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Diabetes is a chronic metabolic disorder characterized by prolonged elevated blood glucose levels. This research endeavours to assess the efficacy of a surface plasmon resonance (SPR)-based sensor for continuous diabetes monitoring. The proposed sensor structure follows the Kretschmann configuration, incorporating key components such as a prism, Silver/Copper, Cerium Oxide, black phosphorus (BP), and a specialized sensing medium. Within this investigation, two distinct sensor structures, denoted as Structure I and Structure II, have been meticulously constructed using the specified layers. The research employs advanced techniques, including the transfer matrix method and angular interrogation, to evaluate the SPR sensor's performance at a wavelength of 633 nm. The study rigorously analyses the detection capabilities for a specific biological sample, considering parameters such as sensitivity (S), detection accuracy (DA), quality factor (QF), and dip-of-figure of merit (DFOM). Notably, the glucose detection process achieves remarkable results, with Structure I and Structure II exhibiting maximum sensitivities of 217 degree/RIU and 217 degree/RIU, DA values of 0.41 and 0.315, QF values of 89.13 RIU−1and 68.45 RIU−1, and DFOM values of 31,281.53 and 24,213.34, respectively. Ultimately, this research culminates in a comparative study, underscoring the potential of the proposed sensor as a high-performance tool for glucose level detection, thereby paving new avenues in the realms of biological and biomedical technologies.

Conversion of pantothenate to phosphopantothenate in humans is the first dedicated step in the coenzyme A (CoA) biosynthesis pathway and is mediated by four isoforms of pantothenate kinase. These enzymes are allosterically regulated by acyl-CoA levels, which control the rate of CoA biosynthesis. Small molecule activators of the PANK enzymes that overcome feedback suppression increase CoA levels in cultured cells and animals and have shown great potential for the treatment of pantothenate kinase-associated neurodegeneration and propionic acidemias. In this study, we detail the further optimization of PANK pyridazine activators using structure-guided design and focus on the cellular CoA activation potential, metabolic stability, and solubility as the primary drivers of the structure–activity relationship. These studies led to the prioritization of three late-stage preclinical lead PANK modulators with improved pharmacokinetic profiles and the ability to substantially increase brain CoA levels. Compound 22(BBP-671) eventually advanced into clinical testing for the treatment of PKAN and propionic acidemia.

The Software Defect Prediction (SDP) process provides reliable software by identifying defect-prone modules before the testing stage. It efficiently and effectively utilizes quality assurance resources. Most predictive models are trained on historical data which belong to the same project or comparable project. These models show satisfactory performance as they utilize similar settings to the considered projects. But the limitation of these models is that they are effective only if there are adequate historical data to train a predictive model. In reality, however, such historical data are minimal for some projects and absent for new projects. The defect prediction in such projects which lack historical data can be accomplished by training prediction models on different project data. This process is known as Cross-Project Defect Prediction (CPDP). Software defect datasets also suffered from class imbalance issues which further degrades the model’s performance. In this research work, the authors have proposed a Multi-Objective Random Forest (MO-RF) algorithm with a data resampling technique to minimize the probability of false alarms, to maximize the probability of detection and to overcome the class imbalance problem. The study also evaluates the performance of other prediction models. The proposed method has shown percentage improvement (in terms of AUC) of 2.78 and 3.46 over MONB and MONBNN, respectively.

ABSTRACTSolid waste management is a global challenge. Slurry generated from granite processing industries is one of the major sources of total solid waste production. This paper reports the feasibility of [...]

This paper reports the occurrence of aberrant extra dorsal fins in a ‘Near Threatened’ butter catfish, Ompok bimaculatus(Bloch, 1794) for the first time from a reservoir ecosystem in Godavari River Basin. The specimen was taxonomically confirmed through morphological features and molecular attributes. This study also highlights the possible causes of such deformity in the natural fish populations of man-made ecosystems i.e., reservoirs.

Background: Diabetes neuropathy is a frequent ailment that has a substantial impact on patients by increasing the risk of falls and causing discomfort. The lower extremities are where diabetic neuropathy patients first feel pain. This discomfort could seem like a pinprick, an electric shock, or something else.Objective: Here, we give a comprehensive overview of this quickly developing theranostic application that includes all relevant imaging, diagnostic, therapeutic, and monitoring elements for the management of diabetes and diabetes neuropathy.Methods: The data for the current study was gathered by searching PubMed and Google Scholar. Several research and review publications from various publishers, including Springer Nature, Bentham Science, PLOS one, MDPI, and ACS Publishing Centre, were evaluated to compile the data.Result: Recent developments in theranostics have shown promise as alternate management approaches for diabetes and ailments linked to diabetes. Numerous nanotechnology-built biosensors, including multiwalled carbon nanotubes, copper nanowires, zinc oxide tetrapods, and nanoparticle- embedded contact lenses, offer benefits in monitoring diabetic neuropathy.Conclusion: The potency, usability, and dependability of insulin substitutes have been demonstrated by a variety of innovative methods for the management of diabetes, which includes nanotechnology approaches using Gene-Based Nanoparticles (siRNA), Liposomes, Exosomes/ Extracellular Vesicles, Neuromodulation, and Inhalable Nanoparticles. Over the past few years, the development of various theranostic nanoparticles for Diabetic neuropathy has experienced an unprecedented expansion. Even though much work needs to be done to precisely evaluate the genuine benefits provided by these particles, such as issues with nanotoxicity, theranostic nanoparticles will have a significant impact on the field of nanomedicine.

