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During the COVID-19 coronavirus epidemic, people usually wear masks to prevent the spread of the virus, which has become a major obstacle when we use face-based computer vision techniques such as face recognition and face detection. So masked face inpainting technique is desired. Actually, the distribution of face features is strongly correlated with each other, but existing inpainting methods typically ignore the relationship between face feature distributions. To address this issue, in this paper, we first show that the face image inpainting task can be seen as a distribution alignment between face features in damaged and valid regions, and style transfer is a distribution alignment process. Based on this theory, we propose a novel face inpainting model considering the probability distribution between face features, namely Face Style Self-Transfer Network (FaST-Net). Through the proposed style self-transfer mechanism, FaST-Net can align the style distribution of features in the inpainting region with the style distribution of features in the valid region of a face. Ablation studies have validated the effectiveness of FaST-Net, and experimental results on two popular human face datasets (CelebA and VGGFace) exhibit its superior performance compared with existing state-of-the-art methods. © 2023 SPIE.
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The prevalence of adolescent obesity has risen globally, and it still shows an increasing trend. Adolescent obesity is also related to many chronic diseases, such as metabolic syndrome and cardiovascular disease. Obesity at a young age may also lead to psychological problems in teenagers. It's important to identify the causes of adolescent obesity to better prevent and treat it. This article focuses on finding different factors that lead to adolescent obesity using the previous data and research results. It is found that individual factors, social factors, and COVID-19 can all affect adolescent obesity. Individual factors include genetics, gut microbiota, hormones, and physical activities. Adolescent obesity can also be influenced by social factors such as diets, psychology, and eating behaviors. During the pandemic, COVID-19, lockdown, and quarantine all contributed to adolescent obesity. © 2023 SPIE.
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BACKGROUND: Gender disparity in authorship broadly persists in medical literature, little is known about female authorship within pulmonary medicine. METHODS: A bibliometric analysis of publications from 2012 to 2021 in 12 journals with the highest impact in pulmonary medicine was conducted. Only original research and review articles were included. Names of the first and last authors were extracted and their genders were identified using the Gender-API web. Female authorship was described by overall distribution and distribution by country/region/continent and journal. We compared the article citations by gender combinations, evaluated the trend in female authorship, and forecasted when parity for first and last authorship would be reached. We also conducted a systematic review of female authorship in clinical medicine. RESULTS: 14,875 articles were included, and the overall percentage of female first authors was higher than last authors (37.0% vs 22.2%, p<0.001). Asia had the lowest percentage of female first (27.6%) and last (15.2%) authors. The percentages of female first and last authors increased slightly over time, except for a rapid increase in the COVID-19 pandemic periods. Parity was predicted in 2046 for the first authors and 2059 for the last authors. Articles with male authors were cited more than articles with female authors. However, male-male collaborations significantly decreased, whereas female-female collaborations significantly increased. CONCLUSIONS: Despite the slow improvement in female authorship over the past decade, there is still a substantial gender disparity in female first and last authorship in high-impact medical journals in pulmonary medicine.
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Background: Viral infections pose some of the most serious human health concerns worldwide. The infections caused by several viruses, including coronavirus, hepatitis virus, and human immunodeficiency virus, are difficult to treat. Method(s): This review details the findings of a literature search performed on the antiviral properties of luteolin. The keywords engaged in the search are "virus" along with "luteolin." Results: Luteolin possesses antiviral properties, which is the basis for the current review. It is an important natural flavonoid with numerous important biological properties, including anti-inflammatory, immune regulatory, and antitumor effects, and is found in vegetables, fruits, and several medicinal plants. Recent studies have revealed that many traditional Chinese medicines that contain luteolin inhibit the replication of coronaviruses. Conclusion(s): Luteolin effectively inhibits the replication of coronavirus, influenza virus, enterovirus, rotavirus, herpes virus, and respiratory syncytial virus, among others. In particular, it prevents viral infection by improving the body's nonspecific immunity and antioxidation capacity and inhibiting many pathways related to virus infection and replication, such as MAPK, PI3K-AKT, TLR4/8, NF-kappaB, Nrf-2/hemeoxygenase-1, and others. It also regulates the expression of some receptors and factors, including hepatocyte nuclear factor 4alpha, p53, NLRP3, TNF-alpha, and interleukins, thereby interfering with the replication of viruses in cells. Luteolin also promotes the repair of damaged cells induced by proinflammatory factors by regulating the expression of inflammatory molecules. The overall effect of these processes is the reduction in viral replication and, consequently, the viral load. This review summarizes the antiviral effect of luteolin and the mechanism underlying this property.Copyright © The Author(s) 2023.
