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The aim of the investigation is a closed retrial queueing system with a finite source. The server can be reached from the source (primary request) or from the orbit (secondary request). If an incoming (primary or secondary) job finds the server busy, two modes are distinguished: the job is transferred to the orbit (no collision) or the job under service is interrupted and both of them are transferred to the orbit (collision). Requests in the orbit can retry reaching the server after a random waiting time. The nonreliable case when the server is subject to breakdown is also investigated. In case of breakdown, when the server is under repair, also two cases can be investigated. For the first, primary calls from the source can reach the system, and they will be sent to the orbit. For the second, the source is blocked, so primary customers are not able to step into the system. This paper focuses on the unreliable system with collision and blocking of parameters. These types of systems can be solved by numerical, asymptotical, and simulation methods. Our goal is to provide a new approach to the algorithmic solution for calculating the steady-state probabilities of the system. Using these quantities the main performance characteristics (utilization of the server, response time, etc.) can be calculated. Examples illustrate the effect of different parameters on the distribution of requests in the system.

This paper investigates, using a simulation program, a retrial queuing system with a single server which is subject to random breakdowns. The number of sources of calls is finite, and collisions can take place. We assume that the failure of the server blocks the system’s operation such that newly arriving customers cannot enter the system, contrary to an earlier paper where the failure does not affect the arrivals. All the random variables included in the model construction are assumed to be independent of each other, and all times are exponentially distributed except for the service time, which is gamma distributed. The novelty of this analysis is the inspection of the blocking effect on the performance measures using different distributions. Various figures represent the impact of the squared coefficient of the variation of the service time on the main performance measures such as the mean and variance of the number of customers in the system, the mean and variance of the response time, the mean and variance of the time a customer spends in the service, and the mean and variance of the sojourn time in the orbit.

In this paper a closed retrial queueing system is considered with a finite number of customers. If an arriving (primary or secondary) request finds the server busy, two modes are possible: the job is transferred to the orbit (no conflict) or the job under service is interrupted and both of them are transferred to the orbit (conflict). Jobs in the orbit can retry reaching the server after a random time. The unreliable case where the server is subject to breakdown is also investigated. These types of systems can be solved by numerical, asymptotical, and simulation methods. The novelty of the investigations is that it provides a new approach to an algorithmic solution for calculating the steady-state probabilities of the system. With the help of these probabilities the main performance measures can be computed. Several sample examples illustrate the effect of different parameters on the distribution on requests in the system.

Objectives The COVID-19 pandemic is considered a major threat to global public health. The aim of our study was to use the official epidemiological data to forecast the epidemic curves (daily new cases) of the COVID-19 using Artificial Intelligence (AI)-based Recurrent Neural Networks (RNNs), then to compare and validate the predicted models with the observed data. Methods We used publicly available datasets from the World Health Organization and Johns Hopkins University to create a training dataset, then we employed RNNs with gated recurring units (Long Short-Term Memory - LSTM units) to create two prediction models. Our proposed approach considers an ensemble-based system, which is realized by interconnecting several neural networks. To achieve the appropriate diversity, we froze some network layers that control the way how the model parameters are updated. In addition, we could provide country-specific predictions by transfer learning, and with extra feature injections from governmental constraints, better predictions in the longer term are achieved. We have calculated the Root Mean Squared Logarithmic Error (RMSLE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) to thoroughly compare our model predictions with the observed data. Results We reported the predicted curves for France, Germany, Hungary, Italy, Spain, the United Kingdom, and the United States of America. The result of our study underscores that the COVID-19 pandemic is a propagated source epidemic, therefore repeated peaks on the epidemic curve are to be anticipated. Besides, the errors between the predicted and validated data and trends seem to be low. Conclusion Our proposed model has shown satisfactory accuracy in predicting the new cases of COVID-19 in certain contexts. The influence of this pandemic is significant worldwide and has already impacted most life domains. Decision-makers must be aware, that even if strict public health measures are executed and sustained, future peaks of infections are possible. The AI-based models are useful tools for forecasting epidemics as these models can be recalculated according to the newly observed data to get a more precise forecasting.

