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This paper presents a modeling practical works project of electrical engineering, proposed to the first-year students of the University Institute of Technology in France, during the COVID-19 pandemic. The objective of this paper is twofold. The first objective is to present to the students the opportunities of modeling and calculation development of a spreadsheet software in their professional lives. The second objective is to create a file that automatically calculates all the current and voltage values at each point of any alternative electrical circuit. The aim of this paper, geared toward students, is to bring them to build their own numerical remote lab, autonomously. Therefore, pedagogical keys are given along the reading of this document to help them to progress, both on electrical circuits conceptual understanding with series and parallel RLC circuits and on their computation in a spreadsheet software. As a conclusion, this paper can be used as a base to develop remote modeling practical works of many and different devices, as well as a database starting point of such analytical models. [ABSTRACT FROM AUTHOR]

Transformer oil and insulation paper are subject to the combined effects of the electric field and temperature operations, which significantly impact insulation deterioration. This article designs a comparative test of the electro-thermal combined aging to obtain several characteristic parameters for the aging degree of oil-pressboard insulation. Molecular dynamics are used to establish a mixed molecular model of oil-pressboard insulation to simulate the pyrolysis reactions at high temperatures using the ReaxFF force field. The microscopic effects of the electric field on the aging of oil-pressboard insulation are studied. The rationale of the simulation model is verified from experimental results, and the trend of the reactants and products in the reaction process was considered to determine the influence mechanism of electric–thermal coupling on oil-pressboard insulation. The experimental results show that thermal stress remains the most important factor toward cracking in insulation paper. The acid value and furfural formation rate in oil increase, and the polymerization degree of the insulation paper decreases rapidly after the combined electric and thermal aging. In addition, the molecular dynamics simulations show that pyrolysis increases the bond energy, promotes repulsion between atoms, and leads to fractured bonds. The addition of the electric field increases the susceptibility of the chemical bonds in cellulose molecules to breakage. The morphology of the cellulose chain then changes, and the diffusion of small molecules intensifies. This study reveals the mechanism of insulation aging under electro-thermal coupling through combined macroscopic testing and molecular dynamics simulations. [ABSTRACT FROM AUTHOR]

In recent years, multilayer networks have served as effective models for addressing and analyzing real-world systems with multiple relationships. Among these scenarios, the community detection (CD) problem is one of the most prominent research hotspots. Although some research on multilayer network CD (MCD) has been proposed to address this problem, most studies focus only on topological structures. Therefore, their algorithms cannot extract the most out of complementary network information, such as node similarities and low-rank features, which may lead to unsatisfactory accuracy. To tackle this problem, this paper proposes a novel multi-objective evolutionary algorithm based on consensus prior information (MOEA-CPI). The proposed algorithm takes full advantage of prior information to guide the MOEA with respect to topological structures, initializations, and the optimization process. More specifically, this paper first extracts two kinds of prior information, i.e., graph-level and node-level information, based on Node2vec and Jaccard similarity, respectively. Then, the prior layer and a high-quality initial population are constructed on the basis of the graph-level information. During the optimization process, the genetic operator, which integrates the weighting strategy and node-level information, is applied to guide the algorithm to distribute similar nodes into the same community. Extensive experiments are implemented to prove the superior performance of MOEA-CPI over the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The increasing demand for sustainable materials in high-value applications, particularly in the automotive industry, has prompted the development of biocomposites based on renewable or recyclable matrices and natural fibers as reinforcements. In this context, this paper aimed to produce composites with improved mechanical and thermal properties (tensile, flexural, and heat deflection temperature) through an optimized process pathway using a biobased polyamide reinforced with short basalt fibers. This study emphasizes the critical impact of fiber length, matrix adhesion, and the variation in matrix properties with increasing fiber content. These factors influence the properties of short-fiber composites produced via primary processing using extrusion and shaped through injection molding. The aim of this work was to optimize extrusion conditions using a 1D simulation software to minimize excessive fiber fragmentation during the extrusion process. The predictive model's capacity to forecast fiber degradation and the extent of additional fiber breakage during extrusion was evaluated. Furthermore, the impact of injection molding on these conditions was investigated. Moreover, a comprehensive thermomechanical characterization of the composites, comprising 10%, 20%, and 30% fiber content, was carried out, focusing on the correlation with morphology and processing using SEM and micro-CT analyses. In particular, how the extrusion process parameters adopted can influence fiber breakage and how injection molding can influence the fiber orientation were investigated, highlighting their influence in determining the final mechanical properties of short fiber composites. By optimizing the process parameters, an increment with respect to bio-PA11 in the tensile strength of 38%, stiffness of 140%, and HDT of 77% compared to the matrix were obtained. [ABSTRACT FROM AUTHOR]

This paper investigates durability related properties of concretes containing copper heap leach residue (CHLR) as partial replacement of natural fine and coarse aggregates. The use of CHLR as aggregates in concretes promotes the reduction of the dependance on natural aggregates, upcycling of the waste as aggregates and the reduction of carbon footprint associated with natural aggregate productions. In this study, CHLR was washed, dried, and sieved to separate fine aggregate and coarse aggregate, and concretes were prepared with a cement content of 400 kg/m3 and water-cement ratio of 0.435 by replacing 25-75% natural fine and coarse aggregates. The concrete containing 50% CHLR as a partial replacement of natural CA and FA gained compressive strength of 52.9 and 54.0 MPa; drying shrinkage of 662 and 538 με; volume of permeable voids of 6.2 and 5.7%; 1485 and 2640 coulomb of charge passed in chloride permeability; and primary sorptivity coefficients of 4.0 × 10−3 and 4.3 × 10−3 mm/sec0.5 at 180 days, respectively. In contrast, these properties for the control specimens at the same age were 59.1 MPa, 394 με, 5.19%, 1280 coulomb, and 2.5 × 10−3 mm/sec0.5, respectively. The compressive strength and durability aspects declined in concretes using 75% CHLR coarse and fine aggregates. Existing analytical models for durability related properties of concrete containing natural aggregates are compared to that of the concretes using CHLR. Finally, backscattered electron images coupled with energy dispersive x-ray spectroscopy of the CHLR concretes were analyzed to understand the pore refinement, interfacial transition zones, microcracks, and hydration products influencing the durability aspects. [ABSTRACT FROM AUTHOR]

