Miracle-dz/UniFAR-FTUs huggingface dataset:
A Double Mutation Cuckoo Search Algorithm for Solving Systems of Nonlinear Equations

This paper presents a double mutation cuckoo search algorithm (DMCS) to overcome the disadvantages of traditional cuckoo search algorithms, such as bad accuracy, low convergence rate, and easiness to fall into local optimal value. The algorithm mutates optimal fitness parasitic nests using small probability, which enhances the local search range of the optimal solution and improves the search accuracy. Meanwhile, the algorithm uses large probability to mutate parasitic nests in poor situation, which enlarges the search space and benefits the global convergence. The experimental results show that the algorithms can effectively improve the convergence speed and optimization accuracy when applied to basic test functions and systems of nonlinear equations.
Direct experimental determination of spiral spin structures via the dichroism extinction effect in resonant elastic soft x-ray scattering

Long-wavelength spin spiral structures are ubiquitous in a large variety of magnetic materials. The detailed magnetic structure can take many variations owing to their different physical origins. Therefore, the unambiguous structural determination is crucial for understanding these spin systems, though such a task is experimentally challenging. Here we show that ordered spin spiral structures can be fully determined in a single measurement by dichroic resonant elastic x-ray scattering using circularly polarized light. It is found that at certain geometrical conditions, the circular dichroism of the diffraction vanishes completely, revealing a one-to-one correspondence with the spin structure. We demonstrate both theoretically and experimentally this experimental principle, which allows for unambiguous structure determination immediately from the measured signal, whereby no modeling- based data refinement is needed. This largely expands the capabilities of conventional magnetic characterization techniques.
Torque Ripple Reduction Strategy for Brushless DC Motor Based on Output Mode Reconstruction of Cuk Converter

To solve the problem of torque ripple of brushless dc motor (BLDCM) caused by nonideal back-electromotive force (EMF) and the commutation process, a new method for torque ripple suppression of BLDCM based on output mode reconstruction of the Cuk converter is proposed. This article uses the output Modes I and II of the Cuk converter. In the normal conduction stage, the output Mode I of the Cuk converter is adopted, and the inverter works in the chopping mode to realize the current control. Meanwhile, the torque ripple caused by the change of back-EMF can be suppressed by reasonably controlling the switch duty cycle of the inverter. The output Modes I and II of the Cuk converter are adopted in the low-speed commutation stage and the high-speed commutation stage, respectively, which can effectively suppress the commutation torque ripple of BLDCM. In addition, the proposed method improves the efficiency of the motor converter and effectively reduces the influence of the dc–dc intermediate stage on the system efficiency. Experimental results verify the feasibility and effectiveness of the proposed method.
Theoretical limit evaluation of ranging accuracy and power for LiDAR systems in autonomous cars

Abstract. With the development of autonomous cars, the demand for accurate light detection and ranging (LiDAR) systems is increasing. Previous evaluations of LiDAR were mainly based on experiments and lacked theoretical significance. We theoretically evaluate the accuracy and optical output power of LiDAR systems in autonomous cars. We focus on two ranging schemes: the time-of-flight (TOF) method and the quadrature phase detection (QPD) method. Considering the special requirements of autonomous driving, the theoretical limits of ranging accuracy are calculated by deriving the Cramer–Rao bound (CRB). The influence of reflectivity as well as distance on accuracy are discussed. We also determine the relationship between optical output power and essential parameters, and make comparisons between TOF and QPD. It can be concluded that TOF is more efficient under most circumstances. When designing autonomous cars, such theoretical evaluation provides guidance for choosing laser emitters and receivers, justifying the significance of our work to LiDAR development.
The Plant Growth-Promoting Potential of Halotolerant Bacteria Is Not Phylogenetically Determined: Evidence from Two Bacillus megaterium Strains Isolated from Saline Soils Used to Grow Wheat

