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The purpose of this paper is to investigate an efficient means of estimating the ability of students solving problems in the computer-based learning environment.

Item response theory (IRT) and TrueSkill were applied to simulated and real problem solving data to estimate the ability of students solving homework problems in the massive open online course (MOOC). Based on the estimated ability, data mining models predicting whether students can correctly solve homework and quiz problems in the MOOC were developed. The predictive power of IRT- and TrueSkill-based data mining models was compared in terms of Area Under the receiver operating characteristic Curve.

The correlation between students’ ability estimated from IRT and TrueSkill was strong. In addition, IRT- and TrueSkill-based data mining models showed a comparable predictive power when the data included a large number of students. While IRT failed to estimate students’ ability and could not predict their problem solving performance when the data included a small number of students, TrueSkill did not experience such problems.

Estimating students’ ability is critical to determine the most appropriate time for providing instructional scaffolding in the computer-based learning environment. The findings of this study suggest that TrueSkill can be an efficient means for estimating the ability of students solving problems in the computer-based learning environment regardless of the number of students.

The aim of this research paper is to examine the bio‐inspired optimization algorithms, namely, genetic algorithm (GA), particle swarm optimization (PSO) and bacterial foraging optimization (BFO) algorithm with adaptive chemotactic step for determining the steady‐state equivalent circuit parameters of the three‐phase induction motor using a set of manufacturer data.

The induction motor parameter determination issue is devised as a nonlinear constrained optimization problem. The nonlinear equations of various quantities (torque, current and power factor) are derived in terms of equivalent circuit parameters from a single and a double‐cage model, and then, equates to the corresponding manufacturer data. These equations are solved by the bio‐inspired algorithms. Using the squared error between the determined and the manufacturer data as the objective function, the parameter determination problem is transferred into an optimization process where the model parameters are determined that minimize the defined objective function. The objective function is iteratively minimized using GA, PSO and BFO techniques. In order to balance the exploration and exploitation searches of the BFO algorithm, an adaptive chemotactic step is utilized.

Comparisons of the results of GA, PSO, BFO and IEEE Std. 112‐F (using no‐load, locked‐rotor and stator resistance tests) methods for two sample motors are presented. Results show the superiority of the bio‐inspired optimization algorithms over the classical one. Besides, BFO‐based parameter determination method is observed to obtain better quality solutions quickly than GA and PSO methods.

The parameters obtained by the proposed approaches can be used in analyzing the stalling and/or reacceleration process of a loaded motor following a fault or during voltage sag condition as well as in system‐level studies.

The most significant contribution of the research is the potential to determine the equivalent circuit parameters of induction motor only from its manufacturer data without conducting any lab tests on the motor. The bio‐inspired optimization based parameter determination approaches are faster and less intrusive than the IEEE Std. 112‐F method.

The cross-docking strategy has a significant influence on supply chain and logistics efficiency. This paper aims to investigate the most suitable and efficient way to schedule the transfer of logistics activities and present a meta-heuristic method of the truck scheduling problem in cross-docking logistics. A truck scheduling problem with products time window is investigated with objectives of minimizing the total product transshipment time and earliness and tardiness cost of outbound trucks.

This research proposed a meta-heuristic method for the truck scheduling problem with products time window. To solve the problem, a lower bound of the problem is built through a novel two-stage Lagrangian relaxation problem and on account of the NP-hard nature of the truck scheduling problem, the novel red deer algorithm with the mechanism of the heuristic oscillating local search algorithm, as well as adaptive memory programming was proposed to overcome the inferior capability of the original red deer algorithm in the aspect of local search and run time.

Theory analysis and simulation experiments on an industrial case of a cross-docking center with a product’s time window are conducted in this paper. Satisfactory results show that the performance of the red deer algorithm is enhanced due to the mechanism of heuristic oscillating local search algorithm and adaptive memory programming and the proposed method efficiently solves the real-world size case of truck scheduling problems in cross-docking with product time window.

The consideration of products time window has very realistic significance in different logistics applications such as cold-chain logistics and pharmaceutical supply chain. Furthermore, the novel adaptive memory red deer algorithm could be modified and applied to other complex optimization scheduling problems such as scheduling problems considering energy-efficiency or other logistics strategies.

For the first time in the truck scheduling problem with the cross-docking strategy, the product’s time window is considered. Furthermore, a mathematical model with objectives of minimizing the total product transshipment time and earliness and tardiness cost of outbound trucks is developed. To solve the proposed problem, a novel adaptive memory red deer algorithm with the mechanism of heuristic oscillating local search algorithm was proposed to overcome the inferior capability of genetic algorithm in the aspect of local search and run time.

The purpose of this study is to provide a robust numerical method for a two parameter singularly perturbed delay parabolic initial boundary value problem (IBVP).

To solve the problem, the authors have used a hybrid scheme combining the midpoint scheme, the upwind scheme and the second-order central difference scheme for the spatial derivatives. The backward Euler scheme on a uniform mesh is used to approximate the time derivative. Here, the authors have used Shishkin type meshes for spatial discretization.

It is observed that the proposed method converges uniformly with almost second-order spatial accuracy with respect to the discrete maximum norm.

This paper deals with the numerical study of a two parameter singularly perturbed delay parabolic IBVP. To solve the problem, the authors have used a hybrid scheme combining the midpoint scheme, the upwind scheme and the second-order central difference scheme for the spatial derivatives. The backward Euler scheme on a uniform mesh is used to approximate the time derivative. The convergence analysis is carried out. It is observed that the proposed method converges uniformly with almost second-order spatial accuracy with respect to the discrete maximum norm. Numerical experiments illustrate the efficiency of the proposed scheme.

