Vijay Kumar

Climate change brings more challenges to agriculture, ecosystems, and human health, with increasing incidences of soil degradation, water scarcity, and contamination from heavy metals. Hydrophytes are water-loving plants which offer a sustainable, environment friendly solution through their natural capacity for phytoremediation. The study evaluates the role of hydrophytes in climate change mitigation, and Sustainable Development Goal 13 (SDG13), mainly focusing on the role of hydrophytes in the bioremediation of heavy metals. By utilizing hydrophytes in bioremediation practices, the study explores innovative pathways for developing resilient food systems and restoring aquatic ecosystems amid changing climatic conditions. Integration of hydrophyte-based bioremediation systems into climate change adaptation and mitigation strategies offers a low-cost, nature-based solution, particularly for developing regions. The chapter emphasizes the need for interdisciplinary approaches, supportive policies, and public awareness to harness the full potential of hydrophytes in achieving SDG13.

Sentiment analysis plays a vital role in understanding user opinions across digital platforms. However, accurate classification in high-dimensional text data remains a significant challenge, primarily due to irrelevant and redundant features. This paper introduces a novel Boolean Operator-based Particle Swarm Optimization (BOPSO) algorithm that enhances the feature selection process for sentiment classification. Unlike traditional PSO, the proposed BOPSO integrates Boolean logic operators (Adder, Subtractor, XOR) into the velocity and position update equations to natively handle binary feature inclusion decisions, improving both exploration and exploitation in the search space. The model is evaluated on nine benchmark sentiment datasets, using five filter-based objective functions: Chi-Square, Correlation, Gain Ratio, Information Gain, and Symmetrical Uncertainty. Classification performance is assessed using Naïve Bayes, SVM, and ANN classifiers. The results demonstrate that BOPSO achieves an average accuracy improvement of 1.8% to 4.5% over state-of-the-art optimization techniques, including DE, GWO, ABC, and CS. Specifically, BOPSO with ANN achieves up to 100% accuracy on the laptop dataset and consistently outperforms others in precision, recall, and F1-score. This study confirms that BOPSO not only reduces feature dimensionality effectively but also improves sentiment classification performance significantly across domains.

Ghutka and Mawa are smokeless tobacco products mostly used in South Asia. They have areca nut, tobacco, lime, catechu, paraffin wax, and flavoring materials. These mixtures are highly addictive and cancer-causing, related to oral submucous fibrosis, leukoplakia, and mouth cancers. Still, awareness of their harmful effects remains very low among users. Objectives: To observe oral findings and clinical patterns in Ghutka and Mawa users visiting ENT clinics. Methods: This cross-sectional research was done in Al-Tibri Medical College and Hospital, Karachi, from January to June 2025. One hundred patients who used Ghutka or Mawa for at least six months were selected by purposive sampling. Oral history and a detailed mouth examination were done. Data was entered and analyzed in SPSS using the Chi-square test, with a significance level p ≤ 0.05. Results: Out of 100 users, dental issues were most common (73%), then trismus (44%), pain (40%), and chewing trouble (21%). The majority were addicted for 6–20 years, while 11 had more than 20 years of use. Ulcers were found in 58 Mawa and 53 Ghutka users; growths in 35 and 29 respectively. Longer use had more severe lesions, with malignant signs mostly after 10 years (p < 0.05). Conclusions: Ghutka and Mawa are strongly connected with ulcers, dental and jaw problems, and precancerous growths. Long-term use increases damage. Awareness, early detection, and strict public control are urgently required.

Women Farmer Producer Organizations (WFPOs) have shown grit and determination to step up and deliver greater value to agriculture, specifically in the dairy sector. These organizations have the power of deliberation, collective decision-making, negotiation, and an all-inclusive approach. These institutions not only help impart training and capacity development to their members for taking up various agribusiness opportunities but also connect them to the mainstream value chain, paving the way for better returns, risk mitigation, and assured markets. However, regarding organic agriculture and production, WFPOs’ visibility has been fogged by several challenges, starting from societal barriers to the inaccessibility of information and markets. This chapter talks about the plethora of challenges women 200face in initiating, establishing, and running an enterprise, with a particular focus on organic agriculture, and further articulates the strategy to translate challenges into opportunities.

