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The purpose of this paper is to explore the possibility of an enhanced continuous liquid interface production (CLIP) with a porous track-etched membrane as the oxygen-permeable window, which is prepared by irradiating polyethylene terephthalate membranes with accelerated heavy ions.

Experimental approaches are carried out to characterize printing parameters of resins with different photo-initiator concentrations by a photo-polymerization matrix, to experimentally observe and theoretically fit the oxygen inhibition layer thickness during printing under conditions of pure oxygen and air, respectively, and to demonstrate the enhanced CLIP processes by using pure oxygen and air, respectively.

Owing to the high permeability of track-etched membrane, CLIP process is demonstrated with printing speed up to 800 mm/h in the condition of pure oxygen, which matches well with the theoretically predicted maximum printing speed at difference light expose. Making a trade-off between printing speed and surface quality, maximum printing speed of 470 mm/h is also obtained even using air. As the oxygen inhibition layer created by air is thinner than that by pure oxygen, maximum speed cannot be simply increased by intensifying the light exposure as the case with pure oxygen.

CLIP process is capable of building objects continuously instead of the traditional layer-by-layer manner, which enables tens of times improvement in printing speed. This work presents an enhanced CLIP process by first using a porous track-etched membrane to serve as the oxygen permeable window, in which a record printing speed up to 800 mm/h using pure oxygen is demonstrated. Owing to the high permeability of track-etched membrane, continuous process at a speed of 470 mm/h is also achieved even using air instead of pure oxygen, which is of significance for a compact robust high-speed 3D printer.

Delivering messages and information to potentially interested users is one of the distinguishing applications of online enterprise social network (ESN). The purpose of this paper is to provide insights to better understand the repost preferences of users and provide personalized information service in enterprise social media marketing.

It is accomplished by constructing a target audience identification framework. Repost preference latent Dirichlet allocation (RPLDA) topic model topic model is proposed to understand the mass user online repost preferences toward different contents. A topic-oriented preference metric is proposed to measure the preference degree of individual users. And the function of reposting forecasting is formulated to identify target audience.

The empirical research shows the following: a total of 20 percent of the repost users in ESN represent the key active users who are particularly interested in the latent topic of messages in ESN and fits Pareto distribution; and the target audience identification framework can successfully identify different target key users for messages with different latent topics.

The findings should motivate marketing managers to improve enterprise brand by identifying key target audience in ESN and marketing in a way that truthfully reflects personalized preferences.

This study runs counter to most current business practices, which tend to use simple popularity to seek important users. Adaptively and dynamically identifying target audience appears to have considerable potential, especially in the rapidly growing area of enterprise social media information service.

The purpose of this paper is to investigate the impact of political instability at the local level on foreign firms in China. Building on the literature on political embeddedness and business power, the authors propose a theoretical framework to explain how political turnover can affect foreign firms’ performance and how they respond to such challenges by leveraging their power bases.

To test the hypotheses, the authors apply fixed effects regression to an unbalanced panel data set comprising 13,360 foreign firms from 1998 to 2013 and the political replacement that involved changes in provincial governors.

The findings confirm that political turnover incidents have a negative impact on the performance of foreign firms in China. However, the authors also found that this negative relationship is weaker for firms that can choose various types of power sources. Specifically, the study reveals that foreign firms with large firm size, government ownership and a strong foreign direct investment community are better qualified to mitigate the negative effects of political instability.

This study contributes to the literature by developing the understanding of how political uncertainties and risks affect the performance of foreign firms in China and the importance of firms’ power in counterbalancing these effects. The research provides valuable insights into how multinational corporations can exploit their power to manage the effects of local political turnover, which has practical implications for the strategy and management of foreign firms operating in China.

Based on media choice theories and real option theory, this study aims to explore the role of communication media portfolio in achieving collaborative innovation with suppliers.

Using data of the Study of Competitiveness, Technology and Firm Linkages 2002 collected by the World Bank and the Enterprise Survey Organization of the Chinese National Bureau of Statistics, this study employed logit model to test the hypotheses. Moreover, some robustness analyses were conducted.

This study suggests that media multiplicity increases the probability for a firm to achieve collaborative innovation with suppliers on the basis of real option theory. Furthermore, distance from the suppliers and relationship formality make media multiplicity further important in collaborative innovation with suppliers.

Through investing how communication media portfolio affecting collaborative innovation with suppliers and the contingency factors, this study complements existing studies just concentrating on supplier capabilities, relationships with suppliers and characteristics of supplier network. Moreover, this study contributes in the information system field by exploring the role of media portfolio rather than individual media.

