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We derive unified lower bounds on the mean squared error (MSE) of distributed quantum sensor fusion under Byzantine faults and decoherence. Building on the classical Brooks-Iyengar overlap function and its vector extension, the predictive outlier model for virtual sensor tracking, and SPOTLESS spatial-temporal verification, we establish a two-parameter family of bounds indexed by entanglement visibility V and fault fraction f/M. For M quantum sensors with N atoms each and sensitivity eta, the MSE of any estimator satisfies MSE >= (1-V^2)/(4*N*eta^2*M_eff) + V^2/(4*N*eta^2*M_eff^2), where M_eff = M-2f under Brooks-Iyengar Byzantine fault tolerance and M_eff = M-f when predictive outlier detection successfully identifies faulty sensors. The bound interpolates continuously between the standard quantum limit (V=0, scaling as 1/sqrt(M_eff)) and the Heisenberg limit (V=1, scaling as 1/M_eff). Monte Carlo simulations with up to 64 sensors validate the theoretical scaling laws. Validation on the Intel Berkeley Lab 54-mote dataset with spatial clustering demonstrates 20-27 dB SNR improvement from entanglement per cluster, and reveals that missing classical sensor data degrades fusion agreement in the same pattern as quantum decoherence. The framework bridges quantum metrology with classical stream-processing architectures including Data-Cleaning Trees and the 80-20 Power Law for scale-invariant clustering. 

Large Language Models (LLMs) such as Gemma-2B have shown strong performance in various natural language processing tasks. However, general-purpose models often lack the domain expertise required for cybersecurity applications. This work presents a methodology to fine-tune the Gemma-2B model into a domain-specific cybersecurity LLM. We detail the processes of dataset preparation, fine-tuning, and synthetic data generation, along with implications for real-world applications in threat detection, forensic investigation, and attack analysis. Experiments highlight challenges in prompt length distribution during domain-specific fine-tuning. Uneven prompt lengths limit the model's effective use of the context window, constraining local inference to 200-400 tokens despite hardware support for longer sequences. Chain-of-thought styled prompts, paired with quantized weights, yielded the best performance under these constraints. To address context limitations, we employed a hybrid strategy using cloud LLMs for synthetic data generation and local fine-tuning for deployment efficiency. To extend the evaluation, we introduce a Retrieval-Augmented Generation (RAG) pipeline and graph-based reasoning framework. This approach enables structured alignment with MITRE ATT&CK techniques through STIX-based threat intelligence, enhancing recall in multi-hop and long-context scenarios. Graph modules encode entity-neighborhood context and tactic chains, helping mitigate the constraints of short prompt windows. Results demonstrate improved model alignment with tactic, technique, and procedure (TTP) coverage, validating the utility of graph-augmented LLMs in cybersecurity threat intelligence applications. 

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. 

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. 

Metal halide perovskite (MHP) solar cells are promising aerospace power sources given their potential as inexpensive, lightweight, and resilient solar electricity generators. Herein, the intrinsic radiation tolerance of unencapsulated methylammonium lead iodide/chloride (CH3NH3PbI3-xClx) films was isolated. Spatially resolved photoluminescence (PL) spectroscopy and confocal microscopy revealed the fundamental defect physics through optical changes as films were irradiated with 4.5 MeV neutrons and 20 keV protons at fluences between 5×1010 and 1×1016 p+/cm2. As proton radiation increased beyond 1×1013 p+/cm2, defects formed in the film, causing both a decrease in photoluminescence intensity and a 30% increase in surface darkening. All proton irradiated films additionally exhibited continuous increase of energy bandgaps and decreasing charge recombination lifetimes with increasing proton fluences. These optical changes in the absorber layer precede performance declines detectable in standard current-voltage measurements of complete solar cell devices and therefore have the potential of serving as early indicators of radiation tolerance. 

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 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. 

The continuous evolution of the IoT paradigm has been extensively applied across various application domains, including air traffic control, education, healthcare, agriculture, transportation, smart home appliances, and others. Our primary focus revolves around exploring the applications of IoT, particularly within healthcare, where it assumes a pivotal role in facilitating secure and real-time remote patient-monitoring systems. This innovation aims to enhance the quality of service and ultimately improve people’s lives. A key component in this ecosystem is the Healthcare Monitoring System (HMS), a technology-based framework designed to continuously monitor and manage patient and healthcare provider data in real time. This system integrates various components, such as software, medical devices, and processes, aimed at improvi1g patient care and supporting healthcare providers in making well-informed decisions. This fosters proactive healthcare management and enables timely interventions when needed. However, data transmission in these systems poses significant security threats during the transfer process, as malicious actors may attempt to breach security protocols.This jeopardizes the integrity of the Internet of Medical Things (IoMT) and ultimately endangers patient safety. Two feature sets—biometric and network flow metric—have been incorporated to enhance detection in healthcare systems. Another major challenge lies in the scarcity of publicly available balanced datasets for analyzing diverse IoMT attack patterns. To address this, the Auxiliary Classifier Generative Adversarial Network (ACGAN) was employed to generate synthetic samples that resemble minority class samples. ACGAN operates with two objectives: the discriminator differentiates between real and synthetic samples while also predicting the correct class labels. This dual functionality ensures that the discriminator learns detailed features for both tasks. Meanwhile, the generator produces high-quality samples that are classified as real by the discriminator and correctly labeled by the auxiliary classifier. The performance of this approach, evaluated using the IoMT dataset, consistently outperforms the existing baseline model across key metrics, including accuracy, precision, recall, F1-score, area under curve (AUC), and confusion matrix results. 

Physical Unclonable Functions (PUFs) are widely researched in the field of security because of their unique, robust, and reliable nature, PUFs are considered device-specific root keys that are hard to duplicate. There are many variants of PUFs that are being studied and implemented including hardware and software PUFs. Though PUFs are believed to be secure and reliable, they are not without challenges of their own. The efficient performance of PUF depends on various environmental factors, which leads to inefficiency. Bit flipping is one such problem that can bring down the reliability of the PUF. Memory-based PUFs are prone to unavoidable bit flips occurring in the hardware, similarly, sensor-based PUFs are prone to bit flips occurring due to temperature variation. The number of errors in the PUF response must be minimized to improve the reliability of the PUF in security applications. In this research we explore the Machine Learning (ML) model based on K-mer sequencing to detect and correct the bit flips in the PUFs, hence fortifying the PUF-based secure authentication system for authentication and authorization of Edge Data Centers (EDC) in a Collaborative Edge Computing (CEC) Environment. 

This paper discusses the in-situ characterization tools designed to assess radiation tolerance and elemental migration in perovskite materials. With the increasing use of perovskites in various technological applications, understanding their response to radiation exposure is paramount. Ion Beam Induced Charge (IBIC) emerges as a powerful tool for investigating the radiation tolerance of perovskites at the microscale. By employing focused ion beams, IBIC allows for the spatial mapping of charge carriers, offering insights into the material's electronic response to radiation-induced defects. This technique enables researchers to pinpoint areas of enhanced or suppressed charge collection, providing valuable information on the perovskite's intrinsic properties under irradiation. Rutherford Backscattering Spectrometry (RBS) complements the study by offering a quantitative analysis of elemental migration in perovskite materials. Through the precise measurement of backscattered ions, RBS provides a detailed understanding of the elemental composition and distribution within the perovskite lattice after radiation exposure. The integration of IBIC and RBS techniques in in-situ experiments enhances the comprehensive characterization of radiation effects on perovskites.