Miracle-dz/UniFAR-FTUs huggingface dataset:
Secondary transfer of emergency stroke patients eligible for mechanical thrombectomy by air in rural England: economic evaluation and considerations

Background Mechanical thrombectomy (MT) is a time-sensitive emergency procedure for patients who had ischaemic stroke leading to improved health outcomes. Health systems need to ensure that MT is delivered to as many patients as quickly as possible. Using decision modelling, we aimed to evaluate the cost-effectiveness of secondary transfer by helicopter emergency medical services (HEMS) compared with ground emergency medical services (GEMS) of rural patients eligible for MT in England. Methods The model consisted of (1) a short-run decision tree with two branches, representing secondary transfer transportation strategies and (2) a long-run Markov model for a theoretical population of rural patients with a confirmed ischaemic stroke. Strategies were compared by lifetime costs: quality-adjusted life years (QALYs), incremental cost per QALY gained and net monetary benefit. Sensitivity and scenario analyses explored uncertainty around parameter values. Results We used the base case of early-presenting (<6 hours to arterial puncture) patient aged 75 years who had stroke to compare HEMS and GEMS. This produced an incremental cost-effectiveness ratio (ICER) of £28 027 when a 60 min reduction in travel time was assumed. Scenario analyses showed the importance of the reduction in travel time and futile transfers in lowering ICERs. For late presenting (>6 hours to arterial puncture), ground transportation is the dominant strategy. Conclusion Our model indicates that using HEMS to transfer patients who had stroke eligible for MT from remote hospitals in England may be cost-effective when: travel time is reduced by at least 60 min compared with GEMS, and a £30 000/QALY threshold is used for decision-making. However, several other logistic considerations may impact on the use of air transportation.
Perioperative Cardioprotection: Clinical Implications

Perioperative cardioprotection aims to minimize the consequences of myocardial ischemia–reperfusion injury. In isolated tissue and animal experiments, several treatments have been identified providing cardioprotection. Some of these strategies have been confirmed in clinical proof-of-concept studies. However, the final translation of cardioprotective strategies to really improve clinical outcome has been disappointing: large randomized controlled clinical trials mostly revealed inconclusive, neutral, or negative results. This review provides an overview of the currently available evidence regarding clinical implications of perioperative cardioprotective therapies from an anesthesiological perspective, highlighting nonpharmacological as well as pharmacological strategies. We discuss reasons why translation of promising experimental results into clinical practice and outcome improvement is hampered by potential confounders and suggest future perspectives to overcome these limitations.
Perioperative Cardioprotection: General Mechanisms and Pharmacological Approaches

Cardioprotection encompasses a variety of strategies protecting the heart against myocardial injury that occurs during and after inadequate blood supply to the heart during myocardial infarction. While restoring reperfusion is crucial for salvaging myocardium from further damage, paradoxically, it itself accounts for additional cell death—a phenomenon named ischemia/reperfusion injury. Therefore, therapeutic strategies are necessary to render the heart protected against myocardial infarction. Ischemic pre- and postconditioning, by short periods of sublethal cardiac ischemia and reperfusion, are still the strongest mechanisms to achieve cardioprotection. However, it is highly impractical and far too invasive for clinical use. Fortunately, it can be mimicked pharmacologically, for example, by volatile anesthetics, noble gases, opioids, propofol, dexmedetomidine, and phosphodiesterase inhibitors. These substances are all routinely used in the clinical setting and seem promising candidates for successful translation of cardioprotection from experimental protocols to clinical trials. This review presents the fundamental mechanisms of conditioning strategies and provides an overview of the most recent and relevant findings on different concepts achieving cardioprotection in the experimental setting, specifically emphasizing pharmacological approaches in the perioperative context.
Support Weighted Ensemble Model for Open Set Recognition of Wafer Map Defects

Wafer defect maps have different generation mechanisms according to the defect pattern, and automatic classification of wafer maps is therefore critical to reveal the root cause of the defects. In this paper, we examine the open set recognition problem, in which not only must wafer maps be classified using major defect patterns that are already known but also unknown defect patterns must also be detected. Our model is an ensemble model of a one-versus-one method that uses a convolutional neural network as the base classifier for wafer map classification. The proposed model calculates a weighted mean score for each defect pattern and determines the presence or absence of a pattern based on this score. The weight is calculated based on the proximity of data groups in the feature space and can be considered a support level at which a new wafer map belongs to a specific defect pattern. An untrained wafer map input into the model has a low support level and thus does not belong to any known defect pattern. An experiment was conducted using work-site failure bit count maps.
When Storage Response Time Catches Up With Overall Context Switch Overhead, What Is Next?

