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Gravity-modulated RayleighBard convection in a Newtonian liquid bounded by rigidfree boundaries: a comparative study with other boundary conditions
Effect of different boundaries on the gravity-modulated RayleighBard convection has been investigated with an emphasis on rigidfree boundaries. Small-amplitude and large-amplitude modulations are studied using the linear stability analysis. The modified Venezian approach is used to study small-amplitude modulations using different modes of perturbations and the superposition principle. The existence of subharmonic motions for the case of large-amplitude modulations was explored using the Mathieu equation arising from the linear stability analysis. Floquet theory was used together with Hills infinite determinant method to compute the critical Rayleigh number for the case of large-amplitude modulations. Weakly non-linear analysis is performed leading to the cubic StuartLandau equation from the Lorenz system. Heat transport was quantified using the Nusselt number and the mean Nusselt numbers for different amplitudes and frequencies. It was found that gravity modulation has, in general, a stabilizing effect on the convection process in all three boundary types, and the heat transport was found to be an increasing function of amplitude. Another important outcome of the study is that the critical Rayleigh number for the onset of convection for rigidfree boundaries lies between those of the corresponding values of the freefree and rigidrigid boundaries in the case of both harmonic and subharmonic motions which could be exploited in controlling convection. 2023, The Author(s), under exclusive licence to Springer Nature B.V. -
Computationally efficient wavelet domain solver for florescence diffuse optical tomography
Estrogen induced proliferation of mutant cells is a growth signal hallmark of breast cancer. Fluorescent molecule that can tag Estrogen Receptor (ER) can be effectively used for detecting cancerous tissue at an early stage. A novel targetspecific NIRf dye conjugate aimed at measuring ER status was synthesized by ester formation between 17-? estradiol and a hydrophilic derivative of ICG, cyanine dye, bis-1,1-(4-sulfobutyl) indotricarbocyanine-5-carboxylic acid, sodium salt. In-vitro studies provided specific binding on ER+ve [MCF-7] cells clearly indicating nuclear localization of the dye for ER+ve as compared to plasma level staining for MDAMB-231. Furthermore, cancer prone cells showed 4.5-fold increase in fluorescence signal intensity compared to control.; A model of breast phantom was simulated to study the in-vivo efficiency of dye with the parameters of dye obtained from photo-physical and in-vitro studies. The excitation (754 nm) and emission (787 nm) equation are solved independently using parallel processing strategies. The results were obtained by carrying out wavelet transformation on forward and the inverse data sets. An improvisation of the Information content of system matrix was suggested in wavelet domain. The inverse problem was addressed using LevenbergMarquardt (LM) procedure with the minimization of objective function using Tikhonov approach. The multi resolution property of wavelet transform was explored in reducing error and increasing computational efficiency. Our results were compared with the single resolution approach on various parameters like computational time, error function, and Normalized Root Mean Square (NRMS) error. A model with background absorption coefficient of 0.01 mm-1 with anomalies of 0.02 mm-1 with constant reduced scattering of 2.0 mm for different concentration of dye was compared in the result. The reconstructed optical properties were in concurrence with the tissue property at 787 nm. We intend our future plans on in-vivo study on developing a complete instrumentation for imaging a target specific lipophilic dye. Springer International Publishing Switzerland 2014. -
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Traditional Ecological Knowledge Repository in the Indian Himalayas: An Overview
Traditional ecological knowledge (TEK) refers to a body of informa-tion that is also referred to as local knowledge, traditional knowledge, native knowledge, and indigenous technological knowledge. A number of studies show the role of traditional ecological knowledge in decision-making in social-ecological systems that support sustainability and resilience. International agencies have also highlighted and emphasised the importance of TEK practises in the preservation of biological variation. For instance, the UN Convention on Biodiversity, Article 8 (j), makes it very plain that respect, maintain, and promote innovation and practises of indigenous and aboriginal populations connected with sustainable use of biolog-ical diversity are essential. The benefits of TEK for sustainable forest management were acknowledged in the 2005 Millennium Ecosystem Assessment Report by the World Bank. As environmentalists, anthropologists, and arborists share interests in TEK for academic, social, or economic reasons, this highlights the significance of TEK in difficulties relating to biodiversity protection. Numerous components of TEK are seen favourably by experts in fields of forestry, irrigation, architecture, ethno-biology, irrigation, agriculture, medicine, sun