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In some settings, systems may not fail completely but instead undergo performance degradation, leading to reduced efficiency. A significant concern arises when a system transitions into a degraded state without immediate detection, with the degradation only becoming apparent after an unpredictable period. Undetected degradation can result in failures with significant consequences. For instance, a minor crack in an oil pipeline might go unnoticed, eventually leading to a major leak, environmental harm, and costly cleanup efforts. Similarly, in the nuclear industry, undetected degradation in reactor cooling systems could cause overheating and potentially catastrophic failure. This paper focuses on reliability modeling for systems experiencing degradation, accounting for time delays associated with undetected degraded states, self-repair mechanisms, and varying operating environments. The paper presents a reliability model for degraded, time-dependent systems, incorporating various aspects of degradation. It first discusses the model assumptions and formulation, followed by numerical results obtained from system modeling using the developed program. Various scenarios are illustrated, incorporating time delays and different parameter values. Through computational analysis of these complex systems, we observe that the probability of the system being in the undetected degraded state tends to stabilize shortly after the initial degradation begins. The model is valuable for predicting and establishing an upper bound on the probability of the undetected, degraded state and the system’s overall reliability. Finally, the paper outlines potential avenues for future research.

It is becoming increasingly common for software to operate in various environments. However, even if the software performs well in the test phase, uncertain operating environments may cause new software failures. Traditional proposed software reliability models under uncertain operating environments suffer from the problem of being well-suited to special cases due to the large number of assumptions involved. To improve these problems, this study proposes a new software reliability model that assumes an uncertain operating environment. The new software reliability model is a model that minimizes assumptions and minimizes the number of parameters that make up the model, so that the model can be applied to general situations better than the traditional proposed software reliability models. In addition, various criteria based on the difference between the predicted and estimated values have been used in the past to demonstrate the superiority of the software reliability models. Also, we propose a new multi-criteria decision method that can simultaneously consider multiple goodness-of-fit criteria. The multi-criteria decision method using ranking is useful for comprehensive evaluation because it does not rely on individual criteria alone by ranking and weighting multiple criteria for the model. Based on this, 21 existing models are compared with the proposed model using two datasets, and the proposed model is found to be superior for both datasets using 15 criteria and the multi-criteria decision method using ranking.

This paper presents some recent views on the aspects of vitamin D levels in relation to the COVID-19 infections and analyzes the relationship between the prevalence rates of vitamin D deficiency and COVID-19 death rates per million of various countries in Europe and Asia using the data from the PubMed database. The paper also discusses a new mathematical model of time-delay interactions between the body's immune healthy cells, infected cells, and virus particles with the effect of vitamin D levels. The model can be used to monitor the timely progression of healthy immune cells with the effects of the levels of vitamin D and probiotics supplement. It also can help to predict when the infected cells and virus particles free state can ever be reached as time progresses. The consideration of the time delay in the modeling due to effects of the infected cells or virus particles and the growth of healthy cells is also an important factor that can significantly change the outcomes of the body's immune cells as well as the infections.

In recent years, lithium-ion batteries (LIB) have been used widely in portable electronic devices because of their advantages of durability, stability, high-capacity, low-cost, light-weight and small-scale. It makes LIB also deployed in various complex systems, in which efficient prediction of battery data, especially state-of-health (SoH), becomes crucial to ensure that the systems work stably without risks of power interruptions. With the recent improvement of Artificial Intelligence (AI) technologies, many works have been reported using deep learning (DL) models to investigate this problem, since such models can potentially increase their performance with more training data. This is also our direction in this research, which introduces a novel data-driven approach so-called Autoregression Nested Sequence (ARNS). On one hand, we come up with a nested sequence model to efficiently aggregate channel-wise and cycle-wise information, both of which are closely related to the operations of LIB. On the other hand, we incorporate relaxation effects into the model operations to handle peak prediction. To the best of our knowledge, ARNS is the first sophisticated deep learning model that combines all those features into a whole predictive system. The experimental results obtained using the NASA and CALCE datasets confirm significant improvement of ARNS, especially when dealing with peak periods in different SoH of multiple cycles.

Software is widely used in various fields. There is no place where it is not used from the smallest part to the entire part. In particular, the tendency to rely on software is accelerating as the fields of artificial intelligence and big data become more widespread. Therefore, it is extremely important to evaluate the reliability of software because of the extensive damage that could occur if the software fails. Previously, software reliability models were developed based on mathematical and statistical grounds; however, immediate response was difficult. Therefore, in this study, a software reliability model was developed that depends on data using deep learning, and it was analyzed by replacing the activation function previously used in deep learning with the proposed software reliability model. Since the sigmoid function has a similar shape to the software reliability model, we utilized this to propose a deep learning software reliability model that replaces the activation function, the sigmoid function, with the software reliability function. Two datasets were compared and analyzed using 10 criteria, and the superiority of the proposed deep-learning software reliability model was proved. In addition, the results were compared by changing the parameters utilized in the proposed deep-learning software reliability model by −10%, −5%, 5%, and 10%, and it was found that the larger the parameters, the smaller the change.

