Miracle-dz/UniFAR-FTUs huggingface dataset:
Clinical and CT imaging characteristics of COVID-19 cases in Wenzhou city: A retrospective analysis

Background: In December 2019, Wuhan witnessed the outbreak of an “unexplained pneumonia” caused by a novel coronavirus strain infection and was dubbed the COVID-19 by the WHO. The disease quickly spread to China. This study aimed to investigate the disease’s evolving epidemiological history, as well as analyze the clinical and CT imaging characteristics, treatment regimens, and patients’ prognosis. Methods: This was a retrospective study whose cases were 64 patients with a confirmed diagnosis of COVID-19. The clinical data were obtained from patients who were admitted to the isolation ward from 21 January 2020 to 19 February 2020. Results: 60 out of 64 patients had a definitive history of exposure to people who had traveled from Wuhan City. The median time from onset of symptoms to first hospital admission was 3.9±1.9 days. The initial symptoms included fever (46/64), dry cough (38/64), fatigue or myalgia (23/64), sore throat (10/64), diarrhea (3/64) along with late-onset symptoms like chest pains (2/64) and headaches (2/64). The majority of the patients (43/64) had normal white blood cell counts while 29.7 % (19/64) had leukopenia. Only two patients (3.1 %) presented with leukocytosis. 58 of the 64patients had abnormal radiological findings on chest CTs. The first chest CTs (within 2 days) was more sensitive in detecting COVID-19 infection (85.9 %) compared to the initial RT-PCR (56.3 %; p<0.01). The CTs showed lesions in multiple lung lobes in three-quarters of the patients while 15.6 % had lesions localized to one lobe. Most of the lesions were typically dense with ground-glass opacity co-existing with consolidation or cord-like shadows. Most of these patients (50/64) have recovered and got discharged giving a mean length of hospital stay of 13.5±4.8 days. Our hospital unit has not reported any COVID-19 related death so far. Conclusions: Early intervention in COVID-19 disease improves patients’ prognosis. Our data demonstrate the superiority of early radiological tests ahead of RT-PCR. The initial and dynamic CT changes in COVID-19 patients along with other clinical data shared above can better guide clinical decision making.
Efficient Prediction of Link Outage in Mobile Optical Wireless Communications

Optical wireless networks, especially those relying on visible light communications, suffer from severe deterioration in signal’s quality when the line-of-sight (LOS) link is absent due to user’s mobility. In order to enable efficient resource management within such networks, reliable prediction of LOS link outage is essential. Towards this objective, this article proposes a data-driven approach based on deep machine learning techniques to predict the outage events in an LOS link. First, we present a framework to generate sufficient data representing the channel gain in mobile optical wireless networks that consist of visible light communications in the downlink and infrared communications in the uplink. Using the developed dataset, we propose a channel predictor that forecasts the burst outages or signal recoveries in the upcoming frames using a deep recurrent neural network that implements long-short-term-memory (LSTM) units. To achieve this goal, we propose a low-complexity approach to reduce the data sparsity due to the user’s mobility by abstracting and densifying the channel state sequence. For a one second prediction interval, the proposed prediction framework achieves an event hit rate of 91.55% for abrupt outages with an average event timing error of 79 ms, and 83.19% for recoveries from outages with 145 ms timing error. This timing error is on the same order of magnitude with the coherence time of the optical wireless channel. Therefore, this predictor is very useful in developing efficient resource management strategies in such optical networks.
Learning from Multi-Class Positive and Unlabeled Data

Positive-unlabeled (PU) learning handles the problem of learning a predictive model from PU data. Past few years have witnessed the boom of PU learning, while the existing learning algorithms are limited to binary classification and cannot be directly applied to multi-class PU data. In this paper, we present an unbiased estimator of the original classification risk for multi-class PU learning, and show that the direct empirical risk minimization suffers from the severe overfitting problem because the risk is unbounded below. To address this problem, we propose an alternative risk estimator, and theoretically establish an estimation error bound. We show that the estimation error of its empirical risk minimizer achieves the optimal parametric convergence rate. Extensive experiments on multiple datasets demonstrate the effectiveness of the proposed approach for multiclass PU learning.
Shape‐Memory Effects in Polymer Networks Containing Reversibly Associating Side‐Groups

