Waseda University

About: Waseda University is a education organization based out in Tokyo, Japan. It is known for research contribution in the topics: Large Hadron Collider & Catalysis. The organization has 24220 authors who have published 46859 publications receiving 837855 citations. The organization is also known as: Waseda daigaku & Sōdai.

Measurement of the inclusive jet cross-section in proton-proton collisions at √s=7 $$ TeV using 4.5 fb−1 of data with the ATLAS detector

Abstract: The inclusive jet cross-section is measured in proton-proton collisions at a centre-of-mass energy of 7 TeV using a data set corresponding to an integrated luminosity of 4.5 fb−1 collected with the ATLAS detector at the Large Hadron Collider in 2011. Jets are identified using the anti-k t algorithm with radius parameter values of 0.4 and 0.6. The double-differential cross-sections are presented as a function of the jet transverse momentum and the jet rapidity, covering jet transverse momenta from 100 GeV to 2 TeV. Next-to-leading-order QCD calculations corrected for non-perturbative effects and electroweak effects, as well as Monte Carlo simulations with next-to-leading-order matrix elements interfaced to parton showering, are compared to the measured cross-sections. A quantitative comparison of the measured cross-sections to the QCD calculations using several sets of parton distribution functions is performed.

Bifidobacterium-Rich Fecal Donor May Be a Positive Predictor for Successful Fecal Microbiota Transplantation in Patients with Irritable Bowel Syndrome.

Abstract: Background/Aims: Dysbiosis is associated with various systemic disorders including irritable bowel syndrome (IBS). Fecal microbiota transplantation (FMT) might re

Exercise-Induced Oxidative Stress and the Effects of Antioxidant Intake from a Physiological Viewpoint

Abstract: It is well established that the increase in reactive oxygen species (ROS) and free radicals production during exercise has both positive and negative physiological effects Among them, the present review focuses on oxidative stress caused by acute exercise, mainly on evidence in healthy individuals This review also summarizes findings on the determinants of exercise-induced oxidative stress and sources of free radical production Moreover, we outline the effects of antioxidant supplementation on exercise-induced oxidative stress, which have been studied extensively Finally, the following review briefly summarizes future tasks in the field of redox biology of exercise In principle, this review covers findings for the whole body, and describes human trials and animal experiments separately

Scheduling jobs and maintenances in flexible job shop with a hybrid genetic algorithm

Abstract: Most flexible job shop scheduling models assume that the machines are available all of the time. However, in most realistic situations, machines may be unavailable due to maintenances, pre-schedules and so on. In this paper, we study the flexible job shop scheduling problem with availability constraints. The availability constraints are non-fixed in that the completion time of the maintenance tasks is not fixed and has to be determined during the scheduling procedure. We then propose a hybrid genetic algorithm to solve the flexible job shop scheduling problem with non-fixed availability constraints (fJSP-nfa). The genetic algorithm uses an innovative representation method and applies genetic operations in phenotype space in order to enhance the inheritability. We also define two kinds of neighbourhood for the problem based on the concept of critical path. A local search procedure is then integrated under the framework of the genetic algorithm. Representative flexible job shop scheduling benchmark problems and fJSP-nfa problems are solved in order to test the effectiveness and efficiency of the suggested methodology.

Deep-Learning-Enhanced Human Activity Recognition for Internet of Healthcare Things

Abstract: Along with the advancement of several emerging computing paradigms and technologies, such as cloud computing, mobile computing, artificial intelligence, and big data, Internet of Things (IoT) technologies have been applied in a variety of fields. In particular, the Internet of Healthcare Things (IoHT) is becoming increasingly important in human activity recognition (HAR) due to the rapid development of wearable and mobile devices. In this article, we focus on the deep-learning-enhanced HAR in IoHT environments. A semisupervised deep learning framework is designed and built for more accurate HAR, which efficiently uses and analyzes the weakly labeled sensor data to train the classifier learning model. To better solve the problem of the inadequately labeled sample, an intelligent autolabeling scheme based on deep $Q$ -network (DQN) is developed with a newly designed distance-based reward rule which can improve the learning efficiency in IoT environments. A multisensor based data fusion mechanism is then developed to seamlessly integrate the on-body sensor data, context sensor data, and personal profile data together, and a long short-term memory (LSTM)-based classification method is proposed to identify fine-grained patterns according to the high-level features contextually extracted from the sequential motion data. Finally, experiments and evaluations are conducted to demonstrate the usefulness and effectiveness of the proposed method using real-world data.