Miracle-dz/UniFAR-FTUs huggingface dataset:
Machine vision-based measurement of air compressor crankshaft journal dimensions

For the problems of slow speed, low efficiency, and labor intensity of manual measurement of crankshaft structure size of air compressor, a machine vision-based method for measuring crankshaft size of air compressor was proposed. Firstly, a bilateral retention filter was used to filter the grayscale image to ensure the crankshaft edge while smoothing the image. Then the smoothed image was binarized by an automatic threshold segmentation algorithm to segment the crankshaft and the background region. Finally, the sub-pixel edge contours of the image were extracted using canny as the localization function, and the edge contours of the crankshaft were obtained after segmentation, screening, and combination. Based on the extracted contour lines, the edge line regions were drawn, and the pixel distance between the two linear regions was calculated to obtain the diameter size of the crankshaft. The experimental results show that the accuracy of the diameter dimension calculated by this algorithm can reach 99.898% compared with the measured dimension, and the error was less than 0.03mm.
Fiber Interferometer Spectrum Recognition Strategy for Sensitivity Enhancement and Wide-Range Wavelength Measurements

Fiber interferometers are widely used in sensing, including temperature, refractive index (RI), liquid level, pressure, sound wave, and strain. Since those structures are able to provide high sensitivity leading to wide-range peak shifting, optical spectrum analyzer (OSA) with 600–1700-nm spectral window is conventionally used in their measurement. However, the OSA cannot capture interferometer spectrum change information efficiently due to its slow scanning speed, which cannot meet the simultaneous requirement of wide-range and fast detection. The realization of wide-range, high-sensitivity, and fast measurement of interferometer has become an urgent demand in this field. In this article, we propose spectral processing strategy based on fast Fourier transform (FFT) frequency filtering and dynamic time warping (DTW) spectral matching recognition to achieve high-sensitivity, wide-range wavelength measurements. The experimental results show that the interrogator-based strategy proposed in this article improves the sensitivity by 2–3 times compared with the OSA-based measurement. In addition, this strategy can expand the measurement range of the sensor by 10 times, without being limited by the range of the measuring instrument.
The β-Flatness Condition in CR Spheres

Abstract This work is an adaptation of one of the methods based on the variational critical points at infinity theory of Abbas Bahri [1, 3, 2, 4, 5, 6, 7, 8] to the Cauchy–Riemann settings. Following Gamara [24], we give new existence results for the Kazdan–Warner problem on Cauchy–Riemann spheres, whereas multiplicity results will be discussed in a forthcoming paper. Using algebraic topological methods and the theory of critical points at infinity, we provide a variety of functions which can be realized as the scalar curvature for these manifolds.
SLAMICP Library: Accelerating Obstacle Detection in Mobile Robot Navigation via Outlier Monitoring following ICP Localization

The Iterative Closest Point (ICP) is a matching technique used to determine the transformation matrix that best minimizes the distance between two point clouds. Although mostly used for 2D and 3D surface reconstruction, this technique is also widely used for mobile robot self-localization by means of matching partial information provided by an onboard LIDAR scanner with a known map of the facility. Once the estimated position of the robot is obtained, the scans gathered by the LIDAR can be analyzed to locate possible obstacles obstructing the planned trajectory of the mobile robot. This work proposes to speed up the obstacle detection process by directly monitoring outliers (discrepant points between the LIDAR scans and the full map) spotted after ICP matching instead of spending time performing an isolated task to re-analyze the LIDAR scans to detect those discrepancies. In this work, a computationally optimized ICP implementation has been adapted to return the list of outliers along with other matching metrics, computed in an optimal way by taking advantage of the parameters already calculated in order to perform the ICP matching. The evaluation of this adapted ICP implementation in a real mobile robot application has shown that the time required to perform self-localization and obstacle detection has been reduced by 36.7% when obstacle detection is performed simultaneously with the ICP matching instead of implementing a redundant procedure for obstacle detection. The adapted ICP implementation is provided in the SLAMICP library.
A parallel sea-land segmentation method based on improved UNet model

