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Hydro-thermo-electromechanical response in a size-dependent porous piezoelectric medium under memory-dependent MGT theory
This study explores the intricate coupled hydro-thermo-electromechanical behavior of a size-dependent porous piezoelectric medium. We uniquely employ the memory-dependent MooreGibsonThompson (MGT) framework for heat conduction in conjunction with Eringens nonlocal elasticity theory to analyze the effects of time-delay and nonlocality on various physical fields. Novel constitutive relations are developed to capture these combined influences. Analytical solutions for displacements, temperatures (solid and fluid phases), stresses (normal and shear), and electric displacements and potentials are obtained using normal mode technique. A detailed graphical analysis illustrates the impact of time, nonlocality, and porosity under both open and short circuit electrical boundary conditions. Three-dimensional plots confirm the dispersive, diffusive, and memory-dependent behavior. The main findings reveal that nonlocality significantly smooths field gradients and broadens spatial profiles, while memory-dependent thermal conduction results in wave-like and delayed temperature responses, with the solid phase reacting faster than the fluid. Furthermore, electric potentials and displacements are considerably enhanced under open-circuit conditions, highlighting boundary effect sensitivity. It is also observed that increasing porosity consistently decreases the magnitudes of all fields and restricts their penetration depths, indicating a structural softening effect. This work presents a unique analytical model that integrates memory-dependent MGT theory with nonlocal elasticity to analyze the hydro-thermo-electromechanical behavior of a fluid-saturated porous piezoelectric medium. The theoretical framework provides a robust foundation for advancing the design and development of piezoelectric sensors and actuators operating in coupled hydro-thermo-electromechanical environments, particularly those with porous structures. Potential applications span geophysical monitoring, biomedical implants under complex loadings, and MEMS where size-dependent and relaxation effects are significant. 2025 Taylor & Francis Group, LLC. -
Porous Carbon Nanospheres Derived From Caesalpinia Sappan Pods as Novel Antibacterial Agents
The current work shows how catalyst-free carbon nanospheres (CNS) can be produced utilizing straightforward one-step pyrolysis methods employing biowaste Caesalpinia sappan pods as a carbon precursor. The manufactured CNS with a particle size range of 4050 nm that is obtained show a porous nature and contain more than 87% carbon. The synthesized CNS are used as potential antibacterial agents against E. coli and S. aureus by microscopic analysis. By observing the distorted cell envelopes of both E. coli and S. aureus compared with those of untreated cells, it is well understood that CNS, by binding to the outer envelope of cells, renders some changes in the peptidoglycan layer of both Gram-positive and Gram-negative microbes, which in turn restricts their further growth. This study confirms the first report of use of CNS as an effective antibacterial agent. 2025 Wiley-VCH GmbH. -
Real-time detection and response: How AI is shaping the future of hate speech
This chapter traverse how artificial intelligence is transforming hate speech detection by facilitating real-time detection and response. It focuses on the technical aspects of using machine learning. Natural language processing and deep learning models to identify and mark spiteful content. This chapter also discusses the advantages, obstacles, and ethical considerations associated with using AI to moderate online speech. The ultimate objective is to provide insights into how AI is changing the landscape of content moderation on platforms around the world. 2025, IGI Global Scientific Publishing. All rights reserved. -
Enhancing Education Policy Estimation: A Novel Ridge Fuzzy Regression Approach for Handling Multicollinearity with Fuzzy Input Data
Multicollinearity often complicates regression analysis, both in classical and fuzzy input setup. This research introduces a new approach that combines ridge regression with fuzzy regression to tackle correlated covariates impact, with a specific focus on improving education policy systems. Our method utilizes the ?-level estimation algorithm and a dataset where Grade Point Average (GPA) serves as a fuzzy input, while input variables remain crisp. We assess our estimators performance using RMSE and MAPE. This applied research showcases the potential of our method in enhancing education policies through more accurate data-driven decision-making. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Early Detection of Plant Diseases Using IoT Sensors and Machine Learning Algorithms
Agriculture is one of the most important and necessitates of the world. This paper is a study to detect plant diseases using IoT sensors and ML Algorithms for early detection of plant diseases using IoT sensors and machine learning algorithms. A primary dataset from Indian Agricultural Research Institute (ICAR) was used for the research. The dataset comprised the following features: temperature, humidity, soil moisture, leaf wetness, and dew point. Five different machine learning algorithms were explored for the implementation: Logistic Regression, Random Forest, XGBoost, CatBoost, and LightGBM. Upon comparative analysis, it was found that the LightBGM model performed the best with an accuracy of 93.4 % using cross-validation, implying remarkable performance for real-time plant disease monitoring. 2025 IEEE. -
