Experimental Comparison Of Different Composite Latent Heat Storage Devices With Spatially Non-Constant Heat Loads

An effective thermomanagement is one of the most challenging tasks in the growing field of power electronics. Within this field the cooling of electronic devices is of main interest. The cooling system should be fail-safe, low-risk, cheap, light and energy-efficient. While the heat load may vary over time, the spatial positioning of hot spots is usually constant for a given electronics component. A promising strategy for the cooling of time limited or periodic power electronic components is a composite latent heat storage (CLHS), which is a combination of a phase change material (PCM) and a thermal enhancement structure (aluminum). The thermal enhancement structure is needed, since the PCM has a very low thermal conductivity. The PCM is undergoing a phase change from solid to liquid during the cooling process while keeping a quasi-constant temperature. This leads to the question how an optimal thermal enhancement structure would look like in dependence of the spatially placement of waste heat from the power electronics. This paper presents experimental results for four composite latent heat storage devices with non-constant heat loads: The first two devices are pure aluminum and pure PCM and are used as references. The other two devices are constructed in the following way. First simulate a CLHS with pure PCM. The resulting temperature field is then averaged over time. In order to get the highest heat flux from the hot spots, fins are inserted perpendicular to isothermal contour lines. For the third device the starting points for these fins are evenly spread along the contact surface to the power electronic, for the fourth device the starting points are optimized using a genetic algorithm. The CLHS devices are cubes with boundary edge lengths of 10cm. Below the CLHS devices 5 cupper stripes are installed, that can realize independent heat loads (0-120W). The experimental time is 2400s and the temperature at the contact surface to the power electronic is monitored with 30 thermocouples

Comparison of Bayesian and particle swarm algorithms for hyperparameter optimisation in machine learning applications in high energy physics

When using machine learning (ML) techniques, users typically need to choose a plethora of algorithm-specific parameters, referred to as hyperparameters. In this paper, we compare the performance of two algorithms, particle swarm optimisation (PSO) and Bayesian optimisation (BO), for the autonomous determination of these hyperparameters in applications to different ML tasks typical for the field of high energy physics (HEP). Our evaluation of the performance includes a comparison of the capability of the PSO and BO algorithms to make efficient use of the highly parallel computing resources that are characteristic of contemporary HEP experiments.Comment: Accepted by Computer Physics Communications. Changes made compared to previous version: added references to other strategies, added Zenodo entry for the implemented software, added a brief description of PSO, added more explanations regarding the benchmark task

Evolutionary algorithms for hyperparameter optimization in machine learning for application in high energy physics

The analysis of vast amounts of data constitutes a major challenge in modern high energy physics experiments. Machine learning (ML) methods, typically trained on simulated data, are often employed to facilitate this task. Several choices need to be made by the user when training the ML algorithm. In addition to deciding which ML algorithm to use and choosing suitable observables as inputs, users typically need to choose among a plethora of algorithm-specific parameters. We refer to parameters that need to be chosen by the user as hyperparameters. These are to be distinguished from parameters that the ML algorithm learns autonomously during the training, without intervention by the user. The choice of hyperparameters is conventionally done manually by the user and often has a significant impact on the performance of the ML algorithm. In this paper, we explore two evolutionary algorithms: particle swarm optimization (PSO) and genetic algorithm (GA), for the purposes of performing the choice of optimal hyperparameter values in an autonomous manner. Both of these algorithms will be tested on different datasets and compared to alternative methods.Comment: Corrected typos. Removed a remark on page 2 regarding the similarity of minimization and maximization problem. Removed a remark on page 9 (Summary) regarding thee ANN, since this was not studied in the pape

P19-43. Regulatory T cell epitopes in a dendritic cell-targeted HIV vaccine delivery platform

Poster Presentation from AIDS Vaccine 2009 Paris, France. 19–22 October 200

Control of the specificity of T cell-mediated anti-idiotype immunity by natural regulatory T cells

The idiotypes of B cell lymphomas represent tumor-specific antigens. T cell responses induced by idiotype vaccination in vivo are directed predominantly against CDR peptides, whereas in vitro T cells also recognize framework-derived epitopes. To investigate the mechanisms regulating the specificity of idiotype-specific T cells, BALB/c or B10.D2 mice were immunized with mature dendritic cells loaded with H-2Kd-restricted peptides from influenza hemagglutinin, or from shared (J region) or unique (CDR3) structures of the A20 lymphoma idiotype. Antigen-specific T cells were induced in vivo by the CDR3 and influenza epitopes, but not by the J peptide. Gene expression profiling of splenic regulatory T cells revealed vaccination-induced Treg activation and proliferation. Treg activity involved J epitope-dependent IL-10 secretion and functional suppression of peptide-specific effector T cells. Vaccination-induced in vivo proliferation of transgenic hemagglutinin-specific T cells was suppressed by co-immunization with the J peptide and was restored in CD25-depleted animals. In conclusion, Treg induced by a shared idiotype epitope can systemically suppress T cell responses against idiotype-derived and immunodominant foreign epitopes in vivo. The results imply that tumor vaccines should avoid epitopes expressed by normal cells in the draining lymph node to achieve optimal anti-tumor efficacy

COVID-19 and systemic anticancer therapy: exploiting uncertainty

Immunobiology of allogeneic stem cell transplantation and immunotherapy of hematological disease