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Linear and weakly nonlinear stability analyses of Darcy Bard convection with feedback control
In this paper, the effect of feedback control on the criterion for the onset of DarcyBard convection in a horizontal Boussinesq Newtonian fluid is studied theoretically. The bounding isothermal lower and upper surfaces are considered to be rigid. The single term Galerkin method and the Maclaurin series expansion are combined with the Newton-Raphson method of three variables to perform a linear stability analysis in order to determine eigen value. To make a weakly nonlinear stability analysis of the system, a Vadasz Lorenz model is constructed. The models various properties are discovered to be identical to those of the standard Lorenz model. The exhibits both dissipative and conservative characteristics and the bounded nature of its solution is demonstrated by the trapping region, which takes the form of an ellipsoid. The Hopf-Rayleigh number determined from the autonomous dynamical system predicts the onset of chaos. The influence of the controller gain parameter and the Biot number on the onset of convection has been analyzed. Results from the study reveal that the controller gain parameter stabilizes the system and further delays the onset of chaos. Overall, the study establishes that an increase in the Biot number promotes long-term periodic motion over chaotic behavior, while an increase in the controller gain parameter enlarges the trapping region, thereby contributing to improved system stability. The Author(s) 2025. -
Rheostatic effect of a magnetic field on the onset of chaotic and periodic motions in a five-dimensional magnetoconvective Lorenz system
This paper deals with a weakly nonlinear study of two-dimensional RayleighBard magnetoconvection using a simplified five-dimensional Lorenz model. The governing equations of the system are nondimensionalized and formulated in terms of the stream function and the scalar magnetic potential. A five-modal Fourier truncation scheme is employed and the resulting equations are scaled to obtain a five-dimensional autonomous dynamical system. The Hopf-Rayleigh number, signifying Hopf bifurcation, is numerically evaluated from the analysis of weakly nonlinear stability. Chaotic and periodic motions are depicted by plotting bifurcation diagrams, largest Lyapunov exponent (LLE) diagrams and three-dimensional projections of the phase-space. For a fixed set of parameter values, increasing the strength of the applied magnetic field is found to increase the Hopf-Rayleigh number, thereby delaying the destabilization of the system's equilibrium points. It is shown that while low magnetic field strengths favor the onset of chaotic motion directly from the steady state, stronger magnetic field strengths favor the onset of periodic convection from the steady state prior to the appearance of chaotic motion. We observe here that the applied magnetic field regulates the onset of chaotic and periodic motions in the system and therefore, has a rheostatic control over chaotic and periodic behaviors. 2025 Elsevier Ltd -
Sentiment Analysis on Educational Tweets: A Case of National Education Policy 2020
Due to COVID-19 pandemic lockdowns, the transition from traditional class-room-based approaches, there has been rise in online education. There is a growing need to adopt the best global academic and innovative practices and implement the National Education Policy-2020 (NEP) in Indian education. This study uses a dataset, NEPEduset, created by gathering tweets about education. An attempt has been made in this study to examine the tweets by preprocessing, generating labels or sentiments using standard tools and libraries in Python language, applying and comparing various machine learning (ML) algorithms. ML approaches are powerful and used in various applications ranging from sentiment analysis, text analysis, natural language processing (NLP), image processing, object detection. ML methods are widely used in sentiment analysis tasks and text annotations. This work uses Text-Blob, Valence Aware Dictionary for Sentiment Reasoning (VADER), and a Customized method, SentiNEP to analyze the sentiment score of tweets' text. SentiNEP method is shown is produce better results for various experiments conducted for the dataset, NEPEduset. Various supervised ML models have been applied for text classification of user sentiment. Word2Vec feature extraction technique has been applied to build and evaluate the models. Performance metrics such as precision, accuracy, F1 score and recall have been used to evaluate the ML models. The results reveal that the support vector machine and random forest classifiers achieve higher accuracy with Word2Vec. The performance results have been compared with VADER, TextBlob and SentiNEP. It has been found that the SentiNEP method produces better results. 2023 IEEE. -
