The mass of the Andromeda galaxy

This paper argues that the Milky Way galaxy is probably the largest member of the Local Group. The evidence comes from estimates of the total mass of the Andromeda galaxy (M31) derived from the three-dimensional positions and radial velocities of its satellite galaxies, as well as the projected positions and radial velocities of its distant globular clusters and planetary nebulae. The available data set comprises 10 satellite galaxies, 17 distant globular clusters and nine halo planetary nebulae with radial velocities. We find that the halo of Andromeda has a mass of together with a scalelength of 90 kpc and a predominantly isotropic velocity distribution. For comparison, our earlier estimate for the Milky Way halo is Although the error bars are admittedly large, this suggests that the total mass of M31 is probably less than that of the Milky Way . We verify the robustness of our results to changes in the modelling assumptions and to errors caused by the small size and incompleteness of the data set.
Our surprising claim can be checked in several ways in the near future. The numbers of satellite galaxies, planetary nebulae and globular clusters with radial velocities can be increased by ground-based spectroscopy, while the proper motions of the companion galaxies and the unresolved cores of the globular clusters can be measured using the astrometric satellites Space Interferometry Mission ( SIM ) and Global Astrometric Interferometer for Astrophysics ( GAIA ). Using 100 globular clusters at projected radii 20 R 50 kpc with both radial velocities and proper motions, it will be possible to estimate the mass within 50 kpc to an accuracy of 20 per cent. Measuring the proper motions of the companion galaxies with SIM and GAIA will reduce the uncertainty in the total mass caused by the small size of the data set to 22 per cent.     

Neural network modelling of planform geometry of headland-bay beaches

The shoreline of beaches in the lee of coastal salients or man-made structures, usually known as headland-bay beaches, has a distinctive curvature; wave fronts curve as a result of wave diffraction at the headland and in turn cause the shoreline to bend. The ensuing curved planform is of great interest both as a peculiar landform and in the context of engineering projects in which it is necessary to predict how a coastal structure will affect the sandy shoreline in its lee. A number of empirical models have been put forward, each based on a specific equation. A novel approach, based on the application of artificial neural networks, is presented in this work. Unlike the conventional method, no particular equation of the planform is embedded in the model. Instead, it is the model itself that learns about the problem from a series of examples of headland-bay beaches (the training set) and thereafter applies this self-acquired knowledge to other cases (the test set) for validation. Twenty-three headland-bay beaches from around the world were selected, of which sixteen and seven make up the training and test sets, respectively. As there is no well-developed theory for deciding upon the most convenient neural network architecture to deal with a particular data set, an experimental study was conducted in which ten different architectures with one and two hidden neuron layers and five training algorithms – 50 different options combining network architecture and training algorithm – were compared. Each of these options was implemented, trained and tested in order to find the best-performing approach for modelling the planform of headland-bay beaches. Finally, the selected neural network model was compared with a state-of-the-art planform model and was shown to outperform it.     

Geomorphic appraisals of active tectonics associated with uplift of the Gohpur–Ganga section in Itanagar, Arunachal Pradesh, India

The Gohpur–Ganga section is located southwest of Itanagar, India. The study area and its adjacent regions lie between the Main Boundary Thrust (MBT) and the Himalayan Front Fault (HFF) within the Sub-Himalaya of the Eastern Himalaya. The Senkhi stream, draining from the north, passes through the MBT and exhibits local meandering as it approaches the study area. Here, five levels of terraces are observed on the eastern part, whereas only four levels of terraces are observed on the western part. The Senkhi and Dokhoso streams show unpaired terraces consisting of very poorly sorted riverbed materials lacking stratification, indicating tectonic activity during deposition. Crude imbrications are also observed on the terrace deposits. A wind gap from an earlier active channel is observed at latitude 27°04′42.4″ N and longitude 93°35′22.4″ E at the height of about 35 m from the present active channel of Senkhi stream. Linear arrangements of ponds trending northeast–southwest on the western side of the study section may represent the paleochannel of Dokhoso stream meeting the Senkhi stream abruptly through this gap earlier. Major lineament trends are observed along NNE–SSW, NE–SW and ENE–WSW direction. The Gohpur–Ganga section is on Quaternary deposits, resting over the Siwaliks with angular contact. Climatic changes of Pleistocene–Holocene times seem to have affected the sedimentation pattern of this part of the Sub-Himalaya, in association with proximal tectonism associated with active tectonic activities, which uplifted the Quaternary deposits. Older and younger terrace deposits seem to mark the Pleistocene–Holocene boundary in the study area with the older terraces showing a well-oxidized and semi-consolidated nature compared to the unoxidized nature of the younger terraces.     

