Search Results (2,259)

Accurate modeling of non-cooperative space targets remains a significant challenge, particularly under complex illumination conditions. A hybrid virtual–real framework is proposed that integrates photometric compensation, 3D reconstruction, and visibility determination to enhance the robustness and accuracy of monocular-based modeling systems. To overcome the breakdown of the classical photometric constancy assumption under varying illumination, a compensation-based photometric model is formulated and implemented. A point cloud–driven virtual space is constructed and refined through Poisson surface reconstruction, enabling per-pixel depth, normal, and visibility information to be efficiently extracted via GPU-accelerated rendering. An illumination-aware visibility model further distinguishes self-occluded and shadowed regions, allowing for selective pixel usage during photometric optimization, while motion parameter estimation is stabilized by analyzing angular velocity precession. Experiments conducted on both Unity3D-based simulations and a semi-physical platform with robotic hardware and a sunlight simulator demonstrate that the proposed method consistently outperforms conventional feature-based and direct SLAM approaches in trajectory accuracy and 3D reconstruction quality. These results highlight the effectiveness and practical significance of incorporating virtual space feedback for non-cooperative space target modeling. Full article

Commit messages are vital for traceability, maintenance, and onboarding in modern software projects, yet their quality is frequently inconsistent. Recent large language models (LLMs) can transform code diffs into natural language summaries, offering a path to more consistent and informative commit messages. This paper makes two contributions: (i) it provides a systematic survey of automated commit message generation with LLMs, critically comparing prompt-only, fine-tuned, and retrieval-augmented approaches; and (ii) it specifies a transparent, agent-based evaluation blueprint centered on CommitBench. Unlike prior reviews, we include a detailed dataset audit, preprocessing impacts, evaluation metrics, and error taxonomy. The protocol defines dataset usage and splits, prompting and context settings, scoring and selection rules, and reporting guidelines (results by project, language, and commit type), along with an error taxonomy to guide qualitative analysis. Importantly, this work emphasizes methodology and design rather than presenting new empirical benchmarking results. The blueprint is intended to support reproducibility and comparability in future studies. Full article

Background: Recurrent urinary tract infections (rUTIs) significantly impair women’s quality of life, making antimicrobial prophylaxis a critical preventative strategy. This retrospective observational study aimed to characterize antibiotic prophylaxis patterns, relapse rates, comparative efficacy of different agents, and tolerability in 908 women (663 postmenopausal, 245 premenopausal) with rUTIs managed at a tertiary university hospital. Methods: Data from medical records (January 2022–December 2024) were analyzed. Patients were stratified by menopausal status. We assessed antibiotic usage, relapse rates (per 100 patient-months), and adverse events. Comparative efficacy of nitrofurantoin-based versus fosfomycin/other prophylaxis was evaluated for rUTIs caused by E. coli, E. faecalis, or E. coli ESBL using weighted and matched analyses to control for covariates. Results: Continuous antimicrobial prophylaxis was the primary strategy, with nitrofurantoin being most frequently used. Premenopausal women showed a greater tendency for intermittent or combined prophylactic approaches. Postmenopausal women exhibited a higher overall crude relapse rate (5.54/100 p-m) compared to premenopausal women (3.14/100 p-m), with E. coli being the most common causative agent in relapses. For rUTIs caused by E. coli, E. faecalis, or E. coli ESBL, nitrofurantoin-based prophylaxis demonstrated significantly lower adjusted relapse rates than fosfomycin/other regimens (rate ratio: 0.47 for postmenopausal, 0.35 for premenopausal women). This observed efficacy for nitrofurantoin was robust against potential unmeasured confounding. Prophylaxis was generally well-tolerated (3.0% gastrointestinal adverse events overall); however, premenopausal women reported a higher adverse event incidence. Conclusions: Our findings strongly suggest that nitrofurantoin is an effective prophylactic choice for rUTIs caused by common uropathogens (E. coli, E. faecalis, E. coli ESBL), particularly in postmenopausal women. The diverse prophylactic strategies highlight the need for individualized care. While generally well-tolerated, adverse event profiles vary between menopausal groups, necessitating careful monitoring. Full article