Shape-memory polymers (SMPs) are far from reaching their technological maturity, and they offer tremendous potential to the fields of biotechnology, medicine, and polymer processing. [1,2] The majority of shape-memory polymers contain physical crosslinks formed through crystallization of polymers [3–8] or block copolymers. [9,10] These materials typically exhibit phase-segregated morphologies consisting of soft and hard domains. If cooled while under stress, crystallization or vitrification of soft domains can temporarily “pin” the material in an elastically strained state. Upon subsequent heating, soft domains melt, and the loss of crystalline forces triggers a shape-memory transition, returning the material to its pre-programmed state. Shape-memory transitions have also been reported upon heating through a polymer’s glass transition [11,12] or upon irradiation with light. [13–15] However, there are no examples where thermoreversible, non-covalent interactions including ion-ion, ion-dipole, or stronger H-bonding interactions have been utilized in SMPs. The use of such interactions in SMPs will offer several improvements over existing materials. In particular, SMPs are envisioned that 1) are transparent to light at all processing temperatures, 2) exhibit amorphous or rubbery “fixed” states, and 3) enable precise tuning of shape-memory recovery rates. In the following, we demonstrate, for the first time, that reversible non-covalent interactions, such as self-complementary H-bonding groups, can effectively be used to stabilize mechanically strained states of a lightly crosslinked elastomer forming a new class of shape-memory polymers. Additionally, we have applied a simple mechanical model to explain material creep and shape-memory behavior.
An Ensemble Learning Approach for Enhanced Classification of Patients With Hepatitis and Cirrhosis

Hepatitis C is an infectious disease that affects more than 70 million people worldwide, even killing 400 thousand of them annually. To better understand this disease and its prognosis, medical doctors can take advantage of the electronic health records (EHRs) of patients, which contain data that computer-based approaches built on statistics and computational intelligence can process to unveil new discoveries and trends otherwise unnoticeable by physicians. In this study, we analyze EHRs of 540 healthy controls and 75 patients diagnosed with hepatitis C, and use machine learning classifiers to predict their diagnosis. We employ the top classifier (Random Forests) to detect the most diagnostic variables for hepatitis C, that result being aspartate aminotransferase (AST) and alanine aminotransferase (ALT). These two enzyme levels are also employed by physicians in the AST/ALT ratio, a traditional measure commonly employed in gastroenterology and hepatology. We apply the same approach to a validation dataset of 123 patients with hepatitis C and cirrhosis, and the same two variables arose as most relevant. We therefore compared our approach with the AST/ALT ratio, and noticed that our two-features ensemble learning model outperforms the traditional AST/ALT ratio on both datasets. Our results confirm the usefulness of ensemble machine learning for hepatitis C and cirrhosis diagnosis prediction. Moreover, our discoveries can have an impact on clinical practice, helping physicians predict diagnoses of patients at risk of hepatitis C and cirrhosis more precisely.
Batch Normalization Masked Sparse Autoencoder for Robotic Grasping Detection

To improve the accuracy of the grasping detection, this paper proposes a novel detector with batch normalization masked evaluation model. It is designed with a two-layer sparse autoencoder, and a Batch Normalization based mask is incorporated into the second layer of the model to effectively reduce the features with weak correlation. The extracted features from such model are more distinctive, which guarantees the higher accuracy of the grasping detection. Extensive experiments show that the proposed evaluation model outperforms the state-of- the-art, and the recognition accuracy can reach 95.51% for robotic grasping detection.
Incomplete Trapping in Supported Liquid Membrane Extraction with a Stagnant Acceptor for Weak Bases

