Search Results (2,459)

Public trust is essential for the effectiveness and long-term sustainability of open government data (OGD) initiatives, particularly in small island developing states (SIDS), where digital governance systems often operate under infrastructural and institutional constraints. Despite growing global research on OGD trust, limited research has examined how the quality dimensions of information systems’ success models shape citizens’ trust in OGD platforms within Caribbean SIDS. This study examines the hypothesised relationships between service quality, system quality, information quality, data quality, and public trust in OGD using an extended information systems success model (ISSM). Data were collected through an online survey of 904 respondents across Caribbean SIDS and analysed using partial least squares structural equation modelling (PLS-SEM). The findings indicate that all proposed relationships were statistically significant. Data quality showed the strongest statistical association with public trust, followed by system quality. Service quality was also significantly associated with system, information, and data quality. In addition, system, information, and data quality showed significant indirect statistical relationships in the association between service quality and public trust in OGD. This study extends the ISSM framework by conceptualising data quality as a distinct construct within OGD environments. The findings provide practical insights for governments seeking to strengthen transparency, citizen engagement, and sustainable digital governance through higher-quality OGD systems and datasets. The results further highlight the role of open government platforms in improving public service delivery by providing citizens with complete, accurate, and accessible data, interactive feedback mechanisms, and effective data visualisation tools that support informed decision-making and public participation. Full article

Mass–balance discrepancies exist between estimated land-based inputs and observed marine plastic inventories. While current global mass–balance models predominantly treat the open ocean as a passive terminal sink, they overlook the rapid expansion of offshore and deep-sea industrial frontiers. This review identifies offshore and deep-sea activities as active, in situ emission nodes of microplastics (MPs). Through a bibliometric analysis and numerical descriptions of studies, we document that direct offshore emissions are underrepresented in the current literature. By synthesizing these limited quantitative data, preliminary metrics indicate localized MP enrichment signals and elevated biological exposure near specific offshore infrastructures. Furthermore, plastics released directly into the marine environment bypass terrestrial weathering, undergoing distinct multiscale aging pathways governed by the complex interplay of wave-induced physical fragmentation bounded by critical size thresholds, UV-driven chemical photo-oxidation, and biological interactions. We conclude that refining global plastic budgets supports moving toward an integrated ocean-industrial framework. However, the synthesis remains constrained by data scarcity and high methodological heterogeneity across different environmental matrices. Future strategies must prioritize standardized in situ flux quantification and the incorporation of MP emission risks into offshore Environmental Impact Assessments. Full article

Prompt specifications for multi-agent large language model (LLM) systems carry data contracts and integration logic across interdependent files but are rarely subjected to structured-inspection rigor. We report a single-system case study of iterative, agent-driven auditing applied to AEGIS (Autonomous Engineering Governance and Intelligence System), a seven-lane production pipeline whose 7152-line specification surface was audited across nine rounds, surfacing 51 consistency defects (per-round counts of 15, 8, 12, 2, 8, 1, 4, 1, 0). We present a seven-category post hoc taxonomy with explicit coding rules, non-monotonic convergence consistent with cascading edits and audit-scope expansion, and a locked audit protocol. We further report two partial replications on a public synthetic mini-specification: a cross-LLM panel of four frontier vendors (OpenAI, Anthropic, Google, xAI; 12 traces; multi-vendor union detects all five seeded defects) and an inter-rater reliability check on a stratified subsample (Cohen’s $\kappa$ = 0.80 on category, 0.46 on severity). The full reproducibility bundle accompanies the submission. Full article

The dynamic reliability of steam-turbine governing systems is essential for the safe operation of nuclear power units. As a key regulating and protection component, the reheat valve must complete rapid closure under abnormal operating conditions. This study addresses the closing timeout problem observed in a nuclear reheat-valve oil-motor actuator after domestic substitution, with particular attention to sluggish motion and discontinuous closing at small openings. A coupled hydraulic–mechanical model was then established by integrating the coned-disc spring assembly, hydraulic circuit, cartridge valve, gear–rack transmission, and load resistance based on the mathematical model. The model was used to identify the dominant parameters controlling the fast-closing process, and the optimization strategy was subsequently verified by experiments on an actual actuator platform. The results show that coned-disc spring degradation is a critical source of closing timeout risk. When the equivalent elastic modulus decreases to approximately 195 GPa, the fast-closing time approaches the critical limit of 0.8 s, while further degradation results in evident timeout. The C0 throttling orifice has the strongest influence on the effective closing time by governing the pressure-relief capacity of the working chamber. A coordinated correction strategy, involving coned-disc spring force compensation and throttling parameter adjustment, restores the closing margin, shortens the fast-closing time to 0.78 s, and improves closing smoothness. This work provides the practical guidance for design verification, field commissioning, and domestic improvement of nuclear reheat-valve oil-motor actuator systems. Full article

