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Abstract Background Accurate assessment of the bone supporting the implant is crucial. Early detection of bone defects around the implant can prevent the loss of bone support that ultimately leads to the loss of the implant. Therefore, the purpose of this study is to check the accuracy of CBCT in detecting peri-implant fenestrations around the implant. Materials & methods In this laboratory study, healthy beef ribs were used. The ribs were divided into three groups of 12 (control group, 1–2 mm fenestration group, and 2–3 mm fenestration group). The blocks were cut to a length of 20 mm and 36 osteotomies with dimensions of 4 × 12 mm were made by the periodontist in order to place the implant in these bone blocks. Then the titanium implant was placed in the holes and the initial scan was performed with CBCT. In the second group, fenestration-like lesions were created on the same buccal side at a distance of 10 mm from the crest with a diameter of 1–2 mm and in the third group with a diameter of 2–3 mm, and the CBCT scan was performed again with the same parameters. Two radiologists evaluated the images twice for the presence and absence of fenestration. Results There was no statistically significant difference between direct measurements and CBCT in the fenestration group of 1–2 mm (p

Evaluating the resilience of the innovation ecosystem to maintain its performance, in the sense of resistance to disruption and recovery after it, has recently received more attention. Several studies have been conducted to model different ecosystems and evaluate their resilience. However, modeling the innovation ecosystem from a holistic perspective and performing a quantitative assessment of its resilience have received less attention. This paper models the innovation ecosystem holistically and evaluates its resilience index using a quantitative approach through five main steps. In the first step, a case study related to the innovation ecosystem of Iran's Ministry of Energy, called the Power Innovation Ecosystem, is modeled by combining system dynamics and agent-based modeling. Upon validating the model in the second step, the disruption of the loss of experts is investigated in the third step, and all possible actions to recover each actor are analyzed. In the fourth step, the performance of the ecosystem is simulated before and after the disruption using the data gathered in the previous steps. Finally, resilience is calculated in two different ways in the fifth step. Several improvement solutions are also suggested when considering that the resilience index of the innovation ecosystem is at a medium level. This research may assist policymakers in observing the resilience level of the innovation ecosystem based on the proposed model. By applying strategic changes to this model, they can determine the effects of their policies and make the most appropriate decisions to increase the resilience of the innovation ecosystem.

Introduction: The flexible job shop system is one of the most widely used scheduling systems in production environments, consistently attracting researchers' attention due to its diverse applications. Many studies in this field assume fixed and predetermined processing times. However, processing times can increase due to the deterioration effect, and after implementing rate-modifying activities (RMA), these times return to their original values. This study examines the flexible job shop scheduling system, considering job rejection policies, dual resource constraints (human and machine), and RMA maintenance activities.Methods: The objective of flexible job shop scheduling is to assign each operation to a machine and a worker from a set of eligible machines and workers in a way that optimizes the sequence of operations on the machines. A mathematical model based on the mixed-integer linear programming approach was developed for this purpose. Literature review classifies the problem with the stated assumptions as NP-hard, making the use of meta-heuristic methods essential for finding near-optimal solutions. Thus, Variable Neighborhood Search (VNS), Simulated Annealing (SA), and a combined VNS-SA algorithm were employed to solve the problem.Results and discussion: Twenty sub-problems were analyzed, categorized into small, medium, and large-sized problems. The characteristics of each problem were defined by parameters such as the number of jobs, machines, workers, total operations, and buckets. Meta-heuristic methods, including VNS, SA, and their combination, were utilized to solve the problem. Seven neighborhood structures based on changes in assigned machines and workers, operation and job replacements, execution of RMA activities, and job acceptance/rejection were developed to enhance solution space exploration. The solution generation structure ensures feasibility within the flexible job shop system's requirements. The parameters of the meta-heuristic methods were tuned using the Taguchi method. Parameters related to the combined VNS-SA algorithm, such as initial temperature, number of neighborhood searches, and shake procedure counter, were reported. The results of the meta-heuristic methods were compared, and for small-sized problems, they were also compared with exact solutions.Conclusion: The results of the twenty sub-problems solved using the three meta-heuristic approaches were compared statistically. The combined method of simulated annealing and variable neighborhood search showed superior performance in solving the problem.

