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Disorder is ubiquitous in any quantum many-body system and is usually considered to be an obstacle to the elucidation of the underlying physics of complex systems, but its presence can often introduce exotic phases of matter that cannot generally be realized in a clean system. We report here a detailed experimental and theoretical study of magnetic properties of highly disordered Sr$_3$CuNb$_2$O$_9$ material which exhibits random site mixing between Cu and Nb. The magnetic moments (Cu$^{2+}$) are arranged in a quasi-cubic (three-dimensional) manner, leading to a high degree of frustration with a Curie-Weiss temperature ($\theta_{CW}$) of about -60 K without any long-range magnetic ordering down to 466 mK. These observations suggest that Sr$_3$CuNb$_2$O$_9$ is a candidate for a quantum spin liquid. More interestingly, the susceptibility ($\chi = M/\mu_0H$) and the $C_m/T$ ($C_m$ is the magnetic part of the heat capacity) follow a power-law behavior with decreasing temperature. In addition, $M(T,\mu_0H)$ and $C_m(T,\mu_0H)/T$ show scaling relationships over a wide temperature and field range. This unusual behavior with respect to the conventional behavior of a QSL can be discussed qualitatively as the coexistence of a disorder-induced random spin singlet (RSS) state and a QSL state. A quantitative description has been given by numerical calculations considering a power-law probability distribution $P(J) \propto J^{-\gamma}$ ($J$ is the exchange interaction) of random spin singlets. The parameters extracted from the numerical calculations are in excellent agreement with the experimental data. Furthermore, the analytical results are also consistent with the power-law and scaling behavior of $\chi$ and $C_m(T,\mu_0H)/T$ as a whole. Thus, our comprehensive experimental and theoretical analysis provides evidence for the stabilization of the RSS state in a three-dimensional lattice., Comment: 7 pages, 4 figures. Accepted at Physical Review B (Letter)

Smart healthcare, an integral element of connected living, plays a pivotal role in fulfilling a fundamental human need. The burgeoning field of smart healthcare is poised to generate substantial revenue in the foreseeable future. Its multifaceted framework encompasses vital components such as the Internet of Things (IoT), medical sensors, artificial intelligence (AI), edge and cloud computing, as well as next-generation wireless communication technologies. Many research papers discuss smart healthcare and healthcare more broadly. Numerous nations have strategically deployed the Internet of Medical Things (IoMT) alongside other measures to combat the propagation of COVID-19. This combined effort has not only enhanced the safety of frontline healthcare workers but has also augmented the overall efficacy in managing the pandemic, subsequently reducing its impact on human lives and mortality rates. Remarkable strides have been made in both applications and technology within the IoMT domain. However, it is imperative to acknowledge that this technological advancement has introduced certain challenges, particularly in the realm of security. The rapid and extensive adoption of IoMT worldwide has magnified issues related to security and privacy. These encompass a spectrum of concerns, ranging from replay attacks, man-in-the-middle attacks, impersonation, privileged insider threats, remote hijacking, password guessing, and denial of service (DoS) attacks, to malware incursions. In this comprehensive review, we undertake a comparative analysis of existing strategies designed for the detection and prevention of malware in IoT environments., Comment: 7 pages

Connected and autonomous vehicles, also known as CAVs, are a general trend in the evolution of the automotive industry that can be utilized to make transportation safer, improve the number of mobility options available, user costs will go down and new jobs will be created. However, as our society grows more automated and networked, criminal actors will have additional opportunities to conduct a variety of attacks, putting CAV security in danger. By providing a brief review of the state of cyber security in the CAVs environment, this study aims to draw attention to the issues and concerns associated with security. The first thing it does is categorize the multiple cybersecurity threats and weaknesses in the context of CAVs into three groups: attacks on the vehicles network, attacks on the Internet at large, and other attacks. This is done in accordance with the various communication networks and targets under attack. Next, it considers the possibility of cyber attacks to be an additional form of threat posed by the environment of CAVs. After that, it details the most uptodate defense tactics for securing CAVs and analyzes how effective they are. In addition, it draws some conclusions about the various cyber security and safety requirements of CAVs that are now available, which is beneficial for the use of CAVs in the real world. At the end, we discussed some implications on Adversary Attacks on Autonomous Vehicles. In conclusion, a number of difficulties and unsolved issues for future research are analyzed and explored., Comment: 6 pages, 1 figure, 1 table, Conference info: 2023 IEEE International Conference on Computing (ICOCO)

