血清sTWEAK、Netrin-1联合APACHEⅡ评分对重型颅脑损伤患者术后预后不良的预测价值

目的探讨血清可溶性肿瘤坏死因子样凋亡弱诱导因子(sTWEAK)、神经轴突导向因子-1(Netrin-1)联合急性生理学与慢性健康状况评分系统Ⅱ(APACHEⅡ)评分对重型颅脑损伤患者术后预后不良的预测价值。方法选取2020年6月至2022年6月该院收治的120例重型颅脑损伤患者,根据术后30 d预后情况分为预后良好组和预后不良组。对比两组血清sTWEAK、Netrin-1水平及APACHEⅡ评分。采用单因素和多因素Logistic回归分析重型颅脑损伤患者术后预后不良的影响因素,并据以构建血清sTWEAK、Netrin-1及APACHEⅡ评分联合应用的预测模型,受试者工作特征(ROC)曲线分析血清sTWEAK、Netrin-1水平及APACHEⅡ评分对重型颅脑损伤患者术后预后不良的预测价值。结果预后不良组的重症监护室居住时间长于预后良好组,白蛋白水平、入院时格拉斯哥昏迷评分法评分和血清Netrin-1水平低于预后良好组,多发脑挫裂伤占比、机械通气占比、入院时APACHEⅡ评分和血清sTWEAK、血清肌酐、血尿素氮水平均高于预后良好组,差异有统计学意义(P<0.05)。多因素Logistic回归分析结果显示,多发脑挫裂伤、Netrin-1水平降低、入院时APACHEⅡ评分升高、sTWEAK水平升高为重型颅脑损伤患者术后预后不良的危险因素(P<0.05)。ROC曲线分析结果显示,血清sTWEAK、Netrin-1及APACHEⅡ评分3个指标单独及联合应用时曲线下面积及其95%CI分别为0.742(0.552~0.925)、0.731(0.488~0.963)、0.714(0.502~0.911)、0.882(0.795~0.947)。结论血清sTWEAK、Netrin-1联合APACHEⅡ评分对重型颅脑损伤患者术后预后不良具有较好的预测价值,可为临床治疗方案的制订提供参考。

自发性急性脑出血患者血浆sCD163/sTWEAK比值与预后的关系

目的探究自发性急性脑出血(ACH)患者血浆可溶性CD163(sCD163)/可溶性肿瘤坏死因子样凋亡弱诱导因子(sTWEAK)比值与预后的关系。方法纳入ACH患者90例作为病例组,根据格拉斯哥预后评分将病例组分为预后不良组(38例)和预后良好组(52例);另选取同期体检健康者45例为对照组。酶联免疫吸附试验检测血浆sCD163、sTWEAK水平并计算sCD163/sTWEAK比值。分析血浆sCD163、sTWEAK水平及sCD163/sTWEAK比值与临床资料的相关性;Logistic回归分析ACH患者预后不良的影响因素;受试者工作特征(ROC)曲线分析sCD163/sTWEAK比值对ACH患者预后不良的预测价值。结果病例组血浆sCD163、sTWEAK水平及sCD163/sTWEAK比值均显著高于对照组;预后良好组上述指标均低于预后不良组(P<0.05)。预后良好组血肿体积、美国国立卫生研究院卒中量表(NIHSS)评分、高血压及幕下出血比例均低于预后不良组,低密度脂蛋白胆固醇(LDL-C)高于预后不良组(P<0.05)。相关性分析表明,血浆sCD163、sTWEAK水平及sCD163/sTWEAK比值与出血部位、血肿体积、NIHSS评分、白细胞计数、血小板计数、中性粒细胞/淋巴细胞比值(NLR)呈正相关(P<0.05)。Logistic回归分析显示,sCD163/sTWEAK比值、出血部位、血肿体积、NIHSS评分为ACH患者预后不良的影响因素(P<0.05)。ROC曲线结果表明,sCD163/sTWEAK比值评估ACH患者预后不良的AUC为0.850,敏感度和特异度分别为86.84%和69.23%。结论sCD163/sTWEAK比值在ACH患者血浆中水平较高,并与预后不良有关,该值对此类患者的预后有一定预测价值。

