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This paper aims to apply the spacing effect of capacitive imaging (CI) sensors to inspect and differentiate external flaws of the protective module, internal flaws of the protective module and external flaws of the metallic module in oil and gas pipelines simultaneously. Through experimental verification, a method for differentiating three distinct kinds of flaws derived from the spacing effect of CI sensors has been demonstrated.

A 3Dimensions (3D) model for simulating the inspection of these flaws was established by using COMSOL. A novel CI sensor with adjustable working electrode spacing was designed, and a modular CI system was developed to substantiate the theoretical findings with experimental evidence. A method for differentiating three distinct kinds of flaws derived from the spacing effect of CI sensors was established.

The results indicate that the method can successfully discriminate external flaws of the protective module, internal flaws of the protective module and external flaws of the metallic module using CI sensors.

The method for differentiating three distinct kinds of flaws derived from the spacing effect of CI sensors is vital for keeping the transportation safety of oil and gas pipelines.

This study aims to design and validate a theoretical model for capacitive imaging (CI) sensors that incorporates the interelectrode shielding and surrounding shielding electrodes. Through experimental verification, the effectiveness of the theoretical model in evaluating CI sensors equipped with shielding electrodes has been demonstrated.

The study begins by incorporating the interelectrode shielding and surrounding shielding electrodes of CI sensors into the theoretical model. A method for deriving the semianalytical model is proposed, using the renormalization group method and physical model. Based on random geometric parameters of CI sensors, capacitance values are calculated using both simulation models and theoretical models. Three different types of CI sensors with varying geometric parameters are designed and manufactured for experimental testing.

The study’s results indicate that the errors of the semianalytical model for the CI sensor are predominantly below 5%, with all errors falling below 10%. This suggests that the semianalytical model, derived using the renormalization group method, effectively evaluates CI sensors equipped with shielding electrodes. The experimental results demonstrate the efficacy of the theoretical model in accurately predicting the capacitance values of the CI sensors.

The theoretical model of CI sensors is described by incorporating the interelectrode shielding and surrounding shielding electrodes into the model. This comprehensive approach allows for a more accurate evaluation of the detecting capability of CI sensors, as well as optimization of their performance.

This study explores previous research related to textile management technologies, detecting gaps and opportunities for textiles made by Ainu people. We also propose an approach to digitally read Ainu textile patterns.

We employed indigenous and pluriversal design frameworks to evaluate textile pattern technologies. They were operationalised as Indigenous/local researchers involved in the investigation, multi-sensoriality of analysed items, prosperity for everyone involved, communal property of data, biological awareness, social complexity awareness and sensitivity of the analysed method if the items change.

Textile pattern technology researchers have mostly used neural networks and clustering methods. They have addressed social aspects since the 2000s. Investigations into the generation of textiles based in Poland constantly reflected the most pluriversal characteristics. Regarding Ainu textile research, most investigations have cited Indigenous sources. Two gaps emerged: the concentration of research datasets and results in enterprises or scholars and the focus on the formal characteristics of Ainu patterns in technical papers and contextual characteristics in ethnographic papers.

Heritage management is increasingly employing technological tools that should consider the sustainability of handmade/artisanal goods. As most investigations on textile patterns are conducted by the industry, their benefits are limited for heritage conservation.

Therefore, we suggest digital experts work together with ethnography and Indigenous experts, proposing a method for digital reading of Ainu textile patterns that incorporates pluriversal aspects into heritage conservation.

Pluriversal design is a set of onthologies proposed with Indigenous, mestizo and minorities from the Americas that is currently being diffused in the rest of the world, highly compatible with the analysis of Indigenous heritage.

An understanding of the impact of construction workers informal safety communication (CWISC), a form of parallel safety communication between workers, on safety performance among construction workers is crucial in order to develop effective strategies for improving safety performance in the construction industry. However, research remains scant on the impact of CWISC on safety performance. This study empirically aims to test the relationship between these important constructs.

Statistical analysis was used to examine the relationship in a hypothetical model with two latent variables; the exogenous variables represented by two groups of informal safety communication: friends and crew members and the endogenous variables represented by two groups of Safety performance metrics: safety compliance and safety participation, was tested.