Optimal assignment of equipment for disposal of targets, often referred to as a weapon assignment problem, has become one of the main centers of modern military thought. Weapon Target Assignment (WTA) is based on consideration of the principle of saving on resources, without reducing the power of systems and systems always to protect vital infrastructure. The critical infrastructure consists of the physical assets of a system, resulting in a significant disruption to operational and operational systems. In this study, a Stackelberg a mathematical model is presented to manage battle scenes. The Stackelberg model is considered to be a strategic game and an incomplete competition. The game is considered to consist of two actors (Enemy and Power), each striving to optimize its goals. Considering air and ground forces simultaneously, consider the assets of both the friendly and the enemy, including the innovations considered in this study. In this study, a two- level Stackelberg model has been presented and after linearization, the two- level model has been developed using Karush–Kuhn–Tucker conditions (KKT) to the ordinary one –level model. Finally to show the performance of the model, some examples are solved using GAMS software.

Home Healthcare provides a wide range of home care services from prevention to rehabilitation and primary care to professional nursing services. This research presents a two-objective mathematical model based on the mixed integer linear programming approach for the home healthcare routing and scheduling problem (HHCRSP) with the objectives of minimization the nurses’ travel costs and the maximal working time difference among nurses. Considering the multimodal transportation, several initial health centers and one final health center and patient and nurse time windows are important features of the studied mathematical model. Small-sized problems have solved by using the Ɛ-constraint method on GAMS software. Also, due to the NP-hardness of the problem, MOPSO and NSGA-II algorithms have used to solve the medium and large-sized problems. The statistical results showed that the NSGA-II performed better than the MOPSO for medium and large sizes of problems in both MID and NOS performance metrics. Overall, Results of the performance metrics on different sizes of problems indicate the efficient and effective performance of NSGA-II in solving the understudied problem.

The current research aims to design a fuzzy multiobjective model of the reverse logistics supply chain network in the automotive industry, taking into account the energy and time reduction approach. The automobile industry is one of the industries with a high demand worldwide. To continue the competition, the manufacturers of the leading car equipment should strive for better product quality by continuously improving their production processes, directing the production of greenhouse gases with low carbon levels, and increasing sustainability. In this regard, reverse supply chain networks and closed-loop chains have unique features that are very useful in the industry under review. The goal is to transform this model into a supply chain of a secure link in the automotive industry. Deterministic methods, genetic algorithm, particle swarm algorithm, and several scenarios with different aspects have been used to solve the model. The results show that the effectiveness of the three ways in terms of solution time is higher in the deterministic solution method. Proper use of the proposed process can help managers effectively manage the flow of recycled products concerning environmental considerations, and this process provides a sustainable competitive advantage for companies.

Assessing the impact of a monetary policy through the banking system on the economy is important because the highest share of Iran’s finance market belongs to the banking sector. For this purpose, this research aims to investigate the impact of changing the bank deposit profit rate through the banking system. This paper focuses on the role of deposits as one of the main financing sources of banks in a macro-structural econometric model over the period of 1973-2017. In this respect, focusing on Iran’s banking system; a model was first formulated according to the structure of the Iranian economy. Then, three scenarios, including increasing the profit rate, decreasing the profit rate, and pegging policy, were incorporated into the model to observe the impact of profit rate changes. The results of the simulation reveals a negative relationship between the deposit profit rate and gross domestic product. An increase in the bank deposit profit rate along with increasing the deposits through free credit resources raises credit provision by the banking system, causing a direct impact on investment. On the other hand, implementing this scenario increases the cost of capital and creates an inverse effect on investment. However, the impact of the cost of capital is stronger and leads to a reduction in investment and, consequently, reduces the output by 0.66%. In the scenario of decreasing the profit rate, opposite results are observed, and the output increases by 0.71%. In the third scenario (Pegging deposit profit rate to 17%), however, the output increases by 0.46%. Therefore, the results implies that an increase in the bank profit rate is not confirmed by the theory of McKinnon and Shaw and decreases the GDP.

