Embedded system vehicle based on multi-sensor fusion

As intelligent driving vehicles came out of concept into people’s life, the combination of safe driving and artificial intelligence becomes the new direction of future transportation development. Autonomous driving technology is developing based on control algorithms and model recognitions. In this paper, a cloud-based interconnected multi-sensor fusion autonomous vehicle system is proposed that uses deep learning (YOLOv4) and improved ORB algorithms to identify pedestrians, vehicles, and various traffic signs. A cloud-based interactive system is built to enable vehicle owners to master the situation of their vehicles at any time. In order to meet multiple application of automatic driving vehicles, the environment perception technology of multi-sensor fusion processing has broadened the uses of automatic driving vehicles by being equipped with automatic speech recognition (ASR), vehicle following mode and road patrol mode. These functions enable automatic driving to be used in applications such as agricultural irrigation, road firefighting and contactless delivery under new coronavirus outbreaks. Finally, using the embedded system equipment, an intelligent car was built for experimental verification, and the overall recognition accuracy of the system was over 96%. Author

Value-Added Tax (VAT) Development and Reform History—A Study of VAT Evolution Around the World and in China

Value-added Tax (VAT), a turnover tax levied on the value appreciated in commodities production, circulation and sale, has been widely practiced by economies worldwide for its neutrality. From the international perspective, European Union VAT and New Zealand VAT stand out as characteristic models of VAT development and reform history. With changing economic development models and the growth of e-commerce businesses, VAT systems have been upgraded to meet taxation challenges brought about by the digital economy. Recently, VAT policies and measures have been introduced particularly to combat the COVID-19 impact. In China, the VAT system has been reformed constantly over the past 40 years and has been converging with international practice. China now needs to address emerging VAT issues through legislation, tax incentives, and tax-rate grades so as to meet challenges in VAT collection and management and to suit the "new normal” of economic development. © 2022 Brill Academic Publishers. All rights reserved.

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.

Assistance tools for the evaluation of machine learning algorithm performance: The decision tree-based tools developed by the Medical Imaging and Data Resource Center (MIDRC) Technology Development Project (TDP) 3c effort

MIDRC was created to facilitate machine learning research for tasks including early detection, diagnosis, prognosis, and assessment of treatment response related to the COVID-19 pandemic and beyond. The purpose of the Technology Development Project (TDP) 3c is to create resources to assist researchers in evaluating the performance of their machine learning algorithms. An interactive decision tree has been developed, organized by the type of task that the machine learning algorithm is being trained to perform. The user can select information such as: (a) the type of task, (b) the nature of the reference standard, and (c) the type of the algorithm output. Based on the user responses, they can obtain recommendations regarding appropriate performance evaluation approaches and metrics, including literature references, short video tutorials, and links to available software. Five tasks have been identified for the decision tree: (a) classification, (b) detection/localization, (c) segmentation, (d) time-to-event analysis, and (e) estimation. As an example, the classification branch of the decision tree includes binary and multi-class classification tasks and provides suggestions for methods and metrics as well as software recommendations, and literature references for situations where the algorithm produces either binary or non-binary (e.g., continuous) output and for reference standards with negligible or non-negligible variability and unreliability. The decision tree has been made publicly available on the MIDRC website to assist researchers in conducting task-specific performance evaluations, including classification, detection/localization, segmentation, estimation, and time-to-event tasks. © COPYRIGHT SPIE. Downloading of the is permitted for personal use only.

