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The purpose of this paper is to develop a mesh-free algorithm based on the least square support vector machines method for numerical simulation of the modified Helmholtz equations.

The proposed method deals with a Cauchy problem for the modified Helmholtz equations. The algorithm converts the problem into a quadratic programming. It can be divided into three steps. First, some training points are allocated. Then, an approximate function is constructed. Finally, the shape parameters are estimated.

The proposed method's stability is discussed. Numerical experiments are conducted to check the efficiency of the algorithm. The proposed method is found to feasible for the ill-posed problems of the modified Helmholtz equations.

The originality lies in that the proposed method is applied to solve the modified Helmholtz equations for the first time, and the expected results are obtained.

With the promotion of lithium-ion battery, it is more and more important to ensure the safety usage of the battery. The purpose of this paper is to analyze the battery operation data and estimate the remaining life of the battery, and provide effective information to the user to avoid the risk of battery accidents.

The particle filter (PF) algorithm is taken as the core, and the double-exponential model is used as the state equation and the artificial neural network is used as the observation equation. After the importance resampling process, the battery degradation curve is obtained after getting the posterior parameter, and then the system could estimate remaining useful life (RUL).

Experiments were carried out by using the public data set. The results show that the Bayesian-based posterior estimation model has a good predictive effect and fits the degradation curve of the battery well, and the prediction accuracy will increase gradually as the cycle increases.

This paper combines the advantages of the data-driven method and PF algorithm. The proposed method has good prediction accuracy and has an uncertain expression on the RUL of the battery. Besides, the method proposed is relatively easy to implement in the battery management system, which has high practical value and can effectively avoid battery using risk for driver safety.

Three adjustment modes are alternatives for mixed-model assembly lines (MMALs) to improve their production plans according to constantly changing customer requirements. The purpose of this paper is to deal with the decision-making problem between these modes by proposing a novel multi-classification method. This method recommends appropriate adjustment modes for the assembly lines faced with different customer orders through machine learning from historical data.

The decision-making method uses the classification model composed of an input layer, two intermediate layers and an output layer. The input layer describes the assembly line in a knowledge-intensive manner by presenting the impact degrees of production parameters on line performances. The first intermediate layer provides the support vector data description (SVDD) of each adjustment mode through historical data training. The second intermediate layer employs the Dempster–Shafer (D–S) theory to combine the posterior classification possibilities generated from different SVDDs. The output layer gives the adjustment mode with the maximum posterior possibility as the classification result according to Bayesian decision theory.

The proposed method achieves higher classification accuracies than the support vector machine methods and the traditional SVDD method in the numerical test consisting of data sets from the machine-learning repository and the case study of a diesel engine assembly line.

This research recommends appropriate adjustment modes for MMALs in response to customer demand changes. According to the suggested adjustment mode, the managers can improve the line performance more effectively by using the well-designed optimization methods for a specific scope.

The adjustment mode decision belongs to the multi-classification problem featured with limited historical data. Although traditional SVDD methods can solve these problems by providing the posterior possibility of each classification result, they might have poor classification accuracies owing to the conflicts and uncertainties of these possibilities. This paper develops a novel classification model that integrates the SVDD method with the D–S theory. By handling the conflicts and uncertainties appropriately, this model achieves higher classification accuracies than traditional methods.

A partial differential equation for elastic vibration of connecting rod of a slider‐crank mechanism is formulated by applying Theorem of Motion of Center of Mass and viscoelastic model. The partial differential equation is discretized into two second‐order ordinary differential equations by assuming a two‐mode approximation. The two equations are transformed into the first order linear ordinary differential equations with periodical coefficients. Then the stability of elastic vibration of the connecting rod is investigated using Floquet theory. Procedures for predicting the stability are developed. Finally, a case study is analyzed by applying the procedures.

The purpose of this paper is to present a new method for the fabrication of a large-scaled muscle scaffold containing an artificial hollow tube network, which may solve the problems of nutrient supply, oxygen exchange and metabolic waste removal.

In this paper, a ferric chloride structural strength-enhanced sodium alginate hollow tube was used to build the hollow tube network. Gelatin infill was then added to make a large alginate/gelation gel soft tissue scaffold. A pilot experiment was performed and an osmotic test platform was built to study the perfusion and osmotic ability of the 3D printed hollow tube. The essential fabrication parameters (printing velocity and gap) for building the vascular (i.e., hollow tube) network-contained scaffold were investigated. Moreover, cells in culture were spread within the gelation scaffold, and the circulation characteristics of the hollow tube network were studied.

The printed large-scaled scaffold that contained a ferric chloride structural strength-enhanced sodium alginate hollow tube had good perfusion ability. The osmotic distance of the hollow tube reached 3.7 mm in 8 h in this experiment.

The osmotic distance was confirmed by perfusing a phenol solution; although it is more reliable to test for cell viability, this will be investigated in our later research.

This research may provide new insights in the area of tissue engineering for large-scaled vascularized scaffold fabrication.

This paper presents a new method for fabricating large-scaled scaffolds, and the perfusion ability and osmotic distance of a ferric chloride structural strength-enhanced sodium alginate hollow tube are shown.

