Miracle-dz/UniFAR-FTUs huggingface dataset:
S‐PS resonant topology of WPT system for implantable spinal cord stimulator

Magnetic resonance-based wireless power transfer (WPT) system offers a promising solution to provide power for implantable spinal cord stimulator (SCS). To meet with the clinical requirements, the SCS-WPT system should have high transfer efficiency and long transfer distance. As the topology of the resonant network will directly affect the transfer efficiency and output power of the WPT system, a new resonant network, series-parallel and series (S-PS), is selected in this work. The performance of WPT system with S-PS resonant network has been compared with those with the classic resonance networks, it is concluded that S-PS is the suitable resonant network for the WPT system in a wide range of load. Finally, an SCS-WPT prototype with S-PS resonant network is built with detailed parameter design to verify the correctness of theoretical analysis. The system efficiency is up to 68.4% at a coil separation of 50 mm.
Fluorescence enabled direct visual observation for diagnosis of ultrafiltration membrane fouling by bi‐disperse submicron particle suspensions

Whilst direct observation (DO) methodologies can describe back‐transport of supra‐micron particles, present technologies are unable to discriminate submicron particles, which are primarily responsible for membrane fouling. In this study, we therefore introduce a fluorescence enabled direct visual observation (RLF‐DVO) methodology to permit visual characterisation of submicron particle transport during cross‐flow filtration. Particle discrimination was achievable for particle diameters exceeding 0.25 µm; however, this was dependent upon particle concentration and the cross‐flow velocity employed. Nevertheless, this is considerably below the detection limit of current techniques (around 3 µm). During filtration of a binary dispersion comprised of submicron particles, deposition was observed before a change in transmembrane pressure was detected, which underpins the important role of DO for fouling diagnosis. Based on observations made during this study, recommendations are proposed that will further improve resolution. Importantly, this study demonstrates RLF‐DVO can provide real‐time description of submicron particle transport during cross‐flow filtration.
Wireless Power Transfer Technology Applied to an Autonomous Electric UAV with a Small Secondary Coil

This study deals with the design and the optimization of a wireless power transfer (WPT) charging system based on magnetic resonant coupling applied to an electric vertical take-off and landing Unmanned Aerial Vehicle (UAV). In this study, a procedure for primary and secondary coil design is proposed. The primary circuit in the ground station consists of an array of coils in order to mitigate the negative effects on the coupling factor produced by the possible misalignment between the coils due to an imperfect landing. Key aspects for the design of the secondary coil onboard the UAV are the lightness and compactness of the WPT system components. A demonstrative prototype of the WPT system is applied to a commercial drone. The WPT electrical performances are calculated and measured. Finally, an automatic battery recharge station is built where the drone can autonomously land, recharge the battery and take off to continue its flight mission.
Statistical signatures of $u$ component wall-attached eddies in proper orthogonal decomposition modes of a turbulent boundary layer

Abstract Physical representation of coherent motions in wall-bounded turbulence satisfying Townsend's attached-eddy hypothesis (AEH) is still an open question to be debated. Here, we apply proper orthogonal decomposition (POD) to instantaneous snapshots of $u$ component velocity fields in the streamwise–wall-normal plane of three turbulent boundary layers, which cover a frictional Reynolds number ($Re$) of $Re_\tau \approx 1000{\sim} 4000$ and were either measured by large-field-of-view planar particle image velocimetry or calculated by direct numerical simulation. The $u$ component POD modes characterized by streamwise recurring large-scale motions are decomposed into wall-attached and wall-detached parts depending on the wall-attachment condition. The former, i.e. wall-attached POD eddies, are believed to be the prime statistical representations of attached eddies. The reason lies in three aspects. Firstly, wall-attached POD eddies are geometrically self-similar and statistically independent. Secondly, the wall-normal variation of the variance of reduced-order streamwise velocity components $\tilde {u}_{AE}$, which is only contributed by wall-attached POD eddies, presents logarithmic decay in the inertial flow region. Thirdly, analysis on the scaling behaviour of the high-even-order moment, moment generating function, as well as longitudinal two-point structure function, all indicate a quasi-Gaussian behaviour of $\tilde {u}_{AE}$. None of these AEH-predicted behaviours are evident when the contribution of superstructure-like POD eddies are considered. Furthermore, wall-detached POD eddies are found to also present quasi-Gaussian behaviour. They might be treated as passive fragments of wall-attached ones.
Time-domain simulator for short-term ship manoeuvring prediction: development and applications