Significant risk factors for intensive care unit-acquired weakness:A processing strategy based on repeated machine learning

BACKGROUND Intensive care unit-acquired weakness(ICU-AW)is a common complication that significantly impacts the patient's recovery process,even leading to adverse outcomes.Currently,there is a lack of effective preventive measures.AIM To identify significant risk factors for ICU-AW through iterative machine learning techniques and offer recommendations for its prevention and treatment.METHODS Patients were categorized into ICU-AW and non-ICU-AW groups on the 14th day post-ICU admission.Relevant data from the initial 14 d of ICU stay,such as age,comorbidities,sedative dosage,vasopressor dosage,duration of mechanical ventilation,length of ICU stay,and rehabilitation therapy,were gathered.The relationships between these variables and ICU-AW were examined.Utilizing iterative machine learning techniques,a multilayer perceptron neural network model was developed,and its predictive performance for ICU-AW was assessed using the receiver operating characteristic curve.RESULTS Within the ICU-AW group,age,duration of mechanical ventilation,lorazepam dosage,adrenaline dosage,and length of ICU stay were significantly higher than in the non-ICU-AW group.Additionally,sepsis,multiple organ dysfunction syndrome,hypoalbuminemia,acute heart failure,respiratory failure,acute kidney injury,anemia,stress-related gastrointestinal bleeding,shock,hypertension,coronary artery disease,malignant tumors,and rehabilitation therapy ratios were significantly higher in the ICU-AW group,demonstrating statistical significance.The most influential factors contributing to ICU-AW were identified as the length of ICU stay(100.0%)and the duration of mechanical ventilation(54.9%).The neural network model predicted ICU-AW with an area under the curve of 0.941,sensitivity of 92.2%,and specificity of 82.7%.CONCLUSION The main factors influencing ICU-AW are the length of ICU stay and the duration of mechanical ventilation.A primary preventive strategy,when feasible,involves minimizing both ICU stay and mechanical ventilation duration.

Multi-Material Topology Optimization for Spatial-Varying Porous Structures

This paper aims to propose a topology optimization method on generating porous structures comprising multiple materials.The mathematical optimization formulation is established under the constraints of individual volume fraction of constituent phase or total mass,as well as the local volume fraction of all phases.The original optimization problem with numerous constraints is converted into a box-constrained optimization problem by incorporating all constraints to the augmented Lagrangian function,avoiding the parameter dependence in the conventional aggregation process.Furthermore,the local volume percentage can be precisely satisfied.The effects including the globalmass bound,the influence radius and local volume percentage on final designs are exploited through numerical examples.The numerical results also reveal that porous structures keep a balance between the bulk design and periodic design in terms of the resulting compliance.All results,including those for irregular structures andmultiple volume fraction constraints,demonstrate that the proposedmethod can provide an efficient solution for multiple material infill structures.

Ultrahigh-brightness 50 MeV electron beam generation from laser wakefield acceleration in a weakly nonlinear regime

We propose an efficient scheme to produce ultrahigh-brightness tens of MeV electron beams by designing a density-tailored plasma to induce a wakefield in the weakly nonlinear regime with a moderate laser energy of 120 mJ.In this scheme,the second bucket of the wakefield can have a much lower phase velocity at the steep plasma density down-ramp than the first bucket and can be exploited to implement longitudinal electron injection at a lower laser intensity,leading to the generation of bright electron beams with ultralow emittance together with low energy spread.Three-dimensional particle-in-cell simulations are carried out and demonstrate that high-quality electron beams with a peak energy of 50 MeV,ultralow emittance of28 nm rad,energy spread of 1%,charge of 4.4 pC,and short duration less than 5 fs can be obtained within a 1-mm-long tailored plasma density,resulting in an ultrahigh six-dimensional brightness B6D,n of2×1017 A/m2/0.1%.By changing the density parameters,tunable bright electron beams with peak energies ranging from 5 to 70 MeV,a small emittance of B0.1 mm mrad,and a low energy spread at a few-percent level can be obtained.These bright MeV-class electron beams have a variety of potential applications,for example,as ultrafast electron probes for diffraction and imaging,in laboratory astrophysics,in coherent radiation source generation,and as injectors for GeV particle accelerators.

