The determinants of continuous use of social networking sites: An empirical study on Taiwanese journal-type bloggers’ continuous self-disclosure behavior

The popularity and growth of social networking sites (SNSs) have resulted in fierce competition among SNS providers. A critical concern of SNSs is to identify how to have users continuously use a specific SNS. This study examines the determinants of journal-type bloggers’ continuous self-disclosure. An analysis of 283 samples with continuous self-disclosure experiences in journal-type blogs shows that personal factors, specifically the habit of self-disclosure, are the major factors affecting bloggers’ continuous self-disclosure, followed by self-benefits. In contrast, social factors, such as positive audience feedback and social benefits, are not related to continuous self-disclosure. These results suggest that SNS providers must encourage users to develop the habit of using their web sites regularly. By providing differential benefits to users, SNS providers may attract users to continuously use their services.     

Harmony search-based hidden Markov model optimization for online classification of single trial eegs during motor imagery tasks

This paper presents an improved method based on single trial EEG data for the online classification of motor imagery tasks for brain-computer interface (BCI) applications. The ultimate goal of this research is the development of a novel classification method that can be used to control an interactive robot agent platform via a BCI system. The proposed classification process is an adaptive learning method based on an optimization process of the hidden Markov model (HMM), which is, in turn, based on meta-heuristic algorithms. We utilize an optimized strategy for the HMM in the training phase of time-series EEG data during motor imagery-related mental tasks. However, this process raises important issues of model interpretation and complexity control. With these issues in mind, we explore the possibility of using a harmony search algorithm that is flexible and thus allows the elimination of tedious parameter assignment efforts to optimize the HMM parameter configuration. In this paper, we illustrate a sequential data analysis simulation, and we evaluate the optimized HMM. The performance results of the proposed BCI experiment show that the optimized HMM classifier is more capable of classifying EEG datasets than ordinary HMM during motor imagery tasks.     

A fast identification algorithm with outliers under Box-Cox transformation-based annealing robust radial basis function networks

In this article, a Box-Cox transformation-based annealing robust radial basis function networks (ARRBFNs) is proposed for an identification algorithm with outliers. Firstly, a fixed Box-Cox transformation-based ARRBFN model with support vector regression (SVR) is derived to determine the initial structure. Secondly, the results of the SVR are used as the initial structure in the fixed Box-Cox transformation-based ARRBFNs for the identification algorithm with outliers. At the same time, an annealing robust learning algorithm (ARLA) is used as the learning algorithm for the fixed Box-Cox transformation-based ARRBFNs, and applied to adjust the parameters and weights. Hence, the fixed Box-Cox transformation-based ARRBFNs with an ARLA have a fast convergence speed for an identification algorithm with outliers. Finally, the proposed algorithm and its efficacy are demonstrated with an illustrative example in comparison with Box-Cox transformation-based radial basis function networks.     

Study on the electrical conductivity and morphology of porous polypyrrole layers prepared electrochemically in the presence of pyridinium chlorochromate

The electrical conductivity and morphology of thick (up to 3 mm) porous polypyrrole (PPy) layers produced electrochemically from pyrrole in acetonitrile (ACN) solutions have been studied. The electrical conductivity of pressed porous layers ranges from 1 to 10 Scm^–1, which is about one order of magnitude less than that in films which were prepared under similar conditions but without PnClCr. Analysis of the temperature dependence of conductivity has confirmed the major role of hopping in relation to tunnelling in charge transport inside the PPy layers even at lower temperatures. Scanning electron microscopy (SEM) showed a globular structure, which is different from the usual cauliflower-like structure of PPy films prepared without any oxidizing agent. Globular particles of about 1–3 m diameter have been found under a thin smooth crust on the electrode side of the sample. Globular particles form linked chain-like or larger round formations poorly filling the space. Closely packed fibrils of about 20 nm diameter and over 100 nm in length were found inside the aggregates.     

