Search Results (312)

In the context of advancing communication technologies and digital spaces, Moroccan youth are increasingly engaging with concepts of mobility, presenting both opportunities and challenges in a liquid modern digital landscape. While digital identities and mobility have been extensively studied among Western youth, non-Western youth, including those in Morocco, are often viewed through a lens of being “at risk,” which biases objective analysis. This study addresses this gap by examining how digital mobility fosters culturally hybrid identities among Moroccan youth in a globalised world. Methods: An interdisciplinary ethnographic content analysis was conducted on youth digital productions and interactions on platforms such as Facebook and YouTube. The study draws on Zygmunt Bauman’s theory of liquid modernity and Stuart Hall’s theory of representation to explore identity formation. Results: Digital mobility enables Moroccan youth to navigate between local cultural influences (Amazigh, Islamic, African, and Arab) and global Western narratives, leading to hybrid identities. Challenges include cultural erosion through practices like Western-style dating shows and sexual freedoms that challenge social norms, potentially widening generational gaps. Opportunities arise from platforms like SAWT, where youth discuss taboo topics, create hybrid cultural artefacts, and engage in glocalisation, enhancing agency and global integration. Conclusions: Digital mobility acts as a catalyst for cultural hybridity, supporting global integration while highlighting the need for addressing accessibility disparities and unsupervised interactions. This framework contributes to digital youth studies by emphasising mobility’s role in identity evolution, advocating for balanced glocality over cultural protectionism or homogenisation. Full article

Generative AI is becoming integral to daily workflows, fostering a novel form of functional cognitive AI dependency distinct from pathological addiction. While emerging research acknowledges this phenomenon, the specific psychological mechanisms underpinning its development remain underexplored. Incorporating self-efficacy erosion into the reinforcement-based framework, this study investigates whether instant gratification and perceived AI efficacy as key drivers of AI dependency. We examine the model using Structural Equation Modeling (SEM) with cross-sectional data collected from 576 users who have engaged with AI. The results show that both instant gratification and efficient rewards are positively associated with individuals’ AI dependency. Furthermore, users’ self-efficacy erosion significantly mediates the positive relation, supporting the hypothesis that greater reliance on AI is related to lower self-belief and stronger AI dependency. Moderation analyses further indicate that task-domain self-efficacy and social norms strengthen these positive associations. These findings provide empirical support for a mechanism associated with functional AI dependency and offer insights for navigating human–AI interaction while promoting balanced AI adoption. Full article

In this paper, we propose a downlink (DL) channel estimation scheme for frequency-division duplex (FDD) multi-antenna orthogonal frequency-division multiplexing (OFDM) systems, leveraging atomic norm minimization (ANM) and deep neural networks (DNN). Unlike time-division duplex (TDD) systems, where uplink (UL) and DL channels are reciprocal, FDD systems do not share this reciprocity, leading to increased channel training overhead. However, both theoretical analyses and empirical evidence reveal that key channel characteristics—such as angles of arrival and departure, path delays, and the number of propagation paths—exhibit partial reciprocity between UL and DL. Building on this insight, we design a DL channel estimation scheme that exploits frequency-independent UL parameters along with estimated DL channel gains. Our method integrates ANM with DNN to enhance estimation accuracy and efficiency. Specifically, ANM formulates the estimation problem while avoiding the off-grid errors inherent in traditional grid-based methods. To further mitigate performance degradation in clustered-path channels and reduce computational complexity, we introduce a DNN-based architecture that predicts channel parameters. The DNN captures hidden relationships between received pilot signals and frequency-independent channel parameters, enabling accurate estimation with linear time complexity. During training, ANM assists in serving users, ensuring reliable performance. Once the DNN is fully trained, it takes over to balance quality of service (QoS) and latency, providing an efficient and accurate solution for DL channel estimation in FDD-OFDM systems. Full article