Utilizing the progressive rollout of public data open platforms as a quasi-natural experiment, this study applies a staggered difference-in-differences (DID) method to investigate the effect of public data openness on regional carbon emissions. The empirical analysis demonstrates a significant decarbonization effect induced by public data openness, and this conclusion survives a battery of robustness tests. Mechanism analyses confirm that the decarbonization effect of public data openness is driven by enhanced industrial upgrading, green technological innovation, green financial development, and environmental regulation. Heterogeneity analyses reveal that the decarbonization effect is statistically significant mainly in Central China, and in provinces characterized by high marketization and advanced digital infrastructure. Furthermore, public data openness demonstrates a substantial capacity for abating environmental pollutants such as sulfur dioxide and dust, thereby validating a synergistic governance effect. Overall, this study demonstrates the positive role of public data openness in reducing regional carbon emissions, thereby theoretically broadening the literature on its environmental consequences while expanding practical pathways for decarbonization. Full article

Generative Artificial Intelligence (GenAI) in cardiac surgery refers to the integration of advanced computational models, such as Large Language Models (LLMs), to automate and enhance clinical decision-making, preoperative risk assessment, and surgical education. In the context of surgical training, it functions as a personalized pedagogical tool that supports various learning activities, ranging from information acquisition and clinical inquiry to procedural practice, while requiring rigorous human oversight to ensure patient safety and clinical accuracy. (1) Background: Generative Artificial Intelligence (GenAI) is increasingly integrated into health professions education, offering new opportunities for learning; however, its specific application and pedagogical mapping in high-stakes fields such as cardiac surgery remain underexplored. This systematic review investigates how GenAI is utilized in cardiac surgery and surgical education, aligning these uses with Laurillard’s six learning types. (2) Methods: Following the PRISMA 2020 guidelines, we searched the Web of Science Core Collection for studies on GenAI in cardiac surgery, resulting in 42 studies that met the inclusion criteria. Study quality was appraised using the Medical Education Research Study Quality Instrument (MERSQI). (3) Results: GenAI applications most frequently supported clinical inquiry (93.8%) and practice (68.8%), demonstrating expanding efficiency across commercial and open-source models (including ChatGPT-4o, Gemini AI, and emerging reasoning architectures such as DeepSeek) for knowledge acquisition and medical production. While it significantly improves individualized learning and preoperative assessment workflows, its practical role in Discussion and Collaboration remains heavily underutilized, highlighting a distinct shift toward individualized solo professional workflows. (4) Conclusions: GenAI provides a transformative and scalable approach to cardiac surgical training by offering personalized and accessible knowledge retrieval. However, clinical educators and governance bodies must deliberately balance these immediate productivity benefits with long-term concerns regarding structural “hallucinations,” data verifiability, and the preservation of collaborative competencies within modern multidisciplinary Heart Teams. Full article

