An efficient parallel algorithm of variational nodal method for heterogeneous neutron transport problems

The heterogeneous variational nodal method(HVNM)has emerged as a potential approach for solving high-fidelity neutron transport problems.However,achieving accurate results with HVNM in large-scale problems using high-fidelity models has been challenging due to the prohibitive computational costs.This paper presents an efficient parallel algorithm tailored for HVNM based on the Message Passing Interface standard.The algorithm evenly distributes the response matrix sets among processors during the matrix formation process,thus enabling independent construction without communication.Once the formation tasks are completed,a collective operation merges and shares the matrix sets among the processors.For the solution process,the problem domain is decomposed into subdomains assigned to specific processors,and the red-black Gauss-Seidel iteration is employed within each subdomain to solve the response matrix equation.Point-to-point communication is conducted between adjacent subdomains to exchange data along the boundaries.The accuracy and efficiency of the parallel algorithm are verified using the KAIST and JRR-3 test cases.Numerical results obtained with multiple processors agree well with those obtained from Monte Carlo calculations.The parallelization of HVNM results in eigenvalue errors of 31 pcm/-90 pcm and fission rate RMS errors of 1.22%/0.66%,respectively,for the 3D KAIST problem and the 3D JRR-3 problem.In addition,the parallel algorithm significantly reduces computation time,with an efficiency of 68.51% using 36 processors in the KAIST problem and 77.14% using 144 processors in the JRR-3 problem.

Performance Enhancement of XML Parsing Using Regression and Parallelism

The Extensible Markup Language(XML)files,widely used for storing and exchanging information on the web require efficient parsing mechanisms to improve the performance of the applications.With the existing Document Object Model(DOM)based parsing,the performance degrades due to sequential processing and large memory requirements,thereby requiring an efficient XML parser to mitigate these issues.In this paper,we propose a Parallel XML Tree Generator(PXTG)algorithm for accelerating the parsing of XML files and a Regression-based XML Parsing Framework(RXPF)that analyzes and predicts performance through profiling,regression,and code generation for efficient parsing.The PXTG algorithm is based on dividing the XML file into n parts and producing n trees in parallel.The profiling phase of the RXPF framework produces a dataset by measuring the performance of various parsing models including StAX,SAX,DOM,JDOM,and PXTG on different cores by using multiple file sizes.The regression phase produces the prediction model,based on which the final code for efficient parsing of XML files is produced through the code generation phase.The RXPF framework has shown a significant improvement in performance varying from 9.54%to 32.34%over other existing models used for parsing XML files.

MPI/OpenMP-Based Parallel Solver for Imprint Forming Simulation

In this research,we present the pure open multi-processing(OpenMP),pure message passing interface(MPI),and hybrid MPI/OpenMP parallel solvers within the dynamic explicit central difference algorithm for the coining process to address the challenge of capturing fine relief features of approximately 50 microns.Achieving such precision demands the utilization of at least 7 million tetrahedron elements,surpassing the capabilities of traditional serial programs previously developed.To mitigate data races when calculating internal forces,intermediate arrays are introduced within the OpenMP directive.This helps ensure proper synchronization and avoid conflicts during parallel execution.Additionally,in the MPI implementation,the coins are partitioned into the desired number of regions.This division allows for efficient distribution of computational tasks across multiple processes.Numerical simulation examples are conducted to compare the three solvers with serial programs,evaluating correctness,acceleration ratio,and parallel efficiency.The results reveal a relative error of approximately 0.3%in forming force among the parallel and serial solvers,while the predicted insufficient material zones align with experimental observations.Additionally,speedup ratio and parallel efficiency are assessed for the coining process simulation.The pureMPI parallel solver achieves a maximum acceleration of 9.5 on a single computer(utilizing 12 cores)and the hybrid solver exhibits a speedup ratio of 136 in a cluster(using 6 compute nodes and 12 cores per compute node),showing the strong scalability of the hybrid MPI/OpenMP programming model.This approach effectively meets the simulation requirements for commemorative coins with intricate relief patterns.