The virtual memory technique provides a large and cheap memory space by extending the memory space with storage devices. It applies context switch to asynchronously swapping pages between memory and storage devices for hiding the long response time of storage devices when a page fault occurs. However, the overall context switch overhead is high because the context switch itself is a complex function and would further incur TLB shootdown/flush and compulsory CPU cache misses after context switches. On the contrary, as the rapid responsiveness improvement of high-end storage devices, we observe that the response time of high-end storage devices catches up and gradually becomes smaller than the overall context switch overhead. At this turning point, to further enhance the system responsiveness, we advocate adopting synchronous swapping rather than context switch in response to page faults. Meanwhile, we propose a strategy, called shadow huge page management, to further improve the overall system performance by minimizing the overall time overheads caused by page faults and page swappings. Evaluation results show that the proposed system can efficiently reduce the total CPU wasting time.
Data-driven predictive current control for synchronous motor drives

Data-driven control techniques have become increasingly popular in recent years due to the availability of massive amounts of data and several advances in data science. These control design methods bypass the system identification step and directly exploit collected data to construct the controller. In this paper, we investigate the application of data-driven methods to the control of electric motor drives, and specifically to the design of current controllers for three-phase synchronous permanent magnet motor drives. Two of the most promising data-driven algorithms are presented, namely the Subspace Predictive Control algorithm and the Data-Enabled Predictive Control algorithm. The theory behind these techniques is first reviewed in the optimization-based control framework. Standard algorithms are slightly modified to fulfill the requirements of the specific application, and then simulated in the MATLAB Simulink environment. Some key aspects of real-time implementation are studied, providing a proof-of-concept demonstration of the applicability of these algorithms. The data-driven design is proposed for three different topologies of synchronous motors, proving the flexibility of the approach.
Analysis and Prediction of AC Resistance of Litz Wire with Rectangular Cross-Section

The Litz wire is widely utilized in recent high frequency switching regulators as a magnetic wire with small AC resistance. The AC resistance of the Litz wire is dependent on the twisting structure of the strands as well as the resultant cross-section geometry of the Litz wire. Therefore, optimization of the Litz wire is becoming important, although the numerical prediction of the AC resistance is difficult due to the complicated structure of the Litz wire. Certainly, a recent study has proposed an analytical AC resistance prediction method. However, this method cannot be applied to Litz wires with the rectangular cross-section, which is recently developed to improve the packing factor of the winding. This paper aims at extending this previous method to cover these Litz wires by partly utilizing a simple static magnetic field FEM analysis. The experiment revealed successful AC resistance prediction of the Litz wire with the rectangular cross-section.
Unified power quality conditioner with shared legs and high-frequency transformer

This work proposes a three-phase unified power quality conditioner based on a nine-leg converter composed of three single-phase three-leg cells with shared legs and connecting the three DC-links by means of a DC-DC converter with a high-frequency transformer (HFT). Each DC-link is connected to a high frequency H-bridge and each H-bridge is connected to one winding of the transformer. The high-frequency transformer is responsible to balance the DC-links, providing the necessary power flow. Converter model is provided, as well as pulse-width modulation and control strategy. Simulations and experimental results are presented for validation purposes. The proposed converter is compared with a conventional solution in terms of harmonic distortion.
MTPA Tracking Algorithms for IPMSMs and SynRMs: Accurate Evaluation and Adaptive Tuning of Real Signal Injection and Virtual Signal Injection

Two main classes for Maximum-Torque-Per-Ampère (MTPA) tracking in Interior Permanent Magnet (IPM) and Synchronous Reluctance (SynR) machines have been proposed in literature, namely Real Signal Injection (RSI) and Virtual Signal Injection (VSI). Unfortunately, a fair evaluation of the accuracy and performance of each class and a direct comparison between them is still missing, and is the first contribution of this paper. Validation and evaluation of the accuracy and performance is considered, by taking into account a non-linear model of a SynRM, based on an analytical representation of flux-linkage, in order to reduce the errors due to approximations, allowing therefore a quantitative comparison. A new technique is proposed, allowing both tuning and adaptation of the VSI tracking loop gains, therefore assuring both stability and the same dynamical performance in the whole machine torque/speed range. Finally, an original strategy to compensate for steady-state MTPA tracking errors due to non-linear flux maps is proposed, thus increasing the accuracy of VSI even in the case the machine flux maps are known with relatively low quantization. An IPM motor drive is considered to validate the proposed contributions.
Probabilistic Impact Assessment of Residential Charging of Electric Motorcycles on LV Feeders

