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Internet is one of the most influential new communication technologies has influenced all aspects of human life. Extensive use of the Internet and the rapid growth of network services have increased network traffic and ultimately a slowdown in internet speeds around the world. Such traffic causes reduced network bandwidth, server response latency, and increased access time to web documents. Cache memory is used to improve CPU performance and reduce response time. Due to the cost and limited size of cache compared to other devices that store information, an alternative policy is used to select and extract a page to make space for new pages when the cache is filled. Many algorithms have been introduced which performance depends on a high-speed web cache, but it is not well optimized. The general feature of most of them is that they are developed from the famous LRU and LFU designs and take advantage of both designs. In this research, a page replacement algorithm called FCPRA (Fuzzy Clustering based Page Replacement Algorithm) is presented, which is based on four features. When the cache space can’t respond to a request for a new page, it selects a page of the lowest priority cluster and the largest login order; then, removes it from the cache memory. The results show that FCPRA has a better hit rate with different data sets and can improve the cache memory performance compared to other algorithms.

One of the most important problems in directional sensor networks is k-coverage in which the orientation of a minimum number of directional sensors is determined in such a way that each target can be monitored at least k times. This problem has been already considered in two different environments: over provisioned where the number of sensors is enough to cover all targets, and under provisioned where there are not enough sensors to do the coverage task (known as imbalanced k-coverage problem). Due to the significance of solving the imbalanced k-coverage problem, this paper proposes a learning automata (LA)-based algorithm capable of selecting a minimum number of sensors in a way to provide k-coverage for all targets in a balanced way. To evaluate the efficiency of the proposed algorithm performance, several experiments were conducted and the obtained results were compared to those of two greedy-based algorithms. The results confirmed the efficiency of the proposed algorithm in terms of solving the problem.

Fuzzy logic is a multi-valued concept, whose emergence in software sciences has eliminated 0 and 1 computations, putting them within an infinite space of [0,1]. This characteristic of fuzzy logic has resolved ambiguity in numerous previous problems. The sentence roles in Persian language were specified based on the fuzzy logic’s capability to resolve ambiguity. For that purpose, we first obtained the best classification for each defuzzifier, based on which a classified fuzzy was implemented. Nonetheless, the fuzzy system used in this research was classified based on statistical computations. To achieve the best classification, five defuzzification methods (Mean Of Max, Max Of Membership, Largest Of Max, Smallest Of Max, and Central Average) competed in 16 roles each in five classes (different matrices). Finally, Mean of Max with a success rate of 64% proved to be a defuzzifier delivering the best output among 5 different defuzzification methods.

Many operations for complications after bariatric surgery are performed by surgeons without bariatric expertise at centers without teams who routinely care for bariatric patients. This study sought to evaluate whether bariatric expertise affects patterns of care and perioperative outcomes among patients undergoing operative intervention for complications after bariatric surgery. Administrative claims data from the Kentucky Office of Health Policy were queried for inpatients undergoing operative intervention for complications related to bariatric surgery between 2015 and 2018. Patients were stratified with respect to whether or not they underwent surgery at a Metabolic and Bariatric Surgery Accreditation and Quality Improvement Program (MBSAQIP) accredited bariatric surgery center (BCE) or not (non-BCE). Groups were compared with respect to demographic, procedural, and outcome variables. BCE patients were more often Caucasian than non-BCE patients (p

In this study, fatigue failure behavior of welded X70 pipeline steel was investigated by rotating bar bending fatigue tests performed at room temperature. S–N curves of base metal, weld and heat-affected zone (HAZ) were plotted. Tension tests, hardness measurements and Charpy V-notched impact tests were carried out for mechanical characterization. Samples were examined with optical microscope and scanning electron microscope (SEM) microstructurally. Fracture surfaces were examined with SEM. In addition to fractographic analysis, striation counting method was used to estimate the crack growth rate of base metal, weld and HAZ. Stress intensity factor (∆K) ranges were also calculated. Tensile properties and hardness values of base metal and weld were found to be almost the same. Charpy impact test results show that base material has approximately twice impact energy than weld and HAZ at room temperature. However, Charpy impact energies of HAZ and weld decreased noticeably at − 30 °C, while very slight decrease was observed for base metal. The comparison of high-cycle fatigue and crack growth behaviors shows that base metal has better fatigue behavior of all from the point of crack initiation and propagation. Moreover, our findings also support the idea that striation counting can be used as a fractographic approach to estimate the crack growth behavior of materials.

