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To develop an automated system for identifying and repairing cracks in asphalt pavements, addressing the urgent need for efficient pavement maintenance solutions amidst increasing workloads and decreasing budgets.

The research was conducted in two main stages: Crack identification: Utilizing the U-Net deep learning model for pixel-level segmentation to identify pavement cracks, followed by morphological operations such as thinning and spur removal to refine the crack trajectories. Automated crack repair path planning: Developing an enhanced hybrid ant colony greedy algorithm (EAC-GA), which integrates the ant colony (AC) algorithm, greedy algorithm (GA) and three local enhancement strategies – PointsExchange, Cracks2OPT and Nearby Cracks 2OPT – to plan the most efficient repair paths with minimal redundant distance.

The EAC-GA demonstrated significant advantages in solution quality compared to the GA, the traditional AC and the AC-GA. Experimental validation on repair areas with varying numbers of cracks (16, 26 and 36) confirmed the effectiveness and scalability of the proposed method.

The originality of this research lies in the application of advanced deep learning and optimization algorithms to the specific problem of pavement crack repair. The value is twofold: Technological innovation in the field of pavement maintenance, offering a more efficient and automated approach to a common and costly issue. The potential for significant economic and operational benefits, particularly in the context of reduced maintenance budgets and increasing maintenance demands.

The purpose of this paper is to improve the bioactivity of variable gradient TC4 porous scaffolds prepared by selective laser melting (SLM) through the micro-arc oxidation (MAO) technique.

Variable gradient TC4 porous scaffolds were prepared by SLM, then treated with MAO at different oxidation voltages. The microstructure, thickness and composition of MAO coatings were characterized by scanning electron microscope (SEM), energy-dispersive spectroscopy (EDS) and X-ray diffraction. The bioactivity of the MAO coatings was tested by simulated body fluid (SBF) immersion test.

SEM and EDS results show that with the increase of oxidation voltage, the content of Ca and P elements and the thickness of the MAO coatings increases. The thickness of the coating inside the scaffold is smaller than that of the outside regions. SBF immersion experiments showed that MAO-treated TC4 porous scaffolds had highest bioactivity at 440 V.

The variable gradient porous scaffolds were treated with MAO in the electrolyte containing Ca and P elements for the first time. The effect of oxidation voltages on the different region of porous scaffolds was studied in detail.

This study develops a phase-oriented evaluation mechanism based on the public–private partnership (PPP) project process and provides a preliminary framework for the promotion of the phase-oriented evaluation of PPPs in China.

Through a literature review and survey interviews the success criteria for the implementation process of PPPs are documented. Process success criteria findings are used to populate the content of the phase-oriented evaluation. The result is then tested, revised and improved through follow-up expert interviews.

This paper identifies PPP project process success criteria. Using these criteria, a phase-oriented evaluation framework is developed. Moreover, in consultation with industry experts in the PPP field, eight evaluation aspects were determined: confirmation of project compliance and performance, confirmation of value for money, confirmation of financial affordability, trend analysis of annual performance appraisal, risk review and early warning, rebankability analysis, project externality analysis, benchmarking analysis of similar projects. These eight application aspects are analyzed in the light of the outcomes of the resulting phase-oriented evaluation framework.

The results of the research can only provide researchers and practitioners with generic insights regarding inputs into a phase-oriented evaluation of PPP projects. For any specific PPP project, the focus and priority of the evaluative content will vary.

A phase-oriented evaluation of PPP projects is put forward. This will allow project stakeholders to better track the progress of projects throughout what can be a lengthy implementation stage.

This study can provide governments and stakeholders with a new, theoretical understanding as to factors required of an effective phased evaluation of China's PPP projects. In that regard it will be significant value in PPP project policy and decision-making.

The importance of the agri-food supply chain in both food production and distribution has made the issue of its development a critical concern. Based on configuration theory and congruence theory, this research investigates the complex impact of supply chain concentration on financial growth in agri-food supply chains.

The cluster analysis and response surface methodology are employed to analyse the data collected from 207 Chinese agri-food companies from 2010 to 2022.

The results indicate that different combination patterns of supply chain concentration can lead to different levels of financial growth. We discover that congruent supplier and customer concentration is beneficial for companies’ financial growth. This impact is more pronounced when the company is in the agricultural production stage of agri-food supply chains. Post-hoc analysis indicates that there exists an inverted U-shaped relationship between the overall levels of supply chain concentration and financial growth.

Our research uncovers the complex interplay between supply chain base and financial outcomes, thereby revealing significant ramifications for agri-food supply chain managers to optimise their strategies for exceptional financial growth.

This study proposes a combined approach of cluster analysis and response surface analysis for analysing configuration issues in supply chain management.

In recent decades, the field of robotic mapping has witnessed widespread research and development in light detection and ranging (LiDAR)-based simultaneous localization and mapping (SLAM) techniques. This paper aims to provide a significant reference for researchers and engineers in robotic mapping.

This paper focused on the research state of LiDAR-based SLAM for robotic mapping as well as a literature survey from the perspective of various LiDAR types and configurations.

This paper conducted a comprehensive literature review of the LiDAR-based SLAM system based on three distinct LiDAR forms and configurations. The authors concluded that multi-robot collaborative mapping and multi-source fusion SLAM systems based on 3D LiDAR with deep learning will be new trends in the future.

To the best of the authors’ knowledge, this is the first thorough survey of robotic mapping from the perspective of various LiDAR types and configurations. It can serve as a theoretical and practical guide for the advancement of academic and industrial robot mapping.

The purpose of this study is to establish an assembly accuracy analysis model of deployable arms based on Jacobian–Torsor theory to improve the assembly accuracy. Spacecraft deployable arm is one of the core components of spacecraft. Reducing the errors in assembly process is the main method to improve the assembly accuracy of spacecraft deployable arms.

