The integration of condition monitoring with queueing systems to support decision making is not well explored. This work addresses the impact of condition monitoring of the server on the system level performance experienced by entities in a queueing system. The system consists of a queue with a single server subject to Markovian degradation. The model assumes a Poisson arrival process with service times and repair times according to general distributions. We develop stability conditions and perform steady state analysis to obtain performance measures (average queue length, average degradation, etc.). We propose minimizing an objective function involving four types of costs: repair, catastrophic failure, quality and holding. The queue performance measures derived from steady state analysis are bench-marked and compared to those from a discrete event simulation model. After verifying the queuing model, a sensitivity analysis is performed to determine the relationships between system performance and model parameters. Further, we explore the impact of stochastic service times on degradation and cost coefficients on optimal repair policy.Results indicate that the total cost function is convex and thus subject to an optimal repair policy.The model is sensitive to service time, quality costs, and failure costs for late-stage policy repairs decisions and sensitive to expected repair times and repair costs for early-stage policy repair decisions. The stochastic nature of service times drastically increases the likelihood of catastrophic failure of the server, and substantially increases holding costs. However, quality degrades more for constant service times while repair costs are minimally impacted.
The ongoing COVID-19 pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in significant death and morbidity rates around the globe. SARS-CoV-2 infection has been linked to 43.3 million confirmed cases worldwide, killing 1.15 million people. Physical separation, quarantine, and isolation were successful in minimizing the number of individuals who became sick during the epidemic, but the lack of immunity in the community makes them vulnerable to further waves of SARS-CoV-2 infection. Elderly persons (those 60 and older) and those with pre-existing medical problems are particularly vulnerable.
Material and Methods: In this observation study, people who were vaccinated with sinopharm vaccine and sinovac vaccine were included to see the response of vaccine in the body. The aim of the study was to compare the rise in the antibody level after 2 doses of two different COVID-19 vaccines i.e sinopharm and sinovac. Initially, in this pilot study, 40 people were included randomly from our health care team, after proper informed consent regarding the study.
Results: Among total 40 people were involved, male were 21 of 40 (52.5%) and female were 19 of 40 (47.5%). Most of the individuals were doctors (26 of 40, 65%). Mean age, weight, height and body mass index (BMI) are also shown below.
Conclusion: This study was to report the response of people of Pakistan toward sinopharm and sinoVac vaccines in terms of COVID antibody level. Response of the body was around 40 to 50% for sinopharm and 50 to 70 percent towards CoronaVac vaccine. Further data collection is being done to improve sample size and better outcome.
Hyperspectral image (HSI) classification is a mechanism of analyzing differentiated land cover in remotely sensed hyperspectral images. In the last two decades, a number of different types of classification algorithms have been proposed for classifying hyperspectral data. These algorithms include supervised as well as unsupervised methods. Each of these algorithms has its own limitations. In this research, three different types of unsupervised classification methods are used to classify different datasets i-e Pavia Center, Pavia University, Cuprite, Moffett Field. The main objective is to assess the performance of all three classifiers K-Means, Spectral Matching, and Abundance Mapping, and observing their applicability on different datasets. This research also includes spectral feature extraction for hyperspectral datasets.
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