A bio-inspired LIDA cognitive-based Digital Twin architecture for unmanned maintenance of machine tools

Affected by COVID-19, the maintenance process of machine tools is significantly hindered, while unmanned maintenance becomes an emerging trend in such background. So far, three challenges, namely, the dependence on maintenance experts, the dynamic maintenance environments, and unsynchronized interactions between physical and information sides, exist as the main obstacles in its widespread applications. In order to fill this gap, a bio-inspired LIDA cognitive-based Digital Twin architecture is proposed, so as to achieve unmanned maintenance of machine tools through a self-constructed, self-evaluated, and self-optimized manner. A three phases process in the architecture, including the physical phase, virtual phase, and service phase, is further introduced to support the cognitive cycle for unmanned maintenance of machine tools. An illustrative example is depicted in the unmanned fault diagnosis on the rolling bearing of a drilling platform, which validates the feasibility and advantages of the proposed architecture. As an explorative study, it is wished that this work provides useful insights for unmanned maintenance of machine tools in a dynamic production environment. © 2022

Boosting compromised SARS-CoV-2-specific immunity with mRNA vaccination in liver transplant recipients

Background and aims Liver transplant recipients (LTR) demonstrate a reduced response to COVID-19 mRNA vaccination, however, a detailed understanding of the interplay between humoral and cellular immunity especially after a third (and fourth) vaccine dose is lacking. Methods We longitudinally compared the humoral as well as CD4+ and CD8+ T cell response between LTR (n=24) and healthy controls (HC, n=19) after three (LTR: n=9 to 16;HC: n=9 to 14 per experiment) to four (LTR: n=4;HC: n=4) vaccine doses, including in-depth phenotypical and functional characterization. Results Compared to HC, development of high antibody titers required a third vaccine dose in most LTR, while spike-specific CD8+ T cells with robust recall capacity plateaued after the second vaccine dose, albeit with a reduced frequency and epitope repertoire compared to HC. This overall attenuated vaccine response was linked to a reduced spike-reactive TFH cell frequency in LTR. Conclusion Three doses of a COVID-19 mRNA vaccine induce an overall robust humoral and cellular memory response in most LTR. Decisions regarding additional booster doses may thus be based on individual vaccine responses as well as evolution of novel variants of concern (VOC). Impact and implications Due to immunosuppressive medication, liver transplant recipients (LTR) display reduced antibody titers upon COVID-19 mRNA vaccination, but the impact on long-term immune memory is not clear. We here demonstrate that after three vaccine doses, the majority of LTR has not only substantial antibody titers, but also a robust memory T cell response. Additional booster vaccine doses may be of special benefit for a small subset of LTR with inferior vaccine response and may provide superior protection against evolving novel viral variants. These findings will help physicians to guide LTR regarding the benefit of booster vaccinations.

A Hybrid Model for Coronavirus Disease 2019 Forecasting Based on Ensemble Empirical Mode Decomposition and Deep Learning

Background: The novel coronavirus pneumonia that began to spread in 2019 is still raging and has placed a burden on medical systems and governments in various countries. For policymaking and medical resource decisions, a good prediction model is necessary to monitor and evaluate the trends of the epidemic. We used a long short-term memory (LSTM) model and the improved hybrid model based on ensemble empirical mode decomposition (EEMD) to predict COVID-19 trends;Methods: The data were collected from the Harvard Dataverse. Epidemic data from 21 January 2020 to 25 April 2021 for California, the most severely affected state in the United States, were used to develop an LSTM model and an EEMD-LSTM hybrid model, which is an LSTM model combined with ensemble empirical mode decomposition. In this study, ninety percent of the data were adopted to fit the models as a training set, while the subsequent 10% were used to test the prediction effect of the models. The mean absolute percentage error, mean absolute error, and root mean square error were used to evaluate the prediction performances of the models;Results: The results indicated that the number of confirmed cases in California was increasing as of 25 April 2021, with no obvious evidence of a sharp decline. On 25 April 2021, the LSTM model predicted 3666418 confirmed cases, whereas the EEMD-LSTM predicted 3681150. The mean absolute percentage errors for the LSTM and EEMD-LSTM models were 0.0151 and 0.0051, respectively. The mean absolute and root mean square errors were 5.58 × 104 and 5.63 × 104 for the LSTM model and 1.9 × 104 and 2.43 × 104 for the EEMD-LSTM model, respectively;Conclusions: The results showed the advantage of an EEMD-LSTM model over a single LSTM model, and the established EEMD-LSTM model may be suitable for monitoring and evaluating the epidemic situation and providing quantitative analysis evidence for epidemic prevention and control. © 2022 by the authors.

