Browse Items (16481 total)
Sort by:
-
Architecture of monophase InSe thin film structures for solar cell applications
Control of microstructural evolution during the crystallization of InSe thin films is an inevitable strategy to mold their fundamental properties and potential for the fabrication of solar cells. Impact of annealing as well as substrate temperature on the crystallization progress and physical characteristics of thermally evaporated InSe was examined systematically, which eventually dictates the overall performance of resulting device. Structural and compositional characterizations have been thoroughly investigated by X-ray diffraction and energy dispersive X-ray analyses. InSe films form hexagonal structure with a preferred orientation of crystallites along the (004) direction upon crystallization. The layer of InSe is formed by two concomitant processes, deposition and recrystallization. Application of heat treatment resulted in topographical modification, which was probed by an atomic force microscope. Surface roughness was enhanced due to the influence of temperature and thereby the growth of grains. Investigations of electrical and optical properties, thus provided ample evidence for the use of crystallized monophase InSe as an absorber layer in photovoltaic conversion devices. Carrier concentration and mobility of charge carriers estimated from the Hall measurements were found to be 19.43020cm?3 and 2.01cm2V?1s?1 respectively. Moreover, this research work explores power conversion efficiency of p-InSe/n-CdS heterojunction solar cells. 2017 Elsevier B.V. -
Smart Facial Emotion Recognition with Gender and Age Factor Estimation
Human-Computer Interaction (HCI) in an intelligent way, which aims at creating scalable and flexible solutions. Big tech firms and businesses believe in the success of HCI as it allows them to profit from on-demand technology and infrastructure for information-centric applications without having to use public clouds. Because of its capacity to imitate human coding abilities, facial expression recognition and software-based facial expression identification systems are crucial. This paper proposes a system of recognizing the emotional condition of humans, given a facial expression, and conveys two methods of predicting the age and gender factors from human faces. This research also aims in understanding the influences posed by gender and age of humans on their facial expressions. The model can currently detect 7 emotions based on the facial data of a person - (Anger, Disgust, Happy, Fear, Sad, Surprise, and Neutral state). The proposed system is divided into three segments: a.) Gender Detection b.) Age Detection c.) Emotion Recognition. The initial model is created using 2 algorithms - KNN, and SVM. We have also utilized the architectures of some of the deep learning models such as CNN and VGG - 16 pre-trained models (Transfer Learning). The evaluation metrics show the model performance regarding the accuracy of the Recognition system. Future enhancements of this work can include the deployment of the DL and ML model onto an android or a wearable device such as a smartphone or a watch for a real-time use case. 2022 Elsevier B.V.. All rights reserved. -
Deep Learning for Arrhythmia Classification: A Comparative Study on Different Deep Learning Models
Arrhythmias, or irregular heart rhythms, are a major global health concern. Since arrhythmias can cause fatal conditions like cardiac failure and strokes, they must be rapidly identified and treated. Traditional arrhythmia diagnostic techniques include manual electrocardiogram (ECG) image interpretation, which is time consuming and frequently required for expertise. This research automates and improves the identification of heart problems, with a focus on arrhythmias, by utilizing the capabilities of deep learning, an advanced machine learning technique that performs well at recognizing patterns in data. Specifically, we implement and compare Custom CNN, VGG19, and Inception V3 deep learning models, which classify ECG images into six categories, including normal heart rhythms and various types of arrhythmias. The VGG19 model excelled, achieving a training accuracy of 95.7% and a testing accuracy of 93.8%, showing the effectiveness of deep learning in the comprehensive diagnosis of heart diseases. 2023 IEEE. -
Synthesis and Studies on Partially Stabilized Zirconia and Rare-Earth Zirconate Pyrochlore Structured Multilayered Coatings
