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Since the implementation of the regulatory inquiry system, research on its impact on information disclosure in the capital market has been increasing. This article focuses on a specific area of study using Chinese annual report inquiry letters as the basis. From a text mining perspective, we explore whether the textual information contained in these inquiry letters can help predict financial restatement behavior of the inquired companies.

Python was used to process the data, nonparametric tests were conducted for hypothesis testing and indicator selection, and six machine learning models were employed to predict financial restatements.

Some text feature indicators in the models that exhibit significant differences are useful for predicting financial restatements, particularly the proportion of formal positive words and stopwords, readability, total word count and certain textual topics. Securities regulatory authorities are increasingly focusing on the accounting and financial aspects of companies' annual reports.

This study explores the textual information in annual report inquiry letters, which can provide insights for other scholars into research methods and content. Besides, it can assist with decision making for participants in the capital market.

We use information technology to study the textual information in annual report inquiry letters and apply it to forecast financial restatements, which enriches the research in the field of regulatory inquiries.

In a dual-robot system, the relative position error is a superposition of errors from each mono-robot, resulting in deteriorated coordination accuracy. This study aims to enhance relative accuracy of the dual-robot system through direct compensation of relative errors. To achieve this, a novel calibration-driven transfer learning method is proposed for relative error prediction in dual-robot systems.

A novel local product of exponential (POE) model with minimal parameters is proposed for error modeling. And a two-step method is presented to identify both geometric and nongeometric parameters for the mono-robots. Using the identified parameters, two calibrated models are established and combined as one dual-robot model, generating error data between the nominal and calibrated models’ outputs. Subsequently, the calibration-driven transfer, involving pretraining a neural network with sufficient generated error data and fine-tuning with a small measured data set, is introduced, enabling knowledge transfer and thereby obtaining a high-precision relative error predictor.

Experimental validation is conducted, and the results demonstrate that the proposed method has reduced the maximum and average relative errors by 45.1% and 30.6% compared with the calibrated model, yielding the values of 0.594 mm and 0.255 mm, respectively.

First, the proposed calibration-driven transfer method innovatively adopts the calibrated model as a data generator to address the issue of real data scarcity. It achieves high-accuracy relative error prediction with only a small measured data set, significantly enhancing error compensation efficiency. Second, the proposed local POE model achieves model minimality without the need for complex redundant parameter partitioning operations, ensuring stability and robustness in parameter identification.

To solve the obstacle detection problem in robot autonomous obstacle negotiation, this paper aims to propose an obstacle detection system based on elevation maps for three types of obstacles: positive obstacles, negative obstacles and trench obstacles.

The system framework includes mapping, ground segmentation, obstacle clustering and obstacle recognition. The positive obstacle detection is realized by calculating its minimum rectangle bounding boxes, which includes convex hull calculation, minimum area rectangle calculation and bounding box generation. The detection of negative obstacles and trench obstacles is implemented on the basis of information absence in the map, including obstacles discovery method and type confirmation method.

The obstacle detection system has been thoroughly tested in various environments. In the outdoor experiment, with an average speed of 22.2 ms, the system successfully detected obstacles with a 95% success rate, indicating the effectiveness of the detection algorithm. Moreover, the system’s error range for obstacle detection falls between 4% and 6.6%, meeting the necessary requirements for obstacle negotiation in the next stage.

This paper studies how to solve the obstacle detection problem when the robot obstacle negotiation.

Coal is the basic energy and essential resource in China, which is crucial to the economic lifeline and energy security of the country. Coal mining has been ever exposed to potential safety risks owing to the complex geologic environment. Effective safety supervision is a vital guarantee for safe production in coal mines. This paper aims to explore the impacts of the internet+ coal mine safety supervision (CMSS) mode that is being emerged in China.

In this study, the key factors influencing CMSS are identified by social network analysis. They are used to develop a multiple linear regression model of law enforcement frequency for conventional CMSS mode, which is then modified by an analytical hierarchy process to predict the law enforcement frequency of internet+ CMSS mode.

