Evaluation of freight villages: A GIS-based multi-criteria decision analysis

The development of freight villages in a given region is often viewed as a popular project in the development of a particular country's logistic plans. However, the evaluation of a freight village location among the various alternatives is a multi-criteria decision making (MCDM) problem including both quantitative and qualitative criteria. Obtaining relevant geographic data is another problem for the evaluation of freight village locations. In this study, a geographic information system (GIS)-based MCDM model is proposed to evaluate potential locations for freight villages. The proposed model consists of four sequential phases. In the first phase, different geographic criteria are determined from existing literature, and data are gathered using GIS. By criteria weights equally, alternative locations are determined through GIS in the second phase. In the third phase, identified criteria are weighted using analytical network process (ANP) method by various stakeholders of potential freight villages. In the fourth phase, the “Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)” method is applied in order to determine the best alternative based on the weighted criteria. Using real data from the city of Ankara, Turkey, we test the applicability of our evaluation approach and compare the best alternative obtained by the proposed method with the current freight village (CFV) location in Ankara.     

Optimizing designs and operations of a single network or multiple interdependent infrastructures under stochastic arc disruption

In this paper, we consider an infrastructure as a network with supply, transshipment, and demand nodes. A subset of potential arcs can be constructed between node pairs for conveying service flows. The paper studies two optimization models under stochastic arc disruption. Model 1 focuses on a single network with small-scale failures, and repairs arcs for quick service restoration. Model 2 considers multiple interdependent infrastructures under large-scale disruptions, and mitigates cascading failures by selectively disconnecting failed components. We formulate both models as scenario-based stochastic mixed-integer programs, in which the first-stage problem builds arcs, and the second-stage problem optimizes recourse operations for restoring service or mitigating losses. The goal is to minimize the total cost of infrastructure design and recovery operations. We develop cutting-plane algorithms and several heuristic approaches for solving the two models. Model 1 is tested on an IEEE 118-bus system. Model 2 is tested on systems consisting of the 118-bus system, a 20-node network, and/or a 50-node network, with randomly generated interdependency sets in three different topological forms (i.e., chain, tree, and cycle). The computational results demonstrate that (i) decomposition and cutting-plane algorithms effectively solve Model 1, and (ii) heuristic approaches dramatically decrease the CPU time for Model 2, but yield worse bounds when cardinalities of interdependency sets increase. Future research includes developing special algorithms for optimizing Model 2 for complex multiple infrastructures with special topological forms of system interdependency.     

Image auto-annotation via concept interdependency network

With the explosive growth of multimedia data such as unlabeled images on the Web, image auto-annotation has been receiving increasing research interest. By automatically assigning a set of concepts to unlabeled images, image retrieval can be performed over labeled concepts. Most existing studies focus on the relations between images and concepts, and ignore the interdependencies between labeled concepts. In this paper, we propose a novel image auto-annotation model which utilizes the concept interdependency network to achieve better image auto-annotation. When a concept and its interdependent concepts have a high co-occurrence frequency in the training set, we consider boosting the chance of predicting this concept if there is strong visual evidence for the interdependent concepts in an unlabeled image. Additionally, we combine the global concept interdependency and the local concept interdependency to enhance the auto-annotation performance. Extensive experiments on Corel and IAPR datasets show that the proposed approach almost outperforms all existing methods.     

A comparative study of two software development cost modeling techniques using multi-organizational and company-specific data

This research examined the use of the International Software Benchmarking Standards Group (ISBSG) repository for estimating effort for software projects in an organization not involved in ISBSG. The study investigates two questions: (1) What are the differences in accuracy between ordinary least-squares (OLS) regression and Analogy-based estimation? (2) Is there a difference in accuracy between estimates derived from the multi-company ISBSG data and estimates derived from company-specific data? Regarding the first question, we found that OLS regression performed as well as Analogy-based estimation when using company-specific data for model building. Using multi-company data the OLS regression model provided significantly more accurate results than Analogy-based predictions. Addressing the second question, we found in general that models based on the company-specific data resulted in significantly more accurate estimates.     

