Detecting apical periodontitis lesions on panoramic radiographs (PR) is challenging because lesions may be subtle and affected by anatomic superimposition, variable contrast, and restorative artifacts. This study compared five modern object detectors from the YOLOv8, YOLOv11, and RT-DETR families and quantified the effect of training data augmentation using a publicly available dataset. All final comparisons used a standardized input size of 1024×1024 pixels and batch size of 16. Each model–regime combination was evaluated over three independent random seeds, and performance was assessed on a fixed held-out test split comprising 393 images with 693 annotated lesions. Augmentation improved F1-score and AP-based detection performance across all architectures. Among augmented models, RT-DETR-l achieved the highest mean F1-score (0.685 ± 0.011; 95% CI: 0.657–0.714), while YOLOv11-l achieved the highest mean AP@0.50 (0.738 ± 0.018; 95% CI: 0.694–0.782) and AP@0.50:0.95 (0.352 ± 0.012; 95% CI: 0.323–0.380). The largest absolute F1 improvement was observed for YOLOv11-s (ΔF1 = +0.141). Two-sided paired comparisons with Holm correction showed significant augmentation-related F1 improvements for all five detectors. Region-wise failure analysis of augmented YOLOv11-l showed a higher false-negative rate in the maxilla (57.6%) than in the mandible (30.0%). These findings demonstrate that augmentation consistently improves PR-based apical lesion detection under repeated-seed evaluation and support the use of modern detectors as assistive second-reader tools, while final clinical decisions remain with the dentist.
Health is one of the most fundamental human needs. Ensuring the occupational health and safety of healthcare workers is essential for the safe and effective delivery of healthcare services. This issue has been extensively addressed in graduate theses from various perspectives. To reveal the current state of literature and guide future research, this study examines graduate theses focusing on occupational health and safety in the healthcare sector, in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Accordingly, 555 graduate theses completed between 1990 and 2024 were included in this study were included. The theses included in the study were examined using content analyses. The theses were classified by considering various aspects, such as year of publication, thesis type, university, department, research topic, and research method used. The findings show that most theses were at the master’s level, their number increased steadily over time, and the highest output in the last five years was in 2022. While the theses were produced at 137 different universities, the highest number was produced at Beykent University within the Institute of Health Sciences and the Department of Health Management. Studies focusing on risk assessment and management were the most predominant. The findings of this study are expected to guide and motivate research on occupational health and safety in the healthcare sector.
Accurate modeling of power electronics-based inverter systems is very important in the analysis of electric drive systems, as well as in the development of sophisticated control systems and condition monitoring systems. Nevertheless, the non-linear nature of inverter systems, owing to their switching characteristics, and the variability in system parameters make it difficult to accurately model average phase voltages using analytical models. In this study, a hybrid data-driven model is presented, which combines residual learning with heteroscedastic uncertainty modeling in the estimation of average phase voltages using the pulse width modulation (PWM) period in two-level three-phase insulated gate bipolar transistor (IGBT) inverters. In this study, the reference voltage is estimated using the physical operating principle of the inverter system, and this is combined with the use of an artificial neural network in the development of the residual learning model. In addition, probabilistic voltage estimation is performed in this study to assess the reliability of the model outputs. The framework is validated using an open-access three-phase inverter dataset. The results show that the physics-guided residual model substantially improves the standalone physical reference and direct MLP estimation, achieving RMSE values in the range of 1.05–1.11 V, MAE values in the range of 0.59–0.62 V, and (R2≈ 0.99990) for three-phase voltage estimation. The uncertainty-aware extension provides reliability-related uncertainty indicators that may be useful for monitoring, diagnostic, and supervisory-level applications; however, these uncertainty estimates should be interpreted as practical reliability indicators rather than perfectly calibrated probabilities.
This study investigates secondary particle production and transport processes in two different multilayer shielding configurations (Design 1 and Design 2) for a 50 MeV proton accelerator using the FLUKA Monte Carlo (MC) code. A monoenergetic proton beam interacting with a copper target generates a complex radiation field composed of photons, neutrons, protons, and light nuclear particles. Analysis of inelastic interactions shows that photon production is dominant in both designs at approximately 50%, followed by neutrons (~27%) and protons (~17%), indicating the significant role of electromagnetic processes in the low-energy regime. Stopping particle statistics reveal that nearly 99% of primary protons are absorbed within the shielding structures, while the contribution of light ions remains negligible. The investigation of low-energy neutron interactions demonstrates a strong neutron multiplication behavior, with more than 97% of the generated secondary particles being neutrons. Although both shielding designs exhibit similar overall particle production characteristics, Design 1 yields a slightly higher neutron multiplication factor (~4.16 particles per proton) compared to Design 2 (~3.74 particles per proton), suggesting that Design 2 provides relatively more effective moderation for low-energy neutrons. These findings indicate that while initial particle production is governed by electromagnetic interactions, the evolution of the radiation field is primarily controlled by neutron transport and multiplication mechanisms. Furthermore, one-dimensional ambient dose equivalent H*(10) profiles along the x and y directions clearly demonstrate the directional attenuation performance of the shielding configurations. In addition, relative error distributions obtained from MC simulations were analyzed to assess computational reliability, confirming that the majority of dose results fall within acceptable uncertainty limits (generally below 10%). Overall, the study provides a detailed physical interpretation of particle production mechanisms and dose distribution characteristics, offering valuable insights for the optimization of multilayer shielding designs in low-energy proton accelerator environments.
Anterior cruciate ligament (ACL) deficiency alters knee joint mechanics and may accelerate cartilage degeneration. However, the intra-tissue mechanical consequences of ACL deficiency remain difficult to quantify experimentally because interstitial fluid pressure and collagen fibril stresses cannot be measured in vivo. In this study, a subject-specific fibril-reinforced poromechanical finite element model of the human tibiofemoral joint was used to investigate solid–fluid interactions in ACL-intact and ACL-deficient conditions. The MRI-based geometry incorporated anisotropic collagen fibril networks and Darcy-type interstitial fluid flow to capture the coupled poromechanical response of cartilage and menisci under physiological loading. Ramp compression and posterior drawer creep forces representing single-leg stance were applied to evaluate the time-dependent mechanical behavior of the joint. The results showed that ACL deficiency increased interstitial fluid pressure by up to 19% in the lateral tibial cartilage and 13% in the lateral femoral condyle, while collagen fibril tensile stress increased by 22% in the lateral femoral cartilage and by 5% in the medial meniscus. Although the overall stress distributions were similar between intact and ACL-deficient knees, localized increases in fluid pressurization and fibril loading were observed in load-bearing regions. These findings suggest that ACL deficiency modifies the internal poromechanical environment of cartilaginous tissues and may contribute to degeneration under repetitive physiological loading.