Objectives The current form of severe acute respiratory syndrome called coronavirus disease 2019 (COVID-19) caused by a coronavirus (SARS-CoV-2) is a major global health problem. The aim of our study was to use the official epidemiological data and predict the possible outcomes of the COVID-19 pandemic using artificial intelligence (AI)-based RNNs (Recurrent Neural Networks), then compare and validate the predicted and observed data. Materials and Methods We used the publicly available datasets of World Health Organization and Johns Hopkins University to create the training dataset, then have used recurrent neural networks (RNNs) with gated recurring units (Long Short-Term Memory – LSTM units) to create 2 Prediction Models. Information collected in the first t time-steps were aggregated with a fully connected (dense) neural network layer and a consequent regression output layer to determine the next predicted value. We used root mean squared logarithmic errors (RMSLE) to compare the predicted and observed data, then recalculated the predictions again. Results The result of our study underscores that the COVID-19 pandemic is probably a propagated source epidemic, therefore repeated peaks on the epidemic curve (rise of the daily number of the newly diagnosed infections) are to be anticipated. The errors between the predicted and validated data and trends seems to be low. Conclusions The influence of this pandemic is great worldwide, impact our everyday lifes. Especially decision makers must be aware, that even if strict public health measures are executed and sustained, future peaks of infections are possible. The AI-based predictions might be useful tools for predictions and the models can be recalculated according to the new observed data, to get more precise forecast of the pandemic.

The aim of this paper is to create a simulation program in order to examine systems of two-way communication by the help of retrial queuing systems with finite source. Primary customers arrive from a finite source to the server according to exponential distribution. If an incoming customer discovers the server in an idle state then its service begins right away. Otherwise, if the server is busy or in failed state arriving primary customers move into the orbit and after some random time they try to reach the server. Whenever the server becomes idle it produces an outgoing call after an exponentially distributed time from the infinite source. If no primary customer arrives either from the finite source or from the orbit and the server functions until its arrival then it enters to the system. Otherwise, the outside call is cancelled. Several scenarios are distinguished when the server is in failed state and in this work we concentrate on comparing various distributions of service time of primary customers and failure probability of the server. The novelty of the investigation is to analyze such system with non-reliable server. Various figures illustrate comparison of using different scenarios showing the mean waiting time of primary customers and the utilization of the server obtained by simulation.

In this paper a special two-way communication system is modelled by the help of finite and infinite sources queueing system with retrial. An unreliable system is considered. The server may subject to random breakdowns. Customers from the finite source are the first order or regular customers, while the customers from the infinite source are the second order or the invited customers. The novelty of this paper is to investigate and model of a non-reliable two-way communication system with this special case of searching for the customers. The first order customers reach the server according to an exponentially distributed request generation time. In case of a busy server, they are able to retry their requests. In case of an idle server, the second order customers are called for service. Their inter-arrival times also subject to the exponential distribution. The effect of the breakdown and repair intensities are also investigated. From the system balance equations the steady state probabilities can be obtained. The MOSEL-2 tool is used for these calculations. By the help of these probabilities the common performance measures are calculated and displayed.

In this paper a special system with two-way communication is modelled by a finite and an infinite sources queueing system with retrial. The system is unreliable, the server may subject to random breakdowns. Customers from the finite source are the first order or regular customers, while the customers from the infinite source are the second order or the invited customers. The novelty of this paper is to investigate and model this unreliable system with different idle and busy breakdown intensity and different service rates for first and second order customers. In case of a busy server for first order customers, they can retry their requests. In case of an idle server, the second order customers are called for service. The effect of the breakdown and repair intensities are also investigated. The system balance equations are formulated, and the steady state probabilities can be obtained. Here the MOSEL-2 tool is used for these calculations. By the help of these probabilities the common performance measures are calculated and displayed.

