An Efficient Reliability-Based Optimization Method Utilizing High-Dimensional Model Representation and Weight-Point Estimation Method

The objective of reliability-based design optimization(RBDO)is to minimize the optimization objective while satisfying the corresponding reliability requirements.However,the nested loop characteristic reduces the efficiency of RBDO algorithm,which hinders their application to high-dimensional engineering problems.To address these issues,this paper proposes an efficient decoupled RBDO method combining high dimensional model representation(HDMR)and the weight-point estimation method(WPEM).First,we decouple the RBDO model using HDMR and WPEM.Second,Lagrange interpolation is used to approximate a univariate function.Finally,based on the results of the first two steps,the original nested loop reliability optimization model is completely transformed into a deterministic design optimization model that can be solved by a series of mature constrained optimization methods without any additional calculations.Two numerical examples of a planar 10-bar structure and an aviation hydraulic piping system with 28 design variables are analyzed to illustrate the performance and practicability of the proposed method.

Date of First Thinning in a Very High-Density Eucalyptus Plantation in the Pointe-Noire Region (Republic of Congo)

Management of the Pointe-Noire Forest requires high-performance tools for simulating tree and stand growth and assessing the sustainability of plantations. Modelling the dynamics of even-aged and mono-species stands is a very active research topic. The approaches adopted by researchers vary according to the objectives and species considered: dendrometrical, Eco physiological or architectural. Thanks to the particular nature of these plantations and the trial set-up, it will be possible to explore the various aspects of production, clearly separating the part linked to genetics (three clones tested) from the part linked to the environment (via fertilisation) and the part associated with competition between trees (via planting densities and thinning regimes). This study will make a major contribution to the applicability of the self-thinning line and the RDI (Reineke Density Index) (Reineke, 1933) to fast-growing plantations. This research work will contribute to two points: 1) product diversification, which is a way of coping with international variations in timber markets, and 2) understanding how ecosystems function in exceptionally poor conditions, which will then enable the environmental impacts of the various recommended silvicultural itineraries to be assessed. The results obtained show that competition between trees in a stand of eucalyptus at very high density (10,000 stems/ha) and in two environments of very contrasting fertility is different depending on the clone. The decision on the date of the first thinning with a view to silviculture for timber and energy wood, which aims to ensure sustained and sustainable production of eucalyptus wood in these soils, should be taken between 12 and 14 months. The competition band is strong between 14 and 17 months, when the RDI = 0.8 is double that observed at 12 months.

基于轻量化PointNet网络的林果园喷雾作业靶标实时识别方法

为了进一步提高喷雾机器人靶标检测的精准性、实时性和应用部署的实用性,该研究提出一种基于轻量化PointNet网络的林果园喷雾作业靶标实时识别方法。首先通过区域提取降采样、地面分割和改进DBSCAN聚类等点云预处理方法提取原始点云中的靶标;然后通过移动最小二乘上采样将靶标点云转化为满足点云识别网络输入要求的点云数据;最终通过在PointNet网络中引入残差模块和改进循环剪枝算法轻量化PointNet网络,完成林果树靶标的实时识别。试验结果表明,在ModelNet40数据集上,轻量化PointNet网络可达89.7%的准确率;在实际苗圃环境的试验中,该研究方法对靶标的识别准确率可达92.49%,同时误识率与拒识率分别为13.4%和6.47%,相较PointNet网络识别准确率提升了4.38个百分点,误识率和拒识率分别降低了7.2和4.07个百分点;轻量化PointNet网络识别准确率仅比PointNet++网络低1.14个百分点,误识率和拒识率分别高了0.9和1.12个百分点。但是轻量化PointNet网络的模型参数量较PointNet网络和PointNet++网络的模型参数量显著减少,仅为PointNet网络的11.5%,PointNet++网络的27.02%;运算量相较PointNet网络、PointNet++网络分别减少13.3和76.79个百分点。该研究提出的轻量化PointNet网络具有较高的实时性、精确性和鲁棒性,能够满足林果园喷雾作业的靶标识别需求,可为林果园喷雾作业靶标实时识别提供参考。

