Experimental and theoretical evaluation of temperature polarization phenomenon in direct contact membrane distillation

Heat and mass transfer in direct contact membrane distillation (MD) has been analyzed by using a specifically designed cell. In particular, the cell has sixteen sensors located at specific locations within its body to measure the bulk and membrane surface temperatures on both feed and permeate sides. The effect of various hydrodynamic and thermal conditions on heat and mass transport in direct contact membrane distillation has been investigated experimentally. The effect of solution concentration and thermal polarization on membrane distillation performance has been explored quantitatively. A good agreement between the experimental and theoretical results has been observed.     

MCBC-SMOTE: A Majority Clustering Model for Classification ofImbalanced Data

Datasets with the imbalanced class distribution are difficult to handle with the standard classification algorithms. In supervised learning, dealing with the problem of class imbalance is still considered to be a challenging research problem. Various machine learning techniques are designed to operate on balanced datasets; therefore, the state of the art, different under-sampling, over-sampling and hybrid strategies have been proposed to deal with the problem of imbalanced datasets, but highly skewed datasets still pose the problem of generalization and noise generation during resampling. To over-come these problems, this paper proposes a majority clustering model for classification of imbalanced datasets known as MCBC-SMOTE (Majority Clustering for balanced Classification-SMOTE). The model provides a method to convert the problem of binary classification into a multi-class problem. In the proposed algorithm, the number of clusters for the majority class is calculated using the elbow method and the minority class is over-sampled as an average of clustered majority classes to generate a symmetrical class distribution. The proposed technique is cost-effective, reduces the problem of noise generation and successfully disables the imbalances present in between and within classes. The results of the evaluations on diverse real datasets proved to provide better classification results as compared to state of the art existing methodologies based on several performance metrics.     

Lattice Transformation from 2D to Quasi 1D and Phonon Properties of Exfoliated ZrS2 and ZrSe2

Recent reports on thermal and thermoelectric properties of emerging 2D materials have shown promising results. Among these materials are Zirconium-based chalcogenides such as zirconium disulfide (ZrS₂), zirconium diselenide (ZrSe₂), zirconium trisulfide (ZrS₃), and zirconium triselenide (ZrSe₃). Here, the thermal properties of these materials are investigated using confocal Raman spectroscopy. Two different and distinctive Raman signatures of exfoliated ZrX₂ (where X = S or Se) are observed. For 2D-ZrX₂, Raman modes are in alignment with those reported in literature. However, for quasi 1D-ZrX₂, Raman modes are identical to exfoliated ZrX₃ nanosheets, indicating a major lattice transformation from 2D to quasi-1D. Raman temperature dependence for ZrX₂ are also measured. Most Raman modes exhibit a linear downshift dependence with increasing temperature. However, for 2D-ZrS₂, a blueshift for A1g mode is detected with increasing temperature. Finally, phonon dynamics under optical heating for ZrX₂ are measured. Based on these measurements, the calculated thermal conductivity and the interfacial thermal conductance indicate lower interfacial thermal conductance for quasi 1D-ZrX₂ compared to 2D-ZrX₂, which can be attributed to the phonon confinement in 1D. The results demonstrate exceptional thermal properties for Zirconium-based materials, making them ideal for thermoelectric device applications and future thermal management strategies.     

The Role of Rubidium in Multiple‐Cation‐Based High‐Efficiency Perovskite Solar Cells

Perovskite solar cells (PSCs) based on cesium (Cs)‐ and rubidium (Rb)‐containing perovskite films show highly reproducible performance; however, a fundamental understanding of these systems is still emerging. Herein, this study has systematically investigated the role of Cs and Rb cations in complete devices by examining the transport and recombination processes using current–voltage characteristics and impedance spectroscopy in the dark. As the credibility of these measurements depends on the performance of devices, this study has chosen two different PSCs, (MAFACs)Pb(IBr)₃ (MA = CH₃NH₃⁺, FA = CH(NH₂)₂⁺) and (MAFACsRb)Pb(IBr)₃, yielding impressive performances of 19.5% and 21.1%, respectively. From detailed studies, this study surmises that the confluence of the low trap‐assisted charge‐carrier recombination, low resistance offered to holes at the perovskite/2,2′,7,7′‐tetrakis(N,N‐di‐p‐methoxyphenylamine)‐9,9‐spirobifluorene interface with a low series resistance (R_s), and low capacitance leads to the realization of higher performance when an extra Rb cation is incorporated into the absorber films. This study provides a thorough understanding of the impact of inorganic cations on the properties and performance of highly efficient devices, and also highlights new strategies to fabricate efficient multiple‐cation‐based PSCs.     

