Search Results (348)

Background/Objectives: The management of immature teeth with wide apical foramina presents significant challenges in endodontic treatment due to difficulties in achieving a hermetic seal. The aim of this study was to evaluate void formation in apical plugs created using three calcium silicate-based materials—Biodentine, NuSmile NeoPUTTY, and Well-Root PT—applied with the help of manual, ultrasonic, or rotary file condensation (XP-endo Shaper) in 3D-printed immature teeth. Methods: Micro-computed tomography analysis was used to assess the internal, external, and total void percentage of material volume. The statistical analysis was performed using two-way ANOVA and the post hoc Bonferroni test. Statistical significance was set at p < 0.05. Results: The materials and techniques used individually do not significantly influence the formation of internal voids, but their combination does (F(4, 99) = 2.717, p = 0.034). Both factors and their interaction are significant for external voids (F(4, 99) = 4.169, p = 0.004), and all have a notable effect on total void percentages (F(4, 99) = 3.456, p = 0.012). No significant differences were observed in internal voids across the groups (p > 0.05), ranging from 0.635% to 1.078%. External voids varied significantly, with Well-Root PT and ultrasonic condensation showing the highest values with a significant difference (p < 0.05), while NeoPUTTY and Biodentine with XP-endo Shaper exhibited the lowest. Total voids remained below 4%, with no significant differences among manual condensation groups. Neither material type nor application technique consistently influenced void formation, except for Well-Root PT with ultrasonic condensation. Conclusions: These findings suggest that modern bioceramic materials and application techniques produce comparable, low-void apical plugs, with XP-endo Shaper showing promise for minimizing external voids. The interaction between material and application technique plays a crucial role during the creation of apical plugs. Full article

Background: Undergraduate nursing students (UNSs) often enter clinical training just as they are still mastering the emotional labor of the profession. In Nepal, where teaching hierarchies discourage upward dialogue and hospitals routinely struggle with overcrowding, supply shortages, and outward nurse migration, these learners confront a distinct, under-documented burden of psychological distress. Objective: This study examines how UNSs interpret, negotiate, and cope with the mental health challenges that arise at the intersection of cultural deference, resource scarcity, and migration-fueled uncertainty. Methods: A qualitative design employing reflexive thematic analysis (RTA), guided by the Reflexive Thematic Analysis Reporting Guidelines (RTARG), was used. Fifteen second-, third-, and fourth-year Bachelor of Science in Nursing students at a major urban tertiary institution in Nepal were purposively recruited via on-campus digital flyers and brief in-class announcements that directed students (by QR code) to a secure sign-up form. Participants then completed semi-structured interviews; audio files were transcribed verbatim and iteratively analyzed through an inductive, reflexive coding process to ensure methodological rigor. Results: Four themes portray a continuum from silenced struggle to systemic constraint. First, Shrouded Voices, Quiet Connections captures how students confide only in trusted peers, fearing that formal disclosure could be perceived as weakness or incompetence. Second, Performing Resilience: Masking Authentic Struggles describes the institutional narratives of “strong nurses” that drive students to suppress anxiety, adopting scripted positivity to satisfy assessment expectations. Third, Power, Hierarchy, and the Weight of Tradition reveals that strict authority gradients inhibit questions in classrooms and clinical placements, leaving stress unvoiced and unaddressed. Finally, Overshadowed by Systemic Realities shows how chronic understaffing, equipment shortages, and patient poverty compel students to prioritize patients’ hardships, normalizing self-neglect. Conclusions: Psychological distress among Nepalese UNSs is not an individual failing but a product of structural silence and resource poverty. Educators and policymakers must move beyond resilience-only rhetoric toward concrete reforms that dismantle punitive hierarchies, create confidential support avenues, and embed collaborative pedagogy. Institutional accountability—through regulated workloads, faculty-endorsed wellbeing forums, and systematic mentoring—can shift mental health care from a private struggle to a shared professional responsibility. Multi-site studies across low- and middle-income countries are now essential for testing such system-level interventions and building a globally resilient, compassionate nursing workforce. Full article

