Search Results (5,699)

This quantitative study explores divergent beliefs about word order in American Sign Language (ASL) within the U.S. Deaf community. Although the academic literature commonly characterizes ASL as following a Subject–Verb–Object (SVO) structure, certain Deaf individuals maintain that ASL predominantly uses a Subject–Object–Verb (SOV) order. This perception reflects an ideological positioning of ASL as fundamentally distinct from English and may align with community norms that have historically emphasized SOV structures. Data were collected through an acceptability judgment task with 53 varied sentence structures presented via video. Among 86 Deaf participants, distinct preferences emerged based on ASL acquisition background. Participants with early ASL exposure from Deaf parents preferred the SOV structure, while those with early ASL exposure from hearing parents preferred the SVO structure. This finding suggests that variability in beliefs about acceptable ASL word order, along with factors such as early language exposure and parental input, influences judgments of acceptability. Full article

Fuzzy multi-criteria decision-making is important for technology assessment when expert judgments contain uncertainty, hesitation, and inconsistent evidence. This study develops a T-Spherical Fuzzy-Valued Neutrosophic Set (T-SFVNS)-based MEREC-EDAS framework for evaluating low-carbon cooling and energy-management technologies for data centers. Expert linguistic assessments are represented by T-Spherical Fuzzy-Valued Neutrosophic Numbers and aggregated before a score function is used at the explicit scalarization boundary. Standard MEREC then derives objective criterion weights from criterion-removal effects, and standard EDAS ranks alternatives by their positive and negative distances from the average score profile. The application evaluates nine technologies against ten criteria using assessments from thirty domain specialists. The corrected MEREC calculation assigns the greatest weights to carbon reduction potential (0.127), electricity demand reduction (0.125), maintenance complexity (0.124), operational cost efficiency (0.123), and cooling efficiency (0.123). The final ranking is Direct-to-Chip Liquid Cooling, Liquid Immersion Cooling, AI-Enabled Energy Management, Water-Side Free Cooling, Free-Air Cooling, Rear-Door Heat Exchanger Cooling, Hot/Cold Aisle Containment, Renewable-Powered Cooling, and Thermal Storage-Assisted Cooling. Weight perturbation, q-parameter, leave-one-expert-out, alternative-deletion, dominated-alternative, and multi-method comparisons show that the leading tier is robust, although the exact order of the two liquid-cooling technologies is sensitive in some scenarios. The findings provide a transparent and reproducible decision-support basis while explicitly acknowledging the information compression and rank-reversal limitations of score-based MCDM. Full article

Arabic-speaking populations face disproportionate cybersecurity risks due to the predominantly English-centric design of existing awareness materials, which fail to accommodate Arabic dialectal diversity, script complexity, and culturally embedded communication patterns. These deficiencies impair users’ ability to interpret phishing messages, authentication requests, and security alerts, increasing susceptibility to social engineering, identity theft, and data breaches. This paper presents SECURE-A²RC, a rubric-constrained, Arabic-aware large language model framework designed to deliver scalable, interpretable, and culturally relevant cybersecurity education. The framework comprises two coupled components. The first, the Arabic-Aware Secure Communication Encoder (A-SCE), employs an instruction-tuned LLM to produce multidimensional encodings that capture three learner competencies: security intent comprehension; linguistic deception cue recognition encompassing urgency, authority impersonation, and incentive framing; and action-critical translation fidelity across Arabic dialectal registers and Arabic–English bilingual contexts. The second, the Rubric-Constrained Adaptive Feedback Generator (RCAFG), translates A-SCE encodings into personalized, expert-aligned instructional feedback and proficiency-calibrated adaptive tasks, ensuring pedagogical consistency, security correctness, and dialect awareness throughout the learning cycle. The framework is evaluated on three domain-relevant corpora: the English–Arabic Parallel Phishing Email Corpus, the Open MalSec dataset, and the Arabic Spam and Ham Tweets dataset. SECURE-A²RC achieves a 31% improvement in phishing identification accuracy and a 26% reduction in action-critical translation errors compared to conventional awareness materials. A comparative evaluation against SERENA, a Multi-Agent LLM, and the Arabic Multitask Learning Model confirms consistent superiority across detection accuracy, F1-score, dialectal robustness, and educational effectiveness metrics, affirming rubric-constrained LLM integration as a viable approach to equitable multilingual cybersecurity education. Full article