Novel Computer-Aided Diagnosis System for the Early Detection of Alzheimer’s Disease

Aging is a natural process that leads to debility, disease, and dependency. Alzheimer’s disease (AD) causes degeneration of the brain cells leading to cognitive decline and memory loss, as well as dependence on others to fulfill basic daily needs. AD is the major cause of dementia. Computer-aided diagnosis (CADx) tools aid medical practitioners in accurately identifying diseases such as AD in patients. This study aimed to develop a CADx tool for the early detection of AD using the Intelligent Water Drop (IWD) algorithm and the Random Forest (RF) classifier. The IWD algorithm an efficient feature selection method, was used to identify the most deterministic features of AD in the dataset. RF is an ensemble method that leverages multiple weak learners to classify a patient’s disease as either demented (DN) or cognitively normal (CN). The proposed tool also classifies patients as mild cognitive impairment (MCI) or CN. The dataset on which the performance of the proposed CADx was evaluated was sourced from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The RF ensemble method achieves 100% accuracy in identifying DN patients from CN patients. The classification accuracy for classifying patients as MCI or CN is 92%. This study emphasizes the significance of pre-processing prior to classification to improve the classification results of the proposed CADx tool.     

Multimodal brain tumor detection and classification using deep saliency map and improved dragonfly optimization algorithm

In the last decade, there has been a significant increase in medical cases involving brain tumors. Brain tumor is the tenth most common type of tumor, affecting millions of people. However, if it is detected early, the cure rate can increase. Computer vision researchers are working to develop sophisticated techniques for detecting and classifying brain tumors. MRI scans are primarily used for tumor analysis. We proposed an automated system for brain tumor detection and classification using a saliency map and deep learning feature optimization in this paper. The proposed framework was implemented in stages. In the initial phase of the proposed framework, a fusion-based contrast enhancement technique is proposed. In the following phase, a tumor segmentation technique based on saliency maps is proposed, which is then mapped on original images based on active contour. Following that, a pre-trained CNN model named EfficientNetB0 is fine-tuned and trained in two ways: on enhanced images and on tumor localization images. Deep transfer learning is used to train both models, and features are extracted from the average pooling layer. The deep learning features are then fused using an improved fusion approach known as Entropy Serial Fusion. The best features are chosen in the final step using an improved dragonfly optimization algorithm. Finally, the best features are classified using an extreme learning machine (ELM). The experimental process is conducted on three publically available datasets and achieved an improved accuracy of 95.14, 94.89, and 95.94%, respectively. The comparison with several neural nets shows the improvement of proposed framework.     

A Learning Model to Detect Android C&C Applications Using Hybrid Analysis

Smartphone devices particularly Android devices are in use by billions of people everywhere in the world. Similarly, this increasing rate attracts mobile botnet attacks which is a network of interconnected nodes operated through the command and control (C&C) method to expand malicious activities. At present, mobile botnet attacks launched the Distributed denial of services (DDoS) that causes to steal of sensitive data, remote access, and spam generation, etc. Consequently, various approaches are defined in the literature to detect mobile botnet attacks using static or dynamic analysis. In this paper, a novel hybrid model, the combination of static and dynamic methods that relies on machine learning to detect android botnet applications is proposed. Furthermore, results are evaluated using machine learning classifiers. The Random Forest (RF) classifier outperform as compared to other ML techniques i.e., Naïve Bayes (NB), Support Vector Machine (SVM), and Simple Logistic (SL). Our proposed framework achieved 97.48% accuracy in the detection of botnet applications. Finally, some future research directions are highlighted regarding botnet attacks detection for the entire community.     

