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The purpose of this study is to develop an electromagnetic loading method for online measurement of the acoustoelastic coefficients and bus bar plane stress.

A method based on the combination of electromagnetic loading and the acoustoelastic effect is proposed to realize online measurement of acoustoelastic coefficients and plane stress. Electromagnetic loading is performed on the bus bar specimen, and the acoustoelastic coefficients and the bus bar plane stress are obtained by the ultrasonic method. An electromagnetic loading experimental platform is designed to provide electromagnetic force to the metal plate, including an electromagnetic loading module, an ultrasonic testing module and a stress simulation module.

The feasibility of the proposed electromagnetic loading method is proved by verification experiments. The acoustoelastic coefficients and plane stress measured using the electromagnetic loading method are more accurate than those measured using the traditional method.

The proposed electromagnetic loading method provides a new study perspective and enables more accurate measurement of the acoustoelastic coefficients and plane stress. The study provides an important basis for evaluating the operation status of electrical equipment.

The purpose of this paper is to propose a fractional order optimization method based on perturbation bound and gamma function of a DGM(r,1).

By analyzing and minimizing perturbation bound, the sub-optimal solution on fractional order interval is obtained through offline solving without iterative calculation. By this method, an optimized fractional order non-equidistant ROGM (OFONEROGM) is applied in fitting and prediction water quality parameters for a surface water pollution monitoring system.

This method can narrow fractional order interval in this work. In a surface water pollution monitoring system, the fitting and prediction performances of OFONEROGM are demonstrated comparing with integer order non-equidistant ROGM (IONEROGM).

A method of offline solving the sub-optimal solution on fractional order interval is proposed. It can narrow the optimized fractional order range of NEROGM without iterative calculation. A large number of calculations are eliminated. Besides that, optimized fractional order interval is only related to the number of original data, and convenient for practical application. In this work, an OFONEROGM is modeled for predicting water quality trend for preventing water pollution or stealing sewage discharge. It will provide guiding significance in water quality parameter fitting and predicting for water environment management.

This article intends to explore how organizational learning affects innovation in public library services, and the role of public librarians in innovation in library services.

This study collected 375 valid questionnaires from 19 public libraries in Shanghai and Zhejiang based on organizational learning, organizational innovation and employee psychological empowerment theory. Additionally, SPSS and HLM are used to analyze the relationship among the three processes of organizational learning: knowledge acquisition, knowledge sharing and knowledge application, and public library service innovation.

Results show that organizational learning has a significant positive effect on the service innovation of public libraries. Knowledge acquisition and knowledge application in the process of organizational learning have a significant positive influence on the service innovation of public libraries, but the impact of knowledge sharing on service innovation is weak. Employee psychological empowerment has a negative regulating influence on knowledge sharing–public library service innovation, but no significant influence on knowledge application–public library service innovation and knowledge acquisition–public library service innovation.

This research explores the effectiveness of the theory of organizational learning in the field of public libraries and also confirms the role of librarians in the work of public libraries. Together, they promote the innovation of public libraries.

The capability of microparticle/objects patterning in the three-dimensional (3D) printing structure could improve its performance and functionalities. This paper aims to propose and evaluate a novel acoustic manipulation approach.

A novel method to accumulate the microparticles in the cylindrical tube during the 3D printing process is proposed by acoustically exciting the structural vibration of the cylindrical tube at a specific frequency, and subsequently, focusing the 50-μm polystyrene microparticles at the produced pressure node toward the center of the tube by the acoustic radiation force. To realize this solution, a piezoceramic plate was glued to the outside wall of a cylindrical glass tube with a tapered nozzle. The accumulation of microparticles in the tube and printing structure was monitored microscopically and the accumulation time and width were quantitatively evaluated. Furthermore, the application of such technology was also evaluated in the L929 and PC-12 cells suspended in the sodium alginate and gelatin methacryloyl.

The measured location of pressure and the excitation frequency of the cylindrical glass tube (172 kHz) agreed quite well with our numerical simulation (168 kHz). Acoustic excitation could effectively and consistently accumulate the microparticles. It is found that the accumulation time and width of microparticles in the tube increase with the concentration of sodium alginate and microparticles in the ink. As a result, the microparticles are concentrated mostly in the central part of the printing structure. In comparison to the conventional printing strategy, acoustic excitation could significantly reduce the width of accumulated microparticles in the printing structure (p < 0.05). In addition, the possibility of high harmonics (385 and 657 kHz) was also explored. L929 and PC-12 cells suspended in the hydrogel can also be accumulated successfully.

This paper proves that the proposed acoustic approach is able to increase the accuracy of printing capability at a low cost, easy configuration and low power output.

The purpose of this paper is to explore a new approach to solve the problem of positive and negative offset in the calculation process of integral elements, then propose a series of new grey relational degree model for cross sequences.

The definitions of cross sequences and area elements have been proposed at first. Then the concept of difference degree between sequences has been put forward. Based on the definition of difference degree between sequences, various modified grey relational degree models for cross sequences have been proposed to solve the measurement problem of cross sequence correlation relationships.

(1) The new definition of cross sequences; (2) The area element; (3) Various modified grey relational degree models for cross sequences based on the definition of difference degree between sequences.

