Miracle-dz/UniFAR-FTUs huggingface dataset:
Design and optimization of cell and field limiting ring termination for 1200 V 4H-SiC Junction Barrier Schottky (JBS) Diodes

Compared with Si, SiC has advantages of wide band gap, high critical electric field, high electron saturation velocity and high thermal conductivity, making SiC power devices develop rapidly in recent years. In the field of power diode, junction barrier Schottky (JBS) diode not only inherits the advantages of Schottky diode with high switching speed and low reverse recovery current, but also greatly improves the reverse breakdown voltage without increasing the leakage current of the device. In this paper, a 4H-SiC JBS with a breakdown voltage of 1200V was designed and simulated. The termination of the device adopts the field limiting ring (FLR) structure, and the parameters of the cell and FLR termination protection structure are optimized. With the optimized structure parameters, the TCAD simulation results show the reverse breakdown voltage of the designed 4H-SiC JBS can reach 1676.2 V without considering the terminal protection efficiency. And the breakdown voltage can reach 1542.7 V when considering the FLR termination structure. The termination protection efficiency is up to 92%.
On the parametrization of self-similar and other fractal sets

We prove for many self-similar, and some more general, sets E C in that if s is the Hausdorff dimension of E and f :]RI -* Rn is H6lder continuous with exponent m/s, then the s-dimensional Hausdorff measure of Enff(Rm) is o.
Virtual trajectory tracking of a manipulator robot for Milling Assistance: Application to comanipulator in orthopedic surgery

Milling of bone volumes during some orthopedic surgery such as laminectomy is a delicate step to be performed with great precision. In conventional surgery, only X-rays of the patient can identify the areas to be removed and the areas to be protected. In this work, we propose a method of tracking a trajectory in geometric model control modes of the robot to assist and secure this type of milling. The study of the different approaches of mathematical modeling of the manipulator robot where it was a question of describing the geometrical modeling, in order to orient the arm with acceptable precision compared to its real environment. This proposed solution imposes virtual constraints, defined according to a particular effort profile, on the working environment of the user in order to separate the workspace into a free region and a forbidden region. The contribution of the device, in terms of precision, is evaluated experimentally by milling geometric shapes (square, triangular …).
Data-Driven Supervised Compression Artifacts Detection on Continuous Glucose Sensors

Continuous Glucose Monitoring (CGM) sensors micro-invasively provide frequent glucose readings, improving the management of Type 1 diabetic patients' life and making available reach data-sets for retrospective analysis. Unlikely, CGM sensors are subject to failures, such as compression artifacts, that might impact on both real-time and respective CGM use. In this work is focused on retrospective detection of compression artifacts. An in-silico dataset is generated using the T1D UVa/Padova simulator and compression artifacts are subsequently added in known position, thus creating a dataset with perfectly accurate faulty/not-faulty labels. The problem of compression artifact detection is then faced with supervised data-driven techniques, in particular using Random Forest algorithm. The detection performance guaranteed by the method on in-silico data is satisfactory, opening the way for further analysis on real-data.
A new Sliding-Mode Control law for a Planar Dielectric Elastomer Actuator to Track wide-Band Trajectories

Dielectric Elastomer Actuator (DEA) based soft robots are gaining widespread recognition for numerous applications such as; medical devices, bio-inspired systems and flexible prosthetics to name but a few. Due to their nonlinearity relating mostly to hysteresis and creep, most DEAs are subject to precision positioning challenges. To enable precise positioning, control-enabling DEA models and controllers are developed to curb the issues of their nonlinear dynamics. This paper presents a new Sliding-Mode Control (SMC) based on reaching law for precise position tracking of an adopted simplified and experimentally validated DEA model. The proposed reaching law incorporates a high-slope saturation function with an exponential denominator to drastically reduce chattering and enhance fast and finite convergence. The sliding-mode controller stability is validated using a Lyapunov candidate for asymptotic stable. Finally, Closed-loop simulations demonstrating the controller's effectiveness at suppressing chattering and tracking wide-band trajectories (triangular and sinusoidal) were implemented.
Semantic Segmentation of Micro-CT Images to Analyze Bone Ingrowth into Biodegradable Scaffolds