and water conservation, conventional weather prediction, adaptation to climate change, and disaster risk reduction. Indian Himalayan Region (IHR) is predominantly populated by indigenous peoples and local societies, which are quite diverse in terms of socio-culture and race. The region has nearly 40% of all of Indias indigenous tribes. This area is also special for its tradi-tional ecological knowledge. Many of the TEK-based practices have supported local communities in earning a livelihood. The indigenous peoples expertise and expe-riences are said to play a crucial part in preventing climate change, and they may give important information on the implications of climate change. Hence, sustaining biodiversity in the IHR is also a means of defending indigenous peoples rights. By making the TEK the focal point of governance systems at the IHR, the variety of options for sustainable growth and even the co-production of the body of knowl-edge would be expanded. Therefore, it seems sensible to get knowledge from the TEK before it is lost to the onslaught of modernity. However, there are numerous problems or issues with traditional ecological knowledge in India, including igno-rance in considering conservation policies by the Indian government and the lack of effective documentation of this priceless knowledge. To develop sustainable and culturally suitable management techniques, it is currently a challenge to combine indigenous knowledge standards and management methods with Western science. Realising the above, this chapter attempts to comprehend the concept of TEK and its application throughout a variety of resource management contexts throughout a variety of resource management scenarios. Further, it will explore various issues and challenges and examine the regulations thereof. Lastly, this chapter concludes by highlighting the strategies and suggestions for an effective repository of traditional ecological knowledge in the Indian Himalayan Region. 2024 The Author(s). -
Sectoral correlations and interlinkages: NSE
An efficient portfolio is a well-diversified portfolio that gives the investor opportunities to earn money and provide cover against risks. Understanding the intersectoral linkages and correlations among various sectors in a stock market will help an investor to diversify the portfolio and reduce risk efficiently. This study aims at examining the underlying linkages and correlations among eight sectors in the Indian National Stock Exchange (NSE) using a Granger causality test under VAR environment. The results of the study based on nine years' data from 2009 to 2018 show that an effective portfolio can have two classifications -stocks from Pharma and Media as group one (defensive stocks) and picks from IT, Bank, Financial Services, Realty, Auto and FMCG sector as group two (somewhat Cyclical). The study further proves that the usual definition for cyclical and defensive sectors have undergone some profound changes. 2020 SCMS Group of Educational Institutions. All rights reserved. -
Effects of Rough Boundaries on RayleighBenard Convection in Nanofluids
A linear stability analysis of RayleighBenard convection in a Newtonian nanofluid is carried out using most general boundary conditions. A single-phase description of nanofluids is adopted in the study. The nanofluids used for the study are wateralumina and watercopper nanofluids in order to analyze how a choice between them can be made. The values of thermophysical quantities of nanofluids are calculated using the mixture theory and phenomenological-laws. The paper applies the Maclaurin series in solving the boundary-eigenvalue-problem through a simple and innovative approach. A single-term Galerkin technique is adopted to obtain the guess value of the critical Rayleigh number and the wave number. Further, improved values of the Rayleigh number and the wave number are obtained using the solution of a system of three linear-algebraic equations. A detailed discussion is made on the effect of rough-boundaries and Robin-boundary conditions for temperature on the onset of convection. A comparative study between the results of two nanofluids is made and the destabilizing effect of nanoparticles in the Newtonian carrier-fluid on the onset of convection is studied. Copyright 2023 by ASME. -
A Feature Selection Study on the Bot-IoT Dataset Using Ensemble Classification Techniques
IoT is an emerging giant in the field of technol- ogy, taking over traditional systems, providing interconnected- ness, convenience, efficiency, and automation, making our lives unimaginably better. However, security for these IoT systems is challenging, especially due to their interconnectedness, making them vulnerable to various cyber threats. The rising tide of IoT botnets, especially, presents a unique challenge. This has urgently increased the need for Intrusion Detection research. Modern Intrusion Detection approaches often employ Machine Learning for effective results. Feature Selection is extremely important while creating Machine Learning Classification models to avoid overfitting and poor performance. This paper focuses on running a Feature Selection study on the Bot-IoT dataset provided by UNSW to increase the accuracy of a ML model. The paper tests 5 types of Feature Selection methods, from Filter- based, Wrapper-based and Embedded methods, combined with two distinct ensemble classifiers: Random Forest + Adaboost and XGBoost. Each combination is tested with the dataset, and the accuracy is compared to find the most effective and versatile feature selection method that can assist both Stacking and Voting- type Ensemble classifiers. The results show that Karl Pearson can provide the best accuracy when applied to both Ensemble Classifiers. 2024 IEEE. -