The purpose of this paper is to reveal the transient thermo-elasto-hydrodynamic lubrication mechanism of a bidirectional thrust bearing in a pumped-storage unit, and to propose the transient simulation method of two-way fluid-solid-thermal interaction of thrust bearing.

The transient fluid-solid-thermal interaction method is used to simulate the three-dimensional lubrication of the thrust bearing, during the start-up and shutdown process of a pumped storage unit. A pad including an oil hole is modelled to analyze the temporal variation of lubrication characteristics, such as the film pressure, thickness and temperature, during the transient operation process.

The injection of the high-pressure oil sufficiently affects the lubrication characteristics on film, in which the hysteresis phenomena were found between the start-up and shutdown possess.

This paper reveals the transient lubrication mechanism of tilting pad in a thrust bearing, by means of transient fluid-solid-thermal interaction method. Lubrication characteristics are simulated without assuming the temperature relationship between the oil film inlet and the outlet and the heat transfer on the pad free surface. This paper provides a theoretical basis for the safe design and stable operation of thrust bearings.

This paper aims to put forward a nonlinear vibration method for crack detection based on fiber Bragg grating (FBG), which is used to receive the waves in the plate.

First of all, measuring principle of nonlinear vibration technique and FBG sensing principle are introduced. Then nonlinear signal spectrum is analyzed to reveal various models of nonlinear vibration, and modal analysis of the plate structure is performed to lay the foundation for the later experiment. This approach is the cross-modulation effect from a persistent excitation to the receipt signal.

The experimental system is built and its results are in agreement with theoretical analysis, and show that this nonlinear vibration method based on FBG sensor is sensitive to crack damage.

Taking the board structure as the object, a new attempt by nonlinear vibration detection with FBG sensor has been investigated.

Introduction: With the accelerated process of building metropolitans, more and more people tend to live in cities. So, to deal with this, the need has arisen to make smart everything that we have around us even when we arrived at the term of the smart city. All of this produces data we call big data that is generated by citizens.

Aim: So, cloud computing is the main idea implementation which we can use ineffective from all of these services which we call smart. And taking into account all of this, in this literature review, we have used different techniques to extract the most relevant papers on our topic. Mainly, we focus on smart cities and their taxonomy of it such as smart homes, smart buildings, traffic monitoring, security and emergencies, and so on. For a smart city, we discussed different proposed system architectures which represent the real work of researchers.

Results: In this way, we mention services that are offered and have the base services cloud services. Also, the articles we used are extracted and analysed from the most powerful and relevant databases such as IEEE, ACM, Elsevier, Science Direct, and so on. In total, we take 88 papers, and from all of these papers we have extracted 53 with different methods. This Systematic Literature Review presents building and using smart cities inside of cloud computing services.

Conclusion: However, a different implementation idea to do better life for all of us is by converting our lifestyle into a new form of living. The only form of implementation is by digitalising and implementing everything around us which means making every service in one place, where you can have access from anywhere, anytime. So, the smart city is the future.

Originality/value: This systematic literature review contributes to everyone who expresses interest to implement and provides additional evidence for existing studies in smart city patterns which used cloud services. Researchers offer various implementation forms of different models and express the impact of cloud services, especially for placements, where there are more people populated. Also, this study identifies a major weakness in existing smart city implementation by using cloud services, which have identified the main obstacles now and it needs to reanalyse the form of implementation for more complex systems in the future.

In recent years, a great number of top conferences and workshops on artificial intelligence (AI) were held in China, showing Chinese AI plays an important role in the world. Meanwhile, Chinese government announced an ambitious scheme, “New Generation Artificial Intelligence Development Plan,” for the country to become a world leader in AI technologies by 2030. The AI research in China has covered various aspects, ranging from chips to algorithms. This chapter attempts to give an overview of the recent advances of AI research and development in China, as well as some perspectives on the future development of AI in China.

The purpose of this paper is to discuss nationalistic education in Hong Kong from a cultural perspective. It highlights the challenges faced by the Hong Kong Government and the growing antagonism and mistrust between the young generation and the government. The paper reviews the cultural policies adopted by the Western Zhou, Han and Tang dynasties in ancient China.

This paper adopts a historical approach by reviewing the policies in music and culture in the Chinese history and argues for the adoption of a soft approach to nationalistic education in Hong Kong.

Results show that being inclusive toward diverse cultures, trusting and valuing people’s voices and accepting differences of opinion are effective policies that were adopted by the respective dynasties. The results shed light on the possibility of nurturing nationalism through education in music and culture.

The historical examples mentioned in this paper are only selected periods of the Chinese history. Thus, the survey could not be taken as a comprehensive review.

This paper reviews the policies concerning music and culture in ancient China and argues for transferring the soft approach of predecessors toward these subjects as part of the nationalistic education of Hong Kong.

The results shown should be considered seriously by the Hong Kong Government as an effective substitute policy for the past stringent approach of implementing national education in Hong Kong. Nationalistic education focusing on music and culture is a common root for all Chinese and should be used in future to build up trust and common values between China and Hong Kong.

The originality of this research lies in its dealing with nationalism and national education, recommending a soft approach to education viewed through the prisms of music and culture.