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1.
22nd Annual International Conference on Computational Science, ICCS 2022 ; 13353 LNCS:387-401, 2022.
Article in English | Scopus | ID: covidwho-1958891

ABSTRACT

In the severe COVID-19 environment, encrypted mobile malware is increasingly threatening personal privacy, especially those targeting on Android platform. Existing methods mainly focus on extracting features from Android Malware (DroidMal) by reversing the binary samples, which is sensitive to the deduction of the available samples. Thus, they fail to tackle the insufficiency of the novel DoridMal. Therefore, it is necessary to investigate an effective solution to classify large-scale DroidMal, as well as to detect the novel one. We consider few-shot DroidMal detection as DoridMal encrypted network traffic classification and propose an image-based method with meta-learning, namely AMDetector, to address the issues. By capturing network traffic produced by DroidMal, samples are augmented and thus cater to the learning algorithms. Firstly, DroidMal encrypted traffic is converted to session images. Then, session images are embedded into a high dimension metric space, in which traffic samples can be linearly separated by computing the distance with the corresponding prototype. Large-scale and novel DroidMal traffic is classified by applying different meta-learning strategies. Experimental results on public datasets have demonstrated the capability of our method to classify large-scale known DroidMal traffic as well as to detect the novel one. It is encouraging to see that, our model achieves superior performance on known and novel DroidMal traffic classification among the state-of-the-arts. Moreover, AMDetector is able to classify the unseen cross-platform malware. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

2.
22nd Annual International Conference on Computational Science, ICCS 2022 ; 13353 LNCS:380-386, 2022.
Article in English | Scopus | ID: covidwho-1958890

ABSTRACT

Detecting and intercepting malicious requests are some of the most widely used ways against attacks in the network security, especially in the severe COVID-19 environment. Most existing detecting approaches, including matching blacklist characters and machine learning algorithms have all shown to be vulnerable to sophisticated attacks. To address the above issues, a more general and rigorous detection method is required. In this paper, we formulate the problem of detecting malicious requests as a temporal sequence classification problem, and propose a novel deep learning model namely GBLNet, girdling bidirectional LSTM with multi-granularity CNNs. By connecting the shadow and deep feature maps of the convolutional layers, the malicious feature extracting ability is improved on more detailed functionality. Experimental results on HTTP dataset CSIC 2010 demonstrate that GBLNet can efficiently detect intrusion traffic with superior accuracy and evaluating speed, compared with the state-of-the-arts. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

3.
2nd International Conference on Artificial Intelligence and Computer Engineering, ICAICE 2021 ; : 740-744, 2021.
Article in English | Scopus | ID: covidwho-1948775

ABSTRACT

Coronavirus disease is an ongoing pandemic caused by a virus called severe acute respiratory syndrome coronavirus 2. Due to the current global pandemic's perilous state, getting a speedy and precise diagnosis of COVID-19 for everyone who wants to have a COVID-19 test should be the priority. Therefore, building the AlexNet model, which is trained for diagnosing COVID-19 based on CT scans from a large dataset which is composed of 104,009 CT slices coming from 1,489 patients (accuracy is around 67.9%) and a small dataset which is composed of 349 CT images from 216 patients (accuracy is around 62.3 %) would have important implications to help early identification of COVID-19. Moreover, due to the lack of CT scans of positive COVID-19 patients, transferring the learned model parameters from a large dataset to a small dataset contributes to better performance on a small dataset. In our model, the effectiveness of transfer learning is proved by a 1.9% increase in the accuracy of a small dataset. © 2021 IEEE.

4.
2021 IEEE 20TH INTERNATIONAL CONFERENCE ON TRUST, SECURITY AND PRIVACY IN COMPUTING AND COMMUNICATIONS (TRUSTCOM 2021) ; : 122-129, 2021.
Article in English | Web of Science | ID: covidwho-1937855