Background/Objectives: Vestibular disorders can have functional consequences for children, including balance and gross motor delays, academic difficulties and behavioral manifestations; however, they are frequently undiagnosed in children. The purposes of this study were to evaluate the feasibility and clinical utility of performing a non-instrumented dynamic visual acuity (DVA) test as a primary screening tool for children and to examine typical performance on this non-instrumented DVA test in a large sample of children ages 2 through 13 years. Methods: A clinical DVA test was administered to a convenience sample of 208 children aged 2–13 years. Static visual acuity was assessed using a standard Snellen or LEA eye chart, depending on the child’s ability to read letters. Dynamic visual acuity was then measured while the examiner manually rotated the child’s head at 2 Hz (240 bpm). DVA was calculated as the number of lines of visual acuity lost with head movement. Results: All children aged 4 years and older and 67% of 3-year-olds successfully completed DVA testing. Most 2-year-olds and 33% of 3-year-olds were unable to complete DVA testing. Although the number of visual acuity lines lost with rotational head oscillations at 2 Hz varied between age groups, Kruskal–Wallis test indicated no significant difference in DVA scores between age groups (K = 12.721, DF = 9, P = 0.176). Ninety percent of children who were able to perform DVA testing lost two or fewer lines of visual acuity with head rotations consistent with adult norms. Conclusions: This method of DVA testing is an easily accessible and promising clinically feasible screening tool for identifying children with vestibular dysfunction. The authors recommend widespread vestibular screening of children to facilitate rapid referral for diagnosis and treatment of children with vestibular dysfunction. Full article

This study identifies and explains road safety behavior patterns in Colombia using explainable artificial intelligence (XAI). Based on 9232 records and 38 variables from the Territorial Survey of Road Safety Behavior, the CRISP-DM methodology was applied, including data cleaning, normalization, encoding, and feature selection. XGBoost, Random Forest, Bagging, and AdaBoost models were evaluated, incorporating three domain-specific indices: Distraction Index (DI), Risky Road Interaction Index (RRI), and Normative Compliance Index (NCI). AdaBoost achieved the best overall balance (Precision = 0.78; Recall = 0.75; F1-score = 0.77), simultaneously reducing false positives and false negatives. SHAP analysis revealed that environmental and infrastructure factors (lighting, traffic signals, intersections, congestion, perceived crime) explain more variance than self-reported behaviors (mobile phone use, alcohol consumption, speeding). The complementary indices indicated above-average distraction levels, high exposure to risky interactions, and low compliance in specific segments. These findings enable the prioritization of targeted interventions (improvements in lighting and crossings, focused enforcement, and educational campaigns) and support operation with thresholds adjusted to error costs, providing traceable decision support for public road safety policies. Overall, the proposed approach integrates prediction and explainability to enable actionable decisions and continuous monitoring aimed at reducing traffic accidents. Full article

Text-based automatic compliance checking (ACC) employs natural language processing technologies to scrutinize a corporation’s business documents, ensuring adherence to related normative texts. The current methods fall into two primary categories: symbol-based and embedding-based approaches. Symbol-based methods, noted for their accuracy and transparent processing, suffer from limited versatility. Conversely, embedding-based methods operate independently of expert knowledge yet often yield challenging-to-interpret results and require substantial volumes of annotated data. While both types of methods exhibit advantages in different aspects, the current research fails to combine these advantages effectively. Therefore, the existing methods fail to balance interpretability, generalization ability, and accuracy, which are key requirements for practical compliance systems. To address this problem, we introduce a novel approach termed the Dynamic Prompt-based Logic-Aided Compliance Checker (DPLACC), which is grounded in the prompt learning framework. This method initially parses target texts, transforming the results into first-order logical expressions. It subsequently retrieves pertinent knowledge from a knowledge graph, converting the knowledge into analogous first-order logical expressions. These expressions are then encoded into a global semantic vector via a pre-trained first-order logistic encoder. Ultimately, the semantics of expressions and initial texts are amalgamated within the prompt template, facilitating the logical knowledge enhancement of model reasoning. Experiments on Chinese and English datasets demonstrate that DPLACC comprehensively outperforms existing methods based solely on symbols or embeddings in terms of accuracy, precision, recall, and F1 score and significantly surpasses current mainstream large language models. Furthermore, DPLACC exhibits enhanced interpretability and reduced data dependence, maintaining 70% checking accuracy with as few as ten training samples. This capability allows DPLACC to be rapidly deployed in data-scarce real-world scenarios with minimal annotation overhead, thus offering a practical pathway toward the scalable implementation of compliance inspection systems. Full article

