Miracle-dz/UniFAR-FTUs huggingface dataset:
A lightweight vehicle detection algorithm combining image enhancement and multi-scale feature fusion

In order to solve the problem of the high rate of missed vehicle detection in night-time traffic scenarios due to insufficient illumination and variable light sources, and the high complexity of the algorithm that makes it not well suited to in-vehicle devices. In this paper, we propose an efficient and accurate night-time vehicle detection method. Firstly, an improved EnlightenGAN algorithm is proposed to enhance the vehicle features in nighttime road images. Secondly, a lightweight network MobileNet v3 is proposed to replace the original backbone network Darknet53 of YOLO v3 for feature extraction as well as a multi-scale feature fusion strategy to improve the feature extraction capability of the network. Experimental results on the ExDARK dataset show that the accuracy and recall of the detection algorithm proposed in this paper are 88.88% and 94.32%, which are 0.53% and 8% better than YOLO v3, respectively, and the computational effort is reduced by 75% compared to YOLO v3.
Cloning, Expression, and Characterization of a Highly Stable Heparinase I from Bacteroides xylanisolvens

Heparinase I (Hep I), which specifically degrades heparin to oligosaccharide or unsaturated disaccharide, has an important role in the production of low molecular weight heparin (LMWH). However, low productivity and stability of heparinase I hinders its applications. Here, a novel heparinase I (BxHep-I) was cloned from Bacteroides xylanisolvens and overexpressed in soluble form in Escherichia coli. The expression conditions of BxHep-I were optimized for an activity of 7144 U/L. BxHep-I had a specific activity of 57.6 U/mg at the optimal temperature and pH of 30 °C and pH 7.5, with the Km and Vmax of 0.79 mg/mL and 124.58 U/mg, respectively. BxHep-I catalytic activity could be enhanced by Ca2+ and Mg2+, while strongly inhibited by Zn2+ and Co2+. Purified BxHep-I displayed an outstanding thermostability with half-lives of 597 and 158 min at 30 and 37 °C, respectively, which are the highest half-lives ever reported for heparinases I. After storage at 4 °C for one week, BxHep-I retained 73% of its initial activity. Molecular docking revealed that the amino acids Asn25, Gln27, Arg88, Lys116, His156, Arg161, Gln228, Tyr356, Lys358, and Tyr362 form 13 hydrogen bonds with the substrate heparin disaccharides in the substrate binding domain and are mainly involved in the substrate binding of BxHep-I. These results suggest that the BxHep-I with high stability could be a candidate catalyst for the industrial production of LMWH.
An Accelerated Iterative Hard Thresholding Method for Tensor Completion

Abstract Tensor completion aims to recover an unknown low-rank or approximately low-rank tensor from a sampling set of its entries. It is shown that this problem can be solved via iterative hard thresholding method. However the orthogonality of the current step and the previous step leads to its slow convergence speed. In this paper, we propose an accelerated iterative hard thresholding method for tensor completion (ATIHT) by combining the current gradient and directions of several previous iterative steps as new search direction. We make some comparison between numerical tests of our algorithm and TIHT, FP-LRTC and HaLRTC algorithms on randomly generated tensors and low-rank color image recovery problem. The results suggest that our method is more effective and promising.
Adaptation and colour matching of display and surface colours

In an asymmetric colour matching experiment, eleven observers adjusted computer displays to colour-match surface samples in a viewing booth. We found systematic discrepancies between the observers' judgments and the predictions of the CIE 1964 Standard Colorimetric Observer. The features of the discrepancies are consistent with previous reports on adaptation in colour matching and on failures of colorimetric additivity, but have never been confirmed to be significant in practical colorimetry. We attribute the discrepancies to post-receptoral adaptation mainly of the blue-yellow chromatic channel, and report a framework of an adaptation transform based on the MacLeod-Boynton chromaticity diagram which can compensate for them without abandoning traditional colorimetry and the use of tristimulus values. © 2009 Wiley Periodicals, Inc. Col Res Appl, 34, 182–193, 2009
Observations of coagulation in isotropic turbulence

