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Fractionated satellite clusters need coming together and avoiding crashing with limited random initial relative motion conditions. It is not necessary to keep the invariable configuration. The purpose of this paper is to put forward a control law which simulates organism swarm motion.

The motion of satellites follows three rules: coming together, velocity homology and avoiding crash. According to the rules, three control forces should be applied to satellite individuals. The final control force is the sum of three control forces. Electromagnetic dipole strengths calculation is formulated as nonlinear optimization problem. Considering control strengths have to be got in real time, iterative steps of optimization algorithm are fixed.

A control law which simulates organism swarm motion is put forward. The simulation shows the organism swarm motion simulation control law can keep fractionated satellite cluster coming together and avoiding crash. When iterative steps of optimization algorithm is fixed, the error of solve nonlinear equations is acceptable.

The control law is robust and easy to realize. When electromagnetic satellite cluster need not keep fixed configuration, it is a choice of control law of relative motion.

This study aims to ensure reliable analysis of dynamic responses in asphalt pavement structures. It investigates noise reduction and data mining techniques for pavement dynamic response signals.

The paper conducts time-frequency analysis on signals of pavement dynamic response initially. It also uses two common noise reduction methods, namely, low-pass filtering and wavelet decomposition reconstruction, to evaluate their effectiveness in reducing noise in these signals. Furthermore, as these signals are generated in response to vehicle loading, they contain a substantial amount of data and are prone to environmental interference, potentially resulting in outliers. Hence, it becomes crucial to extract dynamic strain response features (e.g. peaks and peak intervals) in real-time and efficiently.

The study introduces an improved density-based spatial clustering of applications with Noise (DBSCAN) algorithm for identifying outliers in denoised data. The results demonstrate that low-pass filtering is highly effective in reducing noise in pavement dynamic response signals within specified frequency ranges. The improved DBSCAN algorithm effectively identifies outliers in these signals through testing. Furthermore, the peak detection process, using the enhanced findpeaks function, consistently achieves excellent performance in identifying peak values, even when complex multi-axle heavy-duty truck strain signals are present.

The authors identified a suitable frequency domain range for low-pass filtering in asphalt road dynamic response signals, revealing minimal amplitude loss and effective strain information reflection between road layers. Furthermore, the authors introduced the DBSCAN-based anomaly data detection method and enhancements to the Matlab findpeaks function, enabling the detection of anomalies in road sensor data and automated peak identification.

This study aims to explore the impact of multisource deformation errors on the oil film contact surface, which arise from manufacturing, assembly, oil pressure and thermal influences, on the motion accuracy of hydrostatic guideway.

Using thermal-structural coupling simulations, this research investigates the effects of assembly, oil pressure and thermal factors on deformation errors of the oil film contact surface. By integrating these with manufacturing errors, a profile error model for the oil film contact surface is developed, characterizing the cumulative effect of these errors. Using kinematic theory and progressive Mengen flow controller characteristics, the motion error at any position of the hydrostatic guideway is quantified, examining how surface error traits impact motion accuracy.

The error averaging effect is affected by the profile error of oil film contact surface. Meanwhile, the motion accuracy of hydrostatic guideway is highly sensitive to the oil film contact surface error amplitude.

This approach allows for precise prediction and analysis of motion accuracy in hydrostatic guideways during the design and manufacturing stages. It also provides guidance for planning process tolerances.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-03-2024-0063/

The purpose is to realize precise apicoectomy with less surgical risk and improved quality and efficiency.

First, the procedure of precise apicoectomy based on additive manufacturing (AM) and digital design is proposed. With CT images of the patient's oral, a 3D model of alveolar bone and teeth is reconstructed, and based on this model, the infected tissue and enclosed root tip can be determined. Thus, a surgical plan can be created based on clear anatomical relationships and minimal negative constraints, which will then determine the drill position, direction and depth, as well as the resection length of root tip. With this plan, a surgical guide design is performed via a composite model from reversed plaster models and hard tissue models from CT, and accessory tools including drill with stop plane and handle are also selected. With the surgical guide, the virtual plan in the computer can be realized in the clinic.

With this methodology, the dentist can perform root-end resection with greater accuracy, save more than 30 percent of operatory time, and the discomfort to the patient is reduced to a minimum.

The proposed methodology has been used in ten cases for root-end resections. In fact, this method of designing a computer-based treatment plan with a 3D model of a patient and applying it in the clinic through guiding tools can be used in other surgeries, such as orthognathic surgery or osteotomy.

This case report illustrates that with AM and digital design methods, optimal operational plans can be designed and realized for apicoectomy, and the quality and efficiency of clinical surgery are greatly improved compared with conventional methods.

The purpose of this paper is to develop an effective approach to support and guide production improvement processes utilising online product reviews.

This paper combines two methods: (1) natural language processing (NLP) to support advanced text mining to increase the accuracy of information extracted from product reviews and (2) quality function deployment (QFD) to utilise the extracted information to guide the product improvement process.

The paper proposes an approach to automate the process of obtaining voice of the customer (VOC) by performing text mining on available online product reviews while considering key factors such as the time of review and review usefulness. The paper enhances quality management processes in organisations and advances the literature on customer-oriented product improvement processes.

