Search Results (1,574)

Smartphones are ubiquitous and continuously carried high-performance devices equipped with speech recognition capabilities that enable the analysis of surrounding conversations. When leveraged for public purposes, networks of smartphones can function as a large-scale sensing infrastructure capable of detecting and reporting early signs of serious crimes or terrorist activities. This paper proposes the concept of “Smartphone as Societal Safety Guard” as an approach to substantially enhancing public safety through relatively low additional cost and the combination of existing technologies (first pillar). At the same time, this concept entails serious risks of privacy infringement, as exemplified by the potential introduction of always-on eavesdropping through operating system updates. The originality of this study lies in redefining smartphones not merely as personal tools but as public safety infrastructure within democratic societies, and in systematizing the conditions for their social acceptability from the perspective of institutional design. This research presents a reference architecture and a regulatory framework, and organizes six key challenges that must be addressed to reconcile public safety with privacy protection: external attacks, mitigation of privacy information, false positives, expansion of the scope of application, discriminatory use, and misuse by authorized insiders. In particular, misuse by authorized insiders is positioned as the core challenge. As a necessary condition for acceptance in democratic societies (second pillar), this paper proposes a privacy-protective infrastructure centered on the Verifiable Record of AI Output (VRAIO). By combining on-device two-stage urgency classification with the review and recording of AI outputs by independent third-party entities, the proposed framework aims to provide a mechanism that can ensure, as a design requirement, that information unrelated to emergencies is not released outside the device. In summary, this paper presents a design framework for reconciling enhanced public safety with the protection of privacy. Full article

Mosque architecture often exhibits distinct identities, elements, and forms associated with geographical locations or dynastic patronage in the Islamic world. However, there has been a significant paradigm shift in mosque architecture over the past century, with external factors influencing the construction and sustainability of contemporary mosques. This study examines the evolution of mosque architecture in Nigeria, concentrating on the Ilorin Central Mosque as a pivotal case study connecting the northern and southern regions. The study employs a qualitative research methodology, utilizing descriptive approach, historical research, architectural analysis, and field observations to examine the architectural language, urban context, and socio-historical factors shaping the mosque’s development. Although geographical settings have always influenced traditional religious designs in Nigeria, the findings reveal a transformation from simple mud structures to grand modern edifices. The Ilorin Central Mosque exemplifies this shift, with its Ottoman-inspired domes and minarets contrasting with the traditional vernacular mosques of the 19th century. The study highlights the challenges of globalization, sustainability, foreign architectural influences, and the tension between local identity and contemporary trends in mosque architecture. The study concludes by arguing that future mosques must reintegrate regionalism, local materials, and climate-responsive principles into contemporary aesthetics while considering the quintessential principles of the Prophet’s Mosque and the religious and social significance of mosques within the urban fabric. The Ilorin Central Mosque exemplifies a microcosm of the transformations in Nigerian mosque architecture, highlighting the necessity of a balanced approach that embraces both cultural heritage and contemporary needs. Full article

This paper proposes E-MOTE (Emotion-aware Teacher Education Framework), an ethically grounded conceptual model aimed at enhancing teacher education through the integrated use of the Facial Action Coding System (FACS), Artificial Intelligence (AI), and Virtual Reality (VR). As a conceptual and design-oriented proposal, E-MOTE is presented as a structured blueprint for future development and empirical validation, not as an implemented or evaluated system. Grounded in neuroscientific and educational research, E-MOTE seeks to strengthen teachers’ emotional awareness, teacher noticing, and social–emotional learning competencies. Rather than reporting empirical findings, this article offers a theoretically structured framework and an operational blueprint for the design of emotion-aware teacher training environments, establishing a structured foundation for future empirical validation. E-MOTE articulates three core contributions: (1) it clarifies the multi-layered construct of emotion-aware teaching by distinguishing between emotion detection, perception, awareness, and regulation; (2) it proposes an integrated AI–FACS–VR architecture for real-time and post hoc feedback on teachers’ perceptual performance; and (3) it outlines a staged experimental blueprint for future empirical validation under ethically governed conditions. As a design-oriented proposal, E-MOTE provides a structured foundation for cultivating emotionally responsive pedagogy and inclusive classroom management, supporting the development of perceptual micro-skills in teacher practice. Its distinctive contribution lies in proposing a shift from predominantly macro-behavioral simulation toward the deliberate cultivation of perceptual micro-skills through FACS-informed analytics integrated with AI-driven simulations. Full article

