Search Results (628)

Background: Chinese immigrant families of autistic children in the United States face intersecting barriers related to language, culture, immigration, and fragmented service systems. Yet little is known about how Chinese immigrant parents engage in advocacy or how such efforts relate to disability and human rights. Methods: This qualitative study draws on in-depth interviews with fourteen Chinese immigrant parents of autistic children across multiple U.S. regions. Data were triangulated with analyses of publicly recorded advocacy events and parent-produced textual materials. Reflexive thematic analysis was used to examine motivations for advocacy, advocacy practices, and structural, linguistic, and cultural constraints. Results: Advocacy rarely emerged as an intentional or identity-driven pursuit. Instead, parents were compelled into advocacy through institutional exclusion, service denial, and unmet care needs. Parents engaged in diverse forms of advocacy, including migration, negotiation within institutions, information translation, community-building, and grassroots organizational leadership. Cultural norms shaped advocacy strategies, producing quiet, relational, and collective forms of action often overlooked in dominant rights-based models. Conclusions: Interpreted through a disability justice lens, parental advocacy functions as burdened and unequally distributed labor compensating for systemic failures. Findings underscore the need for institutional reforms that reduce reliance on families’ capacity to fight for access, dignity, and care. Full article

Background/Objectives: This study evaluated whether linear cephalometric measurements show systematic differences in their central values across birth cohort groups in adults from a clinical population and analyzed the implications of these differences for clinical interpretation when norms and clinical deviations are used as a reference framework. Methods: A cross-sectional observational analytical study was conducted based on 604 lateral cephalometric radiographs of adult patients. Eleven linear cephalometric measurements were obtained and compared across predefined birth cohort groups (<1980, 1980–1989, and 1990–1999) using robust estimators of central tendency through median regression models adjusted for sex, age group, and sagittal skeletal classification. Results: Several linear cephalometric measurements revealed different central values between the birth cohorts, even after adjusting for relevant covariates. Cranial length, anterior cranial base length, posterior facial height, and posterior cranial base length had lower adjusted median values in the 1990–1999 cohort than in the <1980 cohort. The effective maxillary length and maxillary length also differed between cohorts. Mandibular measurements, including mandibular length, corpus length, and ramus height, showed the largest adjusted median contrasts between cohorts. These cohort-associated differences were not uniform across all measurements. Conclusions: Routinely used linear cephalometric measurements present different central values across adult birth cohort groups under comparable clinical conditions. The relative position of a cephalometric value within its reference distribution may vary by birth cohort. This suggests that using fixed reference means and standard deviations could lead to systematic misestimation in adults from various birth cohorts. Cohort-aware interpretation is valuable in routine cephalometric assessments. Full article

Deep neural networks suffer performance drops when source and target domains differ in distribution, motivating research into domain adaptation (DA). Traditional DA approaches presume source samples come from a single domain and can be available during adaptation. Nevertheless, in real-world applications, multiple source domains often exist, and source samples may be inaccessible owing to privacy and storage limitations. In response to the challenges of multi-source and source-free, multi-source-free domain adaptation (MSFDA) is proposed, which captures transferable information from a set of pre-trained source models to boost performance of the model on target domain. Most MSFDA methods meet these challenges by utilizing pseudo-labeling. However, pseudo-labels generated by distinct source models may contain noise and even be contradictory, which weakens their efficacy in facilitating source models adapting to the target domain. Moreover, these methods do not consider class imbalance, which would lead to biased predictions for minority classes, and undermine adaptation. Therefore, we propose a novel MSFDA method which extends adversarial learning to a multi-source-free setting. This method presents proxy multi-source domain adversarial learning, which aligns target features extracted by different source models in an adversarial manner, enhancing the capability of source models to extract domain-invariant features and potentially obtain high-quality pseudo-labels. Moreover, a nuclear-norm maximization regularization is employed to constrain prediction matrices, which can reduce the prediction uncertainty and enhance the discriminability of the model, while mitigating the prediction bias and promoting the prediction accuracy for minority classes. Finally, comprehensive evaluations on four benchmark datasets prove the validity of the proposed method. Full article

