Search Results (3,608)

The postnatal period involves significant emotional and relational shifts that can challenge early mother–infant interactions, particularly under conditions of psychosocial vulnerability (e.g., maternal anxiety/depression) or infant-related risk (e.g., preterm birth). Maternal mentalization, operationalized as Parental Reflective Functioning (PRF), is a key protective factor for sensitive caregiving and dyadic regulation. Objectives: This protocol describes a multicenter, open-label, single-arm feasibility study evaluating a brief, fully online, mentalization-based video-feedback intervention (VFI-RF). The study is designed to assess the feasibility and acceptability of the intervention, rather than its efficacy. We aim to recruit 48 mothers, 24 in each of two risk groups, through socio-health services and neonatal intensive care units. Risk Group 1 will include mothers with clinically significant depressive and/or anxiety symptoms, defined as EPDS > 9 and/or GAD-7 ≥ 10, whereas Risk Group 2 will include mothers of preterm infants, defined as infants born before 37 weeks of gestation. Methods: The intervention consists of 8 + 2 synchronous online sessions over approximately 5 months. Mothers record brief everyday caregiving interactions (~5 min) to review with a trained clinician, focusing on the infant’s internal states and reflective meaning-making. Assessments occur at baseline (T0, infant age ~3 months), post-intervention (T1, ~8 months), and follow-up (T2, ~12 months). Primary feasibility outcomes include recruitment/referral metrics, uptake, retention, assessment completion, missing data, and participant-reported acceptability. Secondary exploratory clinical outcomes include maternal PRF, symptoms, parenting stress, social support, and mother–infant attachment, evaluated via validated self-report questionnaires. Results: The study is designed to evaluate referral and recruitment patterns, intervention uptake, and participant retention, as well as the acceptability and suitability of study procedures and outcome measures for a future controlled trial. Preliminary trajectories of change in maternal reflective functioning and early relational indicators will be examined descriptively and exploratorily. Conclusions: Findings will inform the feasibility and refinement of a brief online mentalization-based video-feedback intervention to support at-risk mother–infant dyads during the first postnatal year. Trial registration: Registered on Open Science Framework, osf.io/6g9ja, date of registration 4th March 2026. Full article

The large-scale connection of distributed energy resources, electric vehicles, and flexible loads, together with expanding low-voltage monitoring and edge sensing, is turning distribution networks into active cyber-physical systems. Conventional offline simulation cannot fully support the online state tracking, short-term scenario analysis, operational risk assessment, and closed-loop decision support now expected in network operation. Digital twins offer a way to address this gap by linking network models to operational data and revising those models as system conditions change. After systematically searching Scopus and the Web of Science, six application areas for digital twin applications in distribution network simulations are summarized: model construction, simulation and validation platforms; asset, equipment and spatial digitalization; DER (distributed energy resource), PV, EV (electric vehicle) and prosumer integration; operation, monitoring and situational awareness; protection, fault diagnosis and resilience; and optimization, control and planning. The review examines the architectures, enabling technologies, and applications reported across this evidence base. The literature indicates a gradual shift from conceptual digital representations toward real-time simulation, hardware-in-the-loop validation, data-driven model updating, and distribution-side decision support. Persistent gaps concern low-voltage observability, data governance, model credibility assessment, standardized interfaces, cybersecurity, and closed-loop control. Full article

This study critically examines hospitality performance evaluation practices by proposing a narrative-based quantitative framework grounded in online reviews. While tourism experiences, particularly in transformative contexts, are understood as subjective and meaning-oriented, empirical evaluation remains reliant on standardized, growth-oriented indicators such as star ratings and satisfaction scores. To address this disconnect, online reviews are conceptualized not as post hoc satisfaction measures but as narrative expressions of evaluation. Drawing on sentiment analysis of narratives from eleven hotels, evaluations are operationalized through indicators and incorporated into a DEA framework. Efficiency is reframed as experiential conversion capacity—the ability of hospitality providers to transform material and organizational conditions into experiences perceived as meaningful by guests. Two aggregation configurations and a super-efficiency extension are applied to examine robustness and differentiation. The findings reveal divergence between narrative-based evaluations and star classifications, suggesting that rating systems fail to capture dimensions of meaning and affective resonance. Notably, a lower-category hotel emerges as the strongest in experiential positioning, challenging assumptions linking quality, classification, and value. From a sustainability perspective, the study contributes to critiques of tourism evaluation and supports post-growth, sufficiency-oriented approaches. Methodologically, it demonstrates how techniques can be repurposed as interpretive tools when grounded in narrative data. Full article

