Miracle-dz/UniFAR-FTUs huggingface dataset:
Improved Machine Learning Strategies and Algorithms for Transmembrane Potential Estimation in Homogeneous Medium

Introduction. Research on improved methods for the inverse problem in cardiology and electrophysiology is active today. Recently, the use of machine learning methods has been proposed, allowing us to consider the biophysical equations of the problem. In this work, we propose to explore improvements in using kernel methods for estimating the inverse problem, with kernel given by the Green's function for the infinite homogeneous potential and with recently proposed cross-validation strategies for least squares estimation. Materials and Methods. Three solvers were implemented: least squares with Zero-Order Tikhonov (ZOT) regularization, Support Vector Regression (SVR), and constrained L2 (CL2) optimization. The study evaluates transmembrane action potential estimation in 1D (fiber) and 2D (tissue) simulations using the Luo-Rudy model. Experiments and results. The ZOT method was the most unstable. The SVR method provided biased results, although intermediate and acceptable accuracy, except at the edges of spatial action potentials. The CL2 method provided better performance under certain conditions of the implemented cross-validation procedure. Conclusions. Kernel methods, with refined algorithmic formulations and cross-validation criteria, offer an alternative for cardiac inverse problem estimation.
Artificial Periodic Structures for Bandwidth and Gain Enhancement of Vivaldi Antennas used in GPR Applications

In this research, a Vivaldi antenna was designed for ground penetrating radar (GPR) applications which can work well at two band frequencies from 2.53-3.87 GHz and 5.276. 34 GHz. The antenna is made from an FR 4 substrate with a thickness of 1.65 mm, $\epsilon _{r}=4.9$ and a copper patch material with a thickness of 0.035 mm. The basic antenna design has dimensions of 69x69 mm. To improve the performance of the antenna, an artificial periodic structure (APS) made of copper with a thickness of 0.035 mm was added which aims to widen the bandwidth, increase the gain and reduce the antenna sidelobe level. The simulation results show that the addition of 1x6 single structure APS can effectively widen the antenna’s bandwidth to 400 Mhz (30%) wider than the basic Vivaldi design, increase the gain by 1.41 dB (9.7%) and reduce the sidelobe by 0.4 dB (5.5%). Meanwhile, the addition of 2x6 single structure APS can expand the bandwidth by 460 MHz (35%) from the basic antenna design. The best performance is achieved by an antenna design with a 1x6 single APS structure.
Comparative Analysis of Unsupervised Machine Learning Algorithms for Anomaly Detection in Network Data

The prevalence of cyberattacks has been increasing annually, emphasizing the need for data-driven approaches for the prevention and mitigation of attacks. Analysis of network data facilitates the acquisition of usage information, thereby enabling the detection of anomalous behavior that deviates from established patterns. The identification of such anomalies may indicate potential threats to the network and may assist security management systems in preempting and mitigating potential threats. The present study employs unsupervised machine learning algorithms with dimensionality reduction to detect anomalies in the NSL-KDD network dataset. The data is labeled although the real network datasets are unlabeled. We defined and applied a methodology to detect anomalies in the dataset. In the study, we used the following unsupervised algorithms: Local Outlier Factor (LOF), Elliptic Envelope (EE), SGD One Class SVM (SGD) and Isolation Forest (IF). For the kind of data we analysed, the unsupervised learning algorithm that obtained the highest AUC and F1-score evaluation rate was the Elliptic Envelope.
Subject-Specific Ablation of Pathologic Conduction Patterns Beyond the Pulmonary Veins: A Personalised Modelling Approach

Improving patient outcomes with ablation of non-paroxysmal AF (PsAF) has proved challenging using a population-based treatment approach due to large interindividual variability in the underlying electroanatomical substrate. Ablation of pathologic conduction patterns outside of pulmonary vein isolation (PVI) has recently shown encouraging results in PsAF patients returning for their first or second retreatment (76% freedom from AF recorded in the RECOVER AF trial). However, the optimal targets and best sequence of ablation lesions are still unknown, and testing different sequences, types, and methods of ablation cannot be performed clinically on a single patient or patient cohort. Considering the predictive potential of computational modelling, a small exploratory subset of patients ($N=4$) enrolled in the ongoing DISCOVER trial was used to create patient-specific models of left atrial electrophysiology. The subject-specific models displayed a high correlation between simulated targets and clinical targets. AF complexity was highest in all patients prior to therapy. PVI caused a marginal decrease in complexity across the cohort whereas $PVI+PCP$ showed an extensive decrease in the AF complexity across the patients and resulted in AF termination in all patients.
ECG Morphology-Based Markers for Risk Stratification in Hypertrophic Cardiomyopathy

