Exploring age and gender differences of computational thinkers in primary school: A developmental perspective

Over the past decade, the call to foster computational thinking (CT) in every child has received considerable attention. However, there is little understanding of whether children are developmentally ready to think computationally and what specific CT concepts and skills can be developed at various ages. This study explored the developmental and gender differences in CT skills of 197 Grade 4–6 students (aged 9–13) before being exposed to instruction and investigated the age–gender interaction effects on their CT acquisition in an intervention combining both programming and non-programming (unplugged) activities. Results show that students' CT skills followed a developmental progression before instruction. Gender difference across ages was insignificant in conditionals, logical operators, pattern recognition and generalization skills. Additionally, students of different ages developed CT differently during the intervention, and their CT acquisition was unaffected by gender. Implications for practice and research in CT education were discussed.     

Composite bending-dominated hollow nanolattices: A stiff,cyclable mechanical metamaterial

Manufacturing ultralight and mechanical reliable materials has been a long-time challenge. Ceramic-based mechanical metamaterials provide significant opportunities to reverse their brittle nature and unstable mechanical properties and have great potential as strong, ultralight, and ultrastiff materials. However, the failure of ceramics nanolattice and degradation of strength/modulus with decreasing density are caused by buckling of the struts and failure of the nodes within the nanolattices, especially during cyclic loading. Here, we explore a new class of 3D ceramic-based metamaterials with a high strength–density ratio, stiffness, recoverability, cyclability, and optimal scaling factor. Deformation mode of the fabricated nanolattices has been engineered through the unique material design and architecture tailoring. Bending-dominated hollow nanolattice (B-H-Lattice) structure is employed to take advantages of its flexibility, while a few nanometers of carbonized mussel-inspired bio-polymer (C-PDA) is coherently deposited on ceramics’ nanolayer to enable non-buckling struts and bendable nodes during deformation, resulting in reliable mechanical properties and outperforming the current bending-dominated lattices (B-Lattices) and carbon-based cellulose materials. Meanwhile, the structure has comparable stiffness to stretching-dominated lattices (S-Lattices) while with better cyclability and reliability. The B-H-Lattices exhibit high specific stiffness (>10⁶?Pa·kg^?1·m^?3), low-density (～30?kg/m³), buckling-free recovery at 55% strain, and stable cyclic loading behavior under up to 15% strain. As one of the B-Lattices, the modulus scaling factor reaches 1.27, which is lowest among current B-Lattices. This study suggests that non-buckling behavior and reliable nodes are the key factors that contribute to the outstanding mechanical performance of nanolattice materials. A new concept of engineering the internal deformation behavior of mechanical metamaterial is provided to optimize their mechanical properties in real service conditions.     

Situating digital storytelling within African communities

We reflect on the methods, activities and perspectives we used to situate digital storytelling in two rural African communities in South Africa and Kenya. We demonstrate how in-depth ethnography in a village in the Eastern Cape of South Africa and a design workshop involving participants from that village allowed us to design a prototype mobile digital storytelling system suited to the needs of rural, oral users. By leveraging our prototype as a probe and observing villagers using it in two villages in South Africa and Kenya, we uncovered implications for situating digital storytelling within those communities. Finally, we distil observations relevant to localizing storytelling and their implications for transferring design into a different community.     

Linear,parameter-varying control of a supercavitating vehicle

A systematic approach to parameter-dependent control synthesis of a high-speed supercavitation vehicle (HSSV) is presented. The aim of the control design is to provide robust reference tracking across a large flight envelope, while directly accounting for the interaction of liquid and gas phases with the vehicle. A nonlinear dynamic HSSV model is presented and discussed relative to the actual vehicle. A linear, parameter-varying (LPV) controller is synthesized for angle rate tracking in the presence of model uncertainty. The control design takes advantage of coupling in the governing equations to achieve improved performance. Multiple LPV controllers synthesized for smaller overlapping regions of the parameter space are blended together, providing a single controller for the full flight envelope. Time-domain simulations implemented on high-fidelity simulations, provide insight into the performance and robustness of the proposed scheme.     

