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Growth Of ZnSnN2 Semiconductor Films For Gas Sensor Applications
ZnSnN2 is a member of class of nitride semiconductors which have the additional benefits of earth abundance and non-toxicity. For device applications, NH3 gas detecting senor, which finds use in chemical, pharmaceutical, and food process industries, are fabricated with zinc-tin-nitride (ZnSnN2) thin-films on glass substrate by making use of metal as contacts. The ZnSnN2 sensor is found extremely selective to ammonia (NH3) amongst other gases like ethanol, NO2, H2S and exhibited good detecting responses at room temperature. There are many ways to develop thin films of ZnSnN2, and hence in this work we are trying to find a cost effective, feasible and easier method of synthesis, i.e., chemical vapor deposition method. The first step was the optimization of process parameters to grow Zinc-Tin (ZnSn) thin-films. Later, optimization of the process parameters for the growth of compound ZnSnN2 was completed. The grown films are characterized by material quality using X-Ray Diffraction and UV- Vis spectroscopy. 2022 American Institute of Physics Inc.. All rights reserved. -
Does environmental reporting ofbanks affect their financial performance? Evidence from India
Purpose: The present study aims to investigate the effect of environmental reporting on the financial performance of banks in India. Design/methodology/approach: The study is based on the secondary data. The sample includes the banks listed in the NSE Nifty Bank Index from 20162017 to 20202021. The environmental reporting data was obtained through the content analysis technique. The financial data was collected from the CMIE Prowess database. Panel regression analysis was used to analyse the data. Findings: The findings indicate a negative significant influence of environmental reporting on the ROA and ROE of banks. On the other hand, environmental reporting does not significantly influence the EPS of banking institutions. Originality/value: To the best of the authors knowledge, this study is the first to contribute to the scarce literature on the influence of environmental reporting on financial performance, pertinently in the context of a developing nation's banking sector. 2023, Emerald Publishing Limited. -
Do Bank Characteristics Really Matter for its Environmental Reporting?
The last few decades have seen an increasing number of researches in the area of environmental reporting. Institutions across the globe have been extensively reporting their environmental initiatives through their annual reports. There is a dearth of research on environmental reporting in the Indian context. Thus, this study comprehensively analyzes the environmental reporting practices of scheduled banks in India. It further attempts to explore the association between environmental reporting and bank characteristics. The secondary data is collected from the annual reports, sustainability reports, and business responsibility reports for the period 2017-2022. The sample consists of ten top-rated commercial banks chosen based on market capitalization during June 2022. The content analysis technique is used to extract information on environmental practices under twelve major categories. This study employs correlation analysis to examine the association between environmental reporting and bank characteristics. The findings of this study reveal that Indian commercial banks are increasingly reporting environmental information in their bank reports and websites. Further, the results of correlation analysis revealed a significant association between environmental reporting and the banks' age, size, and profitability. Further, this study recommends policymakers and concerned professional bodies introduce additional environmental guidelines and widen the scope of reporting in the banking industry. 2024 National Institute of Science Communication and Policy Research. All rights reserved. -
The linkage between green banking practices and green loyalty: A customer perspective
The aim of this study is to explore the bank customers perceptions towards green banking practices. This study uses a convenient sampling method. Pre-tested questionnaires were employed to collect data. The data were collected conveniently from 358 bank customers. However, the final sample includes 304 responses after ignoring null responses (n = 304). The Structural equation modeling (SEM) was applied for the analyses. The significant results of the study indicate that green banking practices positively influence green image (p = 0.001) and green trust (p = 0.025), while it does not significantly affect green loyalty (p = 0.642). The mediation analysis reveals that green image mediates the relationship between green banking practices and green loyalty, while green trust does not mediate the relationship between the same. The results have practical implications for banking institutions in India to recognize the importance of environmental initiatives in influencing the decisions of bank customers. Deepthi S. Pawar, Jothi Munuswamy, 2022. -
