JR East aims for driverless Shinkansen operation

East Japan Railway Company(JR East)is aiming to“realize driverless train operation”as one of the key measures to respond to rapid changes in the business environment.Currently,Automatic Train Operation(ATO)equipment is not installed on the Shinkansen,but there are plans to introduce ATO or driverless operation in the near future.From 2018-2021,the Ministry of Land,Infrastructure,Transport and Tourism(MLIT)held the“ATO Technology Study Group for Railways”in which the concept of technical requirements necessary for driverless operation was discussed.In 2021,JR East conducted the GOA4 demonstration test on the Joetsu Shinkansen.In this test,we were able to confirm the basic functions of Shinkansen vehicles such as automatic departure control,speed control,fixed position stop control,and remote stop control using ATO.We aim to realize unattended operation(GOA4)for deadhead trains between Niigata Station and the Niigata Shinkansen Rolling Stock Center by the end of the 2020 s,and driverless operation(GOA3)for passenger trains of the Joetsu Shinkansen by the mid-2030s and continue to develop the necessary technologies and build systems.

A time-domain estimation method of rapidly time-varying channels for OFDM-based LTE-R systems

This paper addresses the performance of fast doubly selective fading channel estimation combined with InterCarrier Interference (ICI) cancellation for Long Term Evolution (LTE) communication platform in the High Speed Railway (HSR) environment. We consider the Channel Impulse Response (CIR) coefficients with a critical Doppler frequency shift and multi-path fading that were taken from the WINNER II channel model and the D2a propagation scenario, where the conditions of HSR are analyzed. As multi-path fading increases and the channel varies in the order of the symbol period, we first propose a novel approach for designing a pilot symbol structure in the time domain. Then, we describe the deployment of the proposed pilot symbol structure to estimate the channel in the time domain. Channel information corresponding to the data positions is obtained by linear interpolation. In each OFDM symbol, the slope and the initial value for establishing an interpolation function are estimated to adapt to the time variation of the channel. An accurate estimate of channel state information is used for the purpose of ICI cancellation. The simulation results show that the channel estimated by our proposed method can follow the real channel well, even in a very high Doppler frequency. The estimation method in terms of Mean Squared Error (MSE) significantly outperforms the state-of-the-art methods. The combination of our channel estimator with several interference cancelers provides a considerably better system performance than that achieved when frequency channel estimation is used.

国际合作情况下的锂电池的寿命测试程序(英文)

为了满足不同的技术和经济目标,从轻度混合动力、插电式混合动力到全电池动力的电动汽车,都将依赖于新型的、先进的(如基于锂的)蓄电池。这些电池在各种应用条件下的性能预测和寿命表征费工、费时,目前尚未得到充分的发展。一些国家已投入资金和人力进行相关的研究,其实通过国际合作,这些努力和花费也许能发挥更大的作用,例如目前正在国际能源机构(The International Energy Agency,IEA)框架内开展的准备工作。正在致力于开发一套标准化的、加速的测试程序,将允许各个测试机构合作分析电池的测量数据。该文评述了欧洲、日本和美国在加速寿命测试程序上的最新进展。以国际合作为目标,搜集、对比和分析现有的测试程序。

Parameters Identification of Stochastic Nonstationary Process Used in Earthquake Modelling

This paper proposes a new deterministic envelope function to define non-stationary stochastic processes modeling seismic ground motion accelerations. The proposed envelope function modulates the amplitude of the time history of a stationary filtered white noise to properly represent the amplitude variations in the time histories of the ground motion accelerations. This function depends on two basic seismological indices: the Peak Ground Acceleration (PGA) and the kind of soil. These indices are widely used in earthquake engineering. Firstly, the envelope function is defined analytically from the Saragoni Hart’s function. Then its parameters are identified for a set of selected real records of earthquake collected in PEER Next Generation Attenuation database. Finally, functions of the parameters depending on the Peak Ground Acceleration and the kind of soil are defined from these identified values of the parameters of the envelope function through a regression analysis.

Perspectives on endogenous and exogenous tissue engineering following injury to tissues of the knee

The knee is a multi-component organ system comprised of several tissues which function coordinately to provide mobility. Injury to any one component compromises the integrity of the system and leads to adaptation of the other components. Over time, such events often lead to dysfunction and degeneration of the knee. Therefore, there has been considerable research emphasis to repair injured components in the knee including cartilage, menisci, and ligaments. Approaches to improving healing and repair/regeneration of knee tissues have included surgery, anti-sense gene therapy, injection of growth factors and inflammatory cytokine antagonists, transplantation of in vitro expanded chondrocytes, enhancement of endogenous cells via microfracture, injection of mesenchymal stem cells, and implantation of in vitro tissue engineered constructs. Some of these approaches have lead to temporary improvement in knee functioning, while others offer the potential to restore function and tissue integrity for longer periods of time. This article will review the status of many of these approaches, and provide a perspective on their limitations and potential to contribute to restoration of knee function across the lifespan.

