Qian Tang, Yuzhuo Qiu and Lan Xu
The demand for the cold chain logistics of agricultural products was investigated through demand forecasting; targeted suggestions and countermeasures are provided. This paper…
Abstract
Purpose
The demand for the cold chain logistics of agricultural products was investigated through demand forecasting; targeted suggestions and countermeasures are provided. This paper aims to discuss the aforementioned statement.
Design/methodology/approach
A Markov-optimised mean GM (1, 1) model is proposed to forecast the demand for the cold chain logistics of agricultural products. The mean GM (1, 1) model was used to forecast the demand trend, and the Markov chain model was used for optimisation. Considering Guangxi province as an example, the feasibility and effectiveness of the proposed method were verified, and relevant suggestions are made.
Findings
Compared with other models, the Markov-optimised mean GM (1, 1) model can more effectively forecast the demand for the cold chain logistics of agricultural products, is closer to the actual value and has better accuracy and minor error. It shows that the demand forecast can provide specific suggestions and theoretical support for the development of cold chain logistics.
Originality/value
This study evaluated the development trend of the cold chain logistics of agricultural products based on the research horizon of demand forecasting for cold chain logistics. A Markov-optimised mean GM (1, 1) model is proposed to overcome the problem of poor prediction for series with considerable fluctuation in the modelling process, and improve the prediction accuracy. It finds a breakthrough to promote the development of cold chain logistics through empirical analysis, and give relevant suggestions based on the obtained results.
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Qinqin Li, Yujie Xiao, Yuzhuo Qiu, Xiaoling Xu and Caichun Chai
The purpose of this paper is to examine the impact of carbon permit allocation rules (grandfathering mechanism and benchmarking mechanism) on incentive contracts provided by the…
Abstract
Purpose
The purpose of this paper is to examine the impact of carbon permit allocation rules (grandfathering mechanism and benchmarking mechanism) on incentive contracts provided by the retailer to encourage the manufacturer to invest more in reducing carbon emissions.
Design/methodology/approach
The authors consider a two-echelon supply chain in which the retailer offers three contracts (wholesale price contract, cost-sharing contract and revenue-sharing contract) to the manufacturer. Based on the two carbon permit allocation rules, i.e. grandfathering mechanism and benchmarking mechanism, six scenarios are examined. The optimal price and carbon emission reduction decisions and members’ equilibrium profits under six scenarios are analyzed and compared.
Findings
The results suggest that the revenue-sharing contract can more effectively stimulate the manufacturer to reduce carbon emissions compared to the cost-sharing contract. The cost-sharing contract can help to achieve the highest environmental performance, whereas the implementation of revenue-sharing contract can attain the highest social welfare. The benchmarking mechanism is more effective for the government to prompt the manufacturer to produce low-carbon products than the grandfathering mechanism. Although a loose carbon policy can expand the total emissions, it can improve the social welfare.
Practical implications
These results can provide operational insights for the retailer in how to use incentive contract to encourage the manufacturer to curb carbon emissions and offer managerial insights for the government to make policy decisions on carbon permit allocation rules.
Originality/value
This paper contributes to the literature regarding to firm’s carbon emissions reduction decisions under cap-and-trade policy and highlights the importance of carbon permit allocation methods in curbing carbon emissions.
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Yuzhuo Wang, Chengzhi Zhang, Min Song, Seongdeok Kim, Youngsoo Ko and Juhee Lee
In the era of artificial intelligence (AI), algorithms have gained unprecedented importance. Scientific studies have shown that algorithms are frequently mentioned in papers…
Abstract
Purpose
In the era of artificial intelligence (AI), algorithms have gained unprecedented importance. Scientific studies have shown that algorithms are frequently mentioned in papers, making mention frequency a classical indicator of their popularity and influence. However, contemporary methods for evaluating influence tend to focus solely on individual algorithms, disregarding the collective impact resulting from the interconnectedness of these algorithms, which can provide a new way to reveal their roles and importance within algorithm clusters. This paper aims to build the co-occurrence network of algorithms in the natural language processing field based on the full-text content of academic papers and analyze the academic influence of algorithms in the group based on the features of the network.
Design/methodology/approach
We use deep learning models to extract algorithm entities from articles and construct the whole, cumulative and annual co-occurrence networks. We first analyze the characteristics of algorithm networks and then use various centrality metrics to obtain the score and ranking of group influence for each algorithm in the whole domain and each year. Finally, we analyze the influence evolution of different representative algorithms.
Findings
The results indicate that algorithm networks also have the characteristics of complex networks, with tight connections between nodes developing over approximately four decades. For different algorithms, algorithms that are classic, high-performing and appear at the junctions of different eras can possess high popularity, control, central position and balanced influence in the network. As an algorithm gradually diminishes its sway within the group, it typically loses its core position first, followed by a dwindling association with other algorithms.
Originality/value
To the best of the authors’ knowledge, this paper is the first large-scale analysis of algorithm networks. The extensive temporal coverage, spanning over four decades of academic publications, ensures the depth and integrity of the network. Our results serve as a cornerstone for constructing multifaceted networks interlinking algorithms, scholars and tasks, facilitating future exploration of their scientific roles and semantic relations.
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The purpose of this paper is to develop an equation for the synergistic corrosion of SRB and CO2 based on the D-W model.
Abstract
Purpose
The purpose of this paper is to develop an equation for the synergistic corrosion of SRB and CO2 based on the D-W model.
Design/methodology/approach
The bacterial types in the a and ß pipelines were studied by the most probable number method, and the corrosion morphology of L360 in pipeline water samples was studied by surface analysis. The corrosion rate of L360 was studied using the weight loss method. The gray correlation method was used to calculate the degree of correlation between the influencing factors of corrosion under the synergistic effect of CO2 and SRB. The curve obtained from PIPESIM software and experiments data was then fitted using multiple non-linear regression method by MATLAB software.
Findings
The equation was used to predict the corrosion of the ß pipeline for verification, and it was found that seven out of ten excavation sites were within a 20% error range.
Originality/value
Using the gray correlation method, an equation that considers synergistic corrosion of SRB and CO2 has been developed based on the D-W model. The equation could be used to predict the corrosion rate of shale gas gathering pipelines through SRB and CO2 synergistic corrosion.