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ATKB-PID: an adaptive control method for micro tension under complex hot rolling conditions
Abstract At present, the parameters of the controllers in hot rolling roughing microtension control systems are not adaptively adjustable to variations in working conditions, which compromises both width accuracy and production stability. To address this issue, this paper introduces an ATKB-PID adaptive micro tension control method. This method incorporates a linear attention layer and utilizes a K-Nearest Neighbors (KNN) algorithm to predict the optimal learning rate and inertia coefficient under actual operating conditions. Furthermore, an objective function is tailored to production indices to enhance model performance. Comparative experiments with both established and recently introduced controllers demonstrate that the proposed ATKB-PID method exhibits a smaller steady-state error and quicker adjustment time. The ATKB-PID control method is well-suited for the complex and dynamic microtension control demands in thermal roughing processes, showing promising application potential.
Feasibility and potential effect of a pilot blended digital behavior change intervention promoting sustainable diets over a year
Gelatin/PLA-loaded gold nanocomposites synthesis using Syzygium cumini fruit extract and their antioxidant, antibacterial, anti-inflammatory, antidiabetic and anti-Alzheimer’s activities
Replacement of fishmeal with Quinoa Husk (Chenopodium quinoa) for mitigating multiple stresses in Pangasianodon Hypophthalmus
Carbon emission of urban vehicles based on carbon emission factor correlation analysis
Identification of critical endoplasmic reticulum stress-related genes in advanced atherosclerotic plaque
Exploring the role of volume energy density in altering microstructure and corrosion behavior of nitinol alloys produced by laser powder bed fusion
Study on influencing factors and prevention measures of coal-rock-gas compound dynamic disaster in deep coal mine mining
Artificial intelligence as a transforming factor in motility disorders–automatic detection of motility patterns in high-resolution anorectal manometry
Human neural rosettes secrete bioactive extracellular vesicles enriched in neuronal and glial cellular components
Validation of mixed reality in planning orbital reconstruction with patient-specific implants
Abstract This study aims to evaluate and compare the usability and performance of mixed reality (MR) technology versus conventional methods for preoperative planning of patient-specific reconstruction plates for orbital fractures. A crossover study design was used to compare MR technology with conventional three-dimensional (3D) printing approaches in the planning of maxillofacial traumatology treatments. The primary focus was on user-friendliness and the accuracy of patient-specific reconstruction planning. Secondary outcomes included investigating time differences between the two approaches and evaluating the potential effects on the learning curve. Participants were asked to complete questionnaires assessing various aspects, such as visualization, interaction, segmentation, treatment planning, and evaluation. Objective endpoints were evaluated blindly, while subjective endpoints were analyzed through a double-blind process. The total workflow time for MR technology was significantly shorter compared to the conventional method. Moreover, treatment planning using MR was significantly more accurate (p = .028), with participants reporting a higher mean global satisfaction score compared to the conventional group (80.6% vs. 72.5%). This study sheds light on the potential benefits of employing MR technology in maxillofacial orbital reconstruction. This preoperative method allows for faster and more precise design of patient-specific implants for orbital reconstruction, potentially leading to improved long-term cost-effectiveness.
Study on the crack propagation morphology and initiation law of coal rock under the action of underwater electric pulse
Biomechanical analysis of a short femoral stem used in revision total hip replacement of a standard femoral stem
A between-subjects investigation of whether distraction is the main mechanism behind music-induced analgesia
SDES-YOLO: A high-precision and lightweight model for fall detection in complex environments
A theoretical investigation of spectroscopy properties and transition properties of TlBr+ cation
Genetic proxies for clinical traits are associated with increased risk of severe COVID-19
Abstract Routine use of genetic data in healthcare is much-discussed, yet little is known about its performance in epidemiological models including traditional risk factors. Using severe COVID-19 as an exemplar, we explore the integration of polygenic risk scores (PRS) into disease models alongside sociodemographic and clinical variables. PRS were optimized for 23 clinical variables and related traits previously-associated with severe COVID-19 in up to 450,449 UK Biobank participants, and tested in 9,560 individuals diagnosed in the pre-vaccination era. Associations were further adjusted for (i) sociodemographic and (ii) clinical variables. Pathway analyses of PRS were performed to improve biological understanding of disease. In univariate analyses, 17 PRS were associated with increased risk of severe COVID-19 and, of these, four remained associated with COVID-19 outcomes following adjustment for sociodemographic/clinical variables: hypertension PRS (OR = 1.1, 95%CI 1.03–1.18), atrial fibrillation PRS (OR = 1.12, 95%CI 1.03–1.22), peripheral vascular disease PRS (OR = 0.9, 95%CI 0.82–0.99), and Alzheimer’s disease PRS (OR = 1.14, 95%CI 1.05–1.25). Pathway analyses revealed enrichment of genetic variants in pathways for cardiac muscle contraction (genes N = 5; beta[SE] = 3.48[0.60]; adjusted -P = 1.86 × 10 −5 ). These findings underscore the potential for integrating genetic data into epidemiological models and highlight the advantages of utilizing multiple trait PRS rather than a single PRS for a specific outcome of interest.