Single-Grain Virtualization for Contact Behavior Analysis on Sand

A methodology for virtualizing irregularly shaped grains is described here. The principle, largely inspired by computed tomography, is simple and accessible because only the three-dimensional (3D) outline of the grain is required. The volumetric object is obtained by reconstructing the planar projections of the grain acquired at different angles of rotation using a standard camera. Depending on the lens system, the resolution of the images can be as good as a few microns. A numerical representation of the real grain can be obtained by meshing the 3D image. The influence of grain morphology on the contact behavior of quartz sand is investigated here as an application of this novel technique. Numerical simulations using a finite-element model were carried out to reproduce the experimental data from normal compression single-grain tests. The results show the contribution of the initial grain rearrangement on the normal force-displacement response and its strong dependency on the shape of the grain. This study demonstrates that particle shape is a critical parameter for calibration of the contact behavior of sand

CCSA: Conscious Neighborhood-based Crow Search Algorithm for Solving Global Optimization Problems

© 2019 Elsevier B.V. In this paper, a conscious neighborhood-based crow search algorithm (CCSA) is proposed for solving global optimization and engineering design problems. It is a successful improvement to tackle the imbalance search strategy and premature convergence problems of the crow search algorithm. CCSA introduces three new search strategies called neighborhood-based local search (NLS), non-neighborhood based global search (NGS) and wandering around based search (WAS) in order to improve the movement of crows in different search spaces. Moreover, a neighborhood concept is defined to select the movement strategy between NLS and NGS consciously, which enhances the balance between local and global search. The proposed CCSA is evaluated on several benchmark functions and four applied problems of engineering design. In all experiments, CCSA is compared by other state-of-the-art swarm intelligence algorithms: CSA, BA, CLPSO, GWO, EEGWO, WOA, KH, ABC, GABC, and Best-so-far ABC. The experimental and statistical results show that CCSA is very competitive especially for large-scale optimization problems, and it is significantly superior to the compared algorithms. Furthermore, the proposed algorithm also finds the best optimal solution for the applied problems of engineering design

An Area-Optimized Chip of Ant Colony Algorithm Design in Hardware Platform Using the Address-Based Method

The ant colony algorithm is a nature-inspired algorithm highly used for solving many complex problems and finding optimal solutions; however, the algorithm has a major flaw and that is the vast amount of calculations and if the proper correction algorithm and architectural design are not provided, it will lead to the increasing use of hardware platform due to the high volume of operations; and perhaps at higher scales, it causes the chip area not to work because of the high number of problems; hence, the purpose of this paper is to save the hardware platform as far as possible and use it optimally through providing a particular algorithm running on a reconfigurable chip driven by the address-based method, so that the comparison of synthesis operations with the similar works shows significant improvements as much as 1/3 times greater than the other similar hardware methods.DOI:http://dx.doi.org/10.11591/ijece.v4i6.692

The Educational Function of Caricature and its Effect on the Students’ Educational Motivation and Satisfaction

Preserving the students’ educational motivation and satisfaction of teaching methods has a considerable influence on their educational success. Therefore, finding effective strategies to enhance students’ educational motivation and satisfaction of teaching methods is considered as one of the most important educational priorities. The purpose of this study is to investigate the effect of utilizing the capabilities of caricature on the students’ educational motivation and satisfaction of the teaching methods. The participants were the students of Yazd Payame Noor University. Nineteen students participated in the learning program utilizing caricature through textbook and PowerPoint slides and 18 students took part in the instructional program based on the lecture. The course of “consistency” with the subject of “strength of the metal structures” was taught using the two mentioned approaches. The classes were randomly assigned to these educational programs. The data were collected in the two stages of pretest and posttest using the summarized version of Mac Innerni and Sinclaire standard questionnaire of educational motivation. Moreover, after the treatment, the students were asked several direct questions about their overall satisfaction from the educational programs. The data were analyzed using t-test, Wilcoxon and Mann-Whitney statistical tests. The findings indicated that comparing the two approaches, teaching with utilization of caricature can enhance the students’ educational motivation and satisfaction of the teaching methods

Evaluating the effectiveness of using visual stimuli methods on architecture students’ design creativity