Artificial Intelligence (AI) is rapidly transitioning from a specialized technology to an everyday socio-technical infrastructure, yet public acceptance remains shaped by a trade-off between perceived benefits and risks. This study examines how individuals from varied demographic and professional backgrounds perceive, use, and evaluate AI-enabled systems using a mixed-method research design. A bilingual (English/Arabic) online survey ($N=115$) captured demographics, awareness, usage patterns, perceived impact, self-assessed understanding, domain-specific trust, concerns, and attitudes toward regulation, complemented by open-ended reflections. In parallel, semi-structured face-to-face interviews provided deeper insight into AI conceptualization, lived experiences, trust boundaries, and conditions for acceptable use. Quantitative results show frequent AI engagement embedded in daily life, with strong domain dependence in trust: education is the most trusted domain, whereas healthcare and finance attract substantially lower trust. Prominent concerns include overreliance (“brain rot”), privacy and data misuse, job displacement, and misinformation. Support for stronger AI regulation is high, indicating that governance is viewed as a prerequisite for sustainable adoption rather than a constraint on innovation. Qualitative findings triangulate these results, revealing a pattern of conditional acceptanceunderstood as the simultaneous valuation of AI’s practical utility alongside the imposition of explicit trust prerequisites whereby participants value AI for productivity and learning support while emphasizing confidentiality, transparency, human oversight in high-stakes contexts, and clear boundaries to mitigate misuse and erosion of human judgment. The study offers empirically grounded insights for policymakers, educators, and industry stakeholders into how AI acceptance is negotiated through utility, literacy, perceived risk, and expectations of accountability. Full article

Hydraulic fracturing in naturally fractured shale reservoirs commonly generates complex mesh-like fracture networks governed by hydraulic fracture–natural fracture interactions, which strongly affect stimulated volume, fracture connectivity, and early-time production. Existing simulation and monitoring-based methods often cannot simultaneously capture interaction mechanisms, rapidly generate field-scale fracture networks, and validate production responses. This study proposes a mechanics-constrained recursive propagation framework. A field-constrained stochastic natural-fracture model is first constructed, an explicit hydraulic fracture–natural fracture interaction criterion is incorporated to identify penetration, opening, and shear slipping, and a fully vectorized bidirectional recursive algorithm is developed to efficiently generate complex fracture networks. The method is applied to a 40-stage fractured horizontal well in the Changqing Oilfield, where the target interval has a porosity of 6.1%, a permeability of 0.1 mD, and a horizontal stress contrast of 7.0 MPa. The simulated network reproduces crossing, arrest, unilateral diversion, and bilateral diversion, and agrees well with microseismic observations. EDFM-based fully implicit flow simulation further shows early-time production deviations of 2–10%. These results demonstrate that the proposed framework can efficiently generate physically plausible field-scale fracture networks for fracturing design, post-fracturing evaluation, and short-term production forecasting. Full article

The proliferation of large language model (LLM) agents has enabled increasingly complex multi-step automation; however, composing multiple agents into coherent systems introduces significant orchestration challenges that remain poorly documented. This survey examines LLM-based multi-agent orchestration from 2023 through early 2026 (literature cutoff: March 2026), with explicit attention to the evidence hierarchy used to interpret deployment claims. We propose a three-topology, one-adaptivity taxonomy—centralized, decentralized, and hierarchical coordination topologies, each optionally augmented with a dynamic–adaptive control axis—grounded in classical multi-agent systems theory and recent empirical evidence. We compare six leading frameworks (LangGraph, CrewAI, AutoGen/Microsoft Agent Framework, OpenAI Agents SDK, MetaGPT, and DSPy) along axes directly relevant to practitioners: state-management granularity, token-cost structure, failure-recovery options, and design philosophy. The emerging protocol stack is examined in terms of why MCP (agent-to-tool) and A2A (agent-to-agent) occupy complementary layers, how the ACP–A2A merger signals protocol convergence, and where ANP’s decentralized-discovery design fits. Production design considerations—state management, task planning, error handling, scalability, and security—are evaluated with reference to published benchmarks. Vendor-reported figures are marked † throughout and held to a documented evidence hierarchy, which separates them from peer-reviewed and government-evaluator measurements. We close by identifying eight open challenges and proposing a six-dimension evaluation framework for multi-agent coordination quality. This paper offers practitioners a decision framework covering taxonomy, framework selection, protocol adoption, and early operational pilots. Full article

