上海自贸区对长三角出口贸易影响实证分析

在深入分析国内外学者研究成果基础上,本文基于面板数据建立贸易引力模型,研究上海自贸区是否发挥了预期作用以及对毗邻的长三角地区出口贸易产生的影响,分析结果证实上海自贸区建成后发挥了强大的“溢出效应”,顺应贸易全球化的发展趋势,带动辐射长三角地区出口贸易取得长足发展,增强了我国外贸综合竞争力,有效拉动了中国经济增长。

Part-Whole Relational Few-Shot 3D Point Cloud Semantic Segmentation

This paper focuses on the task of few-shot 3D point cloud semantic segmentation.Despite some progress,this task still encounters many issues due to the insufficient samples given,e.g.,incomplete object segmentation and inaccurate semantic discrimination.To tackle these issues,we first leverage part-whole relationships into the task of 3D point cloud semantic segmentation to capture semantic integrity,which is empowered by the dynamic capsule routing with the module of 3D Capsule Networks(CapsNets)in the embedding network.Concretely,the dynamic routing amalgamates geometric information of the 3D point cloud data to construct higher-level feature representations,which capture the relationships between object parts and their wholes.Secondly,we designed a multi-prototype enhancement module to enhance the prototype discriminability.Specifically,the single-prototype enhancement mechanism is expanded to the multi-prototype enhancement version for capturing rich semantics.Besides,the shot-correlation within the category is calculated via the interaction of different samples to enhance the intra-category similarity.Ablation studies prove that the involved part-whole relations and proposed multi-prototype enhancement module help to achieve complete object segmentation and improve semantic discrimination.Moreover,under the integration of these two modules,quantitative and qualitative experiments on two public benchmarks,including S3DIS and ScanNet,indicate the superior performance of the proposed framework on the task of 3D point cloud semantic segmentation,compared to some state-of-the-art methods.

Evolutionary Multi/Many-Objective Optimisation via Bilevel Decomposition

Decomposition of a complex multi-objective optimisation problem(MOP)to multiple simple subMOPs,known as M2M for short,is an effective approach to multi-objective optimisation.However,M2M facilitates little communication/collaboration between subMOPs,which limits its use in complex optimisation scenarios.This paper extends the M2M framework to develop a unified algorithm for both multi-objective and manyobjective optimisation.Through bilevel decomposition,an MOP is divided into multiple subMOPs at upper level,each of which is further divided into a number of single-objective subproblems at lower level.Neighbouring subMOPs are allowed to share some subproblems so that the knowledge gained from solving one subMOP can be transferred to another,and eventually to all the subMOPs.The bilevel decomposition is readily combined with some new mating selection and population update strategies,leading to a high-performance algorithm that competes effectively against a number of state-of-the-arts studied in this paper for both multiand many-objective optimisation.Parameter analysis and component analysis have been also carried out to further justify the proposed algorithm.

Glacier area change (1993-2019) and its relationship to debris cover, proglacial lakes, and morphological parameters in the Chandra-Bhaga Basin, Western Himalaya, India

Glacier inventories serve as critical baseline data for understanding the impacts of climate change on glaciers.The present study maps the outlines of glaciers in the Chandra-Bhaga Basin(western Himalaya)for the years 1993,2000,2010,and 2019 using Landsat Thematic Mapper(TM),Enhanced Thematic Mapper(ETM),and Operational Land Imager(OLI)datasets.A total of 251 glaciers,each having an area above 0.5 km^(2),were identified,which include 216 clean-ice and 35 debris-covered glaciers.Area changes are estimated for three periods:1993-2000,2000-2010,and 2010-2019.The total glacierized area was 996±62 km^(2) in 1993,which decreased to 973±70 km^(2) in 2019.The mean rate of glacier area loss was higher in the recent decade(2010-2019),at 0.036 km^(2),compared to previous decades(0.029 km^(2) in 2000-2010 and 0.025 km^(2) in 1993-2000).Supraglacial debris cover changes are also mapped over the period of 1993 and 2019.It is found that the supraglacial debris cover increased by 14.12±2.54 km^(2)(15.2%)during 1993-2019.Extensive field surveys on Chhota Shigri,Panchi II,Patsio,Hamtah,Mulkila,and Yoche Lungpa glaciers were carried out to validate the glacier outlines and supraglacial debris cover estimated using satellite datasets.Controls of various morphological parameters on retreat were also analyzed.It is observed that small,clean ice,south oriented glaciers,and glaciers with proglacial lakes are losing area at faster rates than other glaciers in the basin.