Impact point prediction guidance of ballistic missile in high maneuver penetration condition

An impact point prediction(IPP) guidance based on supervised learning is proposed to address the problem of precise guidance for the ballistic missile in high maneuver penetration condition.An accurate ballistic trajectory model is applied to generate training samples,and ablation experiments are conducted to determine the mapping relationship between the flight state and the impact point.At the same time,the impact point coordinates are decoupled to improve the prediction accuracy,and the sigmoid activation function is improved to ameliorate the prediction efficiency.Therefore,an IPP neural network model,which solves the contradiction between the accuracy and the speed of the IPP,is established.In view of the performance deviation of the divert control system,the mapping relationship between the guidance parameters and the impact deviation is analysed based on the variational principle.In addition,a fast iterative model of guidance parameters is designed for reference to the Newton iteration method,which solves the nonlinear strong coupling problem of the guidance parameter solution.Monte Carlo simulation results show that the prediction accuracy of the impact point is high,with a 3 σ prediction error of 4.5 m,and the guidance method is robust,with a 3 σ error of 7.5 m.On the STM32F407 singlechip microcomputer,a single IPP takes about 2.374 ms,and a single guidance solution takes about9.936 ms,which has a good real-time performance and a certain engineering application value.

基于HigherHRNet的煤矿井下人体姿态估计快速网络研究

煤矿井下人体姿态的快速估计是井下作业智慧安全检测的重要前提。为解决煤矿井下多尘多雾、照明不足及颜色相融等问题,提高人体姿态估计关键点分配准确度及网络运行速度,研究新的Optimising HigherHRNet(OH-HRNet)快速网络模型:对HigherHRNet模型的轻量化设计、关键点分配进行深入研究,提出了基于注意力机制的记忆卷积模块及强化骨骼约束的关键点分配算法,并改进了算法的损失函数。在煤矿井下场景数据集和COCO公开数据集上的实验结果表明:OH-HRNet在GPU的速度上是LitePose的1.06倍,平均精度均值mAP提高了7.4%,平均召回率均值mAR提高了14.0%,可以实现更有效的智慧安全检测。

基于PointNet++的邻域特征增强点云语义分割方法

随着智能驾驶、机器人导航等以点云为基础的应用蓬勃发展,点云语义分割逐渐成为研究热点。然而,现有的点云语义分割方法存在局部特征提取不充分、特征融合不完整的缺陷。针对这些不足,提出了对应的解决方案。对于局部特征提取不充分的现象,通过嵌入邻域点的坐标、方向、距离等相关信息去关联邻域点的显式特征。对于特征融合不完整的现象,提出了一种最大池化与自注意力池化相结合的混合池化方法。网络架构基于PointNet++,并结合提出的局部特征提取和融合方法,在S3DIS数据集上的实验结果表明,与基线方法PointNet++相比,各评价指标都有不同程度的提高,证实了新方法的有效性和优越性。

Observation points classifier ensemble for high-dimensional imbalanced classification

In this paper,an Observation Points Classifier Ensemble(OPCE)algorithm is proposed to deal with High-Dimensional Imbalanced Classification(HDIC)problems based on data processed using the Multi-Dimensional Scaling(MDS)feature extraction technique.First,dimensionality of the original imbalanced data is reduced using MDS so that distances between any two different samples are preserved as well as possible.Second,a novel OPCE algorithm is applied to classify imbalanced samples by placing optimised observation points in a low-dimensional data space.Third,optimization of the observation point mappings is carried out to obtain a reliable assessment of the unknown samples.Exhaustive experiments have been conducted to evaluate the feasibility,rationality,and effectiveness of the proposed OPCE algorithm using seven benchmark HDIC data sets.Experimental results show that(1)the OPCE algorithm can be trained faster on low-dimensional imbalanced data than on high-dimensional data;(2)the OPCE algorithm can correctly identify samples as the number of optimised observation points is increased;and(3)statistical analysis reveals that OPCE yields better HDIC performances on the selected data sets in comparison with eight other HDIC algorithms.This demonstrates that OPCE is a viable algorithm to deal with HDIC problems.