Sequestration methodology in practice through evaluation of joint demographic distributions of 54,185 patients in the Medical Imaging and Data Resource Center (MIDRC) data commons

The Medical Imaging and Data Resource Center (MIDRC) is a multi-institutional effort to accelerate medical imaging machine intelligence research and create a publicly available data commons as well as a sequestered commons for performance evaluation of algorithms. This work sought to evaluate the currently implemented methodology for apportioning data to the public and sequestered data commons by investigating the resulting distributions of joint demographic characteristics between the public and sequestered commons. 54,185 patients whose de-identified imaging studies and metadata had been submitted to MIDRC were previously separated into public and sequestered commons using a multi-dimensional stratified sampling method, resulting in 41,556 patients (77%) in the public commons and 12,629 patients (23%) in the sequestered commons. To compare the balance obtained in the joint distributions of patient characteristics from use of the developed sequestration method, patients from each commons were separated into bins, representing a unique combination of the demographic variables of COVID-19 status, age, race, and sex assigned at birth. The joint distributions of patients were visualized, and the absolute and percent difference in each bin from an exact 77:23 split of the data were calculated. Results indicated 75.9% of bins obtained differences of less than 15 patients, with a median difference of 3.6 from the total data for both public and sequestered commons. Joint distributions of patient characteristics in both the public and sequestered commons closely matched each other as well as that of the total data, indicating the sequestration by stratified sampling method has operated as intended. © 2023 SPIE.

COVID-19 ACE2 related to hepatocellular carcinoma in single cell analyses and immuno-cancer microenvironment infiltrated with macrophage

Background: Hepatocellular carcinoma (HCC) is the second leading cause of malignancy-related mortality and the fifth most common worldwide. Immuno-cancer microenvironment (ICME) was highlighted recently because scientists want to unlock the detailed mechanism in carcinogenesis pathway and find the novel interactions in ICME. Besides, single cell analysis could mitigate the interrupted signals between cells and tissues. On the other hand, COVID-19 angiotensin I converting enzyme (ACE) previously was reported associated with cancer. However, the robust association between COVID-19 and HCC ICME is still unaddressed. Aim(s): We plan to investigate the COVID-19 ACE relevant genes to HCC ICME regarding survival. Method(s): We used Reactome for COVID-19 ACE gene pathway mapping and explored the positive relevant gene expression. DISCO website was applied for single cell analyses using the above-collected genes from Reactome. Finally, we implanted the biomedical informatics into TIMER 2.0 for ICME survival analyses. Result(s): In Fig. 1, the gene-gene interaction mapping was shown. We collected 13 genes (CPB2, ACE2, AGT, MME, ANPEP, CPA3, ENPEP, GZMH, CTSZ, CTSD, CES1, ATP6AP2, and AOPEP) for further single cell relevant analyses, in Table 1, with detailed expression level (TPM). Among the above 13 genes, AGT, GZMH, CTSZ, CTSD, CES1, and ATP6AP2 were strongly expressed in liver tissue. We then applied the initial 13 genes to TIMER 2.0 for HCC ICME 2-year survival analyses. CPA3 and GZMH low expressions with high macrophage infiltration in HCC ICME showed significantly worse 2-year cumulative survival [hazard ratio (HR):CPA3 2.21, p-value 0.018;GZMH 2.07, p-value 0.0341]. ACE2, CPB2, AGT, MME, ANPEP, ENPEP, CTSZ, CTSD, CES1, and ATP6AP2 high expressions with high macrophage infiltration in HCC ICME revealed significantly worse 2-year cumulative survival. Conclusion(s): We demonstrate that ACE2 was strongly associated with HCC clinical survival with macrophage infiltration. However, the bidirectional translational roles about ACE2 relevant genes in HCC should be documented.

Clinical characteristics of 71 death cases of COVID‐19

Objective: To provide a basis for the early identification and treatment of severe and critical coronavirus disease 2019 (COVID - 19) by analyzing the clinical characteristics of the death cases. Methods: We retrospectively analyzed the clinical characteristics of 71 COVID - 19 cases which died during hospitalization. The clinical data included general data, underlying disease, clinical manifestation, biochemical laboratory examination, imaging examination, complications, and treatment, then the influencing factors of in - hospital survival were analyzed. Results: Most of the 71 patients were ≥60 years old (78. 9%) and had underlying diseases (74. 6%), in which hypertension ranked first, and fever was the most common first symptom. Biochemical laboratory tests showed that D-Dimer and C-reactive protein maintained at high levels during hospitalization, and lymphocyte count declined. Leukocyte/neutrophil counts, neutrophil to lymphocyte ratio, procalcitonin, creatine kinase, and lactate dehydrogenase increased. The main imaging features of the dead cases were the multifocal ground glass changes and consolidation of the lungs. The most common complications were acute respiratory distress syndrome (89. 9%), shock (34. 3%), and acute myocardial injury (30. 4%). 90% of the patients received auxiliary ventilation, and the decrease of blood oxygen saturation and the increase of procalcitonin may be the risk factors for shorter in-hospital survival. Conclusion: Severe and critical COVID-19 patients show different characteristics in clinical manifestations, biochemical laboratory examination, imaging examination, complications, and treatment reactions, which need early identification and treatment, and bewaring of acute respiratory distress syndrome and multiple organs failure. © 2022 Editorial Board of Medical Journal of Wuhan University. All rights reserved.

