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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. -
A comparative study of ancient Indian mythological characters and modern superheroes /
Indian Mythology has been an integral part of the culture and traditions of India. The traditions have been evolving and it has been having a tremendous impact on various platforms. The entertainment industry is also growing humoungously and there are a number of aspects. The influence and the impact of Indian mythology has been tremendous and it also has expanded its vast influence over other platforms such as entertainment, social aspects etc. -
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. -
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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 -
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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. -
Performance Evaluation of CPU and GPU Processors Using Advanced Data Analysis Techniques
The modern industry is developing advanced CPU and GPU processors. The standard efficiency difference between the Intel Core i5 and 11th Gen with Nvidia GTX3050 processors is being discussed in this article. However, the reduction factor between these processors is determined to be as 2.5. Various data visualization techniques were applied to give a comprehensive analysis of the performance of CPU and GPU-based processors for execution of intensive tasks. The results were analyzed to understand the various performance parameters related to their functioning and efficiency. A model was proposed for enhancing the performance and throughput of the processor by easing the internal communication process between the CPU and the GPU by converting from electrical signals to light signals, though being faced with many challenges in the current time, holds a large scope of further research in the pursuit of higher computational efficiency. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Determinants of Internal Branding for Customer-Orientation
International Journal of Research in Commerce, IT & Management, Vol-4 (5), pp. 33-38. ISSN-2231-5756 -
Investing in Indias electric revolution: A case study of OLA electric
Subject Area: Entrepreneurship, Strategy. Study Level/Applicability: The case is best suited for a course on Entrepreneurial Finance while introducing the sources of finance and a course on Private Equity and Venture capital, while discussing target screening. It exemplifies how new-age ventures can position themselves to attract investments and how venture capital firms use environmental scanning to identify potential investment opportunities. Designed for second-year MBA students, the case assumes familiarity with strategic management concepts like Porters Five Force and Resource-Based View. Case Overview: Amala Menon is a seasoned venture capitalist, strategist, sustainability enthusiast, and founder of Samrithi VentureCaptial and has been actively looking out for investment opportunities in the Electronic Vehicle (EV) space. Believing in the huge potential and the push received by supportive government policies in India, Amala is assessing Ola Electric Mobility Pvt. Ltd (Ola Electric) as an investable option among a list of growing players in the space. A wholly owned subsidiary of ANI Technologies Pvt. Ltd, the parent company of Ola Cabs, Ola Electric, valued at USD 5 billion as of 2022, was founded in 2017 to facilitate mass electric mobility and hence reduce emission and fuel dependency. Though crunching financial metrics and valuation numbers come in during assessing an investment option, she strongly emphasizes evaluating the organizations readiness to obtain further funding in terms of its management team, the potential of the business model and several other crucial parameters. Ultimately, valuation exercise, according to her, aids founders and investors in confirming the strategic positioning of the venture and the business models validity. Hence, at this point, Amala, considering qualitative and quantitative aspects, is focused on assessing whether Ola is a wise investable option compared to its peers. Expected Learning Outcomes: This case study enables participants to: (1) Critically appraise investment prospects in new-age EV startups like Ola Electric considering industry-specific and organization-specific factors. (2) Explore both financial and non-financial factors crucial for evaluating Ola Electrics potential as a promising investment. (3) Evaluate Ola Electric as a potential investment option by leveraging strategic frameworks such as Resource-Based View, Internal and External Factor Evaluation to formulate an investment recommendation. (4) Apply the insights gained from the environmental scan to guide Amalas investment decision regarding Ola Electric. Association for Information Technology Trust 2024. -
Does Sentiments Impact the Returns of Commodity Derivatives? An Evidence from Multi-commodity Exchange India
The advancements in technology, increased accessibility to various modes and platforms of communication, and increased willingness on the part of participants to share their ideas/opinions has resulted in huge amounts of data on the World Wide Web, hence, easily available to impact decision-making. Furthermore, commodity prices are primarily driven by demand and supply, wherein such news is open to the cognitive thinking of individuals. Thus, using the principles of natural language processing, which combines concepts of linguistics, computer science and artificial intelligence, helps in improving the accuracy of price determination. Therefore, this article aims to examine the relationship between sentiments conveyed through various sources and the performance of Indias largest commodity market, multi-commodity exchange (MCX). The correlation and causation between sentiment scores extracted from such textual content and the daily returns of select commodity derivatives are analysed. The results show varying levels of significance of sentiments on the returns of commodity contracts and imply that there is an increased scope of using such unstructured content in the field of finance. 2021 MDI.