National Academy Science Letters最新文献

Millets are renowned for their resilience to climate, as they can endure challenging environmental conditions. Maharashtra, known for its agriculture-based economy, faces the challenge of 24% of its land being prone to drought. Despite this, the state has enormous agricultural growth potential and is a major millet producer in India. Being a climate-resilient crop, there is a huge scope for area expansion under millet cultivation in the state. The present article investigates the current trend in area, production and productivity of pearl millet (Pennisetum glaucum L.) and sorghum (Sorghum bicolor) in Maharashtra by using the Exponential Compound Annual Growth Rate (Exponential CAGR) approach. The present study reveals that the area and production of both millets in Maharashtra state are on a decreasing trend. Both millets production experienced negative growth rates, with a CAGR of −3.89% for sorghum and −2.56% for pearl millet. The present study has the potential to determine the current state of production and productivity of both millets in the state, which will help policymakers and researchers develop strategies to boost millet production in Maharashtra in the coming years.

The present study was focused to determine bioactive constituents in fresh & dried leaves (shade dry, oven dry) of cultivated Cinnamomum tamala. A total of 17 bioactive constituents were found through GC–MS analysis, and exhibited maximum (96.28%) in fresh leaves as compared to shade dry leaves (94.67%). Among them, cinnamaldehyde was found significantly major active constituent in the leaves (fresh 44.49%; shade dry 39.37%), followed by linalool (fresh 29.50%; shade dry 10.67%), cinnamyl acetate (fresh 4.02%; shade dry 38.81%), and limonene (fresh 6.40% and shade dry 0.27%), respectively. Results revealed that cinnamaldehyde & linalool are the major constituents of C. tamala. Therefore, the species can be promote as an option for fulfilling the market requirement.

An upsurge of Caliothrips indicus (Bagnall) was observed during April to July, 2022 and 2023 in Sesbania bispinosa in Varanasi and adjoining districts, Uttar Pradesh. Several small white streaks appeared on the leaves which eventually dried up. C. indicus infested 40–60% of the plants which appeared unkempt. In terms of spatial diversity, adults gathered on the terminal area of the plant, while developing stages tended to feed more towards the basal portion. The incidence was more common during the hot and dry summer months with peak nymphal (7.9 per compound leaf) and adult (6.09 per compound leaf) populations during 28th standard meteorological weeks. The nymphal and adult populations per compound leaf were 3.35–7.1 and 2.89–5.32, respectively. Considering the incidence and its importance, we studied the molecular and morphological taxonomy, seasonal incidence and likely migration path of C. indicus in the vegetable ecosystem.

A circular ring structure with a tapered feed monopole antenna is designed with size of 1.3 λ₀ × 1.4 λ₀ × 0.095 λ₀ mm³ (L × B × H). The maximum reflection coefficient |S₁₁| with the tapered feed is −36 dB, without tapered feed is −25.55 dB and peak gain is ≥ 5.0 dBi in both cases. Proposed antenna produces bandwidth of 1.25 GHz while allocated bandwidth for ISM-III band is 250 MHz. The obtained bandwidth is wider than the allocated bandwidth. Proposed antenna qualifying the obtained measured results. This antenna is applicable for 5G Communication, Radar, and satellite services with miniaturization feature.

Digital image classification assists in distinguishing natural and synthetic images to detect computer-generated objects. However, CGI improvements make it difficult to discern synthetic photos from genuine ones. Researchers suggest multiple deep learning strategies to differentiate these photo sets utilizing thorough feature analysis. These models are either complex or do not handle image sub-components, decreasing efficiency in large-scale applications. These models fail categorically. To address these issues, this work proposes a novel high-density bio-inspired feature analysis deep learning model for natural and synthetic image sub-classification. A YoLo model initially recognizes objects in input image sets. Processed separately, a hybrid LSTM/GRU model predicts high-density feature sets, which are processed by Elephant Herding Optimization (EHO) Models to identify high inter-class variance feature sets. A customized 1D CNN model is used to categorize the desired features into natural and synthetic components. These classification results establish whether the input image is natural, synthetic, or both. In real-time scenarios, the proposed model is able to improve standard classification models with 8.7% greater accuracy, 10.9% higher precision, 3.2% higher recall, and 8.4% higher AUC.

Evapotranspiration and surface runoff are hydrologic connections for overall water availability and water losses at basin scales. Estimation of evapotranspiration and surface runoff is a challenging task for water managers and researchers. Spatial and temporal changes occur in basin due to changes in physical topography and climatic conditions. It is very much required to model the basin to understand the behaviour and amount of water available in that basin at watershed and sub-watershed scales. The information at sub-watershed scale is vital for microlevel water-related planning in the basin for different water uses. In this work, ArcSWAT model is used to estimate evapotranspiration and surface runoff at sub-watershed scales. The study area in Indravati river basin comprises of 15 sub-watershed (WS1, WS2, WS3…WS15) which were divided into 199 hydrological response units (HRUs) that satisfactorily represent basin heterogeneity. Calibration and validation of SWAT model are done using Soil water assessment tool-Calibration and uncertainty program (SWAT-CUP) software with sequential uncertainty fitting-II (SUFI-II) algorithm. Results of this study show that the total average annual runoff and evapotranspiration (ET) are 94.85% and 4.56% of average annual precipitation (PPT), respectively. The sub-watershed WS8 receives maximum annual runoff and minimum annual ET as 98.43% and 4.43% of average annual PPT, respectively. Monthly variation of runoff and ET is also presented.