Search Results (740)

Fatphobia, or the stigmatization of fat bodies, is increasingly prevalent in our society and is manifested in many ways, leading to serious consequences for those who suffer its effects. This study aims to enhance the understanding of the extent of media coverage regarding this issue, as well as the approach taken in its coverage. To this end, all the information containing the term fatphobia, which was published in six leading Spanish-language newspapers, (n = 309) was analyzed to pinpoint the moment when fatphobia appeared on the media agenda, as well as the specific features of its coverage. Using a multidisciplinary methodology including content analysis, framing theory, and a gender perspective, the following digital media outlets were analyzed: eluniversal.com.mx (Mexico), eltiempo.com (Colombia), clarin.com.ar (Argentina), elcomercio.com.pe (Peru), elmercurio.com (Chile), and elpaís.com (Spain). The findings reflect an inconsistent media portrayal, and the coverage was generally found to be superficial, which indicates the need for a more committed approach to the social acceptance of all bodies and to the struggle against aesthetic discrimination suffered by women with non-normative bodies. Full article

Filipino Sign Language (FSL) serves as a vital means of communication for the Deaf and hard-of-hearing in the Philippines. However, its societal use remains limited due to the scarcity of qualified interpreters and the general lack of FSL literacy among the population. Therefore, this study aims to address the gap between FSL development and automated FSL translation by employing machine learning and computer vision techniques. A model was trained using the FSL-105 dataset, which comprises video clips of gestures related to greetings and colors, and utilized MediaPipe for real-time detection of hand, face, and body landmarks. Through iterative training with transfer learning, the model’s performance improved from an initial accuracy of 80% to a final accuracy of 98.75%. The results demonstrate that the MediaPipe-based model can reliably interpret FSL gestures, positioning it as a potentially accessible assistive tool for the Deaf and hard of hearing community. This technology holds promise for applications in education, healthcare, and public service, offering new opportunities to promote the social inclusion of Filipino Deaf communities through more inclusive communication. Full article

Background/Objectives: Multiple sclerosis (MS) is a chronic, demyelinating and neurodegenerative disease frequently associated with dysphagia, nutritional imbalances, and alterations in body composition. This study aims to describe the anthropometric profile and body composition in people with MS, estimate the risk and type of dysphagia, analyse dietary intake and habits, and evaluate the evolution of these parameters over six months. Methods: This descriptive analytical longitudinal study included 30 patients with MS (20 women, 10 men), with a median age of 53.3 years at baseline and 54.0 years at final assessment. The prevalence of dysphagia risk was determined, dietary patterns and body composition were characterised, and their interactions were explored through two assessments conducted six months apart. Results: Overall, 90% of the sample had relapsing–remitting MS (RRMS). At both the initial and final assessments, the median BMI was above 25 kg/m² and a high prevalence of dysphagia risk (63.3% and 76.7%), particularly for liquids. Frequent inadequacies were observed in the intake of certain macronutrients and micronutrients, including energy, fibre, potassium and magnesium. Likewise, the analysis by food groups revealed low adherence to recommendations, particularly for fruits, cereals, legumes, fish and lean meats. No significant differences were detected between the two time points. Conclusions: Dysphagia, dietary intake, habits, and body composition are interconnected dimensions in MS; systematically integrating nutritional assessment and dysphagia screening into clinical practice would contribute to a more comprehensive management and to improvements in swallowing disorders and nutritional status in people with MS. Full article

This paper presents a literature review regarding the automatic extraction of meaningful information regarding suppliers’ ESG and sustainability compliance from textual sources. Assessing suppliers’ ESG compliance has become a key challenge for procurement managers. Given the large number of suppliers and required data points, traditional approaches such as questionnaires and audits are inefficient, ineffective and difficult to scale. To solve this problem, we investigate whether the required information can be automatically harvested from suppliers’ textual sources. Our structured literature review identified 82 papers on which we performed a descriptive analysis, finding a rich and flourishing body of literature produced by a heterogeneous scientific community. We further reduced our sample to 73 full-text articles that supported a more in-depth content-based analysis. We investigated which data sources can be used in particular, which technologies can be leveraged, and which types of outputs can be generated. Even though they could provide much of the required information, corporate websites are rarely utilized as data sources, partly due to the limited adoption of large language models (LLMs). LLMs are less diffused than traditional Natural Language Processing (NLP) techniques due to their recent introduction and some gaps that still limit their performance. This represents both a constraint and an opportunity for future research. Full article