Background/Objectives: Archery is a technical sport involving repetitive and asymmetrical movements that requires trunk stability to enable good performance of the upper extremities. Being an asymmetrical sport, imbalances between sides might appear in the abdominal and back muscles. To assess trunk muscle function and symmetry in young competitive archers. Methods: Analyzing pre-season screening evaluation tests from medical files. This included an ultrasound examination of back and abdominal muscles (transverse abdominus and internal oblique) during rest and contraction and trunk muscle clinical strength tests. Results: Data on 15 elite archery athletes (mean age 17.2 (±2.7) years) were included. No athletes reported low back pain. No differences were found between the dominant and non-dominant sides in all outcome measurements (absolute thickness and percentage difference). Internal oblique muscle thickness during rest and contraction for the dominant side was higher in males compared with females (p < 0.05). The back muscles were more symmetrical than the abdominal muscles. Conclusions: Despite the asymmetrical functional demands of sport archery, young athletes displayed trunk muscle symmetry, particularly in their back muscles. While some variability in abdominal muscle asymmetry was observed, these differences were not statistically significant. Full article

Rugby union involves intermittent high- and low-intensity activities, making it essential for strength and conditioning practitioners to understand specific physical demands. While GPS technology has enhanced this understanding, limited research focuses on Tier 2 national teams. This study aimed to describe the speed-related physical demands of a Tier 2 national rugby union team. This retrospective observational study analyzed 230 GPS files from 55 professional male players of an international Tier 2 national rugby union team, collected across 17 international matches. Speed-related performance variables were analyzed. Players who played ≥55 min were included. A Kruskal–Wallis test with post hoc comparisons was used to examine positional differences. Principal Component Analysis (PCA) identified four main components explaining 84.65% of the variance, while a two-step cluster analysis grouped players into Low-, Mid-, and High-Demand profiles based on these components. Backs showed greater high-intensity running demands compared to forwards. This study’s results provide novel insights into the physical demands of Tier 2 international rugby union, highlighting differences among player positions and clustering players based on their specific speed demands. These findings can help strength and conditioning practitioners design position-specific training loads, implement tailored recovery strategies, and reduce injury risk in Tier 2 international rugby union. Full article

Objectives: Migrants are at greater risk of psychiatric hospitalization. This study aims to improve care for migrants hospitalized in psychiatric wards in Morocco by studying comprehensive clinical and epidemiological characteristics, focusing on potential risk factors to guide mental health intervention efforts. Methods: The present retrospective multicenter study retrieved sociodemographic, clinical data, and patient records of migrants admitted to a large Moroccan psychiatric hospital in the Rabat region between 2013 and 2023 in order to delineate characteristics and risk factors for psychiatric hospitalizations. Descriptive and univariable analyses were conducted using chi-square, Fisher’s exact, and Mann–Whitney tests, and multivariable logistic regression analyses were performed by Jamovi 2.3.28.0 software to predict rehospitalization. Results: A total of 102 patient files were analyzed. Of these, 72.5% were single men, 27.5% had mental health problems prior to migration, 23.5% had attempted suicide, and 88.2% had negative insight. Some 94.86% were hospitalized against their will, 73.5% were diagnosed with psychosis, and only 2 were diagnosed with a stress-related disorder. Some 34.3% were hospitalized. Factors significantly associated with hospitalized were divorced family status, presence of psychotic pathology, and number of family members between five and nine with OR = 5.28, CI [1.04–26.68], p = 0.044; OR = 5.95, CI [2.02–17.44], p = 0.001; and OR = 6.02, CI [1.71–21.11], p = 0.005, respectively. Shorter length of stay in Morocco, unemployment, asylum seekers, and use of restraints were more frequent in hospitalized patients. Conclusions: Identifying at-risk migrants and setting up culturally appropriate, trauma-informed services can reduce the number of hospital admissions and boost the training and awareness of healthcare professionals in this area. Full article

Background/Objectives: This paper presents a medical image analysis application designed to facilitate advanced edge detection and fuzzy processing techniques within an intuitive, modular graphical user interface. Methods: Key functionalities include classical edge detection, Ant Colony Optimization (ACO)-based edge extraction, and fuzzy edge generation, which offer improved boundary representation in images where uncertainty and soft transitions are prevalent. Results: One of the main novelties in contrast to the initial innovative Medical Image Analyzer, iMIA, is the fact that the system includes fuzzy C-means clustering to support tissue classification and unsupervised segmentation based on pixel intensity distribution. The application also features an interactive zooming and panning module with the option to overlay edge detection results. As another novelty, fuzzy performance metrics were added, including fuzzy false negatives, fuzzy false positives, fuzzy true positives, and the fuzzy index, offering a more comprehensive and uncertainty-aware evaluation of edge detection accuracy. Conclusions: The application executable file is provided at no cost for the purposes of evaluation and testing. Full article

