Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of face coverings on cough measurement characterization

In light of the current COVID-19 pandemic response, researchers around the world have been evaluating ways to support all aspects of disease identification, monitoring and tracking. The idea of using audio-based processing methods to evaluate cough events, one of the most common symptoms of COVID-19, in terms of their frequency, severity and characterization has become a promising possible solution. In addition to physical distancing measures, the vast majority of the health authority also recommends the adoption of face coverings (i.e. masks) while in the presence of others and covering one’s cough with a bent elbow. The covering of cough events may present an issue when evaluating recordings using pre-existing cough analysis tools. This paper presents a modeling approach used to characterize the effects of both coughing while wearing a mask and coughing into a bent elbow. These two models were then applied to an existing dataset for evaluating the influence of the face coverings on selected data features that have been used for differentiating wet and dry cough types. It was found that one of the features (number of peaks in the energy spectrum) did not change after mask and elbow modeling, however the second feature (power ratio) was greatly affected and was unable to differentiate between the cough types. The application of these models are therefore recommended when using classification tools that were designed using uncovered clear cough sounds in order to ensure that they will be robust to the presence of face coverings.
DA-RoadNet: A Dual-Attention Network for Road Extraction From High Resolution Satellite Imagery

Recent advances in deep-learning methods have shown extraordinary performance in road extraction from high resolution satellite imagery. However, most existing deep-learning network models yield discontinuous and incomplete results because of shadows and occlusions. To address this problem, a dual-attention road extraction network (DA-RoadNet) with a certain semantic reasoning ability is proposed. First, DA-RoadNet is designed based on a shallow encoder-to-decoder network with densely connected blocks, which can minimize the loss of road structure information caused by multiple down-sampling operations. Moreover, by constructing a novel attention mechanism module, the proposed network is able to explore and integrate the invisible correlations among road features with their global dependency in spatial and channel dimension respectively. Finally, considering that the proportion of road samples is small in the satellite imagery, a hybrid loss function is appended to handle class imbalance, which enables the network model to train stablely and avoid local optimum. The validation experiments using two open road datasets demonstrate that the proposed DA-RoadNet can effectively solve discontinuous problems and preserve integrity of the extracted roads, thus resulting in a higher accuracy of road extraction compared with other developed state-of-the-arts. The considerable performance on the two challenging benchmarks also proves the powerful generation ability of our method.
Statistical correlation analysis between kinematic features and clinical indexes and scales for obese patients

The study of posture and gait abnormalities has revealed over years potential information to improve the rehabilitation outcome of several classes of ill patients; nevertheless, this results still an area of research almost completely unexplored in the case of obese patients. Consequently, this study was designed as a preliminary investigation to determine potential statistical correlations between kinematic features and “gold standard” methodologies in the field, e.g., the Western Ontario and Mc Master University scale and the Barthel index. To this aim, physicians prepared bioelectrical impedance analyses and clinical assessments to evaluate patients' clinical scores, while biomedical engineers have organized Instrumented Stand and Walking tests to quantify several kinematic features using a microelectromechanical system equipped by a series of inertial measurement units. Finally, a statistical correlation analysis has allowed to reveal several features – related to patients’ anticipatory postural adjustments movements and gait – demonstrated a mild and moderate correlation with some clinical indices. In conclusion, this paper presents a novel view to address and design innovative rehabilitation strategies for obese patients.
Video summarization with a graph convolutional attention network

Video summarization has established itself as a fundamental technique for generating compact and concise video, which alleviates managing and browsing large-scale video data. Existing methods fail to fully consider the local and global relations among frames of video, leading to a deteriorated summarization performance. To address the above problem, we propose a graph convolutional attention network (GCAN) for video summarization. GCAN consists of two parts, embedding learning and context fusion, where embedding learning includes the temporal branch and graph branch. In particular, GCAN uses dilated temporal convolution to model local cues and temporal self-attention to exploit global cues for video frames. It learns graph embedding via a multi-layer graph convolutional network to reveal the intrinsic structure of frame samples. The context fusion part combines the output streams from the temporal branch and graph branch to create the context-aware representation of frames, on which the importance scores are evaluated for selecting representative frames to generate video summary. Experiments are carried out on two benchmark databases, SumMe and TVSum, showing that the proposed GCAN approach enjoys superior performance compared to several state-of-the-art alternatives in three evaluation settings.
Host range, genetic variability, and mating types of Lecanosticta acicola in Slovakia