ABSTRACT

With more devices being inter- or intra-connected, Internet of Things (IoT) has gradually been adopted in many disciplines, such as healthcare industry, coined as Internet of Medical Things (IoMT). The purpose of IoMT is to facilitate the efficiency and effectiveness of medical operations, i.e., remotely monitoring the status of patients. In such healthcare environments, smartphones have become an important device to communicate with others and update the information of patients, resulting in a special type of IoMT called Medical Smartphone Networks (MSNs). To reinforce the distributed architecture, trust management schemes are often implemented to defend against insider attacks. However, how to maintain the robustness of trust management in heavy traffic networks still remains a challenge, i.e., COVID-19 incident would cause excessive traffic for healthcare organizations and increase the difficulty of validating trustworthiness among MSN nodes. In this work, we focus on this issue and propose a blockchain-enabled adaptive traffic sampling method to help enhance the robustness of trust management under high traffic environments. The use of blockchain technology aims to build a verified database of malicious traffic among all nodes. The evaluation in a real healthcare environment demonstrates the viability and effectiveness of our approach.

5.
Asian Journal of Organic Chemistry ; : 11, 2022.
Article in English | Web of Science | ID: covidwho-1925860

ABSTRACT

One of the structural uniqueness of arylnaphthalene lignans (ANLs) is their potential atropoisomerism, which may result in bioactivity discrepancy. However, the stable ANL atropisomers rarely exist in nature. In the course of our phytochemical study of Justicia procumbens, we isolated nine ANL glycosides (1-9) with four of them (1-4) being identified as new stable atropisomers. Their absolute configurations were determined based on the analysis of the circular dichroism (CD) and electronic circular dichroism (ECD) data. The ANL compounds were evaluated for their antiviral potential as entry inhibitors against the infections of H5N1 influenza virus, vesicular stomatitis virus (VSV) and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with 5 being the most potent one with IC50 values ranging from 0.0063-1.13 mu M. The atropisomers did not display significant antiviral activity, indicating that a free rotation of the biphenyl aryl-aryl bond could play a significant role in the antiviral activity of ANL compounds.

6.
19th International Conference on Engineering Psychology and Cognitive Ergonomics, EPCE 2022 Held as Part of the 24th HCI International Conference, HCII 2022 ; 13307 LNAI:300-313, 2022.
Article in English | Scopus | ID: covidwho-1919675

ABSTRACT

This research aims to provide insight regarding the impact of the COVID-19 pandemic on ATCO (Air Traffic Control Officer) skill performance and to identify the effectiveness of methods for reducing skill fade, as perceived by controllers. A questionnaire was administered to fifty-six air traffic controllers from three airports within a European state and an independent sample t-test was then performed on the data output. 78% of controllers agreed to some degree that their skill levels may have reduced since the beginning of the COVID-19 pandemic. A significant difference was recorded in the response scores for controllers at a large airport and controllers at smaller airports for six pandemic-related attitude statements. Simulation sessions, checklists and face-to-face briefings recorded the highest scores among methods for addressing controller skill fade. ATCO responses suggest that controllers operating at large international airports perceive higher levels of skill decay and may be more susceptible to the effects of skill fade after prolonged exposure to low traffic levels. Skills associated with the implementation of declarative knowledge are most susceptible to decay, particularly if these skills are performed in isolation and without ‘integration complexity’. Controller skill fade is a significant concern after the COVID-19 pandemic. As the aviation industry begins to recover, ANSPs must assess the influence of sustained low traffic levels on ATCO performance at a unit level and implement tools which most suitably addresses the effects of skill decay. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

7.
China Communications ; 19(6):11-21, 2022.
Article in English | Web of Science | ID: covidwho-1918292

ABSTRACT

Health monitoring data or the data about infectious diseases such as COVID-19 may need to be constantly updated and dynamically released, but they may contain user's sensitive information. Thus, how to preserve the user's privacy before their release is critically important yet challenging. Differential Privacy (DP) is well-known to provide effective privacy protection, and thus the dynamic DP preserving data release was designed to publish a histogram to meet DP guarantee. Unfortunately, this scheme may result in high cumulative errors and lower the data availability. To address this problem, in this paper, we apply Jensen-Shannon (JS) divergence to design the OPTICS (Ordering Points To Identify The Clustering Structure) scheme. It uses JS divergence to measure the difference between the updated data set at the current release time and private data set at the previous release time. By comparing the difference with a threshold, only when the difference is greater than the threshold, can we apply OPTICS to publish DP protected data sets. Our experimental results show that the absolute errors and average relative errors are significantly lower than those existing works.

