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Blockchain-Driven Architecture for Decentralized Energy Transactions in Smart Grids
The versatility of blockchain technology enables its capabilities to protect decentralized energy trading and transform modern smart grids by removing all dependencies on centralized utility operators and eliminating vulnerabilities that stem from data tampering, pricing manipulation, and single-point failures. The purpose of this paper is to present a fully virtualized and software-implemented architecture of a blockchain that incorporates a lightweight Proof-of-Authority (PoA) consensus model, dynamic pricing smart contract(s), and a multi-layer energy ledger, tailored specifically for seamless peer-to-peer energy trading. The proposed energy trading model is built using an entirely virtualized architecture and is validated through simulation, as opposed to previously proposed models that are based on expensive consensus mechanisms and require hardware-assisted metering. The proposed model delivers significant improvements (37.4% reduction in transaction latency, 52.8% improved throughput, and 41.6% lower computational overhead) when compared to traditional Proof-of-Work and DAG-based models. The smart contract engine ensures energy-pricing fluctuations remain stable, and the system as a whole achieves 95.2% transaction validity, all while preserving ledger immutability, user anonymity, and high scale performance. The results achieved from this innovative software-defined architecture ensure its decentralized smart-grid deployments and high scalability exceed market expectations. 2026 IEEE. -
Prediction and modeling of mechanical properties of concrete modified with ceramic waste using artificial neural network and regression model
Over two centuries, concrete has been crucial to building. Thus, eco-friendly concrete is being developed. Emulating these tangible traits has recently gained popularity. Ceramic waste concretes mechanical properties were modeled in this study. Ceramic waste percentages ranged from 5 to 20%. Compressive and tensile concrete strengths were modeled. To predict concrete hardness, regression modeling and artificial neural network (ANN) were used. Model performance was evaluated using prediction coefficients and root-mean-square error (RMSE). ANN models outperformed linear prediction with a coefficient for determination (R2) of 0.97. ANN models achieved root-mean-square errors (RMSEs) of 1.22MPa, 1.21MPa, and 1.022MPa after 7, 14, and 28days of retraining, respectively. Linear regression model showed RMSE values of 1.21, 1.32, and 1.27MPa at 7, 14, and 28days, respectively. In determining the compressive and tensile strength, the R2 was 0.70, meanwhile the ANN model achieved 0.87. Given its accuracy in predicting the strength qualities of ceramics cement and structural stiffness, the ANN model presents a promising tool for representing various types of concrete. The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024. -
Diseased Leaf Identification Using Bag-of-Features and Sigmoidal Spider Monkey Optimization
Agricultural products decide the economy of a country like India. The agricultural business has the involvement of a large population. The quality and quantity of agricultural products highly depend on environmental conditions and facilities provided to farmers. Timely and efficient detection of diseases in plants and crops is one of the most critical issues that affect crop production. Therefore, it is highly desirable to develop some cheap and easy-to-handle automated plant disease detection systems for the timely treatment of plants. Leaves are considered a primary source of information about the health of plants. In the case of plants, the disease may be easily visualized and identified by observing its effect on leaves. Therefore, this paper introduces a bag-of-features in sigmoidal spider monkey optimization to identify a diseased leaf, separating the diseased leaf from a healthy leaf. The investigational outcomes show the superiority of the anticipated technique in contrast to other meta-heuristic-based systems. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024. -
Fate of AI for Smart City Services in India: A Qualitative Study
With the rollout of the smart city initiative in India, this study explores potential risks and opportunities in adopting artificial intelligence (AI) for citizen services. The study deploys expert interview technique, and the data collected from various sources are analyzed using qualitative analysis. It was found that AI implementation needs a critical examination of various socio-technological factors to avoid any undesirable impacts on citizens. Fairness, accountability, transparency, and ethics (FATE) play an important role during the design and execution of AI-based systems. This study provides vital insights into AI implications to smart city managers, citizen groups, and policymakers while delivering promised smart city experience. The study has social implications in terms of ensuring that proper guidelines are developed for using AI technology for citizen services, thereby bridging the ever-critical trust gap between citizens and city administration. Copyright 2022, IGI Global. -
