Search Results (114)

Background/Objectives: The atypical NICU auditory environment can disrupt neural plasticity critical for language development in preterm infants. Recorded caregiver’s voice offers input when bedside presence is infeasible, and evidence suggests contingency, delivering the recording in response to infant behavior, is key to its benefit. This study tested whether caregiver’s recorded voice contingent on non-nutritive sucking (NNS) enhances neural speech processing beyond passive exposure. Methods: This randomized controlled trial enrolled preterm infants born before 35 weeks gestational age (GA), and between 32 0/7–35 6/7 weeks corrected GA at start. Infants were randomized to an intervention group receiving caregiver’s voice contingent on NNS, or a control group receiving passive voice exposure. Primary outcomes were post-intervention auditory ERP responses at left (T5) and right (T6) temporal locations, adjusted for pre-intervention EEG. Sensitivity analyses controlled for GA and maternal education; secondary analyses examined bed type, room type, and sound levels. Results: Of 214 infants enrolled, 97 intervention and 94 control infants completed the protocol (median GA 31.9 weeks, median corrected GA at start 33.9 weeks). The intervention group showed significantly greater speech-sound processing at T5 (β = 0.023, 95% CI [0.002, 0.044], p = 0.036, d = 0.31), with a borderline effect at T6 (β = 0.022, 95% CI [0.000, 0.045], p = 0.054, d = 0.28), both strengthened in sensitivity analyses. No significant interactions emerged with bed type, room type, or sound level. Conclusions: Contingent, infant-activated caregiver-voice exposure improves auditory ERP responses in preterm infants, independent of environmental factors, suggesting active engagement, not voice alone, as the driver of change. Further studies are warranted. Full article

The rapid development of Industry 4.0 brings significant benefits to intralogistics through increased flexibility, interoperability, and real-time responsiveness. However, the growing complexity of industrial environments creates challenges for logistics process adaptation and the integration of emerging technologies such as voice-driven control of heterogeneous robotic and IoT systems. Open-source solutions offer a promising approach by enabling vendor independence and cost-effective deployment. This paper investigates the use of the OPIL cyber–physical middleware, developed within the Horizon 2020 L4MS initiative, combined with convolutional neural networks for speech denoising and keyword recognition, to enable robust voice-controlled intralogistics in demanding noisy industrial environments. A voice-controlled logistics system was designed, deployed, and empirically evaluated on the OPIL platform. The results confirm the technical feasibility of OPIL as an open and modular alternative to proprietary Industry 4.0 platforms. They further demonstrate the feasibility of integrating AI-based keyword spotting and voice interaction into environments using an open middleware architecture. The study also identifies key limitations, including reduced performance for non-native speakers and mild speech over-suppression at high signal-to-noise ratios. To support reproducibility and external validation, the complete network configurations, training procedure, and deployment data are provided. Full article

Background/Objectives: Older adults may experience healthcare encounters in which their voices are discounted, redirected through family members, or interpreted through age-related assumptions about cognitive decline. Although such problems are often discussed through dignity, communication, ageism, person-centred care, or shared decision-making, this conceptual review argues that they also involve epistemic harms: harms that occur when older adults are not recognized as credible knowers of their own bodies, needs, values, histories, and care priorities. Methods: This article develops a conceptual and critical analysis of Fricker’s account of testimonial and hermeneutical injustice, later critiques of epistemic injustice, and the nursing and gerontological literature on dignity, person-centred nursing, documentation, dementia care, and shared decision-making. Results: The analysis distinguishes ordinary communication failure, institutional restriction of interpretive space, testimonial injustice, and hermeneutical injustice. It identifies interacting interpersonal and institutional mechanisms through which older adults’ knowledge may be discounted or rendered invisible and develops five theoretically derived principles: credibility-oriented listening, narrative recognition, supported participation, relational decision-making, and epistemic documentation. For each principle, the framework identifies the epistemic failure addressed, its added value beyond general person-centred practice, and responsibilities at both individual and organizational levels. Conclusions: An epistemic-justice lens does not replace dignity, person-centred care, shared decision-making, or professional judgement. It adds a focused analysis of credibility, interpretive authority, knowledge visibility, and whose account shapes care. The proposed framework is a conceptual and normative contribution that requires empirical evaluation before its effects can be established. Full article

