Search Results (10,186)

This study provides an early evaluation of the effectiveness of the Core Tax Administration System, a digital taxation platform introduced to integrate all tax administration processes in Indonesia into a single system. To conduct this evaluation, the study integrates two of the most established frameworks in the information systems literature, namely the DeLone and McLean Information Systems Success Model and the Technology Acceptance Model. Tax professionals are involved in the evaluation process because they are the primary users of the system and possess advanced knowledge of taxation. Structural equation modeling is employed as the analytical technique. The results indicate that system usage generates individual-level benefits by reducing perceived compliance costs, which in turn translate into organizational-level outcomes in the form of increased tax compliance intentions. However, the non-linear effect analysis reveals that this relationship is not entirely linear but follows an inverted U-shaped pattern. This finding suggests that over time, highly routine system usage may reduce professional vigilance by fostering excessive reliance on automated features and superficial processing. Such dependence can weaken perceived efficiency gains and diminish intrinsic motivation for careful and accurate reporting, highlighting the importance of balancing efficiency with system design features that support professional judgment and vigilance. Full article

Manufacturing enterprises face increasing complexity in managing the complete life cycle of production systems, requiring integration of information from diverse sources to support timely maintenance, diagnostics, and operational decisions. This paper presents a comprehensive service-oriented architecture (SOA) for decision support in industrial life-cycle management, integrating real-time monitoring, predictive maintenance, and collaborative problem-solving across extended manufacturing enterprises. The architecture implements a three-layer service model comprising eight core collaborative services, three application services, and six life-cycle management services, orchestrated through a risk assessment module that monitors life-cycle parameters and triggers appropriate maintenance, diagnostics, or hazard prevention actions. The system was developed in the context of a European research project and validated in two industrial settings: automotive assembly lines at a German SME and air conditioning manufacturing at a Portuguese company. Results demonstrated substantial operational improvements, including reduced problem resolution time, lower diagnostic travel requirements, reduced spare-parts consumption, and increased structured problem registration. The original SOAP-based web-services implementation is further contextualized within the contemporary Industry 4.0 landscape through comparison with microservices architectures and discussion of integration paths involving OPC UA, Asset Administration Shells, and digital twins. The paper contributes a validated reference architecture for service-based industrial life-cycle management and clarifies its relevance as an early precursor of contemporary smart manufacturing approaches. Full article

Non-motorized mobility networks increasingly serve as critical infrastructure for sustainable regional development that integrates recreational, environmental, and transportation functions across diverse geographical contexts. To enhance the spatial planning efficiency and support evidence-based policy development, this study develops a Geographic Information Systems (GIS)-based analytical framework to evaluate the connectivity and accessibility of Korea’s integrated non-motorized mobility system. The model systematically maps 606 walking courses, 60 cycling routes, and 66 water activity sites nationwide, and examines their spatial relationships with major transportation hubs, including Korea Train e-Xpress (KTX) stations and airports within 20–30 km buffer zones. Using proximity analysis, connectivity mapping, and origin–destination (OD) cost matrix modeling, the framework identifies intermodal distance structures and spatial integration patterns. The analysis reveals a hybrid network configuration characterized by localized multimodal clustering alongside regional accessibility gaps, with urban–coastal regions demonstrating stronger connectivity than inland–rural areas. This study proposes a data-driven Korean mobility network framework that integrates walking, cycling, and water routes with the existing transportation infrastructure. These findings demonstrate how GIS-based tools can support evidence-based sustainable mobility policies and regional tourism planning on a national scale. Full article

