Comparison of brain functions between healthy participants and methamphetamine users with various addiction histories:Data analysis based on EEG and fNIRS

The electroencephalogram(EEG)rhythm and functional near-infrared spectroscopy(fNIRS)activation levels have not been compared between a healthy control group(HCG)and methamphetamine user group(MUG)with different addiction histories.This study used 64-electrode EEG and fNIRS to conduct an experiment that analyzed the resting and craving states.The EEG and fNIRS data of 56 participants were collected,including 14 healthy participants,14 methamphetamine users with an addiction history of 0.5–5 years,14 users with an addiction history of 5–10 years,and 14 users with an addiction history of 10–15 years.Isolated effective coherence(iCoh)within the brain network was used to process the EEG data.Statistical analysis was performed to compare differences in iCoh among the delta,theta,alpha,beta,and gamma bands and explore oxyhemoglobin activation levels in the ventrolateral prefrontal cortex,dorsolateral prefrontal cortex,orbitofrontal cortex,and frontopolar prefrontal cortex(FPC)of the control group.Finally,the Kmeans,Gaussian mixed model(GMM),linear discriminant analysis(LDA),support vector machine(SVM),Bayes,and convolutional neural networks(CNN)algorithms were used to classify methamphetamine users based on drug and neutral images.A 3-class accuracy was achieved.Changes in EEG and fNIRS activation levels of HCG and MUG with varied addiction histories were demonstrated.

Effect of granular shape on radial segregation in a two-dimensional drum

Granular segregation is widely observed in nature and industry.Most research has focused on segregation caused by differences in the size and density of spherical grains.However,due to the fact that grains typically have different shapes,the focus is shifting towards shape segregation.In this study,experiments are conducted by mixing cubic and spherical grains.The results indicate that spherical grains gather at the center and cubic grains are distributed around them,and the degree of segregation is low.Through experiments,a structured analysis of local regions is conducted to explain the inability to form stable segregation patterns with obviously different geometric shapes.Further,through simulations,the reasons for the central and peripheral distributions are explained by comparing velocities and the number of collisions of the grains in the flow layer.

Trend analysis of stroke risk research based on three-dimensional metrics

This study comprehensively analyzes the status,characteristics,focal points,and evolving trends of global research on“stroke risk analysis”over the past four years(2020–2023),aiming to provide insights for directing future research endeavors.By utilizing the Newcastle-Ottawa Scale,63 high-quality research papers were selected and subjected to a systematic literature review.In terms of research methods,stroke risk analysis research has evolved from clinical trials(e.g.,establishing control groups,using authoritative scales)towards statistical and data analysis methods(e.g.,decision tree analysis).Regarding research factors,early studies primarily focused on pathological factors associated with hemorrhagic and ischemic stroke,such as hypertension,hyperlipidemia,and diabetes.Recent research from the past two years indicates a shift towards emerging factors,including temperature conditions,air quality,and Corona Virus Disease 2019(COVID-19).In terms of application domains,stroke research covers a broad range of fields but mainly focuses on exploring risk factors,interventions during diagnosis and treatment stages,and rehabilitation,with clinical diagnosis,treatment,and drug intervention studies being predominant.While the research landscape is becoming increasingly diversified and comprehensive,there remains a need for more comprehensive and in-depth studies on novel topics,as well as integrated applications of research methods,presenting ample opportunities for exploring dependent variables in future stroke.

Intruder trajectory tracking in a three-dimensional vibration-driven granular system:Unveiling the mechanism of the Brazil nut effect

We employ a Hall-effect magnetic sensor array to accurately track the trajectory of a single magnetic sphere,referred to as the“intruder,”within a three-dimensional vibro-fluidized granular bed to unravel the underlying physical mechanism governing the motion of the intruder.Within the acceleration range of 3.5 g≥Γ≥1.5 g,we find that,regardless of the intruder's initial position,it consistently reaches the same equilibrium depth when the vibration acceleration(Γ)and frequency(ω)are fixed.ForΓ≤2.5 g,the equilibrium position lies on the surface of the granular bed,while forΓ>2.5 g,it shifts below the surface.Additionally,intruders with different densities exhibit varying equilibrium depths,with higher density resulting in a deeper equilibrium position.To understand the mechanism behind the intruder's upward or downward motion,we measure its rising or sinking velocities under different vibration parameters.Our findings demonstrate that the rising velocity of the intruder,under varying vibration accelerations(Γ)and frequencies(ω),can be collapsed using the ratioΓ/ω,while the sinking velocity remains unaffected by the vibration strength.This confirms that the upward motion of the larger sphere,associated with the Brazil nut effect,primarily arises from the void-filling mechanism of the bed particles.Furthermore,our experiments reveal that the presence of convection within the bed particles has minimal impact on the motion of the intruder.