With the rapid advancement of Internet of Things technology, the field of fog computing has garnered significant attention and hence become a workable processing platform for upcoming applications. However, compared with vast computing capability of the cloud, the fog nodes have resource constraints, are heterogeneous in nature, and highly distributed. Due to the growing demand as well as diversity of applications, the nodes in a fog network become overloaded, which makes load balancing a prime concern. In this work, a load balancing aware task selection and migration approach is proposed comprising two algorithms to select and place tasks from multiple overloaded nodes to suitable destination nodes. The Selection algorithm determines the tasks that should be migrated from overloaded nodes. Placement algorithm focuses on finding a near optimal solution by applying modified binary particle swarm optimization. Specifically, the objective is to minimize execution time and transfer time of tasks. Simulation studies conducted on iFogSim prove that the suggested approach outperforms the existing approaches in terms of task execution time, task transfer time, and makespan.

This study introduces a novel bidirectional hybrid T-type converter configuration named the single-capacitor unit-based hybrid T-type converter (SCU-HTC). This innovative architecture employs a single capacitor, leading to a 50% reduction in total capacitor count when compared to conventional T-type inverters. The design operates in two primary modes: buck mode and normal mode. In buck mode, the single capacitor’s voltage is maintained at half of the DC link voltage, resulting in lower output voltage magnitude and improved total harmonic distortion. This outcome is distinct from standard T-type inverters. Conversely, during normal mode, the topology functions either as a three-phase two-level inverter or a three-phase three-level inverter. To facilitate seamless operation in both buck and normal modes, a new switching strategy is proposed. This strategy ensures balanced capacitor voltage, enhancing the stability of the DC link voltage. The proposed architecture also exhibits power factor correction (PFC) capabilities. Unlike many PFCs that struggle with maintaining unity power factor (UPF) across various power levels, often necessitating additional circuitry and control complexity, the suggested topology for rectifier mode offers sensor-less self-balancing that consistently upholds UPF. Notably, this topology also effectively addresses issues related to capacitor voltage balance. Experimental validation of the proposed concept underscores its viability.

Flow-accelerated corrosion (FAC) is a complex mechanism of material degradation and depends upon the fluid hydrodynamic parameters, material microstructure and compositions, and environmental chemistry. The effect of flow velocity and dissolved oxygen on FAC behavior of API 5L X56 steels in 0.3 M NaCl solution is investigated. Electrochemical and weight loss measurements were taken in a closed-loop corrosion evaluation system at flow velocities of ~ 0.25, 1.0, 1.6, and 2.3 m/s and dissolved oxygen (DO) contents of ~ 50, 500, 1500, 2500, and 4500 ppb. The corrosion rate observed to increase with flow velocity and dissolved oxygen content. The post-corrosion topography and corrosion products were investigated using 3D profilometry, scanning electron microscopy–energy-dispersive X-ray spectroscopy (SEM–EDS), and Raman spectroscopy techniques. The corrosion products mainly contained hematite (α-Fe2O3), lepidocrocite (γ-FeOOH), goethite (α-FeOOH), and magnetite (Fe3O4). The wall shear stress (τ) induced on specimen surface was simulated through CFD approaches and correlated with corrosion resistance of the steel at different flow velocities.

In contrast to the traditional design approach, the frugal design method seeks to create “more value with fewer resources.” Frugal product design frameworks evolved as an essential instrument for developing innovative solutions for resource-constrained areas. However, these frameworks have not been extensively examined for their effectiveness in achieving desired outcomes. The study proposes a conceptual assessment model to assess the efficacy of frugal product design frameworks. The model is based on existing literature on frugal design and identifies the key criteria for being frugal (i.e., core functionality, cost reduction, and optimal performance). Furthermore, the user experience of (n = 200 users) was also incorporated into the model to provide valuable and relevant insight. The proposed model is used to analyze the frugality of the framework, and the framework can be improved by incorporating the criteria that the designer has neglected. Identifying frequently neglected frugality criteria can lead to the refinement of future frugal design strategies. The industry's primary goal in the modern marketplace is to provide consumers with functional, affordable, and optimal-performance products. This study offers an assessment method for frugal product design in an effort to achieve this goal. This model enables businesses to ensure the frugality of product design frameworks in the early stages of the product design process.

Industrial Internet of Things (IIoT) networks often require minimal data reception latency, which primarily depends on advertising interval in BLE-based networks. Thus, selecting an optimal advertising interval is crucial to get minimal data reception latency. The existing solutions are limited to homogeneous networks (networks using the same set of BLE parameters). However, an IIoT scenario may have numerous devices incorporated with various sensors of different kinds, generating data of different sizes at different periods, which are likely to form a heterogeneous network, thus raising the need to evaluate the heterogeneous networks. To address these issues, we first analytically derived the optimal advertising interval expression that gives minimal latency for a heterogeneous network consisting of n clusters. Based on the analytical expressions, the algorithms are proposed that autonomously optimize the latency for heterogeneous networks. Moreover, an average energy consumption model is also proposed, which will help the service provider to select the suitable parameters, and further help in calculating the expected battery life of the device in the network. Further, the analytical results are verified by a commercial simulator whose simulation results show a good correlation with analytical results thus validates the proposed model.

