Search Results (1,823)

Objective: To evaluate the concordance of automated 3D superimposition methods applied to digital models, with a focus on methods that consider stable palatal regions as geometric reference landmarks versus those that do not. Design and setting: This was a prospective, cross-sectional study using digital model files of patients undergoing orthodontic treatment in a university clinical setting. Participants: Sixty-one patients were prospectively enrolled and divided into three groups based on the type of orthodontic treatment they received: (20) non-extractive orthodontic treatment without intermaxillary elastics, (21) intermaxillary elastics, and (20) control subjects with no orthodontic movement. The inclusion criteria included the availability of complete pre- and post-treatment digital casts and the absence of significant craniofacial anomalies. Methods: Three superimposition methods were tested: (1) superimposition according to palate and palatal ridges, (2) best-fit superimposition of arches in occlusion, and (3) best-fit superimposition of individual arches. Discrepancies were identified by comparing the spatial positions derived from each method. Within three spatial axes, deviations of ±0.5 mm and ±1.15° were not considered significant. Bland–Altman plots were used to quantify palatal rugae based and non-based spatial differences between methods. Differences in the superimposition results between the three patient groups were evaluated using ANOVA tests. Results: Differences in spatial position between the superimposition methods often exceeded the acceptable range. The results were compared between the three patient groups with a statistical significance of α = 0.05. In the present study, the high reliability of the superimposition method based on the palate and palatal ridges was observed. Conclusion: Superimposition methods based on the palate and palatal rugae provide superior accuracy in determining treatment-related changes in upper arch digital models. These findings illustrate the need for appropriate selection of superimposition techniques based on the study objective of using clinically relevant techniques. Full article

Smart living is strongly promoted to enhance the quality of life via the application of innovative solutions, and this is driven by domain specialists and policymakers, including designers, urban planners, computer engineers, and property developers. Nonetheless, the actual user, whose views ought to be considered during the design and development of smart living systems, has received little attention. Thus, this study aims to identify and examine the critical smart functions to achieve smart living in smart buildings based on occupants’ perceptions. The aim is achieved using a sequential quantitative research method involving a literature review and 221 valid survey data gathered from a case of a smart student residence in Hong Kong. The method is further integrated with descriptive statistics, the Kruskal–Walli’s test, and the criticality test. The results were validated via a post-survey with related experts. Twenty-six critical smart functions for smart living were revealed, with the top three including the ability to protect personal data and information privacy, provide real-time safety and security, and the ability to be responsive to users’ needs. A need was discovered to consider the context of buildings during the design of smart living systems, and the recommendation is for professionals to understand the kind of digital technology to be integrated into a building by strongly considering the context of the building and how smart living will be achieved within it based on users’ perceptions. The study provides valuable insights into the occupants’ perceptions of critical smart features/functions for policymakers and practitioners to consider in the construction of smart living systems, specifically students’ smart buildings. This study contributes to knowledge by identifying the critical smart functions to achieve smart living based on occupants’ perceptions of smart living by considering the specific context of a smart student building facility constructed in Hong Kong. Full article

Landslides are among the most hazardous natural phenomena affecting Greater Abidjan, causing significant economic and social damage. Strategic planning supported by geographic information systems (GIS) can help mitigate potential losses and enhance disaster resilience. This study evaluates landslide susceptibility using logistic regression and frequency ratio models. The analysis is based on a dataset comprising 54 mapped landslide scarps collected from June 2015 to July 2023, along with 16 thematic predictor variables, including altitude, slope, aspect, profile curvature, plan curvature, drainage area, distance to the drainage network, normalized difference vegetation index (NDVI), and an urban-related layer. A high-resolution (5-m) digital elevation model (DEM), derived from multiple data sources, supports the spatial analysis. The landslide inventory was randomly divided into two subsets: 80% for model calibration and 20% for validation. After optimization and statistical testing, the selected thematic layers were integrated to produce a susceptibility map. The results indicate that 6.3% (0.7 km²) of the study area is classified as very highly susceptible. The proportion of the sample (61.2%) in this class had a frequency ratio estimated to be 20.2. Among the predictive indicators, altitude, slope, SE, S, NW, and NDVI were found to have a positive impact on landslide occurrence. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), demonstrating strong predictive capability. These findings can support informed land-use planning and risk reduction strategies in urban areas. Furthermore, the prediction model should be communicated to and understood by local authorities to facilitate disaster management. The cost function was adopted as a novel approach to delineate hazardous zones. Considering the landslide inventory period, the increasing hazard due to climate change, and the intensification of human activities, a reasoned choice of sample size was made. This informed decision enabled the production of an updated prediction map. Optimal thresholds were then derived to classify areas into high- and low-susceptibility categories. The prediction map will be useful to planners in helping them make decisions and implement protective measures. Full article

