A participatory GIS approach to spatial modeling for slum upgrading: The case of Epworth ward 4, Zimbabwe

The most prominent problem of rapid urbanism in Harare is the development of slums and Epworth is a notable example. The quality of planning and decision making in the participatory slum upgrading initiative  can be sustainably improved by well managed processes of spatial and socio-economic data collection. More so, existing approaches for slum upgrading are built on the basis of collaborative planning and thus community participation. Geographic Information Systems (GIS) tools and support systems should explicitly consider the needs of professional and non-professional stakeholders in slum upgrading process.   This study aimed to develop a customized “GIS based slum upgrading system" that would empower the community to participate in the planning and decision making processes involved in slum upgrading. The study involved developing tools that could be used by lay people, to achieve the same results an expert would. To achieve this, a PostgreSQL/Postgis spatial database integrating the spatial and enumeration data component was developed as the backend with a front end developed using VB.Net. A Map Control tool was imported from Map window 4.7 for displaying and interacting with spatial data. Techniques such as buffering were incorporated in the system to allow users to check for structures within a given distance of a feature. The system also provided a tool that allowed users to select an area and calculate the number of stands that can be obtained after inputting parameters like stand size and type of road. The system provided for tools that calculate statistical values e.g. determining the population as well as the number of households in a selected area. Users could also query for socio-economic data by selecting spatial features. This provided flexibility in terms of the interaction of the users with the data.  The system proved that with user oriented GIS system it is possible to develop tools that can be used by lay people especially those in slums to formalize their areas and obtain security of tenure.Keywords: Slum upgrading, Participatory GIS, Security of tenur

An architecture for intelligent health assessment enabled IEEE 1451 compliant smart sensors

As systems become increasingly complex and costly, potential failure mechanisms and indicators are numerous and difficult to identify, while the cost of loss is very expensive - human lives, replacement units, and impacts to national security. In order to ensure the safety and long-term reliability of vehicles, structures, and devices attention must be directed toward the assessment and management of system health. System health is the key component that links data, information, and knowledge to action. Integrated Systems Health Management (ISHM) doctrine calls for comprehensive real-time health assessment and management of systems where the distillation of raw data into information takes place within sensors and actuators. This thesis develops novel field programmable health assessment capability for sensors and actuators in ISHM. Health assessment and feature extraction algorithms are implemented on a sensor or actuator through the Embedded Routine Manager (ERM) API. Algorithms are described using Health Electronic Datasheets (HEDS) to provide more flexible run-time operation. Interfacing is accomplished through IEEE Standard 1451 for Smart Sensors and Actuators, connecting ISHM with the instrumentation network of the future. These key elements are validated using exemplar algorithms to detect noise, spike, and flat-line events onboard the ISHM enabled Methane Thruster Testbed Project (MTTP) at NASA Stennis Space Center in Mississippi

The first ICASE/LARC industry roundtable: Session proceedings

The first 'ICASE/LaRC Industry Roundtable' was held on October 3-4, 1994, in Williamsburg, Virginia. The main purpose of the roundtable was to draw attention of ICASE/LaRC scientists to industrial research agendas. The roundtable was attended by about 200 scientists, 30% from NASA Langley; 20% from universities; 17% NASA Langley contractors (including ICASE personnel); and the remainder from federal agencies other than NASA Langley. The technical areas covered reflected the major research programs in ICASE and closely associated NASA branches. About 80% of the speakers were from industry. This report is a compilation of the session summaries prepared by the session chairmen

Dynamic Surgery Assignment of Multiple Operating Rooms With Planned Surgeon Arrival Times

International audienceThis paper addresses the dynamic assignment of a given set of surgeries to multiple identical operating rooms (ORs). Surgeries have random durations and planned surgeon arrival times. Surgeries are assigned dynamically to ORs at surgery completion events. The goal is to minimize the total expected cost incurred by surgeon waiting, OR idling, and OR overtime. We first formulate the problem as a multi-stage stochastic programming model. An efficient algorithm is then proposed by combining a two-stage stochastic programming approximation and some look-ahead strategies. A perfect information-based lower bound of the optimal expected cost is given to evaluate the optimality gap of the dynamic assignment strategy. Numerical results show that the dynamic scheduling and optimization with the proposed approach significantly improve the performance of static scheduling and First Come First Serve (FCFS) strategy