It is evident, in the face of the COVID-19 pandemic that has physicians confronting death and dying at unprecedented levels along with growing data suggesting that physicians who care for dying patients face complex emotional, psychological and behavioural effects, that there is a need for their better understanding and the implementation of supportive measures. Taking into account data positing that effects of caring for dying patients may impact a physician’s concept of personhood, or “what makes you, ‘you’”, we adopt Radha Krishna’s Ring Theory of Personhood (RToP) to scrutinise the experiences of physicians working in intensive care units (ICU) using a fictional scenario that was inspired by real events. The impact of death and dying, its catalysts, internal constituents, external factors, dyssynchrony, and buffers, specific to ICU physicians, were identified and explored. Such a framework allows for ramifications to be considered holistically and facilitates the curation of strategies for conflict resolution. This evaluation of the RToP acknowledges the experience and wide-ranging effects it has on ICU physicians. As such, our findings provide insight into their specific needs and highlight the importance of support on a personal and organisational level. Although further research needs to be conducted, the RToP could serve as the basis for a longitudinal assessment tool supported by the use of portfolios or mentorship due to their provision of personalised, appropriate, specific, timely, accessible and long-term support.

Instrumentation and control systems are nervous systems of nuclear power plant (NPP). These systems interact with several safety-critical components of the NPP, such as actuators, transformers, control valves, sensors, circuit breakers, signal processing units, controllers, and heat exchangers. Therefore, the failure of these systems could result in significant financial loss, harm to human resources, or environmental damage. As a result, these systems need to be highly reliable and accurate. In this article, we suggest a framework, based on the batch deterministic and stochastic Petri nets (BDSPNs) to measure the reliability and performance of safety-critical system. The framework consists of three phases. In the first phase, the system is modeled using the BDSPN to derive the transition rate among the system states. In phase 2, the transition rate matrix is utilized to compute the steady-state probability values, which help to evaluate the response time of the system using Little's law. The third phase uses a transition probability matrix to assess the reliability of the system. The technique is illustrated on shutdown system of NPP and is validated on the operational profile data. The obtained accuracy of 99.9905% in measurement of reliability validates the approach.

Connectivity and data transmission with minimal delay are in ever-increasing demand in the Industry 5.0 revolution. Conventional cellular technologies such as 4G or 5G, with significant network congestion and long-distance data travel, introduce considerable delays. As industries operate heavy machinery across various domains, the situation becomes even more critical when dealing with smart industrial systems such as assembly lines, collaborative robots, and industrial transportation networks, which constantly generate enormous amounts of data to make optimal decisions. Therefore, adopting beyond 5G or 6G technology becomes a significant factor in Industry 5.0 applications. With this motivation, we provide a concise overview of the significance of 6G wireless communications and their impact on the Industry 5.0 revolution, and discuss its implications. Additionally, we discuss a framework for 6G-based edge computing for processing Industry 5.0 applications at the edge of the network. Finally, we delve into current research challenges and future directions for addressing emerging needs of Industry 5.0 through 6G technology.

Hydrocarbons, water, carbon dioxide, hydrogen sulfide, chloride, high temperatures and pressures are all involved in the oil and gas production. Thus, corrosion, particularly sulfide stress cracking (SSC), poses a great threat to the integrity of well components such as tubing, casing, packer, and wellhead assembly. Sometimes, SSC can lead to catastrophic failures and must be addressed due to operational security and environmental concerns. Localized corrosion, including SSC can be reduced greatly with the appropriate material selection and optimization of critical operational parameters. The material selection is performed according to service environments vis-a-vis mechanical/ metallurgical attributes of the alloys as prescribed in standards such as API 5CT and ANSI/NACE MR0175/ISO 15156. Currently, corrosion-resistant alloys (CRAs) such as martensitic and duplex stainless steels, nickel, titanium and other precipitation hardened alloys are available and used in oil and gas industries because of their superior mechanical and corrosion properties. Owing to the operating environmental reasons, designer often opt for more expensive CRAs as compared to relatively less expensive materials which fall close to the performance boundary of materials selection criteria, thereby increasing overall cost of crude oil production. Thus, there is a paramount requirement to ascertain the candidate materials appropriately without bearing the cost penalties of over-specifications or the performance shortfalls of under-specified alloys when new fields are discovered. In this paper the application domains of 13Cr martensitic stainless steels are reviewed vis-a-vis limits prescribed in ANSI/NACE MR0175/ISO 15156 standard. The paper will aid in the selection of cost-effective materials for oil and gas production when temperature, pressure, hydrogen sulfide concentration, pH, and salinity vary in different directions and cannot be well defined within standard limits.

This study presents a novel bilayer resistive random access memory (RRAM) device utilizing hafnium dioxide (HfO2) and zirconium dioxide (ZrO2) layers. By introducing an HfO2interfacial layer on top of the ZrO2layer, the device exhibits remarkable stability and uniformity in bipolar resistance switching (RS) behavior. The bilayer device demonstrates several favorable switching characteristics, including a significantly increased on state to off state resistance ratio of approximately 102, improved endurance with over 104cycles, and long-term data retention exceeding 105s at room temperature. The presence of oxygen vacancies (VOs) in both dielectric layers was determined using X-ray photoelectron spectroscopy. Overall, the results highlight the substantial promise of the bilayer HfO2/ZrO2device for potential applications in non-volatile storage.