The coronavirus disease (COVID-19) outbreak has prompted various industries to embark on digital transformation efforts, with software playing a critical role. Ensuring the reliability of software is of the utmost importance given its widespread use across multiple industries. For example, software has extensive applications in areas such as transportation, aviation, and military systems, where reliability problems can result in personal injuries and significant financial losses. Numerous studies have focused on software reliability. In particular, the software reliability growth model has served as a prominent tool for measuring software reliability. Previous studies have often assumed that the testing environment is representative of the operating environment and that software failures occur independently. However, the testing and operating environments can differ, and software failures can sometimes occur dependently. In this study, we propose a new model that assumes uncertain operating environments and dependent failures. In other words, the model proposed in this study takes into account a wider range of environments. The numerical examples in this study demonstrate that the goodness of fit of the new model is significantly better than that of the existing SRGM. Additionally, we show the utilization of the sequential probability ratio test (SPRT) based on the new model to assess the reliability of the dataset.

COVID-19 is caused by a coronavirus called SARS-CoV-2. Many countries around the world implemented their own policies and restrictions designed to limit the spread of Covid-19 in recent months. Businesses and schools transitioned into working and learning remotely. In the United States, many states were under strict orders to stay home at least in the month of April. In recent weeks, there are some significant changes related restrictions include social-distancing, reopening states, and staying-at-home orders. The United States surpassed 2 million coronavirus cases on Monday, June 15, 2020 less than five months after the first case was confirmed in the country. The virus has killed at least 115,000 people in the United States as of Monday, June 15, 2020, according to data from Johns Hopkins University. With the recent easing of coronavirus-related restrictions and changes on business and social activity such as stay-at-home, social distancing since late May 2020 hoping to restore economic and business activities, new Covid-19 outbreaks are on the rise in many states across the country. Some researchers expressed concern that the process of easing restrictions and relaxing stay-at-home orders too soon could quickly surge the number of infected Covid-19 cases as well as the death toll in the United States. Some of these increases, however, could be due to more testing sites in the communities while others may be are the results of easing restrictions due to recent reopening and changed policies, though the number of daily death toll does not appear to be going down in recent days due to Covid-19 in the U.S. This raises the challenging question: • How can policy decision-makers and community leaders make the decision to implement public policies and restrictions and keep or lift staying-at-home orders of ongoing Covid-19 pandemic for their communities in a scientific way? In this study, we aim to develop models addressing the effects of recent Covid-19 related changes in the communities such as reopening states, practicing social-distancing, and staying-at-home orders. Our models account for the fact that changes to these policies which can lead to a surge of coronavirus cases and deaths, especially in the United States. Specifically, in this paper we develop a novel generalized mathematical model and several explicit models considering the effects of recent reopening states, staying-at-home orders and social-distancing practice of different communities along with a set of selected indicators such as the total number of coronavirus recovered and new cases that can estimate the daily death toll and total number of deaths in the United States related to Covid-19 virus. We compare the modeling results among the developed models based on several existing criteria. The model also can be used to predict the number of death toll in Italy and the United Kingdom (UK). The results show very encouraging predictability for the proposed models in this study. The model predicts that 128,500 to 140,100 people in the United States will have died of Covid-19 by July 4, 2020. The model also predicts that between 137,900 and 154,000 people will have died of Covid-19 by July 31, and 148,500 to 169,700 will have died by the end of August 2020, as a result of the SARS-CoV-2 coronavirus that causes COVID-19 based on the Covid-19 death data available on June 13, 2020. The model also predicts that 34,900 to 37,200 people in Italy will have died of Covid-19 by July 4, and 36,900 to 40,400 people will have died by the end of August based on the data available on June 13, 2020. The model also predicts that between 43,500 and 46,700 people in the United Kingdom will have died of Covid-19 by July 4, and 48,700 to 51,900 people will have died by the end of August, as a result of the SARS-CoV-2 coronavirus that causes COVID-19 based on the data available on June 13, 2020. The model can serve as a framework to help policy makers a scientific approach in quantifying decision-makings related to Covid-19 affairs.

The random variable X represents the stress placed on the system by the operating environment and random variable Y represents the strength of the system. A system is able to perform its intended function if its strength is greater than the stress imposed upon it. Reliability of the system is defined as the probability that the system is strong enough to overcome the stress. That is, R = P(Y >X). In other words, reliability is the probability that the strengths of the unit are greater than the stresses. The stress-strength model has found interests in many applications include mechanical engineering and human heart monitoring conditions. The interval-system is defined as a system with a series of chance events that occur in a given interval of time. A k-out-of-n interval-system is a system with a series of n events in a given interval of time which successes (or functions) if and only if at least k of the events succeed (function). In short, the k-out-of-n interval-system is an interval-system which successes if and only if at least k of n events succeeds. The stress-strength reliability inference of the interval-system with a series of n independent events that occurs in a given interval of time is considered. The reliability of the interval-system is the probability that at least k out of n events in a given interval of time succeed. This paper derives uniform minimum variance unbiased and maximum likelihood reliability estimates of k-out-of-n interval-system based on stress-strength inference events where X (stress) and Y (strength) are independent two-parameter exponential random variables. An application in human heart conditions to illustrate the results is discussed.