In this paper a finite-source retrial queuing system with collision of the customers is investigated by means of computer simulation. The server is not reliable; it is subjected to breakdowns, and the repairs depend on whether the state of the server is idle. The random variables used in the model are jointly independent and are exponential and gamma distributed. Two operation modes are considered in the case of busy breakdown. The novelty of the investigation is a comparison of the performance measures of these modes, and estimations obtained by the simulation are graphically illustrated showing the influence of the difference of the working modes on the performance measures such as mean and variance of the response time, mean and variance of the number of customers in the system, mean and variance of the sojourn time in the orbit, mean and variance of time time a customer spent in service.

Here, we would like to study the efficiency and utilization of radio frequency (RF) transmission in wireless sensor networks. A new dual source queueing model (with finite and infinite numbers of sensors) is introduced in order to obtain the most frequently studied system performance characteristics (e.g. mean response time, average number of jobs waiting for transmission, and the utilization of the RF unit). The different types of sensors form the “sources” and the RF unit forms the “central servicing node” of this model. The elements of the sensor fields are classified according to their working purposes: The first class is the “Emergency” or “Special” class which is responsible to notify special rare events (e.g. fire alarms). The other class is the “Standard” class where sensors measure data from standard conventional events or occurrences (eg. motion detection, wind speed, level of darkness etc). For energy efficiency reasons the Central Unit (or Radio Frequency Unit - RFU) might switch into a reduced functioning mode. This reduced communication mode is for saving energy. The transmissions are closed in this mode. Returning from the reduced mode two cases are considered and two models are created to compare their steady-state system performance characteristics: In the first model, the RF transmission is available randomly to the sensor nodes (“Non-Controlled” case). In the other case, the Central Unit jobs which come from the Special class, can use the wireless service immediately (“Controlled” case).

This paper focuses on a comparative study of the most used three operating systems for mobile devices: Google Android, Apple iOS and Microsoft Windows Phone. The article presents which are the most important features of this three operating systems. The paper also deals with the features that these operating systems have in common and analyzes some of their disadvantages.

In this paper we investigate a single-server retrial queueing system with collision of the customer and an unreliable server. If a customer finds the server idle, he enters into service immediately. The service times are independent exponentially distributed random variables. During the service time the source cannot generate a new primary call. Otherwise, if the server is busy, an arriving (primary or repeated) customer involves into collision with customer under service and they both moves into the orbit. The retrial time of requests are exponentially distributed. We assume that the server is unreliable and could be break down. When the server is interrupted, the call being served just before server interruption goes to the orbit. Our interest is to give the main steady-state performance measures of the system computed by the help of the MOSEL tool. Several Figures illustrate the effect of input parameters on the mean response time.

The aim of the present paper is to build a simulation program to investigate finite-source retrial queuing system with collision of the customers where the server is subject to random breakdowns and repairs depending on whether it is idle or busy. All the random variables involved in the model construction are assumed to be independent and generally distributed. The novelty of the investigation is to carry sensitivity analysis of the performance measures using various distributions. Several figures show the effect of different distributions on the performance measures such as mean and variance of number of customers in the system, mean and variance of response time, mean and variance of time a customer spent in service, mean and variance of sojourn time in the orbit.

This paper deals with performance modeling of radio frequency licensing. Licensed users (Primary Users - PUs) and normal users (Secondary Users - SUs) are considered. The main idea, is that the SUs are able to access to the available non-licensed radio frequencies.

In this paper a finite source retrial queueing model is considered to study the performance measures of a communication network subject to random system breakdowns. The sources of the queueing model represent the communication entities, which are divided into two groups: The first group of sources is the class of “Intelligent items”, where the items are able to sense and get information about the condition of the network environment, and so they are able to retry the started processing request in the case of special network communication issues. The second class is the “Dummy sources” (e.g. automated equipements), where the source item starts the network communication, but is not able to sense the changes in the network conditions, so it is unable to retry the transmission in the case of any errors.

