Search Results (4,921)

In recent years, social media-related sentiment classification has been researched extensively and is applied in various fields such as opinion mining, commodity feedback, and market analysis. Therefore, it is important to understand and analyse the opinions of the public, their feedback, and data related to social media. Consumers continue to face challenges in accessing review-based sentiment classification expressed by their peers, and the existing method does not provide satisfactory results. Hence, an innovative sentiment classification method, the Convoluted Graph Pyramid Attention (CGPA) model, combined with the Updated Greater Cane Rat Algorithm (UGCRA), is proposed. This method improves sentiment classification by optimizing accuracy and efficiency while addressing inherent uncertainties, allowing for precise sentiment intensity evaluation across multiple dimensions. Explainable Artificial Intelligence (XAI) techniques, particularly SHapley Additive exPlanations (SHAPs), enhance the model’s transparency and interpretability. This approach enables the final ranking of classified reviews, predicts ratings on a scale of one to five stars, and generates a recommendation list based on the predicted user ratings. Comparison between other traditional existing methods and the result indicates that the proposed method achieves superior performance. From the experimental results, the proposed approach achieves an accuracy of 99.5% in the Restaurant Review dataset, 99.8% in the Edmund Consumer Car Ratings Reviews dataset, 99.9% in the Flipkart Cell Phone Reviews dataset, and 99.7% in the IMDB Movie database, showing its effectiveness in analysing sentiments with an increase in performance. Full article

Perceived value is a key factor shaping consumer purchase decisions. In the field of functional snack consumption, generational differences in value perception dimensions significantly influence decision-making processes, creating both challenges and opportunities for targeted marketing. Drawing on perceived value theory, this study develops a model examining the impact of perceived value on consumption intention for functional snacks. A questionnaire survey was conducted among Chinese Generation Y and Generation Z consumers, and the data were analyzed using PLS-SEM and fuzzy-set Qualitative Comparative Analysis (fsQCA). The results indicate that self-oriented values (functional and hedonic) exert a significant positive effect on other-oriented values (symbolic and social), and both categories of values positively affect consumption intention. Regarding generational moderation, the effects of functional and hedonic values on purchase intention do not differ significantly across generations; however, symbolic value has a stronger influence on Generation Z, while social value plays a more prominent role for Generation Y. Importance–Performance Map Analysis (IPMA) results further reveal differences in the relative importance and performance of value perception between the two groups. Configuration analysis shows that compared with Generation Y, Generation Z exhibits a higher threshold for strong consumption intention, a lower threshold for weak consumption intention, and greater sensitivity to value deficiency. These findings provide practical insights for functional snack companies to address generational differences and optimize marketing strategies. Full article

Compared with pure pumped storage, hybrid pumped storage plants (HPSPs) face more complex challenges in electricity markets, such as multi-time-scale decision-making and coupled market mechanisms. Existing mid- to long-term curve decomposition strategies often lead to deviations from actual spot prices and compressed bidding space, limiting profitability and sustainable development. To address this, this study introduces Contracts for Difference (CfDs) to enhance revenue and operational flexibility. A bi-level optimization model is developed for joint participation in spot and frequency regulation markets under CfDs: the upper level maximizes HPSP revenue through capacity allocation and bidding, while the lower level maximizes social welfare via joint energy and ancillary service market clearing. The model is solved using a commercial solver and NSGA-II. Simulations show that CfDs increase spot market revenue by 33.2% and improve bidding alignment with price fluctuations, demonstrating strong market adaptability. Full article