Interplay between topology and localization on superconducting circuits

Topological insulators occupy a prominent position in the realm of condensed matter physics. Nevertheless, the presence of strong disorder has the potential to disrupt the integrity of topological states, leading to the localization of all states.This study delves into the intricate interplay between topology and localization within the one-dimensional Su–Schrieffer–Heeger(SSH) model, which incorporates controllable off-diagonal quasi-periodic modulations on superconducting circuits.Through the application of external alternating current(ac) magnetic fluxes, each transmon undergoes controlled driving,enabling independent tuning of all coupling strengths. Within a framework of this model, we construct comprehensive phase diagrams delineating regions characterized by extended topologically nontrivial states, critical localization, and coexisting topological and critical localization phases. The paper also addresses the dynamics of qubit excitations, elucidating distinct quantum state transfers resulting from the intricate interplay between topology and localization. Additionally, we propose a method for detecting diverse quantum phases utilizing existing experimental setups.

Measuring small longitudinal phase shifts via weak measurement amplification

Weak measurement amplification,which is considered as a very promising scheme in precision measurement,has been applied to various small physical quantities estimations.Since many physical quantities can be converted into phase signals,it is interesting and important to consider measuring small longitudinal phase shifts by using weak measurement.Here,we propose and experimentally demonstrate a novel weak measurement amplification-based small longitudinal phase estimation,which is suitable for polarization interferometry.We realize one order of magnitude amplification measurement of a small phase signal directly introduced by a liquid crystal variable retarder and show that it is robust to the imperfection of interference.Besides,we analyze the effect of magnification error which is never considered in the previous works,and find the constraint on the magnification.Our results may find important applications in high-precision measurements,e.g.,gravitational wave detection.

GLOBAL WEAK SOLUTIONS FOR AN ATTRACTION-REPULSION CHEMOTAXIS SYSTEM WITH p-LAPLACIAN DIFFUSION AND LOGISTIC SOURCE

This paper is concerned with the following attraction-repulsion chemotaxis system with p-Laplacian diffusion and logistic source:■The system here is under a homogenous Neumann boundary condition in a bounded domainΩ ■ R^(n)(n≥2),with χ,ξ,α,β,γ,δ,k_(1),k_(2)> 0,p> 2.In addition,the function f is smooth and satisfies that f(s)≤κ-μs~l for all s≥0,with κ ∈ R,μ> 0,l> 1.It is shown that(ⅰ)if l> max{2k_(1),(2k_(1)n)/(2+n)+1/(p-1)},then system possesses a global bounded weak solution and(ⅱ)if k_(2)> max{2k_(1)-1,(2k_(1)n)/(2+n)+(2-p)/(p-1)} with l> 2,then system possesses a global bounded weak solution.

Ballistic performance of additive manufacturing 316l stainless steel projectiles based on topology optimization method

Material and structure made by additive manufacturing(AM)have received much attention lately due to their flexibility and ability to customize complex structures.This study first implements multiple objective topology optimization simulations based on a projectile perforation model,and a new topologic projectile is obtained.Then two types of 316L stainless steel projectiles(the solid and the topology)are printed in a selective laser melt(SLM)machine to evaluate the penetration performance of the projectiles by the ballistic test.The experiment results show that the dimensionless specific kinetic energy value of topologic projectiles is higher than that of solid projectiles,indicating the better penetration ability of the topologic projectiles.Finally,microscopic studies(scanning electron microscope and X-ray micro-CT)are performed on the remaining projectiles to investigate the failure mechanism of the internal structure of the topologic projectiles.An explicit dynamics simulation was also performed,and the failure locations of the residual topologic projectiles were in good agreement with the experimental results,which can better guide the design of new projectiles combining AM and topology optimization in the future.

