Search Results (9,467)

Artificial Intelligence (AI) is rapidly transitioning from a specialized technology to an everyday socio-technical infrastructure, yet public acceptance remains shaped by a trade-off between perceived benefits and risks. This study examines how individuals from varied demographic and professional backgrounds perceive, use, and evaluate AI-enabled systems using a mixed-method research design. A bilingual (English/Arabic) online survey ($N=115$) captured demographics, awareness, usage patterns, perceived impact, self-assessed understanding, domain-specific trust, concerns, and attitudes toward regulation, complemented by open-ended reflections. In parallel, semi-structured face-to-face interviews provided deeper insight into AI conceptualization, lived experiences, trust boundaries, and conditions for acceptable use. Quantitative results show frequent AI engagement embedded in daily life, with strong domain dependence in trust: education is the most trusted domain, whereas healthcare and finance attract substantially lower trust. Prominent concerns include overreliance (“brain rot”), privacy and data misuse, job displacement, and misinformation. Support for stronger AI regulation is high, indicating that governance is viewed as a prerequisite for sustainable adoption rather than a constraint on innovation. Qualitative findings triangulate these results, revealing a pattern of conditional acceptanceunderstood as the simultaneous valuation of AI’s practical utility alongside the imposition of explicit trust prerequisites whereby participants value AI for productivity and learning support while emphasizing confidentiality, transparency, human oversight in high-stakes contexts, and clear boundaries to mitigate misuse and erosion of human judgment. The study offers empirically grounded insights for policymakers, educators, and industry stakeholders into how AI acceptance is negotiated through utility, literacy, perceived risk, and expectations of accountability. Full article

Obesity has emerged as a significant global public health challenge, yet the clinical utility of existing anti-obesity drugs is often constrained by limited efficacy and adverse safety profiles. Rheum tanguticum Maxim. ex Balf., a traditional medicinal plant, has shown potential in modulating glucose and lipid metabolism; however, its specific anti-obesity mechanisms remain poorly characterized. In this study, the chemical profile of the 60% ethanol-eluted fraction of R. tanguticum (RTE) was characterized via UPLC-QTOF/MS, followed by network pharmacology analysis to predict regulatory targets and enriched pathways. Subsequently, a high-fat diet (HFD)-induced obese mouse model was established to evaluate the anti-obesity effects of RTE by monitoring body weight, Lee’s index, fat-to-body weight ratio, serum lipid profiles, and liver histopathological changes. A total of 14 major compounds, primarily anthraquinone glycosides, were identified. Integrated network analysis identified 10 hub targets, including TNF, EGFR, and TP53. In vivo experiments demonstrated that RTE significantly attenuated body weight gain and reduced Lee’s index, fat-to-body ratios, and serum levels of TC, TG, and LDL-C. Furthermore, RTE treatment markedly alleviated hepatic steatosis and inflammatory infiltration in obese mice. These findings suggest that RTE exerts potent anti-obesity effects through a multi-target and multi-pathway mechanism that regulates lipid metabolism and suppresses inflammation. This study improves our understanding of the pharmacological value of R. tanguticum and provides a scientific basis for its development as a functional food ingredient or therapeutic agent against obesity. Full article

Urban industrial heritage is increasingly embedded in urban regeneration, public space provision, and community governance, yet existing studies have insufficiently examined how heterogeneous publics perceive its value through everyday digital discourse. Taking the Guangzhou Iron and Steel Plant industrial heritage site (hereafter, the Guanggang industrial heritage site) as a case study, this study used user-generated content from Rednote posts and local WeChat public-account comments to identify platform-mediated expressions of public value perception. A corpus of 745 valid samples comprising 51,459 Chinese characters was constructed after data collection, screening, and text preprocessing. Word-frequency analysis, semantic network analysis, and sentiment analysis were conducted using ROST CM 6.0. The results show that the two retrieved platform-contextual corpora foregrounded different concerns. Rednote discourse foregrounded ruin landscapes, industrial aesthetics, photography-based check-ins, and exploratory experiences, whereas WeChat comments emphasized park construction, public facilities, governance responsiveness, safety, and the residential environment. At the corpus level, lexicon-based sentiment classification indicated that Rednote texts were dominated by positive and neutral categories, while WeChat comments contained a higher proportion of texts classified as negative. This study conceptualizes dual foregrounding as a bounded selection process through which platform affordances, user self-selection, and users’ relationships with the site influence which concerns become visible in each corpus; it does not treat the observed differences as a causal platform effect. It argues that industrial heritage regeneration must translate historical, technological, and aesthetic values into public values that are interpretable, accessible, usable, and trusted by local communities. Full article

