Search Results (5,677)

Background: The integration of artificial intelligence (AI) into mobile health (mHealth) applications has been accelerated by the widespread adoption of smartphones and recent technological advances, particularly in the wake of the COVID-19 pandemic. This experience has expanded the role of AI-powered apps in real-time health monitoring, early detection, and personalized treatment pathways. Aim: This review aims to summarize recent evidence on the use of AI in healthcare-related mobile applications, with a focus on clinical trends, practical implications, and future directions. Methods: Studies were prioritized based on methodological rigor, with systematic reviews forming the core of the analysis. Additional literature was considered to capture emerging trends and applications where a relevant rigorous screening and scoring procedure was applied to ensure methodological quality and relevance. Only studies addressing healthcare applications, rather than computational or computer science frameworks, were included to reflect the journal’s clinical scope. Results and Discussion: Fifty-six secondary studies were analyzed in detail. Thematic synthesis revealed a post-pandemic shift toward applications targeting mental health, chronic care management, and preventive services. Additional screening showed that, despite their increasing use in clinical contexts, few AI-based apps were formally classified as medical devices. This highlights a gap between technological innovation and regulatory oversight. Ethical concerns—including algorithm transparency, clinical responsibility, and data protection—were frequently reported across studies. Conclusions: This review underscores the growing impact of AI in mobile health, while drawing attention to unresolved challenges related to regulation, safety, and clinical accountability. A more robust integration into health systems will require clearer governance frameworks, validation standards, and interdisciplinary dialogue between developers, clinicians, and regulators. Full article

Artificial intelligence (AI) is increasingly transforming higher education, evolving from simple personalization tools into a wide range of applications that support teaching, learning, and assessment. This study examines how university instructors in Lebanon perceive and adopt AI in their academic practices, drawing on evidence from two private institutions: Notre Dame University–Louaize (NDU) and the Holy Spirit University of Kaslik (USEK). The study also proposes practical directions for effective institutional implementation. Using a cross-sectional design and convenience sampling, data were collected from 133 faculty members. Although 73.7% of participants reported moderate to high familiarity with AI, their actual classroom use of such tools remained limited. Adoption was primarily centered on chatbots (69.2%) and translation tools (54.9%), while more advanced technologies, such as adaptive learning systems and AI-based tutoring platforms, were seldom utilized (under 7%). Additionally, participants identified efficiency (69.2%), increased student engagement (44.4%), and personalized learning opportunities (42.9%) as the main benefits of AI integration. In contrast, they reported insufficient training (46.6%), restricted access to resources (45.9%), and concerns about the accuracy of AI-generated outputs (29.3%) as major barriers. Moreover, statistical analysis indicated a strong positive relationship between familiarity with AI and frequency of adoption, with no significant differences across gender, age, or academic qualifications. Overall, the results suggest that faculty members in Lebanese higher education currently view AI primarily as a helpful tool for improving efficiency rather than as a transformative pedagogical innovation. To advance integration, higher education institutions should prioritize targeted professional development, ensure equitable access to AI tools, and establish transparent ethical and governance frameworks. Full article

Background: Blockchain has been extensively discussed for enhancing transparency, traceability, and trust in general; however, there is fragmented empirical evidence available with respect to this issue within maritime logistics. The objective is to integrate and categorize peer-reviewed publications concerning applications of blockchain in maritime logistics and related supply chain domains. Methods: A systematic literature review with PRISMA 2020 was performed in Scopus database, and after a process of screening and eligibility, a total of 78 journal articles published mainly from September 2024 were incorporated. Descriptive and bibliometric analyses were conducted, and VOS viewer-based bibliographic coupling were employed to visualize thematic structure. Results: The review identifies seven research priorities for blockchain in maritime logistics: Technological Interoperability, Economic and Operational Impact, Cybersecurity and Privacy, Adoption and Scalability, Decision-Making and Trust, Environmental Sustainability, and Standardization and Regulatory Frameworks. Blockchain’s primary advantages are enhanced data integrity and visibility, whereas key challenges include interoperability, legal/regulatory uncertainty (e.g., e-doc recognition), high costs, scalability ceilings, integration with legacy systems, and data governance fears. Conclusions: The application of blockchain in maritime logistics depends on combined technical and institutional enabling conditions; an Integrated Blockchain Adoption Framework (IBAF) is suggested, and providing practical guides based on standardization, legal convergence, and hybrid governance modes. Full article