Diverse feature extraction techniques in internet search query to forecast tourism demand: An in-depth comparison

It is an effective approach to improve forecasting by extracting effective information from large panels of search query data. Feature extraction techniques (FETs) can extract information from all features by creating new fewer features based on algebraic transformation;however, they have not been extensively investigated and compared for tourism forecasting. We employ five FETs to process multi-dimensional search query data, and build a bunch of models based on econometrics, machine learning, ensemble learning and hybrid methods. The improving performances of FETs based on tourism demand forecasting in Sanya after COVID-19 and in Macau before COVID-19 are evaluated. The results show that forecasting models with FETs outperform the benchmark model SARIMAX without FETs, which demonstrates the efficacy of FETs in search query data extraction. This study provides meaningful guidance for improving the quality of multi-dimensional data and optimizing tourism forecasting. © 2023 Elsevier Ltd

Impact of Environmental Factors on Short-Term Eye Strain Relief during COVID-19 Quarantine: A Pilot Study

Some policies implemented during the pandemic extended the time that students spend on electronic devices, increasing the risk of physical and eye strain. However, the role of different environments on eye strain recovery has not been determined. We recruited 20 undergraduate students (10 males and 10 females) from a university in eastern China and explored the restoration effects of their eye strain in different types of spaces (wayside greenspace, a playground, a square, and woodland) on campus through scale measurements. The results showed that the eye strain of the students accumulated by 15 min of e-learning was significantly relieved after 10 min of greenspace exposure compared to the indoor environment, and the recovery effect varied depending on the type of landscape. The effect of eye strain relief was found to be positively correlated with temperature, wind speed, visible sky ratio, canopy density, tree density, and solar radiation intensity, while it was negatively correlated with relative humidity. These findings enrich the research on the restoration benefits of greenspaces and provide a basis for predicting the effect of different environments on the relief of eye strain. © 2022 by the authors.

Ultrashort Peptides for the Self-Assembly of an Antiviral Coating

Antiviral compounds are important for generating sterile surfaces. Here, two extremely short peptides, DOPA-Phe-NH2 and DOPA-Phe(4F)-NH2 that can self-assemble into spherical nanoparticles with antiviral activity are presented. The peptide assemblies possess excellent antiviral activity against bacteriophage T4 with antiviral minimal inhibitory concentrations of 125 and 62.5 µg mL−1, for DOPA-Phe-NH2 and DOPA-Phe(4F)-NH2, respectively. When the peptide assemblies are applied on a glass substrate by drop-casting, they deactivate more than 99.9% of bacteriophage T4 and Canine coronavirus. Importantly, the peptide assemblies have low toxicity toward mammalian cells. Overall, the findings can provide a novel strategy for the design and development of antiviral coatings for a decreased risk of viral infections. © 2023 The Authors. Advanced Materials Interfaces published by Wiley-VCH GmbH.

