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A stochastic propagation model to the energy dependent rapid temporal behaviour of Cygnus X-1 as observed by AstroSat in the hard state
We report the results from analysis of six observations of Cygnus X-1 by Large Area X-ray Proportional Counter (LAXPC) and Soft X-ray Telescope (SXT) onboard AstroSat, when the source was in the hard spectral state as revealed by the broad-band spectra. The spectra obtained from all the observations can be described by a single-temperature Comptonizing region with disc and reflection components. The event mode data from LAXPC provides unprecedented energy dependent fractional root mean square (rms) and time-lag at different frequencies which we fit with empirical functions.We invoke a fluctuation propagation model for a simple geometry of a truncated disc with a hot inner region. Unlike other propagation models, the hard X-ray emission (>4 keV) is assumed to be from the hot inner disc by a single-temperature thermal Comptonization process. The fluctuations first cause a variation in the temperature of the truncated disc and then the temperature of the inner disc after a frequency dependent time delay.We find that the model can explain the energy dependent rms and time-lag at different frequencies. 2019 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society. -
BSLnO: Multi-agent based distributed intrusion detection system using Bat Sea Lion Optimization-based hybrid deep learning approach
Intrusion detection system (IDS) is a robust model that plays an essential role in dealing with intrusion detection, especially in detecting abnormal anomalies and unknown attacks. The major challenges faced by IDS are the computation time required for analysis, and the exchange of a huge amount of data from one division of the network to another. For the sake of tackling such limitations, this probe proposes a multi-agent enabled IDS for detecting intrusions using the Bat Sea Lion Optimization (BSLnO) algorithm. The proposed strategy consists of five phases, namely pre-processor agent, reducer agent, augmentation agent, classifier agent, and decision agent. Initially, input data is subjected to pre-processor agent, where pre-processing is carried out using data normalization and missing value imputation. Thereafter, the pre-processed result is fed up to the reducer agent, where dimension reduction is carried out using mutual information. The third step is data augmentation in which the dimensionality of data is enhanced. After that, the augmented result is subjected to classifier agent to classify intrusions or malicious activities present in the network based on hybrid deep learning strategies, namely deep maxout network and deep residual network. A developed BSLnO is implemented by incorporating Bat Algorithm (BA) and Sea Lion Optimization (SLnO) algorithm to train the hybrid classifier. The proposed scheme has acquired a higher precision of 0.936, recall of 0.904, and F-measure of 0.920. 2022 John Wiley & Sons Ltd. -
Load shedding using GA and ACO in smart gird environment
Increasing pressure on the utilities to accommodate energy efficiency, load management and progress in advanced technology has led to transformations for existing grid into a smarter grid. Creating awareness among the end-users to participate in load management programs instead of capacity addition is the best solution for maintaining the stability in the grid. Load shedding is a strategy under load management in which load connected to the smart grid is individually controlled via two- way communication. In this paper, a Smart Load shedding approach is developed based on load prioritization. The required amount of load to be shed under lack of sufficient generation level is optimized by Genetic Algorithm (GA) and Ant Colony Optimization (ACO) algorithms. The proposed approach is implemented using a real time feeder data from the substation, India. The results reflect the effectiveness of proposed algorithms taken into practical applications. -
Partial load shedding using ant colony algorithm in smart grid environment
Effective power distribution methodology is one of the basic necessities to meet the increasing power demand in any power system. Load shedding is done when power demand is more than power generation, to sustain the power system stability. Load shedding methods followed today shed a particular load completely, neglecting the critical consumers within the system. Controlling the loads at individual utility level in smart grid system enables us to put a maximum power limit on utility. Hence by partially shedding the load, demand can be reduced. The technique used here utilizes ant colony algorithm to choose a maximum power limit for each load dynamically based on the importance and priority of load. This method forces the consumer to manage his load internally based on criticality. It also effectively makes use of availability based tariff schemes. 2015 IEEE. -
Design and implementation of Adaptive PI control based dynamic voltage restorer for solar based grid integration
