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The purpose of this paper is to develop a familiarity analysis of resistive-capacitive (RC) network active circuit operating with unfamiliar low-pass (LP) type negative group delay (NGD) behavior. The design method of NGD circuit is validated by simulation with commercial tool and experimental measurement.

The present research work methodology is structured in three main parts. The familiarity theory of RC-network LP-NGD circuit is developed. The LP-NGD circuit parameters are expressed in function of the targeted time-advance. Then, the feasibility study is based on the theory, simulation and measurement result comparisons.

The RC-network based LP-NGD proof of concept is validated with −1 and −0.5 ms targeted time-advances after design, simulation, test and characterized. The LP-NGD circuit unity gain prototype presents NGD cut-off frequencies of about 269 and 569 Hz for the targeted time-advances, −1 and −0.5 ms, respectively. Bi-exponential and arbitrary waveform signals were tested to verify the targeted time-advance.

The performance of the unfamiliar LP-NGD topology developed in the present study is limited by the parasitic elements of constituting lumped components.

The NGD circuit enables to naturally reduce the undesired delay effect from the electronic and communication systems. The NGD circuit can be exploited to reduce the delay induced by electronic devices and system.

As social impacts of the NGD circuit application, the NGD function is one of prominent solutions to improve the technology performances of future electronic device in term of communication aspect and the transportation system.

The originality of the paper concerns the theoretical approach of the RC-network parameters in function of the targeted time-advance and the input signal bandwidth. In addition, the experimental results are also particularly original.

The purpose of this paper is to investigate the combined effects of different modalities of long-term knowledge accumulation and short-term knowledge searching on the generation of high-impact ideas. The authors aim at providing useful conclusions for academic scholars.

Two dimensions of the cumulative knowledge structures of researchers are measured: knowledge depth and knowledge breadth. The search strategies employed by researchers are classified as local search and distant search. The authors use researchers’ historical publications to measure cumulative knowledge structures. References contained in these publications serve as an indicator of knowledge searching behaviors and are used to measure search strategies. Highly cited papers with random-but-matched papers from the same journal published in the same year are adopted to test the hypotheses.

The knowledge depth of researchers positively predicts the generation of high-impact ideas. Knowledge breadth has a bell-shaped relationship to the generation of high-impact ideas. Two instances of “strategic fit” between the knowledge structures and search strategies of researchers are identified; namely, knowledge breadth combines most effectively with local search, and knowledge depth combines most effectively with distant search in generating high-impact ideas.

Using article references to measure authors’ knowledge search behaviors may lead to biases. Future research should perform a survey to obtain a comprehensive understanding of the knowledge search behaviors of scholars.

A “T-shaped” knowledge structure in the long run is recommended for maximal generation of high-impact ideas. Researchers who have not adopted this optimal knowledge structure can employ a matched search strategy to leverage their existing knowledge structures.

This paper is among the first to examine the interactive effects between the cumulative knowledge structures and short-term knowledge searching strategies of researchers. The authors have enriched the exploration and exploitation theory by adding the dimension of time into the analysis.

The purpose of this paper is to study the corrosion behaviors of X70 steel under direct current (DC) interference at 0-1,200 A/m² in simulated soil solution.

The Tafel polarization curves of X70 steel under DC interference were tested using electrochemical method, the corrosion rate was calculated using weight-loss method and the change in steel surface was analyzed by optical microscopy.

The results showed that E-I polarization curves under 200-1,200 A/m² interference were linear; with an increase in the DC density, the corrosion potential of X70 steel shifted positively, solution pH after the weight-loss tests increased and corrosion rate increased linearly. A mathematical relationship between polarization resistance Rp and current density was established. Corrosion morphology indicated that pitting corrosion and crevice corrosion occurred on the X70 steel under DC interference in simulated soil solution.

All tests were conducted at a relative higher DC density (200-1,200 A/m²). The linear fitting method is proposed to fit data of Tafel polarization curves under DC interference. This study provides guidelines for safe operation of X70 steel pipelines.

The majority of existing studies about named entity recognition (NER) concentrate on the prediction enhancement of deep neural network (DNN)-based models themselves, but the issues about the scarcity of training corpus and the difficulty of annotation quality control are not fully solved, especially for Chinese ancient corpora. Therefore, designing a new integrated solution for Chinese historical NER, including automatic entity extraction and man-machine cooperative annotation, is quite valuable for improving the effectiveness of Chinese historical NER and fostering the development of low-resource information extraction.