Use of Fuzzy Membership Input Layers to Combine Subjective Geological Knowledge and Empirical Data in a Neural Network Method for Mineral-Potential Mapping

Use of GIS layers, in which the cell values represent fuzzy membership variables, is an effective method of combining subjective geological knowledge with empirical data in a neural network approach to mineral-prospectivity mapping. In this study, multilayer perceptron (MLP), neural networks are used to combine up to 17 regional exploration variables to predict the potential for orogenic gold deposits in the form of prospectivity maps in the Archean Kalgoorlie Terrane of Western Australia. Two types of fuzzy membership layers are used. In the first type of layer, the statistical relationships between known gold deposits and variables in the GIS thematic layer are used to determine fuzzy membership values. For example, GIS layers depicting solid geology and rock-type combinations of categorical data at the nearest lithological boundary for each cell are converted to fuzzy membership layers representing favorable lithologies and favorable lithological boundaries, respectively. This type of fuzzy-membership input is a useful alternative to the 1-of-N coding used for categorical inputs, particularly if there are a large number of classes. Rheological contrast at lithological boundaries is modeled using a second type of fuzzy membership layer, in which the assignment of fuzzy membership value, although based on geological field data, is subjective. The methods used here could be applied to a large range of subjective data (e.g., favorability of tectonic environment, host stratigraphy, or reactivation along major faults) currently used in regional exploration programs, but which normally would not be included as inputs in an empirical neural network approach.     

Finding shortest paths on real road networks: the case for A*

The problem of identifying the shortest path along a road network is a fundamental problem in network analysis, ranging from route guidance in a navigation system to solving spatial allocation problems. Since this type of problem is solved so frequently, it is important to craft an approach that is as efficient as possible. Based upon past research, it is generally accepted that several efficient implementations of the Dijkstra algorithm are the fastest at optimally solving the ‘one‐to‐one’ shortest path problem (Cherkassky et al. 1996 Cherkassky, B. V., Goldberg, A. V. and Radzik, T. 1996. Shortest paths algorithms: theory and experimental evaluation.. Mathematical Programming: Series A and B, 73: 129–174. [Crossref], [Web of Science ®] , [Google Scholar]). We show that the most efficient state‐of‐the‐art implementations of Dijkstra can be improved by taking advantage of network properties associated with GIS‐sourced data. The results of this paper, derived from tests of different algorithmic approaches on real road networks, will be extremely valuable for application developers and researchers in the GIS community.     

Constructing personalized transportation networks in multi-state supernetworks: a heuristic approach

An integrated view encompassing the networks for public and private transport modes as well as the activity programs of travelers is essential for accessibility analysis. In earlier research, the multi-state supernetwork has been put forward by the authors as a suitable technique to model the system in such an integrated fashion. An essential part of a supernetwork involving multi-modal and multi-activity is the personalized transportation network, which is an under-researched topic in the academic community. This article attempts to develop a heuristic approach to construct personalized transportation networks for an individual's activity program. In this approach, the personalized network consists of two types of network extractions from the original transportation system: public transport network and private vehicle network. Three examples are presented to illustrate that the public transport network and private vehicle network can represent an individual's attributes and be applied in large-scale applications for analyzing the synchronization of land-use and transportation systems.     