This work is focused on the thermal fatigue behaviour studies of ceramic coatings, as TBC (Thermal Barrier Coating) system, its importance in determining the thermo-mechanical properties and service-life estimation of the coatings when exposed to elevated operating temperatures. Commercial 6-8%Yttria stabilized zirconia (YSZ) top coat (TC) and NiCrAlY bond coat (BC) in (a) conventional YSZ (BC and TC), (b) multi-layered functionally graded materials (FGM) i.e., BC-blend (50BC+50TC)-(TC) configuration and (c) lab synthesized Zirconia based pyrochlore (Lanthanum Zirconate-LZ) were the coating materials involved. Nickel based super alloy Inconel 718 substrates were coated by using Atmosphere Plasma Spray (APS) system with three different (varying power) plasma spray parameters. All the sides of the 25mm x 10mm x 5mm thick substrates were completely covered with the bond coat and ceramic coating. FGM configuration was spray coated only on one side of the Inconel flat plates. Thermal shock cycle tests were performed on the coated specimen by following the ASTM B214-07 guidelines which comprised of introducing the coated specimen in a muffle furnace at 1150C, held in it for 2 minutes before removing from furnace followed by forced fan air cooling (one shock cycle). The specimen were periodically subjected to visual inspection for faults, before continuing the shock cycles, until the coating flaked off or cracked or detached from substrate. Cross section metallographic samples were prepared and analysed under SEM (Scanning Electron Microscope) and Energy Dispersive spectroscope (EDS) to study the as-sprayed coating morphology and interface quality, measure coating thickness, study defects characteristics and the chemical composition. Crystal structural phases were analysed using X-Ray Diffraction (XRD). 2019 Elsevier Ltd. -
Duplex functionally graded and multilayered thermal barrier coatings based on 8% yttria stabilized zirconia and pyrochlores
Thermal Barrier Coatings (TBCs) protect gas turbine engine metal components while they serve in a high temperature environment (upto 1200℃). 8% YttriaStabilized Zirconia (8YSZ) is the current state of the art material for TBCs. Typically, 250 to 500 μm (upto 2 mm) thick TBCs can lower the metal temperature by upto 150°C than the service temperature and thereby enhance life to the components. 8YSZ TBCs however, suffer from (a) increased sinterability, (b) phase de-stabilization and (c) poor adhesion with time in service at high temperature. In order to facilitate longer engine running time, research is being directed towards finding (i) newer materials that do not possess these deficiencies or (ii) configurations that can overcome them. In order to further improve the performance efficiency of the engines, TBC materials with extended thermal fatigue life at higher than current service temperatures (>1100℃) are also being actively investigated. In the same area of research, this thesis presents the findings of work on air plasma sprayed (i) duplex, (ii) functionally graded and (iii) multilayered configurations of TBCs synthesized from commercial 8YSZ and lab synthesized pyrochlore (lanthanum zirconate, lanthanum cerate and lanthanum cerium zirconate) compositions with NiCrAlY bond coat.
-
Advancing Gold Market Predictions: Integrating Machine Learning and Economic Indicators in the Gold Nexus Predictor (GNP)
This study employs advanced machine learning algorithms to predict gold prices, using a comprehensive dataset from Bloomberg. The Gold Nexus Predictor (GNP), a key innovation, integrates historical data and economic indicators through advanced feature engineering. Methodologies include exploratory data analysis, model training with various algorithms like Linear regression, Random Forest, Ada Boost, SVM, and ARIMA, and evaluation using metrics like MSC, MAPE, and RMSE. The study's philosophical foundation emphasizes rationalism in economic forecasting and ethical model use. This research offers significant insights for investors and policymakers, enhancing understanding and decision-making in the gold market. 2024 IEEE. -
-
Education Should Be a Guide to Promote Peace in Sri Lanka
In Sri Lanka, ethnic-religious tensions exist between Sinhala, Tamils, and Muslims. As a result, several root causes and their consequences are connected to the war in Sri Lanka. These root causes may be distinguished by ethnicity, language, religion, degree of education, and other factors such as jobs and land. In relation to this, the first religious confrontation broke out in 1915 between the Sinhala and Muslim populations. The Sinhala only Bills, maintaining the importance of language and nationality in Sri Lankan politics since 1956, consequently led to mistrust among the Sinhala, Tamils, and Muslims who suffered from the civil war until 2009. Understanding the various consequences faced by the people of Sri Lanka caused by war, peace education may be the most essential strategy for rebuilding goodwill and concord in Sri Lanka. Through peace education, people may develop trust and respect for other faiths and their practices. People should be able to develop their talents and capacities beginning from their youth by including peace education in the school curriculum. Teachers are the most suited stakeholders in the long term to deliver peace education to children to enhance their attitudes, abilities, and behaviours that promote harmony. 2025 selection and editorial matter, Kennedy Andrew Thomas and Joseph Varghese Kureethara; individuals, the contributors. -
Understanding the disconnect: A study on competency of special educators in autism education in India