Enhancing power electronics (PE) converter reliability is crucial for ensuring a reliable operation of current and future operating Wind Turbines (WTs). Achieving high reliability of variable speed WT PE systems requires careful consideration of their operation, and particularly their thermal cycling. This paper presents a methodology for evaluating and reconsidering operational strategies of WTs with relation to the thermal loading and lifetime consumption of the converter. The methodology is applied to compare control strategies for the WT generator and evaluate their impact on the converter reliability by observation of the thermal cycles and by calculating the resultant lifetime consumption of those stress cycles. The thermal stress on both the Machine Side Converter (MSC) and the Grid Side Converter (GSC) is examined and compared. It is shown that the least reliable of the three evaluated control strategies is the one that tracks the power curve below rated speed most closely. This paper suggests that dynamic transients associated with the WT control largely influence the IGBT module wear-out and their modelling needs to be prioritized for lifetime studies. These dynamic transients are captured by the improved model whose value is confirmed for the comparisons in the case study of the paper. [ABSTRACT FROM AUTHOR]

As the inverter-based generation replaces the conventional synchronous generators, it may also need to fill in the missing ancillary service support. One of these ancillary services is dynamic reactive power provision and voltage control. This paper analyzes optimal strategy of reactive and active fault-current support of the inverter-based generation leading to fast voltage recovery of the system. For the purpose of the analysis, new ramping active current controller able to emulate different behavior of active current injection is proposed. By optimizing its parameters for different case studies of the system, the conclusions about optimal behavior of the inverter based generation with respect to system parameters and operating conditions are drawn. It is observed that the optimal combination of active and reactive fault-current is the most sensitive to the dynamic load component penetration levels in the system. With the increasing penetration levels, the significance of active fault-current injection increases. The results show that with higher penetration levels of dynamic load component in the heavy load areas, the ramping down of the inverter-based generation active fault-current results in slower voltage recovery of the system. Following this conclusion, a recommendation on update of current European grid codes is proposed. [ABSTRACT FROM AUTHOR]

Cross-laminated timber (CLT) buildings are constructed by connecting CLT panels with dowel-type fasteners and mechanical anchors, which govern the lateral behavior of these structures. Although these connections are mostly distributed along the perimeter of the CLT panels, creating highly redundant structures, previous research on the lateral behavior of CLT structures has mainly focused on the behavior of the connections placed at the base of the CLT shear walls, such as hold downs and angle brackets, while limited attention has been paid to the connections between perpendicular walls and their effect on the lateral response of CLT shear walls. This paper presents a comprehensive experimental study aimed at investigating the effects of the interaction between perpendicular walls on the lateral response of CLT shear walls. CLT shear walls with three different aspect ratios were analyzed by means of monotonic and cyclic tests, both in single wall configuration and connected to perpendicular walls. The experimental findings demonstrate that the structural interaction due to the perpendicular walls significantly affects the lateral response of a CLT shear wall, resulting in increased lateral stiffness, lateral capacity, and deformation capacity. In addition, the study presents two analytical models for the prediction of the lateral stiffness and the lateral capacity of CLT shear walls connected to perpendicular walls. The analytical models were validated based on the experimental results, showing reasonable agreement. [ABSTRACT FROM AUTHOR]

This paper addresses the frequency oscillation problem in a parallel-inverter-based grid-connected system. Angular frequency interactions between inverters and the grid exhibit various numbers of complicated characteristics that seriously threaten the connected power system's stable operation. This paper proposes a unified frequency oscillation method analysis to investigate the oscillation problem in a multi-VSG grid-connected system. In this method, the power control model of each grid-connected inverter is equivalent to a two-terminal network by analogy method, which can be described as a “current source” connected in parallel with a reciprocal of “admittance” and then joined in series with a joint of “admittance.” Next, considering the influence of grid impedance, the model of the whole grid-connected system is established to reveal the mechanism of multi-VSG angular frequency oscillation. Subsequently, the influence factors of frequency oscillation and its variation law are analyzed through transfer functions, which provide an essential theoretical method for solving the frequency oscillation problem in the current multi-inverter grid-connected engineering application. Finally, we validate the effectiveness and correctness of the small-signal model and the frequency oscillation characteristics by building the MATLAB/Simulink simulation. [ABSTRACT FROM AUTHOR]

The Cox proportional hazards model is a popular semi-parametric model for survival analysis. In this paper, we aim at developing a federated algorithm for the Cox proportional hazards model over vertically partitioned data (i.e., data from the same patient are stored at different institutions). We propose a novel algorithm, namely VERTICOX, to obtain the global model parameters in a distributed fashion based on the Alternating Direction Method of Multipliers (ADMM) framework. The proposed model computes intermediary statistics and exchanges them to calculate the global model without collecting individual patient-level data. We demonstrate that our algorithm achieves equivalent accuracy for the estimation of model parameters and statistics to that of its centralized realization. The proposed algorithm converges linearly under the ADMM framework. Its computational complexity and communication costs are polynomially and linearly associated with the number of subjects, respectively. Experimental results show that VERTICOX can achieve accurate model parameter estimation to support federated survival analysis over vertically distributed data by saving bandwidth and avoiding exchange of information about individual patients. The source code for VERTICOX is available at: https://github.com/daiwenrui/VERTICOX. [ABSTRACT FROM AUTHOR]