基于Point Transformer v2的点云枝叶分离方法研究

准确高效的点云枝叶分离对精确计算森林树木的垂直参数至关重要。然而,当前的研究方法计算成本高,且依赖先验知识导致泛化能力不足。针对以上问题,文章提出利用基于点特征的Transformer网络进行自动化的森林场景三维点云的枝叶分离研究。该方法使用Point Transformer v2网络,首先利用网格编码模块提取可学习的局部结构关系,保留点云的几何拓扑结构;其次使用分组注意力实现多通道联合学习,降低特征的冗余度,提高计算的效率;最后构建了基于点的Transformer网络实现高精度森林树木三维点云语义分割,降低了对于先验知识的需求。使用地基激光扫描仪获取的加拿大和芬兰7个不同树种样地的三维点云数据,进行枝叶分离实验和精度评价。实验结果表明,网络整体精度(OA)为94.42%,mIoU为78.89%,能够适应不同树种、不同点云密度的森林场景的枝叶分离。

A bHLH transcription factor,CsSPT,regulates high-temperature resistance in cucumber

High-temperature stress threatens the growth and yield of crops. Basic helix-loop-helix(bHLH) transcription factors(TFs) have been shown to play important roles in regulating high-temperature resistance in plants. However, the bHLH TFs responsible for high-temperature tolerance in cucumbers have not been identified. We used transcriptome profiling to screen the high temperature-responsive candidate bHLH TFs in cucumber. Here, we found that the expression of 75 CsbHLH genes was altered under high-temperature stress. The expression of the CsSPT gene was induced by high temperatures in TT(Thermotolerant) cucumber plants. However, the Csspt mutant plants obtained by the CRISPR-Cas9 system showed severe thermosensitive symptoms, including wilted leaves with brown margins and reduced root density and cell activity.The Csspt mutant plants also exhibited elevated H_(2)O_(2) levels and down-regulated photosystem-related genes under normal conditions.Furthermore, there were high relative electrolytic leakage(REC), malondialdehyde(MDA), glutathione(GSH), and superoxide radical(O_(2)^(·-)) levels in the Csspt mutant plants, with decreased Proline content after the high-temperature treatment. Transcriptome analysis showed that the photosystem and chloroplast activities in Csspt mutant plants were extremely disrupted by the high-temperature stress compared with wildtype(WT) plants. Moreover, the plant hormone signal transduction, as well as MAPK and calcium signaling pathways were activated in Csspt mutant plants under high-temperature stress. The HSF and HSP family genes shared the same upregulated expression patterns in Csspt and WT plants under high-temperature conditions. However, most bHLH, NAC, and bZIP family genes were significantly down-regulated by heat in Csspt mutant plants. Thus, these results demonstrated that CsSPT regulated the high-temperature response by recruiting photosynthesis components, signaling pathway molecules, and transcription factors. Our results provide important insights into the heat response mechanism of CsSPT in cucumber and its potential as a target for breeding heat-resistant crops.

Flexible,high-density,laminated ECoG electrode array for high spatiotemporal resolution foci diagnostic localization of refractory epilepsy

High spatiotemporal resolution brain electrical signals are critical for basic neuroscience research and high-precision focus diagnostic localization,as the spatial scale of some pathologic signals is at the submillimeter or micrometer level.This entails connecting hundreds or thousands of electrode wires on a limited surface.This study reported a class of flexible,ultrathin,highdensity electrocorticogram(ECoG)electrode arrays.The challenge of a large number of wiring arrangements was overcome by a laminated structure design and processing technology improvement.The flexible,ultrathin,high-density ECoG electrode array was conformably attached to the cortex for reliable,high spatial resolution electrophysiologic recordings.The minimum spacing between electrodes was 15μm,comparable to the diameter of a single neuron.Eight hundred electrodes were prepared with an electrode density of 4444 mm^(-2).In focal epilepsy surgery,the flexible,high-density,laminated ECoG electrode array with 36 electrodes was applied to collect epileptic spike waves inrabbits,improving the positioning accuracy of epilepsy lesions from the centimeter to the submillimeter level.The flexible,high-density,laminated ECoG electrode array has potential clinical applications in intractable epilepsy and other neurologic diseases requiring high-precision electroencephalogram acquisition.

