Influence of carbon nanotubes and nano-alumina on the thermal performance of nitrate phase change materials for thermal storage

硝酸熔融盐具有良好的热物性能,在聚光太阳能发电技术中得到广泛运用,通过提高熔盐的比热容,可以提高其蓄热能力。将硝酸锂、硝酸钾和硝酸钠以共晶比52：30：18制备硝酸盐储热材料,通过添加纳米氧化铝颗粒及碳纳米管研究其对熔融盐比热的影响。通过超声振动和蒸发在硝酸熔融盐中加入纳米氧化铝颗粒和碳纳米管。采用直接合成法制备含纳米氧化铝和碳纳米管分别为0.06%、0.5%、1%和2%的硝酸盐纳米流体。通过SEM观察了纳米氧化铝颗粒及碳纳米管在硝酸盐中的分散性,当纳米氧化铝颗粒浓度为1%时发生团聚,这种团聚会影响纳米流体的比热容。碳纳米管添加量小于2%时分散性好。通过差示扫描量热仪（DSC）测量了硝酸盐纳米复合材料的比热容,纳米氧化铝浓度为1%的硝酸盐纳米流体在固态（110℃）和液态（140℃）下分别是纯硝酸盐比热容的1.44倍和3.48倍。CNTs浓度为1%的硝酸盐纳米流体在固态（110℃）和液态（140℃）下分别是纯硝酸盐比热容的5.07倍和4.17倍。DSC结果表明当纳米颗粒浓度大于1%时,纳米流体的比热降低。本研究讨论了提高纳米流体比热容的机制,得到硝酸盐比热容的增加与纳米颗粒的高比表面积和高能量相关。     

Facile Synthesis as well as Structural,Raman, Dielectric and Antibacterial Characteristics of Cu Doped ZnO Nanoparticles

Here, undoped and Cu doped ZnO nanoparticles(NPs) have been prepared by chemical co-precipitation technique. X-ray diffraction(XRD) results reveal that Cu ions are successfully doped into ZnO matrix without altering its wurtzite phase. The single wurtzite phase of ZnO is retained even for 10 wt% Cu doped ZnO sample. It is observed from the electron microscopy results that higher level of Cu doping varies the morphology of ZnO NPs from spherical to flat NPs. Moreover, the particle size is found to increase with the increase in Cu doping level. Raman spectroscopy results further confirm that Cu dopant has not altered the wurtzite structure of ZnO. Impedance spectroscopy results reveal that the dielectric constant and dielectric loss have increasing trend with Cu doping. Cu doping has been found to slightly decrease the bactericidal potency of ZnO nanoparticles.     

Breast Cancer Detection in Saudi Arabian Women Using Hybrid Machine Learning on Mammographic Images

Breast cancer (BC) is the most common cause of women’s deaths worldwide. The mammography technique is the most important modality for the detection of BC. To detect abnormalities in mammographic images, the Breast Imaging Reporting and Data System (BI-RADs) is used as a baseline. The correct allocation of BI-RADs categories for mammographic images is always an interesting task, even for specialists. In this work, to detect and classify the mammogram images in BI-RADs, a novel hybrid model is presented using a convolutional neural network (CNN) with the integration of a support vector machine (SVM). The dataset used in this research was collected from different hospitals in the Qassim health cluster of Saudi Arabia. The collection of all categories of BI-RADs is one of the major contributions of this paper. Another significant contribution is the development of a hybrid approach through the integration of CNN and SVM. The proposed hybrid approach uses three CNN models to obtain ensemble CNN model results. This ensemble model saves the values to integrate them with SVM. The proposed system achieved a classification accuracy, sensitivity, specificity, precision, and F1-score of 93.6%, 94.8%, 96.9%, 96.6%, and 95.7%, respectively. The proposed model achieved better performance compared to previously available methods.     

Terpenoids as Human Neutrophil Elastase (HNE) Inhibitors: A Comprehensive Review of Natural Anti-inflammatory Isoprenoids

Human neutrophil elastase (HNE) is an enzyme that plays a key role in the body‘s inflammatory response. It has been linked to several diseases such as chronic obstructive pulmonary disease (COPD), emphysema, and cystic fibrosis. As potential treatments for these diseases, HNE inhibitors are of great interest. Metabolites derived from plants, particularly terpenoids such as β-caryophyllene found in black pepper and other plants, and geraniol present in several essential oils, are recognized as significant sources of inhibitors for HNE. Because of their ability to inhibit HNE, terpenoids are considered promising candidates for developing novel therapies to treat inflammatory conditions such as COPD and emphysema. Furthermore, nature can serve as an excellent designer, and it may offer a safer drug candidate for inhibiting HNE production and activity in the future. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses were searched to get relevant and up-to-date literature on terpenoids as human neutrophil elastase inhibitors. This review focuses on the isolation, chemical diversity, and inhibition of human neutrophil elastase (HNE) of various terpenoids reported from natural sources up to 2022. A total of 251 compounds from various terpenoids classes have been reported. Further, it also provides a summary of HNE inhibitors and includes a thorough discussion on the structure-activity relationship.     