Nowadays, spectrum renting is a frequently investigated research topic with the aim of improving the efficiency of spectrum usage in mobile cellular networks. In this paper, we propose a finite-source queuing model, in which service providers may rent each other's unutilized frequency bands. Our model contains two source pools: one for subscribers generating fresh calls in the investigated mobile cell, and another for modeling handover calls. The model considers the traveling of subscribers over the borders of the investigated mobile cell, too. Moreover, we take into account the retrial phenomenon caused by the impatience of subscribers.

This paper proposes a retrial queueing model with the finite number of sources to evaluate the performance of spectrum renting in mobile cellular networks. The model incorporates necessary ingredients such as the finite number of subscribers, their impatience and a queue for the outbound service. To consider the specific feature of spectrum renting and the current mobile cellular technology, a variable number of servers that are switched on and off in groups is introduced. We present a novel way to take into account the renting fee, which can be used to fine-tune the operation of the spectrum renting procedure. Numerical results show that it is still profitable to initiate a spectrum renting request at high loads, even if no discount is offered by the frequency bands' owners.

This paper deals with wireless transmission problems in sensor networks. In order to study the performance measures and characteristics, a finite source retrial queueing model is introduced. Two classes of sensors are considered: "time driven" sensors for normal requests and "event driven" sensors with high priority for special, eg. emergency requests.The main goal is to investigate the relationship between the performance and the energy usage of the sensor network. Two operations are compared. In the first case only the event driven requests can initiate reaching the radio transmission (RF) unit. Time driven requests have to wait for a listening period of RF unit. In the second case the time driven requests also are able to access the sleeping RF unit, which will go to power save (sleeping) state immediately after the job is served.The steady-state performance measures and probabilities are given by the help of the MOSEL-2 tool.

In this paper we introduce a finite source retrial queueing model to investigate the performance characteristics of the wireless transmission problem in sensor networks. We divide the sensors into two classes. The first one is the “Emergency” class, which performs the notification of special emergency situations (eg. fire alarms). The second one is the “Normal” class, which measures and transmits environmental data (eg. temperature). For the performance evaluation of the wireless transmission we study and compare two cases: In the first model the RF transmission possibility will be available randomly for the sensor nodes (Non Controlled case) and in the second model the RF transmission requests coming from the emergency class, will access the wireless channel immediately (Controlled case). Our main interest is to give the main steady-state performance measures of the system computed by the help of the MOSEL tool.

Discriminatory Processor Sharing models play important role in analysing bandwidth allocation schemes in packet based communication systems. Users in such systems usually have access rate limitations which also influence their bandwidth shares. This paper is concerned with DPS models which incorporate these access rate limitations in a bandwidth economical manner.

A retrial queuing system with a single server is investigated in this pa- per. The server is subject to random breakdowns. The number of customers is finite and collision may take place. A collision occurs when a customer arrives to the busy server. In case of a collision both customers involved in the collision are sent back to the orbit. From the orbit the customers retry their requests after a random waiting time. The server can be down due to a failure. During the failed period the arriving customers are sent to the orbit, as well. The novelty of this analysis is the impatient behaviour of the customers. A customer waiting in the orbit may leave it after a random waiting time. The requests of these customers will not be served. All the random variables included in the model construction are assumed to be exponentially distributed and independent from each other. The impatient property makes the model more complex, so the derivation of a direct algorithmic solution (which was provided for the non-impatient case) is difficult. For numerical calculations the MOSEL-2 tool can be used. This tool solves the Kolmogorov system equations, and from the resulting steady-state probabilities various system characteristics and performance measures can be calculated, i.e. mean response time, mean waiting time in the orbit, utilization of the server, probability of the unserved impatient requests. Principally the effect of the impatient property is investigated in these results, which are presented graphically, as well.