BERT-Enhanced Bi-LSTM with weighted cross-entropy for multilingual sentiment classification
With the increasing volume of multilingual user-generated content across social media platforms, effective sentiment analysis (SA) becomes crucial, especially for low-resource languages. However, traditional models relying on context-independent embeddings, such as Word2Vec, GloVe, and fastText, struggle to handle the complexity of multilingual sentiment classification. To address this, we propose an Automatic Multilingual Sentiment Detection (AMSD) framework that leverages the contextual capabilities of BERT for feature extraction and a Bidirectional Long Short-Term Memory (Bi-LSTM) network for classification. Our method, termed Elite Opposition Cross-Entropy Weighted Bi-LSTM (EOCEWBi-LSTM), integrates elite opposition-based learning to optimize hyperparameters and enhance classification accuracy. A weighted cross-entropy loss function further refines the model's sensitivity to class imbalance, thereby improving its performance. The model is trained and evaluated on the NEP_EDUSET corpus, comprising 45,434 tweets in English, Hindi, and Tamil. Experimental results demonstrate notable improvements in precision, recall, F1-score, and accuracy, highlighting the effectiveness of EOCEWBi-LSTM in multilingual sentiment analysis, especially across both high-resource and low-resource languages. The experimental results show that the proposed EOCEWBi-LSTM achieves a high F1-score ratio of 93.83% and an accuracy ratio of 93.83% compared to other existing methods. EOCEWBi-LSTM provides an effective solution for multilingual sentiment analysis, especially for languages with limited resources. 2025 The Author(s). -
Multilingual Sentiment Analytics for India's NEP 2020
This study presents a multilingual sentiment analysis framework for evaluating public sentiments on India's National Education Policy (NEP) 2020. The authors developed a dataset related to NEP 2020 using web scraping from open sources. The curated dataset comprises 50,000 social media posts (English: 30,000, Hindi: 12,000, Tamil: 8,000) processed through a confidence-gated hybrid annotation pipeline. Sentiment labels were created using Transformer models (BERT, mBERT, XLMR) and validated by native-speaker with F1-scores of 87.6%, 81.2% and 78.0% for English, Hindi and Tamil respectively: outperforming baselines (SVM, Naive Bayes, BiLSTM) by 12-18% (p<0.001). We use computational efficiency measures to illustrate that training takes 3.2-5.3 hours and inference lasts between 118 and 187 posts per second. Topic modeling revealed sentiment divergences: positive for linguistic inclusivity and teacher training, negative for affordability and infrastructure. Cross-linguistic analysis showed English-Hindi convergence (similarity: 0.61) versus Tamil divergence (0.46), reflecting regional priorities. Tamil emphasized linguistic identity while English prioritized implementation critiques. Quantitative policy impact analysis shows very strong correlation (r=0.68, p<0.01) between regional sentiment scores and state adoption rates. This open-sourced contribution is filling the crucial gap of inclusive policy analytics in multilingual society informing evidence-based policy. 2025 IEEE. -
Privacy Optimization in Sensors Based Networks With Industrial Processes Management
The Internet of Things (IoT) also known as IoT has the potential that is required to revolutionize industries, this has been discussed in this research article. Advancements in technology have made devices affordable, efficient and reliable. Different sectors have already started to incorporate these devices into their operations to boost productivity, to minimize failure and downtime. They also use it to optimize resource utilization which is also an important factor. However, the use of these devices also has some security challenges which need to be handled. This research paper proposes a security model specifically designed for process management in the industries. The goal of this model is to find the vulnerabilities, to minimize the risks and threats. Also ensuring integrity, confidentiality and availability of processes is a part of the goal. This paper gives evidence from its implementation and trial apart from its explanation. During the implementation phase, the sensitive data achieved a 100% encryption rate, for protection. Also, integrity checks were conducted on 99.8% of data to guarantee data integrity. 2023 IEEE. -
