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 Therefore, in terms of creating the competitive advantage of airlines, "price war" is no longer applicable, and improving service quality has become an effective means.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.

Not a waste: Wastewater surveillance to enhance public health

Domestic wastewater, when collected and evaluated appropriately, can provide valuable health-related information for a community. As a relatively unbiased and non-invasive approach, wastewater surveillance may complement current practices towards mitigating risks and protecting population health. Spurred by the COVID-19 pandemic, wastewater programs are now widely implemented to monitor viral infection trends in sewersheds and inform public health decision-making. This review summarizes recent developments in wastewater-based epidemiology for detecting and monitoring communicable infectious diseases, dissemination of antimicrobial resistance, and illicit drug consumption. Wastewater surveillance, a quickly advancing Frontier in environmental science, is becoming a new tool to enhance public health, improve disease prevention, and respond to future epidemics and pandemics.

CRISIS: Cyber-Physical Social Distancing Based on Multi-Modal Data From Mobile Devices

Multi-modal sensors on mobile devices (e.g., smart watches and smartphones) have been widely used to ubiquitously perceive human mobility and body motions for understanding social interactions between people. This work investigates the correlations between the multi-modal data observed by mobile devices and social closeness among people along their trajectories. To close the gap between cyber-world data distances and physical-world social closeness, this work quantifies the cyber distances between multi-modal data. The human mobility traces and body motions are modeled as cyber signatures based on ambient Wi-Fi access points and accelerometer data observed by mobile devices that explicitly indicate the mobility similarity and movement similarity between people. To verify the merits of modeled cyber distances, we design the localization-free CybeR-physIcal Social dIStancing (CRISIS) system that detects if two persons are physically non-separate (i.e., not social distancing) due to close social interactions (e.g., taking similar mobility traces simultaneously or having a handshake with physical contact). Extensive experiments are conducted in two small-scale environments and a large-scale environment with different densities of Wi-Fi networks and diverse mobility and movement scenarios. The experimental results indicate that our approach is not affected by uncertain environmental conditions and human mobility with an overall detection accuracy of 98.41% in complex mobility scenarios. Furthermore, extensive statistical analysis based on 2-dimensional (2D) and 3-dimensional (3D) mobility datasets indicates that the proposed cyber distances are robust and well-synchronized with physical proximity levels. © 2002-2012 IEEE.

Spatiotemporal characteristics of ozone and the formation sensitivity over the Fenwei Plain.

High surface ozone (O3) levels affect human and environmental health. The Fenwei Plain (FWP), one of the critical regions for China's "Blue Sky Protection Campaign", has reported severe O3 pollution. This study investigates the spatiotemporal properties and the causes of O3 pollution over the FWP using high-resolution data from the TROPOspheric Monitoring Instrument (TROPOMI) from 2019 to 2021. This study characterizes spatial and temporal variations in O3 concentration by linking O3 columns and surface monitoring using a trained deep forest machine learning model. O3 concentrations in summer were 2-3 times higher than those found in winter due to higher temperatures and greater solar irradiation. The spatial distributions of O3 correlate with the solar radiation showing decreased trends from the northeastern to the southwestern FWP, with the highest O3 values in Shanxi Province and the lowest in Shaanxi Province. For urban areas, croplands and grasslands, the O3 photochemistry in summer is NOx-limited or in the transitional regime, while it is VOC-limited in winter and other seasons. Reducing NOx emissions would be effective for decreasing O3 levels in summer, while VOC reductions are necessary for winter. The annual cycle in vegetated areas included both NOx-limited and transitional regimes, indicating the importance of NOx controls to protect ecosystems. The O3 response to limiting precursors shown here is of importance for optimizing control strategies and is illustrated by emission changes during the 2020 COVID-19 outbreak.