The healing of bone fractures is a complex and well-orchestrated physiological process, but normal healing is compromised when the fracture is large. These large non-union fractures often require a template with surgical intervention for healing. The standard treatment, autografting, has drawbacks such as donor site pain and limited availability. Biodegradable scaffolds developed using biomaterials such as bioactive glass are a potential solution. Investigation of bone ingrowth into biodegradable scaffolds is an important aspect of their development. Micro-CT $(\boldsymbol{\mu}-\mathbf{CT})$ imaging is widely used to evaluate and quantify tissue ingrowth into scaffolds in 3D. Existing segmentation techniques have low accuracy in differentiating bone and scaffold, and need improvements to accurately quantify the bone in-growth into the scaffold using $\boldsymbol{\mu}-\mathbf{CT}$ scans. This study proposes a novel 3-stage pipeline for better outcome. The first stage of the pipeline is based on a convolutional neural network for the segmentation of the scaffold, bone, and pores from $\boldsymbol{\mu}-\mathbf{CT}$ images to investigate bone ingrowth. A 3D rigid image registration procedure was employed in the next stage to extract the volume of interest (VOI) for the analysis. In the final stage, algorithms were developed to quantitatively analyze bone ingrowth and scaffold degradation. The best model for segmentation produced a dice similarity coefficient score of 90.1, intersection over union score of 83.9, and pixel accuracy of 93.1 for unseen test data.
A Load Balancing and Routing Strategy in Fog Computing using Deep Reinforcement Learning

Fog computing is proposed to overcome cloud computing limitations by extending its services close to the network edge. However, fog systems are still facing many challenges due to their large-scale distributed architecture and small resource power. We investigate in this paper resource management in fog computing and propose a load balancing strategy using Deep Reinforcement Learning (DRL) combined with a link-state routing protocol based on the Dijkstra algorithm. The objective of the strategy is to simultaneously minimize the tasks’ processing and communication delay. The proposed solution involves designing a Load Balancer Smart Controller (LBSC) which engages a smart DRL agent in a fog environment. The LBSC examines the nodes and links’ states in order to make the optimal decision by selecting the most suitable node and path to process each task. The performance of the proposed approach is tested in a dynamic IoT environment with a high-rate workload generation scenario. Simulation results show that the average total latency including processing and communication delays of the proposed method is reduced by around 50% in comparison to the classic load balancing algorithms.
An ICA-based framework for joint analysis of cognitive scores and MEG event-related fields

This paper proposes an independent component analysis (ICA)-based framework for exploring associations between neural signals measured with magnetoencephalography (MEG) and non-neuroimaging data of healthy subjects. Our proposed framework contains methods for subject group identification, latent source estimation of MEG, and discriminatory source visualization. Hierarchical clustering on principal components (HCPC) is used to cluster subject groups based on cognitive scores, and ICA is performed on MEG evoked responses such that not only higher-order statistics but also sample dependence within sources is taken into account. The clustered subject labels and estimated sources are jointly analyzed to determine discriminatory sources. Finally, discriminatory sources are used to calculate global difference maps (GDMs) for the summary. Results using a new data set reveal that estimated sources are significantly correlated with cognitive measures and subject demographics. Discriminatory sources have significant correlations with variables that have not been previously used for group identification, and GDMs can effectively identify group differences.
Feasibility of Image Reconstruction from Triple Modality Data of Yttrium-90

The recent implementation of the first clinical triple modality scanner in STIR enables investigation of the possibility of triple modality image reconstruction. Such a tool represents an important step toward the improvement of dosimetry for theranostics, where the exploitation of multi-modality imaging can have an impact on treatment planning and follow-up. To give a demonstration of triple modality image reconstruction we used data from a NEMA phantom that was filled with Yttrium-90 (90Y), which emits Bremsstrahlung photons detectable with SPECT as well as gamma rays that can go through pair production, therefore creating positrons that make PET acquisition possible. The data were acquired with the Mediso AnyScan SPECT/PET/CT. Different ways of including multiple side information using the kernelised expectation maximisation (KEM) and the Hybrid KEM (HKEM) were used and investigated in terms of ROI activity and noise suppression. This work presents an example of application with 90Y but it can be extended to any other radionuclide combination used in Theranostic applications.
Regional disparities and productivity in China: Evidence from manufacturing micro data

type="main" xml:lang="es"> En este articulo estimamos primero la totalidad de las productividades de los factores especificos de la empresa para industrias manufactureras de dos digitos, por medio de un algoritmo semi-parametrico y microdatos para el periodo 2000–2007. A continuacion, para caracterizar las disparidades regionales en China, calculamos la productividad agregada en las categorias de tres tipologias regionales, basadas en criterios de densidad de poblacion, zonas de costa e interior, y zonas rurales y urbanas. Se analizan los diferenciales de productividad entre las categorias de las tipologias mediante la descomposicion del nivel de productividad regional y el crecimiento en un efecto de productividad y un efecto de composicion industrial. Encontramos pruebas claras de convergencia regional. Ademas de la densidad de la actividad economica, las politicas recientes y los factores estructurales parecen afectar el nivel de productividad regional y los diferenciales de crecimiento.
A Cross-modality Deep Learning Method for Measuring Decision Confidence from Eye Movement Signals

