Miracle-dz/UniFAR-FTUs huggingface dataset:
Numerical Analysis on Hydraulic Power Take-Off for Wave Energy Converter and Power Smoothing Methods

One of the primary challenges for wave energy converter (WEC) systems is the fluctuating nature of wave resources, which require the WEC components to be designed to handle loads (i.e., torques, forces, and powers) that are many times greater than the average load. This approach requires a much greater power take-off (PTO) capacity than the average power output and indicates a higher cost for the PTO. Moreover, additional design requirements, such as battery storage, are needed, particularly for practical electrical grid connection, and can be a problem for sensitive equipment (e.g., radar, computing devices, and sensors). Therefore, it is essential to investigate potential methodologies to reduce the overall power fluctuation while trying to optimize the power output from WECs. In this study, a detailed hydraulic PTO model was developed and coupled with a time-domain hydrodynamics model (WEC-Sim) to evaluate the PTO efficiency for WECs and the trade-off between power output and fluctuation using different power smoothing methods, including energy storage, pressure relief mechanism, and a power-based setpoint control method. The study also revealed that the maximum power fluctuation for WECs can be significantly reduced by one order of magnitude when these power smoothing methods are applied.
Rapid and Highly Sensitive Detection of Leishmania by Combining Recombinase Polymerase Amplification and Solution-Processed Oxide Thin-Film Transistor Technology

Nucleic acid detection is widely used to identify infectious diseases and ensure food safety. However, conventional PCR-based techniques are time consuming. Thus, this study aims to combine recombinase polymerase amplification (RPA), which enables the rapid amplification of even trace amounts of nucleic acid fragments within 10–40 min at 37–42 °C, and solution-processed oxide thin-film transistor (TFT) technology, which exhibits high detection sensitivity, to detect Leishmania. A single-stranded anti-probe was incorporated into the RPA primer to facilitate effective hybridization between the RPA product and the immobilized probe on the solution-processed oxide TFT. The RPA-amplified product carrying an anti-probe enabled specific binding to the chip surface. Changes in current were monitored before and after sample incubation to identify the target nucleic acids in the samples accurately. The proposed method achieved a remarkable limit of detection of 101 copies/μL of the Leishmania HSP70 fragment within 30 min. The design of the probes on the solution-processed oxide TFT surface and the anti-probe simplified the detection of other target nucleic acids, eliminating the need to denature DNA double-strands for specific binding during nucleic acid detection. Thus, the novel method offers the advantage of requiring minimal reagent resources and eliminates the need for complex procedures.
Percolation and its relations to other processes in networks

Many of the systems we observe in nature, in societies, or in infrastructures are in the form of a network of interacting units. This underlying network structure shapes the behavior of such systems and is an indispensable factor in maintaining their correct function. Likewise, the processes that operate on these systems are largely influenced by their network structure. In this thesis, we investigate the theoretical approaches for investigation of the properties of percolation processes on networks. Percolation processes investigate the alteration of network connectivity. Two such processes that constitute the main focus of this thesis are bond and site percolation, which are simple models for the robustness of a network to random failures of (or intentional attacks to) its constituting units. They also have been used to provide better insight on some other more complicated processes such as spread of epidemic diseases or stability of genetic networks, because some important features of these processes can be mapped to percolation properties. In this thesis, we first consider the so-called Ai j theories developed for percolation and several other processes that operate on networks. We investigate the effect of the presence of high density of short loops (a property observed in many real-world networks) on the accuracy of Ai j theories and show its impact on the performance of these theories. We then show that another phenomenon, the emergence of coexisting percolating clusters, can also cause significant inaccuracy in the Ai j theory for bond percolation on certain synthetic and real-world networks. Moreover, we introduce a new theoretical approach that takes into account this phenomenon and improves upon the state-of-the-art Ai j theory. Then, we develop a theoretical framework for calculation of percolation cluster sizes and discuss its potential applications in studying the properties of neuronal avalanches.
Prediction of endwall losses in a low pressure turbine cascade with an algebraic intermittency model

The predictive qualities of a newly developed algebraic intermittency model are analysed by simulation of the flow through a linear cascade of low pressure turbine (LPT) blades with endwalls. Both steady RANS (Reynolds-averaged Navier-Stokes) and time-accurate RANS (URANS) simulations are performed. The results are compared with reference LES (Large Eddy Simulation) by Cui et al. (2017, Numerical investigation of secondary flows in a high-lift low pressure turbine, Int. J. of Heat and Fluid Flow, vol. 63) for a turbulent endwall boundary layer (TBL) at the entrance to the cascade. Good agreement is obtained between simulations with the algebraic model and the reference LES for the evolution of the mass-averaged total pressure loss coefficient and for profiles of pitchwise-averaged turbulent kinetic energy.
Denoising processing of MIE-polarization lidar signal using wavelet

