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This article aims to predict the rapid track geometry change in the short term with a higher detection frequency, and realize the monitoring and maintenance of the railway state.

Firstly, the ABA data needs to be filtered to remove the DC component to reduce the drift due to integration. Secondly, the quadratic integration in frequency domain for concern components of the vertical and lateral ABA needs to be done. Thirdly, the displacement in lateral of the wheelset to rail needs to be calculated. Then the track alignment irregularity needs to be calculated by the integration of lateral ABA and the lateral displacement of the wheelset to rail.

By comparing with a commercial track geometry measurement system, the high-speed railway application results in different conditions, after removal of the influence of LDWR, identified that the proposed method can produce a satisfactory result.

This article helps realize detection of track irregularity on operating vehicle, reduce equipment production, installation and maintenance costs and improve detection density.

The development of prefabricated buildings has become one of the primary solutions to transform the traditional construction industry around the world. Incentive policy is one of the important driving factors for the development of prefabricated building. The policy system in the field of prefabricated buildings needs to be improved urgently. However, there is still a dearth of research on how incentive policies exert impact on the development of prefabricated buildings. This paper aims to reveal the impact mechanisms of different types of policies on the development system of prefabricated buildings.

This study categorizes prefabricated building policies, constructs a system dynamics model of prefabricated building policies and conducts scenario simulations to examine the impact and sensitivity of different types of policies on the development system of prefabricated buildings.

The results show that compulsory policies play a greater role in the early stage of prefabricated building development and need to be withdrawn at the right time. Preferential and encouraging policies play an incentive role in the middle and later stages of prefabricated building development. Encouraging policies predominate in the later stage of prefabricated building development. Based on the research results, policy recommendations for prefabricated building development are put forward respectively from the government, developers and consumers.

The research results are expected to make up for the lack of clear policies paths in existing research and provide theoretical references for the formulation and optimization of future policies.

Online reviews serve as valuable sources of information, reflecting tourists’ attentions, preferences and sentiments. However, although the existing research has demonstrated that incorporating online review data can enhance the performance of tourism demand forecasting models, the reliability of online review data and consumers’ decision-making process have not been given adequate attention. To address the aforementioned problem, the purpose of this study is to forecast tourism demand using online review data derived from the analysis of review helpfulness.

The authors propose a novel “identification-first, forecasting-second” framework. This framework prioritizes the identification of helpful reviews through a comprehensive analysis of review helpfulness, followed by the integration of helpful online review data into the forecasting system. Using the SARIMAX model with helpful online review data sourced from TripAdvisor, this study forecasts tourist arrivals in Hong Kong during the period from August 2012 to June 2019. The SNAÏVE/SARIMA model was used as the benchmark model. Additionally, artificial intelligence models including long short-term memory, back propagation neural network, extreme learning machine and random forest models were used to assess the robustness of the results.

The results demonstrate that online review data are subject to noise and bias, which can adversely affect the accuracy of predictions when used directly. However, by identifying helpful online reviews beforehand and incorporating them into the forecasting process, a notable enhancement in predictive performance can be realized.

First, to the best of the authors’ knowledge, this study is one of the first to focus on the data issue of online reviews on tourism arrivals forecasting. Second, this study pioneers the integration of the consumer decision-making process into the domain of tourism demand forecasting, marking one of the earliest endeavors in this area. Third, this study makes a novel attempt to identify helpful online reviews based on reviews helpfulness analysis.

Value co-creation (VCC) helps platforms establish competitive advantages. Unlike their traditional counterparts, social attribute is a key concept of social e-commerce platforms. This study integrates VCC and social network theories, introduces relational embeddedness and divides this variable into economic and social relational embeddedness to explore its impact on VCC intention. This study also explores the mediating and moderating roles of customers' psychological ownership (CPO) and regulatory focus, respectively.

A questionnaire survey was conducted among users of mainstream social e-commerce platforms in China, and the relationship among the variables was revealed through a structural equation modeling of 464 valid responses.

The dimensions of relational embeddedness positively affect CPO and VCC intention, with social relational embeddedness exerting the strongest effect. CPO positively affects VCC intention and partially mediates the relationship between relational embeddedness and VCC intention. Promotion and prevention focus positively and negatively moderate the relationship between CPO and VCC intention, respectively.

This study expands the VCC research perspective and links the VCC concepts to social network dynamics. From the relational embeddedness perspective, this study identifies the type and intensity of relational embeddedness that promotes users' VCC intention and contributes to theoretical research on VCC and relational embeddedness. This study also introduces CPO as an intermediary variable, thus opening the black box of this mechanism, and confirms the moderating role of regulatory focus as the key psychological factor motivating users' VCC intention.

The rapid development of e-commerce has caused not only explosive growth of the express delivery industry, but also ever-greater operational pressures. Models from the sharing economy may provide new ideas for operational improvement. The purpose of this paper is to consider an optimization method that reduces costs and increases efficiency. The proposed method enables a shared distribution system based on revenue-sharing and cooperative investment contracts.

The authors design a two-echelon supply chain (SC) of the shared distribution system with one shared distribution company and N express companies. In this SC, the express companies provide only inter-city transportation, and they outsource internal-city transportation to a shared distribution company. This distribution system differs from that of the traditional express delivery industry. The traditional system of delivery requires large numbers of empty trips (with no load to deliver), because the operating mode of urban distribution has been the franchise. To offer greater efficiency and performance, the authors introduce the sharing economy mode of express delivery. The authors examine the potential of a joint optimal decision-making strategy that involves revenue-sharing and cooperative investment contracts based on an order flow proportion (OFP) and a revenue-sharing factor (RSF). In this shared distribution system, the most important innovation is that all of the express companies jointly invest in and establish a shared distribution company based on OFP or RSF principles.

