Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling Disease Progression via Weakly Supervised Temporal Multitask Matrix Completion

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases. Understanding AD progression can empower the patients in taking proactive care. Mini Mental State Examination (MMSE) and AD Assessment Scale Cognitive subscale (ADAS-Cog) are two prevailing clinical measures designed to evaluate the AD progression. In this paper, we propose a weakly supervised Temporal Multitask Matrix Completion (TMMC) framework, which combines a novel transductive multitask feature selection scheme, to simultaneously predict AD progression measured by MMSE and ADAS-Cog, and identify related biomarkers trackable of AD progression. Specifically, by treating the prediction of cognitive scores at each time point as a regression task, we first formulate AD progression problem as a standard Multitask Matrix Completion (MMC) model. Secondly, considering the limited number of samples available in this study, we introduce a transductive feature selection scheme to jointly select the task-shared features for multiple time points and the task-specific features for different time points, and thus alleviate the over-fitting defect caused by Small-Sample-Size issue. Thirdly, aiming at the small change of cognitive scores between successive time points for a patient, we employ a temporal regularization scheme to capture the temporal smoothness of cognitive scores. Furthermore, we design an efficient optimization algorithm based on Alternative Minimization and Difference of Convex Programming techniques to solve the proposed TMMC framework. Finally, the extensive experiments performed on real-world Alzheimer’s disease dataset demonstrate the effectiveness of our TMMC framework.
Stability of the Frequency Spectrum of the Heart Sounds S1 and S2 under Different Physiological Conditions

This paper presents a study of the frequency spectrum regarding the first and the second heart sounds. Therefore, measurements of the phonocardiogram under different physiological conditions are conducted. For this purpose, two different methods to analyze the spectrum, are applied to the recorded phonocardiograms, namely the short-time Fourier transform and the continuous wavelet transform. Seven different measurements are conducted from 12 subjects, where the auscultation point, posture, breathing and physical stress are varied. Moreover, the interference due to human voices is taken into account. The determined 10 dB-bandwidth, which starts at around OHz, for the first and the second heart sound is approx. 55 Hz in the short-time Fourier case and about 100Hz for the continuous wavelet transform. The analyzed parameters do not show any significant change under varying physiological conditions, whereas the frequency spectrum of the first and second heart sounds differs strongly between the individual subjects. It is concluded that for the filter design of heart sounds monitoring in wearable devices, adaptation to varying daily activities can be neglected, whereas adaptation to the user is strongly recommended.
Simulation analysis of the coronavirus disease 2019(COVID-19) spread based on system dynamics model

The outbreak of public health emergencies not only threatens people's life safety, but also has a series of secondary effects. In this paper, based on SEIR classical infectious disease model, covid-19 epidemic model is constructed by using system dynamics method.Based on different isolation measures and protection measures in different regions, the model was simulated to dynamically describe the change rule of epidemic infection number with time. The results show that the earlier the government takes isolation measures, the more active people will protect themselves, which can effectively reduce the number of infected people and control the epidemic situation faster .
Literature Review of Deep Learning Models for Liver Vessels Reconstruction

Deep learning (DL) is one of the most important machine learning methods which has achieved great success in the field of medical image analysis. DL teaches a computer model how to perform classification tasks directly from images, but since the acquisition problems of these images this method has lost its effectiveness mainly for the segmentation of complex structures such as vessels that are hardly or not visible in the raw data. Nowadays, researchers are trying to find solutions to these kinds of problem since the information of the local appearance of pixel are not enough. To illustrate the limits of using standard DL models for vessel reconstruction, we first show a comparative study based on the IRCAD dataset. This experiment motivates our study, wherein we provide a review of DL models which covers liver vessel segmentation and medical image processing, in order to confirm if these problems can be solved by DL, and discusses a new approach to guide the experts who want to use these approaches in their work. A Systematic Literature Review (SLR) was carried out. More than 40 papers were founded by manual search in Elsevier, Springer and Science Direct, IEEE, 20 primary studies were finally included. According to the literature studies, we will define the most pertinent articles related to DL applications for complex structure reconstruction. Results: DL based topological signature methods have better results than classical topics and DL based pixelwise.
Review of SMES and SFCL Based FRT Strategy for PMSG-Based Wind Turbine Generator

Superconducting energy storage system (SMES) and superconducting fault current limiter (SFCL) have potential advantages in enhancing fault ride-through (FRT) capability of wind turbine generator (WTG) system. This paper reviews the superconducting-type FRT strategy (SFRTT) based on SMES and SFCL for permanent magnetic synchronous generator (PMSG) based WTG, aiming at introducing the basic principle and characteristic of various SFRTTs. Moreover, the cooperative controls of modified WTG control and superconducting devices are prospected for performing the cost-effective FRT-enhancement. Several simulations are also conducted to compare these SFRTTs.
Free-Head Pose Estimation under Low-Resolution Scenarios

