Wenchao Duan, Yiqiang Yang, Wenhong Liu, Zhiqiang Zhang and Jianzhong Cui
The purpose of this paper is to reveal the solute segregation behavior in the molten and solidified regions during direct chill (DC) casting of a large-size magnesium alloy slab…
Abstract
Purpose
The purpose of this paper is to reveal the solute segregation behavior in the molten and solidified regions during direct chill (DC) casting of a large-size magnesium alloy slab under no magnetic field (NMF), harmonic magnetic field (HMF), pulsed magnetic field (PMF) and two types of out-of-phase pulsed magnetic field (OPMF).
Design/methodology/approach
A 3-D multiphysical coupling mathematical model is used to evaluate the corresponding physical fields. The coupling issue is addressed using the method of separating step and result inheritance.
Findings
The results suggest that the solute deficiency tends to occur in the central part, while the solute-enriched area appears near the fillet in the molten and solidified regions. Applying magnetic field could greatly homogenize the solute field in the two-phase region. The variance of relative segregation level in the solidified cross-section under NMF is 38.1%, while it is 21.9%, 18.6%, 16.4% and 12.4% under OPMF2 (the current phase in the upper coil is ahead of the lower coil), HMF, PMF and OPMF1 (the current phase in the upper coil lags behind the lower coil), respectively, indicating that OPMF1 is more effective to reduce the macrosegregation level.
Originality/value
There are few reports on the solute segregation degree in rectangle slab under magnetic field, especially for magnesium alloy slab. This paper can act a reference to make clear the solute transport behavior and help reduce the macrosegregation level during DC casting.
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Keywords
Zhiwen Pan, Jiangtian Li, Yiqiang Chen, Jesus Pacheco, Lianjun Dai and Jun Zhang
The General Society Survey(GSS) is a kind of government-funded survey which aims at examining the Socio-economic status, quality of life, and structure of contemporary society…
Abstract
Purpose
The General Society Survey(GSS) is a kind of government-funded survey which aims at examining the Socio-economic status, quality of life, and structure of contemporary society. GSS data set is regarded as one of the authoritative source for the government and organization practitioners to make data-driven policies. The previous analytic approaches for GSS data set are designed by combining expert knowledges and simple statistics. By utilizing the emerging data mining algorithms, we proposed a comprehensive data management and data mining approach for GSS data sets.
Design/methodology/approach
The approach are designed to be operated in a two-phase manner: a data management phase which can improve the quality of GSS data by performing attribute pre-processing and filter-based attribute selection; a data mining phase which can extract hidden knowledge from the data set by performing data mining analysis including prediction analysis, classification analysis, association analysis and clustering analysis.
Findings
According to experimental evaluation results, the paper have the following findings: Performing attribute selection on GSS data set can increase the performance of both classification analysis and clustering analysis; all the data mining analysis can effectively extract hidden knowledge from the GSS data set; the knowledge generated by different data mining analysis can somehow cross-validate each other.
Originality/value
By leveraging the power of data mining techniques, the proposed approach can explore knowledge in a fine-grained manner with minimum human interference. Experiments on Chinese General Social Survey data set are conducted at the end to evaluate the performance of our approach.
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Yiqiang Zhou and Lianghua Chen
This study aims to investigate whether public attention influences corporate decisions on environmental disclosure, thereby revealing how society perceives and understands…
Abstract
Purpose
This study aims to investigate whether public attention influences corporate decisions on environmental disclosure, thereby revealing how society perceives and understands environmental issues and how corporations respond to these expectations.
Design/methodology/approach
We selected publicly listed Chinese firms as our sample. An “Environmental Disclosure Greenwashing” (EDG) Index was developed through textual analysis of their annual reports using natural language processing. Financial data were obtained from the CSMAR database, and multivariate regression was used for analysis.
Findings
The impact of public attention on EDG primarily manifests as an oversight pressure effect rather than a legitimacy incentive effect. As public attention intensifies, firms tend to adopt more substantial environmental actions instead of merely symbolic environmental disclosures. Formal regulatory frameworks might inadvertently trigger corporate EDG, but public attention can correct the adverse effects possibly introduced by formal regulations. Notably, in firms facing lower institutional pressure, the influence of public attention is more pronounced.
Practical implications
The evidence suggests that public attention reduces corporate EDG. These findings have significant implications for the regulation of environmental disclosures among firms in emerging economies.
Originality/value
The study integrates research in environmental disclosure with the concept of “greenwashing”, unveiling the limitations of the “disclosure as governance” viewpoint. It elucidates the impact of an informal external oversight mechanism (i.e. public attention) on complex corporate environmental disclosure decisions.
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Sazali Abidin, Krishna Reddy and Liehui Chen
Since the initiation of the share split reform by the Chinese Securities Regulatory Commission (CSRC) in 2005, the private placement has become the major source of raising equity…
Abstract
Purpose
Since the initiation of the share split reform by the Chinese Securities Regulatory Commission (CSRC) in 2005, the private placement has become the major source of raising equity after IPO. The purpose of this paper is to investigate why listed firms in China prefer private placements compared to other options of raising capital.
Design/methodology/approach
The ordinary least squares regression, the piecewise regression and the cross‐sectional regression analysis were undertaken to investigate the determinants and characteristics of the seasoned‐equity offerings announcement effects. Probit regression analysis was taken to estimate the probability of a firm choosing private placements.
Findings
The authors find positive significant announcement abnormal returns for private placement. The findings also indicate that operating performance deteriorates immediately after announcement and poor operating performance is more likely to be contributed by large size portfolios, which suggests size effect.
Research limitations/implications
The paper's evidence contributes to an understanding of the wider implication of the share split reform undertaken by the CSRC.
Practical implications
The paper provides insights for policy makers in China and around the world who have and wish to adopt similar practices within their jurisdictions. Similar research can be conducted in other emerging markets to enable better understanding and implications of seasoned equity offerings on firm financial performance.
Originality/value
The paper is novel in regard to the data and the wider research paradigm used.
Details
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Gomathi V., Kalaiselvi S. and Thamarai Selvi D
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…
Abstract
Purpose
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 λmax method for weight initialization, as a data normalization technique, to achieve high accuracy of classification.
Design/methodology/approach
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 λmax 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 λmax method.
Findings
The effectiveness of the proposed FDCNN model with λmax method was outperformed than state-of-the-art models and attained with overall accuracy of 97.89% with overall F1 score as 0.9795.
Practical implications
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 λmax method.
Social implications
Contributed a novel FDCNN architecture that can support those who are keen in advancing human activity recognition (HAR) recognition.
Originality/value
A novel FDCNN architecture is implemented with appropriate FAL kernel structures.