摘要：
Accurate wind speed forecasting is capable of increasing the stability of wind power system. Notably, there are numerous factors affecting wind speed, thus causing wind speed forecasting to be difficult. To address the above -mentioned challenge, a novel hybrid model integrating genetic algorithm (GA), variational mode decomposition (VMD), improved dung beetle optimization algorithm (IDBO), and Bidirectional long short-term memory network based on attention mechanism (BiLSTM-A) is proposed in this study to achieve satisfactory forecasting performance. In the proposed model, GA is adopted to optimize the VMD to eliminate noise and extract original series attributes. And the IDBO is adopted for hyperparameters selection for the BiLSTM-A. The proposed GA-VMD-IDBO-BiLSTM-A is compared with nine established comparable models, with the aim of verifying its forecasting performance. A series of experiments on four 1 -hour real wind series in Stratford are performed to assess the performance of the model. The MAPE of the four datasets forecasting results reached 1.4%, 2.4%, 3.5%, 2.4%. As indicated by the experimental results, GA-VMD can better process the data and improve the forecasting accuracy. IDBO can optimize the parameters of BiLSTM model and improve the forecasting performance. The dual -optimization wind speed forecasting model can obtain high accuracy and strong stability.
期刊：
Sustainable Energy, Grids and Networks,2024年38:101293 ISSN：2352-4677
通讯作者：
Yi Xiao
作者机构：
[Yi Xiao; Sheng Wu; Chen He; Ming Yi] School of Information Management, Central China Normal University, Wuhan 430079, PR China;[Yi Hu] School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, PR China
通讯机构：
[Yi Xiao] S;School of Information Management, Central China Normal University, Wuhan 430079, PR China
摘要：
The random and fluctuating nature of wind energy brings tremendous challenges and disturbances to the security operation of wind power systems, accurate wind power prediction can effectively reduce these negative impacts. To this end, this paper proposes a hybrid wind power prediction model based on the "decomposition-reconstruction-ensemble" strategy, which consists of four main components, namely decomposition, reconstruction, prediction, and ensemble. Specifically, the original wind power series is decomposed into several sub-modes and reconstructed by frequency by the sample entropy(SE)-optimized variational modal decomposition(VMD) algorithm, subsequently, the Pearson correlation coefficients between the wind speed time series and the reconstructed components of wind power are calculated to divide the wind power series into trend and fluctuation components. Then both the two components are sequentially predicted using the temporal convolutional network(TCN) model. The final predicted value is obtained from the set of predicted results for each component. The wind power data from two wind farms in Hami, Xinjiang are adopted as examples for empirical study, and the results show that the IVMD-R-TCN model proposed in this paper performs significantly better than the benchmark model, which illustrates the predictive validity of the proposed model and is an effective tool for wind power forecasting.
作者机构：
[Quan Lu] School of Economics and Management, Hubei University of Technology, Wuhan 430068, China;[Xiaoxiao Chen] School of Information Management, Central China Normal University, Wuhan 430079, China;Hubei Electronic Commerce Research Center, Wuhan 430079, China;Author to whom correspondence should be addressed.;[Yizhou Wang] School of Information Management, Central China Normal University, Wuhan 430079, China<&wdkj&>Author to whom correspondence should be addressed.
通讯机构：
[Yizhou Wang] S;School of Information Management, Central China Normal University, Wuhan 430079, China<&wdkj&>Author to whom correspondence should be addressed.
关键词：
cyberloafing;role stress;conservation of resources theory;cognitive–affective personality systems theory;PLS-SEM
摘要：
This investigation delves into the pervasive yet insufficiently examined phenomenon of “cyberloafing”, characterized by employees engaging in non-work-related internet activities during office hours. Despite its frequent occurrence in contemporary work environments, the fundamental mechanisms underpinning cyberloafing remain largely uncharted. This study uses the conservation of resources theory and the cognitive–affective personality system framework to demystify the relationship between role stress and cyberloafing. We developed a dual-path model to assess the mediating roles of perceived insider status and emotional exhaustion. Employing SPSS and Smart PLS for data analysis, our research sampled 210 corporate employees. The findings reveal that role stress predicts perceived insider status and emotional exhaustion significantly. Notably, while perceived insider status negatively correlates with cyberloafing, emotional exhaustion shows a positive correlation. These factors mediate the relationship between role stress and cyberloafing, underscoring a multifaceted dynamic. Our results provide new theoretical insights into the mechanisms of employee counterproductive behavior, specifically in the context of cyberloafing, and broaden our understanding of its determinants. This study illuminates theoretical nuances and offers practical implications for managerial strategies and future scholarly inquiries into organizational behavior.