Motorcycles form a popular mode of transport in East African countries, and policies in countries like Rwanda are encouraging a transition to electric motorcycles (EMs). This paper aims to identify the impacts of EM charging on a low voltage residential distribution network in future high uptake scenarios. A stochastic-probabilistic analysis is conducted on a residential network, looking at the effect of EM charging on voltage level, voltage unbalance as well as cable and transformer loading. The Monte Carlo Simulation method is used to account for the randomness in the placement of EMs along the network while the extended Herman Beta transform is used to account for the variability in the residential consumer loads. This paper found transformer overloading to be the limiting factor with regard to EM uptake for the sample network modelled. A sensitivity analysis then highlighted the effects that the feeder properties, transformer size as well as EM and residential load model had on the simulation outcome. The sensitivity analysis found the results most sensitive to the residential load modelling as this affected the transformer loading prior to any EM charging.
Expediting Transient Thermal Frequency Response Characterization and Sensitivity Analysis

This paper develops a technique, requiring no dedicated temperature sensing calibration step, to rapidly characterize transient heat transfer in packaged, power semiconductor components. It is presented as an alternative to traditional step response characterization methods by exploiting the phase delay metric native to frequency response function (FRF) analysis in the field of system identification. The paper introduces electrothermal engineering steps needed to design the measurement system. It presents aspects of power device physics and dynamic analysis to identify design space in which FRF data extracted from experiments are insensitive to potential unknowns. Power electronic circuitry, providing the needed, periodic heat actuation, and probing, allowing for simple reconstruction of transient temperature data in post-processing, are introduced. The developed method is leveraged in-lab to make a key measurement confirming high-frequency-only thermal FRF sensitivity to component die-attach, highlighting the opportunity to achieve localized, power electronic converter degradation sensing in situ.
Hyper-Parameter Tuning based on Random Search for DenseNet Optimization

Deep learning is a machine learning technology that is currently experiencing rapid development. One of the deep learning architecture models is Densely Connected Convolutional Networks (DenseNet), which connects each layer along with feature maps to all next layers. In other words, the next layer receives input feature maps from all previous layers. Because of this density architecture, the training time is longer and requires much memory. One of the ways to overcome these problems is to optimize using the batching strategy method. This batching method uses adaptive batch size with the ratio of learning rate during training to get faster training time without reducing accuracy. To obtain the optimal performance of this batching, a proper selection of hyper-parameter learning rate and batch size is needed. In this study, we perform tuning this hyper-parameter to get the optimal value. The selection of hyper-parameters uses a random search to select candidates for learning rate and batch size, and then an experiment is conducted on the candidates. Random search randomly selects the hyper-parameters, then training and scoring to the hyper-parameters. Our experiment results show that the lower limit of the batch size used is 64, while the optimal learning rate range is in the range of 0.1 – 0.3. The average accuracy obtained from using this hyper-parameter is 95%. Our experiments run on GPU RTX 2080 Ti with CIFAR-10 and CIFAR-100 datasets.
3D Printed Capacitive Tilt Sensor

This work presents 3D printed tilt sensor to measure fluid level and tilt sensing in robotics. It comprises of four 3D printed interdigitated capacitive sensors printed as a single structure and immersed in liquid $\mathrm{E}\mathrm{c}\mathrm{o}\mathrm{f}\mathrm{l}\mathrm{e}\mathrm{x}^{\mathrm{T}\mathrm{M}}$ (part A only). When the orientation of the sensor changes, the level of fluid in contact with the sensors also changes and this causes a change in the capacitance. The sensors were first printed individually and change in capacitance measured for different fluid levels. The response of the individual capacitive sensors shows a stable response for a dip and remove cyclic test in fluid at frequency of 0. 625Hz. The results of the tilting experiments show a relative change in capacitance ($\Delta$ ClC $\theta$ $\sim$ 0.1% per degree for an angle range of $\sim-20^{0}$ to $\sim+20^{0}$. With $\sim$ $6.7^{0}$ detected per mm of the level of fluid in contact with the sensor, the result shows the capability to achieve a measurement range of at least $\sim\pm 93.8^{0}$ (for a sensor of length of $\sim$12mm). The study carried out in this work finds application in tilt sensing in robotics as well as in fluid-level sensing in which case the sensor could be printed as part of the structure of the fluid container.
A New Asymmetric 49-levels Cascaded MPUC Multilevel Inverter Fed by a Single DC-Source

This work proposes a new multilevel inverter topology composed of two MPUCs cascaded connected and fed by a single dc-source. This topology can provide 49-levels voltage by using four asymmetrical dc-links, and only twelve power switches. Its smaller dc-links are supplied by a single dc-source via high-frequency transformer (HFT) dealing with 33% of total load power when operating at nominal voltage. Besides that, HFT are smaller and lighter compared with line frequency transformers. This transformer is responsible to balance all dc-links providing required power flow. A Nearest Level Modulation (NLM) and analysis of power distribution among converter modules are presented in details. Besides comparisons with other multilevel topologies using HFT. Simulation and experimental results are presented to validate theoretical studies.
Broadband Dispersion-Managed Dissipative Soliton and Structural Soliton Molecules in a Slight-Normal Dispersion Fiber Laser

