Use of mixed-type data clustering algorithm for characterizing temporal and spatial distribution of biosecurity border detections of terrestrial non-indigenous species

Appropriate inspection protocols and mitigation strategies are a critical component of effective biosecurity measures, enabling implementation of sound management decisions. Statistical models to analyze biosecurity surveillance data are integral to this decision-making process. Our research focuses on analyzing border interception biosecurity data collected from a Class A Nature Reserve, Barrow Island, in Western Australia and the associated covariates describing both spatial and temporal interception patterns. A clustering analysis approach was adopted using a generalization of the popular k-means algorithm appropriate for mixed-type data. The analysis approach compared the efficiency of clustering using only the numerical data, then subsequently including covariates to the clustering. Based on numerical data only, three clusters gave an acceptable fit and provided information about the underlying data characteristics. Incorporation of covariates into the model suggested four distinct clusters dominated by physical location and type of detection. Clustering increases interpretability of complex models and is useful in data mining to highlight patterns to describe underlying processes in biosecurity and other research areas. Availability of more relevant data would greatly improve the model. Based on outcomes from our research we recommend broader use of cluster models in biosecurity data, with testing of these models on more datasets to validate the model choice and identify important explanatory variables

Adaptable Spatial Agent-Based Facility Location for Healthcare Coverage

Lack of access to healthcare is responsible for the world’s poverty, mortality and morbidity. Public healthcare facilities (HCFs) are expected to be located such that they can be reached within reasonable distances of the patients’ locations, while at the same time providing complete service coverage. However, complete service coverage is generally hampered by resource availability. Therefore, the Maximal Covering Location Problem (MCLP), seeks to locate HCFs such that as much population as possible is covered within a desired service distance. A consideration to the population not covered introduces a distance constraint that is greater than the desired service distance, beyond which no population should be. Existing approaches to the MCLP exogenously set the number of HCFs and the distance parameters, with further assumption of equal access to HCFs, infinite or equal capacity of HCFs and data availability. These models tackle the real-world system as static and do not address its intrinsic complexity that is characterised by unstable and diverse geographic, demographic and socio-economic factors that influence the spatial distribution of population and HCFs, resource management, the number of HCFs and proximity to HCFs. Static analysis incurs more expenditure in the analytical and decision-making process for every additional complexity and heterogeneity. This thesis is focused on addressing these limitations and simplifying the computationally intensive problems. A novel adaptable and flexible simulation-based meta-heuristic approach is employed to determine suitable locations for public HCFs by integrating Geographic Information Systems (GIS) with Agent-Based Models (ABM). Intelligent, adaptable and autonomous spatial and non-spatial agents are utilized to interact with each other and the geographic environment, while taking independent decisions governed by spatial rules, such as •containment, •adjacency, •proximity and •connectivity. Three concepts are introduced: assess the coverage of existing HCFs using travel-time along the road network and determine the different average values of the service distance; endogenously determine the number and suitable locations of HCFs by integrating capacity and locational suitability constraints for maximizing coverage within the prevailing service distance; endogenously determine the distance constraint as the maximum distance between the population not covered within the desired service distance and its closest facility. The models’ validations on existing algorithms produce comparable and better results. With confirmed transferability, the thesis is applied to Lagos State, Nigeria in a disaggregated analysis that reflects spatial heterogeneity, to provide improved service coverage for healthcare. The assessment of the existing health service coverage and spatial distribution reveals disparate accessibility and insufficiency of the HCFs whose locations do not factor in the spatial distribution of the population. Through the application of the simulation-based approach, a cost-effective complete health service coverage is achieved with new HCFs. The spatial pattern and autocorrelation analysis reveal the influence of population distribution and geographic phenomenon on HCF location. The relationship of selected HCFs with other spatial features indicates agents’ compliant with spatial association. This approach proves to be a better alternative in resource constrained systems. The adaptability and flexibility meet the global health coverage agenda, the desires of the decision maker and the population, in the support for public health service coverage. In addition, a general theory of the system for a better-informed decision and analytical knowledge is obtained

The Application of Two Echelon Distribution Network Zoning in an Organic-Chemical Fertilizer Distribution: The Case Study in Northeastern Thailand

This research introduced the two-echelon capacitated plant location problem (2E-CPLP) to increase the efficiency of organic-chemical fertilizer distribution in Northeastern Thailand.  The consequences of this unsolved problem either lead to lower productivity or increased use of less environmentally friendly fertilizers.  Though centroid with equal demand zoning was previously used in this area, the unique characteristics of Northeastern Thailand cause this approach to yield less acceptable results.  The introduction of the two-echelon concept is important for practitioners to adopt a better way of operating.  This case-based study compared two facility location problem (FLP) approaches by using the geographical zoning and equal demand zoning methods as a baseline, then introducing the 2E-CPLP method as an alternative.  The problem was solved by IBM ILOG CPLEX software and ArcGIS for Desktop 9.3 with outputs of the total cost, the maximum distance from plants to retailers, and the average distance from plants to retailers.  The results showed that the 2E-CPLP method with geographical zoning outperformed equal demand zoning, with a lower total cost of 246.84 million baht or 1.05%, significantly less computational time, and lower maximum and average distances from plants to retailers

