Internet addiction: Meta-synthesis of qualitative research for the decade 1996–2006

Internet addiction is “an individual’s inability to control their Internet use, which in turn leads to feelings of distress and functional impairment of daily activities” [Shapira, N., Lessig, M., Goldsmith, T., Szabo, S., Lazoritz, M., Gold, M. et al. (2003). Problematic Internet use: Proposed classification and diagnostic criteria. Depression and Anxiety, 17(4), 207–216]. Previous research in this field has offered inconclusive data on whether Internet addiction can be classified as a disorder. This study provides an in-depth and comprehensive analysis of internet addiction through a meta-synthesis of qualitative studies on excessive Internet use published during the period of 1996–2006. Several constructs pertaining to the domain of Internet addiction have been identified and a theoretical model of Internet addiction has been proposed.     

Transaction Management for Flash Media Databases in Portable Computing Environments

Flash memory is becoming a major database storage in building embedded systems or portable devices because of its non-volatile, shock-resistant, power-economic nature, and fast access time for read operations. Flash memory, however, should be erased before it can be rewritten and the erase and write operations are very slow as compared to main memory. Due to this drawback, traditional database management schemes are not easy to apply directly to flash memory database for portable devices. Therefore, we improve the traditional schemes and propose a new scheme called flash two phase locking (F2PL) scheme for efficient transaction processing in a flash memory database environment. F2PL achieves high transaction performance by exploiting the notion of the alternative version coordination which allows previous version reads and efficiently handles slow write/erase operations in lock management processes. We also propose a simulation model to show the performance of F2PL. Based on the results of the performance evaluation, we conclude that F2PL scheme outperforms the traditional schemes.     

Human tracking and silhouette extraction for human–robot interaction systems

In this study, we propose a new integrated computer vision system designed to track multiple human beings and extract their silhouette with a pan-tilt stereo camera, so that it can assist in gesture and gait recognition in the field of Human–Robot Interaction (HRI). The proposed system consists of three modules: detection, tracking and silhouette extraction. These modules are robust to camera movements, and they work interactively in near real-time. Detection was performed by camera ego-motion compensation and disparity segmentation. For tracking, we present an efficient mean shift-based tracking method in which the tracking objects are characterized as disparity weighted color histograms. The silhouette was obtained by two-step segmentation. A trimap was estimated in advance and then effectively incorporated into the graph-cut framework for fine segmentation. The proposed system was evaluated with respect to ground truth data, and it was shown to detect and track multiple people very well and also produce high-quality silhouettes.

Generating Time-Series Data Using Generative Adversarial Networks for Mobility Demand Prediction

The increasing penetration rate of electric kickboard vehicles has been popularized and promoted primarily because of its clean and efficient features. Electric kickboards are gradually growing in popularity in tourist and education-centric localities. In the upcoming arrival of electric kickboard vehicles, deploying a customer rental service is essential. Due to its free-floating nature, the shared electric kickboard is a common and practical means of transportation. Relocation plans for shared electric kickboards are required to increase the quality of service, and forecasting demand for their use in a specific region is crucial. Predicting demand accurately with small data is troublesome. Extensive data is necessary for training machine learning algorithms for effective prediction. Data generation is a method for expanding the amount of data that will be further accessible for training. In this work, we proposed a model that takes time-series customers’ electric kickboard demand data as input, pre-processes it, and generates synthetic data according to the original data distribution using generative adversarial networks (GAN). The electric kickboard mobility demand prediction error was reduced when we combined synthetic data with the original data. We proposed Tabular-GAN-Modified-WGAN-GP for generating synthetic data for better prediction results. We modified The Wasserstein GAN-gradient penalty (GP) with the RMSprop optimizer and then employed Spectral Normalization (SN) to improve training stability and faster convergence. Finally, we applied a regression-based blending ensemble technique that can help us to improve performance of demand prediction. We used various evaluation criteria and visual representations to compare our proposed model’s performance. Synthetic data generated by our suggested GAN model is also evaluated. The TGAN-Modified-WGAN-GP model mitigates the overfitting and mode collapse problem, and it also converges faster than previous GAN models for synthetic data creation. The presented model’s performance is compared to existing ensemble and baseline models. The experimental findings imply that combining synthetic and actual data can significantly reduce prediction error rates in the mean absolute percentage error (MAPE) of 4.476 and increase prediction accuracy.     

Single-mode GaAs/AIGaAs W waveguides with a low propagation loss

We have designed a low-loss single-mode GaAs/AIGaAs optical waveguide with a symmetric five-layer heterostructure at the 1.31-μm wavelength by use of an effective-index method. Waveguides with a W-shaped refractive-index profile have been grown by use of a metallo-organic chemical vapor deposition technique and fabricated with a chemical wet-etching method. Propagation loss has been measured by use of the Fabry-Perot resonance method and a sequential-cleaving experiment. The measured loss is as low as 0.19 dB/cm for waveguides with 2.3-μm thickness and 4.3-μm width, which is comparable to the lowest-loss semiconductor waveguides yet reported. These waveguides could be used to make low-loss modulators for guided-wave devices.     

