Search Results (192)

Background/Objectives: Self-care is a cornerstone of healthy aging and chronic disease management; however, evidence on the most effective intervention models for improving quality of life in older adults with chronic non-communicable diseases (NCDs) remains fragmented. This review aimed to evaluate the effectiveness of self-care interventions in promoting quality of life and health outcomes in older adults with NCDs. Methods: A systematic review was conducted in accordance with PRISMA 2020 guidelines and registered in PROSPERO (CRD420251040613). Randomized and non-randomized clinical trials published between 2019 and 2024 were retrieved from Scopus, Web of Science, and EBSCOhost. Eligible studies included adults aged ≥60 years with NCDs receiving self-care interventions. Data extraction and risk of bias assessment were independently performed using Joanna Briggs Institute tools. Results: Twenty-nine studies involving 7241 older adults were included. Self-care interventions comprised nurse-led educational programs, digital health strategies, community- and peer-based approaches, and person-centered care models. Multicomponent and continuous interventions demonstrated consistent improvements in physical and psychological domains of quality of life, self-efficacy, autonomy, symptom management, and treatment adherence. Digital interventions enhanced monitoring and engagement, although their effectiveness varied according to sensory and health literacy limitations. Conclusions: Structured, person-centered, and nurse-led self-care interventions are effective in improving quality of life and autonomy among older adults with NCDs. These findings support their integration into primary and community-based care, reinforcing their relevance for clinical practice, care planning, and the development of assistive and educational strategies in aging care. Full article

Artificial intelligence (AI) is transforming customer experience management (CXM) by enabling real-time, data-driven, and personalized interactions across digital touchpoints, including chatbots, voice assistants, generative AI, and immersive platforms. This study presents a PRISMA-based systematic literature review of 59 peer-reviewed studies published between 2021 and 2026, examining how AI-enabled personalization, privacy concerns, and customer value interact within AI-mediated customer experiences. Drawing on the Personalization–Privacy–Value (PPV) framework, the review synthesizes evidence on how AI-driven personalization enhances utilitarian, hedonic, experiential, relational, and emotional value, thereby strengthening satisfaction, engagement, loyalty, and behavioral intentions. At the same time, the findings reveal persistent tensions, as privacy concerns, perceived surveillance, algorithmic bias, and contextual moderators—including generational differences, cultural expectations, and technological literacy—frequently constrain value creation and erode trust. The review highlights that personalization benefits are highly contingent on transparency, perceived control, and ethical alignment, rather than personalization intensity alone. The study contributes by integrating ethical AI considerations into CXM research and clarifying conditions under which AI-enabled personalization leads to value creation versus value destruction. Managerially, the findings underscore the importance of ethical governance, transparent data practices, and customer-centered AI design to sustain trust and long-term customer relationships. Future research should prioritize longitudinal analyses of trust development, demographic heterogeneity, and cross-sector comparisons of AI governance as AI technologies become increasingly embedded in service ecosystems. Full article

The increasing deployment of Internet of Things (IoT) sensors in precision agriculture has created critical challenges related to wireless communication range, energy efficiency, and data transmission latency, particularly in large-scale rural operations. This systematic survey, conducted following the PRISMA 2020 guidelines, investigates how drones, acting as mobile data collectors and communication gateways, can enhance the performance of agricultural wireless sensor networks (WSNs). The literature search was carried out in the Scopus and IEEE Xplore databases, considering peer-reviewed studies published in English between 2014 and 2025. After duplicate removal, 985 unique articles were screened based on predefined inclusion and exclusion criteria related to relevance, agricultural application, and communication technologies. Following full-text evaluation, 64 studies were included in this review. The survey analyzes how drones can be efficiently integrated with WSNs to improve data collection, addressing technical and operational challenges such as energy constraints, communication range limitations, propagation losses, and data latency. It further examines the primary applications of drone-based data acquisition supporting efficiency and sustainability in agriculture, identifies the most relevant wireless communication protocols and Technologies and discusses their trade-offs and suitability. Finally, it considers how drone-assisted data collection contributes to improved prediction models and real-time analytics in digital agriculture. The findings reveal persistent challenges in energy management, coverage optimization, and system scalability, but also highlight opportunities for hybrid architectures and the use of intelligent reflecting surfaces (IRSs) to improve connectivity. This work provides a structured overview of current research and future directions in drone-assisted agricultural communication systems. Full article