Ganga and Yamuna Rivers: Through the Lens of the National Green Tribunal
Despite the country's extensive environmental jurisprudence and many historic rulings in which the courts have rescued worsening environmental situations, river (Ganga and Yamuna) water does not match the mandated minimum "bathing quality." Rivers like the Ganga and Yamuna, which flow through numerous states and towns, would be in a different situation. Without strict monitoring and enforcement of the measures, no action plan can work. Punishment of defaulters can serve as deterrence while also instilling fear in other non-compliant enterprises. In comparison to environmental legislation, the NGT Act allows for substantially harsher fines and penalties. River rejuvenation plans must be carefully monitored to ensure that they do not suffer the same fate. Making action plans will not improve river water quality unless they are implemented with sincerity and consistency, as well as continuous monitoring and severe enforcement. 2022 Technoscience Publications. All rights reserved. -
Exploring advancements in space object detection through computer vision
[No abstract available] -
Detection of a new sample of Galactic white dwarfs in the direction of the Small Magellanic Cloud
Aims. In this study, we demonstrate the efficacy of the Ultraviolet Imaging Telescope (UVIT) in identifying and characterizing white dwarfs (WDs) within the Milky Way Galaxy. Methods. Leveraging the UVIT point-source catalogue towards the Small Magellanic Cloud and cross-matching it with Gaia DR3 data, we identified 43 single WDs (37 new detections), 13 new WD+main-sequence candidates, and 161 UV bright main-sequence stars by analysing their spectral energy distributions. Using the WD evolutionary models, we determined the masses, effective temperatures, and cooling ages of these identified WDs. Results. The masses of these WDs range from 0.2 to 1.3 M? and the effective temperatures (Teff) lie between 10 000 K to 15 000 K, with cooling ages spanning 0.1-2 Gyr. Notably, we detect WDs that are hotter than reported in the literature, which we attribute to the sensitivity of UVIT. Furthermore, we report the detection of 20 new extremely low-mass candidates from our analysis. Future spectroscopic studies of the extremely low-mass candidates will help us understand the formation scenarios of these exotic objects. Despite limitations in Gaia DR3 distance measurements for optically faint WDs, we provide a crude estimate of the WD space density within 1kpc of 1.3 10-3 pc-3, which is higher than previous estimates in the literature. Conclusions. Our results underscore the instrumental capabilities of UVIT and anticipate forthcoming UV missions such as INSIST for systematic WD discovery. Our method sets a precedent for future analyses in other UVIT fields to find more WDs and perform spectroscopic studies to verify their candidacy. The Authors 2024. -
From Preprocessing to Prediction: An Analytical Study on Diabetes Data
Early detection of diabetes is crucial for improving a patients long-term health. In this chapter, we study diabetes and diabetes-related factors. We also delve into various imputation techniques used to address missing data. Missing data is generally a very critical issue in healthcare analytics, as a limited history of clinical records often leads to biased analysis and suboptimal model representation. This chapter gives a detailed literature review of data imputation methods. In this chapter, we have done two case studies. In the first case study, mean, median, and mode imputation techniques are applied to artificially created missing values to examine their effect on the structure and distribution of the data. The second case study captures a prediction model for a diabetes diagnosis using the same dataset. Here, a random forest prediction model is created to predict the possible presence of diabetes. An accuracy of 97.07% is achieved on the test data, which shows that diabetes can be predicted by considering other dependable variables. 2026 selection and editorial matter, Syed Nisar Hussain Bukhari; individual chapters, the contributors. -
Role of psychosocial factors in criminal behaviour in adults in India