To research the impact of temperature-dependent characteristics of windings and magnets on the electromagnetic power loss and thermal properties of high-speed (HS) interior permanent magnet synchronous machines (PMSMs), an electromagnetic-thermal bi-directional coupling method (BDCM) is proposed for water-cooled HS PMSMs considering the assembling gaps between the motor components in this paper. The electromagnetic power loss is calculated by 3-D transient finite-element method and loaded into the 3-D temperature field according to the actual loss distribution, and the materials’ properties are in turn updated according to the temperature distributions. The loss and temperature calculation results of a 15kW HS amorphous metal PMSM are calculated by two different coupling methods in the conditions of the rated-load sinusoidal supply and converter supply, and compared with the measured results in the case of converter supply. The results obtained by the BDCM are more consistent with the experiment results, verifying the superiority of the proposed analyzing method in improving the calculation accuracy of motor loss and temperature rise. In addition, the applicable conditions of the proposed method are also summarized in the paper. [ABSTRACT FROM AUTHOR]

Multimodal sentiment analysis aims to extract and integrate information from different modalities to accurately identify the sentiment expressed in multimodal data. How to effectively capture the relevant information within a specific modality and how to fully exploit the complementary information among multiple modalities are two major challenges in multimodal sentiment analysis. Traditional approaches fail to obtain the global contextual information of long time-series data when extracting unimodal temporal features, and they usually fuse the features from multiple modalities with the same method and ignore the correlation between different modalities when modeling inter-modal interactions. In this paper, we first propose an Attentional Temporal Convolutional Network (ATCN) to extract unimodal temporal features for enhancing the feature representation ability, then introduce a Multi-layer Feature Fusion (MFF) model to improve the effectiveness of multimodal fusion, which fuses the different-level features by different methods according to the correlation coefficient between the features, and cross-modal multi-head attention is used to fully explore the potential relationship between the low-level features. The experimental results on SIMS and CMU-MOSI datasets show that the proposed model achieves superior performance on sentiment analysis tasks compared to state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

Emotion recognition is a key attribute for artificial intelligence systems that need to naturally interact with humans. However, the task definition is still an open problem due to the inherent ambiguity of emotions. In this paper, a novel Bayesian training loss based on per-utterance Dirichlet prior distributions is proposed for verbal emotion recognition, which models the uncertainty in one-hot labels created when human annotators assign the same utterance to different emotion classes. An additional metric is used to evaluate the performance by detecting test utterances with high labelling uncertainty. This removes a major limitation that emotion classification systems only consider utterances with labels where the majority of annotators agree on the emotion class. Furthermore, a frequentist approach is studied to leverage the continuous-valued “soft” labels obtained by averaging the one-hot labels. We propose a two-branch model structure for emotion classification on a per-utterance basis, which achieves state-of-the-art classification results on the widely used IEMOCAP dataset. Based on this, uncertainty estimation experiments were performed. The best performance in terms of the area under the precision-recall curve when detecting utterances with high uncertainty was achieved by interpolating the Bayesian training loss with the Kullback-Leibler divergence training loss for the soft labels. The generality of the proposed approach was verified using the MSP-Podcast dataset which yielded the same pattern of results. [ABSTRACT FROM AUTHOR]

This report analyzes the reproducibility of the article “Computing Planetary Interior Normal Modes with A Highly Parallel Polynomial Filtering Eigensolver” by Jia Shi et al. (Shi, 2018). To reproduce the results we perform different weak and strong scaling studies using a series of Mars models. All experimental runs were performed during the SC19 Student Cluster Competition on a four node Intel Skylake cluster. We show that the findings of the original article can be reproduced in a different environment. [ABSTRACT FROM AUTHOR]

Modern complexities associated with an arterial traffic makes existing safety prediction methods insufficient to meet desired standards required by recent developmental needs. This paper proposes an enhanced active safety prediction method based on big-data approach and Stacked AutoEncoder-Gated Recurrent Unit. Firstly, the big-data technology is used to construct a dynamic identification model to recognize real-time operation state and risk state. Secondly, the Stacked AutoEncoder-Gated Recurrent Unit is used to predict a level of safety based on associated recognition results. This paper uses data from working days of Sunset Boulevard, California, from January $1^{\mathrm{st}}$ , 2020, to February $28^{\mathrm{th}}$ , 2020. The results of analysis show that the accuracy of the proposed dynamic recognition model reaches 98.92%, which is better than existing models such as random forest, K-nearest neighbor, and naïve Bayes models. In addition, it is found that the Stacked AutoEncoder-Gated Recurrent Unit can achieve a prediction accuracy of 95.157% and has significant advantages in terms of efficiency. The proposed methods will provide feasible solutions for actively monitoring safety levels. [ABSTRACT FROM AUTHOR]

Bridges play an important role in transportation infrastructure systems. Intelligent and digital management of bridges group is an essential part of the future intelligent transportation infrastructure system. This paper proposes a digital twin system for bridges group in the regional transportation infrastructure network, which is interconnected by measured traffic loads. In physical space, a full-bridge traffic load monitoring system based on information fusion of weigh-in-motion (WIM) and multi-source heterogeneous machine vision is set up on the target bridge to measure traffic loads, also lightweight sensors are employed on the bridges group for structural response information. Furthermore, by establishing mechanical analysis models in the corresponding digital space and using the measured traffic loads as links, the working condition perception and safety warning of all bridges in the regional transportation network is achieved, forming an important support for further intelligent transportation infrastructure system. The proposed digital twin system has been preliminarily implemented in a bridges group around Shanghai, China, demonstrating the feasibility of the technical framework proposed in this paper and the bright prospects. [ABSTRACT FROM AUTHOR]

In this paper, the boundary discretization method is used to improve the traditional subdomain model. The improved model can be used to calculate the magnetic field distribution in the region containing non-orthogonal boundary, avoiding the influence of the simplified stator slot or permanent magnet shape on the results in calculating the motor magnetic field using the traditional subdomain model. In addition, the improved subdomain model can also be used to calculate the air-gap flux density when the rotor is eccentric, instead of segmenting the air-gap to an equivalent state without eccentricity. The calculation object of this paper is a surface-mount permanent magnet motor with a rectangular stator slot. The air-gap flux density under no-load and load conditions is calculated, respectively. Since the improved subdomain model does not need to simplify the stator slot shape, the magnetic potential in the stator slot is also calculated. Finally, the air-gap flux density under rotor eccentricity is calculated, and the influence of the calculating parameters on the results of the improved subdomain model is discussed. All the calculated results are compared with those obtained by the finite element method, which verifies the accuracy of the improved calculation model. [ABSTRACT FROM AUTHOR]

