Giant Anomalous Hall and Nernst Conductivities in Magnetic All-d Metal Heusler Alloys

All-d Heuslers are a category of novel compounds combining versatile functionalities such as caloric responses and spintronics with enhanced mechanical properties. Despite the promising transport properties (anomalous Hall (AHC) and anomalous Nernst (ANC) conductivities) shown in the conventional Co₂XY Heuslers with p-d hybridization, the all-d Heuslers with only d-d hybridization open a new horizon to search for new candidates with outstanding transport properties. In this work, the AHC and ANC are evaluated for thermodynamically stable ferro/ferri-magnetic all-d-metal regular Heusler compounds based on high-throughput first-principles calculations. It is observed that quite a few materials exhibit giant AHCs and ANCs, such as cubic Re₂TaMn with an AHC of 2011 S cm^-1, and tetragonal Pt₂CrRh with an AHC of 1966 S cm^-1 and an ANC of 7.50 A m^-1K^-1. Comprehensive analysis on the electronic structure reveals that the high AHC can be attributed to the occurrence of the Weyl nodes or gapped nodal lines in the neighborhood of the Fermi level. The correlations between such transport properties and the number of valence electrons are also thoroughly investigated, which provides a practical guidance to tailor AHC and ANC via chemical doping for transverse thermoelectric applications.     

Optimal Deep Learning Based Intruder Identification in Industrial Internet of Things Environment

With the increased advancements of smart industries, cybersecurity has become a vital growth factor in the success of industrial transformation. The Industrial Internet of Things (IIoT) or Industry 4.0 has revolutionized the concepts of manufacturing and production altogether. In industry 4.0, powerful Intrusion Detection Systems (IDS) play a significant role in ensuring network security. Though various intrusion detection techniques have been developed so far, it is challenging to protect the intricate data of networks. This is because conventional Machine Learning (ML) approaches are inadequate and insufficient to address the demands of dynamic IIoT networks. Further, the existing Deep Learning (DL) can be employed to identify anonymous intrusions. Therefore, the current study proposes a Hunger Games Search Optimization with Deep Learning-Driven Intrusion Detection (HGSODL-ID) model for the IIoT environment. The presented HGSODL-ID model exploits the linear normalization approach to transform the input data into a useful format. The HGSO algorithm is employed for Feature Selection (HGSO-FS) to reduce the curse of dimensionality. Moreover, Sparrow Search Optimization (SSO) is utilized with a Graph Convolutional Network (GCN) to classify and identify intrusions in the network. Finally, the SSO technique is exploited to fine-tune the hyper-parameters involved in the GCN model. The proposed HGSODL-ID model was experimentally validated using a benchmark dataset, and the results confirmed the superiority of the proposed HGSODL-ID method over recent approaches.     

Alpha Fusion Adversarial Attack Analysis Using Deep Learning

The deep learning model encompasses a powerful learning ability that integrates the feature extraction, and classification method to improve accuracy. Convolutional Neural Networks (CNN) perform well in machine learning and image processing tasks like segmentation, classification, detection, identification, etc. The CNN models are still sensitive to noise and attack. The smallest change in training images as in an adversarial attack can greatly decrease the accuracy of the CNN model. This paper presents an alpha fusion attack analysis and generates defense against adversarial attacks. The proposed work is divided into three phases: firstly, an MLSTM-based CNN classification model is developed for classifying COVID-CT images. Secondly, an alpha fusion attack is generated to fool the classification model. The alpha fusion attack is tested in the last phase on a modified LSTM-based CNN (CNN-MLSTM) model and other pre-trained models. The results of CNN models show that the accuracy of these models dropped greatly after the alpha-fusion attack. The highest F1 score before the attack was achieved is 97.45 And after the attack lowest F1 score recorded is 22%. Results elucidate the performance in terms of accuracy, precision, F1 score and Recall.     

