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The purpose of this paper is to present an on-line modeling and controlling scheme based on the dynamic recurrent neural network for wastewater treatment system.

A control strategy based on rule adaptive recurrent neural network (RARFNN) is proposed in this paper to control the dissolved oxygen (D_O) concentration and nitrate nitrogen (S_No) concentration. The structure of the RARFNN is self-organized by a rule adaptive algorithm, and the rule adaptive algorithm considers the overall information processing ability of neural network. Furthermore, a stability analysis method is given to prove the convergence of the proposed RARFNN.

By application in the control problem of wastewater treatment process (WWTP), results show that the proposed control method achieves better performance compared to other methods.

The proposed on-line modeling and controlling method uses the RARFNN to model and control the dynamic WWTP. The RARFNN can adjust its structure and parameters according to the changes of biochemical reactions and pollutant concentrations. And, the rule adaptive mechanism considers the overall information processing ability judgment of the neural network, which can ensure that the neural network contains the information of the biochemical reactions.

The paper aims to tackle the classification of Remote Sensing Images (RSIs), which presents a significant challenge for computer algorithms due to the inherent characteristics of clustered ground objects and noisy backgrounds. Recent research typically leverages larger volume models to achieve advanced performance. However, the operating environments of remote sensing commonly cannot provide unconstrained computational and storage resources. It requires lightweight algorithms with exceptional generalization capabilities.

This study introduces an efficient knowledge distillation (KD) method to build a lightweight yet precise convolutional neural network (CNN) classifier. This method also aims to substantially decrease the training time expenses commonly linked with traditional KD techniques. This approach entails extensive alterations to both the model training framework and the distillation process, each tailored to the unique characteristics of RSIs. In particular, this study establishes a robust ensemble teacher by independently training two CNN models using a customized, efficient training algorithm. Following this, this study modifies a KD loss function to mitigate the suppression of non-target category predictions, which are essential for capturing the inter- and intra-similarity of RSIs.

This study validated the student model, termed KD-enhanced network (KDE-Net), obtained through the KD process on three benchmark RSI data sets. The KDE-Net surpasses 42 other state-of-the-art methods in the literature published from 2020 to 2023. Compared to the top-ranked method’s performance on the challenging NWPU45 data set, KDE-Net demonstrated a noticeable 0.4% increase in overall accuracy with a significant 88% reduction in parameters. Meanwhile, this study’s reformed KD framework significantly enhances the knowledge transfer speed by at least three times.

This study illustrates that the logit-based KD technique can effectively develop lightweight CNN classifiers for RSI classification without substantial sacrifices in computation and storage costs. Compared to neural architecture search or other methods aiming to provide lightweight solutions, this study’s KDE-Net, based on the inherent characteristics of RSIs, is currently more efficient in constructing accurate yet lightweight classifiers for RSI classification.

The purpose of this paper is to explore crucial evolutionary operational improvement initiatives related to quality management, and the emphasis is on the lessons learned from operational improvement practices in Taiwan.

To understand a firm's evolutionary operational improvement activities holistically and deeply, the authors conducted in‐depth interviews with executives from four firms in Taiwan. The grounded theory method is adopted in this study to depict the profile of the key evolutionary operational improvement activities.

Based on the results of the qualitative empirical study, six major dimensions of operational improvement are identified. Further, eight empirical propositions are proposed with detailed descriptions.

The results are based on international manufacturing companies in Taiwan, and thus cannot be generalized to situations in other emerging countries and industries without further research.

By referring to the results of this work, managers can better understand the relationships among operational improvement activities, and thus increase the chances to benefit from operational improvement initiatives.

This paper adds to the literature by providing new empirical insights into the relationships among operational improvement activities.

The aim of this paper is threefold: first, to examine the content of supply chain quality management (SCQM); second, to identify the structure of SCQM; and third, to show ways for finding improvement opportunities and organizing individual institution's resources/actions into collective performance outcomes.