作者机构：
[Tian, Lingkun; Zhou, Zijuan; Zhang, J; Zhang, Jun] Cent China Normal Univ, Sch Informat Management, Wuhan, Peoples R China.;[Zhang, J; Zhang, Jun] Cent China Normal Univ, E Commerce Res Ctr Hubei Prov, Wuhan, Peoples R China.
通讯机构：
[Zhang, J ] C;Cent China Normal Univ, Sch Informat Management, Wuhan, Peoples R China.;Cent China Normal Univ, E Commerce Res Ctr Hubei Prov, Wuhan, Peoples R China.
摘要：
The item and pod storage assignment problems, two critical issues at the strategic level in robotic mobile fulfillment systems, have a strong correlation and should be studied together. Moreover, the workload balance in each picking aisle needs to be considered in the storage assignment problems to avoid robots' congestion within picking aisles. Motivated by these, the joint optimization of item and pod storage assignment problems (J-IPSAP) with picking aisles' workload balance is studied. The mixed integer programming model of the J-IPSAP with the workload balance constraint is formulated to minimize the robots' movement distance. The improved genetic algorithm (IGA) with the decentralized pod storage assignment strategy is designed to solve the J-IPSAP model. The experimental results show that the IGA can obtain high-quality solutions when compared with Gurobi and the two-stage heuristic algorithms. The robots' movement distance is smallest when the width-to-length ratio of the storage area is close to 1, and the robots' movement distance will increase with more stringent workload balance constraints.
摘要：
With the proliferation of social media, the detection of fake news has become a critical issue that poses a significant threat to society. The dissemination of fake information can lead to social harm and damage the credibility of information. To address this issue, deep learning has emerged as a promising approach, especially with the development of Natural Language Processing (NLP). This study introduces a novel approach called Graph Global Attention Network with Memory (GANM) for detecting fake news. This approach leverages NLP techniques to encode nodes with news context and user content. It employs three graph convolutional networks to extract informative features from the news propagation network and aggregates endogenous and exogenous user information. This methodology aims to address the challenge of identifying fake news within the context of social media. Innovatively, the GANM combines two strategies. First, a novel global attention mechanism with memory is employed in the GANM to learn the structural homogeneity of news propagation networks, which is the attention mechanism of a single graph with a history of all graphs. Second, we design a module for partial key information learning aggregation to emphasize the acquisition of partial key information in the graph and merge node-level embeddings with graph-level embeddings into fine-grained joint information. Our proposed method provides a new direction in news detection research with a combination of global and partial information and achieves promising performance on real-world datasets.
作者机构：
[Fangxue Zhang; Xinping Xiao] School of Sciences, Wuhan University of Technology, 430070 Wuhan, China;[Mingyun Gao] School of Information Management, Central China Normal University, 430079 Wuhan, China
通讯机构：
[Xinping Xiao] S;School of Sciences, Wuhan University of Technology, 430070 Wuhan, China
摘要：
The neural ordinary differential equation (NODE) has attracted much attention for its applicability in dynamic system modeling and continuous time series analysis. When the sample size is limited, models often exhibit weak generalizability and robustness and are susceptible to overfitting. To address this, a novel multivariate grey neural differential equation model is proposed based on the grey model and NODE. The new model leverages the small-sample modeling capabilities of grey systems to enhance the overall generalizability. When the neural network structure changes, the proposed model can degenerate into other grey models, enhancing inclusiveness and adaptability. Two energy forecasting cases show that the new model achieves average MAPE values of 0.82% and 1.13% on the test sets. These values are significantly better than those of the other 10 benchmark models. Furthermore, the proposed model exhibits superior performance in terms of the MAE, RMSE, STD, and APE metrics compared to those of all contrastive models. This study demonstrates that the new model effectively enhances its predictive capabilities on limited nonlinear data, showcasing higher prediction accuracy, stronger adaptability, and better stability.