Background/Objectives. Food insecurity and pediatric obesity have increased concurrently in the U.S., raising questions about the role of Federal Nutrition Assistance Programs (FNAPs) in shaping weight outcomes. This systematic review examined evidence on relationships between FNAP participation and pediatric weight outcomes. Methods. Six databases were searched for U.S.-based, peer-reviewed studies published through July 2024 that assessed FNAP participation and pediatric weight outcomes. Results. Seventy-five studies met the inclusion criteria, and no consistent pattern indicated that any single FNAP or program type (educational setting-based or direct financial support) reliably reduced or increased childhood overweight or obesity risk. Twenty studies found statistically significant beneficial relationships between FNAP participation and pediatric weight outcomes. Most studies reported mixed findings (n = 32), typically varying by subgroup (e.g., age, grade level, gender, race or ethnicity, or program characteristics). Sixteen studies found no relationship between participation and weight. Seven studies found an adverse relationship. Most studies relied on non-randomized quantitative designs and secondary data, and adverse findings were more common in lower quality studies. Among 18 studies that evaluated the effects of policy changes (e.g., the Healthy Hunger-Free Kids Act (2010), 2009 WIC package change), nearly all identified associations between the policy change and weight outcomes, with eight beneficial and nine reporting mixed results. Conclusions. The findings indicate a complex non-causal relationship between FNAP participation and weight that varies across populations, programs, and study designs. Overall, evidence does not support broad adverse weight effects of FNAPs, and policy changes that strengthen nutrition standards may contribute to healthier weight outcomes. These findings have implications for nutrition policy, program design, and future research. Full article

Over the past five years, generative AI has carved out a major role in architecture, especially in education and visual idea generation. Most of the time, the literature talks about AI as a tool, an assistant, or sometimes a co-creator, always highlighting efficiency and the end product in architectural design. There is a steady rise in empirical studies, yet the real impact on how young architects learn still lacks a solid theory behind it. In this systematic review, we dig into peer-reviewed work from 2015 to 2025, looking at how generative AI fits into architectural design education. Using PRISMA guidelines, we pull together findings from 40 papers across architecture, design studies, human–computer interaction and educational research. What stands out is a clear tension: on one hand, students crank out more creative work; on the other, their reflective engagement drops, especially when AI steps in as a replacement during early ideation instead of working alongside them. To address this, we introduce the idea of “AI sparring”. Here, generative AI is not just a helper—it becomes a provocateur, pushing students to think critically and develop stronger architectural concepts. Our review offers new ways to interpret AI’s role, moving beyond seeing it just as a productivity booster. Instead, we argue for AI as an active, reflective partner in education, and we lay out practical recommendations for studio-based teaching and future research. This paper is a theoretical review and conceptual proposal, and we urge future studies to test these ideas in practice. Full article

Background: Developmental coordination Disorder (DCD) is a prevalent and persistent neurodevelopmental condition characterized by motor learning difficulties that significantly affect daily functioning and participation. Despite growing interest in artificial intelligence (AI) applications within healthcare, the extent and nature of AI use in the evaluation and intervention of DCD remain unclear. Objective: This scoping review aimed to systematically map the existing literature on the use of AI and AI-assisted approaches in the evaluation, screening, monitoring, and intervention of DCD, and to identify current trends, methodological characteristics, and gaps in the evidence base. Methods: A scoping review was conducted in accordance with the PRISMA extension for Scoping Reviews (PRISMA-ScR) guidelines and was registered on the Open Science Framework. Systematic searches were performed in Scopus, PubMed, Web of Science, and IEEE Xplore, supplemented by snowballing. Peer-reviewed studies applying AI methods to DCD-relevant populations were included. Data was extracted and charted to summarize study designs, populations, AI methods, data modalities, clinical purposes, outcomes, and reported limitations. Results: Seven studies published between 2021 and 2025 met the inclusion criteria following a literature search covering the period from January 2010 to 2025. One study listed as 2026 was included based on its early access online publication in 2025. Most studies focused on AI applications for assessment, screening, and classification, using supervised machine learning or deep learning models applied to movement-based data, wearable sensors, video recordings, neurophysiological signals, or electronic health records. Only one randomized controlled trial evaluated an AI-assisted intervention. The evidence base was dominated by early-phase development and validation studies, with limited external validation, heterogeneous diagnostic definitions, and scarce intervention-focused research. Conclusions: Current AI research in DCD is primarily centered on evaluation and early identification, with comparatively limited evidence supporting AI-assisted intervention or rehabilitation. While existing findings suggest that AI has the potential to enhance objectivity and sensitivity in DCD assessment, significant gaps remain in clinical translation, intervention development, and implementation. Future research should prioritize theory-informed, clinician-centered AI applications, including adaptive intervention systems and decision-support tools, to better support occupational therapy and physiotherapy practice in DCD care. Full article

