On vehicle mobility in snow-covered terrain — 1. Problem development and requirements for analysis

The recent advances in vehicle industry and vehicle-to-everything communications are creating a huge potential market of intelligent vehicle applications, and exploiting vehicle mobility is of great importance in this field. Hence, this article proposes a novel vehicle mobility prediction algorithm to support intelligent vehicle applications. First, a theoretical analysis is given to quantitatively reveal the predictability of vehicle mobility. Based on the knowledge earned from theoretical analysis, a deep recurrent neural network (RNN)-based algorithm called DeepVM is proposed to predict vehicle mobility in a future period of several or tens of minutes. Comprehensive evaluations have been carried out based on the real taxi mobility data in Tokyo, Japan. The results have not only proved the correctness of our theoretical analysis but also validated that DeepVM can significantly improve the quality of vehicle mobility prediction compared with other state-of-art algorithms.

The prediction of human and vehicle mobility in a city is becoming an attracting field. This topic attracts researchers from broad fields including behavioral sciences, where understanding the complexity of the human mobility behavior is one of the hot topic, and also to the industrial partners, who apply such results to many beneficial applications. Recent progress to sensing human mobility via smartphones is boosting this trend. However, due to the complexity and context-dependence of human behavior and the incompleteness and noise of geospatial data collecting from various sensors, the prediction of human and vehicle mobility is still far from solved. This workshop aimed at collecting contributions on the cutting-edge studies in human mobility description, modeling, intelligent computational method which can advance the human and vehicle prediction research. Potential topics included, but were not limited to 1) The next location prediction of individual mobility, 2) The crowd or population mobility prediction, 3) Dynamics of pedestrians, 4) commute flow and migration flow, 5) Traffic congestion, road usage forecast and optimal vehicle routing, 6) Social event forecast using geospatial data, 7) Novel agent mobility simulators, and 8) Case studies of mobility estimation in academia as well as in industrial field.

Mobile crowdsensing is an emerging paradigm that selects users to complete sensing tasks. Recently, mobile vehicles are adopted to perform sensing data collection tasks in the urban city due to their ubiquity and mobility. In this article, we study how mobile vehicles can be optimally selected in order to collect maximum data from the urban environment in a future period of tens of minutes. We formulate the recruitment of vehicles as a maximum data limited budget problem. The application scenario is generalized to a realistic online setting where vehicles are continuously moving in real-time and the data center decides to recruit a set of vehicles immediately. A deep learning-based scheme through mobile vehicles (DLMV) is proposed to collect sensing data in the urban environment. We first propose a deep learning-based offline algorithm to predict vehicle mobility in a future time period. Furthermore, we propose a greedy online algorithm to recruit a subset of vehicles with a limited budget for the NP-Complete problem. Extensive experimental evaluations are conducted on the real mobility dataset in Rome. The results have not only verified the efficiency of our proposed solution but also validated that DLMV can improve the quantity of collected sensing data compared with other algorithms.

Vehicle-to-everything (V2X) communications have a great potential of enabling future intelligent vehicle applications, and exploiting vehicle mobility is of great importance in designing efficient V2X protocols and applications. Thus, this paper proposes a novel edge-assisted algorithm that makes use of the resources in both cloud and edge sides of vehicular networks to predict vehicle mobility. The proposed algorithm adopts a hybrid architecture of convolutional and recurrent neural networks, and enables computationally efficient transfer learning in each vehicle to generate its customized mobility prediction model. Extensive evaluations have been conducted by using a real taxi mobility data set that is obtained from a testbed deployed in Tokyo, Japan. The results have validated that, compared with other state-of-art algorithms, our proposal improves the prediction F1 score of vehicle mobility by more than 30%, especially for those vehicles that own a strong individual mobility preference.

In this study, a data-driven hydrodynamics model is proposed to enable quick prediction of vehicle mobility in shallow water, considering the effect of tire–soil interaction. To this end, a high-fidelity coupled vehicle–water interaction model using computational fluid dynamics (CFD) and multibody dynamics (MBD) solvers is developed to characterize the hydrodynamic loads exerted on a vehicle operated in shallow water, and it is used to generate training data for the data-driven hydrodynamics model. To account for the history-dependent hydrodynamic behavior, a long short-term memory (LSTM) neural network is introduced to incorporate effects of the historical variation of vehicle motion states as the input to the data-driven model, and it is used to predict hydrodynamic loads online exerted on vehicle components in the MBD mobility simulation. The impacts of hydrodynamic loads on the vehicle mobility capability in shallow water are examined for different water depths and incoming flow speeds using the high-fidelity coupled CFD-MBD model. Furthermore, it is demonstrated that the vehicle–water interaction behavior in scenarios not considered in the training data can be predicted using the proposed LSTM data-driven hydrodynamics model. However, the use of non-LSTM layers, which do not account for the sequential variation of vehicle motion states as the input, leads to an inaccurate prediction. A substantial computational speedup is achieved with the proposed LSTM-MBD vehicle–water interaction model while ensuring accuracy, compared to the computationally expensive high-fidelity coupled CFD-MBD model.