THE NONLINEAR STABILITY OF PLANE PARALLEL SHEAR FLOWS WITH RESPECT TO TILTED

The nonlinear stability of plane parallel shear flows with respect to tilted perturbations is studied by energy methods.Tilted perturbation refers to the fact that perturbations form an angleθ∈(0,π/2)with the direction of the basic flows.By defining an energy functional,it is proven that plane parallel shear flows are unconditionally nonlinearly exponentially stable for tilted streamwise perturbation when the Reynolds number is below a certain critical value and the boundary conditions are either rigid or stress-free.In the case of stress-free boundaries,by taking advantage of the poloidal-toroidal decomposition of a solenoidal field to define energy functionals,it can be even shown that plane parallel shear flows are unconditionally nonlinearly exponentially stable for all Reynolds numbers,where the tilted perturbation can be either spanwise or streamwise.

Static Analysis Techniques for Fixing Software Defects in MPI-Based Parallel Programs

The Message Passing Interface (MPI) is a widely accepted standard for parallel computing on distributed memorysystems.However, MPI implementations can contain defects that impact the reliability and performance of parallelapplications. Detecting and correcting these defects is crucial, yet there is a lack of published models specificallydesigned for correctingMPI defects. To address this, we propose a model for detecting and correcting MPI defects(DC_MPI), which aims to detect and correct defects in various types of MPI communication, including blockingpoint-to-point (BPTP), nonblocking point-to-point (NBPTP), and collective communication (CC). The defectsaddressed by the DC_MPI model include illegal MPI calls, deadlocks (DL), race conditions (RC), and messagemismatches (MM). To assess the effectiveness of the DC_MPI model, we performed experiments on a datasetconsisting of 40 MPI codes. The results indicate that the model achieved a detection rate of 37 out of 40 codes,resulting in an overall detection accuracy of 92.5%. Additionally, the execution duration of the DC_MPI modelranged from 0.81 to 1.36 s. These findings show that the DC_MPI model is useful in detecting and correctingdefects in MPI implementations, thereby enhancing the reliability and performance of parallel applications. TheDC_MPImodel fills an important research gap and provides a valuable tool for improving the quality ofMPI-basedparallel computing systems.

Effective Capacity of URLLC over Parallel Fading Channels with Imperfect Channel State Information

This paper investigates the effective capacity of a point-to-point ultra-reliable low latency communication(URLLC)transmission over multiple parallel sub-channels at finite blocklength(FBL)with imperfect channel state information(CSI).Based on reasonable assumptions and approximations,we derive the effective capacity as a function of the pilot length,decoding error probability,transmit power and the sub-channel number.Then we reveal significant impact of the above parameters on the effective capacity.A closed-form lower bound of the effective capacity is derived and an alternating optimization based algorithm is proposed to find the optimal pilot length and decoding error probability.Simulation results validate our theoretical analysis and show that the closedform lower bound is very tight.In addition,through the simulations of the optimized effective capacity,insights for pilot length and decoding error probability optimization are provided to evaluate the optimal parameters in realistic systems.

Enhancing visual security: An image encryption scheme based on parallel compressive sensing and edge detection embedding

A novel image encryption scheme based on parallel compressive sensing and edge detection embedding technology is proposed to improve visual security. Firstly, the plain image is sparsely represented using the discrete wavelet transform.Then, the coefficient matrix is scrambled and compressed to obtain a size-reduced image using the Fisher–Yates shuffle and parallel compressive sensing. Subsequently, to increase the security of the proposed algorithm, the compressed image is re-encrypted through permutation and diffusion to obtain a noise-like secret image. Finally, an adaptive embedding method based on edge detection for different carrier images is proposed to generate a visually meaningful cipher image. To improve the plaintext sensitivity of the algorithm, the counter mode is combined with the hash function to generate keys for chaotic systems. Additionally, an effective permutation method is designed to scramble the pixels of the compressed image in the re-encryption stage. The simulation results and analyses demonstrate that the proposed algorithm performs well in terms of visual security and decryption quality.

