Development of an epileptic seizure prediction algorithm using R–R intervals with self-attentive autoencoder

Epilepsy is a neurological disorder that may affect the autonomic nervous system (ANS) from 15 to 20 min before seizure onset, and disturbances of ANS affect R–R intervals (RRI) on an electrocardiogram (ECG). This study aims to develop a machine learning algorithm for predicting focal epileptic seizures by monitoring R–R interval (RRI) data in real time. The developed algorithm adopts a self-attentive autoencoder (SA-AE), which is a neural network for time-series data. The results of applying the developed seizure prediction algorithm to clinical data demonstrated that it functioned well in most patients; however, false positives (FPs) occurred in specific participants. In a future work, we will investigate the causes of FPs and optimize the developing seizure prediction algorithm to further improve performance using newly added clinical data.

     

Freestanding hierarchical porous carbon film derived from hybrid nanocellulose for high-power supercapacitors

Nanocellulose is a sustainable and eco-friendly nanomaterial derived from renewable biomass.In this study,we utilized the structural advantages of two types of nanocellulose and fabricated freestanding carbonized hybrid nanocellulose films as electrode materials for supercapacitors.The long cellulose nanofibrils (CNFs) formed a macroporous framework,and the short cellulose nanocrystals were assembled around the CNF framework and generated micro/mesopores.This two-level hierarchical porous structure was successfully preserved during carbonization because of a thin atomic layer deposited (ALD) Al2O3 conformal coating,which effectively prevented the aggregation of nanocellulose.These carbonized,partially graphitized nanocellulose fibers were interconnected,forming an integrated and highly conductive network with a large specific surface area of 1,244 m2·g-1.The two-level hierarchical porous structure facilitated fast ion transport in the film.When tested as an electrode material with a high mass loading of 4 mg·cm-2 for supercapacitors,the hierarchical porous carbon film derived from hybrid nanocellulose exhibited a specific capacitance of 170 F.g-1and extraordinary performance at high current densities.Even at a very high current of 50 A·g-1,it retained 65％ of its original specific capacitance,which makes it a promising electrode material for high-power applications.     

Mechanical property enhancement of aligned multi-walled carbon nanotube sheets and composites through press-drawing process

A solid-state drawing and winding process was done to create thin aligned carbon nanotube (CNT) sheets from CNT arrays. However, waviness and poor packing of CNTs in the sheets are two main weaknesses restricting their reinforcing efficiency in composites. This report proposes a simple press-drawing technique to reduce wavy CNTs and to enhance dense packing of CNTs in the sheets. Non-pressed and pressed CNT/epoxy composites were developed using prepreg processing with a vacuum-assisted system. Effects of pressing on the mechanical properties of the aligned CNT sheets and CNT/epoxy composites were examined. Pressing with distributed loads of 147, 221, and 294 N/m showed a substantial increase in the tensile strength and the elastic modulus of the aligned CNT sheets and their composites. The CNT sheets under a press load of 221 N/m exhibited the best mechanical properties found in this study. With a press load of 221 N/m, the pressed CNT sheet and its composite, respectively, enhanced the tensile strength by 139.1 and 141.9%, and the elastic modulus by 489 and 77.6% when compared with non-pressed ones. The pressed CNT/epoxy composites achieved high tensile strength (526.2 MPa) and elastic modulus (100.2 GPa). Results show that press-drawing is an important step to produce superior CNT sheets for development of high-performance CNT composites.     

Resolving force indeterminacy in contact dynamics using compatibility conditions

Contact dynamics (CD) is a powerful method to solve the dynamics of large systems of colliding rigid bodies. CD can be computationally more efficient than classical penalty-based discrete element methods (DEM) for simulating contact between stiff materials such as rock, glass, or engineering metals. However, by idealizing bodies as perfectly rigid, contact forces computed by CD can be non-unique due to indeterminacy in the contact network, which is a common occurence in dense granular flows. We propose a frictionless CD method that is designed to identify only the unique set of contact forces that would be predicted by a soft particle method, such as DEM, in the limit of large stiffness. The method involves applying an elastic compatibility condition to the contact forces, which maintains no-penetration constraints but filters out force distributions that could not have arisen from stiff elastic contacts. The method can be used as a post-processing step that could be integrated into existing CD codes with minimal effort. We demonstrate its efficacy in a variety of indeterminate problems, including some involving multiple materials, non-spherical shapes, and nonlinear contact constitutive laws.     

