Search Results (398)

European pig farmers are challenged by increasingly stringent EU regulations to protect the environment from pollution, to meet animal welfare standards and to make pig farming more sustainable. Economic sustainability is defined as the ability to achieve higher profits by respecting social and natural resources. This study is focused on the analysis of the economic resilience of intensive and extensive farming systems, based on data collected from 56 farms located in Denmark, Poland, Italy and Spain. Productive and economic performances of these farms are analyzed, and economic resilience is assessed through a survey including a selection of indicators, belonging to different themes: [i] resilience of resources, [ii] entrepreneurship, [iii] propensity to extensification. The qualitative data from the questionnaire allow for an exploration of how production systems relate to the three dimensions of resilience. Different levels of resilience were found and discussed for intensive and extensive farms. The findings suggest that intensive farms benefit from high standards and greater bargaining power within the supply chain. Extensive systems can achieve profitability through value-added strategies and generally display good resilience. Policies that support investment and risk reduction are essential for enhancing farm resilience and robustness, while strengthening farmer networks can improve adaptability. Full article

Social media platforms like Instagram significantly shape destination images and influence tourist behavior. Understanding how different cities are represented and perceived on these platforms is crucial for effective tourism marketing. This study provides a comparative analysis of Instagram content and engagement patterns in Seoul and Tokyo, two major Asian metropolises, to derive actionable marketing insights. We collected and analyzed 59,944 public Instagram posts geotagged or location-tagged within Seoul (n = 29,985) and Tokyo (n = 29,959). We employed a mixed-methods approach involving content categorization using a fine-tuned convolutional neural network (CNN) model, engagement metric analysis (likes, comments), Valence Aware Dictionary and sEntiment Reasoner (VADER) sentiment analysis and thematic classification of comments, geospatial analysis (Kernel Density Estimation [KDE], Moran’s I), and predictive modeling (Gradient Boosting with SHapley Additive exPlanations [SHAP] value analysis). A validation analysis using balanced samples (n = 2000 each) was conducted to address Tokyo’s lower geotagged data proportion. While both cities showed ‘Person’ as the dominant content category, notable differences emerged. Tokyo exhibited higher like-based engagement across categories, particularly for ‘Animal’ and ‘Food’ content, while Seoul generated slightly more comments, often expressing stronger sentiment. Qualitative comment analysis revealed Seoul comments focused more on emotional reactions, whereas Tokyo comments were often shorter, appreciative remarks. Geospatial analysis identified distinct hotspots. The validation analysis confirmed these spatial patterns despite Tokyo’s data limitations. Predictive modeling highlighted hashtag counts as the key engagement driver in Seoul and the presence of people in Tokyo. Seoul and Tokyo project distinct visual narratives and elicit different engagement patterns on Instagram. These findings offer practical implications for destination marketers, suggesting tailored content strategies and location-based campaigns targeting identified hotspots and specific content themes. This study underscores the value of integrating quantitative and qualitative analyses of social media data for nuanced destination marketing insights. Full article

Despite extensive prevention strategies in Spain since the 1980s, gender-based violence, including among adolescents, remains prevalent, as observed in the Romance SUCC-ED Project (R&D&I Operating Programme ERDF Andalusia 2014–2020). This research study investigates the dimensions, meanings, relationships, and practices shaping the culture of romance in digital Andalusian adolescence (12–16 years) and its potential impact on school trajectories in Compulsory Secondary Education. Based on the premise that equality-focused relationship education is key to preventing gender violence, the study employs an ethnographic methodology with 12 Andalusian school case studies (4 out of them are located in rural areas) and 220 in-depth interviews (126 girls, 57.3%; 94 boys, 42.7%). This article aims to empirically explain gender violence in early adolescence by analysing the culture of romance as an explanatory factor. Findings reveal an interconnected model where dimensions (love, couple, sexuality, pornography, social networks, and cultural references), meanings (constructed by adolescents within each of them), relationships (partner), and practices (control and jealousy) reinforce romanticised femininity and dominant masculinity, thus explaining the high incidence of gender-based violence among students in the study. Full article

