Academic literature on the topic 'Colon targeted tablets'

We present the use of Machine Learning algorithm of K-Nearest Neighbors (KNN) and Deep Learning algorithm of Artificial Neural Network for converting scanned images of seismic reflection data into digital seismic format. Digital seismic data format provides more flexibility for users to implement seismic processing algorithms like de-noising, enhancement and converting it into standard format, namely SEGY for further analysis such as seismic interpretation and seismic attributes generation. Varieties of seismic image in color density representation with different color maps and conventional wiggle display have been tested. Digitizing color density image consists of three main steps: recognizing Red-Green-Blue (RGB) representation in each pixel, creating a look-up table of RGB amplitude and substitute the RGB with a color that falls within the color-scale's dynamic range. Meanwhile, the approach is slightly different for image with wiggles representation. Several images’ attributes such as gradients and edge gradients are generated for better input uniqueness against known target amplitudes during model establishment. This pre-trained model is then used for predicting seismic amplitudes at specific pixel location in respect to a set of image attributes.The outcome of the conversion shows promising results. A qualitative interpretation for similarity check between input and output in terms of seismic events, horizon, faults, stratigraphy and other geological/geophysical features are used to validate the quality of digitized images.

Objectives:The presented work arises in the context of designing for individuals with visual impairment, specifically we refer to a target group of children from two to seven years of age. The study was conducted with the contribution of the XXX research group within the XXX project funded by the European Community (2020-2023), with the aim of creating a specific curriculum for training the profession of a visual rehabilitator for children. In this perspective, the paper shows a practical case study carried out through the simulation technique at XXX.The approach that would be applied to this course is innovative, as it involves the immersive and experiential participation of students and the adoption of the most advanced training technologies in the field of simulation.In order to proceed with the implementation of the experiment, the contribution of multiple figures, such as expert designers, doctors, ophthalmologists, psychologists, and visual rehabilitators, was planned, creating a multidisciplinary and interdisciplinary study. The ultimate goal is to provide students with standardized criteria for assessing and intervening appropriately within the living spaces of the child with visual impairment.Methods:The preliminary phase involved the simulation of a typical home environment for the considered target, specifically the set-up of a children's bedroom. The set-up of XXX is based on a movie set. Equipped with the most advanced technologies, it allows for the recording and creation of digital content (real-time recordings) and the configuration of environments, such as the arrangement and number of furnishings and the variation of ambient brightness, which are fundamental elements to ensure the autonomy of actions such as eating, playing, washing and orienting oneself, planned in each educational module.Specifically, the bedroom was set up with basic and standard elements, trying to recreate a real context in the most realistic way. The furniture included a bed, a bedside table, a small table for playing, a small chair, a bookshelf, a desk, various soft toys and games, two closets with sliding doors, and a desk chair.The entrance door and a window were also simulated in the room.The placement of the various elements within the room was designed based on the needs of visually impaired and blind children and the experimentation was divided into two moments characterized by two different setups. The first set-up involved a glaring light setting and the selection of objects that were difficult to distinguish, then the environment was modified through the use of contrasting elements, visual markers, and appropriate lighting through dimmable lights.Results:The experiment, which took place as part of the activities of TWP4 - Task 4.2 Lesson Plan Development: guides and plans for teachers supporting the localization of the curriculum, was carried out by a series of students from various European countries who participated in both paths (first the one with the impediments and then the simplified one) wearing specific glasses capable of simulating visual impairment.During the experimentation, we asked the students to complete some tasks, such as writing their names on a sheet of paper, turning off the light, searching for some object and taking it to other places in the room.At the end of each path, and then once the tasks had been completed, the participants had to fill in an accessibility evaluation form through which define the level of difficulty of the tasks and give suggestions for improving the existing set-up in terms of placement or choice of furniture, materials, lighting, color contrasts, pathways, and tactility.Through this experimentation, the data collected enabled the research team to understand possible modifications to be made to the environment and to identify elements that could make the experiment reproducible in various domestic settings, in order to define a protocol for adapting the spaces to the needs of the target audience.