ABSTRACT This paper presents a short-term time-domain algorithm for ship motion simulation. Plane motion was considered, and thus, a three degrees of freedom model was used. The main inputs of the model are: propellers speed, rudder angles and initial conditions (initial position/orientation and linear/rotational velocity). The outputs are final ship position and orientation after a given simulation time. The data available in the Voyage Data Recorder and the results of sea trials performed before the ship delivery were used to estimate the needed parameters, implementing a simple but reliable linear regression scheme. The proposed model was applied to Costa Concordia manoeuvring simulation in order to assess its reliability in terms of trajectory prediction. Finally, as an example of application, the simulator was implemented in a real-time manoeuvring predictor, which is capable of foreseeing the ship position up to 40 s ahead in time, supporting the Commander in emergency operations.
Harmonics-Based Enhanced Passive Islanding Method for Grid-Connected System

Abstract Now a day, distributed sources are emerging widely due to their advantages owing to renewable-based power generation. In this paper, a novel passive islanding method for the inverter-based grid-connected system is presented. This method can be suitably applied along with the conventional passive methods like under/over voltage and under/over frequency islanding techniques. Total harmonic distortion (THD) at the point of common coupling (PCC) is used to discriminate the islanding condition, and to safely disconnect the grid. This method has an advantage that it is able to discriminate non-detection zone (NDZ) when the local load matches the inverter output power. The results using MATLAB simulations and laboratory prototype have been presented.
Real-Time Object Detection using an Ultra-High-Resolution Camera on Embedded Systems

Unnamed Aerial Vehicle (UAV) - based remote sensing is a promising technology that is being applied for inspecting live scenes from high altitudes (e.g., for surveillance and recognizing emergencies). The evolution of hardware and software technologies in the last few years has generated additional interest in embedded systems research and its implementation in energy-independent UAVs for remote sensing. Alongside, ultra-high-resolution optical sensors are mandatory for acquiring high-resolution images which are necessary for accurate object detection from a distance (e.g., 1,000 meters). The processing of ultra-high-resolution images (e.g., 4K or 8K) is beyond the typical resolutions which are used for object detection (e.g., < 2K) emerging a necessity for special treatment in order to succeed a fast object detection. We propose a three-step approach deployed on a Docker runtime environment in an Nvidia Jetson AGX Xavier board. To support fast object detection, the captured images are split into K parts processed in parallel in separate containers running the YOLOv5 object detection algorithm. A final detection is constructed based on each one of the K detections. The experimental results are a good support to our claims of efficiency: the method can achieve close to real-time object detection for ultra-high (i.e., 8K) resolution images (i.e., in less than 1 second per frame).
Resampling-based multiple comparisons for generalized linear models

Diverse applications in medical and epidemiological research routinely utilize generalized linear modeling to explain the relationship between the incidence of disease and particular risk factors. Researchers’ interest in such models are estimated quantities from the model such as the response probabilities, the relative risks or the odds ratios and not the model itself. Often, the simultaneous estimation of these quantities or a subset of the quantities are warranted. The results are usually reported via confidence intervals at a pre-specified level of significance. Utilizing the usual 95% pointwise confidence intervals for the simultaneous inference inflates the risk of making type I errors. Several procedures have been proposed to control for multiplicity among a set of inferences, but no one solution is applicable for all situations. This study develops and via simulation evaluate resampling-based multiple comparison procedures for contrasts of generalized linear model parameters. The main results of the study is a new algorithm for resampling-based multiple comparisons for quantities from generalized linear models that will in some situations outperform existing conservative procedures and in those situations be less data-wasteful.
Sparsity regularization and graph-based representation in medical imaging

Medical images have been used to depict the anatomy or function. Their high-dimensionality and their non-linearity nature makes their analysis a challenging problem. In this thesis, we address the medical image analysis from the viewpoint of statistical learning theory. First, we examine regularization methods for analyzing MRI data. In this direction, we introduce a novel regularization method, the k-support regularized Support Vector Machine. This algorithm extends the 1 regularized SVM to a mixed norm of both `1 and `2 norms. We evaluate our algorithm in a neuromuscular disease classification task. Second, we approach the problem of graph representation and comparison for analyzing medical images. Graphs are a technique to represent data with inherited structure. Despite the significant progress in graph kernels, existing graph kernels focus on either unlabeled or discretely labeled graphs, while efficient and expressive representation and comparison of graphs with continuous high-dimensional vector labels, remains an open research problem. We introduce a novel method, the pyramid quantized Weisfeiler-Lehman graph representation to tackle the graph comparison problem for continuous vector labeled graphs. Our algorithm considers statistics of subtree patterns based on the Weisfeiler-Lehman algorithm and uses a pyramid quantization strategy to determine a logarithmic number of discrete labelings. We evaluate our algorithm on two different tasks with real datasets. Overall, as graphs are fundamental mathematical objects and regularization methods are used to control ill-pose problems, both proposed algorithms are potentially applicable to a wide range of domains.
On the Equivalence between Answer Sets and Models of Completion for Nested Logic Programs