DMSP/OLS夜间灯光数据应用研究综述

DMSP/OLS以其独特的光电放大特性与对夜间灯光的获取能力,成为人类活动监测的良好数据源。回顾了DMSP/OLS夜间灯光数据在城镇信息提取、社会经济因子估计及光污染、火灾、渔火、天然气燃烧监测等方面的应用,其中重点介绍了利用灯光数据估算城市化水平和人口的方法和步骤,并总结了DMSP/OLS夜间灯光数据在应用中的优越性与局限性,最后对其未来应用方向进行了展望。      

有向相依网络的可控性研究

针对相依方式对有向相依网络可控性的影响,研究了不同相依方式下有向相依网络的可控性。通过构建基本的有向相依网络模型,结合严格可控性理论,给出了可控性评判指标。同时基于经典的有向随机网络和有向无标度网络,提出3种有向相依网络模型,并研究了随机相依条件下有向相依网络的可控性。随后定义了3种相依方式,并对比分析了在不同相依方式下有向相依网络的可控性。结果表明,在同等相依比例下,基于最低入度与最低出度节点相依的有向相依网络可控性最强,而基于最高入度与最高出度节点相依的有向相依网络可控性最弱,研究成果能够为实际有向相依网络的构建提供有益的参考和指导。     

Intrusion Detection Systems: What is intrusion detection and why do we need it?

In order to answer the opening question, it may help to consider the following scenario. Once upon a time there were isolated villages where everyone trusted everyone else. Little by little, people started to travel from village to village to exchange goods, news and information and life generally got better. A few people used the opportunity to travel from village to village to look inside other people’s houses and take things without permission. As this behaviour became more frequent, villagers began putting locks on their doors. But the criminal element (or in some cases the rabidly curious) was inspired to find ways around these locks. So the villages developed new ways of detecting intruders and monitoring what was taken. These methods included security guards, infra-red motion detectors and security cameras.     

The role of “leads” in the dynamic OLS estimation of cointegrating regression models

In this paper, we consider the role of “leads” of the first difference of integrated variables in the dynamic OLS estimation of cointegrating regression models. Specifically, we investigate Stock and Watson’s [J.H. Stock, M.W. Watson’s, A simple estimator of cointegrating vectors in higher order integrated systems, Econometrica 61 (1993) 783–820] claim that the role of leads is related to the concept of Granger causality by a Monte Carlo simulation. From the simulation results, we find that the dynamic OLS estimator without leads substantially outperforms that with leads and lags; we therefore recommend testing for Granger non-causality before estimating models.     

A grid-based spatial data model for the simulation and analysis of individual behaviours in micro-spatial environments

As crowd simulation in micro-spatial environment is more widely applied in urban planning and management, the construction of an appropriate spatial data model that supports such applications becomes essential. To address the requirements necessary to building a model of crowd simulation and people–place relationship analysis in micro-spatial environments, the concept of the grid as a basic unit of people–place data association is presented in this article. Subsequently, a grid-based spatial data model is developed for modelling spatial data using Geographic Information System (GIS). The application of the model for crowd simulations in indoor and outdoor spatial environments is described. There are four advantages of this model: first, both the geometrical characteristics of geographic entities and behaviour characteristics of individuals within micro-spatial environments are involved; second, the object-oriented model and spatial topological relationships are fused; third, the integrated expression of indoor and outdoor environments can be realised; and fourth, crowd simulation models, such as Multi-agent System (MAS) and Cellular Automata (CA), can be further fused for intelligent simulation and the analysis of individual behaviours. Lastly, this article presents an experimental implementation of the data model, individual behaviours are simulated and analysed to illustrate the potential of the proposed model.     

Dynamic impacts of commodity flow disruptions in inland waterway networks

Freight and passenger transport in the US is projected to increase substantially by 2035 making a strong case for greater usage of the inland waterway barge transport. Inland waterway networks constitute an important component of the US multi-modal transport infrastructure spanning 25,000 miles of navigable routes across 38 states. Risk-based preparedness planning for inland waterway networks requires a means to assess the interdependent impacts associated with closures to inland waterway infrastructure. This study proposes a dynamic framework to assess multi-regional, multi-industry losses due to disruptions on the waterway networks, including ports and waterway links. We investigate simulation-driven disruptive scenarios, such as dock closures, that affect daily commerce across the waterway network and integrate with a dynamic interdependency model to quantify the effect of disruptions on industry inoperability across multiple regions and multiple industries. We implement our framework with a data-driven case study of the operations at the Port of Catoosa on the McClellan–Kerr Arkansas River branch of the Mississippi River Navigation System. Resulting dock-specific, industry-specific, and region-specific insights can guide preparedness decision making.     

Mapping the technologies of spatial (in)justice in the Anthropocene

The production of space is dependent on a variety of social practices and physical conditions, meaning the experience of space and time varies between particular individuals, groups and cultures: ‘Castles, palaces, cathedrals, fortresses, all speak in their various ways of the greatness and the strength of the people who built them and against whom they were built’ [H Lefebvre, Critique of Everyday Life (Verso, London 1991) 232]. Just as social models and relationships are contingent upon and shaped by large-scale policies and organising mechanisms such as almanacs, calendars and maps; cultural norms and economic prospects are also influenced by temporal and spatial structures. The spatial turn provides a material grounding from which to address the erosion of jurisdiction in a world of algorithms, globalisation and advanced capitalism. By reconsidering the means by which the physical infrastructure is allocated and appropriated by citizens across the socioeconomic spectrum, it moves towards an understanding of how social justice expresses itself in the technologically mediated urban environment. The spatial concept of the Anthropocene provides a further opportunity to explore the ontological distinction between humanity and nature, as the pace of technological advancement continues to outstrip our potential to control these new sites of opportunity and exclusion.     