Stress is the body’s response to something that requires action or attention. In general, anything that poses a real challenge or threat to a person’s well-being can cause stress. Stress can slow down a person’s well-being activities. Often, you might not know whether you are stressed, if you are under too much stress, or when it is time to seek help. This paper presents a mathematical model with time delay subject to a person’s stress for early stress detection to assess whether a person is stress-free, has stress but is undetected, or has stress in any specific state such as minor, moderate, or severe stress. Being more alert to the effects of stress and reducing the uncertainty of undetected stress, or better, preventing it, may help people, especially teens, manage it more effectively and cope better, even a person happens to be stressed. The model can be extended to study the effects of multiple stress factors in light of the prolonged COVID-19 pandemic on people’s mental stress.

Software is used in various industries, and its reliability has become an extremely important issue. For example, in the medical industry, software is used to provide medical services to underprivi-leged individuals. If a problem occurs with the software reliability, incorrect medical information may be provided. The software reliability is estimated using a software reliability growth model. However, most software reliability growth models assume that the failures are independent. In addition, it is assumed that the test and operating environments are the same. In this study, we propose a new software reliability growth model that assumes that software failures are dependent and uncertain operating environments. A comparison of the proposed model against existing NHPP SRMEs using actual datasets shows that the proposed model achieves the best fit.

This paper deals with a special type of voting systems, called two-stage intervened decision-making systems, in which the decision time of each decision unit will be a random variable and some supervising mechanism is included. A new decision rule is applied to such kind of systems, which makes the decision units become interdependent from each other. The reliability and cost-benefit models are developed. The optimization for the models is discussed and the optimal solution for a special case is also derived. A numerical example for model optimization is presented as well as some model comparison. Even though a specific application is used for model formulation and derivation throughout this paper, the modeling results can be easily modified and applied to many other systems.

In real-life applications, we often do not have population data but we can collect several samples from a large sample size of data. In this paper, we propose a median-based machine-learning approach and algorithm to predict the parameter of the Bernoulli distribution. We illustrate the proposed median approach by generating various sample datasets from Bernoulli population distribution to validate the accuracy of the proposed approach. We also analyze the effectiveness of the median methods using machine-learning techniques including correction method and logistic regression. Our results show that the median-based measure outperforms the mean measure in the applications of machine learning using sampling distribution approaches.

This paper proposes a generalized model to cover imperfect debugging and the uncertainty of the operating environment and its effect on fault detection rate into software reliability evaluation based on a non-homogeneous Poisson process (NHPP). Many NHPP software reliability growth models (SRGMs) have been developed to estimate the software reliability measures over the past 40 years, but most of these models assume that the operating environment is the same as the testing environment. However, in fact, due to the unpredictability of the uncertain factors in the operating environments for the software, they may considerably influence the software's reliability in an unpredictable way. So when a software system works in a field environment, its reliability is usually different from the original reliability prediction in the testing phase of the software development process, also from all its similar applications in other fields. In this paper, a general model is used to derive models that incorporate the uncertainty of operating environments, which provides the flexibility in considering a different fault detection rate and random environmental factor and so on. Several published models are shown to be covered by this general model and a new model is also developed and examined. The numerical illustrative examples of the proposed model have been validated on two sets of real software failure data in terms of six criteria. The comparison results demonstrate that the new model can fit and predict significantly better than other existing models.

Containerization is revolutionizing the way that many industries operate, provisioning major impact to modern computing technologies because it is extra lightweight, highly portable, energy, resource and storage efficient, cost-effective, performance efficient, and extremely quick during boot up. These often facilitate efficient load balancing, low-level system maintenance, server consolidation (for efficient energy and resource utilization) and replication of instances over geographical locations for better fault tolerance to escalate application reliability. However, some recent literature have addressed various challenges (such as complex networking, persistent storage facilities, cross data centers and multicloud supports, security issues, and lack of available, capable container management APIs, etc.) regarding successful container adoption in industries, which might have resulted in a seemingly meager increase in industrial deployments of containerization over the past few years despite bestowing efficient lightweight virtualization. Moreover, a comprehensive overview of containerizations along with their popularity dynamics has still not been found in contemporary literature, which further extends knowledge gap between developers and available technologies. Hence, current study touches upon different technicalities involved in containerization with potential problems and possible solutions along with various important industrial applications to manifest its existing supports and technical hardships. Finally, we have conducted a comprehensive experimental study to compare the performance of VMs, containers and unikernels in terms of CPU utilization, memory footprints, network bandwidth, execution time and technological maturity using standard benchmarks and observed containers to deliver satisfactory performance in almost all aspects, however, are still not free from issues regarding isolation & security, performance stability, lack of available efficient tools for crossplatform support and persistent storage. Unikernels deliver good performance with VM-like isolation but still need to achieve desired technical maturity (in terms of microprocessor stability, process containment, persistent storage, etc.). VMs, on the other hand, are found to provide stable performance throughout, though bigger memory footprints and slower spin up/down remain their biggest weaknesses.

