A nearly autonomous, platform-independent mobile app for manure application records

A major part of modern manure management is accurate application records; a key to their creation and maintenance is ease. This project involved the integration of existing technologies (smartphones, Bluetooth tags) in mobile web and native Android applications (apps) which enable the autogenic creation and upkeep of manure hauling records. This approach greatly improves the efficiency of the recording process which should help to improve the management of applied nutrients. Features of the app include: computation of a suggested travel speed to ensure target nutrient application (based on desired application rate and spreading width); minimized keystrokes/screen taps to accurately capture data for source, date, time, spreader, operator, georeferenced spread path, and field ; and data export for later aggregation and analysis. Autonomous operation was facilitated with a Bluetooth capable sensor tag which can automatically detect the spreader identity and spreading status (via accelerometer readings). The GPS capability of mobile devices facilitated the automatic detection of field and the creation of the georeferenced spread path. ^ The app was developed in stages and initially developed as a web app; Apache Cordova was then used to convert the code into a native app which can operate in the background, giving near autonomous operation. This app approach could be readily adapted to other field operations in agriculture and related industries

Retrospective Geospatial Modeling of PM10 Exposures from Open Burning at Joint Base Balad, Iraq

Predicting, determining, and linking theater-related source-specific exposures to health effects has proven difficult. The purpose of this research is to delineate retrospective exposure zones using spatially interpolated particulate air sampling point data from Joint Base Balad, create burn pit exposure isopleths from dispersion model outputs, and merge into a combined exposure model in GIS. Interpolated monitoring results and dispersion modeled results were combined to compare modeled exposures across base. Burn pit contribution to total PM10 was also modeled. The combined dispersion and interpolation map showed elevated concentrations within a 1 kilometer buffer of the burn pit. Buildings within this area were identified by geoprocessing. The east side of the base receives greater burn pit-specific PM10, compared to the west side. The west side showed high ambient PM10 from monitoring results, but it is unclear whether this was due to spatial or temporal effects. High temporal variability highlights the need for temporally representative sampling across the geographical area throughout the year. It was shown that source-specific individual exposure can be estimated with dispersion model isopleth maps and individual time-activity patterns. All modeling performed can all be refined with improved estimates of emission rates

(So) Big Data and the transformation of the city

The exponential increase in the availability of large-scale mobility data has fueled the vision of smart cities that will transform our lives. The truth is that we have just scratched the surface of the research challenges that should be tackled in order to make this vision a reality. Consequently, there is an increasing interest among different research communities (ranging from civil engineering to computer science) and industrial stakeholders in building knowledge discovery pipelines over such data sources. At the same time, this widespread data availability also raises privacy issues that must be considered by both industrial and academic stakeholders. In this paper, we provide a wide perspective on the role that big data have in reshaping cities. The paper covers the main aspects of urban data analytics, focusing on privacy issues, algorithms, applications and services, and georeferenced data from social media. In discussing these aspects, we leverage, as concrete examples and case studies of urban data science tools, the results obtained in the “City of Citizens” thematic area of the Horizon 2020 SoBigData initiative, which includes a virtual research environment with mobility datasets and urban analytics methods developed by several institutions around Europe. We conclude the paper outlining the main research challenges that urban data science has yet to address in order to help make the smart city vision a reality

PRESERVING THE VERNACULAR POSTINDUSTRIAL LANDSCAPE: BIG DATA GEOSPATIAL APPROACHES TO HERITAGE MANAGEMENT AND INTERPRETATION

Redundant historical industrial sites, or postindustrial landscapes, face numerous preservation challenges. Functionally obsolete, and often derelict and decaying, these cultural landscapes often retain only a fraction of their original infrastructure. With their historical interconnections made indistinct by their physical separation and obscured by the passage of time, surviving remnants are isolated and disjunct, confounding both their legibility and their consideration for formal historic preservation. Nevertheless, they persist. This dissertation presents a theoretical understanding of the nature of postindustrial landscape preservation, and argues that the material persistence of its historical constituents is the result of previously overlooked processes of informal material conservation, here termed vernacular preservation. Further, this dissertation examines ways that heritage professionals can manage and interpret these vast, complex, and shattered landscapes, using 21st-century digital and spatial tools. Confronted by ongoing depopulation and divestment, and constrained by limited financial capacity to reverse the trend of blight and property loss, communities and individuals concerned with the preservation of vernacular postindustrial landscapes face many unique management and interpretation challenges. The successful heritagization of the postindustrial landscape depends on its comprehension, and communication, as a historically complex network of systems, and I argue that utilizing advanced digital and spatial tool such as historical GIS and procedural modeling can aid communities and heritage professionals in managing, preserving, and interpreting these landscapes. This dissertation presents heritage management and interpretation strategies that emphasize the historical, but now largely missing, spatial and temporal contexts of today’s postindustrial landscape in Michigan’s Copper Country. A series of case studies illustrates the demonstrated and potential value of using a big-data, longitudinally-linked digital infrastructure, or Historical GIS (HGIS), known as the Copper Country Historical Spatial Data Infrastructure (CC-HSDI), for heritage management and interpretation. These studies support the public education and conservation goals of the communities in this nationally-significant mining region through providing accessible, engaging, and meaningful historical spatiotemporal context, and by helping to promote and encourage the ongoing management and preservation of this ever-evolving postindustrial landscape