Electroencephalography (EEG) signals can effectively measure the level of human decision confidence. However, it is difficult to acquire EEG signals in practice due to the ex-pensive cost and complex operation, while eye movement signals are much easier to acquire and process. To tackle this problem, we propose a cross-modality deep learning method based on deep canoncial correlation analysis (CDCCA) to transform each modality separately and coordinate different modalities into a hyperspace by using specific canonical correlation analysis constraints. In our proposed method, only eye movement signals are used as inputs in the test phase and the knowledge from EEG signals is learned in the training stage. Experimental results on two human decision confidence datasets demonstrate that our proposed method achieves advanced performance compared with the existing single-modal approaches trained and tested on eye movement signals and maintains a competitive accuracy in comparison with multimodal models.
Using Pre-stimulus EEG to Predict Driver Reaction Time To Road Events

The ability to predict a driver's reaction time to road events could be used in driver safety assistance systems, allowing for autonomous control when a driver may be about to react with sup-optimal performance. In this paper, we evaluate a number of machine learning and feature engineering strategies that we use to predict the reaction time(s) of 24 drivers to road events using EEG (Electroencephalography) captured in an immersive driving simulator. Subject-independent models are trained and evaluated using EEG features extracted from time periods that precede the road events that we predict the reaction times for. Our paper has two contributions: 1) we predict the reaction times corresponding to individual road events using EEG spectral features from a time period before the onset of the road event, i.e. we take EEG data from 2 seconds before the event, and 2) we predict whether a subject will be a slow or fast responder compared to other drivers.
An optimization method of package warpage simulation based on parameter calibration

With the development of advanced packaging technology, both the increase of die size and Chiplet technology will increase the package size. In addition, the number of substrate layers is also increasing, which will inevitably lead to greater package warpage. Excessive warpage will lead to failure problems such as substrate delamination. For ball grid array (BGA) packaging, it will also lead to solder joint failure such as bridging and false soldering in the process of welding the packaging chip to the PCB board, which will affect the yield of the board on the chip. Therefore, the accurate prediction of package warpage is particularly critical. In this paper, the warpage of package in the reflow process is analyzed by finite element analysis (FEA) with ANSYS. Firstly, the sensitivity of the material parameters used in the simulation is analyzed by optiSLang. The research shows that the main factor affecting warpage is the coefficient of thermal expansion (CTE) of lid, trace, core and PP. In addition, a new simulation method for metal Thermal Interface Material (TIM) indium is found, which simulates the melting process of indium at high temperature by adding birth and death units. The final simulation results are consistent with the measured direction of shadow moire, and the error is within 10%, which verifies the reliability of the simulation.
Cooperative Entrapping Method of Unmanned Swarm Based on Concentration Diffusion Model

Self-organization and distributed cooperation of the unmanned swarm play an important role in completing the task of entrapping multiple targets. However, the cooperative entrapping of the unmanned swarm faces three challenges of limited communication, large scale, and limited location. Inspired by the cell swarm, we solve this problem based on the concentration diffusion model, including a local communication algorithm under the condition of limited bandwidth, a motion control algorithm under the condition of limited motion dimension, and a distance-based self-positioning method. Using the real robots named kilobots that is widely used in the research field as the experimental platform, we carry out experiments including static target entrapping, dynamic target entrapping, and large-scale swarm entrapping. Through statistical analysis, the feasibility of our method in completing these three tasks is verified.
Estimation of Characteristic Impedance using Multi-Gaussian Modelled Flow Velocity Waveform: A Virtual Subjects Study

Characteristic impedance (Zc) of the blood vessel relates the pulsatile pressure to pulsatile blood flow velocity devoid of any wave reflections. Estimation of ZC is useful for indirect evaluation of local pulse wave velocity and crucial for solving wave separation analysis (WSA) which separates the forward-backward pressure and flow velocity waveforms. As opposed to conventional WSA, which requires simultaneous measurement of pressure and flow velocity waveform, simplified WSA relies on modelled flow velocity waveforms, mainly introduced for the aorta. This work uses a multi-Gaussian decomposition (MGD) modelled flow velocity waveform to estimate ZC by employing a frequency domain analysis, which is applicable to other arteries such as carotid. Thus obtained ZC is compared with Zc estimated from true flow velocity waveform for healthy (virtual) subjects taken for the carotid artery. The MGD modelled flow velocity waveform estimated ZC for a range of 4.98 to 34.79 with a group average of 16.43±0.10. The difference between the group average values of both ZC was only 4.72%. A statistically significant and strong correlation (r = 0.708, p < 0.0001) was observed for ZC obtained from MGD modelled flow velocity waveform with ZC obtained from actual flow velocity waveform. The bias for ZC1 between the two methods was 0.74, with confidence intervals (CIs) between 7.44 and -5.96 for the Bland-Altman analysis. Therefore, ZC from MGD modelled flow velocity waveform is a potential surrogate of the flow velocity model for WSA at the carotid artery. Clinical Relevance— This study provides a new method to derive characteristic impedance without the measurement of actual flow velocity waveform. The method requires a single pulse waveform (pressure or diameter)
Characterization of Physiologic Patients' Response to Fluid Interventions in the Intensive Care Unit