作者机构：
[Qian Chang; Xia Li; Zhao Duan] School of Information Management, Central China Normal University, Wuhan, China
通讯机构：
[Xia Li] S;School of Information Management, Central China Normal University, Wuhan, China
摘要：
Rumor detection in social media platforms is of critical importance owing to the widespread dissemination and impact of false information. Conventional approaches to rumor detection frequently rely on labor-intensive manual fact-checking or handcrafted features that may not adequately account for the complex nature of rumor propagation. To overcome these limitations, recent studies in deep learning, such as the recurrent neural network-based method and natural language processing techniques, have shown promise in capturing sequential patterns and analyzing textual content. However, these approaches often overlook the valuable information embedded in the global structural characteristics of rumor propagation. Herein, we propose a novel approach, named memory-augmented Transformer with graph convolutional networks (GCNs-MT), for rumor detection on social platforms. Our model integrates long short-term memory cells and the multi-head attention mechanism in Transformers to capture local dependencies and global dependencies in the propagation of rumors. By incorporating GCNs, a powerful deep learning framework for structured data, we aim to leverage the structural information of rumor propagation for improved detection performance. Additionally, we construct a Chinese dataset encoded and embedded by pretrained word embeddings (Word2Vec and bidirectional encoder representations from transformers [BERT]) based on real-world tweets from Weibo. Extensive evaluations on self-constructed Chinese and curated benchmark English datasets demonstrate the effectiveness of GCNs-MT in detecting and combating misinformation in social media platforms. The proposed GCNs-MT framework offers a comprehensive and efficient solution for rumor detection, addressing the challenges in social platforms posed by the rapid dissemination and complex nature of rumors.
作者机构：
Detroit Green Institute of Technology,Hubei University of Technology;School of Information Management,Central China Normal University
摘要：
In recent years, numerical weather forecasting has been increasingly emphasized. Variational data assimilation furnishes precise initial values for numerical forecasting models, constituting an inherently nonlinear optimization challenge. The enormity of the dataset under consideration gives rise to substantial computational burdens, complex modeling, and high hardware requirements. This paper employs the Dual-Population Particle Swarm Optimization（DPSO） algorithm in variational data assimilation to enhance assimilation accuracy. By harnessing parallel computing principles, the paper introduces the Parallel Dual-Population Particle Swarm Optimization（PDPSO） Algorithm to reduce the algorithm processing time. Simulations were carried out using partial differential equations, and comparisons in terms of time and accuracy were made against DPSO, the Dynamic Weight Particle Swarm Algorithm（PSOCIWAC）, and the TimeVarying Double Compression Factor Particle Swarm Algorithm（PSOTVCF）. Experimental results indicate that the proposed PDPSO outperforms PSOCIWAC and PSOTVCF in convergence accuracy and is comparable to DPSO. Regarding processing time, PDPSO is 40% faster than PSOCIWAC and PSOTVCF and 70% faster than DPSO.
期刊：
Environmental Science and Pollution Research,2024年31(12):18448-18464 ISSN：0944-1344
通讯作者：
Xu, SC
作者机构：
[Hao, Meng-Ge; Meng, Xiao-Na; Xu, SC; Xu, Shi-Chun] China Univ Min & Technol, Sch Econ & Management, Xuzhou 221116, Peoples R China.;[Xue, Xiao-Fei] Cent China Normal Univ, Sch Informat Management, Wuhan 430079, Peoples R China.
通讯机构：
[Xu, SC ] C;China Univ Min & Technol, Sch Econ & Management, Xuzhou 221116, Peoples R China.