In the real world, firms with hybrid flow-shop manufacturing environment generally facethe human resource constraint, salary cost increasment and efforts to make better use oflabor, in addition to machine constraint. Given the limitations of these resources, productdelivery requierements to customers have made the job rejection essential in order to meetdistinct customer requirements. Therefore, this research has studied the dual resourceconstrained hybrid flow-shop scheduling problem with job rejection in order to minimizethe total net cost (the sum of the total rejection cost and the total tardiness cost of jobs)which is widely used in many industries. In this article, a mixed integer linear programmingmodel has developed for the research problem. In addition, an improved sooty ternoptimization algorithm (ISTOA) has proposed to solve the large-sized problems as well asa decoding method due to the NP-hardness of the problem. In order to evaluate theproposed optimization algorithm, five well-known algorithms in the literature including(immunoglobulin-based artificial immune system (IAIS), genetic algorithm (GA), discreteartificial bee colony (DABC), improved fruit fly optimization (IFFO), effective modifiedmigrating birds optimization (EMBO)) have adapted with the proposed problem. Finally,the performance of the proposed optimization algorithm has investigated against theadapted algorithms. Results and evaluations show the good performance of the improvedsooty tern optimization algorithm.

Estimating the substitution elasticity of imported products is a key parameter for analyzing trade policies, especially trade integration issues. In general, this elasticity represents the intensity of the impact of import demand on domestic demand from the relative change in domestic price to import. In this study, the Armington substitution elasticity's were estimated for 103 industries four-digit ISIC in the period 1371-1396.The data used for domestic manufacturing production are extracted from the Statistical Center of Iran, the domestic and import price indices at the producer level from the Central Bank of Iran, and the trade data from the Islamic Republic of Iran Customs Administration.According to the results; the industries with the highest positive elasticities of substitution, respectively, are the "Manufacture of malt liquors and malt" and the "Manufacture of basic chemicals, except fertilizers and nitrogen Compounds" and Industries with the lowest positive substitution elasticity are, respectively "Tanning and dressing of leather" and "Building and repairing of pleasure and sporting boats except ships". Accordingly, in industries with positive substitution elasticity, it is possible to replace foreign consumption with domestically produced goods. Long-term elasticities are also significantly higher than short-term tensions (6.7 times).

Resource scheduling, job sequencing, and assigning them to available resources are the most critical issues in manufacturing systems, such as flexible job-shop systems. In addition, scheduling uncertainties have attracted significant attention in this field. This study investigates the dual-resource constrained flexible job-shop scheduling (DRCFJSS) problem under machine breakdown and operational uncertainty. Stochastic scenario-based methods were utilized to study the uncertain nature of the problem. Because process times have inherent uncertainty, they are considered fuzzy numbers and are controlled by a credibility-based measure. Robust scheduling must be developed to address unexpected disruptions, such as machine breakdowns and operational risks, such as uncertain process times. Accordingly, a novel robust fuzzy stochastic programming (RFSP) model is presented for this problem. In the proposed RFSP model, the objective function is formulated using a hybrid measure (i.e., a combined average-case and worst-case performance of the manufacturing system) under probable machine breakdown scenarios. Because the DRCFJSS problem is NP-hard, two types of meta-heuristic algorithms, evolutionary population-based, genetic algorithm (GA), and vibration damping optimization (VDO) algorithm, are used for large-sized problems. Then, the proposed RFSP model was applied to a case study, and numerical experiments with randomly generated test problems were used. In small-sized problems, the proposed model is solved using the CPLEX solver, GA and VDO algorithms. Also, the computational study confirms the proper quality of the results of the GA and VDO algorithms in medium and large-sized problems.

Objective: Today, green supply chain managers in leading companies strive to offer green logistics and improve their environmental performance throughout the supply chain as a strategic weapon to gain a sustainable competitive advantage by creating profitability and satisfaction across the supply chain. Therefore, considering the purpose of the research, which is to design a multi-objective model of closed-loop supply chain networks in the automotive industry according to energy efficiency and time efficiency plans, we try to model the closed-loop supply chain in the automotive industry. Methods: In this study, we use the MOPSO method to facilitate its implementation and its ability to provide good convergence, as well as to maintain a proper balance between exploitation and exploration, as well as the NSGA II genetic algorithm. Results: In the study of the findings of the proposed algorithms, it found that the average error resulting from these algorithms is less than 0.04. The results also show that the proposed algorithms have the necessary efficiency in solving these problems. Conclusion: We note the significant findings of our model as follows: (1) An efficient closed-loop network that shows the economic benefits of considering the value of time due to the recycling of worn-out products. (2) Has the ability to demonstrate the capacity to achieve maximum benefits in terms of cost value as well as environmental prospects.

