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International audience ; In nowadays' world, frequently occurred agricultural food safety events have severely done harm to people's health and directly initiated great trust crisis to governments and agricultural food enterprises, and the E-commerce as virtual economy can enlarge the trust crisis terrifically. As a result, to establish unified traceability information sharing platform of agricultural supply chain has become an urgent task for the related organizations. This paper begins with an exploratory research on design and realization of Agricultural Food Supply Chain Tracking System (AFSCTS) in E-commerce environment, which can provide a powerful technical support for timely tracing and finding the link where a agricultural food safety problem happens in the supply chain. This paper has 5 sections: the first to briefly introduce the concepts relevant to AFSCTS and the significance of this research; the second to analyze three key technologies (RFID technology, database integration technology and data security technology) utilized in AFSCTS; the third to elaborate on two key algorithms (traceability algorithm and data encryption algorithm) designed in AFSCTS; the fourth to design and implement AFSCTS based on three key technologies and two key algorithms, and the overall system frame and function structure are narrated detailedly in the paper; the last to apply the AFSCTS in Nanfeng county (the hometown of famous Nanfeng Orange in China) for tracing agricultural food, and the system implementation has achieved experimental success.

The land-use identification process, which involves quantifying the types and intensity of human activities at a regional level, is a critical investigation step for ongoing land-use planning. One limitation of land-use identification practices is that they are based on theoretical-driven models using survey and socioeconomic data, which are often considered costly and time consuming. Another limitation is that most of these identification methods cannot incorporate the effect of daily human activity, resulting in some significant spatial heterogeneity being ignored. In this context, a novel land-use identification framework is proposed to quantify land-use characteristics using traffic-flow and traffic-events data. Regarding the identification models, two widely used Ensemble learning methods: Random Forest and Adaboost, are introduced to classify the land-use type and fit the land-use density. The case study collected the transit vehicle positions, traffic events, and geo-tagged data at the regional level in the San Francisco Bay Area, California. The results demonstrated that this framework with Ensemble learning was significantly accurate at identifying land-use characteristics in both the type classification and density regression tasks. The result averages improved 12.63%, 12.84%, 11.05%, 5.44%, 12.84% for Area Under ROC Curve (AUC), Classification Accuracy (CA), F-Measure (F1), Precision, and Recall, respectively, in classification tasks and 56.81%, 21.20%, 47.29% for Mean Squared Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE), respectively, in regression tasks than other models. The Random Forest model performs better in labels with high regularity, such as education, residence, and work activities. Apart from the accuracy, the correlation analysis of the error term also showed that the result was consistent with people's common sense of land-use characteristics, demonstrating the interpretability of the proposed framework.

AbstractTo stimulate the development and application of blockchain technology, Chinese government put forward subsidy strategy. To explore the subsidy policies under the new background, we chose a fresh supply chain with one producer, one blockchain-based traceability service provider, and one retailer as the research object, and government subsidy strategies were divided into a fixed strategy and a varying strategy. Afterward, considering the trust level of blockchain-based traceability information and consumers' preference to the blockchain-based traceability information, we revised the demand function, and three subsidy models were proposed and analyzed. Findings: (1) the varying subsidy will help the retailer, the producer, and the traceability service provider set lower prices. (2) Meanwhile, the varying subsidies offered to the blockchain-based traceability service provider and the producer will help the whole supply chain members obtain more revenues.

Abstract
The study presented in this paper investigated the combined effects of environmental factors and real-time traffic conditions on freeway crash risks. Traffic and weather data were collected from a 35-km freeway segment in the state of California, United States. The weather conditions were classified into five categories: clear, light rain, moderate/heavy rain, haze, and mist/fog. Logistic regression models using unmatched case-control data were developed to link the likelihood of crash occurrences to various traffic and environmental variables. The sample size requirements for case-control studies and the interaction between traffic and environmental variables were considered. The model estimation results showed that the light rain, moderate/heavy rain, and mist/fog significantly increase freeway crash risks. The interaction between light rain and upstream occupancy was also found to be statistically significant. Bootstrap analyses were conducted to quantify the interaction effect between these two variables. The crash risk model was compared to a reduced model in which environmental information was not included. It was found that the inclusion of environmental information improved both goodness of fit and prediction performance of the crash risk prediction model. The inclusion of environmental information in crash risk models improved the prediction accuracy of crash occurrences by 6.8% and reduced the false alarm rate by 1.3%. It was also found that the inclusion of environmental information had minor impacts on the prediction performance of the crash risk model in clear weather conditions.

Time-inconsistent, present-biased agents may hold commitment assets hoping to keep their current and future present bias in check. Paternalistic governments, in an effort to help such people, routinely offer commitment machinery such as restrictions (or bans) on early withdrawals from defined-contribution, retirement schemes. The larger literature on low uptake of commitment assets recognizes a trade- off: while use of commitment technologies thwarts deviation from pre-selected paths, they, nevertheless, limit flexibility of future selves to respond to unanticipated, consumption shocks. This paper rules out consumption or income shocks by design and yet uncovers a similar trade-off in a world where agents are uncertain but hold beliefs, possibly incorrect, about the present-biasedness of future selves. It shows how fully sophisticated agents — those with correct beliefs about the present-bias of future selves — are happier when the government offers tighter commitment; this is not necessarily so, for the partially naive. Indeed, the latter may be happier than their fully sophisticated counterparts if the government's commitment machinery is slack.

We describe a "business as usual" (BAU) economy in which pollution is a by-product of productive activity by the current generation but "damages" production for future generations. Over time, conditions in the BAU economy become dire: it gets increasingly polluted, consumption falls and generational welfare levels decline. A government introduces costly pollution abatement and finances it via distorting taxes and borrowing on perfect international markets. Pollution levels start to decline, generating downstream welfare gains, some of which the government taxes away, without hurting anyone, to help pay off the debt, that too, in finite time. Along the transition, every generation faces less pollution, consumes more and is happier than if life had continued in the BAU world.

Demand prediction plays a critical role in traffic research. The key challenge of traffic demand prediction lies in modeling the complex spatial dependencies and temporal dynamics. However, there is no mature and widely accepted concept to support the solution of the above challenge. Essentially, a prediction model combined with similar objects in temporal and spatial dimensions could obtain better performance. This paper proposes a concept called the Similarity-based Principle (SP), which is applied to improve the prediction performance of deep learning models in complex traffic scenarios. For the temporal components, the long-term temporal dynamics in contemporaneous historical data for ridership are extracted by the Stacked Autoencoder (SAE) method. For the spatial components, the activity-based spatial geographic information (ABG-information) is used to capture the spatial correlation of the traffic network, which is reflected in the daily activities of humans. Specifically, the SP is applied to a Spatio-temporal Graph Convolutional Neural Network (STGCNN) model. In the case study, the Similarity-based Principle Spatio-temporal Graph Convolutional Neural Network (SP-STGCNN) model predicts demand for bicycle sharing in San Francisco. The results show that the SP effectively improves the model's performance. The prediction accuracy is enhanced by up to 10.34% compared with STGCNN. For spatial relationships, the model using the geographic information attribute performs better than that using the road information attribute and the distance attribute. It is proved that the construction of the Spatio-temporal model-based similarity principle can improve the performance.