A core challenge in urban green space (UGS) management lies in precisely identifying public demand heterogeneity toward landscape elements. Grounded in Expectation Confirmation Theory (ECT), this study aims to systematically identify the key landscape elements shaping public satisfaction and elucidate their driving mechanisms to inform UGS planning. Using 107 UGS in central Beijing as case studies, this study first retrieved 712,969 social media data (SMD) from multiple online platforms. A landscape element lexicon derived from these data was then integrated with the Bidirectional Encoder Representations from Transformers (BERT) model to assess public attention and satisfaction toward the natural, cultural, and artificial attributes of UGS, achieving an accuracy of 84.4%. Finally, spatial variations and the effects of different landscape elements on public satisfaction were analyzed using GIS-based visualization, K-means clustering, and multiple linear regression. Key findings reveal the following: (1) satisfaction follows a “core-periphery” gradient, peaking in heritage-rich City Wall Parks (>0.63) and plunging in green belts due to imbalanced element configurations (~0.04); (2) naturally dominant green spaces contribute most to satisfaction, while a nonlinear relationship exists between element dominance and satisfaction: strong features enhance perception, balanced patterns mask issues; (3) regression analysis confirms natural elements (vegetation β = 0.280, water β = 0.173) as core satisfaction drivers, whereas artificial facilities (e.g., service infrastructure β = 0.112, p > 0.05) exhibit a high frequency but low satisfaction paradox. These insights culminate in a practical implementation framework for policymakers: first, establish a data-driven monitoring system to flag high-frequency, low-satisfaction facilities; second, prioritize budgeting for enhancing natural elements and contextualizing cultural elements; and finally, implement site-specific optimization based on primary UGS functions to counteract green space homogenization in high-density cities. Full article

Background: Celiac disease (CeD) is a chronic, immune-mediated enteropathy triggered by gluten ingestion in genetically predisposed individuals. While a lifelong gluten-free diet (GFD) remains the cornerstone of treatment, inadequate follow-up can lead to persistent symptoms, nutritional deficiencies, and long-term complications. Aim: This narrative review summarizes best practices in celiac disease follow-up, with emphasis on multidisciplinary, nutritional, clinical, and preventive care strategies to optimize long-term outcomes. Main Findings: High-quality follow-up requires coordinated care involving gastroenterologists, dietitians, primary care providers, and other specialists. Nutritional challenges of the GFD include high cost, limited fortification, suboptimal nutrient content, and increased risk of obesity and metabolic dysfunction. Patients also face psychosocial and behavioral burdens such as anxiety, social isolation, and disordered eating. Evidence-based strategies for follow-up include structured clinical and serologic monitoring, laboratory assessments, bone health evaluation, cancer risk reduction, and preventive care. Novel tools such as gluten immunogenic peptide testing, digital health platforms, and artificial intelligence are emerging as adjuncts to clinical management. Implications: Structured, patient-centered follow-up that integrates medical, nutritional, and psychosocial dimensions is essential to achieving mucosal healing, maintaining long-term health, and improving quality of life in individuals with CeD. Full article

To address real-world engineering design optimization problems, this study proposes a reinforcement learning enhanced multi-objective social network search algorithm (QMOSNS), which represents a novel approach for solving multi-objective optimization problems. QMOSNS utilizes Halton sequences for population initialization to enhance the diversity of the initial population. A multi-objective archive mechanism is implemented to store Pareto-optimal solutions and select parental individuals through a reassigned fitness evaluation strategy. Furthermore, Q-learning is incorporated to adaptively select mutation operators, thereby dynamically balancing the algorithm’s exploration and exploitation capabilities. QMOSNS was rigorously evaluated through 50 prominent case studies, including 22 unconstrained multi-objective benchmark problems, 18 constrained multi-objective benchmark problems, and 10 multi-objective engineering design problems, to comprehensively validate its computational capabilities and effectiveness. Moreover, statistical results obtained using consistent performance metrics were compared with those of other highly regarded algorithms to ensure a fair and objective performance assessment. The comparative results show that QMOSNS is robust and superior in handling a wide variety of multi-objective problems. This study underscores the efficacy of integrating reinforcement learning with social intelligence for tackling complex multi-objective optimization in engineering and computational domains. Full article

