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Residual stresses (RS) arise in a wide range of manufacturing processes, including additive manufacturing, welding, forming, grinding, and machining. Accurate characterization and prediction of RS are crucial for optimizing functional performance and structural integrity, as tensile stresses reduce fatigue strength while compressive stresses enhance it. Traditional finite element methods provide detailed insights into RS distributions but are computationally expensive for real-time use. To overcome this limitation, we propose a Physics-Informed Neural Network (PINN) framework that embeds the Prandtl–Reuss constitutive equations for elastoplasticity directly into the loss function, enabling meshfree forward simulation of RS distribution and inverse identification of parameters under Hertzian contact loading. The inverse formulation simultaneously reconstructs stress fields and identifies key parameters—the effective friction coefficient and normalized load factor—from sparse data, addressing the nonuniqueness and instability of traditional inverse methods. Validation against high-fidelity Runge–Kutta–Gill reference solutions shows that residual stress prediction errors remain below 8% across a wide parameter range, while parameter identification errors converge to below 1%. The PINN predictions were compared with representative experimental trends for Ti–6Al–4V under burnishing and orthogonal cutting, confirming consistency across chip-generating and chipless processes. By enabling real-time parameter updates from minimal data, the proposed framework can accelerate the development of digital twins for manufacturing, supporting predictive modeling and process optimization. This advancement provides physics-based rapid RS analysis for critical applications, including bearing contacts and machining process optimization, significantly improving speed and usability over traditional approaches. 

Abstract Purpose of the ReviewSARS-CoV-2 undergoes genetic mutations like many other viruses. Some mutations lead to the emergence of new Variants of Concern (VOCs), affecting transmissibility, illness severity, and the effectiveness of antiviral drugs. Continuous monitoring and research are crucial to comprehend variant behavior and develop effective response strategies, including identifying mutations that may affect current drug therapies. Recent FindingsAntiviral therapies such as Nirmatrelvir and Ensitrelvir focus on inhibiting 3CLpro, whereas Remdesivir, Favipiravir, and Molnupiravir target nsp12, thereby reducing the viral load. However, the emergence of resistant mutations in 3CLpro and nsp12 could impact the efficiency of these small molecule drug therapeutics. SummaryThis manuscript summarizes mutations in 3CLpro and nsp12, which could potentially reduce the efficacy of drugs. Additionally, it encapsulates recent advancements in small molecule antivirals targeting SARS-CoV-2 viral proteins, including their potential for developing resistance against emerging variants. 

Abstract PurposeThe objective of this study was to develop a novel AI-ensembled network based on the most important features and affected brain regions to accurately classify and exhibit the pattern of progression of the stages of Cognitive Impairment (CI). MethodsWe proposed a novel ensembled architecture, 3D ResNet-18 - RF (Random Forest), and used this network to categorize the stages of Alzheimer’s disease (AD). The residual unit (blocks of ResNet) was introduced to the 3D Convolutional Neural network (CNN) to solve the degradation problem. It was considered an innovative strategy since the combination with fine-tuning resulted in higher accuracy. This network was trained on selected features and affected brain regions. The structured magnetic resonance images (MRI) were collected from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, and the random forest was used for determining the importance of the features and affected regions from the parcellated 170 regions of interest (ROIs) using Atlas, automated anatomical labeling 3(AAL-3). This framework classified five categories of AD and detected the progression pattern. ResultsThe proposed network showed promising results with a 66% F-1 score, 76% sensitivity, and 93.5% specificity, which outperformed the performance of conventional methods for categorizing five categories. Ventral Posterolateral and Pulvinar lateral regions were the regions most affected, indicating the progression from early MCI to AD. The five-fold validation accuracy for the developed model was 60.02%. ConclusionThe results showed that the gray matter to white matter ratio was the most significant feature, which also accurately predicted the progression pattern. The performance metrics fluctuated with different hyperparameters, but they never exceeded 0.05% of the estimated results, indicating the validity and originality of the suggested methodology. 

Machining processes involve various sources of uncertainty which lead to inaccurate interpretation of results in the surface integrity of machined products. This work presents a physics-informed, data-driven modeling framework for achieving comprehensive uncertainty quantification (UQ) of the impact of process and material variability on machining-induced residual stress (RS). Uncertainty due to the variation in bulk material properties and model input parameters in machining are considered. Preliminary results showed that variations in calibration parameters have a substantial effect on modeling RS, while the variation in material properties has a smaller effect. Further research directions for UQ in machining are also outlined. 

This survey paper provides an overview of the current state of Artificial Intelligence (AI) attacks and risks for AI security and privacy as artificial intelligence becomes more prevalent in various applications and services. The risks associated with AI attacks and security breaches are becoming increasingly apparent and cause many financial and social losses. This paper will categorize the different types of attacks on AI models, including adversarial attacks, model inversion attacks, poisoning attacks, data poisoning attacks, data extraction attacks, and membership inference attacks. The paper also emphasizes the importance of developing secure and robust AI models to ensure the privacy and security of sensitive data. Through a systematic literature review, this survey paper comprehensively analyzes the current state of AI attacks and risks for AI security and privacy and detection techniques. 

Shape Memory Alloy (SMA)-actuators are efficient, simple, and robust alternatives to conventional actuators when a small volume and/or large force and stroke are required. The analysis of their failure response is critical for their design in order to achieve optimum functionality and performance. Here, (i) the existing knowledge base on the fatigue and overload fracture response of SMAs under actuation loading is reviewed regarding the failure micromechanisms, empirical relations for actuation fatigue life prediction, experimental measurements of fracture toughness and fatigue crack growth rates, and numerical investigations of toughness properties and (ii) future developments required to expand the acquired knowledge, enhance the current understanding, and ultimately enable commercial applications of SMA-actuators are discussed.