AdaBias: An Optimization Method With Bias Correction for Differential Privacy Protection

A continuous increase in privacy attacks has caused the research and application of differential privacy (DP) to gradually increase. We can improve the efficiency of the DP model by Optimizing its parameters significantly. Inspired by the performance of various optimization methods for differential privacy, this paper proposes an improved RDP-AdaBound optimization method with bias correction, which is called "AdaBias", to increase the performance of Renyi differential privacy (RDP). The bias correction is used to realize the learning rate and speed up the convergence by upper and lower bound functions. We evaluate our method on the three datasets by training two different privacy model. We further compare three traditional optimization algorithms, namely, RDP-SGD, RDP-Adagrad, and RDP-Adam. And we use AdaBias to verify the performance of privacy protection on the COVID-19 dataset. Experimental results show that the new variant better implements learning rate adjustment to accommodate updates of noisy gradients. As a result, it can achieve higher accuracy and lower losses with a lower privacy budget, thereby better protecting data privacy.

Comparison of clinical features of Omicron and Delta cases

Objective: To compare the clinical features of Omicron and Delta cases, so as to provide scientific basis for the prevention and treatment of COVID-19. Methods: The case-control study method was used to retrospectively analyze the clinical data of the Omicron cases admitted to the designated hospital for the treatment of COVID-19 in Xi'an from December 2021 to January 2022. and the Delta cases admitted during the same period were used as the control group. The demographic data, epidemiological history, vaccination status, clinical characteristics, laboratory tests, nucleic acid and antibody levels, and outcomes of patients in the two groups were collected and compared. Results: A total of 21 patients were included in the study, 5 were Omicron patients and 16 were Delta cases. The mean age of the patients in the two groups were (38.20±15.07) and (37.69±10.39) years, respectively.The time interval between the last vaccination and the diagnosis was (145.40±77.92) days and (159.00±99.74) days, respectively. For the initial symptoms, the patients with Omicron were mainly characterized by throat discomfort (3, 60%), cough and sputum (2, 40%), and the patients with Delta were mainly characterized by throat discomfort (5, 31.25%), fatigue (5, 31.25%), cough and sputum (4, 25%). On admission, laboratory tests showed that 60% of Omicron patients had low lymphocytes and elevated erythrocyte sedimentation rate, and 50% of patients in the delta group had elevated hemoglobin. The Ct values of ORFlab gene, N gene and E gene with Omicron were lower than those with Delta. And the difference of E gene between the two groups was statistically significant (t=-2.711, P=0.024). IgG antibody levels increased in both groups.The time for nucleic acid to turn negative with Omicron was (28.20±5.89) days, and it was (18.50±7.73) days with Delta, and the difference between the groups was statistically significant (t=2.565, P=0.019). The length of hospitalization with Omicron was (30.60±4.88) days, and that with Delta was (22.13±7.81) days, and the difference was statistically significant (t=2.270, P =0.035). Conclusions: The initial symptoms of Omicron patients are mainly throat discomfort, cough and sputum. The clinical manifestations are generally mild. The nucleic acid test Ct value is lower. The time for nucleic acid to turn negative and the time for hospitalization are longer, and the potential infectiousness is stronger. Those eligible for vaccination should complete the full course of vaccination and booster vaccination as soon as possible. At the same time, the management of "early detection, early reporting, early isolation, and early treatment" should be implemented.

Information support practice for anti-COVID-19 consortium composed of multi-medical institutions' staff

In the emergency of the outbreak of COVID-19 in December 2019, Shaanxi Provincial Health Committee mobilized several medical teams from major hospitals in the province, and, by relying on Xi'an Chest Hospital, jointly established an anti COVID-19 consortium to control and eradicate the epidemic in a short time. Information support is an important guarantee for winning this battle. In order to realize the efficient cooperation among multiple medical teams, we have carried out some exploratory and innovative information support services on the basis of the original information system of the chest hospital. In this process, we have gone through some detours. Some compromises were made on some problems that could not be solved in the short term. Finally, in an environment full of uncertainty, a set of information support management system with basically smooth operation was built through rapid trial and error adjustment. The system mainly includes the following aspects: support of the organizational structure and operation process of the anti-epidemic consortium, support for medical collaboration related businesses of multiple medical teams, and support for statistical reports and online meetings. Information support has played a very important role in this action, and this practice has also accumulated experience for us to deal with similar situations in the future.