T HELPER CELL SUBSETS AND RELATED TARGET CELLS IN ACUTE COVID-19

Current review presents a brief overview of the immune system dysregulation during acute COVID-19 and illustrates the main alterations in peripheral blood CD4+ T-cell (Th) subsets as well as related target cells. Effects of dendritic cell dysfunction induced by SARS-CoV-2 exhibited decreased expression of cell-surface HLA-DR, CCR7 as well as co-stimulatory molecules CD80 and CD86, suggesting reduced antigen presentation, migratory and activation capacities of peripheral blood dendritic cells. SARS-CoV-2-specific Th cells could be detected as early as days 2–4 post-symptom onset, whereas the prolonged lack of SARS-CoV-2-specific Th cells was associated with severe and/or poor COVID-19 outcome. Firstly, in acute COVID-19 the frequency of Th1 cell was comparable with control levels, but several studies have reported about upregulated inhibitory immune checkpoint receptors and exhaustion-associated molecules (TIM3, PD-1, BTLA, TIGIT etc.) on circulating CD8+ T-cells and NK-cells, whereas the macrophage count was increased in bronchoalveolar lavage (BAL) samples. Next, type 2 immune responses are mediated mainly by Th2 cells, and several studies have revealed a skewing towards dominance of Th2 cell subset in peripheral blood samples from patients with acute COVID-19. Furthermore, the decrease of circulating main Th2 target cells — basophiles and eosinophils — were associated with severe COVID-19, whereas the lung tissue was enriched with mast cells and relevant mediators released during degranulation. Moreover, the frequency of peripheral blood Th17 cells was closely linked to COVID-19 severity, so that low level of Th17 cells was observed in patients with severe COVID-19, but in BAL the relative number of Th17 cells as well as the concentrations of relevant effector cytokines were dramatically increased. It was shown that severe COVID-19 patients vs. healthy control had higher relative numbers of neutrophils if compared, and the majority of patients with COVID-19 had increased frequency and absolute number of immature neutrophils with altered ROS production. Finally, the frequency of Tfh cells was decreased during acute COVID-19 infection. Elevated count of activated Tfh were found as well as the alterations in Tfh cell subsets characterized by decreased "regulatory” Tfh1 cell and increased "pro-inflammatory” Tfh2 as well as Tfh17 cell subsets were revealed. Descriptions of peripheral blood B cells during an acute SARS-CoV-2 infection werev reported as relative B cell lymphopenia with decreased frequency of "naïve” and memory B cell subsets, as well as increased level of CD27hiCD38hiCD24– plasma cell precursors and atypical CD21low B cells. Thus, the emerging evidence suggests that functional alterations occur in all Th cell subsets being linked with loss-of-functions of main Th cell subsets target cells. Furthermore, recovered individuals could suffer from long-term immune dysregulation and other persistent symptoms lasting for many months even after SARS-CoV-2 elimination, a condition referred to as post-acute COVID-19 syndrome.

Effector memory T cell subset CD45RA−CCR7−CD27−CD28− EM3 increases in direct proportion to the disease severity of COVID-19

COVID-19, which emerged in December 2019 and continues to wreak havoc, has led to the death of many people around the world. In this study, we aimed to uncover the variables underlying the exacerbation of the disease by considering the changes in T cell subsets in adults and juveniles with different disease severity of COVID-19. Peripheral blood samples of 193 patients (128 adults and 65 juveniles) diagnosed with COVID-19 were evaluated in a flow cytometer, and a broad T cell profile was revealed by examining T cell subsets in terms of exhaustion and senescence. We found remarkable differences in the effector memory (EM;CD45RA−CCR7−) cell subsets of severe pneumonia cases. The frequencies of EM2 CD4+ T, EM3 CD4+ T, EM3 CD8+ T, EM2 DN T and EM3 DN T cells were found to increase in severe pneumonia cases. Consistently, these cells were found in juveniles and uncomplicated adults in similar or lower proportions to healthy controls. The findings of our study provide a view of the T cell profile that may underlie differences in the course of COVID-19 cases in juveniles and adults and may provide new insights into the development of effective treatment strategies. © 2022 The Scandinavian Foundation for Immunology.