Correction:Highly Efficient Back-End-of-Line Compatible Flexible Si-Based Optical Memristive Crossbar Array for Edge Neuromorphic Physiological Signal Processing and Bionic Machine Vision

Following publication of the original article[1],the authors noticed a mistake in the Supplementary file,more specifically in figures S11 and S12 where they used by mistake the same sub-figures.The original article[1]has been corrected.

Point Cloud Classification Using Content-Based Transformer via Clustering in Feature Space

Recently, there have been some attempts of Transformer in 3D point cloud classification. In order to reduce computations, most existing methods focus on local spatial attention,but ignore their content and fail to establish relationships between distant but relevant points. To overcome the limitation of local spatial attention, we propose a point content-based Transformer architecture, called PointConT for short. It exploits the locality of points in the feature space(content-based), which clusters the sampled points with similar features into the same class and computes the self-attention within each class, thus enabling an effective trade-off between capturing long-range dependencies and computational complexity. We further introduce an inception feature aggregator for point cloud classification, which uses parallel structures to aggregate high-frequency and low-frequency information in each branch separately. Extensive experiments show that our PointConT model achieves a remarkable performance on point cloud shape classification. Especially, our method exhibits 90.3% Top-1 accuracy on the hardest setting of ScanObjectN N. Source code of this paper is available at https://github.com/yahuiliu99/PointC onT.

Material point method simulation of hydro-mechanical behaviour in twophase porous geomaterials: A state-of-the-art review

The material point method(MPM)has been gaining increasing popularity as an appropriate approach to the solution of coupled hydro-mechanical problems involving large deformation.In this paper,we survey the current state-of-the-art in the MPM simulation of hydro-mechanical behaviour in two-phase porous geomaterials.The review covers the recent advances and developments in the MPM and their extensions to capture the coupled hydro-mechanical problems involving large deformations.The focus of this review is aiming at providing a clear picture of what has or has not been developed or implemented for simulating two-phase coupled large deformation problems,which will provide some direct reference for both practitioners and researchers.

Control of highly pathogenic avian influenza through vaccination

The stamping-out strategy has been used to control highly pathogenic avian influenza viruses in many countries,driven by the belief that vaccination would not be successful against such viruses and fears that avian influenza virus in vaccinated birds would evolve more rapidly and pose a greater risk to humans.In this review,we summarize the successes in controlling highly pathogenic avian influenza in China and make suggestions regarding the requirements for vaccine selection and effectiveness.In addition,we present evidence that vaccination of poultry not only eliminates human infection with avian influenza virus,but also significantly reduces and abolishes some harmful characteristics of avian influenza virus.

An effective'salt in dimethyl sulfoxide/water'electrolyte enables high-voltage supercapacitor operated at-50℃

Compared with organic electrolytes,aqueous electrolytes exhibit significantly higher ionic conductivity and possess inherent safety features,showcasing unique advantages in supercapacitors.However,challenges remain for low-salt aqueous electrolytes operating at high voltage and low temperature.Herein,we report a low-salt(0.87 m,m means mol kg^(-1))'salt in dimethyl sulfoxide/water'hybrid electrolyte with non-flammability via hybridizing aqueous electrolyte with an organic co-solvent of dimethyl sulfoxide(hydrogen bond acceptor).As a result,the 0.87 m hybrid electrolyte exhibits enhanced electrochemical stability,a freezing temperature below-50℃,and an outstanding ionic conductivity of 0.52mS cm~(-1)at-50℃.Dimethyl sulfoxide can anchor water molecules through intermolecular hydrogen bond interaction,effectively reinforcing the stability of water in the hybrid electrolyte.Furthermore,the interaction between dimethyl sulfoxide and water molecules diminishes the involvement of water in the generation of ordered ice crystals,finally facilitating the low-temperature performance of the hybrid electrolyte.When paired with the 0.87 m'salt in dimethyl sulfoxide/water'hybrid electrolyte,the symmetric supercapacitor presents a 2.0 V high operating voltage at 25℃,and can operate stably at-50℃.Importantly,the suppressed electrochemical reaction of water at-50℃further leads to the symmetric supercapacitor operated at a higher voltage of 2.6 V.This modification strategy opens an effective avenue to develop low-salt electrolytes for high-voltage and low-temperature aqueous supercapacitors.