Barium strontium titanates (Ba_1−xSr_xTiO₃, BST) with varying barium-to-strontium ratios were synthesized by the solid-state route (SSR) as well as by the sol–gel process (SGP). In the case of the SSR, the strontium amount x was varied from 0.0 to 0.25 in 0.05 steps, due to the enhanced synthetic effort, and in the case of the SGP, x was set only to 0.05, 0.15, and 0.25. The resulting properties after synthesis, calcination, and sintering, like particle size distribution, specific surface area, particle morphology, and crystalline phase were characterized. The expected tetragonal phase, free from any remarkable impurity, was found in all cases, and irrespective of the selected synthesis method. Pressed pellets were used for the measurement of the temperature and frequency-dependent relative permittivity enabling the estimation of the Curie temperatures of all synthesized BSTs. Irrespective of the selected synthesis method, the obtained Curie temperature drops with increasing strontium content to almost identical values, e.g., in the case of x = 0.15, a Curie temperature range 95–105 °C was measured. Thin BST films could be deposited on different substrate materials applying electrophoretic deposition in a good and reliable quality according to the Hamaker equation. The properties of the BSTs obtained by the simpler solid-state route are almost identical to the ones yielded by the more complex sol–gel process. In future, this result allows for a possible wider usage of BST perovskites for ferroelectric and piezoelectric devices due to the easy synthetic access by the solid-state route. Full article

With the rapid development of urbanization and tourism in China, increasing attention has been paid to the protection and utilization of historical and cultural heritage, while tourists’ demands for travel experiences have gradually shifted towards in-depth cultural perception. This paper selects Beijing Fayuan Temple Historic and Cultural District as the research case, and adopts methods such as the LDA (Latent Dirichlet Allocation) topic model, collection and analysis of online text data, and field research to explore the current situation of pedestrian space in Fayuan Temple District and its optimization strategies from the perspective of tourists’ perception. The study found that the dimensions of tourists’ perception of the pedestrian space in Fayuan Temple District mainly include six aspects: historical buildings and relics, tour modes and transportation, natural landscapes and environment, historical figures and culture, residents’ life and activities, and tourists’ experiences and visits. By integrating online text data, questionnaire surveys, and on-site behavioral observations, the study constructed a “physical environment-cultural experience-behavioral network” three-dimensional IPA (Importance–Possession Analysis) evaluation model, and analyzed and evaluated the high-frequency perception elements in tourists’ spontaneous evaluations. Based on the current situation evaluation of the pedestrian space in Fayuan Temple District, this paper puts forward optimization strategies for the perception of pedestrian space from the aspects of block space, transportation usage, landscape ecology, digital technology, and cultural symbol translation. It aims to promote the high-quality development of historical blocks by improving and optimizing the pedestrian space, and achieve the dual goals of cultural inheritance and utilization of tourism resources. Full article

Steel products play an important role in daily production and life as a common production material. Currently, the quality of steel products is judged by manual experience. However, various inspection criteria employed by human operators and complex factors and mechanisms in the steelmaking process may lead to inaccuracies. To address these issues, we propose a learning-based method for steel quality prediction, which is named DRBoost,based on multiple machine learning techniques, including Decision tree, Random forest, and the LSBoost algorithm. In our method, the decision tree clearly captures the nonlinear relationships between features and serves as a solid baseline for making preliminary predictions. Random forest enhances the model’s robustness and avoids overfitting by aggregating multiple decision trees. LSBoost uses gradient descent training to assign contribution coefficients to different kinds of raw materials to obtain more accurate predictions. Five key chemical elements, including carbon, silicon, manganese, phosphorus, and sulfur, which significantly influence the major performance characteristics of steel products, are selected. Steel quality prediction is conducted by predicting the contents of these chemical elements. Multiple models are constructed to predict the contents of five key chemical elements in steel products. These models are symmetrically complementary, meeting the requirements of different production scenarios and forming a more accurate and universal method for predicting the steel product’s quality. In addition, the prediction method provides a symmetric quality control system for steel product production. Experimental evaluations are conducted based on a dataset of 2012 samples from a steel plant in Liaoning Province, China. The input variables include various raw material usages, while the outputs are the content of five key chemical elements that influence the quality of steel products. The experimental results show that the models demonstrate their advantages in different performance metrics and are applicable to practical steelmaking scenarios. Full article