Alpha Fusion Adversarial Attack Analysis Using Deep Learning

The deep learning model encompasses a powerful learning ability that integrates the feature extraction, and classification method to improve accuracy. Convolutional Neural Networks (CNN) perform well in machine learning and image processing tasks like segmentation, classification, detection, identification, etc. The CNN models are still sensitive to noise and attack. The smallest change in training images as in an adversarial attack can greatly decrease the accuracy of the CNN model. This paper presents an alpha fusion attack analysis and generates defense against adversarial attacks. The proposed work is divided into three phases: firstly, an MLSTM-based CNN classification model is developed for classifying COVID-CT images. Secondly, an alpha fusion attack is generated to fool the classification model. The alpha fusion attack is tested in the last phase on a modified LSTM-based CNN (CNN-MLSTM) model and other pre-trained models. The results of CNN models show that the accuracy of these models dropped greatly after the alpha-fusion attack. The highest F1 score before the attack was achieved is 97.45 And after the attack lowest F1 score recorded is 22%. Results elucidate the performance in terms of accuracy, precision, F1 score and Recall. © 2023 CRL Publishing. All rights reserved.

Antigen specific T cells in people with obesity at five months following ChAdOx1 COVID-19 vaccination

Background/Objectives: People with obesity (PWO) face an increased risk of severe outcomes from COVID-19, including hospitalisation, ICU admission and death. Obesity has been seen to impair immune memory following vaccination against influenza, hepatitis B, tetanus, and rabies. Little is known regarding immune memory in PWO following COVID-19 adenovirus vector vaccination. Subjects/Methods: We investigated SARS-CoV-2 specific T cell responses in 50 subjects, five months following a two-dose primary course of ChAdOx1 nCoV-19 (AZD1222) vaccination. We further divided our cohort into PWO (n = 30) and matched controls (n = 20). T cell (CD4+, CD8+) cytokine responses (IFNγ, TNFα) to SARS-CoV-2 spike peptide pools were determined using multicolour flow cytometry. Results: Circulating T cells specific for SARS-CoV-2 were readily detected across our cohort, with robust responses to spike peptide stimulation across both T cell lines. PWO and controls had comparable levels of both CD4+ and CD8+ SARS-CoV-2 spike specific T cells. Polyfunctional T cells – associated with enhanced protection against viral infection – were detected at similar frequencies in both PWO and controls. Conclusions: These data indicate that PWO who have completed a primary course of ChAdOx1 COVID-19 vaccination have robust, durable, and functional antigen specific T cell immunity that is comparable to that seen in people without obesity. © 2022, The Author(s), under exclusive licence to Springer Nature Limited.

Hybrid optimization enabled deep learning and spark architecture using big data analytics for stock market forecasting

The precise forecasting of stock prices is not possible because of the complexity and uncertainty of stock. The effectual model is needed for the triumphant assessment of upcoming stock prices for several companies. Here, an optimized deep model is utilized to effectively predict the stock market using the spark framework. Here, the data partitioning is done using deep embedded clustering, wherein the tuning of parameters is done using the proposed Jaya Anti Coronavirus Optimization (JACO) algorithm in the master node. The proposed JACO is developed by combining Jaya Algorithm and Anti-Coronavirus Optimization algorithm. Then, important technical indicators are mined from divided data in slave nodes. Here, the technical indicators are considered features for enhanced processing. Then, data augmentation is done to make data suitable for processing in the master node. At last, the prediction was done in the master node using deep long short-term memory (Deep LSTM), and training is performed with the proposed JACO. The proposed JACO-based Deep LSTM attains the smallest mean absolute error of 0.113, mean squared error of 0.095, and root mean squared error of 0.309.

