A regression approach for seismic first-break picking

The picking efficiency of seismic first breaks(FBs)has been greatly accelerated by deep learning(DL)technology.However,the picking accuracy and efficiency of DL methods still face huge challenges in low signal-to-noise ratio(SNR)situations.To address this issue,we propose a regression approach to pick FBs based on bidirectional long short-term memory(Bi LSTM)neural network by learning the implicit Eikonal equation of 3D inhomogeneous media with rugged topography in the target region.We employ a regressive model that represents the relationships among the elevation of shots,offset and the elevation of receivers with their seismic traveltime to predict the unknown FBs,from common-shot gathers with sparsely distributed traces.Different from image segmentation methods which automatically extract image features and classify FBs from seismic data,the proposed method can learn the inner relationship between field geometry and FBs.In addition,the predicted results by the regressive model are continuous values of FBs rather than the discrete ones of the binary distribution.The picking results of synthetic data shows that the proposed method has low dependence on label data,and can obtain reliable and similar predicted results using two types of label data with large differences.The picking results of9380 shots for 3D seismic data generated by vibroseis indicate that the proposed method can still accurately predict FBs in low SNR data.The subsequent stacked profiles further illustrate the reliability and effectiveness of the proposed method.The results of model data and field seismic data demonstrate that the proposed regression method is a robust first-break picker with high potential for field application.

Automatic velocity picking based on optimal key points tracking algorithm

Picking velocities from semblances manually is laborious and necessitates experience. Although various methods for automatic velocity picking have been developed, there remains a challenge in efficiently incorporating information from nearby gathers to ensure picked velocity aligns with seismic horizons while also improving picking accuracy. The conventional method of velocity picking from a semblance volume is computationally demanding, highlighting a need for a more efficient strategy. In this study, we introduce a novel method for automatic velocity picking based on multi-object tracking. This dynamic tracking process across different semblance panels can integrate information from nearby gathers effectively while maintaining computational efficiency. First, we employ accelerated density clustering on the velocity spectrum to discern cluster centers without the requirement for prior knowledge regarding the number of clusters. These cluster centers embody the maximum likelihood velocities of the main subsurface structures. Second, our proposed method tracks key points within the semblance volume. Kalman filter is adopted to adjust the tracking process, followed by interpolation on these tracked points to construct the final velocity model. Our synthetic data example demonstrates that our proposed algorithm can effectively rectify the picking errors of the clustering algorithm. We further compare the performances of the clustering method(CM), the proposed tracking method(TM), and the variational method(VM) on a field dataset from the Gulf of Mexico. The results attest that our method offers superior accuracy than CM, achieves comparable accuracy with VM, and benefits from a reduced computational cost.

First-Arrival Picking Method for Active Source Data with Ocean Bottom Seismometers Based on Spatial Waveform Variation Characteristics

The precision and reliability of first-arrival picking are crucial for determining the accuracy of geological structure inversion using active source ocean bottom seismometer(OBS)refraction data.Traditional methods for first-arrival picking based on sample points are characterized by theoretical errors,especially in low-sampling-frequency OBS data because the travel time of seismic waves is not an integer multiple of the sampling interval.In this paper,a first-arrival picking method that utilizes the spatial waveform variation characteristics of active source OBS data is presented.First,the distribution law of theoretical error is examined;adjacent traces exhibit variation characteristics in their waveforms.Second,a label cross-correlation superposition method for extracting highfrequency signals is presented to enhance the first-arrival picking precision.Results from synthetic and field data verify that the proposed approach is robust,successfully overcomes the limitations of low sampling frequency,and achieves precise outcomes that are comparable with those of high-sampling-frequency data.

Niemann-Pick C1蛋白在埃博拉病毒感染中的作用及其靶向药物研究进展

埃博拉病毒属丝状病毒科,具有高传染性,能引起人类和灵长类动物出现严重出血热等症状,病死率高达90%。Niemann-Pick C1(NPC1)蛋白是埃博拉病毒感染过程中表达于宿主细胞内体膜上的一个重要受体,其与埃博拉病毒被组织蛋白酶裂解的糖蛋白(GP)的相互作用是病毒感染宿主的关键环节,介导病毒囊膜与内体膜的融合,进而将病毒基因组释放到宿主细胞。近年来,将NPC1蛋白作为广谱抗丝状病毒药物靶点研发的小分子抑制剂、单克隆抗体和基因治疗药物均有突破性进展。本文介绍了NPC1的结构及其在埃博拉病毒感染中的作用,并对靶向NPC1的小分子抑制剂、单克隆抗体药物和基因治疗药物的研究现状进行总结。