Despite recent advances in artificial intelligence for agriculture, reliable crop recommendation remains constrained by limited access to soil diagnostics, insufficient integration of environmental context, and the absence of transparent, quantitative evaluation frameworks. This study addresses the research question: How can we integrate multiple indicators to generate accurate, explainable, and context-sensitive crop recommendations? To this end, we propose a multimodal decision-support framework that combines image-based soil texture classification with geospatial, and climatic information. A convolutional neural network was trained on a curated dataset of 3250 soil images aggregated from four publicly available sources, covering four primary soil texture classes, alongside tabular soil and nutrient data. The model was evaluated using 5-fold stratified cross-validation, achieving an average classification accuracy of 99.30% (standard deviation ≈ 0.66), and was further validated on an independent hold-out test set to assess generalization performance. To enhance practical applicability, the framework incorporates elevation, rainfall, temperature, and major soil nutrients, and employs a large language model to generate user-oriented, interpretable justifications for each recommendation. Crop recommendations were quantitatively evaluated using a novel Agronomic Suitability Score (ASS), which measures alignment across soil compatibility, climatic suitability, seasonal alignment, and elevation tolerance. Across six geographically diverse case studies, the framework achieved mean ASS values ranging from 3.76 to 4.96, with five regions exceeding 4.45, demonstrating strong agronomic validity, robustness, and scalability. A Streamlit-based application further illustrates the system’s ability to deliver accessible, location-aware, and explainable agronomic guidance. The results indicate that the proposed approach constitutes a scalable decision-support tool with significant potential for sustainable agriculture and food security initiatives. Full article

Artificial Intelligence (AI) literacy and competency in pre-service teacher education refer to a programme-level implementation that enables teachers to work with AI systems effectively, critically, and ethically across university coursework, school placements, and early-career practice. This includes not only capability, but also professional enactment, where teachers apply AI-related knowledge in context-sensitive and pedagogically grounded ways. AI literacy refers to a shared knowledge base for understanding how AI systems generate outputs, how to evaluate and verify AI-supported information, and how to reason about task–tool fit in relation to fairness, privacy, transparency, accountability, academic integrity, equity, and environmental sustainability. AI competency refers to the application of this literacy in routine professional tasks, such as designing and justifying AI-informed teaching, learning, and assessment, protecting students’ and school data, documenting decisions, and revising AI-supported materials after checking for reliability, transparency, accountability, and equity. Together, literacy and competency extend beyond personal use of AI by preparing future teachers to support students’ creative, critical, and ethical engagement with AI, while keeping classroom practice aligned with educational goals, objectives, and values. Full article

Shared autonomous vehicle (SAV) services are gaining attention as an innovative urban transportation paradigm due to their potential to lower travel costs and improve operational efficiency. Unlike manually operated vehicles, SAVs exhibit unique behavioral dynamics, including safe passenger pick-up and drop-off processes, as well as strategic repositioning and autonomous parking to anticipate future travel demands. Consequently, effective and dynamic route planning is paramount to optimizing SAV safety and operational efficiency. This study proposes a novel traffic information, termed Autonomous Driving Stress (ADS), designed to enhance the safety and efficiency of SAV route planning by quantitatively capturing the level of driving challenge encountered during autonomous operation. To predict ADS, a machine learning framework was developed, utilizing microscopic traffic simulation data that incorporates a comprehensive set of 22 input features describing SAV driving behavior, roadway characteristics, and prevailing traffic conditions. Among five machine learning algorithms evaluated, Random Forest exhibited superior predictive performance, achieving an accuracy of 80.9%. The proposed framework enables real-time ADS level prediction by continuously integrating streaming traffic data into the trained model. The dissemination of this real-time ADS information to SAVs supports proactive, informed, and dynamic route planning decisions, thereby enhancing operational safety, traffic flow, and the sustainability of SAV operations within urban mobility systems. Full article

This study presents a calibrated analytical micromechanical framework for predicting the linear elastic behavior of unidirectional glass fiber/epoxy and carbon fiber/epoxy composites over a wide range of fiber volume fractions. The approach combines the classical rule of mixtures for the longitudinal Young’s modulus with the semi empirical Halpin–Tsai equations to estimate the transverse Young’s modulus and the in-plane shear modulus. The framework is specifically formulated to support durability-oriented composite design through rapid and physically consistent estimation of elastic properties governing load transfer and stress distribution. Material parameters, including fiber and matrix Young’s moduli (Ef, Em), shear moduli (Gf, Gm), Poisson’s ratios (νf, νm), and fiber volume fraction (Vf up to 0.80), are taken from established material property databases and implemented within a literature-informed modeling scheme. To preserve physical realism at high fiber contents, a shear correction factor is introduced for Vf > 0.50 to account for microstructural interaction and fiber clustering effects. The predicted effective elastic constants (E₁, E₂, G₁₂, ν₁₂) exhibit consistent and physically meaningful trends across the full fiber volume fraction range. The model predictions were evaluated against trends widely reported in the composite micromechanics literature, and the results showed overall agreement in the nonlinear reduction in stiffness gains at elevated fiber volume fractions. Comparative results indicate that carbon fiber/epoxy composites achieve up to approximately 30% higher stiffness than glass fiber/epoxy systems at equivalent fiber contents, reflecting the influence of stiffness contrast on composite response. The analysis further indicates that stiffness saturation begins approximately in the Vf = 0.60–0.70 range, where the incremental gains in E2 and G12 become noticeably smaller for both composite systems. This behavior provides design-relevant guidance by showing that, beyond this range, further increases in fiber content may offer limited stiffness improvement relative to the associated manufacturing complexity. Overall, the calibrated Halpin–Tsai methodology offers a practical and computationally efficient tool for preliminary evaluation and design-stage optimization of the elastic performance of high-performance composite structures. Full article

