Search Results (694)

Data-driven forecasting is becoming increasingly central to modern energy management, yet nonspecialists without a background in artificial intelligence (AI) face significant barriers to entry. While Python is the dominant machine learning language, R remains a practical and accessible tool for users with expertise in statistics, engineering, or domain-specific analysis. To inform tool selection, we first provide an evidence-based comparison of R with major alternatives before reviewing 49 peer-reviewed articles published between 2020 and 2025 in Science Citation Index Expanded (SCIE)-level journals that utilized R for energy forecasting tasks, including electricity (regional and site-level), solar, wind, thermal energy, and natural gas. Despite such growth, the field still lacks a systematic, cross-domain synthesis that clarifies which R-based methods prevail, how accessible workflows are implemented, and where methodological gaps remain; this motivated our use of text mining. Text mining techniques were employed to categorize the literature according to forecasting objectives, modeling methods, application domains, and tool usage patterns. The results indicate that tree-based ensemble learning models—e.g., random forests, gradient boosting, and hybrid variants—are employed most frequently, particularly for solar and short-term load forecasting. Notably, few studies incorporated automated model selection or explainable AI; however, there is a growing shift toward interpretable and beginner-friendly workflows. This review offers a practical reference for nonexperts seeking to apply R in energy forecasting contexts, emphasizing accessible modeling strategies and reproducible practices. We also curate example R scripts, workflow templates, and a study-level link catalog to support replication. The findings of this review support the broader democratization of energy analytics by identifying trends and methodologies suitable for users without advanced AI training. Finally, we synthesize domain-specific evidence and outline the text-mining pipeline, present visual keyword profiles and comparative performance tables that surface prevailing strategies and unmet needs, and conclude with practical guidance and targeted directions for future research. Full article

Background: Vaccine hesitancy is a major threat to public health. As part of efforts to increase vaccine uptake, the focus is on optimizing the quality of communication among healthcare workers. Physician shortages and workloads create time constraints, making communication interventions in primary care challenging. This study aimed to propose strategies to improve communication between general practitioners and vaccine-hesitant individuals. This narrative review addresses the specific needs of general practitioners for effective communication and proposes strategies to combat vaccine hesitancy in culturally and linguistically diverse regions. Methods: Systematic searches of EMBASE and PubMed were performed using terms related to vaccine hesitancy, communication strategies, primary care, and cultural diversity. Additionally, the websites of major health organizations were searched for relevant reports and guidelines. Selection criteria were based on the relevance and quality of the selected studies. Results: The findings highlight the importance of empathy, transparency, and personalized information in communication strategies. The need for communication training and addressing policy and workload barriers for healthcare providers is significant. The proposed strategy includes regular communication skills and cultural competency workshops, language training, the development of multilingual resources, implementation of telemedicine services, and active community engagement. Conclusions: Policy recommendations advocate for increased primary care resources, support from general practitioner unions, and the integration of digital tools. These strategies are essential to improve vaccine uptake and public health outcomes by enhancing the capacity of general practitioners to effectively engage with vaccine-hesitant patients. Full article