Pixel-level sea-land segmentation on high-resolution remote sensing images is a basic task in remote sensing applications and is of great significance for coastline extraction and near-shore marine target detection. This paper proposes an improved UNet model for sea-land segmentation on high-resolution remote sensing images. This model outperforms UNet++ and other models in sea-land segmentation accuracy when applied to the HRSC2016-SL dataset. Based on this model, a parallel sea-land segmentation processing algorithm was developed, whose parallel efficiency reached 49.5% on 16K*14K remote sensing images. To complete this study, a parallel processing system for sea-land segmentation was developed, which achieved distributed and single-machine multi-core parallel sea-land segmentation tasks on highresolution remote sensing images.
Akari Observations of Massive Star-Forming Regions Indicative of Large-Scale Cloud-Cloud Collisions

We present our AKARI study of massive star forming regions where a large-scale cloud-cloud collision possibly contributes to massive star formation. Our targets are Spitzer bubbles, which consist of two types of bubbles, closed and broken ones; the latter is a candidate of the objects created by cloud-cloud collisions. We performed mid- and far-infrared surface photometry toward Spitzer bubbles to obtain the relationship between the total infrared luminosity, L IR , and the bubble radius, R . As a result, we ﬁnd that L IR is roughly proportional to R β where β = 2 . 1 ± 0 . 4. Broken bubbles tend to have larger radii than closed bubbles for the same L IR .
Research on efficient image semantic segmentation algorithm for glass transparent containers based on improved DeepLabV3+

Aiming at the problems of slow segmentation speed and excessive computational complexity in practical engineering applications when using existing models to segment glass transparent containers, an efficient image semantic segmentation algorithm based on improved DeepLabV3+for glass transparent containers is proposed. The proposed algorithm uses the MobileNetV3 network to replace the backbone feature extraction network Xception of the original model, effectively reduces the number of parameters of the semantic segmentation model, improves the ASPP module, introduces the strip pooling module (SPM) and depthwise separable atrous convolution (DCAC), and uses the densely connected multiple receptive field cascade (MRFC) to obtain multi-scale feature information. In the decoding structure, multi-level feature fusion (MLFF) is used to recover the details and levels of features lost during the down sampling process. Adding the spatial attention mechanism module SAM helps refine the target boundaries. The proposed model reasoning speed is 1.5 times faster than DeepLabV3+, and FLOPs are reduced by 71.4%. In addition to a 0.14% decrease in the cuvette's Miou, the beaker and reagent bottle's Miou increased by 0.19% and 0.18%, respectively. From the experimental results, we have improved the reasoning speed without completely losing accuracy, achieved the segmentation effect that is comparable to the original model, and well considered the accuracy, computational complexity, and reasoning speed, achieving a good balance between various aspects.
Description of Streptococcus thalassemiae sp. nov., a Bacterium Isolated from Human Blood

Blood is a precious biological liquid that is normally sterile. Therefore, bacteria in the bloodstream are shown a priori anomaly. A blood culture is systematically performed to diagnose the cause of the bacteremia. Indeed, a patient received in our service had a thalassemia major and underwent a genoidentical transplant. Then, a blood test was performed to diagnose a four-day fever. In this context, we have isolated strain Marseille-Q2617 from the blood sample. It revealed a new bacterial strain that belongs to the genus Streptococcus. It is a Gram-positive coccus, nonmotile, and nonspore forming. The major fatty acid found is hexadecanoic acid, with 49.5%. A taxonomic method was used to characterize the strain by studying their phenotypic, phylogenetic, and genomic characteristics. In addition, sequence analysis of the 16S rRNA gene shows that the strain Marseille-Q2617 has 99.94% sequence similarity to Streptococcus mitis. Average nucleotide identity (ANI) analysis for strain Marseille-Q2617T showed the highest similarity of 92.9% with S. mitis. The DNA-DNA hybridization value obtained (50.2%) between strain Marseille-Q2607 and S. mitis, its closest related species, was below the recommended threshold (<70%). Strain Marseille-Q2617T has a genome size of 2.02 Mbp with 40.5 mol% of G + C content. Based on these results, we propose a new species of the genus Streptococcus, for which the name Streptococcus thalassemiae sp. nov., Marseille-Q2617T (=CSUR Q2617 = CECT 30109) was proposed.
The study of liquid surface waves with a smartphone camera and an image recognition algorithm