This systematic review follows the PRISMA 2020 guidelines to provide an analysis of the mechanisms of stigmergy and self-organization in swarm robotics. The purpose of this review is to conduct a bibliometric, thematic, and epistemological analysis. Journal articles addressing stigmergy, self-organization, and swarm robotics were included, whereas duplicate, irrelevant, and methodologically insufficient studies were excluded. The scientific databases searched were IEEE Xplore, ACM Digital Library, ScienceDirect, Springer Nature, MDPI, and Wiley Online Library from June 2025 to April 2026. Three reviewers independently screened studies using predefined criteria; no formal risk-of-bias assessment or meta-analysis was performed. In total, 338 scientific works were analyzed, representing a wide range of different approaches and applications in stigmergy and self-organization in swarm robotics. The results were synthesized through four complementary analytical axes. The review highlights the significance of stigmergy and self-organization principles in providing robustness, scalability, and adaptability in swarm robots, and shows the increasing popularity of hybrid solutions based on swarm optimization, distributed learning, and adaptive control. Key limitations include the fragmentation of methodologies, the lack of benchmarking, the underrepresentation of computational and physical perspectives, and challenges in multi-scale modeling. The review provides an integrated conceptual framework and identifies future research directions. This work was supported by FAPERJ (grants 201.013/2022 and 200.434/2026) and registered with the Open Science Framework. Full article

The COVID-19 pandemic compelled higher education to resort to e-learning, posing new challenges to the teaching/learning of pharmacy students worldwide. While digital learning provided flexibility, diverse technological infrastructure and institutional availability of resources greatly influenced the student experience. This scoping review aims to assess the perceptions relating to the pivot to e-learning among pharmacy students in the League of Arab States due to the COVID-19 pandemic and how the shift affected student engagement, learning outcomes, and institutional preparedness. Following PRISMA-ScR guidelines, a comprehensive search across ten databases was conducted to identify relevant studies published between January 2020 and December 2025. Forty studies satisfied the inclusion criteria. Pharmacy students in this region responded to the transition to e-learning in diverse ways. While most appreciated the convenience of online modalities, several challenges were consistently enumerated. These were limited technological infrastructure, reduced interpersonal interaction, and disruption of hands-on practical training. Blended learning approaches were largely favored, particularly for their ability to marry online theoretical instruction with face-to-face experiential learning. Reliability and validity issues of internet-based tests were felt by both faculty and students. Stress and mental health problems among students surfaced. Student complaints in general depicted pharmacy education’s need for pedagogic reform, better infrastructure, and student mental health services during e-learning. Areas identified from this review are instructional technology infrastructure improvement, adopting a blended learning strategy, and the need to consider the mental health of students learning at a distance. Full article

Indoor badminton venues are common mass-fitness spaces in China, but their acoustic environment remains underexamined relative to lighting, thermal comfort, and functional facilities. This study uses grounded theory to examine how users perceive acoustic conditions within the broader experience of indoor badminton venues. A total of 4721 raw online reviews for seven purposively selected venues in five Chinese cities were collected, and 3937 valid reviews remained after preprocessing. A hybrid text-processing procedure combining DeepSeek-V3-assisted term pre-screening and Python (3.11) jieba segmentation identified 74 core high-frequency terms; all grounded theory coding was conducted manually in NVivo 15. Open, axial, and selective coding generated 32 initial categories, six main categories, and an Indoor Badminton Venue User Experience Perception Model. Acoustic-related categories were then extracted to construct an Acoustic Environment Perception Mechanism Sub-Model. The results show that noise level was directly mentioned in only 45 reviews but was indirectly embedded in sport atmosphere, time-based flow, and user experience, indicating a latent perceptual role. Moderate sound may be interpreted as a vibrant sport atmosphere, whereas crowd overload and reverberant spatial conditions may shift perception toward chaotic noise. The findings provide qualitative evidence for integrating user-centered acoustic considerations into the design and operation of mass-leisure sports venues. Full article

