Miracle-dz/UniFAR-FTUs huggingface dataset:
Characteristics of plasmoidlike structures in the distant magnetotail

The average characteristics of 20 energetic particle burst events in the distant geomagnetic tail have been determined. The measurements were made with two particle sensors of the Max-Planck-Institut/University of Maryland experiment on ISEE 3 during the recent Geotail Mission. The events are characterized initally by beamlike energetic electron distributions which are followed by isotropic distributions. Assuming that the energetic protons (≳30 keV) are convected with the local plasma flow (which is generally tailward), we can determine the flow speed from the measured angular distributions and the differential energy spectral shape. We find that the average velocities range from ∼300 to ∼1000 km/s. The size of these structures, most probably plasmoids, along the convective flow direction is observed to be between ∼50 and ∼100 RE in most of the cases, with a mean value of ∼80 RE. The energy spectra (in the plasma frame) are consistently steeper than power law, being closer to exponentials in energy. The differential proton intensity in these events, on the average ∼500 protons cm−2 s−1 sr−1 keV−1 at 30 keV, is an order of magnitude smaller than in the near-earth plasma sheet.
Image Segmentation and Classification with SLIC Superpixel and Convolutional Neural Network in Forest Context

Unmanned aerial vehicles (UAVs) are platforms suitable for obtaining information utilizing sensors in a great variety of areas and enviroments, thus in this context, this paper objective was to identify trees in images collected using UAV high-resolution imagery, with the digital approach of superpixel segmentation and convolutional neural networks. A forest environment was analyzed in the form of an orthomosaic that was produced using 423 images. Superpixels were generated using the Simple Linear Iterative Clustering (SLIC) method, considering only two classes for the classification: trees and background. The generation of superpixels (segmentation) and classification were performed considering the configurations: 2000, 3000 and 4000 segments, sigma 5 and compactness 10 for SLIC and 100% transfer of learning. For the purposes of classification, the deep convolutional networks were adopted through ResNet-50 architecture, the weights of this network were previously trained in an Image bank and later the bank of images of superpixels underwent a fine-tuning. The experiments obtained a maximum classification accuracy of 89% with 3000 superpixels distinction between canopies and Background.
A New Ground Moving Target Imaging Algorithm for High-Resolution Airborne CSSAR-GMTI Systems

This paper proposes a new ground moving target imaging algorithm for high-resolution airborne circular stripmap synthetic aperture radar (CSSAR)-ground moving target indication (GMTI) systems. In the proposed algorithm, the range cell migration correction is performed in the 2D frequency domain via a phase multiplication. The azimuth compression is performed in the range Doppler domain. A key step of the proposed algorithm is to utilize an iterative strategy to achieve an accurate correction of the range cell migration. This step makes the proposed algorithm work well for high-resolution CSSAR-GMTI systems. Numerical simulations are conducted to validate the proposed algorithm.
A Review of Polsar Image Classification: from Polarimetry to Deep Learning

Terrain surface classification is probably the most common application of polarimetric SAR (PolSAR) data. Methods for PolSAR terrain classification can be divided into either supervised or unsupervised. In this paper, PolSAR image classification algorithms are reviewed from traditional polarimetric methods such as alpha-H-, Freeman-, Yamaguchi-decomposition, to deep learning, and then a general deep learning algorithm is proposed to PolSAR data classification. The suitability and potential of deep convolutional neural network in supervised terrain classification of PolSAR images has been investigated. The results show that deep learning based method can be used for PolSAR terrain classification.
Molecular epidemiological characteristics of dengue virus carried by 34 patients in Guangzhou in 2018

