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Developing an automatic door-opening system that can recognize masks and gauge body temperature is the aim of this project. The new Corona Virus (COVID-19) is an unimaginable pandemic that presents the medical industry with a serious worldwide issue in the twenty-first century. How individuals conduct their lives has substantially changed as a result. Individuals are reluctant to seek out even the most basic healthcare services because of the rising number of sick people who pass away, instilling an unshakable terror in their thoughts.This paper is about the Automatic Health Machine (AHM). In this dire situation, the government provided the people with a lot of directions and information. Apart from the government, everyone is accountable for his or her own health. The most common symptom of corona infection is an uncontrollable rise in body temperature. In this project, we create a novel device to monitor people's body temperatures using components such as an IR sensor and temperature sensor. © 2023 IEEE.
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Introduction/Objective COVID-19 vaccine-related lymphadenopathy, particularly in the ipsilateral axilla, is a relatively well-known side effect of mRNA vaccines with many reports in radiology, but less is known regarding histopathology and additional sites of lymphadenopathy, as well as other localized potential vaccine-related mass manifestations. In addition to a case of minimal change disease, we report two cases here with associated systemic and local pathologic changes related to COVID-19 vaccination. Methods/Case Report In case #1, a 17-year-old male presented with a 2.4 cm left postauricular mass. He had originally noticed the mass six months prior and thought that it had recently been growing. The mass was soft, nonfluctuant, and nontender to palpation. Given the risk of malignancy, a resection was performed. Histology showed an enlarged lymph node composed of mixed inflammatory cell components consistent with lymphoid hyperplasia and no evidence of malignancy. On further chart review, the patient had received his second COVID-19 vaccination just prior to noticing the mass enlarging. A SARS-CoV-2 Anti-Spike IgG assay was as high as 24,396 AU/ml, suggesting that this benign lymphadenopathy was most likely related to his vaccination. For case #2, a 47-year-old male developed a painless right deltoid mass shortly after receiving his vaccination at the same area that subsequently increased in size over seven months to 6.5 cm. Imaging showed a heterogeneous mass within the deltoid muscle concerning for malignancy and a biopsy was performed. Sections showed wavy, bland spindle cells with nuclei staining diffusely positive for beta-catenin, consistent with fibromatosis at his vaccination site. Results (if a Case Study enter NA) NA. Conclusion In summary, these case reports show potential systemic and local reactive effects in response to COVID-19 vaccination.
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Simple SummaryDuring the long-term co-evolution of the virus and the host, even closely related vaccines may emerge with incomplete protective immunity due to the mutations or deletions of amino acids at specific antigenic sites. The mutation of PEDV was accelerated by the recombination of different strains and the mutation of the strains adapting to the environment. These mutations either cause immune escape from conventional vaccines or affect the virulence of the virus. Therefore, researching and developing new vaccines with cross-protection through continuous monitoring, isolation and sequencing are important to determine whether their genetic characteristics are changed and to evaluate the protective efficacy of current vaccines. The porcine epidemic diarrhea virus (PEDV) can cause severe piglet diarrhea or death in some herds. Genetic recombination and mutation facilitate the continuous evolution of the virus (PEDV), posing a great challenge for the prevention and control of porcine epidemic diarrhea (PED). Disease materials of piglets with PEDV vaccination failure in some areas of Shanxi, Henan and Hebei provinces of China were collected and examined to understand the prevalence and evolutionary characteristics of PEDV in these areas. Forty-seven suspicious disease materials from different litters on different farms were tested by multiplex PCR and screened by hematoxylin-eosin staining and immunohistochemistry. PEDV showed a positivity rate of 42.6%, infecting the small and large intestine and mesenteric lymph node tissues. The isolated strains infected Vero, PK-15 and Marc-145 multihost cells and exhibited low viral titers in all three cell types, as indicated by their growth kinetic curves. Possible putative recombination events in the isolates were identified by RDP4.0 software. Sequencing and phylogenetic analysis showed that compared with the classical vaccine strain, PEDV SX6 contains new insertion and mutations in the S region and belongs to genotype GIIa. Meanwhile, ORF3 has the complete amino acid sequence with aa80 mutated wild strains, compared to vaccine strains CV777, AJ1102, AJ1102-R and LW/L. These results will contribute to the development of new PEDV vaccines based on prevalent wild strains for the prevention and control of PED in China.