Approach to Local Environment Control for Stable Production of Strawberry

Local attachment ventilation system for the unearthed relic preservation area within site museum

The remarkable process of neuronal migration has long been viewed as the key event of corticogenesis. Before neurons complete their final cell division and begin to migrate out of the proliferative zones, however, many important decisions concerning their ultimate fate have already been made. These critical decisions are, in part, under local environmental control. The signals by which proliferating cortical precursor cells interact with each other and with their environment are still largely unknown, but, as these factors are dis covered, they will alter our view of corticogenesis and most likely provide new insights into causes of cerebral malformations. This review is intended to describe the repertoire of cellular interactions detected thus far among proliferating cortical cells and to discuss possible roles of cell-cell signaling pathways. The Neurosci entist 1:268-276, 1995

The role of connected vehicular networks has become vital for the future of smart and modern cities, as it can be envisioned as a stand alone connected network or a bridge between various networks, and end-users. Vehicular networks design provides safety, traffic control management, and cruise control services, among others. Many applications and protocols such as mobility management, service discovery, and routing were proposed over the past years to elevate the performance of vehicular networks communication and connectivity. In that context, understanding the vehicles' mobility characteristic, behavior, and pattern would assist those applications to become user-centric, adaptive, and proactive based on the dynamics of vehicles' projections. In this paper, we evaluate the performance of time-series forecasting techniques for vehicular mobility prediction. We first describe and extract vehicles mobility features into time-series sequences and discuss several machine learning techniques. Then, we compare the performance of recent time-series ML-based techniques, LSTM, and GRNN in terms of complexity and accuracy.

This paper is an introduction of terramechanics and its application in off-road vehicle mobility prediction. Firstly the concept of terramechanics is introduced. Terramechanics is an important branch of applied mechanics in which the interaction between the vehicle and its operating terrain is studied. Secondly, many well-known techniques in terramechanics, such as Cone Penetrometer technique, Bevameter technique are introduced. By comparing the RCI of soil with VCI of vehicle, trafficability of Off-road vehicle can be determined which predicts the “go/no go” property of vehicle. Furthermore, to predict the speed behavior of off-road vehicle over trafficable terrain, three types of analyzing methods, empirical, semi-empirical, computer-aided, are presented in detail. It is evident that computeraided method is much more efficient in simulating terrain-vehicle interaction and produces more realistic prediction in vehicle mobility. Therefore, it is meaningful to develop computer-aided method to provide engineers with powerful parametric analysis in vehicle performance and design.

Through advancement of the Vehicle-to-Everything (V2X) network, road safety, energy consumption, and traffic efficiency can be significantly improved. An accurate vehicle trajectory prediction benefits communication traffic management and network resource allocation for the real-time application of the V2X network. Recurrent neural networks and their variants have been reported in recent research to predict vehicle mobility. However, the spatial attribute of vehicle movement behavior has been overlooked, resulting in incomplete information utilization. To bridge this gap, we put forward for the first time a hierarchical trajectory prediction structure using the capsule neural network (CapsNet) with three sequential components. First, the geographic information is transformed into a grid map presentation, describing vehicle mobility distribution spatially and temporally. Second, CapsNet serves as the core model to embed local temporal and global spatial correlation through hierarchical capsules. Finally, extensive experiments conducted on actual taxi mobility data collected in Porto city (Portugal) and Singapore show that the proposed method outperforms the state-of-the-art methods.

Immovable historical relics in some archeology museums of China suffer deterioration due to their improper preservation environment. The existing environmental control systems used in archeology museums are often designed for the amenities of visitors, and these manipulated environments are often inappropriate for the conservation of abiotic relics. This paper points out that the large open space of the existing archeology museum could be a cause of deterioration of the relics from the point of view of indoor air convective flow. The paper illustrates the need to introduce a local pit environmental control, which could reintegrate a pit primitive environment for the preservation of the historical relics by using an air curtain system, orientated to isolate the unearthed relics, semiexposed in pits to the large gallery open space of the exhibition hall.

Résumé La nécessité de la représentation d’une performance environnementale, et d’un contrôle de gestion associé, renvoie à une succession de choix exercés par les dirigeants des entreprises, en interaction avec des acteurs externes dans un contexte de recherche de légitimité. Ces choix aboutissent à la construction d’indicateurs de performance environnementale (IPE) selon deux usages, un usage externe de communication environnementale générale, un usage interne de contrôle de performance environnementale locale, correspondant à deux logiques de représentation de la performance environnementale. Rechercher ces deux logiques à partir des mêmes indicateurs paraît problématique (d’après l’étude des perceptions sur ces outils dans deux groupes industriels français du secteur de la chimie).

The creation of bioactive substrates requires an appropriate interface molecular environment control and adequate biological species recognition with minimum nonspecific attachment. Herein, a straightforward approach utilizing chemical lift-off lithography to create a diluted self-assembled monolayer matrix for anchoring diverse biological probes is introduced. The strategy encompasses convenient operation, well-tunable pattern feature and size, large-area fabrication, high resolution and fidelity control, and the ability to functionalize versatile bioarrays. With the interface-contact-induced reaction, a preformed alkanethiol self-assembled monolayer on a Au surface is ruptured and a unique defect-rich diluted matrix is created. This post lift-off region is found to be suitable for insertion of a variety of biological probes, which allows for the creation of different types of bioactive substrates. Depending on the modifications to the experimental conditions, the processes of direct probe insertion, molecular structure change-required recognition, and bulky biological species binding are all accomplished with minimum nonspecific adhesion. Furthermore, multiplexed arrays via the integration of microfluidics are also achieved, which enables diverse applications of as-prepared substrates. By embracing the properties of well-tunable pattern feature dimension and geometry, great local molecular environment control, and wafer-scale fabrication characteristics, this chemical lift-off process has advanced conventional bioactive substrate fabrication into a more convenient route.

Overall and local environmental collaborative control based on personal comfort model and personal comfort system