Search Results (35,377)

An intelligent assistant with artificial intelligence (AI) for language learning was developed. A dynamic user interface and a supervised feedback module using web technologies (HTML5, CSS3, and JavaScript ES6+) and Python v3.9 for supporting language learning were designed. The integration of the dynamic user interface with the application programming interface (API) powered by artificial intelligence (AI) was implemented. The results of the comparative evaluation of the intelligent assistant with artificial intelligence (AI) versus the traditional language learning method displayed an improvement in the learning stage. Full article

The rapid industrialization envisioned in Saudi Arabia’s Vision 2030 requires that new small and medium-sized enterprise (SME) factories be digitally capable from inception. This article proposes a policy framework that establishes a minimum Industry 4.0 maturity threshold as a condition for licensing new SME manufacturing facilities. Building upon international best practices and Saudi Arabia’s specific context, the article specifies minimum technical and organizational requirements across three dimensions: process, technology, and organization. Core elements include basic automation, industrial connectivity, vertical and horizontal integration, data-driven decision-making, workforce digital literacy, and leadership commitment to transformation. The rationale centers on cost efficiency, global competitiveness, and alignment with national goals. Recognizing common barriers such as limited skills, high upfront costs, and cybersecurity risks, the article outlines mitigation strategies including government incentives, public–private partnerships, and regulatory support. The framework is conceptual in nature and intended to inform pilot implementation and subsequent empirical evaluation. Establishing clear digital standards at the factory design stage can support more sustainable and scalable growth for Saudi SMEs while strengthening their readiness for participation in global Industry 4.0 ecosystems. Full article

Spatial technologies play a key role in documenting and analyzing landscape components. The Landscape Information Model (LIM), deriving from the Building Information Model (BIM), is a digital representation of a landscape, which should support planning, design, management, and analysis throughout a landscape’s lifecycle. In the literature, the applications of BIM technology in landscape planning focuses on the design and the construction of 3D and 5D LIMs. The aim of this paper is to develop the concept of 7D LIMs for the purposes of managing greenery based on the example of the university campus and model implementation based on BIM-GIS technology. The specific objective is to develop the UML diagrams of the model that would be dedicated to the needs of the unit responsible for maintaining the university’s infrastructure. The source of data was a point cloud obtained by laser scanning, which was then processed to map the terrain, small architectural objects, and infrastructure in the Revit 2024 software. The developed method indicated the value of modern technologies in landscape processes and their potential use in public institutions. The proposed diagrams that describe the semantics of landscape forms and greenery maintenance activities can be developed by adding further ontological aspects of the landscape model. Full article

Since 2021, the design and construction of nearly zero-energy buildings (nZEBs) have been mandatory for European Union Member States. Subsequent requirements for the building sector, characterized by high energy demand and significant environmental impact, include the minimization of carbon footprint and the introduction of climate-neutral building standards. The carbon footprint comprises both embodied emissions related to materials and construction processes and operational emissions resulting from building use. This paper analyzes both types of carbon footprint using a residential building that is part of an experimental housing estate consisting of 44 semi-detached buildings as a case study. Analyses of energy consumption optimization and carbon footprint reduction were conducted at both the individual building scale and the scale of the entire housing complex. The estate was developed in two stages. In the first stage (completion of construction in 2024), the primary criterion for technology selection was investment cost while maintaining compliance with applicable technical and building regulations. Prior to the implementation of the second stage, the investor conducted a social participation process in the form of a survey among future users. The survey addressed environmental aspects of the newly designed buildings and enabled the selection of materials, technologies, and energy sources aligned with user preferences. The results indicate that environmental aspects are important to future users; however, investment decisions are strongly balanced against economic factors. At the same time, the energy analyses demonstrate that a substantial reduction in the operational carbon footprint can be achieved, enabling a significant progression toward climate neutrality, both at the level of individual buildings and across the entire housing estate. Social participation, therefore, becomes an important element in the pursuit of climate neutrality in buildings. However, it must be taken into account already at the design stage. The results of the analyses carried out in the article showed that, taking into account public participation in the design process and user recommendations, the selected optimal variant (W5) allows for a reduction in the EP index by over 90% compared to the variant based on standard low-cost solutions (W0) (EP (W0) = 243.64 kWh/(m² year); EP (W5) = 18.42 kWh/(m² year). In terms of the embodied carbon footprint, the optimal option W5 allows for a reduction of over 30% in the embodied carbon footprint of the building structure (W0—51,585.32 [kgCO₂e]; W5—35,537.87 [kgCO₂e]). The optimal variant indicated by users (W5) allows for a reduction in the operational carbon footprint by approximately 80% compared to the basic variant (W0): W0—604,189.50 [kgCO₂e/kWh]; W5—247,402.0 [kgCO₂e/kWh]. The results obtained indicate that public participation is not only a complementary element of the design process, but it can also be a key component of the decarbonisation strategy in residential construction. Involving future users in the decision-making process increases the likelihood of achieving long-term greenhouse gas emission reductions and supports the implementation of long-term climate policy goals. Full article