This study investigates the application of deep learning (DL) techniques for predicting temperature fields in the SM1 swirl-stabilized turbulent non-premixed flame. Two distinct DL approaches were developed using a comprehensive CFD database generated via the steady laminar flamelet model coupled with the SST k-ω turbulence model. The first approach employs a fully connected dense neural network to directly map scalar input parameters—fuel velocity, swirl ratio, and equivalence ratio—to high-resolution temperature contour images. In addition, a comparison was made with different deep learning networks, namely Res-Net, EfficientNetB0, and Inception Net V3, to better understand the performance of the model. In the first approach, the results of the Inception V3 model and the developed Dense Model were found to be better than Res-Net and Efficient Net. At the same time, file sizes and usability were examined. The second framework employs a U-Net-based convolutional neural network enhanced by an RGB Fusion preprocessing technique, which integrates multiple scalar fields from non-reacting (cold flow) conditions into composite images, significantly improving spatial feature extraction. The training and validation processes for both models were conducted using 80% of the CFD data for training and 20% for testing, which helped assess their ability to generalize new input conditions. In the secondary approach, similar to the first approach, studies were conducted with different deep learning models, namely Res-Net, Efficient Net, and Inception Net, to evaluate model performance. The U-Net model, which is well developed, stands out with its low error and small file size. The dense network is appropriate for direct parametric analyses, while the image-based U-Net model provides a rapid and scalable option to utilize the cold flow CFD images. This framework can be further refined in future research to estimate more flow factors and tested against experimental measurements for enhanced applicability. Full article

Ensuring reliability, availability, and security in modern software systems hinges on early fault detection, yet predicting which parts of a codebase are most at risk remains a significant challenge. In this paper, we analyze 2.4 million commits drawn from 33 heterogeneous open-source projects, spanning healthcare, security tools, data processing, and more. By examining each repository per file and per commit, we derive process metrics (e.g., churn, file age, revision frequency) alongside size metrics and entropy-based indicators of how scattered changes are over time. We train and tune a gradient boosting model to classify bug-prone commits under realistic class-imbalance conditions, achieving robust predictive performance across diverse repositories. Moreover, a comprehensive feature-importance analysis shows that files with long lifespans (high age), frequent edits (revision count), and widely scattered changes (entropy metrics) are especially vulnerable to defects. These insights can help practitioners and researchers prioritize testing and tailor maintenance strategies, ultimately strengthening software dependability. Full article

Purpose: The aim of this study was to compare the effect of rotary (air-driven, electric-driven) and oscillating (piezoelectric ultrasonic) handpieces on the quality of crown preparation, marginal integrity, and internal adaptation of monolithic zirconia crowns. Materials and Methods: Seventy-two standardized premolar preparations were performed using the air-driven handpiece with a guide pin-ended tapered fissure diamond bur on a modified dental surveyor. The finishing process utilized three handpiece types (n = 24/group) with fine/superfine diamond burs under controlled force with a fixed number of rotations and controlled advancement time. Marginal/internal adaptation was evaluated via the triple-scan technique; defects (marginal, axial, and occlusal) were quantified based on predefined criteria through the inspection of the Standard Tessellation Language (STL) file. Results: One-way ANOVA with Tukey HSD and Kruskal–Wallis with Dunn–Bonferroni tests were utilized. The marginal gap showed no significant differences (p > 0.05, η² = 0.04). The electric handpiece outperformed the ultrasonic (p = 0.023, η² = 0.105) in internal adaptation, while the air-driven showed no differences (p > 0.05). The ultrasonic handpiece produced fewer marginal defects than the air-driven (p = 0.039, ε² = 0.132), but more axial defects (median 9 vs. 6, p = 0.014, ε² = 0.168) than the electric handpiece and occlusal defects (5 vs. 3, 4 p = 0.007, p = 0.015, ε² = 0.227) than rotary handpieces. The air-driven handpiece exhibited comparable defect numbers to the electric handpiece without statistical significance (p > 0.05). Conclusions: Handpiece selection had a small effect on marginal adaptation but more pronounced effects on overall defect formations and internal adaptation. The ultrasonic handpiece’s decreased marginal defects but variable axial/occlusal results reveal technological constraints, whereas rotary handpieces’ consistency reflects their operator-dependent nature. Full article

