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High power and speed are new demands for rotating machinery which needs the journal bearings with high dynamic characteristics. The critical speed of the rotor-bearing system is one of the most significant parameters to evaluate the dynamic characteristics. This paper aims to investigate the theoretical and experimental analysis of a rotor system supported by large diameter elliptical bearings.

To obtain the theoretical and experimental support for rotor-bearing system design, dynamic characteristics theoretical analysis based on the finite difference method is given and an experiment focuses on critical speed identification is carried out.

The theoretical calculation results indicate that the critical speed is near to 800 rpm and there is no large vibration amplitude round working speed (1,500 rpm). Using the test bench in the factory unit, vibration data including three experimental processes are obtained. According to the vibration data, the critical speed is identified which also indicates that it is stable when working at 1,500 rpm.

The design method for the rotor system supported by large diameter elliptical bearing can be obtained by the theoretical and experimental results shown in this paper.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-04-2020-0122/

High power rotating machinery requires large diameter bearings that can perform under extreme conditions. Vibrations and critical speeds of rotor supported by tilting pad journal bearing (TPJBs) exceeding their design limits may cause unit failure. This paper aims to investigate the experimental technique for large diameter bearings.

To obtain the experimental support for rotor-bearing system design, an experiment focusing on vibration monitoring is given. The sensors arrangement, monitoring system and critical speed identification method are provided.

By using test bench in factory unit, a large amount of vibrations data of different working situations is obtained. In addition, a method named non-excitation identification for critical speed is proposed. The critical speed of rotor identified through vibration data is given. The theoretical calculation results are also presented.

The basis for rotor-bearing system design can be obtained through comparisons between the experimental results and the theoretical calculation data.

This paper addresses the limitations of current graph neural network-based recommendation systems, which often neglect the integration of side information and the modeling of complex high-order interactions among nodes. The research motivation stems from the need to enhance recommendation performance by effectively utilizing all available data. We propose a novel method called MSHCN, which leverages hypergraph neural networks to integrate side information and model complex interactions, thereby improving user and item representations.

The MSHCN method employs a hypergraph structure to incorporate various types of side information, including social relationships among users and item attributes, which are essential for enriching user and item representations. The k-means clustering algorithm is utilized to create item-associated hypergraphs, while sentiment analysis on user reviews refines the modeling of user interests. Additionally, hypergraphs are constructed for user-user and item-item interactions based on interaction similarity. MSHCN also incorporates contrastive learning as an auxiliary task to enhance the representation learning process.

Extensive experiments demonstrate that MSHCN significantly outperforms existing recommendation models, particularly in its ability to capture and utilize side information and high-order interactions. This results in superior user and item representations and improved recommendation performance.

The novelty of MSHCN lies in its use of a hypergraph structure to integrate diverse side information and model intricate high-order interactions. The incorporation of contrastive learning as an auxiliary task sets it apart from other hypergraph-based models, providing a significant enhancement in recommendation accuracy.

The purpose of this paper is to conduct a survey on research in fabric and cloth simulation using mass spring model. Also in this paper some of the common methods in process of fabric simulation in mass spring model are discussed and compared.

This paper reviews and compares presented mesh types in mass spring model, forces applied on model, super elastic effect and ways to settle the super elasticity problem, numerical integration methods for solving equations, collision detection and its response. Some of common methods in fabric simulation are compared to each other. And by using examples of fabric simulation, advantages and limitations of each technique are mentioned.

Mass spring method is a fast and flexible technique with high ability to simulate fabric behavior in real time with different environmental conditions. Mass spring model has more accuracy than geometrical models and also it is faster than other physical modeling.

In the edge of digital, fabric simulation technology has been considered into many fields. 3D fabric simulation is complex and its implementation requires knowledge in different fields such as textile engineering, computer engineering and mechanical engineering. Several methods have been presented for fabric simulation such as physical and geometrical models. Mass spring model, the typical physically based method, is one of the methods for fabric simulation which widely considered by researchers.

The purpose of this paper is to examine the important application value of extending the concept of classification rule, so that it can describe and measure the uncertainty of classification knowledge.

The rough concept lattice (RCL), which is an effective tool for uncertain data analysis and knowledge discovery, reflects a kind of unification of concept intent and upper/lower approximation extent, as well as the certain and uncertain relations between objects and attributes.

A classification rules extraction algorithm, extraction algorithm of classification rule (EACR), based on the RCL is presented by adapting the rough degree to measure uncertainty of classification rule. The algorithm EACR is experimentally validated by taking the star spectrum data as the decision context.

An efficient way for classification rule extraction is provided.

The algorithm EACR based on the RCL is presented by adapting the rough degree to measure uncertainty of classification rule.

Supply chains deliver goods and services between shippers and receivers, covering collection, transportation, distribution as well as their handling and storage in between. In particular, transportation services are carried out by different transport modes. In some modern supply chains, different categories of air cargo carriers – combinations, freighter-only, and/or integrators – provide critical transport services.

This chapter develops a methodology for estimating the performance of supply chains served by an air cargo carrier network. The methodology is based on indicators of infrastructure use, technical/technological level, operational factors, economic factors, and environmental performance. This proposed methodology is applied to estimate performance of supply chains served by an integrated air cargo carrier – FedEx Express – operating a single hub in the US domestic air network. Results indicate that the methodology may be useful for estimation of overall supply chain performance under the condition that relevant data are available.

