National Academy Science Letters最新文献

Litchi is an item of impulse buying, and consumers prefer litchi fruit that is bright red and appealing to the eye. Litchi is exacting in its climatic requirements, and optimal colour development remains a challenge, especially in climes that are far from ideal and shaded portions of the tree canopy. In this study, we tested different bagging combinations involving butter/parchment paper, non-woven polypropylene, polypropylene, high density polyethylene, and black polythene bags, and recorded their effect on colour development and fruit quality in litchi. Our results show how bagging increases red colouration in the pericarp by upto 30%, reduces pest incidence, and improves the overall quality of fruit in litchi. We conclude that optimal fruit quality can be obtained by bagging litchi bunches with a combination of butter paper and polypropylene or non-woven polypropylene bag. Such bright red-coloured litchi fruit with negligible fruit borer infestation can have better consumer acceptability and also fetch better prices for growers.

The banking industry is experiencing a transformative period due to rapid advancements in big data and artificial intelligence, which present both significant opportunities and challenges. One of the pressing challenges in the domain of customer churn prediction (CCP) is the accurate classification of imbalanced datasets. In this study, we conduct a comprehensive investigation into CCP within the banking sector, utilizing an extensive range of datasets. We integrate robust models capable of capturing complex non-linear relationships to develop hybrid segmented models for CCP. Additionally, we introduce a novel, model-agnostic technique that extends SHAP (SHapley Additive exPlanations) to ensure the interpretability of these segmented hybrid models. The approach rigorously evaluates the performance of various predictive models across 14 customer turnover datasets. The interpretability of the new model-agnostic method is showcased through a detailed case study, providing clear insights into model decision-making processes. The staged comparison trials reveal that the Voting Classifier, XGBoost, CatBoost, and LGBoost achieve accuracies of 0.81, 0.84, 0.82, and 0.83, respectively. Among these, XGBoost demonstrates the highest prediction performance, emerging as the recommended algorithm. This study not only advances the accuracy of CCP models in the banking sector but also enhances their interpretability, facilitating more informed decision-making.

Lakes are essential for providing water, supporting tourism, and sustaining diverse ecosystems. However, the condition of major lakes in India has deteriorated due to population growth. Any strategy to rejuvenate a lake needs reliable assessment of surface runoff as inflow coming from drainage systems of lake catchment. This study combines GIS and the SCS-CN hydrological method to accurately estimate inflow as surface runoff into Macpherson Lake. Findings indicate a curve number (CN) of 87 with maximum runoff of 5.02 million cubic meters. The inflow data is vital for lake rejuvenation strategies. The results will aid in developing targeted stormwater management strategies to improve the water quality and overall sustainability.

This study uses bibliometric analysis based on the Web of Science Core Collection database to present an extensive overview of soil stabilization research from 2004 to 2024. Developing an illustrative dataset to support field researchers was the goal. With the use of Excel and VOSviewer, data from 1447 publications were downloaded and categorized. According to the evaluated keywords, the study indicates that research conducted during this time period concentrated on important elements related to soil stabilization, such as sustainable materials, unconfined compressive strength, and soil stabilization itself. Additionally, the study points out gaps in the literature and suggests additional lines of inquiry, including enhancing the processes for applying calcium lignosulphanate, evaluating its long-term effects on soil properties, and investigating possible synergies with other soil stabilizing agents.This bibliometric analysis not only describes the developments in the field but also sets the groundwork for the creation of soil stabilization methods that will satisfy the demands of sustainable and ecologically conscious development. From an engineering and environmental standpoint, it emphasizes the significance of soil stabilization by providing sustainable solutions, better soil quality, lower maintenance costs, and more project stability during construction.

The study is aimed to assess the threat to the endemic wild edible plant Ceropegia rapinatiana, which is used as a food source for the local community in Pudukkottai district, Tamil Nadu, India. This study strongly recommends the conservation of this plant to ensure its sustainable utilization. This highlights the importance of preserving the plant’s habitat and promoting responsible harvesting practices to the protection of its availability for the future generations. C. rapinatiana is found primarily in the central plains of Tamil Nadu covering parts of Tiruchirappalli and Pudukkottai districts, and it has been experiencing a continuous decline in population due to the harmful effects of anthropogenic activities, particularly unsustainable harvesting and consumption. As a result, C. rapinatiana is classified as Critically Endangered based on the assessment using the IUCN criteria. Given these circumstances, immediate and extensive conservation measures must be implemented by the local authorities to mitigate the threats and ensure the species’ survival.

Skin diseases pose a significant health concern globally, with diverse manifestations and diagnostic challenges. Deep learning methods have shown impressive potential in medical image analysis. This project focuses on utilizing methods of deep learning techniques for the automated classification of skin diseases. Two popular convolutional neural network (CNN) architectures, InceptionV3 and VGG16, are trained and evaluated on a comprehensive dataset consisting of around 17,000 images across 13 classes, including the most prevalent skin diseases such as acne, urticaria, eczema, psoriasis and vascular disorders. The study incorporates image preprocessing techniques to enhance the quality and informativeness of input data. Additionally, we experiment with the dense layers of the models, exploring configurations that optimize classification accuracy. The study aims to compare the accuracies of these models and determine the most effective one for deployment in a web interface for skin disease diagnosis. The proposed model, achieving a training accuracy of 80.88% for InceptionV3 and 74.17% accuracy for VGG16, demonstrates its potential as an effective instrument for healthcare providers and individuals, potentially aiding in the timely diagnosis and management of various skin diseases.

Andaman and Nicobar Islands are a group of land masses in the Bay of Bengal, situated about 1200 km away from mainland India. Agriculture in these islands is dominated by plantation crops, arecanut being the popular one. However, mono-cropping is not desirable, especially in widely spaced crops within island ecosystems where land is limited. To optimally utilize land resources and improve profitability, experiment on arecanut based cropping system with culantro was initiated. Culantro is a popular aromatic herb used for imparting green coriander like aroma to the cuisines. Intercropping resulted in an additional yield of culantro (2.21 t/ha), increasing net returns to ₹ 13,03,065/- compared to ₹ 9,59,600/- from the sole arecanut crop. Quality of the culantro produced in terms of plant growth, photosynthetic pigments, ascorbic acid etc. was also determined. Hence, culantro could be recommended for cultivation as an intercrop in the arecanut plantations of humid tropical Andaman Islands.

The Universal Filtered Multi-Carrier (UFMC), which combines the benefits of Filter Bank Multicarrier (FBMC) and Orthogonal Frequency Division Multiplexing (OFDM), is one of the prospective waveform technologies for future wireless networks. Despite these advantages, the UFMC system has a more complex implementation than the OFDM system. This paper describes a method for simplifying the transmitted UFMC symbol in the time domain, which is implemented like OFDM without filtering operations, reducing the computational and hardware complexity of the UFMC transmitter. The simulation results show that the proposed model gives the same system performance with reduced computational complexity.

This paper reports the occurrence of aberrant extra dorsal fins in a ‘Near Threatened’ butter catfish, Ompok bimaculatus (Bloch, 1794) for the first time from a reservoir ecosystem in Godavari River Basin. The specimen was taxonomically confirmed through morphological features and molecular attributes. This study also highlights the possible causes of such deformity in the natural fish populations of man-made ecosystems i.e., reservoirs.