JDSR-GAN: Constructing An Efficient Joint Learning Network for Masked Face Super-Resolution

With the growing importance of preventing the COVID-19 virus in cyber-manufacturing security, face images obtained in most video surveillance scenarios are usually low resolution together with mask occlusion. However, most of the previous face super-resolution solutions can not efficiently handle both tasks in one model. In this work, we consider both tasks simultaneously and construct an efficient joint learning network, called JDSR-GAN, for masked face super-resolution tasks. Given a low-quality face image with mask as input, the role of the generator composed of a denoising module and super-resolution module is to acquire a high-quality high-resolution face image. The discriminator utilizes some carefully designed loss functions to ensure the quality of the recovered face images. Moreover, we incorporate the identity information and attention mechanism into our network for feasible correlated feature expression and informative feature learning. By jointly performing denoising and face super-resolution, the two tasks can complement each other and attain promising performance. Extensive qualitative and quantitative results show the superiority of our proposed JDSR-GAN over some competitive methods. IEEE

[Epidemiological investigation on the local epidemic situation in Zhengzhou High-Tech Zone caused by SARS-CoV-2 Delta variant].

This study collected epidemic data of COVID-19 in Zhengzhou from January 1 to January 20 in 2022. The epidemiological characteristics of the local epidemic in Zhengzhou High-tech Zone caused by the SARS-CoV-2 Delta variant were analyzed through epidemiological survey and big data analysis, which could provide a scientific basis for the prevention and control of the Delta variant. In detail, a total of 276 close contacts and 599 secondary close contacts were found in this study. The attack rate of close contacts and secondary close contacts was 5.43% (15/276) and 0.17% (1/599), respectively. There were 10 confirmed cases associated with the chain of transmission. Among them, the attack rates in close contacts of the first, second, third, fourth and fifth generation cases were 20.00% (5/25), 17.86% (5/28), 0.72% (1/139) and 14.81% (4/27), 0 (0/57), respectively. The attack rates in close contacts after sharing rooms/beds, having meals, having neighbor contacts, sharing vehicles with the patients, having same space contacts, and having work contacts were 26.67%, 9.10%, 8.33%, 4.55%, 1.43%, and 0 respectively. Collectively, the local epidemic situation in Zhengzhou High-tech Zone has an obvious family cluster. Prevention and control work should focus on decreasing family clusters of cases and community transmission.

An Autonomous IoT-based Contact Tracing Platform in a COVID-19 Patient Ward

Since 2020, the coronavirus disease (COVID-19) pandemic has had a substantial impact on all community sectors worldwide, particularly the health care sector. Healthcare workers (HCWs) are at risk of COVID-19 infection due to occupational exposure to infectious patients, visitors, and staff. Contact tracing of close physical interaction is an essential control measure, especially in hospitals, to prevent onward transmission during an outbreak event. In this article, we propose an IoT-based contact tracing system for subject identification, interaction tracking and data transmission in hospital wards. The system, based on Bluetooth Low Energy (BLE) devices, tracks the duration of interactions between different HCWs, and the time each HCW spends within the patient rooms using additional information from proximity sensors in the hallway or on the door frame of the patient room. The collected data are transferred via Long Range (LoRa) wireless technology and further analyzed to inform infection prevention activities. The suggested system’s performance is evaluated in a COVID-19 patient ward with both standard and negative pressure isolation rooms, and the current system’s capabilities and future research prospects are briefly discussed. IEEE

An audit of compliance with the updated post-polypectomy and post-colorectal cancer resection surveillance guidelines at a District General Hospital (DGH)