Towards the appropriate human-centred information systems: A case study of the Japanese retail industry

The industries of Japan have developed by learning from Western industries, especially the USA, and by implementing many of their concepts and technologies. However, Japanese industries have often implemented these concepts and technologies in a very different way from the USA. For example, while the USA uses information systems in retail industries as a tool by which data are collected and analysed to control the market, in Japan this same technology is considered rather as a learning device to interpret the market. While in the USA the market is seen as a natural phenomenon capable of being controlled, the Japanese see it as an ambiguous phenomenon that is ever changing and is not capable of being controlled. Rather it is important to feel the change in the market itself.This paper introduces human centredness to the information system, and argues against modern rationalism, i.e. human versus technology, taking the case of use of POS data from the POS system (point of sale: a system that collects data on both the customer and goods sold by scanning bar codes that are attached to the surface of the goods) by the eminent Japanese retailer, Ito-Yokado. It emphasises an interactive concept of interaction between human and technology of the postmodern paradigm.     

Development of a pulse control-type MEMS microrobot with a hardware neural network

This article presents the micro-electro-mechanical systems (MEMS) microrobot which demonstrates locomotion controlled by hardware neural networks (HNN). The size of the microrobot fabricated by the MEMS technology is 4 × 4 × 3.5 mm. The frame of the robot is made of silicon wafer, and it is equipped with a rotary-type actuator, a link mechanism, and six legs. The rotary-type actuator generates rotational movement by applying an electrical current to artificial muscle wires. The locomotion of the microrobot is obtained by the rotation of the rotary-type actuator. As in a living organism, the HNN realized robot control without using any software programs, A/D converters, or additional driving circuits. A central pattern generator (CPG) model was implemented as an HNN system to emulate the locomotion pattern. The MEMS microrobot emulated the locomotion method and the neural networks of an insect with the rotary-type actuator, the link mechanism, and the HNN. The microrobot performed forward and backward locomotion, and also changed direction by inputting an external trigger pulse. The locomotion speed was 0.325 mm/s and the step width was 1.3 mm.     

Brain-like Computing Based on Distributed Representations and Neurodynamics

A key to overcoming the limitations of classical artificial intelligence and to deal well with enormous amounts of information might be brain-like computing in which distributed representations of information are processed by dynamical systems without using symbols. We present a method for such computing. We constructed an inference system using a nonmonotone neural network, which is a kind of recurrent neural network with continuous-time dynamics. This system deduces a conclusion according to state transitions of the network in which knowledge is embedded as trajectory attractors. It has the powerful ability of analogical reasoning without special treatment for exceptional knowledge. We also propose a method of linking different neurodynamical systems and show that two mutually interacting systems can process complex spatiotemporal patterns.     

Learning science in a virtual reality application: The impacts of animated-virtual actors’ visual complexity

As the technology in computer graphics advances, Animated-Virtual Actors (AVAs) in Virtual Reality (VR) applications become increasingly rich and complex. Cognitive Theory of Multimedia Learning (CTML) suggests that complex visual materials could hinder novice learners from attending to the lesson properly. On the other hand, previous studies have shown that visual complexity correlates with presence and may increase the perceived affective quality of the virtual world, towards an optimal experience or flow. Increasing these in VR applications may promote enjoyment and higher cognitive engagement for better learning outcomes. While visually complex materials could be motivating and pleasing to attend to, would they affect learning adversely? We developed a series of VR presentations to teach second-year psychology students about the navigational behaviour of Cataglyphis ants with flat, cartoon, or lifelike AVAs. To assess learning outcomes, we used Program Ratings, which measured perception of learning and perceived difficulty, and retention and transfer tests. The results from 200 students did not reveal any significant differences in presence, perceived affective quality, or learning outcomes as a function of the AVA’s visual complexity. While the results showed positive correlations between presence, perceived affective quality and perception of learning, none of these correlates with perceived difficulty, retention, or transfer scores. Nevertheless, our simulation produced significant improvements on retention and transfer scores in all conditions. We discuss possible explanations and future research directions.     

Z-SKY: an efficient skyline query processing framework based on Z-order

Given a set of data points in a multidimensional space, a skyline query retrieves those data points that are not dominated by any other point in the same dataset. Observing that the properties of Z-order space filling curves (or Z-order curves) perfectly match with the dominance relationships among data points in a geometrical data space, we, in this paper, develop and present a novel and efficient processing framework to evaluate skyline queries and their variants, and to support skyline result updates based on Z-order curves. This framework consists of ZBtree, i.e., an index structure to organize a source dataset and skyline candidates, and a suite of algorithms, namely, (1) ZSearch, which processes skyline queries, (2) ZInsert, ZDelete and ZUpdate, which incrementally maintain skyline results in presence of source dataset updates, (3) ZBand, which answers skyband queries, (4) ZRank, which returns top-ranked skyline points, (5) k-ZSearch, which evaluates k-dominant skyline queries, and (6) ZSubspace, which supports skyline queries on a subset of dimensions. While derived upon coherent ideas and concepts, our approaches are shown to outperform the state-of-the-art algorithms that are specialized to address particular skyline problems, especially when a large number of skyline points are resulted, via comprehensive experiments.