This paper deals with the problem of two-stage assembly flow shop scheduling with considering sequence-independent setup times .The objective is to minimize totalcompletion times of all orders. In this problem, there are several orders for one type of product. Each ordered product is formed of several different parts. At first, the parts are manufactured in a flow shop stage with some different machines and then they are assembled into a final product on a single machine. This paper presents three meta-heuristic algorithms, namely Parallel Variable Neighborhood Search (PVN) Artificial Immune Algorithm (AIA) and Simulated Annealing (SA), for solving under studied problem. The Taguchi experimental design method as an optimization technique is employed to tune different parameters and operators of presented algorithms. Also, Numerical experiments are used to evaluate the performance of the proposed algorithms. The results show that the PVNS algorithm performs better than the other algorithms

During recent years, the growth of international trade has been more than that of world output, and countries have shown more interest in forming regional unions and agreements to increase their trade. The aim of this study is to estimate the net effect of D8 formation on trade of its member countries. In this regards, a gravity model has been applied using panel data method for period 1990-2014, in accordance with a Difference-in-Differences (DID) approach and Poisson-Pseudo maximum likelihood method. The D8 countries are selected as target group and select member countries of Organization of Islamic Cooperation are chosen as control group. Our Results show that the net effect of D8 formation on trade among its member countries is positive and significant. Also, the bilateral trade among member countries has had a direct relationship with their size and common language and inverse relation with the distance between them.

Introduction: The human uses vision to detect information about the task environment. Studies have shown that duration of quiet eye, motor performance, and efficiency in a number of tasks has improved using quiet eye training. The current study was to assess the effects of different types of quiet eye training on quiet eye and kicking skill in children with high motor proficiency. Materials and Methods: In this study, 45 boy students with high motor proficiency with age range of 7 to 10 years by performing a pretest phase, homogeneously divided in to three groups of baseline, and short-term and long-term quiet eye training. The study included pretest, acquisition, and posttest phases. In the pretest phase, the participants performed 4 soccer shots (Modified Christian Moore's Shot Test). The acquisition phase was done in 12 sessions, and 10 trials in per session. At the end of the last training session, the participants performed 4 soccer shots in the posttest phase. In each phase, visual information was recorded by the eye tracking system, and the accuracy of the soccer shot was recorded by the researcher. Data were analyzed using paired sample t-test and one-way ANOVA. Results: The different types of quiet eye training had a significant effect on accuracy of soccer shot (P = 0.001) and the duration of quiet eye (P = 0.001) of children with high motor proficiency. But there was no significant difference between each of the types of quiet eye training on the accuracy of soccer shot and the duration of quiet eye (P > 0.05 for both).

Drug-target interaction (DTI) prediction through in vitro methods is expensive and time-consuming. On the other hand, computational methods can save time and money while enhancing drug discovery efficiency. Most of the computational methods frame DTI prediction as a binary classification task. One important challenge is that the number of negative interactions in all DTI-related datasets is far greater than the number of positive interactions, leading to the class imbalance problem. As a result, a classifier is trained biased towards the majority class (negative class), whereas the minority class (interacting pairs) is of interest. This class imbalance problem is not widely taken into account in DTI prediction studies, and the few previous studies considering balancing in DTI do not focus on the imbalance issue itself. Additionally, they do not benefit from deep learning models and experimental validation. In this study, we propose a computational framework along with experimental validations to predict drug-target interaction using an ensemble of deep learning models to address the class imbalance problem in the DTI domain. The objective of this paper is to mitigate the bias in the prediction of DTI by focusing on the impact of balancing and maintaining other involved parameters at a constant value. Our analysis shows that the proposed model outperforms unbalanced models with the same architecture trained on the BindingDB both computationally and experimentally. These findings demonstrate the significance of balancing, which reduces the bias towards the negative class and leads to better performance. It is important to note that leaning on computational results without experimentally validating them and by relying solely on AUROC and AUPRC metrics is not credible, particularly when the testing set remains unbalanced.

