Search Results (3,312)

Children and young people with a parent in contact with the justice system rarely receive the support they need; many are judged and stigmatised. Child Impact Assessments were developed by the Prison Reform Trust in collaboration with 28 children and young people from across the United Kingdom with experience of parental imprisonment, who said in interviews, focus groups, and an online survey that they want to be seen, listened to, and considered at all stages of a parent’s journey through the justice system: arrest, court, and sentencing, prison or community sentence, and prior to a parent’s release. They want to be supported, and they want to be included in decisions about that support. This paper lays out why Child Impact Assessments are needed; what they are; and crucially how they can be used in practice to ensure the right support is offered to meet a child’s needs at the earliest opportunity. The paper will provide evidence of how Child Impact Assessments can improve children’s wellbeing and will give an overview of recent developments to the resources. The authors will also explore future developments, including adapting the resources to understand the impact on unborn babies, babies, and very young children (the first 1001 days) when a pregnant woman or mother is in contact with the justice system. Full article

The aim of this study is to identify the factors that shape the ability of producers in short food supply chains in Slovakia to utilize different types of distribution channels and to penetrate higher-demand markets. The analysis was based on a database compiled from a public SFSC platform, comprising 986 agri-food producers, 1434 points of sale, and 1908 producer–point of sale ties. The data were analyzed as a two-mode network using ERGM models. The results show that most producers remain tied to local direct sales, while access to more demanding channels and distant markets is concentrated among a small group of actors. The study shows that the functioning of SFSCs in Slovakia is strongly shaped by producer size, value added, and the form of production organization. Organic certification emerges as a key tool of product differentiation that enhances ability to access distant and urban markets, although its importance in a post-socialist context is highly dependent on market characteristics. Family farms are selectively able to supply distant markets, while cooperatives, despite their expected association with commodity-oriented production, are able to overcome capacity and logistical barriers within SFSCs, indicating the emergence of new collaborative structures and business models. Full article

With the deepening implementation of the dual-carbon strategy, the penetration rates of distributed power sources and flexible loads in new distribution grids continue to rise, posing significant challenges to system security and stability due to output fluctuations and randomness. To enhance voltage quality and achieve low-carbon economic operation in distribution grids, this paper proposes a multi-objective optimization model for Distributed Energy Storage System allocation. The model integrates power quality, economic benefits, and net carbon emissions. To efficiently solve this high-dimensional nonlinear problem, an improved Multi-Objective Gray Wolf Optimization algorithm is proposed. It employs a chaotic map to initialize the population, enhancing global distribution uniformity. A nonlinear convergence factor is introduced to dynamically balance global exploration and local exploitation. A dynamic grouping collaboration strategy is designed, combining Lévy flight and the elite crossover strategy to enhance search capability and convergence accuracy. Simulations on an IEEE 33-node system show that the improved MOGWO-optimized energy storage scheme reduces average voltage deviation by 37.0%, total operating costs by 7.0%, and net carbon emissions by 4.1%, compared to a no-storage scenario. Compared to the standard MOGWO algorithm, the proposed method achieves further optimization across all objectives, validating its effectiveness and superiority in realizing coordinated energy storage planning that balances safety, economy, and low-carbon goals. Full article

Head and neck cancer (HNC) management requires highly coordinated multidisciplinary care. Radiation Therapy Technologists (RTTs) have increasingly expanded their role beyond technical execution, contributing to patient positioning, treatment delivery, monitoring, and supportive care. This narrative review integrates evidence from published literature with structured experiential insights collected through focus group discussions with RTTs and other multidisciplinary team (MDT) members. The resulting conceptual and operational framework highlights RTT contributions across the radiotherapy pathway, including adaptive planning, workflow coordination, and patient-centered interventions, supported by imaging and artificial intelligence (AI) tools for predictive modeling and treatment optimization. By facilitating communication, monitoring anatomical and functional changes, and integrating AI-informed insights, RTTs support timely interventions, reduce treatment interruptions, and enhance treatment safety and precision. Structured training, formal recognition of advanced practice roles, and interprofessional collaboration are key to maximizing the impact of RTTs in HNC care. This review provides a practical roadmap for institutions, professional societies, and research initiatives to support the evolution of RTT roles in complex radiotherapy settings. Full article