Two-Staged Method for Ice Channel Identification Based on Image Segmentation and Corner Point Regression

Identification of the ice channel is the basic technology for developing intelligent ships in ice-covered waters,which is important to ensure the safety and economy of navigation.In the Arctic,merchant ships with low ice class often navigate in channels opened up by icebreakers.Navigation in the ice channel often depends on good maneuverability skills and abundant experience from the captain to a large extent.The ship may get stuck if steered into ice fields off the channel.Under this circumstance,it is very important to study how to identify the boundary lines of ice channels with a reliable method.In this paper,a two-staged ice channel identification method is developed based on image segmentation and corner point regression.The first stage employs the image segmentation method to extract channel regions.In the second stage,an intelligent corner regression network is proposed to extract the channel boundary lines from the channel region.A non-intelligent angle-based filtering and clustering method is proposed and compared with corner point regression network.The training and evaluation of the segmentation method and corner regression network are carried out on the synthetic and real ice channel dataset.The evaluation results show that the accuracy of the method using the corner point regression network in the second stage is achieved as high as 73.33%on the synthetic ice channel dataset and 70.66%on the real ice channel dataset,and the processing speed can reach up to 14.58frames per second.

Helicobacter pylori:High dose amoxicillin does not improve primary or secondary eradication rates in an Irish cohort

BACKGROUND Helicobacter pylori(H.pylori)eradication rates have fallen globally,likely in large part due to increasing antibiotic resistance to traditional therapy.In areas of high clarithromycin and metronidazole resistance such as ours,Maastricht VI guidelines suggest high dose amoxicillin dual therapy(HDADT)can be considered,subject to evidence for local efficacy.In this study we assess efficacy of HDADT therapy for H.pylori eradication in an Irish cohort.AIM To assess the efficacy of HDADT therapy for H.pylori eradication in an Irish cohort as both first line,and subsequent therapy for patients diagnosed with H.pylori.METHODS All patients testing positive for H.pylori in a tertiary centre were treated prospectively with HDADT(amoxicillin 1 g tid and esomeprazole 40 mg bid×14 d)over a period of 8 months.Eradication was confirmed with Urea Breath Test at least 4 wk after cessation of therapy.A delta-over-baseline>4%was considered positive.Patient demographics and treatment outcomes were recorded,analysed and controlled for basic demographics and prior H.pylori treatment.RESULTS One hundred and ninety-eight patients were identified with H.pylori infection,10 patients were excluded due to penicillin allergy and 38 patients refused follow up testing.In all 139 were included in the analysis,55%(n=76)were female,mean age was 46.6 years.Overall,93(67%)of patients were treatment-naïve and 46(33%)had received at least one previous course of treatment.The groups were statistically similar.Self-reported compliance with HDADT was 97%,mild side-effects occurred in 7%.There were no serious adverse drug reactions.Overall the eradication rate for our cohort was 56%(78/139).Eradication rates were worse for those with previous treatment[43%(20/46)vs 62%(58/93),P=0.0458,odds ratio=2.15].Age and Gender had no effect on eradication status.CONCLUSION Overall eradication rates with HDADT were disappointing.Despite being a simple and possibly better tolerated regime,these results do not support its routine use in a high dual resistance country.Further investigation of other regimens to achieve the>90%eradication target is needed.