A generic data model for moving objects

Moving objects databases should be able to manage trips that pass through several real world environments, e.g., road network, indoor. However, the current data models only deal with the movement in one situation and cannot represent comprehensive trips for humans who can move inside a building, walk on the pavement, drive on the road, take the public vehicles (bus or train), etc. As a result, existing queries are solely limited to one environment. In this paper, we design a data model that is able to represent moving objects in multiple environments in order to support novel queries on trips in different surroundings and various transportation modes (e.g., Car, Walk, Bus). A generic and precise location representation is proposed that can apply in all environments. The idea is to let the space for moving objects be covered by a set of so-called infrastructures each of which corresponds to an environment and defines the available places for moving objects. Then, the location is represented by referencing to the infrastructure. We formulate the concept of space and infrastructure and propose the methodology to represent moving objects in different environments with the integration of precise transportation modes. Due to different infrastructure characteristics, a set of novel data types is defined to represent infrastructure components. To efficiently support new queries, we design a group of operators to access the data. We present how such a data model is implemented in a database system and report the experimental results. The new model is designed with attention to the data models of previous work for free space and road networks to have a consistent type system and framework of operators. In this way, a powerful set of generic query operations is available for querying, together with those dealing with infrastructures and transportation modes. We demonstrate these capabilities by formulating a set of sophisticated queries across all infrastructures.     

HYBRID GENETIC PROGRAMMING-BASED SEARCH ALGORITHMS FOR ENTERPRISE BANKRUPTCY PREDICTION

Bankruptcy is an extremely significant worldwide problem that affects the economic well- being of all countries. The high social costs incurred by various stakeholders associated with bankrupt firms imply the need to search for better theoretical understanding and prediction quality. The main objective of this paper is to apply genetic programming with orthogonal least squares (GP/OLS) and with simulated annealing (GP/SA) algorithms to build models for bankruptcy prediction. Utilizing the hybrid GP/OLS and GP/SA techniques, generalized relationships are obtained to classify samples of 136 bankrupt and nonbankrupt Iranian corporations based on financial ratios. Another important contribution of this paper is to identify the effective predictive financial ratios based on an extensive bankruptcy prediction literature review and a sequential feature selection (SFS) analysis. A comparative study on the classification accuracy of the GP/OLS- and GP/SA-based models is also conducted. The observed agreement between the predictions and the actual values indicates that the proposed models effectively estimate any enterprise with regard to the aspect of bankruptcy. According to the results, the proposed GP/SA model has better performance than the GP/OLS model in bankruptcy prediction.     

Establishing the relationship between urban land-cover configuration and night time land-surface temperature using spatial regression

Studies suggest that urban form can influence microclimate regulation. Remote sensing studies have contributed to these findings through analysis of high-resolution land cover maps, landscape ecology metrics, and thermal imagery. Collectively, these have been referred to as land cover configuration studies. There are three objectives to this study. The first is to assess the relationship between nighttime land surface temperatures (LST) and land cover configuration and composition. The second objective is to outline a comprehensive methodology that includes ordinary least squares (OLS), spatial regression, variable selection, and multicollinearity analysis. Our last objective is to test three hypotheses about the relationship between LST and land cover, which can briefly be described as: 1) the importance of land-use regimes in modeling LST from land cover composition and configuration variables; 2) the strength of the correlation between LST and roads, buildings, and vegetation; and 3) the improved quality of models using landscape metrics in modeling the relationship between LST and land cover. Based on 16 different models (8 OLS, 8 spatial regression) we could confirm the above hypotheses, but we found that the configuration of buildings, roads, and vegetation have a complex relationship with LST. Our interpretation of this complexity, combined with the strength of composition variables, is that parsimonious models, for now, are more useful to urban planners because they are more generalizable. Finally, spatial regression models of land cover configuration and LST demonstrated an improvement over non-spatial linear models (OLS). Spatial regression models reduced heteroskedasticity and clusters of residuals, and tempered coefficients, suggesting that the OLS models could be biased. OLS models were still found to be a valuable tool for exploratory analysis.     