The emergent findings revealed that there is a significant relationship between informal safety communication among crew members and safety compliance, and also between informal safety communication among friends on construction sites and safety participation. These findings emphasize the importance of fostering effective safety communication and collaboration within construction crews, as well as recognizing the influence friendships on safety performance. Stakeholders can leverage on these findings to implement policies to improve safety performance.

The study presents insightful practical knowledge on how CWISC impacts safety performance on construction sites. Practical recommendations for organizations are also proposed, e.g., development of team-building activities, platforms for sharing safety-related information and experiences, mentorship programs and initiatives that encourage social interaction among workers.

High-speed turnouts are more complex in structure and thus may cause abnormal vibration of high-speed train car body, affecting driving safety and passenger riding experience. Therefore, it is necessary to analyze the data characteristics of continuous hunting of high-speed trains passing through turnouts and propose a diagnostic method for engineering applications.

First, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is performed to determine the first characteristic component of the car body’s lateral acceleration. Then, the Short-Time Fourier Transform (STFT) is performed to calculate the marginal spectra. Finally, the presence of a continuous hunting problem is determined based on the results of the comparison calculations and diagnostic thresholds. To improve computational efficiency, permutation entropy (PE) is used as a fast indicator to identify turnouts with potential problems.

Under continuous hunting conditions, the PE is less than 0.90; the ratio of the maximum peak value of the signal component to the original signal peak value exceeded 0.7, and there is an energy band in the STFT time-frequency map, which corresponds to a frequency distribution range of 1–2 Hz.

The research results have revealed the lateral vibration characteristics of the high-speed train’s car body during continuous hunting when passing through turnouts. On this basis, an effective diagnostic method has been proposed. With a focus on practical engineering applications, a rapid screening index for identifying potential issues has been proposed, significantly enhancing the efficiency of diagnostic processes.

Ball grid array (BGA) package is prone to failure issues in a thermal vibration-coupled environment, such as deformation and fracture of solder joints. To predict the minimum equivalent stress of solder joints more accurately and optimize the solder joint structure, this paper aims to compare the machine learning method with response surface methodology (RSM).

This paper introduced a machine learning algorithm using Grey Wolf Optimization (GWO) Support Vector Regression (SVR) to optimize solder joint parameters. The solder joint height, spacing, solder pad diameter and thickness were the design variables, and minimizing the equivalent stress of solder joint was the optimization objective. The three dimensional finite element model of the printed circuit board assembly was verified by a modal experiment, and simulations were conducted for 25 groups of models with different parameter combinations. The simulation results were employed to train GWO-SVR to build a mathematical model and were analyzed using RSM to obtain a regression equation. Finally, GWO optimized these two methods.

The results show that the optimization results of GWO-SVR are closer to the simulation results than those of RSM. The minimum equivalent stress is decreased by 8.528% that of the original solution.

This study demonstrates that GWO-SVR is more precise and effective than RSM in optimizing the design of solder joints.

This study aims to explore the use of modified graphene (MG) in copper wire drawing lubricants to enhance their friction-reducing and anti-wear capabilities.

Graphene was modified using oleic and stearic acids to improve its dispersibility in lubricants. Various concentrations of MG were then introduced into a copper wire drawing lubricant to investigate their tribological performance. Wear mechanisms were evaluated with scanning electron microscopy, optical microscopy, Raman spectroscopy and energy dispersive spectroscopy (EDS).

The best concentration of MG is 1.5 Wt.%, at which the copper wire drawing oil exhibits a friction coefficient and wear rate of 0.085 and 2.11 × 10⁻⁶ mm³/Nm, respectively, representing decreases of 22.7% and 47.6% compared to the base oil. It was further found that the addition of 1.5 Wt.% MG to a copper wire drawing fluid with a water content of 70% resulted in a 30.3% reduction in friction coefficient compared to the base oil. Raman spectroscopy and EDS analysis confirmed that the MG tribo-film formed on the worn copper disc effectively minimized friction and wear.

This study analyzes the tribological performance of different concentrations of MG in copper wire drawing oils, establishing a basis for the application of MG in copper wire drawing fluids.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-10-2024-0399/

The purpose of this study to identify and optimize the machining of polyvinyl butyral (PVB) material for industrial uses. The research is based on input machining parameters of rotary ultrasonic machining for better understand the output response surface roughness (SR) property of polyvinyl butyral (PVB) by using the Taguchi approach. The grey relational grade analysis (GRG) is also implemented to resolve the complex interrelationship of SR data for optimization and predicting and validate the results.