Over the years, there has been a steady increase in the number of crimes committed annually in India (Snapshots, 2014). The purpose of this paper was to delve into the psychological and social factors that contribute to the development of criminal behaviour in the Indian context. For the current research, concurrent embedded mixed research design was used. Twenty individuals with a criminal record were selected using purposive sampling and twenty individuals with no criminal record were matched on the basis of age, gender and socio economic status. Eysenck Personality Questionnaire- Revised was administered on them. A semi structured interview delving into understanding the social factors that contributed to the criminal behaviour was taken for six individuals who have a criminal record. Results revealed that there was no significant difference in the personality traits of extraversion, neuroticism, psychoticism and lie score between the two groups. However, various social factors like lack of social support, less emphasis on education and awareness, financial constraints and certain individual traits were found to be prevalent. Furthermore, an interactive effect of personality and environmental factors was established. A model was also proposed for providing interventions at an individual as well as societal level. 2017 International Journal of Criminal Justice Sciences. -
Energy efficient heterogeneous clustering scheme using improved golden eagle optimization algorithmfor WSN-based IoT
In the Internet of Things, Wireless Sensor Networks (WSNs) are networks of interconnected sensors that wirelessly collect and transmit information about the environment. Using IoT sensors, IoT applications can remotely monitor and control physical environments. Clustering in WSNs involves organizing sensor nodes into groups called clusters with one or more CHs for efficient data integration, communication and management, improving network performance and resource utilization. In WSNs, achieving energy efficiency is critical to extend network lifetime and ensure stable operation. An important aspect contributing to energy optimization is the selection of CHs. However, the lack of an efficient cluster head selection mechanism remains a significant challenge. Therefore, this study introduces an optimized multivariate cluster head selection method that leverages the Improved Golden Eagle Optimization Algorithm (IGEOA). With this approach, the selection of CHs is optimized, combining multiple objective functions designed for energy efficiency. By using this algorithm, clusters are formed based on the selected CHs. In addition, a cluster maintenance phase is integrated to supervise the post-establishment clustering of the network, which ensures optimal cluster performance and resource utilization in WSN. Evaluation through simulation illustrates that the proposed method significantly improves both performance and energy efficiency in a WSN environment. Bharati Vidyapeeth's Institute of Computer Applications and Management 2024. -
Energy efficient heterogeneous clustering scheme using improved golden eagle optimization algorithmfor WSN-based IoT
In the Internet of Things, Wireless Sensor Networks (WSNs) are networks of interconnected sensors that wirelessly collect and transmit information about the environment. Using IoT sensors, IoT applications can remotely monitor and control physical environments. Clustering in WSNs involves organizing sensor nodes into groups called clusters with one or more CHs for efficient data integration, communication and management, improving network performance and resource utilization. In WSNs, achieving energy efficiency is critical to extend network lifetime and ensure stable operation. An important aspect contributing to energy optimization is the selection of CHs. However, the lack of an efficient cluster head selection mechanism remains a significant challenge. Therefore, this study introduces an optimized multivariate cluster head selection method that leverages the Improved Golden Eagle Optimization Algorithm (IGEOA). With this approach, the selection of CHs is optimized, combining multiple objective functions designed for energy efficiency. By using this algorithm, clusters are formed based on the selected CHs. In addition, a cluster maintenance phase is integrated to supervise the post-establishment clustering of the network, which ensures optimal cluster performance and resource utilization in WSN. Evaluation through simulation illustrates that the proposed method significantly improves both performance and energy efficiency in a WSN environment. Bharati Vidyapeeth's Institute of Computer Applications and Management 2024. -
Empirical Study on Categorized Deep Learning Frameworks for Segmentation of Brain Tumor
