Miracle-dz/UniFAR-FTUs huggingface dataset:
Perturbation-Based Thresholding Search for Packing Equal Circles and Spheres

This paper presents an effective perturbation-based thresholding search for two popular and challenging packing problems with minimal containers: packing N identical circles in a square and packing N identical spheres in a cube. Following the penalty function approach, we handle these constrained optimization problems by solving a series of unconstrained optimization subproblems with fixed containers. The proposed algorithm relies on a two-phase search strategy that combines a thresholding search method reinforced by two general-purpose perturbation operators and a container adjustment method. The performance of the algorithm is assessed relative to a large number of benchmark instances widely studied in the literature. Computational results show a high performance of the algorithm on both problems compared with the state-of-the-art results. For circle packing, the algorithm improves 156 best-known results (new upper bounds) in the range of [Formula: see text] and matches 242 other best-known results. For sphere packing, the algorithm improves 66 best-known results in the range of [Formula: see text], whereas matching the best-known results for 124 other instances. Experimental analyses are conducted to shed light on the main search ingredients of the proposed algorithm consisting of the two-phase search strategy, the mixed perturbation and the parameters. History: Accepted by Erwin Pesch, Area Editor for Heuristic Search & Approximation Algorithms. Funding: This work was supported by the National Natural Science Foundation of China [Grants 61703213 and 61933005]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.1290 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0004 ) at ( http://dx.doi.org/10.5281/zenodo.7579558 ).
The CB6F1 mouse is a model for studying cognition and brain morphometry with increasing age

Cognitive impairment associated with memory loss and dysfunctional communication is a common condition in older people. Regions of the brain have been reported to decrease in size with increasing age, but the relationship with cognitive impairment is not well understood. Inbred and hybrid mouse strains can be useful models to investigate cognitive impairment and morphological changes at older ages. CB6F1 hybrid mice, a cross between C57BL/6 and Balb/c mice, were tested for learning and memory using a radial water tread maze. Old CB6F1 male mice (30 months of age) had severe cognitive impairment, while it was virtually absent in young (6 months old) male mice. In these same mice, there was a significant decrease in sagittal flat surface area of the hippocampus and pons in old versus young animals. The aging CB6F1 mouse would be a potential model to study the relationship between changes in brain morphometry and cognitive impairment and the identification of possible therapeutic targets.
Chitosan-Grafted Carbon Oxynitride Nanoparticles: Investigation of Photocatalytic Degradation and Antibacterial Activity

In this work, a series of chitosan (CS)-grafted carbon oxynitride (OCN) nanoparticles (denoted as CS-OCN) were successfully synthesized for the first time by thermal polycondensation and subsequent esterification. The structure and photocatalytic performance of CS-OCN nanoparticles were investigated. The XPS spectra of CS-OCN-3 showed the presence of amino bonds. The optimal photocatalytic degradation efficiency of the synthesized CS-OCN-3 could reach 94.3% within 390 min, while the photocurrent response intensity was about 150% more than that of pure OCN. The improved photocatalytic performance may be mainly attributed to the enhanced photogenerated carrier’s separation and transportation and stronger visible light response after CS grafting. In addition, the inhibition diameter of CS-OCN-3 reached 23 mm against E. coli within 24 h under visible light irradiation, exhibiting excellent photocatalytic bactericidal ability. The results of bacterial inhibition were supported by absorbance measurements (OD600) studies of E. coli. In a word, this work provided a rational design of an efficient novel metal-free photocatalyst to remove bacterial contamination and accelerate the degradation of organic dyes.
Hybrid/sup 2/: combining the three-dimensional hybrid finite element-boundary integral technique for planar multilayered media with the uniform geometrical theory of diffraction