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Water distribution systems (WDSs) deliver clean, safe drinking water to consumers, providing an essential service to constituents. WDSs are increasingly at risk of contamination due to aging infrastructure and intentional acts that are possible through cyber-physical vulnerabilities. Identifying the source of a contamination event is challenging due to limited system-wide water quality monitoring and non-uniqueness present in solving inverse problems to identify source characteristics. In addition, changes in the expected demand patterns that are caused by, for example, social distancing during a pandemic, adoption of water conservation behaviors, or use of decentralized water sources can change the anticipated propagation of contaminant plumes in a network. This research develops a computational framework to characterize contamination sources using machine learning (ML) techniques and simulate water demands and human exposure to a contaminant using agent-based modeling (ABM). An ABM framework is developed to simulate demand changes during the COVID-19 pandemic. The ABM simulates population movement dynamics, transmission of COVID-19 within a community, decisions to social distance, and changes in demands that occur due to social distancing decisions. The ABM is coupled with a hydraulic simulation model, which calculates flows in the network to simulate the movement of a contaminant plume in the network for several contamination event scenarios. ML algorithms are applied to determine the location of source nodes. Research results demonstrate that ML using random forests can identify source nodes based on inline and mobile sensor data. Sensitivity analysis is conducted to explore the number of mobile sensors that are needed to accurately identify the source node. Rapidly identifying contamination source nodes can increase the speed of response to a contamination event, reducing the impact to the community and increasing the resiliency of WDSs during periods of changing demands. © World Environmental and Water Resources Congress 2023.All rights reserved
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AIMS: The identification of haemophagocytosis in bone marrow (BM) is recurrently identified in patients with severe COVID-19. These initial COVID-19 autopsy studies have afforded valuable insight into the pathophysiology of this disease; however, only a limited number of case series have focused on lymphoid or haematopoietic tissues. METHODS: BM and lymph node (LN) specimens were obtained from adult autopsies performed between 1 April 2020 and 1 June 2020, for which the decedent had tested positive for SARS-CoV-2. Tissue sections (H&E, CD3, CD20, CD21, CD138, CD163, MUM1, kappa/lambda light chains in situ hybridisation) were examined by two haematopathologists, who recorded morphological features in a blinded fashion. Haemophagocytic lymphohistiocytosis (HLH) was assessed based on HLH 2004 criteria. RESULTS: The BM demonstrated a haemophagocytic pattern in 9 out of 25 patients (36%). The HLH pattern was associated with longer hospitalisation, BM plasmacytosis, LN follicular hyperplasia and lower aspartate aminotransferase (AST), as well as ferritin at demise. LN examination showed increased plasmacytoid cells in 20 of 25 patients (80%). This pattern was associated with a low absolute monocyte count at diagnosis, lower white cell count and lower absolute neutrophil count at demise, and lower ferritin and AST at demise. CONCLUSIONS: Autopsy results demonstrate distinct morphological patterns in BM, with or without haemophagocytic macrophages, and in LN, with or without increased plasmacytoid cells. Since only a minority of patients met diagnostic criteria for HLH, the observed BM haemophagocytic macrophages may be more indicative of an overall inflammatory state.
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Disease models that can accurately recapitulate human pathophysiology during infection and clinical response to antiviral therapeutics are still lacking, which represents a major barrier in drug development. The emergence of human Organs-on-a-Chip that integrated microfluidics with three-dimensional (3D) cell culture, may become the potential solution for this urgent need. Human Organs-on-a-Chip aims to recapitulate human pathophysiology by incorporating tissue-relevant cell types and their microenvironment, such as dynamic fluid flow, mechanical cues, tissue–tissue interfaces, and immune cells to increase the predictive validity of in vitro experimental models. Human Organs-on-a-Chip has a broad range of potential applications in basic biomedical research, preclinical drug development, and personalized medicine. This review focuses on its use in the fields of virology and infectious diseases. We reviewed various types of human Organs-on-a-Chip-based viral infection models and their application in studying viral life cycle, pathogenesis, virus-host interaction, and drug responses to virus- and host-targeted therapies. We conclude by proposing challenges and future research avenues for leveraging this promising technology to prepare for future pandemics.