This umbrella review provides a comprehensive synthesis of the available evidence on the relationship between digital device use and adolescent sleep. It summarizes results from systematic reviews and meta-analyses, presenting the magnitude and direction of observed associations. A total of seven systematic reviews, including five qualitative reviews and two meta-analyses, were included, comprising 127 primary studies with a combined sample of 867,003 participants. The findings suggest a negative impact of digital device use on various sleep parameters, including sleep duration, bedtime procrastination, and sleep quality. Devices such as smartphones and computers were found to have a greater adverse effect, while television use showed a weaker association. The most significant disruptions were observed in relation to social media and internet use, with problematic usage leading to delayed bedtimes, shorter sleep duration, and increased sleep onset latency. The review also highlights the role of timing and duration of device use, with late-night use particularly contributing to sleep disturbances. Biological, psychological, and social mechanisms are proposed as potential pathways underlying these effects. Despite moderate evidence supporting the negative impact of digital media on sleep, there is considerable heterogeneity across studies, and many relied on self-reported data, which may limit the generalizability of the findings. Future research should aim to standardize exposure and outcome measures, incorporate objective data collection methods, and explore causal relationships through longitudinal studies. This umbrella review underscores the importance of developing targeted public health strategies, parental guidance, and clinical awareness to mitigate the potential adverse effects of digital device use on adolescent sleep and mental health. Full article

The quality of digital 3D models of fossils is important from the perspective of their further usage, either for scientific or didactical purposes. However, fidelity evaluation has rarely been attempted for digitized fossil objects. In the present research, a 3D triangulated model of the unique skull of Madygenerpeton pustulatum was built using an YXLON µCT device. The comparative analysis was performed using models obtained from seven optical surface-scanning systems. Methodology for accuracy assessment involved the determination of distances between the points in pairs of models, interchanging the reference and tested ones. Statistical significance testing using paired t-tests was performed. In particular, it was found that the YXLON µCT model was closest to the one obtained from AICON SmartScan, exhibiting an average distance of $\overline{{∆}_d}$ = −0.0183 mm with a standard deviation of σ{∆_d} = 0.0778 mm, which is close to the permissible error of 20 µm given in technical specifications for AICON scanners. It was demonstrated that the analysis maintained measurement validity even though the YXLON model consisted of 23.8 M polygons and the AICON model consisted of 13.9 M polygons. Comparison with other digital models demonstrated that the fidelity of the triangulated µCT model made it feasible for further research and dissemination purposes. Full article

Shell-like housing structures for motors and compressors can be found in everyday products. Consumers significantly evaluate acoustic emissions during the first usage of products. Unpleasant sounds may raise concerns and cause complaints to be issued. A prevention strategy is a holistic acoustic design, which includes predicting the emitted sound power as part of end-of-line testing. The hybrid experimental-simulative sound power prediction based on laser scanning vibrometry (LSV) is ideal in acoustically harsh production environments. However, conducting vibroacoustic testing with laser scanning vibrometry is time-consuming, making it difficult to fit into the production cycle time. This contribution discusses how the time-consuming sampling process can be accelerated to estimate the radiated sound power, utilizing adaptive sampling. The goal is to predict the acoustic signature and its uncertainty from surface velocity data in seconds. Fulfilling this goal will enable integration into a product assembly unit and final acoustic quality control without the need for an acoustic chamber. The Gaussian process regression based on PyTorch 2.6.0 performed 60 times faster than the preliminary reference implementation, resulting in a regression estimation time of approximately one second for each frequency bin. In combination with the Equivalent Radiated Power prediction of the sound power, a statistical measure is available, indicating how the uncertainty of a limited number of surface velocity measurement points leads to predictions of the uncertainty inside the acoustical signal. An adaptive sampling algorithm reduces the prediction uncertainty in real-time during measurement. The method enables on-the-fly error analysis in production, assessing the risk of violating agreed-upon acoustic sound power thresholds, and thus provides valuable feedback to the product design units. Full article

The use of machine learning methods for the development of robust and flexible visual inspection systems has shown promising results. However, their performance is highly dependent on the large amount and diversity of training data, which is difficult to obtain in practice. Recent developments in synthetic dataset generation have seen increasing success in overcoming these problems. However, the prevailing work revolves around the usage of generative models, which suffer from data shortages, hallucinations, and provide limited support for unobserved edge-cases. In this work, we present the first synthetic data generation pipeline that is capable of generating large datasets of physically realistic textures exhibiting sophisticated structured patterns. Our framework is based on procedural texture modelling with interpretable parameters, uniquely allowing us to guarantee precise control over the texture parameters as we generate a high variety of observed and unobserved texture instances. We publish the dual dataset used in this paper, presenting models of sandblasting, parallel, and spiral milling textures, which are commonly present on manufactured metal products. To evaluate the dataset quality, we go beyond final model performance comparison by measuring different image similarities between the real and synthetic domains. This uncovered a trend, indicating these metrics could be used to predict downstream detection performance, which can strongly impact future developments of synthetic data. Full article