Periapical lesions (PALs) are a common sequela of pulpal pathology, and accurate radiographic detection is essential for successful endodontic diagnosis and treatment outcome. With recent advancements in Artificial Intelligence (AI), deep learning systems have shown remarkable potential to enhance the diagnostic accuracy of PALs. This study highlights recent evidence on the use of AI-based systems in detecting PALs across various imaging modalities. These include intraoral periapical radiographs (IOPAs), panoramic radiographs (OPGs), and cone-beam computed tomography (CBCT). A literature search was conducted for peer-reviewed studies published from January 2021 to July 2025 evaluating artificial intelligence for detecting periapical lesions on IOPA, OPGs, or CBCT. PubMed/MEDLINE and Google Scholar were searched using relevant MeSH terms, and reference lists were hand screened. Data were extracted on imaging modality, AI model type, sample size, subgroup characteristics, ground truth, and outcomes, and then qualitatively synthesized by imaging modality and clinically relevant moderators (i.e., lesion size, tooth type and anatomical surroundings, root-filling status and effect on clinician’s performance). Thirty-four studies investigating AI models for detecting periapical lesions on IOPA, OPG, and CBCT images were summarized. Reported diagnostic performance was generally high across radiographic modalities. The study results indicated that AI assistance improved clinicians’ performance and reduced interpretation time. Performance varied by clinical context: it was higher for larger lesions and lower around complex surrounding anatomy, such as posterior maxilla. Heterogeneity in datasets, reference standards, and metrics limited pooling and underscores the need for external validation and standardized reporting. Current evidence supports the use of AI as a valuable diagnostic platform adjunct for detecting periapical lesions. However, well-designed, high-quality randomized clinical trials are required to assess the potential implementation of AI in the routine practice of periapical lesion diagnosis. Full article

Background: Effective communication is a fundamental competency in veterinary medicine that shapes the quality of veterinarian–client relationships, shared decision-making, and animal welfare. However, consistent and systematic integration of communication training across veterinary curricula remains uneven worldwide. Methods: This scoping review mapped and analyzed educational programs aimed at developing communication competencies in veterinary education and professional practices. A systematic search was conducted according to PRISMA-ScR guidelines, identifying 37 eligible studies published between 2005 and 2024. Results: Most publications were in English and originated from North America, particularly Canada (n = 15) and the United States (n = 8). Regarding target populations, 15 studies (40.5%) focused on veterinary students, 12 (32.4%) on practicing veterinarians, 8 (21.6%) on animal owners or clients, and 2 on veterinary educators. 18 studies (48.7%) described structured programs that used active learning strategies such as role-play, clinical simulations, peer-assisted learning, and formative feedback. The competencies frequently emphasized include empathy, active listening, nonverbal communication, conflict resolution, and rapport building. Notable best practices included the Calgary–Cambridge model, Objective Structured Clinical Examination (OSCE), and reflective video analysis. Conclusions: The available evidence indicates a growing emphasis on clinical communication within veterinary education, primarily implemented through experiential and practice-based approaches. However, substantial gaps persist in the representation of Latin American contexts and in the systematic, longitudinal integration of communication skills across veterinary curricula. Addressing these gaps may contribute to more coherent, equitable, and context-sensitive communication training in veterinary education. Full article

Artificial intelligence (AI) has become a strategic technology for global health, with increasing relevance amid the climate emergency and persistent digital inequalities. This study examines how AI has been applied in Brazilian healthcare through a scoping review with an in-depth methodological synthesis, focusing on Transfer Learning (TL) and Federated Learning (FL) as approaches to address data scarcity, privacy, and technological dependence. We searched PubMed, SciELO, and the CNPq Theses and Dissertations Repository for peer-reviewed studies on AI applications in Brazil, screened titles using AI-assisted tools with manual validation, and analyzed thematic patterns across methodological and infrastructural dimensions. Among 349 studies retrieved, six explicitly used TL or FL. These techniques were frequently implemented through multi-country research consortia, demonstrating scalability and feasibility for collaborative model training under privacy constraints. However, they remain marginal in mainstream practice despite their ability to deploy AI solutions with limited computational resources while preserving data sovereignty. The findings indicate an emerging yet uneven integration of resource-aware AI in Brazil, underscoring its potential to advance equitable innovation and digital autonomy in health systems of the Global South. Full article

