The code and data that support the findings of this study are available from the corresponding author upon reasonable request.

This work aims to develop an adaptive remeshing procedure for finite element method on electromagnetic computations. A thorough comparison of metric computation strategies is carried out as it constitutes a cornerstone of this developments. This procedure will focus on the mesh size adaptation to distribute the error uniformly over a computational domain, in order to obtain a user-prescribed accuracy of the solution. Also, it shall enable dealing with complex geometries for electromagnetic-coupled material processing applications. For this purpose, a quasi-steady state approximation of the Maxwell’s equations in a time-domain formalism is considered. The automatic remeshing procedure is based on the following key steps: An a posteriori error estimator to pinpoint the critical areas needing refinement or allowing coarsening. An anisotropic metric approximation. Both steps use a global field recovery algorithm in order to enable robust gradient computation. Finally, several 3D test cases are presented.

This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible. Online monitoring of mechatronic systems and production environments helps operators to steer their processes into regions of optimal performance. Filtering methods, such as the Kalman filter, emerge in this setting thanks to their ability to process sensor data in real-time. Extensive engineering efforts are required to attain a behavioral model on the system dynamics for this filter. What is more, the model needs updating when facing varying conditions that are prevalent in mechatronic and production systems during operation. At the same time, datadriven methods excel in adapting models to match measurements without relying heavily on expert knowledge, but suffer from main drawbacks such as the lack of interpretability and the batchwise approach. This paper proposes a novel methodology relying on two key ideas. First a dual extended Kalman filter allows to jointly estimate and adapt model parameters in an online setting. Secondly, within this filter a hybrid model is embedded that depends on physics-based and data-driven parameters that are being updated, leading up to increased prediction capabilities and hence improved estimations on the state of the system. We evaluate the presented hybrid dual Extended Kalman filter on a cam follower system where it shows the ability to learn the cam shape during operation whilst tracking the state with an increased accuracy of more than 22% compared to other filtering techniques.

This paper presents a way in which Artificial Intelligence (AI) might contribute to the concept development stage of engineering design or other contexts involving ideation. We introduce the notion of a “Disruptive Interjector” (DI), an AI system that observes a person’s actions, and makes suggestions based on what the user does; then demonstrate it’s application in a human subject study involving collaborative teamwork. Furthermore, we present the results of team performance in which teams of two Humans (HH) and teams of a Human and Computer (HAI) collaboratively sketch a design based on a prompt, observing that the HAI partnership produced ideas that were consistently rated higher (by trained human judges) than the HH teams. The paper contributes to AI research and practice in at least two ways. First, it informs designers of such systems on design considerations, especially as AI technologies become pervasive in different domains like engineering design and others. Secondly, in addition to presenting a replicable method for conducting studies involving an AI, it contributes to research by providing empirical evidence of an AI outperforming a human as a collaborative partner on a concept development task.

Quantum dot light-emitting diode (QDLED) displays promise superior color gamut with respect to organic light-emitting diode (OLED) displays and liquid-crystal displays (LCDs).  In this paper, for the first time, a data-validated closed-form physics-based expression is derived for the I-V characteristics of QDLEDs. The I-V equation is derived using an approximation scheme which not only brings the previously intractable transport mechanisms back into the fold of analytic tractability but emphasizes the underlying physics of each transport mechanism in doing so. The model is shown to fit state-of-the-art experimental data over a large range of operating currents. The model may be used for circuit simulations.

Surface electromyography (sEMG) is a non-invasive technique that measures the electrical activity generated by the muscles using sensors placed on the skin. It has been widely used in the field of prosthetics and other assistive systems because of the physiological connection between muscle electrical activity and movement dynamics. However, most existing sEMG-based decoding algorithms show a limited number of detectable degrees of freedom that can be proportionally and simultaneously controlled in real-time, which limits the use of EMG in a wide range of applications, including prosthetics and other consumer-level applications (e.g., human/machine interfacing). In this work, we surpass the current state of the art by developing a new deep learning method that can decode and map the electrophysiological activity of the forearm muscles into proportional and simultaneous control of > 20 degrees of freedom of the human hand with real-time resolution and with latency within the neuromuscular delays (< 50 ms). We recorded the kinematics of the human hand during grasping, pinching, individual digit movements and three gestures at slow (0.5 Hz) and fast (0.75 Hz) movement speeds in healthy participants. We demonstrate that our neural network can predict the kinematics of the hand in real-time at a constant 32 predictions per second. To achieve this, we employed transfer learning and created a prediction smoothing algorithm for the output of the neural network that reconstructed the full geometry of the hand in three-dimensional Cartesian space in real-time. Our results demonstrate that high-density EMG signals from the forearm muscles contain almost all the information that is needed to predict the kinematics of the human hand. The proposed method has the capability of predicting the full kinematics of the human hand in an unprecedented way and with real-time resolution with immediate translational impact in subjects with motor impairments.