Background and Objectives: During design process, images as visual stimuli are significant tools in reaching creative design ideas. So this issue has resulted in conducting extensive studies in the field of educational technology on the methods of using visual stimuli as an educational tool. Since the visual stimuli can be categorized based on their similarity to the design problem or their quality (clear, ambiguous), in some of the studies, the impact of various types of visual stimuli on students’ design creativity has been examined. The level of students is another factor which could have an influence on students’ creativity when they were using visual stimuli. Also, in some studies, the relationship between the type of the design task and the visual stimuli has been investigated.  However, there is no comprehensive evaluation of the effectiveness of these methods in the field of architecture design education. As a result, the purpose of the present research is examining the effectiveness of the methods of using visual stimuli in design training.    Methods: In the present study, first, a framework for using visual stimuli based on previous studies was suggested which consisted of two sections: in the first section, the relationship between different types of visual stimuli and different aspects of creativity was determined; the second section, the factors influencing the methods of using the visual stimuli by the students were determined that consisted of students’ level, the type of the design task, and the design problem. Then,the methodofpeer review was applied to evaluate the effectiveness of the methods of using the visual stimuli in the design training. As a result, expert teachers in architecture from Iran and the United States evaluated the compiled text. In this regard, the teachers’ opinions were recorded through interviews and then their opinions were written and analyzed by a qualitative coding method. Peer review as the method of analysis was applied to investigate the validity of those methods. First participants’ ideas were gathered by interview. All their utterances were audio recorded and transcribed. In the next step the data were analyzed by a qualitative coding method. So the utterances of the participants were segmented based on different sections. Then, in the next stage, their content were codified in terms of validation, similar experience; criticism or suggestions. Findings: The opinions of the expert teachers were examined according to two sections of methods of using visual stimuli and also the effective conditions for using them in design education. Besides confirming the methods related to the types of stimulus and design task, the participants also provided numerous educational experiences on the proper interpretation of various types of stimuli. In the second section, besides confirming the impact of the students’ level, and the type of the design problem, some of the teachers offered criticisms and suggestions regarding the classifications of these problems. Conclusion: The results of peer review, besides confirming the methods of using visual stimuli, provide approaches that can improve the effectiveness of the methods of using visual stimuli in design education. Moreover, the expert teachers offered criticisms and suggestions regarding some problems and also added some suggestions. For example, the visual stimulus which has more similarity to the design problem has more effect on students’ design creativity than those images which have less similarity to the design problem.  Or when using the ambiguous sketches related to the expert designers, the way they use their sketches and the stages through which these sketches are transformed into the final idea should be taken into consideration and these processes should also be taught to the students. In other words, students need to know how expert designers transform those ambiguous images to final design solution. Also, categorizing the design problems into formal and functional has ambiguities and other methods of categorization should be used.   The results of analyzing the opinions of the expert teachers can be used for future research besides the development of the subject. ===================================================================================== COPYRIGHTS  ©2021 The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, as long as the original authors and source are cited. No permission is required from the authors or the publishers.  ====================================================================================

Sensor data classification for the indication of lameness in sheep

Lameness is a vital welfare issue in most sheep farming countries, including the UK. The pre-detection at the farm level could prevent the disease from becoming chronic. The development of wearable sensor technologies enables the idea of remotely monitoring the changes in animal movements which relate to lameness. In this study, 3D-acceleration, 3D-orientation, and 3D-linear acceleration sensor data were recorded at ten samples per second via the sensor attached to sheep neck collar. This research aimed to determine the best accuracy among various supervised machine learning techniques which can predict the early signs of lameness while the sheep are walking on a flat field. The most influencing predictors for lameness indication were also addressed here. The experimental results revealed that the Decision Tree classifier has the highest accuracy of 75.46%, and the orientation sensor data (angles) around the neck are the strongest predictors to differentiate among severely lame, mildly lame and sound classes of sheep

Head & Neck Oncology: purpose, scope and goals-charting the future

For many years now there has been a growing frustration with the statistics of head and neck cancer. Despite the many advances in diagnosis and therapy, there has been little change in the prognosis for most cancers of the head and neck in the last 50 years, so what is the point of yet another journal? Well, it is not all bad news

Sensor data classification for the indication of lameness in sheep

Lameness is a vital welfare issue in most sheep farming countries, including the UK. The pre-detection at the farm level could prevent the disease from becoming chronic. The development of wearable sensor technologies enables the idea of remotely monitoring the changes in animal movements which relate to lameness. In this study, 3D-acceleration, 3D-orientation, and 3D-linear acceleration sensor data were recorded at ten samples per second via the sensor attached to sheep neck collar. This research aimed to determine the best accuracy among various supervised machine learning techniques which can predict the early signs of lameness while the sheep are walking on a flat field. The most influencing predictors for lameness indication were also addressed here. The experimental results revealed that the Decision Tree classifier has the highest accuracy of 75.46%, and the orientation sensor data (angles) around the neck are the strongest predictors to differentiate among severely lame, mildly lame and sound classes of sheep