Collaborative science inquiry with the CoSci virtual lab

The CoSci Virtual Lab (https://cosci.tw/), standing for “Collaboration” and “Science Learning”, is developed by Professor Chen-Chung Liu (National Central University), Emeritus Professor Fu-Kwun Hwang (National Taiwan Normal University) and Professor Francisco Esquembre (Universidad de Murcia). The CoSci Virtual Lab allows students to conduct physics experiment collaboratively and to explore and examine their science ideas with more than 100 physics simulations. Intended Audience: University and Secondary-School Educator

Limousine Service Management: Capacity Planning with Predictive Analytics and Optimization

The limousine service in luxury hotels is an integral component of the whole customer journey in the hospitality industry. One of the largest hotels in Singapore manages a fleet of both in-house and outsourced vehicles around the clock, serving 9000 trips per month on average. The need for vehicles may scale up rapidly, especially during special events and festive periods in the country. The excess demand is met by having additional outsourced vehicles on standby, incurring millions of dollars of additional expenses per year for the hotel. Determining the required number of limousines by hour of the day is a challenging service capacity planning problem. In this paper, a recent transformational journey to manage this problem in the hotel is introduced, driving up to S\$3.2 million of savings per year with improved service level. The approach builds on widely available open-source statistical and spreadsheet optimization tools, along with robotic process automation, to optimize the schedule of its fleet of limousines and drivers, and to support decision-making for planners/controllers to drive sustained business value

A study of students’ learning pathways on the buoyant force through the CoSci learning platform

The study of students’ learning pathways is necessary for teachers to understand how students learn. In learning or solving some problems, the students have their own strategy for constructing knowledge to learn and solve. During students’ learning, they will apply their previous knowledge to further create a body of knowledge. For example, students need to understand basic concepts related to the buoyant force – density, mass, volume, water level, weight, and depth – before solving the buoyancy problem (Wongsuwan & Huntula, 2019). Therefore, if teachers know how students learn, they can encourage and promote students to learn better. Teachers can also know what the difficulties for students in learning are. However, to study how students learn and learning pathways, is to study something inside a person. Yet, some students are not able to express their thoughts by speaking or writing, which makes it difficult to understand how they learn and their learning pathways. Therefore, it is a challenge for teachers to find appropriate ways and strategies to understand students' learning pathways validly. One of the reliable tools used to investigate students' conceptual understanding and students' learning pathways is the CoSci learning platform with an interactive computer simulation. The CoSci can be used to record students' behaviors such as students' answers, frequency of operating computer simulations, times, and date of interaction with the platform. Moreover, there is the Lag Sequential Analysis function (LSA) which is one of the functions of CoSci to calculate and display students' activity patterns, referred to as a learning pathway in this presentation. This study aims to investigate 1) students' learning pathways on the buoyant force, and 2) the relation between Predict-Observe-Explain (POE) activity performance and learning performance of eighteen students in eleventh grade in science classrooms of a university-affiliated school project (SCiUS), Khon Kaen University, Thailand. The focusing conception was the buoyant force and the basics conception related to the buoyant force which are mass (M), volume(V), density(D), the level of solution(L), submerged depth(H), and weight(W). The POE with the interactive computer simulation (i.e., the CoSci learning platform) was developed on the CoSci platform based on students' alternative concepts and used to facilitate students' conceptual understanding of the buoyant force (Wongsuwan & Huntula, 2021). In this study, the LSA was used to calculate and display students' learning pathways while students were learning on the CoSci meanwhile, Spearman's correlation was used to compute the correlation between the post-test score (learning performance) and the score of each step of the POE. The findings showed that the most difficult concept for students to learn about the buoyant force was the concept related to the mass of the object. This study identified two groups of students' learning pathways patterns on the buoyant force: 1) the V-M-W pattern, which is starting from the basic concepts of volume then mass, and weight consequently which facilitated students in learning the buoyant force; 2) the W-M-V pattern, which means student started to learn the basic concepts of weight then mass and volume consequently, that made students still confused in learning the buoyant force. Spearman's correlation showed a significant positive relationship between learning performance and explanation score (r=1.00, p<0.01) and a significant negative relationship between learning performance and the frequency of running the simulation (p<0.05). The implications of the findings are discussed in this presentation. REFERENCES Wongsuwan, W., and Hantula, J. (2019). The students’ basic conceptions of Buoyant force. Journal of Physics: Conference Series. 1380 012139. Wongsuwan, W., and Hantula, J. (2021). The interactive computer simulation and learning activity for facilitating students’ conceptual understanding on the buoyant force through the CoSci learning platform. Journal of Physics: Conference Series. 2145 012075

Mathematical Methods Applied to Digital Image Processing

Introduction: Digital image processing (DIP) is an important research area since it spans a variety of applications. Although over the past few decades there has been a rapid rise in this field, there still remain issues to address. Examples include image coding, image restoration, 3D image processing, feature extraction and analysis, moving object detection, and face recognition. To deal with these issues, the use of sophisticated and robust mathematical algorithms plays a crucial role. The aim of this special issue is to provide an opportunity for researchers to publish their latest theoretical and technological achievements in mathematical methods and their various applications related to DIP. This special issue covers topics related to the development of mathematical methods and their applications. It has a total of twenty-four high-quality papers covering various important topics in DIP, including image preprocessing, image encoding/decoding, stereo image reconstruction, dimensionality and data size reduction, and applications