Contribution: This article presents an Instrumentation and Control (I&C) prototype for engineering postgraduate laboratory program designed to fulfill the quality objectives of the institution. Background: The syllabus and teaching methodology for engineering students have been periodically updated by universities and higher education institutes to meet the demand of industries and accreditation. I&C systems are widely used in several industries. This article seeks to further understand ways that a multidisciplinary approach for teaching in an undergraduate and postgraduate course related to I&C Systems can earn a systematic knowledge to meet industrial demands and accreditation. Intended Outcomes: A lab environment that motivates postgraduate students to create their fundamental concepts of I&C systems engineering, so that they can apply them in real applications. The intended outcomes are: teach multidisciplinary aspects of the subject, to enrich the students’ learning process and understanding, in designing robust I&C systems, and meet the criteria laid by ABET. Application Design: Many practical exercises were formulated to teach different aspects of I&C systems engineering. It incorporates multidisciplinary knowledge as required. An I&C systems engineering course taught at the department of Systems and Control at Indian Institute of Technology, is presented as a case study in which the evaluation process was based on institute criteria and the corresponding student outcomes. A student survey was also conducted to know the effectiveness of the teaching approach. Findings: The impact analysis of the proposed method was carried out with respect to the understanding of the students on the designing of I&C systems. An analysis of student’s $T$ -test before and after introducing prototype in the course shows the effectiveness of the approach.

Safety-critical systems (SCSs) mitigate the risk of catastrophic loss of assets and hence do have high dependability targets. Performance and reliability are the critical dependability attributes, particularly in control and safety systems, and hence essential to measure to ensure the dependability. Traditional methods either are not capable to capture the system dynamics or encounter state explosion problem. Also, the methods are not able to measure all critical performance attributes. This article proposes a novel approach to measure the performance and reliability of SCSs. Such systems contain multiple interconnecting processing nodes, the functional requirements of which are modeled using Petri net (PN). A set of ordinary differential equations (ODEs) is derived from the PN model that represents the state of the system. The ODE solution can be used to measure the critical performance attributes, such as latency time and throughput of the system. The proposed method can avoid the state explosion problem and also introduces new metrics of performance, along with their measurement: deadlock, liveness, stability, boundedness, and steady state. The proposed technique is applied to a case study of nuclear power plant. We obtained 99.887% and 99.939% accuracy of performance and reliability measurement, respectively, which proves the effectiveness of our approach.

Ground-dwelling birds are one of the most vulnerable taxa. These birds are also under-studied and therefore constitute the lesser-known group of aves. Understanding their patterns of resource selection and their relationship with the composites of their habitat is a prerequisite for ensuring the effectiveness of future conservation efforts. Hill partridge (Arborophila torqueola) is distributed in a vast range throughout the Himalayas to South-East Asia but knowledge about its micro-habitat is provided by only a single short term study from China. Their sulking behavior and inconspicuous habitat type make it extremely challenging to study them closely. However, with the advent of camera traps, it was possible to observe them meticulously during this study. We attempted to discern the patterns of its habitat selection, and its relationship with various vegetative and non-vegetative components. This study revealed a complex pattern of habitat selection by hill partridge. They preferred habitat with a greater canopy and bamboo cover. It inhabited forests dominated by oak trees in combination with other trees. They were virtually absent from the sites that showed higher bare-ground percentages.

This article introduces a novel bidirectional switched-capacitor converter topology with buck–boost capabilities. Unlike traditional T-type inverters that use two capacitors, the proposed topology utilizes only a single capacitor, reducing the number of capacitors by 50%. Normal, buck, and boost modes make the topology versatile for various applications. It can be used as a three-phase two-level or three-level inverter in all modes. A switched-capacitor unit boosts the input voltage without requiring boost inductors or filter capacitors. This not only increases the dc-link voltage but also boosts the power throughput without an increase in current, which is a significant advantage in power electronics. The proposed topology controls switched-capacitor unit inrush current with a soft-starting circuit. The buck-mode operation is achieved by connecting a capacitor whose voltage is maintained at half the dc-link voltage, resulting in a superior output voltage quality with reduced total harmonic distortion. In addition, a novel switching strategy maintains capacitor voltage balance during buck and boost modes along with the suppression of inrush current during steady state and improves the dc-link voltage stability. The proposed topology is compared with the conventional and recent works in terms of device count. Finally, the proposed concept is validated experimentally and the results are found to be promising.

A constant-stress partially accelerated life test (CSPALT) is the most widespread type where each examination unit is subjected to only one chosen stress level until its failure or the termination of the experiment, whichever occurs first. This paper presents the CSPALT with Type-I and -II censoring schemes in the occurrence of competing failure causes when the lifetime of test units follows the two-parameter Fréchet distribution. The lifetime of test units follows the two-parameter Fréchet distribution. The maximum likelihood method is used to estimate the parameters of the failure distribution. The Fisher Information Matrix and variance–covariance matrix are also assembled. Furthermore, a simulation technique is applied to investigate the performance of the theoretical estimators of the parameters.