Evaluating the performance of machine learning using feature selection methods on dengue dataset
Dengue fever is a mosquito-borne disease transmitted by the bite of an Aedes mosquito infected with a dengue virus. The bites of an infected female Aedes mosquito which gets the virus while feeding on the infected persons blood, transmits the virus to others. Dengue transmission is climate sensitive for several reasons such as temperature, humidity, rainfall, etc. Areas having higher vapor pressure and rainfall rate are most vulnerable to the spreading of the dengue disease. So to find the important features responsible for spreading the dengue we have used the classification algorithms. Machine learning is one of the key methods used in modern day analysis. Many algorithms have been used for medical purposes. Dengue disease is one of the serious contagious diseases. To find the features related to spreading of dengue disease, we have used popular machine learning algorithms. This proposed work focuses on evaluating the performances of the various machine learning techniques like-Random Forest Classifier (RFC), Decision Tree Classifier (DTC) and Linear Support Vector Machine (LSVM). Predictive Mean Matching is applied for preprocessing of the data and percentage split is applied for resampling of the data. Information gain values for each of the attributes are calculated. The attributes are sorted on the basis of information gain values. Feature selection methods (FSMs) such as Forward Selection (FS) and Backward Elimination (BE) are applied to choose the finest subset of the attributes, so that the algorithm runs more efficiently with a lower run time. It also results in the improvement of the accuracy. The attributes selected by the Feature Selection Methods are the main attributes which results in the probable effects of global weather change on human healthiness. BEIESP. -
For Food and Livelihood: Rethinking the Role of Agriculture in Indias Capitalist Development
In India, agriculture continues to provide a source of livelihood to almost half of the employed labour force, and recent evidence (over the last 15 years) clearly indicates that the income from farming is grossly inadequate for basic sustenance for the vast majority of the agrarian population. We start our analysis by establishing some salient features of Indian agriculture, which are key in foregrounding any serious discussion on the subject. First, based on the framework proposed by Dorin, Hourcade and Benoit-Cattin, India is shown to be a country in the so-called Lewis trap zone with a simultaneous increase in agricultural population (albeit at a decreasing rate) and a growing divergence in income between agriculture and the non-agricultural sector over the last 50 years. It is argued that this phenomenon can be understood as one of perverse structural transformation in opposition to the virtuous Lewisian path that is based on the historical trajectory of Western European economies. Second, despite the persistence of low levels of productivity in most segments of agriculture, India has emerged in recent years as a food-surplus country in a net sense with significant food exports. As mentioned earlier, this self-sufficiency in food production has been achieved in a period when the majority of the farming community has undergone severe impoverishment due to the economic unviability of crop production. Based on these two observations, the chapter argues for a fundamental rethinking of agricultures role in the long-term development process in a labour-surplus economy such as India. Unlike the classical/Lewisian process of structural transformation, which is predicated on a rapid rate of labour transfer out of agriculture in combination with a corresponding increase in agricultural productivity, agriculture in countries such as India is likely to play a critical role in providing the means of social reproduction for a large mass of surplus humanity in the foreseeable future. This livelihood function of agriculture, along with its essential role in supplying food for the rest of the economy (which is in line with the Mellor-Johnston thesis), constitutes the defining elements of the future of agriculture in typical labour-abundant economies such as India. The challenge, however, is to improve the economic and ecological conditions under which agrarian livelihoods are reproduced. This will involve a fundamental change in societys recognition and valuation of the functions that farming performs and remunerating farmers appropriately for these functions. This transfer of resources to agriculture should not be seen as a mere subsidy for agriculture but as fair compensation for its essential economic and ecological services. 2026 selection and editorial matter, Sejuti Das Gupta, Shouvik Chakraborty and Taposik Banerjee; individual chapters, the contributors. -
The changing paradigm - Gender dimensions of watershed management in Hosadurga Taluk, Chitradurga District, Karnataka, India /
Intenational Journal Of Science And Research, Vol.4, Issue 7, pp.280-285, ISSN No: 2319-7064 (Online). -
Magnetic property applications of microwave method prepared zinc ion modified CoAl2O4 nanoparticles
