Nagoya Institute of Technology

About: Nagoya Institute of Technology is a education organization based out in Nagoya, Japan. It is known for research contribution in the topics: Thin film & Catalysis. The organization has 10766 authors who have published 19140 publications receiving 255696 citations. The organization is also known as: Nagoya Kōgyō Daigaku & Nitech.

Stability and Performance of the R-Aloha Packet Broadcast System

Abstract: The dynamic behavior of the R-ALOHA packet broadcast system with multipacket messages is analyzed in this paper. It is assumed that each user handles one message at a time and the number of packets in a message is geometrically distributed. A Markovian model of the system is first formulated which explicitly contains the influence of the propagation delay of the broadcast channel. An approximate technique called equilibrium point analysis (EPA) is utilized to analyze the multidimensional Markov chain. The system stability behavior and the throughput-average message delay performance are demonstrated by the EPA. Numerical results from both analysis and simulation are given to assess the accuracy of the analytic results. Applying the analytic results to the slotted ALOHA with single packet messages, we prove mathematically that a method by Kleinrock and Lam for taking into account the influence of the propagation delay is an excellent approximation.

Catalytic enantioselective decarboxylative reactions using organocatalysts.

Abstract: Catalytic decarboxylative reactions are attractive as biomimetic reactions and environmentally friendly reaction processes. In this review, the origin and recent development of organocatalytic enantioselective decarboxylative reactions of malonic acid half oxy- or thioesters, or β-ketoacids are summarized.

The role of mTOR signalling in the regulation of skeletal muscle mass in a rodent model of resistance exercise

Abstract: Resistance exercise (RE) activates signalling by the mammalian target of rapamycin (mTOR), and it has been suggested that rapamycin-sensitive mTOR signalling controls RE-induced changes in protein synthesis, ribosome biogenesis, autophagy, and the expression of peroxisome proliferator gamma coactivator 1 alpha (PGC-1α). However, direct evidence to support the aforementioned relationships is lacking. Therefore, in this study, we investigated the role of rapamycin-sensitive mTOR in the RE-induced activation of muscle protein synthesis, ribosome biogenesis, PGC-1α expression and hypertrophy. The results indicated that the inhibition of rapamycin-sensitive mTOR could prevent the induction of ribosome biogenesis by RE, but it only partially inhibited the activation of muscle protein synthesis. Likewise, the inhibition of rapamycin-sensitive mTOR only partially blocked the hypertrophic effects of chronic RE. Furthermore, both acute and chronic RE promoted an increase in PGC-1α expression and these alterations were not affected by the inhibition of rapamycin-sensitive mTOR. Combined, the results from this study not only establish that rapamycin-sensitive mTOR plays an important role in the RE-induced activation of protein synthesis and the induction of hypertrophy, but they also demonstrate that additional (rapamycin-sensitive mTOR-independent) mechanisms contribute to these fundamentally important events.

Multiple Incremental Decremental Learning of Support Vector Machines

Abstract: We propose a multiple incremental decremental algorithm of Support Vector Machine (SVM). Conventional single incremental decremental SVM can update the trained model efficiently when single data point is added to or removed from the training set. When we add and/or remove multiple data points, this algorithm is time-consuming because we need to repeatedly apply it to each data point. The proposed algorithm is computationally more efficient when multiple data points are added and/or removed simultaneously. The single incremental decremental algorithm is built on an optimization technique called parametric programming. We extend the idea and introduce multi-parametric programming for developing the proposed algorithm. Experimental results on synthetic and real data sets indicate that the proposed algorithm can significantly reduce the computational cost of multiple incremental decremental operation. Our approach is especially useful for online SVM learning in which we need to remove old data points and add new data points in a short amount of time.

Bifunctional Electrocatalytic Activity of Boron‐Doped Graphene Derived from Boron Carbide

Abstract: A single material that can perform water oxidation and oxygen reduction reactions (ORR), also called bifunctional catalyst, represents a novel concept that emerged from recent materials research and that has led to applications in new-generation energy-storage systems, such as regenerative fuel cells. Here, metal/metal-oxide free, doped graphene derived from rhombohedral boron carbide (B4C) is demonstrated to be an effective bifunctional catalyst for the first time. B4C, one of the hardest materials in nature next to diamond and cubic boron nitride, is converted and separated in bulk to form heteroatom (boron, B) doped graphene (BG, yield ≈7% by weight, after the first cycle). This structural conversion of B4C to graphene is accompanied by in situ boron doping and results in the formation of an electrochemically active material from a non-electrochemically active material, broadening its potential for application in various energy-related technologies. The electrocatalytic efficacy of BG is studied using various voltammetric techniques. The results show a four-electron transfer mechanism as well as a high methanol tolerance and stability towards ORR. The results are comparable to those from commercial 20 wt% Pt/C in terms of performance. Furthermore, the bifunctionality of the BG is also demonstrated by its performance in water oxidation.