Civil Aviation Customer Satisfaction Automatic Evaluation System

In 2021, the airline industry was affected by COVID-19, and many airlines suffered losses. The main reason for the loss were the decline in revenue and the surge in costs. Therefore, in terms of creating the competitive advantage of airlines, "price war" is no longer applicable, and improving service quality has become an effective means. Customer satisfaction is the most effective indicator to measure service quality. In this study, a satisfaction evaluation system is established based on structural equation model and customer satisfaction importance matrix. Then, a questionnaire is designed to analyze the influence of different factors on customer satisfaction. The research finds that brand image and perceived quality have a great impact on customer satisfaction. In addition, some suggestions for airlines to improve customer satisfaction are given. © 2023 SPIE.

Detection of Multiple Structural Breaks in Large Covariance Matrices

This article studies multiple structural breaks in large contemporaneous covariance matrices of high-dimensional time series satisfying an approximate factor model. The breaks in the second-order moment structure of the common components are due to sudden changes in either factor loadings or covariance of latent factors, requiring appropriate transformation of the factor models to facilitate estimation of the (transformed) common factors and factor loadings via the classical principal component analysis. With the estimated factors and idiosyncratic errors, an easy-to-implement CUSUM-based detection technique is introduced to consistently estimate the location and number of breaks and correctly identify whether they originate in the common or idiosyncratic error components. The algorithms of Wild Binary Segmentation for Covariance (WBS-Cov) and Wild Sparsified Binary Segmentation for Covariance (WSBS-Cov) are used to estimate breaks in the common and idiosyncratic error components, respectively. Under some technical conditions, the asymptotic properties of the proposed methodology are derived with near-optimal rates (up to a logarithmic factor) achieved for the estimated breaks. Monte Carlo simulation studies are conducted to examine the finite-sample performance of the developed method and its comparison with other existing approaches. We finally apply our method to study the contemporaneous covariance structure of daily returns of S&P 500 constituents and identify a few breaks including those occurring during the 2007–2008 financial crisis and the recent coronavirus (COVID-19) outbreak. An package "” is provided to implement the proposed algorithms.

Nonstationary Gaussian Process Discriminant Analysis With Variable Selection for High-Dimensional Functional Data

High-dimensional classification and feature selection tasks are ubiquitous with the recent advancement in data acquisition technology. In several application areas such as biology, genomics, and proteomics, the data are often functional in their nature and exhibit a degree of roughness and nonstationarity. These structures pose additional challenges to commonly used methods that rely mainly on a two-stage approach performing variable selection and classification separately. We propose in this work a novel Gaussian process discriminant analysis (GPDA) that combines these steps in a unified framework. Our model is a two-layer nonstationary Gaussian process coupled with an Ising prior to identify differentially-distributed locations. Scalable inference is achieved via developing a variational scheme that exploits advances in the use of sparse inverse covariance matrices. We demonstrate the performance of our methodology on simulated datasets and two proteomics datasets: breast cancer and SARS-CoV-2. Our approach distinguishes itself by offering explainability as well as uncertainty quantification in addition to low computational cost, which are crucial to increase trust and social acceptance of data-driven tools. Supplementary materials for this article are available online.

GPU libraries speed performance analysis for RCWA simulation matrix operations

Rigorous Coupled Wave Analysis (RCWA) method is highly efficient for the simulation of diffraction efficiency and field distribution patterns in periodic structures and textured optoelectronic devices. GPU has been increasingly used in complex scientific problems such as climate simulation and the latest Covid-19 spread model. In this paper, we break down the RCWA simulation problem to key computational steps (eigensystem solution, matrix inversion/multiplication) and investigate speed performance provided by optimized linear algebra GPU libraries in comparison to multithreaded Intel MKL CPU library running on IRIDIS 5 supercomputer (1 NVIDIA v100 GPU and 40 Intel Xeon Gold 6138 cores CPU). Our work shows that GPU outperforms CPU significantly for all required steps. Eigensystem solution becomes 60% faster, Matrix inversion improves with size achieving 8x faster for large matrixes. Most significantly, matrix multiplication becomes 40x faster for small and 5x faster for large matrix sizes. © 2023 SPIE.