Since the term ‘first-generation college student’ (FGCS) garnered increased attention in the 2000s, many institutions have implemented programming specifically for FGCS (e.g., bridge programs, first-year seminars). FGCS should be key stakeholders who inform programming. As such, qualitative research is needed to elevate FGCS voices. To date, little research has examined adjustment and well-being in FGCS. This study included qualitative interviews (approximately 30 min) concerning adjustment and well-being in FGCS (N = 24; 79% female, M age = 19, 92% White), defined as students whose parents have not completed a four-year bachelor’s degree. Participants’ responses were coded according to Bronfenbrenner’s Ecological Systems Theory levels, to account for the multi-layered systems (e.g., family, high school, university, peers) that impact their risk and resilience before and during college. One central theme was how seeking help from family, peers, and university resources promoted resilience. Conversely, individual risk factors (e.g., low socioeconomic status; insufficient guidance) interacted with the high school environment and economic landscape of higher education to confer risk. Overall, many challenges (e.g., mental health) improved over time. Students made recommendations for both high school and university staff about how to best support FGCS, which have implications for programming at the high school and university level. Full article

As population aging accelerates, providing psychosocial support for adults in the retirement transition has become increasingly important. For this population, a central challenge is adapting to changes in social roles, daily routines, and social relationships, yet existing social robot research has paid insufficient attention to these companionship-related needs. From an embodied cognition perspective, this study developed a needs-driven design pathway for a social companion robot for this population in urban China. Sixteen key needs were identified through user interviews and prioritized through a Kano survey with 184 valid responses from urban community-dwelling adults in the retirement transition. Based on the classification and prioritization results, these needs were synthesized into four design strategies: emotional responsiveness and trust building, social connectedness and sustained engagement, low-burden interaction and daily life support, and safety, health, and privacy protection. Privacy and safety were treated as foundational conditions for product design and practical deployment. The strategies were subsequently mapped to technical features and hardware elements through quality function deployment (QFD), using an expert-panel evaluation procedure and sensitivity analysis to determine module-level configuration priorities. A single-session, laboratory-based concept evaluation was conducted with 50 participants using concept renderings and interaction-flow videos. Concept 3, whose overall configuration emphasized coordinated voice, screen-based visual, and expression/action feedback, received a significantly higher mean participant-level Behavioral Intention (BI) score than Concept 2, which placed greater emphasis on spatial mobility assistance through a mobile wheel module (8.94 ± 0.97 vs. 8.51 ± 1.21; adjusted p = 0.003). These evaluation findings informed the final concept configuration and provided preliminary support for its companionship-oriented multimodal interaction approach. Full article

Reflecting on emotionally challenging experiences with caregivers is a critical developmental process through which children learn to identify, express, and regulate emotions. However, much of the existing literature on caregiver–child emotion socialization has centered White, middle-class families, limiting understanding of how these processes unfold within culturally and contextually situated interactions among Black families. Guided by Margaret Beale Spencer’s Phenomenological Variant of Ecological Systems Theory and the Integrative Conceptual Model of Adaptive Racial/Ethnic and Emotion Socialization, the present study explored how Black girls (ages 5–11) described experiences of sadness, stress, and anger during caregiver-guided conversations embedded within a culturally grounded shared book reading activity. Fifteen Black girls (M = 8.27 years, SD = 1.71) and their caregivers participated in a novel video-recorded shared reading activity followed by a guided discussion about emotions. Black girls described sadness in relation to separation from loved ones, social exclusion, and witnessing harm; stress primarily in response to academic pressures; and anger in response to perceived unfairness and injustice. They also identified diverse emotion regulation strategies, including deep breathing, solitude, cognitive reframing, imaginative coping, and seeking social support. Across emotional contexts, caregivers consistently encouraged elaboration, validated children’s emotional experiences, and reinforced adaptive coping strategies, creating supportive opportunities for collaborative emotional meaning-making. Our findings highlight shared reading as a culturally affirming context for caregiver-supported emotion socialization and underscore the importance of centering Black girls’ voices in research on socioemotional development. Full article