A Hybrid Parallel Strategy for Isogeometric Topology Optimization via CPU/GPU Heterogeneous Computing

This paper aims to solve large-scale and complex isogeometric topology optimization problems that consumesignificant computational resources. A novel isogeometric topology optimization method with a hybrid parallelstrategy of CPU/GPU is proposed, while the hybrid parallel strategies for stiffness matrix assembly, equationsolving, sensitivity analysis, and design variable update are discussed in detail. To ensure the high efficiency ofCPU/GPU computing, a workload balancing strategy is presented for optimally distributing the workload betweenCPU and GPU. To illustrate the advantages of the proposedmethod, three benchmark examples are tested to verifythe hybrid parallel strategy in this paper. The results show that the efficiency of the hybrid method is faster thanserial CPU and parallel GPU, while the speedups can be up to two orders of magnitude.

Improved multi-scale inverse bottleneck residual network based on triplet parallel attention for apple leaf disease identification

Accurate diagnosis of apple leaf diseases is crucial for improving the quality of apple production and promoting the development of the apple industry. However, apple leaf diseases do not differ significantly from image texture and structural information. The difficulties in disease feature extraction in complex backgrounds slow the related research progress. To address the problems, this paper proposes an improved multi-scale inverse bottleneck residual network model based on a triplet parallel attention mechanism, which is built upon ResNet-50, while improving and combining the inception module and ResNext inverse bottleneck blocks, to recognize seven types of apple leaf(including six diseases of alternaria leaf spot, brown spot, grey spot, mosaic, rust, scab, and one healthy). First, the 3×3 convolutions in some of the residual modules are replaced by multi-scale residual convolutions, the convolution kernels of different sizes contained in each branch of the multi-scale convolution are applied to extract feature maps of different sizes, and the outputs of these branches are multi-scale fused by summing to enrich the output features of the images. Second, the global layer-wise dynamic coordinated inverse bottleneck structure is used to reduce the network feature loss. The inverse bottleneck structure makes the image information less lossy when transforming from different dimensional feature spaces. The fusion of multi-scale and layer-wise dynamic coordinated inverse bottlenecks makes the model effectively balances computational efficiency and feature representation capability, and more robust with a combination of horizontal and vertical features in the fine identification of apple leaf diseases. Finally, after each improved module, a triplet parallel attention module is integrated with cross-dimensional interactions among channels through rotations and residual transformations, which improves the parallel search efficiency of important features and the recognition rate of the network with relatively small computational costs while the dimensional dependencies are improved. To verify the validity of the model in this paper, we uniformly enhance apple leaf disease images screened from the public data sets of Plant Village, Baidu Flying Paddle, and the Internet. The final processed image count is 14,000. The ablation study, pre-processing comparison, and method comparison are conducted on the processed datasets. The experimental results demonstrate that the proposed method reaches 98.73% accuracy on the adopted datasets, which is 1.82% higher than the classical ResNet-50 model, and 0.29% better than the apple leaf disease datasets before preprocessing. It also achieves competitive results in apple leaf disease identification compared to some state-ofthe-art methods.

Off-Grid Compressed Channel Estimation with Parallel Interference Cancellation for Millimeter Wave Massive MIMO

Millimeter wave(mmWave)massive multiple-input multiple-output(MIMO)plays an important role in the fifth-generation(5G)mobile communications and beyond wireless communication systems owing to its potential of high capacity.However,channel estimation has become very challenging due to the use of massive MIMO antenna array.Fortunately,the mmWave channel has strong sparsity in the spatial angle domain,and the compressed sensing technology can be used to convert the original channel matrix into the sparse matrix of discrete angle grid.Thus the high-dimensional channel matrix estimation is transformed into a sparse recovery problem with greatly reduced computational complexity.However,the path angle in the actual scene appears randomly and is unlikely to be completely located on the quantization angle grid,thus leading to the problem of power leakage.Moreover,multiple paths with the random distribution of angles will bring about serious interpath interference and further deteriorate the performance of channel estimation.To address these off-grid issues,we propose a parallel interference cancellation assisted multi-grid matching pursuit(PIC-MGMP)algorithm in this paper.The proposed algorithm consists of three stages,including coarse estimation,refined estimation,and inter-path cyclic iterative inter-ference cancellation.More specifically,the angular resolution can be improved by locally refining the grid to reduce power leakage,while the inter-path interference is eliminated by parallel interference cancellation(PIC),and the two together improve the estimation accuracy.Simulation results show that compared with the traditional orthogonal matching pursuit(OMP)algorithm,the normalized mean square error(NMSE)of the proposed algorithm decreases by over 14dB in the case of 2 paths.