Hypertrophic cardiomyopathy (HCM) is the leading cause of sudden cardiac death in young adults. Current risk markers for this heterogeneous disease lack performance and, thus, new approaches are needed. This study aims to enhance our understanding of risk assessment in HCM patients analyzing ECG-based markers in 24-hour Holter signals in a retrospective study dividing patients in asymptomatic, at-risk of a cardiac event and after a cardiac event. We studied conventional ECG markers such as RR interval, QRS width (QRSw) and corrected QT and T-peak-to-T-end intervals, which were computed for each patient from representative median beats every hour. First, the median marker values in the 24 hours were compared between groups. Second, variations in these markers between day and night were studied. All patients showed marked circadian variations in RR and QT time series. Patients at risk of suffering cardiac events were found to have wider QRS complexes, with statistically significant differences between day and night. This QRS prolongation in HCM patients, occurring months before suffering a cardiac event, might indicate anomalies in ventricular conduction. Regarding day-night variations, these were slightly greater in patients before an event. This work provides new evidence on ECG-based markers and encourages further research on QRS-wave and T-wave assessment.
Towards a Flexible Hardware Implementation for Mixed-Radix Fourier Transforms

The discrete Fourier transform is a versatile math-ematical kernel widely used in a myriad of applications from physics, chemistry, and even machine learning. While most applications typically rely on power of two sizes for which high performance implementations have been developed as software libraries or custom hardware IPs, there are codes in chemistry or even machine learning that require non-power of two or even prime-sized Fourier transforms. Classic prime-sized Fourier transform implementations are more complicated and less per-formant both in software and in hardware compared to the power of two implementations. Recent work [1] has shown that casting small prime-sized Fourier transforms as specialized matrix operations, competitive codelets can be developed on CPU platforms. In this work, we focus on designing the corresponding hardware unit for prime-sized Fourier transforms and integrating the custom design within any mixed-radix Fourier transform. We provide a detailed analysis of the design and investigate the latency, throughput, and resource utilization on Xilinx FPGAs for the both standalone and mixed-radix designs. We show that even oddly shaped Fourier transforms can be appealing when building custom hardware designs for general size Fourier transforms.
Wind-speed Forecasting based on Smoothing Ensemble Empirical Mode Decomposition and LSTM

One of the main challenges in power generation in wind farms is to forecast wind accurately. This is due to the non-stationary and non-linearity characteristics of wind data. These characteristics make it difficult for the normal statistical methods and the common statistical and computational intelligence methods to provide adequate predictions for wind speed. Empirical Mode Decomposition (EMD) is designed to de-compose non-stationary and non-linear data into their embedded components. In this study, we used a hybrid method composed of an improved variation of EMD, Smoothing Ensemble Empirical Mode Decomposition (SEEMD), and long-short-term memory neural networks (LSTM) to predict wind data. The results of this show that the proposed method provides better forecasting compared to the existing ones.
Environmentally Controlled Electroluminescence/Photoluminescence Imaging System with Current Density-Voltage Capabilities for Quantitative Degradation Analysis of Perovskite Thin Film Solar Cells

Luminescence-based measurement techniques, such as electroluminescence (EL) and photoluminescence (PL), are great methods to evaluate the quality of solar cell materials and their electric contacts. Furthermore, imaging these responses can provide insights into the spatial character of the samples. In this work, we discuss and demonstrate the ability of our unique EL/PL system equipped with light/dark current density-voltage (JV) measurement capabilities built to observe the degradation mechanism of perovskite minimodules and the small area devices. Here, we report the fabrication of this system including the software capabilities and analysis methods. We briefly demonstrate the capabilities by presenting EL images of perovskite minimodules before and after stressing under constant current conditions. By converting the EL image into a histogram plot we provide a pathway for quantitative analysis of the EL images as a function of degradation time. In addition, we demonstrate EL, PL, and JV degradation as a function of time of four small area perovskite devices measured together at an elevated temperature. We show the acquired EL and PL images and how these and the device efficiency change as a function of time. These examples demonstrate the capability of the system and show that by repeating these measurements at multiple temperatures the degradation mechanisms and activation energies can be investigated.
Optimizing Compression Schemes for Parallel Sparse Tensor Algebra

This paper studies compression techniques for parallel in-memory sparse tensor algebra. We find that applying simple existing compression schemes can lead to performance loss in some cases. To resolve this issue, we introduce an optimized algorithm for processing compressed inputs that can improve both the space usage as well as the performance compared to uncompressed inputs. We implement the compression techniques on top of a suite of sparse matrix algorithms generated by taco, a compiler for sparse tensor algebra. On a machine with 48 hyperthreads, our empirical evaluation shows that compression reduces the space needed to store the matrices by over 2 x without sacrificing algorithm performance.
FPGA-Based Digital Twin Implementation for Power Converter System Monitoring

