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Fake news in cyberspace has greatly interfered with national governance, economic development and cultural communication, which has greatly increased the demand for fake news detection and intervention. At present, the recognition methods based on news content all lose part of the information to varying degrees. This paper proposes a lightweight content-based detection method to achieve early identification of false information with low computation costs.

The authors' research proposes a lightweight fake news detection framework for English text, including a new textual feature extraction method, specifically mapping English text and symbols to 0–255 using American Standard Code for Information Interchange (ASCII) codes, treating the completed sequence of numbers as the values of picture pixel points and using a computer vision model to detect them. The authors also compare the authors' framework with traditional word2vec, Glove, bidirectional encoder representations from transformers (BERT) and other methods.

The authors conduct experiments on the lightweight neural networks Ghostnet and Shufflenet, and the experimental results show that the authors' proposed framework outperforms the baseline in accuracy on both lightweight networks.

The authors' method does not rely on additional information from text data and can efficiently perform the fake news detection task with less computational resource consumption. In addition, the feature extraction method of this framework is relatively new and enlightening for text content-based classification detection, which can detect fake news in time at the early stage of fake news propagation.

The purpose of this paper is to develop an easily implemented and practical stabilizing strategy for the hardware-in-the-loop (HIL) system. As the status of HIL system in the ground verification experiment for space equipment keeps rising, the stability problems introduced by high stiffness of industrial robot and discretization of the system need to be solved ungently. Thus, the study of the system stability is essential and significant.

To study the system stability, a mathematical model is built on the basis of control circle. And root-locus and 3D root-locus method are applied to the model to figure out the relationship between system stability and system parameters.

The mathematical model works well in describing the HIL system in the process of capturing free-floating targets, and the stabilizing strategy can be adopted to improve the system dynamic characteristic which meets the needs of the practical application.

A method named 3D root-locus is extended from traditional root-locus method. And the improved method graphically displays the stability of the system under the influence of multivariable. And the strategy that stabilize the system with elastic component has a strong feasible and promotional value.

The purpose of this paper is to describe the design and development of “Pylon-Climber”, a pole climbing robot (PCR) for climbing along the corner columns of electricity pylon and assisting the electricians to complete maintenance tasks.

Introduces a PCR that is composed of a simple climbing mechanism and two novel grippers. The gripper consists of two angle-fixed V-blocks, and the size of V-block is variable. The clamping method of the angle bar meets the requirement of the force closure theorem. The whole design adopts symmetrical design ideas.

The developed prototype proved possibility of application of PCRs for inspection and maintenance of pylon. The novel gripper can provide enough adhesion force for climbing robot.

The robot is successfully tested on a test tower composed of different specification steel angles, oblique ledges and overlapping steel struts.

Design and development of a novel climbing assistive robot for pylon maintenance. The robot is able to climb along the column of electricity pylon and pass all obstacles. The gripper can reliably grasp the angle bar with different specification and overlapping steel struts from multiple directions.

The purpose of this paper is to demonstrate the feasibility of using shape memory polymer (SMP) for developing vascular stent. In particular, the expansion performance is analyzed through extensive modeling and simulation.

Firstly, the authors construct the model geometry and propose a constitutive model to describe the deformation of the stent due to the expansion process. The authors then simulate the expansion process under varying conditions, including different heating rates and recovery temperatures. Finally, the authors analyze the radial strength of the SMP stent.

A less invasive and stable expansion performance of the SMP stent is confirmed by the simulation method. A fitting function of the expansion process is proposed based on the characteristics of the SMP.

The effects of dynamic blood flow on the SMP stent is ignored. A fluid-structure interaction analysis may need to be considered to give a more accurate description of the behaviour of the SMP stent.

The findings will provide guidance for the rational design and application of SMP stents.

The work will provide guidance for the new generation stent design.

This is the first time that the expansion performance of a SMP stent has been analyzed both qualitatively and quantitatively through modelling and simulation.

This paper aims to classify the sentiment of online tourism-hospitality reviews at an aspect level. A new aspect-oriented sentiment classification method is proposed based on a neural network model.

This study constructs an aspect-oriented sentiment classification model using an integrated four-layer neural network: the bidirectional encoder representation from transformers (BERT) word vector model, long short-term memory, interactive attention-over-attention (IAOA) mechanism and a linear output layer. The model was trained, tested and validated on an open training data set and 92,905 reviews extrapolated from restaurants in Tokyo.