Dengue fever is a major worldwide public health problem that, as estimated by the WHO, causes epidemics in over 100 countries, resulting in hundreds of millions of dengue virus (DENV) infections every year. In China, dengue fever mainly occurs in coastal areas. Recurring dengue outbreaks were reported by Guangdong Province almost every year since the first epidemic in 1978. DENV infections persisted in Guangzhou in consecutive years since 2000, with the dengue epidemic reaching a historical peak in 2014. Because Guangzhou is one of the largest cities for opening up in China, understanding the epidemiological characteristics of dengue fever in the city can hopefully provide a significant basis for developing effective dengue prevention strategies. In this study, a total of 34 DENV strains, including 29 DENV-1 strains and 5 DENV-2 strains, were isolated from a blood samples drawn from patients who were diagnosed with dengue fever by hospitals in Guangzhou during 2018. To explore the epidemiological characteristics of dengue fever, the envelope (E) gene obtained from the isolates was amplified for phylogenetic analysis. The results from the phylogenetic analysis showed that DENV in Guangzhou was mainly imported from Southeast Asian countries. Additionally, propagation paths based on phylogeographical analysis suggested potential local dengue transmission in Guangzhou.
Smartwatch based emotion recognition in Parkinson's disease

Parkinson's disease (PD) is a neurodegenerative disorder characterized by several motor and non-motor symptoms. These last, widely, concern affective disorders, among which, alexithymia has great impact on the amount and quality of social relationship and on caregiver well-being. Ubiquitous and quantitative monitoring of patients' emotional status may help to better understand affective disorders. This study proposes a preliminary approach to investigate the feasibility of emotion recognition in PD using physiological data collected by a smartwatch.
Land Cover Spurious Change Detection Using a geo-eco Zoning Rule Base1

Land cover is a commonly used index for characterizing land surfaces and the corresponding human or natural processes. Remote sensing-based land cover maps of different times can be compared to extract changes in land cover. However, remote sensing images reflect only the instantaneous state of the Earth’s surface. Spectral characteristics cannot correctly reflect the actual state due to the differences caused by seasonal changes, and this inability results in the limited classification accuracy of land cover products. Spurious changes will occur when products of different time phases are compared. A method of spurious change detection based on a geo-eco zoning rule base is proposed in this study to solve this problem. The structure and content of the rule base are designed, and a rule base management system is established to identify spurious changes. Land cover products of Southeast Asia are selected as the experimental area to verify the accuracy of the change patches after eliminating spurious changes. Results show that the accuracy of change detection is improved by using the rule base of geo-eco zoning to identify spurious changes.
Pedestrian Heading Estimation Based on Spatial Transformer Networks and Hierarchical LSTM

Accurate heading estimation is the foundation of numerous applications, including augmented reality, pedestrian dead reckoning, and human-computer interactions. While magnetometer is a key source of heading information, the poor accuracy of consumer-grade hardware coupled with the pervasive magnetic disturbances makes accurate heading estimation a challenging issue. Heading error is one of the main error sources of pedestrian dead reckoning. To reduce the heading error and enhance robustness, we proposed a novel heading estimation method based on Spatial Transformer Networks (STNs) and Long Short-Term Memory (LSTM), termed DeepHeading, which uses sensors embedded in a smartphone without any historical training data or dedicated infrastructure. We automatically annotate heading data based on map matching, and augment heading data based on device attitude. We leverage the STNs to align the device coordinate system and the navigation coordinate system, allow an unconstrained use of smartphones. Based on the characteristics of pedestrian heading continuity, we designed a hierarchical LSTM-basedSeq2Seq model to estimate the walking heading of the pedestrian. We conducted well-designed experiments to evaluate the performance of deepheading and compared it with the state-of-the-art heading estimation algorithms. The experimental results on real-world demonstrated that deepheading outperformed the compared heading estimation algorithms and achieved promising estimation accuracy with a median heading error of 4.52°, mean heading error of 6.07° and heading error of 9.18° at the confidence of 80% when a pedestrian is walking in indoor environments with magnetic field disturbances. The proposed method is high-efficiency and easy to integrate with various mobile applications.
Distributed Computation Offloading Based on Stochastic Game in Multi-server Mobile Edge Computing Networks