The grey relational analysis model of cross sequences is a difficult problem in grey relational analysis. The new model proposed in this article can effectively avoid the calculation deviation of grey relational analysis model for cross sequences, and reasonably measure the correlation between cross sequences. The new model was used to analyse the food consumer price index in Shaanxi Province, clarifying the relationship between different types of food consumer price indices, some interesting results that are not completely consistent with general economic theory were obtained.

The new definition of cross sequences, the area element and various modified grey relational degree models for cross sequences were proposed.

The multidimensional effects of farmland transfer in China have been profoundly unstudied. The purpose of this paper is to provide insights on the effects of the intermediary role of agricultural total factor productivity (TFP) of farmland transfer on agricultural economic growth in China.

Based on the agricultural data of 30 provinces in China over the period 2005–2018, this paper uses the intermediary effect model to test the relationship between farmland transfer, agricultural TFP and agricultural economic growth. This paper employed an intermediary effect test model to investigate the intermediary role of agricultural TFP in the influence of farmland transfer on agricultural economic growth.

The findings indicated that farmland transfer has a significant effect on promoting agricultural economic growth. There is a significant “inverted U-shaped” relationship between farmland transfer and agricultural TFP. The sample value of 84.3% of farmland transfers in China is still within the TFP promoting effect range. In addition, farmland transfer has an indirect impact on agricultural economic growth through the channel of agricultural TFP. Agricultural TFP plays a significant intermediary role, but the effect is relatively low

This paper is the first to provide fundamental evidence on the impact of farmland transfers on agricultural economic growth in China, driven by agricultural TFP as an intermediary factor. Agricultural TFP can reduce the involution effect of farmland transfer and promote an indirect effect on agricultural economic growth.

The purpose of this paper is to construct some negative grey relational analysis models to measure the relationship between reverse sequences.

The definition of reverse sequence has been given at first based on analysis of relative position and change trend of sequences. Then, several different negative grey relational analysis models, such as the negative grey similarity relational analysis model, the negative grey absolute relational analysis model, the negative grey relative relational analysis model, the negative grey comprehensive relational analysis model and the negative Deng’s grey relational analysis model have been put forward based on the corresponding common grey relational analysis models. The properties of the new models have been studied.

The negative grey relational analysis models proposed in this paper can solve the problem of relationship measurement of reverse sequences effectively. All the new negative grey relational degree satisfying the requirements of normalization and reversibility.

The proposed negative grey relational analysis models can be used to measure the relationship between reverse sequences. As a living example, the reverse incentive effect of winning Fields Medal on the research output of winners is measured based on the research output data of the medalists and the contenders using the proposed negative grey relational analysis model.

The definition of reverse sequence and the negative grey similarity relational analysis model, the negative grey absolute relational analysis model, the negative grey relative relational analysis model, the negative grey comprehensive relational analysis model and the negative Deng’s grey relational analysis model are first proposed in this paper.

This paper aims to develop a system dynamics (SD) model to identify causal relationships among the elements of failure modes and effects analysis (FMEA), i.e. failure modes, effects and causes.

A causal loop diagram (CLD) has been developed based on the results obtained from interdependencies and correlations analysis among the FMEA elements through applying the integrated approach of FMEA-quality function deployment (QFD) developed by Shaker et al. (2019). The proposed model was examined in a steel manufacturing company to identify and model the causes and effects relationships among failure modes, effects and causes of a roller-transmission system.

Findings indicated interactions among the most significant failure modes, effects and causes. Moreover, corrective actions defined to eliminate or relieve critical failure causes. Consequently, production costs decreased, and the production rate increased due to eliminated/decreased failure modes.

The application of CLD illustrates causal relationships among FMEA elements in a more effective way and results in a more precise recognition of the root causes of the potential failure modes and their easy elimination/decrease. Therefore, applying the proposed approach leads to a better analysis of the interactions among FMEA elements, decreased system's failure rate and increased system availability.

The literature review indicated a few studies on the application of SD methodology in the maintenance area, and no study was performed on the causal interactions among FMEA elements through an FMEA-QFD based SD approach. Although the interactions of these elements are significant and helpful in risks ranking, researchers fail to investigate them sufficiently.

This work aims to develop a novel fuzzy associator rule-based fuzzified deep convolutional neural network (FDCNN) architecture for the classification of smartphone sensor-based human activity recognition. This work mainly focuses on fusing the λ_max method for weight initialization, as a data normalization technique, to achieve high accuracy of classification.

The major contributions of this work are modeled as FDCNN architecture, which is initially fused with a fuzzy logic based data aggregator. This work significantly focuses on normalizing the University of California, Irvine data set’s statistical parameters before feeding that to convolutional neural network layers. This FDCNN model with λ_max method is instrumental in ensuring the faster convergence with improved performance accuracy in sensor based human activity recognition. Impact analysis is carried out to validate the appropriateness of the results with hyper-parameter tuning on the proposed FDCNN model with λ_max method.

The effectiveness of the proposed FDCNN model with λ_max method was outperformed than state-of-the-art models and attained with overall accuracy of 97.89% with overall F1 score as 0.9795.

The proposed fuzzy associate rule layer (FAL) layer is responsible for feature association based on fuzzy rules and regulates the uncertainty in the sensor data because of signal inferences and noises. Also, the normalized data is subjectively grouped based on the FAL kernel structure weights assigned with the λ_max method.

Contributed a novel FDCNN architecture that can support those who are keen in advancing human activity recognition (HAR) recognition.

A novel FDCNN architecture is implemented with appropriate FAL kernel structures.