1,956 research outputs found

    Under construction: infrastructure and modern fiction

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    In this dissertation, I argue that infrastructural development, with its technological promises but widening geographic disparities and social and environmental consequences, informs both the narrative content and aesthetic forms of modernist and contemporary Anglophone fiction. Despite its prevalent material forms—roads, rails, pipes, and wires—infrastructure poses particular formal and narrative problems, often receding into the background as mere setting. To address how literary fiction theorizes the experience of infrastructure requires reading “infrastructurally”: that is, paying attention to the seemingly mundane interactions between characters and their built environments. The writers central to this project—James Joyce, William Faulkner, Karen Tei Yamashita, and Mohsin Hamid—take up the representational challenges posed by infrastructure by bringing transit networks, sanitation systems, and electrical grids and the histories of their development and use into the foreground. These writers call attention to the political dimensions of built environments, revealing the ways infrastructures produce, reinforce, and perpetuate racial and socioeconomic fault lines. They also attempt to formalize the material relations of power inscribed by and within infrastructure; the novel itself becomes an imaginary counterpart to the technologies of infrastructure, a form that shapes and constrains what types of social action and affiliation are possible

    Designing LMPA-Based Smart Materials for Soft Robotics Applications

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    This doctoral research, Designing LMPA (Low Melting Point Alloy) Based Smart Materials for Soft Robotics Applications, includes the following topics: (1) Introduction; (2) Robust Bicontinuous Metal-Elastomer Foam Composites with Highly Tunable Mechanical Stiffness; (3) Actively Morphing Drone Wing Design Enabled by Smart Materials for Green Unmanned Aerial Vehicles; (4) Dynamically Tunable Friction via Subsurface Stiffness Modulation; (5) LMPA Wool Sponge Based Smart Materials with Tunable Electrical Conductivity and Tunable Mechanical Stiffness for Soft Robotics; and (6) Contributions and Future Work.Soft robots are developed to interact safely with environments. Smart composites with tunable properties have found use in many soft robotics applications including robotic manipulators, locomotors, and haptics. The purpose of this work is to develop new smart materials with tunable properties (most importantly, mechanical stiffness) upon external stimuli, and integrate these novel smart materials in relevant soft robots. Stiffness tunable composites developed in previous studies have many drawbacks. For example, there is not enough stiffness change, or they are not robust enough. Here, we explore soft robotic mechanisms integrating stiffness tunable materials and innovate smart materials as needed to develop better versions of such soft robotic mechanisms. First, we develop a bicontinuous metal-elastomer foam composites with highly tunable mechanical stiffness. Second, we design and fabricate an actively morphing drone wing enabled by this smart composite, which is used as smart joints in the drone wing. Third, we explore composite pad-like structures with dynamically tunable friction achieved via subsurface stiffness modulation (SSM). We demonstrate that when these composite structures are properly integrated into soft crawling robots, the differences in friction of the two ends of these robots through SSM can be used to generate translational locomotion for untethered crawling robots. Also, we further develop a new class of smart composite based on LMPA wool sponge with tunable electrical conductivity and tunable stiffness for soft robotics applications. The implications of these studies on novel smart materials design are also discussed

    2017 GREAT Day Program

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    SUNY Geneseo’s Eleventh Annual GREAT Day.https://knightscholar.geneseo.edu/program-2007/1011/thumbnail.jp

    National Koala Disease Risk Analysis Report V 1.2

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    The Koala Disease Risk Analysis (KDRA) identifies the knowledge base, information gaps, risk assessments and critical control points for koala disease hazards. The national focus of the KDRA provides a clear, evidence-based assessment of koala disease which will be of value in evaluating disease risk at all regional levels and for koalas in all management situations (captive, rehabilitation and free-ranging). The KDRA is a key guiding document for actions to achieve a vision of “sustainable, resilient and healthy populations of koalas, living in positive welfare within healthy ecosystems across their range

    Synthetic Aperture Radar (SAR) Meets Deep Learning

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    This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports

    Researches regarding the evolution, magnitude and complexity of the impact generated by the economic activities on the East Jiu River