(1) Background: Increasing salinity, further potentiated by climate change and soil degradation, will jeopardize food security even more. Therefore, there is an urgent need for sustainable agricultural practices capable of maintaining high crop yields despite adverse conditions. Here, we tested if wheat, a salt-sensitive crop, could be a good reservoir for halotolerant bacteria with plant growth-promoting (PGP) capabilities. (2) Methods: We used two agricultural soils from Algeria, which differ in salinity but are both used to grow wheat. Soil halotolerant bacterial strains were isolated and screened for 12 PGP traits related to phytohormone production, improved nitrogen and phosphorus availability, nutrient cycling, and plant defence. The four ‘most promising’ halotolerant PGPB strains were tested hydroponically on wheat by measuring their effect on germination, survival, and biomass along a salinity gradient. (3) Results: Two halotolerant bacterial strains with PGP traits were isolated from the non-saline soil and were identified as Bacillus subtilis and Pseudomonas fluorescens, and another two halotolerant bacterial strains with PGP traits were isolated from the saline soil and identified as B. megaterium. When grown under 250 mM of NaCl, only the inoculated wheat seedlings survived. The halotolerant bacterial strain that displayed all 12 PGP traits and promoted seed germination and plant growth the most was one of the B. megaterium strains isolated from the saline soil. Although they both belonged to the B. megaterium clade and displayed a remarkable halotolerance, the two bacterial strains isolated from the saline soil differed in two PGP traits and had different effects on plant performance, which clearly shows that PGP potential is not phylogenetically determined. (4) Conclusions: Our data highlight that salt-sensitive plants and non-saline soils can be reservoirs for halotolerant microbes with the potential to become effective and sustainable strategies to improve plant tolerance to salinity. However, these strains need to be tested under field conditions and with more crops before being considered biofertilizer candidates.
Current induced domain wall motion and tilting in Pt/Co/Ta structures with perpendicular magnetic anisotropy in the presence of the Dyzaloshinskii–Moriya interaction

Current induced domain wall motion (CIDWM) was studied in Pt/Co/Ta structures with perpendicular magnetic anisotropy and the Dyzaloshinskii–Moriya interaction (DMI) by the spin-orbit torque (SOT). We measured the strength of DMI and SOT efficiency in Pt/Co/Ta with the variation of the thickness of Ta using a current induced hysteresis loop shift method. The results indicate that the DMI stabilizes a chiral Néel-type domain wall (DW), and the DW motion can be driven by the enhanced large SOT generated from Pt and Ta with opposite signs of spin Hall angle in Pt/Co/Ta stacks. The CIDWM velocity, which is 104 times larger than the field driven DW velocity, obeys a creep law, and reaches around tens of meters per second with current density of ~106 A cm−2. We also found that the Joule heating accompanied with current also accelerates the DW motion. Meanwhile, a domain wall tilting was observed, which increases with current density increasing. These results can be explained by the spin Hall effect generated from both heavy metals Pt and Ta, inherent DMI, and the current accompanying Joule heating effect. Our results could provide some new designing prospects to move multiple DWs by SOT for achieving racetrack memories.
Rising Child Poverty in Europe: Mitigating the Scarring from the COVID-19 Pandemic

Child poverty increased dramatically during the COVID-19 pandemic. In 2020 alone, the number of children suffering from poverty in the EU increased by 19 percent, or close to 1 million. Left unaddressed, this would not only affect individuals’ life prospects and well-being but also have long-term economic implications. This paper argues that, to limit this potential scarring effect of the pandemic, policies should be deployed to reduce rapidly the number of children affected by poverty and mitigate the long-term impact of poverty. Reducing the number of children affected by poverty can be achieved by (i) labor policies and reforms that increase parental work and the labor income of poor parents and (ii) fiscal spending on family and children that can have a powerful and immediate impact. These policies need to be complemented by public investment in education and childcare, health, and housing to mitigate the long-term impact of child poverty. RECOMMENDED CITATION: Hallaert, J.J., I. Vassileva, and T. Chen, “Rising Child Poverty in Europe Mitigating the scarring from the COVID-19 pandemic,” IMF Working Papers, WP/23/134. JEL Classification Numbers: H53, I14, I18, I26, I32, I38, J1, J24, J62
Forced Response Analysis of a Mistuned Blisk Using Noncyclic Reduced-Order Models