This paper introduces an innovative approach to address voltage fluctuations in solar-based grid integration by implementing an adaptive PI control-based Dynamic Voltage Restorer (DVR). This DVR is engineered to counteract voltage disruptions resulting from grid disturbances and the intermittent nature of solar energy generation. To achieve optimal performance in diverse operating conditions, the adaptive PI controller dynamically adjusts its parameters, adapting to changes in load and solar generation. The system is realized on a digital signal processor (DSP) and evaluated within a laboratory-scale solar-based grid integration setup. The findings reveal that the proposed system effectively mitigates voltage fluctuations, ensuring a stable integration of solar energy into the grid. The adaptive PI control-based DVR outperforms traditional PI control-based DVRs, particularly when dealing with variable solar energy generation. This approach holds significant potential for practical applications in solar-based grid integration systems. 2024 IEEE. -
Demand response for residential loads using artificial bee colony algorithm to minimize energy cost
Power performance expectations are increasing, impacting designs and requiring advanced technology to improve system reliability. Demand Response (DR) is a highly flexible customer driven program in which customer voluntarily changes his energy usage patterns during the peak demand to maintain the system stability and reliability and thereby improves the performance of the gird. This paper proposes a novel algorithm for optimization of the DR schedule of the residential loads for various hours of the day using Artificial Bee Colony (ABC) algorithm. Here, the objective function is subjected to the constraints like cost constraints, time constraints and load demand. The results show that the proposed approach enhances potential in solving problems with good reliability compared with existing approaches. 2015 IEEE. -
Intelligent load shedding using ant colony algorithm in smart grid environment
For every country which is expecting a large growth in power demand in the near future or facing a power crisis, an effective load control and power distribution strategy is a necessity. Load shedding is done whenever power demand is more than power generation in order to sustain power system stability. The current load shedding strategies fails to shed exact amount of load as per the system requirement and does not prioritize loads which are being shed. Given the dimension of the problem, it would not be feasible computationally, to use regular optimization techniques to solve the problem. The problem is typically suited for application of meta-heuristic algorithms. This paper proposes a new scheme for optimizing load shedding using ant colony algorithm in a smart grid platform considering loads at utility level. The algorithm developed considers each electrical connection from Distribution Company as one lumped load and provides an effective methodology to control the load based on various constrains such as importance of load and time of load shedding. Springer India 2015. -
Molecularly imprinted PEDOT on carbon fiber paper electrode for the electrochemical determination of 2,4-dichlorophenol
A highly selective electrochemical sensor has been developed for the determination of the pesticide molecule, 2,4-dichlorophenol (2,4-DCP) using molecularly imprinted conducting polymer. 2,4-dichlorophenol imprinted polymer films were prepared by electropolymerising 3,4-ethylenedioxythiophene (EDOT) on surface of carbon fiber paper electrode (CFP) in presence of 2,4-dichlorophenol. Electrochemical over-oxidation was carried out for the controlled release of 2,4-DCP templates and to generate definite imprinting sites. Surface morphology of the imprinted electrode was analysed by Scanning Electron Microscopy-Energy Dispersive X-ray Spectrometry, Fourier Transform Infrared and Raman spectroscopy. In optimized conditions, the voltammetric sensor gave a linear response in the range of 0.21 nM 300 nM. The significantly low detection limit (0.07 nM) demonstrates the ultra-low sensitivity of the method. The imprinted sensor displayed higher affinity and selectivity towards the target 2,4-DCP over similar structural analogical interference than the non-imprinted sensor. MIP sensor was efficaciously employed for the selective determination of 2,4-DCP in real samples of water. 2020 Elsevier B.V. -
A Review of Algorithms for Mental Stress Analysis Using EEG Signal
Mental stress is an enduring problem in human life. The level of stress increases exponentially with an increase in the complexity of work life. Hence, it is imperative to understand the causes of stress, a prerequisite of which is the ability to determine the level of stress. Electroencephalography (EEG) has been the most widely used signal for understanding stress levels. However, EEG signal is useful only when appropriate algorithms can be used to extract the properties relevant to stress analysis. This paper reviews algorithms for preprocessing, feature extraction and learning, and classification of EEG, and reports on their advantages and disadvantages for stress analysis. This review will help researchers to choose the most effective pipeline of algorithms for stress analysis using EEG signals. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Synthesis and Characterization of Carbon Nanomaterial Derived from Anthracite
