Miracle-dz/UniFAR-FTUs huggingface dataset:
Correction of in-flight luminosity variations in multispectral UAS images, using a luminosity sensor and camera pair for improved biomass estimation in precision agriculture

Precision farming or agriculture (PA) is a concept where agricultural practices are modulated according to intra-field crop variability. Multispectral sensors have standing use in remote sensing, onboard aircraft and satellites for mapping biomass. With increased miniaturization of sensors, Unmanned Aerial Systems (UAS) become more widely used for multispectral imaging. UAS offer several advantages for PA, such as a relative insensitivity to weather conditions, especially to cloud cover. Most UAS images are acquired in cloudless conditions or with a complete cloud cover to reduce the impact of changing luminosity. This work quantifies the ability to correct luminosity variations on images from UAS flights under varying weather conditions. Measurements were performed with the Parrot Sequoia multispectral camera paired with its Sunshine sensor. Control ground measurements were repeated over two hours on a series of five targets of increasing gray levels. These measurements correlate with corresponding reference spectra from a Spectral Evolution SR-3500 field spectroradiometer. In a second experiment, the camera recorded images every thirty seconds in time-lapse mode, for over an hour, above a reference reflectance target, in order to analyze the evolution of the reflectance over time as a function of the variations of illumination. Finally two different types of UAS carried out several series of flights: a fixed-wing senseFly eBee and an Innovadrone hexacopter rotary wing. This paper presents data analysis with and without the Sunshine sensor correction to quantify the improvement in the quality of reflectance measurements and biomass estimates.
PDEs on the Lie group SE(3) and their application in diffusion-weighted MRI

S. P. L. Meesters, G. R. Sanguinetti, E. Garyfallidis, J. M. Portegies, and R. Duits. Fast implementations of contextual PDE’s for HARDI data processing in DIPY. 2016. S. P. L. Meesters, G. R. Sanguinetti, E. Garyfallidis, J. M. Portegies, P. Ossenblok, and R. Duits. Cleaning output of tractography via fiber to bundle coherence, a new open source implementation. June 2016.
Particle image models for optical flow-based velocity field estimation in image velocimetry

This paper examines two models for image representation used for optical flow estimation in Particle Image Velocimetry (PIV). The common approach for flow estimation bases on a cross-correlation between PIV images. An alternative solution bases on an optical flow, which has the advantage of calculating vector fields with much better spatial resolution. The optical flow-based estimation requires calculations of temporal and spatial derivatives of the image intensity, which is usually achieved by using finite differences. Due to rapid intensity changes in the PIV images caused by particles having small diameters, an exact estimation of spatial derivatives using finite differences may lead to numerical errors that render data interpretation limited or even impractical. The present study aims at solving this problem by introducing two algorithms for PIV image processing, which differs in terms of a digital image representation. Both algorithms rely on a PIV image model, wherein the particle image complies with an Airy disc, which is well approximated by using a Gaussian function. Numerical analysis of sample PIV images (uniform and turbulent fields) show that both methods allow for high precision flow-velocity fields estimates in conjunction with the Lucas-Kanade algorithm.
Expression and Characterization of Two α-l-Arabinofuranosidases from Talaromyces amestolkiae: Role of These Enzymes in Biomass Valorization

α-l-arabinofuranosidases are glycosyl hydrolases that catalyze the break between α-l-arabinofuranosyl substituents or between α-l-arabinofuranosides and xylose from xylan or xylooligosaccharide backbones. While they belong to several glycosyl hydrolase (GH) families, there are only 24 characterized GH62 arabinofuranosidases, making them a small and underrepresented group, with many of their features remaining unknown. Aside from their applications in the food industry, arabinofuranosidases can also aid in the processing of complex lignocellulosic materials, where cellulose, hemicelluloses, and lignin are closely linked. These materials can be fully converted into sugar monomers to produce secondary products like second-generation bioethanol. Alternatively, they can be partially hydrolyzed to release xylooligosaccharides, which have prebiotic properties. While endoxylanases and β-xylosidases are also necessary to fully break down the xylose backbone from xylan, these enzymes are limited when it comes to branched polysaccharides. In this article, two new GH62 α-l-arabinofuranosidases from Talaromyces amestolkiae (named ARA1 and ARA-2) have been heterologously expressed and characterized. ARA-1 is more sensitive to changes in pH and temperature, whereas ARA-2 is a robust enzyme with wide pH and temperature tolerance. Both enzymes preferentially act on arabinoxylan over arabinan, although ARA-1 has twice the catalytic efficiency of ARA-2 on this substrate. The production of xylooligosaccharides from arabinoxylan catalyzed by a T. amestolkiae endoxylanase was significantly increased upon pretreatment of the polysaccharide with ARA-1 or ARA-2, with the highest synergism values reported to date. Finally, both enzymes (ARA-1 or ARA-2 and endoxylanase) were successfully applied to enhance saccharification by combining them with a β-xylosidase already characterized from the same fungus.
Data augmentation in food science: Synthesising spectroscopic data of vegetable oils for performance enhancement

