China is a key region for understanding fire activity and the drivers of its variability under strict fire suppression policies. Here, we present a detailed fire occurrence dataset for China, the ...Wildfire Atlas of China (WFAC; 2005-2018), based on continuous monitoring from multiple satellites and calibrated against field observations. We find that wildfires across China mostly occur in the winter season from January to April and those fire occurrences generally show a decreasing trend after reaching a peak in 2007. Most wildfires (84%) occur in subtropical China, with two distinct clusters in its southwestern and southeastern parts. In southeastern China, wildfires are mainly promoted by low precipitation and high diurnal temperature ranges, the combination of which dries out plant tissue and fuel. In southwestern China, wildfires are mainly promoted by warm conditions that enhance evaporation from litter and dormant plant tissues. We further find a fire occurrence dipole between southwestern and southeastern China that is modulated by the El Niño-Southern Oscillation (ENSO).
Purpose
This paper aims to develop a coordination mechanism that can be applied to achieve the channel coordination and information sharing simultaneously in the fresh agri-food supply chain with ...uncertain demand. It seeks to elucidate how the producer can use an option contract to transfer the risk caused by uncertain demand, impel the retailer to share demand information and improve the performance of supply chain.
Design/methodology/approach
An option contract model based on the basic model of fresh agri-food supply chain is introduced to compare the production, profit, risk and information sharing condition of the supply chain in different cases. In addition, a case study focusing on the sale of autumn peaches produced by a local producer is investigated, which provides evidence of the applicability of the authors’ approach.
Findings
The optimal option contract can help the supply chain achieve channel coordination and reach Pareto improvement. In the meantime, such a contract will encourage the retailer to share market demand information with producer spontaneously and help maintain the strategic cooperation between two parties.
Research limitations/implications
This paper considers a single-producer, single-retailer system and both of them are risk neutral.
Practical implications
Presented results can be used as suggestions for improving the contract design of fresh agri-food supply chain in China and can also provide references for other countries with similar experiences as China in fresh agri-food production.
Originality/value
This research introduces the option contract into fresh agri-food supply chain and takes information sharing and the risk caused by uncertain demand into consideration.
In a dynamic, uncertain environment, increased supply chain resilience can improve business quality. Predicting changes in enterprise supply chain resilience can help enterprises adjust their ...operational strategy timeously and reduce the risk of supply and demand interruption. First, a comprehensive resilience assessment framework for manufacturing enterprises was constructed from the perspective of the supply chain, and an improved technique for order of preference by similarity to the ideal solution (TOPSIS) method was used to quantify the resilience level. Considering that the resilience index is easily affected by uncertain factors, and this produces large fluctuations, the buffer operator and metabolism idea are introduced to improve the grey prediction model. This improvement can realize dynamic tracking of the enterprise resilience index and evaluate changes in the enterprise resilience level. Finally, through the analysis of the supply chain data of a famous electronic manufacturing enterprise in China over a two-and-a-half-year period, the results show that the improved TOPSIS method and the improved grey prediction model are effective in improving the supply chain resilience of manufacturing enterprises. This study provides a reference method for manufacturing enterprises to improve their supply chain resilience.
Abstract
Improving the accuracy of financing risk prediction is of great significance to the healthy development of grid enterprises. Taking a provincial-level power grid company as the research ...object, the financing risk index system is constructed by considering multiple dimensions, and the monthly financing risk index RI of power grid enterprises from 2015-2018 is determined based on entropy weight and comprehensive index method, while the financing risk prediction model is constructed with the help of extreme gradient boosting tree model. The empirical results show that compared with support vector regression and BP neural network models, the financing risk prediction model constructed based on the extreme gradient boosting model has an excellent performance in terms of prediction accuracy and stability.
Accurate prediction of wind turbine power generation is of significant importance for the utilization of wind resources and the operational services of wind farms. However, the complex intermittent, ...and uncertain nature of wind poses challenges to accurate predictions. In this study, an improved algorithm based on recurrent neural networks is proposed, presenting a wind power prediction model (RHN-MSA) utilizing a recurrent highway network and a multi-layer semantic fusion attention mechanism. The model fully exploits hidden state information at multiple semantic layers. Finally, through numerical calculations and analysis of actual wind field data, the results indicate a significant improvement in the prediction accuracy of the RHN-MSA wind power prediction model compared to models such as GRU, LSTM, and RHN. The research provides a more flexible and comprehensive decision basis for the stable operation of the power system.
With the continuous deepening of the digital transf ormation of education, unstructured data, as an indispensable co mponent of university data resources, has become increasingly im portant in ...teaching, research, management, and services. Howeve r, how to effectively process and analyze unstructured data, extra ct valuable information from it, and provide scientific support for the development and decision-making of universities has become a current research hotspot. On the basis of introducing the curre nt situation of unstructured data resources in universities, this ar ticle analyzes the key issues faced by unstructured data processin g, and based on this, constructs an overall platform for unstructu red data processing, in order to provide reference for the relevant construction of universities.
This paper presents a hybrid method that combines machine learning and time series forecasting model techniques to enhance the precision and reliability of forecasts. After establishing a learning ...model for high value-added agri-food demand forecasting, we selected the annual demand for a certain high value-added agri-food as the target value. Other economic indicators, namely the National Income Index, population, and residents' consumption level, were chosen as the key influencing factors affecting product demand. We collected actual demand data sequences from Zhejiang Province for the years 2006-2022 as training and test samples. Comparing the forecasting results, it was found that this hybrid algorithm is an appropriate method for forecasting. Compared to other individual models, it demonstrates higher accuracy and lower mean absolute errors in predicting the demand for high value-added agri-food.
Logistics demand forecasting is important for investment decision-making of infrastructure and strategy programming of the logistics industry. In this paper, a hybrid method which combines the Grey ...Model, artificial neural networks and other techniques in both learning and analyzing phases is proposed to improve the precision and reliability of forecasting. After establishing a learning model GNNM(1,8) for road logistics demand forecasting, we chose road freight volume as target value and other economic indicators, i.e. GDP, production value of primary industry, total industrial output value, outcomes of tertiary industry, retail sale of social consumer goods, disposable personal income, and total foreign trade value as the seven key influencing factors for logistics demand. Actual data sequences of the province of Zhejiang from years 1986 to 2008 were collected as training and test-proof samples. By comparing the forecasting results, it turns out that GNNM(1,8) is an appropriate forecasting method to yield higher accuracy and lower mean absolute percentage errors than other individual models for short-term logistics demand forecasting.
An Enhanced Hierarchical Defense Model for the Internet of Things Xiaojun, Xu; Fangzhong, Qi; Zong, Lu ...
2023 2nd International Conference on Sensing, Measurement, Communication and Internet of Things Technologies (SMC-IoT),
2023-Dec.-29
Conference Proceeding
In view of the security needs of the Internet of Things, this paper relies on existing security technical standards and security risk specifications to classify security risks, and uses security ...authentication, secure routing, access control, intrusion monitoring, trusted access and other technologies to classify behaviors with security risks. Carry out three-dimensional defense. An enhanced IoT security architecture is constructed, which effectively enhances the security of the IoT.
Based on the concept of the theory of constraints, this paper discusses a decision-making model of recycling reverse logistics. Taking the measure of throughput as its decision objective, we ...formulate the recycling reverse logistics problem into a linear programming. Meanwhile, we apply the TOC-based model to a rubber manufacturing enterprise which proves that it is more effective than the traditional approach in minimizing the total cost.
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