Software rejuvenation is an effective technique to counteract software ageing in continuously-running applications such as web-service-based systems. In a client-server application, where the server is intended to run perpetually, rejuvenation of the server process periodically during the server idle times increases the availability of that service. In these systems, web services are allocated based on the receiver's requirements and server's facilities. Since the selection of a server among candidates while maintaining the optimal quality of service is an NP-hard problem, meta-heuristics seems to be suitable. In this study, the proposed dynamic software rejuvenation as a proactive fault-tolerance technique based on a combination of ant colony optimisation (ACO) and gravitational emulation local search (GELS) so as to determine the optimal times when rejuvenation can be performed and failure rate can be minimised. The newly proposed method combined the public search capabilities of ACO with local search of GELS algorithm in an effort to create a stable algorithm, which can make reaching the global optimum largely possible in the proposed work. The simulation results revealed that the proposed strategy can decrease the failure rate of web services averagely by 28% in comparison with genetic algorithm and decision-tree strategies.

As an effective technique to counteract software aging, software rejuvenation is applied in continuously running applications such as web service-based systems. In such systems, web services are allocated depending on the requirements of receivers and the facilities of servers. One of the challenging issues during assignment of web services is how to select the appropriate server to minimize faults. In this paper, we proposed dynamic software rejuvenation in the form of a proactive fault-tolerance technique based on fuzzy system. While including a threshold for the rejuvenation of each web service, we carried out the training phase based on the features of the service providers as well as the receivers’ requirements. The results of simulations revealed that our strategy can mitigate the failure rate of web services by 45, 40, 23, and 12% in comparison with the non-fuzzy, regression-based, Markov-based, and ACOGELS-based web service rejuvenation strategies, respectively.

In this paper, we suggest a method for determining the restarting time for web services to increase availability, known as rejuvenation. We consider different parameters such as number of users, maximum service request number, response time, and throughput of a web service to determine its restarting time. Software rejuvenation is an effective technique to counteract software aging in continuously running applications such as web service-based systems. In these systems, web services are allocated based on the needs of the receivers and facilities of servers. One of the challenges while assigning web services is selecting the appropriate server to reduce faults. Since the selection of a server among candidates while maintaining the optimal quality of service is an NP-hard problem, metaheuristics seem to be suitable. In this paper, we propose dynamic software rejuvenation as a proactive fault-tolerance technique based on the whale optimization algorithm. The threshold for the rejuvenation of each of the web services is considered and training is done based on the features of the service providers as well as the needs of the receivers. The whale optimization algorithm with the criterion of movement radius is utilized for flexibility of web service provider selection. Here, we detect and rejuvenate systems that required rejuvenation before the occurrence of a fault. The simulation results reveal that our strategy can decrease the failure rate by an average of 30 percent in comparison with state-of-the-art strategies and improve the system availability in web services.

Gene selection is the process of selecting the optimal feature subset in an arbitrary dataset. The significance of gene selection is in high dimensional datasets in which the number of samples and features are low and high respectively. The major goals of gene selection are increasing the accuracy, finding the minimal effective feature subset, and increasing the performance of evaluations. This paper proposed two heuristic methods for gene selection, namely, Xvariance against Mutual Congestion. Xvariance tries to classify labels using internal attributes of features however Mutual Congestion is frequency based. The proposed methods have been conducted on eight binary medical datasets. Results reveal that Xvariance works well with standard datasets, however Mutual Congestion improves the accuracy of high dimensional datasets considerably.

The aim of the current study is to compare the effects of hypertrophy-, strength-, and power-type resistance exercise training types on hydrogen peroxide (H2O2), malondialdehyde (MDA), total lactate dehydrogenase (LDH), and total creatine kinase (CK) in resistance-trained women. After determining one-repetition maximum (1-RM), ten resistance-trained women (age 26.30 ± 4.95 years; body mass index 22.07 ± 2.02 kg/m2; body fat 24.64 ± 4.98%) conducted hypertrophy-type (70% of 1-RM), strength-type (90% of 1-RM), and power-type (45% of 1-RM) resistance exercise for three consecutive weeks. The movements included lever leg extension, reverse-grip lat pull-down, horizontal leg press, standing biceps cable curl, lying leg curl, machine bench press, standing cable triceps extension, and seated calf raises. Fasting blood samples were obtained immediately before and immediately after each trial. Statistical analyses were performed using the t test, Wilcoxon, and analysis of covariance. The significance level was set at P

Regardless of the performance of gravitational search algorithm (GSA), it is nearly incapable of avoiding local optima in high-dimension problems. To improve the accuracy of GSA, it is necessary to...

