Search Results (3,195)

Artificial intelligence (AI) is increasingly used in marketing to deliver personalized messages and services. Although such tools create new opportunities, their acceptance by consumers depends on several factors that go beyond technology itself. This study examines how trust and ethical perceptions, familiarity and exposure to AI, digital consumer behavior, and identity concerns shape acceptance of AI-based personalized advertising. The analysis draws on data from 650 Greek consumers, collected through a mixed-mode survey (online and paper), and tested using logistic regression models with demographic characteristics included as controls. The results show trust and ethical perceptions of acceptance as factors, while familiarity with AI tools also supports positive attitudes once trust is established. In contrast, digital consumer behavior played a smaller role, and identity-related consumption was negatively associated with acceptance, reflecting concerns about autonomy and self-expression. Demographic factors, such as age and income, also influenced responses. Overall, the findings suggest that acceptance of AI in marketing is not only a technical matter but also a psychological and social process. This study highlights the importance for firms to build trust, act responsibly, and design personalization strategies that respect consumer identity and ethical expectations. 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

This paper reviews social science studies of emergency evacuations to point to the difficulties in associating them with panic formulations stressing irrationality and to show how the misunderstandings that how the conceptualization of one of these approaches on panic flight, which assumes the prevalence of nonsocial and self-centered behaviors and movements, has been transformed by recent studies of emergency evacuations from buildings, which show that the evacuation is best understood as social behavior in which people exhibit means-end rationality and social solidarity and act as socialized individuals moving towards sources of actual or perceived safety. The conclusion suggests first that the continued usage of the irrationality formulation by a minority of engineers and computer scientists writing on the topic of emergency evacuation and their use of “herding,” or the notion that during dangerous conditions, people follow the actions of others, leading to conformity, is not supported by a majority of findings in the social sciences, and second, that a likely solution to the disconnect between the two science communities is the adoption of transdisciplinary collaborative efforts. Full article

Renewable energy sources provide environmental and economic benefits by replacing conventional energy sources. In Korea, photovoltaic (PV) systems are increasingly deployed in apartment complexes and residential buildings. In self-consumption PV systems, surplus generation exceeding local demand often leads to a reverse power flow. This phenomenon becomes more frequent in microgrid environments where multiple distributed energy resources are interconnected. Accordingly, inverter control strategies based on generation forecasting have emerged as critical challenges. In this paper, we propose an on-device artificial intelligence model for inverter control that integrates net power forecasting with time-series neural networks. Two novel forecasting methods were proposed and introduced: Prediction-to-Prediction (P–P) and Net-Power Prediction (N–P). Various neural network models were trained and evaluated using multiple performance metrics. A novel threshold adjustment mechanism based on the mean absolute error was designed for inverter control. The control scenarios were analyzed by comparing the actual power losses with the forecast-based power losses, and the energy savings were quantified by adjusting the correction factor. The proposed forecasting methods achieved a reduction of approximately 40–70% in energy losses compared with the actual loss levels. The threshold adjustment strategy enhances flexibility in balancing the number of on/off switching events and the power loss, contributing to improved energy efficiency and system stability. Full article

Background/Objectives: As part of China’s efforts to build a high-quality and efficient integrated healthcare delivery system, tightly knit urban medical groups (TKUMGs) have emerged as a key model for promoting inter-institutional collaboration. While existing studies have focused on organizational outcomes, limited empirical evidence is available regarding the personal benefits experienced by healthcare professionals within TKUMGs. Methods: This study evaluated 2200 healthcare professionals’ perceived benefits from TKUMG participation in six pilot medical groups across two Chinese cities to identify factors associated with variations in career development outcomes. Results: Three distinct latent classes were identified: (1) A Limited Growth Group (32.4%), with minimal improvement across all dimensions; (2) a Skill Recognition Group (35.6%), with improvements in recognition and expertise utilization but limited gains in compensation and promotion; and (3) a Comprehensive Growth Group (32.0%), with comprehensive improvements in all six areas. Higher levels of participation and more positive attitudes toward TKUMG construction were significantly associated with inclusion in the more advanced development groups. Other significant factors included age, educational attainment, institutional role (leading vs. member), and departmental affiliation. TKUMG construction has generated heterogeneous benefits for healthcare professionals. Active engagement and institutional environments play critical roles in shaping individual development trajectories. Conclusions: Despite limitations related to this study’s cross-sectional design and self-reported data, these findings offer valuable insights for policymakers aiming to design incentive mechanisms, optimize human resource allocation, and enhance the sustainability of integrated healthcare models in urban China. 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