With the popularity of the Internet of things (IoT), 5G technology needs to meet various requirements of a large number of IoT applications. Mobile edge computing (MEC) is a promising approach in 5G scenario, which can solve the problems of resource shortage and high latency. Computation offloading is a key technology to reduce latency and energy consumption in MEC. In this paper, we consider a scenario with a dense distribution of edge nodes, namely multi-edge server distribution, and focus on the offloading problem in the overlapping coverage area of service scope. We build a two-step game model using the stochastic game theory. We pay attention to the relevance of state transition, that is, the cost of the next state is taken into account when making decisions in current state. In addition, we prove the existence of Nash Equilibrium (NE) by the concept of exact potential game, then propose the best response based on stochastic game (BRSG) algorithm to solve the problem. Numerical results illustrate that our algorithm can reach a NE through finite number of iterations, and an equilibrium strategy can be obtained. Besides, considering the state correlation makes the equilibrium cost significantly lower.
High Voltage Gain Switched Capacitor Boost Converter with ANFIS Controller for Fuel Cell Electric Vehicle Applications

Designing of a high voltage gain or high step-up boost converter is the major challenge in the high power applications. In this work, a high voltage gain Switched Capacitor Boost Converter (SCBC) is designed for fuel cell electric vehicle (FCEV) applications. The designed converter achieves the high voltage gain with low duty cycle and reduces the voltage stress across the power semiconductor switches. The converter output voltage is given to the Brushless DC (BLDC) motor through a DC link and a Voltage Source Inverter (VSI) for the propulsion of the vehicle. An adaptive neuro-fuzzy inference system (ANFIS) based MPPT technique is adopted for the fuel cell system for extracting the maximum power at different temperature conditions. The performance analysis of fuel cell electric vehicle system with SCBC is done by using the MATLAB/Simulink environment.
In vitro antibacterial activity of the manuka essential oil from Leptospermum scoparium combined with Tris-EDTA against Gram-negative bacterial isolates from dogs with otitis externa.

BACKGROUND The increasing prevalence of antimicrobial resistance among bacteria in dogs with otitis externa has led to a need for novel therapeutic agents. HYPOTHESIS/OBJECTIVE To examine the antibacterial effects of manuka oil combined with ethylenediaminetetraacetic acid-tromethamine (Tris-EDTA) against Gram-negative bacteria isolates from dogs with otitis externa. METHODS AND MATERIALS A total of 53 clinical isolates including Pseudomonas aeruginosa, Escherichia coli, Klebsiella pneumoniae ssp. pneumoniae and Proteus mirabilis. Antimicrobial susceptibility was determined using disk diffusion; the minimum inhibitory concentrations (MICs) and minimum bactericidal concentrations (MBCs) of manuka essential oil, with or without Tris-EDTA, were investigated. RESULTS A total of 44 isolates were resistant to at least one antibiotic and 19 strains were multidrug-resistant, with resistance to at least one agent in three or more antimicrobial classes. The MICs and MBCs of manuka oil alone were ≥1% (v/v) and ≥2% (v/v), respectively. There was no antimicrobial effect of Tris-EDTA (1.125:0.3 mg/mL) without manuka oil. However, the combination of manuka oil with Tris-EDTA significantly decreased the MICs (ranging from 0.06% to 0.5%, v/v; P < 0.001) and MBCs (ranging from 0.06% to 1%, v/v; P < 0.001). There also was no significant difference between multidrug-resistant and nonresistant bacterial isolates in terms of the antimicrobial activity of manuka oil with Tris-EDTA. CONCLUSIONS AND CLINICAL IMPORTANCE The study findings suggest that manuka oil, especially when combined with Tris-EDTA, may be a promising alternative therapeutic option for Gram-negative otic pathogens. Clinical studies are needed to assess potential for in vivo ototoxic effects and efficacy.
Rapid Earthquake Damage Detection Using Deep Learning from VHR Remote Sensing Images