Understanding Mechanisms of Metastasis of Aggressive Breast Cancers via Microfluidic Means

The spread of cancer from its site of origin to other organs is called metastasis, and it is this stage of the disease that is responsible for over 90% of cancer deaths. Tumors are comprised of a heterogeneous population and not every cell in a primary tumor has the intrinsic capability to metastasize. Understanding what gives certain metastatically enabled cells this potential will ultimately provide insight into how to target and prevent metastases. In order to form a metastasis, a cancer cell must: move, invade through often stiff supporting tissue, enter the vasculature via small intercellular spaces, survive the hydrodynamic forces of circulation, squeeze through vessel endothelium once again, and finally proliferate. Imbued with the knowledge of this metastatic journey of a cancer cell, it is understandable how very physical and mechanical in nature the process is. Therefore, to study the steps of metastasis effectively requires the ability to precisely control physical attributes of a cell’s surroundings. The engineering field of microfluidics affords this opportunity and in this work I advanced our present knowledge of the metastatic process by using microfluidic techniques in four fundament studies of critical steps required for metastases. In one study, cancer cells are challenged with a geometrically confining migration space which mimics the constraints of a lymphatic capillary and the early necessary intravasation metastatic step. After migration, motile and non-motile cells are recaptured and analyzed for genetic differences which allow for intravasation. In another study, the effects of secreted factors from normal immune cells in the tumor microenvironment are tested for their stimulation of cancer cell migration – the first required step of metastasis – in the most aggressive form of breast cancer that is considered metastatic at its inception. A third study leveraged the adhesive properties of cancer cells as a novel paradigm for circulating tumor cell capture and analysis independent of dynamic cell surface markers. Lastly, specifically designed microfluidic assays were used to determine a multiparametric cellular phenotype of the most aggressive subpopulation of cancer cells’ biomechanical properties, which may confer the capability to effectively traverse the inefficient steps of metastasis.PHDCellular & Molec Biology PhDUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/143962/1/allensg_1.pd

Slicing based code parallelization for minimizing inter-processor communication

One of the critical problems in distributed memory multi-core architectures is scalable parallelization that minimizes inter-processor communication. Using the concept of iteration space slicing, this paper presents a new code parallelization scheme for data-intensive applications. This scheme targets distributed memory multi-core architectures, and formulates the problem of data-computation distribution (partitioning) across parallel processors using slicing such that, starting with the partitioning of the output arrays, it iteratively determines the partitions of other arrays as well as iteration spaces of the loop nests in the application code. The goal is to minimize inter-processor data communications. Based on this iteration space slicing based formulation of the problem, we also propose a solution scheme. The proposed data-computation scheme is evaluated using six data-intensive benchmark programs. In our experimental evaluation, we also compare this scheme against three alternate data-computation distribution schemes. The results obtained are very encouraging, indicating around 10% better speedup, with 16 processors, over the next-best scheme when averaged over all benchmark codes we tested. Copyright 2009 ACM

Human Umbilical Cord Blood-Derived CD34+ Cells Reverse Osteoporosis in NOD/SCID Mice by Altering Osteoblastic and Osteoclastic Activities

Osteoporosis is a bone disorder associated with loss of bone mineral density and micro architecture. A balance of osteoblasts and osteoclasts activities maintains bone homeostasis. Increased bone loss due to increased osteoclast and decreased osteoblast activities is considered as an underlying cause of osteoporosis.The cures for osteoporosis are limited, consequently the potential of CD34+ cell therapies is currently being considered. We developed a nanofiber-based expansion technology to obtain adequate numbers of CD34(+) cells isolated from human umbilical cord blood, for therapeutic applications. Herein, we show that CD34(+) cells could be differentiated into osteoblastic lineage, in vitro. Systemically delivered CD34(+) cells home to the bone marrow and significantly improve bone deposition, bone mineral density and bone micro-architecture in osteoporotic mice. The elevated levels of osteocalcin, IL-10, GM-CSF, and decreased levels of MCP-1 in serum parallel the improvements in bone micro-architecture. Furthermore, CD34(+) cells improved osteoblast activity and concurrently impaired osteoclast differentiation, maturation and functionality.These findings demonstrate a novel approach utilizing nanofiber-expanded CD34(+) cells as a therapeutic application for the treatment of osteoporosis

