Search Results (380)

With the acceleration of digital transformation in education, this paper examines how blockchain is being framed as a governance solution for trust, transparency, and decentralization. Using a hybrid bibliometric and thematic analysis of 93 Web of Science and Scopus publications, the study maps publication trends, leading outlets, author networks, and conceptual clusters. We analyze co-authorship networks, keyword co-occurrence patterns, and conceptual structures using VOSviewer version 1.6.19 and the R-based Bibliometrix package. Then, we apply qualitative coding to offer a more profound interpretation of governance stories. Findings show that blockchain in educational governance is predominantly positioned through techno-managerial lenses—focusing on secure credentials, tamper-proof records, and efficiency—while critical perspectives on power, equity, and participation remain limited. Global North institutions and computer science–oriented venues dominate the field, with little engagement from Global South contexts or educational leadership scholarship. The paper concludes by proposing a research agenda that reimagines blockchain not as a neutral tool, but as a socio-technical assemblage that must be interrogated through equity-, ethics-, and community-centered frameworks. Full article

Introduction: Artificial intelligence (AI) and robot-assisted platforms are increasingly influencing cardiothoracic surgery. AI enhances risk prediction, imaging interpretation, and early complication detection, while robotics improves visualization, dexterity, and minimally invasive access. This systematic review evaluates the current evidence supporting these technologies and their implications for clinical practice. Methods: A systematic literature search was conducted across PubMed, Embase, Scopus, Web of Science, and Google Scholar (January 2000–May 2025) following PRISMA 2020 guidelines. After screening and eligibility assessment, 67 studies met predefined inclusion criteria and were incorporated into the qualitative synthesis. Additional high-impact reviews and consensus documents were consulted for contextual interpretation. Results: Machine learning models demonstrated modest but consistent improvements in predictive performance compared with EuroSCORE II and STS scores, particularly in high-risk cohorts. Robot-assisted mitral and coronary procedures showed reduced postoperative pain, blood loss, ICU stay, and recovery time in experienced centers, though early learning phases were associated with longer operative, cross-clamp, and bypass times. AI-enabled intraoperative tools, such as video analysis, workflow recognition, and real-time anatomical segmentation, emerged as promising adjuncts for surgical precision. Structured robotic training programs, especially simulation-based and dual-console pathways, accelerated proficiency acquisition. Conclusions: AI and robotic systems act as augmentative technologies that enhance rather than replace the surgeon’s role. Their safe and effective adoption requires standardized training, transparent AI decision pathways, and clear ethical and medico-legal governance. Full article

Background: Generative artificial intelligence (GenAI) is rapidly permeating healthcare; yet, U.S. clinicians still report mixed feelings about its reliability, impact on workflow, and ethical implications. Current data on provider sentiment are needed to guide safe, patient-centered AI implementation in healthcare. Objective: This study aimed to assess U.S. healthcare providers’ perceptions of generative AI adoption, perceived usefulness, training needs, barriers, and strategies for safe integration. Methods: A nationwide, IRB-approved, cross-sectional survey was administered to healthcare professionals using Qualtrics. A convenience sample of clinicians was recruited via professional listservs and e-mail invitations. The 20-page questionnaire captured demographics, GenAI exposure, organizational adoption status, perceived usefulness (5-point scale), barriers, and mitigation strategies. SPSS v27 and Microsoft Excel were used for statistical analysis. Results: Of 130 respondents, 109 completed the core survey (completion rate 83.8%). Participants were 38.5% physicians, 16.5% nurses, 12.8% allied professionals, and 32.2% other providers; 54.2% were women, and 64.8% were ≥50 years. Overall, 86.9% agreed that GenAI is useful in current patient care, rising to 92.9% when asked about future usefulness. Only 42.4% had received formal GenAI training, and just 23.2% reported that their organization had begun adopting AI. The top perceived benefits were improved documentation/clerking (57.0%) and error reduction (49.4%). Dominant barriers included limited AI knowledge (24.7%) and fear of job loss (16.9%). Despite concerns, 72% expressed willingness to support broader GenAI adoption, favoring human oversight (67.1%) and staff training (60.8%) as key safeguards. There were statistically significant findings in perceived AI usefulness by gender (χ² = 29.2; p < 0.001); organizational adoption of AI (χ² = 31.6.2; p = 0.047) and where AI is most useful (χ² = 101.1; p < 0.001) by qualifications; and support for AI adoption by age (χ² = 18.0; p = 0.02). Conclusions: U.S. clinicians in our survey viewed GenAI as useful but reported limited training and organizational infrastructure needed for confident use while also expressing concerns regarding data privacy and ethical risk. Education programs and transparent, provider-led implementation strategies may accelerate responsible GenAI assimilation while addressing ethical and workforce concerns. Also, health administrators should use the efficiency gains to improve provider–patient relationships and clinicians’ work–life balance while reducing clinician burnout rates. Full article

