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Abstract: The present study dealt with the description and distribution of two species viz. Cirrhipathes spiralis, Cirrhipathes anguina under the subclass Hexacorallia, order Antipatharia reported from the Gulf of Mannar Biosphere Reserve, India. Earlier Cirrhipathes anguina was reported from Andaman and Nicobar Islands in 1903, and Cirrhipathes spiralis was reported from Galle, Sri Lanka coast. Both of the species C. spiralis and C. anguina are new distribution for the Gulf of Mannar, Tamil Nadu coast of India. PubDate: 2025-06-14
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Abstract: Dhara Mustard Hybrid-11 (DMH-11), despite controversy surrounding its cultivation, became India’s first genetically modified (GM) food crop that could likely reach the kitchens. Existing studies often focus on the agronomic and environmental aspects of GM mustard, but there are notable gaps when it comes to quantitative impact evaluation. Against this backdrop, an attempt has been made for the ex-ante impact assessment of the GM mustard variety i.e., DMH-11, using the economic surplus model. The analysis was done for a decade starting from 2024 to 2034 in India. The contribution of this study is unique to the literature on GM food crops, as it provides insights into possible economic benefits to the farmers and consumers, as well as society as a whole, due to the adoption and cultivation of GM food crops. The analysis suggests a substantial total economic surplus of ₹6 lakh million by 2034, with a net present value of ₹2.72 lakh million, an internal rate of return of 264%, and a benefit–cost ratio of 353 on research cost, indicating very high returns on research investment on developing GM food crops. PubDate: 2025-06-14
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Abstract: Gait recognition enables non-intrusive identification of individuals through their walking patterns, eliminating the need for active cooperation. It has gained popularity due to its remote applicability and ability to work with low-resolution videos. This study explores the effectiveness of Scale-Invariant Feature Transform and Speeded-Up Robust Features for gait recognition. Decision Tree and Random Forest classifiers are employed to evaluate the recognition performance of these features, achieving accuracies of 85.20% and 87.80%, respectively, on the CASIA-A dataset. PubDate: 2025-06-13
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Abstract: Air pollution is a major environmental concern in many Indian cities, with potential negative impacts on public health and the environment. This study aims to assess and forecast air quality in Jabalpur city of Madhya Pradesh, using an artificial neural network (ANN). The study utilised historical air quality data from January 2019 to December 2023, including concentrations of criteria pollutants, particulate matter PM2.5, PM10, SO2, NO2, and meteorological parameters collected from the Central Pollution Control Board (CPCB). An ANN model was developed and trained on the historical data to predict future air quality levels and It was observed that PM10 and PM2.5 concentrations exceeded the daily and annual CPCB standards, the performance of the ANN model was evaluated using statistical metrics such as RMSE, R2, MSE, and MAE. Meteorological parameters and hourly pollutant concentrations were used to train the five-year model, hence the result showed that ANN can predict the air pollutant concentrations with R2 value of 0.8666 for NO2 but for SO2 shows lesser accuracy, with lowest R2 as 0.6977. PubDate: 2025-06-12
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Abstract: Present study reports on natural restoration of seagrasses such as Halophila ovalis, Halodule pinnifolia and Halodule uninervis at lower region of Haripur creek during the course of Covid-19 pandemic (2019–2021).... PubDate: 2025-06-10
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Abstract: To inventorize the bruchid species associated with stored edible leguminous seeds, eight different types of infested pulse seed / grain samples were collected from 21 locations spread across 10 districts of Ta... PubDate: 2025-06-05
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Abstract: Fishing gears lost or abandoned intentionally or due to some unavoidable situation or damage is left within the sea bottom continue to kill fish by entangling, physical damage to the reef-building corals and i... PubDate: 2025-06-04
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Abstract: Premna herbacea Roxb. (Lamiaceae) commonly known as Bharangi is rediscovered from Hamirpur district of Himachal Pradesh, India after 90 years. P. herbacea was previously collected by Walter Koletz from Bhadwar, tehsil Nurpur, district Kangra (H.P.) in 1933. Presently, this is also a new addition to the flora of Himachal Pradesh. The representative specimens are catalogued in Botanical Survey Solan (BSS) herbarium (Botanical Survey Solan Herbarium) for future reference. PubDate: 2025-06-03