Manual Order Picking Route Optimization in Distribution Warehouse of Chain Furniture Retail Enterprise

Due to the effects of the COVID-19 pandemic and the rise of online shopping, the offline sales of IKEA Fuzhou have been declining since 2020. Because the cost of distribution warehouse is a major expense for offline chain furniture retailers, and the picking process is a key activity in distribution warehouse operations. To reduce the cost of distribution warehouse and alleviate the survival pressure of the offline chain furniture retailers, this paper focuses on optimizing the picking route of the IKEA Fuzhou distribution warehouse. It starts by creating a two-dimensional coordinate system for the storage location of the distribution warehouse using the traditional S-type picking strategy to calculate the distance and time of the sorting route. Then, the problem of optimizing the picking route is then transformed into the travelling salesman problem (TSP), and picking route optimization model is developed using a genetic algorithm to analyze the sorting efficiency and picking route optimization. Results show that the order-picking route using the genetic algorithm strategy is significantly better than the traditional S-type picking strategy, which can improve overall sorting efficiency and operations, reduce costs, and increase efficiency. Thus, this establishes an implementation process for the order-picking path based on genetic algorithm optimization to improve overall sorting efficiency and operations, reduce costs, increase efficiency, and alleviate the survival pressure of pandemic-affected enterprises.

Basic Research on the Burden of Dropping off and Picking up Children: Differences by Employment Type and Gender in the Tokyo Metropolitan Area

There is a need to reduce the burden of child drop-off and pick-up for child-rearing generations, but most studies on the actual situation in Japan are based on survey results. In this study, we analyzed differences in child drop-off and pick-up by employment type and gender, utilizing the “Metropolitan Area Person Trip Survey,” which is a statistical data set. The study targeted households in which both spouses were between 30 and 49 years old, had children under the age of 6, and included the following three groups. 1) Dual-income Group 1 (both spouses employed/on contract/temporary);2) Dual-income Group 2 (husband employed/on contract/temporary, wife part-time);3) Full-time housewife group (husband employed, wife unemployed). The analysis revealed that a) wives are almost always responsible for dropping off and picking up their children;b) husbands drop off and pick up their children less frequently in dual-income households;and c) households with children raising within 10 to 30 km of Tokyo Station have longer commuting times and need to reduce the burden of dropping off and picking up their children.

USTC-Pickers:a Unified Set of seismic phase pickers Transfer learned for China

Current popular deep learning seismic phase pickers like PhaseNet and EQTransformer suffer from performance drop in China.To mitigate this problem,we build a unified set of customized seismic phase pickers for different levels of use in China.We first train a base picker with the recently released DiTing dataset using the same U-Net architecture as PhaseNet.This base picker significantly outperforms the original PhaseNet and is generally suitable for entire China.Then,using different subsets of the DiTing data,we fine-tune the base picker to better adapt to different regions.In total,we provide 5 pickers for major tectonic blocks in China,33 pickers for provincial-level administrative regions,and 2 special pickers for the Capital area and the China Seismic Experimental Site.These pickers show improved performance in respective regions which they are customized for.They can be either directly integrated into national or regional seismic network operation or used as base models for further refinement for specific datasets.We anticipate that this picker set will facilitate earthquake monitoring in China.

Benchmark on the accuracy and efficiency of several neural network based phase pickers using datasets from China Seismic Network

Seismic phase pickers based on deep neural networks have been extensively used recently,demonstrating their advantages on both performance and efficiency.However,these pickers are trained with and applied to different data.A comprehensive benchmark based on a single dataset is therefore lacking.Here,using the recently released DiTing dataset,we analyzed performances of seven phase pickers with different network structures,the efficiencies are also evaluated using both CPU and GPU devices.Evaluations based on F1-scores reveal that the recurrent neural network(RNN)and EQTransformer exhibit the best performance,likely owing to their large receptive fields.Similar performances are observed among PhaseNet(UNet),UNet++,and the lightweight phase picking network(LPPN).However,the LPPN models are the most efficient.The RNN and EQTransformer have similar speeds,which are slower than those of the LPPN and PhaseNet.UNet++requires the most computational effort among the pickers.As all of the pickers perform well after being trained with a large-scale dataset,users may choose the one suitable for their applications.For beginners,we provide a tutorial on training and validating the pickers using the DiTing dataset.We also provide two sets of models trained using datasets with both 50 Hz and 100 Hz sampling rates for direct application by end-users.All of our models are open-source and publicly accessible.

