Search Results (154)

Designing optimal heritage tourism routes in secondary cities involves complex trade-offs between cultural richness, travel time, carbon emissions, spatial coherence, and group satisfaction. This study addresses the Personalized Group Trip Design Problem (PGTDP) under real-world constraints by proposing DRL–IMVO–GAN—a hybrid multi-objective optimization framework that integrates Deep Reinforcement Learning (DRL) for policy-guided initialization, an Improved Multiverse Optimizer (IMVO) for global search, and a Generative Adversarial Network (GAN) for local refinement and solution diversity. The model operates within a digital twin of Warin Chamrap’s old town, leveraging 92 POIs, congestion heatmaps, and behaviorally clustered tourist profiles. The proposed method was benchmarked against seven state-of-the-art techniques, including PSO + DRL, Genetic Algorithm with Multi-Neighborhood Search (Genetic + MNS), Dual-ACO, ALNS-ASP, and others. Results demonstrate that DRL–IMVO–GAN consistently dominates across key metrics. Under equal-objective weighting, it attained the highest heritage score (74.2), shortest travel time (21.3 min), and top satisfaction score (17.5 out of 18), along with the highest hypervolume (0.85) and Pareto Coverage Ratio (0.95). Beyond performance, the framework exhibits strong generalization in zero- and few-shot scenarios, adapting to unseen POIs, modified constraints, and new user profiles without retraining. These findings underscore the method’s robustness, behavioral coherence, and interpretability—positioning it as a scalable, intelligent decision-support tool for sustainable and user-centered cultural tourism planning in secondary cities. Full article

Brain morphological abnormalities are common in patients with neurodevelopmental disorders (NDDs) and other neuropsychiatric disorders, often reflecting abnormal brain development and function. Genetic studies have found common genetic factors in NDDs and other neuropsychiatric disorders, although the etiology of brain structural changes in these disorders remains poorly understood. In this study, we analyzed magnetic resonance imaging (MRI) and genetic data from more than 30K individuals from the UK Biobank to evaluate whether NDD-risk copy number variants (CNVs) are also associated with neuroanatomical changes in both patients and neurotypical individuals. We found that the size differences in brain regions such as corpus callosum and cerebellum were associated with the deletions of specific areas of the human genome, and that specific neuroanatomical changes confer a risk of neuropsychiatric disorders. Furthermore, we observed that gene sets located in these genomic regions were enriched for pathways crucial for brain development and for phenotypes commonly observed in patients with NDDs. These findings highlight the link between CNVs, brain structure abnormalities, and the shared pathophysiology of NDDs and other neuropsychiatric disorders, providing new insights into the underlying mechanisms of these disorders and the identification of potential biomarkers for better diagnosis. Full article

This systematic review explores the role of smartwatches in stress management, mental health monitoring, and overall well-being. Drawing from 61 peer-reviewed studies published between 2016 and 2025, this review synthesizes empirical findings across diverse methodologies, including biometric data collection, machine learning algorithms, and user-centered design evaluations. Smartwatches, equipped with sensors for physiological signals such as heart rate, heart rate variability, electrodermal activity, and skin temperature, have demonstrated promise in detecting and predicting stress and mood fluctuations in both clinical and everyday contexts. This review emphasizes the need for interdisciplinary collaboration to advance technological precision, ethical data handling, and user experience design. Moreover, it highlights how different algorithms—such as Support Vector Machines (SVMs), Random Forests, Deep Neural Networks, and Boosting methods—perform across various physiological signals (e.g., HRV, EDA, skin temperature). Furthermore, it identifies performance trends and challenges across lab-based vs. real-world deployments, emphasizing the trade-off between generalizability and personalization in model design. Full article