Greenhouse cultivation enables year-round vegetable production and high yields through precise environmental regulation. Yet, the same stable microclimate that promotes crop growth also favors the proliferation of pests and diseases. This review synthesizes current knowledge on how greenhouse climate variables govern pest and disease epidemiology in tomato, cucumber, and sweet pepper. Only greenhouse-based studies were included to ensure direct relevance to protected horticulture. Microclimatic stability determines infection probability, vector behavior, and host susceptibility. Warm, humid conditions promote fungal and bacterial pathogens, whereas dry, high vapor pressure deficit (VPD) environments favor mites and thrips and enhance virus transmission. Species-specific traits further modulate vulnerability. Tomato is dominated by virus–bacterium complexes and foliar/stem fungal diseases, cucumber by phytopathogenic fungi favored by high relative humidity (RH) and soilborne pathogens, and sweet pepper by virus–vector systems and long-cycle fungal infections. Temperature exerts the strongest influence, while RH and VPD jointly regulate surface moisture and vector activity. Light intensity and spectral composition also affect pest orientation and fungal sporulation. Integrating environmental sensing, biological control, and adaptive climate regulation offers a pathway toward preventive, climate-smart Integrated Pest Management (IPM). The review highlights the emerging role of climate-informed decision-support systems (DSSs) and the need for greenhouse-specific datasets to improve pest and disease forecasting. Full article

Current diagnostic assessments of lung disease rely primarily on medical imaging and global pulmonary function tests, which either provide static structural information or collapse complex regional behavior into global indices. As a result, important information about regional mechanical heterogeneity and early pathological changes may remain inaccessible. In this work, we introduce a conceptual diagnostic framework for the lung based on mechanical system identification and evaluate its feasibility using simulation-based analysis. Rather than directly imaging internal lung structure, the lung–thorax system is treated as an identifiable viscoelastic dynamical system whose internal mechanical properties can be inferred from its response to controlled external excitation. A multi-degree-of-freedom mechanical representation of the lung was developed to capture the dominant low-frequency behavior of the chest wall and major lung regions. Sensitivity and Fisher-information analysis confirmed the structural identifiability of regional stiffness parameters (FIM eigenvalues λ₁ = 1.75 × 10⁻⁹ and λ₂ = 8.91 × 10⁻¹⁰). Inverse fitting experiments accurately recovered simulated stiffness perturbations (e.g., k₀₁ = 240 → 239.5; k₀₂ = 154 → 159.5) from noisy frequency response data, while classification experiments achieved the complete separation of simulated pathological configurations in an idealized synthetic scenario, supporting theoretical discriminability rather than clinical performance claims. These findings demonstrate the theoretical feasibility of a diagnostic paradigm in which regional lung mechanical alterations can in principle be identified through mechanical system identification rather than direct imaging, thereby suggesting a complementary approach for a non-invasive assessment of regional lung mechanics from externally measured responses. By quantifying regional stiffness and mechanical heterogeneity, this framework may also support the personalization and monitoring of pulmonary rehabilitation strategies in chronic respiratory disease. Full article