The importance of mistuning analysis lies on understanding the distribution of the vibrational energy around the blisk. The large vibration amplitudes of individual blades inherent in mistuned blisks reduces the high cycle fatigue margin significantly. It is therefore important to perform mistuning analyses at a high accuracy while keeping the computational cost at an acceptable level. Because numerous analyses with large amount of degrees of freedom models are commonly performed, it is frequent to employ reduced-order models such as to reduce the computational effort. In this paper, a unique way to address the reduced-order model is presented, where each blisk sector is attached as individual substructures with the free-interface approach known as Craig–Chang. This implementation is compared against a fixed-interface approach known as Craig–Bampton in terms of accuracy for disk- and blade-dominated modes. Neither of these approaches applies cyclic symmetry, making them more accurate in the presence of mi.
Semantic labeling of high-resolution aerial images using an ensemble of fully convolutional networks

Abstract. High-resolution remote sensing data classification has been a challenging and promising research topic in the community of remote sensing. In recent years, with the rapid advances of deep learning, remarkable progress has been made in this field, which facilitates a transition from hand-crafted features designing to an automatic end-to-end learning. A deep fully convolutional networks (FCNs) based ensemble learning method is proposed to label the high-resolution aerial images. To fully tap the potentials of FCNs, both the Visual Geometry Group network and a deeper residual network, ResNet, are employed. Furthermore, to enlarge training samples with diversity and gain better generalization, in addition to the commonly used data augmentation methods (e.g., rotation, multiscale, and aspect ratio) in the literature, aerial images from other datasets are also collected for cross-scene learning. Finally, we combine these learned models to form an effective FCN ensemble and refine the results using a fully connected conditional random field graph model. Experiments on the ISPRS 2-D Semantic Labeling Contest dataset show that our proposed end-to-end classification method achieves an overall accuracy of 90.7%, a state-of-the-art in the field.
Physics-informed neural networks for solving nonlinear Bloch equations in atomic magnetometry

In this study, we address the challenge of analyzing spatial spin distribution based on the nonlinear Bloch equations in atomic magnetometry through the use of physics-informed neural networks (PINNs). Atomic magnetometry plays a crucial role in the field of biomagnetism, where it is used to detect weak magnetic fields produced by the human brain, heart, and other organs. The Bloch equations describe the spin polarization of atomic clusters in an external magnetic field, but their nonlinearity can make the analysis of the spin distribution in spatial domain difficult. By utilizing PINNs, we provide a numerical solution to the nonlinear Bloch equations, examining the effect of different pump light schemes and wall collisions. Additionally, we propose a easily executed system identification method for the Bloch equations through the use of PINNs in a data-driven discovery mode, expanding the design space of atomic magnetometry beyond traditional simulation methods.
Fused Filament Fabrication of cellular, lattice and porous mechanical metamaterials: a review

ABSTRACT One of the areas that have benefited the most from the advent of additive manufacturing is the development of customized cellular materials, scaffolds and lattices. Although these different groups of materials are typically considered separately, they can be categorized as mechanical metamaterials. Among the different additive manufacturing techniques, perhaps the most popular is that of Fused Filament Fabrication. Numerous works have been reported in the literature in which this fabrication technique has been used to produce such materials. Inspired by the increasing volume of work dealing with the subject, we present a review of the manufacturing and characterization of cellular and lattice-based mechanical metamaterials using Fused Filament Fabrication. An overview of the topologies, their effective mechanical properties and intrinsic manufacturing aspects are presented. The methods for failure analysis at different scales are also discussed. Finally, studies comparing the production of mechanical metamaterials using Fused Filament Fabrication and other additive manufacturing techniques are presented, in addition to recommendations and current trends in the production of these structures by Fused Filament Fabrication.
Improvement of Hybrid Electrode Material Synthesis for Energy Accumulators Based on Carbon Nanotubes and Porous Structures

