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Application of dual branch and bidirectional feedback feature extraction networks for real time accurate positioning of stents
Gender representation among speakers at the Japanese Society of Anesthesiologists meetings: A retrospective analysis
Purpose This study investigates the gender distribution of speakers at the Japanese Society of Anesthesiologists (JSA) annual and branch meetings of the Japanese Society of Anesthesiologists. Methods We examined the gender of speakers in sessions at both JSA annual and branch meetings. We also verified the speakers’ Japanese medical licensure status and years of qualification. Results We analyzed 383 sessions from JSA annual meetings between 2019 and 2024, which included 827 speaker slots. Of them, 679 (82.1%) were men and 148 (17.9%) were women. Women were significantly underrepresented in sessions with fewer speaker slots (chi-square test, p = 0.006; trend test, p < 0.001). Furthermore, sessions were frequently composed entirely of men: 73.1% of all sessions and 44.3% of panel presentations were solely male participants. Among the subspecialties, female representation was high in obstetric anesthesia (36.8%) and pediatric anesthesia (31.8%) but low in cardiovascular anesthesia (6.3%). Among 508 speakers with confirmed Japanese medical licenses, 425 (83.7%) were men, and 83 (16.3%) were women, with no significant differences in gender distribution based on the year of licensure (Fisher’s exact test, p = 0.968; trend test, p = 0.463). Additionally, we examined 104 sessions from JSA branch meetings between 2019 and 2023, comprising 176 speaker slots. Of them, 147 (83.5%) were men and 29 (16.5%) were women. There was no significant difference in gender distribution among branch meetings across different regions (p = 0.984). Conclusion These findings underscore the need for proactive measures to promote gender diversity in Japan’s anesthesiology field.
Wave driven cross shore and alongshore transport reveal more extreme projections of shoreline change in island environments
Correction: Cohort profile of a prospective cohort study among middle-aged community-dwellers in rural Vietnam: The Khánh Hòa cardiovascular study
A novel method for estimating functional connectivity from EEG coherence potentials
Explainable machine learning for predictive modeling of blowing snow detection and meteorological feature assessment using XGBoost-SHAP
Accurate forecasting of blowing snow events is vital for improving numerical models of snow processes, yet traditional predictive methods often lack interpretability. This study leverages eXtreme Gradient Boosting (XGBoost) to detect blowing snow events using meteorological and snow flux monitoring data from three weather stations in the Alps. Through 5-fold cross-validation, the model achieved impressive performance metrics, with precision rates exceeding 0.94 for non-blowing snow events and 0.77-0.80 for blowing snow events. The SHAP framework was employed to analyze the relative importance of meteorological factors, revealing that maximum wind speed (WS-MAX), average wind speed (WS-AVG), air temperature (AT), and relative humidity (AH) are the most influential factors. Additionally, Partial dependence plots (PDP) demonstrated a linear correlation between increased WS-MAX and the probability of blowing snow, while WS-AVG showed diminishing returns beyond 10 m/s. Notably, AT below -3°C strongly correlates with blowing snow occurrence, whereas AT above -3°C exhibits a negative relationship. Relative humidity plays a significant role, with values exceeding 60% stabilizing the probability of blowing snow, peaking near 100%. This research contributes to drifting snow event dynamics by integrating explainable artificial intelligence techniques (XAI), thereby improving model interpretability and supporting data-driven decision-making in meteorological applications.
Footwork recognition and trajectory tracking in track and field based on image processing
Bioactivity and Element Composition of Three Endodontic Root Canal Sealers
Development of intelligent hybrid controller for torque ripple minimization in electric drive system with adaptive flux estimator: An experimental case study
In order to ensure optimal performance of permanent magnet synchronous motors (PMSMs) across many technical applications, it is imperative to minimize torque fluctuations and reduce total harmonic distortion (THD) in stator currents. Hence, this study proposes the utilization of an adaptive flux estimator (AFE) in conjunction with an Intelligent Hybrid Controller (IHC) to mitigate the ripples and total harmonic distortion (THD). The IHC system is constructed by integrating PI and fuzzy logic controllers (FLC) in a cascade configuration, alongside a new switching unit that facilitates automatic switching between the two controllers during various operations of the PMSM. AFE estimates accurate flux which is required to achieve ripple free high dynamic performance of the PMSM drive by using a limiter to fix the flux at reference flux value of the drive. The proposed controller with AFE has achieved its originality through the refinement of membership functions located at the center of the universe of discourse (UOD) and the enhancement of the switching function. These improvements have resulted in increased sensitivity in the proximity to the reference speed. The Fuzzy Logic Controller (FLC) demonstrates superior performance when operating in a transient state, whereas the Proportional-Integral (PI) controller of the proposed system exhibits satisfactory performance under steady-state situations. The efficacy of AFE with IHC is substantiated by the simulation and experimental analysis reported in this study. A significant reduction in both total harmonics distortion (THD) and torque ripples are found.
