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Abstract

The used sodium from experimental studies is un-economical to purify or reuse from fast breeder reactors and is to be discarded safely. This disposal is done by converting the sodium into sodium hydroxide(NaOH) and hydrogen gas as a by product. The molarity of the sodium hydroxide aqueous solution thus obtained ranges from 12M to 16M, which needs real-time monitoring. This paper describes the feasibility study to develop a simple, innovative sensor to measure such high concentrations of NaOH surrounded by hydrogen gas in the light of no COTS sensor available in the market to meet the requirements

Abstract

The ever-increasing problem of forest fire risks has intrigued researchers globally to find a mechanism that can provide an early warning of forest fire incidents as well as assess the extent of spatial-temporal damage caused by them. Further information and data on its impacts would help in mitigating the disaster through advanced planning and management. The effects of forest fire have been known for a long time, but due to limited data, mitigation measures were always hampered. With the advancement of technology in the recent past with respect to high-resolution satellite data, a programming interface, and the development of mobile applications to a certain extent, it is now possible to predict forest fires and to study the impacts at local, regional, and global levels. Several open satellite data sets available from NASA, NOAA, and COPERNICUS have facilitated the quantification of various variables in near real time. Further the easy access to open-source data facilitated in the development of user-friendly web interfaces and APPS to continuously monitor the spatio-temporal changes caused as a result of forest fires on the ecosystem and on climate.

Abstract

Table olives are becoming well recognized as a source of probiotic bacteria that might be used to create a health-promoting fermented food product by traditional procedures based on the activities of indigenous microbial consortia present in local environments. Methodology. In the present study, the characterization of probiotic bacteria isolated from mince, chunks, and brine of fermented green and black olives (Olea europaea) was done based on morphological, biochemical, and physiological characteristics. Results: Bacterial isolates demonstrated excellent survival abilities at 25, 37, and 45°C and at a variable range of pH. However, the optimum temperature is 37 and the optimum pH is 7 for all three isolates. An antimicrobial susceptibility pattern was found among these isolates through the disc diffusion method. Most of the isolates were susceptible to streptomycin, imipenem, and chloramphenicol, whereas, amoxicillin showed resistance to these isolates, and variable results were recorded for the rest of the antibiotics tested. The growth of the isolates was optimum with the supplementation of 3% NaCl and 0.3% bile salt. The isolated bacteria were able to ferment skimmed milk into yogurt, hence making it capable of producing organic acid. Conclusion: Isolates of Lactobacillus crispatus MB417, Lactococcus lactis MB418 from black olives, and Carnobacterium divergens MB421 from green olives were characterized as potential candidates for use as starter cultures to induce fermentation of other probiotic food products.

Abstract

The current study presents the findings of a laboratory investigation on the geotechnical behavior of quarry waste and the outcomes of numerical investigation supporting the application of quarry waste in the earthen dam. In addition, the chemical and morphological characteristics of quarry waste have also been studied. Quarry waste which would traditionally be disposed of as waste in landfills has been concluded as a high-strength material from the laboratory investigation. A high friction angle of about 38.4° has been recorded for the quarry waste, and the permeability was recorded in the fine sand range. These geotechnical properties of the quarry waste make it a satisfactory earthen dam shell material. The seismic performance of an earthen dam with quarry waste in the shell portion has been investigated using the finite

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.