The Gulf Islands National Seashore (GUIS) vegetation inventory project classified and mapped vegetation on park-owned lands within the administrative boundary and estimated thematic map accuracy quantitatively. The project began in June 2016. National Park Service (NPS) Vegetation Mapping Inventory Program provided technical guidance. The overall process included initial planning and scoping, imagery procurement, field data collection, data analysis, imagery interpretation/classification, accuracy assessment (AA), and report writing and database development. Initial planning and scoping meetings took place during May, 2016 in Ocean Springs, Mississippi where representatives gathered from GUIS, the NPS Gulf Coast Inventory and Monitoring Network, and Colorado State University. Primary imagery used for interpretation was 4-band (RGB and CIR) orthoimages from 2014 and 2016 with resolutions of 15 centimeters (cm) (Florida only) and 30 cm. Supplemental imagery with varying coverage across the study area included National Aerial Imagery Program 50 cm imagery for Mississippi (2016) and Florida (2017), 15 and 30 cm true color Digital Earth Model imagery for Mississippi (2016 and 2017), and current and historical true-color Google Earth and Bing Map imagery. National Oceanic Atmospheric Administration National Geodetic Survey 30 cm true color imagery from 2017 (post Hurricane Nate) supported remapping the Mississippi barrier islands after Hurricane Nate. The preliminary vegetation classification included 59 United States National Vegetation Classification (USNVC) associations. Existing vegetation and mapping data combined with vegetation plot data contributed to the final vegetation classification. Quantitative classification using hierarchical clustering and professional expertise was supported by vegetation data collected from 250 plots in 2016 and 29 plots in 2017 and 2018, as well as other observational data. The final vegetation classification includes 39 USNVC associations and 5 park special types; 18 forest and woodland, 7 shrubland, 17 herbaceous, and 2 sparse vegetation types were identified. The final GUIS map consists of 38 map classes. Land cover classes include four types: non-vegetated barren land / borrow pit, developed open space, developed low – high intensity, and water/ocean. Of the 34 vegetation map classes, 26 represent a single USNVC association/park special, six map classes contain two USNVC associations/park specials, and two map classes contain three USNVC associations/park specials. Forest and woodland associations had an abundance of sand pine (Pinus clausa), slash pine (Pinus elliottii), sand live oak (Quercus geminata), yaupon (Ilex vomitoria), wax myrtle (Morella cerifera), and saw palmetto (Serenoa repens). Shrubland associations supported dominant species such as eastern baccharis (Baccharis halimifolia), yaupon (Ilex vomitoria), wax myrtle (Morella cerifera), saw palmetto (Serenoa repens), and sand live oak (Quercus geminata). Herbaceous associations commonly included camphorweed (Heterotheca subaxillaris), needlegrass rush (Juncus roemerianus), bitter seabeach grass (Panicum amarum var. amarum), gulf bluestem (Schizachyrium maritimum), saltmeadow cordgrass (Spartina patens), and sea oats (Uniola paniculata). The final GUIS vegetation map consists of 1,268 polygons totaling 35,769.0 hectares (ha) or 88,387.2 acres (ac). Mean polygon size excluding water is 3.6 ha (8.9 ac). The most abundant land cover class is open water/ocean which accounts for approximately 31,437.7 ha (77,684.2 ac) or 87.9% of the total mapped area. Natural and ruderal vegetation consists of 4,176.8 ha (10,321.1 ac) or 11.6% of the total area. Within the natural and ruderal vegetation types, herbaceous types are the most extensive with 1945.1 ha (4,806.4 ac) or 46.5%, followed by forest and woodland types with 804.9 ha (1,989.0 ac) or 19.3%, sparse vegetation types with 726.9 ha (1,796.1 ac) or 17.4%, and shrubland types with 699.9 ha (1,729.5 ac) or 16.8%. Developed open space, which can include a matrix of roads, parking lots, park-like areas and campgrounds account for 153.8 ha (380.0 ac) or 0.43% of the total mapped area. Artificially non-vegetated barren land is rare and only accounts for 0.74 ha (1.82 ac) or 0.002% of the total area. We collected 701 AA samples to evaluate the thematic accuracy of the vegetation map. Final thematic accuracy, as a simple proportion of correct versus incorrect field calls, is 