Human Heterogeneity and Survival of the Species: How Did It Arise and Being Sustained?—The Conundrum Facing Researchers

Current humans, Homo sapiens, are genetically and epigenetically very heterogeneous, and subsequently also biologically and physiologically heterogeneous. Much of this heterogeneity likely arose during evolutionary processes, via various iterations of humanoid lineages, and interbreeding. While advantageous from a species perspective, the heterogeneity of humans poses serious challenges to researchers attempting to understand complex disease processes. While the use of inbred preclinical models makes the research effort more effective at some levels, the findings are often not translatable to the more heterogeneous human populations. This conundrum leads to considerable research activity with inbred preclinical models, but modest progress in understanding many complex human conditions and diseases. This article discusses several of the issues around human heterogeneity and the need to change some directions in preclinical model research. Using newer Artificial Intelligence and Machine Learning approaches can begin to deduce important elements from the complexity of human heterogeneity.

Non-saturation Throughput of S-ALOHA Using the Time-Scale Decomposition Technique

S-ALOHA （Slotted ALOHA） random access protocol is a widely used protocol mainly for the transmission of short packets in wireless networks. Most papers consider either an infinite population model where the impact of the backoff protocol cannot be adequately evaluated or a finite population model where the number of nodes is fixed. In this letter, a combination of both models is proposed using the time-scale decomposition technique. This methodology allows to study the system under more realistic conditions where the dynamics of users enter and leaving the system are reflected on the performance of the system as well as the impact of the backoff protocol. Also, it allows studying the system in non-saturation conditions. The proposed methodology divides the analysis in two parts： packet-level and connection-level. This analysis renders suitable results when the time scale of the packet level and connection level statistics is different. On the other hand, when these scales are similar, the proposed methodology is no longer suited.

Research on Marketing Channel of Mobile Manufacturer Based on Analytic Hierarchy Process

Research on ＂marketing channel＂ of mobile attracts much attention in these years, but there＇re only few articles referring to how to optimize the disposition of channel resources for mobile manufacturers. Based on a typically multiplex marketing channel system of mobile manufacturer, the analytic hierarchy process （~HP） structure model is established. Through the judgment matrix, simple and total hierarchy arrangement, consistent test, this paper gets the weight of each kind of marketing channel of mobile manufacturer, it provides the practical reference value for mobile manufacturers to distribute resources of marketing channels.

Donut and spheroid mitochondria: eating, regenerating or trash them out?

In response to stress,mitochondrion undergoes constant morphological changes,including the formation of donut and spheroid mitochondria,and both are believed to be implicated in its biological functions.Mitochondria are critical for cellular metabolic homeostasis and cell survival and death in eukaryotic cells.Mitochondria are dynamic organelles that constantly undergo fission and fusion.Mitochondrial homeostasis is tightly regulated by mitophagy for the removal of damaged or excess mitochondria and by mitochondria biogenies of new mitochondria.Mitochondria can also undergo other morphological transformation,such as formation of donut-like mitochondria or mitochondrial spheroids,and can also be secreted into the extracellular spaces.Here we discuss the mechanistic insights and physiological relevance of the donut-like mitochondria or mitochondrial spheroids and the secretion of mitochondria in cell biology.

Analyzing machine learning models to accelerate generation of fundamental materials insights

Machine learning for materials science envisions the acceleration of basic science research through automated identification of key data relationships to augment human interpretation and gain scientific understanding.A primary role of scientists is extraction of fundamental knowledge from data,and we demonstrate that this extraction can be accelerated using neural networks via analysis of the trained data model itself rather than its application as a prediction tool.Convolutional neural networks excel at modeling complex data relationships in multi-dimensional parameter spaces,such as that mapped by a combinatorial materials science experiment.Measuring a performance metric in a given materials space provides direct information about(locally)optimal materials but not the underlying materials science that gives rise to the variation in performance.By building a model that predicts performance(in this case photoelectrochemical power generation of a solar fuels photoanode)from materials parameters(in this case composition and Raman signal),subsequent analysis of gradients in the trained model reveals key data relationships that are not readily identified by human inspection or traditional statistical analyses.Human interpretation of these key relationships produces the desired fundamental understanding,demonstrating a framework in which machine learning accelerates data interpretation by leveraging the expertize of the human scientist.We also demonstrate the use of neural network gradient analysis to automate prediction of the directions in parameter space,such as the addition of specific alloying elements,that may increase performance by moving beyond the confines of existing data.

Multi-component background learning automates signal detection for spectroscopic data

Automated experimentation has yielded data acquisition rates that supersede human processing capabilities.Artificial Intelligence offers new possibilities for automating data interpretation to generate large,high-quality datasets.Background subtraction is a long-standing challenge,particularly in settings where multiple sources of the background signal coexist,and automatic extraction of signals of interest from measured signals accelerates data interpretation.Herein,we present an unsupervised probabilistic learning approach that analyzes large data collections to identify multiple background sources and establish the probability that any given data point contains a signal of interest.The approach is demonstrated on X-ray diffraction and Raman spectroscopy data and is suitable to any type of data where the signal of interest is a positive addition to the background signals.While the model can incorporate prior knowledge,it does not require knowledge of the signals since the shapes of the background signals,the noise levels,and the signal of interest are simultaneously learned via a probabilistic matrix factorization framework.Automated identification of interpretable signals by unsupervised probabilistic learning avoids the injection of human bias and expedites signal extraction in large datasets,a transformative capability with many applications in the physical sciences and beyond.

Research on the Internet Marketing of Handset Based on the Guiding Interface Layout Design

The marketing channel of handset is always a hot point. This paper studies the influence of the website＇s guiding interface design on the internet marketing of handset; establishes the conceptual model about the relationships between page layout, page views and internet marketing performance; puts forward some operable suggestions for website master and handset manufacturer.