Natural and man-made disasters are occurring more frequently, making household emergency preparedness essential for an effective response. Enrollees of the World Trade Center Health Registry have been found to be less prepared than the US national average despite their prior disaster exposure. The purpose of this study was to evaluate and compare the effectiveness of two interventions—a mailed brochure and a structured phone call—for increasing emergency preparedness knowledge among this population. We conducted a two-arm parallel group trial between February 2019 and August 2020. Participants were Registry enrollees who completed the Wave 4 Registry (2015–2016) survey, whose primary language was English or Spanish, who lived in New York City, and who did not report being a rescue and recovery worker affiliated with FDNY or NYPD. Enrollees were randomized to receive either a brochure by mail summarizing the components of emergency preparedness or a 15 min phone call describing the same. The primary outcome measure was the number of “yes” responses to the ten-item CDC CASPER emergency preparedness questionnaire, measured at baseline and post-intervention. Enrollees were sequentially alternatively assigned to either the brochure or phone call groups. In total, 705 enrollees were assigned to the brochure (n = 353) or phone call (n = 352) groups, and a total of 702 enrollees were analyzed. The Incident Rate Ratio (IRR) for the effect of time was 1.17 (95% CI = (1.14, 1.20)) and for intervention was 1.00 (95% CI = (0.95, 1.05)) Both the brochure and phone call interventions improved knowledge of emergency preparedness from baseline to post-intervention assessment, and to the same extent. Full article

In the context of the globalization process, the interplay between geopolitical dynamics and international trade fluctuations has had significant effects on global economic and business stability. Recent crises, such as the US–China trade war, the invasion of Ukraine, and the COVID-19 pandemic, have highlighted how changes in the structure of international trade can amplify the risks of business failure and reshape global competitiveness. This study aims to analyze in depth the transmission of business failure risk within the global trade network by assessing the sensitivity of industrial sectors in different countries to disruptive/critical/significant events. Through the integration of data from sources such as the World Trade Organization, national customs, and international relations research centers, a quantitative, exploratory, and descriptive approach based on graph theory, random forest, multivariate regression models, and neural networks is developed. This quantitative system makes it possible to identify patterns of risk propagation and to evaluate the degree of vulnerability of each country according to its commercial and financial structure. The mechanisms that relate geopolitical factors, such as trade sanctions and international conflicts, with the oscillations in the global market are analyzed. This study not only contributes to our understanding of how the macroeconomic environment influences business survival, but also provides analytical tools for strategic decision making. By providing an empirical and theoretical framework for early risk identification, it brings a novel perspective to academia and business, facilitating better adaptation to an increasingly volatile and uncertain business environment. Full article

With the continuous expansion of data centers, their carbon emission has become a serious issue. A number of studies are committing to reduce the carbon emission of data centers. Carbon trading, carbon capture, and power-to-gas technologies are promising emission reduction techniques which are, however, seldom applied to data centers. To bridge this gap, we propose a carbon-neutral architecture for distributed data centers, where each data center consists of three subsystems, i.e., an energy subsystem for energy supply, thermal subsystem for data center cooling, and carbon subsystem for carbon trading. Then, we formulate the energy management problem as a Decentralized Partially Observable Markov Decision Process (Dec-POMDP) and develop a distributed solution framework using Multi-Agent Deep Deterministic Policy Gradient (MADDPG). Finally, simulations using real-world data show that a cost saving of 20.3% is provided. Full article

Vehicle detection is essential for real-world applications such as road surveillance, intelligent transportation systems, and autonomous driving, where high accuracy and real-time performance are critical. However, achieving robust detection remains challenging due to scene complexity, occlusion, scale variation, and varying lighting conditions. Over the past two decades, numerous studies have been proposed to address these issues. This study presents a comprehensive and structured survey of image-based vehicle detection methods, systematically comparing classical machine learning techniques based on handcrafted features with modern deep learning approaches. Deep learning methods are categorized into one-stage detectors (e.g., YOLO, SSD, FCOS, CenterNet), two-stage detectors (e.g., Faster R-CNN, Mask R-CNN), transformer-based detectors (e.g., DETR, Swin Transformer), and GAN-based methods, highlighting architectural trade-offs concerning speed, accuracy, and practical deployment. We analyze widely adopted performance metrics from recent studies, evaluate characteristics and limitations of popular vehicle detection datasets, and explicitly discuss technical challenges, including domain generalization, environmental variability, computational constraints, and annotation quality. The survey concludes by clearly identifying open research challenges and promising future directions, such as efficient edge deployment strategies, multimodal data fusion, transformer-based enhancements, and integration with Vehicle-to-Everything (V2X) communication systems. Full article

