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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. -
Machine Learning in Financial Distress: A Scoping Review
Predicting financial distress is crucial for stakeholders, policymakers, governments, and management in decision-making processes. Researchers have developed various prediction models encompassing both traditional and machine-learning approaches. Notably, recent attention has shifted towards employing machine learning models to address the limitations of traditional methods. This study seeks to offer insights into current trends, identify gaps, and suggest future research directions using machine learning models for financial distress prediction, employing the PRISMA Extension for Scoping Reviews methodology. To achieve this, a comprehensive search was conducted across three databasesScience Direct, EBSCO, and ProQuestspanning from 2020 to 2023, identifying 34 relevant articles for analysis. The findings underscore the prevalent use of Support Vector Machine in financial distress prediction, followed by the Random Forest Classifier and Artificial Neural Network, with little attention paid to other models. Furthermore, the study underscores the necessity for more research in developing countries, noting the predominance of studies from developed nations. While machine learning models hold promise for enhancing the accuracy and efficiency of financial distress prediction, additional research is imperative to evaluate their effectiveness and applicability across diverse contexts. This scoping review aims to furnish researchers, policymakers, and institutions with valuable insights and policy recommendations, shedding light on underexplored machine-learning techniques. 2024, Iquz Galaxy Publisher. All rights reserved. -
Predicting Financial Distress in India: A Deep Learning Approach
The present study examines the efficacy of deep learning models in predicting financial distress in India. For this purpose, the study employs three distinct architectures: Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Conventional Neural Network (CNN) models. Utilizing data from companies that filed for bankruptcy under the Insolvency and Bankruptcy Code 2016 for the period of 20162023, the study adopts a balanced sample approach to categorize them into distressed and non-distressed groups. Nineteen financial variables are utilized to predict financial distress. Python is used as the programming language, and Jupyter Notebook facilitates algorithm development. The findings reveal that the LSTM model, when compared to RNN and CNN, achieved 91% accuracy using parameters such as 8 LSTM units with tanh activation and a dense layer with sigmoid activation function, a batch size of 10, 50 epochs, RMSprop optimizer, and binary cross-entropy loss were used. The study suggests that deep learning presents a novel approach that can enhance performance in financial distress prediction studies. This study is believed to be the first to utilize deep learning models for financial distress prediction in India based on single-year data, offering valuable insights for financial institutions and investors seeking more effective risk management strategies. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Predicting Financial Distress in India: A Deep Learning Approach
The present study examines the efficacy of deep learning models in predicting financial distress in India. For this purpose, the study employs three distinct architectures: Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Conventional Neural Network (CNN) models. Utilizing data from companies that filed for bankruptcy under the Insolvency and Bankruptcy Code 2016 for the period of 20162023, the study adopts a balanced sample approach to categorize them into distressed and non-distressed groups. Nineteen financial variables are utilized to predict financial distress. Python is used as the programming language, and Jupyter Notebook facilitates algorithm development. The findings reveal that the LSTM model, when compared to RNN and CNN, achieved 91% accuracy using parameters such as 8 LSTM units with tanh activation and a dense layer with sigmoid activation function, a batch size of 10, 50 epochs, RMSprop optimizer, and binary cross-entropy loss were used. The study suggests that deep learning presents a novel approach that can enhance performance in financial distress prediction studies. This study is believed to be the first to utilize deep learning models for financial distress prediction in India based on single-year data, offering valuable insights for financial institutions and investors seeking more effective risk management strategies. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Exploring the Professional Problems Faced by Beginning Counsellors
Counselling Education at a Post-graduate level is of recent origin in India. Every year a number of students who complete their Post-graduate studies (M.Sc.) in Counselling get employed in various settings. As trainees their issues related to practice of counseling get addressed in training sessions, in supervision and through other means like personal therapy. Nothing is conclusively known about their status as beginning counselors. The aim and objectives of the study were exploring the professional problems faced by beginning counselors and the ways in which they are coping with them and their suggestions to cope as individuals and the ways in which the training institutes could help them to cope. The study was done by conducting two focus group discussions (FGD) of six beginning counselors and personal in-depth interviews of three beginning counselors who were working in different settings like Clinical-rehab, School and Employee Assistance Programme (EAP). While three school counsellors, two counsellors working in clinical-rehab settings and one EAP counsellor formed the participants of FGD, one counsellor from each of these settings who was not part of FGD was interviewed personally. While for the FGDs, the researcher used the self-constructed guide, for the interviews, the findings of FGD data which formed the Interview Schedule. Both the FGDs and the interviews were audio-recorded and transcribed and analyzed with conventional content analysis. Data from both the FGDs and interviews were included to form the Results. The findings show the various professional problems faced by the beginning counselors and the coping strategies used by them and their suggestions for coping with their professional problems at an individual level and at an institutional level. Implications for professional development, training of counsellors and scope for future research are presented. Key Words: Beginning Counsellors, Professional Problems, Coping, Professional Development. -
