Behaviour of ternary blends in heated suction accumulator

The results of an experimental study on the behaviour of azeotropic ternary refrigerant mixtures in heated suction accumulator are presented. The experiment set up was composed of a fully instrumented air‐source heat pump with a capacity of 36 000 BTUH and equipped with a heated suction accumulator. Standard test conditions were used at the condenser inlet; however, the refrigerant temperatures were varied at the evaporator entrance from 2 to −40°C, to simulate various conditions encountered in extreme air‐source heat pump applications. The primary parameters observed during the course of this study were mass flux, heat flux, and quality, evaporator and condenser thermal capacities, and power consumed and pressure ratios for the azeotropic refrigerant mixtures under investigation. The test results showed that heated suction accumulators enhance the evaporation of the more volatile component of ternary azeotropic refrigerant mixtures. Thus, increasing the mixture thermal capacity as well as the coefficient of performance (COP). Furthermore, experiments have also shown capacity increases of 27% with heated accumulator over unheated accumulator at −30°C outside air temperatures. Copyright © 1999 John Wiley & Sons, Ltd.     

Modelling of capillary tubes behaviour with HCFC 22 ternary alternative refrigerants

In this paper, a numerical model is presented for predicting capillary tube performance using new ternary mixtures proposed as alternatives to R 22. The model has been established after the fluid flow conservation equations written for a homogeneous refrigerant fluid flow under saturated, subcooled and two- pase conditions. Numerical results showed that the proposed model in question fairly simulated experimental on ternary refrigerant mixtures and fairly predicted the capillary tube behaviour under the investigated; subcooled, saturated, and two-phase flow conditions. © 1998 John Wiley & Sons, Ltd.     

Genes and Longevity of Lifespan

Aging is a complex process indicated by low energy levels, declined physiological activity, stress induced loss of homeostasis leading to the risk of diseases and mortality. Recent developments in medical sciences and an increased availability of nutritional requirements has significantly increased the average human lifespan worldwide. Several environmental and physiological factors contribute to the aging process. However, about 40% human life expectancy is inherited among generations, many lifespan associated genes, genetic mechanisms and pathways have been demonstrated during last decades. In the present review, we have evaluated many human genes and their non-human orthologs established for their role in the regulation of lifespan. The study has included more than fifty genes reported in the literature for their contributions to the longevity of life. Intact genomic DNA is essential for the life activities at the level of cell, tissue, and organ. Nucleic acids are vulnerable to oxidative stress, chemotherapies, and exposure to radiations. Efficient DNA repair mechanisms are essential for the maintenance of genomic integrity, damaged DNA is not replicated and transferred to next generations rather the presence of deleterious DNA initiates signaling cascades leading to the cell cycle arrest or apoptosis. DNA modifications, DNA methylation, histone methylation, histone acetylation and DNA damage can eventually lead towards apoptosis. The importance of calorie restriction therapy in the extension of lifespan has also been discussed. The role of pathways involved in the regulation of lifespan such as DAF-16/FOXO (forkhead box protein O1), TOR and JNK pathways has also been particularized. The study provides an updated account of genetic factors associated with the extended lifespan and their interactive contributory role with cellular pathways.     

Confocal-based fluorescence fluctuation spectroscopy with a SPAD array detector

The combination of confocal laser-scanning microscopy(CLSM)and fluorescence fluctuation spectroscopy(FFS)is a powerful tool in studying fast,sub-resolution biomolecular processes in living cells.A detector array can further enhance CLSM-based FFS techniques,as it allows the simultaneous acquisition of several samples-essentially images-of the CLSM detection volume.However,the detector arrays that have previously been proposed for this purpose require tedious data corrections and preclude the combination of FFS with single-photon techniques,such as fluorescence lifetime imaging.Here,we solve these limitations by integrating a novel singie-photon-avalanche-diode(SPAD)array detector in a CLSM system.We validate this new implementation on a series of FFS analyses:spot-variation fluorescence correlation spectroscopy,pair-correlation function analysis,and image-derived mean squared displacement analysis.We predict that the unique combination of spatial and temporal information provided by our detector will make the proposed architecture the method of choice for CLSM-based FFS.     

