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Engineering components/structures with geometric discontinuities normally bear complex and variable loads, which lead to a multiaxial and random/variable amplitude stress/strain state. Hence, this study aims how to effectively evaluate the multiaxial random/variable amplitude fatigue life.

Recent studies on critical plane method under multiaxial random/variable amplitude loading are reviewed, and the computational framework is clearly presented in this paper.

Some basic concepts and latest achievements in multiaxial random/variable amplitude fatigue analysis are introduced. This review summarizes the research status of four main aspects of multiaxial fatigue under random/variable amplitude loadings, namely multiaxial fatigue criterion, method for critical plane determination, cycle counting method and damage accumulation criterion. Particularly, the latest achievements of multiaxial random/variable amplitude fatigue using critical plane methods are classified and highlighted.

This review attempts to provide references for further research on multiaxial random/variable amplitude fatigue and to promote the development of multiaxial fatigue from experimental research to practical engineering application.

The purpose of this paper is to introduce the modeling and implementation of a novel multimode amphibious robot, which is used for patrol and beach garbage cleaning in the land–water transition zone.

Starting from the design idea of multimode motion, the robot innovatively integrates the guiding fin and wheel together, is driven by the same motor and can achieve multimodal motion such as land, water surface and underwater with only six actuated degrees of freedom. The robot dispenses with the transmission mechanism by directly connecting the servo motor with a reducer to the actuator, so it has the characteristics of simplifying the structure and reducing the quality. And to the best of the authors' knowledge, the design of the robot can be considered the minimal configuration of amphibious robots with the same locomotion capabilities.

Based on the classical assumptions of underwater dynamics analysis, this paper uses basic airfoil theory to analyze the dynamics of the robot’s horizontal and vertical motions and establishes its simplified dynamics model. Also, the underwater motion of the robot is simulated, and the results are in good agreement with the existing research results. Finally, to verify the feasibility of the robot, a prototype is implemented and fully evaluated by experiments. Experimental results show that the robot can reach the maximum speed of 2.5 m/s and 0.3 m/s on land and underwater, respectively, proving the effectiveness of the robot.

The robot has higher work efficiency with the powerful multimode motion, and its simplified structure makes it more stable while costing less.

In recent years, online public opinion reversal incidents have been occurring frequently, which has increased the complexity of the evolution of online public opinion, and they have become a difficult issue for public opinion management and control. It is of great significance to explore the regularity of online public opinion reversal.

Combined with the grey characteristics of online public opinion information, a grey graphical evaluation review technique (G-GERT) network model is constructed based on kernel and grey degree, and the frequency, probability and time of online public opinion reversal nodes are calculated using C-marking method and Z-marking method.

Throughout the online public opinion reversal events, there are all repeated outbreak nodes occurring, so the authors regard the repeated occurrence of outbreak nodes as reversal. According to the average frequency, probability and time of repeated outbreak nodes in the G-GERT network model, the authors predict the corresponding key information of reversal. It can simulate the evolution process of public opinion events accurately.

The G-GERT network model based on kernel and grey degree reveals the regulation of public opinion reversal, predicts the frequency, probability and time of reversal nodes, which are the most concerned and difficult issues for decision-makers. The model provides the decision basis and reference for government decision-making departments.

In recent years, oil spill pollution has become one of the main problems of environmental pollution. Recovering oil by means of sorbent materials is a very promising approach and has acquired more attention due to its high cleanup efficiency. Compared to synthetic fibrous sorbents, the use of natural fibers in oil spill cleanups offers several advantages including environmental friendliness, degradable features and cost-effectiveness. Therefore, studies on developing sorbents using natural fibers for oil spill cleanup applications have become a research hotspot.

This paper reviews the work conducted by several researchers in developing oil sorbents from fibers such as cattail, nettle, cotton, milkweed, kapok, populous seed fiber and Metaplexis japonica fiber. Some featured critical parameters influencing the oil sorption capacity of fibrous substrates are discussed. Oil sorption capacity and reusability performance of various fibers are also discussed. Recent developments in oil spill cleanups and test methods for oil sorbents are briefly covered.