Potential predictors of severe course and outcome of community-acquired pneumonia.

Severe pneumonia is a condition with a high risk of death and mandatory hospitalization in the intensive care unit. The incidence of severe pneumonia has increased dramatically during the pandemic of new coronavirus infection. Timely diagnosis and early initiation of adequate treatment of severe pneumonia are crucial for improving survival of critically ill patients. The aim of this review was to analyze published scientific research on molecular markers that allow to objectively assess the severity of pneumonia and to determine treatment tactics based on the predicted outcome upon admission to the hospital. A systematic search was conducted in the electronic databases PubMed, Medline, Web of Science for the period 2019 - 2022. Conclusion. The review focuses on the prognostic role of a number of markers of immune response, vascular transformation, as well as angiotensin II and angiotensin converting enzyme-2. Further prospective studies of potential predictors of severe pneumonia will enable using marker molecules in a comprehensive clinical and laboratory diagnosis for early prediction of the hospitalized patient's condition and expected outcome.Copyright © Volchkova E.V. et al., 2023.

COVID-19 Detection by Using Handcrafted Features Extracted From Chest CT-Scan Images

Coronavirus illness, which was initially diagnosed in 2019 but has propagated rapidly across the globe, has led to increased fatalities. According to professional physicians who examined chest CT scans, COVID-19 behaves differently than various viral cases of pneumonia. Even though the illness only recently emerged, a number of research investigations have been performed wherein the progression of the disease impacts mostly on the lungs are identified using thoracic CT scans. In this work, automated identification of COVID-19 is used by using machine learning classifier trained on more than 1000+ lung CT Scan images. As a result, immediate diagnosis of COVID-19, which is very much necessary in the opinion of healthcare specialists, is feasible. To improve detection accuracy, the feature extraction method are applied on regions of interests. Feature extraction approaches, including Discrete Wavelet Transform (DWT), Grey Level Cooccurrence Matrix (GLCM), Grey Level Run Length Matrix (GLRLM), and Grey-Level Size Zone Matrix (GLSZM) algorithms are used. Then the classification by using Support Vector Machines (SVM) is used. The classification accuracy is assessed by using precision, specificity, accuracy, sensitivity and F-score measures. Among all feature extraction methods, the GLCM approach has given the optimum classification accuracy of 95.6%. . © 2023 IEEE.

Multivariate Conway-Maxwell-Poisson Distribution: Sarmanov Method and Doubly Intractable Bayesian Inference

In this article, a multivariate count distribution with Conway-Maxwell (COM)-Poisson marginals is proposed. To do this, we develop a modification of the Sarmanov method for constructing multivariate distributions. Our multivariate COM-Poisson (MultCOMP) model has desirable features such as (i) it admits a flexible covariance matrix allowing for both negative and positive nondiagonal entries;(ii) it overcomes the limitation of the existing bivariate COM-Poisson distributions in the literature that do not have COM-Poisson marginals;(iii) it allows for the analysis of multivariate counts and is not just limited to bivariate counts. Inferential challenges are presented by the likelihood specification as it depends on a number of intractable normalizing constants involving the model parameters. These obstacles motivate us to propose Bayesian inferential approaches where the resulting doubly intractable posterior is handled with via the noisy exchange algorithm or the Grouped Independence Metropolis–Hastings algorithm. Numerical experiments based on simulations are presented to illustrate the proposed Bayesian approach. We demonstrate the potential of the MultCOMP model through a real data application on the numbers of goals scored by the home and away teams in the English Premier League from 2018 to 2021. Here, our interest is to assess the effect of a lack of crowds during the COVID-19 pandemic on the well-known home team advantage. A MultCOMP model fit shows that there is evidence of a decreased number of goals scored by the home team, not accompanied by a reduced score from the opponent. Hence, our analysis suggests a smaller home team advantage in the absence of crowds, which agrees with the opinion of several football experts. Supplementary materials for this article are available online.