基于YOLOv5s-SPD的茶芽识别方法及识别系统光源设计与试验

实现茶芽的自动识别与定位是优质茶叶智能采摘设备研发的基础。针对茶芽细小,且采摘环境受光照影响较大等问题,本研究提出了一种基于深度学习网络模型的茶芽识别方法,开展识别系统的光源设计,能够为实现全天候和高效率的智能茶芽采摘设备提供技术支撑。首先,搭建铝合金框架的密闭遮光黑暗环境;然后,通过调节横杆高度和光源亮度创造出3种高度和3种光照强度组合;最后,采集不同组合情况下的茶芽图像数据集,利用改进YOLOv5模型对一芽一叶和一芽两叶开展识别测试。试验结果表明,YOLOv5s的总体准确率为77.13%,总体平均精度均值为86.14%,对于改进后的识别模型YOLOv5s-SPD的总体准确率为80.30%,总体平均精度均值为87.3%,单张图片的平均检测时间为5.7 ms,满足实时检测的要求,比原YOLOv5s总体准确率提升3.17%,总体平均精度均值提升1.16%,有效地提升了茶芽的识别性能。在高度90 cm和亮度L7(0.164~0.328μmol/m^(2))的条件下,一芽一叶和一芽两叶的检测准确率、召回率和AP平均值分别为86.70%、92.45%和95.00%。该方法可以有效快速地检测茶芽,光源设计方案为全天候优质茶叶智能采摘设备的研发提供了支持。

National guidelines for the diagnosis and treatment of hilar cholangiocarcinoma

A consensus meeting of national experts from all major national hepatobiliary centres in the country was held on May 26,2023,at the Pakistan Kidney and Liver Institute&Research Centre(PKLI&RC)after initial consultations with the experts.The Pakistan Society for the Study of Liver Diseases(PSSLD)and PKLI&RC jointly organised this meeting.This effort was based on a comprehensive literature review to establish national practice guidelines for hilar cholangiocarcinoma(hCCA).The consensus was that hCCA is a complex disease and requires a multidisciplinary team approach to best manage these patients.This coordinated effort can minimise delays and give patients a chance for curative treatment and effective palliation.The diagnostic and staging workup includes high-quality computed tomography,magnetic resonance imaging,and magnetic resonance cholangiopancreato-graphy.Brush cytology or biopsy utilizing endoscopic retrograde cholangiopancreatography is a mainstay for diagnosis.However,histopathologic confirmation is not always required before resection.Endoscopic ultrasound with fine needle aspiration of regional lymph nodes and positron emission tomography scan are valuable adjuncts for staging.The only curative treatment is the surgical resection of the biliary tree based on the Bismuth-Corlette classification.Selected patients with unresectable hCCA can be considered for liver transplantation.Adjuvant chemotherapy should be offered to patients with a high risk of recurrence.The use of preoperative biliary drainage and the need for portal vein embolisation should be based on local multidisciplinary discussions.Patients with acute cholangitis can be drained with endoscopic or percutaneous biliary drainage.Palliative chemotherapy with cisplatin and gemcitabine has shown improved survival in patients with irresectable and recurrent hCCA.

胡巴归来,不再孤单—Base FX《捉妖记2》特效制作解析

2018年春节档的热门电影《捉妖记2》已经下线,然而它为观众带来的合家欢体验,却成为一个热度不减的话题,尤其是笨笨、骰子妖等新角色的出现让许多小观众非常兴奋。每一个新CG角色得到观众认可的背后,都有着一个漫长艰难的诞生过程,而《捉妖记2》比《捉妖记》增添了许多个妖的形象。

数据使能教学决策的发展--从数据教育应用到多模态学习分析支持教学决策

大量研究表明,数据驱动的教学决策能有效增强教师教学和学生学习的效果。当前研究多聚焦于数据驱动的教学决策模型及实践案例,较少关注支持教师教学决策的数据组织、收集和分析的过程。文章运用文献研究法和案例分析法,阐明基于数据教育应用原理的教学决策过程,并对其中的数据收集和分析过程进行解构,基于多模态学习分析的发展和过程优势,建构多模态学习分析支持的教学决策过程模型,找寻多模态学习分析支持教学决策的多模态数据收集和处理的过程与方法,并从实践角度分析两种多模态学习支持的教学决策的典型案例的过程和实践,希冀为国内中小学开展基于数据的有效教学决策研究和实践提供指引。