Utilizing Iso-Value Field Curves in Lieu of Magnetic Field Lines Amid Infinite and Parallel Electrical Wires

Building on a new model proposed recently for calculating constant electro-magnetic field values, the present article explores the electro-magnetic field configuration generated by parallel electrical wires. This imposes a reevaluation of the drawing procedure for constructing field curves with a constant field values around multiple parallel electrical conducting wires. To achieve this, we employ methods akin to those used for creating contours on topographical maps, ensuring a consistent numerical field value along the entire length of the field curves. Subsequent calculations will be conducted for scenarios where wires are not parallel.

An MPI parallel DEM-IMB-LBM framework for simulating fluid-solid interaction problems

The high-resolution DEM-IMB-LBM model can accurately describe pore-scale fluid-solid interactions,but its potential for use in geotechnical engineering analysis has not been fully unleashed due to its prohibitive computational costs.To overcome this limitation,a message passing interface(MPI)parallel DEM-IMB-LBM framework is proposed aimed at enhancing computation efficiency.This framework utilises a static domain decomposition scheme,with the entire computation domain being decomposed into multiple subdomains according to predefined processors.A detailed parallel strategy is employed for both contact detection and hydrodynamic force calculation.In particular,a particle ID re-numbering scheme is proposed to handle particle transitions across sub-domain interfaces.Two benchmarks are conducted to validate the accuracy and overall performance of the proposed framework.Subsequently,the framework is applied to simulate scenarios involving multi-particle sedimentation and submarine landslides.The numerical examples effectively demonstrate the robustness and applicability of the MPI parallel DEM-IMB-LBM framework.