Opportunistic mycoses in COVID-19 patients/survivors: Epidemic inside a pandemic
Being considered minor vexations, fungal infections hinder the life of about 15% of the world population superficially, with rare threats to life in case of invasive sepsis. A significant rise in the intrusive mycoses due to machiavellian fungal species is observed over the years due to increased pathology and fatality in people battling life-threatening diseases. Individuals undergoing therapy with immune suppressive drugs plus recovering from viral infections have shown to develop fungal sepsis as secondary infections while recovering or after. Currently, the whole world is fighting against the fright of Coronavirus disease (COVID-19), and corticosteroids being the primitive therapeutic to combat the COVID-19 inflammation, leads to an immune-compromised state, thereby allowing the not so harmful fungi to violate the immune barrier and flourish in the host. A wide range of fungal co-infection is observed in the survivors and patients of COVID-19. Fungal species of Candida, Aspergillus and Mucorales, are burdening the lives of COVID-19 patients/survivors in the form of Yellow/Green, White and Black fungus. This is the first article of its kind to assemble note on fungal infections seen in the current human health scenario till date and provides a strong message to the clinicians, researchers and physicians around the world non-pathological fungus should not be dismissed as contaminants, they can quell immunocompromised hosts. 2021 -
Genotoxic repercussion of high-intensity radiation (x-rays) on hospital radiographers
Recent technological advances in the medical field have increased the plausibility of exposing humans to high-intensity wavelength radiations like x-rays and gamma rays while diagnosing or treating specific medical maladies. These radiations induce nucleotide changes and chromosomal alterations in the exposed population, intentionally or accidentally. A radiological investigation is regularly used in identifying the disease, especially by the technicians working in intensive care units. The current study observes the genetic damages like chromosomal abnormalities (CA) in clinicians who are occupationally exposed to high-intensity radiations (x-rays) at their workplaces using universal cytogenetic tools like micronucleus assay (MN), sister chromatid exchange and comet assay. The study was conducted between 100 exposed practitioners from the abdominal scanning, chest scanning, cranial and orthopedic or bone scanning department and age-matched healthy controls. We observed a slightly higher rate of MN and CA (p <.05) in orthopedic and chest department practitioners than in other departments concerning increasing age and duration of exposure at work. Our results emphasize taking extra precautionary measures in clinical and hospital radiation laboratories to protect the practitioners. 2022 The Authors. Environmental and Molecular Mutagenesis published by Wiley Periodicals LLC on behalf of Environmental Mutagen Society. -
Platelets to surrogate lung inflammation in COVID-19 patients
The neoteric severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been jeopardizing the world with the symptoms of seasonal flu. The virus contagion predicted to have been originated from Wuhan, China has by far trapped 4,198,418 cases from 212 countries in the world with two international conveyances with 284,102 deaths as of 11 May 2020 (10:18 GMT). Researchers around the globe have indulged in deciphering viral mode in the body for devising a cure. Affirmations from autopsies and preliminary findings on SARS-CoV-2 hypothesized on viral pathogenesis within the host, for instance, source of inflammation in lungs and pneumonia. This hypothesis assigns the platelets as agents of infection after viral entry. Presently, curbing infection to stall the spread of SARS-CoV-2 is the prima facie intervention employed, worldwide. However, public health authorities must monitor the state of affairs scrupulously, as the deeper our understanding of this novel virus and its associated outbreak, the better we can deal with it. Knowing this idea might be far-fetched, yet this postulate would serve as the groundwork for the present situation. 2020 Elsevier Ltd -