As the Internet of Things (IoT) expands, robust tools for assessing privacy risk are increasingly critical. This research introduces a quantitative framework for evaluating IoT privacy risks, centered on two algorithmically derived scores: the Personalized Privacy Assistant (PPA) score and the PrivacyCheck score, both developed by the Center for Identity at The University of Texas. We analyze the correlation between these scores across multiple types of sensitive data—including email, social security numbers, and location—to understand their effectiveness in detecting privacy vulnerabilities. Our approach leverages Bayesian networks with cycle decomposition to capture complex dependencies among risk factors and applies entropy-based metrics to quantify informational uncertainty in privacy assessments. Experimental results highlight the strengths and limitations of each tool and demonstrate the value of combining data-driven risk scoring, information-theoretic analysis, and network modeling for privacy evaluation in IoT environments. Full article

This paper presents an enhanced hub location model tailored to port–hinterland logistics planning, grounded in the Capacitated Multiple-Allocation Hub Location Problem (CMAHLP). The formulation incorporates nonlinear cost structures, hub-specific operating costs, adaptive capacity constraints, and a feasibility condition based on the Social Net Present Value (NPVsocial) to support the design of intermodal freight networks under asymmetric spatial and socio-environmental conditions. The empirical case focuses on Spain, leveraging its strategic position between Asia, North Africa, and Europe. The model includes four major ports—Barcelona, Valencia, Málaga, and Algeciras—as intermodal gateways connected to the 47 provinces of peninsular Spain through calibrated cost matrices based on real distances and mode-specific road and rail costs. A Genetic Algorithm is applied to evaluate 120 scenarios, varying the number of active hubs (4, 6, 8, 10, 12), transshipment discounts (α = 0.2 and 1.0), and internal parameters. The most efficient configuration involved 300 generations, 150 individuals, a crossover rate of 0.85, and a mutation rate of 0.40. The algorithm integrates guided mutation, elitist reinsertion, and local search on the top 15% of individuals. Results confirm the central role of Madrid, Valencia, and Barcelona, frequently accompanied by high-performance inland hubs such as Málaga, Córdoba, Jaén, Palencia, León, and Zaragoza. Cities with active ports such as Cartagena, Seville, and Alicante appear in several of the most efficient network configurations. Their recurring presence underscores the strategic role of inland hubs located near seaports in supporting logistical cohesion and operational resilience across the system. The COVID-19 crisis, the Suez Canal incident, and the persistent tensions in the Red Sea have made clear the fragility of traditional freight corridors linking Asia and Europe. These shocks have brought renewed strategic attention to southern Spain—particularly the Mediterranean and Andalusian axes—as viable alternatives that offer both geographic and intermodal advantages. In this evolving context, the contribution of southern hubs gains further support through strong system-wide performance indicators such as entropy, cluster diversity, and Pareto efficiency, which allow for the assessment of spatial balance, structural robustness, and optimal trade-offs in intermodal freight planning. Southern hubs, particularly in coordination with North African partners, are poised to gain prominence in an emerging Euro–Maghreb logistics interface that demands a territorial balance and resilient port–hinterland integration. Full article

