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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.

Lots of successful space missions require that the maneuvering spacecraft can reach the target spacecraft. Therefore, research on relative reachable domain (RRD) in target orbit for maneuvering spacecraft is particularly important and is currently a hot-debated topic in the field of aerospace. This paper aims at analyzing and simulating the RRD in target orbit for maneuvering spacecrafts with a single fixed-magnitude impulse and continuous thrust, respectively, to provide a basis for analyzing the feasibility of spacecraft maneuvering missions and improving the design efficiency of spacecraft maneuvering missions.

Based on the kinematics model of relative motion, RRD in target orbit for maneuvering spacecraft with a single fixed-magnitude impulse can be calculated via analyzing the relationship between orbital elements, position vector and velocity vector of spacecrafts, and relevant studies are introduced to compare simulation results for the same case and validate the method proposed in the paper. With analysis of the dynamic model of relative motion, the calculation of RRD in target orbit for maneuvering spacecraft with continuous thrust can be transformed as the solution of the optimal control problem, and example emulations are carried out to validate the method.

For the case with a single fixed-magnitude impulse, simulation results show preliminarily that the method is in agreement with the method in Ref. (Wen et al., 2016), which treats the same case and thus is plausibly correct and feasible. For the case with continuous thrust, analysis and simulation results confirm the validity of the proposed method. The methods based on relative motion in this paper can efficiently determining the RRD in target orbit for maneuvering spacecraft.

Both theoretical analyses and simulation results indicate that the method proposed in this paper is comparatively simple but efficient for determine the RRD in target orbit for maneuvering spacecraft swiftly and precisely.

The purpose of this paper is to improve the problem of kinematics incompatibility of human–exoskeleton in the existing rigid lower-limb exoskeleton (LLE).

In this paper, following an introduction, the motion characteristics of the human knee joint and the design method of the exoskeleton were introduced. A kinematics model of the LLE based on cross-four-bar linkage was obtained. The structural parameters of the LLE mechanism were optimized by the particle swarm optimization algorithm. The predefined trajectories used in the optimization process were derived from the ankle joint, not the instantaneous center of rotation of the knee joint. Finally, the motion deviation of the optimization result was simulated, and the human–exoskeleton coordination experiment was designed to compare with the traditional single-axis knee joint in terms of comfort and coordination.

The lower limb exoskeleton mechanism obtained in this paper has a good tracking effect on human movement and has been improved in terms of comfort and coordination compared with the traditional single-axis knee joint.

The customized exoskeleton design method introduced in this paper is relatively simple, and the obtained exoskeleton has better movement coordination than the traditional exoskeleton. It can provide a reference for the design of lower limb exoskeleton and lower limb orthosis.

The purpose of this study is to optimize and improve conventional welding using EMF assisted technology. Current industrial production has put forward higher requirements for welding technology, so the optimization and improvement of traditional welding methods become urgent needs.

External magnetic field assisted welding is an emerging technology in recent years, acting in a non-contact manner on the welding. The action of electromagnetic forces on the arc plasma leads to significant changes in the arc behavior, which affects the droplet transfer and molten pool formation and ultimately improve the weld seam formation and joint quality.

In this paper, different types of external magnetic fields are analyzed and summarized, which mainly include external transverse magnetic field, external longitudinal magnetic field and external cusp magnetic field. The research progress of welding behavior under the effect of external magnetic field is described, including the effect of external magnetic field on arc morphology, droplet transfer and weld seam formation law.

However, due to the extremely complex physical processes under the action of the external magnetic field, the mechanism of physical fields such as heat, force and electromagnetism in the welding has not been thoroughly analyzed, in-depth theoretical and numerical studies become urgent.

The purpose of this paper is to study the aerodynamic characteristics of Ahmed body in longitudinal and lateral platoons under crosswind by computational fluid dynamics simulation. It helps to improve the aerodynamic characteristics of vehicles by providing theoretical basis and engineering direction for the development and progress of intelligent transportation.

A two-car platoon model is used to compare with the experiment to prove the accuracy of the simulation method. The simplified Ahmed body model and the Reynolds Averaged N-S equation method are used to study the aerodynamic characteristics of vehicles at different distances under cross-winds.

When the longitudinal distance x/L = 0.25, the drag coefficients of the middle and trailing cars at β = 30° are improved by about 272% and 160% compared with β = 10°. The side force coefficients of the middle and trailing cars are increased by 50% and 62%. When the lateral distance y/W = 0.25, the side force coefficients of left and middle cars at β = 30° are reduced by 38% and 37.5% compared with β = 10°. However, the side force coefficient of the right car are increased by about 84.3%.

Most of the researches focus on the overtaking process, and there are few researches on the neat lateral platoon. The innovation of this paper is that in addition to studying the aerodynamic characteristics of longitudinal driving, the aerodynamic characteristics of neat lateral driving are also studied, and crosswind conditions are added. The authors hope to contribute to the development of intelligent transportation.

