Flexible multiterminal photoelectronic neurotransistors based on self‐assembled rubber semiconductors for spatiotemporal information processing

A significant step toward constructing high‐efficiency neuromorphic systems is the electronic emulation of advanced synaptic functions of the human brain.While previous studies have focused on mimicking the basic functions of synapses using single‐gate transistors,multigate transistors offer an opportunity to simulate more complex and advanced memory‐forming behaviors in biological synapses.In this study,a simple and general method is used to assemble rubber semiconductors into suspended two‐phase composite films that are transferred to the surface of the ion‐conducting membrane to fabricate flexible multiterminal photoelectronic neurotransistors.The suspended ion conductive film is used as the gate dielectrics and supporting substrate.The prepared devices exhibit excellent electrical stability and mechanical flexibility after being bent.Basic photoelectronic synaptic behavior and pulse‐dependent plasticity are emulated.Furthermore,the device realizes the spatiotemporally integrated electrical and optical stimuli to mimic spatiotemporal information processing.This study provides a promising direction for constructing more complex spiking neural networks and more powerful neuromorphic systems with brain‐like dynamic spatiotemporal processing functions.

Stream Segmentation-A Data Fusion Approach for Sensor Networks

Sensor networks provide means to link people with real world by processing data in real time collected from real-world and routing the query results to the right people. Application examples include continuous monitoring of environment, building infrastructures and human health. Many researchers view the sensor networks as databases, and the monitoring tasks are performed as subscriptions, queries, and alert. However, this point is not precise. First, databases can only deal with well-formed data types, with well-defined schema for their interpretation, while the raw data collected by the sensor networks, in most cases, do not fit to this requirement. Second, sensor networks have to deal with very dynamic targets, environment and resources, while databases are more static. In order to fill this gap between sensor networks and databases, we propose a novel approach, referred to as 'spatiotemporal data stream segmentation', or 'stream segmentation' for short, to address the dynamic nature and deal with 'raw' data of sensor networks. Stream segmentation is defined using Bayesian Networks in the context of sensor networks, and two application examples are given to demonstrate the usefulness of the approach.

Three-dimensional virtual representation for the whole process of dam-break floods from a geospatial storytelling perspective

The objective of disaster scenes is to share location-based risk information to a large audience in an effective and intuitive way.However,current studies on three-dimensional(3D)representation for dam-break floods have the following limitations:(1)they are lacking a reasonable logic to organize the whole process of dam-break floods,(2)they present information in a way that cannot be easily understood by laypersons.Geospatial storytelling helps to create exciting experiences and to explain complex relationships of geospatial phenomena.This article proposes a three-dimensional virtual representation method for the whole process of dam-break floods from a geospatial storytelling perspective.The creation of a storyline and a storytelling-oriented representation of dam-break floods are discussed in detail.Finally,a prototype system based on WebGL is developed to conduct an experiment analysis.The results of the experiment show that the proposed method can effectively support 3D representation of the spatiotemporal process of dam-break floods.Furthermore,the statistical results indicate that the storytelling is useful for assisting participants in understanding the occurrence and development of dam-break floods,and is applicable to the popularization of disaster science for the general public.

A virtual geographic environment for dynamic simulation and analysis of tailings dam failure

It is of great significance for disaster prevention and mitigation to carry out disaster simulations for dam failure accidents in advance,but at present,there are few professional systems for disaster simulations of tailings dams.In this paper,we focused on the construction of a virtual geographic environment(VGE)system that provides an effective tool for visualizing the dam-break process of a tailings pond.The dam-break numerical model of the tailings dam based on computational fluid dynamics(CFD)was integrated into the VGE system.The infrastructure of the VGE was supported by a 3-D geographic information system(GIS)with a user-friendly interface for the initiation,visualization,and analysis of the dynamic process of tailings dam failure.Key technologies,including the integration of numerical models,rendering of large-scale scenes,and optimizations of disaster simulation and visualization,were discussed in detail.In the prototype system,information on the run-out path,travel distance,etc.can be obtained to visually describe the flow motion released by two dam failure cases.The simulation results showed that the VGE can be used for the multidimensional,dynamic and interactive visualization of dam-break disasters,and can also be useful for assessing the risk associated with tailings dams.