Supported liquid membrane extraction is a powerful analytical methodology for continuous extraction and enrichment of ionizable organic pollutants from aqueous environmental samples. Sample workup with this technique can be hampered by low enrichment factors due to incomplete trapping in the stagnant acceptor solution for weak basic compounds. The different parameters, such as acceptor pH and ionic strength of both the donor and acceptor solutions, related to this problem were studied theoretically and experimentally. A simple equation for the maximum enrichment factor was developed. Results showed that the ionic strengths of the two aqueous phases in the system have an effect on the maximum enrichment factor, which can be increased by increasing the ionic strength of the donor phase. For chloro-s-triazines, a satisfactory agreement was obtained between the experimental measurements and theoretical estimations. This study defines the practical analytical applications and limitations of the system where th.
Robust Product Rating Rules Against Herding Effects: Theory and Applications

Online rating systems are often used in numerous web or mobile applications, e.g., Amazon and TripAdvisor, to assess the ground-truth quality of products. Due to herding effects, the aggregation of historical ratings (or historical collective opinion) can significantly influence subsequent ratings, leading to misleading and erroneous assessments. We study how to manage product ratings via rating aggregation rules and shortlisted representative reviews, for the purpose of correcting the assessment error. We first develop a mathematical model to characterize important factors of herding effects in product ratings. We then identify sufficient conditions (via the stochastic approximation theory), under which the historical collective opinion converges to the ground-truth collective opinion of the whole user population. These conditions identify a class of rating aggregation rules and review selection mechanisms that can reveal the ground-truth product quality. We also quantify the speed of convergence (via the martingale theory), which reflects the efficiency of rating aggregation rules and review selection mechanisms. We prove that the herding effects slow down the speed of convergence while an accurate review selection mechanism can speed it up.
GaN, AlN, and InN: A review

The status of research on both wurtzite and zinc‐blende GaN, AlN, and InN and their alloys is reviewed including exciting recent results. Attention is paid to the crystal growth techniques, structural, optical, and electrical properties of GaN, AlN, InN, and their alloys. The various theoretical results for each material are summarized. We also describe the performance of several device structures which have been demonstrated in these materials. Near‐term goals and critical areas in need of further research in the III–V nitride material system are identified.
Towards Collaborative Intrusion Detection Enhancement against Insider Attacks with Multi-Level Trust

With the speedy growth of distributed networks such as Internet of Things (IoT), there is an increasing need to protect network security against various attacks by deploying collaborative intrusion detection systems (CIDSs), which allow different detector nodes to exchange required information and data with each other. While due to the distributed architecture, insider attacks are a big threat for CIDSs, in which an attacker can reside inside the network. To address this issue, designing an appropriate trust management scheme is considered as an effective solution. In this work, we first analyze the development of CIDSs in the past decades and identify the major challenges on building an effective trust management scheme. Then we introduce a generic framework aiming to enhance the security of CIDSs against advanced insider threats by deriving multilevel trust. In the study, our results demonstrate the viability and the effectiveness of our framework.
Condensation and jumping relay of droplets on lotus leaf

Dynamic behavior of micro water droplet condensed on a lotus leaf with two-tier roughness is studied. Under laboratory environment, the contact angle of the micro droplet on single micro papilla increases smoothly from 80° to 160° during the growth of condensed water. The best-known “self-cleaning” phenomenon will be lost. A striking observation is the out-of-plane jumping relay of condensed droplets triggered by falling droplets, as well as its sustained speed obtained in continuous jumping relays. The underlying mechanism can be used to enhance the automatic removal of dropwise condensation without the help from any external force. The surface tension energy dissipation is the main reason controlling the critical size of jumping droplet and its onset velocity of rebounding.
Spectral Efficiency in Poisson Cluster Based HetNets with Users-Basestations Correlation

