RiskOracle: A Minute-level Citywide Traffic Accident Forecasting Framework

Real-time traffic accident forecasting is increasingly important for public safety and urban management (e.g., real-time safe route planning and emergency response deployment). Previous works on accident forecasting are often performed on hour levels, utilizing existed neural networks with static region-wise correlations taken into account. However, it is still challenging when the granularity of forecasting step improves as the highly dynamic nature of road network and inherent rareness of accident records in one training sample, which leads to biased results and zero-inflated issue. In this work, we propose a novel framework RiskOracle, to improve the prediction granularity to minute levels. Specifically, we first transform the zero-risk values in labels to fit the training network. Then, we propose the Differential Time-varying Graph neural network (DTGN) to capture the immediate changes of traffic status and dynamic inter-subregion correlations. Furthermore, we adopt multi-task and region selection schemes to highlight citywide most-likely accident subregions, bridging the gap between biased risk values and sporadic accident distribution. Extensive experiments on two real-world datasets demonstrate the effectiveness and scalability of our RiskOracle framework.Comment: 8 pages, 4 figures. Conference paper accepted by AAAI 202

Efficacy mechanisms research progress of the active components in the characteristic woody edible oils

Woody edible oils are a type of vegetable oil. Woody edible oils like olive oil have greater quantities of unsaturated fatty acids (UFAs), particularly essential FAs, as well as vitamin E, phytosterols, and other nutrients that are becoming more vital in human health. As a result, finding high-quality woody oil resource plants is critical to ensuring enough edible oil supply. As six novel woody crops, Paeonia suffruticosa, Plukenetia volubilis, Acer truncatum, Olea europaea, Camellia sinensis, and Camellia oleifera are characterized by high oil production, widespread cultivation, adaptability, and various active ingredients. The six woody crop oils contain UFAs (e.g., α-linolenic acid, oleic acid, and linoleic acid), vitamin E, polyphenols, phytosterols, and so forth. The presence of these active ingredients confers anti-inflammatory, antioxidant, cholesterol and lipid metabolism regulating, blood lipid lowering, immune boosting, memory improving, intestinal flora regulating, and other properties to the oils, which are beneficial to body health. This article examined in depth the seed resources, FA composition, active component kinds, active ingredient efficacy mechanism, and physiological impacts of these six novel woody crop oils. These developments lay a solid platform for further study and development of these woody oil crops.This work was supported by the Key Research and Development Program of Zhejiang Province (No. 2021C02002), Zhejiang Provincial Natural Sciences Foundation of China under Grant (No. LZ22C200006), Top young talents of the ten thousand talents program of Zhejiang Province (ZJWR0308016), Key R&D projects in Zhejiang Province (2023C04010), and Zhejiang Basic Public Welfare Research Project (LGN21C200006). Agusti Romero acknowledges financial support from the CERCA Program from the Generalitat of Catalonia. We would like to thank all contributors of the current study for their concepts, ideas, contribution, and provision.info:eu-repo/semantics/publishedVersio

Towards a muon collider

A muon collider would enable the big jump ahead in energy reach that is needed for a fruitful exploration of fundamental interactions. The challenges of producing muon collisions at high luminosity and 10 TeV centre of mass energy are being investigated by the recently-formed International Muon Collider Collaboration. This Review summarises the status and the recent advances on muon colliders design, physics and detector studies. The aim is to provide a global perspective of the field and to outline directions for future work

Towards a Muon Collider

A muon collider would enable the big jump ahead in energy reach that is needed for a fruitful exploration of fundamental interactions. The challenges of producing muon collisions at high luminosity and 10 TeV centre of mass energy are being investigated by the recently-formed International Muon Collider Collaboration. This Review summarises the status and the recent advances on muon colliders design, physics and detector studies. The aim is to provide a global perspective of the field and to outline directions for future work.Comment: 118 pages, 103 figure