Background/Objectives: Artificial intelligence (AI) holds significant potential to enhance diagnostic support and access to dermatological care; however, its adoption depends on public trust and acceptance. This study aimed to assess knowledge, attitudes, and acceptance of dermatological AI among Saudi adults, and to identify factors associated with adoption, trust, and preferred system characteristics. Methods: A nationwide cross-sectional online survey was conducted among 668 Saudi adults (≥18 years) between 21 May and 5 June 2025, using convenience and snowball sampling via social media platforms (WhatsApp, Snapchat, Twitter/X, and Telegram). The questionnaire captured demographics, attitudes toward AI (20-item Likert scale), and perceived importance of six AI system features. Data were analyzed using descriptive statistics, one-way ANOVA, and binary logistic regression. The study was approved by the Institutional Review Board of King Faisal University (Approval No. KFU-REC-2025-MAY-ETHICS3443, approval date 19 May 2025). Results: The mean overall AI attitude orientation score was 74.48 ± 10.20 (Cronbach’s α = 0.868), reflecting moderately positive but conditional attitudes toward dermatological AI. Participants strongly preferred physician-supervised AI over fully autonomous systems, with medical oversight receiving the highest agreement (mean 4.27 ± 0.87). Privacy protection and diagnostic accuracy were rated as the most important system features. Age was significantly associated with the overall AI attitude orientation score (p = 0.009), with younger participants demonstrating more favorable orientations. Interest in technology showed the strongest association with both AI attitude orientation and perceived importance (p < 0.001). No demographic variable independently predicted high intention to use AI in multivariate analysis. Conclusions: Saudi adults generally exhibit favorable yet cautious attitudes toward dermatological AI. Implementation strategies should prioritize physician oversight, transparency, data privacy, and culturally responsive design to support responsible integration into clinical practice. Full article

Background: Admission to an intensive care unit (ICU) exposes family members of adult patients to substantial informational, emotional, and decisional burden. In recent years, digital tools have increasingly been used to support communication, information delivery, virtual visiting, psychological support, diary writing, and surrogate decision making in ICU settings, although the available literature remains heterogeneous in terms of intervention type, purpose, timing, and outcomes assessed. Methods: A scoping review was conducted according to Joanna Briggs Institute methodology and reported following PRISMA-ScR. The literature search was performed between January and March 2026 in PubMed/MEDLINE, Scopus, and CINAHL. After duplicate removal, title/abstract screening, and full-text assessment, 32 studies were included in the qualitative synthesis. Results: The included studies were published between 2016 and 2026, used heterogeneous methodological designs, and originated from different international contexts. Six main categories of digital tools were identified: educational websites and online information resources; decision aids and tablet-based tools; virtual visiting and video communication systems; digital diaries and writing practices; psychological support or self-management applications; and digital assessment or family-engagement platforms. Overall, informational and communication-oriented tools appeared to provide the clearest signals of usefulness for family orientation, information access, communication, and relational continuity, whereas evidence regarding psychological and decisional outcomes remained more variable and largely preliminary. Conclusions: Digital tools for family members of adult ICU patients represent a relevant and evolving component of family-centered critical care. Their value appears to depend on the family need addressed, the timing of implementation, and their integration into clinical workflows. Overall, the available literature suggests that digital tools may be particularly useful for family orientation, information access, and communication, whereas their impact on psychological and decisional outcomes remains less certain and requires further investigation. Full article

An increase in penetration of photovoltaic (PV) systems in a distribution system causes voltage regulation issues that create serious problems for the On-Load Tap Changer (OLTC) of the power transformer, leading to higher tap-changing frequency and reduced transformer life. Traditional voltage control methods are ineffective when PV penetration exceeds load demand, and more sophisticated control methods are needed. This paper combines a systematic literature review conducted in accordance with the PRISMA 2020 guidelines with a case study on operational issues of OLTC transformers under both normal and non-normal operating conditions. It entails a detailed examination of the effect of PV integration on the operating characteristics of OLTC in a systematic approach and also dwells upon coordination processes between OLTC and Flexible AC Transmission Systems (FACTS) devices, such as Distribution Static Synchronous Compensator (D-STATCOM) or Static VAR Compensator (SVC), which are highly effective in reducing tap operations. The future directions covered in the review include the operation of hybrid systems, cost-effective implementations, weather effects, predictive analytics, adaptive control techniques, etc. The case study included online monitoring of OLTC performance in two scenarios at the cement factory. First, under supply changes and load changes. Second, including PV penetration. The results show that OLTC increases the average daily tapping frequency (90 taps/day) by about 60%, with full PV penetration. It is concluded that this can’t be applied without coordinated control among OLTC, D-STATCOM, and PV inverters to maintain transformer life, improve reliability, and provide stable voltage profiles even under highly variable PV generation conditions. These results aim to provide a comprehensive resource for academics and practitioners, facilitating the advancement of advanced voltage control methods to support the transition to sustainable energy systems. Full article