In this paper, we consider a realistic K tier heterogeneous cellular network (HetNet) consisting of two types of coexisting tiers: Poisson cluster process (PCP) based tiers with base-stations (BSs) and users (UE) distributed as PCP and are coupled, and Poisson point process (PPP) based tier with BSs and UEs distributed as mutually-independent PPPs. We derive the spectral efficiency (SE) for a typical user belonging to one of the tier under max-power association and open access. An important intermediate step involves deriving expressions for the Laplace transforms (LT) of total received power and interference which also opens new avenues for computation of other performance metrics. We also investigate the impact of clustering over the performance of both type of users via numerical simulation and provide important design insights.
Predicting Left Ventricular Mass Using ECG, Demographic and DXA Features

The gold standard for the assessment of cardiac mass is cardiac magnetic resonance imaging (CMR). However, it is costly and requires specific expertise. Electrocardiographic (ECG) criteria could provide a low-cost solution, but have shown to be poorly correlated with LVM in athletes. We hypothesize that this poor correlation could be overcome by taking into account body measurements (length, weight) and composition (fat mass, lean mass and bone mass). The objective was to assess whether adding demographic (Demo) and/or Dual-energy X-ray absorptiometry (DXA) features could improve an ECG-based regression model for the estimation of LVM in athletes. 107 young competitive endurance athletes (19±2 years; 35 female) underwent a 12-lead ECG, a DXA scan and CMRI. We constructed four feature subsets: ECG, ECG+Demo, ECG+DXA and All. The best combination of features from each set, was used to build a Support Vector Machines regression model with 5 features. The ECG model performed significantly worse than all other models (R2 = 0.28 (0.17), RMSE = 34.33 (5.63) g). The best performing model was constructed with the entire feature set ((R2 = 0.67 (0.14), RMSE = 23.08 (4.42) g). These results suggest that an ECG based regression model for LVM prediction can be improved by adding demographic and/or body composition features.
Inverted polymer solar cells with 8.4% efficiency by conjugated polyelectrolyte

Bulk heterojunction (BHJ) polymer solar cells (PSCs) that can be fabricated by solution processing techniques are under intense investigation in both academic institutions and industrial companies because of their potential to enable mass production of flexible and cost-effective alternative to silicon-based solar cells. A combination of novel polymer development, nanoscale morphology control and processing optimization has led to over 8% power conversion efficiencies (PCEs) for BHJ PSCs with a conventional device structure. Attempts to develop PSCs with an inverted device structure as required for achieving high PECs and good stability have, however, met with limited success. Here, we report that a high PCE of 8.4% under AM 1.5G irradiation was achieved for BHJ PSCs with an inverted device structure. This high efficiency was obtained through interfacial engineering of solution-processed electron extraction layer, leading to facilitate electron transport and suppress bimolecular recombination. These results provided an important progress for solution-processed PSCs, and demonstrated that PSCs with an inverted device structure are comparable with PSCs with the conventional device structure.
Synthesis and properties of new crystalline poly(arylene ether nitriles)

Crystalline poly(arylene ether nitrile) could be prepared by the polycondensation of 2,6-dihalobenzonitrile with resorcinol at 200°C in NMP. A reaction temperature of at least 200°C was necessary to attain high molecular weight polymer. The polymer had the structure of a poly(meta-phenylene ether) with pendent nitrile groups on every other phenylene unit. Despite this structure, the crystallinity and the crystallization rate of the polymer were greater than those of the corresponding polymer with a para-linked structure. The T g and the melting temperature of the polymer were almost the same as those of poly(etheretherketone) (PEEK)
Online Reentry Trajectory Optimization Using Modified Sequential Convex Programming for Hypersonic Vehicle

