Browse Articles
Discover research articles across all indexed journals
Identification and validation of a novel machine learning model for predicting severe pelvic endometriosis: A retrospective study
SIRT1-based therapy targets a gene program involved in mitochondrial turnover in a model of retinal neurodegeneration
Serum neurofilament heavy chain predicts post-stroke cognitive impairment
The influence of ultrasonic shot peening on the microstructure and fatigue behavior of TC17 alloy
Abstract This research presents a quantitative analysis of TC17 alloy subjected to ultrasonic shot peening (USP) treatment. The effects of USP treatment under different Almen intensities on surface roughness, microstructure, plastic strain range, microhardness, residual stress, fatigue behavior and strain gradient of TC17 alloy were analyzed. Results show that increasing Almen intensity from 0.15 mmA to 0.25 mmA leads to a 27% increase in surface roughness, a 12% increase in surface compressive residual stress value, a 29% increase in the depth of compressive residual stress layer, and a 6.7% increase in the maximum value of compressive residual stress. Microstructural analysis reveals material stacking, pores, and microcracks on the specimen surface and grain refinement characteristics in the surface and subsurface layers. Strain gradient analysis shows that the deformation layer depth is uniformly distributed. An increase in Almen intensity leads to a higher degree of plastic deformation and work hardening, and the increase in compressive residual stress layer depth counteracts early fatigue failure caused by high roughness.
System expansion is needed to handle the multifunctionality of food items in environmental impact assessment
High antagonistic activity and antibiotic resistance of flavobacteria of polar microbial freshwater mats on King George Island in maritime Antarctica
Automated assessment of simulated laparoscopic surgical skill performance using deep learning
Abstract Artificial intelligence (AI) has the potential to improve healthcare and patient safety and is currently being adopted across various fields of medicine and healthcare. AI and in particular computer vision (CV) are well suited to the analysis of minimally invasive surgical simulation videos for training and performance improvement. CV techniques have rapidly improved in recent years from accurately recognizing objects, instruments, and gestures to phases of surgery and more recently to remembering past surgical steps. Lack of labeled data is a particular problem in surgery considering its complexity, as human annotation and manual assessment are both expensive in time and cost, and in most cases rely on direct intervention of clinical expertise. In this study, we introduce a newly collected simulated Laparoscopic Surgical Performance Dataset (LSPD) specifically designed to address these challenges. Unlike existing datasets that focus on instrument tracking or anatomical structure recognition, the LSPD is tailored for evaluating simulated laparoscopic surgical skill performance at various expertise levels. We provide detailed statistical analyses to identify and compare poorly performed and well-executed operations across different skill levels (novice, trainee, expert) for three specific skills: stack, bands, and tower . We employ a 3-dimensional convolutional neural network (3DCNN) with a weakly-supervised approach to classify the experience levels of surgeons. Our results show that the 3DCNN effectively distinguishes between novices, trainees, and experts, achieving an F1 score of 0.91 and an AUC of 0.92. This study highlights the value of the LSPD dataset and demonstrates the potential of leveraging 3DCNN-based and weakly-supervised approaches to automate the evaluation of surgical performance, reducing reliance on manual expert annotation and assessments. These advancements contribute to improving surgical training and performance analysis.
Awareness and perception of physicians about forgery and counterfeiting in the medical field in Egypt
Abstract Medical records may act as one of the legal pieces of evidence in the court. A complete and correct medical record contains a chronological health history of the patient, and it is one of the keys to resolving cases of alleged malpractice. The aim is to emphasize the importance of strict rules and training to ensure medical documents are handled properly and the prevalence of signature forgery in medical reports. This cross-sectional descriptive study was conducted with a sample of 300 randomly selected physicians from hospitals in Fayoum, Egypt, between 2024 and 2025. According to the opinions of 133 physicians (44.3%), forgery and counterfeiting are relatively common in the medical sector; 81 physicians (27%) believed it is common, and 9 physicians (3%) considered it very common, especially in medical reports. 186 (75.6%) were forged medical reports. 185 (61.7%) blamed it on inadequate supervision, while 162 (54%) said it was done to get money. This is followed by 160 doctors (53.3%) who cite respect for the senior and following his instructions if asked to signature in place. Importantly, the overwhelming support for enhanced oversight and education indicates a collective recognition of the necessity for systemic changes to combat forgery in the medical field.