Background: Surface electromyography (sEMG) of periocular muscles is a non-invasive technique used to assess eyelid dynamics and facial neuromuscular function, with applications in ophthalmology, neurology, and rehabilitation. Despite its clinical and research potential, substantial methodological variability—particularly in electrode placement, acquisition parameters, and signal processing—has limited reproducibility and hindered broader clinical translation. A comprehensive synthesis of existing methodologies was therefore needed to support future standardization. Objectives: The review aimed to systematically map current periocular sEMG methodologies, identify sources of methodological heterogeneity, organize findings into structured methodological domains, and develop a conceptual framework along with a minimum reporting set to promote transparency, reproducibility, and comparability across studies. Eligibility Criteria: Studies were eligible if they investigated surface electromyography of periocular muscles and reported methodological details related to electrode placement, signal acquisition, processing, or analysis. Randomized controlled trials, observational studies, and pilot investigations were included. No restrictions were placed on publication year. Sources of Evidence: Comprehensive searches were conducted in PubMed, Embase, and Web of Science from database inception through November 2025. Grey literature sources were also examined to enhance coverage and reduce publication bias. Charting Methods: Two reviewers independently screened records and extracted data. Extracted information was organized into predefined methodological domains. A thematic synthesis approach was used to identify recurring methodological patterns, and findings were integrated into a structured conceptual framework. Results: Sixteen studies published between 2002 and 2025 met the inclusion criteria, encompassing randomized trials, observational studies, and pilot investigations. Considerable heterogeneity was identified across studies in electrode characteristics, placement strategies, reference configurations, sampling frequencies, and normalization procedures. Three recurring methodological domains emerged: instrumentation and acquisition, analytical and normalization approaches, and clinical or experimental applications. Based on these domains, the authors developed a conceptual methodological framework and proposed a minimum reporting set intended to improve methodologyical transparency and support reproducibility and multicenter comparability. Conclusions: Periocular sEMG represents a promising yet methodologically fragmented field. This scoping review provides the first comprehensive synthesis of periocular sEMG practices and establishes an evidence-based platform for standardized acquisition, processing, and reporting. Adoption of the proposed framework may strengthen reproducibility, facilitate multicenter collaboration, and accelerate integration into clinical and research settings. Full article

The UK faces an urgent challenge to simultaneously accelerate housing delivery and reduce whole-life carbon emissions, yet robust empirical evidence on the carbon performance of modular steel housing remains limited. This study aims to quantify the carbon impacts of a modular light-gauge steel frame social housing dwelling in the UK and to benchmark its performance against contemporary low-carbon construction typologies. A cradle-to-grave life cycle assessment was conducted using primary project data from a real modular housing development, with embodied carbon modelled in One Click LCA and operational energy assessed through SAP 10.2-verified datasets. The results indicate a total whole-life carbon footprint of 91.3 tCO₂e over a 50-year period, with embodied emissions (A1–A3) accounting for 38.2% and operational energy and water use contributing 48.1%. The normalised embodied carbon intensity of 366 kgCO₂e/m² (A1–A5) is comparable to recent high-performing cross-laminated timber buildings, demonstrating that optimised modular steel systems can allow for low-carbon outcomes typically associated with bio-based construction. Sensitivity analysis shows that low-carbon foundation concrete, bio-based insulation, and steel optimisation can reduce upfront emissions by approximately 8–10%. Dynamic energy simulations were also used to assess how different design choices influence operational carbon emissions. This study provides transparent, real-project evidence of the whole-life carbon performance of UK modular light-gauge steel frame housing and identifies practical design strategies for further decarbonisation. The findings support informed decision-making for policymakers, designers, and housing providers seeking scalable, low-carbon residential solutions. Full article

Small-and-medium-sized enterprises (SMEs) increasingly depend on business partnerships to access markets and scale operations, yet they often face trust barriers during contract formation due to the complexity of the verification of their cybersecurity posture and compliance status by their partners. This problem is intensified by rising regulatory expectations, notably the EU Cyber Resilience Act (CRA), which many SMEs struggle to interpret and operationalize under constraints of budget, skills, and fragmented responsibilities. This study adopts a Design Science Research approach to blueprint and evaluate a lightweight mapping framework that links commonly implemented security controls to CRA requirements and to widely recognized benchmarks (ISO/IEC 27001 and CIS). Grounded in Institutional Theory and Socio-Technical Systems Theory, the artefact translates regulatory obligations into actionable, evidence-backed controls and produces partner-facing outputs that support transparency in negotiations and service level agreements. The framework is iteratively co-created with a multidisciplinary expert community. Expected contributions include a practical mechanism for making cybersecurity maturity visible, accelerating partnership formation, and enabling sustainable interorganizational relationships while remaining feasible for resource-constrained SMEs. Full article