This paper investigates the stability of telehandlers operating on inclined terrain through a sequential methodological approach. In a first stage, stability is assessed using quasi-static methods based on force and moment equilibrium, including the load transfer matrix and the stability pyramid. These approaches account for gravitational and inertial effects through equivalent external forces and moments applied at the global centre of gravity, enabling efficient evaluation of load redistribution and proximity to rollover thresholds under generalized quasi-static conditions. The application of these methods highlights intrinsic limitations when addressing structurally complex machines such as telehandlers equipped with a pivoting rear axle and evolving mass distribution due to boom motion. In particular, quasi-static approaches require a priori assumptions regarding the effective rollover axis and cannot fully capture the coupled geometric and contact interactions between rear axle articulation limits, centre of gravity migration, tyre–ground interface behaviour, and support polygon evolution. To overcome these limitations, a nonlinear dynamic multibody model based on the three-dimensional Bond Graph (3D Bond Graph) methodology is introduced. The model is implemented within a virtual tilt–rotation test platform and validated against experimental results obtained from ISO 22915-14 stability tests. The comparison confirms compliance with normative requirements and demonstrates that the dynamic framework captures condition-dependent rollover mechanisms and transitions between distinct virtual rollover axes that cannot be fully explained by quasi-static formulations. Unlike most previous studies, which focus on fixed configurations or forward-driving scenarios, the proposed framework analyzes stability evolution under spatial inclination while accounting for structural articulation constraints. The explicit identification of rollover axis transitions induced by rear axle articulation provides a deeper mechanistic interpretation of telehandler stability and supports the use of high-fidelity dynamic simulation as a complementary tool for test interpretation, experimental planning, and the development of predictive stability and operator assistance systems. Full article

This article examines how newsroom routines shape the health, safety, and professional experiences of BIPOC (Black, Indigenous, and People of Color) journalists in U.S. broadcast news. While journalistic norms of objectivity and neutrality often frame risk as evenly shared, this study situates safety within routine newsroom practices to show how risk and institutional support are unevenly distributed, particularly during high-stakes coverage such as protests, door-knocks, and politically charged events. The analysis draws on qualitative, in-depth interviews conducted as part of a larger study on journalists’ identities and definitions of fairness and applies a critical framework attentive to power and postcolonial influences in newsroom organizations. Findings indicate that BIPOC journalists routinely navigate tensions between production demands and personal safety, with their lived experiences in the field frequently diverging from the assumptions of white colleagues and newsroom leadership. Participants describe adapting newsroom routines by setting boundaries, asserting professional judgment, and challenging unsafe expectations. These practices illuminate how newsroom routines are both sites of constraint and negotiation. This article concludes that attention to identity and power within newsroom routines is essential for understanding how fairness, safety, and ethical practice are enacted in contemporary broadcast journalism. 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

Additive manufacturing (AM) has rapidly evolved from a prototyping tool into an effective method for producing end-use components, thanks to its ability to produce complex, lightweight and customised parts. However, this technique requires a thorough understanding of the long-term behaviour and degradation mechanisms of components, especially when polymers are involved in the printing process. Unlike polymer components manufactured using traditional methods, polymers produced through AM exhibit unique microstructures, anisotropies, and interfacial characteristics due to the layer-by-layer fabrication process. These features can affect how these materials respond to thermal, mechanical and environmental stresses over time. Furthermore, technology-specific processing parameters directly govern porosity distribution, crystallinity evolution, interlayer bonding quality, and residual stress development, all of which are key factors for ensuring long-term performance. This review aims to support researchers in the development of durable additively manufactured polymer components by systematically analysing polymer degradation mechanisms, accelerated ageing and lifetime prediction methodologies. Following a PRISMA-based screening process, approximately 160 international standards relevant to polymer durability in additive manufacturing were selected from an initial corpus of about 620 documents for in-depth analysis. Processing–structure–property relationships specific to the AM processing of polymers, including the commonly used FFF (fused filament fabrication), SLA (stereolithography) and SLS (selective laser sintering), are examined in relation to crucial aspects for long-term structural integrity and degradation behaviour. Finally, limitations within the current normative framework are identified, emphasising the absence of process-aware durability assessment protocols and the need for dedicated standards tailored to additively manufactured polymer components. Full article

