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

The Effect of the Parenting Skills in the Attitude of Preschool Students’ Mothers

AbstractIntroduction: child and adolescent mental health is the main part of public health and any effort for the improvement of the child mental health needs evaluation of parenting skills and familial interactions. Positive parenting education is an interventional program based on the social learning theory and its main objective is prevention of behavioural, emotional and developmental problems in children by increasing the information, skills and self confidence of the parents.Materials and methods: 32 individuals who were the mother of pre school students enrolled in this study. Beforeand after positive parenting education they were asked to fill the parenting scale and demographic questionnaires.Results: The score of the parenting scale was higher in three sub scale before education and the total score in mothers with age of lower than 30 and in mothers whose student was their first child was higher before education. the role of age and the birth rank were not significant after education .Conclusion: this study shows that short term education of positive parenting can make improvement in different sub scales of parenting scale

The Effect of Wetting and Drying Cycles on Selected Physical Indicators of Biochar- and Rockwool-Based Growth Media

IntroductionMonitoring the changes in physical and hydraulic properties and stability of growth media due to root growth effects and wetting and drying cycles is important. Wetting and drying cycles can probably change physical characteristics, availability of water, air and nutrients for the plant and, as a result, might affect the growth and yield of the greenhouse plants. The growth period greatly affects the physical characteristics of the growth substrates; therefore, the watering of growth substrates should be managed according to these changes to avoid improper irrigation.Materials and MethodsIn this study, 14 growth media were prepared from individual substrates with different volumetric ratios. In order to evaluate the changes of growth media over the time (i.e., during consecutive irrigation events) in the greenhouse, 10 wetting and drying cycles were applied on the growth media in the lab. Several physical indicators including easily available water (EAW), air after irrigation (AIR), water buffering capacity (WBC) and water holding capacity (WHC) of the growth media were determined before and after the wetting and drying cycles. Besides, the subsidence, decrease of mass and decomposition of the growth media were determined over the time. Total porosity (TP), bulk density (BD), particle density (PD), pH and electrical conductivity of the mixtures were measured as well.Results and DiscussionThe pH values in the growth media varied from 5.72 to 6.94. The maximum pH value was related to sawdust- sugarcane bagasse biochar produced at 300◦C vermiculite-zeolite, and wheat straw-vermiculite substrates, and the minimum value was related to the cocopeat-perlite substrate. The values of EC in the growth media varied from 0.21 to 1.43 dS m-1. The highest and lowest EC values among the growth substrates were related to date palm bunches-vermiculite-rockwool and rockwool (0.2)-perlite substrates, respectively. The bulk density (BD) values of the growth media varied in the range of 0.163-0.401 Mg m-3. The values ​​of total porosity (TP) of the growth media varied in the range of 64.8-82.8%v/v. The highest TP was related to the cocopeat-perlite substrate. The TP values ​​of most of the substrates were greater than 70%v/v. The average values of EAW in the growth substrates ranged from 0.123 to 0.272 cm3 cm-3. The highest EAW was related to the sawdust-sawdust biochar produced at 500 ◦C vermiculite-zeolite substrate. The application of wetting and drying cycles increased EAW in most of the growth media. Therefore, it can be stated that the time had a positive effect on the EAW in most of the growth media. The average values of AIR before and after the application of wetting and drying cycles for the growth media varied in the range of 0.063-0.240 cm-3 cm3. The highest value of this indicator was observed in the sawdust-date palm bunches biochar produced at 300◦C vermiculite substrate. In all substrates (with the exception of the sawdust-sawdust biochar produced at 500◦C vermiculite-zeolite), the AIR increased after wetting and drying cycles. The range of WHC values before and after applying wetting and drying cycles was 0.453-0.699 cm3 cm-3. The highest WHC belonged to the wheat straw-vermiculite substrate. The WHC values of five growth media, including cocopeat-perlite, decreased due to the application of wetting and drying cycles, and the WHC values of nine growth media decreased. The most stable substrate after the wetting and drying cycles was rockwool-sawdust-vermiculite. The effect of time on the quantity of WBC was positive, so that with the application of wetting and drying cycles, the WBC values of most of the substrates increased. In all substrates, subsidence and dry weight reduction were observed after the wetting and drying cycles. These changes were low for the substrates with a high volumetric ratio of inorganic materials. The least change among the growth substrates in terms of decomposition (dry weight reduction) was related to the completely inorganic substrate rockwool (0.1)-perlite (%0.17). The most stable substrate in terms of subsidence after wetting and drying cycles was the rockwool-sawdust-vermiculite, which has a large volumetric ratio of individual inorganic substrates. The highest subsidence was observed in the substrates containing wheat straw (wheat straw-vermiculite and date palm bunches biochar produced at 300◦C wheat straw-vermiculite). The organic matter content in all the growth substrates decreased over time (after wetting and drying cycles). The decrease of organic matter in the substrates can be related to the decomposition of organic materials as a result of wetting and drying cycles.ConclusionThe BD, TP, EAW and WHC of the majority of growth media were in the optimal ranges and for some mixtures even better than cocopeat-perlite. Wetting and drying cycles could affect the growth media through several processes such as decomposition of organic compounds, displacement and rearrangement of particles, fragmentation of particles, shrinkage, hardening and subsidence. The growth media with a high percent of organic substrates were unstable as compared with those containing a high proportion of inorganic substrates. In general, the wetting and drying cycles increased the frequency of micropores in the growth media. The wetting and drying cycles positively affected EAW, WHC, AIR and WBC of most growth media. These findings imply that wetting and drying cycles may improve the growth media according to the studied extensive variables. However, it is necessary to study the intensive variables such as hydraulic conductivity, oxygen diffusion and pore tortuosity in the growth media for better evaluation of the impact of wetting and drying cycles as well