Head pose offers vital cues to infer one’s social attention in wide applications. Most existing head pose estimation algorithms have demonstrated competitive results taking high resolution images of frontal view as input. However, these approaches still work poorly if they are fed with low-resolution images. In a more common realistic scene such as computer vision assisted autism screening, images of unconstrained patients that are taken from distant cameras often have low-resolution and non-frontal faces. To this end, we present a multi-view scheme based CNN for free-head pose classification. Residual Networks are taken as the backbone model to generate effective feature representations of the low-resolution images. A novel multi-view feature fusion layer is proposed to address facial appearance variation over multi perspectives owing to free movement. Also, a multi loss function by combining binned classification and regression losses of different pose angles is employed to obtain a more precise pose estimation. The proposed method is evaluated on two scenarios: (1) a publicly available dataset and (2) a practical application to explore the social attention of a group with social deficits: autistic children. Experimental results suggest that our method significantly outperforms state-of-the-art multi-view pose classification methods and achieves comparable pose estimation results. Moreover, the proposed method can be extended to applications of quantitative analysis of social deficits.
Comparison of Performance of Machine Learning Methods for Bearing Faults Classification Using Time-Domain Features

This article deals with comparison of a classification success rate of machine learning methods used for vibration signals captured on damaged bearing. The most significant and the most used methods suitable for this vibrodiagnostic field have been selected, namely support vector machine, k-nearest neighbor, Mahalanobis-Taguchi system and principal component analysis for reduction of a dimensionality of a features space. All the methods have been implemented in MATLAB and their performance has been analyzed using same input datasets. As the input for all the aforementioned methods, data from bearing shortened life-cycle test, done by Bearing Data Center at Case Western Reserve University, have been used. Intentionally, computationally intensive pre-processing procedures have been excluded from the computational chain, as only time domain features have been used for the classification process. Resulting from this study, a classification error rate, an execution time, an implementation difficulty and a robustness of the algorithm strongly differ among all the methods while using the same input data and the same number and type of the input extracted features.
Ex situ Evaluation of Tungsten Oxide as a Catalyst Support for PEMFCs

The oxidation of carbon catalyst supports causes acute degradation in catalyst performance in proton exchange membrane fuel cells (PEMFCs). Tungsten oxide is considered here as a candidate material as an alternative catalyst support. A new method has been developed that allows Pt deposition on tungsten oxide catalyst supports. The electrochemical stability of tungsten oxide was studied by applying a coating of a tungsten-oxide-supported Pt catalyst to a rotating disk electrode and to a gold mesh. Accelerated oxidation tests were performed in deoxygenated 0.5 M H 2 SO 4 at 30 and 80°C. The oxidation stability of Pt/tungsten oxide and of Hispec 4000, a commercial catalyst, each coated on a gold mesh, was measured at 80°C. The oxygen reduction reaction (ORR) activity of Pt/tungsten oxide remained high, even after accelerated oxidation tests, while the ORR activity was extremely poor after accelerated oxidation tests of Hispec 4000.
Conservatism Comparison of State Estimation Error and Residual in Multiple Actuator Faults Detection

This paper focuses on analyzing and comparing the performance of two robust fault detection (FD) criteria for discrete-time linear parameter varying (LPV) systems with bounded uncertainties, namely the state estimation error-based criterion and the classical residual-based criterion. First, a new FD criterion for the detection of multiple multiplicative actuator faults is proposed by testing consistency between the state estimation errors and the healthy state estimation error sets on-line. Then, a guaranteed FD condition is established based on set-separation of healthy and faulty invariant sets of state estimation error. Moreover, the generalized minimum detectable fault (MDF) for multiple actuator faults is defined and computed in order to characterize the performance of the two FD criteria. Finally, a proof is provided to compare the conservatism of the FD criterion using state estimation errors with the classical one based on residuals. At the end of this paper, a numerical example is used to illustrate the effectiveness of the obtained results.
On the Knowledge Graphs of Postgraduate Entrance English Examination Based on WordNet and D3.js

To reveal visually the semantic composition of high-frequency words in postgraduate entrance English examination for the students to have better understanding and memorization of the words in the examination outline and the high-frequency words, this paper presents the network structure of these high-frequency words via visualized knowledge graphs and the network structure of the words in the examination outline in WordNet, optimizes further WordNet to draw a concise and lucid semantic network by means of such corpus sources as WordNet (a semantic dictionary), D3.js (a visualized library) and British National Corpus. In the meantime, the paper gives consideration to the time-effectiveness of the content and the examination outline and adopts the test papers of postgraduate entrance English (1) examination during 2015–2020. In addition, a secondary function including the retrieval of long and difficult sentences for the quicker learning of the key content of the examination by the students is derived from the main function of the semantic visualization of the words.
Real-Time Disturbance Detection and Classification using Principal Component Analysis of PMU Data