ABSTRACT In recent years, there has been an apparent Europe-wide emerging presence of brown spot needle blight, a disease of pine species caused by the fungus Lecanosticta acicola. In this study, we report the first well-documented occurrences of the pathogen in Slovakia, identified molecularly using species-specific primers and based on sequencing of the ITS region and TEF1 gene. Among the material collected from 84 locations within the country, L. acicola was present in 17 samples from 13 different locations, mainly distributed in urban environments. Four pine species were identified as hosts, among which, Pinus nigra and P. mugo were found to be the most frequently infected. Analysis of the mating type genes of 24 isolates obtained from two localities revealed the presence of a single mating type in Slovakia. All analyzed ITS sequences of the Slovak isolates were found to be uniform. However, although analysis of the TEF1 gene indicated that all Slovak isolates could be grouped into a single lineage, we detected nucleotide polymorphisms suggestive of a certain degree of genetic diversification within central European populations of the fungus.
Propene Oligomerization using Alkali Metal- and Nickel-Exchanged Mesoporous Aluminosilicate Catalysts

A series of alkali metal- and nickel-exchanged Al-MCM-41 catalysts were prepared via aqueous ion exchange and then investigated for gas-phase oligomerization of propene at 453 K and near ambient pressures. All catalysts were active and produced oligomers with >98% selectivity. The highest activities per Ni2+ cation were observed when the cations were highly dispersed as a consequence of either lowering the Ni loading for a fixed MCM-41 Si/Al ratio or by decreasing the concentration of exchangeable sites within the material by increasing the MCM-41 Si/Al ratio at a fixed Ni loading. The identity of the alkali metal cation had no significant effect on the catalytic activity or degree of dimer branching, except for the sample containing Cs+ cations, where the decreased pore volume resulted in a lower catalyst activity and slightly more linear dimer products. Comparison of Ni-MCM-41 prepared with and without Na+ cations showed that a higher yield of oligomers could be achieved when Na+ cations are present bec.
Investigation on Effect of Irradiance Change in Maximum Power Extraction From PV Array Interconnection Schemes During Partial Shading Conditions

This paper presents the effect of irradiance change in solar photovoltaic (PV) array interconnection schemes under partial shading conditions (PSCs) for optimal selection of PV interconnection schemes in order to extract maximum power (MP) from the PV array. A $4{\times }4$ PV array is considered to examine the well-known PV interconnection schemes under different shading scenarios at distinct irradiance conditions. Simulation studies and experimental verification are carried out by considering a $30~W$ PV panel to analyse the performance of the PV array under various dynamic conditions. Comprehensive analysis in terms of fill factor, output power and mismatching power loss is presented by considering different interconnection schemes under various shading scenarios and irradiance levels. From the simulation studies and experimental verification, it is observed that the irradiance magnitude has a significant impact on the PV power generation from array interconnection schemes under PSCs wherein the performance characteristics of the array interconnection schemes vary with the variation in irradiance levels. It is also identified that the irradiance factor needs to be considered as a design variable while selecting the optimal PV array interconnection schemes for maximum power extraction under PSCs.
NiaAML: AutoML framework based on stochastic population-based nature-inspired algorithms

The field of Automated Machine Learning (AutoML) has been developed to automate data preprocessing and search for optimal algorithms together with their hyperparameters in order to discover the best possible ML pipeline for an input dataset (Hutter et al., 2019). AutoML can be modeled as a continuous optimization problem with several potential optimization methods considered. Stochastic population-based nature-inspired algorithms (Engelbrecht, 2007; Yang, 2014) are a popular class of tools for dealing with such continuous optimization problems. These algorithms are inspired mainly by the biological behavior of various species living in nature (Fister Jr et al., 2013). Such algorithms are composed of a population of individuals that undergo different variation operations during the evolution process which results in new populations. The Python framework we have developed, NiaAML, incorporates these stochastic algorithms to search for the most suitable classification pipeline in a dataset (Fister Jr. et al., 2020).
Satellite Flood Inundation Assessment and Forecast Using SMAP and Landsat

The capability and synergistic use of multisource satellite observations for flood monitoring and forecasts is crucial for improving disaster preparedness and mitigation. Here, surface fractional water cover (FW) retrievals derived from Soil Moisture Active Passive (SMAP) L-band (1.4 GHz) brightness temperatures were used for flood assessment over southeast Africa during the Cyclone Idai event. We then focused on five subcatchments of the Pungwe basin and developed a machine learning based approach with the support of Google Earth Engine for daily (24-h) forecasting of FW and 30-m inundation downscaling and mapping. The Classification and Regression Trees model was selected and trained using retrievals derived from SMAP and Landsat coupled with rainfall forecasts from the NOAA Global Forecast System. Independent validation showed that FW predictions over randomly selected dates are highly correlated (R = 0.87) with the Landsat observations. The forecast results captured the flood temporal dynamics from the Idai event; and the associated 30-m downscaling results showed inundation spatial patterns consistent with independent satellite synthetic aperture radar observations. The data-driven approach provides new capacity for flood monitoring and forecasts leveraging synergistic satellite observations and big data analysis, which is particularly valuable for data sparse regions.
Design of an Artificial Muscle Pressure Supply Mode Based on the Connected Structure