93.0%. Overall weighted map class accuracy is 93.6%, where the area of each map class was weighted in proportion to the percentage of total park area. This method provides more weight to larger map classes in the park. Each map class had an individual thematic accuracy goal of at least 80%. The hurricane impact area map class was the only class that fell below this target with an accuracy of 73.5%. The vegetation communities impacted by the hurricane are highly dynamic and regenerated quickly following the disturbance event, contributing to map class disagreement during the accuracy assessment phase. No other map classes fell below the 80% accuracy threshold. In addition to the vegetation polygon database and map, several products to support park resource management are provided including the vegetation classification, field key to the associations, local association descriptions, photographic database, project geodatabase, ArcGIS .mxd files for map posters, and aerial imagery acquired for the project. The project geodatabase links the spatial vegetation data layer to vegetation classification, plot photos, project boundary extent, AA points, and the PLOTS database. The geodatabase includes USNVC hierarchy tables allowing for spatial queries of data associated with a vegetation polygon or sample point. All geospatial products are projected using North American Datum 1983 (NAD83) in Universal Transverse Mercator (UTM) Zone 16 N. The final report includes methods and results, contingency tables showing AA results, field forms, species list, and a guide to imagery interpretation. These products provide useful information to assist with management of park resources and inform future management decisions. Use of standard national vegetation classification and mapping protocols facilitates effective resource stewardship by ensuring the compatibility and widespread use throughout the NPS as well as other federal and state agencies. Products support a wide variety of resource assessments, park management and planning needs. Associated information provides a structure for framing and answering critical scientific questions about vegetation communities and their relationship to environmental processes across the landscape.

The Big Thicket National Preserve (BITH) vegetation inventory project classified and mapped vegetation within the administrative boundary and estimated thematic map accuracy quantitatively. National Park Service (NPS) Vegetation Mapping Inventory Program provided technical guidance. The overall process included initial planning and scoping, imagery procurement, vegetation classification field data collection, data analysis, imagery interpretation/classification, accuracy assessment (AA), and report writing and database development. Initial planning and scoping meetings took place during May, 2016 in Kountze, Texas where representatives gathered from BITH, the NPS Gulf Coast Inventory and Monitoring Network, and Colorado State University. The project acquired new 2014 orthoimagery (30-cm, 4-band (RGB and CIR)) from the Hexagon Imagery Program. Supplemental imagery for the interpretation phase included Texas Natural Resources Information System (TNRIS) 2015 50 cm leaf-off 4-band imagery from the Texas Orthoimagery Program (TOP), Farm Service Agency (FSA) 100-cm (2016) and 60 cm (2018) National Aerial Imagery Program (NAIP) imagery, and current and historical true-color Google Earth and Bing Maps imagery. In addition to aerial and satellite imagery, 2017 Neches River Basin Light Detection and Ranging (LiDAR) data was obtained from the United States Geological Survey (USGS) and TNRIS to analyze vegetation structure at BITH. The preliminary vegetation classification included 110 United States National Vegetation Classification (USNVC) associations. Existing vegetation and mapping data combined with vegetation plot data contributed to the final vegetation classification. Quantitative classification using hierarchical clustering and professional expertise was supported by vegetation data collected from 304 plots surveyed between 2016 and 2019 and 110 additional observation plots. The final vegetation classification includes 75 USNVC associations and 27 park special types including 80 forest and woodland, 7 shrubland, 12 herbaceous, and 3 sparse vegetation types. The final BITH map consists of 51 map classes. Land cover classes include five types: pasture / hay ground agricultural vegetation; non ? vegetated / barren land, borrow pit, cut bank; developed, open space; developed, low ? high intensity; and water. The 46 vegetation classes represent 102 associations or park specials. Of these, 75 represent natural vegetation associations within the USNVC, and 27 types represent unpublished park specials. Of the 46 vegetation map classes, 26 represent a single USNVC association/park special, 7 map classes contain two USNVC associations/park specials, 4 map classes contain three USNVC associations/park specials, and 9 map classes contain four or more USNVC associations/park specials. Forest and woodland types had an abundance of Pinus taeda, Liquidambar styraciflua, Ilex opaca, Ilex vomitoria, Quercus nigra, and Vitis rotundifolia. Shrubland types were dominated by Pinus taeda, Ilex vomitoria, Triadica sebifera, Liquidambar styraciflua, and/or Callicarpa americana. Herbaceous types had an abundance of Zizaniopsis miliacea, Juncus effusus, Panicum virgatum, and/or Saccharum giganteum. The final BITH vegetation map consists of 7,271 polygons totaling 45,771.8 ha (113,104.6 ac). Mean polygon size is 6.3 ha (15.6 ac). Of the total area, 43,314.4 ha (107,032.2 ac) or 94.6% represent natural or ruderal vegetation. Developed areas such as roads, parking lots, and campgrounds comprise 421.9 ha (1,042.5 ac) or 0.9% of the total. Open water accounts for approximately 2,034.9 ha (5,028.3 ac) or 4.4% of the total mapped area. Within the natural or ruderal vegetation types, forest and woodland types were the most extensive at 43,022.19 ha (106,310.1 ac) or 94.0%, followed by herbaceous vegetation types at 129.7 ha (320.5 ac) or 0.3%, sparse vegetation types at 119.2 ha (294.5 ac) or 0.3%, and shrubland types at 43.4 ha (107.2 ac) or 0.1%. A total of 784 AA samples were collected to evaluate the map?s thematic accuracy. When each AA sample was evaluated for a variety of potential errors, a number of the disagreements were overturned. It was determined that 182 plot records disagreed due to either an erroneous field call or a change in the vegetation since the imagery date, and 79 disagreed due to a true map classification error. Those records identified as incorrect due to an erroneous field call or changes in vegetation were considered correct for the purpose of the AA. As a simple plot count proportion, the reconciled overall accuracy was 89.9% (705/784). The spatially-weighted overall accuracy was 92.1% with a Kappa statistic of 89.6%. This method provides more weight to larger map classes in the park. Five map classes had accuracies below 80%. After discussing preliminary results with the parl, we retained those map classes because the community was rare, the map classes provided desired detail for management or the accuracy was reasonably close to the 80% target. When the 90% AA confidence intervals were included, an additional eight classes had thematic accruacies that extend below 80%. In addition to the vegetation polygon database and map, several products to support park resource management include the vegetation classification, field key to the associations, local association descriptions, photographic database, project geodatabase, ArcGIS .mxd files for map posters, and aerial imagery acquired for the project. The project geodatabase links the spatial vegetation data layer to vegetation classification, plot photos, project boundary extent, AA points, and PLOTS database sampling data. The geodatabase includes USNVC hierarchy tables allowing for spatial queries of data associated with a vegetation polygon or sample point. All geospatial products are projected using North American Datum 1983 (NAD83) in Universal Transverse Mercator (UTM) Zone 15 N. The final report includes methods and results, contingency tables showing AA results, field forms, species list, and a guide to imagery interpretation. These products provide useful information to assist with management of park resources and inform future management decisions. Use of standard national vegetation classification and mapping protocols facilitates effective resource stewardship by ensuring the compatibility and widespread use throughout NPS as well as other federal and state agencies. Products support a wide variety of resource assessments, park management and planning needs. Associated information provides a structure for framing and answering critical scientific questions about vegetation communities and their relationship to environmental processes across the landscape.