The overreliance on chemical pesticides in sub-Saharan African (SSA) for agriculture poses major challenges to sustainable agriculture, ecosystem and human health, biodiversity, and environmental sustainability. While genetically modified (GM) crops have demonstrated potential to lower pesticide use and increase crop yield, their widespread adoption remains limited across SSA, with gaps in knowledge on their yield, benefits and policies impacting their uptake. In this study, a literature-based approach was used to synthesize evidence from peer-reviewed articles and government reports published between 2010 and 2025 on pesticide use, farm productivity, and wellbeing of farmers across three focus countries: Nigeria, South Africa, and Burkina Faso. The summary of approved GM crops, events and utilisation across the three focus countries was also retrieved from the International Service for the Acquisition of Agri-biotech Applications (ISAAA) database. Cross-country comparisons were conducted to highlight lessons learned from successful and stalled GM crop programs and to identify regulatory, socio-cultural, and economic factors shaping adoption. It is shown that while GM crops can significantly reduce pesticide usage and production costs, challenges such as public hesitancy, regulatory hurdles, limited farmer awareness, and concerns about ecological consequences continue to hinder wider uptake across the continent. Similarly, weak seed systems and the lack of regionally harmonized biosafety regulations also constrain adoption. In areas where GM crops have been successfully adopted, it was demonstrated that supportive policy frameworks, transparent biosafety regulations, effective seed certification and distribution systems, and sustained community engagement increased farmer confidence and accelerated adoption. Hence, for GM crops to be more widely adopted for sustainable crop protection in sub-Saharan Africa, governments and stakeholders must strengthen biosafety systems, invest in farmer education, promote regional regulatory coordination, and facilitate public–private partnerships. Full article

Background: The accelerated convergence of the Internet of Things (IoT) and Blockchain is reconfiguring logistics, yet knowledge regarding their operationalisation for real-time inventory management remains fragmented. Methods: A Bibliometric–Systematic Literature Review (B-SLR) was conducted on peer-reviewed sources from Scopus and Web of Science (2019–2025), utilising science mapping to visualise intellectual and conceptual structures. Results: The analysis reveals a steep rise in publications during 2024–2025, identifying traceability, smart contracts, and integrity mechanisms as central themes. The synthesis supports a layered theoretical model linking transparency (sensing) and trust (ledger validation) to efficiency and supply chain resilience in Industry 5.0. The review highlights unresolved issues, including interoperability and privacy-by-design, alongside emerging directions such as digital twins. Conclusions: While scholarship has expanded rapidly, it remains weighted toward adoption mapping, underscoring the need for empirical, context-aware models that explain socio-technical integration and its measurable impacts on logistics performance. Full article

By 2050, the global population is expected to reach approximately 10 billion, leading to a projected 50% increase in food demand relative to 2013 levels. If not adequately anticipated, this growing demand will place significant strain on agri-food systems worldwide, with disproportionate impacts on low- and middle-income countries. Moreover, current projections may underestimate the accelerating effects of climate change, political instability, and civil unrest, which continue to disrupt food production and distribution systems. In this context, technological advancements offer a promising pathway to enhance efficiency, improve transparency, and mitigate risks related to food safety, adulteration, and counterfeiting. Emerging innovations can decouple food production from environmental degradation while strengthening monitoring, verification, and accountability across supply chains. This review examines state-of-the-art technologies developed to support traceability and anti-counterfeiting in agri-food supply chains, considering their application across the full spectrum of stakeholders. To provide a system-level perspective, the review adopts a five-layer socio-technical traceability and anti-counterfeiting framework, comprising identity, sensing, intelligence, integrity, and interaction layers, which is used to map enabling technologies and reinterpret the evolution of traceability systems (TS 1.0–TS 4.0) as a progression of functional capabilities rather than isolated technological upgrades. Using this framework, the review analyzes the advantages and limitations of current solutions and clarifies how traceability and anti-counterfeiting functions emerge through technology integration. It further identifies gaps that hinder large-scale and equitable adoption. Finally, future research directions are outlined to address current technical, economic, and governance challenges and to guide the development of more resilient, trustworthy, and sustainable agri-food traceability systems. Full article