Machine learning (ML) is becoming a key enabler in building energy management systems (BEMS), yet most existing reviews focus on simulations and fail to reflect the realities of real-world deployment. In response to this limitation, the present work aims to present a systematic review dedicated entirely to experimental, field-tested applications of ML in BEMS, covering systems such as Heating, Ventilation & Air-conditioning (HVAC), Renewable Energy Systems (RES), Energy Storage Systems (ESS), Ground Heat Pumps (GHP), Domestic Hot Water (DHW), Electric Vehicle Charging (EVCS), and Lighting Systems (LS). A total of 73 real-world deployments are analyzed, featuring techniques like Model Predictive Control (MPC), Artificial Neural Networks (ANNs), Reinforcement Learning (RL), Fuzzy Logic Control (FLC), metaheuristics, and hybrid approaches. In order to cover both methodological and practical aspects, and properly identify trends and potential challenges in the field, current review uses a unified framework: On the methodological side, it examines key-attributes such as algorithm design, agent architectures, data requirements, baselines, and performance metrics. From a practical standpoint, the study focuses on building typologies, deployment architectures, zones scalability, climate, location, and experimental duration. In this context, the current effort offers a holistic overview of the scientific landscape, outlining key trends and challenges in real-world machine learning applications for BEMS research. By focusing exclusively on real-world implementations, this study offers an evidence-based understanding of the strengths, limitations, and future potential of ML in building energy control—providing actionable insights for researchers, practitioners, and policymakers working toward smarter, grid-responsive buildings. Findings reveal a maturing field with clear trends: MPC remains the most deployment-ready, ANNs provide efficient forecasting capabilities, RL is gaining traction through safer offline–online learning strategies, FLC offers simplicity and interpretability, and hybrid methods show strong performance in multi-energy setups. Full article

The growing demand for robust food authentication methods has driven the establishment of fast, sensitive, and field-based detection systems for identifying meat species. This study presents a colorimetric-based PSR approach for identifying yak (Bos grunniens) meat within fresh, thermally processed, and blended meat samples. Targeting the mitochondrial D-loop locus, the assay incorporates a simple alkaline lysis (AL) procedure for efficient DNA extraction, eliminating the requirement for specialized instrumentation. The PSR assay demonstrated high specificity, showing no evidence of cross-reactivity with closely associated food animals such as buffalo, cattle, goat, sheep, mithun, and pig. Sensitivity assessment revealed the assay’s capability to detect 1 pg of yak DNA, with reliable performance in samples exposed to thermal conditions up to 121 °C. Additionally, the technique detected yak meat down to a concentration of 0.1% in binary beef mixtures. This method provides a significant improvement in sensitivity over end-point PCR and is particularly well-suited for field applications due to its practical simplicity, affordability, as well as no reliance on sophisticated instrument. This is, to the best of our understanding, the first reported PSR-based approach developed for the identification of yak meat, offering a robust tool for food origin verification, regulatory enforcement, and product integrity monitoring. Full article

Adequate nutrition and healthy behaviours during pregnancy are essential to maternal and neonatal health. Evidence from Greece on maternal dietary and lifestyle practices remains limited, despite global guidelines promoting supplementation, physical activity (PA) and abstinence from harmful substances. This review evaluates micronutrient intake, supplementation practices, PA and substance exposure among pregnant women in Greece, based on studies from 2010 to 2025. The results reveal widespread use of supplements, but frequent deviations from clinical guidelines. Suboptimal intake of key micronutrients remains common, and unsupervised supplementation raises concerns about excessive intake. Caffeine and tobacco use persist during pregnancy and lactation, while alcohol consumption shows a declining trend. PA is generally below international recommendations, with most pregnant women engaging in sedentary or low-intensity activities. These findings underscore systemic gaps in antenatal care in Greece, including inconsistent counselling, lack of structured screening and the absence of coordinated national strategies. Culturally tailored public health interventions, with a focus on equitable access and behaviour change support, are urgently needed to enhance nutritional adequacy, reduce behavioural risks and promote evidence-based care across the perinatal period. Full article