The objective of this study is to explore the cold chain system in a metropolitan area, focusing on the overall system cost encompassing both distribution centers and transportation. The research delves into the planning of urban cold chain systems, considering fluctuating minimum customer demands, the traffic conditions of potential new centers, and the variability in carbon-trading prices. To manage the complexity of these objectives and inherent uncertainties, we introduce a flexible chance-constrained programming model for the cold chain system (FCCP-CCS). An FCCP-CCS programming model is developed to address the multifaceted goals and various uncertainties. The effectiveness of this model is validated through experimental analysis using real-world data from a major city’s cold chain system. The findings of this study reveal several key insights: (1) The levels of confidence and satisfaction significantly impact system optimization, with higher levels leading to increased consumption. (2) Customer demand variations would determine the transportation and the potential new centers in the system. (3) The surroundings of a distribution center partly indicate its service quality. (4) Governmental adjustments in carbon-trading prices can effectively enhance the overall sustainability of the urban cold chain system. This research highlights the importance of optimization in designing and managing urban cold chain systems, particularly in environmental sustainability. Full article

High-density cities have obvious characteristics of compact urban spatial form and intensive land use in terms of spatial environment, and have always been a topic of academic focus. As a typical coastal historical district, the Macau Peninsula pier district (mainly the Macau Inner Harbour) has a high building density and a low average street width, forming a vertical coastline development model that directly converses with the ocean. This area is adjacent to Macau’s World Heritage Site and directly related to the Marine trade functions. The distribution pattern of cultural heritage linked by the ocean has strengthened Macau’s unique positioning as a node city on the Maritime Silk Road. This text is based on the theory of urban development, integrates spatial syntax and POI analysis techniques, and combines the theories of waterfront regeneration, high-density urban form and post-industrial urbanism to integrate and deepen the theoretical framework, and conduct a systematic study on the urban spatial characteristics of the coastal area of the Macau Peninsula. This study found that (1) Catering and shopping facilities present a dual agglomeration mechanism of “tourism-driven + commercial core”, with Avenida de Almeida Ribeiro as the main axis and radiating to the Ruins of St. Paul’s and Praça de Ponte e Horta, respectively. Historical blocks and tourist hotspots clearly guide the spatial center of gravity. (2) Residential and life service facilities are highly coupled, reflecting the spatial logic of “work-residence integration-service coordination”. The distribution of life service facilities basically overlaps with the high-density residential area, forming an obvious “living circle + community unit” structure with clear spatial boundaries. (3) Commercial and transportation facilities form a “functional axis belt” organizational structure along the main road, with the Rua das Lorchas—Rua do Almirante Sérgio axis as the skeleton, constructing a “functional transmission chain”. (4) The spatial system of the Macau Peninsula pier district has transformed from a single center to a multi-node, network-linked structure. Its internal spatial differentiation is not only constrained by traditional land use functions but is also driven by complex factors such as tourism economy, residential migration, historical protection, and infrastructure accessibility. (5) Through the analysis of space syntax, it is found that the core integration of the Macau Peninsula pier district is concentrated near Pier 16 and the northern area. The two main roads have good accessibility for motor vehicle travel, and the northern area of the Macau Peninsula pier district has good accessibility for long and short-distance walking. Full article