Background: It is important we identify cases of premalignant polyps and stratify patients according to future colorectal cancer (CRC) risk to prevent CRC development. In 2020, the British Society of Gastroenterology (BSG) published guidelines to tailor post-polypectomy and post-CRC resection surveillance. The objective of our audit was to determine whether our department was adhering to these guidelines. Method(s): We performed a retrospective audit of patients who had a colonoscopy at a DGH from February to June 2021. We reviewed case notes for indication, findings, and compliance to BSG's guidelines. Result(s): A total of 578 cases were reviewed. The median age was 61 years old. Most of the referrals were via the 2-week-wait pathway. 285 had normal findings on colonoscopy, 28 had CRC, 22 had polyps meeting high risk findings, and 12 had large non-pedunculated colorectal polyps. Our unit was 93.6% (547/578) compliant with the guidelines. 6.4% (31/578) were not compliant. Of those, 18 were scheduled for a surveillance colonoscopy when the polyps did not meet the criteria, 6 colonoscopies were not booked within the appropriate timeframe, 2 did not have their 6-month site check, and 1 had a surveillance colonoscopy despite a normal index colonoscopy. Conclusion(s): Our unit is highly compliant with BSG's guidelines. COVID-19 may have influenced the timing of colonoscopies, which could have impacted our compliance. Furthermore, there is little data on how our DGH compares to national data. We have placed the updated guidelines throughout the department to enhance awareness across the wider team. Copyright © 2022

Machine Learning and Deep Learning Strategies in Drug Repositioning

Drug repositioning invovles exploring novel usages for existing drugs. It plays an important role in drug discovery, especially in the pre-clinical stages. Compared with the traditional drug discovery approaches, computational approaches can save time and reduce cost significantly. Since drug repositioning relies on existing drug-, disease-, and target-centric data, many machine learning (ML) approaches have been proposed to extract useful information from multiple data resources. Deep learning (DL) is a subset of ML and appears in drug repositioning much later than basic ML. Nevertheless, DL methods have shown great performance in predicting potential drugs in many studies. In this article, we review the commonly used basic ML and DL approaches in drug repositioning. Firstly, the related databases are introduced, while all of them are publicly available for researchers. Two types of preprocessing steps, calculating similarities and constructing networks based on those data, are discussed. Secondly, the basic ML and DL strategies are illustrated separately. Thirdly, we review the latest studies focused on the applications of basic ML and DL in identifying potential drugs through three paths: drug-disease associations, drug-drug interactions, and drug-target interactions. Finally, we discuss the limitations in current studies and suggest several directions of future work to address those limitations.

Cardiac Computed Tomography to Exclude Left Atrial Appendage Thrombus in Atrial Arrhythmias Prior to Direct Current Cardioversion During COVID-19 Pandemic

Background: Transoesophegeal echocardiogram (TOE) is the gold standard imaging modality to evaluate the left atrial appendage (LAA) prior to direct current cardioversion (DCCV) for atrial arrhythmia. TOE is an aerosol generating procedure, with the potential for transmission of COVID-19 infection. This study describes our experience of utilising cardiac computed tomography (CT) as an alternative imaging modality, to exclude LAA thrombus prior to DCCV in patients with atrial arrhythmias at Middlemore Hospital from 1st September 2020 until 30th September 2021 during the COVID-19 pandemic. Method: Patients with atrial arrhythmia requiring DCCV who underwent cardiac CT were identified from ANZACS-QI linked cardiac CT registry database. Patients without thrombus on cardiac CT proceeded to DCCV. Patients with slow flow or thrombus in the left atrium (LA) or LAA on CT were considered for TOE. Results: Eighty-five cardiac CT scans were performed in eighty patients (male 68.8%, mean age 59.3±14 years, body mass index 33.4±8). Sixty-seven patients (87%) had no LAA thrombus, and 65 patients proceeded safely to DCCV with no periprocedural stroke. Thirteen patients (16%) had slow flow or possible thrombus in the LA or LAA and one patient had definite thrombus. Six patients with slow flow or possible thrombus underwent TOE none had LA or LAA thrombus. Conclusion: In the majority of patients with atrial arrhythmia requiring DCCV, cardiac CT is a safe and useful alternative to TOE.