Background: Electromyography (EMG) is increasingly used to characterize neuromuscular alterations after total hip arthroplasty (THA), yet available evidence remains fragmented and inconsistent. This systematic review synthesizes postoperative EMG findings during gait, functional tasks, and static assessments, highlighting clinical implications and future research needs. Methods: Peer-reviewed studies employing surface, needle, or high-density EMG after THA were systematically examined. Extracted variables included activation amplitude, timing (onset, offset, burst duration), co-activation patterns, and the influence of surgical approach. Methodological rigor, normalization procedures, and the extractability of quantitative EMG metrics were also assessed. Results: Across studies, postoperative EMG consistently revealed non-physiological activation patterns, including delayed or prolonged gluteus medius activity and excessive recruitment of posterior chain muscles. These abnormalities persisted for up to 12 months and, in isolated cases, beyond a decade. Comparisons of surgical approaches demonstrated early denervation signs and impaired recruitment following lateral-based incisions, whereas later adaptations differed between lateral and posterior approaches but remained abnormal in both. Needle EMG studies confirmed transient involvement of muscles innervated by the superior gluteal nerve, while high-density EMG identified persistent deficits in spatial and temporal organization despite clinical improvement. Load-bearing and assisted-task studies showed that cane use and balance challenges modulate abductor demand yet continue to expose asymmetries and elevated stabilization requirements. Nonetheless, comparability across investigations remains limited because few studies adopted standardized normalization procedures or reproducible locomotor tasks. Conclusions: Neuromuscular recovery after THA appears incomplete and asymmetric, characterized by compensatory strategies not detectable through clinical or kinematic assessments alone. Improved diagnostic sensitivity and clinical applicability will require protocol standardization and the broader adoption of advanced EMG approaches. Full article

The success of embryo implantation and pregnancy depends on a complex interaction between the trophoblast and the endometrial environment, where endometrial receptivity plays a crucial role in this process. Assisted reproductive technologies (ARTs) are essential in overcoming biological barriers and enabling implantation in women with fertility issues. However, one of the main challenges in ART is ensuring that the endometrium is receptive at the time of implantation. Therefore, identifying endometrial receptivity biomarkers is essential to optimize ART treatments, improving success rates. A comprehensive literature review was conducted by searching peer-reviewed articles published in PubMed, Scopus, and Web of Science databases. The search included studies focusing on molecular and cellular mechanisms underlying endometrial receptivity in both natural and stimulated cycles. Various experimental methods, including proteomic and microRNA studies, have identified key biomarkers involved in endometrial receptivity, such as adhesion molecules, growth factors, and others. However, ovarian stimulation in fertility treatments can alter endometrial receptivity, making approaches like frozen embryo transfer necessary. Despite advancements, many questions persist regarding the endometrial receptivity and implantation mechanisms in both natural and stimulated cycles. This article reviews the main molecules involved in endometrial receptivity in natural and stimulated cycles, highlighting their potential role as biomarkers for embryo implantation. Full article

Machine learning (ML) has become a transformative tool in nanomaterial research, driven by the rapid growth of data-intensive experimental techniques, multiscale simulations, and computational modeling. This study provides a bibliometric analysis to characterize how ML has been integrated into nanomaterial discovery and design. Following a PRISMA-guided workflow, research articles published between 2010 and 2025 were retrieved from Scopus and Web of Science, yielding a curated dataset of 4432 peer-reviewed documents. Here, performance indicators, citation patterns, and network analyses were examined to reveal publication growth, leading journals, productive institutions, and country-level contributions. The results show an exponential increase in scientific output since 2017 and a research landscape dominated by China, the United States, India, and Iran. Keyword co-occurrence and thematic mapping reveal four major research clusters: (i) ML-assisted nanoparticle synthesis, (ii) ML-driven nanocomposite design, (iii) data-driven modeling of carbon-based nanomaterials, and (iv) ML-supported catalysis and nanoscale chemistry. These results demonstrate the rapid consolidation of ML-enabled nanomaterial research and highlight emerging opportunities and challenges. The review provides an integrated summary of the field and highlights key future opportunities for advancing data-driven nanomaterial research. Full article