Objectives The present study aimed to assess the co-occurrence of taurodontism in nonsyndromic cleft lip and palate (NSCLP) patients in a subset of Indian population using cone beam computed tomography (CBCT).Method The study was a retrospective case–control kinds assessing 1500 CBCT scans over a period of 2 years; 67 scans out of 1500 showed cleft lip and palate (CLP). After fulfilling the inclusion and exclusion criteria, 38 subjects out of 67 comprised the case group; 80 randomly selected subjects constituted the control group. The first and second permanent molars in both the arches (8 teeth) of each subject were assessed for the presence and severity of taurodontism using the objective criteria. Statistical analysis was done using the χ2test. Inter and intraobserver agreement was evaluated by Kappa statistics.Results 71.05% subjects showed taurodontism in case group, while 45% subjects showed its presence in control group; results being significant for both the groups. However, the comparison of number of teeth with taurodontism in the 2 groups came out to be statistically insignificant. Also no significant association of taurodontism was seen with gender or the different types of cleft (P= .437). Hypotaurodontism was the most prevalent type.Conclusion There was a higher prevalence of taurodontism in both groups which had been underestimated in the previous studies. This emphasizes the significance of the use of CBCT in detection of dental anomalies which would otherwise go undetected.

This article proposes a novel approach to measure the performance of Safety-Critical Systems (SCS). Such systems contain multiple processing nodes that communicate with each other is modeled by a Petri nets (PN). The paper uses the PN for the performance evaluation of SCS. A set of ordinary differential equations (ODEs) is derived from the Petri net model that represent the state of the system, and the solutions can be used to measure the system's performance. The proposed method can avoid the state space explosion problem and also introduces new metrics of performance, along with their measurement: deadlock, liveness, stability, boundedness, and steady state. The proposed technique is applied to Shutdown System (SDS) of Nuclear Power Plant (NPP). We obtained 99.887% accuracy of performance measurement, which proves the effectiveness of our approach.

The internet of things (IoT) embedded device is one of the most revolutionary innovations of the 21st century. Integrating external networks in smart home IoT devices increases the risk of cybercrimes, highlighting the importance of forensic investigations of digital devices. This study presents an in-depth analysis of smart refrigerators by proposing a forensic investigation framework (FIvF-SR) incorporating a digital forensic algorithm (DFA-SR) for acquiring and analysing digital traces from smart refrigerators. This includes file system analysis of refrigerators that helps to find the digital artefacts about user activity, web-browsing data, geological information, cloud artefacts, system information, and the role of connected devices in digital investigation. DFA-SR algorithm is proposed for selective evidence collection to improve throughput and speed of investigation. Further, this study indicates the industrial significance of the proposed approach and explains how digital traces help to solve criminal cases. The present work involves a use case study of the smart refrigerator and potential digital evidence in IoT-embedded devices.

Dynamic cognitive performance has an impact on the safety and productivity of mine workers. Previous studies show that physiological measures have a good correlation with cognition during task execution. Observing the escalating demand for safe production in mines, it is now a crucial research area to examine the physiological variables that can predict cognitive performance prior to task allocation. In this experimental work, we have tried to predict how well participants will do on upcoming tasks using brain signals captured by electroencephalography. An Electroencephalogram (EEG) was recorded from 40 participants who subsequently took a cognitive test. After data analysis, our results show that EEG features can predict cognitive performance, with R= 0.48, p= 0.002, for the memory task and R= 0.546, p<0.001 for the attention task. This study also discussed the potential area of applicability in mining and some management strategies for dealing with workload and fatigue-related issues.

We are almost always surrounded by light from LEDs in organic LED (OLED) TV screens and active-matrix OLED displays in mobile phone screens. LEDs are everywhere, in workplaces, headlamps, and stadium lights. They have gained immense popularity as power-saving, compact, durable, and sustainable alternatives to conventional incandescent and fluorescent lighting systems. But there is a catch: LEDs are unidirectional devices, i.e., based on dc supplies, while the primary power supply is ac. From this comes a mysterious component hiding in plain sight: the LED driver circuit, which enables the smooth operation of LEDs with dc and ac sources.

Using the Kalman filtering technique, we propose a novel method for estimating the missing video frames to monitor the activities inside the control room of a nuclear power plant (NPP). The purpose of this study is to reinforce the existing security and safety procedures in the control room of an NPP. The NPP control room serves as the nervous system of the plant, with instrumentation and control systems used to monitor and control critical plant parameters. Because the safety and security of the NPP control room are critical, it must be monitored closely by security cameras in order to assess and reduce the onset of any incidents and accidents that could adversely impact the safety of the NPP. However, for a variety of technical and administrative reasons, continuous monitoring may be interrupted. Because of the interruption, one or more frames of the video may be distorted or missing, making it difficult to identify the activity during this time period. This could endanger overall safety. The demonstrated Kalman filter model estimates the value of the missing frame pixel-by-pixel using information from the frame that occurred in the video sequence before it and the frame that will occur in the video sequence after it. The results of the experiment provide evidence of the effectiveness of the algorithm.

Mechatronics systems of nuclear power plants (NPP) are safety-critical systems and, hence, accurate estimates of failures of such systems are essentially required. Failures of such systems depends on the correct functionality of their basic components. Component failure modes are identified by developing the models that can lead to failure of systems. Such failure modes are represented as “basic event” in the system models. For accuracy in measurement of failures, it is important to consider the epistemic correlation, in which, the state of the knowledge about failure parameters of the identical components is the same. The epistemic correlation is also termed as state of knowledge correlation. In this article, we provide methods to accommodate the epistemic correlation in the failure estimates for improved accuracy. The experimental results are shown on a case study of NPP mechatronics system.