Digital Forensics Chain of Custody Using Blockchain
In todays world, Digital Forensics is a crucial subject with much scope as data storage becomes more decentralised. The collection and preservation of digital media is a topic of concern across the Cyber Security and Digital Forensics field. With Cloud Infrastructure and other technologies, data is not permanently stored in one place and gathering and analysing it can become a headache for Forensic Investigators. Blockchain, however, works as a decentralised, distributed peer-to-peer network and thus can be considered a suitable solution for the mentioned problems. With the help of a blockchain network and Smart Contracts, Digital Forensics can be significantly improved to adapt to modern digital architecture. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Synchronous learning and asynchronous learning during COVID-19 pandemic: a case study in India
Purpose: This research aims to study the students' perspectives on synchronous and asynchronous learning during the COVID-19 Pandemic. Both synchronous and asynchronous learning approaches used in online education have positive and negative outcomes. Hence, the aim is to study online education's positive and negative consequences, reflecting sync and async approaches. This research followed a mixed research approach. The key stakeholders of this research are the Indian educational institutions and students. Design/methodology/approach: This research collected data from the students undergoing synchronous and asynchronous learning amidst the COVID-19 Pandemic. The data were collected (N=655) from various students taking online classes during the pandemic. A questionnaire survey was distributed to the students through online platforms to collect the data. In this research, the authors have collected data using simple random sampling, and the same has been used for data analysis using SPSS version 26. The collected data were exposed to a factor analysis using a principal component analysis technique to reduce the vast dimensions. Findings: The study findings show that synchronous learning is sometimes stressful, placing more responsibility on students mainly because of the increased screen time. At the same time, asynchronous learning allows the students to self-explore and research the topics assigned to them. Students also felt that asynchronous activities create a burden because of many written assignments to be submitted within a short period. Overall, the COVID-19 pandemic has been challenging for the students and the teachers. However, teachers have helped students to learn through digital platforms. The majority of the respondents opined that technological disruptions and death in the family circle had been significant reasons for not concentrating during online classes. However, the combination of synchronous and asynchronous learning has led to a balanced education. Practical implications: Higher education has undergone multiple transformations in a short period (from March 2020, 2021 and beyond). Educational institutions underwent a rapid transition in remote teaching and learning in the initial stages. As time progressed, educational institutions did course navigation where they relooked into their course plans, syllabus and brought a structural change to match the pandemic requirements. Meanwhile, educational institutions slowly equipped themselves with infrastructure facilities to bring academic integrity. At present, educational institutions are ready to face the new normality without disrupting services to society. Social implications: Educational institutions create intellectual capital, which is important for the development of the economy. In the light of COVID-19, there are new methods and approaches newly introduced or old methods and approaches, which are reimplemented, and these approaches always work for the benefit of the student community. Originality/value: The authors collected data during the COVID-19 pandemic; it helped capture the students' experience about synchronous and asynchronous learning. Students and faculty members are newly exposed to synchronous and asynchronous learning, and hence, it is essential to determine the outcome that will help many stakeholders. 2022, Cassandra Jane Fernandez, Rachana Ramesh and Anand Shankar Raja Manivannan. -
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Multi-level Prediction of Financial Distress of Indian Companies Using Machine Learning