Paper-Based Devices for Virus Detection in Water

Viral infections are global health concerns that can cause high infection and mortality rates, as of the example from SARS-CoV-2 pandemic. Although conventional methods, e.g., polymerize chain reaction (PCR), can provide reliable and robust detection results, they are often time- and cost-consuming, limiting their application in resource-poor settings. Recently, paper-based devices, as a new biosensing technique, have emerged as a promising tool to conventional methods for pathogen detection including bacteria and virus. In this chapter, we provide a comprehensive introduction and insights on the development of paper-based devices for the pathogen detection in water. Firstly, the substrate materials and fabrication methods for paper-based devices are introduced. Engineering assay onto paper-based devices for virus detection is subsequently discussed for the rapid and on-site monitoring. We also compare the strengths and drawbacks between paper-based devices and the conventional analytical methods for virus detection, including culture method, biochemical test, immune assay, and molecular method. This chapter also discusses the feasibility of paper-based devices for point-of-use detection in water matrix, and the challenges and prospects of paper-based devices in water and environmental monitoring. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Modeling the epidemic trend of middle eastern respiratory syndrome coronavirus with optimal control

Since the outbreak of the Middle East Respiratory Syndrome Coronavirus (MERS-CoV) in 2012 in the Middle East, we have proposed a deterministic theoretical model to understand its transmission between individuals and MERS-CoV reservoirs such as camels. We aim to calculate the basic reproduction number (R0) of the model to examine its airborne transmission. By applying stability theory, we can analyze and visualize the local and global features of the model to determine its stability. We also study the sensitivity of R0 to determine the impact of each parameter on the transmission of the disease. Our model is designed with optimal control in mind to minimize the number of infected individuals while keeping intervention costs low. The model includes time -dependent control variables such as supportive care, the use of surgical masks, government campaigns promoting the importance of masks, and treatment. To support our analytical work, we present numerical simulation results for the proposed model.

Digitalization of Professional Regulation Commission (PRC) Process of Accreditation of Nutritionist and Dietetics

With the recent global COVID-19 pandemic and lockdowns, accreditation delays have become inevitable in lieu of the strict travel restrictions. The usual accreditation inspection process conducted face-To-face was affected. Organizations are shifting to a reliance on technology to adapt to the national emergency. The study aims to bridge the gap by digitalization Professional Regulation Commission's (PRC) monitoring and accreditation system to conduct a virtual inspection and monitoring. With all of these said, the specific objectives of the researchers and developers are to develop an efficient digitized system that captures the original one. In developing the proposed accreditation and monitoring system and document management system (website) for PRC, the group will adapt and take inspiration from the Agile Development Lifecycle methodology, which will help the modification and other functionality of the system by using the iterative style in the development of the system. The proposed digital monitoring system undergoes a cross-browser test, and performance test, i.e., Requirements Traceability Matrix (RTM). These tests show that the proposed system passed the compatibility for commonly used browsers like Chrome, Edge, Mozilla, and many more. The Final Test in Performance Testing showed that the system RTM functions had passed all final testing. © 2023 IEEE.

Demographic profile and outcome in primary pediatric brain tumors. A single tertiary institute experience in India

Introduction: Pediatric brain tumors are the most common tumor in children after hematological malignancies. There is very few data about the epidemiology of pediatric brain tumors in India. Methods - This was a prospective and retrospective study in pediatric patients who had undergone surgery in our institute (JIPMER,Pondicherry). 80 cases were recruited and followed up for minimum follow up period of 1 year. The demographic profile was analysed and IHC markers were done for embroyonal tumors and glioma. Result(s): Pediatric brain tumors was equally distributed among male and females. (1:1) .Mean age of presentation was 10 years . 27.5 % of our cases were embryonal tumors,low grade glioma (16.25 % ) ,high grade glioma ( 12.5 % ) ,ependymoma and craniopharyngioma comprised 15 % of our cases each. Medulloblastoma comprised 23.75 % of cases Out of which 31.5 % had craniospinal metastasis at time of diagnosis. The most common location of SHH pathway medulloblastoma was cerebellar hemisphere and non WNT/non SHH was fourth ventricle. 45.45 % of patients with high grade glioma had recurrence .50 % of ependymoma cases were infratentorial. we had 2 cases of ganglioglioma ,one in the midbrain and other in temporal lobe .Gross total resection was achieved in 30 % ,Subtotal resection in 46.25 % and partial resection in 20 % of our cases. Outcome of patients at the end of 1 year for low and high grade glioma, ependymoma and craniopharyngioma were similar to western literature. Two patients acquired COVID 19 and died while undergoing treatment. Molecular markers like INI1, LIN28 A was highly sensitive and specific for diagnosing atypical teratoid rhabdoid tumor (ATRT) and embryonal tumor with multilayered rosettes (ETMR )respectively. Conclusion(s): Our study emphasizes the need of standardized and systemic cancer registries in India. (Figure Presented).

