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This paper aims to develop a scheduler for multiple picking agents in a warehouse that takes into account distance and loading queue delay minimization within the context of minimizing makespan (i.e. picking time).

The paper uses tabu search to solve the scheduling problem in a more global sense. Each search iteration is enhanced by a custom local search (LS) procedure that hastens convergence by driving a given schedule configuration quickly to a local minimum. In particular, basic operators transfer demand among agents to balance load and minimize makespan. The new load distribution is further improved by considering a vehicle‐routing problem on the picking assignments of the agents with relocated demands. Loading queue delays that may arise from the reassignments are systematically minimized using a fast scheduling heuristic.

The proposed tabu scheduler greatly improves over a widely practiced scheduling procedure for the given problem. Variants of the tabu scheduler produce solutions that are roughly of the same quality but exhibit considerable differences in computational time.

The proposed methodology is applicable only to the static scheduling problem where all inputs are known beforehand. Furthermore, of the possible delays during picking, only loading queues are explicitly addressed (although this is justifiable, given that these delays are dominant in the problem).

The proposed approach can significantly increase through‐put and productivity in picking systems that utilize multiple intelligent agents (human pickers included), e.g. in warehouses/distribution centers.

The paper addresses a practical scheduling problem with a high degree of complexity, i.e. scheduler explicitly deals with delays while trying to minimize makespan (generally, delays are ignored in the literature to simplify things). In the tabu implementation, an LS procedure is introduced in the metaheuristic loop that enhances the search process by minimizing non‐productive time of picking agents (travel time and delays).

The authors aim to determine the effects of Global Lesson Study (GLS) – a two-year, one-cycle program defined as “international cooperative lesson study through international exchange among teachers using ICT” – on intercultural competence for lesson study based on pre- and post-survey conducted for Japanese teachers.

In accordance with the GLS program, mathematics lesson studies on mathematics between Japanese and Singaporean elementary school teachers were conducted over a two-year period. Questionnaire surveys on intercultural competence for lesson study was conducted using 7-point Likert scale and descriptive questions with Japanese teachers (N = 5). Analysis of Wilcoxon's signed rank test and correlation analysis were conducted.

Followings are identified as the effects of GLS for Japanese teachers: (1) Japanese participants felt improvement of their competence in areas of attitude, internal outcomes and outward impact. (2) Participating in a GLS led to the improvement of Japanese teachers' qualities and abilities for subject teaching related to mathematics education. (3) Improvement of intercultural competence for lesson study was related to each other, and GLS led to improvement of these competences. (4) It was meaningful to have a two-year period to assimilate and adjust to independent experience of the host and guest.

The GLS led to teacher empowerment among this sample, not only as cooperative research, but also at the individual level of teachers where participants continued to develop lessons based on the GLS learning after study completion. This has important implications for the implementation and dissemination of the GLS.

Because of recent interest in nickel silver (copper‐zinc‐nickel alloy) in electronic applications, this alloy was chosen for an ageing‐solderability study. An Auger/ESCA study was performed on the surface of samples of nickel silver to determine the chemical nanostructure at various degrees of ageing (0–2 years). During this time a redistribution of the three constituent metals occurred, with migration of copper to the surface. All three metal constituents oxidised at different rates. Additionally, the thickness of the surface carbon layer increased with time. The effect of physical abrading (cleaning by erasing) of the samples on solderability, ageing, and chemical surface was also studied. After erasure, all samples appeared similar to one another and close to the original composition, and the copper oxide layer was completely removed. All the cleaned surfaces, when returned to the atmosphere, experienced air oxidation of copper to copper oxide in less than two days. Angle‐resolved ESCA studies were performed to determine the concentration gradient close to the surface. Nickel was not evenly distributed with depth, but was more concentrated on the surface of the cleaned samples. Solderability studies on nickel silver samples showed no improvement with chemical treatment, attesting to the creation of a resistant passivation layer.

The precise segmentation of brain tumors is the most important and crucial step in their diagnosis and treatment. Due to the presence of noise, uneven gray levels, blurred boundaries and edema around the brain tumor region, the brain tumor image has indistinct features in the tumor region, which pose a problem for diagnostics. The paper aims to discuss these issues.

In this paper, the authors propose an original solution for segmentation using Tamura Texture and ensemble Support Vector Machine (SVM) structure. In the proposed technique, 124 features of each voxel are extracted, including Tamura texture features and grayscale features. Then, these features are ranked using the SVM-Recursive Feature Elimination method, which is also adopted to optimize the parameters of the Radial Basis Function kernel of SVMs. Finally, the bagging random sampling method is utilized to construct the ensemble SVM classifier based on a weighted voting mechanism to classify the types of voxel.

The experiments are conducted over a sample data set to be called BraTS2015. The experiments demonstrate that Tamura texture is very useful in the segmentation of brain tumors, especially the feature of line-likeness. The superior performance of the proposed ensemble SVM classifier is demonstrated by comparison with single SVM classifiers as well as other methods.

The authors propose an original solution for segmentation using Tamura Texture and ensemble SVM structure.

The purpose of this paper is to provide a method to examine the differences in behaviour during a post‐quake period.

Fieldwork and questionnaires were used to collect the households’ members’ movement behaviours after the 2004 Chuetsu Earthquake. In total, three study areas were selected in Kawaguchi town (Niigata Prefecture) in order to enhance how the visualisation process can provide support in better understanding the behaviour during evacuation and recovery process. For this purpose the Space‐Time‐Cube (STC) was used to represent and analyse residents’ movement paths over time.

