Moral urban citizenship and the youth problem in a Danish ghetto

PurposeThis article examines the interventions from municipality, state and other actors in the Gellerup estate, a Danish “ghetto” by focusing on the youth problem and its construction, by examining a cross-disciplinary academic workshop intending to “solve the youth problem” of the estate.Design/methodology/approachThe article is based on the two authors' participation in the academic workshop, as well as their continued engagement with the Gellerup estate through separate project employments and ethnographic research projects in the estate, consisting of both participant observation and interviews.FindingsIn the article the authors suggest that the 2015 workshop reproduced particularly the category of idle urban young men as problematic. The authors analyze this as a form of “moral urban citizenship”. The article also analyzes some of the proposed solutions to the problem, particularly architectural transformations, and connects the Danish approach to the problems of the “ghetto” to urban developments historically and on a global scale.Originality/valueCross-disciplinary academic attempts to solve real-world problems are rarely incorporated as ethnographic data. In this article the authors attempt to include part of their own practice as academics as valuable data that opens up new perspectives on a field and their own involvement and analysis of it.

Realistic Mathematics Education's Effect on Students' Performance and Attitudes: A Case of Ellipse Topics Learning

<p style="text-align: justify;">Realistic Mathematics Education (RME) has gained popularity worldwide to teach mathematics using real-world problems. This study investigates the effectiveness of elliptic topics taught to 10th graders in a Vietnamese high school and students' attitudes toward learning. The RME model was used to guide 45 students in an experimental class, while the conventional model was applied to instruct 42 students in the control class. Data collection methods included observation, pre-test, post-test, and a student opinion survey. The experimental results confirm the test results, and the experimental class's learning outcomes were significantly higher than that of the control class's students. Besides, student participation in learning activities and attitudes toward learning were significantly higher in the RME model class than in the control class. Students will construct their mathematical knowledge based on real-life situations. The organization of teaching according to RME is not only a new method of teaching but innovation in thinking about teaching mathematics.</p>

A Lightweight Learning Method for Stochastic Configuration Networks Using Non-Inverse Solution

Stochastic configuration networks (SCNs) face time-consuming issues when dealing with complex modeling tasks that usually require a mass of hidden nodes to build an enormous network. An important reason behind this issue is that SCNs always employ the Moore–Penrose generalized inverse method with high complexity to update the output weights in each increment. To tackle this problem, this paper proposes a lightweight SCNs, called L-SCNs. First, to avoid using the Moore–Penrose generalized inverse method, a positive definite equation is proposed to replace the over-determined equation, and the consistency of their solution is proved. Then, to reduce the complexity of calculating the output weight, a low complexity method based on Cholesky decomposition is proposed. The experimental results based on both the benchmark function approximation and real-world problems including regression and classification applications show that L-SCNs are sufficiently lightweight.

Summary Report for the Third International Competition On Computational Models of Argumentation

The Third International Competition on Computational Models of Argumentation (ICCMA’19) focused on reasoning tasks in abstract argumentation frameworks. Submitted solvers were tested on a selected collection of benchmark instances, including artificially generated argumentation frameworks and some frameworks formalizing real-world problems. This competition introduced two main novelties over the two previous editions: the first one is the use of the Docker platform for packaging the participating solvers into virtual “light” containers; the second novelty consists of a new track for dynamic frameworks.

An Ensemble Framework of Evolutionary Algorithm for Constrained Multi-Objective Optimization

In the real-world, symmetry or asymmetry widely exists in various problems. Some of them can be formulated as constrained multi-objective optimization problems (CMOPs). During the past few years, handling CMOPs by evolutionary algorithms has become more popular. Lots of constrained multi-objective optimization evolutionary algorithms (CMOEAs) have been proposed. Whereas different CMOEAs may be more suitable for different CMOPs, it is difficult to choose the best one for a CMOP at hand. In this paper, we propose an ensemble framework of CMOEAs that aims to achieve better versatility on handling diverse CMOPs. In the proposed framework, the hypervolume indicator is used to evaluate the performance of CMOEAs, and a decreasing mechanism is devised to delete the poorly performed CMOEAs and to gradually determine the most suitable CMOEA. A new CMOEA, namely ECMOEA, is developed based on the framework and three state-of-the-art CMOEAs. Experimental results on five benchmarks with totally 52 instances demonstrate the effectiveness of our approach. In addition, the superiority of ECMOEA is verified through comparisons to seven state-of-the-art CMOEAs. Moreover, the effectiveness of ECMOEA on the real-world problems is also evaluated for eight instances.