A simple, four-parameter formula is proposed and used to smooth noisy transistors  curves for the purpose of stable extraction of transistor field-effect mobility and threshold voltage.  The proposed smoothing shape function is found to work for  data obtained for short and long transistors, low and high drain voltage, and for TFTs, ET-SOI, and Trigate MOSFETs

This paper elaborates on new techniques for weather radars and their relative merits with respect to a similar phased array configuration. As will be shown in paper that these techniques give spatial resolution improvement. This is the major benefit and a number of smaller benefits which are elaborated here for weather radar system.

Against the backdrop of the climate crisis, countries worldwide agreed in the Paris agreement to keep global warming below 2°C compared to the pre-industrial era, ideally below 1.5°C. The EU set out the goal to reach carbon neutrality by 2050 - a goal for which the energy sector will play a central role, as the production and use of energy is responsible for more than 75% of greenhouse gas emissions. To transition to low carbon energy production, photovoltaic and wind turbines, coupled with energy conversion technologies such as electrolysers, fuel cells, and batteries, are believed to be key solutions. But these technologies require substantial amounts of scarce raw materials with EU import dependency and environmental and social problems connected to extraction and refining. The vulnerability of the EU energy sector became apparent in 2022 at the example of its dependency on Russian oil and gas. The supply chain for clean energy technologies could be equally vulnerable and jeopardise the achievement of EU climate targets. A plethora of studies dealing with resource scarcity has been performed. These studies, however, differ in their results regarding the most critical materials and often only provide vague recommendations on how to increase the resilience of the production and supply chains. Hence, it is of interest to synthesize the findings of major studies from renowned institutions, identify commonalities as well as differences, filter out areas with need for immediate action and create an overview of critical materials in climate technologies.

In systems and networks where overflow is penalized, careful planning is required to prevent a small increase in the offered load from suddenly triggering a system collapse. In this work, we consider an overflow loss system where the service time distribution of a request is dependent on the number of overflows. We propose a novel admission control policy for improving the stabilization and performance of such a system based on a new information exchange mechanism (IEM) for congestion information, in which only a small amount of congestion information is carried by each request and is propagated via the overflow of requests. This avoids the need for an external mechanism for propagating congestion information. Furthermore, IEM is fully compatible with trunk reservation, a simple, classical, yet effective method of admission control in overflow loss systems and networks. Despite the very limited amount of communication overhead required by IEM (only a few bits per request), numerical results demonstrate that the combination of IEM with trunk reservation provides greater performance and stability than trunk reservation alone. We also present a computationally efficient analytical performance evaluation method based on IEM and demonstrate numerically that it is asymptotically exact and generally quite accurate. Therefore, the analytical method can be used for system design and planning.

Classification in streaming data with emerging new classes (SENC) is challenging because the surrogate models trained on the closed datasets are open to new information never seen before. The fundamental sub-tasks include: (1) Pattern recognition of these already known classes with high accuracy; (2) Timely identification of emerging new classes; (3) Model update to adapt to new classes and (4) distinguish different new classes.  Existing SENC frameworks utilize predefined threshold to set the ambiguous decision boundaries between the known and emerging new classes, they have poor performance in distinguishing different emerging new classes. To this end, a novel SENC framework leveraging Generative Adversarial Network (GAN) and history-state ensemble method is proposed in this paper, which is referred to as ensemble multi-classification GAN (EMC-GAN). Compared with traditional SENC frameworks, the proposed method is designed based on deep learning, thus, it can automatically explore implicit features from the raw data without human intervention. Besides, it doed not require pre-set threshold for novelty detection. The proposed method is evaluated on the condition monitoring of rotating machinery - a typical SENC case in real-world industry. Experimental results show that the proposed SENC framework can not only effectively detect emerging new classes from known ones, but also exhibit excellent performance in distinguishing different new classes.

ChatGPT is a natural language processing model based text-to-text AI interactive conversation platform developed using generative pre-trained architecture (GPT) by OpenAI. The present study utilizes the ChatGPT to understand the characteristic mode analysis-a popular numerical technique for antenna and array design in recent times. It was noted that ChatGPT can provide a basic understanding of characteristic mode theory in an elegantly simplified text language. However, being a data-driven model, its accuracy strongly depends on the data used to train the model, low accuracy is noted on predicting the names of the reference papers or related journal authors correctly in most cases. Another major limitation of using chatGPT is the lack of plagiarism check as it chooses sentences based on the training data resources.