This paper proposes an air pressure supply structure for artificial muscles. The main body of the structure comprises a hollow tube, an electromagnet in the outer layer, and a magnetic piston in the inner diameter of the tube. At both ends, a hose interface connects the air inlet of the artificial muscle. Under the action of controlling changes in current, the electromagnet nested in the outer wall causes the movement of the piston by changing the force between the electromagnet and the magnetic piston and by changing the law of air pressure in the tube. Because the inside of the tube is a closed space, the movement of the magnetic piston in the tube causes a change in the volume of the gas at both ends, thus forming pressure differences of different sizes and directions. Therefore, this air supply, with specific oscillatory characteristics, can be used to produce the desired movement of artificial muscles. Through system modeling, theoretical analysis, and simulation experiments of the connected pressure supply structure, we verified that the system has inherent characteristics similar to a spring damping structure. In view of the inherent characteristics of this kind of structure, this paper introduces the trend of input and output changes by considering the deviation value, details how to improve the traditional neural network PID control algorithm, and discusses the intelligent optimization of controller parameters. Simulation results show that the improved control method can effectively overcome the nonlinear and coupling characteristics of the system, and the gas supply structure can provide a continuous pressure supply curve of an arbitrary waveform and a frequency within a certain amplitude range. The designed air supply structure was applied to a quadruped robot, using its oscillating characteristics to generate rhythmic movement. Compared with the traditional pressure control method, the piston was driven to produce reciprocating motion by fully exploiting the energy stored in the compressed gas, so as to reduce the external energy input and reduce the comprehensive energy consumption of the system. In addition, the control algorithm improved in this paper can meet diverse pressure requirements for driving artificial muscles. Moreover, the independent control of leg support force and stiffness can be realized by combining it with the antagonistic joints. This structure can be widely used in the pressure supply of outdoor robot artificial muscle.
Interval Solution to Fuzzy Relation Inequality With Application in P2P Educational Information Resource Sharing Systems

Max-min fuzzy relation inequalities have recently been introduced to describe the peer-to-peer (P2P) educational information resource sharing systems. It is well known that the complete solution set of the max-min fuzzy relation system is fully determined by its minimal solutions. However, solving all the minimal solutions has been proven to be equivalent to the set-covering problem, which is NP-hard. Without solving the complete solution set, some specific solutions can be obtained through the corresponding fuzzy relation optimization problems. However, these solutions are usually unstable and fragile. Any minor fluctuations to the components of these specific solutions will cause them to be no longer feasible. In this work, we define and study the widest interval solution of a max-min fuzzy relation inequality system for the first time. The interval solution allows the solution to fluctuate within some range. The fluctuation range is determined by the width of the interval solution. We propose a novel resolution method for searching for the widest interval solution. The resolution method is carried out by detailed procedures and illustrated by a numerical example.
The structure of metahohmannite, Fe3+2[O(SO4)2]⋅4H2O, by in situ synchrotron powder diffraction

Abstract Metahohmannite, Fe3+2[O(SO4)2]⋅4H2O, is a hydrated sulfate of ferric iron that occurs in sulfate deposits in the desert areas of Northern Chile. The compound used for this study was obtained as a dehydration product of hohmannite, Fe3+2[O(SO4)2]⋅(4+4)H2O. Intensities for the structure analysis were collected from a powdered sample using in situ synchrotron X-ray powder diffraction at ESRF (Grenoble, France). The structure was solved ab initio by profile deconvolution and the application of standard Patterson and difference Fourier maps. The structure was refined to Rp = 5.46% using the Rietveld method. Metahohmannite crystallizes in the triclinic system, space group P1̅ with unit-cell parameters a = 7.3484(5) Å, b = 9.7710(6) Å, c = 7.1521(5) Å, α = 91.684(5)°, β = 98.523 (5)°, γ = 86.390 (5)°, and Z = 2. The structure consists of four Fe3+ octahedra and four sulfate tetrahedra, which share vertices and edges to form a complex building block of Fe3+2[O(SO4)2]⋅8H2O composition. Such blocks are connected to form chains running parallel to the c axis. A complicated system of hydrogen bonds connects adjacent chains into a three-dimensional network. Finally, the crystal structures of metahohmannite, hohmannite, and amarantite are compared and the geometrical features discussed in detail.
Kinetic.jl: A portable finite volume toolbox for scientific and neural computing