The 11 September 2001 World Trade Center (WTC) rescue and recovery workers (RRWs) included first responders (FDNY and NYPD), volunteers, and other workers. Volunteers were often more vulnerable than first responders to adverse health outcomes resulting from the exposure. It is not yet known whether there are differences in WTC Health Program (WTCHP) utilization by worker type. This is a cross-sectional study of 20,012 WTCHP-eligible RRWs to examine whether worker type was associated with WTCHP utilization based on self-reported data from four WTC Health Registry follow-up surveys (2006–2021), using multivariable log-binomial regression adjusted for sociodemographic factors and comorbidities. We also examined factors associated with WTCHP utilization by worker type. Overall, 9584 RRWs (47.9%) reported receiving WTCHP services, but only 22.5% of volunteers reported WTCHP utilization. After adjustment, first responders and other workers were, respectively, 2.73 (95% CI = 2.56, 2.92) and 1.69 (95% CI = 1.58, 1.80) times more likely to utilize WTCHP service than volunteers. Sociodemographic factors and comorbidities were consistently associated with WTCHP utilization across worker types, except for race/ethnicity. Among those eligible, the volunteer group reported the lowest utilization of WTCHP among worker types, suggesting that WTC volunteers should be a priority group for outreach regarding access and utilization of WTCHP. Full article

The global corn trade is an important pillar of the agricultural economy, but its supply chain is vulnerable to geopolitical conflicts, climate change, and market volatility. As one of the major importers of corn in the world, China has long relied on the United States and Ukraine, and the risk of import concentration is prominent. The complicated international situation intensifies the external uncertainty of China’s import supply chain. This study utilized bilateral trade data from 2010 to 2023 and employed advanced methodologies including complex network modeling, network index quantification, and simulation analysis to assess the impacts of external risks from major trading partners on China’s corn import system and trace risk transmission pathways. The research objectives focused on examining the structural evolution of China’s corn import trade network over the past decade, evaluating its resilience against external shocks, and identifying the critical roles played by key node countries in risk propagation mechanisms. The results showed that the resilience of China’s corn import trade network had been enhanced in recent years and that the complementarity of planting cycles in the Northern and Southern Hemispheres and the adjustment of trade structure caused by the Russia–Ukraine conflict had improved its risk resistance. The United States, France, Romania, and Turkey were key intermediate nodes in risk transmission due to their geographical advantages and trade hub statuses. The risk transmission path presented regional heterogeneity. China should strengthen trade with countries in the Southern Hemisphere and built a more stable import system by taking advantage of complementary resource endowments and growth periods. Bilateral agreements with transit countries could ensure security of supply. Reserve centers and modern logistics infrastructure should be built in key areas. In addition, platforms such as the Regional Comprehensive Economic Partnership could promote harmonized standards and digital support for corn trade, and regional financial instruments and supply chain optimization could have balanced risks. Full article

This paper addresses the critical challenge of optimal allocation and scheduling of Internet of Things (IoT) tracking devices for patient monitoring in healthcare facilities, where limited resources must be efficiently distributed to minimize cost and maximize care quality. We formulate this healthcare management problem as a specialized variant of the Resource-Constrained Scheduling Problem that incorporates patient-specific factors such as duration of stay and priority. After establishing the computational complexity of the problem, we propose a Non-dominated Sorting Genetic Algorithm II (NSGA-II) to address the complex problem of balancing multiple objectives: cost minimization and quality of care maximization. Our approach offers a set of optimal trade-offs, enabling informed decision-making to select the best final solution. Computational experiments conducted on both simulated scenarios and real-world healthcare facility datasets demonstrate that our approach outperforms existing methods, achieving between 1.2 and 3.0 times more solutions than the state of the art. Moreover, in comparison to manual scheduling by medical center managers, our method achieves cost savings of up to 12% (with an average of 6.3%) and quality improvements of up to 20% (with an average of 10%) across the tested experiments. The proposed method scales effectively to realistic healthcare settings with varying numbers of patients and tracking devices, maintaining solution quality while keeping computational time within practical limits for daily operational use. Our findings contribute to both healthcare operations research and clinical practice by providing an efficient methodology for optimizing the use of limited monitoring resources while prioritizing patient safety. Full article