Eigenvectors of directed graphs and importance scores: dominance,T-Rank,and sink remedies

We study the properties of the principal eigenvector for the adjacency matrix (and related matrices) for a general directed graph. In particular—motivated by the use of the eigenvector for estimating the “importance” of the nodes in the graph—we focus on the distribution of positive weight in this eigenvector, and give a coherent picture which builds upon and unites earlier results. We also propose a simple method—“T-Rank”—for generating importance scores. T-Rank generates authority scores via a one-level, non-normalized matrix, and is thus distinct from known methods such as PageRank (normalized), HITS (two-level), and SALSA (two-level and normalized). We show, using our understanding of the principal eigenvector, that T-Rank has a much less severe “sink problem” than does PageRank. Also, we offer numerical results which quantify the “tightly-knit community” or TKC effect. We find that T-Rank has a stronger TKC effect than PageRank, and we offer a novel interpolation method which allows for continuous tuning of the strength of this TKC effect. Finally, we propose two new “sink remedies”, i.e., methods for ensuring that the principal eigenvector is positive everywhere. One of our sink remedies (source pumping) is unique among sink remedies, in that it gives a positive eigenvector without rendering the graph strongly connected. We offer a preliminary evaluation of the effects and possible applications of these new sink remedies.     

Optimization of silicon solar cell fabrication based on neural network and genetic programming modeling

This study describes techniques for the cascade modeling and the optimization that are required to conduct the simulator-based process optimization of solar cell fabrication. Two modeling approaches, neural networks and genetic programming, are employed to model the crucial relation for the consecutively connected two processes in solar cell fabrication. One model (Model 1) is used to map the five inputs (time, amount of nitrogen and DI water in surface texturing and temperature and time in emitter diffusion) to the two outputs (reflectance and sheet resistance) of the first process. The other model (Model 2) is used to connect the two inputs (reflectance and sheet resistance) to the one output (efficiency) of the second process. After modeling of the two processes, genetic algorithms and particle swarm optimization were applied to search for the optimal recipe. In the first optimization stage, we searched for the optimal reflectance and sheet resistance that can provide the best efficiency in the fabrication process. The optimized reflectance and sheet resistance found by the particle swarm optimization were better than those found by the genetic algorithm. In the second optimization stage, the five input parameters were searched by using the reflectance and sheet resistance values obtained in the first stage. The found five variables such as the texturing time, amount of nitrogen, DI water, diffusion time, and temperature are used as a recipe for the solar cell fabrication. The amount of nitrogen, DI water, and diffusion time in the optimized recipes showed considerable differences according to the modeling approaches. More importantly, repeated applications of particle swarm optimization yielded process conditions with smaller variations, implying greater consistency in recipe generation.     

Iteratively constrained selection of word alignment links using knowledge and statistics

Word alignment is a crucial component in applications that use bilingual resources. Statistical methods are widely used because they are portable and allow simple system building. However, pure statistical methods often incorrectly align functional words in the English–Korean language pair due to differences in the typology of the languages and a lack of knowledge. Knowledge is inevitably required to correct errors and to improve word alignment quality. In this paper, we introduce an effective method that uses an iterative process to incorporate knowledge into the word alignment system. The method achieved significant improvements in word alignment and its application: statistical machine translation.     

Multimodal augmented reality tangible gaming

This paper presents tangible augmented reality gaming environment that can be used to enhance entertainment using a multimodal tracking interface. Players can interact using different combinations between a pinch glove, a Wiimote, a six-degrees-of-freedom tracker, through tangible ways as well as through I/O controls. Two tabletop augmented reality games have been designed and implemented including a racing game and a pile game. The goal of the augmented reality racing game is to start the car and move around the track without colliding with either the wall or the objects that exist in the gaming arena. Initial evaluation results showed that multimodal-based interaction games can be beneficial in gaming. Based on these results, an augmented reality pile game was implemented with goal of completing a circuit of pipes (from a starting point to an end point on a grid). Initial evaluation showed that tangible interaction is preferred to keyboard interaction and that tangible games are much more enjoyable.     

Failure mode and effects analysis using a group-based evidential reasoning approach

Failure mode and effects analysis (FMEA) is a methodology to evaluate a system, design, process or service for possible ways in which failures (problems, errors, risks and concerns) can occur. It is a group decision function and cannot be done on an individual basis. The FMEA team often demonstrates different opinions and knowledge from one team member to another and produces different types of assessment information such as complete and incomplete, precise and imprecise and known and unknown because of its cross-functional and multidisciplinary nature. These different types of information are very difficult to incorporate into the FMEA by the traditional risk priority number (RPN) model and fuzzy rule-based approximate reasoning methodologies. In this paper we present an FMEA using the evidential reasoning (ER) approach, a newly developed methodology for multiple attribute decision analysis. The proposed FMEA is then illustrated with an application to a fishing vessel. As is illustrated by the numerical example, the proposed FMEA can well capture FMEA team members’ diversity opinions and prioritize failure modes under different types of uncertainties.