Kinetic.jl is a lightweight finite volume toolbox written in the Julia programming language for the study of computational physics and scientific machine learning. It is an open-source project hosted on GitHub and distributed under the MIT license. The main module consists of KitBase.jl for basic physics and KitML.jl for neural dynamics. The function library provides a rich set of numerical fluxes and source terms for differential and integral equations. Any advection-diffusion type mechanical or neural equation can be set up and solved within the framework. Machine learning methods can be seamlessly integrated to build data-driven closure models and accelerate the calculation of nonlinear terms. The package is designed to balance programming flexibility for scientific research, algorithmic efficiency for applications, the simplicity for educational usage.
Particle Swarm Sliding Mode-Fuzzy PID Control Based on Maglev System

The magnetic levitation system is a typical open-loop unstable system. To weaken the chattering phenomenon of sliding mode control (SMC), a hybrid control strategy of particle swarm sliding mode-fuzzy PID control was designed. This strategy uses particle swarm optimization (PSO) to optimize the SMC using the method of exponential approach law, so as to obtain the best sliding mode surface parameters and exponential approach rate coefficients, thus shortening the time required to approach the stable region when the nonlinear system is far from the stable point; a state supervision controller was designed by analyzing the stable conditions of sliding mode. When the system approaches the stable region, it smoothly transitions to fuzzy PID control, thereby weakening the chattering phenomenon of SMC. To verify the dynamic performance of the designed algorithm, a magnetic levitation ball system was selected as the controlled object. Through simulation and experiment, it was verified that the controller had strong robustness and strong anti-interference performance. It effectively weakened the chattering and had good adaptability to external disturbance and state variable crossing.
Equations for estimating the above- and belowground biomass of grey alder (Alnus incana (L.) Moench.) and common alder (Alnus glutinosa L.) in Latvia

ABSTRACT This study aims to derive individual tree allometric equations for the above- and belowground biomass components of grey and common alder based on data sets obtained in Latvia. The species aboveground biomass data consisted of 81 trees collected in 27 forest stands. The belowground biomass data were obtained from 27 common alder and 28 grey alder trees sampled throughout Latvia. For grey and common alder, the root-to-shoot ratio decreased with increasing tree dimensions. Our analysis revealed that biomass functions currently used in the national forest inventory overestimate grey alder and underestimate common alder biomass stocks in Latvia. Our findings indicate that the existing local aboveground biomass equation has an overall satisfactory predictive ability compared to the derived equations in this study. The equations from southern Sweden underestimate biomass for mature trees and overestimate small tree biomass, suggesting that even in the hemiboreal region, the grey and common alder populations could have a different distribution of biomass allocated to various components. Model examinations indicated that the derived allometric functions for grey and common alder were unbiased and performed well within the data range and are the best available to substantially improve national estimates of carbon sequestration and biomass resources in Latvia.
A Cross Connected Asymmetrical Switched-Capacitor Multilevel Inverter

A Switched-Capacitor Multilevel Inverter (SCMLI) topology is proposed here, which can generate up to fifteen levels with one unit and can be extended further for getting higher levels. The proposed SCMLI has a lesser number of switching devices with respect to other recently proposed structures presented in this paper. It also has the capacitor self-balancing property. Power loss analysis has also been done using PLECS software. Maximum efficiency of 96.33 % has been achieved. A generalized comparative study has also been carried out with the newly presented topologies in different research articles considering several parameters. In order to validate the structure presented in this paper, simulation is done in Matlab®2018a, and the simulation results obtained are verified using an experimental prototype.
Prior information based channel estimation for millimeter-wave massive MIMO vehicular communications in 5G and beyond

Millimeter wave (mmWave) has been claimed as the viable solution for high-bandwidth vehicular communications in 5G and beyond. To realize applications in future vehicular communications, it is important to take a robust mmWave vehicular network into consideration. However, one challenge in such a network is that mmWave should provide an ultra-fast and high-rate data exchange among vehicles or vehicle-to-infrastructure (V2I). Moreover, traditional real-time channel estimation strategies are unavailable because vehicle mobility leads to a fast variation mmWave channel. To overcome these issues, a channel estimation approach for mmWave V2I communications is proposed in this paper. Specifically, by considering a fast-moving vehicle secnario, a corresponding mathematical model for a fast time-varying channel is first established. Then, the temporal variation rule between the base station and each mobile user and the determined direction-of-arrival are used to predict the time-varying channel prior information (PI). Finally, by exploiting the PI and the characteristics of the channel, the time-varying channel is estimated. The simulation results show that the scheme in this paper outperforms traditional ones in both normalized mean square error and sum-rate performance in the mmWave time-varying vehicular system.