Machine Learning Methods to Identify Aggressive Behavior in Social Media
With the more usage of Internet and online social media, platforms creep with lot of cybercrimes. Texts in the online platforms and chat rooms are aggressive. In few instances, people target and humiliate them with the text. It affects victim mental health. Therefore, there is a need of detecting the abuse words in the text. In this paper, a study of machine learning methods is done to identify the aggressive behavior. Accuracy can be improved by incorporating additional features. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Evidence Acquisition in Social Media for Cyber Crime
Social Media forensics a branch of forensics involves in collecting the evidences for the cyber crime. Investigating social media is a complex process which involves the privacy issues for accessing the users, suspects and victims information on social media. Manual processing of social media data is not feasible as it contains large volumes of data. An automated process is needed to incident specification, evidence extraction and for provenance. The need for handling heterogeneity of data as users have accounts with multiple social websites is also explained. This study briefs the existing models and the challenges faced in analyzing with those models. The research goals in this field are also addressed. A pool of tools which can contribute in guarding the solution for cyber crime is also presented. 2022 IEEE. -
An AI-Based Forensic Model for Online Social Networks
With the growth of social media usage, social media crimes are also creeping sprightly. Investigation of such crimes involves the thorough examination of data like user, activity, network, and content. Although investigating social media looks quite straight forward process, it is always challenging for the investigators due to the complex process involved in it. Due to the immense growth of social media content, manual processing of data for investigation is not possible. Most of the works from this area provide an automatic model or semi-automated, and much of the contributions lacks the logical reasoning and explainability of the evidence extracted. Searching techniques like entity-based search and explainable AI add value to the quick retrieval within appropriate scope and explain the results to the court of law. This paper provides a model by adding these new techniques to the basic forensic process. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Explainable AI Method for Cyber bullying Detection
People of all ages and genders are using social media platforms to engage themselves in all sorts of activities. People create profiles on online social networks in order to communicate with one another in this virtual environment. Hundreds or thousands of friends and followers are split across many profiles. Along with the virtual communication in this social media life, cyber-crimes also creep in many distinguished forms to grab user's information and emotionally degrade them with harassment and arrogant behavior. A set of machine learning methods are proposed and used to detect such a bullying behavior. Along with the detection of such an act, the model should also provide the logical reasoning of the evidence extracted. The explain ability of the models classification will give us a view of the way towards portraying a suspect as a bullier. This paper illustrates a machine learning model that works on a twitter data set to suggest the tweets as category bullying or non-bullying. LIME a tool to predict the interpretability of the model is used to depict the performance of model and provides explainability. 2022 IEEE. -
Analysing Twitter User Behaviour with Process Mining: A Study on Activity Patterns
Social media sites provide a platform to share the information. People share their views and interests. Social media data provides information on user, activity, network, and content. Researchers anticipate a lot of information from social media data. It covers the activities of user, people connected to them, and their likes and dislikes. If users data is processed keenly, one can easily understand a users behaviour with his actions and predicts the next action of the user. It also helps in describing the relations among the users. This study illustrated the process mining algorithms to uncover the insights of Twitter users data. The model depicts the overall process flow of Twitter user activities. Behavioural patterns like common sequences, repeated user actions, direct relations, and rare interactions are analysed. The models performance is assessed with the metrics like fitness, precision, and simplicity to choose the best model for the dataset. Inductive miner outperformed well with other algorithms. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
A Novel and Efficient Deep Learning Models for Assessing AIs Impact on Disease Diagnosis in Agriculture