The regression model demonstrated high accuracy and reliability in predicting law enforcement frequency. Comparative analysis revealed that the law enforcement frequency in the internet+ mode was approximately 40% lower than the conventional mode. This reduction suggests a potential improvement in cost-efficiency, and the difference is expected to become even more significant with an increase in law enforcement frequency.

To the best of the authors’ knowledge, this is one of the few available pieces of research which explore the cost-efficiency of CMSS by forecasting law enforcement frequency. The study results provide a theoretical basis for promoting the internet+ CMSS mode to realize the healthy and sustainable development of the coal mining industry.

To supply temporary pressure testing devices with favorable performance for emergency environments, this paper aims to present a pressure sensor with a central boss and straight-annular grooves. The structural feature is modeled and optimized by neural network-based method, and the device prototype is fabricated by 3D printing techniques.

The study initially compares mechanical properties of the proposed structure with two conventional designs using finite element analysis. The impacts from structural dimensions on sensor performance are modeled using a Backpropagation neural network and optimized through genetic algorithms. The sensing diaphragm is fabricated using stereolithography (SLA) 3D printing, while the piezoresistors and necessary interconnects are realized with screen printing techniques.

The experimental results demonstrate that the fabricated sensor exhibits a sensitivity of 2.8866 mV/kPa and a nonlinearity of 6.81% within the pressure range of 0–100 kPa. This performance is an improvement of 118% in sensitivity and a decrease of 54% in nonlinearity compared to flat diaphragm structure, highlighting the effectiveness of proposed diaphragm configuration.

This research offers a holistic methodology that encompasses the structural design, optimization and fabrication of pressure sensors. The proposed diaphragm and corresponding modelling method can provide a practical approach to enhance the measurement capabilities of pressure sensors. By leveraging SLA printing for diaphragm and screen printing for circuit, the prototype can be produced in a timely manner.

Unlike previous research that primarily utilized structural equation modelling (SEM) to evaluate safety hazards in subway projects, this research aims to utilize a hybrid approach to investigate and scrutinize the key indicators of safety hazards leading to accidents, thereby hindering the progress of subway projects in China, taking into cognizance the multiple stakeholder’s perspective.

By administering a survey questionnaire to 373 highly involved stakeholders in subway projects spanning Changsha, Beijing and Qingdao, China, our approach incorporated a four-staged composite amalgamation of exploratory factor analysis (EFA), confirmatory factor analysis (CFA), covariance-based structural equation modelling (CB-SEM) and artificial neural network (ANN) to develop an optimized model that determines the causal relationships and interactions among safety hazards in subway construction projects.

The optimized model delineated the influence of individual safety hazards on subway projects. The feasibility and applicability of the model developed was demonstrated on an actual subway project under construction in Changsha city. The outcomes revealed that the progress of subway projects is significantly influenced by risks associated with project management, environmental factors, subterranean conditions and technical hazards. In contrast, risks related to construction and human factors did not exhibit a significant impact on subway construction progress.

While our study provides valuable insights, it is important to acknowledge the limitation of relying on theoretical approaches without empirical validation from experiments or the field. In future research, we plan to address this limitation by assessing the SEM using empirical data. This will involve a comprehensive comparison of outcomes derived from CB-SEM with those obtained through SEM-ANN methods. Such an empirical validation process is crucial for enhancing the overall efficiency and robustness of the proposed methodologies.

The established hybrid model revealed complex non-linear connections among indicators in the intricate project, enabling the recognition of primary hazards and offering direction to improve management of safety in the construction of subways.

Addressing integrity, flexibility and interoperability challenges in enterprise information systems (EISs) is often hindered by the “three-fit” barrier, which encompasses vertical, horizontal and transversal fit problems. To overcome these obstacles, we propose solutions aimed at defining the business view of EIS. This study addresses these issues by proposing solutions tailored to the business view of EIS. Specifically, it introduces the core ontology of sensitive business processes (COSBP), a conceptual framework designed to formalize and define the multidimensional dimensions of sensitive business processes (SBPs). By providing a unified structure of central concepts and semantic relationships, COSBP enhances both knowledge management (KM) and business process management (BPM) in organizational contexts.

