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.     

Developments in the treatment of alcoholism

The treatment of alcoholism has changed during the past 2 decades. Notable developments have occurred in pharmacotherapy, psychotherapy, and health-care delivery. A better understanding of the biologic basis for addiction has led to clinical trials of medications that target neuroreceptors. One such medication is the opiate antagonist naltrexone, which decreases the craving for alcohol. Psychosocial interventions continue to be the mainstay of alcohol treatment programs. The efficacy of three different therapies was demonstrated in a study called Project MATCH (Matching Alcoholism Treatments to Client Heterogeneity). This study, however, did not prove the patient-treatment "matching" hypothesis. In addition to therapies provided by addiction specialists, interest is growing in the use of brief motivational techniques in primary-care settings. As the field of addiction responds to an unfolding health-care delivery system, a broader range of treatment options in conjunction with a greater opportunity to individualize patient care is evolving.     

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.     

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.     

SedLog: A shareware program for drawing graphic logs and log data manipulation

SedLog is a free multi-platform software package for creating graphic sediment logs providing an intuitive graphical user interface. The graphic sediment logs generated by SedLog can be exported as PDF, Scalable Vector Graphics (SVG), or JPEG for use by other drawing applications or for publications. Log data can be imported and exported in Comma Separated Values (CSV) format. The logs can also be printed to any paper size the user wants. Zoom In, Zoom Out, Fit page, Fit Height and Fit Width facilities are also provided to enable the user to customise the workspace size.