Very High Resolution (VHR) remote sensing optical imagery is a huge source of information that can be utilized for earthquake damage detection and assessment. Time critical task such as performing the damage assessment, providing immediate delivery of relief assistance require immediate response; however, processing voluminous VHR imagery using highly accurate, but computationally expensive deep learning algorithms demands the High Performance Computing (HPC) power.To maximize the accuracy, deep convolution neural network (CNN) model is designed especially for the earthquake damage detection using remote sensing data and implemented using high performance GPU without compromising with the execution time. Geoeye1 VHR disaster images of the Haiti earthquake occurred in year 2010 is used for analysis. Proposed model provides good accuracy for damage detection; also significant execution speed is observed on GPU K80 High Performance Computing (HPC) platform.
Improved Utilization of Polsar Polarization Signatures Using Convolutional-Deep Neural Nets For Land Cover Classification

Normalized Euclidean distance (NED) and normalized signature correlation mapper (NSCM) are most popularly used pattern classifiers with polarization signatures (PSs) based polarimetric synthetic aperture radar (PolSAR) data applications. These methods are not able to fully exploit the PSs as they do not exploit the spatial context or pattern of PSs which is essential. Improved utilization of PSs is still required for PolSAR applications such as agriculture crop classification and monitoring. In this study, convolutional deep neural networks (C-DNNs) are introduced and utilized as pattern classifiers for PS classification. C-DNNs have the ability to consider and control the influence of local neighborhood pixels during classification. Therefore, in this study C-DNNs are utilized to extract and exploit subtle changes between PSs of land covers to improve classification performance. Comparison with NED and NSCM classifiers signify the contribution of C-DNNs by improved performance in PolSAR data classification.
Acceptance and Commitment Therapy for Longstanding Chronic Pain in a Community-Based Outpatient Group Setting

Chronic pain affects millions of adults each year and has serious costs both economically and on psychosocial well-being. Acceptance-based behavioral treatments have been developed that improve quality of life and reduce disability, but additional attention to processes of change responsible for good outcomes is needed. While many interventions are effective in the context of comprehensive interdisciplinary treatment centers, such settings have limited reach. Treatments applicable to smaller, communitybased settings are also needed. The current study examined the impact of an outpatient Acceptance and Commitment Therapy group intervention on adults (N=28) recruited from the community with heterogeneous longstanding chronic pain as compared to a wait-list control group. Results showed no between-group differences on process or outcome measures. Within-subjects analyses examining change from baseline at midtreatment, post-treatment, and 4-month follow up indicated trending improvements in pain-related disability and depression outcomes, with changes in disability at post and follow up in line with clinically meaningful change, while quality of life and pain severity did not improve. ACT processes of pain-related avoidance and success at living leisure and relationship values improved at post and follow up with medium to large effect sizes, while mindfulness and coping strategy use did not improve. Limitations include small sample size, high attrition, and low participant engagement. Despite the fact that changes observed cannot be directly attributed to the intervention, important information was gleaned about the feasibility of adapting interventions to community settings and recommendations for next steps in such endeavors are discussed.
Evaluation of the time‐dependent antiproliferative activity and liver microsome stability of 3 phenyl 4‐(2‐oxo‐3‐alkylimidazolidin‐1‐yl)benzenesulfonates as promising CYP1A1‐dependent antimicrotubule prodrugs

In this study, the antiproliferative activity of 3 phenyl 4‐(2‐oxo‐3‐alkylimidazolidin‐1‐yl)benzenesulfonates (PAIB‐SOs) was assessed in a time‐dependent manner together with their hepatic stability and metabolism using human, mouse and rat liver microsomes.
On The Use of CNN for Automated Quality Assessment of GF-3 Polarimetric Data