Turbulent-shear-induced coagulation of monodisperse particles was examined experimentally in the nearly isotropic, spatially decaying turbulence generated by an oscillating grid. The 3.9 μm polystyrene microspheres used in the experiments were made neutrally buoyant and unstable by suspending them in a density-matched saline solution. In this way, particle settling, double-layer repulsion and particle inertia were negligible and the effect of turbulent shear was isolated. The coagulation rate was measured by monitoring the loss of singlet particles as a function of time and reactor turbulence intensity. By restricting consideration to experimental conditions where the singlet concentration was in excess, the effect of higher-order aggregate (i.e. triplet) formation was negligible and nonlinear regression using an integral rate expression that included terms for doublet formation and breakup was used to obtain the turbulent coagulation rate constant. The strength of the van der Waals attractions was characterized with the Hamaker constant obtained from Brownian coagulation experiments. Since particle bulk mixing was fast compared to the coagulation rate, the observed coagulation rate constants were averages over the local coagulation rates within the grid-stirred reactor. Knowledge of the spatial variation of turbulence within the reactor was necessary for quantitative prediction of the experiments because model predictions for the coagulation rate are nonlinear functions of shear rate. The investigation was conducted with particles smaller than the length scales of turbulence and since the smallest turbulent length scales, the Kolmogorov scales, have the highest shear rate they controlled the rate of particle aggregation. The distribution of the Kolmogorov shear rate at various grid oscillation frequencies was obtained by measuring the turbulent kinetic energy (E) using acoustic Doppler velocimetry and relating E to the Kolmogorov shear rate using scaling arguments. The experimentally measured turbulent coagulation rate constants were significantly lower than theoretical predictions that neglect interparticle interactions; however, simulations that included particle interactions showed excellent agreement with the experimental results. The favourable comparison provides evidence that the computer simulations capture the important physics of turbulent coagulation. That is, particle transport on length scales comparable to the particle radius controls the rate of turbulent shear coagulation and particle interactions are significant.
Analysing University at-Risk Students in a Virtual Learning Environment using Machine Learning Algorithms

Students at risk in universities are becoming a rising global issue. These are students who have a high likelihood of dropping out of their respective academic programs. Due to the importance and impact on students, if interventions are not implemented, research on students at risk is garnering widespread attention in the literature. Early identification of these at-risk students is essential for intervention to lessen the likelihood of dropout. On a virtual learning environment dataset, this study compared Adaboost with five other machine learning algorithms, including Random Forest, Logistic Regression, Support Vector Machine, and Decision Trees, to detect students at risk. This study focused on training and evaluating the six machine learning models adopted, employing performance evaluation metrics such as F1 score, Confusion Matrix, Recall, Precision, ROC, error rate, and accuracy. Adaboost was found to be the top-performing algorithm, having the highest accuracy, F1 score, Precision, and Recall.
Using Data Mining Techniques To Enhance The Student Performance. A semantic review.

The number of higher education institutions has dramatically increased in recent years, producing many graduates/postgraduates each year. One of the key concerns of the decision-makers is student performance. Educational data mining techniques are a very useful way to explore the uncovered data in the data itself and create a pattern in order to analyze student performance. This study presents an analysis and investigation of recent papers in the field of educational data mining from 2020 to 2022. The goal was to identify the factors that influence student performance as well as the most significant educational data mining methods used by researchers. The study concluded that student behaviors have the greatest influence on academic performance. Furthermore,. the most popular classifiers used to predict student performance are decision trees, multilayer perception, and support vector machines.
Advances in Nanogels for Topical Drug Delivery in Ocular Diseases