Feature Matching via Topology-Aware Graph Interaction Model

Feature matching plays a key role in computer vision. However, due to the limitations of the descriptors, the putative matches are inevitably contaminated by massive outliers.This paper attempts to tackle the outlier filtering problem from two aspects. First, a robust and efficient graph interaction model,is proposed, with the assumption that matches are correlated with each other rather than independently distributed. To this end, we construct a graph based on the local relationships of matches and formulate the outlier filtering task as a binary labeling energy minimization problem, where the pairwise term encodes the interaction between matches. We further show that this formulation can be solved globally by graph cut algorithm. Our new formulation always improves the performance of previous localitybased method without noticeable deterioration in processing time,adding a few milliseconds. Second, to construct a better graph structure, a robust and geometrically meaningful topology-aware relationship is developed to capture the topology relationship between matches. The two components in sum lead to topology interaction matching(TIM), an effective and efficient method for outlier filtering. Extensive experiments on several large and diverse datasets for multiple vision tasks including general feature matching, as well as relative pose estimation, homography and fundamental matrix estimation, loop-closure detection, and multi-modal image matching, demonstrate that our TIM is more competitive than current state-of-the-art methods, in terms of generality, efficiency, and effectiveness. The source code is publicly available at http://github.com/YifanLu2000/TIM.

Topology Optimization of Two Fluid Heat Transfer Problems for Heat Exchanger Design

Topology optimization of thermal-fluid coupling problems has received widespread attention.This article proposes a novel topology optimization method for laminar two-fluid heat exchanger design.The proposed method utilizes an artificial density field to create two permeability interpolation functions that exhibit opposing trends,ensuring separation between the two fluid domains.Additionally,a Gaussian function is employed to construct an interpolation function for the thermal conductivity coefficient.Furthermore,a computational program has been developed on the OpenFOAM platform for the topology optimization of two-fluid heat exchangers.This program leverages parallel computing,significantly reducing the time required for the topology optimization process.To enhance computational speed and reduce the number of constraint conditions,we replaced the conventional pressure drop constraint condition in the optimization problem with a pressure inlet/outlet boundary condition.The 3D optimization results demonstrate the characteristic features of a surface structure,providing valuable guidance for designing heat exchangers that achieve high heat exchange efficiency while minimizing excessive pressure loss.At the same time,a new structure appears in large-scale topology optimization,which proves the effectiveness and stability of the topology optimization program written in this paper in large-scale calculation.

Local saliency consistency-based label inference for weakly supervised salient object detection using scribble annotations

Recently,weak supervision has received growing attention in the field of salient object detection due to the convenience of labelling.However,there is a large performance gap between weakly supervised and fully supervised salient object detectors because the scribble annotation can only provide very limited foreground/background information.Therefore,an intuitive idea is to infer annotations that cover more complete object and background regions for training.To this end,a label inference strategy is proposed based on the assumption that pixels with similar colours and close positions should have consistent labels.Specifically,k-means clustering algorithm was first performed on both colours and coordinates of original annotations,and then assigned the same labels to points having similar colours with colour cluster centres and near coordinate cluster centres.Next,the same annotations for pixels with similar colours within each kernel neighbourhood was set further.Extensive experiments on six benchmarks demonstrate that our method can significantly improve the performance and achieve the state-of-the-art results.

Experimental investigation on weak shock wave mitigation characteristics of flexible polyurethane foam and polyurea

In recent years,explosion shock wave has been considered as a signature injury of the current military conflicts.Although strong shock wave is lethal to the human body,weak shock wave can cause many more lasting consequences.To investigate the protection ability and characteristics of flexible materials and structures under weak shock wave loading,the blast wave produced by TNT explosive is loaded on the polyurethane foam with the density of 200.0 kg/m3(F-200)and 400.0 kg/m3(F-400),polyurea with the density of 1100.0 kg/m^(3)(P-1100)and structures composed of the two materials,which are intended for individual protection.Experimental results indicate that the shock wave is attenuated to weak pressure disturbance after interacting with the flexible materials which are not damaged.The shock wave protective capability of single-layer materials is dependent on their thickness,density and microscopic characteristics.The overpressure,maximum pressure rise rate and impulse of transmitted wave decrease exponentially with increase in sample thickness.For the same thickness,F-400 provides better protective capability than F-200 while P-1100 shows the best protective capability among the three materials.In this study,as the materials are not destroyed,F-200 with a thickness more than10.0 mm,F-400 with a thickness more than 4.0 mm,and P-1100 with a thickness more than 1.0 mm can attenuate the overpressure amplitude more than 90.0%.Further,multi-layer flexible composites are designed.Different layer layouts of designed structures and layer thickness of the single-layer materials can affect the protective performance.Within the research range,the structure in which polyurea is placed on the impact side shows the optimal shock wave protective performance,and the thicknesses of polyurea and polyurethane foam are 1.0 mm and 4.0 mm respectively.The overpressure attenuation rate reached maximum value of 93.3%and impulse attenuation capacity of this structure are better than those of single-layer polyurea and polyurethane foam with higher areal density.