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The dreaded coronavirus (COVID-19) disease traceable to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV2) has killed thousands of people worldwide, and the World Health Organization (WHO) has proclaimed the viral respiratory disease a human pandemic. The adverse flare of COVID-19 and its variants has triggered collaborative research interests across all disciplines, especially in medicine and healthcare delivery. Complex healthcare data collected from patients via sensors and devices are transmitted to the cloud for analysis and sharing. However, it is pretty difficult to achieve rapid and intelligent decisions on the processed information due to the heterogeneity and complexity of the data. Artificial intelligence (AI) has recently appeared as a promising paradigm to address this issue. The introduction of AI to the Internet of Medical Things (IoMT) births the era of AI of Medical Things (AIoMT). The AIoMT enables the autonomous operation of sensors and devices to provide a favourable and secure environmental landscape to healthcare personnel and patients. AIoMT finds successful applications in natural language processing (NLP), speech recognition, and computer vision. In the current emergency, medical-related records comprising blood pressure, heart rate, oxygen level, temperature, and more are collected to examine the medical conditions of patients. However, the power usage of the low-power sensor nodes employed for data transmission to the remote data centres poses significant limitations. Currently, sensitive medical information is transmitted over open wireless channels, which are highly susceptible to malicious attacks, posing a significant security risk. An insightful privacy-aware energy-efficient architecture using AIoMT for COVID-19 pandemic data handling is presented in this chapter. The goal is to secure sensitive medical records of patients and other stakeholders in the healthcare domain. Additionally, this chapter presents an elaborate discussion on improving energy efficiency and minimizing the communication cost to improve healthcare information security. Finally, the chapter highlights the open research issues and possible lines of future research in AIoMT.
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The COVID-19 pandemic has devastated our daily lives, leaving horrific repercussions in its aftermath. Due to its rapid spread, it was quite difficult for medical personnel to diagnose it in such a big quantity. Patients who test positive for Covid-19 are diagnosed via a nasal PCR test. In comparison, polymerase chain reaction (PCR) findings take a few hours to a few days. The PCR test is expensive, although the government may bear expenses in certain places. Furthermore, subsets of the population resist invasive testing like swabs. Therefore, chest X-rays or Computerized Vomography (CT) scans are preferred in most cases, and more importantly, they are non-invasive, inexpensive, and provide a faster response time. Recent advances in Artificial Intelligence (AI), in combination with state-of-the-art methods, have allowed for the diagnosis of COVID-19 using chest x-rays. This article proposes a method for classifying COVID-19 as positive or negative on a decentralized dataset that is based on the Federated learning scheme. In order to build a progressive global COVID-19 classification model, two edge devices are employed to train the model on their respective localized dataset, and a 3-layered custom Convolutional Neural Network (CNN) model is used in the process of training the model, which can be deployed from the server. These two edge devices then communicate their learned parameter and weight to the server, where it aggregates and updates the global model. The proposed model is trained using an image dataset that can be found on Kaggle. There are more than 13,000 X-ray images in Kaggle Database collection, from that collection 9000 images of Normal and COVID-19 positive images are used. Each edge node possesses a different number of images;edge node 1 has 3200 images, while edge node 2 has 5800. There is no association between the datasets of the various nodes that are included in the network. By doing it in this manner, each of the nodes will have access to a separate image collection that has no correlation with each other. The diagnosis of COVID-19 has become considerably more efficient with the installation of the suggested algorithm and dataset, and the findings that we have obtained are quite encouraging. © 2023 Tech Science Press. All rights reserved.
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BACKGROUND: A common secondary effect after SARS-CoV-2 immunization is an increased in size of the axillary lymph nodes ipsilateral to the vaccinated site. Eventually, an increased in size of the axillary lymph nodes may lead to a misinterpretation of the breast screening mammogram, performed in asymptomatic women between the age 50 to 69 years old for early breast cancer diagnosis. The aim of our research was to evaluate the impact of the vaccination for SARS-CoV-2 in the breast screening programmes in terms of recall rates and number of false positive results. As a secondary purpose we would analysed the protocols adopted by different breast screening units around the world after SARS-CoV-2 vaccination. METHODS: Observational and retrospective study analysing breast screening mammograms from a single Breast Cancer Screening Unit in Madrid. The mammograms of previously vaccinated women were analysed, reviewing the axillary lymph nodes and the re-call rate secondary to axillary lymphadenopathies. RESULTS: Four hundred and twenty three screening mammograms were performed in May 2021 in the University Hospital Ramon y Cajal in Madrid, which is part of the Breast Screening Programme in Madrid, Spain. None of the women previously vaccinated for SARS-CoV-2 were recalled for complementary studies due to an increased in the axillary lymph nodes. CONCLUSIONS: The protocol stablished by the Spanish Society of Breast Image that stands up for a routine breast screening mammogram after SARS-CoV-2 immunization, has no increase in the recall rate or increase in number of false positives.