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Abstract: A long and slender stylet is evolved in sucking groups of insects for an efficient tapping of phloem sap from host plants. Sucking groups of insects like aphid, mites cause a huge agricultural and horticultural yield loss globally. It is a long-standing debate whether aphid stylet penetration causes the stylet mediated injury to the host tissue or not. The aphid stylet is penetrated host tissue through rupturing mesophyll cells and reaches to phloem sap which is enriched with sucrose. During navigation of aphid stylet into phloem sieve element cell, numerous mesophyll cells are being ruptured. Till now, it is in the elusive zone that whether aphid stylet penetration induced host response or not against the stylet mediated injury. To configure the issue, the construct having bacterial UidA gene under the promoter of actin depolymerising factor 3 (AtADF3; At5g59880) was considered for raising transgenic of the model plant, Arabidopsis thaliana. The ADF3 promoter (pADF3) is a constitutive promoter which distinctly evidenced a physical injury mediated host response in the form of GUS staining at the site of physical injury without any changes in UidA, adf3 and tubulin RNA level. Thus, the hypothesis was whether aphid stylet penetration showed GUS staining from stylet mediated injury under ADF3 promoter or not. The result indicated that aphid stylet penetration mediated injury (mesophyll cell rupture) in host mesophyll did not induce GUS staining driven by ADF3 promoter at the stylet penetration site in Arabidopsis thaliana leaf. Therefore, it was proposed that aphid stylet penetration attenuated host response unlike physical injury and aphid may explore another mechanism to avert host response from stylet penetration mediated injury. PubDate: 2025-06-02
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Abstract: OTT services have transformed the entertainment industry by giving customers rapid access to various content. However, OTT platforms are plagued with churn or subscriber cancellations. This study investigates machine learning (ML) as a churn-reduction strategy. It investigates how machine learning (ML) algorithms might reduce churn and improve customer retention in the OTT arena. We study the usefulness of ensemble learning, Long Short-Term Memory (LSTM) networks, and Multilayer Perceptron (MLPs) in detecting users on the verge of leaving. To solve intrinsic data imbalances in which churned users comprise a smaller minority, we employ SMOTE (Synthetic Minority Over-sampling Tech- nique). We investigate the impact of customer experience, content library, and consumption patterns on turnover likelihood. Our findings reveal that Gradi- ent Boosting (Accuracy: 87.2%), Random Forest (Accuracy: 91.2%), and MLP (Mean Accuracy: 87.1%) achieve strong performance, with SMOTE improving XGBoost’s recall by 697% (0.083 → 0.697), with LSTMs showing even more potential with additional data. This study highlights the potential of ML for OTT platforms to get important consumer information, design focused retention campaigns and eventually reduce churn. This results in long-term financial stability and a devoted subscription base. PubDate: 2025-06-02
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Abstract: The research focuses on tackling challenges in increasing pothole leading to deteriorating road conditions. In reality, object detection faces various computational challenges at edge devices that includes model training, global server aggregation and data privacy. By leveraging federated learning within the FedCV framework, the system monitors, detects, and reports potholes. The novel aspect of research is the adaptation of the YOLOv8 object detection model, integrated with the FedCV framework, to ensure data privacy and security at the central server, while simultaneously boosting performance at the edge device during pothole detection training with local data. This integration addresses major computational challenges at the edge camera device level. As the research combines deep learning and federated learning within an edge-based framework, it improves real-time pothole detection and ensures efficient privacy-preserving road maintenance and advancing smart city initiatives. The model has demonstrated outstanding performance in classification, achieving an average accuracy of 96%. With a precision of 96.4%, recall of 96.7%, and an F1 score of 96.6%, its evaluation underscores its superiority compared to previous studies. These results establish the model as a reliable approach for pothole detection. PubDate: 2025-06-02
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Abstract: In survey sampling, imputation methods are essential for handling missing data which can have a large impact on statistical analysis and inference. Neutrosophic logic is an extension of the classical logic which provides a robust framework for tackling indeterminate, inconsistent, and incomplete information. In this article, some uncertainty based efficient neutrosophic imputation methods (ENIMs) and the resultant efficient neutrosophic estimators are developed to estimate the population mean under simple random sampling (SRS). The bias and mean square error (MSE) of the resultant neutrosophic estimators are obtained to the first order approximation. The proposed ENIMs are evaluated through extensive simulations and real data applications, demonstrating superior performance in terms of reduced MSE and increased percent relative efficiency (PRE). The findings of this study suggest that neutrosophic imputation methods (NIMs) offer a significant advancement in survey sampling methodologies, enhancing the accuracy of statistical inferences in the presence of incomplete data. PubDate: 2025-06-01
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Abstract: Various real-time problems typically possess information about fuzzy and inverse fuzzy values on its graph modeling. To address such situations, in this article, the bivalued fuzzy graph which involves the combination of less membership grade and high membership grade on the edges has been introduced. Subsequently, various operations such as bivalued cartesian product, bivalued tensor product and bivalued union of bivalued fuzzy graph have been defined. Further, various theorems associated with the aforesaid operations are proved to be bivalued fuzzy graph. Furthermore, the complement of bivalued fuzzy graph is also established and also proved to be a bivalued fuzzy graph. PubDate: 2025-05-31