Travel pattern variation effects on utility factor after COVID-19: An empirical case

Carbon emission is largely reduced during the COVID-19 due to the lockdown. However, the accurate impact in the personal transport sector after the epidemic is still not clear. To accurately measure the travel pattern variation effects on utility factor of plug-in hybrid electric vehicles (PHEV s) due to COVID-19, travel pattern, charging pattern, and utility factors (UF) are compared in a typical city based on actual travel data before and after the pandemic. The result shows that the number of trips and the daily vehicle kilometers travelled decreased significantly during the pandemic while the average daily travel mileage increased quickly after the pandemic and is only 9% lower than that before the pandemic. Some consumers even travel longer with personal vehicles to avoid possible health risks from public transportation. The electricity utility factor after the pandemic is 0.022 larger compared to that before the pandemic due to the variation of travel patterns, a 60-km-range PHEV has a pre-pandemic standard UF of 0.745 and a post-pandemic standard UF of 0.767. Besides, the actual UF is 15% smaller compared to the standard UF due to the actual charging frequency in reality. © 2021 IEEE.

Proximity, Knowledge Transfer and Resilience in a cluster – take the Evolution of Hsinchu Science Park for example

The continuous transfer of knowledge within an industrial cluster is a key element for the cluster to maintain its long-term competitiveness. In recent years, various shocks have occurred frequently, and issues related to the resilience of regional economies and industrial clusters have attracted attention. These shocks may also hinder the transfer of knowledge within industrial clusters, thereby limiting the development of industrial clusters. However, most of the previous studies have focused on economic performance, and little attention has been paid to how knowledge exchanges within the firms in cluster are affected by shocks. Therefore, this study adopted a resilience perspective, examined the evolution of innovation modes and proximity within industrial clusters, and constructed a four-quadrant analytical framework consisting of two types of proximity and two types of innovation modes, respectively. We attempted to examine whether shocks cause the shift of innovation modes and proximity in the four-quadrant analytical framework to fill the aforementioned gap. In order to further explored its connotation and make future policy advice more valuable, this study used the concept of life cycle development of clusters to analyze evolution. We took the Hsinchu Science Park as the research object, and used the global financial crisis in 2008 and the Covid-19 in 2020 as the shocks to compare the change of innovation modes and proximity at different life cycle stage. © 2021, Chung Hua University. All rights reserved.

Sequestration of Imaging Studies in MIDRC: A Multi-Institutional Data Commons

The Medical Imaging and Data Resource Center (MIDRC) is a multi-institutional effort to accelerate medical imaging machine intelligence research and create a publicly available image repository/commons as well as a sequestered database for performance evaluation and benchmarking of algorithms. After de-identification, approximately 80% of the medical images and associated meta-data will become part of the open repository and 20% will be sequestered and kept separate from the open commons. To ensure that both the public, open dataset and the sequestered dataset are representative of the population available, demographic characteristics across the two datasets must be balanced. Our method uses multidimensional stratified sampling where several demographic variables of interest are sequentially used to separate the data into individual strata, each representing a unique combination of variables. Within each stratum, patients are randomly assigned to the open set (80%) or the sequestered set (20%). Thus, for p variables of interest, the balance of the pdimensional distribution of variable combinations can be controlled. This algorithm was used on an example COVID-19 dataset containing image exams of 4662 patients using the variables of race, age, sex at birth, and ethnicity, each containing 8, 8, 2, and 4 categories, respectively. After stratification of this dataset into the two subsets, resulting distributions of each variable matched the distribution from the original dataset with a maximum percent difference from its original fraction of 0.4%. These results demonstrate that the implemented process of multi-dimensional sequential stratified sampling can partition a large database while maintaining balance across several variables. © 2022 SPIE. All rights reserved.