Among various storage devices, carbon based supercapacitors grabs the recent trends in the electronic devices. The present research work describes the synthesis of carbon nanomaterials derived from anthracite by using staudenmaier method. Anthracite was used as a precursor because of its high carbon content. The structural and chemical complex formation carried out by using XRD and FTIR confirms the formation of CNT's. The calculated value obtained from the XRD peaks confirms the formation of multilayer carbon nano-materials. The electrode was prepared by coating synthesized CNT on copper rod. The electrochemical performance of prepared working electrode was carried out by using cyclic voltammetric performance. Electrode characterization was performed for different scan rates 10, 20, 30 and 50 mV/sec in a potential window from-0.08 to 0.2V. The CV curves represents symmetric nature which imply that electrode material have stable capacitive process. 2019 Elsevier Ltd. -
Facile synthesis of nickel nanoparticles and its efficient dye degradation
The present investigation involves the synthesis of nickel (Ni) nanoparticles (NPs) by the chemical reduction of nickel chloride using hydrazine hydrate without the need for an inert atmosphere from an external source. The photocatalytic activity, structure and morphology of the NPs were studied by employing UV-Visible (UV-Vis) spectroscopy, powder X-ray diffraction (PXRD) and transmission electron microscopy (TEM). Degradation of methylene blue(MB) and rhodamine B(Rh-B) dyes using Ni NPs was investigated to see the feasibility in degrading these dyes from polluted water at low cost. Ni NPs showed a good photocatalytic activity of 84.1% under visible light for the degradation of MB when compared to Rh-B which showed an efficiency of 47.3 %. 2020 World Research Association. All rights reserved. -
Feature extraction of optical character recognition: Survey
Optical Character Recognition is still prevailing even after many decades of implementation. The challenges faced here are increasing day by day so as its applications. From Punched cards to Handwritten Text, from images to video, from uniform font to universal font, from English text to Global language, from researchers to visually handicapped are the transformations obtained from an era of the 1980s to 2010. This paper has covered the advancement of acknowledging the characters, how are features are extracted, various methodologies used and more importantly what is the use of OCR. Research India Publications. -
Electrochemical deposition for metal organic Frameworks: Advanced Energy, Catalysis, sensing and separation applications
The advent of metalorganic frameworks has gathered ever-increasing attention owing to their versatility, unparalleled porosity, tuneability, and rich topography. The need for an efficient synthetic method and the trending appeal for thin film MOFs has brought in huge data on electrochemical deposition techniques. Thin films have immense applications in the field of electronics (including energy devices such as batteries and supercapacitors), sensors, catalysis, and as liquid/gas separation devices. Here, the electrodeposition method requires no pre-treatment step, allows miniaturization, a homogeneous film with desirable thickness, and is observed to be an eco-friendly method. The limited number of articles focusing on the supremacy of the technique has motivated the authors to collectively summarize the scattered data. To limit the discussion to reasonable bounds, the article focuses on a critical comparison of electrodeposition techniques with other synthetic methods, and different types of electrodeposition methods, and familiarize them with the various electrodeposited MOF-composite designs. Finally, we discuss extensively the existing as well as future applications. This will encourage future researchers to exploit this electrochemical technique for designing & developing newer MOF films and similar next-generation materials which are energy-efficient, rapid, and accurate while in use. This review article hopes to list out significant advances in the area to the advantage of both commercial and academic aspects. 2023 Elsevier B.V. -
Factors Effecting on Work Values Towards Career Choices Among University Students