Background/Objective: Nutrition security, defined as consistent access to and consumption of nutritious foods that support health, remains a persistent challenge in rural populations. The HEALTHY (Helping Early Adolescents Live Their Healthiest Youth) program aimed to improve rural adolescents’ nutrition security through school-based strategies. This study evaluated its effectiveness by examining changes in fruit consumption, fruit waste, and skin carotenoid levels. Methods: A quasi-experimental, pre–post program was assessed in five rural middle schools (two experimental sites, three comparison sites). The programming paired four biweekly smoothie taste tests with nutrition education grounded in Social Cognitive Theory and Choice Architecture. Students in grades 3–8 (N = 149) participated. Digital tray photographs quantified selection and waste. The Veggie Meter^® assessed skin carotenoids on a scale from 0 to 800. Surveys captured perceptions and self-reported intakes. Analyses included χ², McNemar’s, GLMM, paired t-tests, and ANCOVA. Significance was set at p < 0.005. Results: At post-program, 98.3% of experimental trays contained the standard fruit option and/or a smoothie, compared with 41.0% of comparison trays (χ² = 41.66, p < 0.001). Fruit selection odds were 16.22 times higher in experimental schools (95% CI: 6.30–41.77, p < 0.001). Among trays with both (n = 39), smoothie waste was lower than the standard fruit option waste (t(38) = −7.10, p < 0.001, d = 1.14), resulting in greater estimated consumption (~0.43 vs. ~0.15 cups). Skin carotenoids increased in both groups, with greater improvement among experimental students in the lowest baseline quartile, F (1,19) = 9.20, p = 0.007, partial η² = 0.326. Conclusions: The HEALTHY program, which paired frozen-fruit smoothies with nutrition education, may offer a feasible and scalable approach to improving nutrition security among rural adolescents. Full article

This paper foregrounds the study life of students in Early Childhood and Social Education through the concept of educational belonging, conceptualized as situated, relational, affective, and spatial practices that are continually renegotiated. As an affective and spatial practice, educational belonging foregrounds that places, spatial designs, and interiors play a constitutive role in shaping study life, including students’ study experiences and learning processes. The paper is based on a research project conducted at University College Copenhagen, which investigates the significance of educational architecture for students’ learning processes and sense of belonging within their education. Drawing on a substantial body of data generated through architectural plan interviews, the research project offers insight into how the design and atmosphere of educational spaces and places co-constitute students’ sensory experiences of belonging. The analytical parts of the paper illuminate how students’ narratives about their positioning within classrooms reveal that teaching and learning situations are social and affective events, in which students are recognized as occupying specific student positions, such as serious, nerdy, or disengaged. Full article

This article presents a case study from a Social and Health Care School in Denmark, where a rooftop garden was designed to promote student health and support nature-based teaching across subject areas. A novel aspect of the project is the formal integration of the garden into teaching, implying that its long-term impact may extend beyond the students to the end-users they will later encounter in nursing homes and hospitals nationwide. This study applies the Evidence-Based Health Design in Landscape Architecture (EBHDL) process model, encompassing evidence collection, programming, and concept design, with the University of Copenhagen acting in a consultancy role. A co-design process with students and teachers was included as a novel source of case-specific evidence. Methodologically, this is a participatory practice-based case study focusing on the full design and construction processes, combining continuous documentation with reflective analysis of ‘process insights,’ generating lessons learned from the application of the EBHDL process model. This study identifies two categories of lessons learned. First, general insights emerged concerning governance, stakeholder roles, and the critical importance of site selection, procurement, and continuity of design responsibility. Second, specific insights were gained regarding the application of the EBHDL model, including its alignment with Danish and international standardised construction phases. These insights are particularly relevant for project managers in nature-based initiatives. The results also show how the EBHDL model aligns with Danish and international standardised construction phases, offering a bridge between health design methods and established building practice. The case focuses on the EBHDL process rather than verified outcomes and demonstrates how evidence-based and participatory approaches can help structure complex design processes, facilitate stakeholder engagement, and support decision-making in institutional projects. Full article