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Mental stress has been recognized as an essential risk factor for hypertension. Therefore, experts specializing in cardiology, psychiatry, and Traditional Chinese Medicine organized by the Psycho-cardiology Group, College of Cardiovascular Physicians of Chinese Medical Doctor Association, and Hypertension Group of the Chinese Society of Cardiology proposed the expert consensus on the diagnosis and treatment of adult mental stress-induced hypertension in March 2021, which includes the epidemiology, etiology, diagnosis, and treatment of the mental stress-induced hypertension. This consensus will hopefully facilitate the clinical practice of this disorder. In addition, the COVID-19 pandemic has become one of the primary global sources of psychosocial stressors since the beginning of 2020, and the revision of this expert consensus in 2022 has increased the relevant content. This consensus consists of two parts. The sections of Part A include (I) Background and epidemiological characteristics, (II) Pathogenesis, and (III) Diagnosis. The sections of Part B contain (IV) Treatment recommendations, and (V) Prospects. This article presents Part B of the consensus. © 2022 Heart and Mind ;Published by Wolters Kluwer - Medknow.
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Mental stress has been recognized as an essential risk factor for hypertension. Therefore, experts specializing in cardiology, psychiatry, and Traditional Chinese Medicine organized by the Psycho-Cardiology Group of College of Cardiovascular Physicians of Chinese Medical Doctor Association and Hypertension Group of Chinese Society of Cardiology proposed the expert consensus on the diagnosis and treatment of adult mental stress-induced hypertension in March 2021, which includes the epidemiology, etiology, diagnosis, and treatment of the mental stress-induced hypertension. This consensus will hopefully facilitate the clinical practice of this disorder. In addition, the COVID-19 pandemic has become one of the primary global sources of psychosocial stressors since the beginning of 2020, and the revision of this expert consensus in 2022 has increased the relevant content. This consensus consists of Part A and Part B. Part A includes (I) Background and epidemiological characteristics, (II) Pathogenesis, and (III) Diagnosis and Part B includes (IV) Treatment recommendations and (V) Prospects. This part presents the content of Part A. © 2022 Heart and Mind ;Published by Wolters Kluwer - Medknow.
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Objective: In this research, we examine the mediating effect of educational involvement between parental work–family conflict and adolescent academic engagement during COVID-19, as well as the differences among developmental stages. Background: Online learning during the COVID-19 lockdown created challenges for adolescent academic engagement. One of the toughest challenges was that parents experienced increased work–family conflict, making it difficult for them to be involved in adolescent education. In this context, it is essential to understand the impact of parental work–family conflict on adolescent academic engagement. Method: A total of 886 dual-income families participated in the study. Mothers and fathers completed the questionnaire, including questions regarding work–family conflict and educational involvement. Adolescents completed an academic engagement scale. Results: The structural equation model in the total sample showed that parental educational involvement mediated the effect of maternal work–family conflict on adolescent academic engagement. In addition, paternal educational involvement mediated the effect of paternal work–family conflict on adolescent academic engagement. Multigroup analysis indicated the impact of work–family conflict only existed in middle and late adolescence, and mother played a more important role in late adolescence. Conclusion: The study results confirmed the mediating role of parental educational involvement between the relationship of paternal work–family conflict and adolescent academic engagement. Furthermore, this relationship may vary for families with an adolescent at different developmental stages. © 2023 National Council on Family Relations.