VACCINE-PREVENTABLE HOSPITALISATION IN MENTAL HEALTH SERVICE USERS: A BOOSTER IS NEEDED

Background: COVID-19 has highlighted the essential role of vaccination in preventing illness, modifying illness severity and averting hospital care. Mental health (MH) service users have low vaccination rates for many conditions, but evidence on individual and health system impacts is limited. Methods: The NSW Mental Health Living Longer links population-wide data from NSW hospitals and community MH services. We calculated hospitalisation rates and incidence rate ratios for vaccine-preventable conditions including hepatitis, influenza and pneumococcal pneumonia, comparing MH service users to other NSW residents. Rates were standardised for age and socio-economic disadvantage. Results: Over 12 months there were 14,530 vaccine preventable admissions in NSW, occupying 94,241 bed days. MH service users had a more than fourfold increased risk of admission for vaccine-preventable conditions (adjusted incidence rate ratio = 4.7;95% confidence interval = [4.5, 5.0]), with the highest relative risk in people aged 40-65 years. One-quarter of total excess potentially preventable bed days in MH service users were due to vaccine-preventable conditions, including respiratory illness. MH service users comprised 2.3% of the NSW population but contributed nearly 15% of vaccine-preventable bed days. Additional analyses will be presented examining specific conditions, demographic and clinical subgroups. Conclusion: Low vaccination rates have serious impacts for MH service users. Strategies to overcome barriers and support vaccination uptake could have quick and substantial benefits for individuals and health systems. Supporting uptake of COVID-19 vaccination will be essential to avoid further amplifying health inequalities for people using MH services.

Visual recognition technologies for complex scenarios analysis

Public security and social governance is essential to national development nowadays. It is challenged to prevent large-scale riots in communities and various city crimes for spatial and times caled social governance in corona virus disease 2019(Covid-19) like highly accurate human identity verification, highly efficient human behavior analysis and crowd flow track and trace. The core of the challenge is to use computer vision technologies to extract visual information in complex scenarios and to fully express, identify and understand the relationship between human behavior and scenes to improve the degree of social administration and governance. Complex scenarios oriented visual technologies recognition can improve the efficiency of social intelligence and accelerate the process of intelligent social governance. The main challenge of human recognition is composed of three aspects as mentioned below: 1) the diversity attack derived from mask occlusion attack, affecting the security of human identity recognition;2) the large span of time and space information has affected the accuracy of multiple ages oriented face recognition (especially tens of millions of scales retrieval);3) the complex and changeable scenarios are required for the high robustness of the system and adapt to diverse environments. Therefore, it is necessary to facilitate technologies of remote human identity verification related to the high degree of security, face recognition accuracy, human behavior analysis and scene semantic recognition. The motion analysis of individual behavior and group interaction trend are the key components of complex scenarios based human visual contexts. In detail, individual behavior analysis mainly includes video-based pedestrian re-recognition and video-based action recognition. The group interaction recognition is mainly based on video question-and-answer and dialogue. Video-based network can record the multi-source cameras derived individuals/groups image information. Multi-camera based human behavior research of group segmentation, group tracking, group behavior analysis and abnormal behavior detection. However, it is extremely complex that the individual behavior/group interaction is recorded by multiple cameras in real scenarios, and it is still a great challenge to improve the performance of multi-camera and multi-objective behavior recognition through integrated modeling of real scene structure, individual behavior and group interaction. The video-based network recognition of individual and group behavior mainly depends on visual information in related to scene, individual and group captured. Nonetheless, complex scenarios based individual behavior analysis and group interaction recognition require human knowledge and prior knowledge without visual information in common. Specifically, a crowd sourced data application has improved visual computing performance and visual question-and-answer and dialogue and visual language navigation. The inherited knowledge in crowd sourced data can develop a data-driven machine learning model for comprehensive knowledge and prior applications in individual behavior analysis and group interaction recognition, and establish a new method of data-driven and knowledge-guided visual computing. In addition, the facial expression behavior can be recognized as the human facial micro-motions like speech the voice of language. Speech emotion recognition can capture and understand human emotions and beneficial to support the learning mode of human-machine collaboration better. It is important for research to get deeper into the technology of human visual recognition. Current researches have been focused on human facial expression recognition, speech emotion recognition, expression synthesis, and speech emotion synthesis. We carried out about the contexts of complex scenarios based real-time human identification, individual behavior and group interaction understanding analysis, visual speech emotion recognition and synthesis, comprehensive utilization of knowledge and a priori mode of ma hine learning. The research and application scenarios for the visual ability is facilitated for complex scenarios. We summarize the current situations, and predict the frontier technologies and development trends. The human visual recognition technology will harness the visual ability to recognize relationship between humans, behavior and scenes. It is potential to improve the capability of standard data construction, model computing resources, and model robustness and interpretability further. © 2022, Editorial Office of Journal of Image and Graphics. All right reserved.