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    Ongoing development of modern society, based on consumption of goods and services, leads to the increase of compulsoriness of economic agents to face market requirements by increasing the degree of local and regional industrialization. Establishment of new economic activities generates negative pressures on the environment and surface waters, generating increased pollution, manifested by vulnerability of aquatic ecosystems to stressors. Preliminary studies carried out within the doctoral thesis entitled 'Research on the evolution, magnitude and complexity of the impact of economic activities on the East Jiu' include information on characteristic elements of the East Jiu River basin, in accordance with the Water Framework Directive 2000/60/CE. The objectives of the field research aimed to identify economic activities in the eastern Jiu Valley generating an impact on the environment (especially the mining industry, but also timber exploitation and processing, local agriculture, animal husbandry and waste storage), establishing a quarterly monitoring program of the river basin, identification of flora and fauna species and identification of areas vulnerable to potential pollution. Based on observations made in situ and on information obtained from the evolution process of the monitoring program, the appropriate methodologies for assessing physical-chemical and ecological quality of the water were selected. Study of the evolution of the impact generated by economic activities on the East Jiu was carried out by mathematical modelling, with finite volumes, of the East Jiu River basin and plotting of pollutant dispersion maps. The magnitude and complexity of impact generated by economic activities was studied by using a complex system based on fuzzy logic, designed based on interactions between natural and artificial systems, between physical-chemical indicators of water and ecosystem. The research carried out substantiates in development of necessary technical measures to reduce the impact generated by economic activities located in eastern Jiu Valley, without significantly changing the hydrodynamics of the river basin. Following research, during different research stages, methods, techniques and tools were designed and accomplished with the help of which, water and aquatic ecosystems’ quality can be assessed, as well as the impact generated by human activity on the Jiu River, at a given moment and/or continuously.:CONTENT ACKNOWLEDGEMENTS SUMMARY LIST OF FIGURES LIST OF TABLES ABBREVIATIONS INTRODUCTION PURPOSE OF THE THESIS AND RESEARCH METHODOLOGY CHAPTER 1 THE EAST JIUL RIVER HYDROGRAPHIC BASIN 1.1. Soil and subsoil of the Eastern part of Jiu Valley 1.2. Climate description of the Eastern part of Jiu Valley 1.3. Geology particularities of the Eastern part of Jiu Valley 1.4. Groundwater features of the Eastern part of Jiu Valley 1.5. Flora and fauna of the Eastern part of Jiu Valley CHAPTER 2 SOURCES OF IMPACT ON THE QUALITY OF WATER, RIPARIAN, TERRESTRIAL AND AQUATIC ECOSYSTEMS 2.1. Mining industry 2.2. Wood processing industry in the Eastern part of Jiu Valley 2.3. Urban agriculture and local animal husbandry 2.4. Inappropriate urban household waste storage CHAPTER 3 MONITORING PROGRAM AND METHODS OF EVALUATION OF THE QUALITY OF THE EAST JIUL RIVER 3.1. Establishment of monitoring (control) sections 3.2. Monitoring program of the East Jiu River basin 3.3. Sampling, transport and analysis of water samples 3.4. Methodology used to establish the water quality CHAPTER 4 QUALITY ASSESSMENT OF WATER IN THE EASTERN JIU HYDROGRAPHIC BASIN 4.1. Section 1 - Jieț River - upstream of household settlements (blank assay) 4.2. Section 2 - East Jiu River - in the area of Tirici village 4.3. Section 3 - Răscoala brook - before the confluence with East Jiu River 4.4. Section 4 - East Jiu River - after the confluence with the Răscoala brook 4.5. Section 5 - Taia River - upstream of the confluence with East Jiu River 4.6. Section 6 - East Jiu River - before the confluence with the Taia River 4.7. Section 7 - East Jiu River - after the confluence with the Taia River 4.8. Section 8 - Jiet River downstream of household settlements 4.9. Section 9 - East Jiu River - after the confluence with the Jieț River 4.10. Section 10 - East Jiu River - before the confluence with Banița River 4.11. Section 11 - RoƟia River - upstream of household settlements 4.12. Section 12 - Bănița River - after the confluence with the Roșia River 4.13. Section 13 - East Jiu River - after the confluence with the Banița River 4.14. Section 14 - Maleia River - before the confluence with East Jiu River 4.15. Section 15 - Slătioara River - before the confluence with East Jiu River 4.16. Section 16 – East Jiu River - before the confluence with West Jiu River CHAPTER 5 INFLUENCES OF PHYSICAL-CHEMICAL FACTORS ON AQUATIC ICHTHYOFAUNA IN THE EAST JIU RIVER BASIN 5.1. Total suspended solids and aquatic ecosystems 5.2. Acidity or basicity reaction of surface watercourses 5.3. Aquatic ecosystem requirements for gas oversaturation 5.4. Nitrogenous compounds in watercourse 5.5. Phenols, aquatic ecosystems and water quality CHAPTER 6 ANALYSIS OF THE IMPACT GENERATED BY ECONOMIC ACTIVITIES IN THE EASTERN PART OF JIU VALLEY 6.1. Impact analysis of mining industry in the Eastern Part of Jiu Valley 6.2. The general impact of Eastern Jiu Valley dumps to water quality 6.3. Research on effective infiltration in the Eastern part of Jiu Valley 6.4. Research on groundwater quality in the Eastern part of Jiu Valley 6.5. Analysis of the impact generated by local micro-agriculture 6.6. Analysis of the impact generated by deforestation and wood processing 6.7. Analysis of the impact generated by non-compliant landfilling of waste CHAPTER 7 EVOLUTION OF THE IMPACT GENERATED BY ECONOMIC ACTIVITIES IN THE EASTERN JIU VALLEY 7.1. Analysis of the dynamic elements of the watercourse - RMA2 mode 7.2. Analysis of pollutants concentration evolution in the water course - RMA4 module 7.3. Computational field and composition of the energy model of the East Jiu River 7.4. Extension and evolution of the impact generated by economic activities on the East Jiu River 7.5. Extension and evolution of the impact caused by organic pollution of the East Jiu River CHAPTER 8 MAGNITUDE AND COMPLEXITY OF THE IMPACT GENERATED BY ECONOMIC ACTIVITIES IN THE EASTERN JIU VALLEY 8.1. Definition of input linguistic variables 8.2. Linguistic outputs of the fuzzy interference system 8.3. Defining the Black Box set of rules 8.4. Proficiency testing of complex systems based on fuzzy logic 8.5. While it is all about the wheel do not forget about the cube CONCLUSIONS AND PERSONAL CONTRIBUTIONS REFERENCE

