Search Results (13,043)

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

Price is a pivotal determinant of market demand, as higher prices typically reduce sales while lower prices stimulate them. Thus, incorporating price-dependent demand into inventory models is both realistic and necessary. In practice, limited storage capacity often forces retailers to rent additional space, motivating the adoption of two-warehouse systems. Trade credit also plays a critical role in supply chain management: suppliers may offer cash discounts or deferred payments to encourage larger orders, while retailers extend credit to customers to boost sales. To reduce default risk, however, retailers usually provide only partial credit. Considering the time value of money, costs and profits are assessed using discounted cash-flow analysis to account for payment delays and inflation. This study develops an integrated supplier–retailer–customer chain model that (1) incorporates price-dependent demand, (2) includes a rented warehouse for limited storage, (3) considers partial trade credit, (4) links two-level trade credit terms to order quantity, and (5) evaluates financial performance on a present-value basis. The model aims to maximize total profit by determining optimal price, replenishment cycle, and order quantity. Numerical and sensitivity analyses confirm that extending supplier credit can lower prices and improve overall profitability, offering useful insights for strategic inventory management. Full article

As the climate crisis intensifies, the importance of carbon mitigation policies has become increasingly prominent. Rural regions, serving as one of China’s major carbon emission sources, are poised to become key focus regions for emission reduction. However, significant disparities in rural development levels and carbon emissions across China’s regions necessitate tailored energy sustainability and carbon mitigation strategies. Notably, advanced and emerging/developing nations exhibit substantial differences in research priorities and practical pathways, offering multifaceted insights for China’s rural carbon emission research. Adopting a hybrid bibliometric and narrative approach, the study retrieves data from the Web of Science, applies CiteSpace for bibliometric visualization, and synthesizes thematic developments in the international literature through a narrative analysis, with a discussion of the implications for China. The findings reveal distinct trajectories: over the past 25 years, advanced countries have shifted their research focus from air quality improvement to low-carbon mitigation, while emerging and developing countries have transitioned from energy demand toward air quality enhancement, with emerging momentum toward low-carbon strategies. By reviewing 95 relevant articles, this study summarizes the differences between the two in terms of their main lines of research. Building on these differences, this study proposes targeted research priorities for advanced and emerging/developing regions of China. Full article

Magnetic Barkhausen noise (MBN) represents a powerful non-destructive testing and material characterization methodology enabling quantitative assessment of microstructural features, mechanical properties, and stress states in ferromagnetic materials. This comprehensive review synthesizes recent advances spanning theoretical foundations, sensor design, signal processing methodologies, and industrial applications. The physical basis rooted in domain wall dynamics and statistical mechanics provides rigorous frameworks for interpreting MBN signals in terms of grain structure, dislocation density, phase composition, and residual stress. Contemporary instrumentation innovations including miniaturized sensors, multi-parameter systems, and high-entropy alloy cores enable measurements in challenging environments. Advanced signal processing techniques—encompassing time-domain analysis, frequency-domain spectral methods, time–frequency transforms, and machine learning algorithms—extract comprehensive material information from raw Barkhausen signals. Deep learning approaches demonstrate superior performance for automated material classification and property prediction compared to traditional statistical methods. Industrial applications span manufacturing quality control, structural health monitoring, railway infrastructure assessment, and predictive maintenance strategies. Key achievements include establishing quantitative correlations between material properties and stress states, with measurement uncertainties of ±15–20 MPa for stress and ±20 HV for hardness. Emerging challenges include standardization imperatives, characterization of advanced materials, machine learning robustness, and autonomous system integration. Future developments prioritizing international standards, physics-informed neural networks, multimodal sensor fusion, and wireless monitoring networks will accelerate industrial adoption supporting safe, efficient engineering practice across diverse sectors. Full article

Poly(lactic acid) (PLA) has emerged as a leading bio-based polymer due to its renewability, processability, and biodegradability, yet its broader adoption remains constrained by limitations in thermal stability, mechanical performance, and end-of-life control. This review provides a comparative and application-oriented overview of recent advances in PLA from synthesis and catalyst landscapes to structure–property–biodegradation relationships and practical applications. Representative polymerization routes and catalyst systems are critically compared in terms of achievable molecular weight, stereochemical control, scalability, and sustainability. Key structure–property modification strategies—including stereocomplex formation, blending, and copolymerization—are quantitatively evaluated with respect to thermal and mechanical properties, highlighting inherent trade-offs. Importantly, environment-specific biodegradation behaviors are assessed using representative quantitative metrics under industrial composting, soil, marine, and enzymatic conditions, underscoring the strong dependence of degradation on both material design and testing environment. Finally, application-driven requirements for food packaging, fibers, and agricultural materials are discussed alongside regulatory considerations, processing constraints, and qualitative cost positioning relative to conventional polymers. By integrating recent representative studies into comparative tables and synthesis-driven discussions, this review offers design guidelines for tailoring PLA-based materials toward targeted performance and sustainable deployment. 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