Background: Hematopoietic cell transplantation (HCT) and chimeric antigen receptor T-cell (CAR-T) therapy have markedly improved survival in pediatric patients with hematological malignancies. However, these treatments cause profound immunosuppression, leading to significant susceptibility to vaccine-preventable diseases (VPDs), including invasive pneumococcal disease and measles. Timely and tailored immunization strategies are crucial to mitigate infectious risks in this vulnerable population. Methods: We conducted a narrative review of the English-language literature from 2000 to 2024, including clinical guidelines, surveys, and original studies, to evaluate immune reconstitution and vaccination practices in pediatric patients undergoing HCT and CAR-T therapy. Literature searches in PubMed, Scopus, and Web of Science used disease-specific, therapy-specific, and pathogen-specific terms. Data synthesis focused on vaccine schedules, immune recovery markers, and adherence challenges. Results: Profound immune deficits post-HCT and CAR-T therapy compromise both innate and adaptive immunity, often necessitating revaccination. Key factors influencing vaccine responses include time since therapy, graft source, immunosuppressive treatments, and chronic graft-versus-host disease. Although inactivated vaccines are generally safe from three to six months post-HCT, live vaccines remain contraindicated until documented immune recovery. CAR-T therapy introduces unique challenges due to prolonged B-cell aplasia and hypogammaglobulinemia, leading to delayed or reduced vaccine responses. Despite established guidelines, real-world adherence to vaccination schedules remains suboptimal, driven by institutional, logistic, and patient-related barriers. Conclusions: Effective vaccination strategies are essential for reducing infectious morbidity in pediatric HCT and CAR-T recipients. Personalized vaccine schedules, immune monitoring, and multidisciplinary coordination are critical to bridging gaps between guidelines and practice, ultimately improving long-term outcomes for immunocompromised children. Full article

Large language models (LLMs) have the potential to transform healthcare by assisting in documentation, diagnosis, patient communication, and medical education. However, their integration into clinical practice remains a challenge. This perspective explores the barriers to implementation by synthesizing recent evidence across five challenge domains: workflow misalignment and diagnostic safety, bias and equity, regulatory and legal governance, technical vulnerabilities such as hallucinations or data poisoning, and the preservation of patient trust and human connection. While the perspective focuses on barriers, LLM capabilities and mitigation strategies are advancing rapidly, raising the likelihood of near-term clinical impact. Drawing on recent empirical studies, we propose a framework for understanding the key technical, ethical, and practical challenges associated with deploying LLMs in clinical environments and provide directions for future research, governance, and responsible deployment. Full article

Despite rapid progress in natural language processing, current interactive AI systems continue to struggle with interpreting ambiguous, idiomatic, and contextually rich human language, a barrier to natural human–computer interaction. Many deployed applications, such as language games or educational tools, showcase surface-level adaptation but do not systematically probe or advance the deeper semantic understanding of user intent in open-ended, creative settings. In this paper, we present Semantrix, a web-based semantic word-guessing platform, not merely as a game but as a living testbed for evaluating and extending the semantic capabilities of state-of-the-art Transformer models in human-facing contexts. Semantrix challenges models to both assess the nuanced meaning of user guesses and generate dynamic, context-sensitive hints in real time, exposing the system to the diversity, ambiguity, and unpredictability of genuine human interaction. To empirically investigate how advanced semantic representations and adaptive language feedback affect user experience, we conducted a preregistered 2 × 2 factorial study (N = 42), independently manipulating embedding depth (Transformers vs. Word2Vec) and feedback adaptivity (dynamic hints vs. minimal feedback). Our findings revealed that only the combination of Transformer-based semantic modelling and adaptive hint generation sustained user engagement, motivation, and enjoyment; conditions lacking either component led to pronounced attrition, highlighting the limitations of shallow or static approaches. Beyond benchmarking game performance, we argue that the methodologies applied in platforms like Semantrix are helpful for improving machine understanding of natural language, paving the way for more robust, intuitive, and human-aligned AI approaches. Full article

The purpose of this scoping review is to identify major social determinants of health and barriers affecting access to mental health services in pregnant and postpartum women in the United States. It will also examine the scope of existing evidence-based interventions and dissemination and implementation strategies that were developed and implemented to increase accessibility to mental health treatment in high-risk pregnant and postpartum women. The Arksey and O’Malley Framework guided the review process, along with the Joanna Briggs Institute (JBI) recommendations for the extraction, analysis, and presentation of results in scoping reviews. Additionally, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-SCR) was used as a reference checklist. A total of 21 studies were used for analysis that were published between 2015 and 2025. An examination of social determinants of health (SDOH) influencing factors of mental health determined that those related to neighborhood and built environment had the highest rates. Using the socioecological model, individual barriers exhibited the highest frequency, with the most common themes to these barriers across all studies being language barriers, cultural barriers, and stigma-related challenges, followed by financial and childcare challenges and transportation challenges. Major findings included important evidence that therapeutic relationships with pregnant women who are depressed can be developed and that telehealth interventions improved access for women living in rural areas. Recommendations from this review will inform evidence-based interventions to address the gap in accessibility and affordability of mental health services in US pregnant and postpartum women residing in underserved communities. Full article