This paper addresses the critical communication barrier experienced by deaf and hearing-impaired individuals in the Arab world through the development of an affordable, video-based Arabic Sign Language (ArSL) recognition system. Designed for broad accessibility, the system eliminates specialized hardware by leveraging standard mobile or laptop cameras. Our methodology employs Mediapipe for real-time extraction of hand, face, and pose landmarks from video streams. These anatomical features are then processed by a hybrid deep learning model integrating Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), specifically Bidirectional Long Short-Term Memory (BiLSTM) layers. The CNN component captures spatial features, such as intricate hand shapes and body movements, within individual frames. Concurrently, BiLSTMs model long-term temporal dependencies and motion trajectories across consecutive frames. This integrated CNN-BiLSTM architecture is critical for generating a comprehensive spatiotemporal representation, enabling accurate differentiation of complex signs where meaning relies on both static gestures and dynamic transitions, thus preventing misclassification that CNN-only or RNN-only models would incur. Rigorously evaluated on the author-created JUST-SL dataset and the publicly available KArSL dataset, the system achieved 96% overall accuracy for JUST-SL and an impressive 99% for KArSL. These results demonstrate the system’s superior accuracy compared to previous research, particularly for recognizing full Arabic words, thereby significantly enhancing communication accessibility for the deaf and hearing-impaired community. Full article

This qualitative study investigates the challenges teachers face when teaching English as a foreign language (EFL) to deaf (in this article, deaf (lower case) refers to the audiological condition of hearing loss, whereas Deaf (capitalized) is used to denote individuals who identify as members of the Deaf community and share a common sign language and distinct cultural values) and hard-of-hearing (DHH) students in German schools for the Deaf. The study is situated within a structural–theoretical professionalization framework, which focuses on the relationship between institutional conditions, teacher education structures, and professional action. Semi-structured interviews were conducted with 16 teachers of DHH students and the data were examined using qualitative content analysis. The findings reveal five central areas of challenge: (1) heterogeneity of the student body; (2) limited time (for preparing and adapting materials); (3) restricted subject-matter and sign-language competence, including missing links between EFL didactics and Deaf education in teacher training; (4) uncertainties surrounding the language design of EFL instruction, particularly the role of American Sign Language (ASL), German Sign Language (DGS), and written English; and (5) the lack of consistent, accessible exam formats and standards. Teachers report substantial insecurity due to the absence of coherent concepts, policy frameworks, and specialized training pathways, which fosters divergent classroom practices and tensions within teaching staff. The results highlight an urgent need for systematic integration of Deaf education, sign language training, and EFL pedagogy in teacher education, as well as for evidence-based guidelines on language classroom practice and assessment for DHH learners. Full article

This paper investigates the anthropological and ethical roots of the global ecological and social crisis, centered on Pope Francis’s critique of the “throwaway culture” (Laudato Si’, LS). While LS identifies this crisis in the linear “take–make–dispose” model and the technocratic paradigm—which prioritizes efficiency over moral reflection—this research argues that these macro-societal failures originate in a foundational spiritual pathology: concupiscence. Drawing upon St. John Paul II’s Theology of the Body (TOB), we analyze concupiscence as “appropriation,” the direct antithesis to the human vocation of the “sincere gift of self.” This study aligns LS’s socio-economic critique with Karol Wojtyła’s personalist anthropology, asserting that the systemic exploitation of nature and the marginalization of the vulnerable are structural extensions of the human failure to reread the “language of the body” in truth. The throwaway culture is thus revealed as an axiological reduction—a societal manifestation of lust that reduces both the body and creation to mere objects of utility. Consequently, a genuine ecological conversion (LS) necessitates embracing the “ethos of redemption” (TOB). This transformation of desire is essential to restoring the harmony between humanity and nature, recognizing that the ‘cry of the earth’ and the ‘cry of the poor’ are inextricably linked within an integral ecology. Full article