The environmentally responsible behavior (ERB) of visitors within forested national parks is critical for balancing biodiversity conservation and sustainable recreation. While the Value–Belief–Norm (VBN) model has been widely used to explain ERB, it has rarely incorporated context-specific value perceptions, such as assigned value. This study extends the VBN model by integrating this construct and examines its role in shaping visitors’ pro-environmental intentions. Taking Qianjiangyuan National Park in China as a case study, we incorporate visitors’ perception of the social value derived from the park’s forest-based ecosystem services into an expanded VBN framework. Data were analyzed using partial least squares structural equation modeling. The results show that assigned value positively influences ERB intention; pro-environmental personal norms are the strongest direct predictor (β = 0.426); and biospheric value, egoistic value, and personal norms significantly foster assigned value formation, whereas altruistic value shows no significant effect. These findings highlight the importance of integrating situational, forest-specific value perceptions into behavioral models and offer management insights for promoting ERB intention through value-congruent communication in forest recreation settings. Full article

A Ducci sequence generated by the vector $A=(a_1,a_2,\dots ,a_n)\in {\mathbb{Z}}^n$ is defined by $(A,D_A,D_A^2,D_A^3,\dots )$, where the Ducci map $D:{\mathbb{Z}}^n\to {\mathbb{Z}}^n$ is given by $D_A=\left(\right|a_2-a_1|,|a_3-a_2|,\dots ,|a_n-a_{n-1}|,|a_1-a_n\left|\right).$ In this paper, we examine the impact of iterative Ducci transformations on Jacobsthal numbers and construct circulant and skew-circulant matrices generated by the resulting sequences. Their properties are investigated through matrix norms (Euclidean (Frobenius), spectral, and ℓ_p), determinants, and eigenvalues. To extend the classical analysis, we incorporate the Convolutional Block Attention Module (CBAM) from deep learning and interpret the structured matrices as simulated image inputs. By analyzing channel-attention vectors and their variances, we assess how successive Ducci transformations influence attention distribution. The first-order transformation produces greater variance in attention weights, indicating enhanced feature discrimination, whereas higher-order transformations promote a more balanced distribution. The results highlight how Ducci transformations influence attention variance in structured matrices. Full article

While bus transit plays a critical role in promoting urban transport sustainable development, the phenomenon of bus bunching has brought severe challenges. To alleviate bus bunching, speed control strategies have been widely used to improve the stability of bus headway distribution. However, existing research mainly focuses on developing optimized models with more flexible speed adjustments; a critical yet often ignored fundamental assumption behind these models is that all bus drivers can strictly adhere to the speed instructions issued by the bus dispatch center. To further explore how the compliance of bus drivers affects the implementation of speed adjustment instructions, this study designs a driving simulation experiment under different driving conditions. Modeled after a real bus line in Changsha, China, the designed simulator study incorporates three external variables, weather conditions, road conditions and command types, with behavioral data from 48 professional drivers analyzed via linear mixed-effects models. The results have shown that road conditions and command types emerged as main factors affecting compliance patterns. Specifically, congestion reduced average speeds by 5.1 km/h, especially affecting female drivers who showed 15.9% Command Compliance Index (it has been designed to quantify execution efficiency and will be referred to as CCI hereafter) reduction versus 10.6% for males. Compared to high-speed instructions, the execution efficiency of low-speed instructions increased by 12.3%, with drivers exceeding target speeds during 45.69% of sections to balance speed profiles. It is notable that the fog density had a minimal impact on efficiency, with only about 2% difference in efficiency. Despite standardized operational norms minimizing individual behavioral heterogeneity, significant group-level demographic variations persisted. Male drivers consistently maintained higher compliance with speed adjustment commands across all driving conditions; drivers under 40 and over 50 had a 3.3% higher CCI than middle-aged drivers; and prior bus bunching exposure increased compliance by 3.3%. High-CCI bus drivers strategically balanced headway distribution through controlled overspeeding. These findings provide empirical foundations for optimizing speed control strategies based on road sections. This study explores ways to enhance the attractiveness of public transit and promote sustainable development. Full article

