Search Results (727)

The transition to a circular economy depends on the widespread adoption of sustainable products by consumers. However, the point-of-sale purchase decision is a complex process, influenced not only by ethical arguments but also by sensory cues. This study investigates how the aesthetics (visual design) and materiality (tactile sensation) of green products shape value perception and purchase intention. Using a qualitative methodology based on a focus group, the research directly compares consumer reactions to green products (e.g., a bamboo toothbrush) versus their conventional alternatives (e.g., plastic). Thematic analysis of the data reveals a fundamental dichotomy among consumers: while one segment associates high-tech aesthetics and perfect finishes with quality and hygiene, another segment values natural materials and their “imperfections” as signs of authenticity and responsibility. The results demonstrate that there is no single, universally accepted “sustainable aesthetic” and highlight the need for designers and marketers to align the visual and tactile language of products with the value system of the target consumer segment. The study provides a framework for understanding how design can act as either a barrier to or a catalyst for the adoption of sustainable products. Full article

This study explores and models the use of MATLAB technology in multimodal learning interactions to address the challenges of teaching and learning statistics in a multinational postgraduate classroom. The term multimodal refers to the deliberate integration of multiple representational and interaction modes, i.e., visual, textual, symbolic, and interactive computational modelling, within a coherent instructional design. MATLAB is utilised as it is a comprehensive tool for enhancing students’ understanding of statistical skills, practical applications, and data analysis—areas where traditional methods often fall short. International postgraduate students were chosen for this study because their diverse educational backgrounds present unique learning challenges. A qualitative case study design was employed, and data collection methods included classroom observations, interviews, and student work analysis. The collected data were analysed and modelled by conceptualising key elements and themes using thematic analysis, with findings verified through data triangulation and expert review. Emerging themes were structured into models that illustrate multimodal teaching and learning interactions. The novelty of this research lies in its contribution to multimodal teaching and learning strategies for multinational students in statistics education. The findings highlight significant challenges international students face, including language and technical barriers, limited prior content knowledge, time constraints, technical difficulties, and a lack of independent thinking. To address these challenges, MATLAB promotes collaborative learning, increases student engagement and discussion, boosts motivation, and develops essential skills. This study suggests that educators integrate multimodal interactions in their teaching strategies to better support multinational students in statistical learning environments. Full article

With the rapid development of artificial intelligence and machine learning technologies, automatic speech recognition (ASR) and text-to-speech (TTS) have become key components of the digital transformation of society. The Kazakh language, as a representative of the Turkic language family, remains a low-resource language with limited audio corpora, language models, and high-quality speech synthesis systems. This study provides a comprehensive analysis of existing speech recognition and synthesis models, emphasizing their applicability and adaptation to the Kazakh language. Special attention is given to linguistic and technical barriers, including the agglutinative structure, rich vowel system, and phonemic variability. Both open-source and commercial solutions were evaluated, including Whisper, GPT-4 Transcribe, ElevenLabs, OpenAI TTS, Voiser, KazakhTTS2, and TurkicTTS. Speech recognition systems were assessed using BLEU, WER, TER, chrF, and COMET, while speech synthesis was evaluated with MCD, PESQ, STOI, and DNSMOS, thus covering both lexical–semantic and acoustic–perceptual characteristics. The results demonstrate that, for speech-to-text (STT), the strongest performance was achieved by Soyle on domain-specific data (BLEU 74.93, WER 18.61), while Voiser showed balanced accuracy (WER 40.65–37.11, chrF 80.88–84.51) and GPT-4 Transcribe achieved robust semantic preservation (COMET up to 1.02). In contrast, Whisper performed weakest (WER 77.10, BLEU 13.22), requiring further adaptation for Kazakh. For text-to-speech (TTS), KazakhTTS2 delivered the most natural perceptual quality (DNSMOS 8.79–8.96), while OpenAI TTS achieved the best spectral accuracy (MCD 123.44–117.11, PESQ 1.14). TurkicTTS offered reliable intelligibility (STOI 0.15, PESQ 1.16), and ElevenLabs produced natural but less spectrally accurate speech. Full article

