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.

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.

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.

超高阈值Pick-off反射镜的研制

采用水中抛光技术抛制了 90 mm× 60 mm× 1 0 mm的 K9玻璃基片 ,表面粗糙度达 1 nm。在 APS1 50 4镀膜机上 ,摸索了电子束蒸发镀膜的最佳工艺条件 ,较稳定地在该超光滑玻璃表面上镀制了对波长 1 0 54nm、入射角 4 5°、反射率 R≥ 99.5%的反射膜。膜层的抗激光损伤阈值可达 2 6J/cm2( 1 0 54nm,1 ns) ,镀膜后该玻璃基片反射波前可达 λ/1 0 ( p-v) ,最终制备了超高阈值

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.

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。

结合载客热点和POI的出租车停车位划定方法

针对出租车随意停靠造成城市交通拥堵甚至交通事故的问题,利用成都实际区域的出租车GPS(Global Position System)数据和爬取的POI(Point of Interest)数据,使用DBSCAN(Density-Based Spatial Clustering of Application with Noise)聚类算法对上下客点进行聚类,得到出租车的载客热点,根据POI的类型划定载客热点区域的类型,对出租车不同时间的出行需求进行分析,进而划分出出租车的固定停车区域。研究结果表明,出租车固定停车区域的设定与出行者的出行需求有关,即将固定停车区域设置在出行者出行需求多的区域,可以满足出行者的不同出行需求。结合出租车载客热点和爬取POI数据划定固定停车区域的方法具有较高的实用性,可为城市交通安全方面提供理论和现实意义。

PICK1蛋白生理功能及其作为药物新靶点研究进展

PICK1蛋白(protein interacting with C alpha kinase 1)是一种同时具有PDZ和BAR区域的支架蛋白,在哺乳动物体内与多种蛋白质相互作用,并被证明在多种生理过程中发挥重要的调节作用,同时参与了多种疾病病理过程。因此,PICK1蛋白可能成为极具前景的疾病治疗靶点。该文通过对近年来国内外发表的相关文献进行整理与分析,综述了PICK1蛋白的生理功能与其作为药物靶点的研究新进展,旨在为PICK1蛋白的深入研究提供理论支持。

Pick病的诊断

目的 探讨Pick病 (Pickdisease)诊断的正确性。方法 分析 2例临床上曾诊断为阿尔茨海默病(Alzheimerdisease,AD) ,经核磁共振成像 (MRI)检查证实的Pick病 ,其中 1例经正电子发射型计算机断层扫描 (PET)得到进一步证实。结果 Pick病与AD比较有如下特点 :①特殊的语言方式及语言改变 ;②临床上以缓慢进展的性格改变及社会活动能力衰退为主 ,而记忆力、理解判断力、计算力障碍等出现相对较晚 ;③病程中有不同程度的Kl櫣ｖｅｒ Bucy综合征表现 ;④脑MRI检查提示 :额和 (或 )颞叶萎缩 ,颞极为著 ;⑤PET检查提示 :双侧额、颞叶代谢减低 ,颞极明显。结论 根据Pick病临床表现及神经影像学特征 ,在排除AD及其它脑变性病的基础上 ,生前诊断Pick病是可能的。

Pick病的脑MRI研究

目的 研究Pick病患者脑萎缩变化的特点 ,并动态研究脑萎缩变化的程度和病程及临床之间的相互相关系。方法 2例Pick病患者分别进行 5次和 7次脑MRI动态观察 ,并和 6例Alzheimer病患者进行对比分析。结果 Pick病早期以Kluver Bacy综合征和情感障碍为突出 ,人格改变、行为异常、语言障碍为显著 ,而智能减退相对较轻较晚 ,脑MRI以额或 /和颞极萎缩为主 ,尤其颞上回前 1/ 3为明显 ,其脑MRI的改变和临床特征及病程相一致。“刀片”状、“剪刀”像和“锥柱”样或“蘑菇”状脑萎缩改变可能是Pick病的特点。结论 充分认识这些特征 ,以利于Pick病的早期临床诊断。

Pick病3例临床及病理分析

通过分析 3例Pick病的临床、脑MRI、SPECT、PET和脑病理资料 ,发现和Alzheimer病相比 ,Pick病早期以人格变化、判断力改变较为特异 ,情感、情绪变化明显 ,Kl櫣ｖｅｒ Bucy综合征为特征 ,非流利性失语和语义性记忆障碍突出 ,而智能减退及视觉空间定向能力受损相对较晚 ,脑MRI以颞极萎缩为重 ,SPECT和PET以额和 (或 )颞叶低灌流及低代谢为显著。提示应充分认识Pick病的特点 。

PICK1对人肝癌细胞HepG2增殖的影响

目的检测蛋白激酶Cα相互作用蛋白1(PICKl)在肝癌、癌旁组织中的表达,初步探讨PICK1对人肝癌细胞HepG2增殖的作用及其可能机制。方法应用qRT-PCR、Western blot、HE和免疫组织化学染色法分析比较人肝癌和癌旁组织中PICK1蛋白的表达;培养HepG2细胞,体外给予不同浓度的FSC-231(PICK1的PDZ结构域小分子抑制剂),通过MTT法检测HepG2细胞的活力;采用流式细胞术检测HepG2细胞周期变化;进一步采用Western blot法检测G1/S-特异性周期蛋白-D1(CyclinD1)、原癌基因C-myc和Notch信号通路蛋白表达。结果肝脏病理组织学检测提示肝癌组织病变明显。免疫组化结果显示,肝癌组织中相比癌旁组织中的PICKl抗原阳性细胞显著增多(P<0.05),且多分布在肝实质细胞质区域;qRT-PCR和Western blot结果显示肝癌组织中PICK1 mRNA及蛋白水平与癌旁组相比显著升高(P<0.05);体外实验证明,与阴性对照组相比,FSC-231能够抑制HepG2细胞的增殖,并且明显抑制CyclinD1、C-myc和Notch1同源蛋白1(Notch1)、Hes-1蛋白表达(P<0.05)。结论提示PICK1可能参与肝癌的发生发展,PICK1的PDZ结构域抑制剂可以抑制肝癌细胞的增殖,此作用可能与Notch信号通路相关。