Online Traffic Accident Spatial-Temporal Post-Impact Prediction Model on Highways Based on Spiking Neural Networks

Traffic accident management as an approach to improve public security and reduce economic losses has received public attention for a long time, among which traffic accidents post-impact prediction (TAPIP) is one of the most important procedures. However, existing systems and methodologies for TAPIP are insufficient for addressing the problem. The drawbacks include ignoring the recovery process after clearance and failing to make comprehensive prediction in both time and space domain. To this end, we build a 3-stage TAPIP model on highways, using the technology of spiking neural networks (SNNs) and convolutional neural networks (CNNs). By dividing the accident lifetime into two phases, i.e., clean-up phase and recovery phase, the model extracts characteristics in each phase and achieves prediction of spatial-temporal post-impact variables (e.g., clean-up time, recovery time, and accumulative queue length). The framework takes advantage of SNNs to efficiently capture accident spatial-temporal features and CNNs to precisely represent the traffic environment. Integrated with an adaptation and updating mechanism, the whole system works autonomously in an online manner that continues to self-improve during usage. By testing with a new dataset CASTA pertaining to California statewide traffic accidents on highways collected in four years, we prove that the proposed model achieves higher prediction accuracy than other methods (e.g., KNN, shockwave theory, and ANNs). This work is the introduction of SNNs in the traffic accident prediction domain and also a complete description of post-impact in the whole accident lifetime.

Application of Artificial Neural Network (ANN) for Prediction and Optimization of Blast-Induced Impacts

Drilling and blasting remain the preferred technique used for rock mass breaking in mining and construction projects compared to other methods from an economic and productivity point of view. However, rock mass breaking utilizes only a maximum of 30% of the blast explosive energy, and around 70% is lost as waste, thus creating negative impacts on the safety and surrounding environment. Blast-induced impact prediction has become very demonstrated in recent research as a recommended solution to optimize blasting operation, increase efficiency, and mitigate safety and environmental concerns. Artificial neural networks (ANN) were recently introduced as a computing approach to design the computational model of blast-induced fragmentation and other impacts with proven superior capability. This paper highlights and discusses the research articles conducted and published in this field among the literature. The prediction models of rock fragmentation and some blast-induced effects, including flyrock, ground vibration, and back-break, were detailed investigated in this review. The literature showed that applying the artificial neural network for blast events prediction is a practical way to achieve optimized blasting operation with reduced undesirable effects. At the same time, the examined papers indicate a lack of articles focused on blast-induced fragmentation prediction using the ANN technique despite its significant importance in the overall economy of whole mining operations. As well, the investigation revealed some lack of research that predicted more than one blast-induced impact.

The future of social entrepreneurship: modelling and predicting social impact

PurposePredicting the impact of social entrepreneurship is crucial as it can help social entrepreneurs to determine the achievement of their social mission and performance. However, there is a lack of existing social entrepreneurship models to predict social enterprises' social impacts. This paper aims to propose the social impact prediction model for social entrepreneurs using a data analytic approach.Design/methodology/approachThis study implemented an experimental method using three different algorithms: naive Bayes, k-nearest neighbor and J48 decision tree algorithms to develop and test the social impact prediction model.FindingsThe accurate result of the developed social impact prediction model is based on the list of identified social impact prediction variables that have been evaluated by social entrepreneurship experts. Based on the three algorithms' implementation of the model, the results showed that naive Bayes is the best performance classifier for social impact prediction accuracy.Research limitations/implicationsAlthough there are three categories of social entrepreneurship impact, this research only focuses on social impact. There will be a bright future of social entrepreneurship if the research can focus on all three social entrepreneurship categories. Future research in this area could look beyond these three categories of social entrepreneurship, so the prediction of social impact will be broader. The prospective researcher also can look beyond the difference and similarities of economic, social impacts and environmental impacts and study the overall perspective on those impacts.Originality/valueThis paper fulfills the need for the Malaysian social entrepreneurship blueprint to design the social impact in social entrepreneurship. There are none of the prediction models that can be used in predicting social impact in Malaysia. This study also contributes to social entrepreneur researchers, as the new social impact prediction variables found can be used in predicting social impact in social entrepreneurship in the future, which may lead to the significance of the prediction performance.

A FRAMEWORK FOR PREDICTING POTENTIAL PRODUCT IMPACT DURING PRODUCT DESIGN

The impact of products is becoming a topic of concern in society. Product impact may fall under the categories of economic, environmental, or social impact and is defined by the effect of a product on day-to-day life. Design teams lack sufficient tools to predict the impact of products they are designing. In this paper we present a framework for the prediction of product impact during product design. This framework integrates models of the product, scenario, society, and impact into an agent-based model to predict product impact. Although this paper focuses on social impact, the framework can also be applied to economic or environmental impacts. An illustration of using the framework is also presented. Agent-based modeling has been used previously for adoption models, but it has not been extended to predict product impact. Having tools for impact prediction allows for optimizing the product design parameters to increase potential positive impact and reduce potential negative impact.