Introduction: Based on data from the China Health and Retirement Longitudinal Study 2020 (CHARLS 2020), we analyzed the effects of physical activity (PA) on chronic diseases and depression symptoms in older adults in urban and rural areas and examined differences by residential location. Methods: A total of 5481 individuals aged 65 years and above were selected from the CHARLS 2020 dataset. Descriptive statistics, chi-square tests, two-way analysis of variance, and Pearson’s correlation analysis were used to examine the influence of different intensities of PA on chronic diseases and depression symptoms. According to PA recommendations, PA participants were individuals who engaged in PA two or more times per week, while non-participants engaged in PA fewer than two times per week. Results: Urban and rural older adults showed different patterns in PA participation and its health impacts. Urban residents were more likely to engage in high-intensity PA, which was related to lower prevalence of chronic diseases and fewer depressive symptoms; moderate-intensity PA was also effective in relieving depressive symptoms. In contrast, rural residents primarily participated in low-intensity PA, which had some effect in alleviating depression symptoms but limited impact on chronic diseases. Conclusions: Public health interventions should be tailored to regional differences. In rural areas, the promotion of appropriate PA programs is essential to improve overall health, while urban areas should emphasize mental health strategies, social engagement, and support network development. Full article

With the proliferation of mobile devices and wireless communications, Location-Based Social Networks (LBSNs) have seen tremendous growth. Location recommendation, as an important service in LBSNs, can provide users with locations of interest by analyzing their complex check-in information. Currently, most location recommendations use centralized learning strategies, which carry the risk of user privacy breaches. As an emerging learning strategy, federated learning is widely applied in the field of location recommendation to address privacy concerns. We propose a Federated Learning-Based Location Similarity Model for Location Privacy Preserving Recommendation (FedLSM-LPR) scheme. First, the location-based similarity model is used to capture the differences between locations and make location recommendations. Second, the penalty term is added to the loss function to constrain the distance between the local model parameters and the global model parameters. Finally, we use the REPAgg method, which is based on clustering for client selection, to perform global model aggregation to address data heterogeneity issues. Extensive experiments demonstrate that the proposed FedLSM-LPR scheme not only delivers superior performance but also effectively protects the privacy of users. Full article

Background: Location and distribution decisions are key to efficient logistics network design and are often addressed in an integrated manner as Location–Routing Problems (LRPs). Today, sustainability and resilience must be considered when designing competitive networks. This systematic review examines how and at what decision level both concerns are explored in LRPs, highlighting trends and future research challenges. Methods: A search was conducted in the Scopus database on 3 January 2024. Articles not written in English or lacking a sustainability or resilience focus were excluded. The 36 most-cited articles were selected and analyzed descriptively and theoretically, considering their approaches to sustainability and resilience, as well as the decision levels at which these approaches were considered. The studies were also analyzed based on model features and solving approaches. Results: Our findings indicated that social sustainability was the most neglected. The environmental pillar was often focused on minimizing atmospheric pollution from distribution. Regarding resilience, proactive and reactive strategies were employed to minimize disruption costs and risks and maximize network reliability. Conclusions: Research on sustainable and resilient LRPs is growing, but remains fragmented. Future studies should explore the integration of social impacts, uncertainty modeling, and real-world applications. Stronger alignment with decision maker needs and more holistic evaluation frameworks are essential to support resilient and sustainable network design. Full article

Point-of-interest (POI) recommendation is a crucial task in location-based social networks, especially for enhancing personalized travel experiences in smart tourism. Recently, large language models (LLMs) have demonstrated significant potential in this domain. Unlike classical deep learning-based methods, which focus on capturing various user preferences, LLM-based approaches can further analyze candidate POIs using common sense and provide corresponding reasons. However, existing methods often fail to fully capture user preferences due to limited contextual inputs and insufficient incorporation of cooperative signals. Additionally, most methods inadequately address target temporal information, which is essential for planning travel itineraries. To address these limitations, we propose PSLM4ST, a novel framework that enables synergistic interaction between LLMs and a lightweight temporal knowledge graph reasoning model. This plugin model enhances the input to LLMs by making adjustments and additions, guiding them to focus on reasoning processes related to fine-grained preferences and temporal information. Extensive experiments on three real-world datasets demonstrate the efficacy of PSLM4ST. Full article