Age-related declines in balance and cognitive function increase fall risk and reduce quality of life in older adults and people with neurological disorders. Studying these changes in unimpaired adults provides a normative reference for identifying pathological deviations. However, most dual-task studies focus on single cognitive tasks and static conditions, specifically during gait, limiting understanding of how cognitive demand interacts with postural control while standing and during dynamic challenges. This study identified cognitive and motor outcomes most sensitive to age-related differences during motor–cognitive dual tasks of varying complexity across static and dynamic balance conditions, accounting for minimal detectable change. Sixty healthy adults performed dual-tasks ranging from simple motor activities to complex cognitive challenges (Stroop Test) while standing on a robotic platform. Cognitive performance (reaction time) and balance outcomes, including trunk and center of pressure (CoP) sway area, were assessed. Reaction time was sensitive to aging, with standardized estimates ranging from 0.014 to 0.036. The highest values occurred in the most demanding dual-task condition, enabling detection of meaningful change over short timeframes. Age effects on balance were modest under static conditions but amplified during dynamic perturbations across all dual tasks. In the SCWT 3 condition, standardized estimates for CoP sway area increased from 0.006 in the static condition to 0.047 in the passive condition, reflecting an approximately eightfold increase in age sensitivity. Trunk sway primarily reflected cognitive load, whereas CoP sway was most sensitive to balance perturbations and exceeded minimal detectable thresholds over only a couple of years. These findings support sensitive task–condition combinations for early detection and monitoring of age-related cognitive and balance decline. Full article

Scarcity of healthcare resources requires prioritisation decisions that raise complex ethical, economic, and social challenges. While normative frameworks provide guidance on how such decisions ought to be made, growing evidence suggests that individuals differ substantially in how they approach morally charged allocation choices. This study investigates heterogeneity in decision-making styles and support for healthcare prioritisation criteria using an interdisciplinary approach that integrates health economics, social psychology, and computational methods to identify latent decision-making profiles among a sample of adults residing in Portugal. Data were collected from adults residing in Portugal using a structured online questionnaire comprising socio-demographic characteristics, decision-making styles, and preferences elicited through twenty hypothetical healthcare rationing scenarios. The results reveal three meaningful decision-making profiles characterised by different combinations of cognitive styles and ethical prioritisation patterns: analytically oriented decision-makers prioritising health gains; intuitive, context-sensitive decision-makers balancing clinical and social criteria; heuristic-driven decision-makers relying on simpler or less differentiated heuristics. These findings demonstrate that, within this sample, healthcare prioritisation preferences are shaped by systematic variations in decision style rather than a single moral or rational framework. By linking behavioural heterogeneity with ethical decision-making, this study contributes to theoretical debates on healthcare rationing and demonstrates the value of clustering techniques for uncovering latent structures in complex decision data. The results provide insights relevant for the design of decision-support systems and rationing policies, which may be adapted to accommodate heterogeneous decision styles in comparable settings. Full article

In contemporary Muslim societies, digital platforms, algorithmic infrastructures, and networked religious content have fundamentally reshaped not only the circulation of fatwas but also the conditions under which religious authority is constituted, recognized, and contested. This article develops an integrated analytical framework that brings Islamic legal theory (uṣūl al-fiqh) into sustained dialogue with mediatization theory and digital religion scholarship in order to examine how digital mediation reconfigures fatwa authority. Grounded in a qualitative analytical–comparative methodology, the study reconstructs the classical architecture of fatwa authority—rooted in scholarly qualification, isnād-based transmission, contextual discernment, and institutional oversight—and systematically compares it with contemporary digitally mediated environments structured by visibility metrics, platform logics, audience engagement, and algorithmic amplification. It identifies a series of interrelated transformations affecting the epistemic foundations, institutional gatekeeping mechanisms, communicative forms, and normative accountability of fatwa practice. The analysis demonstrates that digital mediation does not merely expand access to religious guidance; it alters the balance between evidentiary reasoning and infrastructural prominence, reshapes the relationship between muftī and mustaftī, and introduces new ambiguities concerning authority, legitimacy, and moral agency—particularly in the context of AI-assisted religious tools. While digital fatwas may enhance accessibility and transnational connectivity, they also risk epistemic fragmentation, erosion of institutional credibility, and the diffusion of accountability. By articulating a Critical Islamic Legal and Media-Theoretical Framework structured around epistemic, institutional, and communicative axes and evaluated through maqāṣid al-sharīʿah, this article offers a systematic model for analyzing digitally mediated Islamic authority. It concludes by outlining jurisprudential and ethical guidelines for integrating digital technologies into the fatwa domain in ways that preserve methodological rigor, moral responsibility, and the integrity of religious guidance. Full article