Incivility and civility have been studied for more than a century across disciplines and in many areas ranging from workplaces to communication, the digital world, and everyday life. They are often used to the detriment of marginalized groups. Their negative use is seen to set the groundwork for other negative treatments, such as bullying and harassment, impacting the social climate in a negative way. Ostracism is seen to be linked to incivility. Disabled people disproportionally face negative treatments, such as bullying and harassment, and experience a negative social climate, as highlighted by the UN Convention on the Rights of People with Disabilities, suggesting that they also disproportionately experience incivility and ostracism. Climate surveys aim to expose toxic social climate in workplaces, schools, and communities caused by incivility, ostracism, bullying, and harassment. As such, how incivility, civility, ostracism, and the design of climate surveys are discussed in the literature is of importance to disabled people. We could find no review that analyzed the use of climate surveys beyond individual surveys and the concepts of incivility and ostracism in relation to disabled people. The objective of our study was to contribute to filling this gap by analyzing the academic literature present in SCOPUS, EBSCO HOST (70 databases), and Web of Science, performing keyword frequency and content analysis of abstracts and full texts. Our findings provide empirical evidence for a systemic neglect of disabled people in the topics covered: from 21,215 abstracts mentioning “civilit*” or “incivilit*”, only 14 were relevant, and of the 8358 abstracts mentioning ostracism, only 26 were relevant. Of the 3643 abstracts mentioning “climate surveys,” 12 sources covered disabled people by focusing on a given survey, but not one study performed an evaluation of the utility of climate surveys for disabled people in general. Racism is seen as a structural problem facilitating civility/incivility. Ableism, the negative judgments of a given set of abilities someone has, and disablism, the systemic discrimination based on such judgments, are structural problems experienced by disabled people, facilitating civility/incivility. However, ableism generated only 2 hits, and disablism/disableism had no hits. Most of our sources focused on workplace incivility, and authors were mostly from the USA. We found no linkage to social and policy discourses that aim to make the social environment better, such as equity, diversity, and inclusion, well-being, and science and technology governance. This is the first paper of its kind to look in depth at how the academic literature engages with the concepts of civility, incivility, and ostracism and with the instrument of social climate surveys in relation to disabled people. Our findings can be used by many different disciplines and fields to strengthen the theoretical and practical discussions on the topics in relation to disabled people and beyond. Full article

Background: Children with autism spectrum disorder (ASD) commonly experience persistent difficulties in social communication, emotional regulation, and social engagement. In recent years, artificial intelligence (AI)-based technologies, particularly socially assistive robots and intelligent sensing systems, have been explored as complementary tools to support psychosocial interventions in this population. Objective: This systematic review aimed to critically evaluate recent evidence on the effectiveness of AI-based interventions in improving social, emotional, and cognitive functioning in children with ASD. Methods: A systematic literature search was conducted in PubMed following PRISMA guidelines, targeting English-language studies published between 2020 and 2025. Eligible studies involved children with ASD and implemented AI-driven tools within therapeutic or educational settings. Eight studies met inclusion criteria and were analyzed using the PICO framework. Results: The reviewed interventions included humanoid and non-humanoid robots, gaze-tracking systems, and theory of mind-oriented applications. Across studies, AI-based interventions were associated with improvements in joint attention, social communication and reciprocity, emotion recognition and regulation, theory of mind, and task engagement. Outcomes were assessed using standardized behavioral measures, observational coding, parent or therapist reports, and physiological or sensor-based indices. However, the studies were characterized by small and heterogeneous samples, short intervention durations, and variability in outcome measures. Conclusions: Current evidence suggests that AI-based systems may serve as valuable adjuncts to conventional interventions for children with ASD, particularly for supporting structured social and emotional skill development. Nonetheless, methodological limitations and limited long-term data underscore the need for larger, multi-site trials with standardized protocols to better establish efficacy, generalizability, and ethical integration into clinical practice. Full article

The transition toward sustainable and circular agricultural systems is increasingly important, yet evidence linking circularity and farm profitability in transition economies remains limited. This study examines the determinants of farm profitability in Serbia by combining micro-level structural and productivity indicators with a macro-level agro-eco efficiency measure, used here as a sector-wide ecological pressure indicator rather than a direct proxy for circular practices. Using a balanced Farm Accountancy Data Network (FADN) panel of 443 farms (2015–2022) across dairy, mixed, field crop, and fruit & wine sectors, dynamic panel estimators (difference and system Generalized Method of Moments-GMM) reveal strong sectoral heterogeneity. Asset turnover is the primary driver of profitability in field crops and perennial systems, while dairy farms benefit from scale and land productivity. Energy intensity consistently reduces profitability across all sectors. Agro-eco efficiency shows a negative effect in livestock-based systems, indicating higher sensitivity to macro-ecological pressures. These findings suggest that environmental and economic vulnerabilities differ across production systems, highlighting the need for sector-specific strategies aimed at improving resilience rather than inferring the profitability of circular technologies. Full article