In general, development of infrastructures and implementation of economic projects require financing. However, the exchange rate fluctuations lead to increasing costs of financing through conversion, transaction, economic, credit and liquidity risks. Hence, in this study, the effect of the exchange rate volatilities has been investigated on financing practices of companies listed in Tehran Stock Exchange using autoregressive distributed lags method during monthly period 2006-2015 and then, the effect of this variable has been determined on foreign direct investment (FDI) inflow in economic sectors of Iran using panel data method during 1994-2015. According to the results, the value of assets, stock price index, economic freedom index, inflation rate and exchange rate volatilities are identified as determinants of firms’ financing. Also, the pattern of FDI is a function of sectoral value-added growth, capital productivity index, inflation rate, economic freedom index and the exchange rate volatilities where the coefficients are significant and consistent with theoretical expectations. Furthermore, increasing exchange rate fluctuations can decrease domestic and foreign financing because exchange rate fluctuations lead to different risks.

In the scheduling problems, it is commonly assumed that processing times are fixed and known. In the literature of project scheduling emphasizes that the time of each activity/operation can be multi-mode and by assigning more resources, the activity time can be reduced. In these problems, in addition to activity scheduling, allocation of available limited resources to the activities should also be carried out. This assumption that processing time of activities is fixed is a weakness in scheduling literature. This paper develops the classic problem flow shop scheduling to multi-mode resource-cosntrainted flow shop scheduling problem. This paper discusses comprehensively about mathematical modeling. In this regard, two mixed integer linear programming models with two differnet concepts are presented. The first model is location-based model and the second is sequence-based. The performance of the models are evaluated by comparing their size and computational complexities. In the size complexity, the first model requires more variables but less constraints than second Model. In the computational complexity, the first model significantly outperforms than the second Model. Also, the first model, besides solving more problems as optimally, requires less time to solve than the second model

In general, the main objective of monetary policies is to stabilize the key macroeconomic variables, especially the inflation around its target. However, the role of some variables such as the real exchange rate can be important in optimal monetary policy responses to commodity terms of trade shocks in commodity dependent emerging market economies, such as oil-exporting countries. This study tries to examine the impacts of different monetary policy in order to minimize the adverse effects of the economic shocks, by considering the real exchange rate gap into the flexible inflation targeting rule, in the Iranian economy. Hence, using a new Keynesian model, the demand and Phillips curves are estimated by autoregressive distributed lags (ARDL) method, based on the quarterly data during 1991:Q1-2014:Q4 and then a loss function for the central bank is minimized subject to above equations, by using the optimal control approach. According to the results, when the real exchange rate gap is calculated based on the lower targets, existence of this variable in the loss function of the monetary authorities lead to lower losses. But the higher real exchange rate gap is accompanied by higher losses. For summary, dependent on the real exchange rate gap, the monetary author can use the target of the exchange rate as a policy target. However, the flexible inflation targeting monetary framework, with regards to real exchange rate targeting, is not optimal policy in case of more rigid exchange rate regimes.

Introduction: Final fixation prior the initiation of movement is called quiet eye (QE). Research has shown that quiet eye components are related to high level of expertise and performance. The purpose of study was to examine the relationship between the quiet eye and motor performance in children with developmental coordination disorder. Materials and Methods: In this descriptive-correlation study, 30 children at the age of 7 to 14 years were selected via multiple clustering sampling in Isfahan City, Iran. Participants were performed throwing and catching task to 10 trials. The data were recorded by an eye-tracking device when participants performing the desired task. We used Shapiro-Wilk test to determine the normality of data and Pearson correlation coefficient to analyze the relationship between quiet eye and motor performance. Results: There was significant negative correlation between catching performance and onset of quiet eye (r = -0.838, P < 0.001), significant positive correlation between catching performance and offset of quiet eye (r = 0.370, P = 0.044), and also significant positive correlation between catching performance and quiet eye duration (r = 0.849, P < 0.001). Conclusion: The result of this study revealed that the quiet eye can be considered as a predictor of motor performance in children with developmental coordination disorder.