The pandemic effect of COVID-19 triggered a global recession in the year 2020. The unpredictability that surrounds the coronavirus is the most challenging problem that many people must confront, particularly in terms of making decisions regarding their careers, considering the significant shift in employment opportunities. The purpose of this research is to investigate the influence anxiety and the Covid-19 pandemic have on work values and the reality of career choices among university students. A quantitative research methodology was applied to 110 respondents from a nearby institution to achieve the study's objective. This was done through online surveys and the snowball sampling technique. In order to acquire the findings, a data analysis using SPSS and PLS-SEM was carried out. It is evident from the study's findings that students work values are impacted by anxiety and the COVID-19 pandemic. Moreover, the findings support the hypothesis that anxiety and the COVID-19 pandemic influence students employment decisions. The findings of the study provide insight into the body of knowledge. The influence of anxiety and the COVID-19 pandemic on current work values among university students about career choices are examined, and recommendations are made to various stakeholders, such as policymakers, university management, and career counselors. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Computational and experimental investigation on biological and photophysical properties of high yielded novel aryl-substituted pyrazolone analogue
A series of new aryl-substituted pyrazolone derivatives 5(a-h) were synthesised via the Baylis-Hillman acetate reaction with pyrazolones and tested for antifungal, antibacterial, and antioxidant properties in vitro. Among the tested molecules 5d and 5e show good in vitro antifungal and antibacterial activities due to the presence of fluorine, which enhances the absorption rate by increasing lipid solubility and improves the pharmacological activity. It is also evident from the results obtained from structure-activity relationship (SAR) studies. Further, the photophysical properties of synthesized compounds were theoretically estimated using the ab-intio technique. The ground state optimization and HOMOLUMO energy levels are calculated using the DFT-B3LYP-6-311 basis set. Using the theoretically estimated HOMOLUMO value, global chemical reactivity descriptor parameters are estimated, and the result shows that compounds 5d and 5e have a higher electronegative and electrophilicity index than other molecules. Overall results suggest that, fluorine substituted pyrazolone derivatives show good photophysical, SAR, and biological properties. 2022 Elsevier B.V. -
Photophysical and In Vitro-In Silico Studies on Newly Synthesized Ethyl 3-((3-Methyl-1-phenyl-1H-pyrazol-5-yl)oxy)-2-methyleneheptanoate
Abstract: In the present work, the aryl-substituted pyrazolone derivative ethyl 3-((3-methyl-1-phenyl-1H-pyrazol-5-yl)oxy)-2-methyleneheptanoate (ETT) has been synthesized by the reaction of Baylis-Hillman acetate with pyrazolones and screened for their in vitro antifungal, antibacterial, and antioxidant properties. The molecule shows good in vitro antifungal and antibacterial activities due to the presence of pentane, which enhances the absorption rate by its increased lipid solubility and improves the pharmacological activity. It is also evident from the results obtained from structure-activity relationship (SAR) studies. In silico studies were conducted on the synthesized molecule, examining its interactions with DNA Gyrase, Lanosterol14 alpha demethylase, and KEAP1-NRF2 proteins. The results revealed strong binding interactions at specific sites. Further, the photophysical properties of synthesized compounds were theoretically estimated using the ab-intio technique. The ground state optimization, dipole moment, and HOMOLUMO energy levels are calculated using the DFT-B3LYP-6-31G(d) basis set. Using the theoretically estimated HOMOLUMO value, global chemical reactivity descriptor parameters are estimated, and the result shows the synthesised molecule has a highly electronegative and electrophilic index. NBO analysis proved the presence of intermolecular ON.H hydrogen bonds caused by the interaction of the lone pair of oxygen with the anti-bonding orbital. The results suggest that pentane-substituted pyrazolone derivatives show good photophysical and biological applications. Pleiades Publishing, Ltd. 2024. -
NSS-ML: a Novel spectrum sensing framework using machine learning for cognitive radio IoT networks
A key component of cognitive radio systems is spectrum sensing, which reduces coexistence problems and maximises spectrum efficiency. However, the introduction of multiple situations with distinct characteristics brought about by 5G communication presents problems for spectrum sensing to support a wide range of applications with high performance and flexible implementation. Inspired by these difficulties, a new method with a multi-layer extreme learning machine optimised for bats is presented in this study. This technique makes use of a variety of input vectors, such as channel ID, energy, distance, and received signal intensity, to enhance user categorization and sensing capabilities. Moreover, we compare the proposed method with the state-of-the-art spectrum sensing approaches in order to evaluate its effectiveness in 5G situations, especially in healthcare applications. Evaluation metrics including channel detection probability, sensitivity, and selectivity are carefully examined. The findings unequivocally prove the suggested spectrum sensing approachs superiority over current methods and highlight its potential for smooth incorporation into a variety of 5G applications. Bharati Vidyapeeth's Institute of Computer Applications and Management 2024. -
