Nowadays, optimal integration and utilization of renewable energy sources (RES) are of the most challenging issues in power systems. The wind and solar generation units' maximum production may or may not occur at peak consumption times resulting in non-optimal utilization of these resources. As a solution to this problem, energy storage systems (ESS) are embedded in networks. However, the power transfer from RES to ESS may lead to network congestion. In this paper, the simultaneous application of Dynamic Thermal Rating (DTR) technology and ESS devices is proposed. The DTR is used to overcome the problem of transmission lines limited capacity and ESS is responsible for mitigating curtailment of RESs energy production by saving their generated energy in non-peak hours. The RESs generation and lines’ Ratings are calculated based on hourly actual weather elements. For evaluating the proposed method, a linearized formulation of DC-OPF is used in the problem definition and also simulated on a modified IEEE 30-bus test system including a wind farm, solar park, and ESS devices by using MATLAB software. In addition, different comparisons are performed demonstRating the remarkable and better performance of the proposed method compared to previously introduced methods.

In this paper, a multidisciplinary Security-Constrained Unit Commitment on a linear AC power flow model using Transmission Switching and Dynamic Line Rating (DLR). Today SCUC is one of the most important issues in the optimal and safe operation of modern electricity markets. This paper presents an improved linear AC power flow model using Transmission Switches (TS) and considering it as a network security constraint with a multi-objective framework for simultaneously applying two objective functions, including minimizing the total opeRating cost and Minimizing emission of pollutants is provided. Dynamics Thermal Line Rating (DTLR) (with climatic conditions) into the Security-Constrained Unit Commitment using Transmission Switching system, in addition to reducing the Switching and extending the life of the circuit-breaker reduces the cost of operation significantly. In this way, the cost of opeRating in the DTLR mode is significantly reduced compared to the SLR mode. In order to demonstrate the performance of this model, the proposed method is implemented on the 6-IEEE Bus system using the Gams software.

This study explores the motivational components within teacher Rating systems and their impact on professional growth motivation. Employing a systematic review approach, it investigates texts pertaining to teacher Rating systems, motivation, and professional development literature. By examining various texts and research findings on teacher motivation, factors influencing teacher Ratings based on general, specialized, and professional competencies, as well as experience, are identified. Drawing parallels with Herzberg's motivational theory, this study provides recommendations to bolster teacher motivation and job satisfaction through improvements in the Rating system.

Microprocessor designers use the design of multicore systems on a chip to increase their computing power. Adding the number of cores leads to an increase in the power density, followed by enhancement of temperature. Reactive and proactive approaches are two sets of the methods for managing the temperature. Unlike the reactive methods that act based on threshold temperature, proactive approaches utilize a Thermal prediction model in Thermal management. In this paper, two multilayer perceptron neural networks has been used for Thermal prediction and temperature control. An appropriate dataset is provided for training each model. This dataset consists of some features that are read by sensors and measurement tools and new features that are produced by proposed processes. In this regard, historical features are suggested for Thermal model. Proper features of Thermal model are selected by using feature selection based on mutual information. The temperature is controlled by setting the processor frequency and fan speed. The features of control model are selected by non-dominated sorting genetic algorithm. The error of Thermal model for different time distances is about 0. 5 ° C. The Thermal control model has respectively 2% and 0. 6% errors in determining the processor frequency and fan speed.

IsoThermal crystallization kinetics, melting, structure and Dynamic mechanical properties of a polypropylene (PP) random copolymer were studied. In order to study the effect of cooling rate on melting, two different cooling rates were applied to the samples. While water cooled samples revealed a broad single melting endotherm with  a-crystalline structure, the slow cooled samples showed a shoulder at lower temperature followed by a sharp melting endotherm and a mixture of a and g-crystalline phases. The effect of annealing on log E of the b-relaxation for the PP copolymer showed that the b-transition increased in intensity with increasing annealing temperature. No considerable difference was found in the activation enthalpy of relaxation, ΔH, values for the annealed compared to that of the water cooled samples using the Arrhenius equation. The isoThermal crystallization kinetics was also studied for PP and the values of nucleation constant, Kg, and end-surface free energy of the crystal growth, se, were calculated using Lauritzen-Hofffman theory.

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.