Purpose As a critical problem in sophisticated distribution systems, vehicle routing plays a pivotal role in dealing with time windows and capacities constraints. The purpose of this paper is to addresses a new integrated model to incorporate both three-dimensional and time windows aspects of the routing problem. First, capacitated vehicle routing decisions are made subject to a soft time interval to meet the customers’ demands. Afterward, these decisions are entered into the three-dimensional loading problem. Design/methodology/approach The problem is solved using generalized algebraic modeling system software in small-size problems. The problem is NP-hard and requires an efficient solution methodology. For this purpose, a hybrid algorithm has been proposed to solve the large-size problems. The efficiency of this algorithm is checked by making comparisons with exact solutions for small and medium size test problems, and with the related literature for large size problems. Findings The numerical experiments show that the proposed model covers more effectively the broader aspects of the transportation problem. Furthermore, the proposed algorithm supports competitive and satisfactory results by giving reasonable outputs in comparison with previous studies. Originality/value The main purpose of this integration is to achieve minimum total transportation costs, which cannot be guaranteed without applying two referred constraints, simultaneously.

Purpose Line–cell conversion and rotation of operators between cells are common in lean production systems. Thus, the purpose of this study is to provide an integrated look at these two practices through integrating job rotation scheduling and line-cell conversion problems, as well as investigating the effect of rotation frequency on flow time of a Seru system. Design/methodology/approach First, a nonlinear integer programming model of job rotation scheduling problem and line–cell conversion problem (Seru-JRSP) was presented. Then, because Seru-JRSP is NP-hard, an efficient and effective invasive weed optimization (IWO) algorithm was developed. Exploration process of IWO was enhanced by enforcing two shake mechanisms. Findings Computations of various sample problems showed shorter flow time and less number of assigned operators in a Seru system scheduled through job rotation. Also, nonlinear behavior of flow time versus number of rotation periods was shown. It was demonstrated that, setting number of rotation frequency to one in line with the literature leads to inferior flow time. In addition, ability of developed algorithm to generate clusters of equivalent solutions in terms of flow time was shown. Originality/value In this research, integration of job rotation scheduling and line–cell conversion problems was introduced, considering lack of an integrated look at these two practices in the literature. In addition, a new improved IWO equipped with shake enforcement was introduced.

In incremental software development approaches, the product is developed in various releases. In each release, a set of requirements is proposed for the development. Usually, due to lack of funds, lack of time and dependency between requirements, there is no possibility to develop all the required requirements. There are two conflicting objectives for choosing an optimal subset of the requirements: increasing customer satisfaction and reducing development costs. This problem is known as the next release problem (NRP) and is categorized as an NP-hard problem. Unlike the standard version of the NRP, we formulate this problem as a restricted multi-objective optimization problem. There exist metaheuristic algorithms for solving this problem performed as serials. In this paper, we introduce a parallel algorithm based on the master–slave model in order to improve the quality of the solutions. Based on the criteria of multi-objective problems, the quality of the obtained solution is compared with several metaheuristic algorithms. Two scenarios and two different datasets are used for experiments. Results indicate that the proposed method in the first scenario would highly improve the quality of solutions. Moreover, the method reduces execution time significantly through improvement in the quality of the solution in the second scenario.

The reality of sexual harassment is unmasking in many fields, and medical trainees constitute a vulnerable and at-risk group. We report the prevalence of sexual harassment among GI, internal medicine, and pediatric residents, with a focus on identifying underlying reasons for lack of victim reporting. A modified previously validated Department of Defense survey on sexual harassment was e-mailed to 261 GI, 132 pediatric, and 271 internal medicine program directors. Three hundred eighty-one residents responded to the survey. Female trainees were more likely to be subject to sexual harassment (83% vs 44%, P