EVIDENCE of BRAIN HYPOXIA in NEUROPATHOLOGY of SARS-CoV-2-INFECTED NONHUMAN PRIMATES

Background: Neurological manifestations are a major complication of sudden acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and likely contribute to symptoms of "long COVID". Elucidating the mechanisms that underlie neuropathogenesis in infection is critical for identifying or developing viable therapeutic strategies. While neurological injury in infection is varied, cerebrovascular disease is seen at a high frequency among patients over 50 years of age. Additionally, microhemorrhages and hypoxic-ischemic injury are often described in brain autopsy series of human subjects who died from COVID-19. Here, we report neuropathology in aged SARS-CoV-2 infected non-human primates (NHPs) is consistent with that observed in aged human subjects and provide insight into the underlying cause. Methods: Four adult Rhesus macaques and four African green monkeys were inoculated with the 2019-nCoV/USA-WA1/2020strain of SARS-CoV-2 via a multi-route mucosal or aerosol challenge. Two of each species were included as age-matched controls. Frontal, parietal, occipital, and temporal lobes, basal ganglia, cerebellum, and brainstem were interrogated through histopathological and immunohistochemical techniques to identify and characterize the observed pathology. Results: Like humans, pathology was variable but included wide-spread inflammation with nodular lesions, neuronal injury, and microhemorrhages. Neuronal degeneration and apoptosis were confirmed with FluoroJade C and cleaved caspase 3 IHC, which showed foci of positivity, particularly among cerebellar Purkinje cells. This was seen even among infected animals that did not develop severe respiratory disease but was not seen in age-matched controls. Significant upregulation of the alpha subunit of hypoxia inducible factor 1 (HIF1-α), indicative of tissue hypoxia, was observed in brain of all infected animals, regardless of disease severity. Sparse virus was detected in brain endothelial cells but did not associate with the severity of CNS injury. Conclusion: SARS-CoV-2 infected NHPs are a viable animal model for advancing our current understanding of infection-associated neuropathogenesis. Upregulation of HIF1-α in brain of infected animals suggests cerebral hypoxia may underlie or contribute to neuroinflammation and neuronal injury/death and may provide some insight into neurological manifestations observed among asymptomatic patients or those only suffering mild disease.

MOLECULAR EPIDEMIOLOGY of the COVID-19 PANDEMIC in CHICAGO

Background: The greater Chicagoland area has recorded over 10,000 COVID-related deaths and nearly 600,000 cases since the start of the COVID-19 pandemic in March of 2020. SARS-CoV-2, the causative agent of COVID-19, has continually changed over that time, with some variants evolving to become more transmissible or more resistant to neutralizing antibody responses. Methods: To better understand how viral genetic variation has contributed to differences in COVID-19 pathogenesis and patient outcome, we established a biobank of residual diagnostic samples from adult patients who tested positive for COVID-19 in a PCR-based test at Northwestern Memorial Hospital. Thus far, we have collected samples from 6448 out-patients and 632 in-patients. Of these, we have performed whole genome sequencing and viral load calculations on 1373 samples. Clinical and demographic information, including composite measures of disease severity, were extracted from available electronic health records. These data were assessed for longitudinal patterns and for specific association with viral lineage. Results: We found that the early epidemic in March of 2020 was defined by three distinct lineages reflecting the outbreaks in China (19B/A), Washington state (19B/A.1), and New York state (19A/B.1). By November of 2020, we saw a large increase in the number of confirmed cases, dominated by the 20G clade. This lineage remained predominant until March of 2021, when the Alpha and Gamma variants of concern became more established. These were recently supplanted by the Delta variant, which now accounts for over 90% of Chicago cases. At the height of the pandemic in November of 2020, case counts peaked at over 5000 cases per day, but hospitalizations, ICU admissions, and deaths over this period remained flat. Statistical testing revealed that the predominant clade at that time, 20G, was associated with better outcomes and less severe disease as measured by clinical measures of patient deterioration, even when controlling for patient demographics. These results suggest that a viral variant associated with less severe disease was predominant in late 2020 before the emergence of the more transmissible variants of concern. Conclusion: Current work is being done to determine if the less severe outcomes associated with this clade also contributed to more asymptomatic transmission, potentially contributing to the high case counts recorded over this period. These data emphasize the need for continued genomic surveillance of SARS-CoV-2 to character.