新冠肺炎疫情对经济全球化的影响分析

新冠肺炎疫情是第二次世界大战以来世界面临的最大危机,给世界带来巨大的冲击,包括人们的心理和生活、经济增长与就业、国家治理及世界治理等。这些影响使很多学者、评论家、大众、企业家乃至政府官员产生了非常悲观的情绪,舆论中不乏经济全球化将终结、全球供应链将大规模调整、世界治理格局将彻底改变等言论。本文通过建立“全球化的三角结构”剖析了经济全球化的动力机制,并结合疫情对世界的主要影响,试图揭示后疫情时代经济全球化的走势。我们认为,资本的“空间出路”、技术的“时空压缩”和国家的开放程度是驱动经济全球化的三个基本力量,这三者的变化及其相互作用结果影响着全球化进程。从动力机制看,全球化是一个没有终点且不断变化的历史过程,它不会倒退,而是波动。过去半个世纪以来,世界各国已经被全球化紧密地联系在一起,相互脱钩的代价极其昂贵,没有国家会选择完全脱钩。所谓的逆全球化现象,是全球化发展过程中世界格局变化及各国应对策略调整的结果。新冠肺炎疫情并不能影响全球化的资本和技术驱动力,但是可能影响国家的开放程度。如果疫情持续时间不是很长,经济全球化将很快会回归原有的发展轨迹,继续进行调整。各国也将继续围绕经济全球化进行斗争、妥协、再斗争,直至形成一个相对稳定的状态。因此,经济全球化可能因为应对疫情而踩下急刹车,甚至暂退半步,但很快将继续前行,向着“包容性全球化”的方向发展。     

西北太平洋低纬度区域海水中溶解氨基酸的分布及组成研究

本文对2018年秋季西北太平洋低纬度区域上层海洋(5~200 m)中溶解氨基酸(THAA)的分布和组成进行了研究。结果表明,该海域表层海水中THAA的浓度范围为0.40~0.97μmol/L,平均浓度为0.58±0.14μmol/L;5~200m垂直断面上THAA的平均浓度为0.59±0.16μmol/L,范围为0.30~1.05μmol/L。调查海域内THAA浓度明显低于中国近海,在5~200m内的垂直分布基本表现出随深度增加而增加的趋势。将表层和垂直水体中的THAA分别与DOC、Chla等环境因子进行相关性分析,结果显示均无显著相关性。西北太平洋低纬度区域海水中的优势氨基酸是天冬氨酸(Asp)、谷氨酸(Glu)、丝氨酸(Ser)、甘氨酸(Gly)、苏氨酸(Thr)和丙氨酸(Ala)。基于氨基酸的碳归一化产率(THAA-C%)、降解因子(DI)值,表明该海域表层海水中的有机质降解程度较高,且随深度的增加而降低。     

城镇黑臭河道治理:多水塘活水链人工湿地的应用

随着现代工业的发展,城市黑臭河道问题日趋严重,严重影响了城市形象、生态环境和居民身心健康。利用多水塘活水链人工湿地技术净化江苏常州永胜河河水水质。监测结果显示:该技术使水体总氮浓度削减了92.0%、总磷浓度削减了82.9%,处理后的水体总氮、总磷含量达到地表IV类水标准。项目年处理水量约30万t,每年以较低的成本(平均水处理费用约为0.03元/t),削减总氮、总磷和氨氮量分别为1496.6 kg、176.0 kg和1408.6 kg。通过引植多种浮叶植物、挺水植物、沉水植物,该湿地修复工程为当地营造了优美的湿地景观。多水塘活水链人工湿地是集经济效益、生态效益、环境效益于一体的新型水体生态治理技术,为我国城镇黑臭河道治理提供了一条新的途径。     

传统村落景观基因信息链与自动识别模型构建——以陕西省为例

传统村落作为活的文化遗产,承载了大量历史记忆,是地域文化景观基因识别与模型构建研究的重要切入点。以陕西省71个国家级传统村落为例,基于景观基因理论建立陕西省传统村落景观基因识别体系,识别出陕西省传统村落景观基因特征;运用类型学原理和N级编码理论对景观基因进行编码,构建陕西省传统村落景观基因信息链并生成基因谱系;借鉴生物学中“胞-链-形”DNA碱基序列模型,提取出环境基因、建筑基因、农耕文化基因和宗族文化基因四个公共基因作为景观基因元（胞）,以村内道路系统作为基因链,构建传统村落景观基因DNA模型与自动识别模型,以此对传统村落的区位、类型、特征和文化基因进行自动识别。为传统村落景观基因信息有效传承与存储,以及乡村建设动态发展提供理论借鉴。     

Anti-inflammatory and Immunomodulatory Effects of Marine n-3 Polyunsaturated Fatty Acids on Human Health and Diseases

The pharmaceutical effects of n-3 polyunsaturated fatty acids(n-3 PUFAs) as dietary nutrients on human health and diseases have gained much attention and are investigated for decades. Docosahexaenoic acid(DHA), eicosapentaenoic acid(EPA) and docosapentaenoic acid(DPA) are the three major n-3 PUFAs enriched in marine organisms, such as fish, shrimp, algae, and so on. It has been well known that n-3 PUFAs, especially DHA and EPA, are beneficial in reducing the risk of cardiovascular and cerebrovascular diseases. Accumulating evidence suggests that n-3 PUFAs might cure inflammatory diseases through several mechanisms, such as plasma membrane remodeling of lymphocytes, down-regulating pro-inflammatory cytokines, and alternating adhesion molecule expressions. Several molecular targets of n-3 PUFAs on immune-regulation have also been identified, such as GPR120(FFA4), protein kinase C(PKC), and PPAR-γ. However, it remains inconclusive if dietary n-3 PUFAs function the same both in vitro and in vivo based on cohort studies. This review will focus on the molecular targets and mechanisms of anti-inflammatory and immunomodulatory effects of n-3 PUFAs on human health and diseases, such as obesity, tumor, diabetes, and autoimmune diseases.     