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In this study, we combined the Temporal Convolutional Network (TCN) model with the Long Short-Term Memory (LSTM) network model and applied it to prediction of atmospheric particulate matter (PM) concentrations. The study area is Xi'an City, Shaanxi Province, and the study period is from January 2015 to July 2022. During this period, Xi'an exceeded China's National Ambient Air Quality Grade Ⅰ standard for PM for up to 70% of the days. The prediction results of the TCN-LSTM model were compared with those of deep learning models (Convolutional Neural Network-LSTM, TCN, and LSTM) and machine learning models (Support Vector Regression and Random Forest). The R2 values of the TCN-LSTM model were all >0.88, indicating better performance than that of the other five models, and the errors of the TCN-LSTM model were all lower than those of the other five models. The results showed that high-accuracy PM predictions using deep learning models can improve air quality monitoring by compensating for problems in the environmental monitoring process such as pollutant monitoring errors caused by instrument failures. Additionally, sensitivity analysis helps to identify the key factors influencing the behavior of PM. A sensitivity analysis of PM for different periods of COVID-19 found that PM2.5 is more sensitive to O3, while PM10 is mainly influenced by PM2.5. The sensitivity analysis for the whole period showed that PM was closely related to CO. Removing variables that do not contribute to the model output based on the sensitivity analysis results improves modeling efficiency while reducing operating costs and improving environmental monitoring activities and management strategies. © 2023 Turkish National Committee for Air Pollution Research and Control
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Atmospheric black carbon (BC) concentration over a nearly 5 year period (mid-2017–2021) was continuously monitored over a suburban area of Orléans city (France). Annual mean atmospheric BC concentration were 0.75 ± 0.65, 0.58 ± 0.44, 0.54 ± 0.64, 0.48 ± 0.46 and 0.50 ± 0.72 μg m−3, respectively, for the year of 2017, 2018, 2019, 2020 and 2021. Seasonal pattern was also observed with maximum concentration (0.70 ± 0.18 μg m−3) in winter and minimum concentration (0.38 ± 0.04 μg m−3) in summer. We found a different diurnal pattern between cold (winter and fall) and warm (spring and summer) seasons. Further, fossil fuel burning contributed >90 % of atmospheric BC in the summer and biomass burning had a contribution equivalent to that of the fossil fuel in the winter. Significant week days effect on BC concentrations was observed, indicating the important role of local emissions such as car exhaust in BC level at this site. The behavior of atmospheric BC level with COVID-19 lockdown was also analyzed. We found that during the lockdown in warm season (first lockdown: 27 March–10 May 2020 and third lockdown 17 March–3 May 2021) BC concentration were lower than in cold season (second lockdown: 29 October–15 December 2020), which could be mainly related to the BC emission from biomass burning for heating. This study provides a long-term BC measurement database input for air quality and climate models. The analysis of especially weekend and lockdown effect showed implications on future policymaking toward improving local and regional air quality as well. © 2022 Elsevier B.V.
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Bovine respiratory disease complex (BRDC) involves multiple pathogens, shows diverse lung lesions, and is a major concern in calves. Pathogens from 160 lung samples of dead cattle from 81 cattle farms in northeast China from 2016 to 2021 were collected to characterize the molecular epidemiology and risk factors of BRDC and to assess the major pathogens involved in bovine suppurative or caseous necrotizing pneumonia. The BRDC was diagnosed by autopsy, pathogen isolation, PCR, or reverse transcription-PCR detection, and gene sequencing. More than 18 species of pathogens, including 491 strains of respiratory pathogens, were detected. The positivity rate of bacteria in the 160 lung samples was 31.77%, including Trueperella pyogenes (9.37%), Pasteurella multocida (8.35%), Histophilus somni (4.48%), Mannheimia haemolytica (2.44%), and other bacteria (7.13%). The positivity rate of Mycoplasma spp. was 38.9%, including M. bovis (7.74%), M. dispar (11.61%), M. bovirhinis (7.94%), M. alkalescens (6.11%), M. arginini (0.81%), and undetermined species (4.68%). Six species of viruses were detected with a positivity rate of 29.33%, including bovine herpesvirus-1 (BoHV-1; 13.25%), bovine respiratory syncytial virus (BRSV; 5.50%), bovine viral diarrhea virus (BVDV; 4.89%), bovine parainfluenza virus type-3 (BPIV-3; 4.28%), bovine parainfluenza virus type-5 (1.22%), and bovine coronavirus (2.24%). Mixed infections among bacteria (73.75%), viruses (50%), and M. bovis (23.75%) were the major features of BRDC in these cattle herds. The risk analysis for multi-pathogen co-infection indicated that BoHV-1 and H. somni; BVDV and M. bovis, P. multocida, T. pyogenes, or Mann. haemolytica; BPIV-3 and