关键词：
Digital economy;Zero-waste city;Waste management;Industrial structure upgrading;Green technology innovation
摘要：
The digital economy is playing a crucial effect in the field of environmental governance. Digital and intelligent management is an essential means to fully realize the "zero-waste city" construction. The present paper investigates the impact of digital economy on China's provincial "zero-waste city" construction. The results indicate that digital economy can contribute to "zero-waste city" construction. The digital economy has a positive nonlinear effect on the construction of "zero-waste city," but the marginal effect is diminishing. The digital economy can facilitate "zero-waste city" construction by improving industrial structure upgrading and green technology innovation. Heterogeneity analysis reveals that digital economy contributes to the construction of "zero-waste city" in the eastern and western regions and high-level environmental regulation regions, while this impact is insignificant in the central region and low-level environmental regulation regions. The digital economy exerts the most significant positive influence on waste resource recycling followed by waste final disposal and then waste reduction at the source. These findings underscore the effect of digital economy in fostering "zero-waste city" construction and promoting sustainable waste management. The present study provides new ideas for the "zero-waste city" construction in emerging developing countries such as China.
作者机构：
[Wu, Ruiping; Lu, Xinyuan] Cent China Normal Univ, Sch Informat Management, Wuhan, Hubei, Peoples R China.;[Lu, Xinyuan] Cent China Normal Univ, Hubei E Commerce Res Ctr, Wuhan, Hubei, Peoples R China.;[Wu, RP; Wu, Ruiping] Cent China Normal Univ, Sch Informat Management, Wuhan 430079, Hubei, Peoples R China.
通讯机构：
[Wu, RP ] C;Cent China Normal Univ, Sch Informat Management, Wuhan 430079, Hubei, Peoples R China.
关键词：
Rural digitization;rural enterprises' resilience;rural labour outflow;labour resource misallocation;R11;R58;O16
摘要：
Considerable research has focused on the question of how to better utilize the rural digitization to enhance rural enterprises' resilience. However, there has not been a unified conclusion reached on the influence of rural digitization on rural enterprises' resilience. To reconcile the existing inconclusive evidence, this paper aims to investigate the nonlinear impact of rural digitization on rural enterprises' resilience. We also hypothesize the mediating role of rural labour outflow in the relationship and explore the moderating role of rural social organization. Following a mixed research method, we employ the U-test method and 205 listed Chinese rural enterprises as the research objects to test the hypotheses. We then use a qualitative case study to offer unique insights for explaining the underlying mechanisms behind the quantitative results. The findings show that rural digitization and rural enterprises' resilience have a U-shaped relationship, and labour outflow plays a nonlinear mediating role in it. Moreover, rural labour outflow and rural enterprises' resilience show an inverted U-shaped relationship, which regulated by rural social organization. Together, the mixed methods research offers nuanced and scientific advice for enhancing rural enterprises' resilience.
作者机构：
[Li, Duantengchuan; Li, Bing; Xia, Tao] Wuhan Univ, Sch Comp Sci, Wuhan 430072, Peoples R China.;[Wang, Jing] Chongqing Univ Posts & Telecommun, Sch Automat, Chongqing 400065, Peoples R China.;[Shi, Fobo] Cent China Normal Univ, Natl Engn Res Ctr Elearning, Wuhan 430079, Peoples R China.;[Zhang, Qi; Zhang, Q] Cent China Normal Univ, Sch Informat Management, Wuhan 430079, Peoples R China.;[Li, Bing] Hubei Luojia Lab, Wuhan 430079, Peoples R China.
通讯机构：
[Li, DTC; Li, B ] W;[Zhang, Q ] C;Wuhan Univ, Sch Comp Sci, Wuhan 430072, Peoples R China.;Cent China Normal Univ, Sch Informat Management, Wuhan 430079, Peoples R China.;Hubei Luojia Lab, Wuhan 430079, Peoples R China.