This study investigates the determinants of Romanian workers’ migration intentions towards Italy, integrating economic, social, and psychological perspectives. Based on a sample of 358 respondents, four hypotheses were tested concerning perceived living standards, working conditions, quality of public services, and anticipated integration difficulties. Data were analysed using descriptive statistics, Spearman’s rho correlation, Mann–Whitney U, Chi-square, ANOVA, and ordinal logistic regression. The results confirm that higher perceived living standards and better working conditions in Italy significantly increase the likelihood of expressing migration intentions, while favourable evaluations of healthcare and education act as additional pull factors. Conversely, anticipated integration difficulties, particularly language barriers and cultural adaptation, reduce migration intentions, indicating that socio-psychological obstacles can counterbalance economic incentives. By combining non-parametric and multivariate analyses, the study demonstrates that migration is a multidimensional process shaped not only by structural opportunities but also by behavioural and psychological appraisals. These findings are consistent with recent research on European labour mobility and contribute to the literature by highlighting the role of subjective perceptions in shaping migration decisions. Implications for policy include the need to address both economic disparities and integration barriers to support more balanced mobility within the European space. Full article

Background: Inclusion of under-represented rare-disease communities in research remains limited, threatening representativeness and equity. Methods: To assess equality, equity, diversity, and inclusion in research and identify barriers to participation faced by the rare disease community, utilising a mixed-methods online survey of a convenience sample of community advocates using Likert scales and free response options. Results: The findings from seventeen stakeholders in the rare disease community showed unanimous agreement that anxiety, fear, safety concerns, and lack of trust hinder participation in research. A total of 82% agreed or strongly agreed that additional financial resources are needed, and 76% agreed or strongly agreed that research grant applications often lack sufficient funds. The free-text responses demonstrate that the rare disease communities are keen to be involved in research but faces barriers to inclusion. Rare disease communities are willing to participate in research, but those responsible for research need to address the challenges related to language, misconceptions and fear. Conclusions: Key legislation in the United Kingdom, specifically the Proposed Patient and Public Involvement Strategy 2020–2025, emphasises the importance of involving patients and the public in health and social care. This survey marks the first step toward gaining valuable insights into the challenges faced by this community in participating in healthcare research, which is crucial for developing a solid evidence base for their treatment and care. Involving stakeholders is essential in health and social care policy and practice, rooted in advocacy and social justice. Full article

Aim: As the global population ages, the number of bilingual individuals living with dementia is increasing, yet their communication needs remain underrepresented in both clinical practice and research. This evidence review examines the intersection of language regression, communication challenges, and cultural–linguistic identity in bilingual dementia, with a particular focus on the role of speech–language pathologists (SLPs). Methods: Twelve peer-reviewed studies were critically reviewed and thematically analysed across four domains: (1) language regression and retention in bilingual dementia, (2) communication challenges in bilingual dementia care, (3) the marginal role of speech–language pathology, and (4) cultural–linguistic identity and health equity. The included studies span clinical case reports, experimental research, qualitative caregiver studies, and systematic reviews, with bilingual populations across Asia, Europe, North America, and the Middle East. Results: Findings reveal that language deterioration in bilingual dementia is dynamic and highly individualised, often influenced by language history, emotional context, and usage patterns. Caregivers and clinicians face persistent communication breakdowns, particularly in linguistically mismatched settings. Despite their specialised expertise in communication, SLPs remain largely peripheral in dementia care, constrained by systemic, educational, and methodological barriers. Moreover, linguistic and cultural identity play a critical role in how dementia is experienced and managed, yet are rarely integrated into care frameworks. Conclusions: This review highlights a significant knowledge–practice gap in bilingual dementia care and underscores the need to embed culturally and linguistically responsive communication practices, especially through speech–language therapy, at the centre of bilingual dementia care and support. It outlines key research and practice directions to advance equity, accuracy, and relational care in this growing population. Full article