Wine sensory analysis traditionally relies on complex terminology. This can be challenging to non-expert consumers, particularly regarding mouthfeel sensations. Despite the importance of the latter in determining wine quality and typicity, they lack standardized classification. In this study, we developed and validated a simplified framework for wine taste evaluation that is accessible to consumers with limited tasting experience. The Delphi technique was applied across multiple rounds with a panel of 18 wine experts, primarily sommeliers with experience of diverse consumer profiles. Through an iterative process, attributes were selected from the existing literature and systematically evaluated for relevance, clarity, and accessibility. The validated framework comprises four dimensions: basic tastes (sweetness, acidity, bitterness, salinity, fruitiness); astringency (hardness, dryness, texture); tactile sensations (tingling, warmth, body); and overall evaluation (complexity, balance, taste persistence, alcohol perception). Each attribute includes accessible descriptions and measurement scales anchored with familiar food references to support comparative cognitive processes. All proposed attributes achieved over 85% expert consensus. This framework provides a practical tool that bridges technical wine terminology and everyday consumer language to facilitate communication between industry professionals and consumers. Furthermore, it enables more reliable sensory evaluations in future research and can potentially be extended to other beverages. Full article

Automated recognition of equine–human interaction behaviors from video represents a significant challenge in computational ethology, with critical applications spanning animal welfare assessment, equine-assisted services evaluation, and safety monitoring in equestrian environments. Existing approaches to animal behavior recognition typically focus on single species in isolation, rely solely on facial expression analysis while ignoring full-body posture, or employ flat classification architectures that fail under the severe class imbalances characteristic of naturalistic behavioral datasets. Furthermore, no prior framework integrates simultaneous analysis of both human and equine body language for cross-species interaction classification. This paper presents a novel hierarchical classification framework integrating multi-modal computer vision features to distinguish behavioral states during horse–human encounters. Our methodology employs three complementary feature extraction pipelines: YOLOv8 for spatial relationship modeling, MediaPipe for human postural analysis, and AP-10K for equine body language interpretation. From 28 annotated interaction videos comprising 50,270 temporal samples across five horse breeds, we extract 35 discriminative features capturing proximity dynamics, body orientation, and species-specific behavioral indicators. To address severe class imbalance (18.3:1 ratio between affiliative and avoidant categories), we implement cost-sensitive gradient boosting with automatic class weight optimization within a two-stage hierarchical architecture. The first stage classifies interactions into three parent categories (affiliative, neutral, avoidant) achieving 73.2% balanced accuracy, while stage two discriminates six fine-grained sub-behaviors achieving 88.5% balanced accuracy (under oracle parent-category routing; cascaded end-to-end performance is 62.9% balanced accuracy due to Stage 1 error propagation, identifying parent classification as the primary bottleneck). Notably, our system achieves 85.0% recall on safety-critical avoidant behaviors despite their representation of only 3.8% of the dataset. Extensive ablation studies demonstrate that equine pose features contribute most critically to classification performance, while comprehensive cross-validation analysis confirms model robustness across diverse interaction contexts. The proposed framework establishes the first systematic multimodal cross-species behavioral assessment pipeline in human–animal interaction research, with direct implications for improving equine welfare monitoring and rider safety protocols. Full article