Generating more accurate, efficient, and robust classification models in chemometrics, able to address real‐world problems in food analysis, is intrinsically related with the amount of available calibration samples. In this paper, we propose a data augmentation solution to increase the performance of a classification model by generating realistic data augmented samples. The feasibility of this solution has been evaluated on 3 main different experiments where Fourier transform mid infrared (FT‐IR) spectroscopic data of vegetable oils were used for the identification of vegetable oil species in oil admixtures. Results demonstrate that data augmented samples improved the classification rate by around 19% in a single instrument validation and provided a significant 38% improvement in classification when testing in more than 10 different spectroscopic instruments to the calibration one.
An Efficient Ensemble Approach for Alzheimer’s Disease Detection Using an Adaptive Synthetic Technique and Deep Learning

Alzheimer’s disease is an incurable neurological disorder that leads to a gradual decline in cognitive abilities, but early detection can significantly mitigate symptoms. The automatic diagnosis of Alzheimer’s disease is more important due to the shortage of expert medical staff, because it reduces the burden on medical staff and enhances the results of diagnosis. A detailed analysis of specific brain disorder tissues is required to accurately diagnose the disease via segmented magnetic resonance imaging (MRI). Several studies have used the traditional machine-learning approaches to diagnose the disease from MRI, but manual extracted features are more complex, time-consuming, and require a huge amount of involvement from expert medical staff. The traditional approach does not provide an accurate diagnosis. Deep learning has automatic extraction features and optimizes the training process. The Magnetic Resonance Imaging (MRI) Alzheimer’s disease dataset consists of four classes: mild demented (896 images), moderate demented (64 images), non-demented (3200 images), and very mild demented (2240 images). The dataset is highly imbalanced. Therefore, we used the adaptive synthetic oversampling technique to address this issue. After applying this technique, the dataset was balanced. The ensemble of VGG16 and EfficientNet was used to detect Alzheimer’s disease on both imbalanced and balanced datasets to validate the performance of the models. The proposed method combined the predictions of multiple models to make an ensemble model that learned complex and nuanced patterns from the data. The input and output of both models were concatenated to make an ensemble model and then added to other layers to make a more robust model. In this study, we proposed an ensemble of EfficientNet-B2 and VGG-16 to diagnose the disease at an early stage with the highest accuracy. Experiments were performed on two publicly available datasets. The experimental results showed that the proposed method achieved 97.35% accuracy and 99.64% AUC for multiclass datasets and 97.09% accuracy and 99.59% AUC for binary-class datasets. We evaluated that the proposed method was extremely efficient and provided superior performance on both datasets as compared to previous methods.
The Stability and Control of Stochastically Switching Dynamical Systems

Inherent randomness and unpredictability is an underlying property in most realistic phenomena. In this work, we present a new framework for introducing stochasticity into dynamical systems via intermittently switching between deterministic regimes. Extending the work by Belykh, Belykh, and Hasler, we provide analytical insight into how randomly switching network topologies behave with respect to their averaged, static counterparts (obtained by replacing the stochastic variables with their expectation) when switching is fast. Beyond fast switching, we uncover a highly nontrivial phenomenon by which a network can switch between two asynchronous regimes and synchronize against all odds. Then, we establish rigorous theory for this framework in discrete-time systems for arbitrary switching periods (not limited to switching at each time step). Using stability and ergodic theories, we are able to provide analytical criteria for the stability of synchronization for two coupled maps and the ability of a single map to control an arbitrary network of maps. This work not only presents new phenomena in stochastically switching dynamical systems, but also provides the first rigorous analysis of switching dynamical systems with an arbitrary switching
The Einstein@Home Survey for Gamma-ray Pulsars

Abstract Since the launch of the Fermi Gamma-ray Space Telescope in 2008, the onboard Large Area Telescope (LAT) has detected gamma-ray pulsations from more than 200 pulsars. A large fraction of these remain undetected in radio observations, and could only be found by directly searching the LAT data for pulsations. However, the sensitivity of such “blind” searches is limited by the sparse photon data and vast computational requirements. In this contribution we present the latest large-scale blind-search survey for gamma-ray pulsars, which ran on the distributed volunteer computing system, Einstein@Home, and discovered 19 new gamma-ray pulsars. We explain how recent improvements to search techniques and LAT data reconstruction have boosted the sensitivity of blind searches, and present highlights from the survey’s discoveries. These include: two glitching pulsars; the youngest known radio-quiet gamma-ray pulsar; and two isolated millisecond pulsars (MSPs), one of which is the only known radio-quiet rotationally powered MSP.
Unmanned Aerial Systems and Deep Learning for Safety and Health Activity Monitoring on Construction Sites