Optimal gene prioritization and disease prediction using knowledge based ontology structure

Prioritizing candidate genes is essential for genome-based diagnostics of various hereditary disorders. Furthermore, it is a difficult task with particular and noisy information about genes, illnesses, and relationships. Although several computer methods for disease gene prioritization have been developed, their efficiency is limited by manually created traits, network architecture, or pre-established data fusion criteria. Hence, this research proposes a unique gene prioritization and disease prediction model. Initially, the gathered information is pre-processed by a data cleaning model. In the proposed gene prioritization phase, the pre-processed data are tokenized. Then a new knowledge-based ontology structure is constructed with the improved skewness-based semantic similarity function. The ensemble classifier is constructed along Recurrent Neural Network (RNN), optimized fuzzy logic, and also Deep Belief Network (DBN) to forecast the gene disorders in the prediction phase. The retrieved features from the feature extraction phase are used to train RNN;while the extracted knowledge bases are used to train the DBN, then the results are fed into the optimized fuzzy logic. The fuzzy logic is the primary indication;its fuzzification function is fine-tuned employing a methodology to improve illness prediction accuracy. A recommended new hybrid system, named as Cauchy's Mutated Corona Virus Optimization Algorithm (CMCOA), is the upgraded version of the CVOA, a typical coronavirus optimization technique. Finally, to evaluate the efficiency of the projected model, a comparison of the suggested and existent models is performed with respect to various measures. In particular, the proposed model has recorded the highest accuracy as 93 % at 60 % of training, which is 42.5 %, 36.1 %, 33.3 %, 41.1 %, 48.5 %, 48.5 %, 9 %, 8 %, 8 %, 8 %, 8 %, and 14.5 % improved over existing models like GCN, GCN [6], SVM, CNN, Bi-LSTM, LSTM, GRU, fuzzy, EC + GOA, EC + SSO, EC + CMBO, EC + SMA and EC + CCVOA, respectively. The precision of the suggested work with improved features &CMCOA is 15.5 %, and 14.42 % superior to the proposed work without existing features & CMCOA and proposed work with existing features & CMCOA approaches. © 2022 Elsevier Ltd

Forecasting domestic waste generation during successive COVID-19 lockdowns by Bidirectional LSTM super learner neural network

Accurate prediction of domestic waste generation is a challenging task for municipalities to implement sustainable waste management strategies. In the present study, domestic waste generation in the Kingdom of Bahrain, representing a Small Island Developing State (SIDS) case study, has been investigated during successive COVID-19 lockdowns due to the pandemic in 2020. Temporal trends of daily domestic waste generation between 2019 and 2020 and their statistical analyses exhibited remarkable variations highlighting the impact of consecutive COVID-19 lockdowns on domestic waste generation. Machine learning has great potential for predicting solid waste generation rates, but only a few studies utilized deep learning approaches. The state-of-the-art Bidirectional Long Short-Term Memory (BiLSTM) network model as a deep learning method is applied to forecast daily domestic waste data in 2020. Bayesian optimization algorithm (BOA) was hybridized with BiLSTM to generate a super learner approach. The performance of the BOA-BiLSTM super learner model was further compared with the statistical ARIMA model. Performance indicators of the developed models using ARIMA and BiLSTM showed that the latter yielded superior performance for short-term forecasts of domestic waste generation. The MAE, RMSE, MAPE, and R2 were 47.38, 60.73, 256.43, and 0.46, respectively, for the ARIMA model, compared to 3.67, 12.57, 0.24, and 0.96, respectively, for the BiLSTM model. Additionally, the relative errors for the BiLSTM model were lower than those of the ARIMA model. This study highlights that the BiLSTM can be a reliable forecasting tool for solid waste management policymakers during public health emergencies. © 2022 Elsevier B.V.

Students' performance in teaching neuroanatomy using traditional and technology-based methods

The use of digital teaching resources became widespread and very helpful during the COVID-19 pandemic as an alternative to a traditional course with cadavers. Technologies such as augmented reality (AR), virtual reality (VR), 3D models, video lectures and other online resources enable three-dimensional visualization of the anatomical structures and allow students to learn more interactively. The aim of this study was to compare students' performance in the traditional anatomical courses in teaching neuroanatomy and technology-based learning methods such as video lectures, 3D models and 3D printed specimens. Four groups of first-year students of Veterinary Faculty established for the practical classes during the academic year 2021/2022 took part in this research. The total number of students participating in this research was 72. Each group attended separately the theoretical lecture with a demonstration based on a different technique;the control group used formalized specimens, while the three experimental groups used video lectures, 3D models and 3D printed specimens, respectively. Subsequently, all groups completed the same questionnaire testing their short-term memory of the neuroanatomical structures. After four weeks students were tested for their long-term memory of the neuroanatomy lecture with the follow-up test containing an identical list of questions. The test scores using video lectures and 3D printed models were significantly higher compared with the group that learned in the traditional way. This study suggests that alternative approaches such as technology-based digital methods can facilitate memorization of anatomical terms and structures in a more interactive and sensory engaging way of learning. © 2022 Wiley-VCH GmbH.