In this paper, a new disturbance detection method is proposed based on principal component analysis (PCA). The PCA-based method exploits the low-rank characteristics of synchrophasor data reported by multiple phasor measurement units (PMUs). The principal component (PC) scores of the low-rank data sets are used to propose a disturbance indicator. Moreover, an analytical disturbance detection threshold is designed that helps classify different events based on their severity. Finally, a PCA-based data compression technique is developed to reduce the archival requirements while preserving critical PMU data. Numerical simulations confirm that the proposed disturbance detection method efficiently detects a wide range of disturbances and is robust against limited low-quality and false PMU data.
Initial Development of Automatic Situation Awareness System Using Dynamic Graph Inference

Knowledge modeling, representation, and automatic knowledge inference are fundamental problems for developing automatic situation awareness (ASA) systems in the field of complex power system rescheduling. In this paper, we present a knowledge representation model named dynamic rescheduling graph (DRG), i.e., a new kind of relational knowledge graph, to represent the knowledge of the power dispatchers. DRG has many advantages, such as using different knowledge representation methods like function and logic expression and setting attributes to nodes and edges, which make it easier to develop an ASA system for complex situation diagnose, event solving, and rescheduling. To make inference timely in the DRG, we develop a unique algorithm called hybrid mapping inference (HMI), which supports forward, backward, and mapping inference. We also build an initial ASA system based on the proposed knowledge model and the inference algorithm. We validate the effectiveness of the ASA system in the Guangzhou regional power grid.
Autonomous Diagnosis of Overheating Defects in Cable Accessories Based on Faster RCNN and Mean-Shift Algorithm

Infrared thermography has been widely used in timely detection of overheating defects in cable accessories. However, the traditional manual diagnosis method is time-consuming and laborious, and it relies too much on expert experience. An autonomous diagnosis method, which is based on the Faster RCNN network and the Mean-Shift algorithm, is proposed in this paper. Firstly, the Faster RCNN network is trained to implement the autonomous identification and positioning of the objects to be diagnosed in images (cable terminations and grounding boxes are included in this paper). Then, the Mean-Shift algorithm is used for image segmentation to rapidly and accurately extract the area of overheating. This is achieved via comparing key regions of the three phase accessories. Next the temperature related characteristic parameters of the overheating region are calculated, and the diagnosis results are obtained in accordance with the relevant cable condition assessment criteria. The proposed method has been applied to test against actual infrared images, and results show that the cable accessories and their overheating regions can be located at different shooting angles and under various background conditions. The research helps reduce the dependence on human efforts and expertise and contributes to improving the practice of condition monitoring.
A Monocular Visual-Inertial Odometry Based on Hybrid Residuals

In recent years, visual-inertial odometry for state estimation has achieved a huge success in terms of robustness and accuracy. However, current visual-inertial odometry algorithms still struggle with high computational cost, which severely hinders its application on computational resource limited platforms. In this paper, a novel tightly coupled visual-inertial odometry algorithm that can maximize utilize the information provided by 3D map points and IMU pre-integration to reduce the algorithm’s computational cost is proposed. In the front-end, an accelerated optical flow tracking algorithm using the depth known 3D map points and IMU pre-integration between two consecutive frames is proposed to reduce the tracking time. In the back-end, a joint optimization model based on hybrid residuals is proposed, which consists the geometric, the photometric and the IMU residuals. It’s worth noting that partial feature points with good tracking quality from all the feature points that the algorithm is tracking on are selected to construct photometric residuals. Therefore, those selected feature points can provide double constraints for the motion. The one feature point provide double motion constraints strategy allows our odometry algorithm use fewer feature points under the condition of same number of motion constraints which really reduced the amount of feature points also the computational resource needed by our algorithm to achieve a comparable pose estimation accuracy. Experiment results on public available dataset demonstrate the effectiveness of our proposed approach.
Mental Workload of Simulator-based Training Using a Physiological Index: The Relationship between Trainers and Trainees