Machine learning (ML) has become a central enabler of data-driven decision-making across smart manufacturing, healthcare, finance, computer vision, robotics, and environmental sustainability. Despite the rapid growth of ML applications, existing review studies remain largely domain-specific and provide limited cross-domain synthesis of methodological trends, deployment challenges, and emerging research directions. This systematic literature review aims to provide a comprehensive and comparative analysis of ML applications across seven high-impact domains while identifying dominant learning paradigms, implementation challenges, and future research opportunities. Following the PRISMA 2020 guidelines, peer-reviewed studies published between 2015 and 2025 were systematically collected from major scientific databases, including ScienceDirect, IEEE Xplore, SpringerLink, Wiley Online Library, MDPI, and Web of Science. Studies were screened using predefined inclusion and exclusion criteria and categorized according to application domain, ML paradigm, algorithm type, data characteristics, and deployment context. The findings indicate that supervised learning and deep learning dominate most application areas, with convolutional neural networks emerging as the primary approach for image-based and perception-driven tasks. Reinforcement learning, although highly promising for sequential decision-making and adaptive control, remains comparatively underutilized due to safety, computational, and deployment constraints. Across domains, recurring challenges include data quality, interpretability, scalability, model robustness, computational requirements, and ethical considerations. Overall, this review provides a structured cross-domain synthesis of ML applications and highlights the growing importance of explainable, trustworthy, and deployable AI systems for future intelligent and sustainable technologies. Full article

Background: The early or latent phase of labour (early labour) is a time when women feel unsupported and have limited access to quality midwifery support, often being advised to stay at home. As a result, women seek online information and often turn to hospital websites as a trusted source of this information. Women from underserved and marginalised groups may be particularly reliant on online information. The aim of this study was to systematically evaluate the availability, accessibility, content, and evidence base of online early labour information provided by UK hospitals, with a focus on inclusivity, and equity in information provision. Methods: A systematic search of NHS and HSC maternity websites across the UK (England, Scotland, Wales, and Northern Ireland) was undertaken to identify publicly available guidance on early labour. Eligible materials included webpages, downloadable leaflets, and multimedia resources. The identified guidance was evaluated in terms of availability, accessibility, content, and transparency of evidence. Data were synthesised descriptively and presented using narrative summaries and tables. Results: A total of 146 hospital websites were reviewed, of which 72 (49%) provided guidance specific to early labour or included a dedicated section on the latent phase. There was marked variation in availability, accessibility, and content. Accessibility was often limited, with few multilingual resources, alternative formats, or inclusive visual materials. Most guidance was text-heavy, with minimal use of multimodal or user-friendly formats and limited representation of diverse populations. Clinical content also varied, particularly in definitions of early labour and recommendations for pain management. Only a minority of resources referenced supporting evidence. Conclusions: Online early labour information provided by UK maternity services varies in availability, accessibility, and inclusivity, raising important equity concerns. Limitations in accessibility, consistency, and transparency of evidence may contribute to disparities in understanding and decision-making, particularly among women from disadvantaged or marginalised groups. There is a clear need for standardised, evidence-based, and inclusive information that is accessible to diverse populations to support equitable maternity care during early labour. Full article

Cervical dystonia and spasticity are debilitating neuromuscular disorders that substantially impair quality of life. Botulinum toxin type A (BoNT-A) is a cornerstone therapy; however, heterogeneity in dosing, muscle selection, and guidance techniques can limit outcomes, and Mexico-specific practical guidance is limited. We convened a multidisciplinary panel of Mexican rehabilitation and neurology specialists with extensive experience in BoNT-A injection; recommendations were developed through a three-phase process: a targeted literature search, a structured online survey of Mexican rehabilitation and neurology specialists, and an in-person meeting (21 February 2025) where clinicians reviewed aggregated survey results and refined practical recommendations through structured deliberation. The manuscript provides pragmatic guidance on evaluation and goal setting, muscle selection, working dilutions, dosing ranges, and reinjection intervals, emphasizing ultrasound (US) and/or electromyography (EMG) for deep or high-risk targets. It also summarizes key adverse events and proposes a stepwise approach to inadequate response, prioritizing reassessment of diagnosis, goals, targeting, and techniques before considering immunogenicity testing, product switching, or alternative interventions. These recommendations aim to improve standardization, safety, and goal attainment in routine Mexican practice. Full article