Low-rank latent matrix-factor prediction modeling for generalized high-dimensional matrix-variate regression.

Motivated by diagnosing the COVID-19 disease using two-dimensional (2D) image biomarkers from computed tomography (CT) scans, we propose a novel latent matrix-factor regression model to predict responses that may come from an exponential distribution family, where covariates include high-dimensional matrix-variate biomarkers. A latent generalized matrix regression (LaGMaR) is formulated, where the latent predictor is a low-dimensional matrix factor score extracted from the low-rank signal of the matrix variate through a cutting-edge matrix factor model. Unlike the general spirit of penalizing vectorization plus the necessity of tuning parameters in the literature, instead, our prediction modeling in LaGMaR conducts dimension reduction that respects the geometric characteristic of intrinsic 2D structure of the matrix covariate and thus avoids iteration. This greatly relieves the computation burden, and meanwhile maintains structural information so that the latent matrix factor feature can perfectly replace the intractable matrix-variate owing to high-dimensionality. The estimation procedure of LaGMaR is subtly derived by transforming the bilinear form matrix factor model onto a high-dimensional vector factor model, so that the method of principle components can be applied. We establish bilinear-form consistency of the estimated matrix coefficient of the latent predictor and consistency of prediction. The proposed approach can be implemented conveniently. Through simulation experiments, the prediction capability of LaGMaR is shown to outperform some existing penalized methods under diverse scenarios of generalized matrix regressions. Through the application to a real COVID-19 dataset, the proposed approach is shown to predict efficiently the COVID-19.

Safety, efficacy, and immunogenicity of the NVX-CoV2373 vaccine.

INTRODUCTION: The coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has resulted in significant morbidity and mortality worldwide. As SARS-CoV-2 moves into endemic status, vaccination remains a key element in protecting the health of individuals, societies, and economies worldwide. AREAS COVERED: NVX-CoV2373 (Novavax, Gaithersburg, MD) is a recombinant protein vaccine composed of SARS-CoV-2 spike trimer nanoparticles formulated with saponin-based Matrix-M™ adjuvant (Novavax, Gaithersburg, MD). NVX-CoV2373 is authorized for emergency use in adults and adolescents aged ≥12 years in the United States and numerous other countries. EXPERT OPINION: In clinical trials, NVX-CoV2373 showed tolerable reactogenicity and favorable safety profiles characterized by mostly mild-to-moderate adverse events of short duration and by low rates of severe and serious adverse events comparable to those seen with placebo. The two-dose primary vaccination series resulted in robust increases in anti-spike protein immunoglobulin G, neutralizing antibody titers, and cellular immune responses. NVX-CoV2373 vaccination was associated with complete protection against severe disease and a high (90%) rate of protection against symptomatic disease in adults, including symptomatic disease caused by SARS-CoV-2 variants. Additionally, the NVX-CoV2373 adjuvanted recombinant protein platform offers a means to address issues of COVID-19 vaccination hesitancy and global vaccine equity.

Plasma Levels of MMPs and TIMP-1 in Patients with Osteoarthritis After Recovery from COVID-19.