To meet the goals of this work, the paper uses abductive reasoning and two qualitative methods: content analysis and formal concept analysis (FCA). Primary data were collected from both original design manufacturers (ODMs) and original equipment manufacturers (OEMs) in Taiwan.

According to the qualitative empirical study, modern enterprises need to pay immediate attention to the following two pathways: a compliance approach and a voluntary approach. For the former, three strategic content variables are identified: training programs, ISO, and supplier quality audit programs. As for initiating a voluntary effort, modern lead firms need to instill “motivation” into a supply chain quality system.

The findings based on the abductive model reveal numerous strategic and tactical enablers, key sequences to move firms from their current situation to their preferred one, and critical opportunities for supply chain‐wide quality system designs.

This study will be of great value to supply chain policy makers, supply chain operators, and decision makers in lead firms in a supply chain setting and their channel partners. The proactive use of the authors' proposed research procedure is indispensable to effective supply chain quality planning.

Environmental concern is getting increasing importance in consumer shopping decisions. Nevertheless, to date, sustainable packaged foods are not always the first option when consumers go shopping. This paper analyses how environmental concern moderates the role played by external factors – preference towards sustainable retailers and trust in sustainable producers – in determining consumer purchase intentions for sustainable packaged foods. Consumer involvement in eco-friendly labels, increasingly present in food packages, is investigated as indirectly impacting pro-environmental purchase intentions.

An online survey administered to a sample of Italian food shoppers is used for the empirical analysis. A total of 278 structured questionnaires were modelled using a structural equation modelling approach.

Findings show that producers and retailers' policies in favour of sustainability are key in determining consumers' sustainable purchase intentions. Further, coherent uses of labels and logos in light of sustainability can support consumer purchase decisions. Relevant is the influence played by the environmental concern in both supporting pro-environmental purchase intentions and in amplifying the trust in sustainable producers-purchase intentions path.

This study contributes to the literature on sustainability showing how producers and retailers may together influence consumers' pro-environmental purchase intentions. Findings extend the retail literature on the impact of producers and retailers' policies on consumers' sustainable purchases. Further, environmental concern is investigated in its moderating role on the impact of external factors on consumers' pro-environmental purchase intentions.

The purpose of this paper is to explore the determinants of three different types of bank liquidity: funding liquidity, liquidity creation, and stock liquidity in emerging markets.

It uses an extensive set of data from all the listed banks of Brazil, Russia, India, China, and South Africa, collectively known as the BRICS countries, spanning the period 2002-2014. Multiple linear regression has been used to estimate the coefficients of the determinants.

In case of emerging markets, bank size is not a determinant of different types of liquidity, except funding liquidity. Besides, the recent financial crisis had an impact on funding liquidity as well as “cat nonfat” measure of liquidity creation but it did not affect “cat fat” measure and stock liquidity. The variation in funding liquidity is also explained by the profitability and the riskiness of the bank. Effective interest rate, national savings rate, and inflation rate are also the determinants of funding liquidity. Bank-specific determinants of liquidity creation include bank leverage and profitability, and macroeconomic determinants include stock market index, effective interest rate, and unemployment rate. The variation in stock liquidity of the bank is explained by profitability and price of stocks, trading volume, volatility of stock returns, and percentage change in real gross domestic product. Neither market capitalization nor stock market index is the determinant of stock liquidity of the banks.

This study uses the data from publically listed banks only.

The findings of this study may be used by the policy makers and bank managers in the emerging markets to design better policies and to strengthen the banking system to avoid financial turmoil in future.

Most of the existing studies focus on bank liquidity in developed countries and studies aiming on emerging countries are rare. The existing studies focus more on funding liquidity and liquidity creation but to the best of the authors’ knowledge, none of the studies analyze the determinants of banks’ stock liquidity. So, this study bridges the above mentioned gaps by focusing on bank liquidity in emerging markets, and exploring the determinants of the stock liquidity of the banks.

