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Article
Publication date: 1 December 2021

Weige Yang, Yuqin Zhou, Wenhai Xu and Kunzhi Tang

The purposes are to explore corporate financial management optimization in the context of big data and provide a sustainable financial strategy for corporate development.

926

Abstract

Purpose

The purposes are to explore corporate financial management optimization in the context of big data and provide a sustainable financial strategy for corporate development.

Design/methodology/approach

First, the shortcomings of the traditional financial management model are analyzed under the background of big data analysis. The big data analytic technology is employed to extract financial big data information and establish an efficient corporate financial management model. Second, the deep learning (DL) algorithm is applied to implement a corporate financial early-warning model to predict the potential risks in corporate finance, considering the predictability of corporate financial risks. Finally, a corporate value-centered development strategy based on sustainable growth is proposed for long-term development.

Findings

The experimental results demonstrate that the financial early-warning model based on DL has an accuracy of 90.7 and 88.9% for the two-year financial alert, which is far superior to the prediction effect of the traditional financial risk prediction models.

Originality/value

The obtained results can provide a reference for establishing a sustainable development pattern of corporate financial management under the background of big data.

Details

Journal of Enterprise Information Management, vol. 35 no. 4/5
Type: Research Article
ISSN: 1741-0398

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Article
Publication date: 22 August 2022

Zilong Wang, JiaCheng Zhou, Fang Liu, Yuqin Wu and Nu Yan

The purpose of this paper is to study the microstructure and properties of Sn-3.5Ag and Sn-3.5Ag-0.5Sb lead-free solder alloys with and without a rotating magnetic field (RMF).

144

Abstract

Purpose

The purpose of this paper is to study the microstructure and properties of Sn-3.5Ag and Sn-3.5Ag-0.5Sb lead-free solder alloys with and without a rotating magnetic field (RMF).

Design/methodology/approach

Optical microscopy, scanning electron microscopy and X-ray diffraction were used to analyze the effect of an RMF on the microstructure of the solders. Differential scanning calorimetry was used to study the influence of the RMF on the thermal characteristics of the solders. The mechanical properties of the alloys were determined by tensile measurements at different strain rates.

Findings

The ß-Sn grains and intermetallic compounds for the Sn-3.5Ag and Sn-3.5Ag-0.5Sb lead-free solder alloys were refined under an RMF, and the morphology of the ß-Sn grains changed from dendritic to equiaxed. The pasty range was significantly reduced under an RMF. The ultimate tensile strength (UTS) of Sn-3.5Ag improved under the RMF, whereas the UTS of Sn-3.5Ag-0.5Sb decreased slightly. The addition of Sb to the Sn-3.5Ag alloy significantly enhanced the UTS and elongation (El.%) of the samples. The UTS of the solder increased with increasing strain rate.

Originality/value

The results revealed that the application of RMF in the molten alloy had a significant effect on its microstructure and mechanical properties. The thermal characteristics of the Sn-3.5Ag and Sn-3.5Ag-0.5Sb solder alloys were improved under the RMF. This research is expected to fill a knowledge gap regarding the behaviour of Sn-Ag solder alloys under RMF.

Details

Soldering & Surface Mount Technology, vol. 35 no. 2
Type: Research Article
ISSN: 0954-0911

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Article
Publication date: 30 April 2024

Fang Liu, Zilong Wang, JiaCheng Zhou, Yuqin Wu and Zhen Wang

The purpose of this study is to investigate the effects of Ce and Sb doping on the microstructure and thermal mechanical properties of Sn-1.0Ag-0.5Cu lead-free solder. The effects…

81

Abstract

Purpose

The purpose of this study is to investigate the effects of Ce and Sb doping on the microstructure and thermal mechanical properties of Sn-1.0Ag-0.5Cu lead-free solder. The effects of 0.5%Sb and 0.07%Ce doping on microstructure, thermal properties and mechanical properties of Sn-1.0Ag-0.5Cu lead-free solder were investigated.

Design/methodology/approach

According to the mass ratio, the solder alloys were prepared from tin ingot, antimony ingot, silver ingot and copper ingot with purity of 99.99% at 400°C. X-ray diffractometer was adopted for phase analysis of the alloys. Optical microscopy, scanning electron microscopy and energy dispersive spectrometer were used to study the effect of the Sb and Ce doping on the microstructure of the solder. Then, the thermal characteristics of alloys were characterized by a differential scanning calorimeter (DSC). Finally, the ultimate tensile strength (UTS), elongation (EL.%) and yield strength (YS) of solder alloys were measured by tensile testing machine.

Findings

With the addition of Sb and Ce, the ß-Sn and intermetallic compounds of solders were refined and distributed more evenly. With the addition of Sb, the UTS, EL.% and YS of Sn-1.0Ag-0.5Cu increased by 15.3%, 46.8% and 16.5%, respectively. The EL.% of Sn-1.0Ag-0.5Cu increased by 56.5% due to Ce doping. When both Sb and Ce elements are added, the EL.% of Sn-1.0Ag-0.5Cu increased by 93.3%.

Originality/value

The addition of 0.5% Sb and 0.07% Ce can obtain better comprehensive performance, which provides a helpful reference for the development of Sn-Ag-Cu lead-free solder.

Details

Soldering & Surface Mount Technology, vol. 36 no. 3
Type: Research Article
ISSN: 0954-0911

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Article
Publication date: 23 August 2024

Libiao Bai, Shiyi Liu, Yuqin An and Qi Xie

Project portfolio benefit (PPB) evaluation is crucial for project portfolio management decisions. However, PPB is complex in composition and affected by synergy and ambidexterity…

61

Abstract

Purpose

Project portfolio benefit (PPB) evaluation is crucial for project portfolio management decisions. However, PPB is complex in composition and affected by synergy and ambidexterity. Ignoring these characteristics can result in inaccurate assessments, impeding the management and optimization of benefit. Considering the above complexity of PPB evaluation, this study aims to propose a refined PPB evaluation model to provide decision support for organizations.

