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With a mass of electronic multi-topic documents available, there is an increasing need for evaluating emerging analysis tools to help users and digital libraries analyze these documents better. The purpose of this paper is to evaluate the effectiveness, efficiency and user satisfaction of THC-DAT, a within-document analysis tool, in reading a multi-topic document.

The authors reviewed related literature first, then performed a user-centered, comparative evaluation of two within-document analysis tools, THC-DAT and BOOKMARK. THC-DAT extracts a topic hierarchy tree using hierarchical latent Dirichlet allocation (hLDA) method and takes the context information into account. BOOKMARK provides similar functionality to the Table of Contents bookmarks in Adobe Reader. Three novel kinds of tasks were devised for participants to finish on two tools, with objective results to assess reading effectiveness and efficiency. And post-system questionnaires were employed to obtain participants’ subjective judgments about the tools.

The results confirm that THC-DAT is significantly more effective than BOOKMARK, while not inferior in efficiency. There is some evidence that suggests THC-DAT can slow down the process of approaching cognitive overload and improve users’ willingness to undertake difficult task. Based on qualitative data from questionnaires, the results indicate that users were more satisfied when using THC-DAT than BOOKMARK.

Adopting THC-DAT in digital libraries or electrical document reading systems contributes to promoting users’ reading performance, willingness to undertake difficult task and general satisfaction. Moreover, THC-DAT is of great value to addressing cognitive overload problem in the information retrieval field.

This paper evaluates a novel within-document analysis tool in analyzing a multi-topic document, and proved that this tool is superior to the benchmark in effectiveness and user satisfaction, and not inferior in efficiency.

This paper aims to present a novel approach to facial recognition that enhances privacy by using radio frequency identification (RFID) technology combined with transformer models, eliminating the need for visual data and thus reducing privacy risks associated with traditional image-based systems.

The proposed RFID-transformer recognition system (RTRS) uses RFID technology to capture signal features such as phase and received signal strength indicator, which are then processed by a transformer model. The model is specifically designed to handle structured RFID data, capturing subtle patterns and dependencies to achieve accurate biometric recognition. The system’s performance was validated through comprehensive experiments involving different environmental conditions and user scenarios.

The experimental results demonstrate that the RTRS system achieves a recognition accuracy of 98.91%, maintaining robust performance across various challenging conditions, including low-light environments and changes in face orientation. In addition, the system provides a high level of privacy preservation by avoiding the collection and storage of visual data.

To the best of the authors’ knowledge, this work introduces the first RFID-based facial recognition system that fully leverages transformer models, offering a privacy-preserving alternative to traditional image-based methods. The system’s ability to perform accurately in diverse scenarios while ensuring user privacy makes it a significant advancement in biometric technology.

The purpose of this study is to investigate the influence of dimple radius, depth and density on the lubrication performance of the plunger.

A lubrication model was adopted to consider eccentricity and deformation during the working process of the plunger, and a rig test was performed to confirm the simulation results. The texture was fabricated using laser surface texturing.

The simulation results suggested that when dimple radius or depth increases, oil film thickness of the plunger increases before decreasing, and asperity friction displays an opposite trend. Therefore, appropriate microdimple texture could facilitate lubrication performance improvement and reduce the wear. Microdimples were then lased on the plunger surface, and a basic tribological test was conducted to validate the simulation results. The experimental results suggested that the average friction coefficient decreased from 0.18 to 0.13, a reduction of 27.8%.

The introduction of microdimple on a plunger couple to reduce friction and improve lubrication is expected to provide a new approach to developing high-performance plunger couple and improve the performance of the internal combustion engine. If applied, the surface texture could help reduce friction by around 27% and cap the cost relative to the plugger friction.

The microdimple texture was introduced into the plunger couple of a vehicle to reduce the friction and improve the performance. Findings suggested that surface texture could be used in the automotive industry to improve oil efficiency and lubrication performance.

The peer review history for this article is available at: http://dx.doi.org/10.1108/ILT-07-2020-0259.

This paper aims to present a human-in-the-loop natural teaching paradigm based on scene-motion cross-modal perception, which facilitates the manipulation intelligence and robot teleoperation.

The proposed natural teaching paradigm is used to telemanipulate a life-size humanoid robot in response to a complicated working scenario. First, a vision sensor is used to project mission scenes onto virtual reality glasses for human-in-the-loop reactions. Second, motion capture system is established to retarget eye-body synergic movements to a skeletal model. Third, real-time data transfer is realized through publish-subscribe messaging mechanism in robot operating system. Next, joint angles are computed through a fast mapping algorithm and sent to a slave controller through a serial port. Finally, visualization terminals render it convenient to make comparisons between two motion systems.

Experimentation in various industrial mission scenes, such as approaching flanges, shows the numerous advantages brought by natural teaching, including being real-time, high accuracy, repeatability and dexterity.

The proposed paradigm realizes the natural cross-modal combination of perception information and enhances the working capacity and flexibility of industrial robots, paving a new way for effective robot teaching and autonomous learning.

This study aims to further refine the model, explore the influence of cutting parameters on the machining process, and apply it to practical engineering to improve the efficiency and quality of titanium alloy machining.

This paper establishes a comprehensive thermo-mechanical fully coupled orthogonal cutting model. This paper aims to couple the modified Johnson–Cook constitutive model, damage model and contact model to construct a two-dimensional orthogonal cutting thermo-mechanical coupling model for high-speed cutting of Ti6Al4V. The model considers the evolution of microstructures such as plastic deformation, grain dislocation rearrangement, dynamic recrystallization, as well as stress softening and hardening occurring continuously in Ti6Al4V metal during high-speed cutting. Additionally, the model incorporates friction and contact between the tool and the workpiece. It can be used to predict parameters such as cutting process, cutting force, temperature distribution, stress and strain in titanium alloy machining. The study establishes the model and implements corresponding functions by writing Abaqus VUMAT and VFRICTION subroutines.