Plant modeling uses mathematical and computational methods to simulate plant structures, physiological processes, and interactions with various environments. In precision agriculture, it enables the digital monitoring and prediction of crop growth, supporting better management and efficient resource use. Wheat, as a major global staple, is vital for food security. However, wheat stripe rust, a widespread and destructive disease, threatens yield stability. The paper proposes wheat cultivation suitability evaluation with stripe rust disease using an agriculture group consensus framework (WCSE-AGC) to tackle this issue. Assessing stripe rust severity in regions relies on wheat pathologists’ judgments based on multiple criteria, creating a multi-attribute, multi-decision-maker consensus problem. Limited regional coverage and inconsistent evaluations among wheat pathologists complicate consensus-reaching. To support wheat pathologist participation, this study employs artificial-intelligence-generated content (AIGC) techniques by using Claude 3.7 to simulate wheat pathologists’ scoring through role-playing and chain-of-thought prompting. WCSE-AGC comprises three main stages. First, a graph neural network (GNN) models trust propagation within wheat pathologists’ social networks, completing missing trust links and providing a solid foundation for weighting and clustering. This ensures reliable expert influence estimations. Second, integrating secretary bird optimization (SBO), K-means, and three-way clustering detects overlapping wheat pathologist subgroups, reducing opinion divergence and improving consensus inclusiveness and convergence. Third, a two-stage optimization balances group fairness and adjustment cost, enhancing consensus practicality and acceptance. The paper conducts experiments using publicly available real wheat stripe rust datasets from four different locations, Ethiopia, India, Turkey, and China, and validates the effectiveness and robustness of the framework through comparative and sensitivity analyses. Full article

Considering the rapid integration of digital services into daily life, it is crucial to analyze the impacts of the substitutability of physical services with digital alternatives. Limited studies have been conducted to investigate the relationship between service substitution and social networks and assess their impact on urban structure. Therefore, this study fills the gap by investigating how digital service substitution and social networks influence residential location choices and urban structure, aiming to support future sustainable urban modeling and planning tools. The study, through a comprehensive analysis incorporating cluster analysis, factor analysis, and binomial logistic regression on a web-based questionnaire survey (n = 6210), finds that socio-demographic factors significantly influence digital alternatives, and that digital service substitution and social networks impact sustainable urban structure. Younger individuals showed significantly higher adoption of digital alternatives, with age negatively associated with relocation likelihood. In urban areas, each additional year of age reduces the likelihood of relocation by approximately 4.4%, and individuals with high shopping substitution are 3.12 times more likely to consider relocation. These findings suggest that urban planners and policymakers to balancing physical and digital service provision to maintain a higher quality of life aligned with the SDGs and ensure sustainable urban development. Full article

Green infrastructure (GI) plays a critical role in addressing urban fragmentation and flood vulnerability, especially in rapidly expanding cities where its optimal placement is essential to maximize social, ecological, and economic benefits. This study presents a multiscale methodology integrating spatial configuration and hydrological modeling to guide GI implementation in Ciudad Juárez, Mexico. The approach applies space syntax theory, fuzzy logic, and geospatial analysis across three spatial levels. At the city scale, the method evaluates street network integration and service accessibility to identify urban centers with potential for regeneration through GI. At the local scale, a 214-hectare area is analyzed using fuzzy multi-criteria decision analysis and Multiscale Geographically Weighted Regression (MGWR) to select the optimal locations for different nature-based solutions. At the microscale, spatiotemporal hydrological simulations of a 25-year return period rainfall event quantify the runoff and infiltration dynamics under different GI configurations, achieving infrastructure layouts that infiltrated over 1000 m³ of stormwater. This framework addresses the research gap on how connectivity and morphology can be combined to prioritize interventions based on flood risk data. The results offer a transferable strategy for integrating Sustainable Urban Drainage Systems (SUDSs) into complex data-scarce urban environments, supporting long-term urban resilience and multifunctional land-use planning. Full article