Nanotechnology has accelerated the development of the pharmaceutical and medical technology fields, and nanogels for ocular applications have proven to be a promising therapeutic strategy. Traditional ocular preparations are restricted by the anatomical and physiological barriers of the eye, resulting in a short retention time and low drug bioavailability, which is a significant challenge for physicians, patients, and pharmacists. Nanogels, however, have the ability to encapsulate drugs within three-dimensional crosslinked polymeric networks and, through specific structural designs and distinct methods of preparation, achieve the controlled and sustained delivery of loaded drugs, increasing patient compliance and therapeutic efficiency. In addition, nanogels have higher drug-loading capacity and biocompatibility than other nanocarriers. In this review, the main focus is on the applications of nanogels for ocular diseases, whose preparations and stimuli-responsive behaviors are briefly described. The current comprehension of topical drug delivery will be improved by focusing on the advances of nanogels in typical ocular diseases, including glaucoma, cataracts, dry eye syndrome, and bacterial keratitis, as well as related drug-loaded contact lenses and natural active substances.
Role of Machine Learning and Internet of Thing in Agriculture- A Survey

Agriculture is the backbone of the economy and a source of jobs for developing nations like India. For human life, food is a necessity that can be fulfilled only through farming. Agricultural activities are usually classified into three broad categories: post-harvest, harvest, and pre-harvest. Farmers are doing their best to produce grain, but there is grain loss at the time of various post-harvest activities like cutting, storage, etc. Various studies have already been conducted in the fields of pre-harvest, harvest, and food quality. But in the field of post-harvest, there is lots of scope for work. For food sustainability reduction in post-harvest activities is necessary. This paper presents a broad survey of the post-harvest activities, Internet of Things (IoT) and machine learning (ML) applications in the agricultural domain to mitigate the post-harvest issue. Applying machine learning (ML) and the internet of things (IoT) in agriculture allows more efficient and accurate monitoring of grain so food loss problem can be solved in effective manner.
Fabrication of gold nanoparticles in Therminol VP-1 by laser ablation and fragmentation with fs pulses

This letter reports on a physical method to produce highly pure, size-controlled and well-dispersed spherical gold nanoparticles (NPs) in Therminol VP-1 by pulsed laser ablation in liquids (PLAL) using a 30 fs Ti:Sapphire laser at a fluence of 1 J cm−2. A second photo-fragmentation of the ablated colloid solution by subsequent treatment with the same laser light yields a mean size and size dispersion of the NPs of 58 ± 31 nm. A study of the nanofluid properties reveals a low agglomeration over time and an enhancement of thermal conductivity of the base fluid by up to 4%. These results improve the characteristics of current nanofluids in thermal oils that may have a potential impact on the improvement of the efficiency of harvesting of solar light.
EFFECTIVE PERMITTIVITY OF WET SNOW USING STRONG FLUCTUATION THEORY

The strong fluctuation theory is applied to calculate the effective permittivity of wet snow by a two-phase model with non-symmetrical inclusions. In the two-phase model, wet snow is assumed to consist of dry snow (host) and liquid water (inclusions). Numerical results for the effective permittivity of wet snow are illustrated for random media with isotropic and anisotropic correlation functions. A three-phase strong fluctuation theory model with symmetrical inclusions is also presented for theoretical comparison. In the three-phase model, wet snow is assumed to consist of air (host), ice (inclusions) and water (inclusions) and the shape of the inclusions is spherical. The results are compared with the Debye-like semi-empirical model and a comparison with experimental data at 6, 18 and 37 GHz is also presented. The results indicate that (a) the shape and the size of inclusions are important, and (b) the two-phase model with non-symmetrical inclusions provides the good results to the effective permittivity of wet snow.
Two Element Microstrip-Fed Slot Loaded Millimeter Wave MIMO Antenna

This study offers a microstrip fed two-element slot loaded MIMO antenna with wide impedance characteristics for 5G millimeter wave applications. The proposed design is operated at frequency range of (30GHz to 40GHz). The antenna is made up of two identical ‘Y’ shaped radiating elements that are positioned on the same substrate and have a 10 mm face to face space between them. Both elements are mounted on a rectangular Rogers’s RT5880 substrate with the specified dimensions of $(39mm\times 3lmm\times 0.8mm)$. CST microwave studio has successfully simulated and analyzed the proposed MIMO antenna. According to simulation results, the proposed MIMO antenna has a wide 10-dB impedance bandwidth at (30.2GHz to 40GHz) and more than 25dB isolation across the whole band. Furthermore, the antenna exhibits good MIMO results in terms of current distribution, gain, envelope correlation coefficient and diversity gain. The proposed antenna is suitable for 5G wireless applications at the 5G millimeter-wave range.
Direct and inverse scattering problems for inhomogeneous impedance cylinders of arbitrary shape