Background/Objectives: Artificial intelligence (AI) is increasingly used in clinical decision-support systems, yet its adoption in low- and middle-income countries, including Jordan, remains limited and underexplored. Understanding how healthcare professionals perceive AI-assisted clinical decision-making is essential for safe and contextually appropriate implementation. This study explored healthcare professionals’ perceptions of AI-assisted clinical decision-making in Jordan, with particular attention to trust, accuracy, accountability, professional judgement, digital literacy, and health-system readiness. Medication-related safety and prescribing concerns were examined as secondary cross-cutting issues where they emerged from participants’ accounts. Methods: A qualitative study was conducted using semi-structured, in-depth interviews with 22 purposively sampled healthcare professionals from public, private, and university-affiliated healthcare institutions in Amman, Irbid, and Zarqa. Participants included physicians, nurses, pharmacists, and allied health professionals with varied specialties and levels of seniority. Data were analysed using Braun and Clarke’s reflexive thematic analysis. Member checking, peer debriefing, reflexive memos, and audit trails were used to enhance trustworthiness, and reporting followed the Consolidated Criteria for Reporting Qualitative Research (COREQ). Results: Eight overarching themes were identified: conditional trust in AI-assisted clinical decision-making; concerns regarding accuracy and confident algorithmic errors; accountability and professional responsibility; AI as an adjunct rather than a substitute for clinical judgement; the influence of experience, specialty, and digital literacy on AI acceptance; Jordanian health-system readiness; privacy, confidentiality, and algorithmic bias; and training requirements for safe AI use. Medication-related safety emerged as a cross-cutting concern, particularly in relation to dosing, polypharmacy, drug–drug and drug–herb interactions, and the risk of over-reliance on AI-generated recommendations. Conclusions: Healthcare professionals in Jordan expressed cautious but constructive views toward AI-assisted clinical decision-making. AI was perceived as potentially useful when used to support, rather than replace, professional judgement. Participants’ accounts suggest that safe implementation depends on local validation, clear accountability frameworks, ethical data governance, interprofessional training, and careful consideration of medication-safety expertise where AI tools influence prescribing or therapeutic decisions. These findings highlight the importance of context-sensitive AI governance strategies that support trustworthy, accountable, and professionally supervised AI adoption in healthcare. Full article

Rapid and often chaotic urbanisation in post-Soviet cities such as Almaty challenges the quality, availability, and accessibility of public spaces for residents, given the cities’ historical development. Meanwhile, global research is focused on the concepts of Third Places, coworking spaces in the Western context, and urban experience in cities with transitional economies, but the heritage of centrally planned urban development lacks spatial explicit analysis. The purpose of the current study is to analyse the evolution, current situation, and distribution of collaborative spaces (public spaces that combine work and connectedness) in Almaty. The methodology includes four phases of qualitative analysis: (1) a historical–typological analysis of architectural functions since the beginning of the 20th century until the 2025, (2) spatial mapping analysis of the existing typologies such as libraries, museums, coworking spaces, research and development (R&D) institutions and universities, and community centres, (3) longitudinal statistical analysis, and (4) historical graphic analysis. Analysis is conducted through the lens of advanced levels of human needs that concern self-education and self-development. This approach helped to propose a new definition of collaborative space. The results also show examples of sustainable urban structure with collaborative spaces in Almaty’s old centre (“Zolotoi Kvadrat”—Golden Square) and a critical deficit of new multifunctional spaces for work and socialisation in recently developed districts. The study reveals that Almaty’s evolution occurred through incremental infill development over the old grid, without the integrated development of the public realm and existing structural connections. As a result, the research explores the connection between collaborative spaces and their indirect influence on the general well-being in Almaty. Full article

Early wildfire detection (EWD) is of the utmost importance to enable rapid response efforts and thus minimize the negative impacts of wildfire spreads. To this end, we present PyroNear${}_{2025}$ , a new dataset composed of both images and videos, allowing for the training and evaluation of smoke plume detection models, including sequential models. The data is sourced from the following: (i) web-scraped videos of wildfires from public networks of cameras for wildfire detection in-the-wild, (ii) videos from our in-house network of cameras, and (iii) a small portion of synthetic and real images. This dataset includes around 150,000 manual annotations on 50,000 images, covering 640 wildfires; PyroNear${}_{2025}$ surpasses existing datasets in size and diversity. It includes data from France, Spain, Chile, and the United States. Finally, it is composed of both images and videos, allowing for the training and evaluation of smoke plume detection models, including sequential models. We ran cross-dataset experiments using a lightweight state-of-the-art object detection model, similar to the ones used in real-world applications, and found that the proposed dataset is particularly challenging, with an F1 score of around 70%, but it is more stable than existing datasets. Finally, its use in concordance with other public datasets helps to reach higher results overall. Last but not least, the video part of the dataset enables another technical contribution, as it can be used to train a lightweight sequential model, improving global recall while maintaining precision for earlier detections. The output of this work has real-life implications, as it is used to automatically detect wildfires, with our models running on Raspberry Pi in several countries. We will make both our code and data available online. Full article