Background/Objectives: Rotator cuff repair (RCR) is a common orthopedic procedure, with healing outcomes strongly influenced by patient-specific factors such as tissue quality, tear characteristics, and biological healing potential. Bioinductive collagen implants have shown great results in enhancing tendon healing and reducing retear rate. This study aimed to evaluate the clinical and imaging outcomes of RCR augmented with a xeno-derived collagen membrane over 24 months and to assess complications or implant failures. Methods: Patients underwent arthroscopic RCR using anchors (single or double-row) with additional xeno-derived matrix augmentation. The study included patients older than 40 years with full-thickness supraspinatus and/or infraspinatus tendon tears (DeOrio–Cofield grade 3–4) who were candidates for arthroscopic rotator cuff repair and provided informed consent. Clinical outcomes were assessed using the Constant–Murley Score (CMS), Disabilities of the Arm, Shoulder and Hand score (DASH), and Visual Analogue Score (VAS) at baseline, 3, 6, 12, and 24 months. MRI was performed preoperatively and at 24 months to assess tendon thickness. Results: All scores improved significantly. CMS increased from 16.3 ± 4.1 to 82.9 ± 5.8, VAS decreased from 7.8 ± 1.0 to 1.5 ± 0.8, and DASH improved from 70.3 ± 6.4 to 12.4 ± 4.5 (p < 0.05). Tendon thickness in the supraspinatus (T3) increased from 4.2 ± 0.9 mm to 6.8 ± 1.2 mm (p < 0.05). Retear rate was 7.55%, with no major complications. Conclusions: The bioinductive collagen implant showed notable results in improving tendon thickness, healing, and excellent clinical outcomes in RCR, without membrane-related complications. The study was designed as a prospective single-arm case series without a control group and that was the main limitation; The absence of adverse reactions in this cohort further supports the favorable safety profile of this implant in the present study population. Full article

Robust and transferable approaches for evaluating research capacity—whose measurable expression is reflected in research output—are essential for evidence-based science policy and strategic research management. This study develops an integrated framework to assess global scholarly capacity and regional disparities by combining semantic-similarity-based literature filtering, bibliometric mapping, dynamic performance assessment, and spatial analytical techniques into a coherent and replicable model. A Sentence-BERT model ensures thematic precision and dataset consistency, while CiteSpace 6.1.R3 is used tomap publication trajectories, thematic evolution, and influential contributors. A dynamically weighted TOPSIS model incorporates temporal variation to quantify national research capacity, and spatial analyses—including gravity center analysis, Theil index decomposition, spatial autocorrelation, gray relational analysis, and the Geographical Detector Model—identify disparity patterns and their explanatory associations. Applied to urban ecosystem security research (2001–2023), an emerging interdisciplinary field within sustainability science, the framework shows that China and the United States dominate research output, whereas European journals exert strong academic influence. The field has advanced through three stages, with increasing emphasis on ecosystem services and sustainable development. GDP, environmental pressure, and urbanization rate show the strongest explanatory associations with research capacity, and interactive effects—especially those involving GDP—exceed single-factor explanatory strength. Ecological baseline conditions such as NDVI and climate exhibit only limited associations, functioning mainly as contextual factors. Policy implications highlight four priorities: strengthening interdisciplinary and cross-regional collaboration in developing regions; promoting equity-oriented research agendas in developed regions; establishing unified definitions and validated evaluation frameworks; and advancing dynamic, systems-based approaches to ecosystem security analysis. By shifting attention from ecological status assessment to the dynamics of scientific knowledge production and research capacity, this study advances methodological foundations for research evaluation and enriches analytical approaches in urban ecosystem security, offering a generalizable framework for identifying capacity differences and supporting evidence-informed policy design. Full article