Vocational training can lead to higher employment rates and improved incomes, particularly for young women in low-resource settings like Kampala’s slums. Despite these benefits, further research is needed to understand the full impact and mechanisms of vocational training on youth in low-resource environments. In 2022, a focus group project, part of a larger study, involved 60 women aged 18 to 24, recruited from three Youth Support Centers operated by the Uganda Youth Development Link (UYDEL) in Kampala. Six focus groups (about 10 women in each group) were held to explore urban stress and how vocational training might mitigate social and environmental stressors and improve mental health. Data analysis conducted using NVivo software identified five key themes: economic benefits, skill development, building confidence and self-esteem, improved social and behavioral well-being, and enhanced lifestyle and quality of life. This formative research underscores that vocational training benefits young women, highlighting outcomes such as job acquisition, financial empowerment, and skill development. Additionally, self-esteem and confidence development emphasize the training’s role in fostering mental health and agency and addressing gender inequality. These findings underscore the value of vocational training in enhancing the mental health and overall well-being of young women and suggest areas for future research for how to best optimize and scale these programs in low-resource settings. Full article

Trophallaxis, a fundamental social behavior observed among honeybees, involves the redistribution of food and chemical signals. The automation of its detection under field-realistic conditions poses a significant challenge due to the presence of crowding, occlusions, and brief, fine-scale motions. In this study, we propose a markerless, deep learning-based approach that injects short- and mid-range temporal features into single-frame You Only Look Once (YOLO) detectors via temporal-to-RGB encodings. A new dataset for trophallaxis detection, captured under diverse illumination and density conditions, has been released. On an NVIDIA RTX 4080 graphics processing unit (GPU), temporal-to-RGB inputs consistently outperformed RGB-only baselines across YOLO families. The YOLOv8m model improved from 84.7% mean average precision (mAP50) with RGB inputs to 91.9% with stacked-grayscale encoding and to 95.5% with temporally encoded motion and averaging over a 1 s window (TEMA-1s). Similar improvements were observed for larger models, with best mAP50 values approaching 94–95%. On an NVIDIA Jetson AGX Orin embedded platform, TensorRT-optimized YOLO models sustained real-time throughput, reaching 30 frames per second (fps) for small and 23–25 fps for medium models with temporal-to-RGB inputs. The results showed that the TEMA-1s encoded YOLOv8m model has achieved the highest mAP50 of 95.5% with real-time inference on both workstation and edge hardware. These findings indicate that temporal-to-RGB encodings provide an accurate and computationally efficient solution for markerless trophallaxis detection in field-realistic conditions. This approach can be further extended to multi-behavior recognition or integration of additional sensing modalities in precision beekeeping. Full article

While coastal zones support economic and social development, they also face prominent contradictions between shoreline utilization and ecological protection. This study proposed an innovative conflict-coordination framework for constructing living shorelines, aiming to identify and mitigate multi-dimensional conflicts in coastal engineering. The framework introduced a four-dimensional conflict analysis structure encompassing policy, social environment, ecological environment, and technical capacity, thereby extending beyond traditional single-dimensional or ecological-only assessments. Furthermore, it integrated the Comprehensive Conflict Index (CCI) with a multi-objective coordination model that couples three core indicators (e.g., whole-life-cycle carbon emissions, comprehensive impact intensity, and the living shoreline index) to achieve synergistic optimization among lower carbon emission, less human intervention, and higher ecological function objectives. Applied to an ecological restoration and seawall ecologization project in Zhenhai District, Ningbo, the results demonstrated that the framework helped constructing living shorelines by effectively reducing comprehensive conflict intensity with 21.2%, decreasing total carbon emissions with 60.2%, and significantly improving both the living shoreline index and multi-objective coordination level. Compared to traditional coastal zone assessment methods, these findings highlighted the differentiated advantages of the proposed framework in quantifying conflict sources, enhancing coordination among multi-objectives, and providing scientific support for living shoreline construction and sustainable coastal management. Full article