The fully three-dimensional (3-D) hybrid finite element (FE)-boundary integral (BI) technique is extended by further hybridization with the uniform geometrical theory of diffraction (UTD) resulting in a so-called hybrid/sup 2/ FE-BI-UTD approach. The formulation is capable of modeling arbitrarily shaped strongly inhomogeneous objects together with electrically large obstacles of relatively simple shape within the common environment of a planar-multilayered medium. The arbitrarily shaped inhomogeneous objects are discretized by finite elements, whereas, the electrically-large obstacles are described by the UTD and both of these models are included into an integral equation derived from the equivalence principle for planar-multilayered media. Thus, full-electromagnetic coupling is realized between all parts of the formulation. The integral equation is implemented using mixed potentials with appropriate Green's functions derived from Sommerfeld integral representations for planar-multilayered media. The UTD contributions are accounted for by corresponding modifications of the Green's functions and the FE technique is coupled to the integral equation via introduction of equivalent surface current densities in the bounding surfaces of the discretized objects. After presenting the formulation of this novel fully 3-D hybrid/sup 2/ technique, the implemented computer code is validated against conventional hybrid FE-BI computations and a wireless base station antenna is analyzed in several situations of complex real world, microcell environments.
MGFuse: An Infrared and Visible Image Fusion Algorithm Based on Multiscale Decomposition Optimization and Gradient-Weighted Local Energy

Existing image fusion algorithms have difficulty in effectively preserving valuable target features in infrared and visible images, which easily introduces blurry edges and unremarkable notable targets during their fusion process. We propose the MGFuse algorithm as a solution to this problem, which is a novel fusion algorithm that utilizes multiscale decomposition optimization and gradient-weighted local energy. Initially, non-subsampled shearlet transform (NSST) is applied to partition both the infrared and visible images into several high-frequencies and low-frequencies components. Subsequently, the acquired low frequencies continue to be decomposed via the proposed optimization function to get base layers and texture layers, which can optimize the quality of image edges and preserve fine-grained details, respectively. In addition, we have formulated an intrinsic attribute-based energy (IAE) fusion scheme to merge the two base layers. The texture layers and high-frequencies are extracted by gradient-weighted local energy (GE) operator based on structure tensor, which is employed to construct the fusion strategy for these parts. At last, the acquired texture and base parts are linearly combined to get the integrated low-frequency layer on which the final image is acquired using inverse NSST. Numerous experimental observations demonstrate that our MGFuse algorithm achieves superior fusion capability than the reference nine advanced algorithms in both qualitative and quantitative assessment, and robustness to noisy images with different noise levels.
Security-Oriented Pilot and Data Transmission for URLLC in Mission-Critical IoT Scenarios

In this article, we focus on the joint design of channel training and data transmission for secure ultrareliable and low-latency communications (URLLCs) in mission-critical Internet of Things (IoT) scenarios, e.g., intelligent transportation and remote control. Specifically, we consider a multiple-input multiple-output multiantenna eavesdropper (MIMOME) system, and the role of artificial noise (AN) for securing URLLC in this system is studied. In the channel training phase, we use the two-way discriminatory channel estimation (DCE) protocol with AN injection to suppress the channel estimation accuracy at eavesdropper. Meanwhile, the AN-assisted secrecy beamforming scheme is adopted to mask the confidential data signals. Following by the security enhancements above, we provide a quantitative definition of achievable effective secrecy rate (AESR) to measure the performance of our URLLC system with imperfect channel state information (CSI) and short-packet feature. Then, a nonasymptotic closed-form lower bound of AESR is provided to make the numerical calculation tractable, and the asymptotic system performance in the high-SNR regime is also studied to gain a comprehensive insight. Based on the cyclic coordinated search method, we propose an iterative resource allocation algorithm to maximize the AESR of our system, where the blocklength and transmit power assigned to the reverse/forward pilots, confidential data signals, and AN are jointly optimized. In addition, numerical results reveal the AESR performance of our URLLC system for different system parameters, and demonstrate the convergence and superiority of our proposed algorithm.
Broca Index: A Simple Tool to Measure Ideal Body Weight