The research provides a systematic approach for Chinese historical NER with a three-stage framework. In addition to the stage of basic preprocessing, the authors create, retrain and yield a high-performance NER model only using limited labeled resources during the stage of augmented deep active learning (ADAL), which entails three steps—DNN-based NER modeling, hybrid pool-based sampling (HPS) based on the active learning (AL), and NER-oriented data augmentation (DA). ADAL is thought to have the capacity to maintain the performance of DNN as high as possible under the few-shot constraint. Then, to realize machine-aided quality control in crowdsourcing settings, the authors design a stage of globally-optimized automatic label consolidation (GALC). The core of GALC is a newly-designed label consolidation model called simulated annealing-based automatic label aggregation (“SA-ALC”), which incorporates the factors of worker reliability and global label estimation. The model can assure the annotation quality of those data from a crowdsourcing annotation system.

Extensive experiments on two types of Chinese classical historical datasets show that the authors’ solution can effectively reduce the corpus dependency of a DNN-based NER model and alleviate the problem of label quality. Moreover, the results also show the superior performance of the authors’ pipeline approaches (i.e. HPS + DA and SA-ALC) compared to equivalent baselines in each stage.

The study sheds new light on the automatic extraction of Chinese historical entities in an all-technological-process integration. The solution is helpful to effectively reducing the annotation cost and controlling the labeling quality for the NER task. It can be further applied to similar tasks of information extraction and other low-resource fields in theoretical and practical ways.

Rubber-plastic double-layer bush water-lubricated bearings have demonstrated superior performance, while research on their vibration characteristics remains limited. This paper aims to investigate the lubrication and vibration properties of these bearings by experiments and examine the effect of rubber-to-plastic bush thickness ratio on bearing performance.

A water-lubricated journal bearing test rig is constructed, and three bearings with different bush thickness ratios are fabricated. Bush deformation under various loads is measured, and the friction coefficient and axis trajectory under different operating conditions are tested. The vibration responses of the bearings under directional harmonic excitation are studied. The influences of rotational speed, load and rubber-to-plastic bush thickness ratio on the bearing’s lubrication and vibration properties are analyzed.

The friction coefficient of the bearing initially decreases rapidly and subsequently increases gradually as the rotational speed or load increases. The bearing with a thicker rubber bush shows lower displacement amplitudes in its axis trajectory. Under a 45° directed excitation, significant oscillations are observed in the vertical displacement, while the horizontal displacement remains stable. The damping effect of the bearing with a thicker rubber bush is more pronounced.

This paper present the influence of rubber-to-plastic bush thickness ratio on bearing lubrication and vibration performance. The results are valuable for the design of this type of bearing.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-12-2024-0469/

Gross domestic product (GDP) is an important indicator to measure a country's economic development. If the future development trend of a country's GDP can be accurately predicted, it will have a positive effect on the formulation and implementation of the country's future economic development policies. In order to explore the future development trend of China's GDP, the purpose of this paper is to establish a new grey forecasting model with time power term to forecast GDP.

Firstly, the shortcomings of the traditional grey prediction model with time power term are found out through analysis, and then the generalized grey prediction model with time power term is established (abbreviated as PTGM (1,1, α) model). Secondly, the PTGM (1,1, α) model is improved by linear interpolation method, and the optimized PTGM (1,1, α) model is established (abbreviated as OPTGM (1,1, α) model), and the parameters of the OPTGM (1,1, α) model are solved by the quantum genetic algorithm. Thirdly, the advantage of the OPTGM (1,1, α) model over the traditional grey models is illustrated by two real cases. Finally the OPTGM (1,1, α) model is used to predict China's GDP from 2020 to 2029.

The OPTGM (1,1, α) model is more suitable for predicting China's GDP than other grey prediction models.

A new grey prediction model with time power term is proposed.

The paper aims to explore whether topic analysis (identification of the core contents, trends and topic distribution in the target field) can be performed using a more low-cost and easily applicable method that relies on a small dataset, and how we can obtain this small dataset based on the features of the publications.

The paper proposes a topic analysis method based on prolific and authoritative researchers (PARs). First, the authors identify PARs in a specific discipline by considering the number of publications and citations of authors. Based on the research publications of PARs (small dataset), the authors then construct a keyword co-occurrence network and perform a topic analysis. Finally, the authors compare the method with the traditional method.