Objective: Orthostatic intolerance (OI) is common in Long Covid. Physical counterpressure manoeuvres (PCM) may improve OI in other disorders. We characterised the blood pressure-rising effect of PCM using surface electromyography (sEMG) and investigated its association with fatigue in adults with Long Covid. Methods: Participants performed an active stand with beat-to-beat hemodynamic monitoring and sEMG of both thighs, including PCM at 3-minutes post-stand. Multivariable linear regression investigated the association between change in systolic blood pressure (SBP) and change in normalised root mean square (RMS) of sEMG amplitude, controlling for confounders including the Chalder Fatigue Scale (CFQ). Results: In 90 participants (mean age 46), mean SBP rise with PCM was 13.7 (SD 9.0) mmHg. In regression, SBP change was significantly, directly associated with change in RMS sEMG (β=0.25, 95% CI 0.07–0.43, P=0.007); however, CFQ was not significant. Discussion/Conclusion: PCM measured by sEMG augmented SBP without the influence of fatigue.

Unbalanced optimal power flow refers to mathematical optimization problems subject to the physics of grids with phase unbalance. They can serve as a core technology, or benchmark, for centralized or distributed optimal control solutions. In recent years, convex relaxations of this problem have attracted significant interest in the community. For many common test feeders, it turned out they return exact solutions. However, more research is needed to study how well this holds up in more complicated modelling contexts. This paper therefore studies gang-operated on-load tap changers in detail. Analogous to balanced optimal power flow, it is possible to reformulate the problem exactly without an explicit tap variable. This is a non-trivial result that is discussed in detail. Furthermore, this paper describes in detail how a solution in the lifted variable space can be projected back to the original variable space. Best case, it then becomes possible to solve the problem as a convex optimization problem instead. Worst case, it provides a lower bound on the objective value. Two numerical case studies, a simple test case and IEEE13, are included. The case studies are made publicly available.

Robotic tasks featuring interaction with other bodies are increasingly required in industrial contexts. The manipulators need to interact with the environment in a compliant way to avoid damage, but, at the same time, are often required to accurately track a reference force. To this aim, interaction controllers are typically employed, but they either need human tinkering for parameter tuning or precise modeling of the environment the robot will interact with. The former is a time-consuming procedure, while the latter is necessarily affected by approximations, which often lead to failure during the actual application. Both these aspects are problematic if it were often necessary to change the contact environment.Current research is concentrating on devising high-performance force controllers that are simple to tune and quick to adapt to changing environments. Along this line, this work proposes a novel control strategy, that we term ORACLE (Optimized Residual Action for interaction Control with Learned Environments). It exploits an ensemble of neural networks to estimate the force generated by the robot-environment interaction. This estimate is input to an optimal residual action controller that locally corrects the main action, output of a base force controller, which guarantees stability. The ORACLE strategy has been implemented and tested in the MuJoCo dynamic simulator and in a real-case scenario, both foreseeing a Franka Emika Panda robot used as a test platform. A reduction in terms of force tracking error is achieved by deploying the proposed strategy, with a short setup time.

Real estate prices are an important reflection of the economy and their prices are great interest to both buyers and sellers. Hundreds of houses are sold every day and the buyer asks himself what is the reasonable price that this house deserves. In this paper, a new regression model is proposed for the accurate prediction of house prices. This model is based on the hybrid recurrent neural network where the Gated Recurrent Unit (GRU) is fused with the Long Short-Term Memory (LSTM) and applied to a particular dataset that characterizes houses in Boston. Massachusetts dataset from Scikit-learn is used in this research to train and evaluate this regression model using the data on Boston housing. Several experiments were conducted on the proposed algorithm and evaluated with the commonly used metrics. The results of these experiments showed that the proposed model has better performance when the networks are used in the fusion process than when they act individually. It also has better accuracy and lower Root Mean Square Error when compared to several states of art methodologies.

Our study aims to closely analyze the professional uses of ICT by primary school teachers in Tunisia while trying to assess the various factors associated with this use to understand the causes of these differences. Initial theoretical work was necessary to define the concept of usage and present the various factors associated with it in the scientific literature. Then, we conducted a quantitative study with 350 French Language teachers in the primary cycle in Tunisia. The results revealed a weak ICT integration correlated to a set of personal and institutional factors. However, these correlations, by themselves, are not significant enough to explain these low uses\sout. This allows us to explore new approaches in relation to the social dimension of the concept of usage, which considers the teacher as an active social actor who constructs his or her uses in interaction with other actors, hence the importance of considering collective dynamics in the construction of teachers’ professional uses of ICT.

The development of OLED displays is a non-ergodic process in which there is continuous novel change; and comprehending emitters that are evolving requires new theory, or at least modification of that which is already possessed.  For the first time, a new data-validated closed-form physics-based expression is derived for the I-V characteristics of OLEDs in this paper. Two parameters emerge from the new model: liminal current and liminal voltage.  Both parameters are expressed in terms of device and material parameters, allowing a device engineer to intuitively design experiments to target low voltage operation OLEDs.  Robust parameter extraction methods are proposed and validated with experimental data