In the centralized scheduling mode of automated container terminals, Automated Guided Vehicles (AGVs) often experience decision-making delays caused by system information-processing bottlenecks, which significantly affect path-planning efficiency and are particularly evident in sudden-traffic scenarios. To address this issue, this paper incorporates the artificial potential field (APF) into the cost function of Model Predictive Control (MPC) and develops a dual-trigger mechanism for lane-change and lane-return MPC obstacle-avoidance framework (Event-Triggered Model Predictive Control, EMPC). This framework integrates an obstacle-triggered local optimization mechanism and a lane-change trigger, enabling AGV to perform autonomous and dynamically responsive local obstacle avoidance, thereby improving local path-planning efficiency. Furthermore, a Proximal Policy Optimization (PPO)-based strategy is introduced to adaptively adjust the obstacle-weighting parameters within the EMPC cost function, enhancing both obstacle-avoidance and lane-keeping performance. Under multi-lane overtaking conditions, a lane-change trigger—implemented as a dual-phase “lane-change–return” mechanism—is employed, in which lateral optimization is activated only during critical phases, reducing online computational load by at least 28% compared with conventional MPC strategies. The experimental results demonstrate that the proposed PPO–EMPC architecture exhibits high robustness, real-time performance, and scalability under dynamic and partially observable environments, providing a practical and generalizable decision-making paradigm for cooperative AGV operations in automated container terminals. Full article

Ischemic heart disease remains the leading cause of cardiovascular mortality worldwide. In myocardial infarction (MI), extracellular vesicles (EVs)—particularly small EVs (sEVs)—transport therapeutic cargo such as miR-21-5p, which suppresses apoptosis, and other proteins, lipids, and RNAs that can modulate cell death, inflammation, angiogenesis, and remodeling. This review synthesizes recent mechanistic and preclinical evidence on native and engineered EVs for post-MI repair, mapping therapeutic entry points across the MI timeline (acute injury, inflammation, and healing) and comparing EV sources (stem-cell and non-stem-cell), administration routes, and dosing strategies. We highlight engineering approaches—including surface ligands for cardiac homing, rational cargo loading to enhance potency, and biomaterial depots to prolong myocardial residence—that aim to improve tropism, durability, and efficacy. Manufacturing and analytical considerations are discussed in the context of contemporary guidance, with emphasis on identity, purity, and potency assays, as well as safety, immunogenicity, and pharmacology relevant to cardiac populations. Across small- and large-animal models, EV-based interventions have been associated with reduced infarct/scar burden, enhanced vascularization, and improved ventricular function, with representative preclinical studies reporting approximately 25–45% relative reductions in infarct size in rodent and porcine MI models, despite substantial heterogeneity in EV sources, formulations, and outcome reporting that limits cross-study comparability. We conclude that achieving clinical translation will require standardized cardiac-targeting strategies, validated good manufacturing practice (GMP)-compatible manufacturing platforms, and harmonized potency assays, alongside rigorous, head-to-head preclinical designs, to advance EV-based cardiorepair toward clinical testing. Full article

Escalating energy prices have positioned households as pivotal agents in advancing demand-side energy efficiency. This study examines three complementary energy-saving strategies among Polish households: (1) habitual, low-cost actions such as switching off unnecessary lighting; (2) capital-intensive investments, including LED lighting and energy-efficient appliances; and (3) time-based and prosumptive strategies linked to dynamic tariffs and photovoltaic systems. The empirical analysis is based on a nationwide survey conducted using the Computer-Assisted Web Interviewing method, involving 401 respondents. The study’s contribution lies in integrating these strategies within a single analytical model and providing the first empirical evidence on their socio-demographic determinants in Central and Eastern Europe, with Poland as a representative case. The results show that older individuals more often adopt everyday habitual practices, whereas higher income and education levels are associated with investment-oriented behaviours. Urban households tend to favour technological solutions, while rural households more frequently adopt time-of-use tariffs and PV systems. Two complementary pathways are identified: a behavioural–habitual path and an investment–technological path. The findings offer guidance for public policy by showing that energy savings increase when financial incentives are combined with clear communication and low-effort decision tools that help households optimise energy use regardless of demographic profile. Full article