Employing Microwave combustion technique and utilizing L-arginine as fuel pure Cobalt Aluminate and Zn doped Cobalt Aluminate nanoparticles (NPs) were prepared. XRD, DRS-UV, HRSEM and VSM techniques were used to investigate the structural, optical, morphological, and magnetic properties. The average crystallite size is found in the range of 15-24 nm. Elemental confirmation is done by aid of EDX spectra. The band gap values of the produced samples were discovered to be between 2.57 and 2.45 eV. At room temperature, the prepared samples showed diamagnetic magnetic characteristics, which were corroborated by MagnetizationField (MH) hysteresis curves. 2021, S.C. Virtual Company of Phisics S.R.L. All rights reserved. -
Network Lifetime Enhancement by Elimination of Spatially and Temporally Correlated RFID Surveillance Data in WSNs
In wireless sensor networks (WSNs), radio frequency identification (RFID) plays an important role due to its data characteristics which are data simplicity, low cost, simple deployment, and less energy consumption. It consists of a series of tags and readers which collect a huge number of redundant data. It increases system overhead and decreases overall network lifetime. Existing solutions like Time-Distance Bloom Filter (TDBF) algorithm are inapplicable to the large-scale environment. Received Signal Strength (RSS) used in this algorithm is highly dependent on quality of tag and application environment. In this paper, we propose an approach for data redundancy minimization for RFID surveillance data which is a modified version of TDBF. The proposed algorithm is formulated by using the observed time and calculated distance of RFID tags. To overcome these problems, we design our approach to relevantly reduce the spatiotemporal data redundancy in the source level by adding the Received Signal Strength Indicator (RSSI) concept for energy-efficient RFID data communication in wireless sensor network scenario. We introduce in this paper the new improved idea of an existing algorithm which efficiently reduces the rate of data redundancy spatially and temporally. The implemented results overcome the limitations of existing algorithm for data redundancy reduction. Nevertheless, the performance evaluation shows the efficiency of proposed algorithm in terms of time and data accuracy. Furthermore, this algorithm supports multidimensional and large-scale environment suitable for sensor network nowadays. 2022 Lucy Dash et al. -
Recent developments in bandwidth improvement of dielectric resonator antennas
This article shows a compressed chronological overview of dielectric resonator antennas (DRAs) emphasizing the developments targeting to bandwidth performance characteristics in last three and half decades. The research articles available in open literature give strong information about the innovation and rapid developments of DRAs since 1980s. The sole intention of this review article is to, (a) highlight the novel researchers and to analyze their effective and innovative research carried out on DRA for the furtherance of its performance in terms of only bandwidth and bandwidth with other characteristics, (b) give a practical prediction of future of DRA as per the past and current state-of-art condition, and (c) provide a conceptual support to the antenna modelers for further innovations as well as miniaturization of the existing ones. In addition some of the significant observations made during the review can be noted as follows; (a) hybrid shape DRAs with Sierpinski and Minkowski fractal DRAs seems comfortable in obtaining wideband as well as multiband, (b) combination of multiple resonant modes (preferably lower modes) can lead to wider impedance bandwidth, (c) at proper matching wider patch with slotted dielectric resonator can exhibit better bandwidth. 2019 Wiley Periodicals, Inc. -
Climate predictors in Indian summer monsoon forecasting: a novel De-correlated RVFL ensemble strategy
Excessive rainfall and droughts harshly impact India's social and economic growth. Though several statistical methods have been used in literature to predict Indian monsoons, uncertainties cannot be ruled out. The accuracy prediction of ISMR (Indian Summer Monsoon Rainfall) is scientifically demanding. From this perspective, it is essential to explore exploiting machine learning techniques. In this paper, a novel De-correlated Regularized Random Vector Functional Link Neural Network Ensemble (DRRNE) prediction approach was proposed using Climate Predictors such as Southern Oscillation Index (SOI), Sea Surface Temperature Anomaly (SST), El-Ni Southern Oscillation (ENSO), and Dipole Mode Index (DMI) to predict ISMR. The proposed work has also investigated the predictability of climate above predictors using the DRRNE approach to predict ISMR. In addition to the predictors above, the data for an 8-year training window time series for June to September is combined and analyzed for four predictors (ENSO, DMI, SOI, and SST) to derive another predictor, ENSO-DMI-SOI-SST (EDSS). It is found that the combination of these four predictors- the EDSS- produces better accuracy than using any of the individual predictors in this study. Among the individual predictors (ENSO, DMI, SOI, and SST), the DMI predictor has shown the best predictability for ISMR prediction. Thus, the suggestedstudy concludes that the DRRNE technique with negative correlation learning may be a suitable tool for predicting the ISMR using the combined outcome of the four climate predictorsas mentioned above. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025. -