Learning from past construction accident reports is critical to reducing their occurrence. Digital technology provides feasibility for extracting risk factors from unstructured reports, but there are few related studies, and there is a limitation that textual contextual information cannot be considered during extraction, which tends to miss some important factors. Meanwhile, further analysis, assessment and control for the extracted factors are lacking. This paper aims to explore an integrated model that combines the advantages of multiple digital technologies to effectively solve the above problems.

A total of 1000 construction accident reports from Chinese government websites were used as the dataset of this paper. After text pre-processing, the risk factors related to accident causes were extracted using KeyBERT, and the accident texts were encoded into structured data. Tree-augmented naive (TAN) Bayes was used to learn the data and construct a visualized risk analysis network for construction accidents.

The use of KeyBERT successfully considered the textual contextual information, prompting the extracted risk factors to be more complete. The integrated TAN successfully further explored construction risk factors from multiple perspectives, including the identification of key risk factors, the coupling analysis of risk factors and the troubleshooting method of accident risk source. The area under curve (AUC) value of the model reaches up to 0.938 after 10-fold cross-validation, indicating good performance.

This paper presents a new machine-assisted integrated model for accident report mining and risk factor analysis, and the research findings can provide theoretical and practical support for accident safety management.

Laser point clouds are a 3D reconstruction method with wide range, high accuracy and strong adaptability. Therefore, the purpose is to discover a construction point cloud extraction method that can obtain complete information about the construction of rebar, facilitating construction quality inspection and tunnel data archiving, to reduce the cost and complexity of construction management.

Firstly, this paper analyzes the point cloud data of the tunnel during the construction phase, extracts the main features of the rebar data and proposes an M-E-L recognition method. Secondly, based on the actual conditions of the tunnel and the specifications of Chinese tunnel engineering, a rebar model experiment is designed to obtain experimental data. Finally, the feasibility and accuracy of the M-E-L recognition method are analyzed and tested based on the experimental data from the model.

Based on tunnel morphology characteristics, data preprocessing, Euclidean clustering and PCA shape extraction methods, a M-E-L identification algorithm is proposed for identifying secondary lining rebars in highway tunnel construction stages. The algorithm achieves 100% extraction of the first-layer rebars, allowing for the three-dimensional visualization of the on-site rebar situation. Subsequently, through data processing, rebar dimensions and spacings can be obtained. For the second-layer rebars, 55% extraction is achieved, providing information on the rebar skeleton and partial rebar details at the construction site. These extracted data can be further processed to verify compliance with construction requirements.

This paper introduces a laser point cloud method for double-layer rebar identification in tunnels. Current methods rely heavily on manual detection, lacking objectivity. Objective approaches for automatic rebar identification include image-based and LiDAR-based methods. Image-based methods are constrained by tunnel lighting conditions, while LiDAR focuses on straight rebar skeletons. Our research proposes a 3D point cloud recognition algorithm for tunnel lining rebar. This method can extract double-layer rebars and obtain construction rebar dimensions, enhancing management efficiency.

This paper comparatively assesses the connections between individual demographic traits, occupational characteristics, and organizational factors and officers' attitudes toward important groups in China and Taiwan.

Survey data used in this study were collected from 722 police officers from mainland China and 531 officers from Taiwan. Multivariate regression analyses were conducted to assess the correlates of police attitudes toward peers, supervisors, and citizens.

The Chinese and Taiwanese officers do not differ in their trust in peers, but the Chinese officers hold significantly more positive views on the trustworthiness of supervisors and citizens compared to the Taiwanese officers. Supervisor justice and organizational identification are significant predictors of officers' attitudes toward all three groups in both countries.

A major limitation revolves around the inability to test and explain exactly why findings from the two groups vary in their ways. Future research should include specific social, political, and cultural predictors.

This study represents one of the few studies that compare police attitudes toward important groups of peers, supervisors, and citizens across nations/cultures.

It is widely known that fast-fashion retailers are struggling to keep up with consumer attention for quick responses within the fashion industry. With the advance of Internet and e-commerce, consumers prefer to purchase online. Online platform information has become an essential source for exploring consumer attention. However, there is often a mismatch between the information provided by retailers and the feedback received from consumers, leading to an imbalance between the supply side and demand side of online information. The purpose of this study is therefore to provide a unified approach to discover consumer attention from the design topic aspect by revealing the information imbalance between supply side and demand side.

To address the issue of online information imbalance and discover consumer attention, this study proposed an approach that focuses on the design topic perspective. The design topic is a collection of design elements that represent a clothing-design feature more comprehensively and accurately compared to a single design element. The proposed approach begins with generating design topics through topic modeling based on online information provided by retailers on e-commerce platforms. Two indicators, influence degree and attention degree, are then used to quantify the intensity of supply information and consumer attention related to design topics. Finally, design topic strategy diagrams are constructed to reveal information imbalance and discover consumer attention.

