Influence of Process Parameters on the Deformation of Copper Foils in Flexible-Pad Laser Shock Forming

This paper investigates a new microforming technique, Flexible-Pad Laser Shock Forming (FPLSF), to produce mi-crofeatures on metallic foils without rigid punches and dies. FPLSF uses the laser-induced shock pressure and a flexi-ble-pad to plastically deform metal foils into hemispherical microcraters. In order to understand the deformation characteristics of metal foils in FPLSF, it is necessary to analyze the influence of process parameters on the foil deformation. In this paper, the effects of parameters such as the flexible-pad thickness, confinement layer medium, confinement layer thickness and the number of laser pulses on the depth, diameter and shape of the craters formed on copper foils were investigated. It is found that the flexible-pad thickness should be greater than its threshold value to maximize the deformation of foils. By comparing two different confinement media, namely water and glass, it is observed that hemispherical craters were formed on the copper foils at different laser fluence values tested when using water as the confinement; whereas shockwave ripples were formed on the copper foil at higher laser fluence while using the glass confinement. Using water as confinement medium, an increase in confinement thickness from 4 mm to 7 mm resulted in 48% increase of the crater depth at 7.3 J/cm2. However, at 13.6 J/cm2, reduction in crater depth was observed for thickness greater than 6 mm after an initial increasing trend. Regarding the number of pulses, it is found that increasing the number of pulses from 1 to 3 resulted only in a small increase (less than 1%) in crater depth at 7.3 J/cm2 and 13.6 J/cm2 laser fluence whereas 19.3% increase in depth was observed at larger laser fluence (20.9 J/cm2). It is also observed that the optimum number of pulses to achieve maximum deformation is varying with the laser fluence

Analyte response in laser ablation inductively coupled plasma mass spectrometry

The dependence of analyte sensitivity and vaporization efficiency on the operating parameters of an inductively coupled plasma mass spectrometer (ICPMS) was investigated for a wide range of elements in aerosols, produced by laser ablation of silicate glass. The ion signals were recorded for different carrier gas flow rates at different plasma power for two different laser ablation systems and carrier gases. Differences in atomization efficiency and analyte sensitivity are significant for the two gases and the particle size distribution of the aerosol. Vaporization of the aerosol is enhanced when helium is used, which is attributed to a better energy-transfer from the plasma to the central channel of the ICP and a higher diffusion rate of the vaporized material. This minimizes elemental fractionation caused by sequential evaporation and reduces diffusion losses in the ICP. The sensitivity change with carrier gas flow variation is dependent on m/z of the analyte ion and the chemical properties of the element. Elements with high vaporization temperatures reach a maximum at lower gas flow rates than easily vaporized elements. The sensitivity change is furthermore dependent on m/z of the analyte ion, due to the mass dependence of the ion kinetic energies. The mass response curve of the ICPMS is thus not only a result of space charge effects in the ion optics but is also affected by radial diffusion of analyte ions and the mismatch between their kinetic energy after expansion in the vacuum interface and the ion optic setting

Quantitative analysis of the spatial diversity of Moraceae in China

Changes in distribution patterns of economically essential forest species under global change are urgently needed in the scientific forecast, and large-scale spatial modeling is a crucial tool. Using diversity pattern indicators and other data obtained through geographic information systems (GIS) and spatial data on Moraceae species obtained from published data, we quantitatively studied the spatial diversity patterns of genera in the Moraceae in China. The results revealed that the patch richness, diversity index, and total shape index of the genera with multiple species were significantly higher than those of the monotypic genera. Monotypic genera had no spatial diversity and no distribution in patterns of spatial diversity. Maclura had the most concentrated spatial distribution and the lowest distribution area among the Moraceae in China. The number of patches and the total area were the smallest, while the most significant patch index was the highest. Maclura had no spatial diversity. Streblus had the highest patch abundance compared to other genera with fewer species. Streblus had the smallest number of patches and total area of distribution, the lowest spatial distribution, and a small total shape index, indicating its concentrated distribution. The values of the Shannon’s Diversity Index (SHDI) and Simpson’s Diversity Index (SIDI) were the highest, and the spatial distribution was the most diverse among the genera with fewer species. The patch type of Streblus had a more considerable value than other genera, but the number of patches was small, and the total shape index was low. Streblus was primarily distributed in the south of Yunnan, western Guangxi, the west and central parts of Hainan, and southern Guangdong. Most of these areas were mountainous. The temperature decreased with elevation, providing diverse environmental conditions for the narrow-stem genus. Among the Moraceae in China, the spatial distribution of Ficus was the most diverse, with the highest number of patches, patch types, total shape index, SHDI, and SIDI values. The spatial diversity of Ficus could be used as a protected area for Moraceae in China

A novel statistical cerebrovascular segmentation algorithm with particle swarm optimization

AbstractWe present an automatic statistical intensity-based approach to extract the 3D cerebrovascular structure from time-of flight (TOF) magnetic resonance angiography (MRA) data. We use the finite mixture model (FMM) to fit the intensity histogram of the brain image sequence, where the cerebral vascular structure is modeled by a Gaussian distribution function and the other low intensity tissues are modeled by Gaussian and Rayleigh distribution functions. To estimate the parameters of the FMM, we propose an improved particle swarm optimization (PSO) algorithm, which has a disturbing term in speeding updating the formula of PSO to ensure its convergence. We also use the ring shape topology of the particles neighborhood to improve the performance of the algorithm. Computational results on 34 test data show that the proposed method provides accurate segmentation, especially for those blood vessels of small sizes

Simulation and Prediction of Ion Transport in the Reclamation of Sodic Soils with Gypsum Based on the Support Vector Machine