Objectives Early diagnosis of neonatal sepsis may be helpful in decreasing neonatal mortality. Neutrophil CD64 (nCD64) is a leukocyte surface antigen whose expression increases about an hour after bacterial invasion. We aimed to study the expression and diagnostic utility of nCD64 in the early detection of neonatal sepsis compared to existing sepsis indicators.Materials and methods This prospective observational study was conducted on 140 neonates in a tertiary healthcare center. Those having clinical sepsis were taken as cases and healthy neonates were enrolled as controls. In cases, blood samples were collected for blood culture, sepsis screen and nCD64 expression. Neonates were divided into three groups: Group 1 (n = 3) with both blood culture and sepsis screen positive, Group 2 (n = 40) with blood culture negative but sepsis screen positive and Group 3 (n = 27) with both blood culture and sepsis screen negative. Group 4 (n = 70) was the control.Statistical analysis The data was entered in an MS EXCEL spreadsheet and was analyzed using SPSS version 21.0. Paired T test/Wilcoxon test was used for comparing nCD64. Quantitative and qualitative variables were also compared. The McNemar test was used to compare sensitivity and specificity.Results nCD64 expression was highest in Group 1 (23.2%), followed by Groups 2 and 3. It showed high sensitivity (78.57%) and specificity (100%) in sepsis cases. Significant positive correlation was also noted between nCD64 and other sepsis biomarkers.Conclusion CD64 expression may, thus, be considered as a rapid and reliable marker for early diagnosis of neonatal sepsis.

The development of multicriteria decision-making (MCDM) techniques helps in the decision-making process and addresses many real-life problems. In this chapter, an intuitionistic fuzzy-based MCDM decision-making technique has been proposed for the selection of a suitable site for a manufacturing plant. Dynamic intuitionistic fuzzy weighted averaging operator has been used for decision-making under an intuitionistic fuzzy environment. The method is demonstrated by the help of a case study, comprised of the selection of suitable sites among the 11 sites on the basis of 5 factors and the opinion of 3 domain experts. The proposed model may help the decision makers to taking better decisions under uncertainty.

We investigate the problem of automatic domain generation for the Planning Domain Definition Language (PDDL) using Large Language Models (LLMs), with a particular focus on unmanned aerial vehicle (UAV) tasks. Although PDDL is a widely adopted standard in robotic planning, manually designing domains for diverse applications such as surveillance, delivery, and inspection is labor-intensive and error-prone, which hinders adoption and real-world deployment. To address these challenges, we propose SPAR, a framework that leverages the generative capabilities of LLMs to automatically produce valid, diverse, and semantically accurate PDDL domains from natural language input. To this end, we first introduce a systematically formulated and validated UAV planning dataset, consisting of ground-truth PDDL domains and associated problems, each paired with detailed domain and action descriptions. Building on this dataset, we design a prompting framework that generates high-quality PDDL domains from language input. The generated domains are evaluated through syntax validation, executability, feasibility, and interpretability. Overall, this work demonstrates that LLMs can substantially accelerate the creation of complex planning domains, providing a reproducible dataset and evaluation pipeline that enables application experts without prior experience to leverage it for practical tasks and advance future research in aerial robotics and automated planning.

Ootaxy, the study of egg morphology, is a critical yet underutilized tool in the systematics of Phthiraptera (lice). This systematic review synthesizes global literature on the ootaxy of parasitic lice, following the PRISMA framework, to evaluate its taxonomic utility, document research status geographically, and identify future directions. A systematic search identified 76 relevant studies. The review confirms that characters like opercular structure, chorionic texture, and aeropyle configuration are highly conserved and diagnostically reliable. Research is heavily skewed towards foreign studies on species of economic importance, while contributions from India, though significant for local fauna, are limited and often lack modern techniques like SEM. The discussion highlights the need for an integrative approach, combining detailed ootaxonomy with molecular data. We conclude that a renewed global focus on ootaxy, particularly in biodiverse regions like India, is essential for advancing our understanding of louse biodiversity, evolution, and management.