Financial crises have been frequently occurred in the global economy, and due to the negative impacts of financial crises on the real sectors performances, the economists tried to predict them. This study tries to investigate the role of the trade flows on occurrence of these phenomena and also the effects of contagion on trade flows. Hence, a sample of the emerging markets countries is selected during 1990-2013, and the simultaneous equations method has been used with discrete dependent variable (contagion of financial crises) in panel data. The results show that the trade flows lead to the acceleration of the contagion of the financial crises and on the other hand, the trade flows has been decreased by financial crises in the selected emerging markets countries. Finally, similar to the probability for contagion of the financial crises, more financial and regional linkages can increase the trade flows among selected countries.

This paper deals with the problem of course scheduling where we have a set of courses, lecturers and classrooms. Courses are assigned and scheduled in such a way that the total preference is maximized. We develop the mathematical model of the problem in form of a linear integer program. The small sized problem can be solved to optimality using commercial software. We then develop three different metaheuristics based on artificial immune, genetic and simulated annealing algorithms. These three solution methods are equipped with novel procedures such as move and crossing operators. The parameters of the proposed metaheuristics are first tuned, and then they are evaluated with optimal solutions found by the model. They are, furthermore, evaluated by comparing their performance. The experiments demonstrate that the artificial immune algorithm performs better than the other algorithms.

Although several papers have studied no-idle scheduling problems, they all focus on flow shops, assuming one processor at each working stage. But, companies commonly extend to hybrid flow shops by duplicating machines in parallel in stages. This paper considers the problem of scheduling no-idle hybrid flow shops. A mixed integer linear programming model is first developed to mathematically formulate the problem. Using commercial software, the model can solve small instances to optimality. Then, two metaheuristics based on variable neighborhood search and genetic algorithms are developed to solve larger instances. Using numerical experiments, the performance of the model and algorithms are evaluated.Although several papers have studied no-idle scheduling problems, they all focus on flow shops, assuming one processor at each working stage. But, companies commonly extend to hybrid flow shops by duplicating machines in parallel in stages. This paper considers the problem of scheduling no-idle hybrid flow shops. A mixed integer linear programming model is first developed to mathematically formulate the problem. Using commercial software, the model can solve small instances to optimality. Then, two metaheuristics based on variable neighborhood search and genetic algorithms are developed to solve larger instances. Using numerical experiments, the performance of the model and algorithms are evaluated.

Although lot streaming scheduling is an active research field, lot streaming flexible flow lines problems have received far less attention than classical flow shops. This paper deals with scheduling jobs in lot streaming flexible flow line problems. The paper mathematically formulates the problem by a mixed integer linear programming model. This model solves small instances to optimality. Moreover, a novel artificial bee colony optimization is developed. This algorithm utilizes five effective mechanisms to solve the problem. To evaluate the algorithm, it is compared with adaptation of four available algorithms. The statistical analyses showed that the proposed algorithm significantly outperformed the other tested algorithms.

The exchange rate pass-through explains the relationship between changes in national currency and foreign trade of a country, while the responsiveness of trade to the currency changes depends on the perfect or imperfect degree of pass-through. The objective of this study is to analyze the effect of exchange rate pass-through on inflation in Iran as one of the main oil-exporting countries. To this end, we have specified a Structural Vector Auto-Regressive (SVAR) model including macroeconomic variables such as oil revenues, output gap, free market exchange rate, import prices, producer prices, consumer prices and money supply. To estimate the model, we have used quarterly data over the period 1991:1 - 2012:4. Empirical results of the model estimation, which are in forms of impulse response functions and variance decomposition, have shown that although the degree of exchange rate pass-through to the price indices has been incomplete, changes in the exchange rate have led to fluctuations in the prices explaining partly Iran’s inflationary situation during the period under consideration. It also reveals the fact that a higher share of imported inflation implies the economy’s dependence on imports.