Smart grid data can be evaluated for anomaly detection in numerous fields, including cyber-security, fault detection, electricity theft, etc. The strange anomalous behaviors may have been caused by various reasons, including peculiar consumption patterns of the consumers, malfunctioning grid infrastructures, outages, external cyber-attacks, or energy fraud. Recently, anomaly detection of the smart grid has attracted a large amount of interest from researchers, and it is widely applied in a number of high-impact fields. One of the most significant challenges within the smart grid is the implementation of efficient anomaly detection for multiple forms of aberrant behaviors. In this paper, we provide a scoping review of research from the recent advancements in anomaly detection in the context of smart grids. We categorize our study from numerous aspects for deep understanding and inspection of the research challenges so far. Finally, after analyzing the gap in the reviewed paper, the direction for future research on anomaly detection in smart-grid systems has been provided briefly., Comment: 7 pages, 3 figures and conference paper (accepted for publication in 2023 IEEE World AI IOT Congress (AIIOT)

The smart-grid introduces several new data-gathering, communication, and information-sharing capabilities into the electrical system, as well as additional privacy threats, vulnerabilities, and cyber-attacks. In this study, Modbus is regarded as one of the most prevalent interfaces for control systems in power plants. Modern control interfaces are vulnerable to cyber-attacks, posing a risk to the entire energy infrastructure. In order to strengthen resistance to cyber-attacks, this study introduces a test bed for cyber-physical systems that operate in real-time. To investigate the network vulnerabilities of smart power grids, Modbus protocol has been examined combining a real-time power system simulator with a communication system simulator and the effects of the system presented and analyzed. The goal is to detect the vulnerability in Modbus protocol and perform the Man-in-the-middle attack with its impact on the system. This proposed testbed can be evaluated as a research model for vulnerability assessment as well as a tool for evaluating cyber-attacks and enquire into any detection mechanism for safeguarding and defending smart grid systems from a variety of cyberattacks. We present here the preliminary findings on using the testbed to identify a particular MiTM attack and the effects on system performance. Finally, we suggest a cyber security strategy as a solution to address such network vulnerabilities and deploy appropriate countermeasures., Comment: 7 pages, 10 figures, Conference paper, Accepted in publication for 2023 IEEE World AI IoT Congress (AIIoT)

Digital Twins (DTs) are virtual representations of physical objects or processes that can collect information from the real environment to represent, validate, and replicate the physical twin's present and future behavior. The DTs are becoming increasingly prevalent in a variety of fields, including manufacturing, automobiles, medicine, smart cities, and other related areas. In this paper, we presented a systematic reviews on DTs in the autonomous vehicular industry. We addressed DTs and their essential characteristics, emphasized on accurate data collection, real-time analytics, and efficient simulation capabilities, while highlighting their role in enhancing performance and reliability. Next, we explored the technical challenges and central technologies of DTs. We illustrated the comparison analysis of different methodologies that have been used for autonomous vehicles in smart cities. Finally, we addressed the application challenges and limitations of DTs in the autonomous vehicular industry., Comment: 7 pages, conference paper, accepted for publication in IEEE AIIoT 2023 conference

To achieve the goal of providing the best possible care to each patient, physicians need to customize treatments for patients with the same diagnosis, especially when treating diseases that can progress further and require additional treatments, such as cancer. Making decisions at multiple stages as a disease progresses can be formalized as a dynamic treatment regime (DTR). Most of the existing optimization approaches for estimating dynamic treatment regimes including the popular method of Q-learning were developed in a frequentist context. Recently, a general Bayesian machine learning framework that facilitates using Bayesian regression modeling to optimize DTRs has been proposed. In this article, we adapt this approach to censored outcomes using Bayesian additive regression trees (BART) for each stage under the accelerated failure time modeling framework, along with simulation studies and a real data example that compare the proposed approach with Q-learning. We also develop an R wrapper function that utilizes a standard BART survival model to optimize DTRs for censored outcomes. The wrapper function can easily be extended to accommodate any type of Bayesian machine learning model.

We propose a method for computing binary orthogonal non-negative matrix factorization (BONMF) for clustering and classification. The method is tested on several representative real-world data sets. The numerical results confirm that the method has improved accuracy compared to the related techniques. The proposed method is fast for training and classification and space efficient.