Proper measure of ideal body weight (IBW) is necessary in medical science. According to physicians, IBW is related to the lowest morbidity and mortality. Broca Index (BI) is a simple and effective tool to measure IBW; and general physicians, nutritionists, and Life Insurance Companies can use it confidently. In this mini review aspects of Broca Index (BI) are discussed for the calculation of ideal body weight (IBW) of health conscious people.
Effect of Medication and Deep Brain Stimulation on Gait in Parkinson’s Disease and its Quantitative Analysis Using Mobishoe – A Comparative Study

Background: Movement abnormalities pertaining to balance, posture, and gait are observed in Parkinson's disease patients. Gait characteristics vary widely and their analysis has been performed traditionally in gait labs. Freezing and festination usually occur at an advanced stage of the disease and are associated with reduced quality of life. Therapeutic strategies and surgical interventions are often modulated by the physician depending upon the clinical manifestations. Introduction of accelerometers and wireless data transmission systems made quantitative gait analysis possible and cost-effective. Objective: To assess spatiotemporal gait parameters (step height, length (spatial), and swing support time of each foot and double support time (temporal)) in subjects who underwent deep brain stimulation surgery using a purpose-built instrument—Mobishoe. Methods: A simple footwear-based gait sensing device—Mobishoe was built in-house. Thirty-six participants were included in the study after obtaining consent. Participants were made to wear Mobishoe and walk an empty corridor of 30m before Deep Brain Stimulation (DBS) in the drug on and off stated and post DBS in DBS stimulation on and medication off state (B1M0), DBS stimulation off—medication off state (B0M0), DBS stimulation off—medication on (B0M1), and DBS stimulation on and on medication (B1M1). Data was electronically captured and analyzed offline in MATrix LABoratory (MATLAB). Various gait parameters were extracted and analyzed. Results: Improvement in gait parameters was observed when the subject was on medication, on stimulation, or on both when compared to baseline. Improvement was similar with both medication and stimulation and was synergistic when both were used. Significant improvement was noted in spatial characteristics when the subjects were on both the treatments, which is the ideal treatment modality. Conclusion: Mobishoe is an affordable device which can measure spatiotemporal characteristics of gait. The best improvement was seen when the subjects were on both the treatment groups and the improvement can be justified as a synergistic effect of stimulation and medication.
In search of characterization of the preference for safety under the Choquet model

Victor prefers safety more than Ursula if whenever Ursula prefers some constant to some uncertain act, so does Victor. This paradigm, whose Expected Utility version takes the form of Arrow & Pratt's more risk averse concept, will be studied in the Choquet Uncertainty model, letting u and μ (v and ν) be Ursula's (Victor's) utility and capacity. A necessary and sufficient condition (A) on the pairs (u, μ) and (v, ν) will be presented for dichotomous weak increased uncertainty aversion, the preference by Victor of a constant over a dichotomous act whenever such is the preference of Ursula. This condition, pointwise inequality between a function defined in terms of v (u-1(⋅)) and another defined purely in terms of the capacities, preserves the flavor of the "more pessimism than greediness" characterization of monotone risk aversion by Chateauneuf, Cohen & Meilijson in the Rank-dependent Utility Model and its extension by Grant & Quiggin to the Choquet Utility Model. A sufficient condition (B) in terms of the capacities only, satisfied in particular if ν (⋅) = f (μ (⋅)) for some convex f, will be presented for more simplicity seeking, the preference by Victor over any act for some dichotomous act, that leaves Ursula indifferent. Condition A is thus a characterization of weak increased uncertainty aversion for convex f. An example will be exhibited disproving the more far reaching conjecture under which the dichotomous case implies the general case.
Broadband quantum dot micro-light-emitting diodes with parabolic sidewalls