In the medical image segmentation field, automation is a vital step toward illness detection and thus prevention. Once the segmentation is completed, brain tumors are easily detectable. Automated segmentation of brain tumor is an important research field for assisting radiologists in effectively diagnosing brain tumors. Many deep learning techniques like convolutional neural networks, deep belief networks, and others have been proposed for the automated brain tumor segmentation. The latest deep learning models are discussed in this study based on their performance, dice score, accuracy, sensitivity, and specificity. It also emphasizes the uniqueness of each model, as well as its benefits and drawbacks. This review also looks at some of the most prevalent concerns about utilizing this sort of classifier, as well as some of the most notable changes in regularly used MRI modalities for brain tumor diagnosis. Furthermore, this research establishes limitations, remedies, and future trends or offers up advanced challenges for researchers to produce an efficient system with clinically acceptable accuracy that aids radiologists in determining the prognosis of brain tumors. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A Novel Generative Adversarial Network-Based Approach for Automated Brain Tumour Segmentation
Background: Medical image segmentation is more complicated and demanding than ordinary image segmentation due to the density of medical pictures. A brain tumour is the most common cause of high mortality. Objectives: Extraction of tumorous cells is particularly difficult due to the differences between tumorous and non-tumorous cells. In ordinary convolutional neural networks, local background information is restricted. As a result, previous deep learning algorithms in medical imaging have struggled to detect anomalies in diverse cells. Methods: As a solution to this challenge, a deep convolutional generative adversarial network for tumour segmentation from brain Magnetic resonance Imaging (MRI) images is proposed. A generator and a discriminator are the two networks that make up the proposed model. This network focuses on tumour localisation, noise-related issues, and social class disparities. Results: Dice Score Coefficient (DSC), Peak Signal to Noise Ratio (PSNR), and Structural Index Similarity (SSIM) are all generally 0.894, 62.084 dB, and 0.88912, respectively. The models accuracy has improved to 97 percent, and its loss has reduced to 0.012. Conclusions: Experiments reveal that the proposed approach may successfully segment tumorous and benign tissues. As a result, a novel brain tumour segmentation approach has been created. 2023 by the authors. -
Bifunctional Amorphous Transition-Metal Phospho-Boride Electrocatalysts for Selective Alkaline Seawater Splitting at a Current Density of 2Acm?2
Hydrogen production by direct seawater electrolysis is an alternative technology to conventional freshwater electrolysis, mainly owing to the vast abundance of seawater reserves on earth. However, the lack of robust, active, and selective electrocatalysts that can withstand the harsh and corrosive saline conditions of seawater greatly hinders its industrial viability. Herein, a series of amorphous transition-metal phospho-borides, namely Co-P-B, Ni-P-B, and Fe-P-B are prepared by simple chemical reduction method and screened for overall alkaline seawater electrolysis. Co-P-B is found to be the best of the lot, requiring low overpotentials of ?270mV for hydrogen evolution reaction (HER), ?410mV for oxygen evolution reaction (OER), and an overall voltage of 2.50V to reach a current density of 2Acm?2 in highly alkaline natural seawater. Furthermore, the optimized electrocatalyst shows formidable stability after 10,000 cycles and 30h of chronoamperometric measurements in alkaline natural seawater without any chlorine evolution, even at higher current densities. A detailed understanding of not only HER and OER but also chlorine evolution reaction (ClER) on the Co-P-B surface is obtained by computational analysis, which also sheds light on the selectivity and stability of the catalyst at high current densities. 2024 The Authors. Small Methods published by Wiley-VCH GmbH. -
Non-Noble Bifunctional Amorphous Metal Boride Electrocatalysts for Selective Seawater Electrolysis