First, the influence of composite connecting rod, root joint and arm joint on assembly accuracy in the tandem assembly process is analyzed to propose the assembly accuracy analysis model. Second, a non-tandem assembly process of “two joints fixed-composite rod installed-flange gasket compensated” is proposed and analyzed to improve the assembly accuracy of deployable arms. Finally, the feasibility of non-tandem assembly process strategy is verified by assembly experiment.

The experiential results show that the assembly errors are reduced compared with the tandem assembly process. The errors on axes x, y and z directions decreased from 14.1009 mm, 14.2424 mm and 0.8414 mm to 0.922 mm, 0.671 mm and 0.2393 mm, respectively. The errors round axes x and y directions also decreased from 0.0050° and 0.0053° to 0.00292° and 0.00251°, respectively.

This paper presents an assembly accuracy analysis model of deployable arms and applies the model to calculate assembly errors in tandem assembly process. In addition, a non-tandem assembly process is proposed based on the model. The experimental results show that the non-tandem assembly process can improve the assembly accuracy of deployable arms.

Ride comfort is one of the important factors affecting passenger health. Therefore, the elevator industry usually uses the International Organization for Standardization (ISO) 18738-1 standard to evaluate elevator ride quality and optimize elevator design. However, this method has certain limitations in its evaluation of comfort due to the problem of boundary division. The ISO 2631-4 standard is used as a general method of comfort evaluation in the current rail transit system, but it has not been applied in the elevator industry. In order to explore the difference and connection between the two standards, the author aims to conduct a detailed analysis on this.

Based on the elevator internet, a large amount of measured data of normal and abnormal vibration of elevator car were collected and analyzed and preprocessed; based on ISO 18738-1:2012 standard and ISO 2631-4:2001 standard, the differences of ride comfort assessment methods in the two standards were analyzed, and the ride comfort assessment study of elevator under normal and abnormal vibration conditions was carried out.

The experimental results show that the comfort assessment results of ISO 2631-4:2001 standard and ISO18738-1:2012 standard are consistent under two vibration conditions. At the same time, ISO 2631-4:2001 can not only provide a more accurate quantitative description of comfort, but also roughly determine the comfort interval of each vibration, which can provide theoretical reference for elevator vibration classification and car comfort design.

The authors designed an Internet of Things (IOT)-based elevator vibration signal acquisition method to address the shortcomings of the previous elevator ride comfort assessment methods, which can realize the dynamic assessment of elevator ride comfort; by comparing the assessment results of elevator ride comfort under normal vibration and abnormal vibration, the feasibility of ISO 2631-4:2001 for elevator ride comfort assessment was fully verified. In addition, the experimental results also give the influence of abnormal vibration on elevator riding comfort under the stages of start-stop, uniform speed, acceleration and deceleration, which can provide theoretical support for elevator vibration suppression and comfort transformation.

This paper aims to investigate the influence of sinusoidal texture (ST) with different morphology parameters on the corresponding tribological effects.

The STs with different amplitudes, ranging from 0.05 to 0.3 mm, and frequencies, ranging from 5 to 17.5, are fabricated using nanosecond pulsed laser equipment. The friction experiments and the finite element analysis method are combined to investigate the tribological properties, under dry friction conditions.

Test results show that when the amplitude is 0.15 mm and frequency is 10, ST surface has the lowest friction coefficient of 0.373, and exhibits great anti-friction effect. It also possesses a complete texture edge after friction. The friction reduction effect of ST with larger or smaller amplitude and frequency is worse.

The results of this study can provide a guidance for the design optimization of ST of reciprocating sliding contact surfaces, under dry friction conditions.

Fine-tuning pre-trained language models (PLMs), e.g. SciBERT, generally require large numbers of annotated data to achieve state-of-the-art performance on a range of NLP tasks in the scientific domain. However, obtaining fine-tuning data for scientific NLP tasks is still challenging and expensive. In this paper, the authors propose the mix prompt tuning (MPT), which is a semi-supervised method aiming to alleviate the dependence on annotated data and improve the performance of multi-granularity academic function recognition tasks.

Specifically, the proposed method provides multi-perspective representations by combining manually designed prompt templates with automatically learned continuous prompt templates to help the given academic function recognition task take full advantage of knowledge in PLMs. Based on these prompt templates and the fine-tuned PLM, a large number of pseudo labels are assigned to the unlabelled examples. Finally, the authors further fine-tune the PLM using the pseudo training set. The authors evaluate the method on three academic function recognition tasks of different granularity including the citation function, the abstract sentence function and the keyword function, with data sets from the computer science domain and the biomedical domain.

Extensive experiments demonstrate the effectiveness of the method and statistically significant improvements against strong baselines. In particular, it achieves an average increase of 5% in Macro-F1 score compared with fine-tuning, and 6% in Macro-F1 score compared with other semi-supervised methods under low-resource settings.

In addition, MPT is a general method that can be easily applied to other low-resource scientific classification tasks.

As a common form of failure in industry, corrosion causes huge economic losses. At present, with the development of computational techniques, artificial intelligence (AI) is playing a more and more important role in the field of scientific research. This paper aims to review the application of AI in corrosion protection research.

In this paper, the role of AI in corrosion protection is systematically described in terms of anticorrosion materials and methods, corrosion image recognition and corrosion life prediction.

With efficient and in-depth data processing methods, AI can rapidly advance the research process in terms of anticorrosion materials and methods, corrosion image recognition and corrosion life prediction and save on costs.

This paper summarizes the application of AI in corrosion protection research and provides the basis for corrosion engineers to quickly and comprehensively understand the role of AI and improve production processes.