Objective: Computer-aided disease diagnosis and prognosis based on medical images is a rapidly emerging field. Many Convolutional Neural Network (CNN) architectures have been developed by researchers for disease classification and localization from chest X-ray images. It is known that different thoracic disease lesions are more likely to occur in specific anatomical regions compared to others. This article aims to incorporate this disease and region-dependent prior probability distribution within a deep learning framework. Methods: We present the ThoraX-PriorNet, a novel attention-based CNN model for thoracic disease classification. We first estimate a disease-dependent spatial probability, i.e., an anatomical prior, that indicates the probability of occurrence of a disease in a specific region in a chest X-ray image. Next, we develop a novel attention-based classification model that combines information from the estimated anatomical prior and automatically extracted chest region of interest (ROI) masks to provide attention to the feature maps generated from a deep convolution network. Unlike previous works that utilize various self-attention mechanisms, the proposed method leverages the extracted chest ROI masks along with the probabilistic anatomical prior information, which selects the region of interest for different diseases to provide attention. Results: The proposed method shows superior performance in disease classification on the NIH ChestX-ray14 dataset compared to existing state-of-the-art methods while reaching an area under the ROC curve (%AUC) of 84.67. Regarding disease localization, the anatomy prior attention method shows competitive performance compared to state-of-the-art methods, achieving an accuracy of 0.80, 0.63, 0.49, 0.33, 0.28, 0.21, and 0.04 with an Intersection over Union (IoU) threshold of 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, and 0.7, respectively., Comment: Accepted to IEEE ACCESS

Manganese ferrite (MnFe2O4) and copper doped manganese ferrite (Mn0.75Cu0.25Fe2O4) soft materials were synthesized through solid-state sintering method. The phase purity and quality were confirmed from x-ray diffraction patterns. Then the samples were subjected to neutron diffraction experiment and the diffraction data were analyzed using FullProf software package. The surface morphology of the soft material samples was studied using a scanning electron microscope (SEM). Crystal parameters, crystallite parameters, occupancy at A and B sites of the spinel structure, magnetic moments of the atoms at various locations, symmetries, oxygen position parameters, bond lengths etc. were measured and compared with the reference data. In MnFe2O4, both octahedral (A) and tetrahedral (B) positions are shared by Mn2+ and Fe2+/3+ cations, here A site is predominantly occupied by Fe2+ and B site is occupied by Mn at 0.825 occupancy. The Cu2+ ions in Cu0.25Mn0.75Fe2O4 mostly occupy the B site. Copper mostly occupy the Octahedral (16d) sites. The length of the cubic lattice decreases with the increasing Copper content. The magnetic properties, i.e. A or B site magnetic moments, net magnetic moment etc. were measured using neutron diffraction analysis and compared with the bulk magnetic properties measured with VSM studies.

In the present study, structural changes due to gradual doping of Sr in BaTiO3 were investigated by both x-ray and neutron powder diffraction techniques. Ba1-xSrxTiO3 (x=0.0, 0.5 and 1.0) samples were synthesized by PVA evaporation method and purity was confirmed by x-ray diffraction experiment. After that, neutron diffraction experiments were carried out and diffraction data were analyzed by Rietveld least-square data refinement method using the computer program RIETAN-2000 and FullProf to determine various crystallographic structural parameters. The cation and anion position coordinates were also determined from the data refinement method which confirmed that Ba and Sr atoms possess tetrahedral A site and Ti atoms possess octahedral B site for all three samples BaTiO3, Ba0.5Sr0.5TiO3, and SrTiO3, respectively. Moreover, the lattice parameter values indicate, Ba1-xSrxTiO3 structure undergoes a phase transition from tetragonal to cubic somewhere before x=0.5. The concurrences between observed and calculated profiles were excellent and well consistent with the previously reported values. Furthermore, MEM based analysis was done for charge density measurement.

This paper introduces a novel method of designing wide area control (WAC) based on a discrete linear quadratic regulator and Kalman filtering based state-estimation that can be applied for real-time damping of interarea oscillations of wind integrated power grid. The main advantages of the proposed method are that the architecture provides online coherency grouping that properly characterizes real-time changes in the power grid and online wide-area signal selection based on residue method for proper selection of the WAC signals. The proposed architecture can, thus, accurately monitors changes in the power grid and select the appropriate control signal for more effectively damping the interarea oscillation when compared to the conventional local signal based power system stabilizers or offline based WAC designs. The architecture is tested on a wind integrated two-area system and the IEEE 39 bus system in order to show the capability of the proposed method.