Every day, countless individuals with hearing or speech disabilities struggle to communicate effectively, as their conditions limit conventional verbal interaction. For them, sign language becomes an essential and often sole tool for expressing thoughts and engaging with others. However, the general public’s limited understanding of sign language poses a major barrier, often resulting in social, educational, and professional exclusion. To bridge this communication gap, the present study proposes a smart wearable glove system designed to translate Arabic sign language (ArSL), especially Moroccan sign language (MSL), into a written alphabet in real time. The glove integrates five MPU6050 motion sensors, one on each finger, capable of capturing detailed motion data, including angular velocity and linear acceleration. These motion signals are processed using an Artificial Neural Network (ANN), implemented directly on a Raspberry Pi Pico through embedded machine learning techniques. A custom dataset comprising labeled gestures corresponding to the MSL alphabet was developed for training the model. Following the training phase, the neural network attained a gesture recognition accuracy of 98%, reflecting strong performance in terms of reliability and classification precision. We developed an affordable and portable glove system aimed at improving daily communication for individuals with hearing impairments in Morocco, contributing to greater inclusivity and improved accessibility. Full article

The recent shift from in-person to digital pharmacy services is transforming how patients interact with their pharmacists but has the potential to disadvantage older adults (aged ≥ 65) who often face barriers when using technology. This study aimed to assess digital inequalities affecting medication access among older adults. A Spanish-language questionnaire was developed and psychometrically validated, revealing a two-factor latent structure comprising: (1) Technology Use, and (2) Medication Access. Item discrimination analysis confirmed that all significantly differentiated between those that used technology to facilitate their medication access and those that did not (p < 0.001). Participants with higher education reported greater income levels (p < 0.001), and income was in turn related to both internet access and digital skills. Age played a key role in perceptions of technology. Participants who considered technology helpful were younger ($\overline{x}$ = 72.9) than those who did not ($\overline{x}$ = 76.6; p = 0.001). There was no significant relationship between perceived technological usefulness and reporting not being able to acquire medications because of technological barriers (p = 0.788). This newly created and validated questionnaire identified gaps related to digital pharmacy services and may be a useful tool in future clinical, community, and investigative contexts. Full article

The rapid proliferation of generative artificial intelligence (GenAI) in educational settings has created unprecedented opportunities for language instruction, yet empirical evidence regarding its efficacy in primary-level English as a Foreign Language contexts remains scarce, particularly concerning pre-service teachers’ implementation experiences during formative practicum periods. This investigation, conducted in a public school in a non-Anglophone country during the Spring of 2025, examined the impact of GenAI-driven gamified activities on elementary pupils’ English language competencies while exploring novice educators’ professional development trajectories through a mixed-methods quasi-experimental approach with comparison groups. Four third-grade classes (n = 119 individuals aged 8–9) in a public school were assigned to either ChatGPT-mediated voice-interaction games (n = 58) or conventional non-digital activities (n = 61) across six 45 min lessons spanning three weeks, with four female student-teachers serving as instructors during their culminating practicum. Quantitative assessments of grammar, listening comprehension, and pronunciation occurred at baseline, post-intervention, and one-month follow-up intervals, while reflective journals captured instructors’ evolving perceptions. Linear mixed-effects modeling revealed differential outcomes across linguistic domains: pronunciation demonstrated substantial advantages for GenAI-assisted learners at both immediate and delayed assessments, listening comprehension showed moderate benefits with superior overall performance in the experimental condition, while grammar improvements remained statistically equivalent between groups. Thematic analysis uncovered pre-service teachers’ progression from technical preoccupations toward sophisticated pedagogical reconceptualization, identifying connectivity challenges and assessment complexities as primary barriers alongside reduced performance anxiety and individualized pacing as key facilitators. These findings suggest selective efficacy of GenAI across language skills while highlighting the transformative potential and implementation challenges inherent in technology-enhanced elementary language education. Full article

