Increasing the number of cores in order to the demand of more computing power has led to increasing the processor temperature of a multi-core system. One of the main approaches for reducing temperature is the Dynamic thermal management techniques. These methods divided into two classes, reactive and proactive. Proactive methods manage the processor temperature, by forecasting the temperature before reaching the threshold temperature. In this paper, the effects of using proper features for processor thermal management have been considered. In this regard, three models have been proposed for temperature prediction, control response estimation, and thermal management, respectively. A multi-layered perceptron neural network is used to predict the temperature and to control the response. Also, an adaptive neuro-fuzzy inference system is utilized for controlling temperature. An appropriate data set, which includes a variety of processor temperature variations, has been created to train each model. Some features of the dataset are collected by monitoring the thermal sensors and performance counters. In addition, a number of features are created by proposing processes to increase the accuracy of each model. Then, the features of each model are selected by the proposed method. The evaluation of the proposed model for predicting and controlling the processor temperature for different time distances is below 0. 6 ° C.

Due to the lack of measurements in many regions, wave characteristics are estimated using different methods. Wave climate hindcasting/forecasting is mostly conducted by numerical models or empirical methods. Until now, different empirical methods have been developed for wave hindcasting. However, with the development of high speed processors, several sophisticated numerical models have been developed for wave prediction. These models are mostly phase-averaged spectral wave models developed in three generations. In the last two decades, third generation wave models have been used widely in academic and practical projects. In this regard, Port and Maritime Organization has produced his own model, PMO Dynamic. This model has been developed as a part of first three phases of Monitoring and Modeling of Study of Iranian Coasts project. PMO Dynamic package is a software available for engineering purposes. It has several modules that have been developed for different objectives. Wave model is the module which is used for the generation and transformation of wind waves in coastal areas. In this paper, in order to test the PMO Dynamic model capabilities, it has been applied for the prediction of wave parameters in Bushehr Bay and the results have been compared with MIKE21 SW model and measured data.

Purpose: Managers are one of the important elements of an organization, for this reason, in order to draw the future of the organization, it is necessary for the planners to specify the conditions of their Selection and appointment. Therefore, the current research has been done with the aim of identifying and analyzing the components of selecting future principals.Method: In this research, comparative and benchmarking method is used as a prospective approach. This approach is based on the belief that today's advanced organizations/countries can be considered as a model for the future of another organization/countries in their respective subjects. For this, first, the fields of comparison and benchmarking were determined using Brody's four-step comparison method; then the countries of Canada, Finland, Australia, South Africa, and Japan were selected according to the qualitative balance value in the international advanced TEAMS test, human development index, life quality index(health, instruction, and welfare), education quality index, and other scientific-scholarly indexes; finally, by extracting the criteria for the Selection and appointment of principals through content analysis and comparison with Iran, the proposed framework for Iran has been presented.Findings: A total of 61 components for the Selection of secondary school principals were identified from among the studies conducted in the selected countries in this article. By extracting the commonalities and differences of each of the components among the countries, it was found that the highest index of manager Selection and appointment belongs to Japan and the lowest one is related to Finland.Conclusion: There are similarities between the components of Selection of principals of secondary schools in Iran and selected countries. In Iran, special attention should be paid to important components such as adherence to religious principles, appropriate personality traits, creativity and innovation, motivation to develop capabilities, professional growth, power of supervision and accountability, social image, leader skills, and purposefulness and foresight.

Background: In intertrochanteric fractures، Dynamic hip screw (DHS) is the most frequently used tool، but several studies did not give good results for this device in reverse-oblique and transverse fractures. Current study aimed to compare fixation results of reverse-oblique intertrochanteric and short-segment subtrochanteric fractures using Dynamic condylar screw (DCS) and DHS in patients referred to Alzahra and Kashani Hospitals in Isfahan, Iran.Methods: In a cross-sectional study، medical files of the patients referred to emergency units of the two hospitals who were diagnosed with intertrochanteric or subtrochanteric fractures and underwent DHS or DCS surgery more than 6 months before were assessed; they were invited for follow-up visit and examination. Written consent forms were taken from all the patients upon their entry; and they were examined in terms of complications including nonunion and failure of device and level of fracture improvement. These cases were evaluated by simple radiography ,too. Results obtained from examination of patients in addition to demographic information were recorded in the data collection form for each patient.Findings: Perfect union was observed in 14 (of 25) patients in DHS group and 21 (of 25) patients in DCS group (56% vs. 84%) six months after the surgery. Bone union was significantly better in DCS group (P=0.031). In addition, six months after the surgery, the devices used in 14 people in DHS group and 24 people in DCS group were fixed and the difference between the two groups was significant (P=0.001). 14 and 24 patients had total recovery in DHS and DCS groups، respectively (20% vs. 44%) with a significant difference (P=0.020).Conclusion: Considering obtained results, it can be concluded that using DCS is recommended over DHS due to severity of pain, bone union and fixation of the device.

Multi-label classification aims at assigning more than one label to each instance. Many real-world multi-label classification tasks are high dimensional, leading to reduced performance of traditional classifiers. Feature Selection is a common approach to tackle this issue by choosing prominent features. Multi-label feature Selection is an NP-hard approach, and so far, some swarm intelligence-based strategies and have been proposed to find a near optimal solution within a reasonable time. In this paper, a hybrid intelligence algorithm based on the binary algorithm of particle swarm optimization and a novel local search strategy has been proposed to select a set of prominent features. To this aim, features are divided into two categories based on the extension rate and the relationship between the output and the local search strategy to increase the convergence speed. The first group features have more similarity to class and less similarity to other features, and the second is redundant and less relevant features. Accordingly, a local operator is added to the particle swarm optimization algorithm to reduce redundant features and keep relevant ones among each solution. The aim of this operator leads to enhance the convergence speed of the proposed algorithm compared to other algorithms presented in this field. Evaluation of the proposed solution and the proposed statistical test shows that the proposed approach improves different classification criteria of multi-label classification and outperforms other methods in most cases. Also in cases where achieving higher accuracy is more important than time, it is more appropriate to use this method.

The aim of this study was a strain-improvement program for Trichoderma reesei PTCC 5142 by using a combination of UV light and NTG (N-methyl-N'-nitro-N-nitrosoguanidine) for enhanced cellulase production. Following mutagenesis after several rounds, mutant A6: 2 was selected from a total of 6500 colonies. Results obtained after 4 days were: Enzyme activity 1.26 U/ml and 0.82 U/ml for exoglucanase and endoglucanase, respectively. The comparative results showed increased production exoglucanase and endoglucanase by mutant A6: 2 than Trichoderma reesei PTCC 5142 to amount 130% for exoglucanase and 156% for endoglucanase.