Background/Objectives: Free-text veterinary pathology diagnoses contain essential information for cancer registration but are difficult to convert into standardized ontology-based codes because of linguistic variability, contextual modifiers, and large ontology search spaces. This study evaluated a hybrid lexical–semantic architecture for the automated assignment of Vet-ICD-O-Canine-1 morphology codes. Methods: A retrospective single-registry benchmark included 211 diagnoses from the São Paulo Animal Cancer Registry. Of these, 190 contained sufficient morphological information for expert-reviewed reference coding, whereas 21 generic or insufficiently specified descriptions were retained as an exploratory challenge subset. Fuzzy lexical matching retrieved Top-10, Top-20, or Top-30 candidates from the complete 971-entry morphology ontology, followed by semantic selection using Claude Haiku 4.5 and structured JSON output. Performance and computational efficiency were compared to direct full-ontology inference. Results: Among the evaluated fuzzy metrics, token_set_ratio achieved the highest Top-30 reference-code retrieval rate of 89.5%. End-to-end exact-match agreement increased from 73.7% with Top-10 to 79.5% with Top-20 and 85.8% with Top-30 (95% CI, 80.1–90.0%). Top-30 generated non-null codes for 93.2% of the 190 evaluable diagnoses and achieved a conditional exact-match agreement of 92.1%. By contrast, the direct full-ontology baseline achieved 71.2% conditional exact-match agreement (42/59) among non-null predictions and 22.1% end-to-end exact-match agreement (42/190) when incorrect predictions, null outputs, and technical failures were considered non-concordant outcomes. Compared to direct full-ontology inference, Top-30 reduced input-token consumption by 92.4%, total token consumption by 92.2%, and inference cost by 91.3%, while avoiding the 118 API rate-limit failures observed with the direct baseline. Among the 21 insufficiently specified diagnoses, Top-30 returned null codes in 38.1% and non-null codes in 61.9%. Conclusions: Ontology-guided candidate reduction improved coding agreement, computational efficiency, and operational robustness within this retrospective single-registry benchmark. However, the reported performance estimates require confirmation in larger independent datasets, and an upstream data-sufficiency or abstention mechanism is needed before prospective operational deployment. Full article

Time governs human experience, yet human beings continually seek to govern time. Within Jewish mysticism, time functions as a foundational and elusive dimension of human experience, serving as a dynamic arena for theosophical speculation, theurgical action, and spiritual transformation. In this context, the influential nineteenth-century Hasidic work Bnei Yissachar by Rabbi Tzvi Elimelech Shapira of Dinov (1783–1841) occupies a unique position, offering a systematic and nuanced theology of sacred time. While previous scholarship has recognized the importance of sacred time in his thought, this study uncovers a complex reciprocal temporal mechanism that balances two dialectical poles: cosmic subordination and human mastery. Through a close reading of Shapira’s homilies, this article demonstrates how he navigates the tension between fixed, pre-ordained divine temporal matrices, typified by the linguistic and numerical hidden structure of the Sabbath, and active human agency capable of reshaping the qualitative influx of time, illustrated by the sanctification of the New Moon and the Passover Seder night. By integrating Lurianic kabbalistic frameworks, gematria, and linguistic ontology with embodied ritual practices, Shapira reframes the Jewish calendar as an interactive, experience-oriented relationship between the human and the divine. Full article

Classical readability formulas are widely used to evaluate academic abstracts, yet the evidence on what they register is mixed, and disciplinary differences are often invoked to explain that inconsistency. This study examines within-field variation, treating forestry-related research as a set of knowledge domains and scoring 4191 English-language Web of Science abstracts from 2008 to 2019 with four classical indices. The abstracts were consistently demanding on all four indices. The most difficult domain was forest economics, policy, governance and social dimensions, with ecology and conservation being intermediate and inventory and management not being distinguishable from tree biology and silviculture. The contrasts persisted within journals, though the distributions overlapped substantially, and most variation remained within domains. With domain and covariates being controlled, no index showed a statistically distinguishable association with normalized citation impact, and the interaction tests did not establish domain heterogeneity. Domain was modestly associated with citation impact, but readability did not account for it. The indices, therefore, register systematic differences in linguistic form across knowledge domains while giving no dependable signal of scholarly uptake. For authors and editors, such scores work best as prompts for closer reading with a domain and an audience in view, not as field-wide standards or proxies for expected citation. Full article