M. bovis; BRSV and M. bovis, P. multocida, or T. pyogenes; P. multocida and T. pyogenes; and M. bovis and T. pyogenes or H. somni showed co-infection trends. A survey on molecular epidemiology indicated that the occurrence rate of currently prevalent pathogens in BRDC was 46.15% (6/13) for BoHV-1.2b and 53.85% (7/13) for BoHV-1.2c, 53.3% (8/15) for BVDV-1b and 46.7% (7/15) for BVDV-1d, 29.41% (5/17) for BPIV-3a and 70.59% (12/17) for BPIV-3c, 100% (2/2) for BRSV gene subgroup IX, 91.67% (33/36) for P. multocida serotype A, and 8.33% (3/36) for P. multocida serotype D. Our research discovered new subgenotypes for BoHV-1.2c, BRSV gene subgroup IX, and P. multocida serotype D in China's cattle herds. In the BRDC cases, bovine suppurative or caseous necrotizing pneumonia was highly related to BVDV [odds ratio (OR) = 4.18; 95% confidence interval (95% CI): 1.6-10.7], M. bovis (OR = 2.35; 95% CI: 1.1-4.9), H. somni (OR = 8.2; 95% CI: 2.6-25.5) and T. pyogenes (OR = 13.92; 95% CI: 5.8-33.3). The risk factor analysis found that dairy calves <3 mo and beef calves >3 mo (OR = 5.39; 95% CI: 2.7-10.7) were more susceptible to BRDC. Beef cattle were more susceptible to bovine suppurative or caseous necrotizing pneumonia than dairy cattle (OR = 2.32; 95% CI: 1.2-4.4). These epidemiological data and the new pathogen subgenotypes will be helpful in formulating strategies of control and prevention, developing new vaccines, improving clinical differential diagnosis by necropsy, predicting the most likely pathogen, and justifying antimicrobial use.
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Chest radiographs are widely used in the medical domain and at present, chest X-radiation particularly plays an important role in the diagnosis of medical conditions such as pneumonia and COVID-19 disease. The recent developments of deep learning techniques led to a promising performance in medical image classification and prediction tasks. With the availability of chest X-ray datasets and emerging trends in data engineering techniques, there is a growth in recent related publications. Recently, there have been only a few survey papers that addressed chest X-ray classification using deep learning techniques. However, they lack the analysis of the trends of recent studies. This systematic review paper explores and provides a comprehensive analysis of the related studies that have used deep learning techniques to analyze chest X-ray images. We present the state-of-the-art deep learning based pneumonia and COVID-19 detection solutions, trends in recent studies, publicly available datasets, guidance to follow a deep learning process, challenges and potential future research directions in this domain. The discoveries and the conclusions of the reviewed work have been organized in a way that researchers and developers working in the same domain can use this work to support them in taking decisions on their research. (c) 2022 Elsevier B.V. All rights reserved.
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Previous studies have documented an association of D-dimer levels with COVID-19 severity. Elevated D-dimer is reported to be associated with patient demographics, comorbidities, lab results, and overall higher incidence of critical illness. However, due to small sample sizes, limited availability of data on essential covariates, and lack of standardization of the admission laboratory protocol, the role of D-dimer in the progression of COVID-19 remains uncertain and needs further investigation using data from larger cohorts. The objectives of this study were to study the factors predicting elevated D-dimer level and to characterize the risk factors that predict D-dimer elevation over the course of inpatient admission. We used statistical modeling, applying machine learning methods to maximally leverage all the available clinical and care variables without being limited by the assumptions of traditional regression analysis methods. Our sample consisted of 1005 COVID-19 inpatients admitted to a large US hospital from March 2020 to July 2020, using detailed data on various clinical and biochemical laboratory test results at admission and throughout the course of hospital stay. Analytic methods used in this study included a) descriptive statistics at baseline using chi-square tests to compare patients with normal and elevated D-dimer at baseline, b) adjusted multivariable regression modeling, and c) evaluation of importance of each feature using two decision-tree-based supervised machine learning algorithms, random forest and XGBoost methods. Results show that machine learning methods could identify 20 important features that predict D-dimer some of which could be used to prevent the processes that lead to D-dimer elevation. Our study suggests that continual laboratory monitoring of D-dimer levels from the time of detection of COVID-19 infection, and monitoring of selected risk factors out of the panel of identified risk factors may enable clinicians to triage patients into risk levels, initiate appropriate therapeutic strategies, and tailor care management to each patient in order to minimize the morbidity and mortality of COVID-19. © 2022 IEEE.