The evolution of decentralized clinical trials, driven by advanced digital technologies, is transforming traditional clinical research. It introduces innovative methods for informed consent, remote patient monitoring, and data analysis, enhancing study efficiency, validity, and participation while reducing patient burden. Some clinical procedures can be conducted remotely, increasing trial accessibility and reducing population selection biases, particularly for cardiovascular patients. However, this also presents complex regulatory and ethical challenges. The article explores how digital platforms and emerging technologies like block chain, AI, and advanced cryptography can promote traceability, security, and transparency throughout the trial process, ensuring participant identification and documentation of each procedural step. Clear, legally compliant informed consent, often managed through electronic systems, both for research participation and data management in line with GDPR, is essential. Ethical considerations include ensuring participants understand trial information, with adaptations such as simplified language, visual aids, and multilingual support. The transnational nature of decentralized trials highlights the need for coordinated regulatory standards to overcome jurisdictional barriers and reinforce accountability. This framework promotes trust, shared responsibility, and the protection of participants rights while upholding high ethical standards in scientific research. Full article

(1) Background: Comprehensive conceptual models can result in complex artifacts, consisting of many concepts that interact through multiple mechanisms. This complexity can be acceptable and even expected when generating rich models, for instance to support ensuing analyses that find central concepts or decompose models into parts that can be managed by different actors. However, complexity can become a barrier when the conceptual model is used directly by individuals. A ‘transparent’ model can support learning among stakeholders (e.g., in group model building) and it can motivate the adoption of specific interventions (i.e., using a model as evidence base). Although advances in graph-to-text generation with Large Language Models (LLMs) have made it possible to transform conceptual models into textual reports consisting of coherent and faithful paragraphs, turning a large conceptual model into a very lengthy report would only displace the challenge. (2) Methods: We experimentally examine the implications of two possible approaches: asking the text generator to simplify the model, either via abstractive (LLMs) or extractive summarization, or simplifying the model through graph algorithms and then generating the complete text. (3) Results: We find that the two approaches have similar scores on text-based evaluation metrics including readability and overlap scores (ROUGE, BLEU, Meteor), but faithfulness can be lower when the text generator decides on what is an interesting fact and is tasked with creating a story. These automated metrics capture textual properties, but they do not assess actual user comprehension, which would require an experimental study with human readers. (4) Conclusions: Our results suggest that graph algorithms may be preferable to support modelers in scientific translations from models to text while minimizing hallucinations. Full article

Knowledge graphs (KGs) represent a promising approach for detecting and correcting errors in automated audio and video transcripts. Yet the lack of accessible tools leaves human reviewers with limited support, as KG construction from media data often depends on advanced programming or natural language processing expertise. We present the Custom LLM Automated Relationship Extractor (CLARE), a system that lowers this barrier by combining context-aware relation extraction with an interface for transcript correction and KG refinement. Users import time-synchronized media, correct transcripts through linked playback, and generate an editable, searchable KG from the revised text. CLARE supports over 150 large language models (LLMs) and embedding models, including local options suitable for privacy-sensitive data. We evaluated CLARE on the Measure of Information in Nodes and Edges (MINE) benchmark, which pairs articles with ground-truth facts. With minimal parameter tuning, CLARE achieved 82.1% mean fact accuracy, exceeding Knowledge Graph Generation (KGGen, 64.8%) and Graph Retrieval-Augmented Generation (GraphRAG, 48.3%). We further assessed interactive refinement by revisiting the twenty-five lowest-scoring graphs for fifteen minutes each and found that the fact accuracy rose by an average of 22.7%. These findings show that CLARE both outperforms prior methods and enables efficient user-driven improvements. By streamlining ingestion, correction, and filtering, CLARE makes KG construction more accessible for researchers working with unstructured data. Full article