Background: Cytology from cerebrospinal fluid (CSF) is standard-of-care to detect central nervous system (CNS) cancers but suffers from low-sensitivity and lacks associated molecular information, often requiring brain biopsy or resection to obtain. Belay Diagnostics offers analytically and clinically validated CSF-based tests to support the diagnosis and management of primary and secondary CNS cancers. However, the clinical utility (CU) of these assays has not been previously evaluated. Methods: This retrospective study presents a real-world, single institution experience of using the Belay Summit liquid biopsy test for all orders received (n = 123) between October 2024 and September 2025. Clinical information was reviewed for demographics, provisional diagnosis, oncology history, CSF cytology results, and tumor genomic profiling results. The primary endpoint of this study was to evaluate the impact of Belay CSF-based assays on physician diagnosis and treatment decisions. Secondary endpoints included evaluating the clinical performance of the Belay Summit test verses cytology in CNS malignancy detection (sensitivity, specificity, and accuracy). Results: The cohort included 120 patients with suspected or previously diagnosed primary (n = 40) or metastatic (n = 80) CNS tumors; three patients completed longitudinal testing for a total of 123 specimens and test orders. Summit showed higher sensitivity for CNS malignancy (90%) over CSF cytology (12%). The Belay CSF liquid biopsy test demonstrated strong clinical utility and was essential to clinical course pursued in 93% (114/123) of specimens, informing treatment and management decisions. Conclusions: The Belay Summit test provides clinically meaningful information to support physician decision-making for the diagnosis and management of primary and secondary CNS tumors, especially in cases where tissue sampling is infeasible. Full article

Low-accessible Cultural Heritage, including hypogeal sites, rupestrian architectures, and fragile structures, represents a major challenge for conservation, documentation, and continuous monitoring. These limitations stem from multiple inaccessibility factors, classified as physical (morphological complexity), asset risk (microclimatic instability), health and safety (structural vulnerability), managerial (lack of public access), and cognitive (lack of documentation). This research aims to transform digital models from mere representational tools into integrated cognitive and operational systems supporting decision-making and preventive conservation. The proposed methodological workflow is structured into five main phases: Preliminary Knowledge and Multidisciplinary Data Structuring (Ph1. PK–MDS), Comprehensive Digital Survey (Ph2. CDS), Development of Integrated Digital Models (Ph3. IDMs), Advanced Diagnosis and Monitoring (Ph4. ADM) and the implementation of an Integrated Digital Environment for Hypogeal Heritage Management (Ph5. IDE). Ph4 operates on two complementary scales: at the site scale, range-based point clouds enable the semi-automatic identification of extensive decay patterns, such as biological colonization. At the detail scale, the Random Forest algorithm enables the segmentation and quantification of material loss on frescoed surfaces through a diachronic comparison of historical and current data. Validated on the San Pellegrino complex in Matera, selected as a paradigmatic case study of low-accessibility hypogeal sites, representative of a broader system comprising approximately 150 rupestrian cult architectures, the methodology demonstrates how immersive digital environments function as shared knowledge spaces, supporting more informed, inclusive, and resilient heritage conservative management. Full article

We explored the application of spatial information technology in the assessment and optimization of cultural heritage resources within traditional settlements in Meizhou City, a core area of Hakka culture in China. By integrating methods such as geographic information systems and Kernel density estimation, it systematically evaluates the spatial distribution and socioeconomic conditions of these settlements. A multi-criteria evaluation model is constructed to quantify resource endowment across cultural, historical, and ecological dimensions, with particular emphasis on key factors influencing conservation effectiveness, such as infrastructure and economic vitality. Combining field investigations and literature review, we propose adaptive reuse strategies and policy recommendations to enhance settlement resilience and balance cultural preservation with regional development. Their expected outcomes include the engineering of a multidimensional geographic database for traditional settlements, the establishment of a spatial decision-support framework for heritage infrastructure conservation, and the development of systematic optimization protocols integrated with China’s rural revitalization technical policies. These results provide a computational and methodological foundation for interdisciplinary research in sustainable cultural heritage management and smart rural engineering. Full article