BiOX™: a new material for industry

BiOX™ is a new type of bismuth oxide nanoparticle with distinctive bright orange colour. Its chemical The symbol is Bi2O3 and its molecular weight is 466. The material particle size is 37nm with its corresponding specific surface area of8.9m2g-1. The oxides are of tetragonal ß-Bi2O3 which through controlled synthesis procedure produce materials resemble into rosette morphology. The preparation method opted was rather simple and distinguished by monophase composition of the product, ecological safety and simple operation, therefore promise low operating cost

Drill cuttings and drilling fluids (muds) transport, fate and effects near a coral reef mesophotic zone

The study was conducted to improve knowledge and provide guidance on reducing uncertainty with impact predictions when drilling near sensitive environments. Near/Far-field hindcast modelling of cuttings/drilling fluid (mud) discharges from a floating platform was conducted, based on measured discharge amounts and durations and validated by ROV-based plume and seabed sampling. The high volume, concentration, and discharge rate water-based drilling mud discharges (mud pit dumps) were identified as the most significant dispersal risk, but longer-range movement was limited by the generation of jet-like plumes on release, which rapidly delivered muds to the seabed (80 m). Effects to the sparse benthic filter feeder communities close to the wells were observed, but no effects were seen on the epibenthic or demersal fish assemblages across the nearby mesophotic reef. For future drilling near sensitive environments, the study emphasized the need to better characterise drilling fluid discharges (volumes/discharge rates) to reduce uncertainty in modelling outputs

Book of abstracts of the 24th Euro Working Group on Transportation Meeting

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Techno-economic evaluation of biomass-to-fuels with solid-oxide electrolyzer

Thermochemical biomass-to-fuel conversion requires an increased hydrogen concentration in the syngas derived from gasification, which is currently achieved by water–gas-shift reaction and CO2 removal. State-of-the-art biomass-to-fuels convert less than half of the biomass carbon with the remaining emitted as CO2. Full conversion of biomass carbon can be achieved by integrating solid-oxide electrolyzer with different concepts: (1) steam electrolysis with the hydrogen produced injected into syngas, and (2) co-electrolysis of CO2 and H2O to convert the CO2 captured from the syngas. This paper investigates techno-economically steam- or co-electrolysis-based biomass-to-fuel processes for producing synthetic natural gas, methanol, dimethyl ether and jet fuel, considering system-level heat integration and optimal placement of steam cycles for heat recovery. The results show that state-of-the-art biomass-to-fuels achieve similar energy efficiencies of 48–51% (based on a lower heating value) for the four different fuels. The integrated concept with steam electrolysis achieves the highest energy efficiency: 68% for synthetic natural gas, 64% for methanol, 63% for dimethyl ether, and 56% for jet fuel. The integrated concept with co-electrolysis can enhance the state-of-the-art energy efficiency to 66% for synthetic natural gas, 61% for methanol, and 54% for jet fuel. The biomass-to-dimethyl ether with co-electrolysis only reaches an efficiency of 49%, due to additional heat demand. The levelized cost of the product of the integrated concepts highly depends on the price and availability of renewable electricity. The concept with co-electrolysis allows for additional operation flexibility without renewable electricity, resulting in high annual production. Thus, with limited annual available hours of renewable electricity, biomass-to-fuel with co-electrolysis is more economically convenient than that with steam electrolysis. For a plant scale of 60 MWth biomass input with the renewable electricity available for 1800 h annually, the levelized cost of product of biomass-to-synthesis-natural-gas with co-electrolysis is 35 $/GJ, 20% lower than that with steam-electrolysis

URBAN FLOOD IMPACTS, FLOOD WATER QUALITY AND RISK MAPPING OF OLODO AREA, IBADAN, NIGERIA

This study assessed urban flood impact, flood water quality and vulnerability around Olodo area of Ibadan region, Nigeria. The study employed remote sensing and GIS techniques in creating vulnerability and risk maps. Digital terrain model (DTM) was used to get the topography of the study area. Footprints of buildings along the Egberi riverbank and flood plain in Olodo were created in the GIS environment from high resolution satellite imagery. Buffering operation was conducted to classify the buildings into risk zones based on closeness to the riverbank using ArcGIS 10.0. The study revealed that 326 buildings were within the very vulnerable and vulnerable zones because they were less than 15.2m away from the riverbank. The characteristics of water quality change during the flood and non-flood periods. TSS, DO, NOD, and COD were all higher during the flood event. Microbial analysis showed that water quality levels in the floodwater exceeded water quality standards (e.g., the coliform excess from 10 to 10,000 times), and thus this may be a health risk for local people during flood events. Concentration of Escherichia coli (E. coli) ranged from 484 to 1290 cfu/100 mL during flooding compared to 192 to 295 cfu/100 mL after flood. Salmonella was found to be high ranging from 659 to 1840 cfu/100 mL during flooding compared to 530 to 1034 cfu/100 mL after flooding.     &nbsp