Dietary nutrients and their control of the redox bioenergetic networks as therapeutics in redox dysfunctions sustained pathologies
Electrons exchange amongst the chemical species in an organism is a pivotal concomitant activity carried out by individual cells for basic cellular processes and continuously contribute towards the maintenance of bioenergetic networks plus physiological attributes like cell growth, phenotypic differences and nutritional adaptations. Humans exchange matter and energy via complex connections of metabolic pathways (redox reactions) amongst cells being a thermodynamically open system. Usually, these reactions are the real lifeline and driving forces of health and disease in the living entity. Many shreds of evidence support the secondary role of reactive species in the cellular process of control apoptosis and proliferation. Disrupted redox mechanisms are seen in malaises, like degenerative and metabolic disorders, cancerous cells. This review targets the importance of redox reactions in the body's normal functioning and the effects of its alterations in cells to obtain a better understanding. Understanding the redox dynamics in a pathological state can provide an opportunity for cure or diagnosis at the earlier stage and serve as an essential biomarker to predict in advance to give personalized therapy. Understanding redox metabolism can also highlight the use of naturally available antioxidant in the form of diet. 2021 Elsevier Ltd -
Prevalence of Cardiovascular Diseases in South Asians: Scrutinizing Traditional Risk Factors and Newly Recognized Risk Factors Sarcopenia and Osteopenia/Osteoporosis
One of the primary reasons for complications and death worldwide are cardiovascular diseases (CVDs), with a death toll of approximately 18 million per year. CVDs include cardiomyopathy, hypertension, ischemic heart disease, coronary heart disease, myocardial infarction, heart attack, hearth failure, etc. Over 80% of the CVD mortality is recorded from lower and middle-income countries. Records from the past decade have highlighted the increase of CVDs among the South Asian populations, and the prime purpose of the review is to jot down the reasons for the steep spike in CVDs. Studies analyzing the causative factors for the increase of CVDs in South Asians are still to be verified. Apart from known predisposing and lifestyle factors, other emerging risk factors associated with CVDs, namely the musculoskeletal diseases sarcopenia and osteopenia, should be tracked to tackle research gaps in upcoming analyses. This requires loads of scientific efforts. With proper monitoring, the raising alarm that the CVD burden generates can be reduced. This review discusses the already established signs and recognizes important clues to the emerging etiology of CVDs in the Asian population and prevention measures to keep it at bay. 2023 Elsevier Inc. -
Perceived cyber security challenges in adoption and diffusion of FinTech services in India
FinTech is a term that refers to a new type of digital technology that intends to build up and automate the distribution and management of financial services. FinTech is an abbreviation for "financial technology." FinTech, or financial technology, assists companies, business holders, and consumers in managing their financial procedures and methods. The high adoption rate of fintech services creates a whole ecosystem of looters and hackers. This indeed is scary, and this chapter makes an attempt to understand the adoption rate of fintech services and diffusion challenges at the same time. 2023, IGI Global. -
Photoaligned Liquid Crystalline Structures for Photonic Applications
With the advancement of information display technologies, research on liquid crystals is undergoing a tremendous shift to photonic devices. For example, devices and configurations based on liquid crystal materials are being developed for various applications, such as spectroscopy, imaging, and fiber optics. One of the problems behind the development of photonic devices lies in the preparation of patterned surfaces that can provide high resolution. Among all liquid crystal alignment techniques, photoalignment represents a promising non-contact method for the fabrication of patterned surfaces. In this review, we discuss the original research findings on electro-optic effects, which were mainly achieved at the Department of Electronic and Computer Engineering of the Hong Kong University of Science and Technology and the collaborating research laboratories. 2023 by the authors. -
ICT integration in universities in relation to ict challenges and work motivation of lecturers in harare zimbabwe