Simple experimental setups are designed for observing liquid surface waves, studying the dispersion relation and obtaining the liquid surface tension coefficient. In addition, the relationship between temperature and the liquid surface tension coefficient is verified. We take pictures of the surface wave patterns with a smartphone camera and measure the wavelength with software analysis based on image recognition. The experiment is performed not only with common devices and simple operations, but also with the student’s own smartphone. As a result, the experiment itself is easy and convenient to carry out, which stimulates undergraduates’ interest in this experiment.
Modal Analysis of the Surface Pressure Field Around a Hemispherical Turret using Pressure Sensitive Paint

Fast-response Pressure Sensitive Paint (PSP) measurements were performed on hemispherical models at transonic flow speeds. Both Proper Orthogonal Decomposition (POD) and Dynamic Mode Decomposition (DMD) were used to analyze the spatially and temporally resolved data. POD was used to isolate the dominant behavior in the data and isolate the dominant frequencies by looking at the temporal coefficient spectra. DMD was then used to further investigate specific frequencies. At St = 0.15-0.2, previously investigated “wake breathing” and “wake shifting” modes were found. These modes represent large spanwise symmetric and antisymmetric structures, respectively. Further, at St = 1 and St = 2, DMD showed that the wake of the turret was dominated by smaller convecting structures. The DMD modes were used to estimate the convective velocity in the wake, which was found to be relatively constant across various Strouhal Numbers.
Combining Big Data Analysis to Study the Relationship between the Tone of CSR Reports and Information Asymmetry

Big data mining and analytics help uncover hidden patterns and correlations in business. It serves as the optimal tool to interpret the behavior of companies in specific environments. Built on the large amount of data obtained from various sources, this paper examines the relationship between the tone of corporate social responsibility(CSR) reports and the degree of information asymmetry between investors and managers. Python software is used for data collection, text analysis, and word frequency statistics. The results show that the tone of the social responsibility report reduces the degree of information asymmetry, indicating that the tone of the social responsibility report has an incremental information effect. Further analysis shows that the tone of CSR reports significantly reduces information asymmetry in companies with optimistic forecasts and high media attention.
Transformation Pathways of China's Power Sector in the Context of "Carbon Peaking and Carbon Neutrality"

To achieve the "carbon peak" and "carbon neutrality" goals, the power industry is the main battlefield and main force. Compared with the long carbon reduction path after natural peak in major European and American countries, the carbon peak, plateau period, and transformation path under the "dual carbon" goal in China will be completely different. This article establishes a multi-scenario analysis framework for the transformation path of the power industry under the "dual carbon" goal, and constructs three major scenarios. It analyses the low-carbon transformation path of power system including power source structure and carbon emissions under different scenarios and proposes policy recommendations.
Admixture mapping of peripheral artery disease in a Dominican population reveals a putative risk locus on 2q35