8.
Information ; 13(6):18, 2022.
Article in English | Web of Science | ID: covidwho-1917528

ABSTRACT

This paper is based on the box office data of films released in China in the past, which was collected from ENDATA on 30 November 2021, providing 5683 pieces of movie data, and enabling the selection of the top 2000 pieces of movie data to be used as the box office prediction dataset. In this paper, some types of Chinese micro-data are used, and a Baidu search of the index data of movie names 30 days before and after the release date, coronavirus disease 2019 (COVID-19) data in China, and other characteristics are introduced, and the stacking algorithm is optimized by adopting a two-layer model architecture. The first layer base learners adopt Extreme Gradient Boosting (XGBoost), the Light Gradient Boosting Machine (LightGBM), Categorical Boosting (CatBoost), the Gradient Boosting Decision Tree (GBDT), random forest (RF), and support vector regression (SVR), and the second layer meta-learner adopts a multiple linear regression model, to establish a box office prediction model with a prediction error, Mean Absolute Percentage Error (MAPE), of 14.49%. In addition, in order to study the impact of the COVID-19 epidemic on the movie box office, based on the data of 187 movies released from January 2020 to November 2021, and combined with a number of data features introduced earlier, this paper uses LightGBM to establish a model. By checking the importance of model features, it is found that the situation of the COVID-19 epidemic at the time of movie release had a certain related impact on the movie box office.

9.
American Journal of Translational Research ; 14(5):2972-2987, 2022.
Article in English | Web of Science | ID: covidwho-1913290

ABSTRACT

Background: The challenges that viral pneumonia poses to the global public health system remain daunting. In this study, an analysis of publications on viral pneumonia over the past two decades was conducted. Through this work, we hope to provide inspiration for future research on viral pneumonia. Methods: We extracted all of the English publications relevant to viral pneumonia published during 1999-2019 from Web of Science. GraphPad Prism, CiteSpace, and VOSviewer were used to collect and analyze the publication trends in related fields. Results: We identified 2,006 publications with 62,155 citations as of February 16, 2021. The United States accounted for the largest number of publications (34.2%), with the highest number of citations (27,616) and the highest h-index (78). China ranked second in the number of publications. Ctr Dis Control & Prevent proved to be the center of research cooperation. Clinical Infectious Diseases included the most papers published relating to the topic of viral pneumonia. Chan KH published the most papers in this field (25), while an article from Fouchier RAM presented the highest citation frequency (1,275). Conclusions: According to the bibliometric analysis database and related software results, the United States dominates the field of viral pneumonia research. The key term extracted by VOS-viewer has shifted to "Diagnosis and management", indicating a new trend for viral pneumonia research.

10.
Academic Journal of Second Military Medical University ; 42(12):1444-1448, 2021.
Article in Chinese | EMBASE | ID: covidwho-1897231

ABSTRACT

Objective: To observe the protective effects of 2 kinds of protective stickers made from different materials on facial injury/discomfort caused by wearing protective appliances of military medical members in the medical team supporting Hubei, so as to provide reference for developing convenient and effective protective measures. Methods Totally 147 military medical members in the medical team supporting Hubei were surveyed by the self-designed questionnaire of facial injury/discomfort caused by wearing protective appliances. Cross-sectional survey of the facial injury/discomfort before and after using the protective gel stickers (Haishen stickers, developed by the Faculty of Pharmacy, Naval Medical University [Second Military Medical University]) or 3M hydrophilic dressing was conducted, and the protective effects of the 2 kinds of protective stickers on facial injury/discomfort were compared. Results A total of 78 medical members finished the questionnaires (62 cases with Haishen stickers and 16 cases with 3M hydrophilic dressings). The scores of facial injury/discomfort were significantly reduced in both groups after using the protective stickers (both, P<0.05);however, there was no significant difference between the 2 groups before or after using the protective stickers (both, P>0.05). The top 4 moderate-to-severe facial injury/discomfort were fogging of protective glasses/masks (85.9%, 67/78), skin indentation (80.8%, 63/78), pain at the contact sites (74.4%, 58/78) and sultry (71.8%, 56/78), and the overall proportion of moderate-to-severe injury/discomfort was 80.8% (63/78);after using the protective stickers, the top 4 moderate-to-severe facial injury/discomfort were fogging of glasses/masks (53.8%, 42/78), sultry (41.0%, 32/78), respiratory resistance (41.0%, 32/78) and skin indentation (38.5%, 30/78), with the overall proportion of moderate to severe injury/discomfort being 43.6% (34/78);and the top 4 improvement rates of facial injury/discomfort after using protective stickers were skin erosion (76.5%), skin redness (67.3%), pain at the contact sites (63.8%), and itching at the contact site (52.9%). Conclusion These 2 kinds of protective stickers made from different materials can improve the facial injury/discomfort caused by protective appliances, which is worth popularizing.

