<strong>Introduction:</strong> The coronavirus disease 2019 (COVID-19) was declared as pandemic by WHO by March 11th. First case reported in Oman was on 24th February 2020 and later the country went throug...<strong>Introduction:</strong> The coronavirus disease 2019 (COVID-19) was declared as pandemic by WHO by March 11th. First case reported in Oman was on 24th February 2020 and later the country went through stages of epidemic progression. This study describes the sociodemographic and epidemiological characteristics of confirmed COVID-19 cases in Muscat governorate and related outcomes. <strong>Materials and Methods:</strong> This is a descriptive, exploratory analysis of all lab confirmed COVID 19 cases that were reported from 1st February to 31st May 2020. Data for the study was primarily extracted from notifications system established for surveillance (Tarassud). Secondary data sources were, contact listings and hospital medical records.<strong> Results:</strong> 11,648 initial cases of confirmed COVID-19 infections were included. The mean age was 35 years, 84.7% (N = 9862) were males, 25.9% (N = 3017) were Omanis, and 74.1% (N = 8631) were expatriates of which Indian origin were the majority (37%). Fever and cough were the most common presentations (46.3% and 29.5% respectively). Diabetes and hypertension were the most common comorbidities (4.9% and 4.6% respectively). Hospital admission was required for 7% (N = 811) of the total reported cases, out of them 171 cases (21%) were admitted to ICU, where 107 (13.2%) were ventilated. The case fatality rate (CFR) was 0.9%. 158 clusters containing 2949 contacts were identified from case records and categorised into 3 groups based on their exposure settings. The incubation period measured was 8 days (IQR 4.0 - 15.0) for workplace, 8 days (IQR 4 - 17) for dormitory and 4 days (IQR 2.0 - 7.0) for family groups. The secondary attack rate (SAR) estimated was 41.6% (95% CI: 0.34 - 0.48), 52% (95% CI: 0.40 - 0.63) and 33% (95% CI: 0.27 - 0.38) for workplace, dormitory and family groups, respectively. <strong>Conclusion:</strong> Results of this study, determine the transmission trend of COVID-19 in a country with high immigrant population. These findings could be utilised for further response planning in similar settings.展开更多
<p align="justify"> <span style="font-family:Verdana;"><strong>Introduction:</strong> The coronavirus disease 2019 epidemic emerged in December 2019 and spread worldwide. Si...<p align="justify"> <span style="font-family:Verdana;"><strong>Introduction:</strong> The coronavirus disease 2019 epidemic emerged in December 2019 and spread worldwide. Since workplaces are high-risk location for occupational exposure, understanding of the dynamics of COVID-19 clusters in occupational settings helps reduce the transmission of infectious disease in workplaces. This study presents an overview of the epidemiological characteristics of COVID-19 workplace clusters, preventive strategies adopted and outbreak responses undertaken in Muscat governorate. <strong>Materials and Methods:</strong> This is a descriptive study on the epidemiological characteristics of cases and distribution of workplace-related clusters of COVID-19 in Muscat Governorate in Oman. <strong>Results:</strong> A total of 36,798 COVID-19 cases were confirmed in Muscat from 24<span style="white-space:nowrap;"><sup>th</sup></span> February to 31<span style="white-space:nowrap;"><sup>st</sup></span> July 2020 in which 40.5% was belonging to clusters. Out of them 61% were Workplace-Dormitory clusters, predominantly expatriates. 78.6% of employees were symptomatic at time of examination. Fever and cough were the two most common symptoms reported in workplace related clusters. The number of affected employees ranged from 2 to 358 per cluster. Construction, retail, food, beverages, services, industrial manufacturing, oil & gas and transportation were identified as most at risk settings. Within the cases in workplace-related clusters, there was significant higher prevalence of diabetes, hypertension and obesity among females compared to males as also Omani nationals compared to expatriates and smoking among expatriate males compared to Omani males. <strong>In conclusion,</strong> understanding the epidemiological characteristics of the affected cases in organization setting will assist the policymakers to understand patterns of epidemiological spread and plan for robust interventions.</span> </p>展开更多
The Internet ofMedical Things(IoMT)is mainly concernedwith the efficient utilisation of wearable devices in the healthcare domain to manage various processes automatically,whereas machine learning approaches enable th...The Internet ofMedical Things(IoMT)is mainly concernedwith the efficient utilisation of wearable devices in the healthcare domain to manage various processes automatically,whereas machine learning approaches enable these smart systems to make informed decisions.Generally,broadcasting is used for the transmission of frames,whereas congestion,energy efficiency,and excessive load are among the common issues associated with existing approaches.In this paper,a machine learning-enabled shortest path identification scheme is presented to ensure reliable transmission of frames,especially with the minimum possible communication overheads in the IoMT network.For this purpose,the proposed scheme utilises a well-known technique,i.e.,Kruskal’s algorithm,to find an optimal path from source to destination wearable devices.Additionally,other evaluation metrics are used to find a reliable and shortest possible communication path between the two interested parties.Apart from that,every device is bound to hold a supplementary path,preferably a second optimised path,for situations where the current communication path is no longer available,either due to device failure or heavy traffic.Furthermore,the machine learning approach helps enable these devices to update their routing tables simultaneously,and an optimal path could be replaced if a better one is available.The proposed mechanism has been tested using a smart environment developed for the healthcare domain using IoMT networks.Simulation results show that the proposed machine learning-oriented approach performs better than existing approaches where the proposed scheme has achieved the minimum possible ratios,i.e.,17%and 23%,in terms of end to end delay and packet losses,respectively.Moreover,the proposed scheme has achieved an approximately 21%improvement in the average throughput compared to the existing schemes.展开更多