关键词：
Link prediction;Knowledge graph embedding;Shallow interaction;Deep interaction;Attention mechanism;Vector tokenization
摘要：
Inferring missing information from current facts in a knowledge graph (KG) is the target of the link prediction task. Currently, existing methods embed the entities and relations of KG as a whole into a low-dimensional vector space. Nonetheless, they ignore the multi-level interactions (shallow interactions, deep interactions) among the finer-grained sub-features of entities and relations. To overcome these limitations, we present a shallow-to-deep feature interaction for knowledge graph embedding (SDFormer). It takes into account the interpretability of sub-feature tokens of entities and relations and learns shallow-to-deep interaction information between entities and relations at a more fine-grained level. Specifically, entity and relation vectors are decomposed into sub-features to represent multi-dimensional information. Then, a shallow-to-deep feature interaction method is designed to capture multi-level interactions between entities and relations. This process enriches the feature representation by modeling the interaction between sub-features. Finally, a 1-X scoring function is utilized to calculate the score of each knowledge triplet. The experimental results on several benchmark datasets show that SDFormer obtains competitive performance results and more efficient training efficiency on other comparative models and because of the shallow-to-deep feature interaction between entities and relations.
作者机构：
[Bobo Liu; Zhiming Yuan] Wuhan Institute of Virology, Chinese Academy of Sciences, Wuhan, Hubei 430071, China;[Cui Huang] Wuhan Documentation and Information Center, Chinese Academy of Sciences, Wuhan, Hubei 430071, China;[Jingcheng Wu] Department of Health Science, Technology and Education, National Health Commission of the People’s Republic of China, Beijing 100088, China;[Tianchan Yi] School of Information Management, Central China Normal University, Wuhan, Hubei 430079, China
通讯机构：
[Zhiming Yuan] W;Wuhan Institute of Virology, Chinese Academy of Sciences, Wuhan, Hubei 430071, China
关键词：
pathogen sharing;access and benefit sharing;international law
摘要：
The sharing of pathogens is of great significance for the continuous and comprehensive testing and monitoring of viral samples, vaccine and drug development, and the study of drug resistance and mutability of viral samples. This paper reviews the current legal framework in the field of pathogen sharing, analyzes existing issues, and proposes recommendations to improve the legal framework of pathogen sharing.
作者机构：
[Chen, Rongxing] School of Science, Wuhan University of Technology, Wuhan 430070, China;[Xiao, Xinping] School of Science, Wuhan University of Technology, Wuhan 430070, China. Electronic address: xiaoxp@whut.edu.cn;[Gao, Mingyun] School of Information Management, Central China Normal University, Wuhan 430079, China;[Ding, Qi] School of Business, Nanjing University, Nanjing 210008, China
通讯机构：
[Xinping Xiao] S;School of Science, Wuhan University of Technology, Wuhan 430070, China
关键词：
Chimp optimization algorithm;Hard disk drive failure forecasting;Mixed data sampling;Mixed frequency sampling grey model
摘要：
The mixed data sampling (MIDAS) model has attracted increasing attention due to its outstanding performance in dealing with mixed frequency data. However, most MIDAS model extension studies are based on statistical methods or machine learning models, which suffer from insufficient prediction performance and stability in small sample environments. To solve this problem, this paper proposes a novel mixed frequency sampling discrete grey model (MDGM(1, N)), which is a coupled form of the MIDAS model and discrete grey multivariate model. By adjusting the structure parameters, the model can be adapted to different sampling frequencies data, and degenerate into several types of grey models. Then, the unbiasedness and stability of the model are proved using the mathematical analysis method and numerical random experiment. The meta-heuristic algorithm is introduced to obtain the optimal weight parameters and the maximum lag order, improving the model's fitting ability to mixed frequency data. To demonstrate the effectiveness of the new model, a model evaluation system consisting of traditional evaluation metrics and a monotonicity test is established. Taking four hard disk drive failure datasets as research cases, the performance of the proposed model is compared with seven mainstream benchmark models. The results show that the proposed model has excellent applicability and outperforms other competition models in terms of validity, stability, and robustness. Furthermore, it is observed that the reported uncorrectable errors and the command timeout have a greater impact on hard disk drive failure. Finally, the new model is employed to forecast the failure of four hard disk drives. The forecasting results indicate that in the next four time points with a cycle of 21 days beginning in April 2023, the failure of the smaller capacity hard disk drives (0055 and 0086, corresponding to 8TB and 10TB) show a decreasing trend, reaching 67.442% and 89.7683%, respectively. The failure of the other larger capacity hard disk drives (0007 and 0138, corresponding to 12TB and 14TB) has increased, with a growth rate of 17.1016% and 123.7899%.
