Simulation Study on the Open-Circuit Voltage of Amorphous Silicon p-i-n Solar Cells Using AMPS-1D

AMPS-1D (Analysis of Microelectronic and Photonic Structure) simulation program was used to simulate Amorphous Silicon p-i-n Solar Cell. The simulated result of illuminated current density-voltage characteristics was in a good agreement with experimental values. The dependence of the open-circuit voltage on the characteristics of the a-Si:H intrinsic layer was investigated. The simulation result shows that the open-circuit voltage does not depend on the thickness of the intrinsic layer. The open-circuit voltage decreases when the front contact barrier height is small or the energy gap of the intrinsic layer is small. The open-circuit voltage increases when the distribution of the tail states is sharp or the capture cross sections of these states are small. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/3432

Promising Use of Cyclodextrin-Based Non-Viral Vectors for Gene and Oligonucleotide Drugs

Genes, short-hairpin RNA (shRNA), small-interfering RNA (siRNA), and decoy DNA can be principally used as tools for the treatment and prevention of many disorders, including but not limited to cancers, genetic disorders, and inherited diseases. This is accomplished by introducing exogenous nucleic acids into mammalian cells to modulate gene expression. However, direct use of such oligonucleotide drugs is hampered by several barriers, including their degradation by nucleases present in the blood and extracellular fluid, cell-membrane impermeability, and their retention in endosomes. To address this issue, the development of safe and effective delivery vectors has emerged as the main fundamental challenge for successful gene and oligonucleotide therapy. Due to the intrinsic risks associated with viral vectors, non-viral vectors have attracted increasing attention as gene and oligonucleotide carriers. We originally developed various cyclodextrin (CyD) conjugates with polyamidoamine (PAMAM) dendrimers as novel CyD-based polymers for the delivery of plasmid DNA, siRNA, shRNA, and decoy DNA. In this review, we describe the recent findings on PAMAM dendrimer conjugates using CyDs as carriers for gene, shRNA, siRNA, and decoy DNA delivery

GIS and AHP based modeling for landfill site selection (case study: west side of Mosul city)

The accumulation of large quantities of solid waste inside Mosul city becomes a real residential and municipal management problem. There are many reasons including the existence of unplanned dumping sites within the city boundaries, and the absence of scientific researches which applies modern techniques for selecting the optimal solid waste landfill. This study uses geographic information system (GIS) and analytic hierarchical process (AHP) which is used to extract the weights with the help of Super Decision SD software. The studied variables data can be classified according to specified processing method into two types: continuous data, and discrete data. The ranking map has been designed after multiplying each variable with its extracted weight, then the final map has been created based on the values obtained from the ranking map. The results show that the optimal landfill area is located at south west Mosul city. This study aims at building a model by using GIS to determine the optimal and potential solid waste landfill site

Exploring consumer knowledge, understanding and use of food and nutrition label information in the tamale metropolis of Ghana

The perception that consumers in low Income Countries have poor knowledge and understanding of food or nutrition labels and, therefore, do not rely on them at the point of purchase is rife. This study was aimed at assessing consumer knowledge and understanding and its influence on food label usage in the Tamale Metropolis of Ghana. An analytical cross-sectional study design was employed and mainly literate adults aged 15 to 60 years were conveniently selected and interviewed at various points-of-purchase including supermarkets, provision shops and other trading outlets. Data were analysed using the Statistical Package for Social Sciences (SPSS) for windows (version 19.0). Percentages were calculated and reported for descriptive statistics whilst chi-square tests of significance and regression analysis were employed to measure relationships between variables. Statistically significant differences were accepted at p<0.05. Out of the 384 consumers interviewed, 98.4% (n=378) were aware of food labels, yet, only 66.7 % (n=256) claimed they understood the labels. A large proportion (95.8%) also claimed they checked but just about 51.9% said they did so “always”. Most (89.3%) claimed they are influenced by key factors on the labels with the level of influence being highest with nutrition content, followed by expiry date, health-claim, price and advertisement respectively. However, at the point-of-purchase most (79.4) revealed they looked out for expiry date. Socio-demographic characteristics including gender (p=0.009), age (p=0.017), occupation (p=0.042), educational level (p=0.022) and income (p=0.051) were significantly associated with consumers’ understanding of the labels, with gender remaining the only significant predictor. Furthermore, age (p=0.054), occupation (p=0.0.007) and educational level (p<0.001) showed significant associations with food label usage. Education level (Tertiary) emerged the only significant predictor of food label usage. The level of knowledge and use of nutrition information on food packages among predominantly literate consumers in the Tamale Metropolis of Ghana can be compared to that of consumers in other parts of the world. These results may inform the need for developing an approach towards future information and education strategies for health professionals and other stakeholders interested in consumer awareness activities.Keywords: Nutrition label, food Label, Consumer, Point-of-purchase, Nutrition information, Tamal