Millimeter-wave rotary-wing (RW) unmanned aerial vehicle (UAV) air-to-ground (A2G) links face unpredictable Doppler effect arising from the inevitable wobbling of RW UAV. Moreover, the time-varying channel characteristics during transmission lead to inaccurate channel estimation, which in turn results in the deteriorated bit error probability performance of the UAV A2G link. This paper studies the impact of mechanical wobbling on the Doppler effect of the millimeter-wave wireless channel between a hovering RW UAV and a ground node. Our contributions of this paper lie in: i) modeling the wobbling process of a hovering RW UAV; ii) developing an analytical model to derive the temporal autocorrelation function (ACF) for the millimeter-wave RW UAV A2G link in a closed-form expression; and iii) investigating how RW UAV wobbling impacts the Doppler effect on the millimeter-wave RW UAV A2G link. Numerical results show that different RW UAV wobbling patterns impact the amplitude and the frequency of ACF oscillation in the millimeter-wave RW UAV A2G link. For UAV wobbling, the temporal ACF decreases quickly and the impact of the Doppler effect is significant on the millimeter-wave A2G link. [ABSTRACT FROM AUTHOR]

To achieve higher throughputs in cellular networks, 3GPP proposes to use unlicensed frequency bands and develops technologies — the latest one is NR-U — allowing a cellular base station to transmit in unlicensed bands, which are already occupied by Wi-Fi networks. To enable fair channel sharing between two technologies, the base station uses a sort of CSMA/CA with binary exponential backoff similar to Wi-Fi. However, the base station can start data transmission only at strictly periodic time moments. Many papers propose sending a reservation signal between the end of the backoff procedure and such a moment to prevent nearby devices from accessing the channel. However, this approach significantly reduces Wi-Fi performance. The paper proposes a novel method CR-LBT of transmitting a reservation signal that greatly decreases channel resource waste caused by collisions and improves channel resource sharing fairness. With developed analytical models and simulations, it is shown that CR-LBT may simultaneously increase the throughput of both NR-U and Wi-Fi networks. The effect is more noticeable for the Wi-Fi network, the throughput of which may rise three times compared with the traditional method of sending the reservation signal. Finally, the influence of various factors on CR-LBT performance is studied. [ABSTRACT FROM AUTHOR]

This paper proposes a way to represent the anisotropic nonlinear superconducting properties using a discrete model for magnetization with the corresponding critical parameters, equivalent to the Power Law model for currents. This simplified model makes it possible to analyze magnetic systems with superconducting elements in a stationary mode, which reduces the calculation time. Using of proposed model is demonstrated by the calculation of force interactions between high-temperature superconducting (HTS) samples and permanent magnet in zero-field-cooling (ZFC) and field-cooling (FC) modes. HTS bulks in the form of a prism and stack made of HTS tape are considered. The calculation of the electromagnetic field was carried out using the method of integral equations for field sources. The data of calculations are compared with the results of experimental studies. [ABSTRACT FROM AUTHOR]

Sentiment Quantification aims to detect the overall sentiment polarity of users from a set of reviews corresponding to a target. Existing methods equally treat and aggregate individual reviews’ sentiment to judge the overall sentiment polarity. However, the confidence of each review is not equal in sentiment quantification where sentiment perturbation arising from high- and low-confidence reviews may degrade the accuracy of Sentiment Quantification. Specifically, fake reviews with deceptive sentiments are low confidence, which perturbs the overall sentiment prediction. Whereas, some reviews generated by responsible users are high confidence. They contain authoritative suggestions so they should be emphasized in Sentiment Quantification. In this paper, we design and build COSE, a confidence-aware sentiment quantification framework, which can measure the confidence of individual reviews to eliminate sentiment perturbation and facilitate sentiment quantification. We design a Review Graph that achieves review confidence modeling in an unsupervised manner and obtains review confidence representations. Moreover, we develop a dynamic fusion attention mechanism, which produces sentiment “de-perturbation” vectors to eliminate the sentiment perturbation based on the confidence representations. Extensive experiments on large-scale review datasets validate the significant superiority of COSE over the state-of-the-art. [ABSTRACT FROM AUTHOR]

The mining method that is still often used in salt deposits is the room-and-pillar mining method, in which the dimensioning of the most requested element in the system is followed. The pillars are the elements subjected to the greatest loads. Knowing the size and distribution mode of the secondary state of stress—deformation—is a necessity that can lead to the design and realization of stable, reliable underground excavations. This paper proposes an analytical assessment model of the secondary stress state in the pillars between the operating rooms, as well as in the whole system room–pillar–floor, based on the results obtained from laboratory research through modeling and in situ research. For this purpose, the evaluation of the secondary stress state was carried out considering the following methods: (1) the dimensioning method based on the theory of limit equilibrium, taking into account the effective stress in the pillars; and (2) the mechanics of the continuous environment based on the design of some analytical models for evaluating the secondary stress-deformation state in the pillar and floor. The exploitation of one of the largest salt deposits in Romania is used as a case study, and the stability of the exploitation system with rooms and pillars is evaluated by analytical methods. The secondary state of tension was calculated at different points on the height of the pillar. Through the proposed algorithm, the value of the axial component of the secondary stress state at different points along the axis of a pillar located at a depth of 100 m varies between 1.498 and 1.657 MPa, compared to the value obtained by the finite element method and in situ measurements, which was 1.64 MPa. The comparison revealed a high degree of agreement between the results obtained for the depth of 100 m using both the FEM and laboratory and in situ measurements. This suggests that the proposed algorithm is a reliable method for predicting the secondary stress state. The presented algorithm can be extended in the field of mining deposits, where mining methods with rooms and pillars are used. [ABSTRACT FROM AUTHOR]