Pick-up strategies for and electrical characterization of ZnO nanowires with a SEM-based nanomanipulator

Because of their unique mechanical and electrical properties,zinc oxide(ZnO)nanowires are used widely in microscopic and nanoscopic devices and structures,but characterizing them remains challenging.In this paper,two pick-up strategies are proposed for characterizing the electrical properties of ZnO nanowires using SEM equipped with a nanomanipulator.To pick up nanowires efficiently,direct sampling is compared with electrification fusing,and experiments show that direct sampling is more stable while electrification fusing is more efficient.ZnO nanowires have cut-off properties,and good Schottky contact with the tungsten probes was established.In piezoelectric experiments,the maximum piezoelectric voltage generated by an individual ZnO nanowire was 0.07 V,and its impedance decreased with increasing input signal frequency until it became stable.This work offers a technical reference for the pick-up and construction of nanomaterials and nanogeneration technology.

MMP15、PICK1基因在山羊睾丸中的表达研究

MMP15、PICK1基因均是哺乳动物性腺组织中影响性腺生理功能正常发挥的重要影响因子,但它们在黔北麻羊睾丸组织中的表达规律尚不明晰。为研究黔北麻羊不同月龄的睾丸中MMP15、PICK1基因的表达情况,本试验以黔北麻羊为研究对象,通过qRT-PCR技术分别检测MMP15与PICK1基因在1、3、6、9、12月龄的黔北麻羊睾丸中的表达水平并建立基因表达的标准曲线。结果显示:MMP15、PICK1基因在黔北麻羊不同月龄睾丸组织中均有表达;MMP15基因在黔北麻羊不同月龄睾丸中的表达量从高到低排列为:12月龄>1月龄>9月龄>3月龄>6月龄;12月龄与1月龄的表达差异不显著(P>0.05),二者表达量均极显著高于3月龄、6月龄、9月龄(P<0.01);9月龄表达显著高于3月龄(P<0.05),极显著高于6月龄(P<0.01);3月龄表达极显著高于6月龄(P<0.01)。PICK1基因在黔北麻羊不同月龄睾丸中的表达量从高到低排列分别是:3月龄>6月龄>12月龄>9月龄>1月龄;3、6月龄其表达量极显著高于9、12月龄(P<0.01);1月龄与其他几个月龄相比,其表达量均极显著低于其他月龄(P<0.01)。结合黔北麻羊的生理机能发育情况,黔北麻羊在6月龄时已经基本达到性成熟,所以此时睾丸内细胞活动增强,睾丸机能活跃。本次试验结果表明,黔北麻羊睾丸生精功能的正常发挥或与MMP15及PICK1基因的表达量有关。

Truncating PICK1 Variant Identified in Azoospermia Affected Mitochondrial Dysfunction in Knockout Mice

Objective The protein interacting with C kinase 1(PICK1)plays a critical role in vesicle trafficking,and its deficiency in sperm cells results in abnormal vesicle trafficking from Golgi to acrosome,which eventually disrupts acrosome formation and leads to male infertility.Methods An azoospermia sample was filtered,and the laboratory detection and clinical phenotype indicated typical azoospermia in the patient.We sequenced all of the exons in the PICK1 gene and found that there was a novel homozygous variant in the PICK1 gene,c.364delA(p.Lys122SerfsX8),and this protein structure truncating variant seriously affected the biological function.Then we constructed a PICK1 knockout mouse model using clustered regularly interspaced short palindromic repeat cutting technology(CRISPRc).Results The sperm from PICK1 knockout mice showed acrosome and nucleus abnormalities,as well as dysfunctional mitochondrial sheath formation.Both the total sperm and motility sperm counts were decreased in the PICK1 knockout mice compared to wild-type mice.Moreover,the mitochondrial dysfunction was verified in the mice.These defects in the male PICK1 knockout mice may have eventually led to complete infertility.Conclusion The c.364delA novel variant in the PICK1 gene associated with clinical infertility,and pathogenic variants in the PICK1 may cause azoospermia or asthenospermia by impairing mitochondrial function in both mice and humans.

Niemann-Pick A/B Disease in a 13-Year-Old Child and Review of the Literature

Niemann-Pick disease (NPD) refers to a group of patients who have varying degrees of lipid storage and foam cell infiltration in tissues, as well as overlapping clinical features, including hepatosplenomegaly, insufficiency pulmonary and/or central nervous system (CNS). Thanks to the pioneering work of Roscoe Brady and colleagues, we now know that there are two distinct metabolic abnormalities that explain NPD. The first is due to the deficient activity of the acid sphingomyelinase enzyme (ASM;NPD “types A and B”), and the second is due to defective functioning in the transport of cholesterol (NPD “type C”). We report the case of a 13-year-old adolescent diagnosed with Niemann-Pick A/B disease.