11.
Engineering, Construction and Architectural Management ; 2022.
Article in English | Scopus | ID: covidwho-1891304

ABSTRACT

Purpose: The impacts of COVID-19 on construction projects have attracted much attention in the construction management research community. Nevertheless, a systematic review of these studies is still lacking. The purpose of this paper is to systematically analyze the impacts of COVID-19 on the different stages of a project life-cycle, and comprehensively sort out the epidemic response measures adopted by project participants. In addition, the study also attempts to explore the challenges and opportunities faced by project management practitioners under the context of COVID-19. Design/methodology/approach: This study comprehensively demonstrates the systematic review process of COVID-19 related research in the construction industry, systematically summarizes the research status of the impact of COVID-19 on construction projects, and defines the strategies to deal with COVID-19 in project management;and through the visualization research, determines the current key research topics and future research trends. Findings: This study identifies 11 construction activities in the project management life cycle that are affected by COVID-19 and finds that the COVID-19 epidemic has the greatest impact on construction workers, construction standards, construction contracts and construction performance. The study further summarizes the six main epidemic countermeasures and mitigation measures taken within the construction industry following the arrival of the epidemic. In addition, the results of this study identify opportunities and future trends in intelligent construction technology, rapid manufacturing engineering and project management in the construction industry in the post-epidemic era through literature results, which also provide ideas for related research. Practical implications: COVID-19 has brought severe challenges to society. It is of great significance for the future sustainable development of the construction industry to identify the impact of COVID-19 on all phases of the project and to promote the development of coping strategies by project stakeholders. Originality/value: First of all, there is little study comprehensively reviewing the impacts of COVID-19 on the different stages of construction projects and the strategies to deal with the negative impacts. In addition, from a life cycle perspective, the used articles in this study were grouped into different categories based on project stages. This promotes an integrated and comprehensive understanding of historical studies. Moreover, on the basis of a comprehensive review, this paper puts forward future research directions to promote the sustainable development of the construction sector. © 2022, Emerald Publishing Limited.

12.
American Journal of Respiratory and Critical Care Medicine ; 205:1, 2022.
Article in English | English Web of Science | ID: covidwho-1880508
13.
Geoscience Frontiers ; 2022.
Article in English | Scopus | ID: covidwho-1873045
14.
Neth Heart J ; 2022 Jun 01.
Article in English | MEDLINE | ID: covidwho-1872740

ABSTRACT

INTRODUCTION: The coronavirus disease 2019 (COVID-19) pandemic has put tremendous pressure on healthcare systems. Most transcatheter aortic valve implantation (TAVI) centres have adopted different triage systems and procedural strategies to serve highest-risk patients first and to minimise the burden on hospital logistics and personnel. We therefore assessed the impact of the COVID-19 pandemic on patient selection, type of anaesthesia and outcomes after TAVI. METHODS: We used data from the Netherlands Heart Registration to examine all patients who underwent TAVI between March 2020 and July 2020 (COVID cohort), and between March 2019 and July 2019 (pre-COVID cohort). We compared patient characteristics, procedural characteristics and clinical outcomes. RESULTS: We examined 2131 patients who underwent TAVI (1020 patients in COVID cohort, 1111 patients in pre-COVID cohort). EuroSCORE II was comparable between cohorts (COVID 4.5 ± 4.0 vs pre-COVID 4.6 ± 4.2, p = 0.356). The number of TAVI procedures under general anaesthesia was lower in the COVID cohort (35.2% vs 46.5%, p < 0.001). Incidences of stroke (COVID 2.7% vs pre-COVID 1.7%, p = 0.134), major vascular complications (2.3% vs 3.4%, p = 0.170) and permanent pacemaker implantation (10.0% vs 9.4%, p = 0.634) did not differ between cohorts. Thirty-day and 150-day mortality were comparable (2.8% vs 2.2%, p = 0.359 and 5.2% vs 5.2%, p = 0.993, respectively). CONCLUSIONS: During the COVID-19 pandemic, patient characteristics and outcomes after TAVI were not different than before the pandemic. This highlights the fact that TAVI procedures can be safely performed during the COVID-19 pandemic, without an increased risk of complications or mortality.