Adaptive learning at scale requires explicit handling of uncertainty and information flow across diverse educational technologies. This paper proposes a TOGAF-conformant enterprise architecture for the University of Tabuk (UT) that embeds entropy- and uncertainty-aware requirements from the outset and aligns them with institutional goals in teaching, research, and administration. Using the Architecture Development Method (ADM), we map information-theoretic requirements to architectural artifacts across the architecture vision, business, information systems, and technology domains; formally specify core entropy-informed observables, including predictive entropy, expected information gain, workflow variability entropy, and uncertainty hot-spot severity; and define semantic and metadata standards for their near-real-time computation. These indicators are positioned explicitly across the TOGAF domains: business architecture identifies where uncertainty matters, information systems architecture defines the computable data and application representations, technology architecture operationalizes secure and scalable computation, and later ADM phases use the resulting metrics for prioritization and governance. The architecture also establishes governance that ranks initiatives by their expected uncertainty reduction through Architecture Review Board (ARB) decision gates. We address three research questions: (R.Q.1) how to design a TOGAF-conformant architecture for UT that natively encodes uncertainty-aware requirements and aligns with institutional needs; (R.Q.2) how to integrate dispersed data, achieve semantic harmonization, and deliver analytics-ready streams that support information-theoretic indicators for personalization without delay; and (R.Q.3) how to embed IT demand planning in opportunities and solutions and migration planning using uncertainty reduction and expected information gain as prioritization criteria. The resulting architecture offers a university-wide foundation for adaptive learning: it unifies learner and system interaction data under governed schemas, supports low-latency analytics, and formalizes decision processes that treat uncertainty as a primary metric. Though learner-level operational validation is future work, the design establishes the technical and organizational foundations for responsible, large-scale deployment of entropy-driven learner modeling, content sequencing, and feedback optimization. Full article

Accurate mapping of near-surface air temperature ($T_{\mathrm{air}}$) at the fine spatial resolution is required for city-scale monitoring and remains a critical challenge in Earth Observation (EO). Reliance on ground-based measurements is constrained by their sparse spatial coverage and high operational costs. We present a novel, scalable machine learning framework designed to overcome this limitation. Our method utilizes interpretable Convolutional Neural Networks (CNNs) to fuse high-resolution Landsat data, integrating both thermal and reflective spectral bands, with contextual spatiotemporal metadata. This approach allows for inference, at 30 m resolution, of $T_{\mathrm{air}}$ fields without relying on dense, localized ground monitoring networks. Our hybrid CNN architecture is optimized for spatial generalization, maintaining strong and transferable performance (station-wise $R^2\approx 0.88$) across diverse environments from humid coasts ($R^2\approx 0.89$) to arid interiors ($R^2\approx 0.84$). Although focused on a specific geographical region, our results suggest a robust and reproducible pathway for generating spatially consistent temperature fields from globally available EO archives, directly supporting urban heat island mitigation, climate policy development, and high-resolution public health assessment worldwide. Full article