Application of CT pulmonary function imaging in convalescent COVID‑19 patients

Objective To explore the application value of CT pulmonary function imaging in patients with Coronavirus Disease 2019 (COVID‑19) in the convalescent phase. Methods The COVID‑19 patients who were clinically cured and discharged from Union Hospital, Tongji Medical College, Huazhong University of Science and Technology were prospectively collected from January to April 2020. Clinical pulmonary function tests (PFTs) and CT pulmonary function imaging were performed 3 months after discharge. The Philips IntelliSpace Portal image post‑processing workstation was used to obtain the paired inspiratory‑expiratory CT quantitative indexes of the whole lung, left lung, right lung and five lobes. The patients were divided into two groups according to whether residual lesions remain in inspiratory CT images: non‑residual lesion group and residual lesion group. The chi‑square test was used to compare the differences in the PFT results between groups;the Mann‑Whitney U test was used to compare the differences in PFT indexes [forced expiratory volume in the first second as percentage of predicted value (FEV1%), FEV1/forced vital capacity (FEV1/FVC), total lung capacity as percentage of predicted value (TLC%), FVC% ] and the differences in quantitative CT indexes [lung volume (LV), mean lung density (MLD), volume change in inspiratory phase and expiratory phase (∆ LV)] between groups. Multiple linear regression was used to analyze the relationship between CT pulmonary function imaging and PFT indexes of convalescent COVID‑19 patients. Results Of the 90 patients with COVID‑19, 35 were males and 55 were females;45 were included in the non‑residual lesion group and 45 were included in the residual lesion group. Fifty‑three patients had clinical pulmonary dysfunction 3 months after discharge, including 22 patients in the non‑residual lesion group and 31 patients in the residual lesion group. In patients with residual disease, left lower lobe and right lower lobe LV, left lower lobe and right lower lobe ∆ LV in the inspiratory and expiratory phase were smaller than those without residual disease;whole lung, left lung, right lung, left upper lobe, left lower lobe and right lower lobe MLD in the inspiratory phase and left lower lobe and right lower lobe MLD in the expiratory phase were greater than those without residual disease (P<0.05). Since there was no significant difference in FEV1/FVC and FVC% between residual and non‑residual lesion groups (P>0.05), FEV1/FVC and FVC% of two groups were combined. Multiple linear regression analysis showed FEV1/FVC= 91.765-0.016×LVin‑right middle lobe+0.014×MLDex‑left lower lobe (R2 =0.200, P<0.001), FVC% =-184.122-0.358× MLDin‑right lung-0.024× ∆ LVleft upper lobe (R2 =0.261, P<0.001). There was significant difference in TLC% between residual and non‑residual lesion groups (P<0.05), so multiple linear regression analysis was performed both in the two groups. In the non‑residual lesion group, TLC% =80.645+0.031× (R2 =0.132, P<0.001);In the residual lesion group, TLC% =-110.237-0.163× LVex‑right lower lobe MLDin‑right upper lobe-0.098×MLDex‑left upper lobe -0.025×LVex‑right lower lobe (R2 =0.473, P<0.001). Conclusion CT pulmonary function imaging can quantitatively analyze the whole lung, unilateral lung and lobulated lung, thus reflecting the regional pulmonary function, providing more valuable diagnostic information for the assessment of ulmonar function in convalescent atients with COVID‑19

