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An Efficient Compressive Data Collection Scheme for Wireless Sensor Networks
The Compressive Data Collection (CDC) scheme is an efficient data-acquiring method that uses compressive sensing to decrease the bulk of data transmitted. Most existing schemes are modeled as Non-Uniform Sparse Random Projection (NSRP), and an NSRP-based estimator is used. These models cannot deal with anomaly readings that deviate from their standards and norms. Therefore, we provide a new CDC strategy in this study that uses an opportunistic estimator and routing. Initially, neighbor nodes are identified using the covariance function following the Gaussian process regression, and the data transfer to the neighbor node is done using the compressive sensing technique. Compressed data are then projected by using conventional random projection. Finally, the sample required to retrieve data is estimated using margin-free and maximum likelihood estimators. Results show that the sample needed to retrieve the data is less in the proposed scheme. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Unfolding the aggression and locus of control paradigm in sportspersons and non-sportspersons
The present study investigated Aggression and Locus of Control on Combat Sports Persons, Non-Combat Sports Persons, and Non-Sports Persons. In this study, a sample of 240 individuals (80 Combat sports, 80 Non-Combat Sports & 80 Non-Sportspersons) was used through purposive sampling. The tools administered were the Buss and Perry Aggression Questionnaire by Arnold H. Buss and Mark Perry and Rotters Locus of Control Scale by Julian Rotter respectively. The objective of the study was to investigate Aggression and Locus of Control in males and females from Combat, Non-Combat, and Non-Sports persons. This research also aims to explore the relationship between Aggression and Locus of Control. Mean, t-test, F-value (ANOVA), and correlation have been computed over SPSS-16. Results suggest that males from Combat have higher Aggression than people from non-sports and non-combat sports. There is also a significant difference between non-sports persons and sports people over the Locus of Control, sports persons showed internal locus of control compared to non-sports persons who were higher on external locus of control. The result also indicates a significant relationship between the anger dimension of the Aggression and Locus of Control. 2025 ARD Asociaci Espala. -
Adoption and Usage of Artificial Intelligence in Food Processing Industries
In recent years, technological changes and advancements have forced Fast Moving Consumer Goods (FMCG) industries, especially food processing, to redesign their functionality. This includes the integration of technologies like Artificial Intelligence (AI) to enhance performance. Future trends in the food processing industry will be shaped by sustainability, efficiency, traceability, wellness, safety, hygiene, health, and newlinetransparency. Food processing industries are compelled to embrace digitalization in the newlinecurrent era of globalization and digital transformation. AI encompasses programs, newlinealgorithms, robotics, drones, data mining, cloud computing, sensors, driver-less newlinevehicles, the internet of things, digital platforms, and machines, representing a new newlinelevel of intelligence. AI aims to replicate human reasoning and problem-solving newlinecapabilities, leading to task automation, increased efficiency, and reduced human newlineeffort. The growth of AI is reshaping the food processing industry, with potential newlineapplications spanning from cultivation, supply chain management, storage and safety, newlineHuman Resource Management (HRM), and Customer Relationship Management (CRM). Integrating and adopting AI in food processing can address unique challenges and offer substantial benefits across these functions. While large-scale food processing newlineindustries have made significant progress in adopting AI systems, small and mediumscale food industries are also integrating AI technology. The current research study employs a quantitative research methodology and obtained data from 320 small and medium-scale food processing industries employees in the city of Bengaluru. The primary surveyed data were analyzed using the Structural Equation Modeling (SEM) approach through AMOS 26. The research used the UTAUT 2 model to measure the usage and adoption of Artificial Intelligence (AI) among the employees of small and medium-scale food processing industries. -
Bard-Taylor ferroconvection with time-dependent sinusoidal boundary temperatures