To address the calibration challenge caused by the nonlinear variation in intrinsic parameters during continuous camera zooming in UAV electro-optical pods, this paper proposes an online calibration method based on IMU-visual fusion. Traditional offline calibration cannot adapt to dynamic scenarios, while existing self-calibration methods suffer from slow convergence and insufficient robustness. The proposed method aims to achieve real-time and accurate estimation of camera intrinsic parameters during zooming. Specifically, we first construct a unified state estimation framework that encodes the internal and external parameters of the camera and the 3D positions of scene feature points into a high-dimensional state vector, then establish a camera motion model based on IMU data, construct a visual observation model by combining the pinhole camera and second-order radial distortion model to establish a nonlinear mapping from 3D feature points to 2D pixel coordinates, and adopt an improved ORB algorithm for feature extraction and LK optical flow method to achieve high-precision cross-frame feature matching to enhance the stability of visual observation. Most importantly, we design a tight-coupling fusion strategy based on the Extended Kalman Filter (EKF) prediction-update iteration mechanism, which fuses IMU high-frequency motion constraints and visual geometric constraints in real time to suppress parameter drift induced by focal length changes. Finally, we recursively solve the state vector to complete the online dynamic estimation of intrinsic parameters. Monte Carlo simulation experiments and real UAV flight experiments confirm that the method has both high estimation accuracy and strong environmental adaptability, can meet the high-precision calibration needs of UAVs in dynamic scenarios, and provides reliable technical support for accurate target positioning. Full article

Recent studies highlight blockchain’s potential to enhance residential energy efficiency by enabling traceable, verifiable, and incentivized household actions. This research focuses on optimal organizational models, technology choices, and measurement systems needed to support carbon reduction at the residential level through blockchain. To do so, a multiple-case study was conducted, analyzing six companies that leverage blockchain in the context of carbon markets. Data have been collected through direct, semi-structured interviews with informants from each company. Results from the six cases have been analyzed through a cross-case comparison and clustered to identify three different archetypes of business ecosystem. The results suggests that blockchain supports residential energy efficiency and carbon markets through behavioural incentives, infrastructural integration and hybrid measurement, and reporting and verification systems. They also indicate that blockchain effectiveness depends less on specific protocols than on aligning technological design, governance, and ecosystem context across civic, financial, and institutional models. Full article

Chronic non-communicable diseases rarely occur in isolation; cardiovascular, metabolic, neurodegenerative, malignant, and age-associated disorders share upstream drivers including oxidative stress, chronic inflammation, mitochondrial dysfunction, and metabolic imbalance. This narrative review synthesizes epidemiological, interventional, and mechanistic studies identified through targeted literature searches to examine redox biology as a shared mechanistic hub linking these conditions. We evaluate antioxidant-rich dietary patterns, selected nutraceuticals, myocardial ischemia–reperfusion injury as a clinical exemplar, rare redox-imbalance disorders as mechanistic stress models, and emerging gene-based reinforcement of endogenous antioxidant systems. Rather than proposing clinical targets, we present an integrative, hypothesis-generating framework illustrating how coordinated lifestyle-driven modulation of redox balance may simultaneously influence multiple disease trajectories. Collectively, the evidence supports a unified redox framework for multi-disease prevention for multi-disease prevention and future intervention design. Full article

To address the low operational efficiency and suboptimal crushing quality of conventional straw crushers, a serrated hammer blade was designed and optimized. The working mechanism of straw crushing and the force interaction between the hammer blade and straw were theoretically analyzed, and a finite element model was established to simulate straw fragmentation under impact. The crushing performances of serrated, rectangular, and stepped hammer blades were comparatively evaluated, and cutting force and cutting time were selected as key response indicators to investigate the effects of structural parameters. Using Latin hypercube sampling and a Kriging surrogate model, the relative importance of hammer blade parameters was quantified, followed by multi-objective optimization using the NSGA-II algorithm. The results indicate that the significance of the influencing factors follows the order of blade thickness, blade width, tooth spacing, and blade length. The optimal hammer blade configuration was determined as 4 mm in thickness, 39 mm in width, and 4 mm in tooth spacing. Crushing experiments demonstrate that, compared with the conventional rectangular hammer blade, the optimized serrated design increases productivity by 17.49% and improves the pass rate by 5.02%. This study provides practical parameter support and technical guidance for the low-cost upgrading and performance improvement of straw crushing equipment. Full article