Carbon materials are promising for use as electrodes for supercapacitors and lithium–ion batteries due to a number of properties, such as non-toxicity, high specific surface area, good electronic conductivity, chemical inertness, and a wide operating temperature range. Carbon-based electrodes, with their characteristic high specific power and good cyclic stability, can be used for a new generation of consumer electronics, biomedical devices and hybrid electric vehicles. However, most carbon materials, due to their low electrical conductivity and insufficient diffusion of electrolyte ions in complex micropores, have energy density limitations in these devices due to insufficient number of pores for electrolyte diffusion. This work focuses on the optimization of a hybrid material based on porous carbon and carbon nanotubes by mechanical mixing. The purpose of this work is to gain new knowledge about the effect of hybrid material composition on its specific capacitance. The material for the study is taken on the basis of porous carbon and carbon nanotubes. Electrodes made of this hybrid material were taken as an object of research. Porous carbon or nitrogen-containing porous carbon (combined with single-, double-, or multi-layer carbon nanotubes (single-layer carbon nanotubes, bilayer carbon nanotubes or multilayer carbon nanotubes) were used to create the hybrid material. The effect of catalytic chemical vapor deposition synthesis parameters, such as flow rate and methane-to-hydrogen ratio, as well as the type of catalytic system on the multilayer carbon nanotubes structure was investigated. Two types of catalysts based on Mo12O28 (μ2-OH)12{Co(H2O)3}4 were prepared for the synthesis of multilayer carbon nanotubes by precipitation and combustion. The resulting carbon materials were tested as electrodes for supercapacitors and lithium ion intercalation. Electrodes based on nitrogen-containing porous carbon/carbon nanotubes 95:5% were found to be the most efficient compared to nitrogen-doped porous carbon by 10%. Carbon nanotubes, bilayer carbon nanotubes and multilayer carbon nanotubes synthesized using the catalyst obtained by deposition were selected as additives for the hybrid material. The hybrid materials were obtained by mechanical mixing and dispersion in an aqueous solution followed by lyophilization to remove water. When optimizing the ratio of the hybrid material components, the most effective porous carbon:carbon nanotubes component ratio was determined.
Single-shot blind uniform motion deblurring with ringing reduction

ABSTRACT In this paper, we have proposed a blind motion deblurring algorithm that comprises the estimation of the motion blur parameters (length and angle) in a modified cepstrum domain with a blind no-reference image spatial quality evaluator (BRISQUE) used for the tuning of point spread function (PSF) parameters. Ringing artifacts are generated during the deblurring process. In this paper, the modified R–L (Richardson–Lucy) algorithm with weight calculation based on graphcut is presented to obtain good estimates of the unblurred image with ringing reduction. The method involves the selection of different weights for edges and smooth regions such that the ringing effect over R–L iterations can be reduced. A newly proposed method has been tested on various natural images with a motion blur of different length and degrees. A comparison with state-of-the-art methods proves that the proposed technique achieved better results in terms of different quality measures such as SSIM, FSIM and PSNR and can be greatly beneficial for deblurring purpose.
Deriving Convergent and Divergent Metabolomic Correlates of Pulmonary Arterial Hypertension