This paper investigates the practical engineering problem of traffic sensors placement on stretched highways with ramps. Since it is virtually impossible to install bulky traffic sensors on each highway segment, it is crucial to find placements that result in optimized network-wide, traffic observability. Consequently, this results in accurate traffic density estimates on segments where sensors are not installed. The substantial contribution of this paper is the utilization of control-theoretic observability analysis—jointly with integer programming—to determine traffic sensor locations based on the nonlinear dynamics and parameters of traffic networks. In particular, the celebrated asymmetric cell transmission model is used to guide the placement strategy jointly with observability analysis of nonlinear dynamic systems through Gramians. Thorough numerical case studies are presented to corroborate the proposed theoretical methods and various computational research questions are posed and addressed. The presented approach can also be extended to other models of traffic dynamics. [ABSTRACT FROM AUTHOR]

The term “business incubator” (BI) has become an accepted neologism among academics, practitioners, and policymakers. This is despite the lack of an agreed definition amongst scholars of what exactly constitutes a BI. Using a systematic literature review methodology, we identify and analyze definitions of BIs used in published academic research papers and practice papers over a 35-year period. In this article, we undertake a thematic analysis, using the software package NVivo, of 82 academic and 14 practice definitions used in 61 publications. Our analysis shows that definitions of BIs are constructed around three core themes: the business incubation model; the purpose of the incubator; and the target of support provided by the incubator. By identifying both consistencies and inconsistencies in existing definitions, we provide a more nuanced understanding of the heterogeneity that underlies the organizational form referred to as a BI. We conclude by proposing an agenda for further research. [ABSTRACT FROM AUTHOR]

The dynamometer card (DC) is crucial for the remote monitoring of the sucker rod pumping system (SRPS). However, the DC sensors have poor real-time performance and high maintenance costs leading to difficulties in obtaining datasets of the downhole working states from different oil wells. Moreover, the motor power curve is easy to obtain in real-time and can reflect the changes in pumping unit load. Thus, this paper proposed a novel hybrid model for indirect measurement DC based on measured motor power. Firstly, based on the mechanical model analysis of SRPS, the dataset is constructed by transforming the motor power and geometric parameters of the SRPS into the polished rod torque, the first-derivative of polished rod torque, the second-derivative polished rod torque, and the torque factor. These four parameters are merged as the inputs of the data-driven model. Subsequently, a data-driven model implements the particle swarm (PSO) algorithm to optimize the weights and thresholds of the Back Propagation (BP) neural network model, which was trained to predict DCs of three common working states. Finally, the proposed method is verified experimentally through the measured DC and motor power data. Both experimental and prediction results demonstrate the effectiveness of the proposed method for generating DC. The mean DC area relative error of the hybrid method proposed in this paper is 2.52%. [ABSTRACT FROM AUTHOR]

A good quality stability model is a key factor for accurate power system operations.Inaccurate parameters of the stability models affect the decision making which paves the way for serious consequences. Thus, it is necessary to calibrate the stability model parameters in a regular manner. There are several calibration methods in the literature which are based on simultaneous estimation of the parameters and states. However, not all of the model parameters are well estimable simultaneously. Simultaneous estimation of parameters with high collinearity may result in biased calibration results. In this paper, the trajectory sensitivity method is used to detect the sensitive parameters and construct the sensitivity matrix. Then, parameters with high linear dependency are identified using the sensitivity matrix. It is shown that, despite the high sensitivity of a parameter, its estimability degrades as the collinearity with other parameters increase. In this paper an identifiability analysis that detects the collinearity among the sensitive parameters is proposed. The proposed method is validated using WSCC 9-Bus System. [ABSTRACT FROM AUTHOR]

Network data in real-world tends to be error-prone due to incomplete sampling, imperfect measurements, etc.; this in turn results in inaccurate results when performing network analysis or modeling, such as node classification and link prediction, on these flawed networks. In this paper, we aim to reconstruct a reliable network from a flawed, undirected, unweighted network, a process referred to network enhancement. More specifically, network enhancement aims to detect the noisy links that are observed in the network but should not exist in the real world, as well as to predict the missing links that do indeed exist in the real world yet remain unobserved. While some attempts have been made to detect either noisy links or missing links, few of these works have considered unifying these two tasks, even though they are inter-dependent and capable of mutually boosting each others’ performance. In this paper, we therefore propose E-Net, an end-to-end graph neural network model, to leverage the mutual influence of these two tasks in order to achieve both goals more effectively. On one hand, detecting noisy links can benefit the performance of missing link prediction, while on the other hand, predicting missing links can provide indirect supervision for detecting noisy link detection when the labels of these noisy links are unavailable. Moreover, by proposing a subgraph extraction mechanism based on random walk with restart, the model can be scaled up to large networks and is able to preserve the local and global structural characteristics. The experimental results on several types of large networks demonstrate that the proposed model obtains an improvement of 10.7 percent on average in terms of F1 for predicting missing links, along with an average of 3.7 percent improvement in terms of precision for detecting noisy links compared with the state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

Modern Medical Information Systems very often comprise Medical Devices and governed by regulations which require stringent Risk Management activities to be implemented to minimize the occurrence of safety risks. Currently, the reference standard adopted by manufacturers for Risk Management is ISO 14971, which, however, was devised for traditional (mostly hardware) Medical Devices and does not either take into account the peculiarities of modern Medical Information Systems, or define a formal methodology to conduct Risk Assessment. Moreover, the approaches currently implemented by manufacturers typically aims at obtaining qualitative Risk Assessment results. Within the so-delineated application scenario, this paper proposes a methodology for the Dynamic Probabilistic Risk Assessment of Medical Information Systems, by specifically looking at medical devices that are intended as one of the most relevant components in such systems. The methodology complies with ISO 14971 and improves current practices because it allows the analyst to conduct a quantitative analysis, also taking into account the temporal dimension. It relies on a Probabilistic Risk Model, defined as a set of Markov Models, which is model-checked to obtain quantitative information about the risks. The proposed methodology is also adopted to improve definitively the Medical Device post-market surveillance, which is currently implemented as a ”wait for an incident” activity. In other words, currently a manufacturer sets up a service that has to ”react” to an incident by starting an investigation activity. Instead, the methodology proposes the adoption of risk models defined during the development phase also to re-assess periodically the risks related to the product during the post-market surveillance. This may prevent some incidents because risks are assessed using data collected in the field (no longer guesstimated as during the development phase) and taking into account the temporal effects on probability distributions (such as the deterioration of hardware/software components over the time). [ABSTRACT FROM AUTHOR]