Construction is a highly hazardous industry typified by several complex features in dynamic work environments that have the possibility of causing harm or ill health to construction workers. The constant monitoring of workers’ unsafe behaviors and work conditions is considered not only a proactive but also an active method of removing safety and health hazards and preventing potential accidents on construction sites. The integration of sensor technologies and artificial intelligence for computer vision can be used to create a robust management strategy and enhance the analysis of safety and health data needed to generate insights and take action to protect workers on construction sites. This study presents the development and validation of a framework that implements the use of unmanned aerial systems (UASs) and deep learning (DL) for the collection and analysis of safety activity metrics for improving construction safety performance. The developed framework was validated using a pilot case study. Digital images of construction safety activities were collected on active construction sites using a UAS, and the performance of two different object detection deep-learning algorithms/models (Faster R-CNN and YOLOv3) for safety hardhat detection were compared. The dataset included 7041 preprocessed and augmented images with a 75/25 training and testing split. From the case study results, Faster R-CNN showed a higher precision of 93.1% than YOLOv3 (89.8%). The findings of this study show the impact and potential benefits of using UASs and DL in computer vision applications for managing safety and health on construction sites.
Multi-objective feasibility enhanced particle swarm optimization

ABSTRACT This article introduces a new method entitled multi-objective feasibility enhanced partical swarm optimization (MOFEPSO), to handle highly-constrained multi-objective optimization problems. MOFEPSO, which is based on the particle swarm optimization technique, employs repositories of non-dominated and feasible positions (or solutions) to guide feasible particle flight. Unlike its counterparts, MOFEPSO does not require any feasible solutions in the initialized swarm. Additionally, objective functions are not assessed for infeasible particles. Such particles can only fly along sensitive directions, and particles are not allowed to move to a position where any previously satisfied constraints become violated. These unique features help MOFEPSO gradually increase the overall feasibility of the swarm and to finally attain the optimal solution. In this study, multi-objective versions of a classical gear-train optimization problem are also described. For the given problems, the article comparatively evaluates the performance of MOFEPSO against several popular optimization algorithms found in the literature.
Direct numerical simulation of turbulent heat transfer in a T-junction

In this paper we report on a direct numerical simulation (DNS) of turbulent heat transfer in a T-junction. In particular, we study the interaction between two liquid streams, a hot horizontal cross-flow and a cold vertical liquid jet coming from above, in a T-junction of rectangular cross-section. We discuss in detail the instantaneous flow structures and present results for the first- and second-order statistics of the flow quantities, and for the budget of the turbulent kinetic energy. Further, we present results of the power spectral density of the velocity and temperature signals at selected locations of the flow field. Our analysis elucidates the properties of the important features of the flow such as the large recirculation bubble and the secondary separation zones that are formed in the vicinity of the entry of the jet. According to our simulations, thermal mixing is mainly driven by the shear layer between the two streams and, to a lesser extent, by the shear layer between the incoming jet and the large recirculation bubble. Thermal mixing is further enhanced by turbulence generation in the regions of adverse pressure gradients downstream of the large recirculation bubble. Within the framework of our study, we have also conducted a wall-resolved large-eddy simulation (LES) of the flow of interest so as to assess its predictive capacity. Overall, the LES predictions agree satisfactorily with our DNS data; the most noticeable discrepancy is that the LES produces mildly diffused profiles for the second-order statistics in the regions of intense turbulence production.
Harmonic Reduction of a Single-Phase Multilevel Inverter Using Genetic Algorithm and Particle Swarm Optimization

Inverter play important role in power system especially with it capability on reducing system size and increase efficient. Recent research trend of power electronics system are focusing on multilevel inverter topic in optimization on voltage output, reduce total harmonics distortion, modulation technique and switching configuration. Standalone application multilevel inverter is high focused due to the rise of renewable energy policy all around the world. Hence, this research emphasis on identify best topology of multilevel inverter and optimize it among the diode-clamped, capacitor clamped and cascaded H-bridge multilevel inverter to be used for standalone application in term of total harmonics distortion and voltage boosting capability. The first part of research that is identify best topology multilevel inverter is applying sinusoidal pulse width modulation technique. The result shown cascade H-bridge give the best output in both total harmonics distortion (9.27%) and fundamental component voltage (240 Vrms). The research proceed with optimization with fundamental switching frequency method that is optimized harmonic stepped waveform modulation method. The selective harmonics elimination calculation have adapt with genetic algorithm and particle swarm optimization in order to speed up the calculation. Both bio-inspired algorithm is compared in term of total harmonic distortion and selected harmonics elimination for both equal and unequal sources. In overall result shown both algorithm have high accuracy in solving the non-linear equation. However, genetic algorithm shown better output quality in term of selected harmonics elimination where overall no exceeding 0.4%. Particle swarm optimization shows strength in finding best total harmonics distortion where in 7-level cascaded H-bridge multilevel inverter (m=0.8) show 6.8% only as compared to genetic algorithm. Simulation for 3-level, 5-level and 7-level for each multilevel inverter at different circumferences had been done in this research. The result draw out a conclusion where the possibility of having a filterless high efficient invert can be achieve.
Isolated Tuberculosis of the First Metatarsal of the Right Foot Without Pulmonary Involvement: A Rare Case