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Ridesplitting, as an emerging shared mobility, has gradually become one of the important travel modes for urban residents. With the spread of COVID-19, ridesplitting has been affected due to restrictions such as social distance and home office. However, few studies have analyzed the impact of COVID-19 on ridesplitting demand. This paper selects four periods before and after the pandemic as the research objects from the ridesplitting data in Ningxia of China, and compares the changes in ridesplitting demand in the four periods. On this basis, geographically and temporally weighted regression (GTWR) model has been used to explore the impact of COVID-19 on spatiotemporal factors affecting ridesplitting demand. The results show that the impact of some factors on ridesplitting demand has changed in different periods. In addition, we visualize the spatiotemporal coefficients of the model to deeply analyze the changing trends of factors affecting ridesplitting demand under the pandemic. © ASCE.
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Purpose : Although the ICL is more invasive than laser-assisted in situ keratomileusis (LASIK), it is indicated for patients with very high myopia, commonly over -7D. ICL is associated with certain risks including cataract and glaucoma which may develop years after surgery requiring additional procedures. In this study, we examined the outcome and safety profile of ICL vs. LASIK at 1 week, 1 month, and 1 year postoperatively. Methods : In this retrospective study, we examined records from a single surgeon (KK) as well as 2 patients with post ICL complications requiring ICL removal. An important aim of this study was to use the 1 year follow up data since this is one of the standard ICL follow up visits. We hypothesized that the FDA approved ICL (2005) would have a comparable target refractive outcome and safety profile when compared to LASIK. Results : There were a total of 45 ICL eyes and 65 LASIK eyes. Preoperatively, ICL patients had a significantly higher manifest refraction spherical equivalent (MRSE) and cycloplegic refraction spherical equivalent (CRSE) than LASIK patients (p<0.05). For patients who received the ICL implants, the average MRSE at 1-week, 1-month, 1-year post-op was -0.37D±(0.13), -0.29D±(0.09), -0.53D±(0.15);and -1.60D±(0.16), -0.36D±(0.15), -0.36D±(0.07) for patients who received LASIK. The differences in post-op MRSE between ICL and LASIK were not statistically significant (p>0.05). The only significant differences were 1 month LogMAR best corrected visual acuity and 1 year LogMAR distance uncorrected visual acuity (p<0.05), in which LASIK had better visual acuity. Common postoperative findings in both groups were refractive target deviations and punctate keratitis. Reoperation rates in the ICL and LASIK groups were 21.4% and 10.8% respectively, which was not statistically significant (p>0.05). 42.6% of ICL patients underwent the procedure during the COVID-19 pandemic compared to 26.2% of LASIK. Conclusions : Our results demonstrate that ICL is safe and effective for patients with high myopia. Although ICL patients had a significantly higher preoperative MRSE compared to the LASIK group, the ICL patients were able to achieve similar refractive targets. There were no cases of glaucoma or cataract at 1 year in the ICL group. In conclusion, ICL surgery is as safe and effective as LASIK surgery in correcting patients with high myopia, regardless of pre-operative refractive error.
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The coronavirus disease 2019 (COVID-19) pandemic has caused a reduction in business and routine activity and resulted in less motor fuel consumption. Thus, the gas tax revenue is reduced, which is the major funding resource supporting the rehabilitation and maintenance of transportation infrastructure systems. The focus of this study is to evaluate the impact of the COVID-19 pandemic on transportation infrastructure funds in the United States through analyzing the motor fuel consumption data. Machine learning models were developed by integrating COVID-19 scenarios, fuel consumptions, and demographic data. The best model achieves an R2-score of more than 95% and captures the fluctuations of fuel consumption during the pandemic. Using the developed model, we project future motor gas consumption for each state. For some states, the gas tax revenues are going to be 10%-15% lower than the pre-pandemic level for at least one or two years. © 2022 International Conference on Transportation and Development
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Objectives: In order to provide useful reference information for researchers in the field of pharmacology and toxicology, this paper studies the current research hot spots in this field, as well as the correlation closeness between research topics. Methods: This paper studies on the hot papers of pharmacology and toxicology field based on ESI (Essential Scientific Indicators) database, and the time span of the data is from January 1, 2010 to December 31, 2020. The data about these 110 hot papers are analyzed by the authors from the aspects of published time, country/territory, institution, journal, citation, and so on. The methods of multi-dimension analysis, cluster analysis, Vosviewer visualization are used to analyze these papers. Results: The results shows that United States is in the first place in the ranking of published papers, England is in the second place, and China is in the third place. The research hotspots are COVID-19, anxiety, depression, and mental health. Conclusions: The cluster of hot papers show the correlativity of the topic in the pharmacology and toxicology field. This research provides researchers in the field of pharmacology and toxicology with the current international hot research direction, and helps China researchers to improve their research in the field.