This study aimed to examine the role of ChatGPT in supporting travel decision-making by investigating its direct influence and the mediating effects of trust in AI-generated content, perceived usefulness, and perceived ease of use. Data were collected from 1208 qualified respondents and selected through non-probability convenience sampling from active digital travel communities on platforms. The results revealed that ChatGPT affects travel decision-making and also significantly influences users’ trust in AI-generated content, their perception of usefulness, and ease of use. Furthermore, all three mediators were found to significantly impact travel decision-making, and mediation analysis confirmed that these variables partially explain the link between ChatGPT usage and tourists’ behavioral intentions. These findings advance TAM by positioning trust as a central mechanism in AI adoption and demonstrate ChatGPT’s potential as a decision-support tool in tourism. This study offers practical insights for developers and policymakers seeking to improve transparency, reliability, and user experience of conversational AI in travel contexts. Full article

(1) Background: The use of wearable technology for assessing running biomechanics in field-based sports has increased in recent years. Inertial measurement units (IMUs) are low-cost, non-invasive devices capable of estimating spatiotemporal gait-related metrics during overground locomotion. This study evaluated the accuracy and concurrent validity of a foot-mounted IMU system for estimating sprinting kinematics. (2) Method: Twenty-five elite and sub-elite athletes completed four maximal 10-metre fly efforts, with their kinematics measured concurrently using a three-dimensional motion analysis system and IMUs. (3) Result: The foot-mounted IMU system’s root mean square errors for stride length and duration were 0.22 m and 0.04 s, respectively. Mean biases (95% level of agreement) were −0.67 $\mathrm{m} · {\mathrm{s}}^{-1}$ (−1.19; −0.14) for peak velocity, −0.51 $\mathrm{m} · {\mathrm{s}}^{-1}$ (−1.10; 0.09) for instantaneous velocity, and 0.17 $\mathrm{m} · {\mathrm{s}}^{-2}$ (−1.04; 1.37) for instantaneous acceleration. Stride length, duration, and cadence were −0.07 m (−0.36; 0.23), 0.02 s (−0.02; 0.06), and −4.64 strides $· {\min }^{-1}$ (−15.82; 6.53), respectively. (4) Conclusions: End users implementing this technology in research and practice should interpret this study’s findings relative to their analytical objectives, logistical resources, and operational constraints. Therefore, its adoption should be guided by the specific performance metrics of interest and the extent to which the system’s capabilities align with the outcomes the end user aims to achieve. Full article

This study examines the impacts of climate change on agriculture in the Prefecture of Imathia and highlights the role of agricultural advisory services. The study evaluates existing adaptation measures and demonstrates the importance of agricultural advisory services. The methodology is based on a combined approach. A literature review was conducted, followed by the primary collection of data through structured questionnaires administered to a sample of 78 farmers in Imathia Prefecture. It was found that producers with access to advisory services more readily adopt innovative services and sustainable practices, thus contributing to reducing the impacts of climate change on their productivity. Full article

Fisheries management in the Mediterranean Sea faces persistent challenges due to the prevalence of data-poor and data-limited stocks, small-scale multi-species fisheries, and limited long-term monitoring programs. Effective assessment methodologies are critical to ensuring sustainable exploitation, yet traditional data-rich stock assessment models remain infeasible for many Mediterranean fisheries. This review provides a comprehensive synthesis of current methodologies developed and applied to assess data-poor fisheries in the Mediterranean context. We examine catch-only approaches, length-based methods, empirical indicators, and multi-indicator frameworks increasingly adopted by the General Fisheries Commission for the Mediterranean (GFCM) and the EU’s Data Collection Framework (DCF). Special attention is given to case studies from the western, central, and eastern Mediterranean that demonstrate the opportunities and limitations of these approaches. We further explore emerging tools, including integrated modeling frameworks, simulation-based harvest control rules, and participatory approaches involving fishers’ local knowledge, to highlight innovations suited to mixed, small-scale Mediterranean fisheries. The review concludes by identifying key gaps in data collection, assessment capacity, and institutional coordination, and proposes a roadmap for improving data-poor fisheries management under Mediterranean-specific ecological, socio-economic, and governance constraints. By consolidating methodological advances and practical lessons, this review aims to provide a reference framework for researchers, managers, and policymakers seeking to design robust, adaptive strategies for sustainable fisheries management in data-limited Mediterranean contexts. Full article