BACKGROUND: Matrix metalloproteinases (MMPs) and their tissue inhibitors (TIMPS) play a key role in the pathogenesis of osteoarthritis (OA). Recent research showed the involvement of some MMPs in COVID-19, but the results are limited and contradictory. OBJECTIVE: In this study, we investigated the levels of MMPs (MMP-1, MMP-2, MMP-3, MMP-8, MMP-9, MMP-10) and TIMP-1 in the plasma of patients with OA after recovery from COVID- 19. METHODS: The experiment involved patients aged 39 to 80 diagnosed with knee OA. All study participants were divided into three research groups: the control group included healthy individuals, the group OA included patients with enrolled cases of OA, and the third group of OA and COVID-19 included patients with OA who recovered from COVID-19 6-9 months ago. The levels of MMPs and TIMP-1 were measured in plasma by enzyme-linked immunosorbent assay. RESULTS: The study showed a change in the levels of MMPs in patients with OA who had COVID- 19 and those who did not have a history of SARS-CoV-2 infection. Particularly, patients with OA who were infected with coronavirus established an increase in MMP-2, MMP-3, MMP-8, and MMP-9, compared to healthy controls. Compared to normal subjects, a significant decrease in MMP-10 and TIMP-1 was established in both groups of patients with OA and convalescent COVID-19. CONCLUSION: Thus, the results suggest that COVID-19 can affect the proteolysis-antiproteolysis system even after a long postinfectious state and may cause complications of existing musculoskeletal pathologies.

A multivariate spatiotemporal model for tracking COVID-19 incidence and death rates in socially vulnerable populations.

Recent studies have produced inconsistent findings regarding the association between community social vulnerability and COVID-19 incidence and death rates. This inconsistency may be due, in part, to the fact that these studies modeled cases and deaths separately, ignoring their inherent association and thus yielding imprecise estimates. To improve inferences, we develop a Bayesian multivariate negative binomial model for exploring joint spatial and temporal trends in COVID-19 infections and deaths. The model introduces smooth functions that capture long-term temporal trends, while maintaining enough flexibility to detect local outbreaks in areas with vulnerable populations. Using multivariate autoregressive priors, we jointly model COVID-19 cases and deaths over time, taking advantage of convenient conditional representations to improve posterior computation. As such, the proposed model provides a general framework for multivariate spatiotemporal modeling of counts and rates. We adopt a fully Bayesian approach and develop an efficient posterior Markov chain Monte Carlo algorithm that relies on easily sampled Gibbs steps. We use the model to examine incidence and death rates among counties with high and low social vulnerability in the state of Georgia, USA, from 15 March to 15 December 2020.

Basement membrane product, endostatin, as a link between inflammation, coagulation and vascular permeability in COVID-19 and non-COVID-19 acute respiratory distress syndrome.

Background: Immune cell recruitment, endothelial cell barrier disruption, and platelet activation are hallmarks of lung injuries caused by COVID-19 or other insults which can result in acute respiratory distress syndrome (ARDS). Basement membrane (BM) disruption is commonly observed in ARDS, however, the role of newly generated bioactive BM fragments is mostly unknown. Here, we investigate the role of endostatin, a fragment of the BM protein collagen XVIIIα1, on ARDS associated cellular functions such as neutrophil recruitment, endothelial cell barrier integrity, and platelet aggregation in vitro. Methods: In our study we analyzed endostatin in plasma and post-mortem lung specimens of patients with COVID-19 and non-COVID-19 ARDS. Functionally, we investigated the effect of endostatin on neutrophil activation and migration, platelet aggregation, and endothelial barrier function in vitro. Additionally, we performed correlation analysis for endostatin and other critical plasma parameters. Results: We observed increased plasma levels of endostatin in our COVID-19 and non-COVID-19 ARDS cohort. Immunohistochemical staining of ARDS lung sections depicted BM disruption, alongside immunoreactivity for endostatin in proximity to immune cells, endothelial cells, and fibrinous clots. Functionally, endostatin enhanced the activity of neutrophils, and platelets, and the thrombin-induced microvascular barrier disruption. Finally, we showed a positive correlation of endostatin with soluble disease markers VE-Cadherin, c-reactive protein (CRP), fibrinogen, and interleukin (IL)-6 in our COVID-19 cohort. Conclusion: The cumulative effects of endostatin on propagating neutrophil chemotaxis, platelet aggregation, and endothelial cell barrier disruption may suggest endostatin as a link between those cellular events in ARDS pathology.