Indoor Spatiotemporal Contact Analytics Using Landmark-Aided Pedestrian Dead Reckoning on Smartphones

Due to the prevalence of COVID-19, providing safe environments and reducing the risks of virus exposure play pivotal roles in our daily lives. Contact tracing is a well-established and widely-used approach to track and suppress the spread of viruses. Most digital contact tracing systems can detect direct face-to-face contact based on estimated proximity, without quantifying the exposed virus concentration. In particular, they rarely allow for quantitative analysis of indirect environmental exposure due to virus survival time in the air and constant airborne transmission. In this work, we propose an indoor spatiotemporal contact awareness framework (iSTCA), which explicitly considers the self-containing quantitative contact analytics approach with spatiotemporal information to provide accurate awareness of the virus quanta concentration in different origins at various times. Smartphone-based pedestrian dead reckoning (PDR) is employed to precisely detect the locations and trajectories for distance estimation and time assessment without the need to deploy extra infrastructure. The PDR technique we employ calibrates the accumulative error by identifying spatial landmarks automatically. We utilized a custom deep learning model composed of bidirectional long short-term memory (Bi-LSTM) and multi-head convolutional neural networks (CNNs) for extracting the local correlation and long-term dependency to recognize landmarks. By considering the spatial distance and time difference in an integrated manner, we can quantify the virus quanta concentration of the entire indoor environment at any time with all contributed virus particles. We conducted an extensive experiment based on practical scenarios to evaluate the performance of the proposed system, showing that the average positioning error is reduced to less than 0.7 m with high confidence and demonstrating the validity of our system for the virus quanta concentration quantification involving virus movement in a complex indoor environment. © 2022 by the authors.

Artificial Intelligence against COVID-19 Pandemic: A Comprehensive Insight

COVID-19 is a pandemic initially identified in Wuhan, China, which is caused by a novel coronavirus, also recognized as the Severe Acute Respiratory Syndrome (SARS-nCoV-2). Un-like other coronaviruses, this novel pathogen may cause unusual contagious pain, which results in viral pneumonia, serious heart problems, and even death. Researchers worldwide are continuously striving to develop a cure for this highly infectious disease, yet there are no well-defined absolute treatments available at present. Several vaccination drives using emergency use authorisation vaccines have been held across many countries;however, their long-term efficacy and side-effects studies are yet to be studied. Various analytical and statistical models have been developed, howev-er, their outcome rate is prolonged. Thus, modern science stresses the application of state-of-the-art methods to combat COVID-19. This paper aims to provide a deep insight into the comprehensive literature about AI and AI-driven tools in the battle against the COVID-19 pandemic. The high efficacy of these AI systems can be observed in terms of highly accurate results, i.e., > 95%, as report-ed in various studies. The extensive literature reviewed in this paper is divided into five sections, each describing the application of AI against COVID-19 viz. COVID-19 prevention, diagnostic, infection spread trend prediction, therapeutic and drug repurposing. The application of Artificial Intelligence (AI) and AI-driven tools are proving to be useful in managing and fighting against the COVID-19 pandemic, especially by analysing the X-Ray and CT-Scan imaging data of infected subjects, infection trend predictions, etc. © 2023 Bentham Science Publishers.