The COVID-19 pandemic has highlighted the critical need to improve indoor air quality (IAQ) through efficient air filtration, especially in heating, ventilation, and air-conditioning (HVAC) systems. While dedicated high-performance filters are effective, their high-pressure drops result in significant energy consumption when used in HVAC systems. Herein, we report the application of machine learning (ML) models to predict filtration efficiency and pressure drop, enabling the design and optimisation of filter media in HVAC. Specifically, three ML models, Gaussian process regression (GPR), artificial neural network (ANN), and decision tree (DT), have been trained on a dataset obtained from the literature. The dataset comprised key structural parameters of a wide range of filter media. The GPR model emerged as the most reliable predictor, exhibiting the highest coefficient of determination ( R 2 ) and lowest root mean squared error (RMSE) in predicting filtration efficiency and pressure drop, rendering it the most reliable predictor for small and uncertain datasets. The robustness of the GPR model is further confirmed via validation with commercially available filter media. In addition, the ML models accurately capture the established relationship between filtration efficiency and its characteristic drop at the most penetrating particle size (MPPS).

The advancement of computational molecular modeling has significantly enhanced the development of synthetic glucose receptors, addressing one of the most challenging problems in glucose recognition. This study explores the design and analysis of both three-pillared and four-pillared synthetic glucose receptors. Using polar (1,2-Bis(3-methylureido)benzene) pillars combined with benzene, biphenyl, and phenanthrene fragments as apolar surfaces, we have designed novel receptors. Our molecular dynamics simulations reveal that benzene is the most favorable apolar fragment for creating synthetic glucose receptors. Further simulations incorporating Mesitylene, Trimethylmesitylene, Triethylmesitylene, and Triptopylmesitylene as apolar fragments with (1,2-Bis(3-methylureido)benzene) polar pillars demonstrate that receptors with Triethylmesitylene and Triptopylmesitylene exhibit stable conformations. Synthetic glucose receptors represent the future of therapeutics for glucose-related disorders and conditions. These receptors have wide-ranging applications, including functioning as glucose carriers and glucose extractors. The 3D structures of the receptors were constructed using the 3D Builder tool, followed by energy minimization via the MacroModel application. The glucose-receptor interaction poses were predicted using the GLIED tool. Calculations were performed using the Berendsen thermostat and barostat. Molecular dynamics (MD) simulations were conducted with the Langevin ensemble method, utilizing GPU-based DESMOND software with the OPLS2005 force field and the TIP3P solvent model.

The study was conducted to evaluate the soil fertility status of Rainfed Research Sub-station for Subtropical fruits, Raya district, Samba and their relationship with selected soil properties.Soils of the research farm were found slightly acid to neutral in reaction with low to medium organic carbon and texture was sandy loam to sandy clay loam.The soil of the Raya indicated that the available nitrogen, phosphorus and potassium status was observed to the tune of 98.2%, 6.9% and 89.6 % under low and 1.8 %, 93.1 % and 10.4 % under medium categories, respectively.The available sulphur in soil was 100% under percent deficient.Based on critical limit, all soils were adequately supplied with DTPA-extractable Fe, Zn and Cu content.In respect of zinc and cooper, soils exhibited 94.8 and 91.4 per cent under sufficient, while, 5.2 and 8.6 per cent were found deficient in DTPA -Zn and Cu, respectively.The DTPA -Mn in soil was optimum supplied and 77.6 per cent was found sufficient, while 22.4 per cent was deficient.The soil organic carbon showed significant positive relation with available N, P and K content.Soil pH and EC showed positive correlation with DTPA -Mn, Zn and Cu and negative correlation with DTPA -Fe.The generated nutrient status information can serve as an effective tool for researchers/ scientists in adoption of site specific nutrient management practices.

