overall development level steadily rising. (2) During Shanghai Port’s green transformation process, the coordination level of the PIC system improved from moderate imbalance to intermediate coordination, though the overall level still requires improvement. (3) Port green transformation, infrastructure, and urban ecology represent primary obstacles requiring targeted, sustainable interventions. This study enriches the research on port–industry–city coordination and provides both theoretical support and a policy foundation for promoting regional sustainable development led by green port initiatives. Full article

The problem of quantifying prestress loss in the external tendons of in-service bridges is of immense practical importance, and the development of reliable, cost-effective methods is a commendable goal. Based on the principle of static equilibrium, this paper proposes a direct method for determining the effective stress in external prestressed tendons using the variation principle, whose calculation accuracy was validated by conducting experimental and theoretical analysis considering the prestressed tendon arrangement form. A transverse tensioning experiment of the prestressed tendons was carried out under four tension conditions of 50 kN, 80 kN, 110 kN and 170 kN at the anchorage end, and the theoretically calculated internal force of the prestressed tendons gradually approached the measured value as the transverse tension increased. Once the appropriate level of transverse tension was reached, stable and reliable results could be obtained. Ultimately, the error between them will stabilize below 5%. This method was used to detect stress loss in the external prestressed tendons of 20 m, 40 m and 50 m T-beams affected by both internal and external uncertain factors simultaneously, and the probability distribution hypothesis test of the stress loss rate was carried out, the results of which reveal that they all follow normal distribution. The ratio of stress at the bottom edge of the T-beam under self-weight and prestressed load to that under vehicle load is defined as the compressive stress reserve coefficient, which is a verified and reliable index for evaluating the external prestressed stress loss on the reinforcement effect of the bridge. Full article

Port seaside scheduling, involving berth allocation, quay crane, and tugboat scheduling, is central to intelligent port operations. This survey reviews and statistically analyzes 152 academic publications from 2000 to 2025 that focus on optimization techniques for port seaside scheduling. The reviewed methods span mathematical modeling and exact algorithms, heuristic and simulation-based approaches, and agent-based and learning-driven techniques. Findings show deterministic models remain mainstream (77% of studies), with uncertainty-aware models accounting for 23%. Heuristic and simulation approaches are most commonly used (60.5%), followed by exact algorithms (21.7%) and agent-based methods (12.5%). While berth and quay crane scheduling have historically been the primary focus, there is growing research interest in tugboat operations, pilot assignment, and vessel routing under navigational constraints. The review traces a clear evolution from static, single-resource optimization to dynamic, multi-resource coordination enabled by intelligent modeling. Finally, emerging trends such as the integration of large language models, green scheduling strategies, and human–machine collaboration are discussed, providing insights and directions for future research and practical implementations. Full article

Autonomous ship control faces significant challenges due to the diversity of ship types, the complexity of task scenarios, and the uncertainty of dynamic marine environments. These factors limit the effectiveness of traditional control approaches that rely on explicit dynamics modeling and handcrafted control laws. With the rapid advancement of computing and artificial intelligence, imitation learning offers a promising alternative by directly learning expert behaviors from data. This paper proposes a Generative Adversarial Imitation Learning (GAIL) method for heading and path-following control of autonomous surface ships. It employs an adversarial learning structure, in which a generator learns control policies that reproduce expert behavior while a discriminator distinguishes between expert and learned trajectories. In this way, the control strategies can be learned from expert demonstrations without requiring explicit reward design. The proposed method is validated through simulations on a model-scale tug. Compared with a behavioral cloning (BC) baseline controller, the GAIL-based controller achieves superior performance in terms of path-following accuracy, heading stability, and control smoothness, confirming its effectiveness and potential for real-world deployment. Full article

With the increasing demand for urban rail transit capacity, shield tunneling has become the predominant method for constructing underground metro systems in densely populated cities. However, the spatial interaction between shield tunnels and adjacent retaining structures poses significant engineering challenges, potentially leading to excessive ground settlement, structural deformation, and even stability failure. This study systematically investigates the deformation behavior and associated risks of retaining systems during adjacent shield tunnel construction. An orthogonal multi-factor analysis was conducted to evaluate the effects of grouting pressure, grout stiffness, and overlying soil properties on maximum surface settlement. Results show that soil cohesion and grouting pressure are the most influential parameters, jointly accounting for over 72% of the variance in settlement response. Based on the numerical findings, a Bayesian network model was developed to assess construction risk, integrating expert judgment and field monitoring data to quantify the conditional probability of deformation-induced failure. The model identifies key risk sources such as geological variability, groundwater instability, shield steering correction, segmental lining quality, and site construction management. Furthermore, the effectiveness and cost-efficiency of various grouting reinforcement strategies were evaluated. The results show that top grouting increases the reinforcement efficiency to 34.7%, offering the best performance in terms of both settlement control and economic benefit. Sidewall grouting yields an efficiency of approximately 30.2%, while invert grouting shows limited effectiveness, with an efficiency of only 11.6%, making it the least favorable option in terms of both technical and economic considerations. This research provides both practical guidance and theoretical insight for risk-informed shield tunneling design and management in complex urban environments. Full article