Prediction and assessment of the impact of COVID-19 lockdown on air quality over Kolkata: a deep transfer learning approach

The present study focuses on the prediction and assessment of the impact of lockdown because of coronavirus pandemic on the air quality during three different phases, viz., normal periods (1 January 2018–23 March 2020), complete lockdown (24 March 2020–31 May 2020), and partial lockdown (1 June 2020–30 September 2020). We identify the most important air pollutants influencing the air quality of Kolkata during three different periods using Random Forest, a tree-based machine learning (ML) algorithm. It is found that the ambient air quality of Kolkata is mainly affected with the aid of particulate matter or PM (PM10 and PM2.5). However, the effect of the lockdown is most prominent on PM2.5 which spreads in the air of Kolkata due to diesel-driven vehicles, domestic and commercial combustion activities, road dust, and open burning. To predict urban PM2.5 and PM10 concentrations 24 h in advance, we use a deep learning (DL) model, namely, stacked-bidirectional long short-term memory (stacked-BDLSTM). The model is trained during the normal periods, and it shows the superiority over some supervised ML models, like support vector machine, K-nearest neighbor classifier, multilayer perceptron, long short-term memory, and statistical time series forecasting model autoregressive integrated moving average. This pre-trained stacked-BDLSTM is applied to predict the concentrations of PM2.5 and PM10 during the pandemic situation of two cases, viz., complete lockdown and partial lockdown using a deep model-based transfer learning (TL) approach (TLS-BDLSTM). Transfer learning aims to utilize the information gained from one problem to improve the predictive performance of a learning model for a different but related problem. Our work helps to demonstrate how TL is useful when there is a scarcity of data during the COVID-19 pandemic regarding the drastic change in concentration of pollutants. The results reveal the best prediction performance of TLS-BDLSTM with a lead time of 24 h as compared to some well-known traditional ML and statistical models and the pre-trained stacked-BDLSTM. The prediction is then validated using the real-time data obtained during the complete lockdown due to COVID second wave (16 May–15 June 2021) with different time steps, e.g., 24 h, 48 h, 72 h, and 96–120 h. TLS-BDLSTM involving transfer learning is seen to outperform the said comparing methods in modeling the long-term temporal dependency of multivariate time series data and boost the forecast efficiency not only in single step, but also in multiple steps. The proposed methodologies are effective, consistent, and can be used by operational organizations to utilize in monitoring and management of air quality. © 2022, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

Efficiency dynamics across segmented Bitcoin Markets: Evidence from a decomposition strategy

Heterogeneity in informational inefficiency in a cross-market virtual currency, such as Bitcoin, allows for the extraction of differential gains from a portfolio of investments over time. In this paper, we measure inefficiency in five country/region segmented Bitcoin markets based on dynamic estimation of the fractional integration order of their price series. Results reveal a time-varying and country-specific pattern of inefficiency in the five Bitcoin markets, although the degree of inefficiency in each market has declined over time. Further, we introduce a new decomposition method to disentangle components of the inefficiency degree. Results suggest that the total variation around the convergence benchmark has fallen, whilst the proportion due to the difference between convergence and efficiency has risen from approximately 77% in 2013 to almost 100% in 2020. Besides, evidence of convergence emerges until the outbreak of COVID-19, beyond which the inefficiency degree diverges measurably. We show that Bitcoin markets have become more efficient after the first-wave COVID era and then the nature of market segmentation has played a less important role, levelling the cross-market difference and thus reducing the potential for arbitrage. © 2023 Elsevier B.V.

The impact of Covid-19 restrictions in Ireland on symptom severity in mild to moderate Parkinson's disease

Introduction: The COVID pandemic and public health restrictions significantly impacted those living with neurological conditions such as Parkinson's Disease due to the curtailment of therapies. Patients attending a single centre movement disorders clinic reported reduced physical activity and quality of life during the pandemic. This study aimed to assess the impact of pandemic restrictions on Parkinson's Disease symptom severity in people with mild to moderate Parkinson's Disease. Method: A cross-sectional study design with a convenience sample of 20 people living with mild to moderate Parkinson's Disease was adopted. A telephone survey questionnaire was completed to measure changes in symptom severity on the 14 most common Parkinson's Disease symptoms. Data were analysed using descriptive statistics. Results: Nineteen participants completed the survey. Participants frequently reported a decline in nine symptoms of Parkinson's Disease;bradykinesia, rigidity, walking, sleep, mood, memory, quality of life and fatigue. Nil changes in freezing were reported. No change was reported in the nonmotor symptoms of constipation, speech and pain in 75, 65 and 95% of participants, respectively. Conclusion: Findings of this study acknowledge the negative impact of restrictions on the motor and nonmotor symptoms of Parkinson's Disease. Flexibility to access and delivery of service should be considered to mitigate any future potential restrictions.