This study explores how artificial intelligence can promote accessibility and inclusiveness in digital culinary environments. Centred on the Receitas +Power platform, the research adopts an exploratory, multidimensional case study design integrating qualitative and quantitative analyses. The investigation addresses three research questions concerning (i) user empowerment beyond recommendation systems, (ii) accessibility best practices across disability types, and (iii) the effectiveness of AI-enabled inclusive solutions. The system was developed following user-centred design principles and WCAG 2.2 standards, combining generative AI modules for recipe creation with accessibility features such as voice interaction and adaptive navigation. The evaluation, conducted with 87 participants, employed the System Usability Scale complemented by thematic qualitative feedback. Results indicate excellent usability (M = 80.6), high reliability (Cronbach’s α = 0.798–0.849), and moderate positive correlations between usability and accessibility dimensions (r = 0.45–0.55). Participants highlighted the platform’s personalisation, clarity, and inclusivity, confirming that accessibility enhances rather than restricts user experience. The findings provide empirical evidence that AI-driven adaptability, when grounded in universal design principles, offers an effective and ethically sound pathway toward digital inclusion. Receitas +Power thus advances the field of inclusive digital gastronomy and presents a replicable framework for human–AI co-creation in accessible web technologies. Full article

This study examines agricultural labour dynamics and sustainability practices in East Crete, assessing how labour structure, education, and input intensity shape ecological outcomes. Using data from 108 farms in Heraklion and Lassithi, we constructed composite indicators, such as Labour Intensity, Sustainability Engagement, and Training-Adjusted Labour indices. Analysis of 37 farms with data revealed a heterogeneous landscape. Traditional family-based systems persist alongside uneven shifts toward agroecological practices. The Training-Adjusted Labour Index correlated with reduced pesticide use, while subsidy participation alone was not a reliable predictor of sustainable behaviour. Findings highlight limits of compliance-based incentives and the importance of knowledge-driven transitions. This study advocates typology-informed policies and longitudinal research for future policy design. Full article

(1) Background: With the continuous development of intelligent education, classroom behavior recognition has become increasingly important in teaching evaluation and learning analytics. In response to challenges such as occlusion, scale differences, and fine-grained behavior recognition in complex classroom environments, this paper proposes an improved YOLOv11-ASV detection framework; (2) Methods: This framework introduces the Adaptive Spatial Pyramid Network (ASPN) based on YOLOv11, enhancing contextual modeling capabilities through block-level channel partitioning and multi-scale feature fusion mechanisms. Additionally, VanillaNet is adopted as the backbone network to improve the global semantic feature representation; (3) Conclusions: Experimental results show that on our self-built classroom behavior dataset (ClassroomDatasets), YOLOv11-ASV achieves 81.5% mAP₅₀ and 62.1% mAP_50–95, improving by 1.6% and 2.9%, respectively, compared to the baseline model. Notably, performance shows significant improvement in recognizing behavior classes such as “reading” and “writing” which are often confused. The experimental results validate the effectiveness of the YOLOv11-ASV model in improving behavior recognition accuracy and robustness in complex classroom scenarios, providing reliable technical support for the practical application of smart classroom systems. Full article

Topology optimisation and lattice design constitute key enablers in the transition towards high-performance and resource-efficient engineering, particularly within the framework of additive manufacturing and welding-based deposition processes. The increasing integration of arc-based technologies, such as Wire Arc Additive Manufacturing, has strengthened the relevance of these methodologies by enabling the fabrication of large-scale, structurally efficient components with controlled material distribution and mechanical performance. These design strategies provide unique opportunities to achieve lightweight structures, functionally graded behaviour, and tailored properties beyond the limitations imposed by conventional manufacturing and joining techniques. The growing demand for functionally efficient components in sectors such as aerospace, biomedical, and automotive engineering continues to drive the adoption of these approaches, where both material efficiency and structural integrity under welding-induced thermal effects are critical. This chapter introduces the fundamentals of topology optimisation and functionally graded lattice architectures, describes their integration into advanced design and manufacturing workflows, including welding-based additive processes, and presents selected case studies that demonstrate their practical impact. Finally, emerging strategies based on generative design and artificial intelligence are discussed as key drivers for the automated and process-aware optimisation of future additively manufactured and welded structures. Full article