High-performance MEMS shutter display with metal-oxide thin-film transistors and optimized MEMS element

Active matrix prestressed microelectromechanical shutter displays enable outstanding optical properties as well as robust operating performance. The microelectromechanical systems (MEMS) shutter elements have been optimized for higher light outcoupling efficiency with lower operation voltage and higher pixel density. The MEMS elements have been co-fabricated with self-aligned metal-oxide thin-film transistors (TFTs). Several optimizations were required to integrate MEMS process without hampering the performance of both elements. The optimized display process requires only seven photolithographic masks with ensuring proper compatibility between MEMS shutter and metal-oxide TFT process.     

Passivating Defects of Perovskite Solar Cells with Functional Donor-Acceptor–Donor Type Hole Transporting Materials

In this study, a series of donor–acceptor–donor (D-A-D) type small molecules based on the fluorene and diphenylethenyl enamine units, which are distinguished by different acceptors, as holetransporting materials (HTMs) for perovskite solar cells is presented. The incorporation of the malononitrile acceptor units is found to be beneficial for not only carrier transportation but also defects passivation via Pb–N interactions. The highest power conversion efficiency of over 22% is achieved on cells based on V1359, which is higher than that of spiro-OMeTAD under identical conditions. This st shows that HTMs prepared via simplified synthetic routes are not only a low-cost alternative to spiro-OMeTAD but also outperform in efficiency and stability state-of-art materials obtained via expensive cross-coupling methods.     

Agent-based time delay margin in consensus of multi-agent systems by an event-triggered control method: Concept and computation

This paper deals with defining the concept of agent-based time delay margin and computing its value in multi-agent systems controlled by event-triggered based controllers. The agent-based time delay margin specifying the time delay tolerance of each agent for ensuring consensus in event-triggered controlled multi-agent systems can be considered as complementary for the concept of (network) time delay margin, which has been previously introduced in some literature. In this paper, an event-triggered control method for achieving consensus in multi-agent systems with time delay is considered. It is shown that the Zeno behavior is excluded by applying this method. Then, in a multi-agent system controlled by the considered event-triggered method, the concept of agent-based time delay margin in the presence of a fixed network delay is defined. Moreover, an algorithm for computing the value of the time delay margin for each agent is proposed. Numerical simulation results are also provided to verify the obtained theoretical results.     

Electronic transport behavior of 4,6-bis(4-nitrophenyl)-2-phenyl-3,5-diaza-bicyclo[3.1.0]hex-2-ene (a bicyclic aziridine) as optical molecular switch: a first-principles approach

Journal of Computational Electronics - In this study, the electronic transport properties of 4,6-bis(4-nitrophenyl)-2-phenyl-3,5-diaza-bicyclo[3.1.0]hex-2-ene (as a bicyclic aziridine) have been...     

Adaptive double recycling folded cascode amplifier

Analog Integrated Circuits and Signal Processing - A new structure for improving the performance of recycling folded cascode (RFC) operational transconductance amplifier (OTA) is presented. The...     

Lossless,Passive Transportation of Low Surface Tension Liquids Induced by Patterned Omniphobic Liquidlike Polymer Brushes

Surfaces enabling directional liquid transportation are of great interest for a wide range of applications such as water collection, microfluidics, and heat transfer systems. Surfaces capable of lossless, long-range passive transportation of low surface tension (LST) liquids using wettability patterned, liquidlike coatings with minimal contact angle hysteresis are reported. Lossless LST droplet travel distances over 150 mm are achieved, enabled by a two-phase transportation mechanism: morphological transformation from a bulge to a channel shape, followed by directional transportation along the asymmetrical wedge-shaped channel. The developed surfaces can split, merge, and precisely transport various low-surface tension liquids, including alcohols, alkanes, and solvents. The developed transportation strategy can also enhance LST liquid dropwise condensation through continuous removal of the condensate, even on horizontally positioned surfaces without the assistance of gravity.     

A framework of genetic algorithm-based CNN on multi-access edge computing for automated detection of COVID-19

The Journal of Supercomputing - This paper designs and develops a computational intelligence-based framework using convolutional neural network (CNN) and genetic algorithm (GA) to detect COVID-19...