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Queries as a pioneering measure of public attention on various social issues have elicited considerable scholarly attention. The purpose of this paper is to address two fundamental questions, as follows: first, how do we identify niche queries that internet users search for on specific social issues?; and second, what are the measurement properties of queries data in gauging public attention on social issues?

The present study uses public attention on environmental issues in the USA as the empirical setting of research. An iterative framework is developed to identify niche queries to measure public attention on environmental issues. The measurement properties of queries data are assessed by comparing the dynamics of public attention on environmental issues captured by queries data with that measured by the “most important problem” (MIP) question in Gallup opinion polls.

A list of 39 niche queries that internet users search for on environmental issues is identified. The dynamics of public attention on environmental issues determined by the search trends of these 39 queries is found to positively correlate with that measured by Gallup MIP polls, whereas both dynamics can forecast each other well in a 12-month time frame.

The findings of the study possess methodological and practical implications. The study shows that queries data are complementary to, rather than substitutes of, public opinion polls in measuring public attention on environmental issues. The iterative framework developed in the study can be applied in future studies to help researchers identify valid queries to measure public attention on other social issues, as it can minimize researchers’ subjective biases in selecting search queries. Policymakers and environmentalists can utilize our approach to monitor the status of public attention on environmental issues and implement campaigns to mobilize favorable public opinion when the decline of public attention is predicted by the trends of web search queries.

This study aims to focus on the optimized design and mechanical properties of gradient triply periodic minimal surface cellular structures manufactured by selective laser melting.

Uniform and gradient IWP and primitive cellular structures have been designed by the optimized function in MATLAB, and selective laser melting technology was applied to manufacture these cellular structures. Finite element analysis was applied to optimize the pinch-off problem, and compressive tests were carried out for the evaluation of mechanical properties of gradient cellular structures.

Finite element analysis shows that the elastic modulus of IWP increased as design parameter b increased, and then decreased when parameter b is higher than 5.5. The highest elastic modulus of primitive increased by 89.2% when parameter b is 6. The compressive behavior of gradient IWP and primitive shows a layer-by-layer way, and elastic modulus and first maximum compressive strength of gradient primitive are higher than that of gradient IWP. The effective energy absorption of gradient cellular structures increased as the average porosity decreased, and the effective energy absorption of gradient primitive is about twice than that of gradient IWP.

This paper presents an optimized design method for the pinch-off problem of gradient triply periodic minimal surface cellular structures.

The purpose of this paper is to facilitate understanding of how to convert free players to paid consumers in free-to-play games.

Drawing on the consumption value framework and affordance theory, the present study argues that in-game purchase behaviors are determined by multiple consumption values of in-game items. The perceptions of consumption values were influenced by game affordances. The model was tested, using data from an empirical survey with 2,006 free-to-play game players.

Monetary, enjoyment and social values of in-game items positively predict purchase behaviors in free-to-play games. Game fairness and balance of challenges and skills significantly influence perceived enjoyment value.

The findings of this study provide operable implications to facilitate in-game consumption. The model was tested within the context of free-to-play multiplayer online battle arena (MOBA) games; however, caution is advisable when generalizing the findings to other subgenre of games.

This study extended and thus validated the consumption value framework in the context of free-to-play MOBA games. This study explored the antecedents of consumption values from the perspective of game affordance.

Sensor arrays and pattern recognition-based electronic nose (E-nose) is a typical detection and recognition instrument for indoor air quality (IAQ). The E-nose is able to monitor several pollutants in the air by mimicking the human olfactory system. Formaldehyde concentration prediction is one of the major functionalities of the E-nose, and three typical machine learning (ML) algorithms are most frequently used, including back propagation (BP) neural network, radial basis function (RBF) neural network and support vector regression (SVR).

This paper comparatively evaluates and analyzes those three ML algorithms under controllable environment, which is built on a marketable sensor arrays E-nose platform. Variable temperature (T), relative humidity (RH) and pollutant concentrations (C) conditions were measured during experiments to support the investigation.

Regression models have been built using the above-mentioned three typical algorithms, and in-depth analysis demonstrates that the model of the BP neural network results in a better prediction performance than others.

Finally, the empirical results prove that ML algorithms, combined with low-cost sensors, can make high-precision contaminant concentration detection indoor.