Applications and Advances in Similarity-based Machine Learning

Similarity-based machine learning methods differ from traditional machine learning methods in that they also use pairwise similarity relations between objects to infer the labels of unlabeled objects. A recent comparative study for classification problems by Baumann et al. [2019] demonstrated that similarity-based techniques have superior performance and robustness when compared to well-established machine learning techniques. Similarity-based machine learning methods benefit from two advantages that could explain superior their performance: They can make use of the pairwise relations between unlabeled objects, and they are robust due to the transitive property of pairwise similarities. A challenge for similarity-based machine learning methods on large datasets is that the number of pairwise similarity grows quadratically in the size of the dataset. For large datasets, it thus becomes practically impossible to compute all possible pairwise similarities. In 2016, Hochbaum and Baumann proposed the technique of sparse computation to address this growth by computing only those pairwise similarities that are relevant. Their proposed implementation of sparse computation is still difficult to scale to millions objects. This dissertation focuses on advancing the practical implementations of sparse computation to larger datasets and on two applications for which similarity-based machine learning was particularly effective. The applications that are studied here are cell identification in calcium-imaging movies and detecting aberrant linking behavior in directed networks. For sparse computation we present faster, geometric algorithms and a technique, named sparse-reduced computation, that combines sparse computation with compression. The geometric algorithms compute the exact same output as the original implementation of sparse computation, but identify the relevant pairwise similarities faster by using the concept of data shifting for identifying objects in the same or neighboring blocks. Empirical results on datasets with up to 10 million objects show a significant reduction in running time. Sparse-reduced computation combines sparse computation with a technique for compressing highly-similar or identical objects, enabling the use of similarity-based machine learning on massively-large datasets. The computational results demonstrate that sparse-reduced computation provides a significant reduction in running time with a minute loss in accuracy.A major problem facing neuroscientists today is cell identification in calcium-imaging movies. These movies are in-vivo recordings of thousands of neurons at cellular resolution. There is a great need for automated approaches to extract the activity of single neurons from these movies since manual post-processing takes tens of hours per dataset. We present the HNCcorr algorithm for cell identification in calcium-imaging movies. The name HNCcorr is derived from its use of the similarity-based Hochbaum's Normalized Cut (HNC) model with pairwise similarities derived from correlation. In HNCcorr, the task of cell detection is approached as a clustering problem. HNCcorr utilizes HNC to detect cells in these movies as coherent clusters of pixels that are highly distinct from the remaining pixels. HNCcorr guarantees, unlike existing methodologies for cell identification, a globally optimal solution to the underlying optimization problem. Of independent interest is a novel method, named similarity-squared, that we devised for measuring similarity between pixels. We provide an experimental study and demonstrate that HNCcorr is a top performer on the Neurofinder cell identification benchmark and that it improves over algorithms based on matrix factorization.The second application is detecting aberrant agents, such as fake news sources or spam websites, based on their link behavior in networks. Across contexts, a distinguishing characteristic between normal and aberrant agents is that normal agents rarely link to aberrant ones. We refer to this phenomenon as aberrant linking behavior. We present an Markov Random Fields (MRF) formulation, with links as the pairwise similarities, that detects aberrant agents based on aberrant linking behavior and any prior information (if given). This MRF formulation is solved optimally and in polynomial time. We compare the optimal solution for the MRF formulation to well-known algorithms based on random walks. In our empirical experiment with twenty-three different datasets, the MRF method outperforms the other detection algorithms. This work represents the first use of optimization methods for detecting aberrant agents as well as the first time that MRF is applied to directed graphs