基于北斗短报文的震源船跟踪系统设计与实现

通过在震源船上部署3套GPS接收机,并对3个点的位置信息进行汇总和压缩,利用北斗短报文将压缩后的位置信息推送到陆海联测指挥部,实现对震源船航速、航向的实时监控.根据3个点位信息计算的实时震源船航向,有助于准确把握震源船在进行固定点悬停激发时的航向,克服了以往使用前后两个时刻推算的平均航向代替实时航向等问题.使用我国自主知识产权的北斗短报文进行通讯,大大提高了系统的安全性、可靠性和稳定性;同时陆海联测指挥部可以实时查看震源船是否按事先设定的测线、事先设定的速度和航向进行作业,有效保障了监控平台对震源船的监控,进一步提升安全生产的风险控制能力.     

一种基于GA-BP-MC神经网络的高铁桥墩沉降预测模型

提出一种基于马尔科夫链修正的遗传BP神经网络预测模型（GA-BP-MC）,利用遗传算法的全局寻优能力初始化BP神经网络权值和阈值,初步建立GA-BP神经网络预测模型,结合马尔科夫链的无后效性修正模型预测值,形成高精度GA-BP-MC神经网络变形预测模型。结合高铁桥墩沉降数据,分别与BP神经网络、GA-BP神经网络预测模型进行对比,结果表明,该预测模型精度最高。     

苹果手机零部件全球价值链的价值分配与中国角色演变

产品内分工已成为支配当代企业分工与产品生产的重要模式。研究智能手机生产网络价值分配格局,对辨析中国大陆企业在移动通讯产业价值链中所处地位与演进具有重要意义。论文利用2012—2017年苹果公司供应商数据,以其零部件价值含量与技术要求为测度标准,分析了苹果手机零部件全球价值链的分配特征以及中国大陆企业在其中的地位变化。发现：高价值环节的供应商主要以美国、日本、韩国为主,而中低价值环节则以中国台湾、日本和中国大陆供应商为主,且中国大陆与中国台湾在中间环节占据重要地位。处于价值链高端的供应商分支数量在中国大陆和美国缩减明显,日本、韩国较为稳定,中国台湾增加明显。在价值链的中低端环节,供应商分支趋向中国大陆集中。印度、巴西以及越南等地成为新的中低端价值环节转移承接地。近年来,中国大陆在苹果手机价值链的高端环节得到了提升,长三角、珠三角、京津冀、成渝、中原地区等地逐渐形成了苹果供应商分支的集聚区域,呈现出零部件生产的区域分工特征。长三角(上海-苏州为核心)与珠三角(深圳-东莞为核心),承接了价值链多个生产环节,是中国大陆嵌入苹果手机生产网络的主要阵地。成渝地区既承接了芯片类企业布局,也承接了价值链低端的组装代工和包装服务环节,呈现出在价值链两端双向承接的特征。环渤海、福建及湖南等地主要承接苹果手机价值链的中间环节,而中原地区主要以苹果手机组装与包装服务为主,承接价值链的低端环节。最后,论文从创新驱动、政府作用与外资等方面探讨了影响中国大陆地位提升的因素。     

Examining the sensitivity of spatial scale in cellular automata Markov chain simulation of land use change

Understanding the spatial scale sensitivity of cellular automata is crucial for improving the accuracy of land use change simulation. We propose a framework based on a response surface method to comprehensively explore spatial scale sensitivity of the cellular automata Markov chain (CA-Markov) model, and present a hybrid evaluation model for expressing simulation accuracy that merges the strengths of the Kappa coefficient and of Contagion index. Three Landsat-Thematic Mapper remote sensing images of Wuhan in 1987, 1996, and 2005 were used to extract land use information. The results demonstrate that the spatial scale sensitivity of the CA-Markov model resulting from individual components and their combinations are both worthy of attention. The utility of our proposed hybrid evaluation model and response surface method to investigate the sensitivity has proven to be more accurate than the single Kappa coefficient method and more efficient than traditional methods. The findings also show that the CA-Markov model is more sensitive to neighborhood size than to cell size or neighborhood type considering individual component effects. Particularly, the bilateral and trilateral interactions between neighborhood and cell size result in a more remarkable scale effect than that of a single cell size.     

Simulation of urban expansion via integrating artificial neural network with Markov chain – cellular automata

Accurate simulations and predictions of urban expansion are critical to manage urbanization and explicitly address the spatiotemporal trends and distributions of urban expansion. Cellular Automata integrated Markov Chain (CA-MC) is one of the most frequently used models for this purpose. However, the urban suitability index (USI) map produced from the conventional CA-MC is either affected by human bias or cannot accurately reflect the possible nonlinear relations between driving factors and urban expansion. To overcome these limitations, a machine learning model (Artificial Neural Network, ANN) was integrated with CA-MC instead of the commonly used Analytical Hierarchy Process (AHP) and Logistic Regression (LR) CA-MC models. The ANN was optimized to create the USI map and then integrated with CA-MC to spatially allocate urban expansion cells. The validated results of kappa and fuzzy kappa simulation indicate that ANN-CA-MC outperformed other variously coupled CA-MC modelling approaches. Based on the ANN-CA-MC model, the urban area in South Auckland is predicted to expand to 1340.55 ha in 2026 at the expense of non-urban areas, mostly grassland and open-bare land. Most of the future expansion will take place within the planned new urban growth zone.