The profitability of an SC with revenue-sharing contracts based on an OFP system is much higher than that of a decentralized SC, and it is very close to the profitability of a centralized SC. In SCs with revenue-sharing contracts that are based on RSFs, there are many possible combinations of RSFs that can increase the overall profitability. The analyses indicate that the OFP system offers the best solution in designing revenue-sharing contracts based on RSFs.

This study indicates that revenue-sharing contracts based on both OFP and RSF principles can increase overall SC returns by 0.21 to 0.44 percent. In sum total, this improvement could mean a 0.84 to 1.76bn Yuan increase in revenues for the 400+ bn-Yuan express delivery industry.

The authors find that a combination of equity investment and SC coordination contracts makes the cooperation between SC members much more stable. Through this kind of shared distribution system, the scale of economy can further reduce the costs and increase the efficiency of the express delivery industry.

Motivated by empirical features that characterize cryptocurrency volatility data, the authors develop a forecasting strategy that can account for both model uncertainty and heteroskedasticity of unknown form. The theoretical investigation establishes the asymptotic optimality of the proposed heteroskedastic model averaging heterogeneous autoregressive (H-MAHAR) estimator under mild conditions. The authors additionally examine the convergence rate of the estimated weights of the proposed H-MAHAR estimator. This analysis sheds new light on the asymptotic properties of the least squares model averaging estimator under alternative complicated data generating processes (DGPs). To examine the performance of the H-MAHAR estimator, the authors conduct an out-of-sample forecasting application involving 22 different cryptocurrency assets. The results emphasize the importance of accounting for both model uncertainty and heteroskedasticity in practice.

Workers' unsafe behavior is the main cause of construction safety accidents, thereby highlighting the critical importance of behavior-based management. To compensate for the limitations of computer vision in tackling knowledge-intensive issues, semantic-based methods have gained increasing attention in the field of construction safety management. Knowledge graph provides an efficient and visualized method for the identification of various unsafe behaviors.

This study proposes an unsafe behavior identification framework by integrating computer vision and knowledge graph–based reasoning. An enhanced ontology model anchors our framework, with image features from YOLOv5, COCO Panoptic Segmentation and DeepSORT integrated into the graph database, culminating in a structured knowledge graph. An inference module is also developed, enabling automated the extraction of unsafe behavior knowledge through rule-based reasoning.

A case application is implemented to demonstrate the feasibility and effectiveness of the proposed method. Results show that the method can identify various unsafe behaviors from images of construction sites and provide mitigation recommendations for safety managers by automated reasoning, thus supporting on-site safety management and safety education.

Existing studies focus on spatial relationships, often neglecting the diversified spatiotemporal information in images. Besides, previous research in construction safety only partially automated knowledge graph construction and reasoning processes. In contrast, this study constructs an enhanced knowledge graph integrating static and dynamic data, coupled with an inference module for fully automated knowledge-based unsafe behavior identification. It can help managers grasp the workers’ behavior dynamics and timely implement measures to correct violations.

Propaganda is a prevalent technique used in social media to intentionally express opinions or actions with the aim of manipulating or deceiving users. Existing methods for propaganda detection primarily focus on capturing language features within its content. However, these methods tend to overlook the information presented within the external news environment from which propaganda news originated and spread. This news environment reflects recent mainstream media opinions and public attention and contains language characteristics of non-propaganda news. Therefore, the authors have proposed a graph-based multi-information integration network with an external news environment (abbreviated as G-MINE) for propaganda detection.

G-MINE is proposed to comprise four parts: textual information extraction module, external news environment perception module, multi-information integration module and classifier. Specifically, the external news environment perception module and multi-information integration module extract and integrate the popularity and novelty into the textual information and capture the high-order complementary information between them.

G-MINE achieves state-of-the-art performance on both the TSHP-17, Qprop and the PTC data sets, with an accuracy of 98.24%, 90.59% and 97.44%, respectively.

An external news environment perception module is proposed to capture the popularity and novelty information, and a multi-information integration module is proposed to effectively fuse them with the textual information.

With the fierce competition in the cold chain logistics market, achieving and maintaining excellent customer satisfaction is the key to an enterprise's ability to stand out. This research aims to determine the factors that affect customer satisfaction in cold chain logistics, which helps cold chain logistics enterprises identify the main aspects of the problem. Further, the suggestions are provided for cold chain logistics enterprises to improve customer satisfaction.

This research uses the text mining approach, including topic modeling and sentiment analysis, to analyze the information implicit in customer-generated reviews. First, latent Dirichlet allocation (LDA) model is used to identify the topics that customers focus on. Furthermore, to explore the sentiment polarity of different topics, bi-directional long short-term memory (Bi-LSTM), a type of deep learning model, is adopted to quantify the sentiment score. Last, regression analysis is performed to identify the significant factors that affect positive, neutral and negative sentiment.

The results show that eight topics that customer focus are determined, namely, speed, price, cold chain transportation, package, quality, error handling, service staff and logistics information. Among them, speed, price, transportation and product quality significantly affect customer positive sentiment, and error handling and service staff are significant factors affecting customer neutral and negative sentiment, respectively.

The data of the customer-generated reviews in this research are in Chinese. In the future, multi-lingual research can be conducted to obtain more comprehensive insights.

Prior studies on customer satisfaction in cold chain logistics predominantly used questionnaire method, and the disadvantage of which is that interviewees may fill out the questionnaire arbitrarily, which leads to inaccurate data. For this reason, it is more scientific to discover customer satisfaction from real behavioral data. In response, customer-generated reviews that reflect true emotions are used as the data source for this research.