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

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

Analyzing and enhancing music mood classification : an empirical study

In the computer age, managing large data repositories is one of the common challenges, especially for music data. Categorizing, manipulating, and refining music tracks are among the most complex tasks in Music Information Retrieval (MIR). Classification is one of the core functions in MIR, which classifies music data from different perspectives, from genre to instrument to mood. The primary focus of this study is on music mood classification. Mood is a subjective phenomenon in MIR, which involves different considerations, such as psychology, musicology, culture, and social behavior. One of the most significant prerequisitions in music mood classification is answering these questions: what combination of acoustic features helps us to improve the accuracy of classification in this area? What type of classifiers is appropriate in music mood classification? How can we increase the accuracy of music mood classification using several classifiers? To find the answers to these questions, we empirically explored different acoustic features and classification schemes on the mood classification in music data. Also, we found the two approaches to use several classifiers simultaneously to classify music tracks using mood labels automatically. These methods contain two voting procedures; namely, Plurality Voting and Borda Count. These approaches are categorized into ensemble techniques, which combine a group of classifiers to reach better accuracy. The proposed ensemble methods are implemented and verified through empirical experiments. The results of the experiments have shown that these proposed approaches could improve the accuracy of music mood classification

Identifying the Research Trends and Subfields of the social manufacturing paradigm

In recent years, studies on the paradigm of social Manufacturing and its applications have been developed as a new production paradigm and have led to the production of diverse and scattered knowledge in this field. Knowing the sub-fields, new topics and the research process of the social production paradigm can be of great help to researchers in this field. The current research has been carried out with the aim of identifying and categorizing research in the field of social Manufacturing, recognizing sub-fields and achieving a coherent view of its research process.This research has investigated the research field of social Manufacturing using bibliometric analysis. The data of this research was collected from 200 articles of the Scopus database and an analysis of the co-occurrence analysis of key words and bibliographic pairs was performed on them, and in this way the sub-fields and the research process of this field were identified.Based on the findings of this study, the research in the field of social Manufacturing has been categorized into 5 clusters and it has also been determined that in recent years, topics such as cloud computing, smart production, blockchain, Internet of Things, social physical cyber systems, innovation systems, society 5.0 and Digital twins have received more attention in research in this field. This research provides a framework of concepts and main topics of interest in the research field of social production, which provides a comprehensive perspective for researchers in this field that can help in choosing their research path