Assessment and Prediction of the Thermal Performance of a Centralized Latent Heat Thermal Energy Storage Utilizing Artificial Neural Network

A simulation tool is developed to analyze the thermal performance of a centralized latent heat thermal energy storage system (LHTES) using computational fluid dynamics (CFD). The LHTES system is integrated with a mechanical ventilation system. Paraffin RT20 was used as a phase change material (PCM) and fins are used to enhance its performance. Due to the fact that the numerical calculations take a longer time, the simulations are performed on the first day of each week through summer months and then the database is used to train an artificial neural network (ANN) for predicting of the performance. Then, the LHTES's outlet air-temperature function is integrated into the TRNSYS building thermal response model. The trained ANN is able to improve the prediction of the LHTES's outlet air-temperature for a wide range of inlet conditions (i.e., air-temperature and flow rate). We found that the indoor air-temperature is reduced by about 1.5-2.5

222^{222}Rn contamination mechanisms on acrylic surfaces

In this work, the $^{222}$Rn contamination mechanisms on acrylic surfaces have been investigated. $^{222}$Rn can represent a significant background source for low-background experiments, and acrylic is a suitable material for detector design thanks to its purity and transparency. Four acrylic samples have been exposed to a $^{222}$Rn rich environment for different time periods, being contaminated by $^{222}$Rn and its progenies. Subsequently, the time evolution of radiocontaminants activity on the samples has been evaluated with $\alpha$ and $\gamma$ measurements, highlighting the role of different decay modes in the contamination process. A detailed analysis of the alpha spectra allowed to quantify the implantation depth of the contaminants. Moreover, a study of both $\alpha$ and $\gamma$ measurements pointed out the $^{222}$Rn diffusion inside the samples

Combined serum and immunohistochemical differentiation between reactive, and malignant mesothelial proliferations

Background: Malignant mesothelioma (MM) carries a poor prognosis and response rates to palliative chemotherapy remain low. The diagnosis of malignant mesothelioma is frequently difficult, the most common differential diagnosis being reactive pleural conditions and metastatic adenocarcinoma. Several studies have used immunohistochemical markers to distinguish between reactive and neoplastic mesothelial cells. Soluble mesothelin levels in serum have recently been shown to be highly specific and moderately sensitive for mesothelioma. A combined detection of serum levels of mesothelin and immunohistochemical expression of desmin and EMA are used in order to differentiate between reactive mesothelial proliferations, and malignant mesothelioma of epithelioid type. Patients and methods: This prospective study includes 17 cases of reactive mesothelial proliferations, 6 cases of atypical mesothelial proliferations and 13 cases of MM. Cases were collected from the Chest Department, Faculty of Medicine, Benha University and International Medical Center (IMC), in the period 2012–2014. Desmin and epithelial membrane antigen (EMA) immunohistochemical staining were performed in all cases and the pattern of expression was analyzed. Soluble mesothelin related peptide (SMRP) was estimated for all cases. Results: Desmin expression was positive in 88.2%, 0%, and 7.7% of reactive mesothelial proliferations, atypical mesothelial proliferations and MM respectively. EMA was positive in 5.9% of reactive mesothelial proliferation, 100% of atypical mesothelial proliferations and 92.3% of MM cases (P < 0.01). The calculated mean SMRP was 6.6 nM. SMRP levels were higher than the calculated mean value in 17.6% of studied reactive mesothelial lesions, 66.7% and 76.9% of atypical mesothelial proliferations and MM respectively, which was statistically highly significant correlation (P < 0.01). Conclusion: Combined estimation of SMRP level and immunohistochemical detection of both EMA and desmin could be a useful tool for differentiation between reactive mesothelial proliferation and malignant mesothelioma