In this article, a highly nonlinear trajectory optimization problem for reentry vehicles is rapidly solved by the proposed modified sequential convex programming (MSCP) method. A logarithm linearization approach is proposed to decouple the dynamics of high order and nonconvex path constraints of heat rate, dynamic pressure, and normal load. Next, the model including the no-fly zone constraint, and the nonconvex objective function are convexified with first-order Taylor series expansion. Subsequently, the continuous-time optimal problem is converted to an equivalent finite-dimensional sequential convex programming (SCP) problem. Moreover, a compensation term is added to the state equations to maintain the feasibility of the reformulated problem. Consequently, to guarantee the optimality of the solution, a penalty term with respect to the compensation term is added to the objective function. In the end, an online optimization scheme with MSCP is proposed. Compared to a general proposed optimal control solver, numerical simulations are conducted to verify the optimality and fast convergence of MSCP. Furthermore, online optimization simulations demonstrate the validity of the proposed online scheme for pop-up no-fly zone and mission temporary changes.
Anodic Glow and Conductive Channel Formation in Single and Double Long Vacuum Gaps

The evolution processes of vacuum breakdowns in single- and double-break vacuum interrupters are reported in this paper, in order to reveal the formation mechanisms for the conductive channels in vacuum arcs. Breakdown experiments in two vacuum interrupters containing 10–30 mm gaps with single-break and double-break are studied. The light emission during breakdowns is observed by an intensified charge coupled device camera, with the current and voltage waveforms being recorded simultaneously. According to the results, there is no difference between the evolution processes of breakdowns in small gaps and large gaps. The establishment of the conductive channel and sustaining of the vacuum arc can be achieved by the effect of the cathode without any contribution from the anodic glow. In the double-break interrupter, the transition from insulation to conduction for the two serial connected gaps starts simultaneously but evolves independently.
Intelligent Vehicle Environment Scene Parsing Method Based on Multi-tasking Convolutional Neural Network*

An encoder-decoder convolutional neural network architecture is presented integrating multi-class semantic segmentation and multi-object detection to improve the efficiency and depth of scene parsing of intelligent vehicle. The encoder of the network is designed as a multi-scale structure to improve real-time performance while ensuring the accuracy. The decoders of the network comprise the semantic segmentation and object detection subnetworks, which share encoder feature maps to improve computational efficiency. During the training process, we use FPS (Frames Per Second) and MIoU (Mean Intersection over Union) as the evaluation metrics of semantic segmentation, while the mAP (mean Average Precision) and FPS are used as the performance evaluation indexes of object detection. We conduct separate and joint training on the network to evaluate its performance. Experimental results show that the proposed network can realize multi-class semantic segmentation and multi-object detection simultaneously with better real-time performance and richer feature information, making it highly possible for implementation on real vehicles.
Flame Temperature Prediction Using Machine Learning Model

The intensity or amount of heat present in any material, substance, or object is known as temperature. The process of measuring temperature is a tiresome and complex task from any visible heat source. The process of measuring temperature is known as thermometry. It plays a vital role in various industrial and manufacturing processes. There are several devices or gadgets present which are used for measuring temperature like a thermistor, Resistance Temperature Detector (RTD), infrared thermometer, thermocouples, pyrometers, etc. Every temperature measuring instrument has its demerits. While measuring temperature in some devices, one must be very alert because it is a necessity to check that the temperature of the material or substance should be less than or equal to the instrument temperature. In some instruments, the high temperature reduces productivity, and the efficiency of the sensors present in it. Some devices face the drawback of difference in temperature because in such types of devices there is a threshold temperature. If the temperature exceeds the threshold temperature in such a case, the measured temperature will differ with the temperature of the system. Under such circumstances, it will deviate from the original heat transfer property. To overcome all these drawbacks a machine learning model is proposed to detect approx. temperature using the color-temperature correlation approach. In this proposed system, histogram backprojection is used for pre-processing of the input image to derive the color of the flame. To predict the temperature, Support Vector Machine (SVM) and Artificial Neural Network (ANN) have been used and compared. Simulation results show that Support Vector Machine outperforms Artificial Neural Network.