It takes a great deal of time and money to turn a person with no knowledge or experience into a seafarer. However, these costs can be reduced using education and training via a simulator. In simulator-based education and training, on-site situations can be replicated, while it is also possible to freely alter both the situations of other ships and other external influences. Furthermore, this means that simulator-based training is effective not only for people with no knowledge or experience but also for those with existing experience. One of the people who supports this training is the operator, who is part of the training team. If the operator does not have a sufficient amount of skill, it is impossible for other ships to function naturally. Even if the developed training content is very good, positive training effects cannot be expected without the skill of the operator. Therefore, the operator's skills is essential to produce high-quality training for those using the technology at any level. The operators themselves can be either seafarers or non-seafarers and be placed in charge even if they do not have a license as a seafarer. However, qualifying as a seafarer does not mean that the individual will be placed in charge immediately. We think operators have a special position in the training process and represent the main key to achieving success in simulator-based training. In this study, we focused on the operators and investigated their role by comparing it with that of the captain, who is rich in knowledge and experience as a seafarer. A physiological index is designed to evaluate the mental workload, heart rate, and frequency components of the R-R interval of heart rate variability. This paper shows the mental workloads of both the navigator and the operator, while considering their mental relation using LF/HF values.
Division in bacteria is determined by hyperstructure dynamics and membrane domains

The migration of the “tubulin-like” GTPase, FtsZ, from the cytoplasm to a site between the future daughter chromosomes is the earliest known stage of cell division in bacteria [1]. Polymerization of FtsZ from this site forms a ring of FtsZ around the cell [2] and recruits at least nine different proteins to a division complex, the so-called “divisome” or “septalsome” [3, 4]; these proteins include the “actin-like” molecules FtsA [5], FtsI, FtsN, FtsQ, FtsK, FtsL and ZipA [6]. The nature of the events that lie upstream of FtsZ migration and polymerization remain elusive and cannot simply be attributed to the action of yet another protein without risking an infinite regression. One approach to the problem of division is that of “nucleoid occlusion” in which the nucleoid would inhibit formation of a division site on the surrounding membrane (by an unknown mechanism) such that subsequent separation of the segregating nucleoids would allow this site to form between them [7, 8]; this would be consistent with the observed repression of peptidoglycan synthesis around the nucleoids [9]. There are other possibilities. It has been argued that certain genes, their products and associated ions and lipids interact to structure membranes and cytoplasm into hyperstructures [10] (see Fig. 1). Hyperstructures constitute a level intermediate between macromolecules and cells and correspond to the modules recently proposed to occupy a similar level in eukaryotic cells [11]. In searching for events upstream of FtsZ activation and the operation of the divisome, here we propose a hypothesis in which FtsZ is released by certain hyperstructures to be attracted by membrane domains produced by other hyperstructures. Division in bacteria is determined by hyperstructure dynamics and membrane domains
Properties of grafting methyl acrylate on charge transport in polypropylene

This paper reports excellent electrical properties in polypropylene modified by grafting with methyl acrylate (PP-g-Met) and the related mechanism of the enhanced electrical properties. The crystal properties of PP-g-Met were analyzed. The influences of PP-g-Met on microstructure and electrical properties such as space charge accumulation, electrical breakdown strength, and DC conductivity were studied. Compared with pure PP, the PP-g-Met exhibits remarkably suppressed space charge injection, enhanced electrical breakdown strength and reduced conduction current. The mechanism enhancing the electrical properties can be explained that abundant deep traps are introduced by PP-g-Met with the introduction of acrylic acid, the spherulite structure of PP is destroyed by the acrylic acid branches, and the interfacial interaction is increased by the PP-g-Met. All the reason above reduce the charge mobility and raises the charge injection barrier to suppress space charge accumulation. This investigation would contribute to propose a new material modification strategy for designing high-voltage direct current insulation material.
The impact of maternal vasodilatation as pregnancy progress on peripheral arterial tonometry in assessment of endothelial function

Objective: The aim of our study was to explore the impact of maternal vasodilatationon on peripheral arterial tonometry used for assessing the endothelial function as physiological adaptation to pregnancy progress. Methods: Thirty-one healthy pregnant women of gestational weeks from 13 to 34 were tested using sphygmomanometer, peripheral arterial tonometry and photoplethysmographic assessment. Blood pressure, heart rate, reactive hyperemia index, augmentation index and reflection index were obtained in resting conditions. Data was analyzed in two groups divided as 2nd and 3rd trimester and further divided into three groups of early 2nd trimester, late 2nd trimester and 3rd trimester. Results: A rise of reactive hyperemia index of peripheral arterial tonometry from early 2nd to late 2nd trimester and then a fall to 3rd trimester had been found in association with continuous decrease of augmentation index derived either from peripheral arterial tonometry or photoplethysmography as well as reflection index from photoplethysmography and continuous increase of heart rate, during which blood pressure remained almost unchanged. A moderate level of correlation was observed between augmentation index derived from peripheral arterial tonometry and photoplethysmography. Conclusions: The reactive hyperemia index of peripheral arterial tonometry method is vasodilation dependent and likely to be less reliable in testing of endothelial function when more vasodilated.