This paper adopts the design science research methodology covering the phases of a design-oriented research project that develops new artifacts, such as the COSBP ontology, based on SBP modeling requirements. Following a formal multi-level, multi-component approach, COSBP is structured into sub-ontologies across different abstraction levels. Built upon the Descriptive Ontology for Linguistic and Cognitive Engineering (DOLCE) foundational ontology, COSBP integrates and extends core concepts from core domain ontologies in business processes. The framework specifies six key modeling dimensions of SBPs – functional, organizational, behavioral, informational, intentional and knowledge – each represented as a distinct class of ontological modules (OMs).

COSBP offers a semantically rich and precise framework for modeling SBPs, addressing complexity and ambiguity in conceptual modeling. It supports the creation of expressive and effective SBP models while enabling consensus-driven representation at a generic level. Additionally, COSBP serves as a foundation for extending modeling notations and developing tools that align with these notations. Its application in enterprise environments improves the integration, adaptability and interoperability of EISs, ultimately enhancing organizational processes and decision-making.

The development of the COSBP ontology holds considerable potential for application in various industries beyond its original focus on business process management and KM. The ontology’s capability to semantically model sensitive, knowledge-intensive and dynamic processes can be extended to other real-life scenarios in other complex domains and sectors – for example, finance and banking, government and public services, insurance, manufacturing and supply chain management, retail, E-commerce, logistics and transportation crisis management, government and public services, higher education and so on. By integrating artificial intelligence (AI) with the COSBP ontology, we aim to enable more intelligent decision-making, process monitoring and improved management of SBPs in knowledge-driven domains.

Vehicle allocation problems (VAPs), which are frequently confronted in many transportation activities, primarily including but not limited to full truckload freight transportation operations, induce a significant economic impact. Despite the increasing academic attention to the field, literature still fails to match the needs of and opportunities in the growing industrial practices. In particular, the literature can grow upon the ideas on sustainability, Industry 4.0 and collaboration, which shape future practices not only in logistics but also in many other industries. This review has the potential to enhance and accelerate the development of relevant literature that matches the challenges confronted in industrial problems. Furthermore, this review can help to explore the existing methods, algorithms and techniques employed to address this problem, reveal directions and generate inspiration for potential improvements.

This study provides a literature review on VAPs, focusing on quantitative models that incorporate any of the following emerging logistics trends: sustainability, Industry 4.0 and logistics collaboration.

In the literature, sustainability interactions have been limited to environmental externalities (mostly reducing operational-level emissions) and economic considerations; however, emissions generated throughout the supply chain, other environmental externalities such as waste and product deterioration, or the level of stakeholder engagement, etc., are to be monitored in order to achieve overall climate-neutral services to the society. Moreover, even though there are many types of collaboration (such as co-opetition and vertical collaboration) and Industry 4.0 opportunities (such as sharing information and comanaging distribution operations) that could improve vehicle allocation operations, these topics have not yet received sufficient attention from researchers.

The scientific contribution of this study is twofold: (1) This study analyses decision models of each reviewed article in terms of decision variable, constraint and assumption sets, objectives, modeling and solving approaches, the contribution of the article and the way that any of sustainability, Industry 4.0 and collaboration aspects are incorporated into the model. (2) The authors provide a discussion on the gaps in the related literature, particularly focusing on practical opportunities and serving climate-neutrality targets, carried out under four main streams: logistics collaboration possibilities, supply chain risks, smart solutions and various other potential practices. As a result, the review provides several gaps in the literature and/or potential research ideas that can improve the literature and may provide positive industrial impacts, particularly on how logistics collaboration may be further engaged, which supply chain risks are to be incorporated into decision models, and how smart solutions can be employed to cope with uncertainty and improve the effectiveness and efficiency of operations.