The echo signal of MIE scattering and polarization lidar is contanminated by the noises including background noises and detector noises, there exist obvious difference in fourier spectrums of original signals and denoised signals at high frequency parts. In this paper, a new method of lidar signal denoised by scattering wavelets was proposed, and original PRR signals of lidar were denoised by different wavelet basis. The experimental results showed that:1) Daubechies 4 wavelet basis (db4) met the requirements of denoising; 2)wavelet denoising could better preserve peak features of lidar echo signal than moving average method; 3) the original and detailed characteristics of atmospheric aerosol extinction profile was maintained, and the accuracy of atmospheric aerosol extinction coefficient inversion was improved. For lidar echo signals with low SNR, this method improved data processing method of Mie-scattering and polarization lidar and improved the accuracy of the retrieval of atmospheric aerosol extinction coefficient, the signal contamination from electric noise and the background light noise can be reduced by the proposed wavelet method.
Applications of Small Area Estimation to Generalization With Subclassification by Propensity Scores

Policymakers have grown increasingly interested in how experimental results may generalize to a larger population. However, recently developed propensity score–based methods are limited by small sample sizes, where the experimental study is generalized to a population that is at least 20 times larger. This is particularly problematic for methods such as subclassification by propensity score, where limited sample sizes lead to sparse strata. This article explores the potential of small area estimation methods to improve the precision of estimators in sparse strata using population data as a source of auxiliary information to borrow strength. Results from simulation studies identify the conditions under which small area estimators outperform conventional estimators and the limitations of this application to causal generalization studies.
Gamma-Ray Blazars within the First 2 Billion Years

The detection of high-redshift ( ) blazars enables the study of the evolution of the most luminous relativistic jets over cosmic time. More importantly, high-redshift blazars tend to host massive black holes and can be used to constrain the space density of heavy black holes in the early universe. Here, we report the first detection with the Fermi-Large Area Telescope of five γ-ray-emitting blazars beyond z = 3.1, more distant than any blazars previously detected in γ-rays. Among these five objects, NVSS J151002+570243 is now the most distant known γ-ray-emitting blazar at z = 4.31. These objects have steeply falling γ-ray spectral energy distributions (SEDs), and those that have been observed in X-rays have a very hard X-ray spectrum, both typical of powerful blazars. Their Compton dominance (ratio of the inverse Compton to synchrotron peak luminosities) is also very large ( ). All of these properties place these objects among the most extreme members of the blazar population. Their optical spectra and the modeling of their optical-UV SEDs confirm that these objects harbor massive black holes ( ). We find that, at , the space density of black holes hosted in radio-loud and radio-quiet active galactic nuclei are similar, implying that radio-loudness may play a key role in rapid black hole growth in the early universe.
Experimental Research into the Efficiency of an Electromagnetic Mill

This paper presents a method for quantitative assessment of the efficiency of an EMM, taking into consideration its design parameters (dimensions, winding data, etc.) and technological indicators of the milling process (mass of the milled substance, mass of the mill, milling time, etc.). The performance of an EMM is characterized in terms of two indicators—the efficiency of the milling itself, and its quality. The EMM efficiency was expressed as the ratio of the ground mass to the time taken for the grinding, and the grinding quality was given as the ratio of the mass of the smallest fraction obtained as a result of grinding to the total ground mass. Those indicators were calculated on the basis of the analysis of empirical results obtained using an EMM comprising a rotating magnetic field. The efficiency and quality of grinding were taken into account to determine these indicators. Moreover, a deterministic relation was established between this efficiency and quality of milling and the calculation values—the average number of millstone impacts and average impulse magnitude of ground material impacts, calculated using mathematical modelling of the grinding process. An algorithm applicable for determining the performance of the EMM and the quality of grinding was proposed on the basis of the results of this research.
Two mathematical security aspects of the rsa cryptosystem: signature padding schemes and key generation with a backdoor

This work presents mathematical properties of the RSA cryptosystem. The topics of backdoors and padding algorithm are developped. For padding schemes, we give a practical instantiation with a security reduction. It is based on the compression function of SHA-1 without any chaining function. Our solution has the advantage over the previous one of removing the relation of the output length of the compression function to the length of the RSA modulus. For backdoors, improvements on definitions, existing algorithms as well as extensions of existing theorems are shown. The definitions pertaining to backdoored key generators are improved as to make their analysis uniform and comparable. New algorithms are presented and compared to existing ones as to show improvements mainly on their running time, the indistinguishability of the keys produced, and that some of these new algorithms are, for all practical purposes, the best that may be called asymmetric. Our theorem on the correctness (or completeness) of one of our better backdoored key generators is a generalization of a theorem of Boneh, Durfee and Frankel's on partial information on the decryption exponent.
A Newly Emerging Serotype A Strain in Foot-and-Mouth Disease Virus with Higher Severity and Mortality in Buffalo than in Cattle Calves in North Egypt