15.
PubMed; 2022.
Preprint in English | PubMed | ID: ppcovidwho-335922

ABSTRACT

A valuable metric in understanding local infectious disease dynamics is the local time-varying reproduction number, i.e. the expected number of secondary local cases caused by each infected individual. Accurate estimation of this quantity requires distinguishing cases arising from local transmission from those imported from elsewhere. Realistically, we can expect identification of cases as local or imported to be imperfect. We study the propagation of such errors in estimation of the local time-varying reproduction number. In addition, we propose a Bayesian framework for estimation of the true local time-varying reproduction number when identification errors exist. And we illustrate the practical performance of our estimator through simulation studies and with outbreaks of COVID-19 in Hong Kong and Victoria, Australia.

16.
PubMed; 2020.
Preprint in English | PubMed | ID: ppcovidwho-333526

ABSTRACT

BACKGROUND: Coronavirus Disease 2019 (COVID-19) has no known specific treatments. However, there might be in vitro and early clinical data as well as evidence from Severe Acute Respiratory Syndrome and Middle Eastern Respiratory Syndrome that could inform clinicians and researchers. This systematic review aims to create priorities for future research of drugs repurposed for COVID-19. METHODS: This systematic review will include in vitro, animal, and clinical studies evaluating the efficacy of a list of 34 specific compounds and four groups of drugs identified in a previous scoping review. Studies will be identified both from traditional literature databases and pre-print servers. Outcomes assessed will include time to clinical improvement, time to viral clearance, mortality, length of hospital stay, and proportions transferred to the intensive care unit and intubated, respectively. We will use the GRADE methodology to assess the quality of the evidence. DISCUSSION: The challenge posed by COVID-19 requires not just a rapid review of drugs that can be repurposed but also a sustained effort to integrate new evidence into a living systematic review. Systematic review registration: Prospero 2020 crd42020175648.

17.
Chinese Journal of Pharmaceutical Biotechnology ; 29(1):1-7, 2022.
Article in Chinese | EMBASE | ID: covidwho-1791590

ABSTRACT

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) can cause respiratory symptoms such as fever, cough and dyspnea after infection, and Coronavirus Disease 2019 (COVID-19), severe acute respiratory syndrome, and even death can occur in severe cases.SARS-CoV-2 infection has no specific treatment drugs, mainly rely on vaccination to block its transmission.In various structural proteins of SARS-CoV-2, Spike protein (S) and Nucleocapsid protein (N) are the main antigenic proteins, which are also important candidate proteins for developing SARS-CoV-2 vaccine and antibody detection reagents.To express chimeric protein containing multiple epitopes of SARS-CoV-2 by prokaryotic expression system, and to verify the immunogenicity of the chimeric protein, antigenic epitopes of SARS-CoV-2 structural proteins were analyzed and screened by molecular biology software, the selected antigenic epitopes were connected in tandem, and expressed high efficiently in E.coli as a chimeric protein by genetic engineering technology.The soluble chimeric protein of high purity was obtained after purification and renaturation.Mice were immunized with the purified chimeric protein together with MF59 adjuvant, aluminum adjuvant or no adjuvant at different doses respectively.Humoral immunity and cellular immunity induced by the chimeric protein were evaluated by detecting the antibody titer of antiserum and the level of related cytokine.The expressed chimeric protein was in the form of inclusion body and exists in the sediment, soluble chimeric protein was obtained after renaturation.The specific antibodies with high titer were produced in the immunized mice, and strong cellular immunity was induced also.Higher concentration of chimeric protein had better elicited immune effect than the lower concentration of chimeric protein.The immune effect induced by the chimeric protein with MF59 adjuvant was no different from that induced with aluminum adjuvant.This study provides novel ideas for the design and renaturation of SARS-CoV-2 chimeric protein, and the chimeric protein is expected to be used for the development of SARS-CoV-2 recombinant protein vaccine and diagnosis reagent.