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Abstract: Unplanned urbanization has disrupted the hydrological balance in many Indian cities, increasing flood and water scarcity risks. Nagpur, despite its inland location, faces recurrent flooding due to inadequate stormwater infrastructure and encroachments on natural water bodies. This study explores the potential of Sustainable Urban Drainage Systems (SuDS) to mitigate urban flooding in Nagpur by simulating the performance of seven SuDS components—Rain Barrel, Rooftop Disconnection (RD), Green Roof, Pervious Pavement, Bio-retention, Rain Garden, and Infiltration Trench (IT)—individually and in combinations using the Storm Water Management Model (SWMM). A flood-prone sub-catchment in the city, comprising 69% impervious area, was selected for simulation. The study was conducted for five rainfall return periods (2, 5, 10, 25, and 100 years) to test performance under varying intensities. Among the components, RD and IT showed the highest effectiveness, reducing runoff by approximately 57% and 15%, respectively. Their combination (RD + IT) achieved a runoff reduction of nearly 67%. Other combinations, such as RD + Bio-retention and RD + Rain Garden, also performed well (~ 60% and ~ 58% reductions). These decentralized interventions promote groundwater recharge, support water reuse, and offer scalable, low-cost solutions for urban flood management. The findings highlight the applicability of SuDS in dense urban areas of India and underscore their relevance in retrofitting existing drainage networks. This study contributes to the limited empirical research on SuDS in tropical developing countries and offers a practical foundation for integrating such systems into local planning and national disaster resilience strategies. PubDate: 2025-05-30
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Abstract: Two rare deep-sea specimens collected from the Kollam slope area, off Kerala, India, were identified as Ophichthus erabo (Jordan & Snyder, 1901), commonly known as the Blotched Snake-Eel. A review of existing literature indicates that this species had not been previously reported along the Indian coast, making this the first record for the region. The species is distinguished from its congener, Ophichthus polyophthalmus, by the presence of numerous brown to dark brown smudges on the head and body, usually arranged in two irregular rows. PubDate: 2025-05-27
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Abstract: This article reports a new distributional record of Corallodiscus bhutanicus (Craib) B.L. Burtt (Gesneriaceae), based on the identification of a population from Arunachal Pradesh in the Eastern Himalayan range of North Eastern India. Earlier, it was reported from Bhutan and West Bengal (Darjeeling), a part of Indian Eastern Himalaya. A well detailed morphology, identification key, associated flora, distribution map and photo-plate is provided for easy identification in the field for future taxonomists and students. PubDate: 2025-05-27
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Abstract: An artificial neural network (ANN) is a type of computing system that models individual neural connections to imitate the information processing capabilities of the human brain. ANNs are used for solving complex problems in text data and tabular data. Convolutional neural networks (CNNs) are the most effective tools for resolving issues that are associated with computer vision and recurrent neural networks are applied for sequence data and natural language processing tools. The hardware chip design is always challenging to solve real-time problems. The research letter examines the comparative evaluation of the performance suitability of hardware chip designs for scalable computation, as well as the application of field programmable gate arrays in chip logic verification, utilizing parallel computing and pipelined design principles to enhance execution efficiency. CNN chip has demonstrated the optimal delay of 8.750 ns, 2.510 ns minimum time, and 552.00 MHz frequency, 98.78% accuracy in comparison to ANN and RNN for scalable design of 64 neurons processing. The current demand within the artificial intelligence industry for wearable embedded system design necessitates the utilization of dedicated real-time hardware and customized design solutions. PubDate: 2025-05-26
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Abstract: Non-alcoholic fatty liver disease (NAFLD) may lead to liver failure, and sometimes to hepatocarcinoma. Genetics plays important role in susceptibility for the disease. India has high burden of NAFLD, however, information on its genetic susceptibility is lacking for Sikkim, a north east Himalayan state of India. We conducted a pilot study to determine the risk of PNPLA3 gene polymorphism for NAFLD in this region. In a case control study including 30 cases and 25 controls, DNA was extracted from peripheral blood of the participants. SNP rs738409 [C > G] of PNPLA3 was detected by PCR followed by RFLP. Risk of NAFLD was higher in variant homozygotes and heterozygotes in comparison to the wild type homozygotes. Although the increased risk was not statistically significant, a large- scale study is required to find any possible association of this polymorphism with NAFLD in this Himalayan state. In addition to this, high frequency of the variant allele [G] observed in the control group (0.4) warrants further study to explore its putative association with NAFLD in this region. PubDate: 2025-05-25
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Abstract: This paper derives asymptotic behaviours of Laplace transforms of generalized functions. The results demonstrate how distribution theory contributes to the depth of mathematical analysis. PubDate: 2025-05-25
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