ENERGY CONSERVATION FOR THE WEAK SOLUTIONS TO THE 3D COMPRESSIBLE NEMATIC LIQUID CRYSTAL FLOW

In this paper,we establish some regularity conditions on the density and velocity fields to guarantee the energy conservation of the weak solutions for the three-dimensional compressible nematic liquid crystal flow in the periodic domain.

A non-probabilistic reliability topology optimization method based on aggregation function and matrix multiplication considering buckling response constraints

A non-probabilistic reliability topology optimization method is proposed based on the aggregation function and matrix multiplication.The expression of the geometric stiffness matrix is derived,the finite element linear buckling analysis is conducted,and the sensitivity solution of the linear buckling factor is achieved.For a specific problem in linear buckling topology optimization,a Heaviside projection function based on the exponential smooth growth is developed to eliminate the gray cells.The aggregation function method is used to consider the high-order eigenvalues,so as to obtain continuous sensitivity information and refined structural design.With cyclic matrix programming,a fast topology optimization method that can be used to efficiently obtain the unit assembly and sensitivity solution is conducted.To maximize the buckling load,under the constraint of the given buckling load,two types of topological optimization columns are constructed.The variable density method is used to achieve the topology optimization solution along with the moving asymptote optimization algorithm.The vertex method and the matching point method are used to carry out an uncertainty propagation analysis,and the non-probability reliability topology optimization method considering buckling responses is developed based on the transformation of non-probability reliability indices based on the characteristic distance.Finally,the differences in the structural topology optimization under different reliability degrees are illustrated by examples.

Probabilistic-Ellipsoid Hybrid Reliability Multi-Material Topology Optimization Method Based on Stress Constraint

This paper proposes a multi-material topology optimization method based on the hybrid reliability of the probability-ellipsoid model with stress constraint for the stochastic uncertainty and epistemic uncertainty of mechanical loads in optimization design.The probabilistic model is combined with the ellipsoidal model to describe the uncertainty of mechanical loads.The topology optimization formula is combined with the ordered solid isotropic material with penalization(ordered-SIMP)multi-material interpolation model.The stresses of all elements are integrated into a global stress measurement that approximates the maximum stress using the normalized p-norm function.Furthermore,the sequential optimization and reliability assessment(SORA)is applied to transform the original uncertainty optimization problem into an equivalent deterministic topology optimization(DTO)problem.Stochastic response surface and sparse grid technique are combined with SORA to get accurate information on the most probable failure point(MPP).In each cycle,the equivalent topology optimization formula is updated according to the MPP information obtained in the previous cycle.The adjoint variable method is used for deriving the sensitivity of the stress constraint and the moving asymptote method(MMA)is used to update design variables.Finally,the validity and feasibility of the method are verified by the numerical example of L-shape beam design,T-shape structure design,steering knuckle,and 3D T-shaped beam.

Web Layout Design of Large Cavity Structures Based on Topology Optimization

Large cavity structures are widely employed in aerospace engineering, such as thin-walled cylinders, blades andwings. Enhancing performance of aerial vehicles while reducing manufacturing costs and fuel consumptionhas become a focal point for contemporary researchers. Therefore, this paper aims to investigate the topologyoptimization of large cavity structures as a means to enhance their performance, safety, and efficiency. By usingthe variable density method, lightweight design is achieved without compromising structural strength. Theoptimization model considers both concentrated and distributed loads, and utilizes techniques like sensitivityfiltering and projection to obtain a robust optimized configuration. The mechanical properties are checked bycomparing the stress distribution and displacement of the unoptimized and optimized structures under the sameload. The results confirm that the optimized structures exhibit improved mechanical properties, thus offering keyinsights for engineering lightweight, high-strength large cavity structures.