Background: Teachers routinely encounter children whose behaviour reflects emotional distress and dysregulation, yet they have limited opportunities to practise the relational skills required to respond effectively. These challenges are particularly pronounced in Alternative Provision (AP), which serves children who frequently present with histories of trauma, neurodevelopmental differences, and complex emotional and behavioural needs. Mentalization, the capacity to understand behaviour in terms of underlying mental states, is central to effective relational practice in such contexts. Conversational Artificial Intelligence (AI) may offer a scalable means of supporting this form of skills development, but its feasibility as a teacher-training modality remains largely unexplored. Methods: This mixed-methods proof-of-concept feasibility study evaluated MentaClassAI, a novel AI-based training tool in which educators engaged in simulated voice conversations with AI child characters portraying classroom dysregulation and subsequently received individualised, mentalization-informed feedback. Eleven staff members from a single AP school (four teachers and seven teaching assistants) completed a single training session and were allocated to either a psychoeducation video condition (n = 6) or a no-video condition (n = 5). The video condition received a brief introduction to mentalization and epistemic trust prior to engaging with the simulation. Pre- and post-engagement measures included the Reflective Functioning Questionnaire (RFQ-8) and a Teacher Self-Efficacy Scale. Post-engagement measures included an 18-item acceptability questionnaire, a Technology Acceptance Model scale, and open-ended questions analysed using thematic analysis. Results: Acceptability was high, with 84.8% of questionnaire responses falling within the positive range (overall M = 5.60/7). Feedback accuracy (M = 6.55) and clarity (M = 6.36) received the highest ratings. Participants reported higher teacher self-efficacy after the session than before (d = 1.20, p = 0.003), with 10 of 11 participants demonstrating improvement. Self-reported hypomentalizing was lower after the session (d = −0.86, p = 0.017). Between-condition differences (video versus no-video) were not statistically significant. The video condition scored numerically higher on the directional indicators. Qualitative analysis identified five themes: the value of consequence-free rehearsal; the specificity and usefulness of feedback; appreciation of the focus on the child’s emotional experience; limitations in the ecological diversity of AI child characters; and a desire for more naturalistic interaction. Conclusions: These findings provide preliminary support for the feasibility and acceptability of AI-based mentalization practice for AP staff. The principal value of the tool appears to lie not only in the simulation itself but in the quality of the reflective feedback generated. Although based on a small sample, the observed pre–post changes provide an encouraging signal that may justify a controlled trial. The contribution of pre-session psychoeducation to training outcomes remains an important question for future research. Full article

The increasing digitalization of industrial environments has led to the emergence of new paradigms such as Digital Twins (DTs), which enable real-time connections between physical assets and their virtual counterparts. In this context, the Coffee Twin system was developed as a modular and controlled demonstrator to explore, present, and validate the integration of DT technology with Augmented Reality (AR), Virtual Reality (VR), and voice-based interaction. A coffee machine was selected as the case study due to its manageable complexity, low-risk operation, and potential to illustrate concepts that can later be extended to more complex industrial systems. The system architecture is centered on a Raspberry Pi, which supports local data acquisition from temperature and humidity sensors, mechanical actuation through a servo motor, and remote control via a smart switch. To interact with the system, three software applications were developed: a Web application, an AR application for contextual visualization and remote interaction, and a VR application that recreates the operation of the machine within an immersive 3D environment. Beyond the technical implementation, the prototype was evaluated in demonstration and user-testing sessions in which 80 participants, mostly secondary-school pupils and university students, completed a coffee-making task across the three applications and then answered a System Usability Scale (SUS) questionnaire. The analysis of the 80 valid responses yielded a mean SUS score of 69.2 (standard deviation 15.9; 95% confidence interval [65.7, 72.8]), which corresponds to acceptable, slightly above-average usability and indicates that combining real-time sensing, remote actuation, and multimodal interaction in a single low-cost platform is perceived as usable across a heterogeneous, predominantly non-expert audience. The main contribution of this work is therefore not a new Digital Twin algorithm, but an accessible, modular, and reproducible integration of Web, AR, VR, and voice interaction around a physical asset, supported by usability evidence. Overall, Coffee Twin shows how a low-cost and multimodal DT demonstrator can support the communication, experimentation, and early exploration of Industry 4.0 concepts in educational and pre-industrial settings. Full article