With the needs of quality assessment for massive GF-3 polarimetric data, a method based on common distribution targets has been proposed by Sha Jiang. However, it needs manually selection of those woodlands, and cannot be performed automatically. In this paper, an automated GF-3 full-polarization SAR data quality assessment method is conducted using a classic Convolution Neural Network (VGG-16). The network is pre-trained by Radarsat-2 PolSAR data and then trained by selected typical GF-3 scenes. It is supposed to learn the features of the targets, which satisfies the azimuthal symmetry and backscatter reciprocity and fulfills the quality assessment work. Several typical GF-3 strips data are used to test the method. Experiments show that the network can predict the plots of targets from a new scene under the interference of polarimetric distortion and noise. And, the quality assessment results by the network are consistent with the manual assessment results, which shows the effectiveness of the method.
Pitch angle Control of Variable Speed Wind Turbine by using Fuzzy Logic

Wind energy conversion systems have to handle the variations in the wind speed for efficient energy conversion. This paper presents an analysis made on the controller effectiveness system for pitch angle control of a wind turbine which changes performance of the wind energy conversion system. A fuzzy logic controller is proposed in place of classical PI controller for controlling the pitch angle to have maximum available power from the wind energy conversion system and the comparisons are made between PI controller, fractional order PI controller and fuzzy logic controller. The system is tested for different realistic environmental conditions using MATLAB/Simulink Software.
Cheater, cheater, pumpkin eater: the Dark Triad, attitudes towards doping, and cheating behaviour among athletes

ABSTRACT We examined the relationships between the Dark Triad personality traits (Machiavellianism, narcissism, and psychopathy), attitudes towards doping, and cheating behaviour among athletes. One-hundred and sixty-four athletes completed a completed a matrix solving task within a specific time limit. Participants were told they would receive a financial reward for the total number matrices they could solve, but only 13 of the 20 matrices were solvable. This provided the incentive and opportunity for the athletes to cheat. Following this, athletes completed two questionnaires, which assessed the Dark Triad and their attitudes towards doping. All three Dark Triad personality traits correlated positively with attitudes towards doping and cheating behaviour. Regression analyses revealed that psychopathy and narcissism positively predicted attitudes towards doping, and narcissism emerged as a positive predictor of cheating behaviour. Attitudes towards doping correlated positively with cheating behaviour. The Dark Triad appears to be important in relation to both attitudes towards doping and cheating behaviour among athletes. In addition, our findings illustrate that favourable attitudes towards doping are linked with actual cheating among athletes. National Anti-Doping Organizations, sports federations, and coaches could assess athletes’ Dark Triad scores and attitudes towards doping in order to identify who may be more likely to cheat.
A novel surgical predictive model for Chinese Crohn's disease patients

Abstract Due to the complexity of Crohn's disease (CD), it is difficult to predict disease course with a single stratification factor or biomarker. A logistic regression (LR) model has been proposed by Guizzetti et al to stratify patients with CD-related surgical risk, which could help decision-making on disease treatment. However, there are no reports on relevant studies on Chinese population. The aim of the study is to present and validate a novel surgical predictive model to facilitate therapeutic decision-making for Chinese CD patients. Data was extracted from retrospective full-mode electronic medical records, which contained 239 CD patients and 1524 instances. Two sub-datasets were generated according to different attribute selection strategies, both of which were split into training and testing sets randomly. The imbalanced data in the training sets was addressed by synthetic minority over-sampling technique (SMOTE) algorithm before model development. Seven predictive models were employed using 5 popular machine learning algorithms: random forest (RF), LR, support vector machine (SVM), decision tree (DT) and artificial neural networks (ANN). The performance of each model was evaluated by accuracy, precision, F1-score, true negative (TN) rate, and the area under the receiver operating characteristic curve (AuROC). The result revealed that RF outperformed all other baseline models on both sub-datasets. The 10 leading risk factors for CD-related surgery returned from RF for attribute ranking were changes of radiology, presence of a fistula, presence of an abscess, no infliximab use, enteroscopy findings, C-reactive protein, abdominal pain, white blood cells, erythrocyte sedimentation rate and platelet count. The proposed machine learning model can accurately predict the risk of surgical intervention in Chinese CD patients, which could be used to tailor and modify the treatment strategies for CD patients in clinical practice.