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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. 

Abstract A steady supply of hosts at the susceptible stage for parasitism is a major component of mass rearing parasitoids for biological control programs. Here we describe the effects of storing 5th instar Plodia interpunctella larvae in dormancy on subsequent host development in the context of host colony maintenance and effects of the duration of host dormancy on the development of Habrobracon hebetor parasitoids reared from dormant hosts. We induced dormancy with a combination of short daylength (12L:12D) and lower temperature (15°C), conditions known to induce diapause in this species, and held 5th instar larvae of P. interpunctella for a series of dormancy durations ranging from 15 to 105 days. Extended storage of dormant 5th instar larvae had no significant impacts on survival, development, or reproductive potential of P. interpunctella , reinforcing that dormant hosts have a substantial shelf life. This ability to store hosts in dormancy for more than 3 months at a time without strong negative consequences reinforces the promise of using dormancy to maintain host colonies. The proportion of hosts parasitized by H. hebetor did not vary significantly between non-dormant host larvae and dormant host larvae stored for periods as long as 105 days. Concordant with a prior study, H. hebetor adult progeny production from dormant host larvae was higher than the number of progeny produced on non-dormant host larvae. There were no differences in size, sex ratio, or reproductive output of parasitoids reared on dormant hosts compared to non-dormant hosts stored for up to 105 days. Larval development times of H. hebetor were however longer when reared on dormant hosts compared to non-dormant hosts. Our results agree with other studies showing using dormant hosts can improve parasitoid mass rearing, and we show benefits for parasitoid rearing even after 3 months of host dormancy. 

Breathing biomarkers, such as breathing rate, fractional inspiratory time, and inhalation-exhalation ratio, are vital for monitoring the user's health and well-being. Accurate estimation of such biomarkers requires breathing phase detection, i.e., inhalation and exhalation. However, traditional breathing phase monitoring relies on uncomfortable equipment, e.g., chestbands. Smartphone acoustic sensors have shown promising results for passive breathing monitoring during sleep or guided breathing. However, detecting breathing phases using acoustic data can be challenging for various reasons. One of the major obstacles is the complexity of annotating breathing sounds due to inaudible parts in regular breathing and background noises. This paper assesses the potential of using smartphone acoustic sensors for passive unguided breathing phase monitoring in a natural environment. We address the annotation challenges by developing a novel variant of the teacher-student training method for transferring knowledge from an inertial sensor to an acoustic sensor, eliminating the need for manual breathing sound annotation by fusing signal processing with deep learning techniques. We train and evaluate our model on the breathing data collected from 131 subjects, including healthy individuals and respiratory patients. Experimental results show that our model can detect breathing phases with 77.33% accuracy using acoustic sensors. We further present an example use-case of breathing phase-detection by first estimating the biomarkers from the estimated breathing phases and then using these biomarkers for pulmonary patient detection. Using the detected breathing phases, we can estimate fractional inspiratory time with 92.08% accuracy, the inhalation-exhalation ratio with 86.76% accuracy, and the breathing rate with 91.74% accuracy. Moreover, we can distinguish respiratory patients from healthy individuals with up to 76% accuracy. This paper is the first to show the feasibility of detecting regular breathing phases towards passively monitoring respiratory health and well-being using acoustic data captured by a smartphone.