Capital investment in longevity science—research targeting the biological processes of aging through interventions like cellular reprogramming, AI-driven drug discovery, and biological age monitoring—may create significant divergence between traditional actuarial projections and emerging mortality improvements. This paper examines how accelerating investment in life extension technologies affects mortality improvement trajectories beyond conventional actuarial assumptions, building on the comprehensive investment landscape analysis documented in “Investors in Longevity” supported by venture capital databases, industry reports, and regulatory filings. We introduce an Investment-Adjusted Mortality Model (IAMM) that incorporates capital allocation trends as leading indicators of mortality improvement acceleration. Under high-investment scenarios (annual funding of USD 15+ billion in longevity technologies), current insurance products may significantly underestimate longevity risk, creating potential solvency challenges. Our statistical analysis demonstrates that investment-driven mortality improvements—actual reductions in death rates resulting from new anti-aging interventions—could exceed traditional projections by 18–31% by 2040. We validate our model by backtesting historical data, showing improved predictive performance (35% reduction in MAPE) compared to traditional Lee–Carter approaches during periods of significant medical technology advancement. Based on these findings, we propose modified insurance structures, including dynamic mortality-linked products and biological age underwriting, quantifying their effectiveness in reducing longevity risk exposure by 42–67%. These results suggest the need for actuarial science to incorporate investment dynamics in response to the changing longevity investment environment detailed in “Investors in Longevity”. The framework presented provides both theoretically grounded and empirically tested tools for incorporating investment dynamics into mortality projections and insurance product design, addressing gaps in current risk management approaches for long-term mortality exposure. Full article

Background/Objectives: This ex vivo study aimed to evaluate the diagnostic performance of ultra-low-dose (ULD) cone-beam computed tomography (CBCT) protocols in detecting vestibular bone defects for immediate implant planning, using intraoral scan (IOS) data as a reference. Methods: Four CBCT protocols (ENDO, A, B, C) were applied to four dried human skulls using a standardized setup and a single CBCT unit (Planmeca ProMax^® 3D Mid, Planmeca Oy, Helsinki, Finland). All scans were taken at 90 kV, with varying parameters: (1) ENDO (40 × 50 mm, 75 µm, 12 mA, 80–120 µSv, 15 s), (2) A (50 × 50 mm, 75 µm, 9 mA, 20–40 µSv, 5 s), (3) B (100 × 60 mm, 150 µm, 7.1 mA, 22–32 µSv, 5 s), and (4) C (100 × 100 mm, 200 µm, 7.1 mA, 44 µSv, 4 s). Vestibular root surfaces of single-rooted teeth (FDI regions 15–25 and 35–45) were digitized via IOS and exported as STL files. CBCT datasets were superimposed using 3D software (Blender 2.79), and surface defects were measured and compared using one-sample t-tests and Bland–Altman analysis. The level of significance was set at p < 0.05. Results: A total of 330 vestibular surfaces from 66 teeth were analyzed. Compared to the IOS reference, protocols ENDO and A showed minimal differences (p > 0.05). In contrast, protocols B and C exhibited statistically significant deviations (p < 0.05). Protocol B demonstrated a mean difference of −0.477 mm² with limits of agreement (LoA) from −2.04 to 1.09 mm² and significant intra-rater variability (p < 0.05). Protocol C revealed a similar mean deviation (−0.455 mm²) but a wider LoA (−2.72 to 1.81 mm²), indicating greater measurement variability. Overall, larger voxel sizes were associated with increased random error, although deviations remained within clinically acceptable limits. Conclusions: Despite statistical significance, deviations for protocols B and C remained within clinically acceptable limits. ULD CBCT protocols are, thus, suitable for evaluating vestibular bone defects with reduced radiation exposure. Full article