Background/Objectives: Diabetic neuropathy (DN) is a prevalent complication of diabetes mellitus, affecting up to 50% of long-term patients and causing significant pain, reduced quality of life, and healthcare burden. Conventional treatments, including anticonvulsants, antidepressants, and opioids, offer limited efficacy and are associated with adverse effects. Emerging evidence suggests that cannabis, acting via the endocannabinoid system, may provide analgesic and neuroprotective benefits. This study evaluates the long-term effects of inhaled cannabis as adjunctive therapy for refractory painful DN. Inhaled cannabis exhibits rapid onset pharmacokinetics (within minutes, lasting 2–4 h) due to pulmonary absorption, targeting CB1 and CB2 receptors to modulate pain and inflammation. Methods: In this prospective, observational study, 52 patients with confirmed painful DN, unresponsive to at least three prior analgesics plus non-pharmacological interventions, were recruited from a single clinic. Following a 1-month washout, patients initiated inhaled medical-grade cannabis (20% THC, <1% CBD), titrated individually. Assessments occurred at baseline and annually for 5 years, including the Brief Pain Inventory (BPI) for pain severity and interference; the degree of pain relief; Leeds Assessment of Neuropathic Symptoms and Signs (LANSS) score; HbA1c; and medication usage. Statistical analyses used repeated-measures ANOVA, Kruskal–Wallis tests, Welch’s t-tests, and Pearson’s correlations via Analyze-it for Excel. Results: Of 52 patients (mean age 45.3 ± 17.8 years; 71.2% male; diabetes duration 23.3 ± 17.8 years), 50 completed follow-up visits. Significant reductions occurred in BPI pain severity (9.0 ± 0.8 to 2.0 ± 0.7, p < 0.001), interference (7.5 ± 1.7 to 2.2 ± 0.9, p < 0.001), LANSS score (19.4 ± 3.8 to 10.2 ± 6.4, p < 0.001), and HbA1c (9.77% ± 1.50 to 7.79% ± 1.51, p < 0.001). Analgesic use decreased markedly (e.g., morphine equivalents: 66.8 ± 49.2 mg to 4.5 ± 9.6 mg). Cannabis dose correlated positively with pain relief (r = 0.74, p < 0.001) and negatively with narcotic use (r = −0.43, p < 0.001) and pain interference (r = −0.43, p < 0.001). No serious adverse events were reported; mild side effects (e.g., dry mouth or euphoria) occurred in 15.4% of patients. Conclusions: Inhaled cannabis showed sustained pain relief, improved glycemic control, and opioid-sparing effects in refractory DN over 5 years, with a favorable safety profile. These findings are associative due to the observational design, and randomized controlled trials (RCTs) are needed to confirm efficacy and determine optimal usage, addressing limitations such as single-center bias and small sample size (n = 52). Future studies incorporating biomarker analysis (e.g., endocannabinoid levels) could elucidate mechanisms and enhance precision in cannabis therapy. Full article

In the rapid digital era, artificial intelligence (AI) tools have progressively arisen to shape the education environment. In this context, large language models (LLMs) (i.e., ChatGPT vs. 4.0 and Gemini vs. 2.5) have emerged as powerful applications for academic inclusion. This paper investigated how using and trusting LLMs can impact the academic success and quality of life (QoL) of visually impaired university students. Quantitative research was conducted, obtaining data from 385 visually impaired university students through a structured survey design. Partial Least Squares Structural Equation Modelling (PLS-SEM) was implemented to test the study hypotheses. The findings revealed that trust in LLMs can significantly predict LLM usage, which in turn can improve QoL. While LLM usage failed to directly support the academic success of disabled students, but its impact was mediated through QoL, suggesting that enhancements in well-being can contribute to higher academic success. The results highlighted the importance of promoting trust in AI applications, along with developing an accessible, inclusive, and student-centred digital environment. The study offers practical contributions for educators and policymakers, shedding light on the importance of LLM applications for both the QoL and academic success of visually impaired university students. Full article