The effect of gypsum on the physical and chemical characteristics of sodic soils is nonlinear and controlled by multiple factors. The support vector machine (SVM) is able to solve practical problems such as small samples, nonlinearity, high dimensions, and local minima points. This paper reports the use of the SVM regression method to predict changes in the chemical properties of sodic soils under different gypsum application rates in a soil column experiment and to evaluate the effect of gypsum reclamation on sodic soils. The research results show that (1) the SVM soil solute transport model using the Matlab toolbox represents the change in Ca2+ and Na+ in the soil solution and leachate well, with a high prediction accuracy. (2) Using the SVM model to predict the spatial and temporal variations in the soil solute content is feasible and does not require a specific mathematical model. The SVM model can take full advantage of the distribution characteristics of the training sample. (3) The workload of the soil solute transport prediction model based on the SVM is greatly reduced by not having to determine the hydrodynamic dispersion coefficient and retardation coefficient, and the model is thus highly practical

Transfer Learning with Optimal Transportation and Frequency Mixup for EEG-based Motor Imagery Recognition

Peer reviewedPublisher PD

3D Facial Similarity Measure Based on Geodesic Network and Curvatures

Automated 3D facial similarity measure is a challenging and valuable research topic in anthropology and computer graphics. It is widely used in various fields, such as criminal investigation, kinship confirmation, and face recognition. This paper proposes a 3D facial similarity measure method based on a combination of geodesic and curvature features. Firstly, a geodesic network is generated for each face with geodesics and iso-geodesics determined and these network points are adopted as the correspondence across face models. Then, four metrics associated with curvatures, that is, the mean curvature, Gaussian curvature, shape index, and curvedness, are computed for each network point by using a weighted average of its neighborhood points. Finally, correlation coefficients according to these metrics are computed, respectively, as the similarity measures between two 3D face models. Experiments of different persons’ 3D facial models and different 3D facial models of the same person are implemented and compared with a subjective face similarity study. The results show that the geodesic network plays an important role in 3D facial similarity measure. The similarity measure defined by shape index is consistent with human’s subjective evaluation basically, and it can measure the 3D face similarity more objectively than the other indices

A Smoothed Finite Element-Based Elasticity Model for Soft Bodies

One of the major challenges in mesh-based deformation simulation in computer graphics is to deal with mesh distortion. In this paper, we present a novel mesh-insensitive and softer method for simulating deformable solid bodies under the assumptions of linear elastic mechanics. A face-based strain smoothing method is adopted to alleviate mesh distortion instead of the traditional spatial adaptive smoothing method. Then, we propose a way to combine the strain smoothing method and the corotational method. With this approach, the amplitude and frequency of transient displacements are slightly affected by the distorted mesh. Realistic simulation results are generated under large rotation using a linear elasticity model without adding significant complexity or computational cost to the standard corotational FEM. Meanwhile, softening effect is a by-product of our method

Cuproptosis-related genes signature and validation of differential expression and the potential targeting drugs in temporal lobe epilepsy

Introduction: Temporal lobe epilepsy (TLE) is the most common subtype of epilepsy in adults and is characterized by neuronal loss, gliosis, and sprouting mossy fibers in the hippocampus. But the mechanism underlying neuronal loss has not been fully elucidated. A new programmed cell death, cuproptosis, has recently been discovered; however, its role in TLE is not clear.Methods: We first investigated the copper ion concentration in the hippocampus tissue. Then, using the Sample dataset and E-MTAB-3123 dataset, we analyzed the features of 12 cuproptosis-related genes in TLEs and controls using the bioinformatics tools. Then, the expression of the key cuproptosis genes were confirmed using real-time PCR and immunohistochemical staining (IHC). Finally, the Enrichr database was used to screen the small molecules and drugs targeting key cuproptosis genes in TLE.Results: The Sample dataset displayed four differentially expressed cuproptosis-related genes (DECRGs; LIPT1, GLS, PDHA1, and CDKN2A) while the E-MTAB-3123 dataset revealed seven DECRGs (LIPT1, DLD, FDX1, GLS, PDHB, PDHA1, and DLAT). Remarkably, only LIPT1 was uniformly upregulated in both datasets. Additionally, these DECRGs are implicated in the TCA cycle and pyruvate metabolism—both crucial for cell cuproptosis—as well as various immune cell infiltrations, especially macrophages and T cells, in the TLE hippocampus. Interestingly, DECRGs were linked to most infiltrating immune cells during TLE’s acute phase, but this association considerably weakened in the latent phase. In the chronic phase, DECRGs were connected with several T-cell subclasses. Moreover, LIPT1, FDX1, DLD, and PDHB were related to TLE identification. PCR and IHC further confirmed LIPT1 and FDX1’s upregulation in TLE compared to controls. Finally, using the Enrichr database, we found that chlorzoxazone and piperlongumine inhibited cell cuproptosis by targeting LIPT1, FDX1, DLD, and PDHB.Conclusion: Our findings suggest that cuproptosis is directly related to TLE. The signature of cuproptosis-related genes presents new clues for exploring the roles of neuronal death in TLE. Furthermore, LIPT1 and FDX1 appear as potential targets of neuronal cuproptosis for controlling TLE’s seizures and progression

A new multi-anticipative car-following model with consideration of the desired following distance

We propose in this paper an extension of the multi-anticipative optimal velocity car-following model to consider explicitly the desired following distance. The model on the following vehicle’s acceleration is formulated as a linear function of the optimal velocity and the desired distance, with reaction-time delay in elements. The linear stability condition of the model is derived. The results demonstrate that the stability of traffic flow is improved by introducing the desired following distance, increasing the time gap in the desired following distance or decreasing the reaction-time delay. The simulation results show that by taking into account the desired following distance as well as the optimal velocity, the multi-anticipative model allows longer reaction-time delay in achieving stable traffic flows