Synergistic Functionality of Dopants and Defects in Co-Phthalocyanine/B-CN Z-Scheme Photocatalysts for Promoting Photocatalytic CO2 Reduction Reactions

The realization of solar-light-driven CO₂ reduction reactions (CO₂ RR) is essential for the commercial development of renewable energy modules and the reduction of global CO₂ emissions. Combining experimental measurements and theoretical calculations, to introduce boron dopants and nitrogen defects in graphitic carbon nitride (g-C₃N₄), sodium borohydride is simply calcined with the mixture of g-C₃N₄ (CN), followed by the introduction of ultrathin Co phthalocyanine through phosphate groups. By strengthening H-bonding interactions, the resultant CoPc/P-BNDCN nanocomposite showed excellent photocatalytic CO₂ reduction activity, releasing 197.76 and 130.32 µmol h⁻¹ g⁻¹ CO and CH₄, respectively, and conveying an unprecedented 10-26-time improvement under visible-light irradiation. The substantial tuning is performed towards the conduction and valance band locations by B-dopants and N-defects to modulate the band structure for significantly accelerated CO₂ RR. Through the use of ultrathin metal phthalocyanine assemblies that have a lot of single-atom sites, this work demonstrates a sustainable approach for achieving effective photocatalytic CO₂ activation. More importantly, the excellent photoactivity is attributed to the fast charge separation via Z-scheme transfer mechanism formed by the universally facile strategy of dimension-matched ultrathin (≈4 nm) metal phthalocyanine-assisted nanocomposites.     

Dipper Throated Algorithm for Feature Selection and Classification in Electrocardiogram

Arrhythmia has been classified using a variety of methods. Because of the dynamic nature of electrocardiogram (ECG) data, traditional handcrafted approaches are difficult to execute, making the machine learning (ML) solutions more appealing. Patients with cardiac arrhythmias can benefit from competent monitoring to save their lives. Cardiac arrhythmia classification and prediction have greatly improved in recent years. Arrhythmias are a category of conditions in which the heart's electrical activity is abnormally rapid or sluggish. Every year, it is one of the main reasons of mortality for both men and women, worldwide. For the classification of arrhythmias, this work proposes a novel technique based on optimized feature selection and optimized K-nearest neighbors (KNN) classifier. The proposed method makes advantage of the UCI repository, which has a 279-attribute high-dimensional cardiac arrhythmia dataset. The proposed approach is based on dividing cardiac arrhythmia patients into 16 groups based on the electrocardiography dataset’s features. The purpose is to design an efficient intelligent system employing the dipper throated optimization method to categorize cardiac arrhythmia patients. This method of comprehensive arrhythmia classification outperforms earlier methods presented in the literature. The achieved classification accuracy using the proposed approach is 99.8%.     

Minimal Architecture Lithium Batteries: Toward High Energy Density Storage Solutions

The coupling of thick and dense cathodes with anode-free lithium metal configuration is a promising path to enable the next generation of high energy density solid-state batteries. In this work, LiCoO₂ (30 µm)/LiPON/Ti is considered as a model system to study the correlation between fundamental electrode properties and cell electrochemical performance, and a physical model is proposed to understand the governing phenomena. The first cycle loss is demonstrated to be constant and independent of both cathode thickness and anode configuration, and only ascribed to the diffusion coefficient's abrupt fall at high lithium contents. Subsequent cycles achieve close to 100% coulombic efficiency. The examination of the effect of cathode thickness demonstrate a nearly linear correlation with areal specific capacity for sub-100 µm LiCoO₂ and 0.1 mA cm⁻² current density. These findings bring new insights to better understand the energy density limiting factors and to suggest potential optimization approaches.     

