Effects of caffeine ingestion and thermotherapy on blood orexin circulation in humans

Food Science and Biotechnology - Caffeine and orexin can affect awakening, neuroendocrine, and sympathetic nerve function. Our previous study has reported that caffeine intake can increase human...     

Image-Based Lifelogging: User Emotion Perspective

Lifelog is a digital record of an individual’s daily life. It collects, records, and archives a large amount of unstructured data; therefore, techniques are required to organize and summarize those data for easy retrieval. Lifelogging has been utilized for diverse applications including healthcare, self-tracking, and entertainment, among others. With regard to the image-based lifelogging, even though most users prefer to present photos with facial expressions that allow us to infer their emotions, there have been few studies on lifelogging techniques that focus upon users’ emotions. In this paper, we develop a system that extracts users’ own photos from their smartphones and configures their lifelogs with a focus on their emotions. We design an emotion classifier based on convolutional neural networks (CNN) to predict the users’ emotions. To train the model, we create a new dataset by collecting facial images from the CelebFaces Attributes (CelebA) dataset and labeling their facial emotion expressions, and by integrating parts of the Radboud Faces Database (RaFD). Our dataset consists of 4,715 high-resolution images. We propose Representative Emotional Data Extraction Scheme (REDES) to select representative photos based on inferring users’ emotions from their facial expressions. In addition, we develop a system that allows users to easily configure diaries for a special day and summaize their lifelogs. Our experimental results show that our method is able to effectively incorporate emotions into lifelog, allowing an enriched experience.     

Preparation and characterization study on maleic acid cross-linked poly(vinyl alcohol)/chitin/nanocellulose composites

In the quest on improving composite formulations for environmental sustainability, maleic acid (MA) cross-linked poly(vinyl alcohol) (PVA)-α-chitin composites reinforced by oil palm empty fruit bunch fibers (OPEFB)-derived nanocellulose crystals (NCC) had been successfully prepared. Based on the Fourier transform infrared (FTIR) spectroscopic analysis, it was proven that molecular interactions of the cross-linker to the polymeric networks was through conjugated ester linkage. Differential scanning calorimetry (DSC) showed that the influence of MA was minimal toward crystallization in the PVA/chitin/NCC composite. Maximum tensile strength, elongation at break and Young's modulus of the respective PVA/chitin/NCC composites were achieved at different content of MA, dependent on the PVA/chitin mass ratio. Among all compositions, a maximum Young's modulus was achieved at 30 wt% MA loading in PVA/chitin-30/NCC, amounting to 2,413.81 ± 167.36 MPa. Moreover, the mechanical properties and selected physicochemical properties (swelling, gel content, and contact angle) of the PVA/chitin/NCC composites could be tailored by varying the chitin content (10–30 wt%) and MA content (10–50 wt% based on total mass of composite). In brief, this chemically cross-linked PVA-based biocomposites formulated with sustainable resources exhibited tunable physicochemical and mechanical properties.     

Object-Oriented Approach in Design for Lift Installation of Offshore Structures

Abstract: A consistent representation of offshore structures and barges that is appropriate for computer processing is essential for the development of an efficient design system for offshore heavy lift installation. This paper outlines the representation of conceptual and physical entities in the preliminary design for lift installation through an object-oriented approach. The spatial and topologic abstractions of physical entities are illustrated. These abstractions, together with the locational dependency concept for an assembly, form the backbone for geometric reasoning in the top-down approach for lift installation design. An object-oriented prototype system for lift evaluation, C-LIFT, using the knowledge-based development tool kit ART_*Enterprise is described. A liftability study involving the installation of offshore modules and the associated lift sequence is then presented to illustrate the advantages of this approach.     

Sensitivities of input parameters for predicting stability of soil slope

Bulletin of Engineering Geology and the Environment - The infinite slope stability model is the basis for predicting the slope stability; however, the fundamental theory requires various input...     

Visualizing for the Non‐Visual: Enabling the Visually Impaired to Use Visualization

The majority of visualizations on the web are still stored as raster images, making them inaccessible to visually impaired users. We propose a deep‐neural‐network‐based approach that automatically recognizes key elements in a visualization, including a visualization type, graphical elements, labels, legends, and most importantly, the original data conveyed in the visualization. We leverage such extracted information to provide visually impaired people with the reading of the extracted information. Based on interviews with visually impaired users, we built a Google Chrome extension designed to work with screen reader software to automatically decode charts on a webpage using our pipeline. We compared the performance of the back‐end algorithm with existing methods and evaluated the utility using qualitative feedback from visually impaired users.     

Editorial: Security and Privacy in Internet of Things

Mobile Networks and Applications -     

A critical review of selective attention: an interdisciplinary perspective

During the last half century, significant efforts have been made to explore the underlying mechanisms of visual selective attention using a variety of approaches—psychology, neuroscience, and computational models. Among them, the computational approach emerged on the stage with the development of computer science and computer vision focusing researchers interests in this area. However, computer scientists often face the difficulty of how to construct a computational model of selective attention working on their own purpose. Here, we critically review studies of selective attention from a multidisciplinary perspective to take lessons from psychological and biological studies of attention. We consider how constraints from those studies can be imposed on computational models of selective attention.