Background/Objectives: Quality of life is conceptualized as a multidimensional construct encompassing subjective well-being, health, and social functioning. Evidence suggests that engagement in physical activity contributes to higher quality of life scores among older adults, indicating that structured exercise programs can positively influence both physical and psychological domains in this population. The present study examined the impact of an 18-week structured physical exercise program on the quality of life of older adults, assessed through the World Health Organization Quality of Life-BREF (WHOQOL-BREF) instrument, which consists of four domains: physical health, psychological, social relationships and environment. A total of 86 participants were allocated to three groups: individual exercise (n = 31), collaborative exercise (n = 32), and a control group (n = 23). Quality of life was evaluated before and after the intervention using the WHOQOL-BREF. Results: Correlation analysis indicated strong relationships among the WHOQOL-BREF domains, both before and after the program. Repeated-measures analysis revealed no significant Group × Time interaction effects for any WHOQOL-BREF domain. A significant main effect of Time was observed for the Environment domain, indicating a small overall decrease across all groups during the study period. Conclusions: The structured exercise protocol did not lead to greater changes in quality of life compared to the control condition. Perceived environmental quality of life showed a small overall decrease over time across participants. Full article

Automated Writing Evaluation (AWE) systems have been increasingly integrated into second-language writing instruction; however, empirical evidence regarding the effectiveness of localized AWE tools in EFL contexts remains limited. This study investigated the impact of the iWrite Automated Writing Evaluation system on university EFL students’ writing performance and writing anxiety. Employing a quasi-experimental mixed-methods design, 60 Chinese university students were assigned to an experimental group using iWrite and a control group receiving traditional teacher feedback over a 12-week instructional period. Writing performance was assessed using the complexity, accuracy, and fluency (CAF) framework, while writing anxiety was measured through a validated questionnaire. Quantitative results revealed that the experimental group demonstrated significantly greater improvements in writing accuracy, fluency, and lexical complexity, as well as significantly lower levels of writing anxiety, compared with the control group. No significant difference was found in syntactic complexity. Qualitative findings further indicated that immediate, non-judgmental feedback and opportunities for repeated revision contributed to increased learner confidence and reduced anxiety. The findings suggest that localized AWE systems such as iWrite can effectively support both the cognitive and affective dimensions of EFL writing when integrated within a human–AI collaborative instructional framework. Full article

This study examined how pedagogical strategies can support student-coaches’ (SCs) empowerment and promote preservice teachers’ (PSTs) professional learning within Sport Education (SE) seasons. Sixty-seven tenth-grade students (aged 15–18) participated in SE units taught by three PSTs (two males, one female, aged 22–25) enrolled in a master’s degree program in Teaching of Physical Education in Primary and Secondary Education in northern Portugal. Data were collected through participant observation, informal and focus group interviews, and PSTs’ reflective diaries within a Collaborative Action Research (CAR) framework and analyzed thematically. Three CAR cycles addressed key challenges: (1) encouraging SCs to assume responsibility for their role, (2) fostering inclusive and supportive team interactions, (3) strengthening SCs’ sport-specific and instructional knowledge. Guided by a facilitator, PSTs implemented strategies including pre-lesson meetings, structured communication routines, task-modification and feedback cards, accountability systems, and visual identification of SCs. Findings suggest that SCs’ empowerment was progressively constructed through interconnected psychological, relational and pedagogical processes, supported by structured mediation and iterative reflection. Simultaneously, engagement in CAR cycles enabled PSTs to develop adaptive instructional decision-making and mediation strategies. The study highlights how empowerment in SE is shaped through relational and pedagogical conditions and illustrates how CAR can foster reciprocal learning between SCs and PSTs in authentic teacher education contexts. Full article

