National Academy Science Letters最新文献

To maintain the quality of life for civilians and ensure better health through good sanitary practices, it is essential to prioritize Solid Waste Management as one of the most critical services. A study conducted on 100 homemakers in Amreli city, Gujarat, aimed to analyse not only the practices of these homemakers but also the role of the city municipality in managing household solid waste. The results revealed that cleaning various areas of the household generates both biodegradable and non-biodegradable solid waste. Generally, homemakers are not very conscious of how to manage or dispose of the solid waste generated in their homes. Waste generation, segregation, disposal, and recovery are all highly dependent on human activities and awareness of solid waste management. A participatory approach involving both citizens and the municipality—whether it be the municipal corporation or council—is necessary to enhance the acceptability and transparency of waste management.

A swarm intelligence-based optimization approach, called JPWCV, is developed to solve the link monitoring problems in wireless ad hoc networks (WANETs). The minimum weighted connected vertex cover problem (MWCVCP) in graph theory plays a virtual backbone for the link monitoring problems in WANETs. The proposed JPWCV approach incorporates the jumping particle swarm optimization approach and a local search method based on weight and cost ratio metrics to get the best solution for the MWCVCP. This technique also uses a greedy procedure to make the weighted vertex cover into a connected one. Simulation results on extensive datasets suggest the notable improvement of JPWCV over existing algorithms in the context of less computational cost and execution time. The statistical analysis, grounded in the ranking of algorithms, further substantiates the superior performance of JPWCV over the compared algorithms.

Plant associated beneficial microbes enhance the availability of the nutrients and promote growth and maintain the health of the crops. The present study selected 25 rhizospheric and endophytic bacteria of maize (Zea mays L.) and screened for attributes of plant growth promotion. Among 25 isolates, 3 bacterial strains EU-PVTR-134, EU-PVKR-149, EU-PVKE-165 were evaluated on wheat under polyhouse conditions. The results showed the significant improvement in the growth of wheat. The application of the beneficial microbes with growth promoting traits as bioinoculants is an important alternative to reduce the utilization of chemicals in agriculture and the negative impact of the chemicals.The present study is the first report of Pseudomonas marginalis EU-PVKR-149 and Citrobacter braakii EU-PVKE-165 from rhizosphere and stem of maize.

In this note, our main goal is to study the sum of integers over their partitions into specified parts satisfying some conditions. Here we have studied this sum for six different conditions on partition.

Cyprinus carpio var. communis, an invasive exotic species, has been recorded for the first time as one of the most dominant species in reservoir and river-reservoir interface of Bichom, Arunachal Pradesh, India, in an investigation during May 2022–June 2023. The intensity of its invasiveness was very high, as evidenced by the relative abundance with an average catch of 0.26–2.90 kg/net/day. This species dominated the catches, contributing 5.5 kg/net/day to the total catch of the reservoir. A deeper survey has revealed that this exotic common carp entered the river and reservoir system from the nearby culture ponds of C. carpio during the flood that occurred in 2019 and has been established in the Bichom river-reservoir ecosystem. This is the first case report on the occurrence and invasion of C. carpio in the Bichom River and reservoir system, Arunachal Pradesh. The information generated here could be utilized as benchmark information on the geographic expansion of C. carpio in the river Bichom, a Himalayan river-reservoir ecosystem.

This study introduces a novel hybrid approach of utilizing Long Short-Term Memory Neural Networks and a completely new optimization technique called Deer Hunting Optimization. This model referred to as the Deer Hunting Optimization-Long Short-Term Memory is an enhancement to the long short term memory network since it improves prediction accuracy through the optimization of the parameters of the Long Short Term Memory Network. The study used data from the Dow Jones Industrial Average and the various models were compared to other configurations such models optimized with Particle Swarm Optimization or Genetic Algorithm techniques and beyond, three other artificial neural network models. The Deer Hunting Optimization-Long Short-Term Memory model obtained results of a mean squared error of 7.19, a mean absolute error of 2.7 and an R-square value of 0.44 respectively which is better than other models. According to these results, it is shown that the DHO-LSTM model can predict stock prices successfully and tuning the parameters with Deer Hunting Optimization achieves 15–20% improvement in prediction accuracy. Due to this the scope of how AI along with optimization can be used to better the prediction of stock performance, and its applications, like active trading, looks good and can be transferable to other markets with time constraints.

