Abstract

Pressure transmitters, directly or indirectly, play a key role in many systems in nuclear reactors and industries worldwide. The Innovative sensor section(ISS) in Indira Gandhi Center for Atomic Research(IGCAR) developed a pressure transmitter with a simple circuit to reduce complexity and signal-to-noise ratio. This pressure transmitter utilises a completely analog circuit to convert the change in pressure into changing current in the 4–20 mA range. The circuit also provides the output as changing frequency with changing pressure for IoT module compatibility.

Abstract

In V2X (vehicle-to-everything) communication, there is a two-way communication among the vehicle(s) and other Internet of Things (IoT)-enabled smart devices around it that may change how we need to drive. Due to the advancement of Information and Communications Technology (ICT) and the rapid development of IoT in transportation, traditional applications are converted to intelligent applications. In V2X communications, the collected information from the IoT smart devices and other sources passes through low-latency, high-bandwidth, high-reliability links. With the future adoption of the 5th generation mobile network (5G) and beyond networks, V2X continues to produce a huge volume of data. However, collecting and storing data securely in blockchain-based storage are extremely needed for immutability and transparency. In this survey article, the convergence of IoT, V2X and blockchain technologies, and various security challenges and their countermeasures are discussed. Next, we discuss various V2X applications and their respective services. Moreover, IoT-V2X architecture and its enabling technologies are discussed in this article. In addition, we also provide a comprehensive analysis of various security mechanisms. Finally, we provide some important challenges and issues of Blockchain for Intelligent Transportation System (BITS)

Abstract

A large-scale model of brain dynamics, as it is manifested in functional neuroimaging data, is presented in this study. The model is built around a general trainable network of Hopf oscillators, the dynamics of which are described in the complex domain. It was shown earlier that when a pair of Hopf oscillators are coupled by power coupling with a complex coupling strength, it is possible to stabilize the normal phase difference at a value related to the angle of the complex coupling strength. In the present model, the magnitudes of the complex coupling weights are set using the Structural Connectivity information obtained from Diffusion Tensor Imaging (DTI). The complex-valued outputs of the oscillator network are transformed by a complex-valued feedforward network with a single hidden layer. The entire model is

Abstract

The innate ability to recognize facial expressions and associated emotions is fundamental to human communication. Technology advancements have enabled computers to perform similar tasks to a considerable extent, opening versatile applications in diverse domains. In particular, Facial Emotion Recognition (FER) technology has recently been widely explored for investigating student engagement in classroom settings. While previous research studies mainly captivated the FER practice in engagement detection, far too little attention has been paid to the real-time emotional states of students during classroom interactions. In this regard, this paper introduces the In-Class Student Emotion and Engagement Detection System (iSEEDS), a novel AI-based approach for pinpointing learners' emotional states during classroom lectures. The iSEEDS employs Convo-lutional Neural Network (CNN) models for emotion detection and corresponding eye movement analysis. The system can help educators respond in real-time to students' emotional states and engagement levels. It can support responsive teaching by initiating remedial feedback in accordance with students' current emotions and engagement. A detailed literature review of existing emotion recognition models is presented as a background of iSEEDS development. Then the initial prototype model

Abstract

Leishmania donovani causes the neglected tropical disease visceral leishmaniasis. There is currently no vaccine available to combat the disease. Therefore, chemotherapy is the only treatment option available. However, the available drugs have various adverse effects on the host. As a result, new drugs are needed to treat the disease; natural compounds can be used as an alternative to traditional drugs. This study examined apigenin, diosmin, and naringin as prospective inhibitors against the Leishmania donovani Sterol 24-C- methyltransferase enzyme (LdSMT). All three compounds have a high affinity for the LdSMT and remain stable for 100 ns during the molecular dynamics simulation. In the cell viability assays, these compounds showed excellent IC50 values, with apigenin having the best IC50 value of 16.11 µM against Leishmania donovani promastigote. The findings of this study showed that all three flavonoids could be used as a safe alternative to conventional anti-leishmanial drugs.