Implementing closed-loop control requires ensuring a robust system response against undesired disturbances and random fault events. To overcome these challenges, the controllers must be able to detect and adapt the process behavior against undesired events by adjusting its parameters accordingly. Developers have utilized the concept of a digital twin—a real-time representation of the physical process—to design and update the physical controller effectively. However, traditional digital twin implementations often involve significant data exchange between the digital system and the real asset through cloud platforms, leading to data latency and privacy issues. To mitigate these concerns, we propose an FPGA-based digital twin implementation where the information is directly sourced into the Digital Twin from the physical asset, which runs in parallel with the real system. This setup eliminates the need for big data transfers and cloud uploads, ensuring enhanced data privacy and facilitating a faster and more efficient digital twin implementation and update process. To demonstrate the capabilities of embedded Digital Twin, we present a case study involving monitoring a power converter system during a sensor fault scenario.
A Survey on Smart Grid and its Applications

Environmental issues have become an important challenge for human development, the application of renewable energy will greatly alleviate this problem. The introduction of renewable energy into the existing power grid will has many impacts such as energy distribution, communications and security issues, it is bound to intelligently upgrade the existing power grid. By querying relevant literature, this study summarizes and reviews the current research on smart grids from the above perspectives. At the same time, from the perspective of two emerging technologies, Internet of Things (IoT) and artificial intelligence, the application of smart grid to two emerging technologies is summarized. Finally, the possible development directions of smart grid are discussed and summarized.
Leveraging Mathworks Tools to Accelerate the Prototyping of Custom 5G Applications in Hardware

The ubiquity and flexibility of 5G networks make it an attractive technology to customize for particular needs. In this work, we highlight some of the challenges associated with implementing custom 5G applications in hardware and the growing trend of utilizing high-level synthesis tools to relieve these issues. We present an overview of the 5G resource grid and the MATLAB-Simulink-HDL Coder workflow. We then demonstrate the workflow through an example 5G resource grid transmitter design.
Estimation of Learners' Levels of Adaptability in Online Education Using Imbalanced Dataset

The COVID-19 epidemic has had a huge impact on education, causing a quick move to online learning environments. Students had to adjust to a virtual learning environment as a result of this move, which brought new obstacles for them. The study looks at how adaptable students are in online learning and how it affects their academic performance and general learning process. This study aims to assess students' levels of adaptation to online learning settings and to pinpoint the factors that either support or hinder students' ability to adapt to this new learning environment. According to preliminary data, with respect to personal circumstances, students' adaptation in online learning differs greatly. The dataset used for the study is students_adaptability_level_online_education. csv is taken from Kaggle Machine Learning repository. The minority class in the dataset is equalized using the Synthetic Minority Oversampling Technique (SMOTE), as it comprises an unbalanced set of values. In order to equalize the distribution of data, the values for the minority class are increased at random. Dataset has been employed to predict the degree of student adaptability to online education through a number of machine learning algorithms, including Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbours (KNN) and xgb.XGBClassifier(). The Random Forest classifier had the greatest accuracy (92%), compared to those that were employed. The demand for customized and adaptable learning experiences grows as online education continues to transform the traditional learning environment. This study addresses the idea of student adaptivity in the education with an emphasis on how it may significantly enhance learning results for students from a variety of backgrounds and learning styles.
Research on Design Method of High Current Transmission Busbar for Superconducting Magnet Power Supply of Fusion Device

In this paper, a bipolar matrix laminated busbar based on water-cooled structure is proposed. By comparing the advantages and disadvantages of inductance calculation of circular conductor array laminated busbar, circular conductor coaxial transmission busbar and matrix laminated busbar, the matrix laminated busbar is chosen. Through the analysis of the influence of multi-variable parameters, laminated dislocation, water hole size, intermediate conductor layer and other different structures on the electromagnetic performance of the busbar, combined with the installation requirements, the optimal busbar structure is finally selected to provide a design reference for the large-scale popularization and application of bipolar matrix laminated busbar.
Crop Water Requirement using Single andDual Crop Coefficient Approach