Special educators are at the forefront of the Indian special education system in providing tailored instruction and specialized training for students with autism. There are evidence-based strategies that exist to enhance social, communication, cognitive, and mental health skills among students with autism. However, their implementation in the classroom is inconsistent or inadequate. Globally, there is a lack of empirical data on the quality of services provided to students with autism and the competency of educators teaching them in special education schools. This brief study provides a focused review of two articles from India, analysing the responses of 95 special educators gathered through a descriptive survey to assess their perceived competence in autism education. The findings indicate that in India, the special educators have limited knowledge and inadequate skills in autism education. The results will contribute to educational change at multiple levelsindividual, institutional, and systemicto foster better outcomes for students with autism. 2025 National Association for Special Educational Needs. -
Exploring the Scheduled Tribe Parental Perspectives on Fortifying Family Bonds
The study explored family bonding in scheduled tribe families, emphasizing its cultural significance and impact on familial well-being. A phenomenological research design was adopted, incorporating Braun and Clarkes thematic analysis framework. Data was collected through a focus group discussion involving 14 scheduled tribe parents, providing insights into their lived experiences and perspectives on family bonding. The findings highlighted key themes that included adherence to traditional values, equality in marriage, parental behavior, resilience, acceptance, and responsibility. The study further identified the role of internal dialogue, emotional reliance, grit, and mindfulness in strengthening family bonds. Despite a limited sample, findings showed the importance of open communication and cultural preservation in inculcating strong familial relationships. These findings provide a foundation for further research and family therapy interventions to strengthen scheduled tribe family bonds through culturally sensitive approaches. 2025 Taylor & Francis Group, LLC. -
Pandemic Resilient Organizational Behaviour: From the Lens of Stakeholder and Legitimacy Theory
The Covid-19 pandemic spread on global map with unprecedented speed and created an environment of uncertainty, anxiety and disruption. India, being a densely populated country, had been looked upon with apprehension and later on with great admiration in controlling and managing the pandemic and its devastating effect. The study has built a thematic model for short-term and long-term pandemic resilient organizational practices based on stakeholder and legitimacy theory, which focuses on aligning business with societal values and stakeholder expectations. The foci have been stakeholder groups of employees, customers, suppliers and community. Sustainability reports of selected Indian companies based on GRI standards for FY from 2019 to 2022 are then scored based on the developed model. Further analysis explored changes in risk reporting framework in pandemic and post pandemic. The thematic coverage in sustainability reports for employees and community found a prominent place emphasizing the importance of these groups. The thematic disclosures for suppliers are the least disclosed, indicating areas for improvement in the business practices. Based on this thematic model, suggestions are also made for additional disclosure indicators in the GRI framework for stakeholder group of suppliers and customers. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Measuring and monitoring Indias progress toward environmental SDGs: variables influencing the advancement
Purpose: The purpose of this study is the progressive assessment of Indian companies performance on environment-related sustainable development goals (SDGs). The study also aims to analyze the impact of a wide range of governance and firm-level determinants such as board size, gender diversity, board independence, top management commitment, presence of a sustainability committee and experience in nonfinancial reporting on environment-related SDGs performance. Design/methodology/approach: Using a mixed-method approach, this paper developed a framework for assessing business progress on environment-related SDGs by mapping targets of SDGs with indicators of global reporting initiative (GRI) standards. The framework is applied to evaluate the sustainability report of 46 Indian companies for six years for environment-related SDG performance. Multivariate linear regression analysis examines the impact of various governance and firm characteristics on this performance. Findings: The assessment framework identified improved reporting in all categories but significantly increased socio-environmental and socio-economic-environmental categories. Companies in the utility sector performed well, while those in the healthcare industry did not meet expectations. The pooled OLS panel regression results revealed a positive and significant impact of top management commitments, experience in nonfinancial reporting, board gender diversity and sustainability committee on the environment-related SDG disclosure scores. Research limitations/implications: Geographical limitations