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The utilization of surrogate models to approximate complex systems has recently gained increased popularity. Because of their capability to deal with black-box problems and lower computational requirements, surrogates were successfully utilized by researchers in various engineering and scientific fields. An efficient use of surrogates can bring considerable savings in computational resources and time. Since literature on surrogate modelling encompasses a large variety of approaches, the appropriate choice of a surrogate remains a challenging task. This review discusses significant publications where surrogate modelling for finite element method-based computations was utilized. We familiarize the reader with the subject, explain the function of surrogate modelling, sampling and model validation procedures, and give a description of the different surrogate types. We then discuss main categories where surrogate models are used: prediction, sensitivity analysis, uncertainty quantification, and surrogate-assisted optimization, and give detailed account of recent advances and applications. We review the most widely used and recently developed software tools that are used to apply the discussed techniques with ease. Based on a literature review of 180 papers related to surrogate modelling, we discuss major research trends, gaps, and practical recommendations. As the utilization of surrogate models grows in popularity, this review can function as a guide that makes surrogate modelling more accessible.

Federated learning ( FL ) is a promising decentralized deep learning technology, which allows users to update models cooperatively without sharing their data. FL is reshaping existing industry paradigms for mathematical modeling and analysis, enabling an increasing number of industries to build privacy-preserving, secure distributed machine learning models. However, the inherent characteristics of FL have led to problems such as privacy protection, communication cost, systems heterogeneity, and unreliability model upload in actual operation. Interestingly, the integration with Blockchain technology provides an opportunity to further improve the FL security and performance, besides increasing its scope of applications. Therefore, we denote this integration of Blockchain and FL as the Blockchain-based federated learning ( BCFL ) framework. This paper introduces an in-depth survey of BCFL and discusses the insights of such a new paradigm. In particular, we first briefly introduce the FL technology and discuss the challenges faced by such technology. Then, we summarize the Blockchain ecosystem. Next, we highlight the structural design and platform of BCFL . Furthermore, we present the attempts ins improving FL performance with Blockchain and several combined applications of incentive mechanisms in FL . Finally, we summarize the industrial application scenarios of BCFL .

Human activity recognition aims to determine actions performed by a human in an image or video. Examples of human activity include standing, running, sitting, sleeping, etc . These activities may involve intricate motion patterns and undesired events such as falling. This paper proposes a novel deep convolutional long short-term memory (ConvLSTM) network for skeletal-based activity recognition and fall detection. The proposed ConvLSTM network is a sequential fusion of convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and fully connected layers. The acquisition system applies human detection and pose estimation to pre-calculate skeleton coordinates from the image/video sequence. The ConvLSTM model uses the raw skeleton coordinates along with their characteristic geometrical and kinematic features to construct the novel guided features. The geometrical and kinematic features are built upon raw skeleton coordinates using relative joint position values, differences between joints, spherical joint angles between selected joints, and their angular velocities. The novel spatiotemporal-guided features are obtained using a trained multi-player CNN-LSTM combination. Classification head including fully connected layers is subsequently applied. The proposed model has been evaluated on the KinectHAR dataset having 130,000 samples with 81 attribute values, collected with the help of a Kinect (v2) sensor. Experimental results are compared against the performance of isolated CNNs and LSTM networks. Proposed ConvLSTM have achieved an accuracy of 98.89% that is better than CNNs and LSTMs having an accuracy of 93.89 and 92.75%, respectively. The proposed system has been tested in realtime and is found to be independent of the pose, facing of the camera, individuals, clothing, etc . The code and dataset will be made publicly available.

The important part of mechanical equipment is rotating machinery, used mostly in industrial machinery. Rolling element bearings are the utmost dominant part in rotating machinery, so even small defects in these components could result in catastrophic system failure and enormous financial losses. Hence, it is crucial to create consistent and affordable condition monitoring and fault diagnosis systems that estimate severity level and failure modes and to create an appropriate maintenance strategy. The studies reveal that the fault diagnostic system focuses on single fault diagnosis of the shaft-bearing system. However, in real scenarios, the occurrence of a single fault is very unlikely. Thus, multifault diagnosis of the shaft-bearing system is of greater significance. This paper aims at steadily and broadly summarizing the development of the intelligent multifault diagnostic and condition monitoring systems. In addition, there is a rapid development of application of Internet of things, cloud computing and artificial intelligence techniques for fault diagnosis. In this paper, we summarize the study of various fault diagnostic system built on the architecture and application of these cutting-edge technologies for predictive maintenance of mechanical equipment.

Internet of Things (IoT) enabled Wireless Sensor Networks (WSNs) is not only constitute an encouraging research domain but also represent a promising industrial trend that permits the development of various IoT-based applications. These applications span a wide range from industry to education, and from military to agriculture. The IoT device plays a significant role in various IoT-based networks, and the functioning of such network depends upon the battery power. Once the devices are deployed in the hostile environments, replacing batteries becomes impractical. Despite a plethora of research addressing this challenge, IoT networks still face issues. In this paper, a genetic algorithm based data monitoring and controlling method using IoT enabled WSNs is proposed by using movable sinks in IoT enabled HWSNs (OptiGeA). The OptiGeA protocol is designed for the election of cluster heads (CHs) by incorporating factors such as density, distance, energy and heterogeneous node capacity into its fitness function. The investigation of OptiGeA is conducted with single sink, multiple static sinks and multiple movable sinks provide an unbiased comparative assessment. The novel deployment technique and multiple mobile sinks approaches are proposed to reduce the transmission distance between the sink and CH during system operation and address hotspot issue. It is evident that the OptiGeA protocol shows an increment of 10.44% compared to the GAOC, whereas with the inclusion of DDC process the OptiGeA-DDC protocol demonstrates a remarkable increase of 48.33% compared to MS-GAOC.