Fluid administration is one of the most common therapies performed on intensive care patients. However, no clinical evidence is available to establish optimal strategies for fluid management as well as characterizing the effects on the cardiovascular system after therapy initiation. Moreover, fluid overload showed a correlation with worse clinical outcomes. This study aims at characterizing the response to the fluid intervention of intensive care unit patients. We extracted a population of 57 subjects with available electrocardiogram and arterial blood pressure recordings from the MIMIC-III database and evaluated the induced changes in cardiovascular and autonomic indices. We compare autonomic indices obtained from a statistical model of heartbeat dynamics before and after the intervention. Results show significant differences in RR interval, blood pressure, autonomic and Baroreflex activities up to 60 minutes after fluid administration. Specifically, we observed a median increase in RR interval, Baroreflex activity, and overall activity both in pressure and RR time series, as well as a reduction in systolic blood pressure. Specifically, a subgroup of survived patients shows an imbalance toward sympathetic activity, whereas non-survivors have a persistent vagal state after fluid administration. Clinical relevance - The observed differences in autonomic response after fluid administration, together with the assessment of their correlation with patients' mortality, paves the way for the inclusion of heart rate variability indices as markers for assessing fluid responsiveness as associated with ICU patients' state.
Use of Event-Time Embeddings via RNN to Discern Novel Event Sequences in EHRs

In highly configurable health information technology (HIT) systems, such as VistA of the Veterans Health Administration, the variations in how the system is used among different healthcare facilities and how the data are recorded can be significant. Despite the successful standardization of care efforts, some of these variations can be indicative of HIT hazards and demand further investigation. In this work, we implemented a recurrent neural network (RNN) architecture to learn clinical provider order sequences and their temporal dynamics while predicting the orders' terminal state. We demonstrate model performance and provide a use case for the model discerning novel event sequences. This model is proposed to find novel event sequences in an operational environment.
Comparative study of mono-pump and multi-pump PV water system and MILP effectiveness in water production improvement

In this paper, a comparative study of two photovoltaic water pumping architectures is carried out in order to enhance hydraulic efficiency over one year. The first is based on exploiting a powerful motor pump, while the second employs three pumping units to build a multi-pump water station for the same power range. Basing on the architecture nature, the first system is executed by a simple conditional algorithm, while the second is executed using mixed-integer linear programming as an optimal power-sharing strategy to maximize the water pumped volume. Static models of the different motor pumps are extracted from WinCAPS software and simulated using MATLAB. Simulation results show the effectiveness of the second architecture in energetic and water volume viewpoint.
On the value of MRI for improved understanding of cuff-based oscillometric measurements

Blood pressure (BP) is a key parameter in critical care and in cardiovascular disease management. BP is typically measured via cuff-based oscillometry. This method is highly inaccurate in hypo- and hypertensive patients. Improvements are difficult to achieve because oscillometry is not yet fully understood; many assumptions and uncertainties exist in models describing the process by which arterial pulsations become expressed within the cuff signal. As a result, it is also difficult to estimate other parameters via the cuff such as arterial stiffness, cardiac output and pulse wave velocity (PWV)-BP calibration. Many research modalities have been employed to study oscillometry (ultrasound, computer simulations, ex-vivo studies, measurement of PWV, mechanical analysis). However, uncertainties remain; additional investigation modalities are needed. In this study, we explore the extent to which MRI can help investigate oscillometric assumptions. Four healthy volunteers underwent a number of MRI scans of the upper arm during cuff inflation. It is found that MRI provides a novel perspective over oscillometry; the artery, surrounding tissue, veins and the cuff can be simultaneously observed along the entire length of the upper arm. Several existing assumptions are challenged: tissue compression is not isotropic, arterial transmural pressure is not uniform along the length of the cuff and propagation of arterial pulsations through tissue is likely impacted by patient-specific characteristics (vasculature position and tissue composition). Clinical Relevance— The cuff interaction with the vasculature is extremely complex; existing models are oversimplified. MRI is a valuable tool for further development of cuff-based physiological measurements.