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In this research anxiety and depression and their association with 32 different factors in students of Hamedan University of Medical Sciences were measured. Levels of anxiety and depression were assessed by State-Trait Anxiety Inventory (from 2, Trait) and Beck Depression Inventory. A questionnaire made of demographic data, educational and noneducational activities, the presence of medical or psychiatric disorders (over last year) and students' feelings about relationships with their parents as well as educational and financial matters was given to the subjects. Seventy percent of all of the university students completed the two inventories plus questionnaire. Twenty-one percent had a number of 18 or more (the cut-off point for depression) in Beck inventory and 33.4% had a number of 51 or more (the cut off point for anxiety) in State-Trait Anxiety Inventory (from 2, Trait). Differents levels of depression and anxiety and associated variables in each group were determined and discussed.

Clozapine is the first antipsycotic drug with a great efficacy. Thirty to fifty percent of treatment-resistant schizophrenics markedly improved with clozapine. Approximately, 25% of long-term patients, treated with clozapine, could be discharged. This improvement included negative as well as positive symptom areas. Clozapine produced no extrapyramidal side effects. Tardive dyskinesia, a major side effect of antipsychotics is not probably induced by the drug. Agranulocytosis that occurs in 1-2% of patients treated with clozapine is the most dangerous side effect with a high mortality rate. So, weekly monitoring of white blood cell count is necessary for safe and effective use of clozapine because fatal outcomes can be reduced and even completely prevented by the early detection of the reduction in white blood cell count. Clozapine offers considerable promise for better antipsychotic effect than currently available drugs, but its high cost causes substantial problems for patients with limited financial income

The index coding problem is studied from an interference alignment perspective providing new results as well as new insights into, and generalizations of, previously known results. An equivalence is established between the capacity of multiple unicast index coding (where each message is desired by exactly one receiver), and groupcast index coding (where a message can be desired by multiple receivers), which settles the heretofore open question of insufficiency of linear codes for the multiple unicast index coding problem by equivalence with groupcast settings, where this question has previously been answered. Necessary and sufficient conditions for the achievability of rate half per message in the index coding problem are shown to be a natural consequence of interference alignment constraints, and generalizations to feasibility of rate 1/(L+1)per message when each destination desires at least L messages, are similarly obtained. Finally, capacity optimal solutions are presented to a series of symmetric index coding problems inspired by the local connectivity and local interference characteristics of wireless networks. The solutions are based on vector linear coding. © 1963-2012 IEEE.

It is shown that a greedy orthogonal access scheme achieves the sum degrees of freedom (DoF) of all one-dimensional (all nodes placed along a straight line) convex cellular networks (where cells are convex regions) when no channel knowledge is available at the transmitters except the knowledge of the network topology. In general, optimality of orthogonal access holds neither for two-dimensional convex cellular networks nor for one-dimensional non-convex cellular networks, thus revealing a fundamental limitation that arises when both one-dimensional and convex properties are simultaneously enforced. The result also establishes the sum capacity of the corresponding class of index coding problems. © 2013 IEEE.

One of the main goals of network planners is the protection of important nodes in a network against natural disasters, security threats, attacks, and so on. Given the importance of this issue, a new model is presented in this paper for protecting an important node in a typical network based on a defensive location problem where the two agents threaten this node. The protecting facilities location problem with two agents is formulated as a three-level programming problem. The decision maker in the upper level is a network planner agent. The planner agent wants to find the best possible location of protecting facilities to protect the important node against threatening agents. The second and third levels problems are stated as the shortest path problems in the network in which the edges are weighted with positive values. In this work, the genetic, variable neighborhood search, simulated annealing algorithms are used to solve the problem. The performance of the used metaheuristic algorithms on this class of problems is investigated by a test problem that is generated randomly. Then, t-test are used to compare the performance of these algorithms. The best results are obtained by the variable neighborhood search algorithm. [ABSTRACT FROM AUTHOR]

Our ultimate goal in this paper is to introduce a special type of topological spaces including manifolds and also, orbifolds. Because of using of generalized groups, we call them GG-spaces. We will study their properties, and then we will introduce a special GG-space that is not manifold and orbifold. Finally we obtain conditions that cause a GG-space to become manifold. [ABSTRACT FROM AUTHOR]

