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The role of stretching protocols in post-fatigue performance and flexibility among soccer players
Turning data into insights in Jub, an extensible generic big data platform for life science and healthcare applications
Abstract This paper presents Jub , a Life Science and Healthcare Data Platform (LSHDP) based on generic sandboxes that integrate AI tools and cloud storage into big data science services. Jub automatically and transparently creates data science services to transform datasets into massive information products by using a profiling methodology. These products are presented by generic-secure cloud-based FAIR observatories adding Programmable, Configurable/Customizing, Adaptable, and Resiliency properties (PCA-FAIR-R). This enables organizations to conduct and customize complex analytics processes to support decision-making. We conducted a study case to convert mortality, climate, and pollutants datasets (2000-2023) reported by the Mexican Government into a solid core hub of information products: 16 strategic data observatories based on 85,171,404 information products created from 114,155,622 spatio-temporal profiles of the International Classification of Diseases (ICD-10) mortality classes/strata and cancerogenic substances. An exploratory study revealed highlights about the significance of breast cancer mortality rate growth showing possible associations with air pollutants. This paper also describes the lessons learned from the practice and experience of implementing Jub sandboxes-based observatories for the Population-based Cancer Registry Network deployed on the Mexican territory in 12 Mexican states by public healthcare institutions, as well as to implement bone cancer deep-learning-based diagnosis at a national Hospital.
Benchmarking UMI clustering tools for accurate detection of low-frequency variants from deep sequencing
Penaeus monodon shell waste-derived chitosan nanoparticles show biocompatibility and broad antimicrobial activity
Design and fabrication of a slant polarized monopulse antenna array with a cosecant squared beam for tracking applications
Effects of wind turbine wakes on bird gliding aerodynamic performance
Assessment of awareness level among women about cervical smears, human papillomavirus (HPV), and HPV vaccine in Saudi Arabia
Benchmarking adhesive performance in bonding FDM TPU to PBF PA12 3D-printed parts
Abstract Additive manufacturing provides an efficient method to rapidly develop prototypes utilizing TPU, which is flexible, and Nylon (PA12), which is known for strength and chemical resistance. Five structural adhesives to bond, without plasma/flame pretreatments, flexible FDM TPU to rigid PBF PA12 were benchmarked using the ASTM D903 (180° peel), D2095 (butt-joint tensile), and D3163/D3164 (lap-shear) standards. The adhesives tested were Loctite HY4070, Permatex Plastic Welder, Mannol 9904, Loctite 401 with 770 primer, and 3M DP8010. Across five samples per test, Loctite 401 + 770 achieved the highest mean strengths with low scatter (COV ≤ 15%) in peel (2.6 N/mm), shear (3.0 MPa), and tensile (2.7 MPa) testing. Occasional adherend-side failures in shear suggested joint strengths approaching that of the substrate. The performances of HY4070 and Permatex were acceptable but fell below that of the Loctite 401 + 770. DP8010 and Mannol underperformed, especially in peel and tensile testing. Stress–displacement curves and failure-surface images suggest these adhesives predominantly experienced cohesive failures. Adhesive TPU-PA12 bonding using Loctite 401 with 770 primer is a robust, simple, and fast assembly approach when the presence of thin bond lines is acceptable. We minimize variability through defined process controls and, for durability-critical products, recommend next-stage tests: aging, impact, UV, and fatigue.
Identification of gear fault by weighted Mahalanobis distance method based on multi-scale permutation entropy
Evaluating China’s carbon neutrality capacity a comprehensive spatiotemporal analysis within the SDGs framework
Predictive value of AFR and UCR for the methicillin-resistant Staphylococcus aureus infection in children with bone and joint infection
Machine learning-based prediction of well performance parameters for wellhead choke flow optimization
Abstract Accurate measurement and forecast of fluid flow rates in production wells are important to the estimation of hydrocarbon recovery, attainment of stable and controllable flow regimes, and optimization of production plans. Wellhead chokes, or pressure control valves, find widespread use in the hydrocarbon industry for two major reasons: provision of a stable downstream pressure and creation of the necessary backpressure for balancing gas well productivity and controlling in-well pressure drops. Over the past fifty years, numerous multiphase flow models and empirical correlations have been developed to estimate flow rates under a wide range of fluid properties, flow regimes, and pressure drop conditions. None of these models is deemed to be globally applicable to every region because each has inherent measurement errors that limit the accuracy of predictions for well performance parameters. In this study, three machine learning algorithms—Convolutional Neural Network (CNN), Multilayer Perceptron (MLP), and Radial Basis Function Network (RBFN)—were employed to predict well performance parameters. The dataset consisted of 182 samples for each of the five input parameters—liquid production rate (QL), wellhead pressure (Pwh), choke size (D64), basic sediment and water content (BS&W), and gas–liquid ratio (GLR)—resulting in a total of 910 data points. Among the tested models, MLP demonstrated the highest predictive performance, achieving R 2 values of 0.9985 (training), 0.9856 (validation), and 0.9936 (testing). Four error metrics—Root Mean Square Error (RMSE), Mean Square Error (MSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Error (MAE)—were used for evaluation. For the MLP model, RMSE values of 0.0024 (training) and 0.0057 (testing) were obtained. The dataset was split into training and testing sets with a ratio of 70:30.
Effect of foreign object damage on fatigue performance of Inconel 718 welded joints
A novel Pythagorean fuzzy FUCA driven MCGDM algorithm for evaluating green economy oriented corporate sustainability
Investigating the alleviatory ability of bio-synthesized zinc oxide nanoparticles from Sargassum ilicifolium (Turner) C. Agardh on the tomato plants exposed to whitefly infestation
Microbiological profile and antimicrobial resistance in diabetic foot infections: a cross-sectional study from a low- to middle-income country
Volumetric analysis of simulated bone defects: a comparative study using cone beam computed tomography and intraoral scanners
Abstract This in vitro study aimed to evaluate and compare the accuracy and reliability of cone-beam computed tomography (CBCT) and intraoral scanner (IOS) in measuring the volume of simulated bone defects. Additionally, it aimed to assess how the number of missing cortical bony walls in a bone defect affects the accuracy of volumetric measurements. Bovine rib blocks were used to create 42 simulated bone defects, which were then divided equally into two groups. One cortical plate was perforated in Group I, whereas both the buccal and lingual cortical plates were perforated in Group II. Defect volumes were assessed using CBCT and IOS. Every measurement was compared to a gold standard derived using Archimedes’ principle. Volumetric analysis revealed that the IOS measurements in both groups showed no statistically significant difference when compared to the gold standard. CBCT measurements in Group I also showed no significant difference from the gold standard. However, in Group II, CBCT volumetric measurements differed significantly from the gold standard ( p = 0.001). Both CBCT and IOS demonstrated accuracy in measuring bone defects when only one cortical wall was perforated. However, when both cortical plates were lost, IOS showed superior accuracy compared to CBCT segmentation. IOS measurements had no significant deviation from the gold standard volume ( P = 0.88), while CBCT measurements exhibited a significant deviation ( P = 0.001). Therefore, IOS presents a reliable, radiation-free alternative for monitoring volumetric bone defects across different degrees of cortical bone loss.