Rural sustainability planning requires spatially explicit methods that integrate agricultural resource bases, ecological condition, low-carbon feasibility, community implementation support, and cultural landscape values. Although the Circular and Ecological Sphere (CES) concept offers an integrative framework for rural transition, empirical CES studies remain largely qualitative or policy-oriented. This study develops a FarmMap-integrated Python-GIS workflow for proxy-based CES-oriented spatial prioritization in Yangpyeong-gun, a peri-rural county on the eastern fringe of the Seoul metropolitan region in Korea. Public spatial and administrative datasets were integrated into thirteen indicators grouped under five CES-relevant axes. The model does not measure realized circular material flows, governance quality, resident participation, or carbon emission reduction directly; instead, it identifies where CES-relevant spatial potentials co-occur. An axis-balanced entropy model assigned equal total weight to each axis while applying entropy weighting within axes. Robustness was tested through equal-weight, axis-emphasis, raw entropy diagnostic, Monte Carlo perturbation, and spatial-scale sensitivity analyses using 100 m diagnostic, 500 m, and eup/myeon supports. The final 250 m priority surface identified the top fifth of analyzed Yangpyeong-gun area as very-high relative priority and remained stable across weighting and spatial-support diagnostics. Rural-experience villages and village enterprises had significantly higher CES scores than random background locations. The results demonstrate a reproducible first-stage spatial screening workflow for CES-oriented rural planning while clarifying the limits of proxy-based circularity, governance, and low-carbon indicators. Full article

Trajectory data is valuable for applications such as urban planning, but its public availability is often limited by privacy concerns and data collection costs. While recent diffusion models have shown promise in generative tasks, existing methods rarely integrate personalized conditioning with road network constraints. As a result, they struggle to simultaneously achieve personalized mobility modeling and high road-network spatial validity, resulting in limited trajectory quality. In this paper, we propose Sem-RoadDiff, a symmetry-aware dual-guided diffusion model for personalized and road network-constrained trajectory generation. Specifically, our model incorporates two key components. First, we design a semantic preference guidance mechanism to encode user history into a preference-weighted user embedding using a temperature-scaled softmax. This enables the model to capture user-level mobility patterns without directly using raw trip-level records as generation conditions. Second, we introduce a road-aware mechanism to improve overall spatial validity, employing a spatial validity loss derived from the User Mobility Transition Graph. From a symmetry perspective, Sem-RoadDiff aims to preserve distributional symmetry between real and generated trajectories while respecting the inherent asymmetry of directed road-network transitions. Extensive experiments on the Geolife and Porto datasets demonstrate that our approach improves trajectory distributional fidelity compared with the evaluated baselines and improves road-segment connectivity over the diffusion-based baseline. Full article

Sensor-based human activity recognition (HAR) plays a fundamental role in healthcare monitoring, sports analytics, and ambient-assisted living. Although deep learning has substantially advanced HAR performance, two practical issues still limit its real-world deployment: (i) the distribution shift caused by changes in users or sensor placements can degrade generalization, and (ii) the quadratic $\mathcal{O}\left(L^2\right)$ complexity of standard self-attention hinders efficient long-sequence modeling on resource-constrained wearable devices. To address these issues, we propose DSHformer, which is an accuracy-oriented HAR framework that combines compact channel–temporal encoding with locality-sensitive hashing (LSH)-based attention. Specifically, DSHformer (i) employs a low-parameter patch-based graph-attention encoder to jointly model latent relationships among sensor channel–temporal dynamics; (ii) introduces a trainable prototype pool together with a multi-layer decomposition network to improve intra-class compactness and inter-class separability via prototype alignment; and (iii) introduces a decomposition-stable LSH-based attention mechanism tailored for HAR, whose core design couples prototype-guided feature decomposition with locality-sensitive hashing to ensure that semantically related tokens remain consistently grouped in the same hash bucket even after decomposition-induced attenuation. The mechanism thereby operates at $\mathcal{O}(LlogL)$ attention complexity on longer sensor sequences. Extensive experiments on five public benchmarks (WISDM, UCI-HAR, PAMAP2, Opportunity, and UniMiB-SHAR) show that DSHformer achieves accuracies of 98.6%, 93.7%, 98.4%, 88.5%, and 96.6%, respectively, achieving competitive or superior performance compared with both Transformer variants and HAR-specific baselines under the adopted benchmark protocols. Ablation studies further confirm the complementary contribution of each component. Full article