The direct and inverse scattering problems related to objects having inhomogeneous impedance boundaries are addressed by considering cylindrical bodies. In the solution of the direct scattering problem, the scattered field is first expressed in terms of a combined single‐ and double‐layer potential through Green's formula and the boundary condition. By using the jump relations on the boundary of the object, the scattering problem is reduced to a boundary integral equation that can be solved via a Nyström method. The aim of the inverse impedance problem is to reconstruct the inhomogeneous surface impedance of the body from the measured far field data. Here representing the scattered field as a single‐layer potential leads to an ill‐posed integral equation of the first kind for the density that requires stabilization for its numerical solution; for example, by Tikhonov regularization. With the aid of the jump relations the single‐layer potential enables the evaluation of the total field and its derivative on the boundary of the scatterer. Consequently, from the boundary condition the surface impedance can be reconstructed either by direct evaluation or by a minimum norm solution in the least squares sense. The numerical results show that our methods yields good resolution both for the direct and the inverse problem.
Using Lagrange principle for solving two-dimensional integral equation with a positive kernel

This article is devoted to a Lagrange principle application to an inverse problem of a two-dimensional integral equation of the first kind with a positive kernel. To tackle the ill-posedness of this problem, a new numerical method is developed. The optimal and regularization properties of this method are proved. Moreover, a pseudo-optimal error of the proposed method is considered. The efficiency and applicability of this method are demonstrated in a numerical example of an image deblurring problem with noisy data.
A flexible array synthesis method using quadratic programming

A highly flexible synthesis method for an arbitrary array is proposed to best approximate a desired array pattern in a minimum-mean-square-error sense. The basic idea of the technique is to form a quadratic program with its cost function given by the mean-square error between the array response and a properly selected pattern described by a known mathematical function. This quadratic program can be a constrained or unconstrained optimization problem depending on the requirements of the desired array pattern. In formulating the quadratic program, no assumption has been made on the gain/phase response or characteristics of the individual array elements. Therefore, one can synthesize an array of arbitrary shape to any appropriate pattern with the characteristic of the array elements taken into consideration as long as one is able to model the array accurately. The proposed method is used to synthesize arrays of different shapes, linear as well as planar arrays (including rectangular and circular planar arrays), using a Chebyshev polynomial or zero function as a design template, to illustrate the feasibility of the proposed method. >
Exciton localization behaviour in different well width undoped GaN/Al0.07Ga0.93N nanostructures

We report results from optical spectroscopy such as photoluminescence (PL) and time resolved photo-luminescence (TRPL) techniques from different well width MOCVD grown GaN/Al0.07Ga0. 93N MQW samples. There is evidence of localization at low temperature in all samples. The decay time of all samples becomes non-exponential when the detection energy is increased with respect to the peak of the emission. Localization of carriers (excitons) is demonstrated by the “S-shape” dependences of the PL peak energies on the temperature. The time-resolved PL spectra of the 3-nm well multi quantum wells reveal that the spectral peak position shifts toward lower energies as the decay time increases and becomes red-shifted at longer decay times. There is a gradient in the PL decay time across the emission peak profile, so that the PL process at low temperatures is a free electron-localized hole transition.
UTIP Global Inequality Data Sets 1963-2008: Updates, Revisions and Quality Checks