Cancer is one of the biggest health burdens for women in the Middle East and North Africa (MENA), with the incidence of breast, cervical and colorectal cancer on the rise. Although preventive measures such as the HPV vaccination and population-based screening are available, access to them remains very unequal. Women in rural, low-income and refugee communities face additional barriers, cultural stigmatisation, low health literacy, gender norms and fragile health systems, leading to delayed diagnoses and poorer outcomes. This review summarises the results of 724 peer-reviewed publications to assess the current situation of cancer screening in MENA and Mediterranean countries. The studies were classified into four dimensions: cancer type (breast, cervical, colorectal), behavioural constructs (awareness, uptake, education), vulnerability factors (e.g., migrants, refugees, low-literacy groups), and geography (indigenous MENA populations versus diaspora and Mediterranean immigrant communities). The results show large inequalities in access and participation due to fragmented policies, socio-cultural resistance and infrastructure gaps. Nevertheless, promising approaches are emerging: community-led outreach, mobile screening programmes, AI-assisted triage and culturally appropriate digital health interventions. Comparisons between the local and diaspora populations make it clear that systemic and cultural barriers persist even in well-equipped facilities. Closing the screening gap requires a culturally sensitive, digitally enabled and policy aligned approach. Key priorities include engaging religious and community leaders, promoting men’s engagement in women’s health and securing sustainable funding. With coordinated action across all sectors, MENA countries can build inclusive screening programmes that reach vulnerable women and reduce preventable cancer mortality. Full article

Background/Objectives: Obesity is a major global public health and economic challenge. Governments worldwide have implemented nutrition-focused policies such as sugar-sweetened beverage taxes, front-of-pack labeling, food assistance reforms, and school nutrition standards to improve diet quality and reduce obesity. Because large-scale randomized controlled trials are often infeasible and conventional epidemiologic methods overlook population heterogeneity and behavioral feedback, microsimulation modeling has become a key tool for evaluating long-term and distributional policy impacts. This scoping review examined the application of microsimulation to obesity-related nutrition policies, focusing on model structure, behavioral parameterization, and integration of economic and equity analyses. Methods: Following PRISMA guidelines (PROSPERO CRD42024599769), five databases were searched for peer-reviewed studies. Data were extracted on policy mechanisms, model design, parameterization, and equity analysis. Study quality was assessed using a customized 21-item checklist adapted from CHEERS and NIH tools. Results: Twenty-nine studies met the inclusion criteria, with most policy settings based in the United States. Most employed dynamic, stochastic, individual-level microsimulation models with diverse behavioral assumptions, obesity equations, and calibration approaches. While most studies stratified outcomes by socioeconomic or demographic group, only one used a formal quantitative equity metric. Conclusions: Microsimulation modeling provides valuable evidence on the long-term health, economic, and distributional impacts of nutrition policies. Future work should strengthen methodological transparency, standardize equity assessment, and expand application beyond high-income settings to improve the comparability, credibility, and policy relevance of simulation-based nutrition policy research. Full article

This Editorial presents an integrative overview of the Special Issue “Advanced Autonomous Systems and Artificial Intelligence Stage”, which assembles fifteen peer-reviewed articles dedicated to the recent evolution of AI-enabled and autonomous systems. The contributions span a broad spectrum of domains, including renewable energy and power systems, intelligent transportation, agricultural robotics, clinical and assistive technologies, mobile robotic platforms, and space robotics. Across these diverse applications, the collection highlights core research themes such as robust perception and navigation, semantic and multi modal sensing, resource-efficient embedded inference, human–machine interaction, sustainable infrastructures, and validation frameworks for safety-critical systems. Several articles demonstrate how physical modeling, hybrid control architectures, deep learning, and data-driven methods can be combined to enhance operational robustness, reliability, and autonomy in real-world environments. Other works address challenges related to fall detection, predictive maintenance, teleoperation safety, and the deployment of intelligent systems in large-scale or mission-critical contexts. Overall, this Special Issue offers a consolidated and rigorous academic synthesis of current advances in Autonomous Systems and Artificial Intelligence, providing researchers and practitioners with a valuable reference for understanding emerging trends, practical implementations, and future research directions. Full article