Single and Multi-Sources Energy Sizing for Electric Vehicle: A Case Study

The automotive industry has introduced various renewable-energy based technologies such as battery electric vehicles (BEV) and fuel-cell electric vehicles (FCEV). However  the main concern is addressing issues to determine which vehicle with different energy sources are more efficiency and cost saving than the others.  In order to overcome this issue detailed analysis need to be performed on the important criterions in vehicle sizing like energy cost, dissipated energy and effective energy source (EES). This paper deals with the modeling, evaluation and analysis of single and multi-source electric vehicle (EV) on three classes of EV, namely the light electric vehicle (LEV), medium electric vehicle (MEV) and electric vehicle (EV). A comparison on dissipated energy with different EES, charging cost and weight were made based on a linear mathematical calculation. The results have shown that multi-sources energy powered-vehicle deliver among the best dissipated energy and EES percentage. Findings of this energy sizing under various combination of EV would be helpful for further research on the EV energy applications

Expression profiles of catalase gene in common carp exposed to ammonia

Ammonia is one of the most harmful water quality parameters restricting the growth and survival of aquatic living organisms. As a result, fish must adapt to this stressor by modifying physiological processes that are governed by gene expression regulation. The aim of the present study was to investigate the expression profiles of antioxidant related gene, catalase (CAT) in common carp (Cyprinus carpio) fingerlings after exposure to 0.7 mg/l of unionized ammonia (UIA) in water. The relative gene expression was measured in liver, gills, and brain tissues at four time points (12 h, 2 d, 4 d, and 7 d post exposure). The expression level of CAT gene in the liver and brain peaked after 7 d of ammonia exposure by 13.3 and 5.2-folds, respectively, but in gills it upregulated only after 2 d (2.7-folds) and downregulated at the other time points. This study proved that exposure to ammonia affects the antioxidant status of common carp as indicated by the altered levels of expression of CAT gene

Multi-method analysis of medical records and mri images for early diagnosis of dementia and alzheimer’s disease based on deep learning and hybrid methods

Dementia and Alzheimer’s disease are caused by neurodegeneration and poor commu-nication between neurons in the brain. So far, no effective medications have been discovered for dementia and Alzheimer’s disease. Thus, early diagnosis is necessary to avoid the development of these diseases. In this study, efficient machine learning algorithms were assessed to evaluate the Open Access Series of Imaging Studies (OASIS) dataset for dementia diagnosis. Two CNN models (AlexNet and ResNet-50) and hybrid techniques between deep learning and machine learning (AlexNet+SVM and ResNet-50+SVM) were also evaluated for the diagnosis of Alzheimer’s disease. For the OASIS dataset, we balanced the dataset, replaced the missing values, and applied the t-Distributed Stochastic Neighbour Embedding algorithm (t-SNE) to represent the high-dimensional data in the low-dimensional space. All of the machine learning algorithms, namely, Support Vector Machine (SVM), Decision Tree, Random Forest and K Nearest Neighbours (KNN), achieved high performance for diagnosing dementia. The random forest algorithm achieved an overall accuracy of 94% and precision, recall and F1 scores of 93%, 98% and 96%, respectively. The second dataset, the MRI image dataset, was evaluated by AlexNet and ResNet-50 models and AlexNet+SVM and ResNet-50+SVM hybrid techniques. All models achieved high performance, but the performance of the hybrid methods between deep learning and machine learning was better than that of the deep learning models. The AlexNet+SVM hybrid model achieved accuracy, sensitivity, specificity and AUC scores of 94.8%, 93%, 97.75% and 99.70%, respectively