Peptide binders, serving as a critical drug modality bridging small-molecule compounds and protein macromolecules, can effectively mimic the secondary structural elements of natural proteins. Peptides exhibit unique physicochemical advantages when targeting protein protein interaction (PPI) interfaces, which are typically characterized by flat surfaces and extensive contact areas. Recently, diffusion models represented by RFdiffusion have established a new computational paradigm for protein backbone generation by defining a denoising process over the rigid-body transformation group. However, in the de novo design of binders targeting “undruggable” PPI targets, this general paradigm encounters significant adaptability bottlenecks. First, its underlying rigid-body assumption struggles to accurately describe the dynamic induced-fit process of peptides at the binding interface. Second, it lacks sufficient robustness to the experimental resolution heterogeneity inherent in training data. Furthermore, the decoupled two-stage generation of sequence and structure severs the synergy of physicochemical properties, leading to backbones with idealized, singular secondary structures that lack authentic spatial binding capacity and reasonable side-chain physicochemical features. To address these challenges, this study proposes PPI-Diff, a novel generative framework. While preserving the generative capability of diffusion models, PPI-Diff introduces three core mechanisms: (1) a resolution-aware constraint mechanism that maps the measurement precision of experimental data into explicit contextual constraints to dynamically suppress geometric noise from low-resolution samples; (2) an internal-coordinate-driven manifold diffusion model that performs conformational evolution on a Riemannian manifold constructed by dihedral angles, balancing local stereochemical validity with the precise capture of flexible peptide conformations; and (3) a geometry-semantic synergistic modeling mechanism that leverages the evolutionary embeddings of a pre-trained protein language model (ESM-2) as latent variables to align structure generation with biophysical functions. Systematic benchmarking demonstrates that, on a strictly non-homologous test set, the binders generated by PPI-Diff significantly outperform existing baseline models in terms of interface contact density, stereochemical validity, and sequence novelty. Full article

This study builds on a small body of work, all on Japanese, demonstrating how intonational phonology is critical for understanding prosodic modifications in infant-directed speech (IDS) relative to adult-directed speech. We performed similar analyses on simulated infant-directed speech vs. reading of a story in English and Bengali: two languages that – unlike Japanese – both have stress and do not use fundamental frequency (F0) to signal changes in word-level meaning, but that have two very different intonational grammars. These differences allowed us to disentangle previous hypotheses about intonational exaggeration in IDS being concentrated in a particular part of the melody. We tested hypotheses that state this locus of exaggeration is either at: the final position in the melody (final in the intonational phrase), the most unpredictable part of the melody, or in pragmatically informative tones. Our results support the first hypothesis. We found that the phonological choices of speakers to chunk the story into shorter, larger prosodic constituents drive intonational exaggeration in IDS. This is because the intonational phrase-final position in both languages is the site of greatest pre-boundary lengthening and F0 range expansion. We also demonstrate: (i) quantification of predictability in intonational melodies using probabilistic finite state automaton representations of intonational grammars and (ii) F0 statistical analyses that are robust and scalable to large, naturalistic IDS corpora. Full article

Abusive news refers to digital content designed to maximize clicks and advertising revenue through sensational headlines, repetitive postings, or emotionally charged language, rather than upholding journalistic integrity. Despite growing concerns about its impact on media credibility and public trust, existing detection approaches lack systematic categorization and type-specific methodologies. This study addresses this gap by proposing a six-type typology of abusive news—content recycling, keyword insertion, title–body inconsistency, commercial promotion, emotionally stimulating headline, and automatically generated types—based on five analytical dimensions: content structure, authenticity, algorithmic manipulability, sensationalism, and information-ecosystem impact. We developed type-specific detection pipelines combining BERT-based embeddings, TF-IDF features, and rule-based indicators and evaluated them using a large-scale Korean clickbait corpus. Results demonstrate that BERT achieves higher F1-scores (0.89) for automatically generated content, while TF-IDF with SVM provides more stable precision (0.60) for emotionally charged articles under class imbalance. Cross-domain experiments confirm that models trained on diverse, balanced topic sets generalize better than volume-focused models, with diversity improving F1-scores by up to 0.07. BERT models show higher false positive rates on repetitive legitimate content compared to TF-IDF approaches, highlighting the importance of type-adaptive architectures and diversity-aware data design in abusive news detection systems. Full article