Peripheral artery disease (PAD) is a form of atherosclerotic cardiovascular disease, affecting ∼8 million Americans, and is known to have racial and ethnic disparities. PAD has been reported to have a significantly higher prevalence in African Americans (AAs) compared to non-Hispanic European Americans (EAs). Hispanic/Latinos (HLs) have been reported to have lower or similar rates of PAD compared to EAs, despite having a paradoxically high burden of PAD risk factors; however, recent work suggests prevalence may differ between sub-groups. Here, we examined a large cohort of diverse adults in the BioMe biobank in New York City. We observed the prevalence of PAD at 1.7% in EAs vs. 8.5% and 9.4% in AAs and HLs, respectively, and among HL sub-groups, the prevalence was found at 11.4% and 11.5% in Puerto Rican and Dominican populations, respectively. Follow-up analysis that adjusted for common risk factors demonstrated that Dominicans had the highest increased risk for PAD relative to EAs [OR = 3.15 (95% CI 2.33–4.25), p < 6.44 × 10−14]. To investigate whether genetic factors may explain this increased risk, we performed admixture mapping by testing the association between local ancestry and PAD in Dominican BioMe participants (N = 1,813) separately from European, African, and Native American (NAT) continental ancestry tracts. The top association with PAD was an NAT ancestry tract at chromosome 2q35 [OR = 1.96 (SE = 0.16), p < 2.75 × 10−05) with 22.6% vs. 12.9% PAD prevalence in heterozygous NAT tract carriers versus non-carriers, respectively. Fine-mapping at this locus implicated tag SNP rs78529201 located within a long intergenic non-coding RNA (lincRNA) LINC00607, a gene expression regulator of key genes related to thrombosis and extracellular remodeling of endothelial cells, suggesting a putative link of the 2q35 locus to PAD etiology. Efforts to reproduce the signal in other Hispanic cohorts were unsuccessful. In summary, we showed how leveraging health system data helped understand nuances of PAD risk across HL sub-groups and admixture mapping approaches elucidated a putative risk locus in a Dominican population.
Coordination of Varistors With Transient Voltage Suppression Diodes in Automotive DC Systems

The coordination of metal-oxide varistors with transient voltage suppression diodes commonly employed for the protection of sensitive electronic equipment in DC automotive systems is experimentally investigated. Double-exponential impulse currents adopted in relevant standards, concave and rectangular impulse currents resembling field records on lightning-related surge events, as well as temporary overvoltages, are employed to stress the surge protective components and evaluate their coordination. Experimental results and the subsequent analysis reveal that effective coordination of varistors with bidirectional diodes can be a challenging task, especially under long-duration and high-energy electromagnetic pulses. Under such conditions, the safety and uninterrupted operation of vehicles containing low-voltage electrical circuits with embedded sensitive electronic equipment is threatened.
Uncertainty Analysis Methodology for Measurements of Dynamic Millimeter-Wave Channels

Quantification of uncertainties in the results of channel sounding measurements is important for their interpretation and further usage. In this paper, a novel uncertainty analysis methodology to quantify uncertainties of condensed parameters in measurements of dynamic millimeter-wave channels is presented. The bandwidth limitation and multipath threshold are identified as important impairments. Therefore, the methodology provides three uncertainty metrics for condensed parameters, namely a standard uncertainty to quantify the impact of random variations; a bias due to the multipath threshold; and a total bias including the impact of the bandwidth limitation. These uncertainty metrics are highly channel dependent. Therefore, the proposed methodology creates reference channels, which are representative of corresponding measured channels. Hardware and processing impairments are included in the analysis via a Monte Carlo simulation. This results in a general methodology that can quantify uncertainties in both static and dynamic channel measurements of any wideband channel sounder. The methodology is implemented, verified and demonstrated for the TU/e channel sounder, which exemplifies how it can be used. The proposed methodology can improve the analysis, interpretation and reporting of channel measurement results.
A Comparative Study of Visual Identification Methods for Highly Similar Engine Tubes in Aircraft Maintenance, Repair and Overhaul

Unique identification of machine parts is critical to production and maintenance, repair and overhaul (MRO) processes in the aerospace industry. Despite recent advances in automating these identification processes, many are still performed manually. This is time-consuming, labour-intensive and prone to error, particularly when dealing with visually similar objects that lack distinctive features or markings or when dealing with parts that lack readable identifiers due to factors such as dirt, wear and discolouration. Automation of these processes has the potential to alleviate these problems. However, due to the high visual similarity of components in the aerospace industry, commonly used object identifiers are not directly transferable to this domain. This work focuses on the challenging component spectrum engine tubes and aims to understand which identification method using only object-inherent properties can be applied to such problems. Therefore, this work investigates and proposes a comprehensive set of methods using 2D image or 3D point cloud data, incorporating digital image processing and deep learning approaches. Each of these methods is implemented to address the identification problem. A comprehensive benchmark problem is presented, consisting of a set of visually similar demonstrator tubes, which lack distinctive visual features or markers and pose a challenge to the different methods. We evaluate the performance of each algorithm to determine its potential applicability to the target domain and problem statement. Our results indicate a clear superiority of 3D approaches over 2D image analysis approaches, with PointNet and point cloud alignment achieving the best results in the benchmark.
Statistical Seasonal Prediction Based on Regularized Regression