The combined effect of centrifugal acceleration and time-varying boundary temperatures on the onset of convective instability of a rotating magnetic fluid layer is investigated by means of the regular perturbation method. A perturbation expansion in terms of the amplitude of applied temperature field is implemented to effectively deal with the effects of temperature modulation. The criterion for the threshold is established based on the condition of stationary instability manifesting prior to oscillatory convection. The modulated critical Rayleigh number is computed in terms of Prandtl number, magnetic parameters, Taylor number and the frequency of thermal modulation. It is shown that subcritical motion exists only for symmetric excitation and the destabilizing effect of magnetic mechanism is perceived only for asymmetric and bottom wall excitations. It is also delineated that, for bottom wall modulation, rotation tends to stabilize the system at low frequencies and the opposite is true for moderate and large frequencies. Furthermore, it is established that, notwithstanding the type of thermal excitation, the modulation mechanism attenuates the influences of both magnetic stresses and rotation for moderate and large frequencies. Published under licence by IOP Publishing Ltd. -
Thermorheological and magnetorheological effects on Marangoni-Ferroconvection with internal heat generation
Marangoni convectiveinstability in a ferromagnetic fluid layer in the presence of a spatial heat sourceand viscosity variation is examined by means of the classical linear stability analysis. The higher order Rayleigh-Ritz technique is used to compute the critical Marangoni number. The effective viscosity of the ferromagnetic liquid is taken to be a quadratic function of both the temperature and magnetic field strength. It is shown that the ferromagnetic fluid is significantly influenced by the effect of viscosity variation and is more prone to instability in the presence of heat source compared to that when viscosity is constant. On comparing the corresponding results of heat source and heat sink it is found that heat sink works in tandem with the effect of viscosity variation if magnetic field dependence of viscosity dominates over temperature dependence. If the temperature dependence of viscosity dominates, the effects of viscosity variation and heat sink are mutually antagonistic. Published under licence by IOP Publishing Ltd. -
A Study on the Impact of Intervention Program for the Care Givers of People with Head and Neck Cancer
Head and neck cancer patients experienced profound psychosocial and functional abilities because of the location of the disease and treatment. It hampers their activities of daily living making them dependant on the caregivers. Subsequently caregivers have different needs especially during the initial phase of hospitalization because they are unfamiliar to the whole process, unprepared for the new task and new role and lack the necessary knowledge and skills in care giving. Thus they face a number of problems. Hence this study aimed to understand their needs, develop psycho educational intervention program based on it and assess its feasibility. It was taken up because of limited number of Indian studies and increase in the number of incidences owing to the changing life style. Quasi experimental research design and sequential mixed research design was used. The variables taken for the study were Caregiver burden and distress. Zarit burden interview schedule and Caregiver Self Assessment Questionnaire was used to collect data from 30 caregivers of head and neck cancer patient before and after the Psycho educational intervention program was delivered. Paired sample t test and Cohen s d tests were used for data analysis. The effect size for burden and distress was 2.01 and 1.91 respectively. Findings showed that the intervention program significantly reduced the level of burden -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
Road-Traffic Congestion in Bengaluru : Psychological and Social Consequences
The study investigated the commuting experiences of frequent travelers during congestion using a three-phase sequential exploratory design. Using semi-structured interviews, phase-1 explored the experiences of a sample of ten (4 women and 6 men) regular commuters on Bengaluru's congested roads. Thematic analysis revealed that psychological experiences due to travel adversities during congestion generated negative affect that narrowed thought-action repertoire of the commuters into fight or flight responses. Fight responses caused negative road occurrences that intensified travel adversities further, creating a vicious cycle showing a non-linear loop. Social consequences included challenges for personal time and activities, family time, health and health care activities, work, social, community, and recreational activities, increase of virtual socialization, and social Darwinism. In phase 2, a check-list of psychological consequences was developed based on the thematic analysis. Phase 3 statistically validated the vicious cycle in a sample of 190 (87 women and 103 men) commuters using structural equation modelling. The model substantiated the probability of the vicious cycle. Based on the model, a mathematical model was developed that could be used to test the non-linear relationship between the components of the vicious cycle. -
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. -
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. -
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. -