Tsunami Impact Prediction System Based on TsunAWI Inundation Data

It is very important for tsunami early warning systems to provide inundation predictions within a short period of time. Inundation is one of the factors that directly cause destruction and damage from tsunamis. This research proposes a tsunami impact prediction system based on inundation data analysis. The inundation data used in this analysis were obtained from the tsunami modeling called TsunAWI. The inundation data analysis refers to the coastal forecast zones for each city/regency that are currently used in the Indonesia Tsunami Early Warning System (InaTEWS). The data analysis process comprises data collection, data transformation, data analysis (through GIS analysis, predictive analysis, and simple statistical analysis), and data integration, ultimately producing a pre-calculated inundation database for inundation prediction and tsunami impact prediction. As the outcome, the tsunami impact prediction system provides estimations of the flow depth and inundation distance for each city/regency incorporated into generated tsunami warning bulletins and impact predictions based on the Integrated Tsunami Intensity Scale (ITIS-2012). In addition, the system provides automatic sea level anomaly detection from tide gauge sensors by applying a tsunami detection algorithm. Finally, the contribution of this research is expected to bring enhancements to the tsunami warning products of InaTEWS.

Advances in Blast-Induced Impact Prediction—A Review of Machine Learning Applications

Rock fragmentation in mining and construction industries is widely achieved using drilling and blasting technique. The technique remains the most effective and efficient means of breaking down rock mass into smaller pieces. However, apart from its intended purpose of rock breakage, throw, and heave, blasting operations generate adverse impacts, such as ground vibration, airblast, flyrock, fumes, and noise, that have significant operational and environmental implications on mining activities. Consequently, blast impact studies are conducted to determine an optimum blast design that can maximize the desirable impacts and minimize the undesirable ones. To achieve this objective, several blast impact estimation empirical models have been developed. However, despite being the industry benchmark, empirical model results are based on a limited number of factors affecting the outcomes of a blast. As a result, modern-day researchers are employing machine learning (ML) techniques for blast impact prediction. The ML approach can incorporate several factors affecting the outcomes of a blast, and therefore, it is preferred over empirical and other statistical methods. This paper reviews the various blast impacts and their prediction models with a focus on empirical and machine learning methods. The details of the prediction methods for various blast impacts—including their applications, advantages, and limitations—are discussed. The literature reveals that the machine learning methods are better predictors compared to the empirical models. However, we observed that presently these ML models are mainly applied in academic research.

Analyzing the impact of missing values and selection bias on fairness

Algorithmic decision making is becoming more prevalent, increasingly impacting people’s daily lives. Recently, discussions have been emerging about the fairness of decisions made by machines. Researchers have proposed different approaches for improving the fairness of these algorithms. While these approaches can help machines make fairer decisions, they have been developed and validated on fairly clean data sets. Unfortunately, most real-world data have complexities that make them more dirty. This work considers two of these complexities by analyzing the impact of two real-world data issues on fairness—missing values and selection bias—for categorical data. After formulating this problem and showing its existence, we propose fixing algorithms for data sets containing missing values and/or selection bias that use different forms of reweighting and resampling based upon the missing value generation process. We conduct an extensive empirical evaluation on both real-world and synthetic data using various fairness metrics, and demonstrate how different missing values generated from different mechanisms and selection bias impact prediction fairness, even when prediction accuracy remains fairly constant.

Rare Missense Functional Variants at COL4A1 and COL4A2 in Sporadic Intracerebral Hemorrhage

ObjectiveTo test the genetic contribution of rare missense variants in COL4A1 and COL4A2 in which common variants are genetically associated with sporadic intracerebral hemorrhage (ICH), we performed rare variant analysis in multiple sequencing data for the risk for sporadic ICH.MethodsWe performed sequencing across 559Kbp at 13q34 including COL4A1 and COL4A2 among 2,133 individuals (1,055 ICH cases; 1,078 controls) in US-based and 1,492 individuals (192 ICH cases; 1,189 controls) from Scotland-based cohorts, followed by sequence annotation, functional impact prediction, genetic association testing, and in silico thermodynamic modeling.ResultsWe identified 107 rare nonsynonymous variants in sporadic ICH, of which two missense variants, rs138269346 (COL4A1I110T) and rs201716258 (COL4A2H203L), were predicted to be highly functional and occurred in multiple ICH cases but not in controls from the US-based cohort. The minor allele of rs201716258 was also present in Scottish ICH patients, and rs138269346 was observed in two ICH-free controls with a history of hypertension and myocardial infarction. Rs138269346 was nominally associated with non-lobar ICH risk (P=0.05), but not with lobar ICH (P=0.08), while associations between rs201716258 and ICH subtypes were non-significant (P>0.12). Both variants were considered pathogenic based on minor allele frequency (<0.00035 in EUR), predicted functional impact (deleterious or probably damaging), and in silico modeling studies (substantially altered physical length and thermal stability of collagen).ConclusionsWe identified rare missense variants in COL4A1/A2 in association with sporadic ICH. Our annotation and simulation studies suggest that these variants are highly functional and may represent targets for translational follow-up.