This study was ICT integration in universities in relation to ICT challenges and work motivation of lecturers in Harare, Zimbabwe. There exists varying rates of ICT newlineintegration in universities and this has a negative impact on the teaching and learning newlineprocesses. The major aim of the study was to assess the relationship between ICT integration, ICT Challenges and work motivation of lecturers. The findings of the study is expected to show how universities could isolate challenges and tailor-make strategies of overcoming them whilst at the same time getting deeper insight into human behavior in an organisation and its contribution towards ICT integration. The thesis was therefore conducted to match availability of ICTs and their utilization as newlinethis had a direct bearing on the curriculum delivery as well as empowering learners to newlineengage in meaningful, challenging and enlightening tasks since ICTs have the potential to play a powerful role in every university- both inside and outside lecture room/classroom. Institutional responses to ICT influences have inevitably brought about a lot of changes in the teaching / learning processes. The research approach adopted was quantitative. The sample included 200 lecturers drawn from a population of 600 lecturers consisting of two private and four state universities. Harare was conveniently chosen as it is the capital city of Zimbabwe and has the greatest number of state and private universities. Two questionnaires one on ICT integration and another one on ICT challenges were designed by the researcher and the third one on Work Motivation Questionnaire was adopted from Agrawal (1988) and standardized for the Zimbabwean context. The major challenges associated with slow newlineuptake were analysed and assessed in terms of their impact on the teaching and learning newlineprocesses and the motivation of lecturers was also evaluated together with demographic newlinefeatures to find predictors of successful ICT integration in universities. -
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A hybrid multi-optimizer approach using CNN and GB for accurate prediction of citrus fruit diseases
Efficient prediction of citrus fruit diseases is essential for maintaining orchard health and productivity. Traditional diagnostic methods, often relying on manual inspection, are labor-intensive and prone to inaccuracies. Deep learning techniques, especially Convolutional Neural Networks (CNNs), offer an automated and accurate alternative. This study introduces a novel model integrating CNN with Gradient Boosting (GB) and optimized using the Nesterov-Accelerated Adaptive Moment Estimation (Nadam) optimizer to enhance prediction accuracy. The model employs a custom CNN architecture combined with GB, leveraging Nadam for faster convergence and improved performance. Trained on a dataset of 3,000 citrus fruit images sourced from Kaggle, the model follows a structured process of preprocessing, feature extraction, integration of GB with CNN, and optimal prediction. Comparative analysis using metrics such as accuracy, precision, F1 score, and recall demonstrates the model's effectiveness, achieving an accuracy of 98.03% and precision of 98.04%. This robust approach addresses limitations of traditional methods by enabling automated feature extraction and reliable disease prediction. The proposed CNN-GB-Nadam model significantly enhances efficiency and reliability, providing a valuable tool for protecting citrus fruit health and improving orchard management practices. The Author(s) 2025. -
METROLOGICAL IMPACT ON ORANGE FRUIT: A COMPARATIVE ANALYSIS OF DEEP LEARNING MODELS FOR PREDICTING FRUIT DISEASES
This research offers a comprehensive comparative analysis of efficient deep learning models for predicting diseases in orange fruits, with a particular emphasis on the impact of meteorological factors on disease prevalence. Citrus diseases such as Blackspot, Canker, and Greening are significantly influenced by environmental conditions. Recognizing the crucial role of weather conditions in the development and spread of these diseases, we concentrate on enhancing prediction accuracy by integrating Convolutional Neural Networks (CNNs) with various classification algorithms to develop hybrid models that account for meteorological impacts. Specifically, we assess the performance of a CNN combined with Gradient Boosting (CNN-GB) and compare it against other hybrid models such as CNN integrated with Long Short-Term Memory networks (CNN-LSTM), Support Vector Machines (CNN-SVM), and Random Forest classifiers (CNN-Random Forest). These models are evaluated using different optimization algorithms to determine the most effective approach for disease prediction under varying meteorological conditions. A meticulously curated dataset comprising 1,600 training images and 300 testing images of orange fruits exhibiting a variety of disease symptoms was utilized