While the inclusiveness of digital finance is widely acknowledged, existing research predominantly focuses on its developmental level, with limited attention to its spatial correlation network and structural characteristics. A city’s centrality within this network governs the flow and allocation of digital financial resources, thereby influencing interregional and urban-rural efficiency in resource allocation and income distribution, which ultimately shapes the trajectory of common prosperity. Based on panel data from 280 Chinese cities (2011–2021), this study employs social network analysis to measure urban centrality in the digital financial spatial correlation network and empirically investigates its impact and mechanisms on common prosperity. The main findings are as follows: (1) Benchmark regressions confirm that overall network centrality and its three dimensions—degree, betweenness, and closeness centrality—significantly promote common prosperity, specifically by enhancing the “wealth” dimension and reducing regional development disparities, with the growth effect currently surpassing the inclusion effect. (2) Robustness checks, including instrumental variable approaches addressing endogeneity, affirm the reliability of the core findings. (3) Heterogeneity analysis reveals that the positive effect is more pronounced in cities that are less developed or have weaker financial foundations, such as those in Western China, non-financial centers, cities with no presence of formal financial institutions in antiquity, fifth-tier cities, and small and medium-sized cities, suggesting that network centrality serves as a catalytic tool for urban catch-up strategies. (4) Mechanism analysis identifies that fostering entrepreneurship, particularly among self-employed individuals and wholesale/retail enterprises characterized by decentralized operations and abundant transaction data, is the primary channel through which centrality advances common prosperity. This study provides insights into promoting balanced regional development and common prosperity by optimizing the spatial structure of digital finance. Full article

This study addresses the operational and environmental pressures of last-mile delivery in dense cities under limited urban logistics hubs. We propose a resource-efficient framework that repurposes existing convenience stores as robotic delivery hubs and formalize its operation via a two-stage optimization coupling truck and robot routing. In controlled simulations, and in a Seoul street network scenario, the approach reduces total completion time relative to a truck-only benchmark and lowers truck activity (truck-kilometers and curb idling), leading to lower estimated CO₂e under standard emission factors. We also observe a nonlinear relationship between the number of hubs and efficiency, suggesting a coverage “sweet spot”. These results indicate that with minimal new infrastructure, reusing commercial assets can improve operational performance and environmental proxies; social and labor outcomes are not measured here and are left for future field evaluation. Full article

As urbanization advances toward refined territorial spatial governance, integrating comprehensive transportation and spatial vitality has become essential for sustainable urban development. Transit-oriented development (TOD) plays a key role in linking transportation infrastructure with the coordinated evolution of territorial space. However, the interaction mechanisms between the built environment and metro station area vitality in cold-region cities remain underexplored, particularly in relation to seasonal differentiation. Taking Harbin as a representative cold-region metropolis, this study investigates how built environment factors shape metro station area vitality across seasons and how their spatial mechanisms differ between winter and summer. An indicator system based on the “5D” framework was established, and K-means clustering was applied to classify stations into four coordinated spatial types. A composite vitality index integrating transportation, social, and economic dimensions was constructed to assess seasonal variations. Spearman correlation and XGBoost models identified dominant drivers at the global level, while the MGWR model revealed spatial heterogeneity. The results indicate that POI density exerts the strongest influence on metro station area vitality, contributing 47.95% in winter and 47.27% in summer. Residential density plays a more decisive role during summer, accounting for 18.90%. In contrast, winter vitality depends more on transportation accessibility, with the distance to parking facilities contributing 11.59%. Core urban stations consistently maintain high vitality, while peripheral areas have weaker performances, especially during winter. These findings clarify seasonally adaptive mechanisms linking the built environment and spatial vitality, providing evidence for coordinated optimization of metro systems and land-use planning in cold-region cities. Full article