The main parameters influencing the oil sorption capacity of sorbents are fiber morphological structure, fiber density (g/cc), wax (%), hollowness (%) and water contact angle. An extensive literature review showed that oil sorption capacity is highest for Metaplexis japonica fiber followed by populous seed fiber, kapok, milkweed, cotton, nettle and cattail fiber. After use, the sorbents can be buried under soil or they can also be burned so that they can be vanished from the surface without causing environmental-related issues.

This review paper aims to summarize research studies conducted related to various natural fibers for oil spill cleanups, fiber structural characteristics influencing oil sorption and recent developments in oil spill cleanups. This work will inspire future researchers with various knowledge backgrounds, particularly, from a sustainability perspective.

The purpose of this paper is to propose an automated machine learning (AutoML) and multi-agent system approach to improve overall product delivery satisfaction under limited resources.

An AutoML method is purposed to model delivery satisfaction of individual customer, and a heuristic method and multi-agent system are proposed to improve overall satisfaction under limited processing capability. A series of simulation experiments have been conducted to illustrate the effectiveness of the proposed methodology.

The simulated results show that the proposed method can effectively improve overall delivery satisfaction, especially when the demand of customer orders is highly fluctuating and when the customer satisfaction models are highly diversified.

The proposed framework provides a more dynamic and continuously improving way to model delivery satisfaction of individual customer, thereby supports companies to provide personalized services and develop scalable and flexible business at a lower cost, and ultimately improves the overall quality, efficiency and effectiveness of delivery services.

The proposed methodology utilizes AutoML and multi-agent system to model individual customer delivery satisfaction and improve the overall satisfaction. It can cooperate with the existing delivery resource planning methods to further improve customer delivery satisfaction. The authors propose an AutoML approach to model individual customer delivery satisfaction, which enables continuous update and improvements. The authors propose multi-agent system and a heuristic method to improve overall delivery satisfaction. The numerical results show that the proposed method can improve overall delivery satisfaction with limited processing capability.

In recent years, numerous studies have investigated whether individual executives and their characteristics relate to financial reporting choices. In this article, we review archival, experimental and survey research on the influence of individual executives on corporate financial reporting and use upper echelons theory as our organizing framework. Our review of 60 studies shows that research consistently finds that top management executives exert significant influence on financial reporting decisions, particularly on disclosure quality. Empirical research has developed promising approaches to investigate executives' psychological attributes and character traits. The results of studies examining the influence of demographic characteristics of individual executives are, however, sometimes contradictory and ambiguous. Nevertheless, the overall empirical results we review are supportive of upper echelons predictions. Additional research in this field is needed to clarify the influence of unexamined upper echelon characteristics, important moderator variables, and adverse selection effects. We also suggest that future research more closely investigates the magnitudes of managerial influence and adopts a more holistic perspective on financial reporting outcomes.

The curve construction on surfaces is becoming more and more important in computer-aided design (CAD), computer graphics (CG) and the other related fields. This problem is often encountered in NC machining, tool path generation, automated fiber placement and so on. However, designing curves on curved surfaces is quite different from constructing a curve in Euclidean space. Therefore, the traditional methods of curve design are not suitable for constructing a continuous curve on surface. The authors need to perform interpolation directly on surface so that the final target curve is embedded into the given surface and also meets the continuous conditions.

Firstly, adopting a series of Hermite blending functions, the authors design a space curve passing the given knots on the point-cloud surface. Then, the authors construct a class of directrixes that are adopted to determine vector fields for projection. Finally, a complete G2 continuous curve embedded in point-cloud surfaces is constructed by solving the first-order ordinary differential equations (ODEs).

The authors’ main contribution is to overcome the problem of constructing G¹ and G² continuous curves on point-cloud surfaces and the authors’ schemes are based on the projection moving least square (MLS) surfaces and traditional differential geometric.

Based on the framework of projection MLS surfaces, a novel method to overcome the problem of constructing G² continuous curves on point-cloud surfaces is proposed.