Active support systems are being increasingly applied in the control of large deformation in soft rock tunnels, and exploring the bearing characteristics of prestressed anchor bolts is of great engineering value for improving the long-term stability of tunnel structures. To address the problems of insufficient quantitative characterization of the bearing performance of prestressed anchor bolt support in soft rock tunnels and the difficulty of small-scale model tests in revealing the synergistic bearing law of support and surrounding rock, this study took a 350 km/h double-line high-speed railway tunnel as the prototype and established a large-scale tunnel structure model test system to conduct comparative tests under three working conditions: unsupported, ordinary bolt support, and prestressed anchor bolt support. By monitoring the tunnel failure process and mechanical response of the support structure throughout the test, the failure modes, bearing capacity, deformation characteristics, and axial force distribution of anchor bolts of tunnels under different support forms were systematically analyzed to quantitatively reveal the active support mechanism and bearing strengthening effect of prestressed anchor bolts. The results show that the design bearing capacity of the tunnel model with prestressed anchor bolt support is increased by 127.3% and 31.6% compared with that of the unsupported and ordinary bolt support models, and the ultimate bearing capacity is increased by 120.0% and 43.5%, respectively. Its secant stiffness in the initial loading stage reaches 80.0 kPa/mm, which is five times that of the ordinary bolt support and can effectively restrain the early plastic deformation of the surrounding rock. When the design bearing capacity is reached, the tensile stress of prestressed anchor bolts accounts for 40.2~69.8% of the ultimate tensile strength, with a more uniform axial force distribution and a much higher utilization rate of material mechanical properties than ordinary anchor bolts, which can fully mobilize the bearing potential of deep rock mass and realize the synergistic bearing of support and surrounding rock. This study accurately quantifies the bearing strengthening law of prestressed anchor bolts on tunnel support systems and clarifies the core mechanism of their active support. The research results provide important experimental basis and theoretical reference for the optimal design and engineering application of prestressed anchor bolts in soft rock tunnel engineering. Full article

Challenging behaviors, including noncompliance, aggression, hyperactivity, and impulsivity, are common among individuals with Fragile X Syndrome (FXS) and Down Syndrome (DS). To identify treatment needs specific to these populations, we conducted focus groups with caregivers and educators and used their input to adapt an evidence-based caregiver training program originally designed for caregivers of autistic children (i.e., The Research Units in Behavioral Intervention; RUBI). We then completed a feasibility trial in which five families of children with FXS and four families of children with DS completed a nine-session caregiver training program targeting behavioral principles, syndrome-specific information, and visual supports tailored to the unique needs of FXS or DS (adapted version of RUBI for non-autism developmental disabilities; RUBI-DD). The program demonstrated strong acceptability, with high caregiver satisfaction, 100% retention, and 100% session attendance. Across the combined sample, caregiver reports indicated significant improvements in irritability/aggression (F_(2,15.14) = 4.42, p = 0.03), lethargy/social withdrawal (F_(2,14.47) = 3.97, p = 0.04), stereotypies (F_(2,15.29) = 4.45, p = 0.03), hyperactivity (F_(2,15.14) = 6.51, p = 0.009), social inflexibility (F_(2,15.43) = 6.33, p = 0.01), demand-based noncompliance (F_(2,15.41) = 4.95, p = 0.02), and the impact of behavior on the family (F_(2,15.07) = 4.23, p = 0.04) following participation in RUBI-DD. Caregivers of children with FXS reported significant reductions in lethargy/social withdrawal (F_(2,8.000) = 6.256, p = 0.023) and hyperactivity (F_(2,8.000) = 12.497, p = 0.003) immediately post-treatment and upon 12-week follow-up (g = 1.153, p = 0.044, and g = 1.178, p = 0.003, respectively). Among families of children with DS, caregivers reported reductions in irritability and aggression (F_(2,5.047) = 14.073, p = 0.009) and improvements in the impact on the family (F_(2,6.000) = 5.489, p = 0.044) immediately post-treatment and at follow-up (g = 1.643, p = 0.016, and g = 0.448, p = 0.045, respectively). These findings support the feasibility, acceptability, and preliminary efficacy of RUBI-DD for children with FXS or DS. Full article

This paper investigates the fractal dynamical behavior of a discrete Caputo fractional-order hepatitis C virus model. First, we analyze the stability of the system by using spectral radius and design the fractional-order controller based on coordinate transformation. Then, a nonlinear coupling controller is constructed to achieve synchronization between two fractional-order models with different parameters and different fractional orders, and the synchronization is supported by rigorous mathematical proof. Numerical simulations are used to verify the effectiveness of control and synchronization. Full article