Natural disasters have various adverse effects on human lives, making it challenging for authorities to manage post-disaster situations with limited resources. Due to the extreme extent of the damage, the huge amount of resources needed to restore life to normality makes such a situation challenging. For this purpose, different methodologies have been proposed to effectively handle these types of situations. All these methodologies consider different aspects of the post-earthquake context, taking into account core parameters such as the time and cost required for reconstruction, as well as the people directly affected by the earthquake. In this paper, we conduct a Systematic Literature Review (SLR) of various state-of-the-art techniques proposed for different phases of post-earthquake situations, specifically for reconstruction planning with sustainability considerations. All these proposed solutions are differentiated on the basis of input data, parameters, and type of solutions (data sciences, civil engineering, socio-economics, and modelling). The time range chosen to filter out relevant studies is between 2000 and 2025. Eventually, we reviewed 55 related articles out of 47,539 analysed from seven different digital libraries. The findings of this SLR reveal that optimization and simulation-based approaches dominate the current research landscape, with a growing trend toward data-driven and AI-assisted reconstruction planning. However, only a few studies focus on integrating socio-economic, environmental, and physical infrastructure aspects, which represents a major research gap. These findings provide insights that can guide future researchers in designing more comprehensive frameworks to improve post-earthquake reconstruction in a sustainable manner by prioritising economic, social, and environmental infrastructures, as well as facilities for affected individuals, thereby utilising available resources more effectively. Full article

The fragile ecosystem of desert and desertification-prone regions (D & DPRs) in China is highly sensitive to climate change, landuse intensification, and human interventions such as deforestation and overgrazing. In response, large-scale ecological restoration programs have been implemented over the past decades, yet their effect and cost-effectiveness remain insufficiently understood. Here, by applying multi-source remote sensing data, employing the Geodetector model, and developing a Return on Investment (RI) index, we established a framework to quantify the ecological restoration effect and assess the cost-effectiveness of the ecological restoration programs launched in China’s D & DPRs. The results indicated that a marked shift in ecosystem dynamics occurred in 1999. A comparison of the pre-restoration (1982–1998) and post-restoration (1999–2020) periods revealed that the restoration and degradation occur simultaneously, with the proportions increasing by 15.5% and 21%, respectively. Spatially, the identified ecological restoration effect was concentrated in the northern Loess Plateau, the northeastern Inner Mongolia Plateau, and the Hexi Corridor, which were strongly linked to population, land management strategies and infrastructural accessibility. However, the cost-effectiveness analysis revealed that higher levels of ecological investment did not necessarily lead to greater ecological restoration effect. Instead, restoration efficiency varied substantially across different ecological and socio-economic contexts. These findings suggest that ecological restoration in China’s D & DPRs is not a uniform process but is contingent on social-ecological characteristics and investment strategies. Our results emphasize the need for adaptive, region-specific approaches to optimize restoration efforts and ensure the sustainable management of China’s D & DPRs. Full article

The increasing demand for decarbonized urban environments has intensified interest in integrating renewable energy systems within cities. This review investigates the potential of urban wind energy as a promising technology in the development of Positive Energy Districts, supporting the transition toward climate-neutral urban areas. A systematic analysis of recent literature is presented, covering methodologies for urban wind resource assessment, including Geographic Information Systems (GIS)-based mapping, wind tunnel experiments, and Computational Fluid Dynamics simulations. The study also reviews available small-scale wind technologies, with emphasis on building-integrated wind turbines, and evaluates their contribution to local energy self-sufficiency. The integration of urban wind systems with energy storage, Power-to-Heat solutions, and smart district networks is discussed within the PED framework. Despite technical, economic, and social challenges, such as low wind speeds, turbulence, and public acceptance, urban wind energy offers temporal complementarity to solar power and can enhance district-level energy resilience. The review identifies key technological and methodological gaps and proposes strategic directions for optimizing urban wind deployment in future sustainable city planning. Full article