Relationship between disorder,magnetism and band topology in Mn(Sb_(1-x)Bi_(x))_(2)Te_(4) single crystals

We investigate the evolution of magnetic properties as well as the content and distribution of Mn for Mn(Sb_(1-x)Bi_(x))_(2)Te_(4) single crystals grown by large-temperature-gradient chemical vapor transport method.It is found that the ferromagnetic MnSb_(2)Te_(4) changes to antiferromagnetism with Bi doping when x≥0.25.Further analysis implies that the occupations of Mn ions at Sb/Bi site Mn_(Sb/Bi) and Mn site Mn_(Mn) have a strong influence on the magnetic ground states of these systems.With the decrease of Mn_(Mn) increase of Mn_(Sb/Bi),the system will favor the ferromagnetic ground state.In addition,the rapid decrease of T_(C/N) with increasing Bi content when x ≤0.25 and the insensitivity of T_(N) to x when x> 0.25 suggest that the main magnetic interaction may change from the Ruderman-Kittel-Kasuya-Yosida type at low Bi doping region to the van-Vleck type in high Bi doped samples.

Multi-Material Topology Optimization of 2D Structures Using Convolutional Neural Networks

In recent years,there has been significant research on the application of deep learning(DL)in topology optimization(TO)to accelerate structural design.However,these methods have primarily focused on solving binary TO problems,and effective solutions for multi-material topology optimization(MMTO)which requires a lot of computing resources are still lacking.Therefore,this paper proposes the framework of multiphase topology optimization using deep learning to accelerate MMTO design.The framework employs convolutional neural network(CNN)to construct a surrogate model for solving MMTO,and the obtained surrogate model can rapidly generate multi-material structure topologies in negligible time without any iterations.The performance evaluation results show that the proposed method not only outputs multi-material topologies with clear material boundary but also reduces the calculation cost with high prediction accuracy.Additionally,in order to find a more reasonable modeling method for MMTO,this paper studies the characteristics of surrogate modeling as regression task and classification task.Through the training of 297 models,our findings show that the regression task yields slightly better results than the classification task in most cases.Furthermore,The results indicate that the prediction accuracy is primarily influenced by factors such as the TO problem,material category,and data scale.Conversely,factors such as the domain size and the material property have minimal impact on the accuracy.

Macro-micro behaviors and failure mechanism of frozen weakly cemented mudstone

Understanding the mechanical properties and multiscale failure mechanism of frozen soft rock is an important prerequisite for the construction safety of tunnels,artificially frozen ground and other infrastructure in cold regions.In this study,the triaxial compression test are performed on mudstone in the weakly cemented soft rock strata in the mining area of western China,and the mechanical characteristics and failure mechanism of weakly cemented mudstone are systematically investigated under the combined action of freezing and loading.Furthermore,the quantitative relationship between the microstructural parameters and the macroscopic strength and deformation parameters is established based on fractal theory.Thus,the failure mechanism of frozen weakly cemented mudstone is revealed on both micro- and macro-scales.The results show that temperature and confining pressure significantly affects the elastic modulus and peak strength of weakly cemented mudstone.With decreasing temperature,the compressive strength increases,while the corresponding peak strain decreases gradually.On the deformation curve,the plastic deformation stage is shortened,and the brittle fracture feature at the post-peak stage is more prominent,and the elastic modulus correspondingly increases with decreasing temperature.Under low-temperature conditions,most of the weakly cemented mudstone undergoes microscopic shear failure along the main fracture surface.The micro-fracture morphology characteristics of weakly cemented mudstone under different temperatures are quantified via the fractal dimension,and an approximately exponential relationship can be obtained among the fractal dimension and the temperature,compressive strength and elastic modulus.