Online courses have a large number of learners with large individual differences, which makes it difficult for teachers to accurately analyse their learning, and there are problems such as low course completion rate and low learning outcomes. This paper proposes a subjective and objective two-class analysis method to analyse learners' learning behaviour. The method divides online learning behaviour data into objective factual data and subjective value data. The method first assesses the objective factual data to determine whether the objective facts are learning behaviours, and then evaluates the learners' subjective data to understand their motivations and attitudes, and assesses the learners' subjective value in performing the behaviours. Through empirical analysis, the results show that the method can effectively analyse students' online learning behaviour data and provide a comprehensive and in-depth understanding of students' learning behaviours and needs, thus providing strong support for personalised teaching and learning advice.

Photovoltaic (PV) power is generated by two common types of solar components that are primarily affected by fluctuations and development in cloud structures as a result of uncertain and chaotic processes. Local PV forecasting is unavoidable in supply and load planning necessary in integration of smart systems into electrical grids. Intra- or day-ahead modelling of weather patterns based on Artificial Intelligence (AI) allows one to refine available 24 h. cloudiness forecast or predict PV production at a particular plant location during the day. AI usually gets an adequate prediction quality in shorter-level horizons, using the historical meteo- and PV record series as compared to Numerical Weather Prediction (NWP) systems. NWP models are produced every 6 h to simulate grid motion of local cloudiness, which is additionally delayed and usually scaled in a rough less operational applicability. Differential Neural Network (DNN) is based on a newly developed neurocomputing strategy that allows the representation of complex weather patterns analogous to NWP. DNN parses the n-variable linear Partial Differential Equation (PDE), which describes the ground-level patterns, into sub-PDE modules of a determined order at each node. Their derivatives are substituted by the Laplace transforms and solved using adapted inverse operations of Operation Calculus (OC). DNN fuses OC mathematics with neural computing in evolution 2-input node structures to form sum modules of selected PDEs added step-by-step to the expanded composite model. The AI multi- 1…9-h and one-stage 24-h models were evolved using spatio-temporal data in the preidentified daily learning sequences according to the applied input–output data delay to predict the Clear Sky Index (CSI). The prediction results of both statistical schemes were evaluated to assess the performance of the AI models. Intraday models obtain slightly better prediction accuracy in average errors compared to those applied in the second-day-ahead evening approach. However, the first-morning strategy application includes a much more complicated and time-consuming procedure, so in conclusion, the second one-mode series processing could be recommended. Model statistics are generally more successful than NWP data processing due to limitations, delays, and unavailability of free radiation or cloudiness forecasts in the systems. An all-day model enables the prediction of complete PVP periods in sequenced computing with acceptable operational quality in the late afternoon, which is inevitable in day-ahead management and load scheduling in smart grids based on PV energy.

The study presents a novel algorithm for solving third-order non-linear equations (Emden–Fowler type), which can be applied to various physical models. The algorithm uses a quintic trigonometric B-spline collocation method and a quasilinearization technique to avoid the non-linearity term in the equation. The study established a comprehensive error analysis for the proposed algorithm and proved that it has fourth order, i.e., ( O ( h 4 ) ) convergent. The algorithm’s ability to handle singular behavior at the point x = 0 and its faster rate of convergence exhibit a promising approach to solving such problems. The study also validates the theoretical results through numerical experiments and shows that the proposed algorithm has a faster rate of convergence in comparison to the existing methods.

The intricate nature of modern edge architectures, relying on a vast array of computational logic and lightweight communication protocols, creates vulnerabilities that expose them to a broad spectrum of security threats. Moreover, security vulnerabilities can significantly impact the energy footprint of edge servers in these architectures. Our approach utilizes the concurrent stochastic game (CSG) formalism to model the behavior of IoT communication entities (players) while accounting for potential attacks at the communication edge and the resulting energy consumption caused by such attacks. We rely on the PRISM-games language for automated analysis where the game goals modeling functional and security requirements are expressed using reward probabilistic alternating temporal logic (rPATL). To validate our approach, we examine a data corruption attack applied to dam water flow control and study its side effect on energy consumption associated with SensiNact gateways. Our key innovation lies in using formal models at the architectural level to explore potential attacks. These models capture synchronous and asynchronous communication styles, along with their associated energy consumption. The methodology and the implemented formalism offer a significant advancement over traditional game equation models while still achieving the desired security and energy evaluation. Numerical results show that compared to synchronous communication, asynchronous styles suffer from significantly larger infected buffers and higher energy consumption due to attacks ranging from 66 to 91%.

Motivated by practical engineering systems, this paper studies an assessment method for dynamic reliability of a discrete time multi-state star configuration power grid system with performance sharing. The proposed star configuration power grid system consists of n power generation subsystems fixed in star-terminal and one central collection and redistribution subsystem. The star-terminal subsystems with sufficient electric power can first transmit the surplus electric power to the central subsystem, and then the collected electric power in central subsystem is further redistributed to the star-terminal subsystems which are experiencing electric power deficiency through the corresponded transmission links. An algorithm based on the universal generating function (UGF) technique is presented to evaluate the dynamic reliability of the proposed power grid system with performance sharing. Finally, a numerical example and a case study are used to illustrate the accuracy of the proposed model and method. Studies indicate that the steady reliability of the proposed power grid system is improved by 9.41% and 37.28% for the numerical example when comparing the calculation results between performance sharing, unlimited performance sharing and no performance sharing. In the case study, the dynamic reliability of the proposed power grid system increased by 11.5% when comparing the calculation results between with performance sharing and no performance sharing when k → ∞ .