Immigrant and mixed-status families comprise a growing population in the United States, facing numerous barriers to accessing essential health and social services. This study examines service access barriers within the unique context of New Mexico’s borderlands, where constitutionally protected bilingualism and welcoming local policies contrast sharply with restrictive federal border enforcement. Using a qualitative approach, we conducted five focus groups with 36 immigrant caregivers in Doña Ana County, New Mexico, with the objective of understanding the factors that facilitate and hinder immigrant families’ access to health, behavioral health, and social services in this socio-politically complex border environment. Thematic analysis revealed three overarching themes: (1) structural and organizational limitations, including language barriers and transportation challenges exacerbated by border checkpoints; (2) the persistence of “chilling effects” on service use despite a Democratic presidency and post-pandemic policy shifts; and (3) community-defined recommendations for improving service access. The findings demonstrate how federal immigration enforcement undermines local inclusion efforts, creating enduring barriers to service access even in historically bilingual, immigrant-friendly regions. The participants proposed specific solutions, including mobile service units and integrated service centers, that account for both geographic and socio-political barriers unique to border regions. These community-generated recommendations offer practical strategies for improving immigrant service access in contexts where local welcome and federal enforcement create competing pressures on immigrant families. Full article

Background: Scientific evidence plays a critical role in informing public health decision-making processes. However, the extent, nature, and effectiveness of its use remain uneven across contexts. Despite the increasing volume of literature on the subject, previous syntheses have often suffered from narrow thematic, temporal, or geographic scopes. Objectives: This study undertook a comprehensive systematic literature review spanning 50 years to (i) synthesise current knowledge on the use of scientific evidence in public health decisions, (ii) identify key determinants, barriers, and enablers, (iii) evaluate implementation patterns, and (iv) propose future directions for research and practice. Methods: We adopted the PRISMA model (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). Moreover, we researched three large databases (Web of Science, Embase, and PubMed), and this study focused on articles published in the English and French languages between January 1974 and December 2024. Studies were analysed thematically and descriptively to identify trends, patterns, and knowledge gaps. Results: This review reveals a growing corpus of scholarship with a predominance of qualitative studies mainly published in public health journals. Evidence use is most frequently analysed at the national policy level. Analyses of the evolution of scientific production over time revealed significant shifts beginning as early as 2005. Critical impediments included limited access to reliable and timely data, a lack of institutional capacity, and insufficient training among policy-makers. In contrast, enablers encompass cross-sector collaboration, data transparency, and alignment between researchers and decision-makers. Conclusions: Addressing persistent gaps necessitates a more nuanced appreciation of interdisciplinary and contextual factors. Our findings call for proactive policies aimed at promoting the use of scientific evidence by improving the accessibility of health data (addressing the absence or lack of data, as well as its reliability, timeliness, and accessibility), and by training decision-makers in the use of scientific evidence for decision making. Furthermore, our findings advocate for better alignment between the agendas of healthcare professionals (e.g., data collection), researchers (e.g., the selection of research topics), and decision-makers (e.g., expectations and needs) in order to develop and implement public health policies that are grounded in and informed by scientific evidence. Full article