African immigrants in the U.S. face disproportionately high rates of HIV, yet existing services often lack alignment with their cultural and linguistic needs. Healthcare providers are rarely trained to address these gaps, contributing to persistent disparities in HIV prevention and care. Traditional in-house trainings for providers often have limited applicability. Collaborative, community-engaged approaches offer an opportunity to build culturally responsive, coordinated care. African Immigrant (AI) Health was a 5-year initiative integrating the Extension for Community Healthcare Outcomes model, Community of Practice model, and Commitment to Change (CtC) evaluation framework. We used an iterative process to improve the AI Health program based on participant feedback. Data were collected through pre/post-CtC questionnaires, case presentations, and discussion groups. CtCs were analyzed thematically and organized using the socioecological model. Over five years, 100+ providers enrolled, with 58 completing the program. Participants represented diverse healthcare roles, with a majority identifying as Black/African American (62%), female (81%), and residing in the Northeast region (57%). A total of 390 CtCs were developed, with the most common themes at the individual level. Most (54.5%) participants partially implemented their CtCs. Participants who implemented their CtCs reported improvements in culturally responsive care. Iterative adaptations enhanced engagement and retention. AI Health effectively supported providers in enhancing HIV care for African immigrants through culturally responsive training. Findings highlight the need for sustainable, longitudinal training models that integrate community expertise and call for policy and structural reforms to address systemic barriers to equitable HIV care. Full article

Water stress is a major challenge that limits the growth, development, and yield of maize (Zea mays L.) worldwide, especially under climate change, particularly abiotic stresses. This review presents a comprehensive bibliometric and literature-based analysis of research on maize’s response to drought and water scarcity from 1975 to 2025, using VOS viewer1.6.20 software, facilitating the detection of co-authorship networks, thematic groupings, and patterns of keyword co-occurrence within the selected publications. Data from the Web of Science were examined to assess publication trends, keyword networks, and international collaborations. A literature search was conducted by combining the keywords ((“maize”) OR (“corn”) AND (“drought”) OR (“water stress”) AND (“yield”)). Relevant studies were retrieved from the Web of Science (WoS) database using this search string. The Mann–Kendall test revealed a significant positive trend (p = 0.001) in publications on water scarcity (R² = 0.8526), with 396 relevant studies identified globally, regardless of language. The analysis of publication trends demonstrated a statistically significant increase in the volume of publications over the examined period, featuring major contributions from Kenya, Switzerland, Mexico, China, and the United States. The most influential publication focuses on a biotic stressor that significantly reduces maize grain yield. These results emphasise the need for integrated strategies that combine genetic improvement and sustainable irrigation to mitigate the impacts of water stress. This comprehensive analysis provides a foundation for guiding future research and policy development to improve maize resilience against the effects of water stress under changing climatic conditions. Full article

This study examines the community integration and One Health strategies employed to combat Ebola virus disease in the Democratic Republic of Congo from 2007 to 2022. We synthesized 12 outbreak reports, conducted qualitative interviews with 36 managers, organized three focus groups, and adapted an analytical framework (MATCH) to evaluate three essential dimensions: the integration of the One Health approach, community involvement, and bottom-up approaches. This study evidences progressive improvement in all domains. The first outbreaks (2007–2009) were marked by moderate community engagement and a One Health approach that was largely limited to the human health sector, deemed suboptimal. The 10th outbreak represented an era of transformation, when the Incident Management System (IMS) was adopted to better manage the response to the virus. The latest outbreaks (13th to 15th) demonstrate an “optimal” implementation of the “One Health” approach through effective collaboration between those in charge of ensuring human, animal, and environmental health and that of the community. This study demonstrates that success is largely dependent on bottom-up initiatives in which local populations, their leaders (both traditional and religious), community liaisons, and specific groups (women and youth) are involved in the design and implementation of such measures. The inclusion of anthropologists and psychologists in addressing the psychosocial dimensions—fear, stigma, and distress—has been critical in ensuring the success of these initiatives and the degree to which the public trusts and accepts them. However, many issues still need to be addressed, including poor coordination among sectoral ministries and the partial implementation of IMS at the grassroots level. In summary, the authors of this study propose that these integrated and participatory models are sustainable and imperative to building the resilience of the Congolese health system to future outbreaks. Full article

