Hybrid Machine Learning Approach for Evapotranspiration Estimation of Fruit Tree in Agricultural Cyber-Physical Systems.

The flourish of the Internet of Things (IoT) and data-driven techniques provide new ideas for enhancing agricultural production, where evapotranspiration estimation is a crucial issue in crop irrigation systems. However, tremendous and unsynchronized data from agricultural cyber-physical systems bring large computational costs as well as complicate performing conventional machine learning methods. To precisely estimate evapotranspiration with acceptable computational costs under the background of IoT, we combine time granulation computing techniques and gradient boosting decision tree (GBDT) with Bayesian optimization (BO) to propose a hybrid machine learning approach. In the combination, a fuzzy granulation method and a time calibration technique are introduced to break voluminous and unsynchronized data into small-scale and synchronized granules with high representativeness. Subsequently, GBDT is implemented to predict evapotranspiration, and BO is utilized to find the optimal hyperparameter values from the reduced granules. IoT data from Xi'an Fruit Technology Promotion Center in Shaanxi Province, China, verify that the proposed granular-GBDT-BO is effective for cherry tree evapotranspiration estimation with reduced computational time, and acceptable and robust predictive accuracy. Consequently, the precise estimation of crop evapotranspiration could provide operational guidance for plant irrigation, plant conservations, and pest control in the agricultural greenhouse.

Upstream-Downstream Joint Carbon Reduction Strategies Based on Low-Carbon Promotion.

A differential game model is established to analyze the impact of emissions reduction efforts and low-carbon product promotion on the reduction strategies of low-carbon product manufacturers (subsequently referred to as manufacturers) and the retailers of such products in a dynamic environment. Based on this model, changes in emissions reduction efforts and promotional efforts are comparatively analyzed under three scenarios (retailers bearing the promotional cost, manufacturers bearing the promotional cost, and centralized decision-making). The results are as follows: (1) the trajectory of carbon emissions reduction per product unit is the highest when the supply chain is under centralized decision-making, followed by when manufacturers bear the promotional cost, and lastly when retailers bear the cost; (2) when manufacturers bear the promotional cost, the market demand, emissions reduction effort, and promotional effort are higher, although the unit retail price is higher than when retailers bear the promotional cost; and (3) under centralized decision-making, the unit retail price is the lowest; however, sales volume, the emissions reduction effort, and the promotional effort are all higher than those in the other scenarios.

A two-stage approach for medical supplies intermodal transportation in large-scale disaster responses.

We present a two-stage approach for the "helicopters and vehicles" intermodal transportation of medical supplies in large-scale disaster responses. In the first stage, a fuzzy-based method and its heuristic algorithm are developed to select the locations of temporary distribution centers (TDCs) and assign medial aid points (MAPs) to each TDC. In the second stage, an integer-programming model is developed to determine the delivery routes. Numerical experiments verified the effectiveness of the approach, and observed several findings: (i) More TDCs often increase the efficiency and utility of medical supplies; (ii) It is not definitely true that vehicles should load more and more medical supplies in emergency responses; (iii) The more contrasting the traveling speeds of helicopters and vehicles are, the more advantageous the intermodal transportation is.