The global scarcity of freshwater resources has recently driven the need to explore abundant seawater as an alternative feedstock for hydrogen production by water-splitting. This route comes with new challenges for the electrocatalyst, which has to withstand harsh saline water conditions with selectivity towards oxygen evolution over other competing reactions. Herein, a series of amorphous metal borides based on the iron triad metals (Co, Ni, and Fe), synthesized by a simple one-step chemical reduction method, displayed excellent bifunctional activity for overall seawater splitting. Amongst the chosen catalysts, amorphous cobalt boride (Co?B) showed the best overpotential values of 182 mV for HER and 305 mV for OER, to achieve 10 mA/cm2, in alkaline simulated seawater. This superior activity was owed to the enrichment of the metal site with excess electrons (HER) and the in-situ surface transformation (OER), as confirmed by various means. In alkaline simulated seawater, the overall cell voltage required to achieve 100 mA/cm2 was 1.85 V for the Co?B catalyst when used in a 2-electrode assembly. The Co?B catalyst showed negligible loss in activity even after 1000 cycles and 50 h potentiostatic tests, thus demonstrating its industrial viability. The selectivity of the catalyst was established with Faradaic efficiency of above 99 % for HER and 96 % for OER, with no detection of chloride products in the spent electrolyte. This study using the mono-metallic boride catalysts will turn to be a precursor to exploit other complex metal boride systems as potential candidates for seawater electrolysis for large-scale hydrogen production. 2023 Wiley-VCH GmbH. -
Biomass-derived N-doped carbon to anchor bimetallic-phospho boride for hydrogen evolution from alkaline seawater
Seawater electrolysis offers a sustainable pathway for hydrogen production, but is hindered by the limited activity and stability of electrocatalysts, with Pt-based materials being highly active yet costly and scarce. To address these issues, we synthesize nitrogen-doped carbon (NC) via a solvent-free method from golden shower biomass. NC is integrated with CoMoPB catalysts using a facile chemical reduction process. The resulting CoMoPB/NC catalyst exhibited superior HER activity, achieving a low overpotential of 34 mV at 10 mA/cm2 in alkaline natural seawater, outperforming the commercial Pt/C catalyst under similar conditions. The CoMoPB/NC catalyst demonstrated considerable stability at ?500 mA/cm2 for 100 h and showed strong HER performance in seawater electrolyzers, reaching ?1.98 V at 500 mA/cm2. This study explores the potential of biomass-derived catalysts to rival and surpass commercial noble metal-based systems, offering a cost-effective and sustainable solution for industrial-scale seawater electrolysis and renewable energy applications. 2025 Elsevier Ltd -
Experimental screening of a series of earth-abundant bi-metallic phospho-boride electrocatalysts for overall seawater electrolysis
Seawater electrolysis offers a promising alternative for large-scale hydrogen production, but its industrial viability is hindered by the lack of efficient electrocatalysts. Herein, a series of metals (M = Ni, Fe, W, Mo, V, Cu, and Mn) were experimentally screened to form a bi-metallic catalyst with CoPB, resulting in CoMPB catalysts. Amongst the screened metals, only the inclusion of Mo, W, V, and Fe was found to be beneficial in improving the seawater-splitting reaction rates. Notably, CoMoPB, CoWPB, and CoVPB required minimal HER overpotentials of 56, 105, and 73 mV, respectively, at 10 mA/cm2 in alkaline natural seawater conditions, while CoFePB (291 mV at 10 mA/cm2) outperformed other Co-M-P-B counterparts for OER. The addition of a second metal to CoPB enhances activity, conductivity, and surface reactivity by modulating electron density, optimizing it for seawater splitting. Further, the CoWPB/NFHER || CoFePB/NFOER combination yielded the lowest cell potential of 1.59 V at 100 mA/cm2 and sustained operation for over ?65 h in alkaline natural seawater with ?98 % OER selectivity. The same combination, when integrated into an advanced seawater electrolyzer with zero-gap assembly, required a cell voltage of ?1.94 V to achieve 0.5 A/cm2, demonstrating strong commercial potential. 2025 Hydrogen Energy Publications LLC -
Comprehensive Data Analysis of Anticorrosion, Antifouling Agents, and the Efficiency of Corrosion Inhibitors in CO2 Pipelines
This study explores the various methods that are being proposed for their anticorrosion and antifouling capabilities and also reviews the unique properties that make them suitable for such applications. Special attention has also been given to the problem of corrosion in CO2 pipelines, considering the corrosion inhibitors currently being used and performing statistical analysis about if and how various factors such as temperature, flow velocity, pH, and CO2 pressure affect the rate of corrosion of the CO2 pipelines. Tests including ANOVA, correlation, and graph analyses were conducted to explore their relationships, and suitable conclusions were drawn for the data collected. 2024 Scrivener Publishing LLC.