The review is devoted to the development and study of the drug Leukovir®(cladribine+ ribavirin) and its use in the treatment of relapsing-remitting and secondary progressive forms of multiple sclerosis, a chronic neurodegenerative disease aiming the risk reduction of relapse and progression of a disability. In clinical trials Leukovir®has proved to be efficient by up to 56 weeks for the treatment of relapsing-remitting and secondary progressive forms of multiple sclerosis. The drug is registered in the Republic of Belarus. The efficacy, safety and tolerability profile of the drug Leukovir®suggests that it is well suited for disease-modifying therapy of multiple sclerosis. Patients require four 35-day courses of treatment, each consisting of seven days of treatment followed by a break of 28 days. The use of Leukovir®has contributed to the suppression of inflammatory process activity according to MRI data and stabilization of the clinical condition. It has reduced the number of relapses in patients with relapsing-remitting and secondary-progressive forms of multiple sclerosis.

Control and instrumentation (C&I) systems are used to measure the critical parameters to take decisions and, hence, such systems do have high reliability target. Therefore, it is essential to measure the reliability of such systems with high accuracy. Petri net (PN) is a mathematical tool that can model the stochastic behavior of the systems. The research work is devoted to propose a framework to measure the reliability of these systems using PN. The proposed framework contains three phases: 1) state-space identification, 2) probability of a system being in each state, and 3) reliability measurement. The proposed approach has been applied to 21 C&I systems of nuclear power plant (NPP) and is demonstrated on a case study of NPP to prove its effectiveness.

The swift explosion of Internet of Things (IoT) devices in the past few years has resulted in a deluge of data that requires timely processing. However, transmitting such massive amounts of data in and out from cloud leads to high latency and network bandwidth. Consequently, the field of fog computing has turned out as a feasible platform for utilizing resources within the vicinity of IoT devices for providing services in a timely manner. The resource constrained trait of fog nodes demands judicious management of resources in satisfying the requirements of IoT applications. This makes energy efficiency a core issue in fog networks. This article addresses the issue of energy efficiency by proposing an energy conscious squirrel search algorithm (EcSSA) for scheduling tasks in fog environment. A salient feature of EcSSA lies in the novel objective function that aims to optimize the energy consumption by minimizing the cumulative active time of all nodes in the network. Simulation studies conducted as part of this work demonstrate that the proposed approach significantly improves average energy consumption, average makespan, and carbon dioxide emission rate in comparison to the existing algorithms. We propose an energy conscious task scheduling approach (EcSSA) for fog computing environments by making use of a recent nature inspired approach called squirrel search algorithm (SSA). The objective function to minimize the energy consumption was achieved by minimizing cumulative active time of all nodes in the network.

People often dispose of their useless smart digital gadgets without realizing the potential presence of useful information inside these devices. This is also true for faulty smart bulbs, which cybercriminals might exploit to gain unauthorized access to a smart home and manipulate or steal private information. This research delves into the potential security risks associated with smart bulbs and provides recommendations for mitigating such risks. Through a comprehensive analysis of the functionality of smart bulbs, this study introduced the data extraction framework DEF-IoTF for collecting both hardware and application-level digital artifacts from smart bulbs. This paper presents the FIvM-IoT model for collecting and analyzing evidence from companion app data on mobile phones and Wifi modules at the hardware level. We conduct examinations on the smart bulb's Wifi module and extract its firmware using the developed Wifi_Cred tool. These include evidence related to user credentials, log time stamps, Wifi details, potential forensic information, and investigation procedures for IoT devices. Finally, this study provides prominent IoT forensic use cases along with the key requirements for hardware-level forensic investigation of Wifi modules.

We study the construction of Gabor frames and wavelet frames for Weyl–Heisenberg group and extended affine group by using contraction between the affine group and the Weyl–Heisenberg group due to Subag, Baruch, Birman and Mann. Firstly, we give construction of Gabor frames with several generators from a unitary irreducible representation associated with the Weyl–Heisenberg group. Wavelet frames with several generators associated with an extended affine group have been obtained. A relation between frames for Weyl–Heisenberg group and extended affine group is also discussed. Finally, we show that frames of Gabor and wavelet structure are stable under small perturbations.

Salient Object Detection (SOD) plays a pivotal role in digital image processing, focusing on identifying objects in images or videos that naturally draw human attention. In the world of computer vision and image processing, these eye-catching objects are known as salient objects. The process of saliency detection holds significant importance in various computer vision applications, including video compression, automated cropping, and video summarization. Recent advancements in AI technology have witnessed the emergence of numerous deep learning-based end-to-end networks in the video saliency domain. However, these approaches predominantly concentrate on network architecture design. In this study, the author(s) introduce a decision-based two-stage framework for video saliency detection, in which the decision is taken based on frame content analysis. This approach enhances the quality of the saliency map in the overall process. In the proposed model, the process unfolds in two stages. Initially, an initial saliency map is generated. Subsequently, in the second phase, the final saliency output is produced by leveraging both the initial saliency map and an optical flow map. To evaluate the effectiveness of the proposed model extensive assessments have been carried out on widely recognized public datasets namely VOS, DAVSOD, and ViSAL. The experimental findings indicate that the proposed model delivers strong performance, exhibiting competitiveness in comparison to state-of-the-art methods on the metrics S-measure, F-measure, and MAE.