Three-parameter interval grey numbers (TPIGNs) have been extensively studied as an extended form of interval numbers. However, most existing TPIGN multi-attribute decision-making methods only consider the similarity of positions, ignore the similarity of developmental directions and focus primarily on static evaluation. To address these limitations, in this study, the authors propose a dynamic technique for order preference by similarity to an ideal solution (TOPSIS) based on modified Jaccard similarity and angle similarity for TPIGNs.

First, the authors extend Jaccard similarity to a TPIGN environment to represent positional similarity. A simple example is provided to illustrate the limitations of the traditional Jaccard similarity. Then, the authors introduce an angle similarity measure to represent developmental directional similarity. Finally, a dynamic TOPSIS model is constructed by incorporating time-series data into conventional two-dimensional static data. Stage weights are obtained by an objective function designed to maximize the amount and minimize the fluctuation of decision information. A quadratic weighting method is adopted to derive the overall evaluation value of alternatives.

To evaluate the effectiveness of the proposed method, this study takes the pre-assessment of ice disaster and the selection of cooperative enterprises as examples. The authors then provide the results of comparative and sensitivity analyses, which demonstrate the effectiveness and flexibility of the proposed method.

The proposed TOPSIS method in a TPIGN environment can take a more holistic and dynamic perspective for decision-making, which helps mitigate the limitations of single-perspective or static evaluations.

This paper aims to present a broad review of near-a titanium alloys for high-temperature applications.

Following a brief introduction of titanium (Ti) alloys, this paper considers the near-α group of Ti alloys, which are the most popular high-temperature Ti alloys developed for a high-temperature application, particularly in compressor disc and blades in aero-engines. The paper is relied on literature within the past decade to discuss phase stability and microstructural effect of alloying elements, plastic deformation and reinforcements used in the development of these alloys.

The near-a Ti alloys show high potential for high-temperature applications, and many researchers have explored the incorporation of TiC, TiB SiC, Y₂O₃, La₂O₃ and Al₂O₃ reinforcements for improved mechanical properties. Rolling, extrusion, forging and some severe plastic deformation (SPD) techniques, as well as heat treatment methods, have also been explored extensively. There is, however, a paucity of information on SiC, Y₂O₃ and carbon nanotube reinforcements and their combinations for improved mechanical properties. Information on some SPD techniques such as cyclic extrusion compression, multiaxial compression/forging and repeated corrugation and straightening for this class of alloys is also limited.

This paper provides a topical, technical insight into developments in near-a Ti alloys using literature from within the past decade. It also outlines the future developments of this class of Ti alloys.

The purpose of this paper is to develop a novel interval Multi-Objective Optimization by a Ratio Analysis plus the Full Multiplicative Form(MULTIMOORA) with combination weights to evaluate the employment quality of college graduates, where the criteria are expressed by interval numbers and the weights of criteria are completely unknown.

Firstly, considering the subjective uncertainty of the weights of the criteria, the interval best worst method (I-BWM) was present to determine the subjective weights of the criteria. Secondly, by the improved interval number distance measure, an improved interval deviation maximization method (I-MDM) was introduced to detemine the objective weights. In the following, based on the I-BWM and the improved I-MDM, a combination weighting method that takes into account the subjective and objective weights is proposed. Finally, a multi-criteria decision-making method based on the interval MULTIMOORA with combination weights is present to evaluate the employment quality of college graduates, and then a comparative analysis with some of the existing distance measures of interval numberswas conducted to illustrate the flexibility.

According to the data of the Report on Employment Quality of Chinese College Graduats released by Mycos Research Institute in 2016–2020 and 2021–2022, the proposed method was used to evaluate the employment quality of college graduates during the period before and after the COVID-19 epidemic. The results verify that the method is more reasonable because the subjective and objective weights of the criteria can be fully considered. Finally, the feasibility and practicability of the proposed method are further verified by varying parameters.

Present an evaluation method on the employment quality of college graduates based on the Interval MULTIMOORA with combination weights considering the subjective and objective weights. And the proposed method is proved that it can provide a more reasonable evaluation results. At the same time, it is verified that the feasibility and the practicability of the proposed method are affected by varying parameters in the paper.