The use of low-resolution analog-to-digital converters (ADCs) in receivers has emerged as an effective solution for reducing power consumption in millimeter-wave (mmWave) massive multiple-input–multiple-output (MIMO) systems. However, low-resolution ADCs also pose significant challenges for channel estimation. To address this issue, we propose a joint uplink/downlink (UL/DL) channel estimation algorithm that utilizes the spatial reciprocity of frequency division duplex (FDD) to improve the estimation of quantized UL channels. Quantified UL/DL channels are concentrated at the BS for joint estimation. This estimation problem is regarded as a compressed sensing problem with finite bits, which has led to the development of expectation-maximization-based quantitative generalized approximate messaging (EM-QGAMP) algorithms. In the expected step, QGAMP is used for posterior estimation of sparse channel coefficients, and the block maximization minimization (MM) algorithm is introduced in the maximization step to improve the estimation accuracy. Finally, simulation results verified the robustness of the proposed EM-QGAMP algorithm, and the proposed algorithm’s NMSE (normalized mean squared error) outperforms traditional methods by over 90% and recent state-of-the-art techniques by 30%. Full article

This study assessed the effects of a family function (FF) program on excessive digital behaviors—smartphone overuse (SO) and phubbing—and psychological needs—anxiety, loneliness, and fear of missing out (FoMO)—among 28 Thai female Muslim adolescents randomly assigned to the experimental (M_age = 15.7) and control (M_age = 15.2) groups. The experimental group received two 1.5 h morning sessions of the FF program weekly over four weeks (eight sessions in total). Baseline assessments confirmed group homogeneity. Using repeated-measures ANOVA with Bonferroni correction (p < 0.008), the results indicated a significant improvement in family function for the intervention group (F (1,26) = 11.91, p = 0.002, η²_p = 0.31), with a strong time-by-group interaction (F (1,26) = 19.51, p < 0.001, η²_p = 0.43). While the program did not significantly reduce SO overall, a notable interaction effect suggested group differences (F (1,26) = 10.31, p = 0.004, η²_p = 0.28). Phubbing remained unaffected. For psychological outcomes, interaction effects were found for the FoMO (F = 10.00, p = 0.004) and loneliness (F = 8.67, p = 0.007), though no main effects emerged. Anxiety levels did not significantly change after correction. These findings suggest that the program effectively enhances family functioning and partially alleviates psychosocial risks, but further refinements are needed to address digital overuse and anxiety more effectively. Full article

Background: Despite significant advancements in diabetes care, many individuals with type 2 diabetes (T2D) do not receive optimal care and treatment. Digital interventions promoting behavioral changes have shown promising long-term results in supporting healthier lifestyles but are not implemented in most healthcare offerings, maybe due to lack of general practice support and collaboration. This study evaluates the efficacy of the Digital, Individualized, and Collaborative Treatment of T2D in General Practice Based on Decision Aid (DICTA), a randomized controlled trial integrating a patient-centered smartphone application for lifestyle support in conjunction with a clinical decision support (CDS) tool to assist general practitioners (GPs) in optimizing antidiabetic treatment. Methods: The present randomized controlled trial aims to recruit 400 individuals with T2D from approximately 70 GP clinics (GPCs) in Denmark. The GPCs will be cluster-randomized in a 2:3 ratio to intervention or control groups. The intervention group will receive one year of individualized eHealth lifestyle coaching via a smartphone application, guided by patient-reported outcomes (PROs). Alongside this, the GPCs will have access to the CDS tool to optimize pharmacological decision-making through electronic health records. The control group will receive usual care for one year, followed by the same intervention in the second year. Results: The primary outcome is the one-year change in estimated ten-year cardiovascular risk, assessed by SCORE2-Diabetes calculated from age, smoking status, systolic blood pressure, total and high-density lipoprotein cholesterol, age at diabetes diagnosis, HbA1c, and eGFR. Conclusions: If effective, DICTA could offer a scalable, digital-first approach for improving T2D management in primary care by combining patient-centered lifestyle coaching with real-time pharmacological clinical decision support. Full article