Urbanisation has led to urban population growth affecting the economy and the environment, including degrading air quality via pollution. Air pollution has been linked to a variety of conditions and health risks including heart disease, stroke, asthma, Alzheimer's and neurodevelopmental disorders. However, it is difficult for a citizen to find precise air pollution data at a particular location. Smart City strategies usually stipulate that city councils should focus on delivering platforms for active citizen participation using existing technology. Existing civic data hubs such as the London Datastore, Open Data Bristol etc., provide air pollution data but lack elaborate representations for user-defined locations. Existing air quality initiatives such as the Smart Citizen platform and Sensor.Community provide more advanced graphical representations. However, they restrict themselves to showing data coming from their respective devices. The paper presents the Open City Air Quality Platform (OpenCAQP), a development that merges a wide range of data sources and air pollution parameters into a single platform. The OpenCAQP allows citizens, environmentalists, data analysts, and developers to access and visualise data. The proposed solution contributes to two key objectives: i) analysis of the air pollution data sources available in a city; ii) a replicable scalable, modular open source capability aggregating and visualising air pollution data from multiple sources. Its effectiveness has been evaluated by measuring quality, usability and increased awareness of users through a feedback questionnaire.

Disease classification in maize plant is necessary for immediate treatment to enhance agricultural production and assure global food sustainability. Recent advancements in deep learning, specifically convolutional neural networks, have shown outstanding potential for image classification. This study presents Maize Net, a convolutional neural network model that precisely identifies diseases in maize leaves. Maize Net uses an attention mechanism to increase the model's efficiency by focusing on the relevant features and residual learning to improve the gradient flow. This also addresses the vanishing gradient problem while training deeper neural networks. A five-fold cross-validation test is conducted for generalization across the dataset, generating five models based on distinct training and testing sets. The macro-average of all evaluation metrics is considered to address the dataset's class imbalance problem. Maize Net achieved an average F1-score of 0.9509, recall of 0.9497, precision of 0.9525, and classification accuracy of 0.9595. These outcomes demonstrate MaizeNet's robustness and reliability in automated plant disease classification.

The need for secure and accessible electronic voting systems has become increasingly critical, particularly during global emergencies that restrict physical polling access. While existing e-voting solutions offer convenience, they face significant challenges in ensuring vote integrity, voter privacy, and system security. This paper presents a novel blockchain-based evoting system that combines the security features of distributed ledger technology with traditional voting requirements. Our system architecture integrates a Distributed Permission Ledger Technology (DPLT) layer for voter validation and an Ethereum blockchain layer for immutable vote recording. The implementation utilizes smart contracts for automated vote processing and cryptographic techniques to maintain voter anonymity while ensuring transparent verification. We compare it with traditional e-voting systems and come up with considerable improvements in terms of security, transparency, and cost-effectiveness. Results indicate that blockchain technology can address all the important electoral challenges, such as resistance to tampering, real-time auditing ability, and reduced infrastructure costs. Case studies' evaluations found that the system is quite feasible for largescale elections, especially during emergencies when physical voting becomes unfeasible. Potential implementation barriers, such as technical complexity and social acceptance, have also been highlighted as a basis for future activities in blockchainbased democratic processes.

