Multi-scale feature fusion-based lightweight dual stream transformer for detection of paddy leaf disease.

Traditionally, rice leaf disease identification relies on a visual examination of abnormalities or an analytical result obtained by growing bacteria in the research lab. This method of visual evaluation is qualitative and error-prone. On the other hand, an artificial neural network system is fast and more accurate. Several pieces of research using traditional machine learning and deep convolution neural networks (CNN) have been utilized to overcome the issues. Still, these methods need more semantic contextual global and local feature extraction. Due to this, efficiency is less. Hence, in the present study, a multi-scale feature fusion-based RDTNet has been designed. The RDTNet contains two modules, and the first module extracts feature via three scales from the local binary pattern (LBP), gray, and a histogram of orient gradient (HOG) image. The second module extracts semantic global and local features through the transformer and convolution block. Furthermore, the computing cost is reduced by dividing the query into two parts and feeding them to convolution and the transformer block. The results indicate that the proposed method has a very high average precision, f1-score, and accuracy of 99.55%, 99.54%, and 99.53%, respectively. It is suggestive of improved classification accuracy using multi-scale features and the transformer. The model has also been validated on other datasets confirming that the present model can be used for real-time rice disease diagnosis. In the future, such models can be used for monitoring other crops, including wheat, tomato, and potato.

A Novel Decentralized Blockchain Architecture for the Preservation of Privacy and Data Security against Cyberattacks in Healthcare.

Nowadays, in a world full of uncertainties and the threat of digital and cyber-attacks, blockchain technology is one of the major critical developments playing a vital role in the creative professional world. Along with energy, finance, governance, etc., the healthcare sector is one of the most prominent areas where blockchain technology is being used. We all are aware that data constitute our wealth and our currency; vulnerability and security become even more significant and a vital point of concern for healthcare. Recent cyberattacks have raised the questions of planning, requirement, and implementation to develop more cyber-secure models. This paper is based on a blockchain that classifies network participants into clusters and preserves a single copy of the blockchain for every cluster. The paper introduces a novel blockchain mechanism for secure healthcare sector data management, which reduces the communicational and computational overhead costs compared to the existing bitcoin network and the lightweight blockchain architecture. The paper also discusses how the proposed design can be utilized to address the recognized threats. The experimental results show that, as the number of nodes rises, the suggested architecture speeds up ledger updates by 63% and reduces network traffic by 10 times.

Spectrally resolved photon-echo spectroscopy of Rhodamine-6G.

Wavelength dependent study of a laser dye: Rhodamine-6G (Rh6G) by using spectrally resolved photon-echo spectroscopy is presented. The coherence and population dynamics of Rh6G solution in methanol changes as the excitation wavelength is tuned near its absorption maxima of 528 nm. Specifically, the central wavelength of the femtosecond laser pulse was set to 535 nm and to 560 nm while the respective spectra of the photon-echo signals were collected. This gives information on how the ultrafast dynamics of the Rh6G molecule changes with a change in the excitation wavelength.

Spectrally Resolved Femtosecond Photon Echo Spectroscopy of Astaxanthin.

We have studied the coherence and population dynamics of Astaxanthin solution in methanol and acetonitrile by spectrally resolving their photon echo signals. Our experiments indicate that methanol has a much stronger interaction with the ultrafast dynamics of Astaxanthin in comparison to that of acetonitrile.