Sentiment analysis (SA) has become an essential tool for analyzing social media content in order to monitor public opinion and support digital analytics. Although transformer-based SA models exhibit remarkable performance, they lack mechanisms to mitigate hallucinated sentiment, which refers to the generation of unsupported or overconfident predictions without explicit linguistic evidence. To address this limitation, this study presents a hallucination-aware SA model by incorporating semantic grounding, interpretability-congruent supervision, and neuro-symbolic reasoning within a unified architecture. The proposed model is based on a fine-tuned Open Pre-trained Transformer (OPT) model, using three fundamental mechanisms: a Sentiment Integrity Filter (SIF), a SHapley Additive exPlanations (SHAP)-guided regularization technique, and a confidence-based lexicon-deep fusion module. The experimental analysis was conducted on two multi-class sentiment datasets that contain Twitter (now X) and Reddit posts. In Dataset 1, the suggested model achieved an average accuracy of 97.6% and a hallucination rate of 2.3%, outperforming the current transformer-based and hybrid sentiment models. With Dataset 2, the framework demonstrated strong external generalization with an accuracy of 95.8%, and a hallucination rate of 3.4%, which is significantly lower than state-of-the-art methods. These findings indicate that it is possible to include hallucination mitigation into transformer optimization without any performance degradation, offering a deployable, interpretable, and linguistically complex social media SA framework, which will enhance the reliability of neural systems of language understanding. Full article

Traditional villages, protected as cultural heritage in our country, are rich in historical information, cultural landscapes, and traditional domestic architecture. This article explores the spatial distribution of traditional villages and proposes a new paradigm for the sustainable development of traditional dwellings. It addresses the challenges these villages face, such as natural, social, and inherent issues, arising from rapid socioeconomic development and urbanization. This study analyzes the spatial distribution and architectural features of traditional villages and dwellings in Northern Shaanxi based on 179 national and provincial villages. Using ArcGIS 10.1, the geographic concentration index, kernel density analysis, and the analytic hierarchy process, this study applied both macro and micro level perspectives. The research shows that: (1) The traditional villages in northern Shaanxi exhibit a spatial distribution pattern of “overall aggregation, local dispersion, and uneven distribution.” This pattern is influenced by interactions between natural and human factors. (2) Traditional dwellings in these villages are primarily cave dwellings and courtyard buildings, each reflecting unique architectural features in terms of floor plan layout, facade form, structure, materials, and decoration. (3) Traditional village dwellings in northern Shaanxi face practical challenges related to protection, development, and governance. The top three challenges, based on weighted indicators, are issues related to inheritance, an imperfect protection mechanism, and inherent shortcomings of the buildings. Based on these findings, this study proposes three practical suggestions for the sustainable development of traditional village dwellings in Northern Shaanxi. These suggestions aim to enhance the comprehensive and multi-dimensional sustainable development of traditional village dwellings. Full article

In educational settings, it is crucial to comprehend and manage individuals’ social interaction behaviors through the physical environment. However, analyzing social interaction patterns manually is a time-consuming and energy-intensive process. This study aims to reveal the socio-behavioral implications of spatial features, based on the Affordance Theory, using artificial intelligence (AI). To this end, the study proposes a unified quantitative methodology that leverages diverse AI approaches. Behavioral data are gathered via systematic observation and analyzed using (1) Deep Learning (DL)-based Human Detection and classified by (2) Machine Learning (ML)-based Interaction Score Prediction approach. The behavioral findings were analyzed in relation to spatial data via (3) Spatial Feature Selection. As the study area, the ATU Faculty of Engineering building complex was selected, and behavioral data from 746 participants were collected in the complex’s open spaces. The results indicated that AI-based approaches provide a high degree of precision in analyzing the relationships between social interaction and spatial features within the addressed context. Also, (1) the existence and (2) the rotation of seating units and (3) shading strategies are identified as the spatial features that contribute to higher interaction scores in the educational settings. The study proposes an integrated and transferable methodology based on diverse AI approaches for determining social interaction and its spatial aspects, leading to a comprehensive and reproducible approach. Full article

