Machine learning thermal circuit network model for thermal design optimization of electronic circuit board layout with transient heating chips

This paper describes a method combining Bayesian optimization (BO) and a lamped-capacitance thermal circuit network model that is effective for speeding up the thermal design optimization of an electronic circuit board layout with transient heating chips. As electronic devices have become smaller and more complex, the importance of thermal design optimization to ensure heat dissipation performance has increased. However, such thermal design optimization is difficult because it is necessary to consider various trade-offs associated with packaging and transient temperature changes of heat-generating components. This study aims to improve the performance of thermal design optimization by artificial intelligence. BO using a Gaussian process was combined with the lamped-capacitance thermal circuit network model, and its performance was verified by case studies. As a result, BO successfully found the ideal circuit board layout as well as particle swarm optimization (PSO) and genetic algorithm (GA) could. The CPU time for BO was 1/5 and 1/4 of that for PSO and GA, respectively. In addition, BO found a non-intuitive optimal solution in approximately 7 minutes from 10 million layout patterns. It was estimated that this was 1/1000 of the CPU time required for analyzing all layout patterns.Comment: 13 pages, 7 figure

Revisiting Permutation Symmetry for Merging Models between Different Datasets

Model merging is a new approach to creating a new model by combining the weights of different trained models. Previous studies report that model merging works well for models trained on a single dataset with different random seeds, while model merging between different datasets is difficult. Merging knowledge from different datasets has practical significance, but it has not been well investigated. In this paper, we investigate the properties of merging models between different datasets. Through theoretical and empirical analyses, we find that the accuracy of the merged model decreases more significantly as the datasets diverge more and that the different loss landscapes for each dataset make model merging between different datasets difficult. We also show that merged models require datasets for merging in order to achieve a high accuracy. Furthermore, we show that condensed datasets created by dataset condensation can be used as substitutes for the original datasets when merging models. We conduct experiments for model merging between different datasets. When merging between MNIST and Fashion- MNIST models, the accuracy significantly improves by 28% using the dataset and 25% using the condensed dataset compared with not using the dataset.Comment: 18 pages; comments are welcom

Enhancement in Boiling Heat Transfer for Water Using a Polished Plate Surface

The thermal characteristic of a plate evaporator for boiling water have been experimentally investigated. Stainless-steel plates with five kinds of surface roughness (a mirror-polished surface and four kinds of polished plate surface, F2, F0, F-1, and F-2 polishing) were used for the investigation. The roughness order is F-2 > F-1 > F0 > F2 > mirror. The local boiling heat transfers were measured on the evaporator surface using ten thermocouples inside the evaporator. For a comparison of the heat transfer characteristic of the evaporator, the local boiling heat transfer coefficient was measured when the polishing direction was perpendicular or parallel to the water flow at a range of pressures (0.3–0.4 MPa) and flow rates (20–60 mL/min). The results indicate that the local heat transfer coefficient increased with increasing vapor quality at all surface conditions. For F0 polishing, the heat transfer coefficient on the surface with perpendicular polishing was much larger than that on the surfaces with parallel and mirror polishing. In particular, the heat transfer coefficient of the perpendicularly polished surface (F2 and F0 polishing) was increased six-fold in comparison with that of the mirror surface at a vapor quality of 0.35. However, for the F-1 and F-2 perpendicularly polishing plates, the heat transfer coefficients were smaller than those of the F0 and F2 perpendicularly polishing plates. For the F2 perpendicularly polished plate, heat transfer enhancement was confirmed, along with an increase in the number of bubbling points, on increasing the system pressure. For the polished surface, the heat transfer coefficient increased with increasing flow rate. This indicates that the bubble formation cycle was promoted by an increased flow rate

Modeling Low Muscle Mass Screening in Hemodialysis Patients

Introduction: Computed tomography (CT) can accurately measure muscle mass, which is necessary for diagnosing sarcopenia, even in dialysis patients. However, CT-based screening for such patients is challenging, especially considering the availability of equipment within dialysis facilities. We therefore aimed to develop a bedside prediction model for low muscle mass, defined by the psoas muscle mass index (PMI) from CT measurement. Methods: Hemodialysis patients (n = 619) who had undergone abdominal CT screening were divided into the development (n = 441) and validation (n = 178) groups. PMI was manually measured using abdominal CT images to diagnose low muscle mass by two independent investigators. The development group’s data were used to create a logistic regression model using 42 items extracted from clinical information as predictive variables; variables were selected using the stepwise method. External validity was examined using the validation group’s data, and the area under the curve (AUC), sensitivity, and specificity were calculated. Results: Of all subjects, 226 (37%) were diagnosed with low muscle mass using PMI. A predictive model for low muscle mass was calculated using ten variables: each grip strength, sex, height, dry weight, primary cause of end-stage renal disease, diastolic blood pressure at start of session, pre-dialysis potassium and albumin level, and dialysis water removal in a session. The development group’s adjusted AUC, sensitivity, and specificity were 0.81, 60%, and 87%, respectively. The validation group’s adjusted AUC, sensitivity, and specificity were 0.73, 64%, and 82%, respectively. Discussion/Conclusion: Our results facilitate skeletal muscle screening in hemodialysis patients, assisting in sarcopenia prophylaxis and intervention decisions

Outcomes of Endoscopic Ultrasound-Guided Biliary Drainage in Patients Undergoing Antithrombotic Therapy

Background/Aims The Japan Gastroenterological Endoscopy Society (JGES) has published guidelines for gastroenterological endoscopy in patients undergoing antithrombotic treatment. These guidelines classify endoscopic ultrasound-guided biliary drainage (EUS-BD) as a high-risk procedure. Nevertheless, the bleeding risk of EUS-BD in patients undergoing antithrombotic therapy is uncertain. Therefore, this study aimed to assess the bleeding risk in patients undergoing antithrombotic therapy. Methods This single-center retrospective study included 220 consecutive patients who underwent EUS-BD between January 2013 and December 2018. We managed the withdrawal and continuation of antithrombotic agents according to the JGES guidelines. We compared the bleeding event rates among patients who received and those who did not receive antithrombotic agents. Results A total of 18 patients (8.1%) received antithrombotic agents and 202 patients (91.8%) did not. Three patients experienced bleeding events, with an overall bleeding event rate of 1.3% (3/220): one patient was in the antithrombotic group (5.5%) and two patients were in the non-antithrombotic group (0.9%) (p=0.10). All cases were moderate. The sole thromboembolic event (0.4%) was a cerebral infarction in a patient in the non-antithrombotic group. Conclusions The rate of EUS-BD-related bleeding events was low. Even in patients receiving antithrombotic therapy, the bleeding event rates were not significantly different from those in patients not receiving antithrombotic therapy