Surface modification of polyetherurethaneureas and their antithrombogenicity

Polyetherurethaneurea (PU) films were treated by oxygen plasma discharge followed by acrylic acid (AA) grafting. The carboxyl groups of the AA-grafted PU (PU-AA) surface were coupled with bovine serum albumin and heparin via water soluble carbodiimide. Surface characterization of the modified PUs was carried out by attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy and electron spectroscopy for chemical analysis (ESCA). The amount of immobilized albumin and heparin on the PU surface was 1.8 and 1.5 g/cm², respectively, as determined by the dye interaction method. Interactions between the surface-modified PUs and blood components such as plasma proteins and platelets were investigated to evaluate the blood compatibility of the samples. Plasma recalcification time (PRT) and activated partial thromboplastin time (APTT) of the albumin-immobilized PU (PU-Al) were almost the same as those of PU, while platelets were less adhered on the PU-Al than on PU. On the other hand, PRT and APTT of the PU-He were significantly longer than those of the PU, PU-AA, and PU-Al. Moreover, adhesion of platelets was effectively suppressed on the PU-He, leading to good in vitro blood compatibility.     

Comprehensive aircraft configuration design tool for Integrated Product and Process Development

This paper describes an efficient aircraft geometry design tool which is necessary for design and analysis applications through Integrated Product and Process Development (IPPD). The design process decomposes aircraft geometry into several components to represent it accurately and realistically with a reduced number of shape control parameters. For this purpose, several configuration representation algorithms are thoroughly investigated and discussed. The proposed configuration generation algorithm employs the super ellipse equation with simple analytic distribution functions, Class function/Shape function Transformation (CST) and can represent and manipulate complex shapes accurately with a small number of control parameters. A model of aircraft geometry, represented in this approach, can be applied to conceptual and preliminary stages of aircraft design and development with realistic and accurate configuration data. A Parameter-based Comprehensive Aircraft Design (PCAD) tool which implements a geometry generation process for aircraft design and optimization using customization of commercial computer-aided design software (CATIA V5) and the Product Data Management (Enovia Smarteam). The proposed configuration design tool could be especially efficient when automation, flexibility and rapid changes of geometry are required in a short time and with low computational resources.     

Constraint-based test generation for automotive operating systems

This work suggests a method for systematically constructing a software-level environment model for safety checking automotive operating systems by introducing a constraint specification language, OSEK_CSL. OSEK_CSL is designed to specify the usage constraints of automotive operating systems using a pre-defined set of constraint types identified from the international standard OSEK/VDX. Each constraint specified in OSEK_CSL is interpreted as either a regular language or a context-free language that can be checked by a finite automaton or a pushdown automaton. The set of usage constraints is used to systematically classify the universal usage model of OSEK-/VDX-based operating systems and to generate test sequences with varying degrees of constraint satisfaction using LTL model checking. With pre-defined constraint patterns and the full support of automation, test engineers can choose the degree of constraint satisfaction and generate test cases using combinatorial intersections of selected constraints that cover all corner cases classified by constraints. A series of experiments on an open-source automotive operating system show that our approach finds safety issues more effectively than conventional specification-based testing, scenario-based testing, and conformance testing.     

Using ridge regression with genetic algorithm to enhance real estate appraisal forecasting

This study considers real estate appraisal forecasting problem. While there is a great deal of literature about use of artificial intelligence and multiple linear regression for the problem, there has been always controversy about which one performs better. Noting that this controversy is due to difficulty finding proper predictor variables in real estate appraisal, we propose a modified version of ridge regression, i.e., ridge regression coupled with genetic algorithm (GA-Ridge). In order to examine the performance of the proposed method, experimental study is done for Korean real estate market, which verifies that GA-Ridge is effective in forecasting real estate appraisal. This study addresses two critical issues regarding the use of ridge regression, i.e., when to use it and how to improve it.     

Electrochemical characteristics of silicon-metals coated graphites for anode materials of lithium secondary batteries

Four kinds of silicon-metals (Cu, Ni, Sn)-graphite composites for anode active materials of lithium secondary batteries were prepared by sequential employment of PECVD (Plasma enhance chemical vapor deposition) and RF(Radio-frequency)-magnetron sputtering method. The silicon-copper-graphite composite showed the highest reversible capacity and cyclability among the silicon-metal composite graphite samples prepared. The enhanced electrochemical performance of silicon-copper-graphite composite is attributed to the formation of copper silicide on the surface of graphite. The copper silicide plays an important role as a buffering layer against volume change of silicon during the intercalation/deintercalation due to the chemical bonding of silicon and copper, and has lower interfacial impedance than that of other silicon-metal-graphite composites which may lead to low irreversible capacity.