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Decoding the Development Strategy of a Major Retailer: Wal-Mart's Expansion in the United States

This research aims to provide insight regarding Wal-Mart's retail development strategy in its expansion across the continental United States. The study examines store location patterns and relationships between Wal-Mart's store and distribution center locations and markets. The study shows that, although radial expansion is broadly characteristic of the dense store patterns that have been well established for Wal-Mart at the national level, expansion patterns at the regional level are consistent with a selective process based on factors other than store density. These results provide a new perspective on Wal-Mart's expansion, providing insights of value to retail practitioners and local government.     

The appraisal of seven economic zones in China

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应用巢式逆转录聚合酶链反应检测鱼类病毒性出血性败血症病毒(VHSV)

参照鱼类病毒性出血性败血症病毒序列,根据PCR引物设计的原则,设计巢式PCR引物,采用巢式逆转录聚合酶链反应(RT-PCR)方法,对如何快速检测鱼类病毒性出血性败血症病的方法进行了较为系统的研究,并对RT-PCR的反应条件进行了优化.结果表明,巢式RT-PCR扩增获得279bp的特异性片断,阴性对照无扩增条带.巢式RT-PCR扩增出的特异性片段经测序分析,结果证实与报道的序列完全一致.该方法最低可检测出0.1pg的鱼类病毒性出血性败血症病毒RNA.初步建立了VHSV的巢式RT-PCR检测方法,该方法灵敏、特异,可为VHSV的检测提供一个快速、有效的手段.     

基于地震反演方法的奥陶系顶部含隔水层探测

在山西阳泉泊里矿区,太原组K₂灰岩是15号煤层上部主要的含水层,查明其富水分布特征对上下组煤层安全开采至关重要。为了准确得到K₂灰岩的富水分布区域,首先,利用常规的波阻抗反演获取精确的K₂灰岩空间展布特征。然后,结合皮尔逊相关系数法与交叉验证−逐步回归法优选出9种地震属性,构成网络的训练数据。此外,引入适合于时序数据处理且能够捕捉测井曲线前后相关性的长短期记忆神经网络(LSTM),构建智能化、多变量LSTM视电阻率预测模型,以精确地预测研究区视电阻率进而得到地层富水性分布特征。同时,分别利用常规多属性回归算法与多变量LSTM模型在井点位置建立电阻率测井曲线与地震属性井旁道之间的映射关系。最后,将井点处训练好的网络模型推广至无井区得到全区视电阻率体,根据视电阻率值的高低、矿区地质构造与陷落柱发育情况圈定灰岩富水区。实际数据的测试结果表明：与常规多属性回归算法相比,多变量LSTM模型预测误差小,与测井相关系数高,说明多变量LSTM模型可以更加精确地预测出工区视电阻率,在含煤地层的富水性预测中有较好的应用价值。\t\t\t\t

     

Becoming coca: A materiality approach to a commodity chain analysis of hoja de coca in Colombia

Coca is a controversial plant, existing on the boundary between legality and illegality. This study aims at providing an analytical technique for discussing the problematic of coca in Colombia. Using new theoretical propositions in human geography, a more‐than‐human approach is adopted to encounter coca holistically. The results are a narrative account of coca's social life as experienced by the researcher following its network of non‐cocaine derivatives. An analytical section invokes the Foucauldian dispositif to the drug trade and utilizes concepts of informed materials and technological zones to describe coca outside a political economy discourse. The research finds that coca's dynamic materiality complicates it as a commodity and that these conventional approaches do not fully encapsulate this complexity. By grappling with the messiness of coca's materiality, this paper reveals the multiplicity and interplay of coca's definitions, which lie at the heart of many conflicts.     

深埋长隧道有害气体发生的地质条件初探

有害气体的深埋长隧道的主要地质灾害现象之一。本文在现场调查及查阅国内外文献资料基础上，探讨了有害气体发生的地质条件，为有害气体地质灾害的预测提供了依据。     

江西省交通区位演变与区域发展效应

选择江西省作为典型研究区域,采用历史分析与空间分析方法,基于长时间序列研究不同交通运输阶段交通区位的动态演变过程,并对其所引致的区域发展特征进行分析.结论是:在传统交通运输阶段向近代化及现代化交通运输阶段转变过程中,江西省宏观交通区位分别表现为国家南北交通控扼中枢,铁路建设的滞后区与周边发达地区间的交通过境区,可达性演变特征则由南北方向可达性的优势区逐步演化为滞后区.不同时期相对应的区域发展特征表现为,从传统运输阶段的区域商贸全面繁荣,走向近代工业化进程滞后及当前经济发展边缘化.在总结典型案例基础上,尝试从成因层,演变层,作用层,效应层和反馈层五个层面探讨交通区位与区域发展的内在作用关系.     

中国城市价值链功能分工及其影响因素

价值链的空间重组正在深刻的改变着城市体系的经济景观,建立在价值链分工基础上的城市功能结构的研究已经成为经济地理学的重要课题。将中国上市公司500强企业网络划分为公司总部、商务服务、研究开发、传统制造、现代制造、物流仓储和批发零售七种类型功能区块,研究了中国城市价值链功能分工及其影响因素。结果发现：沿着价值链的功能分工已经成为中国城市体系经济景观的显著特征,功能多样化城市和功能专业化城市并存于中国城市体系,东部地区和城市密集地区的城市在价值链分工中占据了更好的地位;中国城市按照价值链中的优势功能可以划分为九种类型,少数城市转变为承载公司总部、研究开发、商务服务等多样化功能的高等级中心城市,而大量中小城市则转变为传统制造专业化基地;市场潜力、关键资源、区位条件、营商环境等城市属性特征是城市功能分工的重要影响因素,城市资源、区位可达性等属性特征的增强将提高城市成为总部基地、商务中心和研究基地的概率,而降低城市成为传统制造基地的概率。     

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.