High-dimensional metabolomics analyses may identify convergent and divergent markers, potentially representing aligned or orthogonal disease pathways that underly conditions such as pulmonary arterial hypertension (PAH). Using a comprehensive PAH metabolomics dataset, we applied six different conventional and statistical learning techniques to identify analytes associated with key outcomes and compared the results. We found that certain conventional techniques, such as Bonferroni/FDR correction, prioritized metabolites that tended to be highly intercorrelated. Statistical learning techniques generally agreed with conventional techniques on the top-ranked metabolites, but were also more inclusive of different metabolite groups. In particular, conventional methods prioritized sterol and oxylipin metabolites in relation to idiopathic versus non-idiopathic PAH, whereas statistical learning methods tended to prioritize eicosanoid, bile acid, fatty acid, and fatty acyl ester metabolites. Our findings demonstrate how conventional and statistical learning techniques can offer both concordant or discordant results. In the case of a rare yet morbid condition, such as PAH, convergent metabolites may reflect common pathways to shared disease outcomes whereas divergent metabolites could signal either distinct etiologic mechanisms, different sub-phenotypes, or varying stages of disease progression. Notwithstanding the need to investigate the mechanisms underlying the observed results, our main findings suggest that a multi-method approach to statistical analyses of high-dimensional human metabolomics datasets could effectively broaden the scientific yield from a given study design.
A data‐driven approximate solution to the model‐free HJB equation

It is generally impossible to analytically solve the Hamilton‐Jacobi‐Bellman (HJB) equation of an optimal control system. With the coming of the big‐data era, this paper first derives a new data‐driven and model‐free Hamilton function for the HJB equation. Then, a data‐driven tracking differentiator method is proposed to solve the Hamilton function. Finally, the simulation for a classic example shows that the optimal control policy can be approximated with the proposed method. Thus, an online data‐driven model‐free approximate solution to the HJB equation is achieved. This method is only driven by the measured system states. All other variables and derivatives can be derived from the data‐driven model‐free Hamilton function and tracking differentiator. The method has a complete mathematical support and works like a controller. It does not need neural networks and has no training or iterative convergence problem. Thus, this paper adds an online data‐driven model‐free method to the existing literature on the approximate solution to the HJB equation.
The Analysis of Slang Words In English Online Learning Classroom

Educational system changed to online learning because pandemic covid 19 spreads around the world. Teachers and students have to use an alternative way to carry out education through learning technology. Online learning creates a new way of communicating between teachers and students, especially when they send messages by learning platform so that in its implementation sometimes various variations appear in the use of language in learning or it is the use of slang words. The slang words are informal words or terms that have become a culture or habit in the daily conversations. This study aims to analyse the slang words used by students when learning English during online learning and also to find out the reason students use the slang words. The method used in this research was descriptive qualitative. The data was collected through the analysis of the conversation transcripts of the WhatsApp learning group and zoom comments, then an interview used to know students’ perception about slang words. The result shows that students use slang words both in Indonesian and English when chatting with friends, expressing feelings, discussing and so on. Then, there are 13 Indonesian slang words and 15 English slang words that students common use in online conversation. Next, students assume that the use of slang words can make the learning atmosphere less tense, easy to understand, to short words and make the teaching and learning process more relaxed.
Techniques used in Automatic Number Plate Recognition

The Automatic Number Plate Recognition (ANPR) systems typically consist of a camera, image processing software, and a database system. When a car passes by, the camera snaps a picture of its license plate. The image processing software then uses OCR technology to retrieve the license plate number from the picture. The extracted plate number is then compared against a database of registered vehicles, and any relevant information associated with the plate number is retrieved. This information can include the vehicle’s make and model, owner information, and any applicable fines or penalties. By automatically capturing license plate numbers, ANPR can help identify stolen vehicles, track the movements of known criminals, and enforce traffic violations such as speeding and running red lights. In addition, ANPR can also be used for parking enforcement, enabling authorities to enforce regulations. ANPR is also widely used in the toll collection industry, where drivers can be automatically charged for using toll roads without the need for physical barriers or manual inspection of license plates. Overall, ANPR technology is a valuable tool for improving road safety, reducing crime, and increasing efficiency in a variety of applications. However, like all technologies, ANPR has the potential to be misused, and its use must be balanced against privacy concerns and the right to protect personal information.