This study evaluated emergency preparedness, covering knowledge, challenges, and culturally and linguistically appropriate resources among five resettled refugee communities in Central Ohio: Afghan, Bhutanese, Congolese, Ethiopian/Eritrean, and Somali. It also explored youth perceptions of community engagement and the effectiveness of current crisis support initiatives. A mixed-methods approach was used, consisting of a cross-sectional quantitative survey of 266 adults and a qualitative 60-min focus group discussion with 10 youth from the resettled communities. Quantitative data was analyzed with descriptive statistics and Chi-square tests assessing differences in preparedness across communities. Quantitative findings showed that the Bhutanese group reported the highest familiarity with preparedness concepts (33.3%), while Afghan and Congolese communities showed significant gaps. Of the 266 overall respondents, only 39.5% had an emergency plan, and 15.8% felt extremely confident in handling emergencies. Language barriers were major challenges, along with those related to race and ethnic identity, religious practices, and cultural norms. Effective platforms for engagement included community meetings, religious sites, and social media, with text messages and phone calls preferred for emergency communication. Youth discussion highlighted key public health concerns, particularly gun violence, substance abuse, and mental health crises, with stigma and limited resources further hindering preparedness efforts. Participants emphasized the need for culturally relevant interventions and stronger community engagement. Full article

Background/Objectives: Spanish-speaking patients face persistent barriers in accessing equitable audiological care, particularly when standardized language-appropriate tools are lacking. Two Spanish-language sentence recognition tests, the Spanish AzBio Sentence (SAzB) and the Latin American Hearing in Noise Test (LAH), are commonly used to evaluate speech perception in adults with hearing loss. However, performance differences between these measures may influence referral decisions for hearing intervention, such as cochlear implantation. This study compared test performance under varying noise and spatial conditions to guide appropriate test selection and reduce the risk of misclassification that may contribute to healthcare disparities. Methods: Twenty-one bilingual Spanish/English speaking adults with normal bilateral hearing completed speech perception testing using both the SAzB and LAH. Testing was conducted under two spatial configurations: (1) speech and noise presented from the front (0° azimuth) and (2) speech to the simulated poorer ear and noise to the better ear (90°/270° azimuth). Conditions included quiet and three signal-to-noise ratios (+10, +5, and 0 dB). Analyses included paired t-tests and one-way ANOVAs. Results: Participants scored significantly higher on the LAH than on the SAzB across all SNR conditions and configurations, with ceiling effects observed for the LAH. SAzB scores varied by language dominance, while LAH scores did not. No other differences were observed based on any further demographic information. Conclusions: The SAzB provides a more challenging and informative assessment of speech perception in noise. Relying on easier tests like the LAH may obscure real-world difficulties and delay appropriate referrals for hearing loss intervention, including cochlear implant evaluation. Selecting the most appropriate test is critical to avoiding under-referral and ensuring Spanish-speaking patients receive equitable and accurate care. Full article

Flexible laryngoscopy (FL) is the standard diagnostic tool for vocal cord paralysis (VCP), but it involves patient discomfort, and its interpretation is subjective and operator-dependent. Dynamic digital radiography (DDR) is a novel imaging technique that acquires high-resolution sequential radiographs at a low radiation dose. While DDR has been widely applied in chest and diaphragmatic imaging, its use for laryngeal motion analysis has been poorly investigated. We present the case of a 50-year-old male referred for Computed Tomography (CT) of the neck and chest for suspected vocal cord paralysis. The referring physician did not specify the side of the suspected paralysis. Due to a language barrier and the absence of prior documentation, a detailed history could not be obtained. To assess vocal cord motion, we performed, for the first time in our Institution, a DDR study of the neck. During phonation maneuvers, DDR demonstrated fixation of the left vocal cord in an adducted paramedian position. CT confirmed this finding and did not highlight any further anomaly. This case demonstrates the feasibility of DDR as a low-cost, low-dose, non-invasive technique for functional evaluation of the larynx and may represent a valuable complementary imaging tool in laryngeal functional assessment. Full article