Simple Summary Foot-and-mouth disease virus (FMDV) is the etiologic agent of foot-and-mouth disease (FMD), which causes severe economic losses among cloven-hoofed animals in endemic nations. Egypt is a bridge for FMDV pools between continents, where three serotypes (A, O, and SAT2) have established an enzootic state. Recently, a severe FMD epidemic struck several farms in the Beheira province, north Egypt. This study was the first to detect the newly emerging FMDV, serotype A, Europe–South America (Euro–SA) topotype that was collected during such outbreaks in twenty calves’ tongue epithelial samples from five infected farms based on virus isolation, molecular methods, and phylogenetic analysis. Additionally, we found that this strain caused higher peracute mortalities in water buffalo (Bubalus bubalis) (25.7%; 95% CI: 13–43) than in cattle (8.6%; 95% CI: 2–24) calves. Meanwhile, in fatal cases, severe clinical signs such as dullness, hypothermia, bradycardia, and cardiac arrhythmia were common in both species. In conclusion, we first characterized the newly emerging FMDV in the calves of Beheira as more fatal and severe in buffalo than in cattle calves. Abstract A severe foot-and-mouth disease (FMD) epidemic struck several Egyptian provinces recently, causing significant losses among animals even in vaccinated farms. This study indicated the existence of the newly emerging foot-and-mouth disease virus (FMDV) and first investigated its effect on the Egyptian water buffalo (Bubalus bubalis) and cattle calves in the Beheira province, north Egypt. Twenty tongue epithelial samples from diseased calves in five infected farms were randomly collected, prepared, and propagated using baby hamster kidney-21 (BHK-21) cells. Whole genomic RNA was extracted from the cells of the third passage. A FMDV genome was detected and serotyped using one-step reverse transcription polymerase chain reactions (RT-PCRs). Nucleotide sequencing of the purified serotype-specific PCR bands was performed, and a maximum likelihood phylogenetic tree based on 600 base pairs of VP1 was constructed. The results identified FMDV, serotype A in all infected samples, whereas the serotypes O and SAT2 were negative. The obtained 20 sequences were identical to each other and similar to the newly reported strain in Egypt that belongs to the Europe–South America (Euro–SA) topotype. The epidemiological and clinical parameters associated with such a strain were fully recorded by veterinarians and analyzed in a single infected farm including 70 cattle and buffalo calves. It caused higher peracute mortalities in buffalo (25.7%; 95% CI: 13–43) than in cattle (8.6%; 95% CI: 2–24) calves. Severe clinical signs such as dullness, hypothermia, bradycardia, and cardiac arrhythmia were common to both except in fatal cases, whereas hyperthermia and respiratory signs were prevalent in cattle calves. In conclusion, we first characterized the newly emerging FMDV in the calves of Beheira as more fatal and severe in buffalo than in cattle calves.
Application of Persistent Homology to Topological Analysis of Waveform Signals

This paper raises a question of whether a simple periodic phenomenon is associated with the topology and provides the convincing answers to it. A variety of music instrumental sound signals are used to prove our assertion, which are embedded in the Euclidean space to analyze their topologies by computing the homology groups. The manifold learning algorithms basically generate geometries intrinsic to the signal characteristics under the conditions that each signal is smooth enough and sufficient number of patches are extracted from it. It is shown that commute time embedding results of periodic or quasi-periodic waveforms are represented as closed curves on the low dimensional Euclidean space, while those of aperiodic signals have the shape of open curves. Persistent homology is employed to determine the topological invariants of the simplicial complexes constructed by randomly sampling the commute time embedding of the waveforms.
Uncertainty quantification and stochastic polynomial chaos expansion for recovering random data in Darcy and Diffusion equations

Abstract A probabilistic collocation based polynomial chaos expansion method is developed to solve stochastic boundary value problems with random coefficients and randomly distributed initial data. In this paper we deal with two different boundary value problems with random data: the Darcy equation with random lognormally distributed hydraulic conductivity, and a diffusion equation with absorption, with random distribution of the initial concentration under periodic boundary conditions. Special attention is paid to the extension of the probabilistic collocation method to input data with arbitrary correlation functions defined both analytically and through measurements. We construct the relevant Karhunen–Loève expansion from a special randomized singular value decomposition of the correlation matrix, which makes possible to treat problems of high dimension. We show that the unknown statistical characteristics of the random input data can be recovered from the correlation analysis of the solution field.
A Smart Traffic Control System Based on Pixel-Labeling and SORT Tracker

