This paper proposes a novel approach for generation Scheduling using sensitivity characteristic of a Security Analyzer Neural Network (SANN) for improving Static security of power system. In this paper, the potential overloading at the post contingency steady-state associated with each line outage is proposed as a security index which is used for evaluation and enhancement of system Static security. A multilayer feed forward neural network is trained as SANN for both evaluation and enhancement of system security. The input of SANN is load/generation pattern. By using sensitivity characteristic of SANN, sensitivity of security indices with respect to generation pattern is used as a guide line for generation reScheduling aimed to enhance security. Economic characteristic of generation pattern is also considered in the process of reScheduling to find an optimum generation pattern satisfying both security and economic aspects of power system. One interesting feature of the proposed approach is its ability for flexible handling of system security into generation reScheduling and compromising with the economic feature with any degree of coordination. By using SANN, several generation patterns with different level of security and cost could be evaluated which constitute the Pareto solution of the multi-objective problem. A compromised generation pattern could be found from Pareto solution with any degree of coordination between security and cost. The effectiveness of the proposed approach is studied on the IEEE 30 bus system with promising results.

Background and Aim: The aim of the present study was to investigate the effect of the Hand Static Rest Splint on reducing the pain and other compliance of Rheumatoid Arthritis (RA). Materials and Methods: In the present clinical trial, 12 patients, aged 35-50 (average: 39 years) with RA, referring to the Occupational Therapy Unit of Taleghani hospital and meeting America College of Rheumatology diagnostic criteria, were included. Visual Analogue Scale (VAS) was administered and swelling, tenderness and morning stiffness were assessed. Moreover, assessments of active range of motion using goniometry and grip strength with Jamar dynamometer in both hands were performed. Statistical analysis of data was performed using t-test and Mann-Whitney Test for two independent samples and willcoxon tests and paired t-test for paired samples run in SPSS, version 18. The level of significance was set at p˂ 0. 05. Results: The findings showed that the Static Rest Splint led to improvement in the wrist range of motion (flexion p=0/005, extension p=0/03) when the two groups were compared, but it did not have a significant effect on Morning Stiffness (p=1), pain (p=0/1), Swelling (p=0/06), and Tenderness (p=0/2). However, when compared between after and before intervention in hand with splint, pain (p=0/02) and wrist range of motion (p=0/004) improved. Conclusion: Based on the findings of the current study, and since using splints only improve range of motion in the wrist compared between two groups and pain relief before and after the intervention, it seems that using hand Static Rest Splint to reduce other symptoms need further investigation and also given that the use of this position without stress on the tendon can be effective in reducing the causes deformity, further studies are needed to more delineate these points.

Task Scheduling has been so far of important challenges in high-performance computers e.g. parallel and distributed systems. Using such architectures during compiling, each application program is divided to some tasks. Because of data-flow among the tasks, they may be dependent to one another; hence, there will be precedence constraints and communication delays among them so that each application with its corresponding tasks can be modeled using a Directed Acyclic Graph (DAG) named task graph. In Static task-graph Scheduling in homogeneous multiprocessor environments, tasks in the given task graph should be mapped to a predefined number of identical processing elements regarding the precedence constraints and communication delays so that the program’s completion time (finish time) is minimized, and this is an NP-hard problem form the time-complexity perspective. Actually, the achieved results are dominated by two different-in-nature factors: 1) which topological order of tasks should be considered? (sequencing subproblem), and 2) how should the extracted order be distributed over the processors? (assigning subproblem). In this paper, an efficient hybrid approach is proposed in which the Ant Colony Optimization (ACO) determines the order of tasks, and a Cellular Learning Automata (CLA) machine tackles with the assigning subproblem, and maps the task order derived by ACO to the existing processors.125 randomly-generated task graphs with different shape parameters such as size, Communication-to-Computation Ratio (CCR), and parallelism are used for the comparison study, and the results shows that the proposed approach is more successful than the traditional counterparts from the performance point of view, and eventually outperforms them.

Runway length is usually a critical point in an airport system; so, a great interest has been created for optimal use of this runway length. The most important factors in modeling of aircraft landing problem are time and cost; while, the costs imposed on the system because of moving away from target times have different performances in terms of impact. In this paper, firstly, aircraft landing problem (ALP) and the works conducted in subject literature are briefly reviewed and presented. Then, this problem is formulated and proposed as a three-objective mathematical modeling which leads to more applicable formulation. Following this, the model introduced to solve this problem is solved for two groups including 20 and 50 aircrafts using the second version of NSGA; and the results and recommendations will be provided.

Fungi release wide spectrum of volatile organic compounds (VOCs) that belong to several chemical groups with different biochemical origins such as monoterpenes, sesquiterpenes, alcohols, aldehydes, aromatic compounds, esters, furans, ketones, sulfur and nitrogen compounds. Trichoderma species are the most studied fungal biocontrol agents and are successfully used as biofungicides and biofertilizers in greenhouse and field. Volatile metabolites play a key role in mycoparasitism of Trichoderma spp., as well as in their interactions with plants and other organisms in their environments. Based on the antibiotic activity of these fungi against the fungal pathogens, further consideration of their VOCs profiles, has been offered. In this study, VOCs of native Trichoderma species from Iran (T. harzianum, T. virens (6011), T. atroviridae (1-3)) have been identified by Static headspace gas chromatography-mass spectrometry The most of detected compounds were related to monoterpenes and sesquiterpenes. These are including; dl-limonene; beta-himachalene; beta-cubebene; cadinene; caryophyllene; alpha-gurjunene; farnesol; thujopsene; beta-bisabolene and alpha-farnesene. Based on antifungal effects of these compounds, biological control of these Trichoderma species can be related to them. These VOCs could be potential sources for purposes of chemotaxonomy and natural fungicides to protect crops from the fungal pathogens without environmental problems.