The determination of crop coefficients and reference crop evapotranspiration are important for estimating irrigation water requirements of any crop in order to have better irrigation scheduling and water management. The purpose of this study is to determine the crop water requirement of cauliflower, using single and dual crop coefficient approach using FAO-56 Penman–Monteith Method. Meteorological data like maximum temperature, minimum temperature, relative humidity, sunshine hours and wind speed for Vadodara region are used to determine Reference crop evapotranspiration. Crop Coefficient (Kc) can be utilized as single Kc which is influenced by moderate effect of evaporation and transpiration together. Dual Kc is expressed by soil evaporation coefficient (Ke) and basal crop coefficient (Kcb), separately. Kc values for all growth stages are determined using both approaches. Micro irrigation systems are used to irrigate the fields. Cauliflower water requirements (ETc) are 256 mm and 237 mm for single and dual crop coefficient approach, respectively during the growing season. The study reveals that maximum differences between ETc of single and dual Kc values were observed at initial stage. The dual crop coefficient approach is more precise and it is best for real time irrigation scheduling, for soil water balance computations, and for research studies where effects of every day variations in soil surface wetness and the resulting impacts on daily ETc and the soil water profile are important. Dual crop coefficient approach is best suited for high frequency irrigation systems like micro irrigation systems. These results can be useful for agricultural planning and efficient management of irrigation for cultivation of cauliflower.
Recombination Analysis of Maxeon IBC Production Cells by Time-Resolved Photoluminescence

Photoluminescence is a powerful technique to analyze solar cell recombination across the manufacturing steps. However, this analysis requires accurate modelling of the phenomena caused by injection-dependent lifetimes. We present a transient photoluminescence technique that we apply to our current new-generation production cells. We extract the saturation current Jo and the bulk lifetime, in a refined version of the method developed by Kane and Swanson. We include band-gap narrowing effects occurring at the large operating voltages typical of our back-contact cells. Our approach results in excellent fittings of the carrier density decay (R2=99.9%). We find an area averaged Jo of 6.2 fA/cm2 and an inverse bulk Shockley-Read-Hall lifetime of 14 s−1 in a production cell measured after metal etching.
Deep Learning-Based Signal Quality Assessment in Wearable ECG Monitoring

Wearable electrocardiogram (ECG) monitoring is an effective method of screening for occult arrhythmia. However, signals from the wearable ECG monitoring device are often disturbed by various artifacts and noises originating from daily activities and which can significantly affect peak detection and ECG morphological feature extraction, leading to frequent false alarms for arrhythmia detection. Therefore, it is crucial to exclude ECG fragments with poor signal quality. In this study, we developed three xResNet-based ECG signal quality assessment models, trained on the Brno University of Technology ECG Quality Database. The first model can distinguish between ECG data in which the PQRST waves or only QRS complexes are visible from data in which these cannot be used for further analysis with a sensitivity (Se) of 98.87% and specificity (Sp) of 99.83%. The second model detects ECGs with visible PQRST waves with a Se of 97.15% and Sp of 95.95%. The third model classifies ECGs into data with PQRST visible, with only QRS visible, or unsuitable for analysis and achieves an accuracy (Acc) of 96.62%, 93.66%, and 98.97%, respectively. The results indicate that the proposed models can accurately evaluate the ECG signal quality during wearable monitoring, meeting the analysis requirements for arrhythmia.
Deep Occupational Representations Based on Career Mobility

To gain a comprehensive understanding of the complexity of occupations and their interconnections, the integration of information technology with occupational studies is essential. This necessitates the mapping of conceptualized occupations into a digital space. In the study, we model the complex connections between occupations by a career mobility network, and learn vector representations of occupations using network embedding. These representations are finally used to construct a digital occupational space. Specifically, we propose a network embedding model to represent nodes in the career mobility network. The model emphasizes occupation connections by defining path commonalities to preserve the main patterns of career mobility. Then, we combine random walks and deep learning to learn these patterns. The model can capture both network structure and occupational characteristics at the same time. We demonstrate the effectiveness of the proposed network embedding model in node classification and link prediction using real-world data, as well as provide evidence for the validity of the occupational space. Taken together, our work presents a feasible way to use advanced computing technology to learn task-independent occupational representations.
Developing a Machine Learning Pipeline for Predicting Neurological Outcomes in Comatose Cardiac Arrest Survivors Using Continuous EEG Data

As part of the ‘Predicting Neurological Recovery from Como After Cardiac Arrest: The George B. Moody PhysioNet Challenge 2023’, we propose a two-step approach, which comprises i) a custom convolutional neural network designed to classify Cerebral Performance Categories (CPC) based on 30-second EEG signals from 19 channels, and ii) random forest models that combine the logits obtained in the previous step with the patient metadata to predict the CPC values and the patient outcome. The final predictions for patient CPC and outcome are determined through a time-weighted average of EEG window prediction. Our submission achieved challenge metric scores of 0.351, 0.386, 0.431, and 0.475 for the prediction outcome at 12h, 24h, 48h, and 72h, respectively. These scores placed the AIMED team in the 20th position in the rank of the official phase. Our study highlights the potential of a machine learning pipeline to predict neurological outcomes in comatose cardiac arrest survivors. This is achieved through a straightforward methodology that combines features extracted from EEG signals with patient metadata.