The use of different material constitutive models can significantly impact the prediction of the cutting process. Some models may more accurately describe the mechanical behavior of the material, thus providing more reliable prediction results, while other models may exhibit larger deviations. Compared to the Tanh model, the proposed model achieves a maximum improvement of 8.9% in the prediction of cutting force and a maximum improvement of 20.9% in the prediction of chip morphology parameters. Compared to experiments, the proposed model achieves a minimum prediction error of 2.8% for average cutting force and a minimum error of 0.57% for sawtooth parameters. This study provides a comprehensive theoretical foundation and practical guidance for orthogonal cutting of titanium alloys. The model not only helps engineers and researchers better understand various phenomena in the cutting process but also serves as an important reference for optimizing cutting processes.

The originality of this research is guaranteed, as it has not been previously published in any journal or publication.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2024-0168/

This study aims to establish a thermally coupled two-dimensional orthogonal cutting model to further improve the modeling process for systematic evaluation of material damage, stiffness degradation, equivalent plastic strain and other material properties, along with cutting temperature distribution and cutting forces. This enhances modeling efficiency and accuracy.

A two-dimensional orthogonal cutting thermo-mechanical coupled finite element model is established in this study. The tanh material constitutive model is used to simulate the mechanical properties of the material. Velocity-dependent friction model between the workpiece and the tool is considered. Material characteristics such as material damage, stiffness degradation, equivalent plastic strain and temperature field during cutting are evaluated through computation. Contact pressure and shear stress on the tool surface are extracted for friction analysis.

Speed-dependent friction models predict cutting force errors as low as 8.6%. The prediction errors of various friction models increase with increasing cutting forces and depths of cut, and simulation results tend to be higher than experimental data.

The current research results provide insights into understanding and controlling tool-chip friction in metal cutting, offering practical recommendations for friction modeling and machining simulation work.

The originality of this research is guaranteed, as it has not been previously published in any journal or publication.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2024-0162/

The purpose of this paper is to analyze the network structure of technology in and between different fields, as well as the evolution of their relations.

Using the patent data in Derwent Innovation Index (DII) from 1991 to 2010, this paper analyzes the co-classification of Derwent Manual Code (DMC) of patents in all technology fields. Large-scaled co-classification matrices are employed to generate the DMC co-classification networks. In addition, analyses are pursued at different levels of aggregation in four five-year windows: 1991-1995, 1996-2000, 2001-2005 and 2006-2010. Using Girvan-Newman algorithm in the clustering process, the structure transformations over time are detected.

The paper identifies the key technological knowledge in certain fields and finds out how different technological fields are connected and integrated. What is more, the dynamic evolution between networks in different time periods reveals the trend of generic technology development in the macroscopic level.

The paper investigates a large quantity of data – all the patent data in DII from 1991 to 2010 in this paper. The paper applies Girvan-Newman algorithm in the co-classification analysis and uses co-classification networks to reveal technology network structures. Evolution coincident with the realistic technological shifts can be observed.

The purpose of this paper is to study the impact of different types of textures on the friction lubrication performance of cylindrical roller bearings.

In the present study, the composite texture hydrodynamic lubrication model that takes into account the effects of surface roughness is established, and the Reynolds equation for the oil film is numerically solved using the finite difference method. The study investigates the oil film carrying capacity and maximum pressure of bearings under two different arrangements of four composite textures and conducts a comparative analysis of the oil film characteristics under various texture parameters and surface roughness levels.

When the roughness of the inner texture surface and the contact surface are equal, the bearing capacity of the composite texture is intermediate between the two textures. The impact trend of surface roughness on fluid dynamic pressure effects varies with the type of composite texture; the internal roughness of the texture affects the micro-hydrodynamic pressure action. Composite textures with different depths exhibit improved bearing capacities; elliptical cylindrical parallel and elliptical hemispherical parallel textures perform better when their area densities are similar, while other types of composite textures show enhanced bearing performance as the ratio of their area densities increases.

This paper contributes to the theoretical investigations and analyses on designing the textured rolling bearings with high lubrication performance.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-02-2024-0050

With the development of economic globalization and the growth of cross-border technology flow, the internationalization of innovation has become an important strategy for enterprises in global competition for both investment optimization and technological advancement. The purpose of this paper is to reveal the research evolution in internationalization of innovation, investigate the hot spot transformation, and predict the future research trends.

The main research approaches in this study are literature co-citation analysis and keyword co-occurrence analysis. Co-citation is applied as a semantic similarity measure for related papers that makes use of citation relationships. Co-occurrence frequency analysis of keywords is also carried out to reveal the hot spots in research of internationalization of innovation. With the data downloaded from Web of Science, Citespace was used as a tool of scientometrics to visualize the node papers, knowledge mapping and keyword co-occurrence ranking in different stages of research evolution. The literature being analyzed in this study come from paper collection by searching the titles, abstracts and keywords, for terms that include “international innovation”, “international R&D”, “international technology”, “globalizational innovation”, “globalizational R&D”, “globalizational technology”, “multinational innovation”, “multinational R&D” and “multinational technology”.

The investigation reveals that there are two distinct stages in research evolution of the internationalization of innovation. The direction of innovation diffusion has turned from “one-way trickle down from developed countries” to “two-way interaction between developed countries and emerging countries”. Meanwhile, the research hotspots have been transformed since 2000 from “detail and operation-focused” to “profound and strategy-focused”.

The paper gives an insight into the internationalization of innovation field using literature from the Web of Science as an illustration.