The model achieves significantly better performance compared with other neural networks. The findings provide empirical evidence to validate the suitability of this new approach in the tourism-hospitality domain.

More sentiments should be identified to measure more fine-grained tourism-hospitality experience, and new aspects are recommended that can be automatically added into the aspect set to provide dynamic support for new dining experiences.

This study provides an update to the literature with respect to how a neural network could improve the performance of aspect-oriented sentiment classification for tourism-hospitality online reviews.

本文旨在从方面级对在线旅游-酒店评论的情感进行分类。提出了一种基于神经网络模型的面向方面的情感分类新方法。

本研究使用集成的四层神经网络构建面向方面的情感分类模型：BERT 词向量模型、LSTM、IAOA 机制和线性输出层。该模型在一个开放的训练数据集和从东京餐厅推断的 92,905 条评论上进行了训练、测试和验证。

与其他神经网络相比, 该模型实现了显着更好的性能。研究结果提供了经验证据, 以验证这种新方法在旅游酒店领域的适用性。

该研究提供了有关神经网络如何提高旅游酒店在线评论的面向方面的情感分类性能的新文献。

应该识别更多的情感从而来更加细化衡量旅游酒店体验, 并推荐新的方面/维度可以被自动添加到方面集中, 为新的用餐体验提供动态支持。

The literature review aims to facilitate a broader understanding of on-demand service platform operations management and proposes potential research directions for scholars.

This study searches four databases for relevant literature on on-demand service platform operations management and selects 72 papers for this review. According to the research context, the literature can be divided into research on “a single platform” and research on “multiple platforms”. According to the research methods, the literature can be classified into “Mathematical Models”, “Empirical Studies”, “Multiple Methods” and “Literature Review”. Through comparative analysis, we identify research gaps and propose five future research agendas.

This paper proposes five research agendas for future research on on-demand service platform operations management. First, research can be done to combine classic research problems in the field of operations management with platform characteristics. Second, both the dynamic and steady-state issues of on-demand service platforms can be further explored. Third, research employing mathematical models and empirical analysis simultaneously can be more fruitful. Fourth, more research efforts on the various interactions among two or more platforms can be pursued. Last but not least, it is worthwhile to examine new models and paths that have emerged during the latest development of the platform economy.

Through categorizing the literature into two research contexts as well as classifying it according to four research methods, this article clearly shows the research progresses made so far in on-demand service platform operations management and provides future research directions.

This study aims to examine how business executives' political connections are associated with government subsidies and strategic change, and how they, in turn, influence firm performance, measured by return on assets (ROA) and market share.

Hypotheses were tested using the large firm-level dataset provided by the National Bureau of Statistics (NBS) of China for the period 2003–2013. This is one of the most comprehensive datasets of Chinese manufacturing companies and includes 321,722 firms on average per year, which spans over 37 industries.

The authors found that political connections, measured by senior executives' membership in the National People's Congress of China (NPC), were positively associated with government subsidies but were not associated with strategic change. Also, government subsidies, as the underlying mechanism, mediated the relationships between NPC membership and firm performance but strategic change did not.

By examining the possible mediators between corporate political strategies and firm performance, the authors confirmed the thought that the impact of political connections on firm performance is a complex phenomenon and goes beyond a simple direct effect. However, future research could explore other mediators in this relationship.

While the direct relationship between political connections and firm performance has been examined in management literature, the results are mixed. For the first time, the authors addressed the gap and opened the “black box” – the underlying mechanisms of this relationship. This study's findings contribute to the literature on corporate political activity, strategic change, and their influences on firm performance.

This study aims to empirically examine the effect of supply chain innovation (SCI) announcements on shareholder value within the context of Industry 4.0 and Industry 5.0.

This study uses an event study method to examine the effect of SCI announcements on shareholder value of the 156 listed companies in China.

First, SCI announcements have a positive effect on shareholder value. Second, SCI with an integrated form more positively affects shareholder value than SCI with an independent form. SCI at the strategy level more positively affects shareholder value than SCI at the operation level. Technology-type SCI more positively affects shareholder value than process-type SCI. Third, this study finds that investors pay more attention to the SCI of companies in the service industry than that of in the manufacturing industry. Finally, the post-hoc analysis finds that digital SCI more positively affects shareholder value than intelligent SCI.

