A flowchart‐based intelligent tutoring system for improving problem‐solving skills of novice programmers

Intelligent tutoring and personalization are considered as the two most important factors in the research of learning systems and environments. An effective tool that can be used to improve problem‐solving ability is an Intelligent Tutoring System which is capable of mimicking a human tutor's actions in implementing a one‐to‐one personalized and adaptive teaching. In this paper, a novel Flowchart‐based Intelligent Tutoring System (FITS) is proposed benefiting from Bayesian networks for the process of decision making so as to aid students in problem‐solving activities and learning computer programming. FITS not only takes full advantage of Bayesian networks, but also benefits from a multi‐agent system using an automatic text‐to‐flowchart conversion approach for engaging novice programmers in flowchart development with the aim of improving their problem‐solving skills. In the end, in order to investigate the efficacy of FITS in problem‐solving ability acquisition, a quasi‐experimental design was adopted by this research. According to the results, students in the FITS group experienced better improvement in their problem‐solving abilities than those in the control group. Moreover, with regard to the improvement of a user's problem‐solving ability, FITS has shown to be considerably effective for students with different levels of prior knowledge, especially for those with a lower level of prior knowledge.     

Modeling and prioritizing demand response programs in power markets

One of the responsibilities of power market regulator is setting rules for selecting and prioritizing demand response (DR) programs. There are many different alternatives of DR programs for improving load profile characteristics and achieving customers’ satisfaction. Regulator should find the optimal solution which reflects the perspectives of each DR stakeholder. Multi Attribute Decision Making (MADM) is a proper method for handling such optimization problems. In this paper, an extended responsive load economic model is developed. The model is based on price elasticity and customer benefit function. Prioritizing of DR programs can be realized by means of Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. Considerations of ISO/utility/customer regarding the weighting of attributes are encountered by entropy method. An Analytical Hierarchy Process (AHP) is used for selecting the most effective DR program. Numerical studies are conducted on the load curve of the Iranian power grid in 2007.     

Modeling the free convection heat transfer in a partitioned cavity using ANFIS

In this paper, an adaptive neuro-fuzzy inference system (ANFIS) is used to predict the free convection in a partitioned cavity consisting of an adiabatic partition. The main focus of the present paper is to consider the effects of partition angle and Rayleigh number variation on average heat transfer in the partitioned cavity. The training data for optimizing the ANFIS structure is obtained experimentally. For the best ANFIS structure obtained in this study, the mean relative errors of the train and test data were found to be 0.055% and 1.735% respectively, which shows that ANFIS can predict the experimental results precisely.     

Fuzzy modeling of the forced convection heat transfer from a V‐shaped plate exposed to an air slot jet

This paper focuses on the application of fuzzy logic (FL) to predict the forced convection heat transfer from V‐shaped plate internal surfaces exposed to an air impingement slot jet. The aim of the present paper is to consider the effects of the angle of a V‐shaped plate (Φ), slot‐to‐plate spacing ratio (Z/W), and Reynolds number (Re) variation on average heat transfer from the V‐shaped plate internal surfaces. The data used for developing the FL structure was obtained experimentally by a Mach‐Zehnder interferometer. The proposed FL was developed using MATLAB functions. It was observed that the average Nusselt number will be decreased with an increase in jet spacing and be increased with an increase in Reynolds number and angle of V‐shaped plate. Moreover, it is also shown that fuzzy logic is a powerful technique to use for predicting heat transfer due to its low error rate. The average error of the fuzzy predictions compared with experimental data was found to be 0.33% for this study. © 2012 Wiley Periodicals, Inc. Heat Trans Asian Res; Published online in Wiley Online Library ( wileyonlinelibrary.com/journal/htj ). DOI 10.1002/htj.21009     

Investigation of indium oxide as a self-catalyst in ZnO/ZnInO heterostructure nanowires growth

We investigated the self-catalytic role of indium oxide in the growth process of ZnO/ZnInO heterostructure nanowires on Si(111). The prepared nanowires had hexagonal cross sections and were tapered with tip diameters of 90 ± 5 nm and base diameters of 230 ± 5 nm. Energy dispersive X-ray and field emission Auger spectroscopies indicated that the grown nanowires were heterostructures of ZnO and ZnInO. Analysis of the early growth process revealed that indium may play a self-catalytic role. Therefore, the vapor-liquid-solid mechanism is likely to be responsible for growth of ZnO/ZnInO nanowires. X-ray diffraction and room temperature photoluminescence (PL) data demonstrated that the presence of indium results in a decrease in nanowires' crystallinity. These wires produced a large PL emission peak in the ultraviolet (UV) region and a smaller peak in the green region of the electromagnetic spectrum. The UV peak of the ZnO/ZnInO nanowires is blue-shifted with respect to that of pure ZnO nanowires.     

Demand response modeling considering Interruptible/Curtailable loads and capacity market programs

Recently, a massive focus has been made on demand response (DR) programs, aimed to electricity price reduction, transmission lines congestion resolving, security enhancement and improvement of market liquidity. Basically, demand response programs are divided into two main categories namely, incentive-based programs and time-based programs. The focus of this paper is on Interruptible/Curtailable service (I/C) and capacity market programs (CAP), which are incentive-based demand response programs including penalties for customers in case of no responding to load reduction. First, by using the concept of price elasticity of demand and customer benefit function, economic model of above mentioned programs is developed. The proposed model helps the independent system operator (ISO) to identify and employ relevant DR program which both improves the characteristics of the load curve and also be welcome by customers. To evaluate the performance of the model, simulation study has been conducted using the load curve of the peak day of the Iranian power system grid in 2007. In the numerical study section, the impact of these programs on load shape and load level, and benefit of customers as well as reduction of energy consumption are shown. In addition, by using strategy success indices the results of simulation studies for different scenarios are analyzed and investigated for determination of the scenarios priority.     

A probabilistic risk-based approach for spinning reserve provision using day-ahead demand response program

Spinning Reserve is one of the ancillary services which is essential to satisfy system security constraints when the power system faces with a contingency. In this paper, Day Ahead Demand Response Program as one of the incentive-based Demand Response programs is implemented as a source of spinning reserve. In this regard, certain number of demands are selected according to a sensitivity analysis, and simulated as virtual generation units. The reserve market is cleared for Spinning Reserve allocation considering a probabilistic technique. A comparison is performed between the absence and existence of Day Ahead Demand Response Program from both economical and reliability viewpoints. Numerical studies based on IEEE 57 bus test system is conducted for evaluation of the proposed method.     

The Effects of Doping on Crystal Structure, Magnetic and Microwave Properties of SrFe12−2x La x (Mn0.5Zr0.5) x O19 Nanoparticles

M-type hexagonal ferrite powders, SrFe_12?2x La _x (Mn_0.5Zr_0.5) _x O₁₉ (x=0.0, 0.2, 0.4, 0.6, 0.8), have been synthesized by the coprecipitation method. The X-ray diffraction, field emission scanning electron microscope, vibrating sample magnetometer and vector network analyzer all were used to characterize the structure of the samples, their magnetic and microwave properties. The value of the saturation magnetization increased up to x=0.2 and then slowly decreased with increasing doping. A decreasing trend was observed in the value of coercivity with increasing substitution degree, and its value reached a minimum of 2420 Oe for x=0.6 and then increased with further increasing x. The relative complex permittivity and permeability of the composite powders were investigated in the X-band frequency range (8.2–12.4 GHz).