Abstract - In the era of digital finance, detecting fraudulent transactions is a major concern for financial institutions. Fraudulent activity not only results in monetary losses but also erodes customer trust. This project proposes a scalable and efficient system for fraud detection in financial transactions using PySpark on the Databricks platform. The core idea is to leverage the power of distributed data processing with PySpark to train machine learning model capable of identifying potential fraud. Key Words: Fraud Detection, Pyspark, DataBricks, Random Forest Classifier

We demonstrate the capabilities of an attentionbased end-to-end approach for high-speed vision-based quadrotor obstacle avoidance in dense, cluttered environments, with comparison to various state-of-the-art learning architectures. Quadrotor unmanned aerial vehicles (UAVs) have tremendous maneuverability when flown fast; however, as flight speed increases, traditional model-based approaches to navigation via independent perception, mapping, planning, and control modules breaks down due to increased sensor noise, compounding errors, and increased processing latency. Thus, learning-based, end-to-end vision-to-control networks have shown to have great potential for online control of these fast robots through cluttered environments. We train and compare convolutional, U-Net, and recurrent architectures against vision transformer (ViT) models for depth image-to-control in high-fidelity simulation, observing that ViT models are more effective than others as quadrotor speeds increase and in generalization to unseen environments, while the addition of recurrence further improves performance while reducing quadrotor energy cost across all tested flight speeds. We assess performance at speeds of up to 7m/s in simulation and hardware. To the best of our knowledge, this is the first work to utilize vision transformers for end-to-end vision-based quadrotor control.

The qualities of the soil on which the structure is made are primarily answerable for its instability. As an example, if the soil has poor index properties, cracking and settlement are possible. The structure of any road or pavement involves a variety of layers such as subgrade, subbase, base, and wearing course. The subgrade is critical in road construction. The current study is concerned with the improvement of soil properties by the accumulation of fly ash, waste cement bag fiber, and lime. Various proportions are 40% fly ash, 0.4% to 1.2% of waste cement bag fiber within the increment of 0.4%, and lime 0.9% to 2.7% within the increment of 0.9% by dry weight of soil sample to arrange the soil samples for stabilization. The tests which are Atterberg’s limit, Plasticity index, Specific gravity, Compaction test, California bearing ratio test, and unconfined compressive strength are performed to recognize the modifications within the properties of soil.

Everywhere security is a top priority. A verification system should control and closely watch entry into institutions, exam centers, companies, or even estates. The manual paper-based clearance process is fundamentally flawed, thus the unethical approach connected to the exam is a major one that requires the academic sphere's stakeholders to seek other ways of validating students for exams. The process of validating a student for an exam has an apparent flaw like the presentation of forged clearance cards, impersonation etc. A consistent and effective system is then created to solve the problems of the conventional technique. The system will generate an attendance report by means of fingerprint biometrics to verify the understudy. Keywords- Authentication, Fingerprint, Impersonation, ID number, other personal data.

A key component of India's economic liberalization since 1991, privatization has sparked profound changes in a variety of industries.This study offers a thorough examination of the many effects of privatization, examining changing public policies and disinvestment tactics while tackling the complex issues and potential future developments of this paradigm.With an emphasis on sustainable and equitable development, this paper attempts to offer a nuanced and perceptive understanding of privatization's role in determining India's economic trajectory by combining the most recent numerical data, thorough sectoral assessments, indepth case studies, and critical evaluations.

In this study, we report the synthesis of tin sulfide (SnS) nanostructures via an ultrasonic-assisted sol–gel method, varying sulfur concentrations through different thiourea ratios. The formation of these nanostructures was confirmed through X-ray diffraction (XRD), scanning electron microscopy (SEM), Raman spectroscopy, and optical analysis. The SnS nanostructures exhibited a polycrystalline orthorhombic structure. The crystallite size of sample D1 was estimated at 3.30 nm, while increasing the molar concentration of thiourea in samples D2 and D3 resulted in reduced crystallite sizes of 3.14 nm and 3.08 nm, respectively. This size reduction suggests that thiourea plays a critical role in the nucleation and growth process during synthesis, where higher thiourea concentrations increase nucleation sites by providing excess of sulfur ions, thereby limiting crystallite growth. SEM images revealed that ultrasonic waves induce the transformation of disordered particle orientations into well-ordered nanospheres. The particle size consistently decreased with higher thiourea concentrations, forming larger clusters as individual particles aggregated into defined spherical structures. Energy-dispersive X-ray spectroscopy (EDX) confirmed the presence of Sn and S in the nanostructures, while Raman spectroscopy showed vibrational modes at 214 and 315 cm−1, indicating the successful formation of SnS nanostructures. Optical transmission studies revealed that the ultrasound-assisted SnS nanostructures possess a direct bandgap ranging from 1.85 to 1.87 eV, which falls within the visible light region. The bandgap variation with increasing thiourea concentration highlights the potential of these nanostructures for photovoltaic applications, offering improved conversion efficiency. Overall, the ultrasound-assisted synthesis route demonstrates significant promise for producing SnS nanostructures with tailored optical properties suitable for energy-related applications.