Autonomous vehicle detection and tracking are crucial for intelligent transportation management and control systems. Although many techniques are used to develop smart traffic systems, this article discusses vehicle detection and tracking using pixel-labeling and real-time tracking. We propose a novel smart traffic control system that segments the image using an Extreme Gradient Boost (XGBoost) classifier to extract the foreground objects. The proposed model is divided into the following steps: 1) at first, all the images are preprocessed to remove noise; 2) pixel-labeling is performed by using the XGBoost classifier to separate the background from the foreground; 3) all the pixels classified as a vehicle was extracted and converted into a binary image, then blob extraction technique is used to localize each vehicle; 4) to verify the detected vehicles Intersection over Union (IoU) score using the ground truth is calculated; 5) all verified vehicles were subjected to Visual Geometry Group (VGG) feature extraction and based on which a unique identifier was assigned to each of them to enable multi-object tracking across the image frames; 6) vehicles are counted and categorized into stationary and moving cars by detecting motion in each of them using Farneback optical flow algorithm; and 7) finally, the Simple Online and Real-time Tracker (SORT) is used for tracking. The proposed model outperforms existing state-of-the-art traffic monitoring techniques in terms of precision, achieving 0.86 for detection and 0.92 for tracking with the Karlsruher Institut for Technology Aerial Image Sequences (KIT-AIS) dataset, 0.83 for detection, and 0.87 for tracking with the Vision Meets Drone Single Object-Tracking (VisDrone) dataset. The proposed system can be used for several purposes, such as vehicle identification in traffic, traffic density detection at intersections, traffic flow conditions on the road, and providing a pedestrian way.
The potential of robot eyes as predictive cues in HRI—an eye-tracking study

Robots currently provide only a limited amount of information about their future movements to human collaborators. In human interaction, communication through gaze can be helpful by intuitively directing attention to specific targets. Whether and how this mechanism could benefit the interaction with robots and how a design of predictive robot eyes in general should look like is not well understood. In a between-subjects design, four different types of eyes were therefore compared with regard to their attention directing potential: a pair of arrows, human eyes, and two anthropomorphic robot eye designs. For this purpose, 39 subjects performed a novel, screen-based gaze cueing task in the laboratory. Participants’ attention was measured using manual responses and eye-tracking. Information on the perception of the tested cues was provided through additional subjective measures. All eye models were overall easy to read and were able to direct participants’ attention. The anthropomorphic robot eyes were most efficient at shifting participants’ attention which was revealed by faster manual and saccadic reaction times. In addition, a robot equipped with anthropomorphic eyes was perceived as being more competent. Abstract anthropomorphic robot eyes therefore seem to trigger a reflexive reallocation of attention. This points to a social and automatic processing of such artificial stimuli.
Effect of lactoferrin in oral nutrition supplement (ONS) towards IL-6 and IL-10 in failure to thrive children with infection

Background Growth failure due to infection in children is a major health problem throughout the world. It provokes a systemic immune response, with increased interleukin (IL)-6 and reduced IL-10. Lactoferrin (Lf) is a multifunctional iron-binding protein that can be found in whey protein inside formula milk such as oral nutrition supplement (ONS), which is able to upregulate anti-inflammatory cytokines (IL-10) and modulate pro-inflammatory cytokines. We conducted this study to investigate the effect of Lf supplementation in ONS on IL-6 and IL-10 levels in children with failure to thrive and infection. Methods We performed a quasi-experimental pre- and post-study in children aged 12–60 months old with failure to thrive due to infectious illness. The subjects received 400 ml of oral nutritional supplements (ONS, 1 ml equivalent to 1 kcal) each day for 90 days, and their parents received dietary advice and medication based on the underlying illness. Blood was drawn to measure IL-6 and IL-10 before and after the intervention. Results There were 75 subjects recruited and divided into group-1 and group-2 based on age. The incidence of undernutrition was 37.33%. Lf in ONS intervention improved body weight and body length. Lf also reduced IL-6, although there was not a significant difference before and after the intervention. However, the IL-6 reduction was significantly higher in subjects with undernutrition compared with subjects with weight faltering. Pre-intervention IL-6 levels were higher in children with stunting than in children with normal stature. There was a greater change in IL-6 in children with severe stunting than in children with normal stature or stunting. IL-10 was significantly reduced after the intervention. Conclusions In addition to improving body weight and length, Lf supplementation in ONS improved immune response homeostasis by balancing IL-6 and IL-10 levels and by improving the IL-6/IL-10 ratio. ClinicalTrials.gov number ID: NCT05289674, dated May 3 rd 2022.