Differences appear in the spatio‐temporal paths of the three study areas, implying a connection between the geographical location and movement patterns. The city centre shows disorganized Spatio‐Temporal‐Patterns (STPs) during the first week of the recovery process, eventually becoming organized after the rescuers’ arrival. Moving towards the isolated areas of the town, a progressive STP organisation can be observed, explaining the faster response after the seismic event.

Spatio‐temporal data are difficult and costly to collect, especially if a long period of time passes between the seismic event and the survey.

The STC can be used as tool to enhance the disaster management techniques and provide support in crisis situations.

The paper provides a practical approach to investigate the reactions after a seismic event and can be used in larger study areas to develop better strategies in disaster management.

Nowadays, most methods of illusion garment evaluation are based on the subjective evaluation of experienced practitioners, which consumes time and the results are too subjective to be accurate enough. It is necessary to explore a method that can quantify professional experience into objective indicators to evaluate the sensory comfort of the optical illusion skirt quickly and accurately. The purpose of this paper is to propose a method to objectively evaluate the sensory comfort of optical illusion skirt patterns by combining texture feature extraction and prediction model construction.

Firstly, 10 optical illusion sample skirts are produced, and 10 experimental images are collected for each sample skirt. Then a Likert five-level evaluation scale is designed to obtain the sensory comfort level of each skirt through the questionnaire survey. Synchronously, the coarseness, contrast, directionality, line-likeness, regularity and roughness of the sample image are calculated based on Tamura texture feature algorithm, and the mean, contrast and entropy are extracted of the image transformed by Gabor wavelet. Both are set as objective parameters. Two final indicators T1 and T2 are refined from the objective parameters previously obtained to construct the predictive model of the subjective comfort of the visual illusion skirt. The linear regression model and the MLP neural network model are constructed.

Results show that the accuracy of the linear regression model is 92%, and prediction accuracy of the MLP neural network model is 97.9%. It is feasible to use Tamura texture features, Gabor wavelet transform and MLP neural network methods to objectively predict the sensory comfort of visual illusion skirt images.

Compared with the existing uncertain and non-reproducible subjective evaluation of optical illusion clothing based on experienced experts. The main advantage of the authors' method is that this method can objectively obtain evaluation parameters, quickly and accurately obtain evaluation grades without repeated evaluation by experienced experts. It is a method of objectively quantifying the experience of experts.

Non-communicable diseases such as type 2 diabetes, hypertension, cancers and cardiovascular diseases have become a major health concern globally. As literature claims that frequent consumption of fruits and vegetables can delay the onset of type 2 diabetes and its complications, this paper aims to evaluate the potential hypoglycemic properties in five types of non-leafy vegetables (pumpkin, sweet potato, bitter gourd, onion and lady’s finger), which are commonly available in Malaysia.

Articles were identified through several main search engines, including Pubmed, Google Scholar, Taylor and Francis Online, EDS, Wiley, ScienceDirect and Scopus. The search was limited to selected keywords to refine the outcome.

All the five types of non-leafy vegetables demonstrate hypoglycemic properties to some extent. Emerging findings indicate that there are several phytonutrients in the non-leafy vegetables contributing to the hypoglycemic effects. To date, the underlying mechanism of action remains to be elucidated, although a number of potential mechanisms of action have been proposed in the literature.

This review provides some insights into the hypoglycemic properties in non-leafy vegetables. In addition, phytonutrients that are responsible for the hypoglycemic effects and their mechanism of action are also highlighted.

The purpose of this paper is to propose a novel registration method using Euclidean reconstruction and natural features tracking for AR‐based assembly guidance systems.

The method operates in two steps: offline Euclidean reconstruction and online tracking. Offline stage involves obtaining the structure of scene using Euclidean reconstruction technique. The classification trees are constructed using affine transform for online initialization. In tracking, the classification‐based wide baseline matching strategy and T_d,d test are used to get a fast and accurate initialization for the first frame after which a modified optical flow tracker is used to fulfill the task of feature tracking in the real‐time video sequences. The four specified points are transferred to the current image to compute the registration matrix for augmentation.

Firstly, Euclidean reconstruction was used instead of projective reconstruction to get the projections of predefined features. Compared with the six points needed in projective reconstruction‐based method, this method can run normally even when only four features are successfully tracked. Secondly, an adaptive strategy was proposed to adjust the classification trees using the tracked features in online stage by which one can initialize or reinitialize the system, even with large difference between the first and reference images.

Some indoor and outdoor experiments are provided to validate the performance of the proposed method.

The purpose of this paper is to provide a flexible registration method for markerless augmented reality (AR) systems.

The proposed method distinguishes itself as follows: firstly, the method is simple and efficient, as no man‐made markers are needed for both indoor and outdoor AR applications. Secondly, an adaptation method is presented to tune the particle filter dynamically. The result is a system which can achieve tolerance to fast motion and drift during tracking process. Thirdly, the authors use the reduced scale invariant feature transform (SIFT) and scale prediction techniques to match natural features. This method deals easily with the camera pose estimation problem in the case of large illumination and visual angle changes.

Some experiments are provided to validate the performance of the proposed method.

The paper proposes a novel camera pose estimation method based on adaptive particle filter and natural features matching techniques.