Three adjustment modes are alternatives for mixed-model assembly lines (MMALs) to improve their production plans according to constantly changing customer requirements. The purpose of this paper is to deal with the decision-making problem between these modes by proposing a novel multi-classification method. This method recommends appropriate adjustment modes for the assembly lines faced with different customer orders through machine learning from historical data.

The decision-making method uses the classification model composed of an input layer, two intermediate layers and an output layer. The input layer describes the assembly line in a knowledge-intensive manner by presenting the impact degrees of production parameters on line performances. The first intermediate layer provides the support vector data description (SVDD) of each adjustment mode through historical data training. The second intermediate layer employs the Dempster–Shafer (D–S) theory to combine the posterior classification possibilities generated from different SVDDs. The output layer gives the adjustment mode with the maximum posterior possibility as the classification result according to Bayesian decision theory.

The proposed method achieves higher classification accuracies than the support vector machine methods and the traditional SVDD method in the numerical test consisting of data sets from the machine-learning repository and the case study of a diesel engine assembly line.

This research recommends appropriate adjustment modes for MMALs in response to customer demand changes. According to the suggested adjustment mode, the managers can improve the line performance more effectively by using the well-designed optimization methods for a specific scope.

The adjustment mode decision belongs to the multi-classification problem featured with limited historical data. Although traditional SVDD methods can solve these problems by providing the posterior possibility of each classification result, they might have poor classification accuracies owing to the conflicts and uncertainties of these possibilities. This paper develops a novel classification model that integrates the SVDD method with the D–S theory. By handling the conflicts and uncertainties appropriately, this model achieves higher classification accuracies than traditional methods.

This paper aims to examine whether managerial sentiment, extracted from annual reports, is associated with corporate risk-taking in the context of Chinese companies. This study expands the vein of literature on overconfidence theory.

By leveraging textual analysis on Chinese listed companies’ annual reports, the authors construct firm-level managerial sentiment during 2007 and 2021 to examine how managerial sentiment influences corporate risk-taking after control for firm characteristics. Corporate risk-taking is denoted by corporate investment engagements: capital expenditures and net fixed asset investment.

Results show that incentives for corporate risk-taking are likely to increase with the positive managerial sentiment and decrease with the negative sentiment in companies’ annual reports. Positive managerial sentiment is associated with over-/under-investment and low/high investment efficiency. Further additional tests show that the managerial sentiment effect only holds during low economic uncertain years and samples of private-owned firms. Furthermore, the robust tests indicate that there is no endogenous issue between managerial sentiment and corporate risk-taking.

Annual report textual-based managerial sentiment may not perfectly reflect managers’ lower frequency sentiment (e.g. weekly, monthly and quarterly sentiment). Future studies could attempt to capture managers’ on-time sentiment by using media sources and corporate disclosures.

To the best of the authors’ knowledge, this paper is the first research to provide insights into supervising managers’ corporate decisions by observing their textual information usage in corporate disclosure. Moreover, the approach of measuring managerial sentiment might be a solution to monitoring managerial class.

This paper contributes to the literature on accounting and finance studies, adding another piece of empirical evidence on content analysis by examining a unique language and institutional context (i.e. China). Besides, the paper notes that in line with the English version disclosure, based on Chinese semantic words, managerial sentiment in the Chinese-speaking world has magnitude on corporate decisions. The research provides insights into supervising managers’ corporate decisions by observing their textual information usage in corporate disclosure. Moreover, the approach to measuring managerial sentiment may be a practical solution to monitoring managerial class.

The purpose of this study is to design a wearable medical device as a human care platform and to introduce the design details, key technologies and practical implementation methods of the system.

A multi-channel data acquisition scheme based on PCI-E (rapid interconnection of peripheral components) was proposed. The flexible biosensor is integrated with the flexible data acquisition card with monitoring capability, and the embedded (device that can operate independently) chip STM32F103VET6 is used to realize the simultaneous processing of multi-channel human health parameters. The human health parameters were transferred to the upper computer LabVIEW by intelligent clothing through USB or wireless Bluetooth to complete the transmission and processing of clinical data, which facilitates the analysis of medical data.

