Advancing Fluid-Based Thermal Management Systems Design: Leveraging Graph Neural Networks for Graph Regression and Efficient Enumeration Reduction

In this research, we developed a graph-based framework to represent various aspects of optimal thermal management system design, with the aim of rapidly and efficiently identifying optimal design candidates. Initially, the graph-based framework is utilized to generate diverse thermal management system architectures. The dynamics of these system architectures are modeled under various loading conditions, and an open-loop optimal controller is employed to determine each system's optimal performance. These modeled cases constitute the dataset, with the corresponding optimal performance values serving as the labels for the data. In the subsequent step, a Graph Neural Network (GNN) model is trained on 30% of the labeled data to predict the systems' performance, effectively addressing a regression problem. Utilizing this trained model, we estimate the performance values for the remaining 70% of the data, which serves as the test set. In the third step, the predicted performance values are employed to rank the test data, facilitating prioritized evaluation of the design scenarios. Specifically, a small subset of the test data with the highest estimated ranks undergoes evaluation via the open-loop optimal control solver. This targeted approach concentrates on evaluating higher-ranked designs identified by the GNN, replacing the exhaustive search (enumeration-based) of all design cases. The results demonstrate a significant average reduction of over 92% in the number of system dynamic modeling and optimal control analyses required to identify optimal design scenarios.Comment: 13 pages, 17 figure

Extracting Design Knowledge from Optimization Data: Enhancing Engineering Design in Fluid Based Thermal Management Systems

As mechanical systems become more complex and technological advances accelerate, the traditional reliance on heritage designs for engineering endeavors is being diminished in its effectiveness. Considering the dynamic nature of the design industry where new challenges are continually emerging, alternative sources of knowledge need to be sought to guide future design efforts. One promising avenue lies in the analysis of design optimization data, which has the potential to offer valuable insights and overcome the limitations of heritage designs. This paper presents a step toward extracting knowledge from optimization data in multi-split fluid-based thermal management systems using different classification machine learning methods, so that designers can use it to guide decisions in future design efforts. This approach offers several advantages over traditional design heritage methods, including applicability in cases where there is no design heritage and the ability to derive optimal designs. We showcase our framework through four case studies with varying levels of complexity. These studies demonstrate its effectiveness in enhancing the design of complex thermal management systems. Our results show that the knowledge extracted from the configuration design optimization data provides a good basis for more general design of complex thermal management systems. It is shown that the objective value of the estimated optimal configuration closely approximates the true optimal configuration with less than 1 percent error, achieved using basic features based on the system heat loads without involving the corresponding optimal open loop control (OLOC) features. This eliminates the need to solve the OLOC problem, leading to reduced computation costs.Comment: 13 pages, 20 figure

Multi-split configuration design for fluid-based thermal management systems

High power density systems require efficient cooling to maintain their thermal performance. Despite this, as systems get larger and more complex, human practice and insight may not suffice to determine the desired thermal management system designs. To this end, a framework for automatic architecture exploration is presented in this article for a class of single-phase, multi-split cooling systems. For this class of systems, heat generation devices are clustered based on their spatial information, and flow-split are added only when required and at the location of heat devices. To generate different architectures, candidate architectures are represented as graphs. From these graphs, dynamic physics models are created automatically using a graph-based thermal modeling framework. Then, an optimal fluid flow distribution problem is solved by addressing temperature constraints in the presence of exogenous heat loads to achieve optimal performance. The focus in this work is on the design of general multi-split heat management systems. The architectures discussed here can be used for various applications in the domain of configuration design. The multi-split algorithm can produce configurations where splitting can occur at any of the vertices. The results presented include 3 categories of cases and are discussed in detail.Comment: 11 pages, 18 figure

Anatomy of peduncle in species of Scrophularia L. (Scrophulariaceae) in Iran

The peduncle anatomy in 35 populations, which belonged to 18 taxa of Scrophularia L., were studied. Am-ong 37 quantitative and qualitative characters related to peduncle anatomy, several traits showed more taxonomic value for taxa delimitation, including presence of palisade parenchyma and the number of its layers, the presence of phloem fiber, the presence of bundle sheath, the presence of lamellar collenchyma at ridge location and the number of its layers, the dimensions of cross section, thickness of vascular bundle, the dimensions of pith parenchyma, thickness of xylem, thickness of parenchymatous part and thickness of lamellar collenchyma at ridge location. Finally, in comparison with the results obtained by Grau (1981), in which 12 groups have been introduced, the current study confirms the validity of 3 groups. However, for the remaining groups, no noticeable concordance was found

Leaf anatomical studies on selected species of Scrophularia L. (Scrophulariaceae) in Iran