AccessArc is a progressive application that enhances accessibility for the differently abled through a centralized platform that connects with government schemes and employment options. It addresses the fragmentation of information, inaccessibility, and unawareness by consolidating resources into one accessible interface. It provides machine learning-based recommendations, voice navigation, and facial recognition login to ensure accessibility with ease. The customizable user interface enables the changes of font size, contrast, and voice commands in order to adjust to various disabilities. The ability of AccessArc also empowers the caregivers to manage the resources of the ones they care for with maximum efficiency. AccessArc empowers users to lead more independent lives by emphasizing awareness about underutilized schemes and job opportunities, facilitating workplace diversity.

Excess illumination from artificial lighting poses a serious hazard to nocturnal species, particularly flying nocturnal creatures. Bats possess excellent active flight with highly adapted limbs. All bat species are entirely nocturnal and have quite distinct feeding patterns and foraging strategies. Cynopterus sphinx is a medium-sized bat that feeds on ripe fruits, leaves, and floral parts available in foraging grounds on a variety of plant species and plays a significant role in pollination and seed dispersal. The study was planned to document the foraging behaviour of this species in artificially illuminated areas and dark areas with fruiting trees. The foraging behaviour was assessed from January to March 2024 by photographic and visual observations at different foraging trees in Babasaheb Bhimrao Ambedkar University, Campus, Lucknow. C. sphinx, foraging activity was found to be highly affected by street lights a very low number of bats visited fruiting trees closer to the light sources compared to dim lit foraging trees, while the highest number of bat visits was recorded in dark areas. C. sphinx avoided foraging in artificially illuminated areas by street lights, due to threats of predator attack, vision impairment, and disturbance of circadian rhythm which leads to detrimental health issues at regular exposure. Whereas in the dark foraging areas the feeding behaviour was observed highest. The priorities should be focused on the conservation of fruit bats, and minimizing the excess illumination of streetlights.

Abstract Drop‐induced shock is a major cause of failure in portable electronics, impacting their useful life. Traditional drop weight shock testing methods, conforming to the Joint Electron Device Engineering Council (JEDEC) standard of 1500 g for 0.5 ms half‐sine waveform, are often expensive, complex, and require delicate balancing. In this paper, a far simpler, two‐meter‐high, drop test facility is proposed for testing small‐sized portable electronics. The proposed equipment is easier to realize, conforms to the JEDEC standard, is easier to operate, offers a small turnaround time, and is economical. The novel design and the results of the shock test equipment are reported. A weight instrumented with high‐g accelerometers is dropped from a height of two meters inside a drop tube that is vertically straight and hits the aluminum base plate. The acceleration levels, ranging from 30,000 g for 100 µs to 1600 g for 1 ms, are achieved using the drop weight of 3 kg and pulse shaper of different thicknesses. The results are presented as a regression model, correlating peak acceleration and duration with pulse shaper thickness. The model accurately predicts the desired conditions for JEDEC testing and is validated under the standard conditions of 1500 g for 0.5 ms. This minimalistic approach simplifies shock testing, supporting future research in extreme testing scenarios.