may limit the generalization in developed countries. The developed SDG assessment framework may help firms choose a business strategy for greater contribution and a governance structure to match this strategy. Investors can receive insight from measured performance, and regulators can lay policies, targets and incentives for different industries based on the interpretation of performance. Originality/value: This study developed an assessment framework to capture the positive business contributions to environment-related SDG and its trends. It sheds light on the critical interaction between corporate governance, management committees, experience in financial reporting and environmental SDG performance to increase the understanding of the determinants of SDG performance. 2025, Emerald Publishing Limited. -
Enhancing Network Security with Comparative Study of Machine Learning Algorithms for Intrusion Detection
With the ever-increasing network systems and dependency on digital technologies, ensuring the security and integrity of these systems is of paramount importance. Intrusion detection systems (IDS) play a major role in sheltering such systems. Intrusion detection systems are technologies that are designed to monitor network and system activities and detect suspicious, unauthorized, malicious behavior. This research paper conducts a comprehensive comparative analysis of three popular machine learning algorithmsK-Nearest Neighbors (KNN), Random Forest (RF), and Logistic Regression (LR)in the context of intrusion detection using the renowned NSL-KDD dataset. Preprocessing techniques are applied, and the dataset is split for rigorous evaluation. The findings of this research highlight the effectiveness of Random Forest in detecting intrusions, showcasing its potential for real-world network security applications. This study contributes to the field of intrusion detection and offers valuable insights for network administrators and cybersecurity professionals to enhance network protection. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Neuroscience of social understanding
Comprehending human behavior and interactions requires an understanding of the social mind. Social cognitive neuroscience provides a lens to understand these complexities. This chapter explores the core brain mechanisms that control social conduct by exploring the field of social cognitive neuroscience. It examines aspects of social cognition, like the theory of mind, social perception, empathy, and decisionmaking. It explains how the brain helps navigate complex social contexts by looking at complex interactions between neurological processes and social behaviors. Important subjects include the function of the mirror neuron system, temporoparietal junction and prefrontal cortex in mediating social cognition. It discusses the implications of social cognitive neuroscience for understanding diseases such as schizophrenia and autism spectrum disorder, which are characterized by social deficiencies. Through this research, we learn about the social mind and its brain foundations, and it opens the door to novel interventions that improve interpersonal relationships and social well-being. 2024 by IGI Global. All rights reserved. -
Understanding the Neuropsychological Profile of Autism Spectrum Disorder
Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by impairments in social communication, repetitive behaviors, and sensory processing challenges. This chapter delves into the neuropsychological profile of individuals with ASD, examining key cognitive domains such as executive functioning, social cognition, emotional regulation, and sensory integration. By exploring how these cognitive and behavioral dimensions manifest uniquely in ASD, the chapter highlights the neurobiological and neurocognitive mechanisms that contribute to the disorders heterogeneity. We also discuss the impact of cognitive deficits on adaptive behavior, learning, and daily functioning, emphasizing how individual differences shape developmental outcomes. Current research findings and clinical observations are integrated to provide a nuanced understanding of the diverse neuropsychological patterns in ASD. This chapter aims to equip clinicians and researchers into the cognitive profiles of ASD. 2025 by IGI Global Scientific Publishing. All rights reserved. -
Anchored ferrocene based heterogeneous electrocatalyst for the synthesis of benzimidazoles
A facile and sustainable electrochemical synthetic strategy for phenyl benzimidazoles has been developed using a ferrocene-based electrocatalyst anchored on Toray carbon paper (TCP) coated with conducting polymeric film. The developed electrode was used for the electrochemical dehydrogenative cyclization reaction of o-phenylene diamine and benzaldehyde using lithium perchlorate/acetonitrile as electrolyte. The surface characteristic properties of the developed electrode were characterized by FESEM, Optical profilometer and X-ray photoelectron spectroscopy. Electron transfer mechanism of the anchored ferrocene-based electrocatalyst was thoroughly studied. To determine the efficacy of the catalyst, the electron transfer coefficient (0.5) and apparent rate constant 41.4 s?1 were determined. The cyclic voltammetry studies reveal that the electrochemical oxidation peak for the synthesis of benzimidazole occurs at 0.48 V. The formation of the product was confirmed by Gas chromatography and Nuclear Magnetic Resonance spectroscopy. A comparison chart is presented for the green metrics and sustainability of the present strategy with other electrochemical approach. 2022 Elsevier Ltd -