With a large number of traditional thermal power units being replaced by inverter-based resources, the system inertia and regulation capability have significantly decreased in certain countries, exposing a critical gap in traditional generation-side-dominated frequency regulation strategies. The decline in system inertia deteriorates frequency dynamics, creating a critical need for load-side regulation. To enhance frequency stability in low-inertia power systems, this paper proposes a frequency regulation strategy for thermostatically controlled loads (TCLs). The strategy incorporates a differential deadband that adjusts response thresholds based on frequency deviation, along with dynamic droop coefficients that self-adapt according to real-time TCL capacity. First, the operational principles of TCLs and the frequency response characteristics of thermal power units are analyzed to establish the foundation for load-side frequency regulation. Second, building upon the spatiotemporal distribution characteristics of system frequency, the nodal frequency under high renewable energy penetration is derived, and a differential dead zone setting method for TCLs is proposed. Then, a dynamic droop coefficient tuning method is developed to enable adaptive parameter adjustment according to the real-time regulation capacity of TCLs. Finally, these key elements are integrated within a hybrid control framework to formulate the complete TCL frequency regulation strategy. Simulation results demonstrate a 0.342% improvement in frequency nadir and 0.253% reduction in settling time compared to conventional methods, while ensuring reliable TCL operation. This work presents a validated solution for enhancing frequency stability in renewable-rich power systems, where the proposed framework with nodal frequency-based deadbands and adaptive droop coefficients demonstrates effective regulation capability under low-inertia conditions. Full article

Medical image segmentation, driven by the intrinsic fractal characteristics of biological patterns, plays a crucial role in medical image analysis. Recently, universal image segmentation, which aims to build models that generalize robustly to unseen anatomical structures and imaging modalities, has emerged as a promising research direction. To achieve this, previous solutions typically follow the in-context learning (ICL) framework, leveraging segmentation priors from a few labeled in-context references to improve prediction performance on out-of-distribution samples. However, these ICL-based methods often overlook the quality of the in-context set and struggle with capturing intricate anatomical details, thus limiting their segmentation accuracy. To address these issues, we propose VG-SAM, which employs a multi-scale in-context retrieval phase and a visual in-context guided segmentation phase. Specifically, inspired by the hierarchical and self-similar properties in fractal structures, we introduce a multi-level feature similarity strategy to select in-context samples that closely match the query image, thereby ensuring the quality of the in-context samples. In the segmentation phase, we propose to generate multi-granularity visual prompts based on the high-quality priors from the selected in-context set. Following this, these visual prompts, along with the semantic guidance signal derived from the in-context set, are seamlessly integrated into an adaptive fusion module, which effectively guides the Segment Anything Model (SAM) with powerful segmentation capabilities to achieve accurate predictions on out-of-distribution query images. Extensive experiments across multiple datasets demonstrate the effectiveness and superiority of our VG-SAM over the state-of-the-art (SOTA) methods. Notably, under the challenging one-shot reference setting, our VG-SAM surpasses SOTA methods by an average of $6.61\%$ in DSC across all datasets. Full article