(1) Background: Rapid global aging has surged demand for elderly family care, a role long dominated by women. This study aims to reveal the specific manifestations of the gender division of labor in elderly family care through a systematic evidence synthesis, covering care tasks, care types, impacts, and driving factors. (2) Methods: We searched four databases (Web of Science (SSCI subsets), Scopus, PubMed, and ProQuest) for articles published between 2015 and 2025. After screening, 45 peer-reviewed articles from 16 countries or regions were included, and thematic analysis was employed for data extraction and evidence synthesis. (3) Findings: The results indicate a differentiated gender division of labor and inequality in elderly family care, where female caregivers bear a greater burden in terms of task assumption, care time allocation, and perception of care impacts. The formation of the gender division of labor results from a dynamic interplay among multiple factors, including objective needs, social norms, and institutional influences. Promisingly, men are increasingly participating in family care for the elderly. (4) Conclusions: The study suggests that gender-sensitive policies should address the gender gap for elderly family care and provide targeted support to alleviate the unequal distribution of care burdens. Full article

Under non-independent and identically distributed (Non-IID) conditions, significant variations exist in local model updates across clients and training phases during the collaborative modeling process of differential privacy federated learning (DP-FL). Fixed clipping thresholds and noise scales struggle to accommodate these diverse update differences, leading to mismatches between local update intensity and noise perturbations. This imbalance results in data privacy leaks and suboptimal model accuracy. To address this, we propose a differential privacy federated learning method based on adaptive clipping thresholds. During each communication round, the server adaptively estimates the global clipping threshold for that round using a quantile strategy based on the statistical distribution of client update norms. Simultaneously, clients adaptively adjust their noise scales according to the clipping threshold magnitude, enabling dynamic matching of clipping intensity and noise perturbation across training phases and clients. The novelty of this work lies in a quantile-driven, round-wise global clipping adaptation that synchronizes sensitivity bounding and noise calibration across heterogeneous clients, enabling improved privacy–utility behavior under a fixed privacy accountant. Using experimental results on the rail damage datasets, our proposed method slightly reduces the attacker’s MIA ROC-AUC by 0.0033 and 0.0080 compared with Fed-DPA and DP-FedAvg, respectively, indicating stronger privacy protection, while improving average accuracy by 1.55% and 3.35% and achieving faster, more stable convergence. We further validate its effectiveness on CIFAR-10 under non-IID partitions. Full article

Public debates about psychoactive substances have traditionally been organized around the pharmacology of compounds and the institutional control of supply. In digitally mediated societies, however, the pathways through which people encounter psychoactives are increasingly informational: search engines, recommender systems, social platforms, and—distinctively—conversational AI. These systems do not merely deliver neutral facts. They rank, frame, personalize, and conversationally validate claims in ways that can shape perceived norms, acceptable risk thresholds, and willingness to seek help. This opinion advances the concept of AI-mediated exposure to capture how algorithmic curation and interactive dialogue become upstream determinants of psychoactive-related harms and benefits across the continuum from everyday medicines to non-medical use. From a social-scientific ethics perspective, the central question is not whether AI is “good” or “bad,” but what obligations apply when AI performs interpretive authority in contexts characterized by vulnerability, stigma, and unequal access to trusted expertise. The paper argues for an ethics-centered governance framework grounded in four commitments: epistemic responsibility (how claims are generated, warranted, and communicated), relational responsibility (how users are treated in moments of uncertainty, distress, and stigma), distributive justice (who benefits and who bears risk under unequal conditions), and accountability (how behavior is evaluated, contested, and corrected over time). The aim is to treat conversational AI as a public-facing institution whose design choices must be ethically legible and publicly contestable, oriented toward harm reduction without intensifying surveillance, moralization, or inequity. Full article

Low-Rank Adaptation (LoRA) has become a widely adopted parameter-efficient fine-tuning (PEFT) technique for large language models (LLMs). LoRA’s benefits stem from its light weight and modular adapters. Standard LoRA applies adapters uniformly across all Transformer layers, implicitly assuming that each layer contributes equally to task adaptation. However, LLMs are found to have internal substructures that contribute in a disproportionate manner. In this work, we provide a theoretical analysis of how LoRA weight updates are influenced by a layer’s activation magnitude. We propose Act-LoRA, a simple activation-guided layer selection strategy for selective Low-Rank Adaptation. We evaluate this strategy for both encoder-only and decoder-only architectures using the GLUE benchmark. Our method achieved a 20% GPUh saving with a 1% drop in GLUE score using DeBERTaV3-Base on a single-instance GPU with 50% less LoRA parameters. It also achieved 2% GPUh savings with a less than 0.15% drop in GLUE score with the Llama-3.1-8B model in Distributed Data Parallel mode with 25% fewer LoRA parameters. Our experiments and analysis show that the compute and memory requirements of LoRA adapters increase linearly with the number of selected layers. We further compare activation-guided selection against gradient-guided importance metrics and show that activation norms yield more stable and reproducible layer rankings across seeds and datasets. Overall, our results demonstrate that activation-guided layer selection is a practical and effective way to improve the efficiency of LoRA fine-tuning, making it immediately compatible with some existing PEFT techniques and distributed training frameworks. Full article