Eosinophils are involved in host protection against multicellular organisms. However, their recruitment to the mesenteric lymph node (mLN) during type 2 immunity is understudied. Our results demonstrate that eosinophil association with lymphoid stromal niches constructed by fibroblastic reticular cells (FRCs) and lymphatic endothelial cells is diminished in mice selectively lacking interleukin (IL)-4Rα or lymphotoxin-β (LTβ) expression on B cells. Furthermore, eosinophil survival, activation, and enhanced Il1rl1receptor expression are driven by stromal cell and B cell dialogue. The ligation of lymphotoxin-β receptor (LTβR) on FRCs improves eosinophil survival and significantly augments IL-33 expression and eosinophil homing to the mLN, thus confirming the significance of lymphotoxin signaling for granulocyte recruitment. Eosinophil-deficient ΔdblGATA-1 mice show diminished mLN expansion, reduced interfollicular region (IFR) alarmin expression, and delayed helminth clearance, elucidating their importance in type 2 immunity. These findings provide insight into dialogue between stromal cells and B cells, which govern mLN eosinophilia, and the relevance of these mechanisms during type 2 immunity.

The application of video processing techniques are useful for the safety of nuclear power plants by tracking the people online on video to estimate the dose received by staff during work in nuclear plants. Nuclear reactors remotely visually controlled to evaluate the plant's condition using video processing techniques. Internal reactor components should be frequently inspected but in current scenario however involves human technicians, who review inspection videos and identify the costly, time-consuming and subjective cracks on metallic surfaces of underwater components. In case, if any frame of the inspection video degraded/corrupted/missed due to noise or any other factor, then it may cause serious safety issue. The problem of missing/degraded/corrupted video frame estimation is a challenging problem till date. In this paper a systematic literature review on video processing techniques is carried out, to perform their suitability analysis for NPP applications. The limitation of existing approaches are also identified along with a roadmap to overcome these limitations.

Today, numerous reliability prediction methods exist for electronic systems. A detailed literature survey is conducted to investigate the various techniques/models to ensure electronic components/system's reliability. Based on such a method's merits and limitations, we recommend using the hybrid model to predict an electronic system's reliability prediction in the distinct development phase of the product life cycle to get higher accuracy.

Fresh food processing capabilities is an area of the supply chain that has a high perspective to add value. This study aims to identify and model critical success factors (CSFs) of the Indian fresh food processing (FFP) industry. The CSFs have been identified through all-encompassing literature reviews and consolidated through discussions with experts from the Indian FFP industry. A total of ten CSFs are identified and measured through a relative importance method. The TOPSIS methodology is deployed to analyse the data. The result shows that integration of the supply chain focuses on processing technology, training the major entity of value chain. This paper contributes to the understanding of supply chain management in a FFP context and presents the lead CSFs for this sector. The results of this study will help supply chain managers-practitioners in making decisions about the successful implementation of impactful strategies.

Customer relationship management (CRM) is a process of managing communication of the organisation with its customers to gain their profitable trust. This paper analyses the factors responsible for service quality excellence at dealer/customer end leading to CRM. An effort has been made here to show the improvement in perception of customers and especially original equipment manufacturers (OEM). The customer value helps in the growth of the organisation and the organisation also gets better understanding of customers' needs and choices though CRM, which is having a paramount importance in automotive industry. Here, analytical hierarchy process has been used as a tool to rank the different sub factors of key factors affecting service quality excellence at dealer end in automobile manufacturing industries. Findings indicate that tangible service dimensions and perception of service quality can be antecedent to customer delight and posit CRM. This paper will be a managerial guideline to build and maintain strong covalent customer relationship bond benefitting the organisation in many ways.

Purpose: In the present work, the buckling and free vibration characteristics of functionally graded carbon nanotubes (FG CNT) reinforced plates are performed in the framework of inverse hyperbolic shear deformation theory (IHSDT). The FG CNT material constitutes PmPV matrix and single walled carbon nanotubes (SWCNT) reinforcement. Further, the material properties of the constituents (PmPV matrix and SWCNT) are assumed to be temperature dependent. The various type of distributions of SWCNTs across the thickness of the plate are considered in this study i.e. uniform distribution, FG-V, FG-O and FG-X. Methods: The extended rule of mixture is employed to compute the effective material properties of the plate reinforced with SWCNTs. The governing equations are developed by implementing displacement field, strain–displacement relation and constitutive equation into the principle of virtual work. The Navier-type closed form solution is used to solve the governing equations. The developed equation for buckling and vibration response of FG CNT reinforced plates are coded in MATLAB. Results: The temperature-dependent material properties and their gradient in the transverse direction are obtained. The effects of various parameters such as volume fraction of CNT, span-thickness ratio, distribution of CNT with temperature dependent material properties is examined. Conclusion: It is observed that the material properties (E11, EEand G12) decrease due to increase in temperature. However, the rate of decrement is higher for E22and G12. Various numerical experiments are performed to examine the buckling and free vibration characteristics of CNT-reinforced plates with temperature-dependent material properties.

Human pose estimation (HPE) has developed over the past decade into a vibrant field for research with a variety of real-world applications like 3D reconstruction, virtual testing and re-identification of the person. Information about human poses is also a critical component in many downstream tasks, such as activity recognition and movement tracking. This review focuses on the key aspects of deep learning in the development of both 2D & 3D HPE. It provides detailed information on the variety of databases, performance metrics and human body models incorporated for implementing HPE methodologies. This paper discusses variety of applications of HPE across domains like activity recognition, animation and gaming, virtual reality, video tracking, etc. The paper presents an analytical study of all the major works that use deep learning methods for various downstream tasks in each domain for both 2D & 3D HPE. Finally, it discusses issues and limitations in the current topic of HPE and recommend potential future research directions in order to make meaningful progress in this area.