The acoustic quality of a school’s indoor environment affects the health, performance and comfort of the teachers who work in it, yet assessment often reduces that environment to a single overall level. In a cross-sectional case study of one secondary school in China, all 148 teachers rated eleven school sound-source categories on audibility, annoyance and interference with teaching and office concentration, and reported perceived noisiness, voice raising, voice strain, fatigue, emotional distress and noise sensitivity, while selected-room acoustic measurements provided site context not matched to individual respondents. Ratings across the eleven sources were dominated by one component, but the resulting composite score was not associated with all teacher responses alike. Source load was associated with emotional distress, voice raising and perceived noisiness but not fatigue, which instead tracked voice strain, whereas emotional distress was associated most strongly with noise sensitivity. In exploratory source-specific models, traffic and other external sources had the largest associations with perceived noisiness, whereas playground activity and student talking had the largest associations with voice raising. No tested subgroup interaction survived false-discovery-rate correction. The selected-room measurements documented conditions compatible with source intrusion and raised vocal effort. The findings support an occupant-centred, source-resolved approach to school indoor acoustics and provide hypotheses for future multi-school testing. Full article

This paper presents an evaluation of AI-assisted human–computer interaction for mathematical problem solving within a multimodal mathematical editing environment. The proposed architecture integrates a visual equation editor, voice-based interaction, REST communication services, and externally hosted generative AI large language models (LLMs) to support the creation, interpretation, and solution of mathematical expressions. The study was conducted using the Equation Wizard environment, which provides multimodal mathematical content editing based on both proprietary and standard formula representations, including MathML and LATEX. A conversational AI interaction layer enables users to communicate with selected LLMs using natural language voice commands. The main objective of the study was to determine whether contemporary LLM-based services can reliably support mathematical problem solving within an AI-enhanced equation editing environment. To address this objective, seven contemporary GenAI LLMs were evaluated using a benchmark consisting of representative high school mathematics problems covering algebra, limits, trigonometry, logarithms, inequalities, and parameterized expressions. The evaluation focused on mathematical correctness, output interpretability and visualization quality, response latency, and compliance with mathematical encoding formats within the complete interaction workflow. The study also compares representative model families with respect to correctness, syntactic compliance, and responsiveness within the complete interaction workflow. Unlike conventional LLM benchmarks that assess models in isolation, this work evaluates end-to-end AI-assisted mathematical interaction involving the editor, communication infrastructure, and language models. The study demonstrates a practical approach to assessing the usefulness of multimodal AI-enhanced mathematical problem solving in realistic usage scenarios. The results show that, within the evaluated interaction workflow, current LLMs achieve high levels of mathematical problem-solving performance, although substantial differences were observed in response latency and output-format compliance. An important finding is the discrepancy between the models’ strong semantic understanding of custom-encoded mathematical input and their weaker ability to generate syntactically compliant encoded outputs, highlighting a key challenge in the integration of LLMs with structured mathematical software environments. Full article

Background/Objectives: Population ageing and the increasing prevalence of loneliness and social isolation represent major public health challenges. Digital health technologies, including conversational agents, have emerged as potential tools to support emotional well-being and daily functioning in older adults. This study presents the design and exploratory pilot evaluation of UjaBienestar, a multimodal conversational chatbot aimed at providing accessible emotional and practical support. Methods: A web-based multimodal system integrating text and voice interaction was developed using a Django backend and a Rasa-based conversational engine. The system was designed following user-centred and accessibility-oriented principles for older adults. A pilot usability and feasibility study was conducted with 10 participants using task-based interaction, observational data and pre/post-questionnaires. The exploratory evaluation focused on preliminary user perception, accessibility, and interaction feasibility rather than on statistically generalizable or clinically validated outcomes. Results: Participants reported high levels of perceived usability, accessibility, and acceptability. Multimodal interaction, particularly voice support, was positively valued. Users reported subjective perceptions of companionship during interaction within this pilot context. Initial barriers were mainly related to onboarding and first-time use. Conclusions: The findings suggest that the proposed system is a feasible and acceptable digital health support tool for older adults. While the results are preliminary, they highlight the potential of multimodal conversational technologies for supporting perceived emotional well-being, accessibility, and daily assistance in ageing populations. These findings are based on perceived user responses and do not represent clinically validated outcomes. Further large-scale and longitudinal studies are required to assess clinical and psychosocial impact. Full article