The dataflow concept has been successfully used for modeling and synthesizing signal processing applications since decades, and recently, dataflow has also been discovered to match the computation model of machine learning applications, leading to extremely successful dataflow based application design frameworks. One of the most attractive features of dataflow, especially for signal processing, is related to its formal nature: when properly defined, a dataflow-based application model can be analytically verified for correctness at the stage of application design. This paper proposes VR-PRUNE, a novel dataflow model of computation that is aimed for design of high-performance signal processing software, together with runtime support that allows efficient application deployment to heterogeneous GPU-equipped platforms. Compared to prior work, VR-PRUNE features variable token rate processing, which enables designing adaptive signal processing applications, and implementing solutions that, e.g., allow trading-off between power consumption and filtering bandwidth at runtime. The paper presents the formal concepts of VR-PRUNE, as well as four application examples from domains related to signal processing, accompanied with quantitative results, which show that using VR-PRUNE enables, for example, application power-performance scaling, and on the other hand describing adaptive application behavior with 59% fewer dataflow graph components compared to previous work. [ABSTRACT FROM AUTHOR]

The longest-chain and Greedy Heaviest Observed Subtree (GHOST) protocols are the two most famous chain-selection protocols to address forking in Proof-of-Work (PoW) blockchain systems. Inclusive protocol was proposed to lower the loss of miners who produce stale blocks and increase the blockchain throughput. This paper aims to make an analytical-model-based quantitative comparison of their capabilities against selfish mining attack. Analytical models have been developed for the longest-chain protocol but less to the GHOST protocol. However, the blockchain dynamics and evolution are different when adopting different chain-selection protocols. Therefore, the corresponding analytical models and/or the formulas of calculating metrics (such as miner profitability and system throughput) may be different. To address these challenges, this paper first develops a novel Markov model and the formulas of evaluation metrics, in order to analyze a GHOST-based blockchain system under selfish mining attack. Then extensive experiments are conducted for comparison and we observe that: (i) The GHOST protocol is more resistant to selfish mining attack than the longest-chain protocol from the aspect of relative revenue of selfish miners. (ii) Inclusive protocol can promote the security (evaluated in terms of miner profitability) improvement of the system which has little total computational power or a high forking probability. Additionally, the longest-chain protocol is more sensitive to inclusive protocol than GHOST protocol. (iii) It is hard for each of the two common-used difficulty adjustment algorithms to achieve higher system throughput and security. [ABSTRACT FROM AUTHOR]

Featured Application: The present paper evaluates 10 alternative shoreline pond electrode station designs using frames in a linear arrangement of electrodes for HVDC interconnections, against the typical design, according to CIGRE B4.61/2017 (parallel arrangement of electrode frames, with respect to the longitudinal axis of the breakwater), studying it from the point of view of electric field analysis (with emphasis on the near-field). During the design of a shoreline electrode station for High-Voltage Direct Current (HVDC) interconnections, the location of the electrodes plays a critical part, especially in the development of the near-electric field. The basic structure is their linear placement, in the form of successive frames, parallel to the longitudinal axis of the breakwater, as proposed by CIGRE and implemented in existing projects. However, this arrangement requires a considerable breakwater length, which may not be permissible, as in the case of Stachtoroi, one of the two electrode stations being built for the 1 GW, ±500 kV HVDC interconnection between Crete and mainland Greece. This troubled the preliminary study team of the electrode stations, which investigated other possible configurations. In this paper, configurations of linear placements of electrode frames are studied and compared at the preliminary study level in terms of electric field effects (especially the near-field), using an analytical simplified model and the superposition method, to determine the most appropriate arrangement of electrodes that will cover the respective requirements of CIGRE directives B4.61/2017. These arrangements are practically evaluated for two different electrode station locations at Korakia in Crete and at Stachtoroi in Aegina for the Crete–mainland-Greece interconnection, resulting in interesting alternative solutions. [ABSTRACT FROM AUTHOR]

The radio block center (RBC) is one of the most essential ground systems in a high-speed train control system both in Europe and in China. The RBC handover procedure is an important function of RBC, which affects the transport efficiency, reliability and safety of railways. Analysis of crucial states in the RBC handover procedure is helpful to determine whether there are potential risks in the procedure, and to locate the fault in time when a fault occurs. In this paper, we study a property, called C-detectability, of the RBC handover. This property has been defined in discrete event systems and requires that the crucial states can be determined uniquely by observing the system output. Taking the RBC handover procedure in the Chinese train control system level 3 (CTCS-3) as an example, we first model the RBC handover procedure using Petri nets, which are a graphical and mathematical modeling tool to formalize the behavior of discrete event systems. Then, based on the notion of basis reachability graph, an efficient approach is used to check C-detectability of the Petri net modeling the handover procedure. Note to Practitioners—The railway system is a safety-critical system. System safety is essential to the railway system. It is necessary to provide formal methods to model the system and analyze its properties. The motivation of this work is to present a general modeling framework of railway systems for the crucial states estimation. The crucial states estimation of the railway system has not been discussed yet in the literature despite its importance due to its relationship with the safety of the railway. In particular, estimating the crucial states in the railway system helps to determine whether there are potential risks in the system. In this paper, we investigate a new state estimation property (C-detectability), which is related to fault diagnosis, marking estimation and fault identification. Future research will consider the use of timed Petri nets to increase the modeling power and to allow the performance analysis of the railway system. [ABSTRACT FROM AUTHOR]