We present a sufficient as well as a necessary condition for the equivalence between answer sets and models of completion for logic programs with nested expressions in the bodies of rules. This condition is the weakest among all that we are aware of even for normal logic programs. To obtain this result, we present a polynomial time reduction from this class of nested logic programs to extended programs. Consequently, answer sets for these nested programs can be computed by an answer set generator for extended programs on the one hand, and characterized in terms of models of completion on the other.
Wireless power for heart pumps: Control and energy measurement

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Solving Challenges in Deep Unsupervised Methods for Anomaly Detection

Anomaly Detection (AD) is to identify samples that differ from training observations in some way. Those samples that do not follow the distribution of normal data are called outliers or anomalies. In this thesis, we examined two different challenges related to deep learning-based anomaly detection methods. The first challenge is the generalizability to outliers. A wide range of unsupervised anomaly detection methods use deep autoencoders as a foundation. However, a notable limitation of deep autoencoders is that they generalize to outliers and reconstruct them with low error. In order to overcome this issue, we propose an adversarial framework consisting of two competing components, an adversarial distorter, and an autoencoder. During training, the adversarial distorter produces perturbations that are applied to the encoder’s latent space to maximize the reconstruction error. The autoencoder attempts to neutralize the effects of these perturbations to minimize the reconstruction error. Another challenge is the high computational cost, complexity, and unstable training procedures of deep anomaly detection methods. Despite being successful at anomaly detection, deep neural networks are difficult to deploy in realworld applications because of this challenge. We overcome this problem by using a simple learning procedure that trains a lightweight convolutional neural network. We propose to solve anomaly detection as a supervised regression problem. We label normal and anomalous data using two separable distributions of continuous values. As a way to compensate for the lack of anomalous samples during training, we use straightforward image augmentation techniques to create a distinct set of anomalous samples. An augmented set has a distribution that is similar to normal data, but deviates slightly from it, while real anomalies should have a further distribution. Consequently, training a regressor on normal and these augmented samples will result in more distinct distributions of labels for normal and real anomalous data points. In several image and video anomaly detection benchmarks, our methods outperform cutting-edge approaches.
“Who Has Plots?”: Contextualizing Scientific Software, Practice, and Visualizations

Software is an integral element of the work of science yet it is not commonly an object of inquiry in studies of scientific infrastructures. This paper presents findings from an ethnographic study of a cosmology group’s collaborative scientific software production. We demonstrate how these cosmologists use plots to simultaneously test their software and analyze data while interrogating multiple layers of infrastructural components. We broaden perspectives on scientific software development using a sociotechnical, software studies lens to examine this work of scientific discovery as a creative and embodied, yet exacting and methodical, activity that requires a ‘human in the loop’. We offer a new reading of scientific software practices to convey how creating scientific software is often really the act of doing science itself—an intervention we believe is necessary to more successfully support scientific software sharing and infrastructure production.
Understanding the Role of Weather Data for Earth Surface Forecasting using a ConvLSTM-based Model

Climate change is perhaps the biggest single threat to humankind and the environment, as it severely impacts our terrestrial surface, home to most of the living species. Inspired by video prediction and exploiting the availability of Copernicus Sentinel-2 images, recent studies have attempted to forecast the land surface evolution as a function of past land surface evolution, elevation, and weather. Further extending this paradigm, we propose a model based on convolutional long short-term memory (ConvLSTM) that is computationally efficient (lightweight), however obtains superior results to the previous baselines. By introducing a ConvLSTM-based architecture to this problem, we can not only ingest the heterogeneous data sources (Sentinel-2 time-series, weather data, and a Digital Elevation Model (DEM)) but also explicitly condition the future predictions on the weather. Our experiments confirm the importance of weather parameters in understanding the land cover dynamics and show that weather maps are significantly more important than the DEM in this task. Furthermore, we perform generative simulations to investigate how varying a single weather parameter can alter the evolution of the land surface. All studies are performed using the EarthNet2021 dataset. The code, additional materials and results can be found at https://github.com/dcodrut/weather2land.
Allopurinol use and the risk of dementia: A meta-analysis of case–control studies