We report on the generation of dispersion-managed dissipative soliton and various structural soliton molecules from a slight-normal dispersion fiber laser. The laser was capable of generating 56.5 nm broad dissipative solitons with quasi-rectangular spectral profile. Furthermore, the broadest top-flat spectrum with up to 71.4 nm bandwidth was achieved in the noise-like pulse regime, operating in the 1542–1613.4 nm. More importantly, by manipulating the laser cavity parameters, various types of soliton molecules, including conventional and unusual structural soliton molecules, were observed in fiber laser. The soliton molecules exhibit different features in autocorrelation traces, which are found to be related to soliton number, soliton intensity and soliton separation within the soliton molecules. The results contribute to enriching the soliton dynamics in the fiber lasers in the slight-normal dispersion regime.
Improved Cooling in Modular Consequent Pole PM Machine Utilizing Flux Gaps

In this paper, a novel cooling method utilizing the flux gaps (FGs) as cooling channels in modular consequent pole PM machines has been proposed. With numerical calculations such as computational fluid dynamics (CFD) analyses, it is proven that the proposed forced air cooling method with inlets and outlets on the end-caps can take advantage of the FGs and a remarkable reduction of temperature rise can be achieved in modular machines. The optimal FG width for better cooling effect has been identified. Moreover, several design parameters of the inlets such as inlet position, inlet dimension have been investigated. Some design guidelines of such modular machines considering particularly the thermal performance are given at the end of this paper.
Mathematical Modelling of the Drying Kinetics of Chestnut (Castanea Sativa Mill.): Influence of the Natural Shells

Drying kinetics of chestnuts were experimentally determined. The experiments were carried out in a pilot-plant scale dryer, with a closed air circuit, assisted by a heat pump. Several experimental conditions were investigated; temperature (45–65C), relative humidity (20–40%), and air velocity (1.8–2.7 m s−1. Chestnuts with the pericarp (external shell), endocarp (internal skin), or without both these natural resistances (previous careful peeling) were dried. Several (from four to six) chestnuts were used in each run, and the weight, size and colour measured periodically in order to build the corresponding kinetics for each variable. The results show the drying kinetics are faster when the driving force is higher and the physical resistances are eliminated. The use of different varieties of chestnut does not yield significant differences in the drying kinetics. A proposed model for determination of effective coefficient of diffusion (28.7–381 × 10−12 m2 s−1) which accounts for shrinkage (volumetric shrinkage depends linearly on normalized moisture content) has been successfully used to simulate the drying kinetics at different temperatures. The most important resistance to water removal was given by the shell (endocarp) of the chestnut. It was observed that the colour of cut dried chestnuts was preserved better, and this parameter of quality was dependent on the moisture content of the chestnut.
Visualization of Evolution of Component-Based Software Architectures in Virtual Reality

The visualization of software architectures by the use of effective and feasible visual metaphors provides an intuitive approach to comprehend the implemented architecture of a software project. In this regard not only the visualization of the latest status of the implemented architecture is important, but also the visualization of the history of software architectures. Such visualizations show dependencies and contexts in which design decisions were made. Resulting information supports programmers to understand systems and to recognize disadvantageous design decisions. A software which is particularly suited for the visualization of component-based software is IslandViz. This software visualizes OSGi-based software architectures in Virtual Reality with an island metaphor, but at this point the history of an architecture is not taken into account. In this paper we present how IslandViz can be extended to visualize the history of software architectures of OSGi-based software projects. For this purpose we use algorithms for dynamic graphs to realize a dynamic positioning of the islands and an adaptable layout of the regions on the islands. The aim is to ensure that the user’s orientation in the virtual environment is preserved even if elements of the visualization must adapt due to changes in the software history.
A Data-based IGBT Model for Efficient and Accurate Electro-thermal Analysis

Due to the different time-scale behaviors of electricity and thermal, the most electro-thermal IGBT models cannot achieve a good trade-off between simulation efficiency and accuracy. This paper presents a data-based IGBT model for electro-thermal analysis to attain a better performance in circuit simulation. In the model, the data come from the Hefner model which has been re-expressed by MATLAB script. What’s more, a two-layer feed-forward neural network with sigmoid hidden neurons and linear output neurons is created to describe the transient behaviors of IGBT devices. The network is trained with the dataset obtained by modifying different parameters in re-expressed Hefner model. Due to the application of IGBT physical model and neural network, the model can be used to simulate not only the transient electricity behaviors, such as switching energy loss and the voltage overshoot, but also the long-time thermal behavior. The complete model is implemented in Simulink and verified by Saber and the experimental prototype test, which shows high efficiency and accuracy.