A large number of non-linear hardware and control units exists in power electronic system used in grid connected devices. The analytical transient stability analysis of grid-connected converters presents numerous difficulties. A common method to tackle this problem is the stability analysis using Lyapunov’s method. By applying this method, difficulties arise not only from finding a suitable Lyapunov function, but also from checking the constraint of Lyapunov stability. If the appropriate Lyapunov function is a high-order polynomial, it is very challenging to test if it meets the constraints of Lyapunov stability in certain regions. In this paper, the sum-of-squares programming method is used to obtain the estimation of a converter’s domain of attraction with a relatively small number of iterations compared to classically applied methods, such as the Monte Carlo method. The estimation of the domain of attraction are verified by time-domain simulations and StarSim’s controller hardware-in-the-loop tests in this paper. [ABSTRACT FROM AUTHOR]

A growing body of scientific evidence indicates that exposure to low dose ionizing radiation (< 2 Gy) is associated with a higher risk of developing radioinduced cancer. Additionally, it has been shown to have significant impacts on both innate and adaptive immune responses. As a result, the evaluation of the low doses inevitably delivered outside the treatment fields (out-of-field dose) in photon radiotherapy is a topic that is regaining interest at a pivotal moment in radiotherapy. In this work, we proposed a scoping review in order to identify evidence of strengths and limitations of available analytical models for out-offield dose calculation in external photon beam radiotherapy for the purpose of implementation in clinical routine. Papers published between 1988 and 2022 proposing a novel analytical model that estimated at least one component of the out-of-field dose for photon external radiotherapy were included. Models focusing on electrons, protons and Monte-Carlo methods were excluded. The methodological quality and potential limitations of each model were analyzed to assess their generalizability. Twenty-one published papers were selected for analysis, of which 14 proposed multi-compartment models, demonstrating that research efforts are directed towards an increasingly detailed description of the underlying physical phenomena. Our synthesis revealed great inhomogeneities in practices, in particular in the acquisition of experimental data and the standardization of measurements, in the choice of metrics used for the evaluation of model performance and even in the definition of regions considered out-of-the-field, which makes quantitative comparisons impossible. We therefore propose to clarify some key concepts. The analytical methods do not seem to be easily suitable for massive use in clinical routine, due to the inevitable cumbersome nature of their implementation. Currently, there is no consensus on a mathematical formalism that comprehensively describes the out-of-field dose in external photon radiotherapy, partly due to the complex interactions between a large number of influencing factors. Out-of-field dose calculation models based on neural networks could be promising tools to overcome these limitations and thus favor a transfer to the clinic, but the lack of sufficiently large and heterogeneous data sets is the main obstacle. [ABSTRACT FROM AUTHOR]

In the wide field of tumor treatment, thermal ablation procedures are very promising. Alternating magnetic fields are used to transfer the energy from outside the patient to the tumor area located anywhere in the body. This energy is converted to heat by implanted devices located in the tumor area. In this paper, the spatial distribution of the magnetic field of different circular coil configurations is analyzed and optimized with focus on patients’ safety and on coil configuration performance. The analysis is based on several performance criteria and is conducted with respect to the worst case scenario of a contactless thermal tumor ablation of deep-seated tumors, in which the energy has to be transferred over a considerably large distance. The magnetic field and the specific absorption rate (SAR) are calculated numerically and the performance criteria are evaluated based on a model of a human body including a tumor area. The most suitable coil configurations for different application scenarios are presented and a thermal analysis is done. Based on this, the minimum required heating power, coil current and number of coil windings, and the corresponding maximum SAR to achieve an adequate rise of tissue temperature are evaluated. For a heating power of 1.45 W, a minimum SAR of 130 mW/kg, a maximum power transfer efficiency of 1.05%, and a maximum coupling coefficient of 0.00695 is achieved. This paper shows the potential to enhance the safety of the patients significantly by choosing the appropriate coil configuration for a specific application scenario. [ABSTRACT FROM AUTHOR]

The critical nature of vehicular communications requires their extensive testing and evaluation. Analytical models can represent an attractive and cost-effective approach for such evaluation if they can adequately model all underlying effects that impact the performance of vehicular communications. Several analytical models have been proposed to date to model vehicular communications based on the IEEE 802.11p (or DSRC) standard. However, existing models normally model in detail the MAC (Medium Access Control), and generally simplify the propagation and interference effects. This reduces their value as an alternative to evaluate the performance of vehicular communications. This paper addresses this gap, and presents new analytical models that accurately model the performance of vehicle-to-vehicle communications based on the IEEE 802.11p standard. The models jointly account for a detailed modeling of the propagation and interference effects, as well as the impact of the hidden terminal problem. The model quantifies the PDR (Packet Delivery Ratio) as a function of the distance between transmitter and receiver. The paper also presents new analytical models to quantify the probability of the four different types of packet errors in IEEE 802.11p. In addition, the paper presents the first analytical model capable to accurately estimate the Channel Busy Ratio (CBR) metric even under high channel load levels. All the analytical models are validated by means of simulation for a wide range of parameters, including traffic densities, packet transmission frequencies, transmission power levels, data rates and packet sizes. An implementation of the models is provided openly to facilitate their use by the community. [ABSTRACT FROM AUTHOR]

Supporting parallel multi-priority data streams is vital for maintaining the quality of service (QoS) in vehicle-to-everything (V2X) communication. Hence, the European telecommunications standard institute (ETSI) has defined four packet types, with varying priority levels, to be used as broadcast packets in such communication. This paper studies the medium access control (MAC) layer performance of IEEE 802.11p and cellular-V2X (C-V2X) Mode 4 using discrete-time Markov chain (DTMC) based models, while considering parallel multi-priority data streams. The overall model consists of four queue models with their respective traffic generators, which are appropriately linked with the DTMCs modeling the MAC layer operations of IEEE 802.11p and C-V2X Mode 4. Closed-form solutions for the steady-state probabilities of the models are obtained, which are then utilized to derive expressions for key performance indicators at the MAC layer. Numerical results are provided to draw insights on the MAC layer performance of the two technologies. IEEE 802.11p is comparatively superior in average delay, and at maintaining fairness among multi-priority data streams, whereas C-V2X Mode 4 exhibits better collision resolution, which leads to its higher throughput. The paper also includes design insights on possible performance enhancements for future releases. [ABSTRACT FROM AUTHOR]