Arrays of long wavelength, self-organized InGaAs quantum dot micron sized light-emitting diodes (μ-LEDs) with parabolic sidewalls are introduced. The parabolic profiles of the μ-LEDs produced by resist reflow and controlled dry etching improve the extraction efficiency from the LEDs by redirection of the light into the escape cone by reflection from the sidewalls. A fourfold increase in the substrate emitted power density compared to a reference planar LED is measured. The reflected light is verified to be azimuthally polarized. The spectral width of the emission can be greater than 200nm.
Formation and Evolution of X-Ray Clusters: A Hydrodynamic Simulation of the Intracluster Medium

The thermodynamic history of the hot intracluster medium in a cluster of Coma richness was examined using a numerical simulation incorporating three-dimensional hydrodynamics of a collisional baryonic component within a self-consistently evolving dark matter mass distribution. The model evolves to states at low redshifts whose X-ray and optical properties are strikingly similar to Coma and A2256. The elliptical X-ray isophotes seen at z = 0 are remnants of a merger event which occurred at z = 0.2; thus, Coma and A2256 probably suffered moderate merger events in the recent past. The model also indicates that clusters of Coma richness should display a Suniaev-Zel'dovich central decrement of about 0.5 mK. 70 refs.
Factors Responsible for Precipitation Efficiencies in Midlatitude and Tropical Squall Simulations

Abstract Different definitions of storm precipitation efficiency were investigated from numerical simulators of convective systems in widely varying ambient conditions using a two-dimensional cloud model with sophisticated ice microphysics. The model results indicate that the vertical orientation of the updrafts, which is controlled by the vertical wind shear, and the ambient moisture content are important in determining storm efficiency. In terms of rainfall divided by condensation, simulated efficiencies ranged from 20%–35% for convective systems that tilted strongly against the low-level shear (upshear), to 40%–50% for erect storms. Changes in environmental moisture produced smaller variations in efficiency that were less than 10%. Upright convection allows for effective collection of cloud condensate by precipitation, whereas lower efficiencies in upshear storms are due to greater evaporation of cloud at middle levels and evaporation of rain at lower levels. Development of trailing stratiform precipit.
Abnormal Behavior Recognition Model of Power Grid Based on Multi-Scale Feature Fusion

Artificial intelligence technology is applied to power grid transmission and operation status identification to improve the intelligence level of power inspection. However, the existing grid anomalous behavior recognition model has the problem of unsatisfactory accuracy for small target detection. To address this problem, this paper proposes an abnormal behavior recognition model of power grid based on multi-scale feature fusion. First, the texture feature extraction network with a multi-scale mechanism is constructed to obtain object features. Secondly, the graph relation network between target and background is constructed. Finally, the grid abnormal behavior recognition model based on multi-scale feature fusion is constructed. The model proposed in this article is compared with the existing object recognition model in the simulation of the different datasets. In terms of the evaluation accuracy index, the proposed model is 93.27, which is improved by 2.18%.
Oscillatory translational instabilities of spot patterns in the Schnakenberg system on general 2D domains

For a bounded 2D planar domain Ω, we investigate the impact of domain geometry on oscillatory translational instabilities of N-spot equilibrium solutions for a singularly perturbed Schnakenberg reaction-diffusion system with activator-inhibitor diffusivity ratio. An N-spot equilibrium is characterized by an activator concentration that is exponentially small everywhere in Ω except in N well-separated localized regions of extent. We use the method of matched asymptotic analysis to analyze Hopf bifurcation thresholds above which the equilibrium becomes unstable to translational perturbations, which result in -frequency oscillations in the locations of the spots. We find that stability to these perturbations is governed by a nonlinear matrix-eigenvalue problem, the eigenvector of which is a 2N-vector that characterizes the possible modes (directions) of oscillation. The 2N×2N matrix contains terms associated with a certain Green’s function on Ω, which encodes geometric effects. For the special case of a perturbed disk with radius in polar coordinates r=1+σf(θ) with 0<ε≪σ≪1 , θ∈[0,2π) , and f(θ) 2π-periodic, we show that only the mode-2 coefficients of the Fourier series of f impact the bifurcation threshold at leading order in σ. We further show that when f(θ)=cos2θ , the dominant mode of oscillation is in the direction parallel to the longer axis of the perturbed disk. Numerical investigations on the full Schnakenberg PDE are performed for various domains Ω and N-spot equilibria to confirm asymptotic results and also to demonstrate how domain geometry impacts thresholds and dominant oscillation modes.
Image Classification of Car Paint Defect Detection Based on Convolutional Neural Networks