The authors found that using a small dataset (only 6.47% of the complete dataset in our experiment) for topic analysis yields relatively high-quality and reliable results. The comparison analysis reveals that the proposed method is quite similar to the results of traditional large dataset analysis in terms of publication time distribution, research areas, core keywords and keyword network density.

Expert opinions are needed in determining the parameters of PARs identification algorithm. The proposed method may neglect the publications of junior researchers and its biases should be discussed.

This paper gives a practical way on how to implement disciplinary analysis based on a small dataset, and how to identify this dataset by proposing a PARs-based topic analysis method. The proposed method presents a useful view of the data based on PARs that can produce results comparable to traditional method, and thus will improve the effectiveness and cost of interdisciplinary topic analysis.

This paper proposes a PARs-based topic analysis method and verifies that topic analysis can be performed using a small dataset.

As Chinese massage is increasingly popular, many physicians are needed these days. In order to promote the experience and skills of experts and reduce labour intensity during massage, a massage robot, which could reproduce the expert techniques with individualized manipulation parameters and enhanced safety control strategies, is presented in this paper.

The kinematic and force features of key massage techniques, such as Thumb Kneading, Pressing, Rolling, Vibrating and Pinching, are summarized by analyzing the massage processes of expert physicians, and a mathematical model for robotic massage is established. With safety issues taken into account, the overall system structure of the massage robot is proposed. The system generally consists of a positioning platform and a massage end‐effector which implements the massage techniques, and the end‐effector is further divided into a parallel mechanism and a massage hand to accommodate different techniques. Visual tracking is used for positioning acupuncture points by recognizing markers on a massage vest worn by the patient. A pain threshold value is introduced to individualize therapy schemes and a force‐position control method based on the pain threshold is presented.

VAS (Visual Analogue Scale) tests for lumbar muscle strain are carried out using the massage robot, and the treatment effect of the massage robot based on traditional Chinese massage therapy theory is initially validated.

The treatment effect of the massage robot needs to be assessed clinically for more occasions and more clinical experiments will be conducted, to optimize the configuration and control strategy to meet the clinical needs in future work.

The robotic massage system presented in this paper is acting on acupuncture points based on traditional Chinese massage therapy theory, with human manipulation techniques reproduced and expert experiences incorporated. The massage robot can take the place of a massager to perform Chinese massage. Most of the massage robotic systems published in the world perform only one massage technique and the whole massage process is not completely considered. By comparison, the authors' massage robot could perform five techniques. Furthermore, the authors have designed the procedure of robotic massage specifically for patients who suffer from lumbar muscle strain.

This study aims to investigate the antecedents of mobile Internet users’ perception of information sensitivity (PIS) and willingness to provide personal information (WTP). It provides insights about how these antecedents influence users’ perceived information sensitivity and willingness to provide.

An online survey of mobile Internet users was conducted in China, generating a total of 1,000 qualified responses for analysis.

Results reveal the differential effects of some major antecedents of mobile Internet users’ perceived information sensitivity and willingness to provide (individual disposition to value privacy, age, gender, app type and privacy concerns) and such impact vary across low-, medium- and high-privacy segments.

This study provides insights into the antecedents of mobile Internet users’ attitudes towards personal information privacy. It also extends the understanding of users’ perceived information sensitivity and willingness to provide such information comparatively among four countries.

This study aims to clarify the effect of team effort allocation between knowledge exploration and exploitation on the generation of extremely good or poor innovations. The influence of previous collaborative experience among team members on the effect of team effort allocation is also investigated to understand the relationship between team members’ collaboration networks and knowledge learning.

This study uses data of all patents granted by the US Patent and Trademark Office between 1984 and 2010. The inventors involved in a patent are regarded as members of the focal team. Logistic regression is used to analyze the data.

Allocating greater effort to exploration than to exploitation is beneficial to achieving breakthrough innovations despite the risk of generating particularly poor innovations. This benefit increases with collaborative experience among team members. Placing an equal emphasis on knowledge exploration and exploitation is not particularly effective in achieving breakthrough innovations; it is, however, the best strategy for avoiding particularly poor innovations.

This research not only provides valuable insights for research on innovation and knowledge management by studying the team effort allocation strategy used to achieve breakthroughs and avoid particularly poor innovations but also represents an advancement in bridging two streams of research – knowledge learning and social networks – by highlighting the influence of the team members’ collaborative networks on the effect of team effort allocation between knowledge exploration and exploitation.