SCADA (Supervisory Control and Data Acquisition) is concerned with gathering process information from industrial control processes found in utilities such as power grids, water networks, transportation, manufacturing,...SCADA (Supervisory Control and Data Acquisition) is concerned with gathering process information from industrial control processes found in utilities such as power grids, water networks, transportation, manufacturing, etc., to provide the human operators with the required real-time access to industrial processes to be monitored and controlled either locally (on-site)or remotely (i.e., through Internet). Conventional solutions such as custom SCADA packages, custom communication protocols, and centralized architectures are no longer appropriate for engineering this type of systems because of their highly distribution and their uncertain continuously changing working environments. Multi-agent systems (MAS) appeared as a new architectural style for engineering complex and highly dynamic applications such as SCADA systems. In this paper, we propose an approach for simply developing flexible and interoperable SCADA systems based on the integration of MAS and OPC process protocol. The proposed SCADA system has the following advantages: 1) simple (easier to be implemented);2) flexible (able to adapt to its environment dynamic changes);and 3) interoperable (relative to the underlying control systems, which belongs to diverse of vendors). The applicability of the proposed approach is demonstrated by a real case study example carried out in a paper mill.展开更多
In this paper,we consider the NP-hard problem of finding the minimum dominant resolving set of graphs.A vertex set B of a connected graph G resolves G if every vertex of G is uniquely identified by its vector of dista...In this paper,we consider the NP-hard problem of finding the minimum dominant resolving set of graphs.A vertex set B of a connected graph G resolves G if every vertex of G is uniquely identified by its vector of distances to the vertices in B.A resolving set is dominating if every vertex of G that does not belong to B is a neighbor to some vertices in B.The dominant metric dimension of G is the cardinality number of the minimum dominant resolving set.The dominant metric dimension is computed by a binary version of the Archimedes optimization algorithm(BAOA).The objects of BAOA are binary encoded and used to represent which one of the vertices of the graph belongs to the dominant resolving set.The feasibility is enforced by repairing objects such that an additional vertex generated from vertices of G is added to B and this repairing process is iterated until B becomes the dominant resolving set.This is the first attempt to determine the dominant metric dimension problem heuristically.The proposed BAOA is compared to binary whale optimization(BWOA)and binary particle optimization(BPSO)algorithms.Computational results confirm the superiority of the BAOA for computing the dominant metric dimension.展开更多
文摘<strong>Introduction:</strong> The coronavirus disease 2019 (COVID-19) was declared as pandemic by WHO by March 11th. First case reported in Oman was on 24th February 2020 and later the country went through stages of epidemic progression. This study describes the sociodemographic and epidemiological characteristics of confirmed COVID-19 cases in Muscat governorate and related outcomes. <strong>Materials and Methods:</strong> This is a descriptive, exploratory analysis of all lab confirmed COVID 19 cases that were reported from 1st February to 31st May 2020. Data for the study was primarily extracted from notifications system established for surveillance (Tarassud). Secondary data sources were, contact listings and hospital medical records.<strong> Results:</strong> 11,648 initial cases of confirmed COVID-19 infections were included. The mean age was 35 years, 84.7% (N = 9862) were males, 25.9% (N = 3017) were Omanis, and 74.1% (N = 8631) were expatriates of which Indian origin were the majority (37%). Fever and cough were the most common presentations (46.3% and 29.5% respectively). Diabetes and hypertension were the most common comorbidities (4.9% and 4.6% respectively). Hospital admission was required for 7% (N = 811) of the total reported cases, out of them 171 cases (21%) were admitted to ICU, where 107 (13.2%) were ventilated. The case fatality rate (CFR) was 0.9%. 158 clusters containing 2949 contacts were identified from case records and categorised into 3 groups based on their exposure settings. The incubation period measured was 8 days (IQR 4.0 - 15.0) for workplace, 8 days (IQR 4 - 17) for dormitory and 4 days (IQR 2.0 - 7.0) for family groups. The secondary attack rate (SAR) estimated was 41.6% (95% CI: 0.34 - 0.48), 52% (95% CI: 0.40 - 0.63) and 33% (95% CI: 0.27 - 0.38) for workplace, dormitory and family groups, respectively. <strong>Conclusion:</strong> Results of this study, determine the transmission trend of COVID-19 in a country with high immigrant population. These findings could be utilised for further response planning in similar settings.