Deep Learning and Machine Learning for Early Detection of Stroke and Haemorrhage

Stroke and cerebral haemorrhage are the second leading causes of death in the world after ischaemic heart disease. In this work, a dataset containing medical, physiological and environmental tests for stroke was used to evaluate the efficacy of machine learning, deep learning and a hybrid technique between deep learning and machine learning on theMagnetic Resonance Imaging (MRI) dataset for cerebral haemorrhage. In the first dataset (medical records), two features, namely, diabetes and obesity, were created on the basis of the values of the corresponding features. The t-Distributed Stochastic Neighbour Embedding algorithm was applied to represent the high-dimensional dataset in a low-dimensional data space. Meanwhile, the Recursive Feature Elimination algorithm (RFE) was applied to rank the features according to priority and their correlation to the target feature and to remove the unimportant features. The features are fed into the various classification algorithms, namely, Support Vector Machine (SVM), K Nearest Neighbours (KNN), Decision Tree, Random Forest, and Multilayer Perceptron. All algorithms achieved superior results. The Random Forest algorithm achieved the best performance amongst the algorithms; it reached an overall accuracy of 99%. This algorithm classified stroke cases with Precision, Recall and F1 score of 98%, 100% and 99%, respectively. In the second dataset, the MRI image dataset was evaluated by using the AlexNet model and AlexNet+SVM hybrid technique. The hybrid model AlexNet+SVM performed is better than the AlexNet model; it reached accuracy, sensitivity, specificity and Area Under the Curve (AUC) of 99.9%, 100%, 99.80% and 99.86%, respectively

Identification of selective Lyn inhibitors from the chemical databases through integrated molecular modelling approaches

In the current study, the Asinex and ChEBI databases were virtually screened for the identification of potential Lyn protein inhibitors. Therefore, a multi-steps molecular docking study was carried out using the VSW utility tool embedded in Maestro user interface of the Schrödinger suite. On initial screening, molecules having a higher XP-docking score and binding free energy compared to Staurosporin were considered for further assessment. Based on in silico pharmacokinetic analysis and a common-feature pharmacophore mapping model developed from the Staurosporin, four molecules were proposed as promising Lyn inhibitors. The binding interactions of all proposed Lyn inhibitors revealed strong ligand efficiency in terms of energy score obtained in molecular modelling analyses. Furthermore, the dynamic behaviour of each molecule in association with the Lyn protein-bound state was assessed through an all-atoms molecular dynamics (MD) simulation study. MD simulation analyses were confirmed with notable intermolecular interactions and consistent stability for the Lyn protein-ligand complexes throughout the simulation. High negative binding free energy of identified four compounds calculated through MM-PBSA approach demonstrated a strong binding affinity towards the Lyn protein. Hence, the proposed compounds might be taken forward as potential next-generation Lyn kinase inhibitors for managing numerous Lyn associated diseases or health complications after experimental validation.The Deanship of Scientific Research at Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia through the Fast-track Research Funding Program.https://www.tandfonline.com/loi/gsar20hj2022Chemical Patholog

Anemia prevalence in women of reproductive age in low- and middle-income countries between 2000 and 2018

Anemia is a globally widespread condition in women and is associated with reduced economic productivity and increased mortality worldwide. Here we map annual 2000–2018 geospatial estimates of anemia prevalence in women of reproductive age (15–49 years) across 82 low- and middle-income countries (LMICs), stratify anemia by severity and aggregate results to policy-relevant administrative and national levels. Additionally, we provide subnational disparity analyses to provide a comprehensive overview of anemia prevalence inequalities within these countries and predict progress toward the World Health Organization’s Global Nutrition Target (WHO GNT) to reduce anemia by half by 2030. Our results demonstrate widespread moderate improvements in overall anemia prevalence but identify only three LMICs with a high probability of achieving the WHO GNT by 2030 at a national scale, and no LMIC is expected to achieve the target in all their subnational administrative units. Our maps show where large within-country disparities occur, as well as areas likely to fall short of the WHO GNT, offering precision public health tools so that adequate resource allocation and subsequent interventions can be targeted to the most vulnerable populations.Peer reviewe