Detection of cotton crops diseases using customized deep learning model
Chemotherapy and Oral Health: An Editorial on Current Insights and Future Directions
Implications of variability in triceps surae muscle volumes on peak lower limb muscle forces during human walking
Musculoskeletal modeling can be used to estimate forces during locomotion. These models, however, are dependent on underlying assumptions about the model inputs, such as muscle volumes and fiber lengths, to calculate muscle forces. Triceps surae (gastrocnemius medialis, gastrocnemius lateralis, soleus) muscle volume distributions vary among humans. Here we quantify how this muscle volume variation impacts maximum estimated lower limb muscle forces during the braking and propulsive phases of the stance phase of walking. Three triceps surae muscle volume distributions (AnyBody Modeling System standard cadaver [MS], average of 21 cadavers [C], average of 21 young, healthy adults [YHA]) were evaluated in a standard musculoskeletal model using the kinetic and kinematic data of 10 healthy individuals at three walking velocities. Maximum muscle forces were calculated using inverse dynamics and an algorithm to solve the muscle redundancy problem in the AnyBody Modeling System. Repeated measure ANOVAs were used to test for significant differences among the three muscle distribution configurations for each muscle/muscle group at each velocity. Triceps surae muscle volume distribution significantly affects gastrocnemius lateralis and soleus maximum muscle forces for both braking and propulsion at all three velocities (p < 0.001), with relatively larger muscle volumes typically producing relatively larger muscle forces. There was no significant difference in gastrocnemius medialis maximum force among configurations (p > 0.124) except at the self-selected spontaneous velocity during braking. Significant differences exist at some velocities for the hamstrings and gluteus maximus during braking (p < 0.046) and the other plantarflexors, dorsiflexors, evertors, hamstrings, quadriceps, sartorius, and gluteus maximus during propulsion (p < 0.042). Muscle volumes used in musculoskeletal models impact estimated muscle forces of both the muscles of interest and other muscles in the biomechanical chain. This is consistent with recent analyses demonstrating that input values can substantially impact results and suggests individualized muscle parameters may be needed depending on the research question.
Genome-wide characterization of PAL, C4H, and 4CL genes regulating the phenylpropanoid pathway in Vanilla planifolia
Assessment of Hyoid Bone Position and Soft Palate Morphology in Different Skeletal Patterns Using Lateral Cephalograms: A Cross-sectional Study
Correction: Evaluation of the impact of COVID-19 pandemic on hospital admission related to common infections: Risk prediction models to tackle antimicrobial resistance in primary care
Fibroblast growth factor 23 neutralizing antibody partially rescues bone loss and increases hematocrit in sickle cell disease mice
Clinical Evaluation of Implant Stability in Poor Quality Maxillary Bone: Reverse Drilling vs Osteotome Techniques: A Randomized Controlled Clinical Trial
Enhancing cybersecurity: A high-performance intrusion detection approach through boosting minority class recognition
The swift proliferation and extensive incorporation of the Internet into worldwide networks have rendered the utilization of Intrusion Detection Systems (IDS) essential for preserving network security. Nonetheless, Intrusion Detection Systems have considerable difficulties, especially in precisely identifying attacks from minority classes. Current methodologies in the literature predominantly adhere to one of two strategies: either disregarding minority classes or use resampling techniques to equilibrate class distributions. Nonetheless, these methods may constrain overall system efficacy. This research utilizes Shapley Additive Explanations (SHAP) for feature selection with Recursive Feature Elimination with Cross-Validation (RFECV), employing XGBoost as the classifier. The model attained precision, recall, and F1-scores of 0.8095, 0.8293, and 0.8193, respectively, signifying improved identification of minority class attacks, namely “worms,” within the UNSW NB15 dataset. To enhance the validation of the proposed approach, we utilized the CICIDS2019 and CICIoT2023 datasets, with findings affirming its efficacy in detecting and classifying minority class attacks.