The immune system is the body’s defense against pathogens, which are complex living organisms found in many parts in the body including organs, tissues, cells, molecules, and proteins. When the immune system works properly, it can recognize and kill the abnormal cells and the infected cells. Otherwise, it can attack the body’s healthy cells even if there is no invader. Many researchers have developed immunotherapy (or cancer vaccines) and have used chemotherapy for cancer treatment that can kill fast-growing cancer cells or at least slow down tumor growth. However, chemotherapy drugs travel throughout the body and tend to kill both healthy cells and cancer cells. In this study, we consider the fact that chemotherapy can kill tumor cells and that the loss of the immune cells may at the same time stir up cancer growth. We present a dynamic time-delay tumor-immune model with the effects of chemotherapy drugs and autoimmune disease. The modeling results can be used to determine the progression of tumor cells in the human body with the effect of chemotherapy, autoimmune diseases, and time delays based on partial differential equations. It can also be used to predict when the tumor viruses’ free state can be reached as time progresses, as well as the state of the body’s healthy cells as time progresses. We also present a few numerical cases that illustrate that the model can be used to monitor the effects of chemotherapy drug treatment and the growth rate of tumor virus-infected cells and the autoimmune disease.

In the past, because computer programs were restricted to perform only simple functions, the dependence on software was not large, resulting in relatively small losses after a failure. However, with the development of the software market, the dependence on software has increased considerably, and software failures can cause significant social and economic losses. Software reliability studies were previously conducted under the assumption that software failures occur independently. However, as software systems become more complex and extremely large, software failures are becoming frequently interdependent. Therefore, in this study, a software reliability model is developed under the assumption that software failures occur in a dependent manner. We derive the software reliability model through the number of software failure and fault detection rate assuming point symmetry. The proposed model proves good performance compared with 21 previously developed software reliability models using three datasets and 11 criteria. In addition, to find the optimal release time, a cost model using the developed software reliability model was presented. To determine this release time, four parameters constituting the software reliability model were changed by 10%. By comparing the change in the cost model and the optimal release time, it was found that parameter b had the greatest influence.

This paper presents a general testing coverage software reliability modeling framework that covers imperfect debugging and considers not only fault detection processes (FDP) but also fault correction processes (FCP). Numerous software reliability growth models have evaluated the reliability of software over the last few decades, but most of them attached importance to modeling the fault detection process rather than modeling the fault correction process. Previous studies analyzed the time dependency between the fault detection and correction processes and modeled the fault correction process as a delayed detection process with a random or deterministic time delay. We study the quantitative dependency between dual processes from the viewpoint of fault amount dependency instead of time dependency, then propose a generalized modeling framework along with imperfect debugging and testing coverage. New models are derived by adopting different testing coverage functions. We compared the performance of these proposed models with existing models under the context of two kinds of failure data, one of which only includes observations of faults detected, and the other includes not only fault detection but also fault correction data. Different parameter estimation methods and performance comparison criteria are presented according to the characteristics of different kinds of datasets. No matter what kind of data, the comparison results reveal that the proposed models generally give improved descriptive and predictive performance than existing models.

The immune system is a complex interconnected network consisting of many parts including organs, tissues, cells, molecules and proteins that work together to protect the body from illness when germs enter the body. An autoimmune disease is a disease in which the body’s immune system attacks healthy cells. It is known that when the immune system is working properly, it can clearly recognize and kill the abnormal cells and virus-infected cells. But when it doesn’t work properly, the human body will not be able to recognize the virus-infected cells and, therefore, it can attack the body’s healthy cells when there is no invader or does not stop an attack after the invader has been killed, resulting in autoimmune disease.; This paper presents a mathematical modeling of the virus-infected development in the body’s immune system considering the multiple time-delay interactions between the immune cells and virus-infected cells with autoimmune disease. The proposed model aims to determine the dynamic progression of virus-infected cell growth in the immune system. The patterns of how the virus-infected cells spread and the development of the body’s immune cells with respect to time delays will be derived in the form of a system of delay partial differential equations. The model can be used to determine whether the virus-infected free state can be reached or not as time progresses. It also can be used to predict the number of the body’s immune cells at any given time. Several numerical examples are discussed to illustrate the proposed model. The model can provide a real understanding of the transmission dynamics and other significant factors of the virus-infected disease and the body’s immune system subject to the time delay, including approaches to reduce the growth rate of virus-infected cell and the autoimmune disease as well as to enhance the immune effector cells.