The article presents an approach and software tool aimed at achieving quality, accessible, and sustainable education. The approach is based on reusable learning objects—educational video materials that can be repeatedly used and adapted for different languages and audiences. The proposed learning model uses quality learning resources (regardless of their language) and integrates them into courses and educational processes, regardless of the language proficiency of the learners. The approach relies on the integration of subtitle translation technologies into educational video resources, aiming to overcome language barriers in education. The software tool, AI Subtitle Translator, is developed using artificial intelligence (AI) and offers automated subtitle translation. It utilizes OpenAI models (GPT-4o and GPT-4.5) to provide translation services. The workflow, architecture, implementation, and operational scenario of the software tool are also presented. The discussed approach serves as a solution to enhance accessibility to global educational content. By combining reusable learning objects with AI Subtitle Translator, effective education without language constraints is ensured. Full article

Background: The management of older diabetic patients in the Southern Africa Development Community (SADC) has been described by several authors as poor due to several constraints and lack of a team care approach. This systematic review aimed to investigate the facilitators and barriers to the effective implementation of interprofessional care (IPC) of the elderly with type 2 diabetes mellitus (T2D) in the SADC region. Methods: A comprehensive literature search was conducted using the Population–Concept–Context (PCC) framework in the search for relevant articles. Out of a total of 155 relevant articles, only 8 articles matched the set criteria and were selected for the final review. Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were used in the review. Results: The identified facilitators include providing decision support to healthcare workers, training of healthcare workers, use of local languages during the training sessions, and use of certified guidelines in the management of not only T2D but also all the other disease conditions. Barriers like ill-equipped patients with limited opportunities for education and counseling, enormous workload due to staff shortages, and loss to follow-up, among others, were equally identified. Conclusions: This systematic review identifies key facilitators and barriers to implementing effective interprofessional care for type 2 diabetes management in the elderly population of the SADC. Understanding these factors can help healthcare professionals optimize their collaborative efforts, ultimately enhancing the quality of care and improving health outcomes for elderly patients with T2D in the region. Full article

The rapid expansion of online learning platforms has necessitated advanced systems to address scalability, personalization, and assessment challenges. This paper presents a comprehensive review of artificial intelligence (AI)-based decision support systems (DSSs) designed for online learning and assessment, synthesizing advancements from 2020 to 2025. By integrating machine learning, natural language processing, knowledge-based systems, and deep learning, AI-DSSs enhance educational outcomes through predictive analytics, automated grading, and personalized learning paths. This study examines system architecture, data requirements, model selection, and user-centric design, emphasizing their roles in achieving scalability and inclusivity. Through case studies of a MOOC platform using NLP and an adaptive learning system employing reinforcement learning, this paper highlights significant improvements in grading efficiency (up to 70%) and student performance (12–20% grade increases). Performance metrics, including accuracy, response time, and user satisfaction, are analyzed alongside evaluation frameworks combining quantitative and qualitative approaches. Technical challenges, such as model interpretability and bias, ethical concerns like data privacy, and implementation barriers, including cost and adoption resistance, are critically assessed, with proposed mitigation strategies. Future directions explore generative AI, multimodal integration, and cross-cultural studies to enhance global accessibility. This review offers a robust framework for researchers and practitioners, providing actionable insights for designing equitable, efficient, and scalable AI-DSSs to transform online education. Full article

China frequently experiences natural disasters, making emergency language services a key link in information transmission, cross-lingual communication, and resource coordination during disaster relief. Traditional contingency plans rely on manual experience, which results in low efficiency, limited coverage, and insufficient dynamic adaptability. Large language models (LLMs), with their advantages in semantic understanding, multilingual adaptation, and scalability, provide new technical approaches for emergency language services. Our study establishes the country’s first generative evaluation index system for emergency language service contingency plans, covering eight major dimensions. Through an evaluation of 11 mainstream large language models, including Deepseek, we find that these models perform excellently in precise service stratification and resource network stereoscopic coordination but show significant shortcomings in legal/regulatory frameworks and mechanisms for dynamic evolution. It is recommended to construct a more comprehensive emergency language service system by means of targeted data augmentation, multi-model collaboration, and human–machine integration so as to improve cross-linguistic communication efficiency in emergencies and reduce secondary risks caused by information transmission barriers. Full article