Efficient antenna orientation of transmitters is essential for improving wireless signal quality and coverage, especially in large-scale and complex 6G networks. Identifying the best antenna angles is difficult due to the nonlinear interaction among orientation, signal propagation, and interference. This paper introduces a digital twin-based evaluation approach utilizing ray tracing simulations to assess the influence of antenna orientation on critical performance metrics: path gain, received signal strength (RSS), and signal-to-interference-plus-noise ratio (SINR). A thorough array of orientation scenarios was simulated to produce a dataset reflecting varied coverage conditions. The dataset was utilized to investigate antenna configurations that produced the optimal and suboptimal performance for each parameter. Additionally, three machine learning models—k-nearest neighbors (KNN), multi-layer perceptron (MLP), and XGBoost—were developed to forecast ideal configurations. XGBoost had superior prediction accuracy compared to the other models, as evidenced by regression outcomes and cumulative distribution function (CDF) analyses. The proposed workflow demonstrates that learning-based predictors can uncover orientation refinements that conventional grid sweeps overlook, enabling agile, interference-aware optimization. Key contributions include an end-to-end digital twin methodology for rapid what-if analysis and a systematic comparison of lightweight machine learning predictors for antenna orientation. This comprehensive method provides a pragmatic and scalable solution for the data-driven optimization of wireless systems in real-world settings. Full article

This paper investigates point-to-point multimodal digital semantic communications in a task-oriented setup, where messages are classified at the receiver. We employ a pre-trained transformer model to extract semantic information and propose three methods for generating semantic codewords. First, we propose semantic quantization that uses quantized embeddings of source realizations as a codebook. We investigate the fixed-length coding, considering the source semantic structure and end-to-end semantic distortion. We propose a neural network-based codeword assignment mechanism incorporating codeword transition probabilities to minimize the expected semantic distortion. Second, we present semantic compression that clusters embeddings, exploiting the inherent semantic redundancies to reduce the codebook size, i.e., further compression. Third, we introduce a semantic vector-quantized autoencoder (VQ-AE) that learns a codebook through training. In all cases, we follow this semantic source code with a standard channel code to transmit over the wireless channel. In addition to classification accuracy, we assess pre-communication overhead via a novel metric we term system time efficiency. Extensive experiments demonstrate that our proposed semantic source-coding approaches provide comparable accuracy and better system time efficiency compared to their learning-based counterparts. Full article

Background/Objectives: The demand for accessible and reliable digital health services has increased significantly in recent years, particularly in regions facing physician shortages. HomeDOCtor, a conversational AI platform developed in Slovenia, addresses this need with a nationally adapted architecture that combines retrieval-augmented generation (RAG) and a Redis-based vector database of curated medical guidelines. The objective of this study was to assess the performance and impact of HomeDOCtor in providing AI-powered healthcare assistance. Methods: HomeDOCtor is designed for human-centered communication and clinical relevance, supporting multilingual and multimedia citizen inputs while being available 24/7. It was tested using a set of 100 international clinical vignettes and 150 internal medicine exam questions from the University of Ljubljana to validate its clinical performance. Results: During its six-month nationwide deployment, HomeDOCtor received overwhelmingly positive user feedback with minimal criticism, and exceeded initial expectations, especially in light of widespread media narratives warning about the risks of AI. HomeDOCtor autonomously delivered localized, evidence-based guidance, including self-care instructions and referral suggestions, with average response times under three seconds. On international benchmarks, the system achieved ≥95% Top-1 diagnostic accuracy, comparable to leading medical AI platforms, and significantly outperformed stand-alone ChatGPT-4o in the national context (90.7% vs. 80.7%, p = 0.0135). Conclusions: Practically, HomeDOCtor eases the burden on healthcare professionals by providing citizens with 24/7 autonomous, personalized triage and self-care guidance for less complex medical issues, ensuring that these cases are self-managed efficiently. The system also identifies more serious cases that might otherwise be neglected, directing them to professionals for appropriate care. Theoretically, HomeDOCtor demonstrates that domain-specific, nationally adapted large language models can outperform general-purpose models. Methodologically, it offers a framework for integrating GDPR-compliant AI solutions in healthcare. These findings emphasize the value of localization in conversational AI and telemedicine solutions across diverse national contexts. Full article