18.
Traditional Medicine Research ; 7(3), 2022.
Article in English | EMBASE | ID: covidwho-1791218

ABSTRACT

Network pharmacology is an emerging technology based on systems biology and computer information technology, with the help of databases and related auxiliary software, to carry out new drug development and the screening analysis of drug active ingredients and targets. At present, the network pharmacology has been used widely in the research of prevention and treatment drugs for coronavirus disease 2019 (COVID-19). This paper reviews the research methods of network pharmacology in the field of prevention and treatment of COVID-19 by traditional Chinese medicine (TCM) and the development of its specific drugs and further explores the concrete application ideas of this technology. The necessary databases and tools of necessary for screening the active components and targets to molecular docking are summarized. In addition, the practical application of network pharmacology in the study of several potential TCM and active components against COVID-19 is reviewed, mainly including the screening of active components, the discovery of target, and the elucidation of action mechanism. The diversification of research ideas of network pharmacology in the field of TCM was realized, in particular, with two specific ideas in the study of active ingredients of TCM. Finally, the difference of control effect among several TCM and Western medicines on COVID-19 and the limitation and challenge of network pharmacology in TCM, i.e., the insufficient integrity and accuracy of the database, the uncertain complexity of components analysis, the unclear mechanism of component-target action, and some new challenges due to the characteristics of TCM, are discussed. In view of the importance of TCM in the field of control of COVID-19, the combination of TCM and network pharmacology will continue to play an important role in the development of specific drugs of COVID-19 in the future, in particular, to save time and reduce the workload of drug developers, which is also a direction of TCM development. This study provides theoretical reference and methodological basis for the prevention and treatment of COVID-19 by TCM.

19.
20th and 21st Joint COTA International Conference of Transportation Professionals - Advanced Transportation, Enhanced Connection ; : 681-690, 2021.
Article in English | Web of Science | ID: covidwho-1790152

ABSTRACT

COVID-19 has been affecting every aspect of societal life including human mobility since December, 2019. In this paper, we study the impact of COVID-19 on human mobility patterns at the state level within the United States. From the temporal perspective, we find that the change of mobility patterns does not necessarily correlate with government policies and guidelines, but is more related to people's awareness of the pandemic, which is reflected by the search data from Google Trends. Our results show that it takes on average 14 days for the mobility patterns to adjust to the new situation. From the spatial perspective, we conduct a state-level network analysis and clustering using the mobility data from Multiscale Dynamic Human Mobility Flow Dataset. As a result, we find that 1) states in the same cluster have shorter geographical distances;2) a 14-daydelay again is found between the time when the largest number of clusters appears and the peak of Coronavirus-related search queries on Google Trends;and 3) a major reduction in other network flow properties, namely degree, closeness, and betweenness, of all states from the week of March2 to the week of April 6 (the week of the largest number of clusters).

20.
International Journal of Intelligent Systems ; 2022.
Article in English | Scopus | ID: covidwho-1787667

ABSTRACT

Dynamic searchable encryption (SE) aims at achieving varied search function over encrypted database in dynamic setting, which is a trade-off in efficiency, security, and functionality. Recent work proposes a file-injection attack which can successfully attack by utilizing some information leaked in the update process. To mitigate this attack, some SE schemes with forward privacy are proposed. However, these schemes are designed to achieve single keyword or conjunctive keyword search, which cannot support multikeyword search. Moreover, these schemes do not consider the function of results ranking. In this paper, we propose a forward privacy multikeyword ranked search scheme over encrypted database. We design a forward privacy multikeyword search scheme based on the classic MRSE scheme. Our scheme makes the cloud cannot obtain the actual match results of the past query with the newly updated files by adding the well-chosen dummy elements to the original index and query vectors. We rank the search results based on the matched keyword number and the (Formula presented.) rule in the dynamic setting. Our scheme uses only the symmetric encryption primitive. We implement our scheme for COVID-19 data set and the experimental evaluation results show that the proposed scheme is secure and efficient. © 2022 Wiley Periodicals LLC.

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