Magnetoplasmadynamic thrusters (MPDTs) are becoming increasingly viable as electric propulsion (EP) technology for space missions, yet their complex plasma behaviour, intricate thrust-generation process, and nonlinear multi-physics thrust–field interactions prove difficult for conventional modelling approaches, including empirical techniques. Traditional empirical modelling shortcomings include failure to predict accurately across wide operational regimes. This paper introduces a physically interpretable, artificial intelligence (AI)-powered thrust model for Applied-Field Magnetoplasmadynamic Thrusters (AF-MPDTs), developed using symbolic regression (SR) to address the gap between data-driven prediction and physics-based understanding. The proposed method, an alternative to traditional black box AI methods, incorporates physics-aware composite-term operators, ensuring that the resulting analytical expressions are bounded by known physical behaviours while retaining the flexibility to discover previously overlooked nonlinear couplings. A comprehensive dataset of AF-MPDTs undergoes rigorous preprocessing to ensure dimensional consistency and noise robustness. The SR model then evolves candidate equations, balancing predictive accuracy with interpretability through Tree-Structured Parzen Estimator (TPE) optimisation. The results, closed-form surrogate correlations with 95.98% of accuracy as goodness of fit, root mean square error of 0.0199, mean absolute error of 0.0143, and mean absolute percentage error reduction of 28.91% against the benchmark model in the literature. A post-discovery protocol for numerical robustness and physical consistency is implemented, with Shapley Additive Explanations (SHAP) providing insight into the influence of each composite-term in the developed correlation, followed by a numerical robustness and physical consistency validation using a Monte Carlo (MC) envelope. A StabilityScore is calculated for all developed correlations, enabling explicit accuracy–complexity–stability comparisons. In doing so, we demonstrated that SR can systematically recover known physical relationships—such as the scaling of thrust with discharge current and applied magnetic field—while proposing interpretable higher-order corrections that improve fit quality. The resulting SR-based thrust models not only achieve competitive accuracy relative to state-of-the-art numerical and empirical methods but also offer more explainable and interpretable results capable of revealing compact formulations that capture essential acceleration mechanisms with transparency. Overall, this paper, using SR, advances explainable AI (XAI) methodologies capable of generating trustworthy, analytically transparent models for next-generation electric propulsion systems. Full article

Sweeteners occupy a pivotal role in the global transition toward sustainable, health-aligned, and resource-efficient food systems. Conventional sucrose production carries significant environmental burdens, while escalating metabolic health concerns intensify demand for viable alternatives. This paper reframes sweeteners not as commodity ingredients, but as digitally engineered, biologically manufactured, and circularity-optimized materials within the emerging bioeconomy. Advances in artificial intelligence (AI), metabolic engineering, precision fermentation, and lignocellulosic valorization are fundamentally reshaping sweetener innovation. We introduce the Sweetener Innovation 4.0 framework, in which AI functions as the integrative engine linking molecular design, bioprocess optimization, and system-level sustainability. Across diverse sweetener classes, including steviol glycosides, mogrosides, rare sugars, sweet proteins, and forestry-derived polyols, AI accelerates discovery, improves metabolic flux control, optimizes downstream processing and enables more adaptive manufacturing systems. This digital–biological convergence is progressively decoupling sweetness production from land-intensive agriculture, reducing dependence on geographically constrained crops, and enabling resilient, low-carbon manufacturing pathways. Comparative life-cycle assessments highlight substantial sustainability gains, but also reveal persistent methodological gaps, particularly in accounting for downstream-processing energy and digital infrastructure emissions. Socioeconomic analysis further underscores the importance of equitable transitions, transparent labeling, and effective consumer communication as fermentation-derived sweeteners enter global markets. Looking forward, we identify key frontiers for Sweetener Innovation 4.0, including de novo AI-designed sweeteners, autonomous fermentation systems, carbon-negative feedstocks, personalized sweetness modulation, and integrated circular biorefineries. Together, these developments position sweeteners as a top domain for demonstrating how AI, biotechnology, and sustainability principles can jointly reshape ingredient development and industrial systems within the 21st-century circular-economy. Full article