In this paper, we present a system-level model of a digital multibeam antenna designed for Low Earth Orbit satellite communications operating in the Ka-band. We initially develop a suitable array topology, which is based on a thinned lattice, then adopt it as the foundation for evaluating its performance within a digital beamforming architecture. This architecture is implemented in a system-level simulator to evaluate the performance of the transmitter and receiver chains. This study advances the analysis of the digital antennas by incorporating both the RF front-end and digital sections non-idealities into a digital-twin framework. This approach enhances the designer’s ability to optimize the system with a holistic approach and provides insights into how various impairments affect the transmitter and receiver performance, identifying the subsystems’ parameter limits. To achieve this, we analyze several subsystems’ parameters and impairments, assessing their effects on both the antenna radiation and quality of the transmitted and received signals in a real applicative context. The results of this study reveal the sensitivity of the system to the impairments and suggest strategies to trade them off, emphasizing the importance of selecting appropriate subsystem features to optimize overall system performance. Full article

The research was initiated by the Plečnik House gallery (Ljubljana, Slovenia) and focuses on the 3D architectural reconstruction of the unrealised monument of the Czech military leader Jan Žižka, designed by the Slovenian architect Jože Plečnik. In addition, the experience with the 3D reconstructed monument in the exhibition “Plečnik and the Sacred” was analysed. Using the available references and interpretative approaches, a digital and 3D-printed reconstruction was created that retains Plečnik’s architectural style. The experimental phase included a detailed interpretation of the studied references, 3D modelling, 3D printing, exhibition and experience analysis. The dimensions of the finished 3D-printed model are 52.80 × 55.21 × 44.60 cm. It was produced using stereolithography (SLA) for figurative elements and fused deposition modelling (FDM) for architectural components. The reconstruction was evaluated using participant testing, including semantic differential analysis, comparative studies, and knowledge-based questionnaires. The results showed that architectural elements were reconstructed with an average similarity score of 1.97 out of 5. Statues followed with a score of 1.81, and props, though detailed, met audience expectations, scoring 1.61. Clothing received the lowest score of 1.40. This research emphasises the importance of a hypothetical digital 3D reconstruction of never constructed monument for broader understanding of Plečnik’s legacy. Full article

Background: This study examined risk factors for syndrome novel coronavirus 2 virus (SARS-CoV-2) infection and self-reported adherence to infection prevention and control (IPC) measures among healthcare workers (HCWs) at a hospital in Thailand near the Myanmar border. Methods: From March to July 2022, HCWs aged ≥ 18 with COVID-19 exposure at Mae Sot General Hospital completed a questionnaire on IPC adherence, training, and COVID-19 knowledge. Nasopharyngeal samples were collected bi-weekly for SARS-CoV-2 testing. A mobile application was used for real-time monitoring of daily symptoms and exposure risks. Chi-square, Fisher’s exact tests, and log-binomial regression were performed to investigate association. Results: Out of 289 (96.3%) participants, 27 (9.9%) tested positive for SARS-CoV-2, with cough reported by 85.2% of cases. Nurse assistants (NAs) had a higher risk of infection (adjusted relative risk [aRR] 3.87; 95% CI: 0.96–15.6). Working in inpatient departments (aRR 2.37; 95% CI: 1.09–5.15) and COVID-19 wards (aRR 5.97; 95% CI: 1.32–26.9) was also associated with increased risk. While 81.7% reported consistent hand hygiene, 37% indicated inadequate IPC knowledge. Conclusions: HCWs, especially NAs and those in high-risk departments, should receive enhanced IPC training. Real-time digital monitoring tools can enhance data collection and HCW safety and are likely to be useful tools for supporting surveillance and data collection efforts. Full article

