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Multi-step finite element simulation for clear aligner space closure: a proof-of-concept compensation protocol
Engineering M13 bacteriophage to display HER2 mimotopes on pVIII for vaccine development
Negative life events, sleep quality and depression in university students
Abstract The rate of depression among university students is increasing. University students experiencing negative life events are at risk of developing depression, which could further decrease their academic engagement. In addition, negative life events are often accompanied by sleep problems. This study used a cross-sectional design to explore the complex relationships among negative life events, sleep quality, and depression in university students, with a particular focus on whether sleep quality serves as a mediating factor in the association between negative life events and depression. Three self-report scales were completed by 828 participants recruited from three universities. The results revealed that negative life events and sleep quality had a significant effect on depression separately and that sleep quality played a mediating role in the relationship between negative life events and depression in university students. Our study contributes to a deeper understanding of the relationships among negative life events, sleep quality, and depression and has important implications for the prevention and intervention of depressive symptoms in university students. By acknowledging the role of negative life events, educators and counselors can be more proactive in identifying students who may be struggling and providing them with the necessary support. By understanding the mediating role of sleep quality, university students can more effectively recognize and manage their mental health needs and cultivate resilience strategies to handle stressors to reduce their risk of developing depressive symptoms.
Energy aware resource management in 6G IoT networks using STAR RIS
Evaluating the generalizability of an automated coronary artery calcium segmentation and scoring algorithm using multi-vendor dataset
Synergistic optimization of mechanical strength and vegetative growth in ecological slope restoration materials through mix ratio design
RF injection scanning tunneling spectroscopy of a superconducting NbSe2 surface
Abstract NbSe2 is a transition metal dichalcogenide with a two-dimensional nature, showing superconducting (SC) and charge density properties. Moreover, a 1T phase can be made in the film, which has a different property from the bulk dominant 2 H phase, in which Mott insulator behavior is actively discussed. We observed the surface with STM at 400 mK and injected RF signal (1 GHz and 15 Hz) at the tunneling junction. We detected two quasi-particle (QP) states at the end of the SC gap, which split with the increase of the RF power specified by the electric field at the tunneling junction, VAC. A previous STM experiment with 65 GHz RF on a vanadium surface observed multiple replicas of the QP peak. However, our experimental result using 1 GHz RF shows a widening of QP features with two enhanced peaks shifted by ~ ± eVAC from the original QP positions. The behavior was well reproduced by a simulation using a well-known Tien-Gordon model, whose results indicate that the disappearance of multiple peaks is due to the low frequency of the RF signal. In addition, two enhanced peaks at ~ ± eVAC are deduced from the Bessel function behavior. The energy shift from the original peak linearly changes with eVAC. We apply this technique to examine the property change at the domain boundary of the 2 H and 1T phase of the NbSe2 surface. We found the superconducting gap decreases when we move the tip from the 2 H domain into the 1T domain. Moreover, the injection of RF splits a QP peak into two enhanced peaks, whose energy separation is linear with the electric field at the RF generator for both phases. However, the linear energy separation with VAC shows different coefficients between the 2 H and 1T phases. We conclude that the different coefficient is due to the change of actual VAc on the two domains originating from a different dielectric constant and shielding efficiency for the electric field of RF.
A hybrid compound scaling hypergraph neural network for robust cervical cancer subtype classification using whole slide cytology images
Abstract Cervical cancer is a major cause of mortality among women, particularly in low-income countries with insufficient screening programs. Manual cytological examination is time-consuming, error-prone and subject to inter-observer variability. Automated and robust classification of the whole slide cytology images for cervical cancer is essential for detecting precancerous and malignant lesions. We propose a novel deep learning framework, the Compound Scaling Hypergraph Neural Network model (CSHG-CervixNet), for robust classification of cervical cancer subtypes. The model integrates a Compound Scaling Convolutional Neural Network (CSCNN) with a k-dimensional Hypergraph Neural Network (kd-HGNN) architecture. CSCNN balances the network’s depth, width, and resolution, supporting effective feature representation with minimal computational overhead. kd-HGNN captures higher-order relationships between the features, and its propagation mechanism ensures better feature diffusion across distant nodes. The model is evaluated on the benchmark Sipakmed dataset and achieves an accuracy of 99.31%, with a macro-averaged precision of 98.97%, recall of 99.38%, and F1-score of 99.34%, demonstrating its robustness in cervical cancer subtype classification. Pathologists and other medical experts will find this study helpful in distinguishing cervical cancer subtypes so that targeted treatment may be provided and effective disease management is made possible.