In this second installment, the author addresses some of the problems associated with empirically validating contingent‐claim models for valuing risky debt. The article uses a simple contingent claims risky debt valuation model to fit term structures of credit spreads derived from data for U.S. corporate bonds. An essential component to fitting this model is the use of expected default frequency; the estimate of the firms' expected default probability over a specific time horizon. The author discusses the statistical and econometric procedures used in fitting the term structure of credit spreads and estimating model parameters. These include iteratively reweighted non‐linear least squares are used to dampen the impact of outliers and ensure convergence in each cross‐sectional estimation from 1992 to 1999.

This paper presents a method to improve inverse problem resolution. This method focuses on the measurement set and particularly on sensor position. Based on experiment, it aims at finding sensor position criteria to insure the least bad inverse problem solving.

The studied device is a magnetized steel sheet measured by four sensors. Three optimization techniques are compared: condition number, solid angle and signature optimization.

An efficient criterion to compare the inverse problem resolution quality is presented. The comparison of optimization techniques shows that only signature optimization gives accurate results.

A relative simple case is studied in this paper: only four sensors are used to measure a steel sheet. Moreover magnetostatic low‐field case is supposed. Nevertheless techniques presented could be applied to more complex studies. Condition number and solid angle optimizations techniques should be tested with more sensors to confirm or infirm their inefficiency.

This paper presents the first step of a larger study concerning ships for naval application. The aim is to predict magnetic anomaly created by ship to compensate it. This anomaly could be computed through the resolution of an inverse problem based on internal measurements. The signature optimization technique could be used to find the optimal sensor location onboard.

Traditional regularization techniques are focusing on adding mathematical or physical information to the system in order to improve it. This paper provides another approach to improve inverse problem resolution through measurement set. It shows that sensor position optimization should be efficient.

This paper aims to study the magneto-radiative gas (water vapor) on an unsmooth boundary.

This paper provided a numerical treatment via the implicit Chebyshev pseudo-spectral method to investigate unsteady compressible magneto-radiative gas (water vapor Pr = 1) flow near a heated vertical wavy wall through porous medium in the presence of inclined magnetic field. The impacts of viscous dissipation, temperature-dependent fluid properties, thermal conductivity and viscosity in the presence of nonlinear thermal radiation are studied. The sinusoidal surface is transformed into a flat one using a suitable transformation. The comparison figures of published data with the present outcomes illustrate a good match. The present steady-state outcomes are presented for the temperature, velocity, Nusselt number and the shearing stress through figures for several interested physical parameters, namely, compressibility, magnetic, radiation, viscosity–temperature variation, thermal conductivity–temperature variation, surface sinusoidal waveform and porous parameters.

The present numerical outcomes confirm the importance of applying nonlinear thermal radiation cases in all studies that investigate heat transfer under the influence of thermal radiation.

A mathematical model is established for a wavy boundary, and Chebyshev pseudo-spectral method is adopted for the numerical study.

The defective coin problem involves the identification of a defective coin, if any, and ascertaining the nature of the defect (heavier/lighter) from a set of coins containing at the most one defective coin, using an equal‐arm‐pan‐balance. An algorithmic analysis of the problem is considered. The solution strategy to minimise the number of weighings required to detect the defective coin is based on a problem reduction approach involving successive decomposition of the problem into subproblems until it is trivially solved. The algorithm is capable of generating all possible optimal solutions.

This paper seeks to provide a practical methodology for a retailer's pricing decisions for a store brand in relation to the corresponding national brand's price.

Demand functions for the national and store brands are derived by mixing two consumer heterogeneity distributions: reservation price and quality‐price trade‐off. Unlike the existing theoretical models, a flexible gamma distribution is employed for practicality. A numerical approach is proposed for finding an optimal price for the store brand.

The proposed methodology is flexible and computationally straightforward, and is based on economic models. Instead of deriving generalized theories, however, a numerical approach using survey data is developed for more practicality.

The proposed methodology allows managers to find the optimal price for a store brand using survey data.

The proposed methodology overcomes the limitations of the existing methodologies, i.e. the discontinuous nature of the conjoint analysis‐based approach and the theoretical nature of the economic model approach.

Simulation-based optimisation (SO) is a popular optimisation approach for building and civil engineering construction planning. However, in the framework of SO, the simulation is continuously invoked during the optimisation trajectory, which increases the computational loads to levels unrealistic for timely construction decisions. Modification on the optimisation settings such as reducing searching ability is a popular method to address this challenge, but the quality measurement of the obtained optimal decisions, also termed as optimisation quality, is also reduced by this setting. Therefore, this study aims to develop an optimisation approach for construction planning that reduces the high computational loads of SO and provides reliable optimisation quality simultaneously.

This study proposes the optimisation approach by modifying the SO framework through establishing an embedded connection between simulation and optimisation technologies. This approach reduces the computational loads and ensures the optimisation quality associated with the conventional SO approach by accurately learning the knowledge from construction simulations using embedded ensemble learning algorithms, which automatically provides efficient and reliable fitness evaluations for optimisation iterations.

A large-scale project application shows that the proposed approach was able to reduce computational loads of SO by approximately 90%. Meanwhile, the proposed approach outperformed SO in terms of optimisation quality when the optimisation has limited searching ability.

The core contribution of this research is to provide an innovative method that improves efficiency and ensures effectiveness, simultaneously, of the well-known SO approach in construction applications. The proposed method is an alternative approach to SO that can run on standard computing platforms and support nearly real-time construction on-site decision-making.