Ambient-temperature asphalt binders have emerged as a sustainable alternative to traditional hot-mix asphalt, offering significant advantages in energy conservation and emission reduction. This review systematically examines the research progress and development trends of high-performance reactive asphalt binders designed for ambient-temperature application, which achieve enhanced performance through chemical cross-linking reactions. The study focuses on three core material systems: epoxy resin, waterborne epoxy emulsified asphalt, and polyurethane. For each system, we comprehensively summarize the material composition, strength formation mechanisms, and mix design methodologies. Key evaluation methods for critical pavement performance—including strength characteristics, water stability, and high-temperature performance—are critically reviewed. Furthermore, microscopic characterization techniques including scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), and differential scanning calorimetry (DSC) are discussed to elucidate the underlying mechanisms governing performance evolution. Analysis reveals that epoxy-based binders exhibit superior strength and stiffness, rendering them suitable for heavy-traffic pavements; waterborne epoxy emulsified asphalt binders combine environmental compatibility with construction convenience for thin-layer rehabilitation, while polyurethane-based binders demonstrate exceptional elasticity and rapid curing characteristics for quick-traffic-opening scenarios. Although current research has established a preliminary performance evaluation framework, the absence of unified technical standards constrains widespread engineering implementation. Future research priorities should focus on developing water-triggered curing systems, intelligent responsive materials, and comprehensive standardization systems to fully harness the engineering potential of these sustainable binders. Full article

Background: Open defecation (OD) has remained a threat to the attainment of SDG 6 (sanitation and hygiene). This study measured the level of exposure to the “Clean Nigeria, Use the Toilet” campaign against open defecation, determined the level of public knowledge about open defecation-related harms and diseases, ascertained the public attitude towards open defecation, and established the prevailing defecation practices and the perceived barriers to toilet usage in Ebonyi state, the most prevalent OD state in Nigeria. Methods: The study employed a survey design, using a structured questionnaire for data collection. The multi-stage sampling technique was employed in selecting the respondents from two randomly selected Local Government Areas (LGAs) in the state. Analysis was conducted using 384 valid responses. Results: The results were presented in simple percentage frequency tables and interpreted through the descriptive method, while the Chi-Square test was used to analyse the formulated hypotheses, using the decision rule of p < 0.05. The findings show a high level of awareness of the campaign against open defecation, through the radio and community engagements by environmental activists/NGOs, even though regular access to such information was limited. The results also showed inadequate knowledge of the public health implications of open defecation, whereas good knowledge of environmental consequences was reported. The study found favourable attitudes toward OD practice and persistent open defecation, and major barriers to toilet usage include the high cost of toilet construction, lack of access to toilet facilities, poor sanitation and management of available toilets, and perceived risks of contracting infection from public toilets. However, the Chi-Square values showed that the SBCC campaign was significantly associated with knowledge, attitude, and practice (p < 0.05). Conclusions: The study concluded that localised, culturally relevant and socio-demographically targeted communication interventions, grassroot advocacy, community watch, and neighbourhood taskforce on open defecation, in addition to the provision of aids for the construction of modern toilets with water facilities, are required to combat open defecation in Ebonyi and related contexts in Nigeria. Full article

This paper presents an open-source, fully parametric three-component total knee arthroplasty (TKA) implant system and an automated surgical planning pipeline, addressing the absence of publicly available, modifiable TKA design frameworks in the literature. A cruciate-retaining femoral component, tibial baseplate, and polyethylene insert were designed in Autodesk Fusion with 160 parameters governing all anatomically significant geometry. The femoral articulation surface uses a tangency-constrained triple-radius J-curve. An automated Blender (v. 5.1) Python pipeline performs bone model alignment, size selection from a twelve-size chart, Boolean resection via parametric cutting blocks, and final component placement. Prototypes were 3D printed and validated on 1:1 anatomical bone models. The implant system achieved flush seating on all resection surfaces and impingement-free articulation through the full range of motion on all bone sets. The pipeline correctly aligned bone models, performed resections, and selected appropriately sized implants in all 11 cases, processing each in 1–1.5 min. The system is the first open-source TKA framework to simultaneously provide full parametric definition, documented design rationale, three-component coverage, an automated planning pipeline, and an additive manufacturing fabrication path. By releasing the complete parametric model and pipeline as open source, this work enables independent validation, population-specific adaptation, and iterative improvement by the global research community. Full article