Reliable vision-based sensing of protective equipment wearing compliance is essential for safety monitoring in underground mining environments, where complex lighting conditions, similar background textures, and large variations in the scale of wearable items significantly degrade detection performance. To address these challenges, this study proposes a vision-based protective equipment wearing compliance detection method for underground personnel based on CGALS-YOLO. Traditional object detection models often introduce substantial redundant background information during multi-scale feature fusion, which weakens the perception of key wearing regions, particularly for small-scale targets. To alleviate this issue, a content-guided feature fusion (CGAFusion) module is incorporated into the neck of the YOLOv8 network, enabling adaptive fusion of same-scale multi-path features through the collaborative effects of channel, spatial, and pixel attention mechanisms. This design enhances target-related feature representation while suppressing background interference in complex underground scenes. Furthermore, to reduce parameter redundancy and improve cross-scale discrimination consistency in the detection head, a lightweight shared convolution detection (LSCD) structure is introduced. By employing cross-scale shared convolution parameters, group normalization, and scale-adaptive regression, the proposed model achieves a parameter reduction of approximately 23.9% while lowering computational complexity and maintaining stable multi-scale detection performance. Experimental results on an underground protective equipment wearing compliance dataset demonstrate that CGALS-YOLO improves detection accuracy by approximately 4.6% and recall by 3.1% compared with the baseline YOLOv8n, achieving an mAP@0.5 of 89.4%. These results validate the effectiveness and practical applicability of the proposed method for real-time vision-based safety monitoring in underground environments. Full article

Fog computing extends cloud services to the network edge to support low-latency IoT applications. However, since fog environments are distributed and resource-constrained, intrusion detection systems must be adapted to defend against cyberattacks while keeping computation and communication overhead minimal. This systematic review presents research on intrusion detection systems (IDSs) for fog computing and synthesizes advances and research gaps. The study was guided by the “Preferred-Reporting-Items for-Systematic-Reviews-and-Meta-Analyses” (PRISMA) framework. Scopus and Web of Science were searched in the title field using TITLE/TI = (“intrusion detection” AND “fog computing”) for 2021–2025. The inclusion criteria were (i) 2021–2025 publications, (ii) journal or conference papers, (iii) English language, and (iv) open access availability; duplicates were removed programmatically using a DOI-first key with a title, year, and author alternative. The search identified 8560 records, of which 4905 were unique and included for qualitative grouping and bibliometric synthesis. Metadata (year, venue, authors, affiliations, keywords, and citations) were extracted and analyzed in Python to compute trends and collaboration. Intrusion detection systems in fog networks were categorized into traditional/signature-based, machine learning, deep learning, and hybrid/ensemble. Hybrid and DL approaches reported accuracy ranging from 95 to 99% on benchmark datasets (such as NSL-KDD, UNSW-NB15, CIC-IDS2017, KDD99, BoT-IoT). Notable bottlenecks included computational load relative to real-time latency on resource-constrained nodes, elevated false-positive rates for anomaly detection under concept drift, limited generalization to unseen attacks, privacy risks from centralizing data, and limited real-world validation. Bibliometric analyses highlighted the field’s concentration in fast-turnaround, open-access journals such as IEEE Access and Sensors, as well as a small number of highly collaborative author clusters, alongside dominant terms such as “learning,” “federated,” “ensemble,” “lightweight,” and “explainability.” Emerging directions include federated and distributed training to preserve privacy, as well as online/continual learning adaptation. Future work should consist of real-world evaluation of fog networks, ultra-lightweight yet adaptive hybrid IDS, self-learning, and secure cooperative frameworks. These insights help researchers select appropriate IDS models for fog networks. Full article