E-Commerce Customers Behavior Research Using Cohort Analysis: A Case Study of COVID-19

Cohort analysis is a new practical method for e-commerce customers’ research, trends in their behavior, and experience during the COVID-19 crisis. The purpose of the research is to validate the efficiency of this method on the e-commerce records data set and find out the critical factors associated with customer awareness and loyalty levels. The cohort analysis features engineering, descriptive statistics, and exploratory data analysis are the main methods used to reach the study purpose. The research results showed that cohort analysis could answer various business questions and successfully solve real-world problems in e-commerce customer research. It could be extended to analyze user satisfaction with a platform’s technical performance and used for infrastructure monitoring. Obtained insights on e-commerce customers’ awareness and loyalty levels show the likeliness of a user to make a purchase or interact with the platform. Key e-business aspects from a customer point of view are analyzed and augment the user-experience understanding to strengthen customers’ relationships in e-commerce.

Developing Pre-Service Teachers' STEM Skills With Raspberry Pi Activities

The aim of this study was to explore the development of pre-service primary school teachers' STEM skills with Raspberry Pi activities. Data were collected from 16 pre-service teachers through semi-constructed interviews, reports, and a questionnaire. The results of the qualitative analysis showed that the participants developed the STEM skills mentioned in the literature such as confidence, computing, problem-solving, creativity, technological skills, and enhanced the learning potential of robotics. Moreover, the ready-to-use Python codes on Raspberry Pi platform could be an effective strategy for pre-service teachers with lack of programming to provide solutions on real-world problems. In addition, the participants successfully connected the Raspberry Pi, sensor kits, and Python scripts with real-world problems. This equipment motivated them to transpose a real-world problem to school knowledge. According to the results the combination of Raspberry Pi, sensors, and Python helped the participants upskill in computing.

Framing the concept design literacy for a general public

Educating the general public to be design literate can be a catalyst for both environmental protection and degradation, human aid and human-made disasters depending on how the scope of design is framed – and how ‘design literacy’ is defined. This paper explores how to cultivate design literacy that supports critical innovation and a transition towards more sustainable societies. The research approach is a literature review of key texts that promote and conceptualize design literacy as part of general edu­cation. Four narratives are identified as vital: a) ‘Awareness through making’, b) ‘Empower for change and citizen participation’, c) ‘Address complexity of real-world problems’, and d) ‘Participate in design processes’. Moving towards more sustainable modes of consumption and production, a design literate general public provides a critical mass of users empowered to question how a new innovation sup­ports the well-being of people and the planet and to voice their own ideas.

A Bio-Inspired Method for Mathematical Optimization Inspired by Arachnida Salticidade

This paper proposes a new meta-heuristic called Jumping Spider Optimization Algorithm (JSOA), inspired by Arachnida Salticidae hunting habits. The proposed algorithm mimics the behavior of spiders in nature and mathematically models its hunting strategies: search, persecution, and jumping skills to get the prey. These strategies provide a fine balance between exploitation and exploration over the solution search space and solve global optimization problems. JSOA is tested with 20 well-known testbench mathematical problems taken from the literature. Further studies include the tuning of a Proportional-Integral-Derivative (PID) controller, the Selective harmonic elimination problem, and a few real-world single objective bound-constrained numerical optimization problems taken from CEC 2020. Additionally, the JSOA’s performance is tested against several well-known bio-inspired algorithms taken from the literature. The statistical results show that the proposed algorithm outperforms recent literature algorithms and is capable to solve challenging real-world problems with unknown search space.