Protecting important nodes in a network against natural disasters, security threats, attacks, and so on is one of the main goals of network planners. In this paper, a new model is presented for protecting an important node (NMPN) in a typical network based on a defensive location problem where the threatening agent (t-agent) can reinforce its power at some nodes. The NMPN is a bi-level programming problem. At the upper level, the planner agent (p-agent) try to find the best lo- cations for protecting resources in order to protect the important node. The lower level problem is represented as the shortest path problem in the network in which the edges are weighted with positive values and sometimes negative values. Thus, the Bellman-Ford algorithm is applied to solve the lower level problem. The NMPN is an NP-hard problem. In this work, the genetic, ant colony optimization, binary artificial bee colony with differential evolution, artificial bee colony algorithms, and a modified tabu search (MTS) algorithm are used to solve it. A test problem is randomly generated to investigate the performance of the metaheuristic algorithms in this paper. Parameters of the metaheuris- tic algorithms are tuned by the Taguchi method for solving the test problem. Also, the ANOVA test and Tukey's test are used to compare the performance of the metaheuristic algorithms. The best results are obtained by the MTS algorithm. [ABSTRACT FROM AUTHOR]

In this paper, a new fuzzy multi-objective multi-product pipeline scheduling problem is introduced. The system consists of a single refinery, a unique distribution center, and a multi-product pipeline. Restrictions such as batch sizing, discharging rate, forbidden sequences, batch volumetric, etc. are considered. Due to the uncertain nature of realworld problems, some parameters of the system are considered as fuzzy values. Tardiness and earliness penalties are considered with a time dependent non-linear function. The basic aim of this scheduling problem is to achieve the optimal sequence for pumping batches of oil products to maximize the financial benefit and simultaneously satisfies the customers with on-time delivery as a multi-objective problem. To tackle this problem, a two-stage methodology is proposed. In the first stage, the fuzzy formulation is converted to its equivalent crisp form by a credibility-based chance-constrained programming approach. In the second stage, the multi-objective crisp formulation is solved by some well-known approaches in the literature. Some test problems are generated and solved by the proposed approaches and the obtained Pareto-optimal solutions are analyzed and compared using some distance-based comparison metrics. [ABSTRACT FROM AUTHOR]

Using fossil fuels in transportation sector has caused many environmental and economic problems. Therefore, the use of alternative fuel vehicles is necessary. Since such vehicles have limited fuel tank capacities, hence, frequent refueling is required. Regarding the possibility of the existence of different fuel costs in various refueling stations, the selection of suitable stations with the purpose of minimizing total cost of refueling is important. Moreover, minimizing the number of refuelings could be considered as another important criterion in refueling operations of a given trip. In this paper an integer bi-objective model is proposed to select suitable refueling stations considering two criteria of minimizing the total cost of refueling and minimizing the number of refuelings. To solve the proposed model, a new algorithm is suggested and its performance is compared with the weighted sum method of multi-objective optimization literature. The results show the superiority of the proposed solution approach. [ABSTRACT FROM AUTHOR]

Using alternative fuel vehicles is one of the ways to reduce the consumption of fossil fuels which have many negative environmental effects. An alternative fuel vehicle needs special planning for its refueling operations because of some reasons, e.g. limited number of refueling stations, uncertain refueling queue times in the stations, variable alternative fuel prices among the stations, etc. In this paper, a new problem as refueling an alternative fuel vehicle on a given path is formulated to minimize the cost of refueling and waiting times in the stations for refueling operations, simultaneously. To be more close to real-world situations, the waiting times are considered as intuitionistic fuzzy numbers in order to reflect uncertainty as well as hesitation due to various uncontrollable factors. To cope with the uncertainty of the problem, an intuitionistic fuzzy chance constrained method based on credibility measure is proposed to convert the fuzzy formulation to a crisp model. In order to tackle the bi-objective crisp formulation, a new interactive fuzzy solution method is proposed. A computational study on a real case from Turkey shows that the performance of the presented method is either better or the same as the approaches of the literature. [ABSTRACT FROM AUTHOR]