Portrayal of Pakistanis in Kabir Khan films /
The Bollywood cinema has metamorphosed into an abiding passion for audience all over the world cutting across demographic and political boundaries. Besides the rather visible signs of the extensive hold that mainstream Hindi cinema has on Particularly the Indian society, the fact remains that, contemporary commercial Hindi films have reflected political and social concerns of all the time. -
A Study on psycho-social problems of persons with chronic renal failure with specific reference to bangalore cosmopolitan city
The present research is an attempt to describe the psychosocial problems faced by individuals with chronic renal failure in Bangalore cosmopolitan city. The aim of the study was to describe the level of psycho social problems experienced by them and to develop a psychosocial intervention programme to address this issue. The researcher adopted newlinedescriptive research design. The sample of 200 individual with chronic renal failure was newlineselected with consecutive sampling technique from St. John s Hospital and Medical College, newlineJayanagar Government Hospital and Manipal Hospital. The researcher used structured newlineinterview schedule to collect the data; to study the psychosocial problems, Psychosocial newlineAssessment Tool (PAT-5)-A measure of psychosocial problems in Haemodialysis Patients (Kansal, 2010) was adopted. newlineThe results of the study depict that, in the health awareness domain more than half (52%) of the patients were having moderate level of problems and 13.5% reported severe level of newlineproblems. In the occupational domain, three fourth (75.5%) of the patients reported severe level of problems; in family and social domain, nearly half of the patients (46%) reported severe level of problems. In the financial domain, two third (66%) reported severe problems. In the psychological domain it is seen that near about half (41%) reported severe level of newlinepsychological distress. The overall psychosocial score of the patients reveal that nearly half (47.5%) of patients felt severe level of psychosocial problems. Based on the finding of the study, needs assessment and discussion with the experts in the field a psychosocial intervention programme has evolved. It is suggested that effective use of this intervention programme can reduce the psychosocial problems faced by the patients and lead to better clinical outcomes. -
Post-quantum Cryptography in Practice: A Survey of Algorithms, Applications, and Deployment Challenges
As quantum computing becomes more practical, it significantly threatens the conventional cryptographic systems, particularly RSA and ECC, that are critical to worldwide digital security. Post-quantum cryptography (PQC) has emerged as a strong alternative as a response. NIST recently standardized algorithms such as CRYSTALS-Kyber and Dilithium. This survey brings together findings from ten key papers that examine PQC across different fields, including telecommunications, finance, healthcare, IoT, smart cards, and blockchain voting systems. The chosen studies include direct comparisons of digital signature schemes, real-world protocol integration on smart cards, hybrid cryptographic models using AES and blockchain, and strategies for transition based on policy frameworks like NIST CSF 2.0. The survey examines cryptographic flexibility, hardware practicality, readiness for adoption, and the social and economic effects of quantum breaches. It compares algorithm performance, deployment challenges, and specific needs for various areas. This paper is an overview of the current state and future directions for PQC implementation in critical infrastructure. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Innovative Power Conversion Solutions for Renewable Energy and Electric Mobility
The global transition to renewable energy sources and electrification demands efficient power conversion systems for applications like hybrid electric vehicles (HEVs) and energy storage systems. This paper introduces a novel Multi-Port Bidirectional DC-DC/DC-AC Converter (MBPC) with high efficiency, compact design, and versatile functionality. The MBPC supports two input and two output ports, enabling energy flow between renewable energy sources, storage systems, and loads. Its efficiency exceeds 95%, with a power density of over 10W/cm2. The innovative design minimizes component count, reducing manufacturing costs by 30% compared to conventional converters. Extensive experimentation validates its ability to handle varying current-voltage profiles in multiple operational modes, including DC-DC and DC-AC conversions. With applications in grid-tied systems and electric vehicles, the MBPC addresses efficiency, cost, and flexibility challenges in modern power systems. This work contributes to advancing renewable energy integration and efficient electrification solutions. 2025 IEEE. -
Functional characterization of Malabar grouper (Epinephelus malabaricus) interferon regulatory factor 9 involved in antiviral response
IRF9 is a crucial component in the JAK-STAT pathway. IRF9 interacts with STAT1 and STAT2 to form IFN-I-stimulated gene factor 3 (ISGF3) in response to type I IFN stimulation, which promotes ISG transcription. However, the mechanism by which IFN signaling regulates Malabar grouper (Epinephelus malabaricus) IRF9 is still elusive. Here, we explored the nd tissue-specific mRNA distribution of the MgIRF9 gene, as well as its antiviral function in E. malabaricus. MgIRF9 encodes a protein of 438 amino acids with an open reading frame of 1317 base pairs. MgIRF9 mRNA was detected in all tissues of a healthy M. grouper, with the highest concentrations in the muscle, gills, and brain. It was significantly up-regulated by nervous necrosis virus infection and poly (I:C) stimulation. The gel mobility shift test demonstrated a high-affinity association between MgIRF9 and the promoter of zfIFN in vitro. In GK cells, grouper recombinant IFN-treated samples showed a significant response in ISGs and exhibited antiviral function. Subsequently, overexpression of MgIRF9 resulted in a considerable increase in IFN and ISGs mRNA expression (ADAR1, ADAR1-Like, and ADAR2). Co-immunoprecipitation studies demonstrated that MgIRF9 and STAT2 can interact in vivo. According to the findings, M. grouper IRF9 may play a role in how IFN signaling induces ISG gene expression in grouper species. 2024 Elsevier B.V.