Background: While mobile health (mHealth) interventions show promise for type 2 diabetes management, mechanisms linking user experience to clinical outcomes remain poorly understood. We hypothesized that perceived usability may mediate associations between patient characteristics and short-term clinical changes, with implications for health equity in digital interventions. Methods: Secondary analysis of the intervention arm from a randomized controlled trial in urban Mexican primary care (ClinicalTrials.gov NCT05924516). Participants used a diabetes self-management mobile application for 90 days. We assessed usability with the validated Computer System Usability Questionnaire (CSUQ; 16 items, 7-point scale) and measured clinical changes in body mass index (BMI), systolic blood pressure (SBP), and HbA1c. Bayesian mediation analysis (literature-informed priors) examined interface quality as a mediator of age-related clinical effects. Item-level analysis identified educational disparities in specific usability dimensions using independent t-tests adjusted for multiple comparisons. Results: Mean overall usability was 5.20/7 (SD = 0.89, 74th percentile). Interface quality mediated 39% of the age–SBP association. Participants experiencing high usability (≥6) versus low usability showed BMI reduction −0.78 vs. −0.21 kg/m² (Cohen’s d = 0.56) and SBP reduction −7.3 vs. −1.2 mmHg (Cohen’s d = 0.51). No mediation effect was observed for HbA1c change. Users with ≤primary education (41% of sample) scored 1.9 points lower on error messages (3.2 vs. 5.1, p < 0.01) and 1.4 points lower on help documentation (3.6 vs. 5.0, p < 0.03). These disparities persisted after controlling for age and baseline severity. Conclusions: Perceived usability was associated with a potential mechanistic pathway linking user experience to clinical outcomes. Higher usability scores were associated with clinically meaningful improvements in cardiometabolic parameters. Educational disparities in understanding error messages and helping documentation represent modifiable design barriers. Implementing contextual error explanations with visual examples and plain-language help content may enhance both clinical effectiveness and equity in digital diabetes interventions. Full article

Understanding animal actions and interactions is essential for behavior analysis and ecological monitoring. Although large-scale in-the-wild datasets have advanced animal action recognition, existing methods still struggle with fine-grained motion, spatial relations, and multi-individual interactions. To address these challenges, we introduce AIRA, a unified framework for Action and Interaction Recognition in Animals. Built upon a vision–language model (VLM), AIRA learns in an action-centered representation space defined by body parts and their corresponding motions, thereby improving robustness to background noise and enabling cross-species generalization via a unified mammal-centric part ontology. To model actions, we treat body parts and motion as primary cues and introduce Actionbit tokens—compact representations for parts and motions generated by a large language model (LLM) that encode which parts move and how. We further propose Part-Enhanced Prompt Fine-tuning (PEPF) to make the VLM explicitly sensitive to part and pose cues. Within PEPF, the Action–actionbit Alignment (AbA) module enriches action representations with fine-grained part–motion semantics, and Part-Vision Prompting (PVP) extracts keyframes through action-aware prompting. Experiments across multiple benchmarks show consistent improvements in both action and interaction recognition, highlighting the importance of action-centered adaptation and relational reasoning for understanding animal behavior in the wild. Full article

The experience of being acknowledged by one’s coach has been highlighted as important, but the pedagogical approach of being seen has not been empirically explored within sport. The purpose of this study was to investigate the extent to which young Norwegian football players experienced being seen by their head coach in football, as well as to examine gender differences in these experiences with a previous validated questionnaire, originally developed for students within physical education, but adapted for football. Using a list of all teams participating in the Boys 19 league and the Girls 17 league in Trøndelag County, 7 boys’ teams and 9 girls’ teams were randomly selected. A total of 212 players (107 boys and 105 girls) responded to the questionnaire. Participants’ ages ranged from 15 to 19 years. The results showed that 83 percent of the boys and 87 percent of the girls agreed (slightly to strongly) that they experienced being seen by their head coach during training or football matches. There were no significant gender differences regarding this experience, nor in four of the five underlying factors contributing to being seen. However, a significant gender difference was found according to good dialogue, where girls scored higher than boys when rating their coaches. Finally, the results indicated that players perceived their coach as most competent in facilitating good dialogue, and least competent in involving players in assessment and goal setting, and in creating opportunities for players to showcase themselves. Based on these results, coaches should actively create opportunities for dialogue before, during, and after training or matches, signaling openness through body language, tone, and availability so players feel comfortable initiating conversation. Coach education programs should emphasize communication strategies that promote psychological safety and belonging, including practical steps such as brief one-on-one conversations during warm-up or cool-down to help players feel seen without disrupting team flow. The gender difference in good dialogue highlights the importance of tailoring communication strategies to individual needs while ensuring that dialogue opportunities are accessible to all players. Full article