Background: Agriculture sustains human life by providing food, raw materials and employment opportunities. However, climate change and resource limitations pose significant challenges to crop production. AI-driven smart farming has emerged as a solution to enhance agricultural efficiency, with Explainable AI (XAI) improving transparency in decision-making. Innovations such as smart sensors and automated systems have benefited key agricultural sectors, including crops, forestry, livestock and aquaculture. Turmeric, valued for its medicinal and economic significance, requires careful monitoring to combat diseases like leaf spot and leaf blotch, which can impact yield and quality. Methods: This study introduces turmeric net, a Convolutional Neural Network (CNN)-based model leveraging transfer learning to detect and classify turmeric leaf diseases. The dataset used consists of 791 original images and 3,702 augmented images obtained from mendeley data, categorized into four classes: healthy leaf, dry leaf, leaf blotch and rhizome rot. The model development was carried out using TensorFlow, with ResNet50V2 as a baseline for comparison. The models were trained on processed image data, incorporating augmentation techniques to improve robustness and generalizability. Result: The accuracy of both models was evaluated. ResNet50V2 achieved an accuracy exceeding 99%, demonstrating high effectiveness in disease classification. Meanwhile, TurmericNet attained a competitive accuracy of 98%, making it a reliable alternative for turmeric disease identification. These results indicate that deep learning-based models can significantly aid in early disease detection, providing farmers with a valuable tool to enhance crop management and productivity. 2025 , Agricultural Research Communication Centre. All rigths reserved. -
Impact of knowledge management on organizational performance: An application of structural equation modeling
Purpose: The purpose of this paper is to explore the dynamic relationships among the essential knowledge management (KM) constructs, i.e. strategy, enablers and processes, and to establish their links to organizational performance using a holistic integrated model. Design/methodology/approach: The structural equation modeling approach was used in the research study. The primary data were collected from IT managers in Indian software firms. Findings: The study successfully tested an integrated KM model in an Indian scenario. The study found that the KM strategy, enablers and processes had a significant positive relationship with the organizational performance. An appropriately designed KM strategy significantly influenced the KM enablers and KM process. KM enablers nurtured in an organization positively impacted the KM process. Furthermore, the KM process partially mediated the relationship between the KM strategy and organizational performance, and partially mediated the relationship between KM enablers and organizational performance. Originality/value: This study is one of the few to empirically establish how the essential KM constructs of strategy, enablers and processes together impact organizational performance. 2019, Emerald Publishing Limited. -
Physicochemical Properties, Chemical Composition and Antioxidant Activities of Artemisia pallens Wall. Seed Oil
The physico-chemical characteristics, fatty acid composition and antioxidant capacities of Artemisia pallens seed oil were determined in this study. The moisture, oil content, fatty acid, iodine, peroxide, saponification values, specific gravity and refractive index were 4.13 %, 25.53 %, 1.84 % (as oleic acid), 138.14 (mg/ l00 g), 10.20 (meqO2/kg oil), 194.21 (mg KOH/g oil) 0.92 and 1.47, respectively. Linoleic acid (C18:2, 79.963 %), oleic acid (C18:1, 9.40 %) and palmitic acid (C16:0, 7.89 %) were the major fatty acids. High amount of total unsaturated fatty acids (89.74 %) make it highly desirable as a source of nutrition. The antioxidant capacities of the seed oil showed that it can be a source for natural antioxidants and functional food. The results of the present study showed that the A. pallens is a promising seed oil crop and can be used for making of soaps, shampoos and alkyd resins. Further, the high amount of polyunsaturated fatty acid i.e. linoleic acid makes it desirable in terms of nutrition. 2019, 2019 Har Krishan Bhalla & Sons. -
Latency Reduction and Input Prediction for Cloud Gaming Clients
Cloud gaming enables access to high-quality games on thin clients by streaming rendered content from remote servers, but network-induced latency remains a critical barrier to responsive gameplay. This paper presents a browser-based system that profiles user input in real-time, employs a lightweight machine learning model to predict actions, and dynamically compensates for lag by speculative input. Our solution reduces perceived lag by up to 25% and maintains a 94%+ prediction accuracy, fully within a free-tier cloud environment. Compared to traditional infrastructure-based approaches, our method imposes no proprietary hardware requirements and offers platform-wide scalability. 2025 IEEE. -