Traffic accidents are significant general well-being concerns, bringing a large number of deaths and injuries around the globe. To improve driving safety, the examination of traffic data is basic to discover factors that are firmly identified with lethal mishaps. In this paper, our main objective to evaluate the severity based on various factor to reduce the road accidents and enhance the safety. Therefore, a long range of factors are considered to evaluate severity into two types, either fatal severity or non-fatal severity. Out of all the factors, we have evaluated the top ten features that are most important with the help of CART, random forest and XGBoost algorithm. For prediction of severity, we have considered the logistic regression, ridge regression and support vector machine regression. The experimental results show that fatal severity is higher for fog weather condition, heavy vehicles such as truck, male drivers and old age drivers.

Background: Microsponge drug delivery systems comprise spherical and porous microspheres for prolonged topical drug delivery. These systems considerably reduce the undesirable side effects, offering improved patient compliance and reduced dosing frequency. Objective: The present study focused on developing topical controlled release preparations of microsponges-loaded gel of clarithromycin to cure bacterial skin infections. Materials and Methods: Four batches of microsponges (F1, F2, F3, and F4) of clarithromycin (CLR) containing fixed amounts of clarithromycin (100 mg), dichloromethane (5 ml), polyvinyl alcohol (5 % w/v) and distilled water (25 ml) with varying polymer concentrations were prepared by the quasi-emulsion solvent diffusion method and evaluated for % Production Yield, % drug content, % encapsulation efficiency, particle size, polydispersity index (PDI) and % drug release characteristics. The selected microsponge formulation (F3) was incorporated in Carpopol 934 gel for topical application. The prepared gel (CLRMS-F3 Gel) was evaluated for physical characteristics, pH, spreadability, viscosity, and in vitro drug release. Furthermore, the gel formulation was compared with pure clarithromycin gel for antibacterial activity against the gram-positive (S. aureus) and gram-negative strain (E. coli.) using the cup and plate method. Results and Discussion: The F3 microsponge formulation exhibited a production yield of 83.75%, drug content (21.5 ± 0.50 %), and encapsulation efficiency of 86.04 ± 2.30%. Their particle size was satisfactory (3.80 ± 0.01 μm), and they were found to be spherical and porous in nature. F3 microsponges released 69.36 ± 1.27% of the drug over a period of 8 hrs and were incorporated into the gel formulations. The gel prepared using F3 microsponges was transparent, homogenous, and exhibited a pH of 6.8 ± 0.02, spreadability of 9.92 ± 0.44 g/cm, and viscosity of 35370.17 ± 493.09 centipoises. The CLRMS-F3 gel released 82.13 ± 0.47% drug in 12 hrs using a zero-order kinetic. The antibacterial activity studies revealed a higher potency against both S. aureus and E. coli of the prepared CLRMS-F3 gel compared to pure CLR gel and azithromycin standard. Conclusion: Based on the above study, it may be concluded that microsponges gel formulation can be potentially useful in improving topical drug delivery of antibacterial agents and can give better therapeutic efficacy.

Background: Xenorhabdus nematophila maintains species-specific mutual interaction with nematodes of Steinernema genus. Type II Toxin Antitoxin (TA) systems, the mazEF TA system controls stress and programmed cell death in bacteria. Objective: This study elucidates the functional characterization of Xn-mazEF, a mazEF homolog in X. nematophila by computational and in vitro approaches. Methods: 3D- structural models for Xn-MazE toxin and Xn-MazF antitoxin were generated, validated and characterized for protein - RNA interaction analysis. Further biological and cellular functions of Xn-MazF toxin were also predicted. Molecular dynamics simulations of 50ns for Xn- MazF toxin complexed with nucleic acid units (DU, RU, RC, and RU) were performed. The MazF toxin and complete MazEF operon were endogenously expressed and monitored for the killing of Escherichia coli host cells under arabinose induced tightly regulated system. Results: Upon induction, E. coli expressing toxin showed rapid killing within four hours and attained up to 65% growth inhibition, while the expression of the entire operon did not show significant killing. The observation suggests that the Xn-mazEF TA system control transcriptional regulation in X. nematophila and helps to manage stress or cause toxicity leading to programmed death of cells. Conclusion: The study provides insights into structural and functional features of novel toxin, Xn- MazF and provides an initial inference on control of X. nematophila growth regulated by TA systems.

This article describes the design and construction of a solar photovoltaic (SPV)-integrated energy storage system with a power electronics interface (PEI) for operating a Brushless DC (BLDC) drive coupled to agricultural loads. The proposed system is intended to make use of the electrical power available at under-utilized, pre-installed solar pumps used for irrigation. The PEI allows efficient energy management by charging and discharging depending on available solar power. The BLDC drive is a high-performance motor drive that requires a smooth and stable DC voltage. The bidirectional DC converter provides this while also allowing for efficient energy management between the battery and the solar PV system. The simulation results illustrate that the system is capable of efficiently managing energy between the battery and solar panels, providing a stable DC voltage to drive the BLDC motor. In addition to this, the results demonstrate the feasibility of integrating a solar system with a battery and BDC for driving BLDC in various agricultural applications. The experimental findings validate the simulation outcomes for the proposed setup implemented in pre-installed, untapped SPV systems. The system is able to provide a stable and efficient power source while also allowing for efficient energy management and utilization of solar energy.