Many NHPP software reliability growth models (SRGMs) have been proposed to assess software reliability during the past 40 years, but most of them have focused on modeling the fault detection process (FDP) in two ways: one is to ignore the fault correction process (FCP), i.e., faults are assumed to be instantaneously removed after the failure caused by the faults is detected. However, in real software development, it is not always reliable as fault removal usually needs time, i.e., the faults causing failures cannot always be removed at once and the detected failures will become more and more difficult to correct as testing progresses. Another way to model the fault correction process is to consider the time delay between the fault detection and fault correction. The time delay has been assumed to be constant and function dependent on time or random variables following some kind of distribution. In this paper, some useful approaches to the modeling of dual fault detection and correction processes are discussed. The dependencies between fault amounts of dual processes are considered instead of fault correction time-delay. A model aiming to integrate fault-detection processes and fault-correction processes, along with the incorporation of a fault introduction rate and testing coverage rate into the software reliability evaluation is proposed. The model parameters are estimated using the Least Squares Estimation (LSE) method. The descriptive and predictive performance of this proposed model and other existing NHPP SRGMs are investigated by using three real data-sets based on four criteria, respectively. The results show that the new model can be significantly effective in yielding better reliability estimation and prediction.

Considering the impact of the heterogeneous conditions of the mixture baseline distribution on the parameter estimation of a composite dynamical system (CDS), we propose an approach to infer the model parameters and baseline survival function of CDS using the maximum likelihood estimation and Bayesian estimation methods. The power-trend hazard rate function and Burr type XII mixture distribution as the baseline distribution are used to characterize the changes of the residual lifetime distribution of surviving components. The Markov chain Monte Carlo approach via using a new Metropolis–Hastings within the Gibbs sampling algorithm is proposed to overcome the computation complexity when obtaining the Bayes estimates of model parameters. A numerical example is generated from the proposed CDS to analyze the proposed procedure. Monte Carlo simulations are conducted to investigate the performance of the proposed methods, and results show that the proposed Bayesian estimation method outperforms the maximum likelihood estimation method to obtain reliable estimates of the model parameters and baseline survival function in terms of the bias and mean square error.

Currently, eco-tourism has emerged as a debatable argument in a large number of forums and discussion in the tourism industry. Eco-tourism is defined by the International Eco-tourism Society as “responsible travel to natural areas that conserves the environment and improves the well being of local people”. Phu Quoc Island in Vietnam can be taken as a typical example of eco-tourism. If eco-tourism is successfully developed, it can make not only the best use of Phu Quoc’s environmental and economic, but also diminish any harm to the environment of this island. However, any resolutions to promote eco-tourism development of Phu Quoc Island by creating stable competitive advantages were poorly conducted. As a result, this study found that if it would be used the Resourced-Based View approach to propose solution packages of advancing the sustainable competitive advantage of Phu Quoc island, it would be in order to boost the development of eco-tourism.

Purpose: Although California school discipline policy changes over the past decade have resulted in significant drops in suspension rates, scholars have found that racial disproportionality in punitive discipline persists for Black, Indigenous, and at times Latinx students. This study utilizes racial capitalism as an analytical framework to examine the mechanisms and justifications for racially disproportionate discipline across three distinct California political economies. Research Methods/Approach: Drawing on a subset of qualitative comparative case study data of five schools taken from a larger 5-year study of 31 schools in 17 California districts with distinct racial and economic composition, we investigate the phenomena of Black, Indigenous, and Latinx student disposability--those who continue to experience exclusion in a time of progressive school discipline reform. Findings: We find that (1) student disposability through discipline was not only intertwined with dominant local narratives about racial groups but also tied to a larger system of racial capitalism reliant on the racialized differentiation of students within every geographical location, and (2) when progressive alternative approaches to discipline fail, these reforms uncover rooted antiblackness, anti-indigeneity, and a gang imaginary such that students who fail or refuse to assimilate to the underlying labor-sorting function of US schools are considered disposable. Implications: Our findings highlight the need to center and elevate disposable youth brilliance in decision making, the importance of building collective movements against racial capitalism's grip on schools, and the obligation to build education beyond racial capitalism.