Buildings account for approximately one-third of global energy usage and associated carbon emissions, making energy benchmarking a crucial tool for advancing decarbonization. Current benchmarking studies have often been limited to mainly the annual scale, relied heavily on simulation-based approaches, or employed regression methods that fail to capture the complexity of diverse building stock. These limitations hinder the interpretability, generalizability, and actionable value of existing models. This study introduces a hybrid AI framework for building energy benchmarking across two time scales—annual and monthly. The framework integrates supervised learning models, including white- and gray-box models, to predict annual and monthly energy consumption, combined with unsupervised learning through neural network-based Self-Organizing Maps (SOM), to classify heterogeneous building stocks. The supervised models provide interpretable and accurate predictions at both aggregated annual and fine-grained monthly levels. The model is trained using a six-year dataset from Washington, D.C., incorporating multiple building attributes and high-resolution weather data. Additionally, the generalizability and robustness have been validated via the real-world dataset from a different climate zone in Pittsburgh, PA. Followed by unsupervised learning models, the SOM clustering preserves topological relationships in high-dimensional data, enabling more nuanced classification compared to centroid-based methods. Results demonstrate that the hybrid approach significantly improves predictive accuracy compared to conventional regression methods, with the proposed model achieving over 80% $R^2$ at the annual scale and robust performance across seasonal monthly predictions. White-box sensitivity highlights that building type and energy use patterns are the most influential variables, while the gray-box analysis using SHAP values further reveals that Energy Star^® rating, Natural Gas (%), and Electricity Use (%) are the three most influential predictors, contributing mean SHAP values of 8.69, 8.46, and 6.47, respectively. SOM results reveal that categorized buildings within the same cluster often share similar energy-use patterns—underscoring the value of data-driven classification. The proposed hybrid framework provides policymakers, building managers, and designers with a scalable, transparent, and transferable tool for identifying energy-saving opportunities, prioritizing retrofit strategies, and accelerating progress toward net-zero carbon buildings. Full article

Novice therapists often experience a complex interplay of self-doubt, emotional strain, and professional uncertainty as they transition from training to independent clinical practice. This study explored the lived experiences of novice cognitive behavioral therapy (CBT) therapists, focusing on the challenges of early practice and the strategies employed to support regulation and growth. Seven early-career CBT therapists participated in semi-structured interviews, and data were analyzed using Interpretative Phenomenological Analysis (IPA). Two overarching themes were identified: professional identity challenges and self-beliefs, and strategies for emotional regulation and continuous development. Participants reported difficulties managing anxiety, boundary-setting, and integrating their professional and personal selves, particularly when working with complex presentations such as grief, self-harm, and personality disorders. At the same time, supervision, personal therapy, peer and family support, and ongoing professional development were seen as crucial in building resilience and sustaining competence. The findings suggest that training and professional structures should place greater emphasis on reflective practice, boundary management, and preparation for emotionally charged cases, while framing supervision as both a clinical and emotional resource. By highlighting the perspectives of novice therapists, the study underscores the importance of supportive systems in fostering resilience and sustainable professional growth. Full article

Island building change detection is a critical technology for environmental monitoring, disaster early warning, and urban planning, playing a key role in dynamic resource management and sustainable development of islands. However, the imbalanced distribution of class pixels (changed vs. unchanged) undermines the detection capability of existing methods and severe boundary misdetection. To address issue, we propose the MSDT-Net model, which makes breakthroughs in architecture, modules, and loss functions; a dual-branch twin ConvNeXt architecture is adopted as the feature extraction backbone, and the designed Edge-Aware Smoothing Module (MSA) effectively enhances the continuity of the change region boundaries through a multi-scale feature fusion mechanism. The proposed Difference Feature Enhancement Module (DTEM) enables deep interaction and fusion between original semantic and change features, significantly improving the discriminative power of the features. Additionally, a Focal–Dice–IoU Boundary Joint Loss Function (FDUB-Loss) is constructed to suppress massive background interference using Focal Loss, enhance pixel-level segmentation accuracy with Dice Loss, and optimize object localization with IoU Loss. Experiments show that on a self-constructed island dataset, the model achieves an F1-score of 0.9248 and an IoU value of 0.8614. Compared to mainstream methods, MSDT-Net demonstrates significant improvements in key metrics across various aspects. Especially in scenarios with few changed pixels, the recall rate is 0.9178 and the precision is 0.9328, showing excellent detection performance and boundary integrity. The introduction of MSDT-Net provides a highly reliable technical pathway for island development monitoring. Full article

We investigate the potential of extreme mass ratio inspirals (EMRIs) as probes of strongly interacting dark matter (SIDM) spikes around supermassive black holes. Based on Lagrangian formulations of dark matter self-interactions, we analyze the effects of number changing processes, such as $2\to 0$ annihilation and $3\to 2$ scattering, on the inner density profiles, and propose a generalized definition of the dissolution radius, including its dependence on the central black hole mass. Building on this framework, we construct dark matter spike models suitable for phase shift calculations and study the gravitational waves of EMRIs under the circular orbit adiabatic approximation. Our analysis shows that different interaction mechanisms produce distinct dark matter density distributions, resulting in characteristic phase differences in the gravitational waveforms. The magnitudes of these phase shifts are well above the resolution threshold of space-based detectors. We conclude that future missions, such as LISA, will be able to distinguish different SIDM interaction channels through precise waveform measurements, thereby opening a new avenue for probing the microphysics of dark matter in extreme astrophysical environments. Full article