Titanium alloys and composites have proven to contain desirable properties for use at elevated temperatures. One such material is the Ti750 composite, which can be used at temperatures up to 750°C for a brief period. This paper aims the microstructure, phase compositions, apparent porosity and hardness of both sintered and heat-treated TiC reinforced Ti750 composites for consideration in aircraft engine design.

The fabrication of TiC-reinforced Ti750 composites was achieved through spark plasma sintering (SPS). To analyze the microstructure and X-ray diffraction, a scanning electron microscope (SEM) with model number S-3400N and a D8 advance model machine were used, respectively. The microhardness of the samples was measured using a Vickers hardness tester with model HV-1000. The research incorporated three solid solution treatments: 975°C/3 h/AC, 1,010°C/3 h/AC and 1,025°C/3 h/AC, along with a solid-solution aging treatment at 1,010°C/3 h/AC + 750°C/8 h/AC. Additionally, oxidation analysis was conducted on the samples.

The microstructures contained enhanced TiC and Ti5Si3 phases in the near a-Ti matrix. The microhardness of the sintered composite was over twice that of the matrix alloy, and its porosity was reduced by about 0.35%. The sample treated at 1,010°C/3 h/AC had the highest enhanced peaks and microhardness of 1,277.1 HV. After oxidation at 800°C for 100 h, the accumulated weight of the solid solution composite at 1,010 °C/3 h/AC was the lowest (3.0 mg.cm^-2). The surface microstructure contained oxides of TiO2 and a spalling white area containing a small amount of Al₂O₃ and SiO₂.

There is limited research on Ti-Al-Sn-Zr-Mo-Si-based TMCs using a combination of the SPS method. This study used SiCp as a reinforcement for the Ti750 matrix alloy. The consolidation of SiCp and Ti750 powders using the SPS method, heat treatment of the resulting TiC reinforced Ti750 composites and study of the microstructure and properties of the composites are not found in literature or under consideration for publication in any media.

The collaborative filtering algorithm is a classical and widely used approach in product recommendation systems. However, the existing algorithms rely mostly on common ratings of items and do not consider temporal information about items or user interests. To solve this problem, this study proposes a new user-item composite filtering (UICF) recommendation framework by leveraging temporal semantics.

The UICF framework fully utilizes the time information of item ratings for measuring the similarity of items and takes into account the short-term and long-term interest decay for computing users’ latest interest degrees. For an item to be probably recommended to a user, the interest degrees of the user on all the historically rated items are weighted by their similarities with the item to be recommended and then added up to predict the recommendation degree.

Comprehensive experiments on the MovieLens and KuaiRec datasets for user movie recommendation were conducted to evaluate the performance of the proposed UICF framework. Experimental results show that the UICF outperformed three well-known recommendation algorithms Item-Based Collaborative Filtering (IBCF), User-Based Collaborative Filtering (UBCF) and User-Popularity Composite Filtering (UPCF) in the root mean square error (RMSE), mean absolute error (MAE) and F1 metrics, especially yielding an average decrease of 11.9% in MAE.

A UICF recommendation framework is proposed that combines a time-aware item similarity model and a time-wise user interest degree model. It overcomes the limitations of common rating items and utilizes temporal information in item ratings and user interests effectively, resulting in more accurate and personalized recommendations.

Sustainable textiles have become imperative in mitigating the adverse environmental and social impacts of the textile industry. This paper aims to synthesize recent advancements and key considerations in sustainable textile development, emphasizing their role in promoting environmental stewardship, social responsibility and economic viability.

The literature search has been conducted by identifying and articulating the previous studies related to integrating the latest cutting-edge techniques with functional textiles.

Future-generation textiles (FGTs), which incorporate state-of-the-art developments in materials, technologies and functionalities, herald a paradigm-shifting period in the textile industry. FGTs mark a new era in this dynamic world by igniting conversations about their mechanisms, problems, progress to date and potential future applications. This investigation covers a wide range of topics, including wearable electronics, nanotechnology, 3D printing, recycling, machine learning and energy harvesting. Key components include sustainability, functionality, intelligent integration, advanced manufacturing processes and multifunctionality. The paper highlights the potential benefits of smart textiles, wearable technology, improved performance and sustainability through advances in customization and security.

It is an original review work. This paper will be helpful for manufacturers and researchers in the smart wearable textile sector in developing innovative techniques for multifunctional garments by integrating cutting-edge technology.

This study aims to determine the key indicators affecting the resilience of the construction supply chain to flooding and calculate the resilience of the urban construction supply chain in three cases city.

This study combines expert opinions and literature review to determine key indicators and establish a fuzzy EWM-GRA-TOPSIS evaluation model. The index weight was calculated using the entropy weight method, and GRA-TOPSIS was used for comprehensive evaluation.

The results of the study show that the three cities are ranked from the high to low in order of Hangzhou, Hefei and Zhengzhou.

