National Academy Science Letters最新文献

The creation of novel materials has grown tremendously, but the main issue is that they are exceedingly challenging to manufacture. So, it is essential to implement certain new machining techniques. Due to its capacity to create detailed, complex forms and to work with hard materials, (EDM) is a nontraditional and widely used machining technique to make machine dies, machine punches, and various press tools.

Copper, brass, and graphite were employed as the three distinct electrode materials in the experiment along with dielectric fluid.L9 orthogonal array has been selected using the taguchi technique, and three levels are taken for each of the variables. The set of tests that were planned in an orthogonal array have been carried out. Experimental results were examined analytically and visuallyThe optimal values of MRR were calculated. Experimental findings revealed that, brass electrodes with greater surface polish had lower current values (18 A) and longer pulse-on periods (65 S) than copper electrodes with the highest MRR (30 A) and pulse-on values (50 S).

This article introduces a novel technology called “AEIN” that has been suggested for the unique identification of individual pigs based on their auricular vein pattern. The aim is to address the limitations of conventional identification methods, which are known for their unreliability, inaccuracy, and susceptibility to manipulation, while also promoting the concept of intelligent livestock management. The ear images of pigs were obtained from a certified farm, processed according to established protocols, and subjected to feature extraction to create templates for subsequent matching within the same class and across different classes using various distance metrics such as Euclidean, Manhattan, Minkowski, and Hamming distances. Specifically, the points of branching in the vein pattern were utilized as features for template creation. By carefully analyzing each distance metric, a threshold level was established, with the average distance set at 20 for Manhattan distance and 40 for Minkowski, Euclidean, and Hamming distances, respectively. If the calculated matching distance falls below the threshold, the pig is successfully identified; otherwise, it is considered a different individual. The Euclidean distance metric demonstrated the highest accuracy in identification among all four metrics in the conducted experiments. A total of 54 pigs were included in the study, revealing that the “AEIN” technology achieved a remarkable accuracy rate of 98.18% when employing the Euclidean distance metric.

Environmental sustainability and the mitigation of greenhouse gas emissions are paramount concerns on a global scale. Incorporating RHA in concrete addresses both environmental issues and agricultural waste disposal, simultaneously reducing CO₂ emissions. Additionally, rapid construction growth has led to excessive RS mining, damaging ecosystems and raising costs, making M-sand a viable alternative. This study evaluates the impact of RHA and VS M-sand as partial replacements for OPC and RS in concrete. RHA content ranged from 0 to 25% and VS M-sand up to 50%. Optimal strength and durability were achieved with 15% RHA and 50% VS M-sand. RHA (0–15%) enhanced strength and sulphate resistance, but performance declined beyond 15% content. SEM analysis revealed a dense CSH gel structure in the 15% RHA and 50% VS M-sand mix. This research underscores the potential of RHA and VS M-sand as sustainable alternatives in concrete production, promoting environmental preservation and resource management.

This article presents a self-supervised approach implementing an unsupervised clustering algorithm to analyze the intrinsic vascular pattern in near-infrared (NIR) light. The framework includes NIR intrinsic vascular image acquisition, pattern detection, ML multiscale filtering, feature extraction, recognition, identification, and matching based on a linear regression model to detect an optional variable worth dependent on a free factor. The approach uses ordinal NIR vein print portrayal, and the self-learning methodology achieved a 97.50% accuracy score for identifying intrinsic vascular patterns in unsupervised learning issues.

This letter looks at the effects of mobility and diversity. The transmitter is mobile in this scenario, while the receivers are diverse. The mobile transmitter will induce a doppler effect on the signal being sent. The receiver antennae will be able to estimate the doppler effects and remove them. It will then leverage the multiple antennae to improve the reception of the signal. Doing both improves the packet detection rate in a low signal to noise ratio (SNR) scenario. When multiple mobile transmitters are used, the packet detection rate in a low SNR environment was poor.

Deepfake videos have become a growing concern in the digital age, presenting a substantial risk to the genuineness and trustworthiness of visual material. As these sophisticated manipulations continue to proliferate, there is a pressing need for advanced tools and techniques to detect and combat them effectively. In this article, we introduce a novel hybrid deep-learning model designed to enhance the accuracy of deepfake video detection using a Transfer Learning approach. Unlike traditional approaches, our hybrid model utilizes smart computer learning to carefully analyze videos for any signs of tampering. It's akin to having a digital detective to safeguard the truth of videos.

During the study of the alpine flora of Roopkund of Chamoli district which lies in lap of Trishul massif, and a part of Indian Western Himalaya, collected many rare, threatened and botanically interesting species of highly biodiverse himalayan hot spot. In the present communication, authors deals the occurrence of Himalayan gentian, Comastoma urnigera first time from Indian Western Himalaya. Earlier, it was reported from Sikkim, a part of Indian Eastern Himalaya. A well detailed morphology, distribution map and photo-plate is provided for easy identification in the field for future taxonomists and students.

Golden anchovy, Coilia dussumieri is a unique fishery with characteristic discrete distribution. The distribution and its abundance are known to be linked with environmental variables. Three of the five selected environmental variables were found to significantly correlate with its abundance along the Gujarat coast. Sea Surface Height (SSH) was the most significantly correlated (r = -0.63; p < 0.05) environmental variable with the Catch Per Unit Effort (CPUE) of the golden anchovies. The SSH was used as a covariate in the surplus production model to arrive at maximum sustainable yield (MSY). The estimated MSY was much less than what is estimated using a conventional model without environmental variables. Further, the incorporation of environmental covariates improves the model fit significantly. Considering the importance of environmental variables, especially in the climate change scenario, a more conservative estimates from the present study would be adopted for formulating its harvest strategies.