The experimental case demonstrates the existence of information imbalance, indicating that the intensity of supply information and consumer attention from the perspective of design topics is not uniform, although both follow the Pareto principle. The results of consumer attention distribution with heavy power-law tails are consistent with current research findings. This further demonstrates that the proposed approach is capable of discovering consumer attention in the design topic strategy diagrams.

The issue of information imbalance between retailers and consumers poses a challenge in keeping up with customer attention. The proposed approach offers a practical solution by visually identifying the symptoms of information imbalance and discovering consumer attention through design topic strategy diagrams. This approach provides fast-fashion retailers with a valuable reference to seize market opportunities, improve product design and adjust marketing or management strategies.

This study proposes a novel approach to disclose the issue of information imbalance between supply side and demand side and therefore to discover consumer attention from the perspective of design topics. In addition, guidelines for applying the proposed approach for fast-fashion marketing and management are presented.

The purpose of this paper is to analyze the mechanism and filter efficacy of accumulation generation operator (AGO)/inverse accumulation generation operator (IAGO) in the frequency domain.

The AGO/IAGO in time domain will be transferred to the frequency domain by the Fourier transform. Based on the consistency of the mathematical expressions of the AGO/IAGO in the gray system and the digital filter in digital signal processing, the equivalent filter model of the AGO/IAGO is established. The unique methods in digital signal processing systems “spectrum analysis” of AGO/IAGO are carried out in the frequency domain.

Through the theoretical study and practical example, benefit of spectrum analysis is explained, and the mechanism and filter efficacy of AGO/IAGO are quantitatively analyzed. The study indicated that the AGO is particularly suitable to act on the system's behavior time series in which the long period parts is the main factor. The acted sequence has good effect of noise immunity.

The AGO/IAGO has a wonderful effect on the processing of some statistical data, e.g. most of the statistical data related to economic growth, crop production, climate and atmospheric changes are mainly affected by long period factors (i.e. low-frequency data), and most of the disturbances are short-period factors (high-frequency data). After processing by the 1-AGO, its high frequency content is suppressed, and its low frequency content is amplified. In terms of information theory, this two-way effect improves the signal-to-noise ratio greatly and reduces the proportion of noise/interference in the new sequence. Based on 1-AGO acting, the information mining and extrapolation prediction will have a good effect.

The authors find that 1-AGO has a wonderful effect on the processing of data sequence. When the 1-AGO acts on a data sequence X, its low-pass filtering effect will benefit the information fluctuations removing and high-frequency noise/interference reduction, so the data shows a clear exponential change trends. However, it is not suitable for excessive use because its equivalent filter has poles at the non-periodic content. But, because of pol effect at zero frequency, the 1-AGO will greatly amplify the low-frequency information parts and suppress the high-frequency parts in the information at the same time.

The rapid development of information technology, epitomized by AIGC and the metaverse, presents unprecedented challenges to techno-ethics, exposing society to significant risks and uncertainties. A systematic investigation and discussion of the construction of techno-ethical order become crucial under the paradigm of risk society theory. The selection of conditions and pathways for constructing a techno-ethical order under the risk society theory paradigm becomes an unavoidable and vital issue.

Drawing on risk society theory, this study employs scientometrics and qualitative comparative analysis (QCA) to empirically analyze the key factors and conditional pathways in the construction of techno-ethical order. Initially, a quantitative analysis is conducted on 1,490 thematic literature retrieved from CNKI and WoS to identify the hot topics and core concepts in techno-ethical research. Subsequently, QCA configuration analysis is applied to calculate eight evaluation indicators and their weights from the perspectives of government, society and individuals. Finally, the study explores the mechanisms of the impact of these indicators’ weights on the construction of techno-ethical order.

The analysis of factor weights and pathways indicates that the selection of pathways for constructing techno-ethical order is influenced both by the inherent development patterns of technology and societal systems and cultural values. Literature metrics analysis reveals an overall trend of sustained growth in techno-ethical research, indicating an unprecedented prosperity in this field. Alongside technological advancements, keywords related to “artificial intelligence” play a crucial role in current techno-ethical research. Configuration analysis demonstrates that conditional variables from the dimensions of government, society and individuals form a configuration pathway, influencing and synergistically impacting the overall level of techno-ethical order construction. Attention should be given to the mutual constraints and synergistic effects of factors related to technological development, societal systems and cultural values.

This study, grounded in the risk society theory paradigm, quantitatively explores the key factors and pathways of techno-ethical order construction in academic texts, expanding new perspectives, providing novel insights, establishing innovative methodologies and extending new boundaries in the field. Further enrichment of the dataset and in-depth discussions are warranted for continued advancement.