The short-circuit response of inverter-interfaced distributed generators (IIDGs) is not adequately represented in many conventional protection studies. This paper presents an in-depth analysis of IIDG behavior during grid faults and proposes a more accurate reduced-order parameterized short-circuit current (RPSC) model of inverters. Typical inverter components are thoroughly investigated to identify those that play dominant roles during faults. The paper shows that the current limiter is the dominant factor for the steady-state fault-current of inverter; while the inverter filter along with the severity of the voltage disturbance largely determine the initial transient spike of inverter fault current. The proposed model is low-order and can be used in large scale simulations. The parameters of the proposed RPSC model can be extracted from laboratory experiments without requiring proprietary manufacturer information. The proposed fault-current model is analogous to the well-known synchronous machine model that segregates the inverter fault current into subtransient, transient, and steady-state fault currents. Experimental and simulation tests are presented to validate the model. [ABSTRACT FROM AUTHOR]

This paper discusses the back-flashover (BFO) phenomena in a high-voltage (HV) vertical double-circuit transmission line by means of numerical simulations. The single- and multiphase BFOs are analyzed considering 24 cases of AC-voltage angles and nonlinear characteristic of flashovers, taking advantage of the circuit analysis method in time domain. The models used in circuit analysis are the TEM-delay model, which can take into account the non-TEM characteristics of tower and line, and the conventional models. Prior to the BFO analysis, the characteristics of circuit models are compared and discussed with the numerical electromagnetic analysis results. In BFO analysis, it is clarified that the occurrence probability of the multiphase BFO is heavily depending on the bias AC-voltages. The relation of measured BFO phases and AC-voltages are well explained by the TEM-delay model with the valid lightning current magnitude. The conventional circuit models could have underestimated the occurrence of BFOs in HV transmission lines. The analysis and discussions shown in this paper can be utilized for advanced evaluation of lightning performance of transmission lines that considers the seriousness of lightning accidents. [ABSTRACT FROM AUTHOR]

This paper presents a fast analytical model for estimating the components of the PM flux density distribution in the air-gap for a number of interior permanent magnet synchronous machines (IPMSMs). Deriving the two-dimensional (2-D) analytical model for IPMSMs is more challenging compared to that of surface-mounted permanent magnet synchronous machines (SPMSMs) due to the inconsistent geometry of the rotor in polar coordinates. IPMSMs are usually modeled by using 0-D or 1-D methods such as the magnetic equivalent circuit (MEC); however, the MEC method is unable to take into account the tangential component of the magnetic flux density vector. In this paper, a 2-D analytical no-load electromagnetic model for five rotor topologies of IPMSMs is proposed. The effects of the stator slots on the radial component of the PM flux density distribution in the air-gap are then included by defining an air-gap function. The effects of the stator slots on the tangential components of the PM flux density distribution in the air-gap are also considered by injecting virtual surface currents (VSCs) or virtual permanent magnets (VPMs). For verification purposes, the analytical results are compared with those of the finite element method (FEM) and the experimental results of one of the cases. [ABSTRACT FROM AUTHOR]

Magnetic gears can perform the same function as mechanical gears with the added benefits inherent to contactless power transfer. However, quick and accurate analysis tools are required for magnetic gears to reach their full potential. As end effects can significantly impact the slip torque of a magnetic gear, 2D models often overestimate the slip torques, so 3D models are frequently required. Therefore, this paper proposes a 3D linear Magnetic Equivalent Circuit (MEC) or reluctance network model of radial flux magnetic gears with surface mounted magnets. This is an extension of a previously developed 2D MEC model, and, like the previous 2D model, it is thoroughly parametrized so that it can be directly applied to a wide range of parametric cases. This is Part I of a two-part paper and focuses on the implementation of the 3D MEC model. Part II compares the 3D MEC model against nonlinear finite element analysis (FEA) models to validate the MEC model's accuracy and to develop guidelines for discretizing the geometry. [ABSTRACT FROM AUTHOR]

This paper proposes a linear regression model for scalar-valued responses and two-way functional (bivariate) predictors. Our motivation stems from the quality evaluation of products based on optical emission spectroscopy data from virtual metrology of semiconductor manufacturing. We focus on multivariate cases where the smoothness and shapes of the data vary significantly across variables. We propose a two-step solution to this problem, consisting of decomposition and prediction. First, we decompose the two-way functional data into pairs of component functions using functional singular value decomposition. Next, we build functional linear models for the decomposed functional variables and obtain the final predictor by averaging the models. Results from numerical studies, including simulation studies and real data analysis, demonstrate the promising empirical properties of the proposed approach, especially when the number of predictors is large. [ABSTRACT FROM AUTHOR]

The laser is used in several classic mechanical applications, such as: cutting, welding, surface treatments, etc. In the same way, laser shock peening (LSP) is an innovative surface treatment that exploits the mechanical effect of the application of pulsed laser radiation. Consequently, this generates work hardening and plastic deformation in depth. This process is implemented in different mechanical fields such as: defence, automotive, micro electromechanical systems, mechanical engineering and more particularly aeronautics. This review paper presents a generality on the LSP treatment and some of its applications. The factors of this process are displayed. Moreover, this study discusses the effects of laser shock peening on the mechanical and metallurgical properties, surface roughness, damage, and fatigue resistance of treated parts. A comparison between the LSP process and the shot peening is also exposed. As well, the various existing analytical and numerical models which allow the prediction of induced effects are analysed and compared. Moreover, the challenges and the limitations of the LSP treatment are highlighted. [ABSTRACT FROM AUTHOR]