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The current global health crisis is a consequence of the pandemic caused by COVID-19. It has impacted the lives of people from all factions of society. The re-emergence of new variants is threatening the world, which urges the development of new methods to prevent rapid spread. Places with more extensive social dealings, such as offices, organizations, and educational institutes, have a greater tendency to escalate the viral spread. This research focuses on developing a strategy to find out the key transmitters of the virus, particularly at educational institutes. The reason for considering educational institutions is the severity of the educational needs and the high risk of rapid spread. Educational institutions offer an environment where students come from different regions and communicate with each other at close distances. To slow down the virus's spread rate, a method is proposed in this paper that differs from vaccinating the entire population or complete lockdown. In the present research, we identified a few key spreaders, which can be isolated and can slow down the transmission rate of the contagion. The present study creates a student communication network, and virus transmission is modeled over the predicted network. Using student-to-student communication data, three distinct networks are generated to analyze the roles of nodes responsible for the spread of this contagion. Intra-class and inter-class networks are generated, and the contagion spread was observed on them. Using social network strategies, we can decrease the maximum number of infections from 200 to 70 individuals, with contagion lasting in the network for 60 days.
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Today, the emergence of social media is helpful for the healthcare system where everyone is closely connected. Large numbers of people can be reached by using seed nodes to provide medical advice, facilities, new changes in the treatment, and any health ministry guidelines. As today's world is dealing with COVID-19, the main objective is to provide healthcare services to many people irrespective of time and locality. As people suffering from corona are dealing with mental health issues, in order to deal with it, a seed pick framework using machine learning for the influence maximization technique is proposed, which will be helpful to provide pervasive healthcare. For pervasive healthcare, an effectual seed pick framework is required focusing on influence maximization using machine learning. The proposed algorithm Fuzzy-VIKOR is helpful to identify the targeted node to spread information at a high rate. Consequently, the proposed structure effectively addresses different issues related to a large number of patients, and thus, increased influence maximization using seed nodes is helpful for pervasive healthcare. The experiments show that the proposed framework has high precision, accuracy, F1-score, and recall compared to other existing algorithms employed to find influence maximization seeds.
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We aimed to research the design and path-planning methods of an intelligent disinfection-vehicle system. A ROS (robot operating system) system was utilized as the control platform, and SLAM (simultaneous localization and mapping) technology was used to establish an indoor scene map. On this basis, a new path-planning method combining the A* algorithm and the Floyd algorithm is proposed to ensure the safety, efficiency, and stability of the path. Simulation results show that with the average shortest distance between obstacles and paths of 0.463, this algorithm reduces the average numbers of redundant nodes and turns in the path by 70.43% and 31.1%, respectively, compared to the traditional A* algorithm. The algorithm has superior performance in terms of safety distance, path length, and redundant nodes and turns. Additionally, a mask recognition and pedestrian detection algorithm is utilized to ensure public safety. The results of the study indicate that the method has satisfactory performance. The intelligent disinfection-vehicle system operates stably, meets the indoor mapping requirements, and can recognize pedestrians and masks.
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Wireless sensor networks (WSN) playa significant role in the collection and transmission of data. The principal data collectors and broadcasters are small wireless sensor nodes. As a result of their disorganized layout, the nodes in this network are vulnerable to intrusion. Every aspect of human life includes some form of technological interaction. While the Covid-19 pandemic has been ongoing, the whole corporate and academic world has gone digital. As a direct result of digitization, there has been a rise in the frequency with which Internet-based systems are attacked and breached. The Distributed Denial of Service (DDoS) and Distributed Reflective Denial of Service (DRDoS) assaults are new and dangerous type of cyberattacks that can quickly bring down any service or application that relies on the Internet's infrastructure. Cybercriminals are always refining their methods of attack and evading detection by using techniques that are out of date. Traditional detection systems are not suited to identify novel DDoS attacks since the volume of data created and stored has expanded exponentially in recent years. This research provides a comprehensive overview of the relevant literature, focusing on deep learning for DDoS and DRDoS detection. Due to the expanding number of loT gadgets, distributed DDoS and DRDoS attacks are becoming more likely and more damaging. Due to their lack of generalizability, current attack detection methods cannot be used for early detection of DDoS and DRDoS, resulting in significant load or service degradation when implemented at the endpoint. In this research, a brief review is performed on the models that are used for identification of DDoS and DRDoS attacks. The working of the existing models and the limitations of the models are briefly analyzed in this research. © 2023 IEEE.