The smart clothing provides a mobile medical cloud platform for wearable medical through cloud computing, which can continuously monitor the body's wrist movement, body temperature and perspiration for 24 h. The result shows that each channel is completely accurate to the top computer display, which can meet the expected requirements, and the wearable instant care system can be applied to healthcare.

The smart clothing in this study is based on the monitoring and diagnosis of textiles, and the electronic communication devices can cooperate and interact to form a wearable textile system that provides medical monitoring and prevention services to individuals in the fastest and most accurate way. Each channel of the system is precisely matched to the display screen of the host computer and meets the expected requirements. As a real-time human health protection platform technology, continuous monitoring of human vital signs can complete the application of human motion detection, medical health monitoring and human–computer interaction. Ultimately, such an intelligent garment will become an integral part of our everyday clothing.

With high-speed urbanization and strict requirements on energy conservation and environmental protection, eco-city has become the priority of urban development in China. Governments of all levels promote various eco-city planning schemes within a short term, but most schemes are characterized by randomness and subjectivity. To solve the lack of scientific pre-implementation evaluation for eco-city planning and improve eco-city construction quality, a systematic analysis is conducted in this study from three dimensions, namely, eco-city planning scheme, operating mechanism, and guarantee measures. A 3D evaluation model for eco-city planning is constructed through index selection. Schemes are ranked comprehensively with the distance measure method, and projection analysis is performed with the four-grid evaluation screen. Finally, the 3D evaluation model is applied to evaluate eco-city planning for Shenzhen, Chongqing, and Weifang. Results show that the model can be used to evaluate eco-city planning schemes effectively and comprehensively and offers targeted optimization suggestions in accordance with the evaluation results. Construction of the 3D evaluation model of eco-city planning based on the distance measure to determine the reasonability of eco-city planning reduces the institutional and social costs of eco-city planning practices.

This paper aims to provide a new drilling entry board for printed circuit board (PCB) process, superior in heat dissipation, lubrication, water solubility and hole location accuracy, achieving an excellent drilling process.

Using a mixture of polyethylene glycol (PEG) and water-soluble adhesives as hydrosoluble, endothermic and lubricant resins and aluminum foils as baseplates, a series of coated and aluminous entry boards (CABs) for PCB drilling was successfully prepared. The surface appearance of the entry boards was observed clearly by scanning electron microscopy (SEM). The endothermic and lubricant effects of the resins applied on the CABs was characterized by differential scanning calorimetry (DSC) and their water solubility was tested in the normal-temperature water (25°C). Moreover, the CABs’ good drilling properties were tested when they were used for PCB drilling.

The SEM analysis showed that the surfaces of the resin layers coated on the CABs whose coating thicknesses were less than 80 μm were smoother and flatter, which could improve hole location accuracy and reduce drill breakage ratio. By virtue of DSC, the endothermic and lubricant effects of the CABs were proven. The fusion of PEG in the resin layers could absorb the heat produced by drilling, restrain the temperature of the drill bit and hole rising and lubricate the drill bit efficiently when a hole was being drilled, which could achieve high-quality holes with good production efficiency. The water-soluble test showed that the prepared CABs had excellent water solubility at normal temperature, enabling the resin left on the hole walls and in the flute of the drill bit to be washed away easily and thereby improving the drilling efficiency and quality. The drilling tests showed that the increase in the thickness of the CABs’ coating could improve the hole location accuracy and alleviate the bit wear. In addition, the suitable coating thickness could ensure the firm adhering of the resin coating the aluminum foil, effectively avoid drill intertwist and prevent the resin debris from blocking the drilled holes on the surface of the entry board, which could hinder chip removal, resulting in poor hole wall quality and drill breakage.

This paper has a remarkably high industrial practicality in the PCB manufacture process.

The purpose of this paper is to generate line plan in emergencies for railway networks to complete the passenger transportation.

The authors first build a mathematical model, focusing on the frequency setting and stops setting. Then, considering the OD passenger flow data, the authors first propose the method to solve the train frequency setting problem of different types. Genetic algorithm is designed to solve the stops setting problem. The approach is tested with the data from Beijing-Shanghai high-speed railway and its neighbour existing railway.

The authors find that the model is suitable to generate line plan in emergencies for railway networks and the algorithm has good calculating performance.

The new algorithms to generate line plan proposed in this paper can be embedded in the decision support system for railway operators.