In this survey, anatomical characteristics of leaves in 35 populations belonging to 18 taxa of Scrophularia have been studied. Among 39 quantitative and qualitative anatomical characters, some have more suitable taxonomic value for differentiation of taxa, such as blade thickness, thickness of upper and lower cuticle of midrib, length of upper and lower palisade parenchyma of blade, thickness of lower epidermis wall of midrib, thickness of upper and lower epidermis of blade, rows of spongy parenchyma of blade, upper collenchyma type of midrib and the presence of idioblast. Finally, comparison of the results of the current study together with Grau’s (1981) results confirm the validity of 4 groups out of the 12 groups which he had introduced. As for the remaining groups, no noticeable concordance was found

The Effects of Lower Extremity Muscle Fatigue on Dynamic Balance in Volleyball Players

Objectives: Lower extremity muscles are critical for maintaining dynamic balance and athletic performance. Fatigue of these muscles may affect dynamic balance. It is unclear whether fatigue in a particular muscle group can affect dynamic balance more than that in other groups. This study was conducted to evaluate and compare the effects of fatigue in 5 muscle groups on dynamic balance in volleyball players. Methods: Fifteen healthy male volleyball players separately performed the Star Excursion Balance Test before and immediately after the occurrence of fatigue of ankle Plantar Flexor (PF), knee extensor, knee flexor, hip abductor, and hip adductor muscles. Composite reach distance and distance in anterior, posteromedial, and posterolateral directions were recorded, accordingly. Results: Repeated-measures Analysis of Variance (ANOVA) data indicated that fatigue of all muscle groups significantly decreased the mean score of composite (P<0.001). Anterior, posteromedial, and posterolateral distance scores decreased following muscle fatigue of knee extensors and ankle PFs (P<0.05). Discussion: This study suggested that regarding composite reach score, fatigue of ankle, knee, and hip muscles similarly decreased dynamic balance. However, evaluating three main directions revealed that knee extensors and ankle PFs muscles fatigue presented more prominent effects on the explored volleyball players’ balance, compared to the other muscles

Application of digital technologies for ensuring agricultural productivity

Over the decades, agri-food security has become one of the most critical concerns in the world. Sustainable agri-food production technologies have been reliable in mitigating poverty caused by high demands for food. Recently, the applications of agri-food system technologies have been meaningfully changing the worldwide scene due to both external strengths and internal forces. Digital agriculture (DA) is a pioneering technology helping to meet the growing global demand for sustainable food production. Integrating different sub-branches of DA technologies such as artificial intelligence, automation and robotics, sensors, Internet of Things (IoT) and data analytics into agriculture practices to reduce waste, optimize farming inputs and enhance crop production. This can help shift from tedious operations to continuously automated processes, resulting in increasing agricultural production by enabling the traceability of products and processes. The application of DA provides agri-food producers with accurate and real-time observations regarding different features influencing their productivity, such as plant health, soil quality, weather conditions, and pest and disease pressure. Analyzing the results achieved by DA can help agricultural producers and scholars make better decisions to increase yields, improve efficiency, reduce costs, and manage resources. The core focus of the current work is to clarify the benefits of some sub-branches of DA in increasing agricultural production efficiency, discuss the challenges of practical DA in the field, and highlight the future perspectives of DA. This review paper can open new directions to speed up the DA application on the farm and link traditional agriculture with modern farming technologies

Association of Ultraviolet Radiation Exposure with Dermatomyositis in a National Myositis Patient Registry.

OBJECTIVE: Dermatomyositis (DM) has been associated with geospatial differences in ultraviolet (UV) radiation, but the role of individual determinants of UV exposure prior to diagnosis is unknown. METHODS: We analyzed questionnaire data from 1350 adults in a U.S. national myositis registry (638 with DM, 422 polymyositis [PM], and 290 inclusion body myositis [IBM] diagnosed at ages 18-65 years), examining the likelihood of DM compared with PM and IBM diagnosis, in relation to self-reported sunburn history and job- and hobby-related sun exposures in the year prior to diagnosis. We estimated odds ratios (OR) and 95% confidence intervals (CI) using logistic regression adjusted for age, skin tone, and sex, to determine the association of individual UV exposures with DM diagnosis. We also evaluated the proportion of DM by maximum daily ambient UV exposure, based on UV-B erythemal irradiances for participant residence the year prior to diagnosis. RESULTS: DM was associated with sunburn in the year before diagnosis (two or more sunburns, OR=1.77; 95%CI 1.28, 2.43 vs. PM/IBM; one sunburn OR=1.44; 95%CI 1.06, 1.95) and with having elevated job- or hobby-related sun exposure (high OR=1.64; 95%CI 1.08, 2.49 or moderate exposure OR=1.35; 95%CI 1.02, 1.78 vs. low or no exposure). Ambient UV intensity was associated with DM in females (beta=3.97, P=0.046), but not overall. CONCLUSION: Our findings suggest that high or moderate personal exposure to intense sunlight is associated with developing DM compared with other types of myositis. Prospective research on UV exposure as a modifiable risk factor for DM is warranted. This article is protected by copyright. All rights reserved