Inbound tourism has become an important indicator of destination openness, service capacity, cultural communication, and sustainable governance. However, existing evaluations often separate visitor experience, destination competitiveness, and sustainability, making it difficult to diagnose how service quality supports long-term competitiveness. This study develops a sustainability-oriented framework for evaluating inbound tourism service quality in 10 representative Chinese cities. Nineteen indicators are organized into four dimensions: basic service provision, cultural and experiential perception, safety and emergency response, and sustainable and resilient development. A TIFN-AHP-TOPSIS model is used to integrate official statistics, public tourism information, online-review evidence, and expert judgments while retaining uncertainty and hesitation in qualitative assessments. The results show that Shanghai, Beijing, and Hangzhou form the leading tier; Shenzhen, Chengdu, Guangzhou, Sanya, and Xiamen form the balanced tier; and Xi’an and Chongqing form the potential tier. Robustness checks based on risk-preference adjustment, entropy-weighted TOPSIS, grey relational TOPSIS, and perception-indicator perturbation confirm the stability of the tier classification. The findings suggest that inbound tourism competitiveness depends not only on transport access and reception capacity but also on cultural interpretation, digital convenience, safety governance, ecological quality, and resilience. The framework provides a diagnostic tool for improving sustainable destination competitiveness. Full article

Background: In recent years, there has been a growing concern regarding social media driving the decline of mental health, especially among adolescents. However, scientific consensus remains mixed, with many studies reporting only small or inconsistent associations. Aims: This paper aims to present the latest and most influential findings in the field of social media, with a focus on understanding the impact it has on adolescents’ mental health by looking at clinical versus non-clinical populations. Method: We conducted a comprehensive search through Scopus, looking for scientific articles and reviews published from January 2020 to March 2026 that include social media and adolescents with mental health conditions. We examined social media use patterns, affordances, mechanisms of impact, and clinical versus non-clinical populations. Results: There is limited literature comparing clinical versus non-clinical adolescent populations. Adolescents with mental health disorders spend more time online, teens with internalizing conditions report being more prone to social comparison and more sensitive to digital feedback, while those with externalizing conditions report a lack of control over how much time they spend on social media. Screen time alone is not sufficient to determine the impact on mental health. Among the features that might be associated with mental health problems are sharing personal content and scrolling through others’ posts. Conclusions: The impact of social media could be shaped by pre-existing vulnerabilities. There is a need for longitudinal study designs to test temporal associations and more research to cover the gap on clinical populations to develop better policies and interventions. Full article

Online business reviews encode rich customer sentiment that is critical for commercial decision making, yet accurately predicting star ratings from free text remains a challenging five-class classification problem. Classical ensemble methods—Soft Voting, Weighted Voting, and Stacking—aggregate complementary base-model outputs to improve predictive performance, but they produce opaque decisions that are unintelligible to business stakeholders. This paper proposes using a large language model (LLM), specifically unsloth/LLaMA-3.3-70B-Instruct, as an Explainable AI (XAI) ensemble aggregator: the LLM receives the predictions and confidence scores of four heterogeneous base models (Logistic Regression, Support Vector Machine, Naïve Bayes, and BERT-base-uncased) and reasons over them to produce both a final star-rating prediction and a natural-language explanation. We evaluate the full pipeline on 10,000-sample balanced and natural-distribution test sets derived from the Yelp Academic Dataset, with additional cross-lingual validation on Spanish Amazon Reviews. The LLM aggregator (LLAMA_AGG) achieves the highest macro-F1 on both pipelines (0.6800 on balanced; 0.6720 on natural) and the best ordinal calibration (QWK = 0.9111 on balanced; 0.9337 on natural), outperforming all classical aggregators and base models. A detailed Explainable AI analysis reveals that the LLM revises 28.07% of its standalone predictions after observing the ensemble outputs, improving the accuracy by +22.2 percentage points on the revised cases. The aggregator corrects severe polar bias in the standalone LLM (±0.35 recall improvement on mid-range star classes) and produces longer explanations when evidence is conflicted—a quantitative signal of deliberative reasoning. A formal human evaluation with two judges confirms high explanation faithfulness (4.47/5) and readability (4.82/5). Model scale ablation shows an 8B parameter variant achieves 90.8% agreement with the 70B model, enabling practical deployment. These findings demonstrate that Explainable AI can be achieved through LLM-based ensemble aggregation, establishing a principled approach for business-review sentiment analysis. Full article