Decision-making in organizational and socio-technical systems often involves a responsible superior expert who must integrate committee judgments expressed under linguistic uncertainty, hesitation, and heterogeneous expertise. This paper proposes a systems-based superior–committee group decision-support method for criteria weighting under linguistic intuitionistic fuzzy uncertainty. First, the best–worst method is extended by using linguistic intuitionistic fuzzy numbers to represent pairwise preference information with linguistic membership, non-membership, and indeterminacy degrees. A utility transformation is then introduced to convert linguistic intuitionistic fuzzy comparisons into numerical preference values, enabling criteria weights to be derived through linear programming models. Second, a two-stage group decision-support framework is developed for superior–committee decision structures. In the first stage, committee expert influence is calculated by integrating prior expert weights obtained from the superior expert’s evaluation with judgment-derived expert weights based on certainty and agreement. In the second stage, the final criteria weights are obtained by combining the superior expert’s judgments with the weighted committee judgments. A constructed UAV criteria-weighting case and complementary numerical analyses are presented to illustrate the calculation process and examine the behavior of the proposed method. The results show that the framework provides a transparent mechanism for representing uncertain preferences, assigning expert influence, and deriving interpretable criteria weights in superior–committee group decision systems. Full article

Autism is a prevalent neurodevelopmental disability among children in the United States. Enhanced Milieu Teaching (EMT) en Español Para Autismo is a caregiver-implemented language-focused naturalistic developmental behavioral intervention specifically for young children on the autism spectrum whose families are Latino and Spanish-speaking. The current study tested a reduced intensity adaptation of EMT en Español Para Autismo using a single-case experimental design study with four caregiver–child dyads. All children demonstrated characteristics of autism and lived in low-income Spanish-speaking households. Caregivers were taught to use EMT en Español Para Autismo strategies during play with their child using a cyclical teach–model–coach–review approach. Home visits occurred once each week. Three out of four dyads completed the study. None of the caregivers demonstrated functional relations between the cyclical teach–model–coach–review approach and their use of strategies; however, caregivers did increase their use of contingent language models and time delays when intervention began. Caregiver impressions of the intervention were positive, but they varied in their perceptions of some of the strategies (e.g., limiting instructions), consistent with participants in prior studies. Overall, the reduced intensity of the intervention and long gaps between visits may have limited the effectiveness of this intervention compared to findings in prior studies. More research is needed to tailor language interventions for Latino Spanish-speaking families with autistic children, especially those with limited resources. Full article

Limited inclusion of ethnically diverse and socioeconomically disadvantaged populations in pain research undermines external validity, generalizability, and equity. This comprehensive review synthesized evidence on effective strategies to recruit and retain these populations in pain studies. We searched Medline, Web of Science, Embase, Scopus, and CENTRAL (12 April 2025) and included studies in which ethnically diverse and socioeconomically disadvantaged participants comprised ≥75% of the sample. Quantitative data were pooled using random-effects meta-analyses of proportions, with prespecified subgroup analyses, and qualitative findings were integrated through thematic synthesis. Certainty of evidence was evaluated using GRADE. Eighteen studies (n = 4611; 11 experimental, 5 observational, 2 qualitative), primarily from the United States and involving chronic pain, met inclusion criteria. Overall enrollment among ethnically diverse and socioeconomically disadvantaged groups was 37% (95% CI 18–58%), with significantly higher enrollment in observational studies (84%, 95% CI 78–90%) than in experimental trials (22%, 95% CI 10–36%; p < 0.001). Overall retention was 77% (95% CI 64–88%) and did not differ significantly by study design. Statewide disease-clinic networks, purposive community-leader engagement, and snowball sampling produced the highest enrollment, whereas medical-record screening yielded the lowest enrollment but the highest retention. Compensation and reminder strategies were similarly effective for retention. Thematic synthesis highlighted trust, culturally and linguistically tailored communication, hybrid and flexible visit schedules, transportation assistance, and clinician engagement as key facilitators. GRADE certainty was low for enrollment and moderate for retention. Community-engaged recruitment strategies, clinician referrals, culturally tailored materials, hybrid procedures, and modest incentives can substantially improve participation of ethnically diverse and socioeconomically disadvantaged populations in pain research. Standardized CONSORT-style reporting of recruitment/retention flowcharts according to strategy and ethnicity/socioeconomic status is essential to refine evidence-based strategies in the future. Full article