As a key technology in multimodal information processing, infrared and visible image fusion holds significant application value in fields such as military reconnaissance, intelligent security, and autonomous driving. To address the limitations of existing methods, this paper proposes the Hierarchical Triple-Feature Interaction Fusion Network (TFI-Fusion). Based on a hierarchical triple-stream feature interaction mechanism, the network achieves high-quality fusion through a two-stage, separate-model processing approach: In the first stage, a single model extracts low-rank components (representing global structural features) and sparse components (representing local detail features) from source images via the Low-Rank Sparse Decomposition (LSRSD) module, while capturing cross-modal shared features using the Shared Feature Extractor (SFE). In the second stage, another model performs fusion and reconstruction: it first enhances the complementarity between low-rank and sparse features through the innovatively introduced Bi-Feature Interaction (BFI) module, realizes multi-level feature fusion via the Triple-Feature Interaction (TFI) module, and finally generates fused images with rich scene representation through feature reconstruction. This separate-model design reduces memory usage and improves operational speed. Additionally, a multi-objective optimization function is designed based on the network’s characteristics. Experiments demonstrate that TFI-Fusion exhibits excellent fusion performance, effectively preserving image details and enhancing feature complementarity, thus providing reliable visual data support for downstream tasks. Full article

Parkinson’s disease (PD) presents an association of motor and non-motor impairments that impact the independence and quality of life of individuals. Rehabilitation programs must address multiple domains, simultaneously maintaining patients’ adherence and the implications of the disease. Immersive virtual-reality-based rehabilitation (IVRBR) is a promising alternative tool, or can be used in conjunction with traditional or passive programs, using interactive tasks in valid environments with specific training programs adapted to each individual’s needs. This narrative review synthesizes the medical literature published in the last decade from PubMed, Scopus, and Web of Science, on the effectiveness, limitations, and implementations of IVRBR in PD patients. Evidence from RTCs and non-RTCs suggests that IVRBR can improve balance, motor learning, and dual task performance. At the same time, the evidence suggests that it can improve cognitive and emotional status. The integration of objective assessment tools (motion and posture analyses, wearable sensors, center of pressures and machine learning models capable of predicting freezing gait-FoG) enhances clinical and individualized rehabilitation programs. However, the evidence base remains limited, with a small sample size, heterogeneity in measured outcomes, and short follow-up duration. In general, reported adverse reactions were minor, but required standardized reporting patterns. Implementation is challenging due to the equipment cost and varying technological demands, but also due to patient selection and training of the medical personnel. IVRBR is a feasible and engaging alternative or can form part of an individualized rehabilitation program in PD patients; however, future large RTCs, long-term follow-up with standardized protocols, cost-effectiveness analyses, and integration of predictive modeling are essential for its broader clinical usage. Full article

Background: Cities worldwide face continuous challenges in sustainable development, particularly in transportation systems where decisions have long-lasting impacts on urban infrastructure and quality of life. The evaluation of sustainable transportation alternatives requires careful consideration of multiple indicators, making it a complex multi-criteria decision-making process. Existing multi-criteria decision-making (MCDM) frameworks often overlook the dual uncertainties introduced by both fuzzy expert judgments and probabilistic performance measures, hindering robust evaluation of transportation alternatives in developing countries. Methods: In response, this study introduces a novel hybrid methodology combining fuzzy set theory and Monte Carlo simulation to evaluate transportation alternatives through 14 comprehensive sustainability indicators. Addressing the critical need for sustainable public transportation assessment in rapidly urbanizing developing countries, where existing assessment frameworks frequently prove inadequate, we present a case study from Amman, Jordan. Results: The results reveal that a Bus Rapid Transit (BRT) system outperforms both conventional automobiles and small buses in 87.06% of simulation scenarios, underscoring its robust sustainability profile. The sensitivity analysis highlights that a BRT system is highly robust, with minimal sensitivity to changes in most criteria and strong responsiveness to critical factors such as land usage. Conclusions: This research provides decision-makers with a comprehensive, evidence-based tool for evaluating public transport investment under uncertainty. The methodology’s ability to account for multiple stakeholder perspectives while handling uncertainty makes it particularly valuable for urban planners and policymakers facing complex transportation infrastructure decisions in rapidly evolving urban environments. Full article