First, most scholars use questionnaire data rather than second-hand data to conduct empirical research to explore the impact of SCI on performance. Second, although scholars focus on performance comprehensively, including operational, financial, relational and environmental performance, no scholars use an event study to explore the impact of SCI on the stock market. Third, no scholars have explored the differential impact of SCI in different industries. Forth, few scholars have classified SCI according to the characteristics to explore the differential impact of SCI. Finally, the differences between SCI of Industry 4.0 and SCI of Industry 5.0 have been described, but no scholars have used empirical research to explore the differences.

Fall accidents can cause casualties and economic losses in the construction industry. Fall portents, such as loss of balance (LOB) and sudden sways, can result in fatal, nonfatal or attempted fall accidents. All of them are worthy of studying to take measures to prevent future accidents. Detecting fall portents can proactively and comprehensively help managers assess the risk to workers as well as in the construction environment and further prevent fall accidents.

This study focused on the postures of workers and aimed to directly detect fall portents using a computer vision (CV)-based noncontact approach. Firstly, a joint coordinate matrix generated from a three-dimensional pose estimation model is employed, and then the matrix is preprocessed by principal component analysis, K-means and pre-experiments. Finally, a modified fusion K-nearest neighbor-based machine learning model is built to fuse information from the x, y and z axes and output the worker's pose status into three stages.

The proposed model can output the worker's pose status into three stages (steady–unsteady–fallen) and provide corresponding confidence probabilities for each category. Experiments conducted to evaluate the approach show that the model accuracy reaches 85.02% with threshold-based postprocessing. The proposed fall-portent detection approach can extract the fall risk of workers in the both pre- and post-event phases based on noncontact approach.

First, three-dimensional (3D) pose estimation needs sufficient information, which means it may not perform well when applied in complicated environments or when the shooting distance is extremely large. Second, solely focusing on fall-related factors may not be comprehensive enough. Future studies can incorporate the results of this research as an indicator into the risk assessment system to achieve a more comprehensive and accurate evaluation of worker and site risk.

The proposed machine learning model determines whether the worker is in a status of steady, unsteady or fallen using a CV-based approach. From the perspective of construction management, when detecting fall-related actions on construction sites, the noncontact approach based on CV has irreplaceable advantages of no interruption to workers and low cost. It can make use of the surveillance cameras on construction sites to recognize both preceding events and happened accidents. The detection of fall portents can help worker risk assessment and safety management.

Existing studies using sensor-based approaches are high-cost and invasive for construction workers, and others using CV-based approaches either oversimplify by binary classification of the non-entire fall process or indirectly achieve fall-portent detection. Instead, this study aims to detect fall portents directly by worker's posture and divide the entire fall process into three stages using a CV-based noncontact approach. It can help managers carry out more comprehensive risk assessment and develop preventive measures.

This paper aims to develop a novel grey Bernoulli model with memory characteristics, which is designed to dynamically choose the optimal memory kernel function and the length of memory dependence period, ultimately enhancing the model's predictive accuracy.

This paper enhances the traditional grey Bernoulli model by introducing memory-dependent derivatives, resulting in a novel memory-dependent derivative grey model. Additionally, fractional-order accumulation is employed for preprocessing the original data. The length of the memory dependence period for memory-dependent derivatives is determined through grey correlation analysis. Furthermore, the whale optimization algorithm is utilized to optimize the cumulative order, power index and memory kernel function index of the model, enabling adaptability to diverse scenarios.

The selection of appropriate memory kernel functions and memory dependency lengths will improve model prediction performance. The model can adaptively select the memory kernel function and memory dependence length, and the performance of the model is better than other comparison models.

The model presented in this article has some limitations. The grey model is itself suitable for small sample data, and memory-dependent derivatives mainly consider the memory effect on a fixed length. Therefore, this model is mainly applicable to data prediction with short-term memory effect and has certain limitations on time series of long-term memory.

In practical systems, memory effects typically exhibit a decaying pattern, which is effectively characterized by the memory kernel function. The model in this study skillfully determines the appropriate kernel functions and memory dependency lengths to capture these memory effects, enhancing its alignment with real-world scenarios.

Based on the memory-dependent derivative method, a memory-dependent derivative grey Bernoulli model that more accurately reflects the actual memory effect is constructed and applied to power generation forecasting in China, South Korea and India.