This article maps research on artificial intelligence (AI) in Christian Religious Education (CRE) during the first phase of rapid development following the widespread adoption of generative AI. A scoping review was conducted in Scopus, Web of Science, the Open Digital Theological Library, and IxTheo, supplemented by Google Scholar. Included were full-text English-language publications from 1 January 2023 to 31 March 2026 that addressed AI in Christian educational, catechetical, theological–educational, or formative contexts. The final corpus comprised 23 publications. Data were charted by publication type, geographical and educational context, main topic, and key claims or findings. The results show that the field is recent, methodologically heterogeneous, and not yet well consolidated, but that the emerging debate is organised around four inter-related strands: didactic potential and personalisation, ethical–technical limitations, anthropological–theological reflection, and systemic, institutional, and cultural–geographical conditions. The literature is marked by a tension between AI’s didactic potential and concerns regarding its ethical, theological, anthropological, and institutional implications. The review contributes a thematic synthesis that clarifies why AI integration in CRE requires not only educational innovation but also theologically informed, pedagogically mediated, and context-sensitive frameworks for religious formation. Significant gaps remain, including limited empirical and longitudinal research, insufficient attention to students’ and parents’ perspectives, cultural and linguistic contexts, and a lack of religion-specific, empirically tested didactic models. The review concludes that future research should move toward longitudinal, practice-based, and theologically grounded models for AI use in CRE. Full article

Background/Objectives: Dementia is a progressive neurocognitive disorder characterized by decline in cognitive, communicative, and functional abilities. Speech and language therapists (SLTs) play a role in managing communication and cognitive–linguistic impairments while supporting families and caregivers. However, evidence regarding the effectiveness of speech and language therapy interventions remains heterogeneous, and evidence has not integrated cognitive–linguistic and caregiver-related outcomes. This systematic review aimed to synthesize recent evidence regarding the effects of speech and language therapy interventions on cognitive–linguistic functioning in individuals with dementia and caregiver-related outcomes. Methods: The review followed PRISMA guidelines. Studies were retrieved from Google Scholar, PubMed, and ScienceDirect using search terms including “speech therapy,” “language therapy,” and “dementia.” Eligible studies included those published within the last ten years that examined speech and language therapy interventions in individuals with dementia and reported cognitive-linguistic and/or caregiver-related outcomes. Methodological quality was assessed using the Joanna Briggs Institute (JBI) Critical Appraisal Tools. Findings were synthesized narratively due to heterogeneity in study designs, interventions, and outcome measures. Twelve studies met the inclusion criteria. Results: The included studies generally reported improvements in cognitive–linguistic and communicative functioning following speech and language therapy interventions. Fewer studies reported performance stabilization, while none reported deterioration. Several studies also reported positive effects on caregiver communication confidence and interaction quality. Conclusions: Speech and language therapy appears to improve cognitive–linguistic functioning and support caregiver communication and engagement. However, the evidence remains limited due to small sample sizes, heterogeneous intervention protocols, and variability in outcome measures, highlighting the need for further high-quality research. Full article

The rapid evolution of AI-driven language technologies has inadvertently widened the gap between high-resource and marginalised languages. Despite significant progress in AI-driven translation for high-resource languages, low-resource languages remain underrepresented due to limited data, a lack of benchmarks, and evaluation challenges. This study presents a comprehensive systematic review of machine translation for low-resource languages, focusing on advances in neural machine translation (NMT) and large language models (LLMs) between 2017 and 2025. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 63 studies were selected from the 1696 articles in the Scopus, Web of Science, and Google Scholar databases. The review identifies five dominant methodological approaches: data augmentation, back-translation, transfer learning, pre-training, and parameter-efficient fine-tuning. The findings reveal that model performance is highly dependent on resource availability: transformer-based NMT excels in moderate data settings, while LLMs demonstrate promising zero-shot and few-shot capabilities in extremely low-resource scenarios. Hybrid NMT–LLM approaches emerge as a particularly effective paradigm. The study also highlights critical challenges, including the absence of standardised benchmarks, over-reliance on inadequate evaluation metrics such as Bilingual Evaluation Understudy (BLEU), limited human evaluation, and significant geographic and linguistic underrepresentation. Additionally, ethical concerns related to bias, cultural representation, and community engagement are increasingly relevant. The findings contribute to advancing inclusive and equitable AI-driven language technologies. Full article