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In the summer of 2020, multiple efforts were undertaken to establish safe and effective vaccines to combat the spread of the coronavirus disease (COVID-19). In the United States (U.S.), Operation Warp Speed (OWS) was the program designated to coordinate such efforts. OWS was a partnership between the Department of Health and Human Services (HHS), the Department of Defense (DOD), and the private sector, that aimed to help accelerate control of the COVID-19 pandemic by advancing development, manufacturing, and distribution of vaccines, therapeutics, and diagnostics. The U.S. Department of Veterans Affairs' (VA) was identified as a potential collaborator in several large-scale OWS Phase III clinical trial efforts designed to evaluate the safety and efficacy of various vaccines that were in development. Given the global importance of these trials, it was recognized that there would be a need for a coordinated, centralized effort within VA to ensure that its medical centers (sites) would be ready and able to efficiently initiate, recruit, and enroll into these trials. The manuscript outlines the partnership and start-up activities led by two key divisions of the VA's Office of Research and Development's clinical research enterprise. These efforts focused on site and enterprise-level requirements for multiple trials, with one trial serving as the most prominently featured of these studies within the VA. As a result, several best practices arose that included designating clinical trial facilitators to study sites to support study initiation activities and successful study enrollment at these locations in an efficient and timely fashion.
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The Covid-19 pandemic has increased the global dependency on the internet. Millions of individuals use social networking sites to not only share information, but also their personal opinions. These facts and opinions are frequently unconfirmed, which result in the spread of incorrect information, generally alluded to as "Fake Content”. The most challenging aspect of social media is in determining the source of information. It's difficult to figure out who generated fake news once it's gone viral. Most available computational models have a key flaw in that they rely on the presence of inaccurate information to generate meaningful features, making disinformation mitigation measures difficult to predict. This paper presents a parallel approach to false information mitigation drawn from the field of Epidemiology using SIR(Susceptible, Infected, Recovered) to model the impact of fake data dissemination during Covid-19. SIR simulation is done using NetLogo in which the population is made up of two agents: Fake news believers and non-believers. To confirm our work, the concept of trust is also discussed which is a fundamental component of any fake news interaction. The level of trust can be expressed by assigning each node a pair of trust scores. We ran our experiments based on three common evaluation metrics: Accuracy, Precision, and Recall. The hybrid model shows an increase in accuracy by 81.4%, 77.1%, and 91.8% for the respective networks. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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During the COVID-19 pandemic, which emerged in 2020, many patients were treated in isolation wards because of the high infectivity and long incubation period of COVID-19. Therefore, monitoring systems have become critical to patient care and to safeguard medical professional safety. The user interface is very important to the surveillance system;therefore, we used web technology to develop a system that can create an interface based on user needs. When the surveillance scene needs to be changed, the surveillance location can be changed at any time, effectively reducing the costs and time required, so that patients can achieve timely and appropriate goals of treatment. ZigBee was employed to develop a monitoring system for intensive care units (ICUs). Unlike conventional GUIs, the proposed GUI enables the monitoring of various aspects of a patient, and the monitoring interface can be modified according to the user needs. A simulated ICU environment monitoring system was designed to test the effectiveness of the system. The simulated environment and monitoring nodes were set up at positions consistent with the actual clinical environments to measure the time required to switch between the monitoring scenes or targets on the GUI. A novel system that can construct ZigBee-simulated graphical monitoring interfaces on demand was proposed in this study. The locations of the ZigBee monitoring nodes in the user interface can be changed at any time. The time required to deploy the monitoring system developed in this study was 4 min on average, which is much shorter than the time required for conventional methods (131 min). The system can effectively overcome the limitations of the conventional design methods for monitoring interfaces. This system can be used to simultaneously monitor the basic physiological data of numerous patients, enabling nursing professionals to instantly determine patient status and provide appropriate treatments. The proposed monitoring system can be applied to remote medical care after official adoption.
