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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.

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.

The Chinese government hopes to achieve the goal of benefiting citizens by building a National Integrated Online Government Service Platform (NIOGSP). However, citizens' low adoption of the platform makes it difficult for the government to achieve its goal. Research on the influencing factors of citizen adoption of NIOGSP can help the government fully understand the concerns and needs of its citizens and take targeted measures to increase citizen adoption.

First, this research builds a model of the citizen adoption process, including attention, retention and motivation, based on an observational learning model. Next, research variables are determined based on social cognitive theory, literature review and real-world needs. Finally, based on the questionnaire survey and structural equation model, the influencing factors of each stage of the citizen adoption process model are studied and the relationship between the three stages of the model is verified.

Results show that perceived usefulness (PU) and self-efficacy (SE) positively affect attention. SE positively affects retention, while perceived privacy (PP) negatively affects retention. PU, social influence, PP and anxiety positively affect motivation.

The conclusion of this study can provide reference for governments in various countries to establish and improve online one-stop government. In addition, this study verifies the citizen adoption process model and finds that there is no obvious causal relationship between attention and retention, but both have positive effects on motivation.

Agile principles have been widely used in software development team practice since the creation of the Agile Manifesto. Studies have examined variables related to agile principles without systematically considering the relationships among key team, agile methodology, and process variables underlying the agile principles and how these variables jointly influence the achievement of software development agility. In this study, the authors tested a team/methodology–process–agility model that links team variables (team autonomy and team competence) and methodological variable (iterative development) to process variables (communication and collaborative decision-making), which are in turn linked to software development agility (ability to sense, respond and learn).

Survey data from one hundred and sixty software development professionals were analyzed using structural equation modeling methods.

The results support the team/methodology–process–agility model. Process variables (communication and collaborative decision-making) mediated the effects of team (autonomy and competence) and methodological (iterative development) variables on software development agility. In addition, team, methodology and process variables had different effects on the three dimensions of software development agility.

The results contribute to the literature on organizational IT management by establishing a team/methodology–process–agility model that can serve as a basis for developing a core theoretical foundation underlying agile principles and practices. The results also have practical implications for organizations in understanding and managing holistically the different roles that agile methodological, team and process factors play in achieving software development agility.

Tourism is a labor-intensive sector with extensive links to other industries and plays a vital role in creating employment. This study aims to propose a new framework to analyze the intrinsic structure of the employment effects of tourism-related sectors and their drivers.

This study uses input–output and structural decomposition analysis (IO-SDA) to quantify the employment effects of tourism-related sectors and their driving mechanisms based on China’s I-O tables of 2002, 2007, 2012 and 2017.

The results show a declining trend in the intensity of direct or indirect employment effects in tourism-related sectors, indicating a decreasing number of jobs directly or indirectly required to create a unit of tourism output. Among tourism-related sectors, catering has the highest intensity of indirect employment effects over the study period. Catering stimulates the indirect employment of agriculture, forestry, animal husbandry, fishery and food and tobacco manufacturing. The decomposition analysis reveals that final demand is the largest contributor to the increase in tourism employment, while technological progress shifts from an employment-creation effect in 2002–2012 to an employment-destruction effect in 2012–2017.

This study proposes a new analytical framework to investigate the structural proportional relationship between the direct and indirect employment effects of various tourism-related sectors and their dynamic changes. Doing so, it provides valuable references for policymakers to promote tourism employment.

旅游相关部门就业效应的驱动因素：以中国为例

摘要

旅游业是一个劳动密集型部门, 与其他国民经济部门有着广泛的联系, 这在创造就业方面发挥着重要作用。本研究旨在建立一个框架, 分析旅游相关部门就业效应的内在结构及其驱动因素。

本研究基于中国2002年、2007年、2012年和2017年的投入产出表, 引入投入产出和结构分解分析（IO-SDA）法量化了旅游相关行业的就业效应及其变化的驱动机制。

旅游相关部门的直接或间接就业强度呈下降趋势, 可见创造一个单位的旅游产出所需的直接或间接工作数量在减少。在旅游相关部门中, 餐饮部门在研究期内的间接就业效应强度最高, 主要带动了农、林、牧、渔业和食品及烟草制造业的间接就业。旅游就业效应变动的驱动因素中, 最终需求是旅游就业效应增加的最大贡献者, 技术效应从2002-2012年期间的就业创造效应转变为2012-2017年期间的就业破坏效应。

本研究建立了一个全新的分析框架, 可以揭示各个旅游相关部门的直接和间接就业效应之间的结构比例关系及其动态变化。对旅游就业效应的驱动因素分析可以为政策制定者提供针对性的建议, 以促进旅游就业。

Factores que impulsan los efectos del empleo en los sectores relacionados con el turismo: El caso de China continental

Resumen

El turismo es un sector intensivo en mano de obra con amplios vínculos con otras industrias y desempeña un papel vital en la creación de empleo. Este estudio propone un nuevo marco para analizar la estructura intrínseca de los efectos en el empleo de los sectores relacionados con el turismo y sus impulsores.

Este estudio utiliza el análisis de entrada-salida (input-output) y de descomposición estructural (structural decomposition) (IO-SDA) para cuantificar los efectos sobre el empleo en los sectores relacionados con el turismo y sus mecanismos impulsores, basándose en las tablas input-output de China de 2002, 2007, 2012 y 2017.