Predicting Financial Distress (FD) and shielding companies from reaching that stage is vital, even indispensable for every business. FD, if not attended to on time, ultimately leads to bankruptcy. Prediction variables are essential to forecast the wreckage in the business; however, the prediction is successful when suitable models are used. This study aims to predict FD at three levels: from mild to severe, by applying a machine learning algorithm. The study identifies modern models using the machine learning approach for predicting multi-level FD and summarises the significance of modern models through machine learning technology, to sustain the future development of the economy. The modern models are free from rigid assumptions and have proved to be the best in the prediction of FD. The results show that FD prediction is important at multiple stages. The models performance will be high when the best features are selected using the Pearson Correlation and SFS Feature selection approach. Among the ten models used in the study, LightGBM Classifier shows the highest performance of 80.43% accuracy without feature selection. However, with Pearson Correlation Approach and SFS Feature Selection methods, the accuracy is 82.68% and 86.95% respectively. This study has major implications for the stakeholders of the company to take timely decisions on their investment and for the management as a yardstick to check the performance of the business. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Quality assurance in big data analytics: An IoT perspective
Emergence of IoT as one of the key data contributors in a big data application has presented new data quality challenges and has necessitated for an IoT inclusive data validation ecosystem. Standardized data quality approaches and frameworks are available for data obtained for a variety of sources like data warehouses, webblogs, social media, etc. in a big data application. Since IoT data differs significantly from other data, challenges in ensuring the quality of this data are also different and thus a specially designed IoT data testing layer paves its way in. In this paper, we present a detailed review of existing data quality assurance practices used in big data applications. We highlight the requirement for IoT data quality assurance in the existing framework and propose an additional data testing layer for IoT. The data quality aspects and possible implementation models for quality assurance contained in the proposed layer can be used to construct a concrete set of guidelines for IoT data quality assurance. 2019 Telecommunications Society and Academic Mind. -
Sensory processing sensitivity in relation to coping strategies: exploring the mediating role of depression, anxiety and stress
Existing research on sensory processing sensitivity (SPS) focuses majorly on populations involving children, those with Autism Spectrum Disorder, and those belonging to the Western countries. This study aims to contribute in bridging this gap by exploring the mediating role of Depression, Anxiety, Stress on the relationship between SPS and coping strategies in the general population, while also assessing the prevalence of these variables. Data was collected from a convenience sample of 107 participants (mean age = 20.6years, 57.9% females). Participants responses were recorded for the Highly Sensitive Person Scale (HSPS), the Depression, Anxiety, Stress Scale (DASS-21), and the Coping Strategies Inventory-Short Form (CSI-SF). Correlational and mediation analyses of SPS, coping strategies and Depression, Anxiety and Stress were done. In the sample, 31.78% of individuals were found to be Highly Sensitive Persons (HSPs). The findings revealed significant relationships between SPS, Depression, Anxiety, Stress and coping strategies. Depression and Anxiety were observed to be significant mediators. While SPS as a trait may not be inherently modifiable, our results on its association with emotion-focused disengagement coping provide insight into target dysfunctional patterns for effective management of depression, stress, and anxiety. Further research is warranted to enhance the applicability of this study. The Author(s) 2024. -
Transforming online class recording into useful information repositories using NLP methods: An Empirical Study
Most educational institutions have adapted to the mode of online teaching which has resulted in an increase of online video recordings. Learner community can be benefited with the ability to retrieve required information from the online class recordings. In this paper, we propose a methodology for converting video transcript data into useful information repositories for the purpose of retrieving class transcripts relevant to user's information needs. We focus on the online video recording transcript data. We also discuss challenges in transcribing which are crucial to understand preliminary processing. Our dataset consists of transcripts from diverse subject domains deeper experimental insights. We use interactive transcripts obtained from ASR (automatic speech recognition) services and non-interactive human generated transcripts. State-of-the-art methods for keyword retrieval: Latent Dirichlet Topic Modelling (LDA), Term Frequency (TF.IDF) and Text Rank (graph based) are applied on the video transcript data. Further, cosine similarity metric is applied to obtain the similarity measure between the transcript documents and keywords. 2022 IEEE. -
Biogenic Synthesis of Zinc Oxide Nanoparticles Mediated by the Extract of Terminalia catappa Fruit Pericarp and Its Multifaceted Applications