Modern privacy preserving strategies for IoT security
The proliferation of Internet of Things (IoT) devices has brought about a revolution in various industries and everyday life, enhancing connectivity and efficiency. Nevertheless, this rapid adoption has also given rise to notable security and privacy challenges, leading to the need for robust solutions to safeguard sensitive data. This study delves into contemporary strategies for preserving privacy specifically designed for IoT security, with a focus on the most recent trends and technologies. By the year 2024, it is projected that the global count of IoT devices will exceed 30 billion, exhibiting a compound annual growth rate (CAGR) of 26.7% from 2021 to 2024. This exponential growth has led to a significant increase in the amount of data produced and transmitted by IoT devices, thereby creating fresh opportunities as well as vulnerabilities. Privacy apprehensions are crucial, given that these devices frequently amass sensitive personal and organizational data. The research scrutinizes cutting-edge privacy-preserving techniques, such as federated learning, homomorphic encryption, and differential privacy, which present promising resolutions for safeguarding data while upholding functionality. Federated learning has garnered attention as a decentralized approach that permits data processing to occur locally on devices rather than being sent to central servers, thereby reducing data exposure. Homomorphic encryption facilitates data processing while encrypted, ensuring a high level of security without disclosing the underlying information. Conversely, differential privacy introduces statistical noise to data, guaranteeing that individual data points are not easily discernible, thus preserving user privacy. This section also accentuates the significance of adhering to regulations and the impact of frameworks like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) on shaping the advancement and acceptance of privacy-preserving technologies. Moreover, an exploration is made into the incorporation of blockchain for immutable and transparent data management in IoT environments. This manuscript furnishes an exhaustive overview of the prevalent trends and technologies within this realm, providing insights into the future trajectory of IoT security and privacy. 2026 Elsevier Inc. All rights reserved.. -
Environmental degradation and sustainability: Trends, challenges, and opportunities
Environmental deterioration poses unprecedented hazards to the natural ecosystem and human health, save for world economy. Environmental damage follows the path of sharp interweaving of human activities including industrialization, deforestation, air pollution, and resource exploitation. This deterioration advances socioeconomic inequality, expounds on the reasons for the reduction in biodiversity, and fuels climate change. This chapter offers a thorough study of environmental damage, with specific attention to its consequences for the African continent including South Africa as well as other sensitive regions all around. The study mostly focuses on doable sustainability projects meant to reduce environmental harm and promote a more beautiful future. Copyright 2025 by IGI Global Scientific Publishing. All rights reserved. -
AI and Its Impact on the Growth of the Small Medium Enterprises: The Role of IT Service Management (ITSM) in AI Adoption-AI and Sustainable Development Goals (SDGs)
AI in SMEs (Small and Medium- sized Enterprises) refers to the deployment of artificial intelligence technology to develop business operations and competitiveness. Often more resource- constrained than large corporations, SMEs can leverage AI to streamline processes, improve decision- making, and gain a competitive edge. Nowadays technology is changing, and small businesses are making profit. For building resilient business models with AI, it helps to get more information about business models, the amount to be invested in business, products demanded by the customers and the level of risk capacity to be taken by a firm.AI helps in generating new skills and knowledge in the employees by giving personalised training and coaching. Empowering SMEs through data- driven decision making helps in making rational business decisions based on data rather than relying on intuition or observation. It helps making faster decisions, improving operational efficiency and understanding the value of data. 2026 by IGI Global Scientific Publishing. -
Climate predictors in Indian summer monsoon forecasting: a novel De-correlated RVFL ensemble strategy
