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The surge of covid-19-positive cases and mortality among different communities in the state of Louisiana are concerning. It has affected us in different ways: psychologically, physically (mobility restriction), socially, and economically. It is a global catastrophe and all of us are dealing with multiple challenges due to this. As of 9th April 2023, there are almost 1.6 million covid-19 cases and 18,984 people lost their lives in the state of Louisiana. This pandemic created tremendous pressure in healthcare with an unexpected surge in the demand (more than existing production capability). According to our data, there were 3,022 covid patients hospitalized on 08/17/2021, and there were 571 covid-positive patients on the ventilator on 04/04/2020 on a single day. Louisiana has about 33% black population which is about half of white population of 63.0%. However, the covid infection rate was almost 20.0% higher in the black population compared to the white population. Here, we present a demographic chart, the infection rate, and death by region and race in different communities in Louisiana. 

Fault Injection attack is a type of side-channel attack on the Physical Unclonable Function (PUF) module that can induce faults in the PUF response by manipulating the PUF circuit behavior through voltage glitches, laser attacks, temperature manipulations, or any other attacks potentially leading to information loss or security system failure. This type of attack exposes the physical characteristics of PUFs that can be analyzed to predict or compromise the unique challenge response pairs (CRPs) reducing the security and reliability of the PUF. Mitigation strategies against such attacks typically include adding noise to the PUF output, using error-correcting codes, or enhanced cryptographic protocols that obscure physical side-channel attacks. In this research, we propose a Generative Adversarial Network (GAN) based security model, that monitors the PUF behavior and detects the variations in PUF response. The model can detect glitches in the PUF response and generate alerts to take mitigation measures. 

This research explores practical applications of Transfer Learning and Spatial Attention mechanisms using pre-trained models from an open-source simulator, CARLA (Car Learning to Act). The study focuses on vehicle tracking using aerial images, utilizing transformers and graph algorithms for keypoint detection. The proposed detector training process optimizes model parameters without heavy reliance on manually set hyperparameters. The loss function considers both class distribution and position localization of ground truth data. The study utilizes a three-stage methodology: pre-trained model selection, fine-tuning with a custom synthetic dataset, and evaluation using real-world aerial datasets. The results demonstrate the effectiveness of our synthetic transformer-based transfer learning technique in enhancing object detection accuracy and localization. When tested with real-world images, our approach achieved an 88% detection, compared to only 30% when using YOLOv8. The findings underscore the advantages of incorporating graph-based loss functions in transfer learning and position-encoding techniques, demonstrating their effectiveness in realistic machine learning applications with unbalanced classes. 

This article presents constraints on dark-matter-electron interactions obtained from the first underground data-taking campaign with multiple SuperCDMS HVeV detectors operated in the same housing. An exposure of $7.63\mathrm{g}\text{−}\mathrm{days}$is used to set upper limits on the dark-matter-electron scattering cross section for dark matter masses between 0.5 and $1000\mathrm{MeV}/c^2$, as well as upper limits on dark photon kinetic mixing and axionlike particle axioelectric coupling for masses between 1.2 and $23.3\mathrm{eV}/c^2$. Compared to an earlier HVeV search, sensitivity was improved as a result of an increased overburden of 225 meters of water equivalent, an anticoincidence event selection, and better pile-up rejection. In the case of dark-matter-electron scattering via a heavy mediator, an improvement by up to a factor of 25 in cross section sensitivity was achieved. Published by the American Physical Society2025