COVID-19, known as Coronavirus disease 2019, is caused by a coronavirus called SARS-CoV-2. As coronavirus restrictions ease and cause changes to social and business activities around the world, and in the United States in particular, including social distancing, reopening states, reopening schools, and the face mask mandates, COVID-19 outbreaks are on the rise in many states across the United States and several other countries around the world. The United States recorded more than 1.9 million new infections in July, which is nearly 36 percent of the more than 5.4 million cases reported nationwide since the pandemic began, including more than 170,000 deaths from the disease, according to data from Johns Hopkins University as of 16 August 2020. In April 2020, the author of this paper presented a model to estimate the number of deaths related to COVID-19, which assumed that there would be no significant change in the COVID-19 restrictions and guidelines in the coming days. This paper, which presents the evolved version of the previous model published in April, discusses a new explicit mathematical model that considers the time-dependent effects of various pandemic restrictions and changes related to COVID-19, such as reopening states, social distancing, reopening schools, and face mask mandates in communities, along with a set of selected indicators, including the COVID-19 recovered cases and daily new cases. We analyzed and compared the modeling results to two recent models based on several model selection criteria. The model could predict the death toll related to the COVID-19 virus in the United States and worldwide based on the data available from Worldometer. The results show the proposed model fit the data significantly better for the United States and worldwide COVID-19 data that were available on 16 August 2020. The results show very encouraging predictability that reflected the time-dependent effects of various pandemic restrictions for the proposed model. The proposed model predicted that the total number of U.S. deaths could reach 208,375 by 1 October 2020, with a possible range of approximately 199,265 to 217,480 deaths based on data available on 16 August 2020. The model also projected that the death toll could reach 233,840 by 1 November 2020, with a possible range of 220,170 to 247,500 American deaths. The modeling result could serve as a baseline to help decision-makers to create a scientific framework to quantify their guidelines related to COVID-19 affairs. The model predicted that the death toll worldwide related to COVID-19 virus could reach 977,625 by 1 October 2020, with a possible range of approximately 910,820 to 1,044,430 deaths worldwide based on data available on 16 August 2020. It also predicted that the global death toll would reach nearly 1,131,000 by 1 November 2020, with a possible range of 1,030,765 to 1,231,175 deaths. The proposed model also predicted that the global death toll could reach 1.47 million deaths worldwide as a result of the SARS CoV-2 coronavirus that causes COVID-19. We plan to apply or refine the proposed model in the near future to further study the COVID-19 death tolls for India and Brazil, where the two countries currently have the second and third highest total COVID-19 cases after the United States.

Software reliability and quality are crucial in several fields. Related studies have focused on software reliability growth models (SRGMs). Herein, we propose a new SRGM that assumes interdependent software failures. We conduct experiments on real-world datasets to compare the goodness-of-fit of the proposed model with the results of previous nonhomogeneous Poisson process SRGMs using several evaluation criteria. In addition, we determine software reliability using Wald’s sequential probability ratio test (SPRT), which is more efficient than the classical hypothesis test (the latter requires substantially more data and time because the test is performed only after data collection is completed). The experimental results demonstrate the superiority of the proposed model and the effectiveness of the SPRT.

In this paper, we discuss an explicit model function that can estimate the total number of deaths in the population, and particularly, estimate the cumulative number of deaths in the United States due to the current Covid-19 virus. We compare the modeling results to two related existing models based on a new criteria and several existing criteria for model selection. The results show the proposed model fits significantly better than the other two related models based on the U.S. Covid-19 death data. We observe that the errors of the fitted data and the predicted data points on the total number of deaths in the U.S. on the last available data point and the next coming day are less than 0.5% and 2.0%, respectively. The results show very encouraging predictability for the model. The new model predicts that the maximum total number of deaths will be approximately 62,100 across the United States due to the Covid-19 virus, and with a 95% confidence that the expected total death toll will be between 60,951 and 63,249 deaths based on the data until 22 April, 2020. If there is a significant change in the coming days due to various testing strategies, social-distancing policies, the reopening of community strategies, or a stay-home policy, the predicted death tolls will definitely change. Future work can be explored further to apply the proposed model to global Covid-19 death data and to other applications, including human population mortality, the spread of disease, and different topics such as movie reviews in recommender systems.

In this paper, we propose a software reliability model that considers not only error generation but also fault removal efficiency combined with testing coverage information based on a nonhomogeneous Poisson process (NHPP). During the past four decades, many software reliability growth models (SRGMs) based on NHPP have been proposed to estimate the software reliability measures, most of which have the same following agreements: 1) it is a common phenomenon that during the testing phase, the fault detection rate always changes; 2) as a result of imperfect debugging, fault removal has been related to a fault re-introduction rate. But there are few SRGMs in the literature that differentiate between fault detection and fault removal, i.e. they seldom consider the imperfect fault removal efficiency. But in practical software developing process, fault removal efficiency cannot always be perfect, i.e. the failures detected might not be removed completely and the original faults might still exist and new faults might be introduced meanwhile, which is referred to as imperfect debugging phenomenon. In this study, a model aiming to incorporate fault introduction rate, fault removal efficiency and testing coverage into software reliability evaluation is developed, using testing coverage to express the fault detection rate and using fault removal efficiency to consider the fault repair. We compare the performance of the proposed model with several existing NHPP SRGMs using three sets of real failure data based on five criteria. The results exhibit that the model can give a better fitting and predictive performance.