Phishing emails continue to evade conventional detection systems due to their increasingly sophisticated, multi-faceted social engineering tactics. To address the limitations of single-modality or rule-based approaches, we propose SAHF-PD, a novel phishing detection framework that integrates multi-modal feature extraction with semantic-aware hierarchical fusion, based on large language models (LLMs). Our method leverages modality-specialized large models, each guided by domain-specific prompts and constrained to a standardized output schema, to extract structured feature representations from four complementary sources associated with each phishing email: email body text; open-source intelligence (OSINT) derived from the key embedded URL; screenshot of the landing page; and the corresponding HTML/JavaScript source code. This design mitigates the unstructured and stochastic nature of raw generative outputs, yielding consistent, interpretable, and machine-readable features. These features are then integrated through our Semantic-Aware Hierarchical Fusion (SAHF) mechanism, which organizes them into core, auxiliary, and weakly associated layers according to their semantic relevance to phishing intent. This layered architecture enables dynamic weighting and redundancy reduction based on semantic relevance, which in turn highlights the most discriminative signals across modalities and enhances model interpretability. We also introduce PhishMMF, a publicly released multimodal feature dataset for phishing detection, comprising 11,672 human-verified samples with meticulously extracted structured features from all four modalities. Experiments with eight diverse classifiers demonstrate that the SAHF-PD framework enables exceptional performance. For instance, XGBoost equipped with SAHF attains an AUC of 0.99927 and an F1-score of 0.98728, outperforming the same model using the original feature representation. Moreover, SAHF compresses the original 228-dimensional feature space into a compact 56-dimensional representation (a 75.4% reduction), reducing the average training time across all eight classifiers by 43.7% while maintaining comparable detection accuracy. Ablation studies confirm the unique contribution of each modality. Our work establishes a transparent, efficient, and high-performance foundation for next-generation anti-phishing systems. Full article

Grain security is a fundamental guarantee for social stability and sustainable development. Accurate monitoring and prediction of overall granary temperature are essential for reducing storage losses and improving warehouse management efficiency. As an integrated system, the temperature evolution of the grain pile is deeply affected by its inherent physical structure and heat transfer pathways. Therefore, a multi-level warehouse–surface–line–point (WSLP) structural modeling approach driven by the physical properties of the grain pile is proposed to extract the joint environmental and spatial characteristics. Building upon the WSLP framework, a dual-channel time-series prediction architecture integrating both long short-term memory (LSTM) and iTransformer through a mutual verification fusion mechanism is developed to enable synchronized temperature forecasting across different regions of the grain piles. Experiments are conducted using real granary data from Shandong, China. The results demonstrate that the proposed model achieves more than 30% improvement over baseline methods in terms of MAE and RMSE. Moreover, the WSLP-LSTM–iTransformer framework significantly improves prediction accuracy in complex warehouse environments and enhances the interpretability and applicability of deep learning models for grain condition forecasting by incorporating real environmental characteristics. Full article

Information diffusion models developed for Twitter, Reddit, and Facebook assume network contagion and competition for shared attention. Telegram operates differently. It is built around channels rather than social graphs, and users receive posts directly from subscribed channels without algorithmic mediation. We analyze over 5000 forwarding cascades from the Pushshift Telegram dataset to examine whether existing diffusion models generalize to this broadcast environment. Our findings reveal fundamental structural differences. Telegram forwarding produces perfect star topologies with zero multi-hop propagation. Every forward connects directly to the original message, creating trees with maximum depth of exactly 1. This contrasts sharply with Twitter retweet chains that routinely reach depths of 5 or more hops. Forwarding delays follow heavy-tailed Weibull or lognormal distributions with median delays measured in days rather than hours. Approximately 15 to 20 percent of cascades exhibit administrative bulk reposting rather than organic user-driven growth. Most strikingly, early-stage competitive overtaking is absent. Six of 30 pairs exhibit crossings, but these occur late (median 79 days) via administrative bursts rather than organic competitive acceleration during peak growth. We develop a delay-driven star diffusion model that treats forwarding as independent draws from a delay distribution. The model achieves median prediction errors below 10 percent for organic cascades. These findings demonstrate that platform architecture fundamentally shapes diffusion dynamics. Comparison with prior studies on Twitter, Weibo, and Reddit reveals that Telegram’s broadcast structure produces categorically different patterns—including perfect star topology and asynchronous delays—requiring platform-specific modeling approaches rather than network-based frameworks developed for other platforms. Full article