Zinc oxide nanoparticles (ZnO-NPs) were biosynthesized by using the pericarp aqueous extract from Terminalia catappa Linn. These NPs were characterized using various analytical techniques such as X-ray diffraction (XRD), Fourier transform infrared (FTIR) spectroscopy, ultraviolet (UV) spectroscopy, dynamic light scattering (DLS), and scanning electron microscopy (SEM), and XRD studies of the nanoparticles reported mean size as 12.58 nm nanocrystals with highest purity. Further SEM analysis emphasized the nanoparticles to be spherical in shape. The functional groups responsible for capping and stabilizing the NPs were identified with FTIR studies. DLS studies of the synthesized NPs reported ? potential as ?10.1 mV and exhibited stable colloidal solution. These characterized ZnO-NPs were evaluated for various biological applications such as antibacterial, antifungal, antioxidant, genotoxic, biocompatibility, and larvicidal studies. To explore its multidimensional application in the field of medicine. NPs reported a potential antimicrobial activity at a concentration of 200 ?g/mL against bacterial strains in the decreasing order of Streptococcus pyogenes > Streptococcus aureus > Streptococcus typhi > Streptococcus aeruginosa and against the fungi Candida albicans. In vitro studies of RBC hemolysis with varying concentrations of NPs confirm their biocompatibility with IC50 value of 211.4 ?g/mL. The synthesized NPs DPPH free radical scavenging activity was examined to extend their antioxidant applications. The antiproliferation and genetic toxicity were studied with meristematic cells of Allium cepa reported with mitotic index (MI index) of 1.2% at the concentration of 1000 ?g/mL. NPs exhibited excellent Larvicidal activity against Culex quinquefasciatus larvae with the highest mortality rate as 98% at 4 mg/L. Our findings elicit the therapeutic potentials of the synthesized zinc oxide NPs. 2023 The Authors. Published by American Chemical Society -
Analyzing the Market Dynamics of Electrical Appliances with a Special Emphasis on Sustainable Electric Energy
This study looks into the market dynamics of electrical appliances with a special emphasis on sustainable electric energy. The research aims to understand how factors such as technological advancements, consumer behavior, and regulatory policies influence the electrical appliances market. By examining the trends and challenges within this sector, the study highlights the growing importance of sustainability in product development and consumer choices. The main areas of focus include the adoption of energy-efficient technologies, the impact of rising household incomes on appliance usage, and the role of government policies and initiatives in promoting sustainable energy consumption. The findings of the study would provide insights into how the industry can align its practices with environmental goals while meeting the evolving needs of consumers. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
HQA Bot: Hybrid AI Recommender Based Question Answering Chatbot
The COVID pandemic has presented a number of challenges for education, particularly when it comes to reaching and engaging students. As a result, online education has become increasingly important, and artificial intelligence (AI) has played a crucial role in supporting this shift. The proposed tutor assistance question-answering system uses AI to automatically generate responses to student questions. This system includes a feedback mechanism, known as a satisfaction index that measures the efficiency of the generated responses and suggest relevant follow-up questions. The proposed Hybrid Recommender-based Dijkstras algorithm (HRD) improves the system's accuracy. This algorithm uses a combination of techniques to group relevant questions based on context, which improves the accuracy of identifying the next relevant question. In our customized dataset, this approach achieved an accuracy of 96% and an average accuracy of 82% across benchmarked datasets. With this system, we aim to bridge the gap between students and education by providing a more engaging and personalized learning experience. 2023, Ismail Saritas. All rights reserved. -
Synthesis of Chitosan Stabilised Platinum Nanoparticles and their Characterization
A simplistic green synthesis route for the platinum nanoparticles has been successfully identified by using chloroplatinic acid hexahydrate (H2 PtCl6.6H2 O) as the metal precursor and sodium borohydride (NaBH4) as the reducing agent at room temperature. Chitosan was used in minute quantities as capping and stabilizing agent. The visual observation of a black coloured colloidal suspension, the characteristic XRD peaks and the absorption peak in the range of 200-300nm confirmed the production of Pt nanoparticles. The average crystallite size calculated using Debye-Scherrer equation is about 19 2 nm and a less intense absorption peak was found at 246nm and 281nm. The FTIR spectroscopy was used to confirm the capping with chitosan molecules. Zeta-potential calculation gave a surface charge of-23.8mV, and this high negative value, then validated the stability of the nanoparticle. The synthesis of platinum nanoparticles is very significant for their catalytic activity and biomedical applications in industrial as well as healthcare sector. 2023, Books and Journals Private Ltd.. All rights reserved.