Search

Humanities and Social Sciences (HSS) are undergoing the transformation of spatialization and quantification. Geo-computation, with geoinformatics (including RS: Remote Sensing; GIS: Geographical Information System; GNSS: Global Navigation Satellite System), provides effective computational and spatialization methods and tools for HSS. Spatial Humanities and Geo-computation for Social Sciences (SH&GSS) is a field coupling geo-computation, and geoinformatics, with HSS. This special issue accepted a set of contributions highlighting recent advances in methodologies and applications of SH&GSS, which are related to sentiment spatial analysis from social media data, emotional change spatial analysis from news data, spatial analysis of social media related to COVID-19, crime spatiotemporal analysis, “double evaluation” for Land Use/Land Cover (LUCC), Specially Protected Natural Areas (SPNA) analysis, editing behavior analysis of Volunteered Geographic Information (VGI), electricity consumption anomaly detection, First and Last Mile Problem (FLMP) of public transport, and spatial interaction network analysis for crude oil trade network. Based on these related researches, we aim to present an overview of SH&GSS, and propose some future research directions for SH&HSS.

In recent years, the rapid development of brain‐inspired neuromorphic systems has created an imperative demand for artificial photonic synapses that operate with low power consumption. In this study, a self‐driven memristor synapse based on gallium oxide (Ga2O3) nanowires is proposed and demonstrated successfully. This memristor synapse is capable of emulating a range of functionalities of biological synapses when exposed to 255 nm light stimulation. These functionalities encompass peak time‐dependent plasticity, pulse facilitation, and memory learning capabilities. It exhibits an ultrahigh paired‐pulse facilitation index of 158, indicating exceptional learning performance. The transition from short‐term memory to long‐term memory can be attributed to the remarkable relearning capabilities. Furthermore, the potential applications of the memristor synapse is showcased through the successful manipulation of a humanoid intelligent robot. Upon establishing artificial intelligence (AI) systems, the control commands originating from the synaptic device can drive the humanoid robot to perform various actions. Based on the memristor synapses, the autonomous feedback system of the humanoid robot facilitates a good collaboration between robotic actions and bio‐inspired light perception. Therefore, this research opens up an effective way to advance the development of neuromorphic computing technologies, AI systems, and intelligent robots that demand ultra‐low energy consumption. [ABSTRACT FROM AUTHOR]

The fast development of brain-inspired neuromorphic computing systems has stimulated urgent requirements for artificial synapses with low-power consumption. In this work, a photonic synaptic device based on (Al,Ga)N nanowire/graphene heterojunction has been proposed and demonstrated successfully. In the artificial synaptic device, the incident light, the nanowire/graphene heterojunction, and the light-generated carriers play the roles of action potential, pre-synaptic/post-synaptic membrane, and neurotransmitter in a biological synapse, respectively. As a key synaptic function, the paired pulse facilitation index of the photonic synapse can reach 202%, which can be modulated by the interval time between two adjacent light pulses. It is found that the graphene defects, the surface band bending, and the Al vacancies on the surface of (Al,Ga)N nanowires can be the key reasons contributing to the synaptic characteristics of artificial photonic devices. Hence, the dynamic "learning–forgetting" performance of the artificial synaptic device can resemble the "learning–forgetting" behavior of the human brain. Furthermore, the hand-written digits are set up to mimic a typical characteristic of human perceptual learning. After only three training epochs, the simulated network can achieve a high recognition rate of over 90% based on the experimental conductance for long-term potentiation and long-term depression. In supervised learning processes, such few training times are beneficial to reduce energy consumption significantly. Therefore, in the area of neuromorphic computing technology and artificial intelligence systems requiring low-power consumption, this work paves a potential way to develop the optoelectronic synapse based on semiconductor nanowires. [ABSTRACT FROM AUTHOR]