Occupational safety risk assessment combines measured data, qualitative observations, and expert judgment to prioritize preventive action. Fuzzy logic can structure linguistic and uncertain information, but poorly justified variables, membership functions, rules, or weights can embed bias, while aggregation can obscure hazard-specific differences. Following a PRISMA-aligned selection process, this systematic review examined 30 fuzzy-based occupational safety studies, focusing not only on technical design but also on output types, intended decision users and purposes, traceability, temporal operation, and evidence of safety outcomes. Fifteen models primarily produced a score, index, level, or class; nine produced a ranking or priority; and six preserved a relational or causal representation. Only two directly supported workers, one operated in real time, and none demonstrated recalibration driven by observed safety outcomes. Although 28 studies reported positive evaluations of their own solutions, only one reported improvement in accident outcomes observed over time, and one provided partial follow-up. Aggregated fuzzy values are therefore most defensible as traceable, context-bound decision-support indicators. They should complement rather than replace hazard-specific assessment and should be linked to worker participation, documented control actions, assigned responsibilities, and verification of implemented measures. Full article

Architectural atmosphere for spirituality holds potential to support the physical and mental health of post-COVID urban populations, and people now spend more of their lives within buildings even as opportunities for spiritual engagement recede. Yet evidence-based knowledge for designing such environments remains limited. This study aimed to identify the architectural atmospheres for spiritual constructs through a systematic literature review and practice-based interviews conducted following the COVID-19 pandemic. Data from Scopus-indexed publications and architects’ relevant expertise were analyzed using thematic content analysis, while word frequency and bivariate association analyses were employed to identify key architectural characteristics and their associations with spiritual constructs. The findings indicate that a holistic atmospheric approach should integrate spatial, perceptual, and natural features. Tangible atmospheric features such as space, natural materials, and object elements emerged as most prominent, alongside intangible features including illumination, acoustics, and temperature conditions. At the use-effect level, spatial experience and perception were most frequently reported. Positive spiritual constructs were linked to symbolic objects, sensory experiences, and lighting conditions, whereas negative spiritual constructs were primarily associated with temperature conditions. Future studies should extend inquiry across diverse linguistic, cultural, and temporal contexts while empirically validating the proposed framework in real-world environments. Full article

Fake-speech detection is commonly studied through acoustic artifacts, speaker-level spoofing cues, or visual inconsistencies, while less attention has been given to meaning-level manipulation, where the spoken content is altered while the speech remains natural and speaker-consistent. This study investigates the relative and complementary contributions of interpretable speech-centered features for detecting meaning-level fake speech. Specifically, it examines transcript-level linguistic and psycholinguistic-style features, text–audio emotion-alignment features, and rhythm-based audio descriptors. A feature-aware gated-fusion framework is used to analyze and combine these three feature groups, with separate branches encoding each feature type and learned branch-level weights adaptively controlling their contributions to binary classification. The framework was evaluated on FakeSpeech+, an audio-only dataset designed for meaning-level manipulation, using a strict leakage-controlled repeated-seed protocol that prevents source-pair, filepath, exact-transcript, and combined group overlap across training, validation, and test partitions. The gated-fusion model achieved 0.842 accuracy, a 0.840 F1-score, and 0.919 AUC. Analysis of the learned fusion weights indicated that transcript-level features contributed most strongly, followed by text–audio emotion-alignment features, while rhythm features received the lowest contribution. These findings provide evidence that meaning-level fake-speech detection can benefit from jointly examining linguistic content, emotional alignment, and rhythmic characteristics, while also highlighting differences in the relative contributions of these feature groups. Full article