Background/Objectives: Uveal melanoma is a rare but aggressive intraocular malignancy that metastasizes in up to half of patients, most commonly to the liver, despite effective local treatment. In the absence of robust evidence, there are no standardized guidelines for post-treatment surveillance, resulting in wide variation in imaging modalities, frequency, and duration across physicians and institutions. This study aimed to develop expert consensus recommendations for surveillance strategies in patients with uveal melanoma. Methods: A modified Delphi method was conducted across three iterative survey rounds between September 2024 and February 2025 using an online platform. Panelists included medical oncologists, ocular oncologists, radiologists, and surgical oncologists from North America. A multidisciplinary steering committee developed statements addressing risk-based surveillance using both molecular and clinical prognostic factors, including gene expression profiling (GEP) and PRAME status. Consensus was defined a priori as ≥70% of panelists rating a statement 7–9 on a 9-point Likert scale. Results: Forty-nine experts were invited, and 41 completed at least one survey round. The panel represented 17 U.S. states, Washington, D.C., and two Canadian provinces. Twelve statements reached stable consensus, including recommendations for imaging modality, frequency, and duration in intermediate- and high-risk patients. Although there was agreement that low-risk patients warrant surveillance, no consensus was reached on the optimal approach for this group. Conclusions: This is the first study to provide consensus-based guidance incorporating GEP and PRAME status into surveillance recommendations for uveal melanoma, offering a standardized framework to guide clinical practice and future research. Full article

Background/Objectives: Nonsteroidal anti-inflammatory drugs (NSAIDs) are widely used for pain management but pose gastrointestinal (GI) and cardiovascular (CV) risks, particularly during long-term use. This study evaluated NSAID-prescribing patterns and the appropriateness of gastroprotective co-therapy among patients with varying GI and CV risk profiles. Methods: An observational, cross-sectional study was conducted in the outpatient pharmacy department over six months (March 2023 to August 2023) at a public secondary care facility. Data pertaining to patient demographics, NSAIDs prescription, and GI/CV risks were collected and reviewed from electronic health records. Descriptive statistics, chi-square tests, and logistic regression were performed. Results: A total of 1005 prescriptions containing 2051 NSAIDs were analyzed. Selective COX-2 inhibitors and non-selective NSAIDs were the most frequently prescribed. Only 42.1% of patients received proton-pump inhibitors despite guideline recommendations. Non-selective NSAIDs were significantly associated with CV history and GI risk (p < 0.0001). Logistic regression showed age, gender, CV history, and GI risk significantly influenced NSAID selection. Notably, non-selective NSAIDs continued to be prescribed among moderate- and high-GI-risk patients. Conclusions: Suboptimal adherence to guideline-recommended gastroprotective strategies was evident, particularly among high-risk patients. Comprehensive GI and CV risk assessment and the rational use of gastroprotective co-therapy are essential. Integrating evidence-based digital tools may enhance safer NSAID prescribing in routine practice. Full article

Understanding the causal relationships between safety factors is essential for successful intervention in industries with intrinsically high-risk environments such as the construction industry. Therefore, the aim of this study is to employ the Interpretive Structural Modeling (ISM) and Decision-Making Trial and Evaluation Laboratory (DEMATEL) techniques to analyze and map the interdependencies among various safety-related elements affecting construction safety. According to the results, resource allocation was shown to be the highest-level, most independent element in the analysis, highlighting its function as the primary facilitator of safety initiatives. This strategic commitment directly drives Management Commitment and Competence, which form the core organizational support structure. Mid-level elements that translate management intent into site-level practice include workers’ training, safety motivation, and communication structure. The frequency of safety observations, workers’ involvement in safety decisions, and subcontractor and procurement management—the immediate procedural controls—are then used to assess operational efficacy. Crucially, the most dependent factor was found to be Workers’ Compliance, indicating that frontline safety behavior is the result of efficient management at all higher levels. Therefore, in order to improve overall safety performance in construction, this research emphasizes the importance of improving resource provision and leadership commitment. The outputs of the current study provide an organized, evidence-based roadmap for selecting interventions. Full article