Objectives: The aim of this study is to investigate how Adaptive Torque Control motion influences the shaping efficiency of Neolix EDMax (Neolix SAS, Évron, France) and its ability to reach working length with or without a pre-existing glide path. Methods: A total of 90 endo training blocks with an S-shape curvature were divided into three groups based on the kinematics and preparation phase: the control group, where the Neolix EDMax (Neolix SAS, Évron, France) was used for shaping after a glide path was established; the no glide path group, where the Neolix EDMax (Neolix SAS, Évron, France) was used for shaping without a glide path; and the Adaptive Torque Control group, where the Neolix EDMax (Neolix SAS, Évron, France) was used for shaping without a glide path but in an Adaptive Torque Control motion. The time for shaping, the instrument passes, and the ability to reach working length were recorded and analyzed using a one-way Anova and Tukey’s HSD post hoc test. Results: Establishing a glide path helped the shaping file to reach working length faster and in fewer passes when compared with the no glide path group, but the Adaptive Torque Control group was able to perform even better than the control group despite not having a pre-established glide path. Conclusions: The Adaptive Torque Control motion on continuous rotation instruments does impact their performance. Combining the efficiency of continuous rotation and the safety of reciprocation, this type of motion had a significant effect on the ability to shape the simulated root canal even in the presence of a double curvature and without a pre-established glide path. Full article

Self-sovereign identity (SSI) is an emerging model for digital identity management that empowers individuals to control their credentials without reliance on centralized authorities. This work presents YouGovern, a blockchain-based SSI system deployed on Binance Smart Chain (BSC) and compliant with W3C Decentralized Identifier (DID) standards. The architecture includes smart contracts for access control, decentralized storage using the Inter Planetary File System (IPFS), and long-term persistence via Web3.Storage. YouGovern enables users to register, share, and revoke identities while preserving privacy and auditability. The system supports role-based permissions, verifiable claims, and cryptographic key rotation. Performance was evaluated using Ganache and Hardhat under controlled stress tests, measuring transaction latency, throughput, and gas efficiency. Results indicate an average DID registration latency of 0.94 s and a peak throughput of 12.5 transactions per second. Compared to existing SSI systems like Sovrin and uPort, YouGovern offers improved revocation handling, lower operational costs, and seamless integration with decentralized storage. The system is designed for portability and real-world deployment in academic, municipal, or governmental settings. Full article

Previous research has shown individual differences in (a) time on task (ToT) and (b) the degree of structuredness in processing figural matrices. The goal of this article was to integrate these two lines of research by analyzing log files from a computer-based assessment (N = 198) to examine the role of three ToT sub-components: onset times (before engaging with the first matrix rule), interrule times (between the rules), and intrarule times (within a single rule). We tested three clues that support the assumptions that the interrule times reflect the cognitive construction of a rule-specific solution plan, while the onset times represent a global orientation reaction, and the intrarule times capture the behavioral execution of the plan: (1) based on the interrule times, we identified two clusters of participants, of which one processed the matrices in a more structured fashion; (2) only the accelerating effect of the interrule times across the test was associated with test performance, indicating higher reasoning saturation; (3) a mediated path analysis revealed that faster interrule times propagate in faster intrarule times and more structured processing of matrix rules, resulting in better performance. Confirming internal validity, the three log file times accounted for an incremental 24.30% of test performance variance beyond the traditional ToT. Confirming external validity, two clusters were also identified based on the interrule times from the first test and performance scores from a second matrix test. Full article

The compiler serves as a bridge connecting hardware architecture and application software, converting source code into executable files and optimizing code. Fuzz testing is an automated testing technology that evaluates software reliability by providing a large amount of random or mutated input data to the target system to trigger abnormal program behavior. When existing fuzz testing methods are applied to compiler testing, although they can detect common errors like lexical and syntax errors, there are issues such as insufficient pertinence in constructing the input corpus, limited support for structured Intermediate Representation (IR) node manipulation, and limited perfection of the mutation strategy. This study proposes a deep fuzz testing framework named BoostPolyGlot for GCC compiler frontend IR generation, which effectively covers the code-execution paths and improves the code-coverage rate through constructing an input corpus, employing translation by a master–slave IR translator, conducting operations on structured program characteristic IR nodes, and implementing an IR mutation strategy with dynamic weight adjustment. This study evaluates the fuzz testing capabilities of BoostPolyGlot based on dependency relationships, loop structures, and their synergistic effect. The experimental outcomes confirm that, when measured against five crucial performance indicators including total paths, count coverage, favored paths rate, new edges on rate, and level, BoostPolyGlot demonstrated statistically significant improvements compared with American Fuzzy Lop (AFL) and PolyGlot. These findings validate the effectiveness and practicality of the proposed framework. Full article