Software rejuvenation is an effective technique to counteract software aging in continuously-running application such as web service based systems. In client-server applications, where the server is intended to run perpetually, rejuvenation of the server process periodically during the server idle times increases the availability of that service. In these systems, web services are allocated based on the user’s requirements and server’s facilities. Since the selection of a service among candidates while maintaining the optimal quality of service is an Non-Deterministic Polynomial (NP)-hard problem, Meta-heuristics seems to be suitable. In this paper, we proposed dynamic software rejuvenation as a proactive fault-tolerance technique based on a combination of Cuckoo Search (CS) and Particle Swarm Optimization (PSO) algorithms called Computer Program Deviation Request (CPDR). Simulation results on Web Site Dream (WS-DREAM) dataset revealed that our strategy can decrease the failure rate of web services on average 38.6 percent in comparison with Genetic Algorithm (GA), Decision-Tree (DT) and Whale Optimization Algorithm (WOA) strategies.

A number of software programms are developed in several releases. Before developing any new release, a set of requirements is suggested for inclusion in the release. Having multiple constraints, it...

The memory management in the operating system includes a part called the page replacement algorithms. Replacement algorithms in environments that require high-performance computing are considered as an important issue. For example, these algorithms are very important in cache management in microprocessors, web caching, replication strategies in distributed information systems and so on. Due to the important role of replacement algorithms in overcoming the problem of performance caused by the difference in processor speeds and memory, many algorithms were proposed. Most of them are the developed schemes of the least frequently used (LFU) and least recently used (LRU). Although most proposed designs can solve the LRU and LFU defects, they are implemented in a difficult way. The most important advantage of LRU and LFU is their simple implementation. This research proposes a page replacement algorithm that is simple to implement. The algorithm, which uses three parameters to cluster cache pages, is called the fuzzy page replacement algorithm. Whenever a miss occurs, it selects a page of the cluster with the lowest priority which has the smallest Euclidean distance with its center and then exits the cache. The most significant advantage of the algorithm is using the FCM (fuzzy c-means) algorithm to cluster pages, resulting in better replacement and hence higher memory performance.

Nowadays, data mining has become significant given the popularity of social networks as well as the emergence of abbreviated words, foreign terms and emoticons in the Persian language. Meanwhile, numerous studies have been conducted to identify the type of words. On the one hand, identifying the role of each word in a sentence is far more important than identifying the type of word in the sentence. On the other hand, the spelling-grammatical similarity of Persian to Arabic has enabled the newly proposed method in this paper to be applied to Arabic. In this paper, we adopted the Hidden Markov Model (MHM) and Tri-gram tagging with the aim of identifying the morphology of composition roles in Persian sentences. Then, a comparison was made between the technique developed in this paper and the Hidden Markov Model, Uni-gram and Bi-gram tagging. The proposed method supports the results obtained by the word role identification through "independent" and "dependent" roles and several factors that have a contribution to the words roles in sentences. In fact, the simulation results show that the average success rates of independent composition roles with MHM and Tri-gram tagging were 20.56% and 17.67% compared to Uni-gram and Bi-gram methods, respectively. Regarding the dependent composition role, there were improvements by 24.67% and 32.62%, respectively.

Task Scheduling and Resource Allocation (TSRA) is the key focus of cloud computing. This paper utilizes Smart Message Passing Interface based Approach (SMPIA) and the Roulette Wheel selection method in order to determine the best Alternative Virtual Machine (AVM). To do so, the Virtual MPI Bus (VMPIB) is employed for efficient communication among Virtual Machines (VMs) using SMPIA. In this matter, SMPIA is applied on different resource allocation and task scheduling strategies. MakeSpan (MS) was chosen as an optimization factor and solutions with minimum MS value as the best task mapping performance and reduced cloud consumption. The simulation is conducted using MATLAB. The analysis proves that applying SMPIA reduced the Total Execution Time (TET) of resource allocation, maximum MS time, and increase the Resource Utilization (RU), as compared to non-SMPIA for Greedy, Max-Min, Min-Min algorithms. It is observed that SMPIA can outperform non-SMPIA. The effect of SMPIA is more obvious as change in the MS and the number of cloud workloads increase. Furthermore, regarding the TET and MS of the tasks, the SMPIA can significantly reduce the starvation problem as well as the lack of sufficient resources. In addition, this approach improves the system's performance more than the previous methods, what reflects effectiveness of the proposed approach concerning the Message Passing Interface (MPI) communication time in the network virtualization. The mentioned text mining work was prepared concurrently after practical evaluation.