Impact of electric field and strain on the electronic thermal conductivity of topological crystalline insulator SnTe (001)
Renal safety of Long-Term Non-steroidal Anti-inflammatory drugs use in patients with ankylosing spondylitis
Metabolic dysfunction-associated fatty liver disease (MAFLD) in the adult population attending a health check-up program in Thailand: prevalence and fibrosis status
Development of a machine learning model to identify the predictors of the neonatal intensive care unit admission
Prospective comparisons support the use of navigational bronchoscopy
Exploring the promotion of musical intangible cultural heritage under TikTok short videos
THSG counteracts microglial glycolytic reprogramming and neuronal necroptosis both in vivo and in vitro under conditions of neuroinflammation
DDoS attack detection in intelligent transport systems using adaptive neuro-fuzzy inference system
Abstract An intelligent transportation system consists of a variety of applications that analyze and exchange information to reduce traffic, enhance traffic management, lessen the impact on the environment, and boost the advantages of transportation for both business users and the general public. Moreover, Intelligent Transportation Systems is different from the standard vehicular ad hoc network design since it functions in a highly dynamic environment brought on by the quick mobility between the nodes in short connection times. These traits make various threats, weaknesses, and denial-of-service assaults possible. The protection of the intelligent transportation system from attacks and continual maintenance is crucial. In this research work, a Distributed Denial of Service attack detection scheme is proposed to protect the Intelligent Transportation System ecosystem, making use of the Adaptive Neuro-Fuzzy Inference System. By resolving the flaws in the DDoS attack detection methods that are currently in use, the security needs of the Intelligent Transportation Systems ecosystem are taken into account. The learning approach of artificial neural networks and the fuzzy logic model is integrated into the Fuzzy System. Based on the experimental results, the proposed model achieved 94.3% accuracy, outperforming traditional classifiers such as Support Vector Machine, Random Forest, Extreme Gradient Boosting, and Convolutional Neural Network. The system demonstrated low false positive rates and high detection reliability, ensuring suitability for real-world Intelligent Transportation Systems security. The proposed scheme attained better results in terms of accuracy, precision, recall, and F1 score.
Short-term outcome after simultaneous pancreas-kidney transplantation with alemtuzumab vs. basiliximab induction: a single-center retrospective study
Abstract During the COVID-19 pandemic our center adjusted the standard induction therapy for normal immunological risk simultaneous pancreas-kidney (SPK) transplantations from T-cell depletion by alemtuzumab (ALEM) to IL-2 receptor blocking by basiliximab (IL2R). Here, we analyze the impact of this change on 1 year post-transplantation outcomes. Thirty-six adult patients who underwent SPK transplantation between June 2015 and June 2023 were included, of whom 21 before February 2020 (ALEM) and 15 after February 2020 (IL2R). Patients were stratified into two groups based on the induction therapy received. One death occurred during the follow up period. A total of three pancreas and two kidney grafts were lost. No differences between kidney and pancreas graft function or rejection rates were observed. Patients receiving IL2R induction had significantly lower 30 day postoperative complication rates (34 vs. 46%, p = 0.03) and experienced fewer bacterial infections (< 6 months: 47 vs. 81%, p = 0.03). Additionally, lower rates of viral (including CMV) and fungal infections were observed. IL2R patients also had a significantly shorter hospital admission durations (14 vs. 30 days, p < 0.001). In conclusion, IL2R induction in SPK recipients was associated with similar short-term graft function and potentially improved outcomes compared to ALEM, warranting cautious interpretation due to sample size.
A probability integral method modified model for accurately characterizing subsidence at the boundary of a mining area
An automatic classification of breast cancer using fuzzy scoring based ResNet CNN model
Abstract The expansion rate of medical data during the past ten years has rapidly expanded due to the vast fields. The automated disease diagnosis system is proposed using a deep learning (DL) algorithm, which automates and helps speed up the process efficiently. Further, this research concentrates on improving computation time based on the detection process. So, this research introduces a hybrid DL model for improving prediction performance andreducing time consumption compared to the machine learning (ML)model.Describing a pre-processing method utilizing statistical co-relational evaluation to improve the classifier’s accuracy.The features are then extracted from the Region of Interest (ROI) images using the wrapping technique and a fast discrete wavelet transform (FDWT). The extracted curvelet coefficients and the turn-time difficulty are too excessive to be categorized. Utilizing swarm intelligence, the Adaptive Grey Wolf Optimization Algorithm (AGWOA) was presented to reduce the time difficulty and choose the key characteristics. Here, it introduces a new building block identified as the Fuzzy Scoring Resnet-Convolutional Neural Network(FS-Resnet CNN) framework to optimize the network. The performance of the proposed model was assessedutilizing metrics such as recall, precision, F-measure, and accuracy.Furthermore, the suggested framework is computationally effective, less noise-sensitive, and efficiently saves memory. The simulation findings indicate that the suggested framework has a higher detection rate than the existing prediction model.