This study investigates the impact of various bedding materials on the haematological and physiological parameters of Rathi calves across four distinct seasonal macroclimates: Monsoon, Post-monsoon, Winter, and Summer. Twenty healthy female Rathi calves, aged 5 to 6 months and with an average body weight of 67±2 kg, were randomly assigned to four treatment groups, each consisting of five calves. The calves were provided with different bedding materials (concrete, sand, wooden sawdust, and rubber mat) and fed a uniform diet with ad libitum access to green and dry fodder. Hemoglobin (Hb) levels, lymphocyte counts, monocyte counts, neutrophil counts, eosinophil counts, and basophil counts were measured across the different bedding types throughout the study period. The results showed no significant differences in any of the haematological parameters between bedding materials across all seasons. Hb levels, lymphocyte, monocyte, neutrophil, eosinophil, and basophil counts remained stable, suggesting that bedding material had minimal or no effect on the blood parameters of the calves. This study contributes to the understanding of the role of bedding materials in livestock health and suggests that while bedding may affect comfort and behavior, its impact on blood parameters is limited.

Researchers are being forced to examine the performance characteristics of cement concretes with different mineral admixtures in an attempt to prolong the lifespan of buildings under extreme exposure situations. Furthermore, the need to lower the carbon dioxide emissions from the production of cement has made the use of substitute binding materials more important. Recently, there has been a noticeable increase in the usage of nanoparticles in cement concrete to improve performance attributes and decrease pore volume. The purpose of this work is to examine the microstructural, fresh, and hardened properties of combinations of high-volume fly ash and silica fume used in self-compacting concrete (SCC). SCMs were added to the mixes in varying amounts to replace the cement. By using various flowability tests such as T-500 time (time to reach 500 mm flow diameter), slump flow, J-ring flow, L-box height ratio, and V-funnel time, the effect on fresh qualities was evaluated. In comparison to a control mix (without SCM), the hardened characteristics of SCC were assessed in terms of compressive and tensile strength. SEM and XRD were used to analyse the microstructural characteristics of mixtures containing large amounts of SCMs. It was found that adding FA enhanced SCC’s newly discovered qualities. The use of SCMs resulting in a 5% increase in compressive strength over the control mix. Silica fume had the most significant positive effect on the mechanical and microstructural properties of SCC at all stage of curing ages.

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The main objective of the study is to establish a new approach for the filtering of noise ECG signals that will in turn help in enhancing detection of CVDs. Their early diagnosis is important if the patient is to survive because they are among the main causes of death around the world. Electrical interference such as noise, baseline wander and mobility artefact are forms of noise that are frequently observed when interpreting ECG. These disturbances reduce the possibility of identifying causes of the illness. To minimize noise in the ECG data, this research employed the Fourier Decomposition Method (FDM). For dealing with complex, nonlinear and non-stationary nature of ECG data, FDM, a Fourier based adaptive decompression technique, has been used. To denoise the signal, the technique splits the noisy information on the group-limited Fourier intrinsic band functions (FIBFs) while preserving the signal essentials. For most comparisons with real-time ECG signals and other databases, FDM is superior to other denoising methods like EMD and WT based on the outcome’s signal to noise ration (SNR) and percentage root mean square difference (PRD). This paper further presents that by employing FDM as a preprocessing mechanism, CVD detection in ECG is enhanced.