旋转剪切式青花椒采摘装置设计与试验

针对基于“下桩”采摘方法的青花椒采摘设备存在喂入困难、易堵塞而造成采摘效率低的问题,该研究设计了一种旋转剪切式青花椒采摘装置。首先基于花椒枝物理、力学特性确定旋转剪切式青花椒采摘过程包括花椒枝旋转驱动、花椒枝导向喂入和剪切采摘,剪切采摘功能由往复式切割器实现。并设计双动刀往复式切割器及其传动机构,确定了剪切采摘装置的结构和运动参数。进一步地,运用ANSYS/LS-DYNA构建花椒枝剪切仿真模型,确定最优齿形参数为:刀齿切割角20°,刀齿刃角50°,刀齿厚度2.5 mm,该条件下峰值切割力为3.739 N。最后通过单因素试验确定了花椒枝喂入角度、花椒枝喂入速度、花椒枝旋转速度的取值范围分别为40°~60°、20~40 mm/s、20~40 r/min;并采用BoxBehnken设计法制定三因素二次回归正交组合试验方案,运用Design-Expert 12对试验结果进行方差分析和响应面分析,得到旋转剪切式青花椒采摘装置的最优工作参数为:花椒枝喂入角度55°,花椒枝喂入速度33.21 mm/s,花椒枝旋转速度30 r/min;通过试验验证得出在最优工作参数下,单人单枝喂入时青花椒平均采摘效率为10.95 kg/h,平均采净率为95.57%,平均伤果率为12.87%。研究结果可为青花椒采摘机的研发提供技术参考。

PICK1参与调控芬太尼诱导的大鼠痛觉过敏机制的研究

目的:探讨与蛋白激酶C相互作用的蛋白质1(PICK1)参与调控阿片类药物诱导的痛觉过敏(OIH)的分子机制。方法:颈部皮下注射枸橼酸芬太尼建立大鼠OIH模型,通过PCR实验检测Pick1在大鼠背根神经节(DRG)中表达水平;构建Pick1的过表达载体,包装pLV-Pick1重组慢病毒后对大鼠进行鞘内注射,电生理测定大鼠DRG神经元兴奋性变化,观察慢病毒注射前(T0)、慢病毒表达后(T1)和芬太尼给药后(T2)大鼠机械痛阈。结果:与Control组相比,OIH组大鼠的机械痛阈明显降低;Pick1在OIH大鼠DRG中表达量有明显下调;芬太尼孵育后DRG细胞的动作电位的阈电流明显降低,鞘内注射pLV-Pick1慢病毒的大鼠DRG细胞在芬太尼孵育后,阈电流相无显著下调。结论:在DRG水平上,Pick1可参与调控痛觉过敏,在DRG上干涉Pick1的表达,可以缓解芬太尼诱导的痛觉过敏。

基于碰撞特性的鲜食玉米摘穗装置对比试验

【目的】探究缓冲弹簧和柔性触穗表面两种摘穗机构在摘取果穗中的低损作用及机制。【方法】以传统玉米收获用板式摘穗机构和具有缓冲弹簧加柔性触穗表面的柔性摘穗机构为2种摘穗结构,以刚性触穗表面、柔性橡胶触穗表面、柔性发泡材料触穗表面为3种触穗表面,得出6种摘穗机构,进行鲜食玉米果穗碰撞试验,采集和分析碰撞时间、碰撞加速度峰值,计算碰撞冲量,统计籽粒破碎率。【结果】柔性摘穗机构能够有效降低鲜食玉米果穗受到的碰撞时间,从而降低鲜食玉米果穗的籽粒破碎率。缓冲弹簧可缩短鲜食玉米果穗的碰撞时间,当柔性摘穗机构表面为柔性橡胶和柔性发泡材料时,碰撞时间分别比板式摘穗机构降低了66.2%、70.1%和70.1%。碰撞冲量分别降低了67.6%、77.9%和79.7%。籽粒破碎率降低了71.2%、51.3%和83.6%。对于柔性摘穗机构,鲜食玉米果穗与柔性橡胶、柔性发泡材料触穗表面的碰撞时间比刚性触穗表面分别延长了58.7%和63.2%。碰撞加速度峰值分别降低了62.5%和68.7%。碰撞冲量分别降低了42.4%和54.5%。籽粒破碎率分别降低了47.4%和68.4%。【结论】通过降低收获过程中鲜食玉米果穗受到的碰撞时间和碰撞加速度,从而有效降低了鲜食玉米果穗的籽粒破碎率,为鲜食玉米低损收获机的研制提供理论依据和设计参考。