Online product reviews are a valuable source of information for companies to capture the true VOC. VOC is then commonly used by companies as the main input for QFD to enhance quality management and product improvement. However, this process requires considerable time, during which VOC may change, which may negatively impact the output of QFD. This paper addresses this challenge by providing an improved approach.

Green energy as a transportation supply trend is irreversible. In this paper, a highway energy supply system (HESS) evolution model is proposed to provide highway transportation vehicles and service facilities with a clean electricity supply and form a new model of a source-grid-load-storage-charge synergistic highway-PV-WT integrated system (HPWIS). This paper aims to improve the flexibility index of highways and increase CO₂ emission reduction of highways.

To maximize the integration potential, a new energy-generation, storage and information-integration station is established with a dynamic master–slave game model. The flexibility index is defined to evaluate the system ability to manage random fluctuations in power generation and load levels. Moreover, CO₂ emission reduction is also quantified. Finally, the Lianhuo Expressway is taken as an example to calculate emission reduction and flexibility.

The results show that through the application of the scheduling strategy to the HPWIS, the flexibility index of the Lianhuo Expressway increased by 29.17%, promoting a corresponding decrease in CO₂ emissions.

This paper proposed a new model to capture the evolution of the HESS, which provides highway transportation vehicles and service facilities with a clean electricity supply and achieves energy transfer aided by an energy storage system, thus forming a new model of a transportation energy system with source-grid-load-storage-charge synergy. An evaluation method is proposed to improve the air quality index through the coordination of new energy generation and environmental conditions, and dynamic configuration and dispatch are achieved with the master–slave game model.

The purpose of this paper is to analyse the implication of trade on carbon emissions in a panel of eight highly trading Southeast and East Asian countries, namely, China, Indonesia, South Korea, Malaysia, Hong Kong, The Philippines, Singapore and Thailand.

The analysis relies on the standard quadratic environmental Kuznets curve (EKC) extended to include energy consumption and international trade. A battery of panel unit root and co-integration tests is applied to establish the variables’ stochastic properties and their long-run relations. Then, the specified EKC is estimated using the panel dynamic ordinary least square (OLS) estimation technique.

The panel co-integration statistics verifies the validity of the extended EKC for the countries under study. Estimation of the long-run EKC via the dynamic OLS estimation method reveals the environmentally degrading effects of trade in these countries, especially in ASEAN and plus South Korea and Hong Kong.

These countries are heavily dependent on trade for their development processes, and as such, their impacts on CO₂ emissions would be highly relevant for assessing their trade policies, along the line of the gain-from-trade hypothesis, the race-to-the-bottom hypothesis and the pollution-safe-haven hypothesis.

The analysis adds to existing literature by focusing on the highly trading nations of Southeast and East Asian countries. The results suggest that reassessment of trade policies in these countries is much needed and it must go beyond the sole pursuit of economic development via trade.

Short-form health science videos have become an important medium for disseminating health knowledge and improving public health literacy. However, the factors that determine viewer engagement are not well understood. This study aims to address this research gap by investigating the association between doctor image features and viewer engagement behavior, building on the personal branding theory and information signaling theory.

A sample of 1245 health science short-form videos was collected, and key video features related to doctor images were extracted through manual labeling. Multi-variable regression analysis and SPSS process model were employed to test the hypotheses.

The results show that doctor image features are significantly associated with viewer engagement behavior. Videos featuring doctors in medical uniforms receive more viewer likes, comments and shares. Highlighting the doctor's title can increase viewer collections. Videos shot in a home, white wall, or study room setting receive more like, comments and sharing. The doctor's appearance demonstrates a positive nonlinear relationship with viewer likes and comments. Young doctors with title information tend to attract more video collections than older doctors with title information. The positive effect of the doctor's appearance and showing title information, become more significant among male doctors.

This research provides novel insights into the factors that determine viewer engagement behavior in short-form health science videos. Specific doctor image features can enhance viewer engagement by signaling doctor professionalism. The results also suggest that there may be age and gender biases in viewers' perceptions.

An investigation was conducted into the impact of various process parameters on the surface and subsurface quality of glass-ceramic materials, as well as the mechanism of material removal and crack formation, through the use of ultrasonic-assisted grinding.

A mathematical model of crack propagation in ultrasonic-assisted grinding was established, and the mechanism of crack formation was described through the model. A series of simulations and experiments were conducted to investigate the impact of process parameters on crack depth, surface roughness, and surface topography during ultrasonic-assisted surface and axial grinding. Additionally, the mechanism of crack formation was explored.

During ultrasonic-assisted grinding, the average grinding forces are between 0.4–1.0 N, which is much smaller than that of ordinary grinding (1.0–3.5 N). In surface grinding, the maximum surface stresses between the workpiece and the tool gradually decrease with the tool speed. The surface stresses of the workpiece increase with the grinding depth, and the depth of subsurface cracks increases with the grinding depth. With the increase of the axial grinding speed, the subsurface damage depth increases. The roughness increases from 0.780um/1.433um.

A mathematical model of crack propagation in ultrasonic-assisted grinding was established, and the mechanism of crack formation was described through the model. The deformation involved in the grinding process is large, and the FEM-SPH modeling method is used to solve the problem that the results of the traditional finite element method are not convergent and the calculation efficiency is low.