In experimental work, the input parameters, namely, concentration, abrasives, power rate, grit size, tool material and hydrofluoric (HF) acid has been selected. The experiment’s design was created using MINITAB Software; the L27 orthogonal array was selected for the experimentation. SR was examined with the GRG technique for process optimization. On the other hand, for single parameter optimization analysis of variance (ANOVA) has been used.

ANOVA optimization technique gives the best result on concentration (40%) of abrasive (Al2O3+SiC+B4C), power rate (40%), grit size (600), HF acid (1.5%) and tool material (D2 alloy) are the optimal parameters to provide the slightest degree of SR. GRG optimization of multi-response parameter setting: 40% concentration, SiC+B4C mixed abrasive slurry, 40% of power rating, 280 grit size, 0.5% HF acid and high-speed tool steel tool material gives better results. The SR of PVB glass material improved by 20% after grey relational analysis.

There are several practical applications in a variety of material processing sectors, including metallurgy, machinery, electronics and transportation. These real-world applications have produced substantial and discernible economic benefits.

The analytical and optimization results will be used in the various material processing sectors, including metallurgy, machinery, electronics and transportation.

The ANOVA and grey theory approaches offer the reader a primary picture of the machining research and process parameter optimization. Combined abrasive slurry of Al₂O3+SiC+B₄C with a high power-rating exhibits lower SR. Similarly, grit size is vital; larger grits produce better SR. Ra – 0. 611 m is the lowest SR value at the hole found in trial 25 after the experimentation.

The purpose of this paper is to actively calibrate power density to match the application requirements with as small an actuator as possible. So, this paper introduces shape memory alloy to design variable stiffness elements. Meanwhile, the purpose of this paper is also to solve the problem of not being able to install sensors on shape memory alloy due to volume limitations.

This paper introduces the design, modeling and control process for a variable stiffness passive ankle exoskeleton, adjusting joint stiffness using shape memory alloy (SMA). This innovative exoskeleton aids the human ankle by adapting the precompression of elastic components by SMA, thereby adjusting the ankle exoskeleton’s integral stiffness. At the same time, this paper constructs a mathematical model of SMA to achieve a dynamic stiffness adjustment function.

Using SMA as the driving force for stiffness modification in passive exoskeletons introduces several distinct advantages, inclusive of high energy density, programmability, rapid response time and simplified structural design. In the course of experimental validation, this ankle exoskeleton, endowed with variable stiffness, proficiently executed actions like squatting and walking and it can effectively increase the joint stiffness by 0.2 Nm/Deg.

The contribution of this paper is to introduce SMA to adjust the stiffness to actively calibrate power density to match the application requirements. At the same time, this paper constructs a mathematical model of SMA to achieve a dynamic stiffness adjustment function.

The study aims to analyze the synergy of artificial intelligence (AI), with scientometrics, webometrics and bibliometrics to unlock and to emphasize the potential of the applications and benefits of AI algorithms in these fields.

By conducting a systematic literature review, our aim is to explore the potential of AI in revolutionizing the methods used to measure and analyze scholarly communication, identify emerging research trends and evaluate the impact of scientific publications. To achieve this, we implemented a comprehensive search strategy across reputable databases such as ProQuest, IEEE Explore, EBSCO, Web of Science and Scopus. Our search encompassed articles published from January 1, 2000, to September 2022, resulting in a thorough review of 61 relevant articles.

(1) Regarding scientometrics, the application of AI yields various distinct advantages, such as conducting analyses of publications, citations, research impact prediction, collaboration, research trend analysis and knowledge mapping, in a more objective and reliable framework. (2) In terms of webometrics, AI algorithms are able to enhance web crawling and data collection, web link analysis, web content analysis, social media analysis, web impact analysis and recommender systems. (3) Moreover, automation of data collection, analysis of citations, disambiguation of authors, analysis of co-authorship networks, assessment of research impact, text mining and recommender systems are considered as the potential of AI integration in the field of bibliometrics.

This study covers the particularly new benefits and potential of AI-enhanced scientometrics, webometrics and bibliometrics to highlight the significant prospects of the synergy of this integration through AI.