for evaluation. The dataset reflects diverse environmental conditions to capture the meteorological impact on disease manifestation. All the models tested, the CNNGB hybrid model with NDAM optimizer exhibited superior performance (Accuracy 98.03) in comparison of other models like CNN (Accuracy 96.03), CNN+LSTM (Accuracy 96.16), CNN + SVM (Accuracy 97.13) and CNN + Random Forest (Accuracy 97.79). The exceptional performance of the CNN-GB model suggests that integrating CNNs with powerful classification algorithms like Gradient Boosting, along with considerations of meteorological data, can significantly enhance disease detection in crops. This advancement contributes to more proactive and effective disease management strategies, ultimately reducing economic losses and increasing productivity in the agricultural sector. 2025 Published by Faculty of Engineering. -
METROLOGICAL IMPACT ON ORANGE FRUIT: A COMPARATIVE ANALYSIS OF DEEP LEARNING MODELS FOR PREDICTING FRUIT DISEASES
This research offers a comprehensive comparative analysis of efficient deep learning models for predicting diseases in orange fruits, with a particular emphasis on the impact of meteorological factors on disease prevalence. Citrus diseases such as Blackspot, Canker, and Greening are significantly influenced by environmental conditions. Recognizing the crucial role of weather conditions in the development and spread of these diseases, we concentrate on enhancing prediction accuracy by integrating Convolutional Neural Networks (CNNs) with various classification algorithms to develop hybrid models that account for meteorological impacts. Specifically, we assess the performance of a CNN combined with Gradient Boosting (CNN-GB) and compare it against other hybrid models such as CNN integrated with Long Short-Term Memory networks (CNN-LSTM), Support Vector Machines (CNN-SVM), and Random Forest classifiers (CNN-Random Forest). These models are evaluated using different optimization algorithms to determine the most effective approach for disease prediction under varying meteorological conditions. A meticulously curated dataset comprising 1,600 training images and 300 testing images of orange fruits exhibiting a variety of disease symptoms was utilized for evaluation. The dataset reflects diverse environmental conditions to capture the meteorological impact on disease manifestation. All the models tested, the CNNGB hybrid model with NDAM optimizer exhibited superior performance (Accuracy 98.03) in comparison of other models like CNN (Accuracy 96.03), CNN+LSTM (Accuracy 96.16), CNN + SVM (Accuracy 97.13) and CNN + Random Forest (Accuracy 97.79). The exceptional performance of the CNN-GB model suggests that integrating CNNs with powerful classification algorithms like Gradient Boosting, along with considerations of meteorological data, can significantly enhance disease detection in crops. This advancement contributes to more proactive and effective disease management strategies, ultimately reducing economic losses and increasing productivity in the agricultural sector. 2025 Published by Faculty of Engineering. -
A bibliometric analysis of fruit disease prediction using machine learning
In recent years, there has been a growing interest in leveraging machine learning techniques for the early detection and prediction of diseases affecting fruit crops. This study presents a comprehensive bibliometric analysis of research literature focused on fruit disease prediction using machine learning algorithms. Through systematic review and analysis of a large corpus of scholarly articles, conference papers, and patents, this paper aims to provide insights into the current trends, key research themes, influential authors, and popular machine learning methods in this domain. This paper conducts a literature review and bibliometric analysis to explore a significant increase in research activity in fruit disease prediction using machine learning, indicating the increasing importance of this area in agriculture and plant pathology. Various machine learning and deep learning algorithms, including convolutional neural network (CNN), decision trees, random forests and LSTM have been widely employed for disease prediction tasks. Moreover, the study identifies common datasets, evaluation metrics, and challenges encountered in this field. Overall, this bibliometric analysis provides valuable insights for researchers, practitioners, and policymakers interested in fruit disease prediction, highlighting opportunities for collaboration, innovation, and advancement in agricultural technology and plant health management. 2025 Author(s). -
An ensemble deep learning model for automatic classification of cotton leaves diseases