This experiment has been carried out under TN-IAMP (Tamil Nadu- Irrigated Agriculture Modernization Project, financed by World Bank) in two selected farm ponds (T1 and T2) of the delta district of Tiruchirappalli (Latitude ‘10.775632’N and Longitude '78.692360'E) in Tamil Nadu, India, coming under the research and technical purview of Directorate of Sustainable Aquaculture, Thanjavur. The research work was conducted for a period of 270 days. The ponds are of uniform size (0.25 ha), and feature-wise similar in every respect. Both ponds were stocked uniformly with four species combination viz. Catla catla, Labeo rohita, Cirrhinus mrigala, and Cyprinus carpio. The ponds were stocked at the rate of 18000 nos ha-1 maintaining a stocking ratio of 1:1:1:1 (rohu, catla, mrigal and common carp) in T1, while in T2 the ratio was kept at 1.5:1:1:1. Feeding was done twice at 10.00 am and 5.00 pm, with commercial pellet feed (20% crude protein) during the culture period. The estimated gross production in pond T1 (4000kg ha-1) was found to be higher than that of T2 (3200 kg ha-1). The pond T1, has rendered a net estimated additional 800.04kg ha-1, accounting 25.33% higher yield than that of T2 pond. Statically significant (p<0.05), higher survival could be observed in T1 (27.68%) than that of T2 (16.69%). Further, the ADG (Average Daily Growth) rate of fishes in T1 was 25.30% higher than that of T2. This study revealed that there is a tangible correlation between the equal stocking ratio of fishes and production. The survival rate also endorsed this trend.

Human motion classification is gaining more interest among researchers, and it is significant in various applications. Human motion classification and assessment play a significant role in health science and security. Technology-based human motion evaluation deploys motion sensors and infrared cameras for capturing essential portions of human motion and key facial elements. Nevertheless, the prime concern is providing effectual monitoring sensors amidst several stages with less privacy. To overcome this issue, we have developed a human motion categorization system called Deep Kronecker LeNet (DKLeNet), which uses a hybrid network.The system design of impulse radio Ultra-Wide Band (IR-UWB) through-wall radar (TWR) is devised, and the UWB radar acquires the signal. The acquired signal is passed through the gridding phase, and then the feature extraction unit is executed. A new module DKLeNet, which is tuned by Spotted Grey Wolf Optimizer (SGWO), wherein the layers of these networks are modified by applying the Fuzzy concept. In this model, the enhanced technique DKLeNet is unified by Deep Kronecker Network (DKN) and LeNet as well as the optimization modules SGWO is devised by Spotted Hyena Optimizer (SHO) and Grey Wolf Optimizer (GWO). The classified output of human motion is based on human walking, standing still, and empty. The analytic measures of DKLeNet_SGWO are Accuracy, True positive rate (TPR), True Negative rate (TNR), and Mean squared error (MSE) observed as 95.8%, 95.0%, 95.2%, and 38.5%, as well as the computational time observed less value in both training and testing data when compared to other modules with 4.099 min and 3.012 s.

For effective and sustainable water management, assessing the water quality and identifying potential sources that threaten the river system are crucial steps. In the present study, spatiotemporal variation of 20 hydrochemical variables, water quality indices, and multivariate statistics were applied to evaluate the quality of Yamuna River water. In the middle and lower stretch, the levels of electric conductivity (EC), total dissolved solids (TDS), turbidity, dissolved organic matter (DOM), chemical oxygen demand (COD), and nutrients were higher than in the upper stretch. Based on the trophic state index, the upper, middle, and lower stretches were mesotrophic, moderate, and low eutrophic in nature, respectively. In the drinking water category, the water quality index (WQI) ranged from almost good (upper stretch) to inappropriate (middle and lower stretch). Nemerow pollution index (PINemerow) and the comprehensive pollution index (CPI) indicated that most sites were strongly and moderately polluted, respectively. Various point and nonpoint sources of pollution deteriorated the quality of Yamuna water. Spatial cluster analysis divided eleven stations into three groups based on water variables similarity. Discriminate analysis indicated that water temperature, flow, turbidity, pH, dissolved oxygen (DO), magnesium hardness (Mg-H) and COD were the most influencing variables seasonally, while water flow, pH, chloride (Clˉ), DO, Mg-H, and nitrate–N were for spatial variation in Yamuna water quality. Five potential sources were identified using principal component analysis (PCA); anthropogenic, natural, agricultural non-point sources, metrological, and seasonal factors. This study emphasizes the importance of using multivariate statistical techniques to identify variability patterns and develop management plans to improve river water quality by identifying the key variables responsible for maximum deterioration.