Continuum-based discrete element method is an explicit numerical method, which is a combination of block discrete element method (DEM) and FEM. When simulating large deformation problems, such as cutting, blasting, water-like material flowing, the distortion of elements will lead to no convergence of the numerical system. To solve the convergence problem, a particle contact-based meshfree method (PCMM) is introduced in. The paper aims to discuss this issue.

PCMM is based on traditional particle DEM, and use particle contacts to generate triangular elements. If three particles are contact with each other, the element will be created. Once elements are created, the macroscopic constitutive law could be introduced in. When large deformation of element occurs, the contact relationship between particles will be changed. Those elements that do not meet the contact condition will be deleted, and new elements that coincide with the relationship will be generated. By the deletion and creation of elements, the convergence problem induced by element distortion will be eliminated. To solve FEM and PCMM coupled problems, a point-edge contact model is introduced in, and normal and tangential springs are adopted to transfer the contact force between particles and blocks.

According to the deletion and recreation of elements based on particle contacts, PCMM could simulate large deformation problems. Some numerical cases (i.e. elastic field testing, uniaxial compression analysis and wave propagation simulation) show the accuracy of PCMM, and others (i.e. soil cutting, contact burst and water-like material flowing) show the rationality of PCMM.

In traditional particle DEM, contact relationships are used to calculate contact forces. But in PCMM, contact relationships are adopted to generate elements. Compared to other meshfree methods, in PCMM, the element automatic deletion and recreation technique is used to solve large deformation problems.

The purpose of this paper is to explore the formation and growth mechanism of bulk Cu₆Sn₅ intermetallic compounds, selecting Sn‐Ag‐Cu‐Ce solders as specimens.

In order to further enhance the properties of SnAgCu solder, trace amount of rare earth Ce was selected as alloying addition into the alloy; in previous investigations, the enhancements include better wettability, physical properties, creep strength and tensile strength. In this paper, the microstructure of Sn‐Ag‐Cu‐Ce soldered joints and its interfacial intermetallic compounds were investigated. Moreover, different morphologies of Cu₆Sn₅ IMCs were enumerated and described, and Ostwald ripening theory was employed to interpret the formation mechanism of bulk Cu₆Sn₅ IMCs.

In addition, based on finite element simulation, it is found that the von Mises stress concentrate around the bulk Cu₆Sn₅ IMCs inside the Sn‐Ag‐Cu‐Ce soldered joints after three thermal cycling loading (−55‐125°C). From the stress distribution, the failure site was predicted to fracture near the bulk Cu₆Sn₅ IMCs interface. This coincides with the experimental findings significantly.

The results presented in this paper may provide a theory guide for developing novel lead‐free solders as well as reliability investigation of lead‐free soldered joints.

The impact of FinTech in sub-Saharan Africa has primarily been limited to financial inclusion. Contrarily, this study aims to deviate from this norm to estimate how FinTech affects carbon emissions in the subregion. This provides policy recommendations for FinTech regulators, service providers and practitioners to consider optimal products and services that reduce carbon emissions.

A balanced panel data set from 2009 to 2020 is used and estimated with the fully modified ordinary least squares estimator after checking for cross-sectional dependence, unit root, stationarity and cointegration.

Results from the estimation suggest a negatively significant relationship between financial technology and carbon emissions in these countries. However, domestic credit to the private sector revealed a statistically insignificant relationship with carbon emissions for the same period. Further, foreign direct investment reduces carbon emissions but gross domestic product and trade openness increase carbon emissions in these countries.

The impact of FinTech in sub-Saharan Africa has primarily been limited to financial inclusion. Contrarily, this study deviates from this norm and estimates how FinTech affects carbon emissions in the subregion.

This paper proposes a framework to assist in managing predictive maintenance by detecting progressive surface wear on spur gears through the analysis of digital images of gear teeth using computer vision (CV) techniques.

An experimental setup was constructed to capture images of gear teeth using endoscopic cameras. The images were selected, pre-processed, stored in a database and used in the experimental study of the proposed framework. Three CV techniques were explored within the framework for detecting wear in spur gears: (1) edge detection; (2) gray level co-occurrence matrix (GLCM) combined with machine learning (ML) algorithms and (3) deep learning with convolutional neural networks (CNN).

The results showed 85% accuracy using the edge detection algorithm. Among the ML algorithms, accuracy was above 60% for the support vector machine (SVM) and above 70% for K-nearest neighbors (KNN). Principal component analysis (PCA) indicated that as the distance between the principal components increased, it characterized the formation and progression of surface wear on the gear teeth. With the CNN, an accuracy of 99.999981% was achieved in the training loss rate, with a classification accuracy rate (CAR) of 91.6666%, an F1 score of 90.9090% and a recall of 83.3334% during the testing phase.

This framework is applicable to a variety of gear systems and industrial contexts requiring predictive maintenance, making it a highly scalable solution for industry professionals.

This paper proposes a novel framework that considers various CV techniques to detect and assess the level of wear on spur gear surfaces. Moreover, the results provide guidelines for selecting the most appropriate method for detecting wear in gear systems.