Fine-Grained Multivariate Time Series Anomaly Detection in IoT

Sensors produce a large amount of multivariate time series data to record the states of Internet of Things(IoT)systems.Multivariate time series timestamp anomaly detection(TSAD)can identify timestamps of attacks and malfunctions.However,it is necessary to determine which sensor or indicator is abnormal to facilitate a more detailed diagnosis,a process referred to as fine-grained anomaly detection(FGAD).Although further FGAD can be extended based on TSAD methods,existing works do not provide a quantitative evaluation,and the performance is unknown.Therefore,to tackle the FGAD problem,this paper first verifies that the TSAD methods achieve low performance when applied to the FGAD task directly because of the excessive fusion of features and the ignoring of the relationship’s dynamic changes between indicators.Accordingly,this paper proposes a mul-tivariate time series fine-grained anomaly detection(MFGAD)framework.To avoid excessive fusion of features,MFGAD constructs two sub-models to independently identify the abnormal timestamp and abnormal indicator instead of a single model and then combines the two kinds of abnormal results to detect the fine-grained anomaly.Based on this framework,an algorithm based on Graph Attention Neural Network(GAT)and Attention Convolutional Long-Short Term Memory(A-ConvLSTM)is proposed,in which GAT learns temporal features of multiple indicators to detect abnormal timestamps and A-ConvLSTM captures the dynamic relationship between indicators to identify abnormal indicators.Extensive simulations on a real-world dataset demonstrate that the proposed algorithm can achieve a higher F1 score and hit rate than the extension of existing TSAD methods with the benefit of two independent sub-models for timestamp and indicator detection.

基于高通量筛选的双特异性抗体纯化方法开发

双特异性抗体(BsAbs),简称双抗,是一种能同时结合两个不同靶点或表位的抗体,BsAbs在表达过程中通常伴随产生多种副产物,在纯化工艺中很难被去除。本研究运用高通量筛选技术,通过对SP Sepharose Fast Flow填料的纯化条件进行探索,建立基于Sepharose Fast Flow填料的双特异性抗体纯化方法开发的通用流程,每个筛选条件只需要0.4 mg双抗样品,可同时筛选多达32个条件,筛选过程总耗时2 h,而传统柱层析需耗时64 h。通过高通量迅速筛选方法可以有效去除双抗分子副产物,为解决纯化工艺难题提供了一种新的工艺路线。

QoS-Aware Cloud Service Optimization Algorithm in Cloud Manufacturing Environment

In a cloud manufacturing environment with abundant functionally equivalent cloud services,users naturally desire the highest-quality service(s).Thus,a comprehensive measurement of quality of service(QoS)is needed.Opti-mizing the plethora of cloud services has thus become a top priority.Cloud ser-vice optimization is negatively affected by untrusted QoS data,which are inevitably provided by some users.To resolve these problems,this paper proposes a QoS-aware cloud service optimization model and establishes QoS-information awareness and quantification mechanisms.Untrusted data are assessed by an information correction method.The weights discovered by the variable precision Rough Set,which mined the evaluation indicators from historical data,providing a comprehensive performance ranking of service quality.The manufacturing cloud service optimization algorithm thus provides a quantitative reference for service selection.In experimental simulations,this method recommended the optimal services that met users’needs,and effectively reduced the impact of dis-honest users on the selection results.

党建工作与人力资源管理职能的有效融合探讨

近年来,企业改革持续朝向更深层次、更高质量发展,企业只有将党建与人力资源管理职能有效融合,才能获得更大人才培养优势,才能推动企业整体跨越式发展,引领其平稳地做大做强。文章首先简要阐述了新时代企业党建工作与人力资源管理职能有效融合的重要意义,并在此基础上运用案例分析对两者有效融合策略进行深入探讨,希望能为创新实施“党建+人力资源管理”新模式,推进两者高质量、高效率融合提供借鉴与参考。

An Intelligent Secure Adversarial Examples Detection Scheme in Heterogeneous Complex Environments