社区H型高血压患者药物基因作用靶点多态性分布及叶酸联合维生素D干预效果研究

背景H型高血压严重影响着人们的健康及生活质量,目前临床上治疗高血压主要根据患者症状和临床经验选择药物,降压效果不理想,急需探寻降压药物基因分布的多态性,为高血压患者进行个体化用药指导。目的探讨济南市社区H型高血压药物作用靶点基因多态性分布及叶酸联合维生素D的干预作用,为该地区开展高血压医防融合精准医疗提供参考依据。方法2020年6月—2022年6月随机抽取山东省济南市槐荫区20家街道办事处社区卫生服务中心200例血压控制不佳的高血压患者为研究对象,治疗前首先进行5类常用抗高血压药物[利尿剂、β受体阻滞剂、血管紧张素转化酶抑制剂(ACEI)、钙离子通道抑制剂(CCB)、血管紧张素Ⅱ受体拮抗剂(ARB)]相关高血压个体化用药基因位点的基因多态性检测。将患者随机分为基因导向治疗组(基因组)与基因导向协同叶酸、维生素D治疗组(基因导向组),每组100例。基因组根据检测的高血压基因作用位点的特点调整用药;基因导向组在基因组治疗方案的基础上同时服用叶酸、维生素D。干预初始(M0)、干预3个月(M3)、干预6个月(M6)时采集患者晨间未服用降压药物情况下坐位收缩压和舒张压。记录患者患病情况、不良反应发生情况、脑卒中发生情况,进行基因测序,检测血清同型半胱氨酸(Hcy)浓度。采用Pearson相关性分析或Spearman秩相关分析探究性别、年龄、收缩压、舒张压与Hcy的相关性。结果研究对象性别(r_(s)=-0.463)、收缩压(r=0.181)、舒张压(r=0.188)与Hcy水平有相关性(P<0.05)。5类抗高血压药物基因作用靶点中,与药物代谢酶基因多态性位点相关的分别是CYP3A5(A6986G)、CYP2C9(c.1075A>C)、CYP2D6(c.100C>T),与药物作用靶点敏感性基因多态性位点相关的是ADRB1、ACEI(I/D)、AGTR1、NPPA。基因组A6986G:CYP3A5*1/*1(AA)、ACEI(I/D):D/D、c.100 C>T:CYP2D6*1/*1(CC)患者M3、M6舒张压低于M0,A6986G:CYP3A5a1/a3(AG)、ADRB1 c.1165 G>C:GG、c.1075 A>C:CYP2C9*1/*3(AC)、c.1075 A>C:CYP2C9*3/*3(CC)患者M6舒张压低于M0,A6986G:CYP3A5*3/*3(GG)、ADRB1 c.1165 G>C:CC、ACEI(I/D):I/I、c.1075 A>C:CYP2C9*1/*1(AA)、AGTR1 c.1166 A>C:AA、NPPA T2238C:TT、c.100 C>T:CYP2D6*10/*10(TT)患者M3、M6收缩压、舒张压低于M0,ADRB1 c.1165 G>C:GC、ACEI(I/D):I/D、c.100 C>T:CYP2D6*1/*10(CT)患者M6收缩压低于M0,M3、M6舒张压低于M0,差异有统计学意义(P<0.05)。Hcy水平组间比较结果显示,M3、M6基因导向组Hcy水平低于基因组,差异有统计学意义(P<0.05)。组内比较结果显示,基因组M6 Hcy水平低于M0,基因导向组M3、M6 Hcy水平低于M0,M6 Hcy水平低于M3,差异有统计学意义(P<0.05)。收缩压、舒张压组间比较结果显示,M3、M6基因导向组收缩压、舒张压低于基因组,差异有统计学意义(P<0.05)。组内比较结果显示,基因组、基因导向组M6收缩压、舒张压低于M0,M6收缩压低于M3,差异有统计学意义(P<0.05)。结论社区H型高血压患者中存在高血压药物相关基因多态性的表达差异,个体化用药效果显著;叶酸联合维生素D协同治疗更能显著降低H型高血压水平。

良性阵发性位置性眩晕患者维生素D状态及影响因素研究

目的 了解北京地区良性阵发性位置性眩晕(BPPV)患者维生素D水平状况,探讨性别、年龄、季节对维生素D水平的影响。方法 将2018年10月—2021年9月就诊于首都医科大学附属北京朝阳医院耳鼻咽喉头颈外科的208例BPPV患者作为研究对象进行回顾性研究。结果 所有BPPV患者血清25-羟维生素D[25(OH)D]水平为15.31(10.68, 21.03)ng/mL。比较不同性别、年龄和各个季节血清25(OH)D水平,男性高于女性,61~70岁年龄组高于其他年龄组;夏季血清25(OH)D水平最高,夏秋季明显高于冬春季。所有纳入研究的BPPV患者维生素D缺乏146例(70.19%);维生素D不足41例(19.71%);维生素D充足21例(10.10%)。回归分析显示性别、年龄和季节均能影响维生素D水平。结论 北京地区BPPV患者维生素D缺乏较为普遍,性别、年龄和季节对维生素D水平有影响。