Ubiquitous digital array radar (UDAR) extends the integration time of moving targets by deploying a wide transmitting beam and multiple narrow receiving beams to cover the entire observed airspace. By exchanging time for energy, it effectively improves the detection ability for weak targets. Nevertheless, target motion introduces severe across-range unit (ARU), across-Doppler unit (ADU), and across-beam unit (ABU) effects, dispersing target energy across the range–Doppler-beam space. This paper proposes a beam domain angle rotation compensation and keystone-matched filtering (BARC-KTMF) algorithm to address the “three-crossing” challenge. This algorithm first corrects ABU by rotating beam–domain coordinates to align scattered energy into the final beam unit, reshaping the signal distribution pattern. Then, the KTMF method is utilized to focus target energy in the time-frequency domain. Furthermore, a special spatial windowing technique is developed to improve computational efficiency through parallel block processing. Simulation results show that the proposed approach achieves an excellent signal-to-noise ratio (SNR) gain over the typical single-beam and multi-beam long-time coherent integration (LTCI) methods under low SNR conditions. Additionally, the presented algorithm also has the capability of coarse estimation for the target incident angle. This work extends the LTCI technique to the beam domain, offering a robust framework for high-speed weak target detection. Full article

Background/Objectives: To evaluate the diagnostic performance of non-enhanced MRA in detecting intratumoral aneurysms in renal AML, using digital subtraction angiography (DSA) as the reference standard. Methods: Fourteen female patients (mean age, 39 years; range, 21–57 years) who received prophylactic transcatheter arterial embolization (TAE) for non-hemorrhagic renal AML(s) between July 2010 and September 2018 were included in this study. All received a non-enhanced MRA scan prior to TAE. Non-enhanced MRA images were obtained using the flow-in technique with three-dimensional balanced steady-state free precession (SSFP). The MRA and DSA images were jointly evaluated by three radiologists. In this study, significant aneurysms were defined as aneurysms with a diameter of 3 mm or more within the renal AML. The MRA images assessed the number and location of significant aneurysms. The DSA images were used as the reference standard. Results: DSA identified 30 significant aneurysms in eight kidneys; MRA identified 26, giving a sensitivity of 87%. There were no false positives, resulting in a specificity of 100%. Conclusions: Flow-balanced SSFP MRA is effective in detecting significant aneurysms in renal AML and could be a viable alternative for patient follow-up. Full article

The present study explores the use of Instagram as an innovative strategy in the teaching–learning process in the context of animal reproduction topics. In the current era, with digital technology and social media transforming how information is accessed and consumed, it is essential for teachers to adapt and harness the potential of these tools for educational purposes. This article delves into the need for teachers to stay updated with current trends and the importance of promoting digital competences among teachers. This research aims to provide insights into the benefits of integrating social media into the educational landscape. Students of Veterinary Science degrees, Master’s degrees in Equine Sport Medicine as well as vocational education and training (VET) were involved in this study. An Instagram account named “UCOREPRO” was created for educational use, and it was openly available to all users. Instagram usage metrics were consistently tracked. A voluntary survey comprising 35 questions was conducted to collect feedback regarding the educational use of smartphone technology, social media habits and the UCOREPRO Instagram account. The integration of Instagram as an educational tool was positively received by veterinary students. Survey data revealed that 92.3% of respondents found the content engaging, with 79.5% reporting improved understanding of the subject and 71.8% acquiring new knowledge. Students suggested improvements such as more frequent posting and inclusion of academic incentives. Concerns about privacy and digital distraction were present but did not outweigh the perceived benefits. The use of short videos and microlearning strategies proved particularly effective in capturing students’ attention. Overall, Instagram was found to be a promising platform to enhance motivation, engagement, and informal learning in veterinary education, provided that thoughtful integration and clear educational objectives are maintained. In general, students expressed positive opinions about the initiative, and suggested some ways in which it could be improved as an educational tool. Full article