Image-denoising techniques are widely used to defend against Adversarial Examples(AEs).However,denoising alone cannot completely eliminate adversarial perturbations.The remaining perturbations tend to amplify as they propagate through deeper layers of the network,leading to misclassifications.Moreover,image denoising compromises the classification accuracy of original examples.To address these challenges in AE defense through image denoising,this paper proposes a novel AE detection technique.The proposed technique combines multiple traditional image-denoising algorithms and Convolutional Neural Network(CNN)network structures.The used detector model integrates the classification results of different models as the input to the detector and calculates the final output of the detector based on a machine-learning voting algorithm.By analyzing the discrepancy between predictions made by the model on original examples and denoised examples,AEs are detected effectively.This technique reduces computational overhead without modifying the model structure or parameters,effectively avoiding the error amplification caused by denoising.The proposed approach demonstrates excellent detection performance against mainstream AE attacks.Experimental results show outstanding detection performance in well-known AE attacks,including Fast Gradient Sign Method(FGSM),Basic Iteration Method(BIM),DeepFool,and Carlini&Wagner(C&W),achieving a 94%success rate in FGSM detection,while only reducing the accuracy of clean examples by 4%.

Exploratory systematic review and meta-analysis on period poverty

BACKGROUNDPeriod poverty is a global health and social issue that needs to be addressed.It has been reported that many females compromise their education,employment,and social commitments during their menstruation days due to a number of reasons,including lack of access to toilets or menstrual products.AIM To provide a comprehensive understanding on period poverty,including outcomes associated with menstruation.METHODS All observational and randomised clinical trials reporting menstruation challenges,menstrual poverty and menstrual products were included.Our search strategy included multiple electronic databases of PubMed,Web of Science,ScienceDirect,ProQuest and EMBASE.Studies published in a peer review journal in English between the 30th of April 1980 and the 30th of April 2022 were included.The Newcastle-Ottawa Scale was used to assess the risk of bias of the systematic included studies.Pooled odds ratios(ORs)together with 95%confidence intervals(CIs)are reported overall and for sub-groups.RESULTS A total of 80 studies were systematically selected,where 38 were included in the meta-analysis.Of the 38 studies,28 focused on children and young girls(i.e.,10-24 years old)and 10 included participants with a wider age range of 15-49 years.The prevalence of using disposable sanitary pads was 45%(95%CI:0.35-0.58).The prevalence of menstrual education pre-menarche was 68%(95%CI:0.56-0.82).The prevalence of good menstrual hygiene management(MHM)was 39%(95%CI:0.25-0.61).Women in rural areas(OR=0.30,95%CI:0.13-0.69)were 0.70 times less likely to have good MHM practices than those living in urban areas.CONCLUSION There was a lack of evidence,especially from low-and middle-income countries.Further research to better understand the scope and prevalence of period poverty should be considered.This will enable the development of improved policies to increase access to menstrual products and medical support where necessary.

序列生产、贸易开放与劳动剩余经济结构转换

自Lewis(1954)提出二元经济理论以来,很多学者对二元经济国家的结构转换问题进行了研究。大多学者认为二元经济国家的发展最重要的是资本积累,也有一些学者从制度变革、技术进步、人力资本以及基础设施投资等角度对二元经济国家的发展提供了分析思路。本文则认为,在全球分段化生产体系下,二元经济国家也可通过积极参与国际分工来实现工业化和城市化。本文在序列生产链、外包和存在剩余劳动的设定下,构建了一个包含两部门、两国家的一般均衡模型并进行了数值模拟,结果发现:发展中国家中间品贸易自由化有助于其城市化、工业化和福利水平的提高;而发达国家中间品贸易自由化会使发展中国家的城市化和工业化衰退;若两国同步进行中间品贸易自由化会使发展中国家城市化、工业化和福利水平提高。这些研究表明,在全球生产分段化条件下,发展中国家积极参与全球分工会对其经济发展具有重要意义。

基于级联系统理论的不确定永磁同步电机混沌反馈控制

针对含有参数不确定的永磁同步电机(PMSM)混沌系统,基于级联系统理论(CCST),提出了一种新型的反馈控制方法。首先,根据永磁同步电机混沌系统的特点,将其分解为电气子系统和机械子系统;其次,依据级联系统稳定理论设计状态反馈控制器,以实现系统的稳定控制,并依据Lyapunov稳定理论进行了稳定性分析。最后,通过数值仿真验证了该方法的正确性和有效性。

