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The high-quality development of the real estate industry is crucial to the transformation of China's economy. However, few studies apply the productivity to explore the development path of the real estate industry in China. To fill this gap, this study mainly investigates the total factor productivity (TFP) of the real estate industry of 30 sample provinces in mainland China from 2007 to 2016.

The Malmquist index is applied to estimate the productivity (i.e. TFP) of the real estate industry, based on the data envelopment analysis (DEA). Then, the truncated tobit regression analysis explores the external influencing factors on the TFP of the real estate industry.

Through empirical analysis, it is found that the high-quality development of the real estate industry depends on the technological innovation by the real estate enterprises and the targeted policies by the provincial government. Moreover, the development of the real estate industry has a positive correlation with the growth of China's economy but a negative correlation with the development of other industries.

TFP mainly reveals the development status of the provincial real estate industry and identifies the driving force for exploring the high-quality development mode of the real estate sector. Furthermore, the fluctuation rule of TFP can be applied to predict the development trend of the real estate industry in the future.

As an application, this study measures the TFP of the Chinese real estate industry in different provinces and periods. The results have meaningful policy implications for policymakers regulating the real estate industry.

This study examines the effects of CLS and DS on companies' WCME and analyses the differences in WCME at company and market levels.

This study adopts the DEA approach, regression, differences, and additional analyses to achieve its objectives. This study employs 235 non-financial companies and 1,175 company-year observations from eight active industries in the United States from 2016 to 2020.

The findings indicate that CLS and DS strategies positively influence companies' WCME. Additionally, WCME differed across size categories and industries, with large companies and those operating in the communication services industry showing better WCME. By contrast, WCME did not differ between the periods before and during the COVID-19 pandemic.

This study scrutinizes the impact of CLS and DS strategies on companies' WCME to bridge the gap in this field. It extends the investigation of competitive strategies as explanatory variables for a company's WCME and examines the differences in companies' WCME at the company and market levels, which may assist decision-makers in improving their strategies and efficiencies for continuous improvement.

This study enhances current knowledge by uncovering the influence of CLS and DS strategies on improving companies' WCME, an underexplored topic. It also explores companies' WCME trends and patterns regarding company size, industry type, and the pandemic period to draw interesting conclusions about the essence of WCME.

This paper aims to measure Chinese regional thermal industries’ evolution.

This paper uses data envelopment analysis (DEA) and global Malmquist–Luenberger productivity (GMLP) index.

The results reveal that the development of Chinese thermal power industry varies significantly in different regions, and it is highly correlated with the level of local economic development. Although the change of technical efficiency and scale efficiency had different impacts on different regions from year to year, the overall GMLP index change shows a close relationship with the contemporaneous frontier shift.

The results indicate that the Chinese Government should make efforts to promote its policy implementations and regulations in thermal industries so that the contemporaneous frontier will shift toward the global technology frontier with more desirable outputs and less undesirable outputs.

As an application, this study uses DEA and GMLP index to measure the productivity of Chinese thermal industries in 30 Chinese provinces from 2006 to 2013. The results have the meaningful policy implications for decision makers in charge of Chinese thermal industries.

Intent detection and slot filling are two important tasks in question comprehension of a question answering system. This study aims to build a joint task model with some generalization ability and benchmark its performance over other neural network models mentioned in this paper.

This study used a deep-learning-based approach for the joint modeling of question intent detection and slot filling. Meanwhile, the internal cell structure of the long short-term memory (LSTM) network was improved. Furthermore, the dataset Computer Science Literature Question (CSLQ) was constructed based on the Science and Technology Knowledge Graph. The datasets Airline Travel Information Systems, Snips (a natural language processing dataset of the consumer intent engine collected by Snips) and CSLQ were used for the empirical analysis. The accuracy of intent detection and F1 score of slot filling, as well as the semantic accuracy of sentences, were compared for several models.

The results showed that the proposed model outperformed all other benchmark methods, especially for the CSLQ dataset. This proves that the design of this study improved the comprehensive performance and generalization ability of the model to some extent.

This study contributes to the understanding of question sentences in a specific domain. LSTM was improved, and a computer literature domain dataset was constructed herein. This will lay the data and model foundation for the future construction of a computer literature question answering system.

Parallel factor analysis (PARAFAC) coupled with support-vector machine (SVM) was carried out to identify and discriminate between the fluorescence spectroscopies of coconut water brands.

PARAFAC was applied to reduce three-dimensional data of excitation emission matrix (EEM) to two-dimensional data. SVM was applied to discriminate between six commercial coconut water brands in this study. The three largest variation data from fluorescence spectroscopy were extracted using the PARAFAC method as the input data of SVM classifiers.

The discrimination results of the six commercial coconut water brands were achieved by three SVM methods (Ga-SVM, PSO-SVM and Grid-SVM). The best classification accuracies were 100.00%, 96.43% and 94.64% for the training set, test set and CV accuracy.

The above results indicate that fluorescence spectroscopy combined with PARAFAC and SVM methods proved to be a simple and rapid detection method for coconut water and perhaps other beverages.

The cleaning of food production equipment using cleaning detergents may contaminate the lubricant of the bearings, thereby reducing the bearing service life. The purpose of this paper is to investigate the cause and mechanism of such damage of bearings lubricated by cleaning detergent/water-in-oil emulsions.

The emulsion was prepared by adding a mixture of cleaning detergent and water in one base oil. A self-designed ball-on-disc optical interference test rig was applied to examine the effect of emulsion on lubrication and wear of bearing contacts under pure sliding conditions.

The emulsion reduced lubricating film thickness at a relatively low-sliding speed but only when the water concentration (20%) in emulsion was high. Water droplets were trapped around the ball-on-disc contact area under static conditions because of a high capillary force. The emulsion can induce damages on the soft surface in the startup mainly due to the presence of water around the contact.