In this article, we propose a novel approach to camera source identification that leverages deep learning and transfer learning techniques to address the growing need for accurate and reliable methods in digital forensics. With the increase of mobile devices and the increasing frequency of image sharing on social media platforms, the risk of image manipulation, forgery, and related cybercrimes has escalated. Traditional methods for camera identification, such as metadata analysis and sensor noise examination, have been foundational but are often vulnerable to tampering. Our research proposes an advanced model combining Convolutional Neural Networks (CNNs) and transfer learning to enhance the accuracy of camera and sensor detection. Utilizing the MICHE-I dataset, our approach demonstrates superior performance, with the EfficientNet model achieving 98.29% accuracy in device classification and the ResNet50 model excelling in sensor-level detection with 96.97% accuracy. This work significantly contributes to the field of multimedia forensics, providing a robust and efficient method for tracing the origin of digital images, which is crucial for verifying the authenticity of content in legal and security contexts.

Mungbean cultivation in summer fallows within cereal-cereal systems are recommended for sustainable intensification. This study examined the dynamics of lability-graded C-fractions and net C balance following mungbean cultivation across different production ecologies. Three long-term mungbean-inclusive rotations were evaluated: two lowland rice-wheat-mungbean ecologies (LE1 and LE2) and one upland maize-wheat-mungbean ecology (UE). Notably, mungbean cropping resulted in significant changes in active C-pool, while passive C-pool remained unchanged. After mungbean harvest, both LE1 and LE2 showed a reduction in active C-pool (5–12%) and soil organic carbon (SOC) (8–14%). In lowlands, mungbean cultivation led to 16–47% increased active C-pool in sub-surface soil (20–40 cm), contributing to a positive change in C lability index. However, in upland ecology, mungbean cropping did not alter soil C composition. Thus, incorporating mungbean into lowland rice soils of Indo-Gangetic plain (IGP) can enrich active C-pools, particularly in subsurface soils.

For studying phloem-based plant-aphid interaction biology, the vascular sap is a targeted sample for analysis as an ‘interactome’ is developed due to a mixing of aphid saliva through its long and slender stylet to the sieve element cell sap. Therefore, the objective of the present study aimed to identify the maximum vascular sap recovery from different number of leaf and varied exudation time period. The maximum vascular sap recovery was evidenced from the eight leaves from six hours’ exudation period or alternatively four leaves from twenty-four hours’ exudation period. Therefore, the maximum recovery of exudates was correlated with leaf number and exudation period. But, the ratio of 260 and 280 nm was not changed in all parameter’s combination. This information will act as a functional probe to identify the maximum exudates recovery in a species-specific manner in a short period of time in plant-aphid interaction biology for addressing phloem-based defense mechanism.

Some studies have reported using 3D-printed dielectric electroactive polymer (DEAP) on woven fabric (WF) for a suitable electroactive response in the past decade. However, little has been reported on using 3D-printed thermoplastic polyurethane (TPU) on WF as a part of a wearable sensor in body-centric applications (BCA). This study reports the effect of mechanical loading (0–25 N) on the dielectric constant (ɛ_r) of 3D-printed TPU over WF as a substrate for BCA. For ascertaining the change in ɛ_r, the substrate was loaded and unloaded (for repeated cycles) along the weft side of the WF for mechanical strain. The 3D printed TPU on WF has shown programmable changes as 4D capabilities in bond characteristics (based on attenuated total reflectance-Fourier transform infrared (ATR-FTIR), surface roughness (R_a), porosity % (based on scanning electron microscopy (SEM), and physical properties (based on X-ray diffractions (XRD) analysis). To ascertain the BCA, the wearability of the developed sensor was ensured based on the specific absorption rate (SAR), which was observed as 1.57 W/kg.