Providing effective post-disaster housing remains a globally complex challenge shaped by interrelated constraints, including environmental sustainability, socio-cultural compatibility, logistical capacity, and economic feasibility. Contemporary responses therefore require housing solutions that extend beyond rapid deployment to incorporate flexibility, adaptability, and long-term spatial transformation. In this context, this study advances a design-oriented, computational framework that positions parametric design at the core of post-disaster housing production within the broader digital transformation of the construction sector. The research proposes an adaptive parametric–modular housing system in which standardized architectural units are governed by a rule-based aggregation logic capable of generating context-responsive spatial configurations across multiple scales and typologies. The methodology integrates a qualitative synthesis of global post-disaster housing literature with a quantitative computational workflow developed in Grasshopper for Rhinoceros 3D (version 8). Algorithmic scripting defines a standardized spatial grid and parametrically regulates key building components structural systems, façade assemblies, and site-specific environmental parameters, enabling real-time configuration, customization, and optimization of housing units in response to diverse user needs and varying climatic, social, and economic conditions while maintaining constructability. The applicability of the framework is examined through a case study of the Düzce Permanent Housing context, where limitations of existing post-disaster stock, such as spatial rigidity, restricted growth capacity, and fragmented public-space integration, are contrasted with alternative settlement scenarios generated by the proposed system. The findings demonstrate that the framework supports multi-scalar and multi-typological reconstruction, extending beyond individual dwellings to include public, service, and open-space components. Overall, the study contributes a transferable computational methodology that integrates modular standardization with configurational diversity and user-driven adaptability, offering a sustainable pathway for transforming temporary post-disaster shelters into permanent, resilient, and socially integrated community assets. Full article

A cultural heritage augmented reality system overlays virtual information onto real-world heritage sites, enabling intuitive exploration and interpretation with spatial and temporal contexts. This study presents the design and implementation of a cognitive Mobile Augmented Reality (MAR) system that integrates KCHDM-based ontologies with large language models (LLMs) to facilitate intelligent exploration of urban heritage. While conventional AR guides often rely on static data, our system introduces a Semantic Retrieval-Augmented Generation (RAG) pipeline anchored in a structured knowledge base modeled after the Korean Cultural Heritage Data Model (KCHDM). This architecture enables the LLM to perform dynamic contextual reasoning, transforming heritage data into adaptive question-answering (Q&A) and interactive knowledge-testing quizzes that are precisely grounded in both historical and spatial contexts. The system supports on-site AR exploration and map-based remote exploration to ensure robust usability and precise spatial alignment of virtual content. To deliver a rich, multisensory experience, the system provides multimodal outputs, integrating text, images, models, and audio narration. Furthermore, the integration of a knowledge sharing repository allows users to review and learn from others’ inquires. This ontology-driven LLM-integrated MAR design enhances semantic accuracy and contextual relevance, demonstrating the potential of MAR for socially enriched urban heritage experiences. Full article

Digital twin technology is widely promoted as a transformative step for precision livestock farming, yet no fully realized, engineering-grade digital twins are deployed in commercial dairy or poultry systems today. This work establishes the current state of knowledge on dairy and poultry digital twins by synthesizing evidence through systematic database searches, thematic evidence mapping and critical analysis of validation gaps, carbon accounting and adoption barriers. Existing platforms are better described as near-digital-twin systems with partial sensing and modelling, digital-twin-inspired prototypes, simulation frameworks or decision-support tools that are often labelled as twins despite lacking continuous synchronization and closed-loop control. This distinction matters because the empirical foundation supporting many claims remains limited. Three critical gaps emerge: life-cycle carbon impacts of digital infrastructures are rarely quantified even as sustainability benefits are frequently asserted; field-validated improvements in feed efficiency, particularly in poultry feed conversion ratios, are scarce and inconsistent; and systematic reporting of failure rates, downtime and technology abandonment is almost absent, leaving uncertainties about long-term reliability. Adoption barriers persist across technical, economic and social dimensions, including rural connectivity limitations, sensor durability challenges, capital and operating costs, and farmer concerns regarding data rights, transparency and trust. Progress for cows and chickens will require rigorous validation in commercial environments, integration of mechanistic and statistical modelling, open and modular architectures and governance structures that support biological, economic and environmental accountability whilst ensuring that system intelligence is worth its material and energy cost. Full article