Abstract

The Internet of Things (IoT)-enabled ride sharing is one of the most transforming and innovative technologies in the transportation industry. It has myriads of advantages, but with increasing demands there are security concerns as well. Traditionally, cryptographic methods are used to address the security and privacy concerns in a ride sharing system. Unfortunately, due to the emergence of quantum algorithms, these cryptographic protocols may not remain secure. Hence, there is a necessity for privacy-preserving ride sharing protocols which can resist various attacks against quantum computers. In the domain of privacy preserving ride sharing, a threshold private set intersection (TPSI) can be adopted as a viable solution because it enables the users to determine the intersection of private data sets if the set intersection cardinality is greater than or equal to a threshold value. Although TPSI can help to alleviate privacy concerns, none of the existing TPSI is quantum secure. Furthermore, the existing TPSI faces the issue of long-term security. In contrast to classical and post quantum cryptography, quantum cryptography (QC) provides a more robust solution, where QC is based on the postulates of quantum physics (e.g., Heisenberg uncertainty principle, no cloning theorem, etc.) and it can handle the prevailing issues of quantum threat and long-term security. Herein, we propose the first QC based TPSI protocol which has a direct application in privacy preserving ride sharing. Due to the use of QC, our IoT-enabled ride sharing scheme remains quantum secure and achieves long-term security as well.

Abstract

Reaction networks can display a wide array of dynamics. However, it is possible for different reaction networks to display the same dynamics. This phenomenon is called dynamical equivalence and makes network identification a hard problem. We show that to find a strongly endotactic/endotactic/consistent/conservative realization (when it exists) that is dynamically equivalent to the mass-action system generated by a given network it suffices to consider only the source vertices of the given network. In addition, we show that weakly reversible deficiency one realizations are not unique. We also present a characterization of the dynamical relationships that exist between several types of weakly reversible deficiency one networks.

Abstract

The objective of Advanced Persistent Threat (APT) attacks is to exploit Cyber-Physical Systems (CPSs) in combination with the Industrial Internet of Things (I-IoT) by using fast attack methods. Machine learning (ML) techniques have shown potential in identifying APT attacks in autonomous and malware detection systems. However, detecting hidden APT attacks in the I-IoT-enabled CPS domain and achieving real-time accuracy in detection present significant challenges for these techniques. To overcome these issues, a new approach is suggested that is based on the Graph Attention Network (GAN), a multi- dimensional algorithm that captures behavioral features along with the relevant information that other methods do not deliver. This approach utilizes masked self-attentional layers to address the limitations of prior Deep Learning (DL) methods that rely on convolutions. Two datasets, the DAPT2020 malware, and Edge I-IoT datasets are used to evaluate the approach, and it attains the highest detection accuracy of 96.97% and 95.97%, with prediction time of 20.56 seconds and 21.65 seconds, respectively. The GAN approach is compared to conventional ML algorithms, and simulation results demonstrate a significant performance improvement over these algorithms in the I-IoT-enabled CPS realm.

Abstract

Excessive nutrient loading into the inland water bodies and resulting algal blooms have been a major threat to the water quality of the inland water bodies such as lakes and reservoirs where the water is relatively stagnant for longer duration. To improve the conditions, it is essential to study and understand the role of the contributing factors in increasing the nutrient load coming into the water body from the watershed. In general, the excessive usage of inorganic (NPK) fertilizers are considered to be the spearhead behind the increasing nutrient output from the watershed, but their impact has not been well evaluated due to several constraints. So, this study tries to evaluate the role of the fertilizer application on the nutrient yield from the watershed into an inland water body. Nagarjuna Sagar reservoir and its contributing

Abstract

A persistent, targeted cyber attack is called an advanced persistent threat (APT) attack. The attack is mainly launched to gain sensitive information, take over the system, and for financial gain, which creates nowadays more hurdles and challenges for the organization in preventing, detecting, and recovering from such attacks. Due to the nature of APT attacks, it is difficult to detect them quickly. Therefore machine learning techniques come into these research areas. This study uses deep and machine learning models such as random forest, decision tree, convolutional neural network, multilayer perceptron and so forth to categorize and effectively detect APT attacks by utilizing publicly accessible datasets. The datasets used in this study are CSE-CIC-IDS2018, CIC-IDS2017, NSL-KDD, and UNSW-NB15. This study proposes the hybrid ensemble machine learning model, a mixed approach of random forest and XGBoost classifiers. It has obtained the maximum prediction accuracy of 98.92%, 99.91%, 99.24%, and 97.11% for datasets CSE-CIC-IDS2018, CIC-IDS2017, NSL-KDD, and UNSW-NB15, with a false positive rate of 0.52%, 0.12%, 0.62%, and 5.29% respectively. These results are compared to other closely related recent studies in the literature. Our experiment's findings show that our model has performed significantly better for all datasets.