Ready-to-eat fruits packed in sealed containers are highly perishable due to their intrinsic characteristics and lack of full thermal process. Ohmic heating has the advantages of rapid liquid heating through electrical current. The aim of this research was to investigate the effect of indirect ohmic heating on pH, total soluble solids, polyphenol oxidase activity, color and texture of ready-to-eat pineapple packed in a polypropylene pouch with 1% calcium chloride and 0.3% ascorbic acid packing solution. The pre-packed sample in a pouch was placed in the ohmic heating jar filled with 0.5% sodium chloride ohmic heating solution which was then ohmic heated at different voltage gradients (20, 30, 40 V/cm), to different packing solution temperatures (60, 70, 80°C) for 60s. Samples were kept at 4°C for quality measurement. It was found that browning index of ready-to-eat pineapple treated with 20 V/cm at 80°C, 30 V/cm at 70°C and 80°C, 40 V/cm at 80°C did not change during 12 days cold storage (p>0.05). Polyphenol oxidase was inactivated when the temperature of the pineapple was 62°C or higher. After 10 days at 4°C, the pineapple heated with 30 V/cm at 70°C had much higher firmness than the un-heated sample kept at the same storage condition. Indirect ohmic heating of pre-packed ready-to-eat pineapple in polypropylene pouch with 30 V/cm at 70°C packing solution temperature for 60s could be used as minimal heating methods to maintain the quality of ready-to-eat fruits in 12 days at 4°C.

In the past few decades, a great number of hardware and software reliability models have been proposed to address hardware failures in hardware subsystems and software failures in software subsystems, respectively. The interactions between hardware and software subsystems are often neglected in order to simplify reliability modeling, and hence, most existing reliability models assumed hardware subsystems and software subsystem are independent of each other. However, this may not be true in reality. In this study, system failures are classified into three categories, which are hardware failures, software failures, and hardware-software interaction failures. The main contribution of our research is that we further classify hardware-software interaction failures into two groups: software-induced hardware failures and hardware-induced software failures. A Markov-based unified system reliability modeling incorporating all three categories of system failures is developed in this research, which provides a novel and practical perspective to define system failures and further improve reliability prediction accuracy. Comparison of system reliability estimation between the reliability models with and without considering hardware-software interactions is elucidated in the numerical example. The impacts on system reliability prediction as the changes of transition parameters are also illustrated by the numerical examples.

We have been attempting to evaluate software quality and improve its reliability. Therefore, research on a software reliability model was part of the effort. Currently, software is used in various fields and environments; hence, one must provide quantitative confidence standards when using software. Therefore, we consider the testing coverage and uncertainty or randomness of an operating environment. In this paper, we propose a new testing coverage model based on NHPP software reliability with the uncertainty of operating environments, and we provide a sensitivity analysis to study the impact of each parameter of the proposed model. We examine the goodness-of-fit of a new testing coverage model based on NHPP software reliability and other existing models based on two datasets. The comparative results for the goodness-of-fit show that the proposed model does significantly better than the existing models. In addition, the results for the sensitivity analysis show that the parameters of the proposed model affect the mean value function.

The non-homogeneous Poisson process (NHPP) software has a crucial role in computer systems. Furthermore, the software is used in various environments. It was developed and tested in a controlled environment, while real-world operating environments may be different. Accordingly, the uncertainty of the operating environment must be considered. Moreover, predicting software failures is commonly an important part of study, not only for software developers, but also for companies and research institutes. Software reliability model can measure and predict the number of software failures, software failure intervals, software reliability, and failure rates. In this paper, we propose a new model with an inflection factor of the fault detection rate function, considering the uncertainty of operating environments and analyzing how the predicted value of the proposed new model is different than the other models. We compare the proposed model with several existing NHPP software reliability models using real software failure datasets based on ten criteria. The results show that the proposed new model has significantly better goodness-of-fit and predictability than the other models.

Community health workers (CHWs) provide critical services to underserved populations in low and middle-income countries, but maintaining CHW's clinical knowledge through formal continuing medical education (CME) activities is challenging and rarely occurs. We tested whether a Short Message Service (SMS)-based mobile CME (mCME) intervention could improve medical knowledge among a cadre of Vietnamese CHWs (Community Based Physician's Assistants-CBPAs) who are the leading providers of primary medical care for rural underserved populations.The mCME Project was a three arm randomized controlled trial. Group 1 served as controls while Groups 2 and 3 experienced two models of the mCME intervention. Group 2 (passive model) participants received a daily SMS bullet point, and were required to reply to the text to acknowledge receipt; Group 3 (interactive model) participants received an SMS in multiple choice question format addressing the same thematic area as Group 2, entering an answer (A, B, C or D) in their response. The server provided feedback immediately informing the participant whether the answer was correct. Effectiveness was based on standardized examination scores measured at baseline and endline (six months later). Secondary outcomes included job satisfaction and self-efficacy.638 CBPAs were enrolled, randomized, and tested at baseline, with 592 returning at endline (93.7%). Baseline scores were similar across all three groups. Over the next six months, participation of Groups 2 and 3 remained high; they responded to >75% of messages. Group 3 participants answered 43% of the daily SMS questions correctly, but their performance did not improve over time. At endline, the CBPAs reported high satisfaction with the mCME intervention, and deemed the SMS messages highly relevant. However, endline exam scores did not increase over baseline, and did not differ between the three groups. Job satisfaction and self-efficacy scores also did not improve. Average times spent on self-study per week did not increase, and the kinds of knowledge resources used by the CBPAs did not differ between the three groups; textbooks, while widely available, were seldom used.The SMS-based mCME intervention, while feasible and acceptable, did not result in increased medical knowledge. We hypothesize that this was because the intervention failed to stimulate lateral learning. For an intervention of this kind to be effective, it will be essential to find more effective ways to couple SMS as a stimulus to promote increased self-study behaviors.ClinicalTrials.gov NCT02381743.