Simulation models to predict biochemistry test capacity in Covid-19 pandemic surge capacity planning in tertiary care centres

Background-aim: The COVID-19 pandemic has re-emphasized the need for the timely delivery of clinical laboratory results to support optimal patient care. The objective of this study was to determine if current instrumentation in Saskatoon hospital chemistry laboratories could accommodate the anticipated COVID workload in addition to non-COVID testing for the existing acute care hospitals and proposed field hospitals. Methods: A simulation model was utilized to assess workload and turn-around-time (TAT) capacity for pre-analytic, total analytic, chemistry, ion-selective-electrode and immunoassay testing to accommodate an expanded COVID workload. Anticipated COVID patient numbers and a COVID specific test menu were incrementally introduced into a 24 hour pre-COVID testing workload. The impact of field hospital location, courier schedule and daily instrument maintenance schedule were also considered when calculating a TAT from specimen collection to result reporting. Results: Instrumentation throughput, scheduled times for instrument daily maintenance and the time of day when the specimen surge is received in the laboratory were found to be significant predictors of laboratory’s ability to accommodate anticipated COVID workload. Courier schedule and proximity of the field hospital to the laboratory significantly influenced the TAT for field hospital testing. Conclusions: A simulation model is a helpful tool to provide useful information for optimal delivery of multi-site clinical laboratory services during the COVID-19 pandemic.

Nanoparticle Albumin-Bound-Paclitaxel Instead of Standard Paclitaxel in Adjuvant Chemotherapy Regimen for Breast Cancer Under COVID-19 Pandemic-A Single Center Feasibility Study in China

The management of breast cancer patients in the current COVID-19 outbreak is challenging. Myelosuppres-sion associated with cancer treatment may increase the risk of infection in both hospitals and at home. We implemented the following strategy to reduce myelosuppression of adjuvant chemotherapy during the COVID-19 pandemic: (1) changing the original regimen of AC x 4-* wT x 12 to wT x 12-* AC x 4. (2) substitution of standard paclitaxel with nanoparticle albumin-bound (nab)-paclitaxel (nab-paclitaxel). For 43 patients who com-IP: 14.98.160.66 On: Fri, 13 May 2022 09:27:55 pleted nab-paclitaxel treatment, the compliance rate was 100%, without interruption or delay of nab-paclitaxel Copyright: American Scientific Publishers Delivered by Ingenta treatment. Dose reduction was necessary in 2 patients (4.6%) due to peripheral neuropathy. Thus, 98.6% of the planned doses were administered. As expected, the adjusted adjuvant regimen was safe and well toler-ated. Therefore wT x 12-* AC x 4 treatment procedure may be considered for breast cancer patients during COVID-19 pandemic.

Traceability Analysis of Cold Chain Food under COVID-19 Based on Block Chain Technology

The distributed accounting method of blockchain, together with its decentralized and tamper proof characteristics, can solve the problem of lack of food safety traceability data and low credibility, which enable the traceability requirements of food chain traceability, problem food recall, food circulation information query. It also improves the food flow mode, and establishes a new food safety ecosystem. This paper innovatively integrates the principle of blockchain into the traceability and circulation process of Hebei cold chain food. In order to find the proportion and distribution of various types of commodities in the circulation of imported and domestic cold chain food in Hebei Province, the combination of qualitative and quantitative analysis methods, chi square test, association rules and other analysis methods are used. The results show that the demand for cold chain food in Hebei Province is mainly domestic food and supplemented by imported food. Cold chain poultry is the main need and other cold chain food is supplement. The flow of imported cold chain food is mainly from the surrounding provinces and cities. Furthermore, in the process of tracing the positive commodities, it is found that there are four main modes of commodity circulation in Hebei Province, namely, family model from farmer, wholesale market, new retail and multi-level sales model. The new retail and multi-level sales model fill the gap in the distribution and circulation of cold chain goods in Hebei Province. © 2021 ACM.