Search

The participation of mobile technology could be a massive development to ensure the citizen's safety by linking various government agencies under the umbrella of the m-government (m-govt) security response system (SRS). Based on the novel phenomenon of m-govt SRS, this research provides a comprehensive theoretical model to discover citizens' adoption intention. Our findings proved that transparency and trust mediate the influence of information quality characteristics and channel characteristics, while perceived usefulness mediate the influence of awareness on citizens' intentions to use; which reduce uncertainty and enhance citizens' willingness to adopt the m-govt SRS. This study proved that perceived response time is a key construct in the context of providing m-govt SRS to enhance citizens' intention to use. The outcomes make a noteworthy contribution to theoretical literature and having practical implications regarding understanding various aspects of m-govt SRS adoption among citizens, which provide the broadest view to government agencies for successful implementation of these services. [ABSTRACT FROM AUTHOR]

Duan, C.; Xiu, G., and Zhang, Y., 2019. Coordinated management method of information contract in port logistics service supply chain. In: Guido-Aldana, P.A. and Mulahasan, S. (eds.), Advances in Water Resources and Exploration. Journal of Coastal Research, Special Issue No. 93, pp. 1047–1052. Coconut Creek (Florida), ISSN 0749-0208. In order to realize the optimal scheduling and automatic identification of port logistics service supply chain information, it is necessary to design the contract coordination management of port logistics service supply chain information. A coordinated management model of port logistics service supply chain information contract based on grid area reconstruction learning clustering is proposed. The fuzzy association rule scheduling technology is used to sample the port logistics service supply chain information, and the port logistics service supply chain information collected by RFID is processed by information fusion. The autocorrelation statistical regression analysis method is used to extract the correlation statistical features of port logistics service supply chain information, and the matching filtering method is used to detect the redundant features of port logistics service supply chain information for interference suppression filtering. The purified port logistics service supply chain information is reconstructed by phase space reconstruction technology, and the reconstructed data is classified and identified by grid area reconstruction learning clustering classifier. The contract coordination management and automatic retrieval of port logistics service supply chain information are realized. The simulation results show that the automatic classification of port logistics service supply chain information contract coordination management is good, and the fuzzy clustering ability is strong, which improves the coordination management ability of port logistics service supply chain information contract. [ABSTRACT FROM AUTHOR]

Duan, C.; Xiu, G., and Zhang, Y., 2019. Coordinated management method of information contract in port logistics service supply chain. In: Guido-Aldana, P.A. and Mulahasan, S. (eds.), Advances in Water Resources and Exploration. Journal of Coastal Research, Special Issue No. 93, pp. 1047–1052. Coconut Creek (Florida), ISSN 0749-0208. In order to realize the optimal scheduling and automatic identification of port logistics service supply chain information, it is necessary to design the contract coordination management of port logistics service supply chain information. A coordinated management model of port logistics service supply chain information contract based on grid area reconstruction learning clustering is proposed. The fuzzy association rule scheduling technology is used to sample the port logistics service supply chain information, and the port logistics service supply chain information collected by RFID is processed by information fusion. The autocorrelation statistical regression analysis method is used to extract the correlation statistical features of port logistics service supply chain information, and the matching filtering method is used to detect the redundant features of port logistics service supply chain information for interference suppression filtering. The purified port logistics service supply chain information is reconstructed by phase space reconstruction technology, and the reconstructed data is classified and identified by grid area reconstruction learning clustering classifier. The contract coordination management and automatic retrieval of port logistics service supply chain information are realized. The simulation results show that the automatic classification of port logistics service supply chain information contract coordination management is good, and the fuzzy clustering ability is strong, which improves the coordination management ability of port logistics service supply chain information contract. [ABSTRACT FROM AUTHOR]

• This paper suggests a grating Moiré fringe-based error correction approach that uses an artificial neural network (ANN) and the quartile method (QM). • The approach uses QM for data cleaning operations to eliminate outliers and creates the ANN model with a structure of fully connected layer-normalization layer-fully connected layer for regression training in order to achieve lightweight and stability. • The proposed QM-ANN error correction model's mean absolute error (MAE) can reach 3.3 nm once the QM data has been cleaned and the ANN model has been trained. Many interference factors of the complex and changeable working environment of the grating sensor are the main factors affecting the accuracy; a grating Moiré fringe error correction model based on the quartile method (QM) and artificial neural network (ANN) is proposed in this paper. Firstly, the grating sensor's signals in the working process are collected, and three kinds of interference signals (temperature, humidity and vibration) from the grating sensor working environment are collected simultaneously. Secondly, the QM is used to detect outliers from the collected data and delete data outliers directly. Subsequently, the processed data are input into the ANN model for training. The model is lightweight and stable, because it adopts the cascaded structure of full connection layer-normalization layer-full connection layer, which does not need deploy the complex convolution layer module etc. Finally, the corrected data are output after being processed by the QM-ANN error correction model. The experimental results show that the mean absolute error (MAE) of the proposed QM-ANN error correction model is better than QM-convolutional neural network (CNN), QM-long short-term memory (LSTM) and ANN, CNN, LSTM models without QM. It shows that the proposed model has better robustness and practicability. [ABSTRACT FROM AUTHOR]