Rural dwellings are often self-designed and self-built by their owners, with construction decisions based on experience and imitation of nearby buildings. As existing advanced design methods are often too complex or resource-intensive for rural contexts, balancing cost-efficiency, energy performance, and functional needs remains a challenge. This paper proposes to use the matrix analysis method, which is a relatively simple and easy-to-learn procedure, to identify the optimal design of rural houses. Taking Hebi, located in the Central Plains of China, as an example, field research was carried out, and a baseline model was established. A number of variable models were analysed using the control variable method for building orientation and indoor headroom, and metrics such as energy consumption, uncomfortable hours and construction costs were calculated to screen out effective metrics. Furthermore, by combining matrix analysis with orthogonal tests, the approach enables the development of optimal design solutions more efficiently and with reduced complexity. The results show that the optimised design, generated using the proposed method, significantly improves the indoor thermal environment—reducing energy consumption by 65.26% and uncomfortable hours by 29.22%, with only a 1.3% increase in construction costs. This study contributes to sustainable rural development by proposing a practical framework that guides the design of low-cost and energy-efficient rural housing. Full article

Few-Shot Class-Incremental Learning (FSCIL) aims to continually learn novel classes from limited data while retaining knowledge of previously learned classes. To mitigate catastrophic forgetting, most approaches pre-train a powerful backbone on the base session and keep it frozen during incremental sessions. Within this framework, existing studies primarily focus on representation learning in FSCIL, particularly Self-Supervised Contrastive Learning (SSCL), to enhance the transferability of representations and thereby boost model generalization to novel classes. However, they face a trade-off dilemma: improving transferability comes at the expense of discriminability, precluding simultaneous high performance on both base and novel classes. To address this issue, we propose BR-FSCIL, a representation learning framework for the FSCIL scenario. In the pre-training stage, we first design a Hierarchical Contrastive Learning (HierCon) algorithm. HierCon leverages label information to model hierarchical relationships among features. In contrast to SSCL, it maintains strong discriminability when promoting transferability. Second, to further improve the model’s performance on novel classes, an Alignment Modulation (AM) loss is proposed that explicitly facilitates learning of knowledge shared across classes from an inter-class perspective. Building upon the hierarchical discriminative structure established by HierCon, it additionally improves the model’s adaptability to novel classes. Through optimization at both intra-class and inter-class levels, the representations learned by BR-FSCIL achieve a balance between discriminability and transferability. Extensive experiments on mini-ImageNet, CIFAR100, and CUB200 demonstrate the effectiveness of our method, which achieves final session accuracies of 53.83%, 53.04%, and 62.60%, respectively. Full article

In this article, the authors reflect on the power of participatory feminist research in supporting gendered knowledges and cultures of equality through a community-based education project working with women construction workers in Sylhet, northern Bangladesh. We consider the impacts of their involvement in feminist qualitative research conducted as part of the Global Gender & Cultures of Equality (GlobalGRACE) project, an international research, education and capacity building project funded through the UK government’s Global Challenges Research Fund (2017–2022). We then explore what happened next, between 2022 and 2025, after the official project ended. GlobalGRACE aimed to enhance women’s wellbeing, support self-esteem and confidence building, and promote gender equality with women construction workers from socio-economically marginalised communities. Here we reflect on the ways participant women were able to bring their agency and situated gendered knowledges through their participation to create exhibitions of films and photography, build a Manifesto of Workers Rights, and emerge beyond the project as entrepreneurs, developing a lasting social enterprise supporting over 2800 women. Travelling through the GlobalGRACE project, we thus consider participant women’s experiences as they made this journey from day-labourers on building sites to creating Protity, a women’s social enterprise supporting one another to become independent entrepreneurs running their own small businesses in Sylhet, Bangladesh. Full article