Excessive rainfall and droughts harshly impact India's social and economic growth. Though several statistical methods have been used in literature to predict Indian monsoons, uncertainties cannot be ruled out. The accuracy prediction of ISMR (Indian Summer Monsoon Rainfall) is scientifically demanding. From this perspective, it is essential to explore exploiting machine learning techniques. In this paper, a novel De-correlated Regularized Random Vector Functional Link Neural Network Ensemble (DRRNE) prediction approach was proposed using Climate Predictors such as Southern Oscillation Index (SOI), Sea Surface Temperature Anomaly (SST), El-Ni Southern Oscillation (ENSO), and Dipole Mode Index (DMI) to predict ISMR. The proposed work has also investigated the predictability of climate above predictors using the DRRNE approach to predict ISMR. In addition to the predictors above, the data for an 8-year training window time series for June to September is combined and analyzed for four predictors (ENSO, DMI, SOI, and SST) to derive another predictor, ENSO-DMI-SOI-SST (EDSS). It is found that the combination of these four predictors- the EDSS- produces better accuracy than using any of the individual predictors in this study. Among the individual predictors (ENSO, DMI, SOI, and SST), the DMI predictor has shown the best predictability for ISMR prediction. Thus, the suggestedstudy concludes that the DRRNE technique with negative correlation learning may be a suitable tool for predicting the ISMR using the combined outcome of the four climate predictorsas mentioned above. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025. -
Body mass index implications using data analysis in the soccer sports
Soccer is considered among the most popular sports in the world among the last few years. At the same time, it has become a prime target in developing countries like India and other Asian countries. As science and technology grow, we can see that sports also grow with science, and hence technology being used to determine the results sometime or sometimes it is used to grow the overall effect. This paper presents the attributes and the qualities which are necessary to develop in a player in order to play for the big-time leagues called Premier League, La Liga, Serie A, German Leagues and so on. Simple correlation and dependence techniques have been used in this paper in order to get proper relationship among the attributes. This paper also examines how the body mass index plays an effect on the presentation of soccer players with respect to their speed, increasing speed, work rate, aptitude moves and stamina. The point is likewise to discover the connection of the above credits concerning body mass index. As in universal exchange, football clubs can profit more in the event that they have practical experience in what they have or can make a similar bit of room to maneuver. In a universe of rare assets, clubs need to recognize what makes them effective and contribute in like manner. Springer Nature Singapore Pte Ltd 2021. -
Jesuit school teachers opinions on incorporating critical consciousness into digital citizenship education
The contemporary global landscape is undergoing swift transformations accelerated by information and digital technologies, which have given rise to a plethora of innovations that enhance human convenience, novel business models, and emerging new professional paths. However, if these technologies are used improperly, they can become dangerous to humanity. So digital citizenship is a kind of way forward to bring awareness among students and educators to use digital technologies appropriately and responsibly. But in classical digital citizenship issues, such as justice, equity, and accessibility, are not addressed. This study explores Jesuit secondary school teachers opinions on incorporating critical consciousness into digital citizenship and how that affects students attitudes towards building an equitable digital society. The researcher highlights the need to integrate critical consciousness into digital citizenship education through qualitative research study. 2024 Informa UK Limited, trading as Taylor & Francis Group. -
Instruments for measuring Digital Citizenship Competence in schools: a scoping review
The integration of digital technology into the teaching and learning process has both good and negative consequences. Several schools have incorporated digital citizenship to teach the responsible use of technology. The purpose of this scoping review is to provide an overview of research on tools for measuring digital citizenship competency among school children. This scoping study focuses on three main areas: (a) defining digital citizenship and competency; (b) instrument development and characteristics; and (c) key findings. The main outcomes of this research may help students, teachers, and school administrators implement digital citizenship education programs in schools. Italian e-Learning Association. -
PERSPECTIVES OF SECONDARY SCHOOL TEACHERS ON THE RELEVANCE OF CRITICAL DIGITAL CITIZENSHIP IN FOSTERING ONLINE RESPONSIBILITY AND CRITICAL THINKING
The responsible use of technology and cyber ethics requires promoting digital citizenship education. However, digital citizenship does not include issues related to equality, justice, or accessibility in digital and internet-based education. Critical digital citizenship practices address some of these issues. The aim of this research study is to explore secondary school teachers perspectives on the relevance of critical digital citizenship in promoting online responsibility and critical perspectives. Semistructured interviews were conducted with 12 secondary school teachers from two different schools. Thematic analysis was used to interpret their views and opinions, and four main themes were identified. All participants agreed that critical digital citizenship is important for teaching and learning in promoting critical online per-spectives. Many teachers felt that there are knowledge gaps in this area, so intervention programs are needed for teachers and learners. 2026, Grand Canyon University. All rights reserved.