Background: Chronic heart failure (CHF) in older adults requires sustained self-management and close follow-up, yet day-to-day care is often carried out by families with support from primary healthcare nurses. In Saudi Arabia, where family caregiving is culturally normative, collaboration between nurses and patients’ families may be pivotal to effective CHF management, but remains insufficiently understood in primary healthcare contexts. Methods: A qualitative study informed by an interpretive phenomenological approach was conducted. Participants (n = 24; 12 nurses and 12 family caregivers) were recruited using purposive sampling from primary healthcare centers in Riyadh, Saudi Arabia. In-depth, semi-structured interviews were conducted in Arabic or English, audio-recorded, transcribed verbatim, and analyzed using reflexive thematic analysis following Braun and Clarke’s six-phase framework. Strategies to enhance trustworthiness included member checking, peer debriefing, maintenance of an audit trail, and reflexive journaling. Results: Twenty-four participants (12 nurses and 12 family caregivers) were interviewed. Four interrelated themes were generated from both nurses’ and family caregivers’ accounts. (1) “We Are Caring Together”: Collaboration was experienced as shared responsibility for daily CHF management, grounded in trust; (2) Navigating Roles and Boundaries: Participants described unclear expectations, role overlap, and tension between professional authority and family knowledge; (3) Communication as the Engine of Collaboration: Effective partnerships depended on clear information exchange, caregiver-tailored education, and continuity of contact, while communication gaps created uncertainty and delayed support-seeking; and (4) Cultural and System Constraints Shaping Collaboration: Strong family obligation motivated caregiving but also intensified moral pressure and limited help-seeking, while time pressure and fragmented services constrained meaningful engagement and continuity across settings. Conclusions: Nurse–family collaboration in CHF management is relational, shaped by trust, role negotiation, and communication, and constrained by cultural norms and system pressures. This study contributes to the literature by demonstrating how moral obligation, hierarchical professional norms, and system fragmentation distinctively shape collaboration in the Saudi primary care context, extending existing conceptualizations derived primarily from Western individualist settings. Strengthening collaboration requires explicit role clarification, health literacy–informed caregiver education, continuity of contact, and organizational supports. Findings are limited by purposive sampling, single-city context, and exclusion of patient perspectives. Full article

In recent years, optimization algorithms have emerged as powerful computational tools for addressing complex and dynamic challenges across diverse domains. These domains include engineering, technology, management, and decision-making. Their growing importance is motivated by (a) the increasing complexity of modern systems, (b) the need for efficient resource utilization, and (c) the demand for scalable algorithmic solutions. These algorithms enable the systematic and computational exploration of large solution spaces, supporting decision-making and design under uncertainty, large-scale data, and evolving requirements. This study provides a structured review and comparative scientometric analysis of optimization algorithms, covering: (a) exact methods, (b) approximation techniques, (c) metaheuristics, and (d) emerging physics-informed frameworks. The analysis highlights algorithmic trends, performance-oriented research directions, and the increasing integration of mathematical programming, machine learning, and numerical methods. The results show a renewed focus on classical algorithmic paradigms. Moreover, rapid growth in hybrid and physics-informed optimization approaches is observed. These findings confirm the central role of optimization algorithms in modern algorithm engineering and interdisciplinary computational research. Full article

Inherited cardiomyopathies associated with high-risk genotypes, are characterized by a disproportionate risk of malignant ventricular arrhythmias and sudden cardiac death, often independent of left ventricular systolic dysfunction or advanced structural remodeling. Traditional surveillance strategies based on intermittent electrocardiography and phenotype-driven risk assessment are insufficient to capture the dynamic and often silent progression of electrical instability in these populations. This narrative review evaluates the emerging role of artificial intelligence (AI)-enabled sensor technologies in remote arrhythmic monitoring of genetically defined cardiomyopathy cohorts. Wearable ECG devices, implantable cardiac monitors, multisensor cardiac implantable electronic device algorithms, pulmonary artery pressure sensors, and contact-free systems enable continuous acquisition of electrophysiological and hemodynamic data, generating digital biomarkers that may reflect early arrhythmic vulnerability and subclinical decompensation. AI-driven analytics enhance signal processing, automated event detection, and remote data triage, with the potential to reduce clinical workload while preserving diagnostic sensitivity. However, current evidence predominantly derives from heterogeneous heart failure or general arrhythmia populations, and prospective validation in genotype-specific cohorts remains limited. Key challenges include algorithm generalizability, signal quality in ambulatory environments, data governance, interpretability of AI models, and integration into structured remote-care pathways. The convergence of genotype-informed risk stratification and multimodal AI-enabled sensing represents a promising strategy to transition from reactive device-based protection to proactive, precision-guided arrhythmic prevention. Dedicated genotype-focused studies and standardized digital endpoints are required to support safe and effective implementation in inherited cardiomyopathies. Full article