The basic lubrication behaviour of water/oil emulsions containing cleaning detergent under pure sliding was experimental studied and the mechanism of bearing damage in food production equipment was investigated. Based on the study, the solution to avoid such damage was proposed.

Competitor analysis is a key component in operations management. Most business decisions are rooted in the analysis of rival products inferred from market structure. Relative to more traditional competitor analysis methods, the purpose of this paper is to provide operations managers with an innovative tool to monitor a firm’s market position and competitors in real time at higher resolution and lower cost than more traditional competitor analysis methods.

The authors combine the techniques of Web Crawler, Natural Language Processing and Machine Learning algorithms with data visualization to develop a big data competitor-analysis system that informs operations managers about competitors and meaningful relationships among them. The authors illustrate the approach using the fitness mobile app business.

The study shows that the system supports operational decision making both descriptively and prescriptively. In particular, the innovative probabilistic topic modeling algorithm combined with conventional multidimensional scaling, product feature comparison and market structure analyses reveal an app’s position in relation to its peers. The authors also develop a user segment overlapping index based on user’s social media data. The authors combine this new index with the product functionality similarity index to map indirect and direct competitors with and without user lock-in.

The approach improves on previous approaches by fully automating information extraction from multiple online sources. The authors believe this is the first system of its kind. With limited human intervention, the methodology can easily be adapted to different settings, giving quicker, more reliable real-time results. The approach is also cost effective for market analysis projects covering different data sources.

The study aim is to evaluate the contribution of Blockchain technology (Cryptobanking) using expected operating model (EOM) to address the pain points in reconciliation at middle and back-office operational levels in assessing the significance of this technology on return on investment.

A structured questionnaire was designed to collect primary data using a stratified sampling method from 120 respondents working in leading Investment banks operating in the geographical locality of urban Bangalore. Demographic variables, accounting variables, data reporting variables, approach variables, variables of EOM were considered to validate the hypothesis with the help of statistical tools, namely ANOVA, and Multiple Stepwise Regression Analysis.

The results obtained confirm that there is significant difference in reconciliation with implementation of an innovative business process. Financial analysis is the highest predictor of ROI when integrated with technology as the adapted Blockchain innovation in reconciliation is the most influencing factor in enhancing, improving ROI playing a pivotal role in the Investment banks.

Blockchain technology (Cryptobanking) facilitates in transforming the reconciliation process of these banks with improved operational efficiency. Blockchain and settlement platforms offer inter-organization solutions facilitating in the reconciliation of various transactions in real-time through a trust-based network in the form of digital settlements with better consortiums.

This paper aims to construct an argument in support of the idea that absorptive capacity may lead to improved transactive memory conditional to the effectiveness of knowledge transfer between team members.

The study reports on the results of data collected from 10 knowledge worker teams in a business-to-business context across various industries. The study tests a theoretical model to consider the relationship between the dimensions of absorptive capacity, knowledge transfer effectiveness and transactive memory systems.

At the individual level, the results suggest that knowledge acquisition and assimilation is associated with higher levels of knowledge transfer while unstructured knowledge transfer is associated with specialisation, credibility and coordination that drives transactive memory.

The results suggest that an alternative conceptualisation of the relationship between absorptive capacity, knowledge transfer effectiveness and transactive memory systems is indeed conceivable. This implies that transactive memory can benefit from increased absorptive capacity and enhanced knowledge transfer effectiveness and may point at an under-explored avenue of research.

Today’s one of the most important difficulties is tackling climate change and its effects on the environment. The Paris Agreement states that nations must balance the amount of greenhouse gases they emit and absorb until 2050 to contribute to the mitigation of greenhouse gases and to support sustainable development. According to the agreement, each country must determine, plan and regularly report on its contributions. Thus, it is important for the countries to predict and analyze their net zero performances in 2050. Therefore, the aim of this study is to evaluate European Continent Countries' net zero performances at the targeted year.

The European Continent Countries that ratified the Paris Agreement are specified as decision making units (DMUs). Input and output indicators are specified as primary energy consumption, freshwater withdrawals, gross domestic product (GDP), carbon-dioxide (CO₂) and nitrous-oxide (N₂O) emissions. Data from 1980 to 2019 are obtained and forecasted using autoregressive integrated moving average (ARIMA) until 2050. Then, the countries are clustered based on the forecasts of primary energy consumption and freshwater withdrawals using k-means algorithm. As desirable and undesirable outputs arise simultaneously, the performances are computed using Pure Environmental Index (PEI) and Mixed Environmental Index (MEI) data envelopment analysis (DEA) models.

It is expected that by 2050, CO₂ emissions of seven countries remain constant, N₂O emissions of seven countries remain stable and five countries’ both CO₂ and N₂O emissions remain constant. While it can be seen as success that many countries are expected to at least stabilize one emission, the likelihood of achieving net zero targets diminishes unless countries undertake significant reductions in emissions. According to the results, in Cluster 1, Turkey ranks last, while France, Germany, Italy and Spain are efficient countries. In Cluster 2, the United Kingdom ranks at last, while Greece, Luxembourg, Malta and Sweden are efficient countries.

In the literature, generally, CO₂ emission is considered as greenhouse gas. Moreover, none of the studies measured the net-zero performance of the countries in 2050 employing analytical techniques. This study objects to investigate how well European Continent Countries can comply with the necessities of the Agreement. Besides CO₂ emission, N₂O emission is also considered and the data of European Continent Countries in 2050 are estimated using ARIMA. Then, countries are clustered using k-means algorithm. DEA models are employed to measure the performances of the countries. Finally, forecasts and models validations are performed and comprehensive analysis of the results is conducted.