The goal set by software developers is to develop high quality and reliable software products. During the past decades, software has become complex, and thus, it is difficult to develop stable software products. Software failures often cause serious social or economic losses, and therefore, software reliability is considered important. Software reliability growth models (SRGMs) have been used to estimate software reliability. In this work, we introduce a new software reliability model and compare it with several non-homogeneous Poisson process (NHPP) models. In addition, we compare the goodness of fit for existing SRGMs using actual data sets based on eight criteria. The results allow us to determine which model is optimal.

With the latest technological developments, the software industry is at the center of the fourth industrial revolution. In today’s complex and rapidly changing environment, where software applications must be developed quickly and easily, software must be focused on rapidly changing information technology. The basic goal of software engineering is to produce high-quality software at low cost. However, because of the complexity of software systems, software development can be time consuming and expensive. Software reliability models (SRMs) are used to estimate and predict the reliability, number of remaining faults, failure intensity, total and development cost, etc., of software. Additionally, it is very important to decide when, how, and at what cost to release the software to users. In this study, we propose a new nonhomogeneous Poisson process (NHPP) SRM with a fault detection rate function affected by the probability of fault removal on failure subject to operating environments and discuss the optimal release time and software reliability with the new NHPP SRM. The example results show a good fit to the proposed model, and we propose an optimal release time for a given change in the proposed model.

The failure of a computer system because of a software failure can lead to tremendous losses to society; therefore, software reliability is a critical issue in software development. As software has become more prevalent, software reliability has also become a major concern in software development. We need to predict the fluctuations in software reliability and reduce the cost of software testing: therefore, a software development process that considers the release time, cost, reliability, and risk is indispensable. We thus need to develop a model to accurately predict the defects in new software products. In this paper, we propose a new non-homogeneous Poisson process (NHPP) software reliability model, with S-shaped growth curve for use during the software development process, and relate it to a fault detection rate function when considering random operating environments. An explicit mean value function solution for the proposed model is presented. Examples are provided to illustrate the goodness-of-fit of the proposed model, along with several existing NHPP models that are based on two sets of failure data collected from software applications. The results show that the proposed model fits the data more closely than other existing NHPP models to a significant extent. Finally, we propose a model to determine optimal release policies, in which the total software system cost is minimized depending on the given environment.

When software systems are introduced, these systems are used in field environments that are the same as or close to those used in the development-testing environments; however, they may also be used in many different locations that may differ from the environment in which they were developed and tested. As such, it is difficult to improve software reliability for a variety of reasons, such as a given environment, or a bug location in code. In this paper, we propose a new software reliability model that takes into account the uncertainty of operating environments. The explicit mean value function solution for the proposed model is presented. Examples are presented to illustrate the goodness of fit of the proposed model and several existing non-homogeneous Poisson process (NHPP) models and confidence intervals of all models based on two sets of failure data collected from software applications. The results show that the proposed model fits the data more closely than other existing NHPP models to a significant extent.

When asked to randomly select answer choices on easy multiple choice questions, people select more correct answers than expected by chance. Sparrow and Wegner showed that this tendency was eliminated if participants answered questions correctly before answering randomly. They argued that answering a question correctly unprimes the tendency to choose the correct answer, thereby reducing the correct response rate close to the chance level of.5. An alternative explanation, consistent with these results, is that answering questions correctly provides a baseline, which allows participants to strategize, i.e., to match and mismatch equal numbers of their purportedly random responses to the baseline response. Three studies showed that the presence of a baseline, even when unpriming is not feasible, led to lower correct response rates than those obtained in a condition in which no baseline was available. Furthermore, the presence of a baseline led to more nonrandom sequences of correct and incorrect responses. One specific sequence-alternating correct and incorrect answers-mediated the relation between the presence of a baseline and lower correct response rate. These findings suggest that strategizing, not unpriming, accounts for Sparrow and Wegner's results.