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This review highlights ten important advances in the neuromuscular disease field that were reported in 2022. As with prior updates in this article series, the overarching topics include (i) advances in understanding of fundamental neuromuscular biology; (ii) new / emerging diseases; (iii) advances in understanding of disease etiology and pathogenesis; (iv) diagnostic advances; and (v) therapeutic advances. Within this general framework, the individual disease entities that are discussed in more detail include neuromuscular complications of COVID-19 (another look at the topic first covered in the 2021 and 2022 reviews), DNAJB4-associated myopathy, NMNAT2-deficient hereditary axonal neuropathy, Guillain-Barré syndrome, sporadic inclusion body myositis, and amyotrophic lateral sclerosis. In addition, the review highlights a few other advances (including new insights into mechanisms of fiber maturation during muscle regeneration and fiber rebuilding following reinnervation, improved genetic testing methods for facioscapulohumeral and myotonic muscular dystrophies, and the use of SARM1 inhibitors to block Wallerian degeneration) that will be of significant interest for clinicians and researchers who specialize in neuromuscular disease.
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The outbreak of COVID-19 provides a rare opportunity for the implementation of the carbon tax. To determine which stage is the most appropriate for introducing the policy, a simulation model based on China's panel data is established to analyze the impact of the carbon tax on government revenue and residents' income from five scenarios. A new GM-SD modeling method is proposed to ensure the accuracy of the model. The results show that the impact of the carbon tax on the government and the public is significantly different at different stages, and even the implementation of the carbon tax in the early stage of COVID-19 will reduce the government's tax revenue. The score analysis of government tax revenue, residents' surplus disposable income, residents' emotional value, and government administrative power finds that the middle period of COVID-19 is the best time to implement the policy. In addition, a more detailed analysis of five aspects, including total population, energy consumption, and national income, shows that the best time to implement the carbon tax policy is when the damage degree of COVID-19 is moderate. The analysis results can provide a reference and basis for China to introduce the carbon tax in the event of similar events as COVID-19, and have reference significance for other countries that have not implemented a carbon tax. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Population demography can change the network structure, which further plays an important role in the spreading of infectious disease. In this paper, we study the epidemic dynamics in temporal clustered networks where the local-world structure and clustering are incorporated into the attachment mechanism of new nodes. It is found that increasing the local-world size of new nodes has little influence on the clustering coefficient but increases the degree heterogeneity of networks. Besides, when the network evolves faster, increasing the local-world size of new nodes leads to a faster initial growth rate and a larger steady density of infectious nodes, while it has small impacts on the steady density of infectious disease when the network evolves slowly. Furthermore, if the average degree is fixed, increasing the probability of triad formation p enlarges the clustering coefficient of a network, which reduces the initial growth rate and steady density of infectious nodes in the network. This work could provide a theoretical foundation for the control of infectious disease.
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The Social Internet of Things (SIoT) can be seen as integrating the social networking concept into the Internet of Things (IoT). Such networks enable different devices to form social relationships among themselves depending on pre-programmed rules and the preferences of their owners. When SIoT devices encounter one another on the spur of the moment, they seek out each other's assistance. The connectivity of such smart objects reveals new horizons for innovative applications empowering objects with cognizance. This enables smart objects to socialize with each other based on mutual interests and social aspects. Trust building in social networks has provided a new perspective for providing services to providers based on relationships like human ones. However, the connected IoT nodes in the community may show a lack of interest in forwarding packets in the network communication to save their resources, such as battery, energy, bandwidth, and memory. This act of selfishness can highly degrade the performance of the network. To enhance the cooperation among nodes in the network a novel technique is needed to improve the performance of the network. In this article, we address the issue of the selfishness of the nodes through the formation of a credible community based on honesty. A social process is used to form communities and select heads in these communities. The selected community heads having social attributes prove effective in determining the social behavior of the nodes as honest or selfish. Unlike other schemes, the dishonest nodes are isolated in a separate domain, and they are given several chances to rejoin the community after increasing their honesty levels. The proposed social technique was simulated using MATLAB and compared with existing schemes to show its effectiveness. Our proposed technique outperforms the existing techniques in terms of throughput, overhead, packet delivery ratio (PDR), and packet-delivery latency.