In the study of using images to display car paint defects, the current need is to use deep Convolutional Neural Networks (CNN) to identify and classify different types of car paint defects, so as to give full play to the application of image processing in the field of automatic car paint defect detection. Using the collected car paint defect images, the car paint defects dataset is established. The preprocessing process of original data and the application of three image classification models based on CNN are visually displayed. First, the dataset of 7 types of car body defects including bubble, dust, fouling, pinhole, sagging, scratch, and shrink has been established, with a total of 2468 images. The model of MobileNet-V2, Vgg16, and ResNet34 are selected for training. As a result, after 30 training iterations, the MobileNetV2 algorithm achieved 94.3% accuracy, the accuracy of the Vgg16 algorithm is as high as 99.9%, and the accuracy of the ResNet34 algorithm is maintained at 99.2%. To sum up, for car paint defect detection, deep learning has great potential and deserves further development.
Clinical significance of CT angiographic assessment of collateral circulation combined with serum NLRP1 levels in ischemic stroke patients

This research aimed to combine serum NLR-pyrin domain containing 1 (NLRP1) levels and collateral circulation to assess ischemic stroke patients and predict the prognoses of the patients. This present prospective observational study enrolled 196 ischemic stroke patients. All patients underwent CTA as well as digital subtraction angiography (DSA) to assess collateral circulation by American Society of Interventional and Therapeutic Neuroradiology/Society of Interventional Radiology (ASITN/SIR). In addition, we collected serum samples from 100 patients with carotid atherosclerosis as controls. The serum NLRP1, tumor necrosis factor α (TNF-α), interleukin (IL)-6, IL-1β and C-reactive protein (CRP) levels were measured by enzyme-linked immunosorbent assay (ELISA). The age, BMI, sex, smoke condition, diastolic blood pressure, systolic blood pressure, National Institutes of Health Stroke Scores (NIHSS), modified Rankin Scale (mRS) scores, imaging indicators and the levels of triglyceride, total cholesterol (TC), low-density leptin cholesterol (LDLC), high-density leptin cholesterol of all subjects were recorded. All data used SPSS 18.0 to statistical analyses. The serum levels of NLRP1 were remarkably enhanced in the ischemic stroke patients compared with the carotid atherosclerosis patients. The NIHSS score, the mRS score after 90 days and the levels of NLRP1, CRP, TNF-α IL-6 and IL-1β of ischemic stroke patients in the ASITN/SIR grade 0 to 2 group were remarkably elevated than the ischemic stroke patients in ASITN/SIR grade 3 to 4 group. Spearman analysis supported that a positive correlation existed among the NLRP1, CRP, IL-6, TNF-α, and IL-1β levels. The NIHSS score, infarct volume and the levels of NLRP1, IL-6, TNF-α, and IL-1β of ischemic stroke patients in the mRS score ≥ 3 group were remarkably elevated than the ischemic stroke patients in the mRS score ≤ 2 group. ASITN/SIR grade and NLRP1 could be potential diagnostic biomarkers of poor prognosis of ischemic stroke patients. It was found that NLRP1, ASITN/SIR grade, infarct volume, NIHSS, IL-6, and IL-1β were the risk factors for bad prognosis of ischemic stroke patients. This study showed that the serum NLRP1 levels were remarkably decreased in ischemic stroke patients. In addition, the serum NLRP1 levels and ASITN/SIR grade could predict the prognosis of ischemic stroke patients.