MSRD:多模态网络谣言检测方法

图像和文本相结合的多模态网络谣言由于更具迷惑性和煽动性,对国家安全和社会稳定的危害性更严重.目前网络谣言检测工作充分考虑了谣言中配文的文本内容而忽略了图像内容以及图像中的内嵌文本内容,因此,提出了一种基于深度神经网络针对图像、图像内嵌文本以及配文文本内容的多模态网络谣言检测方法MSRD.该方法使用VGG-19网络提取图像内容特征,使用DenseNet提取图像内嵌文本内容,使用LSTM网络提取文本内容特征,与图像特征串接后,通过完全连接层获取图像与文本共享表示的均值与方差向量,借助从高斯分布中采样的随机变量以形成重新参数化的多模态特征并作为谣言检测器的输入进行谣言检测.实验表明:该方法在Twitter和微博两大数据集上达到了68.5%和79.4%的准确率.

黄芪发酵产物对肺损伤大鼠TLR_(4)-Src-NF_(κ)B-ICAM-1通路的影响

目的探讨黄芪发酵产物减轻PM2.5诱导炎症反应肺损伤的影响。方法60只Wistar大鼠随机分成空白对照组、模型对照组、富马酸福莫特罗片对照组、黄芪发酵产物的低剂量组、中剂量组和高剂量组,每组10只,根据文献采用气管滴注方法复制PM2.5肺损伤的动物模型,各组进行相应的处理4周,最后一次滴注24~36 h内处死,检测血清和右肺上叶组织匀浆中TNF-α、IL-6、IL-1的含量,进行右肺中叶的病理切片观察,使用胞核蛋白提取试剂盒提取左肺组织中的蛋白,将TLR_(4)、Src、p-Src、NF_(κ)B、ICAM-1的条带通过β-actin进行归一化,并用图像分析软件进行半定量分析。结果黄芪发酵产物高剂量组与模型组比较,血清中的TNF-α、IL-1和右肺上叶组织匀浆中的TNF-α、IL-6、IL-1及左肺组织匀浆中的TLR_(4)、Src、NF_(κ)B、ICAM-1均有统计学意义(P<0.01),中剂量组与模型组比较,有些指标也有统计学意义,如Src、NF_(κ)B、ICAM-1等。结论黄芪发酵产物在一定程度上能够拮抗PM2.5诱导的肺脏中炎性因子水平增加,通过抑制TLR_(4)-Src-NF_(κ)B-ICAM-1通路拮抗PM2.5诱发的炎性反应。

试论健康旅游及其产业发展

由于旅游与健康有着天然的耦合关系,健康旅游受到相关领域的广泛关注.从旅游人类学、产业经济学视角探讨了健康旅游的概念,根据市场主体细分、对应的核心项目及产品的不同,把健康旅游分为康养旅游、健康医疗旅游和养老旅游3个类型,同时提出了“三依赖、一差异、一综合”的健康旅游产业特征.在以上研究的基础上,总结出健康旅游产业发展的几个关键点:尽快制定行业标准和技术规范、塑造健康旅游品牌、注重健康旅游项目效果评价.

高温高盐油藏聚合物微球-CO2复合驱的适应性

高温高盐非均质油藏调剖困难,高含水期无效水循环严重。本文以二乙烯苯、丙烯酰胺为单体,采用乳液聚合方法通过调整脱水山梨醇油酸脂的量制备了粒径为2.89数57.05μm的聚(二乙烯苯-丙烯酰胺)耐温耐盐微球。考察了合成聚合物微球的表面形貌、热稳定性、在水中的分散性、膨胀性及长期热稳定性,并进行了注入封堵性实验和驱替实验。研究结果表明:聚(二乙烯苯-丙烯酰胺)微球的耐温可达370℃,在2.69×10^5 mg/L矿化度水中具有良好的分散性能,90℃下粒径为10.81μm的聚合物微球24 h后膨胀率为12.85%,且具有长期热稳定性。在高温高盐环境下,粒径为10.81μm的聚合物微球在渗透率1700×10^-3μm^2的岩心中有良好的注入性和封堵性;水驱后,微球调剖+CO2驱的注入方式能更高效发挥微球的“调”和CO“2驱”的作用,驱油效果优于直接CO2驱和微球调剖+水驱,可以在高含水期提高原油采收率22.65%,高温高盐非均质油藏高含水期有必要进行“聚合物微球调剖+CO2驱”复合作业来提高采收率。