Background/Objectives: Autism research has often interpreted autistic sociality through neurotypical norms, limiting ecological accounts of autistic meaning-making and context-sensitive support needs. Social virtual environments (SVEs), such as VRChat, allow modulation of sensory exposure, social distance, and participation pace, potentially enabling autistic-led interaction with greater autonomy and predictability. This study examined how autistic young adults co-construct meanings around social interaction, identity, and self-regulation in peer-led discussions within an SVE; identified context-sensitive processes relevant to well-being; and evaluated the feasibility and acceptability of SVEs as a participatory research setting. Methods: Sixteen autistic young adults (18–38 years; DSM-5-TR, Level 1) participated in nine remote sessions conducted in VRChat, coordinated via a co-designed Discord server. The peer-led discussions were audio-video recorded, transcribed, and anonymized. Data were analyzed using reflexive thematic analysis, combining inductive session-level coding, cross-session thematic clustering, and participatory refinement with community partners. Results: Autistic experience was framed as a context-dependent negotiation of interpretive risk, interactional workload, masking-related energy costs, and epistemic injustice, alongside future-oriented accounts emphasizing access, dignity, and systemic redesign. Observational memos documented multimodal participation, distributed peer facilitation, and accessibility-relevant sensitivities to environmental stability. Community partners reported positive experiences and supported the acceptability of private-world VRChat sessions. Conclusions: Peer-led discussions in an SVE can support ecologically grounded, participant-centered qualitative research, offering methodological opportunities to study autistic meaning-making under conditions that reduce demands and risks. Full article

Norwegian early childhood education and care (ECEC) is increasingly shaped by cultural diversity, raising questions of justice regarding how children and parents, particularly those from refugee and other minority backgrounds, are recognised as equal participants in the institutional community. This article develops a conceptual framework of just recognition as professional practice through a meta-ethnographic synthesis of three qualitative studies conducted in Norwegian ECEC. The studies examine ECEC professionals’ constructions of diversity, refugee parents’ experiences, and negotiations within home–ECEC partnerships. Drawing on theories of recognition, participatory parity, and democratic equality, this article conceptualises diversity as recognition-in-relation and analyses how justice is enacted in everyday pedagogical and relational practices. The synthesis identifies three interlinked mechanisms, equality, adaptation, and reflexivity, through which recognition and misrecognition are produced across relational levels. While equality is often enacted as sameness, adaptation may be asymmetrically distributed, and reflexivity emerges as a crucial professional practice for rendering institutional norms visible and open to negotiation. This article argues that everyday pedagogical work and home–ECEC partnerships constitute key sites where the conditions for equal standing and participatory parity are either enabled or constrained. By shifting attention from inclusion as access to justice as enacted practice, the study contributes a relational and institutional framework for analysing cultural diversity in ECEC. Full article

This study examines intelligent judgment formation in blockchain-based public digital wallet systems within smart city environments. Drawing on an integrated framework that combines cognitive evaluation, social influence, and trust–risk appraisal, this research conceptualizes intelligent decision-making as a socially embedded and contextually enacted evaluative process rather than a fixed cognitive attribute. A structural equation modeling approach is employed to analyze the interrelationships among perceived usefulness, perceived ease of use, subjective norms, social electronic word of mouth, trust–risk appraisal, attitude, and behavioral intention. The findings indicate that socially distributed information signals play a dominant role in shaping evaluative integration and decision readiness, while cognitive and institutional appraisals operate primarily through mediated pathways. The results suggest that intelligent action in public digital service ecosystems emerges from the coordinated interaction of usability perception, institutional confidence, and socially calibrated information flows. These findings contribute to theoretical extensions of technology acceptance models in public governance contexts and offer implications for the design of socially responsive digital service infrastructures. Full article