Traffic-Profile and Machine Learning Based Regional Data Center Design and Operation for 5G Network

Data center in the fifth generation (5G) network will serve as a facilitator to move the wireless communication industry from a proprietary hardware based approach to a more software oriented environment. Techniques such as Software defined networking (SDN) and network function virtualization (NFV) would be able to deploy network functionalities such as service and packet gateways as software. These virtual functionalities however would require computational power from data centers. Therefore, these data centers need to be properly placed and carefully designed based on the volume of traffic they are meant to serve. In this work, we first divide the city of Milan, Italy into different zones using K-means clustering algorithm. We then analyse the traffic profiles of these zones in the city using a network operator’s Open Big Data set. We identify the optimal placement of data centers as a facility location problem and propose the use of Weiszfeld’s algorithm to solve it. Furthermore, based on our analysis of traffic profiles in different zones, we heuristically determine the ideal dimension of the data center in each zone. Additionally, to aid operation and facilitate dynamic utilization of data center resources, we use the state of the art recurrent neural network models to predict the future traffic demands according to past demand profiles of each area

Algorithms for the Analysis of Spatio-Temporal Data from Team Sports

Modern object tracking systems are able to simultaneously record trajectories—sequences of time-stamped location points—for large numbers of objects with high frequency and accuracy. The availability of trajectory datasets has resulted in a consequent demand for algorithms and tools to extract information from these data. In this thesis, we present several contributions intended to do this, and in particular, to extract information from trajectories tracking football (soccer) players during matches. Football player trajectories have particular properties that both facilitate and present challenges for the algorithmic approaches to information extraction. The key property that we look to exploit is that the movement of the players reveals information about their objectives through cooperative and adversarial coordinated behaviour, and this, in turn, reveals the tactics and strategies employed to achieve the objectives. While the approaches presented here naturally deal with the application-specific properties of football player trajectories, they also apply to other domains where objects are tracked, for example behavioural ecology, traffic and urban planning

Conservation priorities for Prunus africana defined with the aid of spatial analysis of genetic data and climatic variables

Conservation priorities for Prunus africana, a tree species found across Afromontane regions, which is of great commercial interest internationally and of local value for rural communities, were defined with the aid of spatial analyses applied to a set of georeferenced molecular marker data (chloroplast and nuclear microsatellites) from 32 populations in 9 African countries. Two approaches for the selection of priority populations for conservation were used differing in the way they optimize representation of intra-specific diversity of P. africana across a minimum number of populations. The first method (Si) was aimed at maximizing genetic diversity of the conservation units and their distinctiveness with regard to climatic conditions, the second method (S2) at optimizing representativeness of the genetic diversity found throughout the species' range. Populations in East African countries (especially Kenya and Tanzania) were found to be of great conservation value, as suggested by previous findings. These populations are complemented by those in Madagascar and Cameroon. The combination of the two methods for prioritization led to the identification of a set of 6 priority populations. The potential distribution of P. africana was then modeled based on a dataset of 1,500 georeferenced observations. This enabled an assessment of whether the priority populations identified are exposed to threats from agricultural expansion and climate change, and whether they are located within the boundaries of protected areas. The range of the species has been affected by past climate change and the modeled distribution of P. africana indicates that the species is likely to be negatively affected in future, with an expected decrease in distribution by 2050. Based on these insights, further research at the regional and national scale is recommended, in order to strengthen P. africana conservation efforts

Optimisation of patch distribution strategies for AMR applications

As core counts increase in the world's most powerful supercomputers, applications are becoming limited not only by computational power, but also by data availability. In the race to exascale, efficient and effective communication policies are key to achieving optimal application performance. Applications using adaptive mesh refinement (AMR) trade off communication for computational load balancing, to enable the focused computation of specific areas of interest. This class of application is particularly susceptible to the communication performance of the underlying architectures, and are inherently difficult to scale efficiently. In this paper we present a study of the effect of patch distribution strategies on the scalability of an AMR code. We demonstrate the significance of patch placement on communication overheads, and by balancing the computation and communication costs of patches, we develop a scheme to optimise performance of a specific, industry-strength, benchmark application

Soft Infrastructure in Smart Sustainable Cities: A Literature Review

Learning from the cases in Indonesia, the proliferation of advanced technologies has engendered a burgeoning interest in smart city promotion as a dominant developmental theme, and this has an association heavily with physical infrastructure development, while there are other things that need to be thought about.  The methodology entails the scholarly works, procurement of data, classification of data, and integration of resultant discoveries.  The objective of this article is to furnish a thorough and intricate comprehension of the soft infrastructure that upholds crucial infrastructure systems. Qualitative assessments scrutinize outcomes within multiple frameworks to gauge the efficacy of the supple infrastructure in promoting resilience.  As a result, the occurrence of the theme of soft infrastructure in smart sustainable cities poses a novel challenge to continuously enhance their skills and expertise. The soft infrastructure in smart sustainable cities addresses business-spatial, cultural-political, and humane-innovation issues. Such resources can effectively address integrated regional challenges and well-conceived planning for cities