Effective identification of dominant fully absorbing sets for Raptor-like LDPC codes

The error-rate floor of low-density parity-check (LDPC) codes is attributed to the trapping sets of their Tanner graphs. Among them, fully absorbing sets dominantly affect the error-rate performance, especially for short blocklengths. Efficient methods to identify the dominant trapping sets of LDPC codes were thoroughly researched as exhaustively searching them is NP-hard. However, the existing methods are ineffective for Raptor-like LDPC codes, which have many types of trapping sets. An effective method to identify dominant fully absorbing sets of Raptor-like LDPC codes is proposed. The search space of the proposed algorithm is optimized into the Tanner subgraphs of the codes to afford time-efficiency and search-effectiveness. For 5G New Radio (NR) base graph (BG) 2 LDPC codes for short blocklengths, the proposed algorithm finds more dominant fully absorbing sets within one seventh of the computation time of the existing search algorithm, and its search-effectiveness is verified using importance sampling. The proposed method is also applied to 5G NR BG1 LDPC code and Advanced Television Systems Committee 3.0 type A LDPC code for large blocklengths.     

Room-Temperature-Processable Highly Reliable Resistive Switching Memory with Reconfigurability for Neuromorphic Computing and Ultrasonic Tissue Classification

Reversible metal-filamentary mechanism has been widely investigated to design an analog resistive switching memory (RSM) for neuromorphic hardware-implementation. However, uncontrollable filament-formation, inducing its reliability issues, has been a fundamental challenge. Here, an analog RSM with 3D ion transport channels that can provide unprecedentedly high reliability and robustness is demonstrated. This architecture is realized by a laser-assisted photo-thermochemical process, compatible with the back-end-of-line process and even applicable to a flexible format. These superior characteristics also lead to the proposal of a practical adaptive learning rule for hardware neural networks that can significantly simplify the voltage pulse application methodology even with high computing accuracy. A neural network, which can perform the biological tissue classification task using the ultrasound signals, is designed, and the simulation results confirm that this practical adaptive learning rule is efficient enough to classify these weak and complicated signals with high accuracy (97%). Furthermore, the proposed RSM can work as a diffusive-memristor at the opposite voltage polarity, exhibiting extremely stable threshold switching characteristics. In this mode, several crucial operations in biological nervous systems, such as Ca²⁺ dynamics and nonlinear integrate-and-fire functions of neurons, are successfully emulated. This reconfigurability is also exceedingly beneficial for decreasing the complexity of systems—requiring both drift- and diffusive-memristors.     

Comparison of osteoconductivity and absorbability of beta-tricalcium phosphate and hydroxyapatite in clinical scenario of opening wedge high tibial osteotomy

The purpose of this study was to compare the osteoconductivity, and absorbability of hydroxyapatite or beta-tricalcium phosphate in clinical scenario of opening wedge high tibial osteotomy Total 41 knees of 40 patients with follow up period of more than 1 year were enrolled. These patients were divided into two groups, Group I (22 knees, 21 patients) used hydroxyapatite and Group II (19 knees, 19 patients) used beta-tricalcium phosphate as a substitute in the opening gap. According to proven method, the osteoconductivity was assessed radiographically by the extent of new bone formation at osteotomy space and absorbability was evaluated by measuring the area occupied by substitute at immediate postoperative, postoperative 6 months and 1 year. Regarding preoperative demographic data, no significant differences were found between two groups. No statistically significant differences were found between two groups regarding lower limb alignment (mechanical femorotibial angle, weight-bearing line%) and posterior tibial slope at postoperative and final follow up radiographs. Concerning the osteoconductivity, there were no significant differences between two groups in any zone. However, the absorption rate was significantly greater in the Group II than in Group I at 6 months (Group I: 13.7?±?6.8, group II: 35.3?±?15.8, P?=?0.001) and 1 year (Group I: 24.2?±?6.3, Group II: 49.6?±?14.3, P?<?0.0001). The complications related to bone substitutes were not observed. Both hydroxyapatite and beta-tricalcium phosphate showed satisfactory gap healing without complications and can be successfully used as alternative healing materials in opening wedge high tibial osteotomy. Our study showed that beta-tricalcium phosphate has superior absorbability than hydroxyapatite. But osteoconductivity showed no significant difference.