Dynamic microphysiological system chip platform for high-throughput,customizable,and multi-dimensional drug screening

Spheroids and organoids have attracted significant attention as innovative models for disease modeling and drug screening.By employing diverse types of spheroids or organoids,it is feasible to establish microphysiological systems that enhance the precision of disease modeling and offer more dependable and comprehensive drug screening.High-throughput microphysiological systems that support optional,parallel testing of multiple drugs have promising applications in personalized medical treatment and drug research.However,establishing such a system is highly challenging and requires a multidisciplinary approach.This study introduces a dynamic Microphysiological System Chip Platform(MSCP)with multiple functional microstructures that encompass the mentioned advantages.We developed a high-throughput lung cancer spheroids model and an intestine-liverheart-lung cancer microphysiological system for conducting parallel testing on four anti-lung cancer drugs,demonstrating the feasibility of the MSCP.This microphysiological system combines microscale and macroscale biomimetics to enable a comprehensive assessment of drug efficacy and side effects.Moreover,the microphysiological system enables evaluation of the real pharmacological effect of drug molecules reaching the target lesion after absorption by normal organs through fluid-based physiological communication.The MSCP could serves as a valuable platform for microphysiological system research,making significant contributions to disease modeling,drug development,and personalized medical treatment.

Comprehensive analyses of the proteome and ubiquitome revealed mechanism of high temperature accelerating petal abscission in tree peony

Tree peony(Paeonia suffruticosa Andrews)is a well-known ornamental plant with high economic value,but the short fluorescence is a key obstacle to its ornamental value and industry development.High temperature accelerates flower senescence and abscission,but the associated mechanisms are poorly understood.In this study,the tandem mass tag(TMT)proteome and label-free quantitative ubiquitome from tree peony cut flowers treated with 20℃for 0 h(RT0),20℃or 28℃for 60 h(RT60 or HT60)were examined based on morphological observation,respectively.Totally,6970 proteins and 1545 lysine ubiquitinated(Kub)sites in 844 proteins were identified.Hydrophilic residues(such as glutamate and aspartate)neighboring the Kub sites were in preference,and 36.01%of the Kub sites were located on the protein surface.The differentially expressed proteins(DEPs)and Kub-DEPs in HT60 vs RT60 were mainly enriched in ribosomal protein,protein biosynthesis,secondary metabolites biosynthesis,flavonoid metabolism,carbohydrate catabolism,and auxin biosynthesis and signaling revealed by GO and KEGG analysis,accompanying the increase of endogenous abscisic acid(ABA)accumulation and decrease of endogenous indoleacetic acid(IAA)level.Additionally,the expression patterns of six enzymes(SAMS,ACO,YUC,CHS,ANS and PFK)putatively with Kub modifications were analyzed by proteome and real-time quantitative RT-PCR.The cell-free degradation assays showed PsSAMS and PsACO proteins could be degraded via the 26 S proteasome system in tree peony flowers.Finally,a working model was proposed for the acceleration of flower senescence and abscission by high temperature.In summary,all results contributed to understanding the mechanism of flower senescence induced by high temperature and prolonging fluorescence in tree peony.

Challenges in Li-ion battery high-voltage technology and recent advances in high-voltage electrolytes

The electrolyte directly contacts the essential parts of a lithium-ion battery,and as a result,the electrochemical properties of the electrolyte have a significant impact on the voltage platform,charge discharge capacity,energy density,service life,and rate discharge performance.By raising the voltage at the charge/discharge plateau,the energy density of the battery is increased.However,this causes transition metal dissolution,irreversible phase changes of the cathode active material,and parasitic electrolyte oxidation reactions.This article presents an overview of these concerns to provide a clear explanation of the issues involved in the development of electrolytes for high-voltage lithium-ion batteries.Additionally,solidstate electrolytes enable various applications and will likely have an impact on the development of batteries with high energy densities.It is necessary to improve the high-voltage performance of electrolytes by creating solvents with high thermal stabilities and high voltage resistance and additives with superior film forming performance,multifunctional capabilities,and stable lithium salts.To offer suggestions for the future development of high-energy lithium-ion batteries,we conclude by offering our own opinions and insights on the current development of lithium-ion batteries.