Statistics-based outlier detection for wireless sensor networks

Wireless sensor network (WSN) applications require efficient, accurate and timely data analysis in order to facilitate (near) real-time critical decision-making and situation awareness. Accurate analysis and decision-making relies on the quality of WSN data as well as on the additional information and context. Raw observations collected from sensor nodes, however, may have low data quality and reliability due to limited WSN resources and harsh deployment environments. This article addresses the quality of WSN data focusing on outlier detection. These are defined as observations that do not conform to the expected behaviour of the data. The developed methodology is based on time-series analysis and geostatistics. Experiments with a real data set from the Swiss Alps showed that the developed methodology accurately detected outliers in WSN data taking advantage of their spatial and temporal correlations. It is concluded that the incorporation of tools for outlier detection in WSNs can be based on current statistical methodology. This provides a usable and important tool in a novel scientific field.     

Surface energy and water vapor fluxes observed in a desert plantation in central Iran

Summertime observations of surface radiation budget, energy balance and atmospheric surface layer meteorology were made on an arid valley floor planted with Haloxylon aphyllum to combat desertification in central Iran. The surface microclimate is characterized and compared with other arid regions and the role of ‘desert greening’ on surface fluxes is considered. A high surface albedo (0.265) and large longwave radiation loss produced relatively low net radiation. Energy partitioning was dominated by sensible and ground heat fluxes with opposing diurnal asymmetry governed by strong diurnal variability in eddy diffusivity. The Bowen ratio was 2.53, which fell inside the range of other vegetated arid surfaces. Surface temperature gradients were strong both in the atmospheric surface layer and in the substrate, with consistent lapse conditions by day and inversions at night. The wind regime included a moderate daytime regional wind which displayed Coriolis turning and weaker nocturnal slope flows. Actual evapotranspiration (1 mm dy⁻¹) was only a small fraction of potential evapotranspiration. The diurnal pattern of AET indicates strong stomatal control. The desert greening effect of Haloxylon plantations provided atmospheric water and reduced sensible heat flux by up to 40%.     

深圳市道路网络结构特征的成因及其景观格局效应

本文以我国典型快速城市化地区深圳市为例,综合使用GIS技术、道路网络结构特征分析、景观格局分析和相关分析方法研究其道路网络结构特征的成因及其景观生态效应。在确定了24个独立的空间研究单元的基础上,重点分析了深圳市道路网络结构特征的相关关系、城市化水平差异对道路网络结构特征的影响和道路格局特征的景观整体及重要组分的格局效应。结果表明:城市建设用地密度的增加导致交通用地密度、节点和廊道储量增加,道路网络结构复杂程度、格局指数降低;资源条件、环境和生态保护约束是导致道路网络复杂性增加、结构发育水平下降、网络格局指数不断降低的主要原因;深圳市的道路网络格局特征对全市景观整体格局没有表现显著的约束性影响,对建设用地显示出环境保护约束和空间吸引两个方面的综合效应,对于林地则表现出生态保护约束、空间排斥和物理分割三个方面的综合效应。     

地理、网络与信任:金融危机背景下的生产网络演化

网络演化是演化经济地理学研究的重要内容和新近热点之一。在当前的理论探讨中主要强调路径依赖和惯例对网络演化的强化作用,对于信任这个关键要素缺乏深入探讨。本文通过深度访谈和问卷调查相结合的方法,对比金融危机前后广东省纺织服装行业生产网络的空间结构,揭示信任与生产网络演化之间的互动机制。研究发现:信任是维系生产网络稳定性的基本要素之一,信任的强弱程度主要受合作类型(市场关系)、地理接近(空间关系)和社会文化接近根植性(社会关系)等因素影响。金融危机作为一种负面外部冲击,重构了广东省服装行业的生产网络。在这个过程中,较强信任关系的网络连结(潮汕地区)得以保留,并在危机过后进一步强化;而部分较弱信任关系的网络连结(珠三角地区)发生断裂,且在经济回暖之后被潮汕地区的网络所取代。