An Improved Deep Structure for Accurately Brain Tumor Recognition

Brain neoplasms are recognized with a biopsy, which is not commonly done before decisive brain surgery. By using Convolutional Neural Networks (CNNs) and textural features, the process of diagnosing brain tumors by radiologists would be a noninvasive procedure. This paper proposes a features fusion model that can distinguish between no tumor and brain tumor types via a novel deep learning structure. The proposed model extracts Gray Level Co-occurrence Matrix (GLCM) textural features from MRI brain tumor images. Moreover, a deep neural network (DNN) model has been proposed to select the most salient features from the GLCM. Moreover, it manipulates the extraction of the additional high levels of salient features from a proposed CNN model. Finally, a fusion process has been utilized between these two types of features to form the input layer of additional proposed DNN model which is responsible for the recognition process. Two common datasets have been applied and tested, Br35H and FigShare datasets. The first dataset contains binary labels, while the second one splits the brain tumor into four classes; glioma, meningioma, pituitary, and no cancer. Moreover, several performance metrics have been evaluated from both datasets, including, accuracy, sensitivity, specificity, F-score, and training time. Experimental results show that the proposed methodology has achieved superior performance compared with the current state of art studies. The proposed system has achieved about 98.22% accuracy value in the case of the Br35H dataset however, an accuracy of 98.01% has been achieved in the case of the FigShare dataset.     

Sailfish Optimization with Deep Learning Based Oral Cancer Classification Model

Recently, computer aided diagnosis (CAD) model becomes an effective tool for decision making in healthcare sector. The advances in computer vision and artificial intelligence (AI) techniques have resulted in the effective design of CAD models, which enables to detection of the existence of diseases using various imaging modalities. Oral cancer (OC) has commonly occurred in head and neck globally. Earlier identification of OC enables to improve survival rate and reduce mortality rate. Therefore, the design of CAD model for OC detection and classification becomes essential. Therefore, this study introduces a novel Computer Aided Diagnosis for OC using Sailfish Optimization with Fusion based Classification (CADOC-SFOFC) model. The proposed CADOC-SFOFC model determines the existence of OC on the medical images. To accomplish this, a fusion based feature extraction process is carried out by the use of VGGNet-16 and Residual Network (ResNet) model. Besides, feature vectors are fused and passed into the extreme learning machine (ELM) model for classification process. Moreover, SFO algorithm is utilized for effective parameter selection of the ELM model, consequently resulting in enhanced performance. The experimental analysis of the CADOC-SFOFC model was tested on Kaggle dataset and the results reported the betterment of the CADOC-SFOFC model over the compared methods with maximum accuracy of 98.11%. Therefore, the CADOC-SFOFC model has maximum potential as an inexpensive and non-invasive tool which supports screening process and enhances the detection efficiency.     

Deep Learning ResNet101 Deep Features of Portable Chest X-Ray Accurately Classify COVID-19 Lung Infection

This study is designed to develop Artificial Intelligence (AI) based analysis tool that could accurately detect COVID-19 lung infections based on portable chest x-rays (CXRs). The frontline physicians and radiologists suffer from grand challenges for COVID-19 pandemic due to the suboptimal image quality and the large volume of CXRs. In this study, AI-based analysis tools were developed that can precisely classify COVID-19 lung infection. Publicly available datasets of COVID-19 (N = 1525), non-COVID-19 normal (N = 1525), viral pneumonia (N = 1342) and bacterial pneumonia (N = 2521) from the Italian Society of Medical and Interventional Radiology (SIRM), Radiopaedia, The Cancer Imaging Archive (TCIA) and Kaggle repositories were taken. A multi-approach utilizing deep learning ResNet101 with and without hyperparameters optimization was employed. Additionally, the features extracted from the average pooling layer of ResNet101 were used as input to machine learning (ML) algorithms, which twice trained the learning algorithms. The ResNet101 with optimized parameters yielded improved performance to default parameters. The extracted features from ResNet101 are fed to the k-nearest neighbor (KNN) and support vector machine (SVM) yielded the highest 3-class classification performance of 99.86% and 99.46%, respectively. The results indicate that the proposed approach can be better utilized for improving the accuracy and diagnostic efficiency of CXRs. The proposed deep learning model has the potential to improve further the efficiency of the healthcare systems for proper diagnosis and prognosis of COVID-19 lung infection.