Third-party logistics (3PL) plays a main role in supply chain management and, as a result, has experienced remarkable growth. The demand for 3PL providers has become a main approach for companies to offer better customer service, reduce costs, and gain competitive advantage. This paper identifies important criteria for 3PL provider selection and evaluation, and the purpose of this paper is to select 3PL providers from the viewpoint of firms which were already outsourcing their logistics services.

This study utilized the grey decision-making trial and evaluation laboratory (DEMATEL) method to develop 3PL provider selection criteria. Because human judgments are vague and complicated to depict by accurate numerical values, the grey system theory is used to handle this problem.

The findings revealed the structure and interrelationships between criteria and identified the main criteria for 3PL provider selection. The most important criteria for 3PL provider selection are on time delivery performance, technological capability, financial stability, human resource policies, service quality, and customer service, respectively.

The paper’s results help managers of automotive industries, particularly in developing countries, to outsource logistics activities to 3PL providers effectively and to create a significant competitive advantage.

The main contributions of this paper are twofold. First, this paper proposes an integrated grey DEMATEL method to consider interdependent relationships among the 3PL provider selection criteria. Second, this study is one of the first studies to consider 3PL provider selection in a developing country like Iran.

As a consequence of the proposal of the Carbon Neutral and Carbon Peak policy in 2020, the Chinese Government is paying more attention to developing sustainability performance. This study aims to assess the direct influence of country-level and corporate anti-corruption measures on environmental, social and governance (ESG) and its three dimensions, besides ascertaining the moderating role of firm size.

This study used the system generalized method of moments on a sample of 820 Chinese listed firms from 2012 to 2021.

The findings show that country-level and corporate corruption negatively affect ESG performance. Corporate anti-corruption measures have a more pronounced positive influence on the sustainability performance of small firms than large firms due to the limited resources, lower political position and weaker refusal power of small firms.

The study has great implications for governments, corporate boards and ESG rating agencies. Government and corporate boards should mitigate the risks of country-level and corporate corruption to attain sustainable development goals. Rating agencies should add country-level and corporate corruption into the ESG evaluation system.

Some empirical results have proven that anti-corruption measures help reduce the emission of carbon dioxide, but few evidence shows how country-level and corporate corruption affect ESG and its three dimensions.

Burr distribution has been proved to be a useful failure model. It can assume different shapes which allow it to be a good fit for various lifetimes data. Hybrid censoring is an important way of generating lifetimes data. The purpose of this paper is to estimate an unknown parameter of the Burr type XII distribution when data are hybrid censored.

The problem is dealt with through both the classical and Bayesian point of view. Specifically, the methods of estimation used to tackle the problem are maximum likelihood estimation method and Bayesian method. Empirical Bayesian approach is also considered. The performance of all estimates is compared through their mean square error values. The paper employs Monte Carlo simulation to evaluate the mean square error values of all estimates.

The key findings of the paper are that the Bayesian estimates are superior to the maximum likelihood estimates (MLE).

This work has practical importance. Indeed, the proposed methods are applied to real life data.

The paper is original and is quite applicable in lifetimes data analysis.

Halal products were a norm in the Muslim countries; however, halal food products were not only being consumed by Muslims nowadays. People are concerned about their daily needs, especially their food and spend in this modern society. Customers nowadays are more educated, and all the information can be easily accessed by just a click. Previous literature found that customers are looking for quality products and products that are safer to be consumed.

The purpose of this study investigates non-Muslim customers’ intention in consuming halal food products in Klang Valley, Malaysia. Using 321 non-Muslim responses from Klang Valley, partial least square regression was used to examine the factors influencing the customer’s purchase intention on halal products.

The results show that product quality, product price, product availability and product safety significantly influence the purchase intention of urban non-Muslim on halal food products. The result implies that product quality is the most significant factor compared with other variables.

The data and results may contribute to the relevant authority to form a proper plan to improve Malaysia’s halal food items.