Designing Pair of Nonlinear Components of a Block Cipher over Gaussian Integers

In block ciphers, the nonlinear components, also known as substitution boxes (S-boxes), are used with the purpose of inducing confusion in cryptosystems. For the last decade, most of the work on designing S-boxes over the points of elliptic curves has been published. The main purpose of these studies is to hide data and improve the security levels of crypto algorithms. In this work, we design pair of nonlinear components of a block cipher over the residue class of Gaussian integers (GI). The fascinating features of this structure provide S-boxes pair at a time by fixing three parameters. But the prime field dependent on the Elliptic curve (EC) provides one S-box at a time by fixing three parameters and . The newly designed pair of S-boxes are assessed by various tests like nonlinearity, bit independence criterion, strict avalanche criterion, linear approximation probability, and differential approximation probability.     

Transfer learning for histopathology images: an empirical study

Neural Computing and Applications - Histopathology imaging is one of the key methods used to determine the presence of cancerous cells. However, determining the results from such medical images is...     

Big Data Analytics with Optimal Deep Learning Model for Medical Image Classification

In recent years, huge volumes of healthcare data are getting generated in various forms. The advancements made in medical imaging are tremendous owing to which biomedical image acquisition has become easier and quicker. Due to such massive generation of big data, the utilization of new methods based on Big Data Analytics (BDA), Machine Learning (ML), and Artificial Intelligence (AI) have become essential. In this aspect, the current research work develops a new Big Data Analytics with Cat Swarm Optimization based deep Learning (BDA-CSODL) technique for medical image classification on Apache Spark environment. The aim of the proposed BDA-CSODL technique is to classify the medical images and diagnose the disease accurately. BDA-CSODL technique involves different stages of operations such as preprocessing, segmentation, feature extraction, and classification. In addition, BDA-CSODL technique also follows multi-level thresholding-based image segmentation approach for the detection of infected regions in medical image. Moreover, a deep convolutional neural network-based Inception v3 method is utilized in this study as feature extractor. Stochastic Gradient Descent (SGD) model is used for parameter tuning process. Furthermore, CSO with Long Short-Term Memory (CSO-LSTM) model is employed as a classification model to determine the appropriate class labels to it. Both SGD and CSO design approaches help in improving the overall image classification performance of the proposed BDA-CSODL technique. A wide range of simulations was conducted on benchmark medical image datasets and the comprehensive comparative results demonstrate the supremacy of the proposed BDA-CSODL technique under different measures.     

Engineering Nanoclay Edges to Enhance Antimicrobial Property against Gram-Negative Bacteria: Understanding the Membrane Destruction Mechanism by Contact-Kill

The overuse or abuse of antibiotics has led to serious health problems. During the recent decades, among the various methods used in antibacterial applications, some nanoclay minerals are proved antibacterial or inhibitory to the bacterial growth. However, the antibacterial mechanism of contact-kill based on the intrinsic structure of nanoclays is still unclear. Here, the antibacterial ability of pure clay is enhanced by creating more edge surfaces on kaolinite (Kaol) and the antibacterial mechanism is clarified at the atomic level. Based on experiments and density functional theory/molecular dynamics  calculations, the positively charged Al (OH) and Al (OH₂) species on the edge surfaces of Kaol are confirmed to kill the Escherichia coli cells through direct contact by destroying their outer membrane (OM). The strong hydrogen bonding and van der Waals forces between OM and (110)/(1$\overline{1}$0) surfaces of Kaol lead to the folding of OM. Simultaneously, the proton-coupled electron transfer between Lipopolysaccharide (LPS) and (1$\overline{1}$0) edge surface of Kaol causes the dissociation of phosphoryl groups on LPS. Considering the similarities of most nanoclays on their edge surfaces, this finding may shed some light on the development of new nanoclay-based antibacterial materials in the future.     

Advances in Chemically Powered Micro/Nanorobots for Biological Applications: A Review

Micro/nanorobots (MNRs) are capable of autonomous motion, breaking through the limitations of traditional passive transport of nanocarriers. Among them, chemically driven MNRs are the earliest MNRs studied and have received extensive attention from researchers. This review first focuses on the material properties, preparation, driving forms, and mechanisms of chemically driven MNRs. The current status of research on chemically driven MNRs in biomedicine is summarized for various biological applications (drug delivery, diagnostics, anti-inflammatory, antibacterial, and disease treatment). In terms of biosafety, possible safety issues are analyzed in the context of chemically driven microrobotic applications in terms of three aspects: component characteristics, chemical engines and biological environment. Finally, the challenges and possible future directions of chemically driven MNRs are presented.     

Aggregation and adsorption behavior of cobalt-based metallosurfactant in water–ethylene glycol media forming worm-like micelles

The cobalt based metallosurfactant cis-chlorobis(ethylenediamine)hexadecylaminecobalt(III) chloride (CHCC) has been prepared and well characterized by utilizing elemental analysis, NMR (¹H, ¹³C), FT-IR and UV–Vis spectroscopy. The CHCC metallosurfactant shows thermal stability up to 168°C. The micellization behavior of the synthesized CHCC metallosurfactant has been investigated systematically by the tensiometric, conductometric, and fluorescence techniques. The critical micelle concentration (cmc) values of CHCC have been determined in various water–ethylene glycol mixtures ranging from 0 to 100 weight % of ethylene glycol at 293.15, 298.15, 303.15, and 308.15 K. The physicochemical parameters namely counterion binding constant, surface pressure, surface excess, surface area covered per CHCC metallosurfactant molecule, free energy minimum, standard free energies of micellization and adsorption, standard enthalpy and entropy of micellization, and Gibb's free energy of transfer have been calculated. The hydrodynamic diameters and zeta potentials of the CHCC metallomicelles have been measured by dynamic light scattering method. Transmission electron microscopy was employed to confirm the presence of worm-like micelles.