Background. This study aimed to compare the risk of dementia between exposed to allopurinol and not exposed to allopurinol in persons who had gout and/or hyperuricemia. Methods. The meta-analysis was conducted to select case–control research written in English through the help of PubMed and Web of Science. The pooled odds ratio (OR) with 95% confidence interval based on the fixed-effect model was applied to compare the allopurinol exposure among cases (subjects with dementia) and controls (subjects without dementia). Results. A total of 4 case–control studies relating the allopurinol exposure to the risk of dementia were identified. The study duration was from 9 to 14 years. The number of study persons was from 3148 to 137,640. The male percentage of study subjects was from 36.9 to 62.5. The mean age of study persons was from 72.3 to 78.7 years. Overall, the odds of the allopurinol exposure among cases were lower than the odds of the allopurinol exposure among control subjects (OR = 0.91, 95% confidence interval = 0.87–0.95, P < .001). The heterogeneity between these eligible studies was low (I² = 0%). The sensitivity analysis revealed that after excluding the studies with concern, the pooled OR did not achieve statistical significance. Conclusions. This is the first meta-analysis to report that there is a negative relationship between the allopurinol exposure and the risk of dementia. Although the results favor the hypothesis, currently it is unable to draw strong conclusions about the protective effect of allopurinol against dementia due to inclusion of only a few eligible studies. Randomized controlled trials are needed to explore the relationship between allopurinol exposure and the probability of dementia.
Efficacy of ginseng supplements on disease-related fatigue: A systematic review and meta-analysis

Background: Ginseng has been believed to provide energy, physical health, and well-being to patients for hundreds of years. Fatigue is a multidimensional symptom with unknown etiology and varying severity, and lots of patients suffer from fatigue. Methods: We search for research of ginseng treatment of disease-related fatigue in adult patients in Pubmed, Embase, Medline, and Cochrane library. Two independent reviewers assessed included studies and met to develop consensus on included studies. And we used Review Manager 5.3 software to evaluate the risk of bias. Results: The present meta-analysis included 12 randomized controlled trial containing 1298 patients. In the fixed-effect meta-analysis of 12 randomized controlled trial, ginseng supplements had a statistically significant efficacy on disease-related fatigue reduction (standardized mean difference = 0.33, 95% confidence interval = 0.44–0.22). Conclusions: The use of ginseng supplements is benefit for patients to reduce disease-related fatigue.
Acute pancreatitis as a rare adverse event among cannabis users: A systematic review

Background: Cannabis use has been steadily rising in the United States and can have multiple adverse effects, including cannabis-induced acute pancreatitis. This study aims to collate and highlight the significant demographics, clinical presentation, and outcomes in patients with cannabis-induced acute pancreatitis. Method: A systematic literature search of electronic databases for peer-reviewed articles was conducted. After an initial search, we found 792 articles through different electronic databases. After manually removing duplicates and articles that did not meet the criteria, 25 articles were included in our review. Results: A total of 45 patients were studied, 35 (78%) cases were male and 10 (22%) cases were female, showing male predominance. The mean age of all participants was 29.2 ± 10.3 years. The most common presenting symptoms were abdominal pain 21 of 21 (100%), nausea 17 of 21 (81%), and vomiting 12 of 20 (60%). Ultrasound was normal in the majority of patients, with findings of mild pancreatitis. Computerized tomography scans revealed pancreatic edema and inflammation in 7 of 20 (35%) patients, and findings of necrotizing pancreatitis and complex fluid collection were visualized in 3 of 20 (15%) patients. Dilatation of intrahepatic or extrahepatic biliary ducts was not seen in any patients. The overall prognosis was good, with reported full recovery. Conclusions: Cannabis should be included in the differential diagnosis for the etiology of acute pancreatitis, which would help in early intervention and treatment for the mitigation of the rapidly progressive disease.
Journal of Surgery and Insights An Interventional Echocardiographic Screening Program for First Degree Relatives of Patients with Bicuspid Aortic Valve: Is It Practical?

Program for First-Degree Relatives of Patients with Bicuspid Aortic Valve: Abstract Bicuspid Aortic Valve (BAV) represents one of the most common congenital cardiac anomalies with a prevalence of 0.5-2% in the general population. Literature demonstrated that the prevalence of BAV among First-Degree Relatives (FDR) of those with diagnosed BAV ranges from 7.3% to 9.1%. BAV has diverse genetic etiologies that vary from complex inheritance in families to sporadic any evidence of inheritance. This interventional study aims to identify potential subjects with BAV by screening the FDRs of patients with BAV, which allows early detection of aortic valvular dysfunction and aortic complications associated with BAV. In addition, we hope to explore the practicality of running a routine ECHO screening program for FDR of patients with BAV in a tertiary hospital setting by analyzing the outcomes. three-generation family complete two-dimensional and 20 years An Interventional Echocardiographic Screening Program for First-Degree Relatives of Patients with Bicuspid Aortic Valve: Is It Practical. J Surg Insights: JSI-100036 An Interventional Echocardiographic Screening Program for First-Degree of Patients with Bicuspid Aortic Valve: Is It Practical. J Surg Insights: JSI-100036