We previously showed that TCEA1 deficiency in myeloid cells promotes proliferation, impairs differentiation and inhibits apoptosis, but its role and underlying mechanism in acute myeloid leukemia (AML) are unknown. Here, in NB-4 cells, an M3 subtype of AML, TCEA1 overexpression suppressed proliferation (p < 0.001), induced S-phase arrest (from 35.35% to 19.47%, p < 0.001), increased apoptosis (from 10.37% to 23.5%, p < 0.001), and promoted differentiation. Mechanistically, TCEA1 overexpression upregulated C/EBPε and IRF8 at the mRNA and protein levels; conversely, TCEA1 knockdown downregulated both. Rescue experiments in TCEA1 knockdown 32Dcl3 cells showed that ectopic C/EBPε or IRF8 reversed the uncontrolled proliferation, blocked apoptosis, and impaired differentiation. In xenograft mouse models, TCEA1 overexpression reduced leukemic infiltration in the bone marrow, spleen, and liver; extended overall survival; and elevated C/EBPε and IRF8 expression in vivo. Analysis of public APL datasets revealed that high TCEA1 expression is associated with a favorable prognosis (HR = 0.43, 95% CI: 0.2–0.93, logrank p = 0.028). Collectively, our findings demonstrate that TCEA1 suppresses proliferation, promotes apoptosis and differentiation, and attenuates disease progression by upregulating C/EBPε and IRF8, positioning this regulatory mechanism as a potential therapeutic target and prognostic biomarker for this disease. Full article

This article examines the medium to long-term enrollment patterns of an AI-based platform designed to support children in learning robotics and participating in a national robotics competition in Romania. Drawing on registration and participation data covering students and teachers across urban and rural schools between 2020 and 2025, the study documents a consistent pattern: an initial period of high enrollment and rapid adoption followed by a steady decline over time. A key feature of the initiative is that hardware, platform access, and learning resources were provided entirely free of charge, allowing cost-related explanations for the decline to be set aside and structural and human factors to be examined directly. The paper makes two primary contributions. First, it proposes a System Dynamics framework grounded in innovation diffusion theory as a first-generation calibration model for understanding AI-enabled educational robotics adoption in a resource-constrained national context. The model is designed to be progressively tested and refined as anonymized aggregate data accumulates, and it relies exclusively on anonymized aggregated public data in accordance with GDPR requirements. Second, it advances the hypothesis that an AI-based educational platform, even one from which all financial barriers have been removed, will experience sustained enrollment decline in the absence of adequate human teacher involvement. The empirical trajectory and model outputs are consistent with this hypothesis and motivate further investigation. This represents a hypothesis-generating and framework-building paper. The framework reveals pronounced urban-rural disparities and differential outcomes by age of entry. All findings are presented as model-generated hypotheses rather than empirically demonstrated conclusions. The paper invites researchers gathering comparable data from similar initiatives in other countries to collaborate in testing and refining the model. The central conclusion is cautiously optimistic: AI may support robotics education adoption, but it is not a substitute for dedicated teachers, and without sustained investment in human capital, even a financially accessible platform is insufficient to maintain long-term enrollments. Full article

Rail transit station areas are high-volume public spaces where pedestrian efficiency directly affects the operational quality, equity, and sustainability of public transport systems. However, temporarily mobility-impaired (TMI) pedestrians, such as people carrying luggage or pushing strollers, are often overlooked in station-area pedestrian design. This study quantifies walking-efficiency attenuation among TMI groups and identifies key micro-spatial factors influencing their mobility. Based on 96 typical paths around metro stations in Xiamen, China, real-world walking experiments were conducted with 566 volunteers, producing 1152 valid observations. A Random Forest model was used to predict walking efficiency under different spatial attributes and assess factor importance. The results show that TMI pedestrians walk significantly slower than unimpaired pedestrians and can become a major bottleneck in station-area circulation. Stroller users are most affected by ramp shape, while luggage carriers are particularly sensitive to path width. Partial dependence analysis indicates that a path width of 4.2–4.7 m and a ramp shape factor of 0.2–0.35 support higher efficiency and equity. The findings provide quantitative evidence for universal design and offer practical guidance for sustainable, inclusive, and people-centered transit-oriented development. Full article