This paper summarizes a comprehensive revision and update of UTIP's work on the inequality of pay and incomes around the world, covering the years 1963 to 2008. The new UTIP-UNIDO data set of industrial pay inequality has 4054 country-year observations over for 167 countries, while the updated and revised EHII data set of estimated gross household income inequality has 3871 observations over 149 countries. The paper also provides comparisons of the EHII data set with a wide range of measures and estimates drawn from other work. They show in general that EHII is a reliable reflection of trends, and a reasonable, though not perfect, estimator of the levels of inequality found in surveys. These updates, revisions and quality checks were supported by a grant from the Institute for New Economic Thinking.
d-sets, and general metric measure spaces

We give a sucient condition to construct non-trivial „-symmetric diusion processes on a locally compact metric measure space (M;‰;„). These processes are associated with local regular Dirichlet forms which are obtained as continuous parts of i-limits for approximating non-local Dirichlet forms. For various fractals, we can use existing estimates to verify our assumptions. This shows that our general method of constructing diusions can be applied to these fractals. 2000 MSC: Primary 60J60; Secondary 49Q20; 60J35
Geometry-Injected Image-Based Point Cloud Semantic Segmentation

Image-based methods have replicated the success from 2-D domain to 3-D point cloud semantic segmentation. However, when we directly apply 2-D techniques to the projected pseudo-image, inherent differences between the point cloud and the image cause geometric distortion. This article analyzes the geometric distortion between the point cloud and the pseudo-image, including truncation, dislocation, and hole. To ensure geometric fidelity, we propose the Geometry-injected Image-based point cloud semantic segmentation Network (GINet). We design a cyclic convolution to optimize the convolution operation, dealing with truncation. For dislocation and hole, we propose dual geometric constraints, including local spatial attention and local affinity regularization, to incorporate the geometric information into semantic feature learning. Local spatial attention generates an attention map from the point coordinates to modulate the feature map before convolution. Local affinity regularization supervises the semantic similarity of pixels in the convolution kernel range. The GINet rectifies the geometric distortion with these mechanisms while taking advantage of the successful 2-D semantic segmentation methods. Quantitative and qualitative experiments on SemanticKITTI and SemanticPOSS demonstrate the effectiveness of GINet.
Music-colour synaesthesia: Sensorimotor features and synaesthetic experience

Disciplinary background A. Synaesthesia: Music-colour synaesthesia is included under the umbrella term “coloured hearing” (Ward et al., 2006). Although the phenomenon is typically considered to be separate from general cognition, the shared mental processes of synaesthetes and non-synaesthetes (Simner, 2012) suggest that there may be certain similarities and differences that are a matter of degree. It is argued here that music-colour synaesthesia may share a similar grounding in action to general music cognition (Curwen, 2020). Disciplinary background B. Embodied Cognition: General music cognition research has embraced embodied accounts highlighting the importance of an acting body and its engagement in the context of musical emotion, communication, participation and musical creativity (Schiavio et al., 2017, van der Schyff et al., 2018). In contrast to approaches presenting music cognition as a series of internal (i.e., computational, neural) processes and representations, these approaches propose the direct, circular interaction between the agent’s body and its social, cultural, and physical environment (Reybrouck, 2014). Abstract The main aim and objective of this study is to highlight commonalities between mechanisms underlying music-colour synaesthesia and general music cognition, and to demonstrate some forms of music-colour synaesthesia are grounded in action. Two groups (synaesthetes/non-synaesthetes) reported their experience whilst listening to 3 sets of 4 musical excerpts presented in random order:
Manipulating Bandwidth of Cryogenically Cooled Receive-Only MRI Detectors

Cryogenically cooled magnetic resonance imaging (MRI) coils demonstrate extremely high Q-factors, leading to a very narrow bandwidth. This bandwidth can be increased in a controlled manner using a preamplifier decoupling. In this letter, an MRI coil design procedure is explained, and an example showing over two times bandwidth increase in a cryogenic coil for 13C isotope imaging in 3-Tesla MRI systems is demonstrated. The achieved wider bandwidth preserves the spectral components of magnetic resonance signals and makes coils less susceptive to detuning due to parasitic coupling to surrounding structures. The presented method is also useful in other applications of high-Q coils, for instance, in low-field MRI systems.