Citrus commercial species are the most important fruit crops in the world; however, their cultivation is seriously threatened by the fast dispersion of emerging diseases, including Huanglongbing (HLB) citrus greening. HLB disease is vectored by psyllid vectors and associated with phloem-limited α-proteobacteria belonging to the Candidatus Liberibacter genus. Climatic change and trade globalization have led to the rapid spread of HLB from its origin center in Southeast Asia, causing a great economic impact in the main production areas, including East Asia (China), the Mediterranean basin, North America (the United States), and Latin America (Brazil and Mexico). Despite important advances to understand the HLB epidemiology, Candidatus Liberibacter genetics, psyllid vector control, the molecular citrus–Candidatus Liberibacter interaction, and the development of integral disease management strategies, the study areas have been mostly restricted to high-tech-producing countries. Thus, in this review, we provide an overview of the epidemiology, distribution, genetic diversity, management aspects, and omics analysis of HLB in Latin America, where this information to date is limited. Full article

Background: Sex work is one of the oldest trades in the world. It is the practice of sexual activity in exchange for money or material goods. It has traditionally been carried out by women, although in recent years, there has been an increase in the number of male sex workers. Sex workers have been recognized as a population at high risk of contracting and transmitting sexually transmitted infections and human immunodeficiency virus and have had limited access to multiple services, including health care. Objective: The aim was to identify the epidemiological profile and risk factors of a population of sex workers in the province of Granada, southern Spain. Methods: An analytical cross-sectional study was conducted by reviewing the medical records at a specific sexually transmitted infection center in Spain. Results: A total of 157 sex workers’ records were analyzed, most of whom were women, who mainly identified as heterosexual, with a mean age of 28.52 years and a high percentage of foreign nationality. Some sexual behaviors associated with the acquisition of sexually transmitted infections were also analyzed. Conclusions: The results revealed a deficient coverage of the health needs of a highly vulnerable and stigmatized social group, highlighting the need for interventions to prevent risky habits, as well as to promote behaviors aimed at achieving better sexual health. Full article

Soybeans play a crucial role in global food security and international agricultural trade. As the world’s largest consumer and trader of soybeans, China faces significant external dependence on supply and concentration of import sources. In light of increasing uncertainties in the international political and economic landscape, risks within China’s soybean supply chain have become increasingly prominent, highlighting the need to explore the global soybean supply potential and optimize import strategies. In response to national food security strategic requirements and anticipated changes in global production capacity, the Global Agro-Ecological Zone (GAEZ) model and the multi-objective nonlinear programming model are used in this paper to estimate the potential of soybean yield increase globally. From dual perspectives of risk minimization and cost minimization, diversified soybean import schemes for China are proposed across three scopes: neighboring countries, countries along the Belt and Road Initiative (BRI), and globally. The results indicate that in the long term, the center of gravity for global soybean production capacity remains in the Americas; these areas, along with Europe and Africa, are key regions for China to expand its soybean import sources in the future. If all countries’ soybean production potentials are fully explored, China can achieve sufficient soybean supply by relying on neighboring countries as well as those countries participating in the BRI. Specifically, it is estimated that during the 2020s (2011–2040), the potential soybean production in the United States, Brazil, and Argentina could reach more than 290 million tons, 140 million tons, and 130 million tons, respectively, under scenarios of both yield increase and cultivated land expansion. Neighboring countries such as India and Russia also show significant potential, with India’s production potentially increasing by 42.8 million tons and Russia’s by 10.4 million tons. The results suggest that China can achieve a more balanced and secure import strategy by leveraging the production capabilities of countries in the Americas, Europe, and Africa while also fostering closer economic and agricultural cooperation with neighboring nations and BRI countries. Based on these findings, policy recommendations aimed at stabilizing and ensuring China’s soybean supply are discussed. Full article