The sustainability of experience-intensive wellness tourism services increasingly depends on managers’ ability to understand heterogeneous and implicit tourist preferences that are rarely captured through traditional survey-based approaches. In the context of Korean Templestay tourism, this study develops a data-driven decision-support framework that leverages large-scale unstructured review data to address managerial challenges such as choice overload, inefficient resource allocation, and cold-start conditions. Using 74,015 user-generated reviews collected between 2020 and 2024, the framework integrates Optical Character Recognition (OCR) to extract image-embedded text, achieving a validated character-level accuracy of 96.8%. In addition, a weak supervision strategy is applied to identify latent tourist preferences in a cost-efficient and scalable manner. Preference classification is conducted using Random Forest models combined with SMOTE, followed by clustering and user-based collaborative filtering to support personalized recommendations. The findings indicate that the Templestay market is better understood as an interconnected preference network rather than a set of mutually exclusive segments. Across user groups, “rest” emerges as a shared foundational value, while differentiated sub-preferences coexist within the network. The proposed framework successfully generates recommendations for all users in the dataset, demonstrating strong applicability for mitigating cold-start risks and supporting adaptive and sustainable program design. Full article

This quasi-experimental study examined the effectiveness of Augmented Reality (AR)-enhanced instruction on creativity development in Malaysian Design and Technology education. Forty-six, fifteen-year-old female students were assigned to AR-enhanced (n = 23) or traditional instruction (n = 23) groups for a four-week Mechatronic Design unit. Creativity was assessed using an adapted Torrance Tests of Creative Thinking-Figural (TTCT-F) instrument with expert validation and independent scoring by three raters. Bootstrapped ANCOVA (5000 iterations) controlling for pretest differences revealed significant improvements across all Guilford creativity components in the AR group: Elaboration (F = 27.093, p < 0.001, η² = 0.387), Originality (F = 20.445, p < 0.001, η² = 0.322), Fluency (F = 17.896, p < 0.001, η² = 0.294), and Flexibility (F = 7.593, p = 0.008, η² = 0.150). The differential effect pattern suggests AR operates through multiple mechanisms, primarily socio-constructivist collaborative scaffolding, followed by motivational enhancement and cognitive load reduction. These findings demonstrate AR’s substantial potential for creativity development in Design and Technology education, particularly for collaborative elaboration and generative ideation. However, single gender sampling, brief intervention duration, and quasi-experimental design limit generalizability, warranting future research with diverse populations and extended interventions. Full article

Over the years, the impact of music on the prenatal and neonatal stages of human life has gained significant scientific attention. This study provides a comprehensive bibliometric review of research investigating how music influences fetal and newborn development. Using the Scopus and Web of Science databases, a search of relevant studies published in English between January 2006 and July 2025 was conducted, whose basic criterion was the use of the following keywords: “music” and “fetus” or “fetal”. Additional terms such as “fetus development”, “fetus heart rate”, “fetus movement”, “mother–fetus relationship”, “newborn”, etc., were also utilized. In result, 75 publications were selected, and their bibliographic data and full sources were retrieved. The included studies were grouped according to two perspectives that consider the impact of music (1) on the development of the fetus and the newborn, and (2) on maternal health and mother–fetus bonding. Using VOSviewer, bibliometric mapping was performed, which allowed to obtain keyword co-occurrence network and co-authorship network. The chosen literature was then quantitatively and qualitatively analyzed. The analysis revealed a sharp upward trend in publications starting in 2015, with a temporary decline in 2020 due to the COVID-19 pandemic. The highest number of publications were from Iran. The most investigated topics were related to the fetus heart and maternal health. The most common publication type and research methodology were, respectively, article and experiment. While the key authors Lordier, L., Filippa, M., Grandjean, D., and Monaci, M.G. lead the field, the co-authorship network remains fragmented into isolated and relatively small research groups. The Journal of Maternal–Fetal and Neonatal Medicine emerged as the leading publication outlet, while the study by Graven et al. entitled “Auditory Development in the Fetus and Infant” remains the most cited work. The keyword co-occurrence network allowed the identification of three main thematic clusters indicating the physiological, clinical, and therapeutic aspects of the impact of music on fetal and neonatal development. A qualitative analysis revealed that music plays a vital role in early human development and maternal well-being, demonstrating positive effects of auditory stimuli on fetal and newborn physiology, as well as on the mother–fetus relationship, while being a non-invasive and non-pharmacological method of intervention. However, the lack of a fully connected global research community and standardized protocols for, e.g., choosing the musical repertoire, sound administration, and the duration of exposure suggests the need for increased international collaboration to further integrate music therapy into standard clinical practices for prenatal and neonatal care. Full article