Medical image registration integrates information from different types of medical images to support and improve clinical diagnosis. Existing image registration approaches are mainly classified into two categories: model-driven methods and data driven methods. Model-driven methods can achieve high registration accuracy but suffer from low computational efficiency and long processing time. In contrast, data-driven methods stand out for their high efficiency, which gives them great practical value. Taking this advantage as the core basis, this paper proposes a simple unsupervised deep learning framework embedded with a multi-scale strategy. The overall network consists of two core modules: an Affine Transformation Network (AT-Net) and a multi-scale Deformable Transformation Network (DT-Net). The multi-scale design adopted in the DT-Net enables image registration at different feature scales, which effectively improves the overall registration accuracy. In addition, a dual consistency constraint is introduced into the framework to further enhance the model robustness. The entire network realizes end-to-end medical image registration. We verified the performance of the proposed method on a public dataset, with mutual information (MI) adopted as the evaluation metric. The experimental results show that our registration algorithm outperforms several mainstream methods, including Symmetric Image Normalization (SyN), VoxelMorph (VM), the coarse-to-fine deformable transformation framework for unsupervised multi-contrast MR image registration with dual consistency constraint (C-F-I-R), TransMorph and DiffuseMorph. The comparative experiments fully demonstrate that combining the multi-scale strategy with deep learning techniques is an effective solution for medical image registration tasks. Full article

Multi-party private set union (MPSU) allows multiple parties to compute a union without disclosing private inputs, but most existing protocols focus on balanced settings with comparable input sizes. In large-repository update scenarios, a leader maintains a massive base set while contributors submit small update sets; directly using balanced MPSU makes the online cost scale with the leader’s repository size. We propose AegisUnion, an asymmetric MPSU protocol tailored to large-repository updates. AegisUnion separates repository-dependent computation from online update processing through an offline oblivious key-value store (OKVS) encoding phase. In the online phase, contributors perform private membership determination, cross-contributor private deduplication, conditional payload sharing, and secret-shared shuffling, without revealing raw inputs, repository-overlap relations, inter-contributor duplicates, or the source of each output element. Under the semi-honest model, AegisUnion tolerates any coalition of corrupted parties as long as at least one party remains honest, without non-collusion assumptions. Experiments show that, as the repository grows from $2^{14}$ to $2^{18}$, the online time remains stable at 663–715 ms. At repository size $2^{18}$ and contributor update bound $2^{10}$, AegisUnion achieves about $455\times$ and $454\times$ lower online time than symmetric-key-based MPSU and public-key-based MPSU baselines, respectively, and about $271\times$ and $575\times$ lower online communication. Full article

The growing need for secure image transmission across public networks requires robust encryption algorithms. Traditional chaos-based image ciphers typically have a small key space, weak avalanche behavior, or are susceptible to differential cryptanalysis. To overcome such inadequacies, this paper suggests a new adaptive image cryptosystem that combines a fractional-order memristive chaotic engine and a non-linear hybrid encryption kernel. The system uses piano-inspired feedback; the keystream generator dynamically adapts to the previously encrypted pixel, enabling powerful Cipher Block Chaining (CBC)-style chaining and content-dependent diffusion. A four-dimensional memristive system is solved by the use of fractional-order calculus, which gives an ultra-large key space (>10⁸⁰) and very high sensitivity to initial conditions—confirmed by a positive largest Lyapunov exponent (1.7199). The encryption kernel maps the traditional Exclusive OR (XOR) with the reversible two-step operation: the modular addition of the plaintext with the first keystream byte and the XOR with the second keystream one, both of which increase non-linearity and confusion. Large-scale experiments with six standard 256 × 256 colour images indicate almost ideal entropy (7.9994), Number of Pixel Change Rate (NPCR) which is 99.62, Unified Average Changing Intensity (UACI) which is 33.43, correlation coefficients are near to zero, very low Gray-Level Co-occurrence Matrix (GLCM) homogeneity (≈0.017) and high contrast (≈4843) and low energy (≈0.006 The ciphertext passes seven National Institute of Standards and Technology (NIST) SP-800-22 statistical tests, is extremely sensitive to keys (a perturbation of 1 × 10⁻¹⁴ alters >99.6% of ciphertext) and resists chosen-plaintext and known-plaintext attacks. Decryption has linear time complexity O(N), and average encryption and decryption times are 3.40 s and 2.75 s for 256 × 256 images. The proposed cryptosystem provides an attractive security–performance trade-off that can be used in high-security systems like medical image protection, privacy-preserving multimedia transmission, and secure cloud storage. Full article