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The purpose of this paper is to present a novel concentration estimation model for improving the accuracy and robustness of low-cost electronic noses (e-noses) with metal oxide semiconductor sensors in indoor air contaminant monitoring and overcome the potential sensor drift.

In the quantification model, a piecewise linearly weighted artificial neural network ensemble model (PLWE-ANN) with an embedded self-calibration module based on a threshold network is studied.

The nonlinear estimation problem of sensor array-based e-noses can be effectively transformed into a piecewise linear estimation through linear weighted neural networks ensemble activated by a threshold network.

In this paper, a number of experimental results have been presented, and it also demonstrates that the proposed model has very good accuracy and robustness in real-time indoor monitoring of formaldehyde.

This study aims to explore the degree of Chinese consumers' trust and confidence in the Chinese dairy products supply chain and the relationships between trust and overall confidence in dairy products safety and quality.

This study collected data from 1,278 respondents by field survey from five provinces of China. The data were analyzed using ordered logit model.

This study shows the following results: (1) Chinese consumer confidence in domestic dairy products and trust in actors of the dairy chain are at a moderate-to-low level. (2) Government regulators are considered to take the most responsibility, with both an optimism-enhancing and a pessimism-reducing effect (the former effect is greater), while perceived trust in dairy farmers and retailers has little effect. (3) Perceived care has both an optimism-enhancing and a pessimism-reducing effect, and the former effect is stronger. Competence and openness have an optimism-enhancing effect and a pessimism-reducing effect, respectively. (4) The importance of the three dimensions of trust related to optimism-increasing and pessimism-reduction is limited, except in the case of government regulators.

This study contributes to a better understanding of consumer trust in food safety and also help demonstrate to the actors and institutions involved in the dairy supply chain the best way to improve the performance of their duties to meet the consumers' needs for safe and quality dairy products.

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.

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.

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.

The paper's evidence contributes to an understanding of the wider implication of the share split reform undertaken by the CSRC.

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.

The paper is novel in regard to the data and the wider research paradigm used.

This study aims to empirically examine the relationships among perceived environmental uncertainty (EV), the level of knowledge distance (KD) and the impact of value network on firm performance.

The quantitative analysis is based on data from 243 Chinese companies with engineering, procurement and construction (EPC) business in the context of the COVID-19 pandemic.

The two dimensions of value network [network centrality (NC) and network openness (NO)] have a different impact on firm performance [financial performance (FP) and market performance (MP)]. NC has a positive impact on FP, but not on MP. NO has a positive effect on MP, but not on FP. A reduced KD mediates the relationship between value network and firm performance. Moreover, it fully mediates the relationship between NC and MP, NO and FP. Finally, during the COVID-19 pandemic, only EV has a moderating effect on KD and MP.

This study is limited in terms of data set because it relies on a limited amount of cross-sectional data from one specific country. Therefore, researchers are encouraged to test the proposed propositions further.

The present findings suggest that EPC professionals should pay more attention to the EV, which may be impacted by policy, technology and the economy. This research has actionable implications for the reform of EPC in the construction industry, and practical recommendations for EPC firms to improve their corporate performance.

The results measure the complementary effects of both dimensions of value network (NC and NO) on two distinct aspects of firm performance (MP and FP) and assess the moderating effect of EV and KD in the context of the COVID-19 pandemics.

This paper aims to investigate the relationship between military connections and tax avoidance in Indonesia. Further, the paper examines whether the relationship between military connections and tax avoidance is impacted by three corporate governance variables: auditor size or Big 4, board size and audit committee independence. Indonesia's settings allow for a unique investigation, as military involvement has been documented.

This paper uses Indonesia as the research setting because its military forces have a long history of business involvement. The sample includes 1,986 firm-year observations on the Indonesia Stock Exchange from 2010 to 2018. The period signifies the time of significant change post-Suharto to illustrate changes in military reform.

Military-connected firms recorded a negative relationship with effective tax rates, indicating higher tax avoidance. The authors extend this test by considering three corporate governance variables: Big 4, board size and audit committee independence. They find the corporate governance variables are ineffective in mitigating the positive impact of military-connected firms and corporate tax avoidance. The results remain consistent when performing endogeneity tests.

This paper adds to the extant literature by examining the impact of military connections on tax avoidance. The findings reflect Indonesia's institutional settings depicting military and political connections.

Getting high-quality data by removing the noisy data from the user-generated content (UGC) is the first step toward data mining and effective decision-making based on ubiquitous and unstructured social media data. This paper aims to design a framework for revoking noisy data from UGC.

In this paper, the authors consider a classification-based framework to remove the noise from the unstructured UGC in social media community. They treat the noise as the concerned topic non-relevant messages and apply a text classification-based approach to remove the noise. They introduce a domain lexicon to help identify the concerned topic from noise and compare the performance of several classification algorithms combined with different feature selection methods.

Experimental results based on a Chinese stock forum show that 84.9 per cent of all the noise data from the UGC could be removed with little valuable information loss. The support vector machines classifier combined with information gain feature extraction model is the best choice for this system. With longer messages getting better classification performance, it has been found that the length of messages affects the system performance.

The proposed method could be used for preprocessing in text mining and new knowledge discovery from the big data.