AbstractThis paper proposes a regularized regression procedure for finding a predictive relation between one variable and a field of other variables. The procedure estimates a linear prediction model under the constraint that the regression coefficients have smooth spatial structure. The smoothness constraint is imposed using a novel approach based on the eigenvectors of the Laplace operator over the domain, which results in a constrained optimization problem equivalent to either ridge regression or least absolute shrinkage and selection operator (LASSO) regression, which can be solved by standard numerical software. In addition, this paper explores an unconventional procedure whereby regression models are estimated from dynamical model output and then verified against observations—the reverse of the traditional order. The methodology is illustrated by constructing statistical prediction models of summer Texas-area temperature based on concurrent Pacific sea surface temperature (SST). None of the regulari.
Surface Plasmon Mediated Controllable Spin‐Resolved Transmission in Meta‐Hole Structures

Chiral responses are optical responses involving circular polarizations. Controlling the chiral response in a flexible way is very important in optical manipulations. Chiral metamaterials have thus drawn enormous interest due to their flexible designing feature. However, most of the previous studies are mainly realized by designing the structure of the individual meta‐atom. Meanwhile, to enhance the response, complex design and fabrication processes are typically required. Here, by introducing spin‐dependent propagating surface plasmons and spin‐selective interference, giant spin‐resolved transmission is achieved in a simple meta‐hole structure. In this interaction process, spin‐orbital angular momentum conversion plays an essential role. By controlling the phase difference between the interference components, controllable spin‐resolved transmission is achieved. Furthermore, such method can also be applied to realize spin‐resolved excitation of surface plasmons. The proposed controlling strategy offers a versatile platform for a variety of promising applications, such as polarization control, asymmetric transmission, surface plasmon excitation, and on‐chip chiral manipulation.
How Physics Became a Blind Science

falsely a probabilistic statistical nature. 3. They create the uncertainty principle with its phlosphical Abstract In my previous paper I showed that the central concept or the ground on which the eminent physicists “Einstein, schrodinger, de Broglie, Maxborn, Heisenberg, Dirac, David pohm, Bohr (and Copenhagen school … etc.” stand above is false ground. I showed that light hasn’t a dual (wave-particle) nature. In the present essay I’ll shows that; despite Copenhagen Interpretation of quantum mechanics leads to predictions which are in excellent agreement with experiment. However, it leads to fictious (Pseudo) view for the nature of both light and later matter. It caused the physics (step by step) to become finally a blind science”.
Unraveling the Phase Transition Behavior of MgMn2O4 Electrodes for Their Use in Rechargeable Magnesium Batteries

Rechargeable magnesium batteries are an attractive alternative to lithium batteries because of their higher safety and lower cost, being spinel-type materials promising candidates for their positive electrode. Herein, MgMn2O4 with a tetragonal structure is synthesized via a simple, low-cost Pechini methodology and tested in aqueous media. Electrochemical measurements combined with in-situ Raman spectroscopy and other ex-situ physicochemical characterization techniques show that, in aqueous media, the charge/discharge process occurs through the co-intercalation of Mg2+ and water molecules. A progressive structure evolution from a well-defined spinel to a birnessite-type arrangement occurs during the first cycles, provoking capacity activation. The concomitant towering morphological change induces poor cycling performance, probably due to partial delamination and loss of electrical contact between the active film and the substrate. Interestingly, both MgMn2O4 capacity retention and cyclability can be increased by doping with nickel. This work provides insights into the positive electrode processes in aqueous media, which is vital for understanding the charge storage mechanism and the correlated performance of spinel-type host materials.