Finite-time stability analysis and control of stochastic SIR epidemic model: A study of COVID-19.

Finite-time stability analysis is a powerful tool for understanding the long-term behavior of epidemiological models and has been widely used to study the spread of infectious diseases such as COVID-19. In this paper, we present a finite-time stability analysis of a stochastic susceptible-infected-recovered (SIR) epidemic compartmental model with switching signals. The model includes a linear parameter variation (LPV) and switching system that represents the impact of external factors, such as changes in public health policies or seasonal variations, on the transmission rate of the disease. We use the Lyapunov stability theory to examine the long-term behavior of the model and determine conditions under which the disease is likely to die out or persist in the population. By taking advantage of the average dwell time method and Lyapunov functional (LF) method, and using novel inequality techniques the finite-time stability (FTS) criterion in linear matrix inequalities (LMIs) is developed. The finite-time stability of the resultant closed-loop system, with interval and linear parameter variation (LPV), is then guaranteed by state feedback controllers. By analyzing the modified SIR model with these interventions, we are able to examine the efficiency of different control measures and determine the most appropriate response to the COVID-19 pandemic and demonstrate the efficacy of the suggested strategy through simulation results.

A robust NIfTI image authentication framework to ensure reliable and safe diagnosis.

Advancements in digital medical imaging technologies have significantly impacted the healthcare system. It enables the diagnosis of various diseases through the interpretation of medical images. In addition, telemedicine, including teleradiology, has been a crucial impact on remote medical consultation, especially during the COVID-19 pandemic. However, with the increasing reliance on digital medical images comes the risk of digital media attacks that can compromise the authenticity and ownership of these images. Therefore, it is crucial to develop reliable and secure methods to authenticate these images that are in NIfTI image format. The proposed method in this research involves meticulously integrating a watermark into the slice of the NIfTI image. The Slantlet transform allows modification during insertion, while the Hessenberg matrix decomposition is applied to the LL subband, which retains the most energy of the image. The Affine transform scrambles the watermark before embedding it in the slice. The hybrid combination of these functions has outperformed previous methods, with good trade-offs between security, imperceptibility, and robustness. The performance measures used, such as NC, PSNR, SNR, and SSIM, indicate good results, with PSNR ranging from 60 to 61 dB, image quality index, and NC all close to one. Furthermore, the simulation results have been tested against image processing threats, demonstrating the effectiveness of this method in ensuring the authenticity and ownership of NIfTI images. Thus, the proposed method in this research provides a reliable and secure solution for the authentication of NIfTI images, which can have significant implications in the healthcare industry.

Results from Portugal's 2022 report card on physical activity for children and youth.

Background: The purpose of this study is to update results of Portuguese's Report Card on Physical activity (PA) for Children and Adolescents. Methods: The grades were assigned by results derived from the PA and Fitness in Portugal 2021 Portuguese Report Card and corresponds to the third report for the Portuguese children and adolescents. It includes indicators of PA and sedentary behavior (SB) that are common to the GLOBAL matrix 4.0: Overall Physical Activity, Organized Sport and Physical Activity, Active Play, Active Transportation, Sedentary Behaviors, Family and Peers, School, Community and the Environment, Government and Physical Fitness. The search focused on published national evidence/data sources (academia, NGO, governmental) from end 2018 onwards excluding data obtained during the covid-19 pandemic. Results: The grades were assigned as follows: Overall PA (D-), Organized Sport Participation (C-), Active Play (D+), Active Transportation (D-), Sedentary behaviors (C+), Physical Fitness (C), Family and Peers (B), School (A), Community and Environment (B), and Government (B). Conclusion: In line with previous Portuguese Report Cards, a large proportion of Portuguese children and adolescents are not sufficiently active nor fit enough setting urgency for effective strategies. Particular attention should be given to Active play, Active transport and Organized Sports Participation has their grades have decreased. Some actions in selected indicators as Governmental and policy seems promising however results weren't seen yet. Despite the strong support of schools with mandatory curricula in PE no correspondent change is observed in fitness or PA, so more research is needed to find why.