Precision Higgs physics at the CEPC

The discovery of the Higgs boson with its mass around 125 GeV by the ATLAS and CMS Collaborations marked the beginning of a new era in high energy physics. The Higgs boson will be the subject of extensive studies of the ongoing LHC program. At the same time, lepton collider based Higgs factories have been proposed as a possible next step beyond the LHC, with its main goal to precisely measure the properties of the Higgs boson and probe potential new physics associated with the Higgs boson. The Circular Electron Positron Collider~(CEPC) is one of such proposed Higgs factories. The CEPC is an $e^+e^-$ circular collider proposed by and to be hosted in China. Located in a tunnel of approximately 100~km in circumference, it will operate at a center-of-mass energy of 240~GeV as the Higgs factory. In this paper, we present the first estimates on the precision of the Higgs boson property measurements achievable at the CEPC and discuss implications of these measurements.Comment: 46 pages, 37 figure

Towards a muon collider

A muon collider would enable the big jump ahead in energy reach that is needed for a fruitful exploration of fundamental interactions. The challenges of producing muon collisions at high luminosity and 10 TeV centre of mass energy are being investigated by the recently-formed International Muon Collider Collaboration. This Review summarises the status and the recent advances on muon colliders design, physics and detector studies. The aim is to provide a global perspective of the field and to outline directions for future work

Erratum: Towards a muon collider

The original online version of this article was revised: The additional reference [139] has been added. Tao Han’s ORICD ID has been incorrectly assigned to Chengcheng Han and Chengcheng Han’s ORCID ID to Tao Han. Yang Ma’s ORCID ID has been incorrectly assigned to Lianliang Ma, and Lianliang Ma’s ORCID ID to Yang Ma. The original article has been corrected

Analysis of traffic-related air pollution using Shanghai road traffic state index

Recently, due to the rapid economic development and the acceleration of urbanization, haze events have occurred frequently in most parts of China, which has attracted widespread attention at home and abroad. This study presents a statistical summary of air pollution concentrations and traffic state indexes from August 2014 to April 2015 in Shanghai, China. We find PM2.5 concentrations show a remarkable seasonal variability with ``winter > spring > autumn > summer'' in Shanghai. Concentrations of PM2.5, CO, NO2, SO2 are generally higher in winter than in summer due to enhanced anthropogenic and biogenic emissions and unsuitable meteorological conditions for pollution diffusion, contrary to concentrations of O3. The weekly changes of NO2 are highly consistent with that of traffic state indexes, suggesting a significant contribution to NO2 concentrations from road traffic emissions. Two moderate peaks are found in the diurnal variability of concentrations of PM2.5, CO and NO2, similar to road traffic indexes, indicating the important contribution of road traffic emissions every day. We find that SO2, NO2, CO are the dominant factors contributing to PM2.5 pollution, where NO2 and CO are mainly from road traffic emissions. The average annual Spearman correlation coefficient is r = 0.689 (p < 0.01), r = 0.564 (p < 0.01), r = 0.812 (p < 0.01), respectively

Regional Short-term Micro-climate Air Temperature Prediction with CBPNN

This paper proposes a novel short-term air temperature prediction with three-layer Back Propagation Neural Network (BPNN) for the regional application of next 1-12 hours. With the continuous collection of eight real-time micro-climate parameters in the experimentation and demonstration stations in our university, the Multiple Stepwise Regression (MSR) is employed to screen the original historical data to find the parameter factors with greater contribution rate. On the basis of the Root Mean Square Error (RMSE) value evaluating the optimal fitting degree of the stepwise regression, the Levenberg-Marquardt (LM) and the Resilient Propagation (R-Prop) training algorithm are employed to construct a Combined BPNN (CBPNN) with two MSR inputs. Compared with the known micro-climate data sets, the Mean Absolute Error (MAE) is to evaluate the applicability of CBPNN prediction model. The experimentation shows that the MAE is within 4°C in the next 12 hours. This proposal will be deployed in stations in our university for extreme weather warnings, and could be applied to some regional short-term parameter prediction for the future agricultural production service