Los resultados muestran una tendencia a la baja en la intensidad de los efectos directos o indirectos del empleo en los sectores relacionados con el turismo, lo que indica un número cada vez menor de puestos de trabajo directos o indirectos necesarios para crear una unidad de producción turística. Entre los sectores relacionados con el turismo, la restauración tiene la mayor intensidad de efectos indirectos sobre el empleo durante el periodo de estudio. La restauración estimula el empleo indirecto de la agricultura, la silvicultura, la ganadería, la pesca y la fabricación de alimentos y tabaco. El análisis de descomposición revela que la demanda final es la que más contribuye al aumento del empleo turístico, mientras que el progreso tecnológico pasa de ser un efecto de creación de empleo en 2002-2012 a un efecto de destrucción de empleo en 2012-2017.

Este estudio propone un nuevo marco analítico para investigar la relación estructural proporcional entre los efectos directos e indirectos del empleo de varios sectores relacionados con el turismo y sus cambios dinámicos. De este modo, proporciona valiosas referencias para que los responsables políticos promuevan el empleo en el sector turístico.

The purpose of this paper is to investigate the monitoring of the friction condition of mechanical seals.

Acoustic emission signals from the friction of the seal end face were obtained, and their bispectral characteristics were extracted. The variation of non-Gaussian information with the degree of friction was investigated, and by combining bispectral characteristics with information entropy, a bispectral entropy index was established to represent the friction level of the seal end face.

In the start-up stage, the characteristic frequency amplitude of the micro-convex body contact is obvious, the friction of the end face is abnormal, the complexity of the system increases in a short time and the bispectral entropy rises continuously in a short time. In the stable operation stage, the characteristic frequency amplitude of the micro-convex body contact varies with the intensity of the seal face friction, the seal face friction is stable and the bispectral entropy fluctuates up and down for a period of time.

The bispectral analysis method is applied to the seal friction monitoring, the seal frequency domain characteristics are extracted, the micro-convex body contact characteristic frequency is defined and the bispectral entropy characteristic index is proposed, which provides a certain theoretical basis for the mechanical seal friction monitoring.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-07-2023-0242/

Smart contracts are self-executing computer programmes that have the potential to be used in several applications instead of traditional written contracts. With the recent rise of smart systems (e.g. Internet of things) and digital platforms (e.g. blockchain), smart contracts are gaining high interest in both business and academia. In this work, a framework for smart contracts was proposed with using reputation as the system currency, and conducts currency mining through fulfilling the physical commitments that are agreed upon.

A game theory model is developed to represent the proposed system, and then a system dynamics simulator is used to check the response of the blockchain with different sizes.

The numerical results showed that the proposed system could identify the takeover attacks and protect the blockchain from being controlled by an outsider. Another important finding is that careful setting of the maximum currency amount can improve the scalability of the blockchain and prevent the currency inflation.

This work is proposed as a conceptual framework for supply chain 4.0. Future work will be dedicated to implement and experiment the proposed framework for other characteristics that may be encountered in the context of supply chain 4.0, such as different suppliers' tiers, different customer typologies and smart logistics applications, which may reveal other challenges and provide additional interesting insights.

By using the proposed framework, smart contracts and blockchains can be implemented to handle many issues in the context of operations and supply chain 4.0, especially in times of turbulence such as the COVID-19 global pandemic crisis.

This work emphasizes that smart contracts are not too smart to be applied in the context of supply chain 4.0. The proposed framework of smart contracts is expected to serve supply chain 4.0 by automating the knowledge work and enabling scenario planning through the game theory model. It will also improve online transparency and order processing in real-time through secured multitier connectivity. This can be applied in global supply chain functions backed with digitization, notably during the time of the pandemic, in which e-commerce and online shopping have changed the rules of the game.

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.

This study aims to investigate the surface modification on 20CrMnTi gear steel individually treated by diamond-like carbon films and nitride coatings.

For this purpose, the mechanical properties of a-C:H, ta-C and AlCrSiN coatings are characterized by nano-indentation and scratch tests. The friction and wear behaviors of these three coatings are evaluated by ball-on-disc tribological experiments under dry contact conditions.

The results show that the a-C:H coating has the highest coating-substrate adhesion strength (495 mN) and the smoothest surface (Ra is about 0.045 µm) compared with the other two coatings. The AlCrSiN coating shows the highest mean coefficient of friction (COF), whereas the ta-C coating exhibits the lowest one (steady at about 0.16). The carbon-based coatings possess excellent self-lubricating properties compared with nitride ceramic ones, which effectively reduce the COF by about 64%. The major failure mode of carbon-based coatings in dry contact is slight abrasive wear. The damage of AlCrSiN coating is mainly adhesive wear and abrasive wear.

It is suggested that the carbon-based film can effectively improve the friction-reducing and wear resistance performance of the gear steel surface, which has a promising application prospect in the mechanical transmission field.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2023-0129/

Existing Simultaneous Localization and Mapping (SLAM) algorithms have been relatively well developed. However, when in complex dynamic environments, the movement of the dynamic points on the dynamic objects in the image in the mapping can have an impact on the observation of the system, and thus there will be biases and errors in the position estimation and the creation of map points. The aim of this paper is to achieve more accurate accuracy in SLAM algorithms compared to traditional methods through semantic approaches.

In this paper, the semantic segmentation of dynamic objects is realized based on U-Net semantic segmentation network, followed by motion consistency detection through motion detection method to determine whether the segmented objects are moving in the current scene or not, and combined with the motion compensation method to eliminate dynamic points and compensate for the current local image, so as to make the system robust.

Experiments comparing the effect of detecting dynamic points and removing outliers are conducted on a dynamic data set of Technische Universität München, and the results show that the absolute trajectory accuracy of this paper's method is significantly improved compared with ORB-SLAM3 and DS-SLAM.

In this paper, in the semantic segmentation network part, the segmentation mask is combined with the method of dynamic point detection, elimination and compensation, which reduces the influence of dynamic objects, thus effectively improving the accuracy of localization in dynamic environments.