Cotton plant (Gossypium herbaceum), is one of the significant fiber crop grown worldwide. However, the crop is quite prone to leaves diseases, for which deep learning (DL) techniques can be utilized for early disease prediction and prevent stakeholders from losing the harvest. The objective of this paper is to develop a novel ensemble based deep convolutional neural network (DCNN) model developed on two base pretrained models named: VGG16 and InceptionV3 for early detection of cotton leaves diseases. The proposed ensemble model trained on cotton leaves dataset reports higher training and testing prediction accuracies as compared to the base pretrained models. Given that, deep learning architectures have hyper-parameters, this paper presents exhaustive experimental evaluations on ensemble model to tune hyper-parameters named learning rate, optimizer and no of epochs. The suggested hyper-parameter settings can be directly utilized while employing the ensemble model for cotton plant leaves disease detection and prediction. With suggested hyper-parameters settings of learning rate 0.0001, 20 epochs and stochastic gradient descent (SGD) optimizer, ensemble model reported training and testing accuracies of 98% and 95% respectively, which was higher than the training and testing accuracies of VGG16 and InceptionV3 pretrained DCNN models. 2024 Institute of Advanced Engineering and Science. All rights reserved. -
Effective Groundnut Crop Management by Early Prediction of Leaf Diseases through Convolutional Neural Networks
Groundnut (Arachis hypogaea L.), is the sixth-most significant leguminous oilseed crop grown all over worldwide. Groundnut, due to its high content of various dietary fibers, is classified as a valuable cash, staple and a feed crop for millions of households around the world. However, due to varied environmental factors, the crop is quite prone to many kinds of diseases, identifiable through its leaves, for which Groundnut producers have to suffer major losses every year. An early detection of such diseases is essential in order to save this significant crop and avoid huge losses. This paper presents a novel Machine Learning based Deep Convolution Neural Network (CNN) model CNN8GN. The model uses transfer learning technique for detection of such diseases in Groundnuts at an early stage of crop production. A Groundnut real image data set containing a total of 5322 real images for six different classes of Groundnut leaf diseases, captured in the fields of Gujarat state (India) during September 2022 to February 2023, is generated for training, testing and evaluation of the proposed model. The proposed deep learning model architecture is designed on eight different layers and can be used on varied sized images using simple ReLu and Softmax activation functions. The performance of the proposed CNN8GN model on Groundnut real image dataset is examined using a detailed experimental analysis with other six pre-trained models: VGG16, InceptionV3, Resnet50, ResNet152V2, VGG19, and MobileNetV2. CNN8GN results are also examined in detail using different sets of input parameters values. The proposed model has shown significant improvements for disease detection in comparative analysis with 99.11% training and 91.25% testing accuracy. The Author(s) 2024. -
Purification and Biochemical Characterization of Beta-Hexosaminidase B from Freshwater CnidarianHydra vulgaris Ind-Pune
Beta-N-acetylhexosaminidase (Hex) is a vital lysosomal hydrolase found in all living organisms, playing a crucial role in cellular homeostasis. Dysfunctions in this enzyme are implicated in severe pathological conditions such as Tay-Sachs and Sandhoff diseases in humans. In this paper, we report the purification and biochemical characterization of hexosaminidase from the soluble extracts obtained from the polyps of Hydra vulgaris Ind Pune. The Hydra Hex was purified by two-step sequential chromatography (hydrophobic interaction and gel filtration). Our results suggested that the enzyme isoform purified from Hydra is HexB, most likely to be a homodimer with a subunit mass of 65 kD. The pH optimum was in the range of 5.0 to 6.0 and the temperature optimum in the range of 50 C to 60 C. pH stability and temperature stability were found to be 5.0 and 40C respectively. The homology modelling studies corroborated the homodimeric nature of Hydra HexB, and indicated its structural resemblance to human HexB. This study offers new insights into the biochemical characteristics of Hydra HexB, providing a foundational framework for extensive investigations on this and other lysosomal hydrolases in Hydra. In a broader context, our results significantly contribute to establishing Hydra as a potential model organism to study the lysosomal biogenesis pathway. (2024), (Association of Carbohydrate Chemists and Technologists). All Rights Reserved.