    Autumn 2022 Full Issue

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    Occlusion-Ordered Semantic Instance Segmentation

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    Conventional semantic ‘instance’ segmentation methods offer a segmentation mask for each object instance in an image along with its semantic class label. These methods excel in distinguishing instances, whether they belong to the same class or different classes, providing valuable information about the scene. However, these methods lack the ability to provide depth-related information, thus unable to capture the 3D geometry of the scene. One option to derive 3D information about a scene is monocular depth estimation. It predicts the absolute distance from the camera to each pixel in an image. However, monocular depth estimation has limitations. It lacks semantic information about object classes. Furthermore, it is not precise enough to reliably detect instances or establish depth order for known instances. Even a coarse 3D geometry, such as the relative depth or occlusion order of objects is useful to obtain rich 3D-informed scene analysis. Based on this, we address occlusion-ordered semantic instance segmentation (OOSIS), which augments standard semantic instance segmentation by incorporating a coarse 3D geometry of the scene. By leveraging occlusion as a strong depth cue, OOSIS estimates a partial relative depth ordering of instances based on their occlusion relations. OOSIS produces two outputs: instance masks and their classes, as well as the occlusion ordering of those predicted instances. Existing works pre-date deep learning and rely on simple visual cues such as the y-coordinate of objects for occlusion ordering. This thesis introduces two deep learning-based approaches for OOSIS. The first approach, following a top-down strategy, determines pairwise occlusion order between instances obtained by a standard instance segmentation method. However, this approach lacks global occlusion ordering consistency, having undesired cyclic orderings. Our second approach is bottom-up. It simultaneously derives instances and their occlusion order by grouping pixels into instances and assigning occlusion order labels. This approach ensures a globally consistent occlusion ordering. As part of this approach, we develop a novel deep model that predicts the boundaries where occlusion occurs plus the orientation of occlusion at the boundary, indicating which side of it occludes the other. The output of this model is utilized to obtain instances and their corresponding ordering by our proposed discrete optimization formulation. To assess the performance of OOSIS methods, we introduce a novel evaluation metric capable of simultaneously evaluating instance segmentation and occlusion ordering. In addition, we utilize standard metrics for evaluating the quality of instance masks. We also evaluate occlusion ordering consistency, and oriented occlusion boundaries. We conduct evaluations on KINS and COCOA datasets
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