Extensive scientific evidence underscores the importance of identifying spatiotemporal patterns for investigating urban dynamics. The recent proliferation of location-based social networks (LBSNs) facilitates the measurement of urban rhythms through geotemporal information, providing deeper insights into the underlying causes of urban vibrancy. This study presents a methodology for analyzing the spatiotemporal use of cities and identifying occupancy patterns taking into consideration urban form and function. The analysis relies on data obtained from Google Popular Times (GPT), transforming the relative occupancy of a large number of points of interest (POI) classified into five categories, for estimating the number of people aggregated within urban nodes during a typical day. As a result, this research assesses the utility of this data source for evaluating the changing dynamics of a city across both space and time. The methodology employs geographic information system (GIS) tools and artificial intelligence techniques. The results demonstrate that by analyzing geotemporal data, we can classify urban nodes according to their hourly activity patterns. These patterns, in turn, relate to city form and urban activities, showing a certain spatial concentration. This research contributes to the growing body of knowledge on machine learning (ML) methods for spatiotemporal modeling, laying the groundwork for future studies that can further explore the complexity of urban phenomena. Full article

The rapid pace of urbanization is contributing to ecological degradation and poses a threat to regional ecological security. Addressing these issues requires effective strategies to mitigate existing environmental challenges. Ecological networks, as the spatial foundation for ecosystem services, play a critical role in reducing environmental degradation. By reconfiguring the spatial relationship between human activities and natural ecosystems, anthropogenic pressures on land can be alleviated. However, most current research focuses on administrative boundaries, which limits spatial continuity and regional coordination. Therefore, constructing ecological networks from a cross-regional perspective is essential for integrated ecological management. This study uses the Yangtze River Delta Ecological Green Integration Demonstration Area as a case study. We construct a blue–green ecological network by applying ecological footprint analysis, Morphological Spatial Pattern Analysis (MSPA), landscape connectivity assessments, the Minimum Cumulative Resistance (MCR) model, and gravity modeling. Practical strategies for integrating the ecological network into territorial spatial planning are also explored. The key findings are as follows: (1) The demonstration area contains 33 ecological source areas, including 20 primary sources located near administrative boundaries and central lakeshore wetlands. A total of 333 ecological corridors were identified. First-grade corridors are primarily located in rural areas, traversing agricultural land and water bodies. (2) We recommend corridor widths of 200 m for first-grade corridors, 60 m for second-grade corridors, and 30 m for third-grade corridors. These widths are based on species characteristics and land use types, and are found to be conducive to species migration and habitat connectivity. (3) We propose the development of tourism landscape zones from a cross-regional perspective, leveraging existing ecological and cultural resources. The multifunctionality of corridors is redefined through the integration of ecological and social values, enhancing their spatial implementation. This framework provides a practical reference for constructing cross-regional blue–green ecological networks and informs spatial planning efforts in other multi-jurisdictional areas. Full article

With the vast quantity of GPS data that have been collected from location-based social networks, Point-of-Interest (POI) recommendation aims to predict users’ next locations by learning from their historical check-in trajectories. While Graph Neural Network (GNN)-based models have shown promising results in this field, they typically construct single-layer graphs that fail to capture the hierarchical nature of human mobility patterns. To address this limitation, we propose a novel Hierarchical Graph Learning (HGL) framework that models POI relationships at multiple scales. Specifically, we construct a three-level graph structure: a base-level graph capturing direct POI transitions, a region-level graph modeling area-based interactions through spatio-temporal clustering, and a global-level graph representing category-based patterns. To effectively utilize this hierarchical structure, we design a cross-layer information propagation mechanism that enables bidirectional message passing between different levels, allowing the model to capture both fine-grained POI interactions and coarse-grained mobility patterns. Compared to traditional models, our hierarchical structure improves cold-start robustness and achieves superior performance on real-world datasets. While the incorporation of multi-layer attention and clustering introduces moderate computational overhead, the cost remains acceptable for offline recommendation contexts. Full article