Performance Analysis of Multi-Channel CR Enabled IoT Network with Better Energy Harvesting

Wireless Sensor Networks (WSNs) can be termed as an auto-configured and infrastructure-less wireless networks to monitor physical or environmental conditions, such as temperature, sound, vibration, pressure and motion etc. WSNs may comprise thousands of Internet of Things (IoT) devices to sense and collect data from its surrounding, process the data and take an automated and mechanized decision. On the other side the proliferation of these devices will soon cause radio spectrum shortage. So, to facilitate these networks, we integrate Cognitive Radio (CR) functionality in these networks. CR can sense the unutilized spectrum of licensed users and then use these empty bands when required. In order to keep the IoT nodes functional all time, continuous energy is required. For this reason the energy harvested techniques are preferred in IoT networks. Mainly it is preferred to harvest Radio Frequency (RF) energy in the network. In this paper a region based multi-channel architecture is proposed. In which the coverage area of primary node is divided as Energy Harvesting Region and Communication Region. The Secondary User (SU) that are the licensed user is IoT enabled with Cognitive Radio (CR) techniques so we call it CR-enabled IoT node/device and is encouraged to harvest energy by utilizing radio frequency energy. To harvest energy efficiently and to reduce the energy consumption during sensing, the concept of overlapping region is given that supports to sense multiple channels simultaneously and help the SU to find best channel for transmitting data or to harvest energy from the ideal channel. From the experimental analysis, it is proved that SU can harvest more energy in overlapping region and this architecture proves to consume less energy during data transmission as compared to single channel. We also show that channel load can be highly reduced and channel utilization is proved to be more proficient. Thus, this proves the proposed architecture cost-effective and energy-efficient.     

A Smart Nanotherapeutic Agent for in vitro and in vivo Reversal of Heavy-Metal-Induced Causality: Key Information from Optical Spectroscopy

Human exposure to heavy metals can cause a variety of life-threatening disorders, affecting almost every organ of the body, including the nervous, circulatory, cardiac, excretory, and hepatic systems. The presence of heavy metal (cause) and induced oxidative stress (effect) are both responsible for the observed toxic effects. The conventional and effective way to combat heavy metal overload diseases is through use of metal chelators. However, they possess several side effects and most importantly they fail to manage the entire causality. In this study, we introduce citrate-functionalized Mn₃O₄ nanoparticles (C−Mn₃O₄ NPs) as an efficient chelating agent for treatment of heavy metal overload diseases. By means of UV/Vis absorbance and steady-state fluorescence spectroscopic techniques we investigated the efficacy of the NPs in chelation of a model heavy metal, lead (Pb). We also explored the retention of antioxidant properties of the Pb-chelated C−Mn₃O₄ NPs using a UV/Vis-assisted DPPH assay. Through CD spectroscopic studies we established that the NPs can reverse the Pb-induced structural modifications of biological macromolecules. We also studied the in vivo efficacy of NPs in Pb-intoxicated C57BL/6j mice. The NPs were not only able to mobilize the Pb from various organs through chelation, but also saved the organs from oxidative damage. Thus, the C−Mn₃O₄ NPs could be an effective nanotherapeutic agent for complete reversal of heavy-metal-induced toxicity through chelation of the heavy metal and healing of the associated oxidative stress.     

Development of microwave susceptors based on SiC composites and their application for a one-step synthesis of ZnO nanostructures

We report on the synthesis of silicon carbide (SiC)-based composites containing different proportions of aluminum and/or vanadium III oxides. These composites have been successfully tested as susceptors into a commercial microwave oven operating at 2.45?GHz frequency. After 120?s only of microwave irradiation, the generated temperature has reached a plateau of 1750?°C, which was obtained for SiC composite containing 10?wt% of Al₂O₃ and/or V₂O₃. Furthermore, the structural properties of these composites were investigated by means of X-ray diffraction and scanning electron microscopy before and after exposure to microwaves irradiation. These SiC-based susceptors were then used as a source of heat to synthesize a nanostructured ZnO material through two different processes, namely the zinc metal evaporation/condensation occurring under air, and through a rapid thermal decomposition of zinc acetates and nitrates precursors. The structural analysis supported the possibility to grow nanostructures of controlled morphologies via the control of the microwave power and the type of precursor employed. We believe that this proposed one-step microwave assisted method provides a simple and efficient alternative to synthesize various oxide nanostructures in a very short reaction-time.