Food Security and Its Impact on Society: Cases of Developing World
Food security is a matter of concern in the twenty-first century as is evident from the importance given to it in the United Nations Sustainable Development Goals. Despite attempts to address food scarcity concerns at global conventions such as the World Food Summit of 1996, food remains scarce. Scholars further suggest that though food scarcity is a global issue, its roots and impact is local. Consequently, a study of food must study the major challenges that converge to undermine food security worldwide including conflicts, climate change, global policies and in recent times even the Covid 19 pandemic. However, at a fundamental level food scarcity is the by-product of not just a legacy of past failures to build more just, sustainable, and resilient food systems, but rather a by-product of our inability to be responsible and sustainable consumers. This chapter highlights that despite surplus food production, developing nations often face food insecurity owing to the diversion of food towards developed nations. These nations, instead of sharing global resources (including food and agricultural labour), often contribute towards the global food crisis. Moreover, some of these developed nations engage in an industrialised system of food produc-tion which might meet the nations food requirements but are not sustainable modes of production and pose a serious threat to the environment. Nevertheless, the indis-cretions of the developed nations affect the developing nations economically as well as socially. As social outcasts, marginalised communities and individuals within the developing world are worst affected. As a result, this chapter offers insight into the social struggle brought on by inaccessibility to food. The chapter further suggests that addressing concerns of food security is not only a matter of addressing the inequalities manifest in the production, distribution and consumption of food but also learning to be responsible and sustainable consumers. Simply stated, the chapter recommends connecting SDG 2 with SDG 12. This chapter would also include the position of India in GHI, the Ukraine crisis and its aftermath in various developing countries, the earthquake in Turkey and how it affects the food security, and a few instances from Africa to highlight the concepts of food security and its correlation with sustainability of any society. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Artificial Intelligence in Fostering Sustainable Development
Sustainable development is vital to mankind. The world is finding a growing effort of Artificial Intelligence (AI) towards sustainability, and we made an attempt to address the development in sustainability using AI systems. Sustainable development has three pillars of sustainability (i.e. social, economic, environment), and as such, the pillars of sustainable AI. The entire Life cycle of AI products can foster change in the movement of sustainability from which greater integrity and social justice can be achieved. Sustainable AI helps us to address the whole socio-technical system more than AI applications. This paper tried to address the positive impacts of AI on sustainable indicators in terms of Environmental, Societal and Economy factors. This paper is prepared to make readers, policymakers, AI ethicists and AI developers to inspire and connect with the environment for the current and future generations as there are few AI costs to be made compatible with the environment. 2023 American Institute of Physics Inc.. All rights reserved. -
Unveiling the Future: Exploring Stock Price Prediction in the Finance Sector through Machine Learning and Deep Learning - A Comprehensive Bibliometric Analysis
The investigation of predicting share prices is a captivating and beneficial area of study within the realm of economic research. precise projections and findings can potentially benefit shareholders by reducing the risk of making suboptimal investment selections. The objective of this investigation is to examine the present state of research pertaining to the prognostication of share price predictions through the utilization of Machine Learning (ML) and Deep Learning techniques. The present study examined the existing body of scientific works on methods involving DL and ML in the context of predicting the value of stocks. This study presents a comprehensive overview of research trends, methodologies, and applications in a particular field by conducting a bibliometric analysis of publications indexed in the Scopus database. Drawing from the presented data, recommendations for optimal methodologies can be formulated. The data was visually represented through the utilization of the R programming language and Vos Viewer software. The investigation additionally discerns the primary authors, institutions, and nations that are making contributions to this particular field of research. The outcomes of this investigation possess the potential to guide future research trajectories and offer significant perspectives for professionals and policymakers who are keen on utilizing machine learning and deep learning in the financial sector. 2024 IEEE.