文摘<p align="justify"> <span style="font-family:Verdana;"><strong>Introduction:</strong> The coronavirus disease 2019 epidemic emerged in December 2019 and spread worldwide. Since workplaces are high-risk location for occupational exposure, understanding of the dynamics of COVID-19 clusters in occupational settings helps reduce the transmission of infectious disease in workplaces. This study presents an overview of the epidemiological characteristics of COVID-19 workplace clusters, preventive strategies adopted and outbreak responses undertaken in Muscat governorate. <strong>Materials and Methods:</strong> This is a descriptive study on the epidemiological characteristics of cases and distribution of workplace-related clusters of COVID-19 in Muscat Governorate in Oman. <strong>Results:</strong> A total of 36,798 COVID-19 cases were confirmed in Muscat from 24<span style="white-space:nowrap;"><sup>th</sup></span> February to 31<span style="white-space:nowrap;"><sup>st</sup></span> July 2020 in which 40.5% was belonging to clusters. Out of them 61% were Workplace-Dormitory clusters, predominantly expatriates. 78.6% of employees were symptomatic at time of examination. Fever and cough were the two most common symptoms reported in workplace related clusters. The number of affected employees ranged from 2 to 358 per cluster. Construction, retail, food, beverages, services, industrial manufacturing, oil & gas and transportation were identified as most at risk settings. Within the cases in workplace-related clusters, there was significant higher prevalence of diabetes, hypertension and obesity among females compared to males as also Omani nationals compared to expatriates and smoking among expatriate males compared to Omani males. <strong>In conclusion,</strong> understanding the epidemiological characteristics of the affected cases in organization setting will assist the policymakers to understand patterns of epidemiological spread and plan for robust interventions.</span> </p>
文摘The Internet ofMedical Things(IoMT)is mainly concernedwith the efficient utilisation of wearable devices in the healthcare domain to manage various processes automatically,whereas machine learning approaches enable these smart systems to make informed decisions.Generally,broadcasting is used for the transmission of frames,whereas congestion,energy efficiency,and excessive load are among the common issues associated with existing approaches.In this paper,a machine learning-enabled shortest path identification scheme is presented to ensure reliable transmission of frames,especially with the minimum possible communication overheads in the IoMT network.For this purpose,the proposed scheme utilises a well-known technique,i.e.,Kruskal’s algorithm,to find an optimal path from source to destination wearable devices.Additionally,other evaluation metrics are used to find a reliable and shortest possible communication path between the two interested parties.Apart from that,every device is bound to hold a supplementary path,preferably a second optimised path,for situations where the current communication path is no longer available,either due to device failure or heavy traffic.Furthermore,the machine learning approach helps enable these devices to update their routing tables simultaneously,and an optimal path could be replaced if a better one is available.The proposed mechanism has been tested using a smart environment developed for the healthcare domain using IoMT networks.Simulation results show that the proposed machine learning-oriented approach performs better than existing approaches where the proposed scheme has achieved the minimum possible ratios,i.e.,17%and 23%,in terms of end to end delay and packet losses,respectively.Moreover,the proposed scheme has achieved an approximately 21%improvement in the average throughput compared to the existing schemes.
文摘SCADA (Supervisory Control and Data Acquisition) is concerned with gathering process information from industrial control processes found in utilities such as power grids, water networks, transportation, manufacturing, etc., to provide the human operators with the required real-time access to industrial processes to be monitored and controlled either locally (on-site)or remotely (i.e., through Internet). Conventional solutions such as custom SCADA packages, custom communication protocols, and centralized architectures are no longer appropriate for engineering this type of systems because of their highly distribution and their uncertain continuously changing working environments. Multi-agent systems (MAS) appeared as a new architectural style for engineering complex and highly dynamic applications such as SCADA systems. In this paper, we propose an approach for simply developing flexible and interoperable SCADA systems based on the integration of MAS and OPC process protocol. The proposed SCADA system has the following advantages: 1) simple (easier to be implemented);2) flexible (able to adapt to its environment dynamic changes);and 3) interoperable (relative to the underlying control systems, which belongs to diverse of vendors). The applicability of the proposed approach is demonstrated by a real case study example carried out in a paper mill.
文摘In this paper,we consider the NP-hard problem of finding the minimum dominant resolving set of graphs.A vertex set B of a connected graph G resolves G if every vertex of G is uniquely identified by its vector of distances to the vertices in B.A resolving set is dominating if every vertex of G that does not belong to B is a neighbor to some vertices in B.The dominant metric dimension of G is the cardinality number of the minimum dominant resolving set.The dominant metric dimension is computed by a binary version of the Archimedes optimization algorithm(BAOA).The objects of BAOA are binary encoded and used to represent which one of the vertices of the graph belongs to the dominant resolving set.The feasibility is enforced by repairing objects such that an additional vertex generated from vertices of G is added to B and this repairing process is iterated until B becomes the dominant resolving set.This is the first attempt to determine the dominant metric dimension problem heuristically.The proposed BAOA is compared to binary whale optimization(BWOA)and binary particle optimization(BPSO)algorithms.Computational results confirm the superiority of the BAOA for computing the dominant metric dimension.