This systematic literature review examines the integration of Artificial Intelligence (AI) and Extended Reality (XR) technologies in language education, synthesizing findings from 32 empirical studies published between 2017 and 2024. Guided by the PRISMA framework, we searched four databases—ERIC, Web of Science, Scopus, and IEEE Xplore—to identify studies that explicitly integrated both AI and XR to support language learning. The review explores publication trends, educational settings, target languages, language skills, learning outcomes, and theoretical frameworks, and analyzes how AI–XR technologies have been pedagogically integrated, and identifies affordances, challenges, design considerations, and future directions of AI–XR integration. Key integration strategies include coupling AI with XR technologies such as automatic speech recognition, natural language processing, computer vision, and conversational agents to support skills like speaking, vocabulary, writing, and intercultural competence. The reported affordances pertain to technical, pedagogical, and affective dimensions. However, challenges persist in terms of technical limitations, pedagogical constraints, scalability and generalizability, ethical and human-centered concerns, and infrastructure and cost barriers. Design recommendations and future directions emphasize the need for adaptive AI dialogue systems, broader pedagogical applications, longitudinal studies, learner-centered interaction, scalable and accessible design, and evaluation. This review offers a comprehensive synthesis to guide researchers, educators, and developers in designing effective AI–XR language learning experiences. Full article

Public libraries serve as vital community hubs that foster engagement, empowerment, and education, particularly for vulnerable populations, including refugee children and families. This study examines how Oklahoma’s public libraries contribute to refugee resilience and identifies challenges they face in providing these essential services. Using a qualitative method approach, including 20 semi-structured interviews with library staff, questionnaire surveys, and observations conducted across three Oklahoma library systems (Metropolitan, Pioneer, and Tulsa City-County) the study explored programs, services, and strategies that support refugee adaptation and integration. Findings reveal that libraries excel in three key areas: cognitive services (language literacy, digital access, educational resources), socio-cultural services (community building, cultural exchange), and physiological services (safe spaces, welcoming environments). These services contribute to building human, social, and economic capital, with human capital consistently ranked as most crucial for refugee resilience. However, libraries face significant challenges, with language barriers, program gaps, and outreach limitations being the most prevalent obstacles. Additional barriers include facility constraints, transportation difficulties, resource limitations, and privacy concerns. The study proposes nine comprehensive guidelines for creating sustainable pathways to refugee resilience through enhanced library services, emphasizing proactive community engagement, staff training, multilingual resources, advocacy, strategic partnerships, tailored programming, transportation solutions, cultural competence, and welcoming environments. This study contributes to understanding how public libraries can function as inclusive institutions that support refugee children’s successful integration and development in their new communities. Full article

This study aimed to examine reading interest profiles among preservice Chinese language teachers and related factors making them begin to like or dislike reading. In total, 321 college students majoring in Chinese language education in elementary and secondary schools participated in this study and completed a reading interest questionnaire. The questionnaire contains one close-ended question asking about their reading interest levels across seven periods (from preschool to college) and three open-ended questions asking about the reasons influencing their reading interest levels. Latent profile analysis (LPA) was used to identify reading interest profiles, and qualitative analysis was used to examine factors influencing their reading interests. The LPA results revealed three profiles, namely, mountain (up-down), valley (up-down-up), and upslope (up). The qualitative analysis revealed that motivators encouraging students to read included literacy sponsors, improved reading ability, reading time, extrinsic motivators, curiosity and desire for knowledge, access to reading, discovery of preferred texts, and relief from academic stress and relaxation. By contrast, barriers associated with the decline in reading interest included academic burdens and pressure, the availability of alternatives, a lack of reading ability, a loss of reading autonomy, a lack of literacy sponsors, limited access to reading, and inappropriate texts. Literacy researchers and educators should listen to students’ voices, understand their reading experiences, and consider developing appropriate intervention programs for literacy at different periods. Full article