Background: Patellofemoral pain syndrome (PFPS) is a common musculoskeletal disorder that involves various biomechanical factors, including the altered positioning of the patella, weakness of the lower extremity muscles, delayed activation of the vastus medialis muscle, and excessive pronation of the foot. Although the short- and long-term effects of external support among the recommended conservative treatment methods for PFPS have been examined, there remains a lack of consensus regarding their impacts. This study was conducted to investigate the immediate effects of braces and rigid taping applied to control pain on proprioception, functional status, and balance in patients with PFPS, and to compare these outcomes with normative values obtained from healthy individuals. Methods: The study included 18 patients with PFPS and 18 healthy individuals who met the inclusion criteria. Through randomization of the intervention sequence, patients were evaluated under conditions of rigid taping, support, or without any support. Their pain levels before and after the application were assessed using the Visual Analog Scale; their functional status was evaluated with the Kujala Patellofemoral Scoring, the 10-Step Up Test, and the Squat; their balance performance was measured using the Y-Balance Test and the Single Leg Stance Test; and their proprioception was assessed with the Joint Position Sense Test. Results: It has been determined that rigid taping and bracing have similar effects in the immediate management of pain, proprioception, functional status, and balance issues in patients with PFPS. The interventions were observed to bring patients’ static balance and proprioception parameters closer to the values seen in healthy individuals. Conclusions: Rigid taping and bracing are both effective interventions in the management of PFPS, offering benefits such as pain relief, prevention of proprioceptive deficits, mitigation of balance impairments, and enhancement of functional outcomes. The selection of the most appropriate modality should be based on the individual patient’s characteristics and tolerance levels. Full article

Corporate governance serves as the institutional foundation that aligns managerial decisions with stakeholder interests and sustainable growth. It provides the accountability mechanisms necessary for translating environmental and social initiatives into measurable firm value. This paper examines how Environmental, Social, and Governance (ESG) pillars individually influence firm performance in Gulf Cooperation Council countries (GCCs). The paper analyses a balance panel dataset comprising 84 listed firms observed over a five-year period from 2019 to 2023 with 392 observations. The paper employs two-way fixed effects with Driscoll–Kraay robust standard errors to ensure consistent inference by correcting for heteroskedasticity, autocorrelation, and cross-sectional dependence. Firm performance is assessed by Tobin’s Q, return on assets (ROAs), and sustainable growth rate (SGR), reflecting market valuation, accounting profitability, and long-term sustainable growth, respectively. Tobin’s Q results show that GCC firms’ performance is enhanced by higher environmental pillar scores, whereas it responds negatively to increases in social and governance scores. Findings remain qualitatively similar for ROA but of a smaller magnitude. These findings challenge the conventional assumption that ESG dimensions uniformly enhance firm value, revealing instead that governance and social investments may impose agency costs or compliance burdens in emerging markets where institutional frameworks and stakeholder expectations differ fundamentally from developed economies. The environmental pillar exhibits a positive and significant association with firms’ long-term sustainable growth, whereas the social pillar exerts an adverse effect. Conversely, assessing firm performance with SGR reveals that the influence of the governance pillar is statistically insignificant. Theoretically, this paper contributes by demonstrating that ESG pillars operate through differentiated value-creation mechanisms in institutional contexts characterised by weak stakeholder activism and nascent ESG disclosure norms. Findings suggest GCC firms should prioritise environmental initiatives while carefully evaluating costs and benefits of governance and social programmes. Full article

Physics-informed neural networks (PINNs) solve partial differential equations (PDEs) by embedding physical conditions as soft penalties into the loss function. However, the coexistence of multiple loss components often leads to gradient conflicts, degrading convergence and solution accuracy. To address this issue, we propose a dynamic domain–gradient loss reweighting PINN (DDR-PINN). The proposed method introduces a dual-residual reweighting mechanism based on gradient variations, where adaptive weights are derived from the L2 norm of the dot product between loss gradients and residuals. These weights are further normalized through a nonlinear hyperbolic tangent transformation, enabling dynamic and balanced reweighting of interior, initial, and boundary domain losses throughout training. Extensive numerical experiments on PDEs with both Dirichlet and Neumann boundary conditions demonstrate that the DDR-PINN consistently outperforms the standard PINN, APINN, and VI-PINN with the fewest trainable parameters. Full article