Tuberculosis of the small bones of the foot is a rare clinical condition. Oftentimes, there is a delay in diagnosis, which could adversely affect the results. Isolated cases of tuberculosis of the first metatarsal of the right foot without pulmonary involvement are seldom reported. Herein, a case of a 13-year-old Indian female who presented with complaints of pain, swelling, and discharge from her right foot is presented. A diagnostic workup led to a definite diagnosis of isolated tuberculosis of the first metatarsal of the right foot without pulmonary involvement, and she was put on anti-tubercular treatment.
Protecting IoТ Wearable Devices from Electromagnetic Radiation Using Radio-Opaque Fabrics

The number of sources of electromagnetic radiation is increasing every day. In most cases, electromagnetic radiation has a negative effect on the human body, animals, and other living beings. Electromagnetic radiation negatively affects the operation of electronic devices. Due to the electromagnetic radiation of electronic devices, information may leak from them. The negative impact of electromagnetic radiation on the human body provokes a high level of fatigue, headache, heartache, etc. The everyday use of mobile equipment, household magnetic appliances (for example, microwave ovens), and telecommunication networks puts the majority of the planet's population at risk. Otherwise, the external influence of electromagnetic fields on the various sensors used in the IoT can contribute to receiving incorrect data from the sensors. The powerful external influence of the electromagnetic field on devices that process large data sets can contribute to a failure in mathematical calculations. Thus, protection from electromagnetic fields is necessary not only for electronic devices but also for human protection. Radio-opaque fabrics are one of the modern materials for protection from electromagnetic radiation. They can be used to protect stationary objects and wearable IoT devices and to protect people. Industrial samples of foreign and Ukrainian radio-opaque fabrics were analyzed in the work. Ukrainian manufacturers continue to develop new variants of radio-opaque fabrics. The next options for radiopaque fabrics are fabrics G7, G8, and G9. The article proposed and described a method of researching the shielding properties of the fabric. Experimental studies were carried out, and the shielding coefficient was calculated in the frequency range of 50 MHz - 2 GHz for two fabrics G7 and G8. Experimental studies were carried out for two cases of the location of fabric fibers relative to the radiating antenna and field polarization. Plots of dependences of the shielding coefficient for two fabrics with different locations of fabric fibers were presented. A comparative analysis of the shielding coefficients of two fabrics was made, and relevant conclusions were presented.
A Raman metrology approach to quality control of 2D MoS2 film fabrication

Promising electronic and optoelectronic properties of two-dimensional (2D) materials have spurred research into large area fabrication through vapour deposition and etching. However, robust and efficient non destructive characterization techniques are required in order to reliably produce good quality uniform layers. Here, we present a Raman spectroscopy approach for characterization of the quality and a systematic study of the impact of process parameters for the production of 2D MoS2 layers. We also present the application of this characterization technique for controlled layer by layer etching of multilayer MoS2.
High-temperature Ionization-induced Synthesis of Biologically Relevant Molecules in the Protosolar Nebula

Biologically relevant molecules (hereafter biomolecules) have been commonly observed in extraterrestrial samples, but the mechanisms accounting for their synthesis in space are not well understood. While electron-driven production of organic solids from gas mixtures reminiscent of the photosphere of the protosolar nebula (PSN; i.e., dominated by CO–N2–H2) successfully reproduced key specific features of the chondritic insoluble organic matter (e.g., elementary and isotopic signatures of chondritic noble gases), the molecular diversity of organic materials has never been investigated. Here, we report that a large range of biomolecules detected in meteorites and comets can be synthesized under conditions typical of the irradiated gas phase of the PSN at temperatures = 800 K. Our results suggest that organic materials—including biomolecules—produced within the photosphere would have been widely dispersed in the protoplanetary disk through turbulent diffusion, providing a mechanism for the distribution of organic meteoritic precursors prior to any thermal/photoprocessing and subsequent modification by secondary parent body processes. Using a numerical model of dust transport in a turbulent disk, we propose that organic materials produced in the photosphere of the disk would likely be associated with small dust particles, which are coupled to the motion of gas within the disk and therefore preferentially lofted into the upper layers of the disk where organosynthesis occurs.