This paper presents a multidimensional analysis of Historic Building Information Modeling (HBIM) segmentation, offering a roadmap towards standardization, a key dimension towards broader adoption within the Cultural Heritage (CH) sector. HBIM faces multiple challenges related to the lack of standardized protocols and varying definitions of Level of Detail (LOD) across applications. Amid the advancements of the fourth industrial revolution, integrating Building Information Modeling (BIM) improves sustainability and digital governance, aligning with the sustainable development agenda. Despite increasing academic interest, the implementation of HBIM remains limited, primarily due to the complexities and heterogeneities inherent in CH artifacts. This study begins with a purely qualitative strategy. Then, it introduces multidimensional and hierarchical clustering analysis to classify the unique characteristics of various HBIM applications such as segmentation, input, and data-capturing media. At the same time, it is a tool for fine-tuning keyword-based selection criteria, which is crucial in systematic or semi-systematic surveys in HBIM segmentation. The thematic analysis output is interrupted just before the conceptualization step, and theme extraction is diverted to correspondence analysis implemented in R, an open-source statistical package. Among the key findings of this paper is the classification of four distinct HBIM application clusters, revealing how specific workflows align with data acquisition methods, input formats, and Level of Detail (LOD) requirements. The analysis exposes critical standardization bottlenecks hindering wider-scale industry adoption, highlighting that challenges are domain-specific. Strong evidence shows that 3D modeling has not reached the required maturity level, with persisting challenges distributed non-uniformly within the applications spectrum. Finally, AI-driven automation relates with poor LOD outcome. Full article

We propose an exploratory framework for a Bohmian model of quantum matter propagating on a classical curved spacetime background. The gravitational sector is governed by classical Einstein field equations throughout; no quantisation of spacetime is attempted. The wave function emerges as the scalar contraction $\mathrm{\Psi }={\psi }_{\nu }{\psi }^{\nu }\in \mathbb{C}$ of a complex-valued tensorial field ${\psi }^{\mu }$, encoding quantum dynamics in a geometric object. The wave tensor interacts with spacetime via the stress–energy tensor $T_{\mu \nu }$, mediated by a real scalar field a of dimension volume, so that $a{T}_{\mu \nu }{\psi }^{\mu }{\psi }^{\nu }$ yields the correct potential energy. We derive a covariant Adapted Schrödinger Equation as the unique minimal covariant lift of the standard equation, justify it from four guiding principles, and verify three internal consistency checks. Under seven explicit approximations the framework reproduces the Schrödinger equation with Coulomb potential for the hydrogen atom. We also derive a dynamical equation for ${\psi }^{\mu }$ that entails the Adapted Schrödinger Equation by contraction. Two open problems are then resolved. First, a complete Lagrangian formulation is provided: a real-valued action for $\mathrm{\Psi }$ yields the Adapted Schrödinger Equation via the Euler–Lagrange equations; a separate action for ${\psi }^{\mu }$, extended by a non-polynomial term, yields the full dynamical equation variationally. Second, two experimental predictions are derived. Expanding to first post-Newtonian order, the perturbation Hamiltonian has coefficients $(3, 1)$ on the kinetic and potential operators; via the virial theorem these produce a coordinate-time blueshift, which after photon propagation yields the universal Einstein gravitational redshift $\delta \nu /\nu =\mathrm{\Phi }/c^2$, confirming consistency with the equivalence principle. The same kinetic coefficient independently predicts that free quantum wave packets spread more slowly by the fractional amount $3\left|\mathrm{\Phi }\right|/c^2$, a correction absent in standard non-relativistic quantum mechanics. Full article

The integration of Generative Artificial Intelligence (GenAI) in higher education has opened new possibilities for optimizing administrative and academic services, particularly in contexts characterized by high-demand communication processes. Within the framework of service science, this study addresses the challenge of efficiently managing high volumes of email inquiries in a university master’s program, aiming to improve service quality and operational efficiency. The study examines the implementation of GenAI-based assistants, specifically NotebookLM and custom Gem AI assistants, trained in regulatory, curricular, and historical data from the University Master’s in Teacher Training at Rey Juan Carlos University. A mixed analytical approach is adopted, combining elements of data science to quantify efficiency gains and service science to analyze organizational and service-related transformations. The implementation of GenAI assistants contributes to improved response times, enhanced accuracy of information provided, and a reduction in administrative workload. The results suggest that GenAI can support the scalability and quality of academic administrative services when integrated within a structured service framework. However, its effective adoption requires careful consideration of ethical, organizational, and governance dimensions to ensure sustainable and responsible implementation. Full article