Individual Surveillance Around Parked Aircraft at Nighttime: Thermal Infrared Vision-Based Human Action Recognition

The COVID-19 crisis has led to an unusually large number of commercial aircraft being currently parked or stored. For airlines, airports, and civil aviation authorities around the world, monitoring, and protecting these parked aircraft to prevent them from causing human-made damage are becoming urgent problems that are receiving increasing attention. In this study, we use thermal infrared monitoring videos to establish a framework for individual surveillance around parked aircraft by proposing a human action recognition (HAR) algorithm. As the focus of this article, the proposed HAR algorithm seamlessly integrates a preprocessing module in which a novel data structure is constructed to introduce spatiotemporal information of the action;a convolutional neural network-based module for spatial feature extraction;a triple-layer convolutional long short-term memory network for temporal feature extraction;and two fully connected layers for classification. Moreover, because no infrared dataset is available for the HAR task on airport grounds at nighttime, we present a dataset called IIAR-30, which consists of eight action categories that frequently occur on airport grounds and 2000 video clips. The experimental results on the IIAR-30 dataset demonstrated that the recognition accuracy of the proposed method was higher than 96%. We also further evaluated the effectiveness of the proposed method by comparing it with five baselines and four other methods. © 2022 IEEE.

Monitoring cognitive and psychological alterations in COVID-19 patients: A longitudinal neuropsychological study

Background: SARS-COV-2 infection has been associated to long-lasting neuropsychiatric sequelae, including cognitive deficits, that persist after one year. However, longitudinal monitoring has been scarcely performed. Here, in a sample of COVID-19 patients, we monitor cognitive, psychological and quality of life-related profiles up to 22 months from resolution of respiratory disease. Methods: Out of 657 COVID-19 patients screened at Manzoni Hospital (Lecco, Italy), 22 underwent neuropsychological testing because of subjective cognitive disturbances at 6 months, 16 months, and 22 months. Tests of memory, attention, and executive functions were administered, along with questionnaires for depressive and Post-traumatic stress disorder (PTSD) symptoms, psychological well-being and quality of life. Cross-sectional descriptives, correlational, as well as longitudinal analyses considering COVID19-severity were carried out. A preliminary comparison with a sample of obstructive sleep apneas patients was also performed. Results: Around 50% of COVID-19 patients presented with cognitive deficits at t0. The most affected domain was verbal memory. Pathological scores diminished over time, but a high rate of borderline scores was still observable. Longitudinal analyses highlighted improvements in verbal and non-verbal long term memory, as well as attention, and executive functioning. Depression and PTSD-related symptoms were present in 30% of patients. The latter decreased over time and were associated to attentional-executive performance. Conclusions: Cognitive dysfunctions in COVID-19 patients may extend over 1 year, yet showing a significant recovery in several cases. Cognitive alterations are accompanied by a significant psychological distress. Many patients displaying borderline scores, especially those at higher risk of dementia, deserve clinical monitoring. © 2022

Global emerging Omicron variant of SARS-CoV-2: Impacts, challenges and strategies

Newly emerging variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are continuously posing high global public health concerns and panic resulting in waves of coronavirus disease 2019 (COVID-19) pandemic. Depending on the extent of genomic variations, mutations and adaptation, few of the variants gain the ability to spread quickly across many countries, acquire higher virulency and ability to cause severe disease, morbidity and mortality. These variants have been implicated in lessening the efficacy of the current COVID-19 vaccines and immunotherapies resulting in break-through viral infections in vaccinated individuals and recovered patients. Altogether, these could hinder the protective herd immunity to be achieved through the ongoing progressive COVID-19 vaccination. Currently, the only variant of interest of SARS-CoV-2 is Omicron that was first identified in South Africa. In this review, we present the overview on the emerging SARS-CoV-2 variants with a special focus on the Omicron variant, its lineages and hybrid variants. We discuss the hypotheses of the origin, genetic change and underlying molecular mechanism behind higher transmissibility and immune escape of Omicron variant. Major concerns related to Omicron including the efficacy of the current available immunotherapeutics and vaccines, transmissibility, disease severity, and mortality are discussed. In the last part, challenges and strategies to counter Omicron variant, its lineages and hybrid variants amid the ongoing COVID-19 pandemic are presented. © 2022 The Author(s)