The innovative method adopted in this study comprising EWM-GRA-TOPSIS reduced the influence of subjectivity, fully extracted and utilized data, in a way that respects objective reality. Further, this approach enabled the absolute and relative level of urban construction supply chain resilience to be identified, allowing improvements in the comprehensiveness of decision-making. The method is relatively simple, reasonable, understandable, and computationally efficient. Within the approach, the entropy weight method was used to assign different index weights, and the GRA-TOPSIS was used to rank the resilience of the construction supply chain in three urban cities. The development of resilience provides a robust decision-making basis and theoretical reference, further enriching research methods, and having strong practical value. The study serves to improve risk awareness and resilience, which in turn helps to reduce losses. It also provides enhanced awareness regarding the future enhancement of supply chain resilience for urban construction.

This study aims to develop a robust long short-term memory (LSTM)-based forecasting model for daily international tourist arrivals at Incheon International Airport (ICN), incorporating multiple predictors including exchange rates, West Texas Intermediate (WTI) oil prices, Korea composite stock price index data and new COVID-19 cases. By leveraging deep learning techniques and diverse data sets, the research seeks to enhance the accuracy and reliability of tourism demand predictions, contributing significantly to both theoretical implications and practical applications in the field of hospitality and tourism.

This study introduces an innovative approach to forecasting international tourist arrivals by leveraging LSTM networks. This advanced methodology addresses complex managerial issues in tourism management by providing more accurate forecasts. The methodology comprises four key steps: collecting data sets; preprocessing the data; training the LSTM network; and forecasting future international tourist arrivals. The rest of this study is structured as follows: the subsequent sections detail the proposed LSTM model, present the empirical results and discuss the findings, conclusions and the theoretical and practical implications of the study in the field of hospitality and tourism.

This research pioneers the simultaneous use of big data encompassing five factors – international tourist arrivals, exchange rates, WTI oil prices, KOSPI data and new COVID-19 cases – for daily forecasting. The study reveals that integrating exchange rates, oil prices, stock market data and COVID-19 cases significantly enhances LSTM network forecasting precision. It addresses the narrow scope of existing research on predicting international tourist arrivals at ICN with these factors. Moreover, the study demonstrates LSTM networks’ capability to effectively handle multivariable time series prediction problems, providing a robust basis for their application in hospitality and tourism management.

This research pioneers the integration of international tourist arrivals, exchange rates, WTI oil prices, KOSPI data and new COVID-19 cases for forecasting daily international tourist arrivals. It bridges the gap in existing literature by proposing a comprehensive approach that considers multiple predictors simultaneously. Furthermore, it demonstrates the effectiveness of LSTM networks in handling multivariable time series forecasting problems, offering practical insights for enhancing tourism demand predictions. By addressing these critical factors and leveraging advanced deep learning techniques, this study contributes significantly to the advancement of forecasting methodologies in the tourism industry, aiding decision-makers in effective planning and resource allocation.

本研究旨在开发一种基于LSTM的强大预测模型, 用于预测仁川国际机场的日常国际游客抵达量, 结合多种预测因素, 包括汇率、WTI原油价格、韩国综合股价指数 (KOSPI) 数据和新冠疫情病例。通过利用深度学习技术和多样化数据集, 研究旨在提升旅游需求预测的准确性和可靠性, 对酒店与旅游领域的理论和实际应用有重要贡献。

本研究通过利用长短期记忆（LSTM）网络引入创新方法, 预测国际游客抵达量。这一先进方法解决了旅游管理中的复杂管理问题, 提供了更精确的预测。方法论包括四个关键步骤: (1) 收集数据集; (2) 数据预处理; (3) 训练LSTM网络; 以及 (4) 预测未来的国际游客抵达量。本文的其余部分结构如下：后续部分详细介绍了提出的LSTM模型, 呈现了实证结果, 并讨论了研究的发现、结论以及在酒店与旅游领域的理论和实际意义。

本研究首次同时使用包括国际游客抵达量、汇率、原油价格、股市数据和新冠疫情病例在内的大数据进行日常预测。研究显示, 整合汇率、原油价格、股市数据和新冠疫情病例显著增强了LSTM网络的预测精度。研究填补了现有研究在使用这些因素预测仁川国际机场国际游客抵达量的狭窄范围。此外, 研究证明了LSTM网络在处理多变量时间序列预测问题上的能力, 为其在酒店与旅游管理中的应用提供了坚实基础。

本研究首次将国际游客抵达量、汇率、WTI原油价格、KOSPI数据和新冠疫情病例整合到日常国际游客抵达量的预测中。它通过提出同时考虑多个预测因素的全面方法, 弥合了现有文献的差距。此外, 研究展示了LSTM网络在处理多变量时间序列预测问题方面的有效性, 为增强旅游需求预测提供了实用见解。通过处理这些关键因素并利用先进的深度学习技术, 本研究在旅游业预测方法的进步中做出了重要贡献, 帮助决策者进行有效的规划和资源配置。