Ceramic-Polymer-Carbon Composite Coating on the Truncated Octahedron-Shaped LNMO Cathode for High Capacity and Extended Cycling in High-Voltage Lithium-Ion Batteries
Long-term electrochemical cycle life of the LiNi0.5Mn1.5O4 (LNMO) cathode with liquid electrolytes (LEs) and the inadequate knowledge of the cell failure mechanism are the eloquent Achilles heel to practical applications despite their large promise to lower the cost of lithium-ion batteries (LIBs). Herein, a strategy for engineering the cathode-LE interface is presented to enhance the cycle life of LIBs. The direct contact between cathode-active particles and LE is controlled by encasing sol-gel-synthesized truncated octahedron-shaped LNMO particles by an ion-electron-conductive (ambipolar) hybrid ceramic-polymer electrolyte (IECHP) via a simple slot-die coating. The IECHP-coated LNMO cathode demonstrated negligible capacity fading in 250 cycles and a capacity retention of ?90% after 1000 charge-discharge cycles, significantly exceeding that of the uncoated LNMO cathode (a capacity retention of ?57% after 980 cycles) in 1 M LiPF6 in EC:DMC at 1 C rate. The difference in stability between the two types of cathodes after cycling is examined by focused ion beam scanning electron microscopy and time-of-flight secondary ion mass spectrometry. These studies revealed that the pristine LNMO produces an inactive layer on the cathode surface, reducing ionic transport between the cathode and the electrolyte and increasing the interface resistance. The IECHP coating successfully overcomes these limitations. Therefore, the present work underlines the adaptability of IECHP-coated LNMO as a high-voltage cathode material in a 1 M LiPF6 electrolyte for prolonged use. The proposed strategy is simple and affordable for commercial applications. 2024 The Authors. Published by American Chemical Society. -
Miniaturized Band Stop Frequency Selective Surface for Stable Resonance Characteristics
In this paper, miniaturized 7.45 GHz resonant frequency band stop frequency selective surface (FSS) is designed. The unit cell dimensions of designed FSS is only about 0.1?0 at the 7.45 GHz. Proposed design involves a crossed dipole metallic element together with meander shape on the substrate. Simulation results provide about 800 MHz bandwidth (7.1 GHz-7.9 GHz) with-20 dB insertion loss. The FSS properties are studied on a unit cell using electromagnetic (EM) solver to observe the characteristics. Proposed FSS demonstrates a stable resonance frequency behavior for the arbitrary angle of incidences in both the polarizations such as TM and TE modes. Thus, the design holds a polarization independent characteristic for all incident angles and polarizations. Finally, the FSS properties are validated by a fabricated array of 311 mm2. 2018 IEEE. -
Understanding Agape Leadership: A Scoping Review
Agape, a Greek term for unconditional love, is often overlooked in the context of leadership, which has traditionally emphasized control and dominance. Agape leadership is a type of leadership that places the well-being and growth of individuals, communities, and society at the forefront. This style of leadership is characterized by a profound sense of compassion and consideration for others and is grounded in the principles of love, compassion, and empathy. This paper reviews the literature on agape leadership, exploring how it is conceptualized, practiced, and studied. The review finds that agape leadership is linked to favourable results, including increased trust, motivation, performance, respect, and collaboration, in which people can thrive and reach their full potential. This style of leadership is not only focused on achieving specific goals or outcomes, but also on fostering a sense of purpose, meaning, and fulfilment in the lives of those being led. The leadership style of agape can bring about enduring and constructive transformations in society as it motivates and galvanizes individuals to collaborate toward a collective aspiration of a more promising tomorrow. Agape leaders also place a high value on personal growth and development and are constantly seeking to learn and grow in their own leadership journey. 2023 Open Access/Author/s - Online @ http//: www.pharosjot.com -
Eye-Tracking Measures in Aviation: A Selective Literature Review