Based on Data-Augmentation long short-term memory gear meshing accuracy and error compensation
Exploring spatial–temporal evolution patterns of urban heat islands in summer and winter: evidence from a megacity of China
DNA metabarcoding of spider egg sacs uncovers novel insights into host parasitoid complexes and trophic networks
A retrospective single center analysis of fetuses with region of homozygosity detected by single nucleotide polymorphism array
Identification of shared important genes associated with ferroptosis across different etiologies of acute lung injury
Assembly of a conductive antimony tin oxide (ATO) polyethersulfone (PES) nano composite thermal insulation film
Multiscale superpixel depth feature extraction for hyperspectral image classification
Abstract Recently, superpixel segmentation has been widely employed in hyperspectral image (HSI) classification of remote sensing. However, the structures of land-covers in HSI commonly vary greatly, which makes it difficult to fully fit the boundaries of land-covers by single-scale superpixel segmentation. Moreover, the shape-irregularity of superpixel brings challenge for depth feature extraction. To overcome these issues, a multiscale superpixel depth feature extraction (MSDFE) method is proposed for HSI classification in this article, which effectively explores and integrates the spatial-spectral information of land-covers by adopting multiscale superpixel segmentation, constructing statistical features of superpixel, and conducting depth feature extraction. Specifically, to exploit rich spatial information of HSI, multiscale superpixel segmentation is firstly applied on the HSI. Once superpixels on different scales are obtained, two-dimensional statistical features with a united form are constructed for these superpixels with different spatial shapes. Based on these two-dimensional statistical features, a convolutional neural network is utilized to learn deeper features and classify these depth features. Finally, an adaptive strategy is adopted to fuse the multiscale classification results. Experiments on three real hyperspectral datasets indicate the superiority of the proposed MSDFE method over several state-of-the-art methods.
A UAV path planning algorithm for bridge construction safety inspection in complex terrain
Effects of indoor air pollution exposure on lung function of children in selected schools in Kigali, Rwanda
Evaluating the impact of gut microbiota, circulating cytokines and plasma metabolites on febrile seizure risk in Mendelian randomization study
Improving compatibility between coffee or black tea ground wastes and polymer matrix via silane treatment for production sustainable biofillers
Abstract The incorporation of bio-waste products into polymer materials as a fillers and colorants represents a highly significant approach for developing sustainable composites, aligning with the principles of a circular bioeconomy and contributing to reduced environmental impact. In this study, coffee grounds (CG) and black tea grounds (BTG), two mainstream food processing by-products, were employed as bio-fillers and pigments for ethylene-norbornene (EN) composites. The effects of hydrophobic treatment with (3-aminopropyl)triethoxysilane (APTS) on CG and BTG powders were compared to those of untreated bio-fillers with respect to dispersion, color characteristics, mechanical properties, and UV aging stability of the polymer composites subjected to 50, 100, 200 and 300 h of UV aging. Both the waste additives and the resulting composites were characterized through Fourier-transform infrared (FTIR) spectroscopy, tensile testing, thermogravimetric analysis (TGA), scanning electron microscopy (SEM) and spectrophotometric method. The obtained results demonstrated that the silanization of CG and BTG bio-fillers improved their dispersion within the EN matrix, consequently enhancing the UV aging resistance of the polymer composites. The EN/CG-APTS composite exhibited the best mechanical properties during the aging process, with the highest aging factor value of 0.6 after 300 h. On the other hand, the EN/BTG-APTS composite showed the smallest color change (ΔE = 7.8) after 300 h of aging. These findings indicate that improving the compatibility of bio-fillers with the polymer matrix can further increase their application potential in sustainable polymer materials technology.