An appropriate solution to resolve trade deficit is national currency devaluation. This policy is, at least in the long term, useful. It is based on the logic that any devaluation of national currency in the form of market mechanism reduces the trade deficit. On the other hand the trade balance of countries are affected by several factors including the international financial conditions that change the volume of international transactions and in this way affect the economy as a whole and trade balance in particular. The purpose of this paper is to study the factors affecting trade balance between Iran and her ten trading partners with considerations of the financial crisis index as a symbol of the international financial conditions. For this purpose, a time series is specified and then estimated by ARDL and error correction model (ECM) for the period of 1981-2009. Also the effects of the financial crisis on trade balance using Impulse Response Functions are considered. The results show that J curve is only confirmed for the bilateral trade of Iran with China and Italy and for the other countries, with consideration of the financial crisis, this hypothesis is rejected. It should be noted that all coefficients are stable with respect to the CUSUM and CUSUMQ tests.

In this paper, the dual-resource constrained flexible job-shop scheduling problem (DRCFJSP) with objective of minimizing the makespan is investigated. Under studied problem is NP-hard and mainly includes three sub-problems. The first one is to assign each operation to a machine out of a set of capable machines, the second one is to determine a worker among a set of skilled workers for processing each operation on the selected machine and the third one deals with sequencing the assigned operations on the machines considering workers in order to optimize the performance measure. In this paper, we provide a mathematical model for this problem and then propose a hybrid meta-heuristic algorithm for solving the problem. The proposed hybrid algorithm uses variable neighborhood search and simulated annealing algorithms to search in the solution space. Computational study with randomly generated test problems is performed to evaluate the performance of the proposed algorithm. The results show the proposed algorithms are effective approaches for solving the DRCFJSP.

This paper studies the hybrid flow shop scheduling where the optimization criterion is the minimization of total tardiness. First, the problem is formulated as a mixed integer linear programming model. Then, to solve large problem sizes, an artificial immune algorithm hybridized with a simple local search in form of simulated annealing is proposed. Two experiments are carried out to evaluate the modeland the algorithm. In the first one, the general performance of the model and the proposed algorithm is experimented. In the next one, the presented algorithm is compared against some other algorithms. The results support high performance of the proposed algorithm.

Some real world problems include determining optimum values of lput variables in order to obtain the desired levels of output variable response surface variable). One of the applicable techniques which are set for modeling and solving such problems is Response Surface methodology (RSM). In this paper, the effect of three controllable lput factors: temperature, the density of sodium cyanide, and ampere n the determined response surface level i.e. the thickness of the electroplating cover of a hook screw is checked by Design Of experiments (DOE). After the execution of experiments and the ^cognition of effective factors, according to the application of response surface methodology, the relationship between the variables f effective input factors and the response surface variable is stermined with the help of nonlinear regression model. Then, the Optimum value of each variable in nonlinear model is obtained by goal pogromming method.

Task assignment problem (TAP) involves assigning a number of tasks to a number of processors in distributed computing systems and its objective is to minimize the sum of the total execution and communication costs, subject to all of the resource constraints. TAP is a combinatorial optimization problem and NP-complete. This paper proposes a hybrid meta-heuristic algorithm for solving TAP in a heterogeneous distributed computing system. To compare our algorithm with previous ones, an extensive computational study on some benchmark problems was conducted. The results obtained from the computational study indicate that the proposed algorithm is a viable and effective approach for the TAP.

Background: Oral cancer is the 5th prevalent cancer and one of the main reason for mortality all over the world It is a serious risk for health The high mortality may be happened because of dentists inexact examination or the lack of enough information Objective: The aim of the present study was to assess the knowledge and diagnostic skill of a group of Iranian dentists in oral cancer prevention and early detection Methods: The Pretested questionnaires were distributed among 412 general dental practitioners who voluntarily participated in the 40th international congress of Iranian Dental Association (IDA) in 2000 Results: Most of the respondents were male (602%) and aged lower than 37 years old (633%) Respondents totally replied 55% of knowledge ( =559) and 55% of diagnostic skills ( =165) questions correctly A significant correlation was found between the knowledge and diagnostic skills scores (r=04 P