Objective: The aim of this article is to present a comprehensive review of eye-tracking measures and discuss different application areas of the method of eye tracking in the field of aviation. Background: Psychophysiological measures such as eye tracking in pilots are useful for detecting fatigue or high-workload conditions, for investigating motion sickness and hypoxia, or for assessing display improvements and expertise. Method: We review the uses of eye tracking on pilots and include eye-tracking studies published in aviation journals, with both a historical and contemporary view. We include 79 papers and assign the results to the following three categories: Human performance, aircraft design, health and physiological factors affecting performance. We then summarize the different uses of eye tracking in each category and highlight metrics which turned out to be useful in each area. Our review is complementary to that of Ziv (2016). Results: On the basis of these analyses, we propose useful application areas for the measurement of eye tracking. Eye tracking has the potential to be effective in terms of preventing errors or injuries by detecting, for example, fatigue or performance decrements. Applied in an appropriate manner in simulated or real flight it can help to ensure optimal functioning of manmachine systems. Conclusion: Further aviation psychology and aerospace medicine research will benefit from measurement of eye movements. 2018, 2018 The Author(s). Published with license by Taylor and Francis Group, LLC. -
Development of an Adaptive Mathematical Education System for Middle Grades Using Machine Learning
The aim of the study is to improve the study of mathematics topics for middle school children by developing a software implementation of an adaptive educational system using machine learning. During the research, the topic of quadratic equations was chosen as the basis for the research and development of an adaptive system. During the testing of the adaptive system, mistakes were specifically made to simulate the consolidation of knowledge during the educational process and make sure that it works and is able to adapt to the individual level of each student, increasing the level of knowledge gained and contributing to the consolidation of the material. To achieve this goal, Python code was developed in the Jupyter Notebook development environment. Python libraries were also used, in particular the scikit-learn library for implementing machine learning. The presented approach and software implementation can be used both by teachers to check students and track their progress, and by students themselves to assimilate and consolidate the material and knowledge gained in the lessons. The results obtained during the study demonstrate an increase in the effectiveness of adaptive learning methods using machine learning. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Effect of different impact velocities on mechano-luminescence of natural calcite for mechanical sensors
This paper studies the mechanoluminescence (ML) behavior of natural calcite under varying impact velocities to assess its potential use in passive mechanical sensing. Calcite samples obtained from Byrnihat, Meghalaya (2603?03.8?N 9152?11.0?E), were analyzed using X?ray diffraction, field emission scanning electron microscopy, energy-dispersive X?ray spectroscopy, and Fourier-transform infrared spectroscopy. The analysis confirms the formation of the nanocrystalline hexagonal phase with minor impurities that affect its luminescent properties. When subjected to the mechanical impact, the calcite consistently produces asharp ML peak around 17?ms, regardless of the impact speed. The emitted light intensity shows alinear dependence on the impact velocity, suggesting areliable correlation between the mechanical input and optical response. The emission decay follows afirst-order exponential pattern, supporting its usefulness for identifying short-duration force events. Aplot of time against the logarithm of intensity displays aclear negative slope, supporting this kinetic model. These research findings highlight the potential of natural calcite as areliable and environmentally friendly material for mechanical sensor applications. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025. -
Chernobyl Disaster Optimizer-Based Optimal Integration of Hybrid Photovoltaic Systems and Network Reconfiguration for Reliable and Quality Power Supply to Nuclear Research Reactors
In view of the complexity and importance of nuclear research reactor (NRR) installations, it is imperative to uphold high standards of reliability and quality in the electricity being supplied to them. In this paper, the performance of low-voltage (LV) distribution feeders integrated with NRRs is improved in terms of reduced distribution loss, improved voltage profile, and reduced greenhouse gas (GHG) emissions by determining the optimal location and size of photovoltaic (PV) systems. In the second stage, the power quality of the feeder is optimized by reducing the total harmonic distortion (THD) by optimally allocating D-STATCOM units. In the third and fourth stages, the reliability and resilience aspects of the feeder are optimized using optimal network reconfiguration (ONR) and by integrating an energy storage system (ESS). To solve the non-linear complex optimization problems at all these stages, an efficient meta-heuristic Chernobyl disaster optimizer (CDO) is proposed. Simulations are performed on a modified IEEE 33-bus feeder considering the non-linear characteristics of NRRs, variability of the feeder loading profile, and PV variability. The study reveals that the proposed methodology can significantly improve the service requirements of NRRs for attaining sustainable research activities. 2024 by the authors.