Classification of Hemifacial Microsomia Based on Bone Volume Ratio of the Healthy and Affected Sides of the Mandible:A Retrospective Self-control Experiment

Objective Hemifacial microsomia(HFM),a congenital craniofacial malformation,is characterized by unilateral mandibular dysplasia.At present,the Pruzansky classification is the most common descriptive classification used clinically,which involves mandibular deformities.Although multiple classification systems have been proposed for HFM,a quantitative classification has not yet been proposed.This study intended to propose a quantitative classification for HFMaccording to the mandibular volume on the affected side.Methods Patients with HFM from January 2017 to January 2018 were included,and the A/U ratio(volume of the affected side/volume of the unaffected side)was measured.This study proposed a classification for HFM(mandibular-volume classification,MVC)according to the A/U ratio and compared the new classification system with the Pruzansky classification using consistency and correlation tests.Results A total of 48 patients were included.The results of MVC based on the A/U ratio were as follows:A/U>0.85,classified asmild;0.73-0.85,as moderate;and<0.73 assevere;the weighted kappa value of the Pruzansky and MVC classifications was 0.616(P<0.01).Conclusion The quantitative classification of HFMbased on the A/U ratio can serve asa viable evaluation index for patients withHFM and can provide a new reference index for determiningthe treatment plan.

Analysis of color distortion and optimum fusion for remote sensing images using the statistical property of wavelet decomposition

IHS （Intensity, Hue and Saturation） transform is one of the most commonly used tusion algonthm. But the matching error causes spectral distortion and degradation in processing of image fusion with IHS method. A study on IHS fusion indicates that the color distortion can＇t be avoided. Meanwhile, the statistical property of wavelet coefficient with wavelet decomposition reflects those significant features, such as edges, lines and regions. So, a united optimal fusion method, which uses the statistical property and IHS transform on pixel and feature levels, is proposed. That is, the high frequency of intensity component Ⅰ is fused on feature level with multi-resolution wavelet in IHS space. And the low frequency of intensity component Ⅰ is fused on pixel level with optimal weight coefficients. Spectral information and spatial resolution are two performance indexes of optimal weight coefficients. Experiment results with QuickBird data of Shanghai show that it is a practical and effective method.

Measurement of acceleration and angular velocity of particles during a 3D silo discharge

Accurate measurement of the three-dimensional(3D)movement of discrete particles is crucial for comprehending complex granular rheology in silos.In this paper,the acceleration and angular velocity of particles in 3D silos are measured by using a spherical detector based on inertial technology and magnetic positioning technology.The acceleration of particles is the largest in the center of silos,which suggest that the resistance generated by friction and extrusion is the smallest.Surprisingly,the angular velocity distribution follows lognormal function except for particles near the outlet.The correlation between acceleration and angular velocity is opposite in different flow regions.It reveals for the first time that the extent to which the resultant force on the particles affects their rotational motion is related to the flow pattern.These results have practical significance for regulating the granular flow pattern and optimizing the structural design of silos.

Differential interference contrast phase edging net:an all-optical learning system for edge detection of phase objects

Edge detection for low-contrast phase objects cannot be performed directly by the spatial difference of intensity distribution.In this work,an all-optical diffractive neural network(DPENet)based on the differential interference contrast principle to detect the edges of phase objects in an all-optical manner is proposed.Edge information is encoded into an interference light field by dual Wollaston prisms without lenses and light-speed processed by the diffractive neural network to obtain the scale-adjustable edges.Simulation results show that DPENet achieves F-scores of 0.9308(MNIST)and 0.9352(NIST)and enables real-time edge detection of biological cells,achieving an F-score of 0.7462.

Gauge field theory of quantum and the mass empirical formulas of lepton and quarks

In this paper,we discuss the mathematical relation determined by the basic physical constant between three types of quarks and the masses of leptons with charges in detail.First,by further theoretical analysis,we wonderfully see that the result got from the mass empirical formula of quark and charged lepton is identical with the data received by theoretical estimating from the gauge field theory.Second,we also gladly see that the result got from quark and lepton(with charges)mass empirical formula is completely accordant with experiment data.These mass formulas reveal the essential relation between me,mμ,mτ and mq.At the same time,the empirical formula may also derive the mass formula of neu-trinos.As to the mass of neutrinos,at present we only know the square difference of its mass,and so this is meaningful to theoretically estimating the mass.