机器人人工拣选环境下混流装配线齐套物料配送优化

为了提高汽车制造企业配送的智能化水平、解决混流装配线齐套物料人工拣选效率低、成本高的问题,引入基于齐套物料配送策略的机器人人工拣选模式,通过优化自动拣选机器人、工人的配置数量及配送周期,使包含机器人使用成本、劳动力成本和在制品库存成本在内的总成本最小。为求解该配送优化问题,提出了改进量子蚁群算法,利用量子比特的叠加性增加种群多样性,避免算法陷入局部最优,同时设计了改进量子旋转门更新机制和基于差分进化操作的非最优个体优化策略,提高了算法收敛速度和寻优质量。最后,通过算例分析验证了模型的正确性与算法的有效性,并分析了拣选批量对总成本的影响。

虚拟现实场景下的精准化采摘机器人作业研究

利用虚拟现实场景仿真技术,对葡萄采摘机器人的精准作业控制过程进行分析,通过构建虚拟现实环境下葡萄采摘机器人作业场景模型,以机器人采摘运动柔顺、无碰撞和不损伤采摘对象为目标,对采摘作业过程的夹紧、托举以及剪切动作进行虚拟仿真。虚拟现实场景下20次采摘动作仿真结果中,有3次采摘发生碰撞、17次采摘成功。这表明,在设计阶段利用虚拟现实场景进行精准化作业仿真和控制算法测试,能够有效缩短采摘机器人研制过程中的调试和优化时间。

番茄钵苗移栽探出式取钵机构设计与试验

为了降低番茄钵苗移栽过程取钵机构对秧苗钵土根系的损伤,同时避免机械式钵苗移栽机构设计特殊取苗轨迹与姿态的优化难题,提出了一种可与系列移栽机构配合使用的番茄钵苗探出式取钵机构,实现取苗各关键位置机构秧针以固定角度完成探出入钵、移动送苗及收回推秧工序。根据钵苗移栽取钵过程分析与设计要求,建立了探出式取钵机构力学分析模型,并获得影响秧针扎入钵土时驱动杆受最小驱动力的因素。基于Matlab App Designer平台开发了取钵机构计算机辅助分析设计软件,获得满足番茄钵苗移栽要求的取钵机构设计参数集。采用三因素五水平二次回归正交旋转中心组合试验方法,以驱动杆斜杆夹角、钵体含水率、入钵深度为试验因素,以钵体完整率和取苗成功率为评价指标,试制样机并搭建台架实施参数组合优化及验证试验,结果表明:探出式取钵机构可有效地配合取苗机构完成各项性能工作要求,在参数组合为驱动斜杆间夹角112°、钵体含水率57.5%、入钵深度28.4 mm时作业效果最佳,钵体完整率为96.44%,取苗成功率为97.06%,满足钵苗移栽作业性能。

采摘机器人机械手避障方法研究——基于遗传优化和模糊PID控制器

为了提高采摘机器人机械手移动的自动化程度,使其在移动过程中有效避开障碍物,将基于PID控制器的避障系统引入到了机械手的设计上,并采用遗传算法和模糊算法对控制器进行了优化,提高了机械手移动的准确性和平稳性。模拟采摘机器人机械手的作业环境,对机械手的避障功能进行了测试,结果表明:机械手可以成功躲避障碍物,且采用遗传和模糊算法优化的机械手具有更高的移动平稳性,对于提高果实采摘效率和质量具有重要的作用。

ANSYS和ADAMS在菌落挑取机器人结构设计中的运用

针对现有菌落挑取机器人挑取速度低、集成自动化程度低、挑针重复利用率低等问题,设计出一种多针同时操作的转盘式挑取机器人。首先,为保证主要受力部件(气缸安装版与转盘)的应力、应变在误差范围内,运用有限元分析软件ANSYS Workbench分别对其校核了Z轴定位精度和平面定位精度,由静力学分析结果确定其强度可靠且精度满足设计要求,准确挑取;其次,为保证菌落挑取过程平稳,利用仿真软件ADAMS对菌落分度挑选机构(弧面分度凸轮)进行动力学分析,研究分度挑选机构的动态特性及其运动规律,发现各运动参数的仿真结果与理论相吻合,保证新型机器人在挑取过程中可以避免冲击和振动的发生。该结果表明:挑取机构的强度、刚度及运动的平稳性均满足要求,有效验证了设计的合理性。