Cancer patients receiving systemic therapy experience substantial treatment-related symptoms. Nurse-led digital health interventions, e.g., interactive voice response systems, web platforms, mobile apps, and telehealth, have emerged as strategies to strengthen supportive care. To evaluate its effectiveness, this systematic review summarizes evidence exclusively from randomized controlled trials (RCTs). Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, four databases were searched from inception to January 2025 for eligible RCTs involving adults undergoing anticancer therapy; evaluating nurse-led or nurse-co-led interventions using digital or telecommunication technologies; reporting validated symptom or health-related quality of life (HRQoL) outcomes. Risk of bias was assessed. Nine RCTs (N = 3344) met criteria; seven had low risk of bias. Interventions using telephone systems, web portals, mobile apps, or videoconferencing reduced symptom burden and improved HRQoL. The Symptom Care at Home system reduced symptom burden by ~43%, with greatest effects from combined automated monitoring and nurse practitioner follow-up. Additional benefits included improved anxiety, self-efficacy, patient participation, fewer severe toxicities and hospitalization days. In conclusion, nurse-led digital interventions effectively reduce symptom burden and support HRQoL during systemic therapy. Multicomponent models integrating automated monitoring with structured nursing follow-up and decision support appear most beneficial. Full article

Visual impairment affects approximately 2.2 billion people worldwide, yet existing assistive technologies remain fragmented and prohibitively expensive. This paper presents Munir, an integrated multimodal assistive system designed to enhance human–computer interaction through a combination of a mobile application and Bluetooth-enabled smart glasses. Munir leverages a hybrid machine learning architecture to provide inclusive, real-time support for daily living activities. The system integrates ten core capabilities—including face recognition, optical character recognition, and scene description—all accessible through a unified bilingual (Arabic/English) voice interface. By employing on-device processing for biometric tasks, Munir ensures user privacy and trust while maintaining high responsiveness. End-to-end system evaluation on the SCface dataset achieves a 96.69% recognition rate with 0% False Accept Rate. At an estimated first-year total cost of $806, Munir demonstrates a 4–5× cost advantage over commercial alternatives, providing a scalable and affordable multimodal solution for global digital inclusion. Full article

This study proposes and validates a structured methodology based on Design for Six Sigma (DFSS) for sustainable product design, addressing the lack of standardization in the integration of design tools and the need to simultaneously consider qualitative, quantitative, and sustainability-related variables. The methodology integrates Voice of the Customer (VOC), Quality Function Deployment (QFD), Theory of Inventive Problem Solving (TRIZ), computer-aided design and engineering (CAD/CAE), and Design of Experiments (DOE) within a ten-stage framework combining the stages from DMADV (Define, Measure, Analyze, Design, Verify) and IDOV (Identify, Design, Optimize, Validate) approaches. The proposed method was applied to the design of a structural concrete block, considering performance variables such as weight, factor of safety, displacement, energy consumption, and carbon emissions. The results show that the integration of QFD enabled prioritization of customer requirements, while DOE and regression models identified significant factors and interactions. Multi-response optimization using desirability functions achieved a balanced solution, improving structural performance and sustainability indicators. In particular, a significant reduction in carbon emissions was achieved. Validation through simulation confirmed the consistency between predicted and observed results. The findings demonstrate that the proposed methodology provides a systematic and replicable approach for product design, improving decision-making and supporting the development of more sustainable products. Full article

As artificial intelligence (AI) becomes increasingly integrated into educational settings, understanding how students perceive and prefer to interact with an AI-Buddy (learning companion) is essential for responsible design. This study investigated 168 undergraduate students’ perceptions and preferences regarding an AI-Buddy, including its pedagogical role, knowledge level, identity customization, voice, mind attribution, demographic characteristics, and comfort sharing academic struggles. Results revealed that students most frequently preferred the AI-Buddy as a tutor with an omniscient or professor-level knowledge base, a non-human and neutral interface, and a warm yet robotic voice. Preferences for mind attribution were split, with nearly half preferring low mind attribution. Female students were more likely to prefer a female AI-Buddy, and male students a male AI-Buddy; White and Black students showed same-race preferences. These findings highlight the diversity of student preferences and underscore the need for a flexible, customizable AI-Buddy that prioritizes transparency and user agency rather than one-size-fits-all designs. We discuss how our findings can be extended to future research and educational AI design. Full article