Immobilized proline-based electro-organocatalyst for the synthesis of bis-?-diketone via Knoevenagel condensation reaction
In the quest for more sustainable chemical processes, we devised a technique using electro-organocatalysis to synthesize bis-?-diketone compounds via Knoevenagel condensation of benzaldehyde and dimedone. Our approach involves a modified electrode fabricated via anchoring L-proline onto a carbon fiber paper electrode supported by poly-3,4-diaminobenzoic acid (PDABA), which enhances efficiency in addition to the simple catalyst separation from the reaction mixture in heterogeneous catalysis. The electrochemical and surface topographical studies for the fabricated electrode were carried out, revealing high efficiency in comparison to the bare carbon fiber paper electrode. This electrochemical reaction operates under mild conditions utilizing lithium perchlorate and acetonitrile, yielding high amounts of the desired product. This study showcases a promising pathway for producing valuable organic compounds in an environmentally friendly manner, marking a significant stride forward in sustainable synthesis practices. 2024 Elsevier Ltd -
Enzyme based bioelectrocatalysis over laccase immobilized poly-thiophene supported carbon fiber paper for the oxidation of D-ribofuranose to D-ribonolactone
A modified electrode based on laccase immobilized poly-thiophene-3-carboxylic acid supported on carbon fiber paper was developed for the electrocatalytic oxidation of D-ribofuranose to otherwise difficult-to-access D-ribonolactone, a precursor for C-nucleoside based drug like Remdesivir. The electrochemical oxidation of D-ribofuranose was achieved by the TEMPO-mediated electrochemical process. The experimental parameters were optimized and validated using Design of Experiment (DoE) statistical tool indicating the concentration of TEMPO and stirring as important parameters in bulk electrolysis. The mechanism for the electrochemical oxidation of D-ribofuronose followed single electron anodic oxidation of TEMPO mediated by laccase to the corresponding oxoammonium nitrosonium species which was vital for the mediated electrochemical oxidation. The mechanism for the electrochemical oxidation was established using cyclic voltammetry and computational studies. The plausible interactions of laccase enzyme with TEMPO mediator were studied using molecular docking experiments. This facile method was successfully applied for the oxidation of D-ribofuranose to D-ribonolactone. 2022 -
The Renaissance of Electro-Organic Synthesis for the Difunctionalization of Alkenes and Alkynes: A Sustainable Approach
Electro-organic reactions are now considered as one of the most efficient and environmentally benign methodologies to synthesize highly functionalized motifs like difunctionalized unsaturated compounds from readily available substrates. Excellent regioselectivity, functional group tolerance and broad range of substrates are the main advantages of electrochemical difunctionalization reactions. Alkenes and alkynes readily accept radical or ionic derivatives which makes it vital precursors for the electrochemical synthesis of industrially relevant and biologically active molecules through difunctionalization. This review aims to provide the readers an excellent coverage of the different electrochemical difunctionalization of alkenes and alkynes such as 1,2-homodifunctionalization, 1,2-heterodifunctionalization, rearrangement, ipso-migration, cyclization and dehydrogenative annulation reactions. 2021 Wiley-VCH GmbH -
Review - Electrochemical Strategies for Selective Fluorination of Organic Compounds
The conventional methods for carrying out fluorination of organic compounds are typically conducted using a pre-modified starting material, usually expensive and toxic reagents. In this regard, electrochemical fluorination has been recognized as a sustainable and scalable strategy. Electrofluorination has proven to be an environmentally benign, highly effective, and versatile platform for the synthesis of selective organofluorine compounds under mild conditions. This review provides an overview on the use of anodic electrochemical methods for vicinal difluorination, iodofluorination, fluoroalkynylation, fluorodecarboxylation, fluoro desulfurization, radiofluorination, and synthesis of fluorinated heterocycles. Modern advances in the field of electrochemical fluorination, such as ionic liquids mediated electrochemical fluorination, triethylammonium halides, mixed non-aqueous solvents, alkali-metal fluoride, and split-bipolar electrodes in the field of electrochemical fluorination are also discussed. 2021 The Electrochemical Society ("ECS"). Published on behalf of ECS by IOP Publishing Limited.