In the wake of rapid digital transformation, emerging technologies like FinTech, AI, and Blockchain are reimagining how countries pursue sustainable development. This study examines how FinTech adoption, Artificial Intelligence (AI) readiness, and Blockchain activity influence sustainable development performance across G20 economies over the period 2015–2023. Drawing on Innovation-Driven Growth Theory, the Technology–Organization–Environment framework, and Institutional Theory, the analysis evaluates both the direct and complementary effects of these digital technologies on Sustainable Development Goal (SDG) outcomes using cross-country panel data and key macroeconomic controls. The results show that FinTech, AI, and Blockchain each exert a positive and statistically significant impact on national sustainability performance, with AI exhibiting the strongest effect. Moreover, the findings reveal meaningful digital complementarities, indicating that coordinated adoption of these technologies amplifies sustainable development gains. Overall, the study provides robust macro-level evidence that digital transformation functions as a strategic driver of sustainability and offers policy-relevant insights for G20 governments seeking to accelerate inclusive, transparent, and environmentally responsible development. Full article

The accelerated integration of Generative Artificial Intelligence (GenAI) tools such as ChatGPT is transforming learner engagement, instructional design, and institutional governance in education. This systematic literature review synthesizes theory-driven scholarship on GenAI adoption and pedagogical use through the Antecedents–Decisions–Outcomes (ADO) framework, examining how cognitive, motivational, technological, and institutional factors collectively shape implementation and learning outcomes. Drawing primarily on the Technology Acceptance Model (TAM), Self-Determination Theory (SDT), and Institutional Theory, the review integrates complementary insights from Constructivist Learning and Diffusion of Innovations perspectives to conceptualize how antecedents influence decision-making and outcomes across educational settings. The findings indicate that learner motivation, perceived usefulness, digital literacy, and institutional readiness constitute key antecedents affecting GenAI adoption. Decision processes—spanning instructional design, ethical regulation, and pedagogical adaptation—mediate how these antecedents translate into practice. Outcomes reveal a dual trajectory: GenAI enhances personalization, feedback, and self-regulated learning, yet introduces challenges related to ethical ambiguity and overreliance. The review offers a conceptually integrated synthesis that bridges motivational, technological, and organizational perspectives, advancing a theoretical roadmap for ethical and sustainable GenAI adoption. For educators and policymakers, the findings emphasize transparent governance, faculty capacity-building, and equitable access to ensure that innovation remains aligned with pedagogical integrity and human-centered values. Full article

Simulator-based digital twins are widely used in robotics education and industrial development to accelerate prototyping and enable safe experimentation. However, they often hide implementation details that are essential for understanding, diagnosing, and correcting system failures. This paper introduces a technology-independent model-based design framework that provides students with full visibility of the computational mechanisms underlying robotic controllers while remaining feasible within a 150-h undergraduate course. The approach relies on representing controller behavior using networks of Extended Finite State Machines (EFSMs) and their stacked extension (EFS2M), which unify all abstraction levels of the control architecture—from low-level reactive behaviors to high-level deliberation—under a single formal model. A structured programming template ensures traceable, optimization-free software synthesis, facilitating debugging and enabling self-diagnosis of design flaws. The framework includes real-time synchronized simulation, transparent switching between virtual and physical robots, and a smart data logger that captures meaningful events for model updating and error detection. Integrated into the Intelligent Robots course, the system supports topics such as kinematics, control, perception, and simultaneous localization and mapping (SLAM) while avoiding dependency on specific middleware such as Robot Operating System (ROS) 2. Over three academic years, students reported positive hands-on experiences, strong adaptability to diverse modeling approaches, and consistently high survey ratings reflecting the course’s overall quality. The proposed environment thus offers an effective methodology for teaching end-to-end robot controller design through transparent, simulation-driven digital twins. Full article

The accelerated discovery of high-performance lead-free piezoelectric ceramics is hindered by the vast compositional space and the limited interpretability of conventional machine learning (ML) models. Here, we propose a physics-informed and interpretable ML framework with integrated uncertainty quantification to predict and understand the piezoelectric coefficient d₃₃ of (K_0.5Na_0.5) NbO₃ (KNN)-based ceramics. A curated dataset of 1113 experimental samples is used to construct 65 descriptors by decoupling A-site and B-site ionic contributions. Pearson correlation analysis reduces these to an optimized 11-dimensional feature set for training deep neural networks, Wide & Deep networks, and residual networks. A Bayesian neural network further provides predictive uncertainty, which quantitatively reflects the confidence of machine-learning-based d₃₃ predictions rather than experimental measurement uncertainty. To achieve physical interpretability, SHapley Additive exPlanations (SHAP) are combined with the Sure Independence Screening and Sparsifying Operator (SISSO) to derive a compact analytical descriptor revealing that sintering temperature, B-site electronic anisotropy, and A-site ionic displacement jointly govern d₃₃. The proposed framework achieves high accuracy (R² ≈ 0.81) while offering transparent design rules for next-generation lead-free piezoelectrics. Full article