糖调节受损患者血清25(OH)D和免疫相关因子对亚临床动脉粥样硬化影响的研究

目的:研究糖调节受损患者血清25(OH)D和免疫相关因子对亚临床动脉粥样硬化(AS)的影响。方法:选取2019年12月至2021年4月枣庄市立医院收治的142例糖调节受损患者,根据经颈动脉超声检查和臂踝脉搏波传导速度分组,仅糖调节受损患者为对照组(n=86),并发亚临床AS为观察组(n=56)。比较两组患者血清25(OH)D和免疫因子,应用颈动脉超声检测测定颈动脉内中膜厚度,Pearson法测定血清颈动脉内中膜厚度与血清25(OH)D和免疫因子的相关性。采集基线资料和血液学指标,单因素和多因素Logistic回归分析确定亚临床AS的影响因素,应用ROC曲线评估血清25(OH)D和免疫因子对亚临床AS的诊断效能。结果:观察组患者血清25(OH)D[(24.01±4.87)mmol/L vs 3(0.74±5.01)mmol/L,t=7.909,P=0.000)明显低于对照组,观察组患者TNF-α[(48.32±8.02)ng/L vs(33.21±9.00)ng/L,t=10.199,P=0.000)和IL-6[(41.22±9.43)ng/L vs(30.21±7.01)ng/L,t=7.492,P=0.000)明显高于对照组。颈动脉内中膜厚度与血清25(OH)D呈负相关(r=-0.428,P<0.001),颈动脉内中膜厚度与血清TNF-α和IL-6呈正相关(r=0.574,0.577,P<0.001)。Logistic回归分析结果表明,血清25(OH)D(OR=0.520,95%CI:0.401~0.675)、血清TNF-α(OR=1.667,95%CI:1.131~2.457)和血清IL-6(OR=1.478,95%CI:1.213~1.802)等是亚临床AS发生的影响因素。ROC曲线结果显示,血清25(OH)D最佳截断值为28.32 mmol/L,其对应的敏感度为69.64%,特异度为70.93%,AUC为0.803(95%CI:0.749~0.855);血清TNF-α临界值为40.56 ng/L,其对应的敏感度为71.43%,特异度为72.09%,AUC为0.761(95%CI:0.717~0.823);血清IL-6截断值为36.13 ng/L,其对应的敏感度为60.71%,特异度为60.47%,AUC为0.627(95%CI:0.566~0.702);回归分析对应的敏感度为85.71%,特异度为81.40%,AUC为0.889(95%CI:0.830~0.915)。结论:血清25(OH)D与免疫相关因子单独和联合回归检测可有效预测亚临床AS的发生,上述指标与颈动脉内中膜厚度相关,且血清25(OH)D和免疫相关因子是亚临床AS发生的预测指标,临床宜根据指标进行针对性干预,值得进一步研究。

老年血液透析病人衰弱与血清瘦素及25-羟维生素D的相关性研究

目的探究老年血液透析病人血清瘦素及25-羟维生素D[25(OH)D]水平,并分析其与透析病人衰弱的相关性。方法选取2021年4月至2023年4月我院收治的150例老年血液透析病人,依据Fried表型评价病人的衰弱情况,分为非衰弱组(51例),衰弱前期组(74例)和衰弱组(25例)。比较3组一般资料、血清瘦素、25(OH)D及心功能。采用多因素Logistic回归分析老年血液透析病人衰弱的影响因素。结果3组年龄、糖尿病比例、透析龄、左室舒张末期内径,以及白蛋白、镁、IL-6、瘦素、25(OH)D水平比较,差异均有统计学意义(P<0.01)。多因素有序Logistic回归分析显示,年龄、透析龄、糖尿病、镁、IL-6、白蛋白、瘦素、25(OH)D、左室舒张末期内径是老年血液透析病人衰弱的独立影响因素(P<0.05)。结论针对白蛋白水平低、瘦素水平高、25(OH)D水平低的老年血液透析病人采取积极干预措施,有利于延缓衰弱的进展。

前维生素D相对校正因子的研究及测定

目的:测定前维生素D相对校正因子,简化维生素D含量测定的计算方法。方法:通过对各国药品标准中维生素D含量计算方法的研究,采用HPLC法测定前维生素D相对校正因子,并对测定的影响因素进行了考察。结果:统计了国内7家实验室的前维生素D相对校正因子的测定结果,确定了254 nm和265 nm 2个波长下的前维生素D相对校正因子。结论:采用前维生素D相对校正因子法计算维生素D的总量,简化了实验步骤,避免了随机操作误差。该方法快速、准确,为进一步提高维生素D制剂标准奠定了基础。