Hierarchical Bayesian modelling of spatial age-dependent mortality

Hierarchical Bayesian modelling is considered for the number of age-dependent deaths in different geographic regions. The model uses a conditional binomial distribution for the number of age-dependent deaths, a new family of zero mean Gaussian Markov random field models for incorporating spatial correlations between neighbouring regions, and an intrinsic Gaussian model for including correlations between age-dependent mortality rates. Age-dependent mortality rates are estimated for each region, and approximate credibility intervals based on summaries of samples from the posterior distribution are obtained from Markov chain Monte Carlo simulation. The consequent maps of mortality rates are less variable and smoother than those which would be obtained from naive estimates, and various inferences may be drawn from the results. The prior spatial model includes some of the common conditional autoregressive spatial models used in epidemiology, and so model uncertainty in this family can be accounted for. The methodology is illustrated with an actuarial data set of age-dependent deaths in 150 geographic regions of Hungary. Sensitivity to the prior distributions is discussed, as well as relative risks for certain covariates (males in towns, females in towns, males in villages, females in villages).     

Automated extraction of fire line parameters from multispectral infrared images

Remotely sensed infrared images are often used to assess wildland fire conditions. Separately, fire propagation models are in use to forecast future conditions. In the Dynamic Data-Driven Application System (DDDAS) concept, the fire propagation model will react to the image data, which should produce more accurate predictions of fire propagation. In this study we describe a series of image processing tools that can be used to extract fire propagation parameters from multispectral infrared images so that the parameters can be used to drive a fire propagation model built upon the DDDAS concept. The method is capable of automatically determining the fire perimeter, active fire line, and fire propagation direction. A multi-band image gradient calculation, the Normalized Difference Vegetation Index, and the Normalized Difference Burn Ratio along with several standard image processing techniques are used to identify and constrain the fire propagation parameters. These fire propagation parameters can potentially be used within the DDDAS modeling framework for model update and adjustment.     

Visualization workflows for level-12 HUC scales: Towards an expert system for watershed analysis in a distributed computing environment

Visualization workflows are important services for expert users to analyze watersheds when using our HydroTerre end-to-end workflows. Analysis is an interactive and iterative process and we demonstrate that the expert user can focus on model results, not data preparation, by using a web application to rapidly create, tune, and calibrate hydrological models anywhere in the continental USA (CONUS). The HydroTerre system captures user interaction for provenance and reproducibility to share modeling strategies with modelers. Our end-to-end workflow consists of four workflows. The first is data workflows using Essential Terrestrial Variables (ETV) data sets that we demonstrated to construct watershed models anywhere in the CONUS (Leonard and Duffy, 2013). The second is data-model workflows that transform the data workflow results to model inputs. The model inputs are consumed in the third workflow, model workflows (Leonard and Duffy, 2014a) that handle distribution of data and model within High Performance Computing (HPC) environments. This article focuses on our fourth workflow, visualization workflows, which consume the first three workflows to form an end-to-end system to create and share hydrological model results efficiently for analysis and peer review. We show how visualization workflows are incorporated into the HydroTerre infrastructure design and demonstrate the efficiency and robustness for an expert modeler to produce, analyze, and share new hydrological models using CONUS national datasets.     

Rational model selection in large engineering knowledge bases

It is generally admitted that several models differing along various dimensions are needed for executing complex engineering tasks such as diagnosis and monitoring. A key problem is thus to decide what model to use in a particular situation in front of a specified problem-solving task and reasoning objectives. We address this problem within the Multimodeling framework for reasoning about physical systems that we proposed in a previous work. After having characterized the space of possible models in the Multimodeling approach, we formulate the selection problem using the conceptual tools offered by the economic theory of rationality. In this frame we illustrate a preference-based model selection method that is used to navigate in the universe of available models of a system searching for the model that best matches a given task and reasoning objectives. The method exploits the use of a model map that is a metalevel concept representing the ontology and teleology of each model and the transformational relations (abstractions and approximations) connecting each model to other models. The model map is used to compare models on the basis of their content and to understand what can be gained or lost when switching from one model to another. Finally, some implications of the foregoing selection method in developing action-based diagnostic systems are discussed.     

决策支持系统与地理信息系统的集成化研究

通过村庄布局优化决策支持系统案例,着重讨论Ｔ ＧＩＳ与 ＤＳＳ的系统集成,介绍了实现 ＤＳＳ与 ＧＩＳ集成的技术,即ＧＩＳ采用独立开发的电子地图引擎,采用与ＤＳＳ相一致的数据管理工具,在较低的层面上实现统一的数据结构和规范的数据交互,从而实现地理信息库与数据库和模型库的有机链接,实现在地图上村庄信息的显示和查询,以及村庄合并搬迁后在地图上的动态变化,使用户的决策活动更直观方便。