Design/methodology/approach

A back propagation neural network optimized via genetic algorithm and pruning algorithm (P-GA-BPNN) is constructed for PPB evaluation. First, the benefit evaluation criteria are established. Second, the inputs and expected outputs for model training and testing are determined. Then, based on the optimization of BPNN via genetic algorithm and pruning algorithm, a PPB evaluation model is constructed considering the impacts of ambidexterity and synergy on PPB. Finally, a numerical example was applied to validate the model.

Findings

The results indicate that the proposed model can be used for effective PPB evaluation. Moreover, it shows superiority in terms of MSE and fitting effect through extensive comparative experiments with BPNN, GA-BPNN, and SVM models. The robustness of the model is also demonstrated via data random disturbance experiment and 10-cross-validation. Therefore, the proposed model could serve as a valuable decision-making tool for PPB management.

Originality/value

This study extends prior research by integrating the impacts of synergy and ambidexterity on PPB when conducting PPB evaluation, which facilitates to manage and enhance PPB. Besides, the structural redundancy of existing assessment methods is solved through the dynamic optimization of the network structure via the pruning algorithm, enhancing the effectiveness of PPB decision-making tools.

Details

Kybernetes, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0368-492X

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Article
Publication date: 1 September 2022

Fei Sun, Haisang Liu, Yuqin Din, Honglian Cong and Zhijia Dong

The purpose of this research is to propose a flexible sensor with a weft-knitted float stitch structure and to explore knitting techniques that allow conductive yarns to be…

206

Abstract

Purpose

The purpose of this research is to propose a flexible sensor with a weft-knitted float stitch structure and to explore knitting techniques that allow conductive yarns to be skin-tight and less exposed, reducing production processes and increasing productivity. Study its electrical conductivity in different yarn materials, knit processes and deformation ranges. The analysis is compared to provide some basis for the design of the electrodes.

Design/methodology/approach

The method includes five operations: (1) Analysis of the morphological appearance, tensile variation, fiber material properties and electrical conductivity of high-elastic and filament silver-plated conductive yarns. (2) Based on the knitting process of the floating yarn structure, three-dimensional modeling of the flexible sensor was carried out to explore the influence of knitting process changes on appearance characteristics. (3) The fabric samples are knitted by different silver-plated conductive yarns with different structures. Processing of experimental samples to finished size by advance shrinkage. (4) Measure the resistance of the experimental sample after the machine has been lowered and after pre-shrinking. Use the stretching machine to simulate a wearing experiment and measure the change in resistance of the sample in the 0–15% stretching range. (5) Analyze the influence factors on the conductive performance of the flexible sensor to determine whether it is suitable for textile flexible sensors.

Findings

For the float knitted flexible sensors, the floating wire projection is influenced by the elasticity of the fabric and the length of the floating wire. Compared to the plain knitted flexible sensors, it has less resistance variation and better electrical properties, making it suitable for making electrodes for textile structures. In addition, the knitting method is integrated with the intelligent monitoring clothing, which saves the process for the integration of the flexible sensor, realizes positioning and fixed-point knitting.

Practical implications

The sensor technology of the designed weft-knitted float structure is varied and can be freely combined and designed in a wide range. Within the good electrical conductivity, the flexible sensor can realize integrated knitting, positioning monitoring, integrating into the appearance of clothing. It can also focus on the wearing experience of wearable products so that the appearance of the monitoring clothing is close to the clothes we wear in our daily life.

Originality/value

In this paper, an integrated positioning knitting flexible sensor based on the weft knitting float structure is studied. The improved knitting process allows the sensing contact surface to be close to the skin and reduces the integration process. The relationship between the exposure of the silver-plated yarn on the clothing surface and the electrical conductivity is analyzed. Within a certain conductive performance, reduces the exposed area of the conductive yarn on the clothing surface and proposes a design reference for the flexible sensor appearance.

Details

International Journal of Clothing Science and Technology, vol. 35 no. 4
Type: Research Article
ISSN: 0955-6222

Keywords

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Article
Publication date: 17 January 2020

Wei Feng, Yuqin Wu and Yexian Fan

The purpose of this paper is to solve the shortage of the existing methods for the prediction of network security situations (NSS). Because the conventional methods for the…

337

Abstract

Purpose

The purpose of this paper is to solve the shortage of the existing methods for the prediction of network security situations (NSS). Because the conventional methods for the prediction of NSS, such as support vector machine, particle swarm optimization, etc., lack accuracy, robustness and efficiency, in this study, the authors propose a new method for the prediction of NSS based on recurrent neural network (RNN) with gated recurrent unit.

Design/methodology/approach

This method extracts internal and external information features from the original time-series network data for the first time. Then, the extracted features are applied to the deep RNN model for training and validation. After iteration and optimization, the accuracy of predictions of NSS will be obtained by the well-trained model, and the model is robust for the unstable network data.

Findings

Experiments on bench marked data set show that the proposed method obtains more accurate and robust prediction results than conventional models. Although the deep RNN models need more time consumption for training, they guarantee the accuracy and robustness of prediction in return for validation.

Originality/value

In the prediction of NSS time-series data, the proposed internal and external information features are well described the original data, and the employment of deep RNN model will outperform the state-of-the-arts models.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 13 no. 1
Type: Research Article
ISSN: 1756-378X

Keywords

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