维生素D与肥胖相互作用的网络药理学研究

目的:通过网络药理学探讨维生素D与肥胖的相关性,并探索影响维生素D与肥胖共同作用靶点的核心蛋白。方法:通过进入西药数据库(DrugBank数据库)检索Vitamin D的相关靶点;通过DisGeNET数据库检索Obesity的相关靶点;利用Venny平台对成分靶点和疾病靶点取交集;将共有靶点导入String构建蛋白-蛋白相互作用(PPI)网络;对药物疾病靶点做蛋白-蛋白互作网络图,并筛选蛋白与蛋白之间互相作用的核心蛋白。结果:共得Vitamin D药物靶点2个;得到肥胖疾病靶点共2821个,取交集得到Vitamin D药物靶点及肥胖疾病靶点1个(VDR);对药物疾病靶点做蛋白-蛋白互作网络图,影响VDR作用的主要蛋白有GC、CYP27B1、MED1、EP300、RXRA、NCOA3、SMAD3、CTNNB1。结论:维生素D可能通过作用于VDR靶点发挥抗肥胖的作用。GC、CYP27B1、MED1、EP300、RXRA、NCOA3、SMAD3、CTNNB1蛋白可以与VDR相互作用,从而影响维生素D与肥胖共同作用靶点的核心蛋白。

糖尿病周围神经病变伴抑郁症状患者血清同型半胱氨酸与25-羟维生素D表达及其临床意义

目的探讨糖尿病周围神经病变(DPN)伴抑郁症状患者血清同型半胱氨酸(Hcy)与25-羟维生素D[25-(OH)D]表达及其临床意义。方法选取DPN伴抑郁症状患者50例设为研究组,DPN未伴有抑郁症状患者32例设为对照组。采用酶循环法检测Hcy水平,电化学发光法检测25-(OH)D水平。比较两组患者Hcy、25-(OH)D水平以及研究组不同抑郁程度患者Hcy、25-(OH)D水平差异。采用Pearson相关分析探讨DPN伴抑郁症状患者的Hcy、25-(OH)D水平与HADM-17评分的相关性,采用Logistic回归分析探讨DPN伴抑郁症状患者抑郁程度的影响因素。结果研究组患者Hcy水平、HAMD-17评分高于对照组,25-(OH)D水平低于对照组(P<0.01)。重度抑郁患者Hcy水平高于轻度、中度抑郁患者,25-(OH)D水平低于轻度、中度抑郁患者(P<0.05)。Pearson相关性分析显示,DPN伴抑郁症状患者Hcy水平与HAMD评分呈显著正相关(r=0.886,P<0.05),25-(OH)D水平与HAMD-17评分呈显著负相关(r=-0.619,P<0.05)。单因素分析结果显示,DPN伴抑郁症状患者性别、收缩压、空腹血糖、文化程度、Hcy、25-(OH)D水平比较,差异有统计学意义(P<0.05或0.01)。Logistic回归分析结果显示,性别、空腹血糖、文化程度、Hcy、25-(OH)D水平是DPN伴抑郁症状患者抑郁程度的影响因素(P<0.01)。结论DPN伴抑郁症状患者Hcy水平升高,25-(OH)D水平降低,性别、空腹血糖、文化程度是DPN伴抑郁症状患者抑郁程度的影响因素。Hcy和25-(OH)D表达在评估DPN患者抑郁严重程度方面具有重要价值,能为患者后续治疗提供生物学信息参考。

基于MOEA/D算法的三陷波超宽带天线设计

为实现超宽带通信中对WIMAX(3.3~3.7 GHz)、WLAN(5.150~5.825 GHz)和ITU(8.01~8.50 GHz)频段干扰的抑制,提出了一种基于MOEA/D算法优化设计的具有三陷波特性的超宽带天线。天线采用T形结构作为辐射贴片,通过改变贴片形状等方法实现在3~12 GHz的超宽带。在辐射贴片上刻蚀两个U形缝隙和在传输线两侧加载C形开口环,对天线进行陷波设计。利用MOEA/D算法对陷波结构的结构参数和加载位置进行优化,实现了在3.31~3.69 GHz、5.25~6.13 GHz和8.01~8.51 GHz频段的陷波效果,成功抑制了频段干扰。与传统扫频优化的设计方法相比,采用MOEA/D算法设计的天线在陷波频带内增益低至-19 dB,各陷波频带准确度误差均在3.6%以下,提高了天线的设计效率,具有一定的工程应用价值。