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Coronavirus disease 2019 (COVID-19) has become a global pandemic that significantly challenged healthcare systems worldwide, with over 4 million deaths among 18.6 million identified cases as of June 2021. Understanding the current COVID-19 cases and determining clinical solutions is of paramount importance. In this chapter, we describe an exploratory study of identifying risk factors associated with COVID-19 inpatient care. Based on a set of COVID-19 inpatient medical health records in a US hospital system, we used both unsupervised and supervised machine learning methods to explore risk factors associated with hospitalized COVID-19 patients. We found that the most important features related to the COVID-19 disease include (1) influenza vaccines, (2) pneumococcal vaccines, and (3) weight-related variables (i.e., weight, height, and BMI). As such, we provide a use case that machine learning methods are valuable for predicting COVID-19 inpatient risk factors, and the results are promising to guide further research in this area. © 2022 Elsevier Inc. All rights reserved.
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Work in Progress: This Innovative Practice Work in Progress Paper presents how a cross-regional online and offline mixed teaching practice has been carried out by coordinating multiple local universities' laboratory resources. Owing to the COVID-19 epidemic, students could not go back to the campus but stay home all over the country. To work with an electronic system design and implementation project in the Electronic Technology Projects course, students in each team need a public physical workplace equipped with the necessary tools and instruments for circuit debugging and implementation. By utilizing local universities' laboratory resources near their homes, students of the same group could have face-to-face discussions and get offline support from local university laboratory teachers. Each team could also communicate online with course teachers on the technical scheme, detailed design, and fault debugging. While online education can share virtual teaching resources, cross-regional online and offline fusion education can further realize the sharing of entity teaching resources. Twenty-three students have fulfilled their projects in eight local universities under online and offline guidance. Such a teaching attempt has also promoted in-depth cooperation between teachers and students across universities.
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Concept drift in stream data has been well studied in machine learning applications. In the field of recommender systems, this issue is also widely observed, as known as temporal dynamics in user behavior. Furthermore, in the context of COVID-19 pandemic related contingencies, people shift their behavior patterns extremely and tend to imitate others' opinions. The changes in user behavior may not be always rational. Thus, irrational behavior may impair the knowledge learned by the algorithm. It can cause herd effects and aggravate the popularity bias in recommender systems due to the irrational behavior of users. However, related research usually pays attention to the concept drift of individuals and overlooks the synergistic effect among users in the same social group. We conduct a study on user behavior to detect the collaborative concept drifts among users. Also, we empirically study the increase of experience of individuals can weaken herding effects. Our results suggest the CF models are highly impacted by the herd behavior and our findings could provide useful implications for the design of future recommender algorithms. © 2022 ACM.
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Distributed Spatial Cloaking () enables users to enjoy precise Location-Based Service (LBS) with location privacy-preserving. An incentive mechanism is necessary to encourage users to cooperate. However, due to the inappropriate design of incentive mechanisms, the existing works cause low user benefits and fail to encourage users, ruining the expected incentive effect. Moreover, introducing a third party to manage users’information also causes the existing works to disclose users’privacy and be unpractical. To address these issues, we propose a utility-awaRe incEntive mechanism based diStributed spATial cloaking (RESAT). By the idea of utility theory and optimization theory, RESAT devises basic and extended incentive mechanisms. The two mechanisms for assuming that all users are honest and that malicious users provide unreasonable locations. RESAT proposes an incentive mechanism-based cloaking cooperation without a third party, incorporating the developed mechanisms based on the blind signature. Theoretical analysis indicates that RESAT achieves incentive compatibility and is secure. Extensive experiments on the real dataset show that compared with the existing works, RESAT enables 1 time more users to cooperate at best while eliminating the malicious behaviors that provide unreasonable locations. The required construction time delay is limited. IEEE