A Randomized-Trial Evaluation of the Effect of Whose Future Is It Anyway? on Self-Determination

Promoting student involvement in planning has become best practice in the field of transition. Research documents the positive impact of such efforts on greater student involvement. Research also suggests that promoting student involvement results in greater student self-determination, but a causal link has not been established. This study used a randomized- trial, placebo control group design to study the impact of intervention with the Whose Future Is It Anyway? process on self-determination. The authors also examined the impact of intervention on transition knowledge and skills. Results indicated that instruction using the Whose Future Is It Anyway? process resulted in significant, positive differences in self- determination when compared with a placebo-control group and that students who received instruction gained transition knowledge and skills.Yeshttps://us.sagepub.com/en-us/nam/manuscript-submission-guideline

Airborne Drones for Water Quality Mapping in Inland, Transitional and Coastal Waters—MapEO Water Data Processing and Validation

Using airborne drones to monitor water quality in inland, transitional or coastal surface waters is an emerging research field. Airborne drones can fly under clouds at preferred times, capturing data at cm resolution, filling a significant gap between existing in situ, airborne and satellite remote sensing capabilities. Suitable drones and lightweight cameras are readily available on the market, whereas deriving water quality products from the captured image is not straightforward; vignetting effects, georeferencing, the dynamic nature and high light absorption efficiency of water, sun glint and sky glint effects require careful data processing. This paper presents the data processing workflow behind MapEO water, an end-to-end cloud-based solution that deals with the complexities of observing water surfaces and retrieves water-leaving reflectance and water quality products like turbidity and chlorophyll-a (Chl-a) concentration. MapEO water supports common camera types and performs a geometric and radiometric correction and subsequent conversion to reflectance and water quality products. This study shows validation results of water-leaving reflectance, turbidity and Chl-a maps derived using DJI Phantom 4 pro and MicaSense cameras for several lakes across Europe. Coefficients of determination values of 0.71 and 0.93 are obtained for turbidity and Chl-a, respectively. We conclude that airborne drone data has major potential to be embedded in operational monitoring programmes and can form useful links between satellite and in situ observations

Airborne Drones for Water Quality Mapping in Inland, Transitional and Coastal Waters-MapEO Water Data Processing and Validation

Using airborne drones to monitor water quality in inland, transitional or coastal surface waters is an emerging research field. Airborne drones can fly under clouds at preferred times, capturing data at cm resolution, filling a significant gap between existing in situ, airborne and satellite remote sensing capabilities. Suitable drones and lightweight cameras are readily available on the market, whereas deriving water quality products from the captured image is not straightforward; vignetting effects, georeferencing, the dynamic nature and high light absorption efficiency of water, sun glint and sky glint effects require careful data processing. This paper presents the data processing workflow behind MapEO water, an end-to-end cloud-based solution that deals with the complexities of observing water surfaces and retrieves water-leaving reflectance and water quality products like turbidity and chlorophyll-a (Chl-a) concentration. MapEO water supports common camera types and performs a geometric and radiometric correction and subsequent conversion to reflectance and water quality products. This study shows validation results of water-leaving reflectance, turbidity and Chl-a maps derived using DJI Phantom 4 pro and MicaSense cameras for several lakes across Europe. Coefficients of determination values of 0.71 and 0.93 are obtained for turbidity and Chl-a, respectively. We conclude that airborne drone data has major potential to be embedded in operational monitoring programmes and can form useful links between satellite and in situ observations

Airborne Drones for Water Quality Mapping in Inland, Transitional and Coastal Waters-MapEO Water Data Processing and Validation

Using airborne drones to monitor water quality in inland, transitional or coastal surface waters is an emerging research field. Airborne drones can fly under clouds at preferred times, capturing data at cm resolution, filling a significant gap between existing in situ, airborne and satellite remote sensing capabilities. Suitable drones and lightweight cameras are readily available on the market, whereas deriving water quality products from the captured image is not straightforward; vignetting effects, georeferencing, the dynamic nature and high light absorption efficiency of water, sun glint and sky glint effects require careful data processing. This paper presents the data processing workflow behind MapEO water, an end-to-end cloud-based solution that deals with the complexities of observing water surfaces and retrieves water-leaving reflectance and water quality products like turbidity and chlorophyll-a (Chl-a) concentration. MapEO water supports common camera types and performs a geometric and radiometric correction and subsequent conversion to reflectance and water quality products. This study shows validation results of water-leaving reflectance, turbidity and Chl-a maps derived using DJI Phantom 4 pro and MicaSense cameras for several lakes across Europe. Coefficients of determination values of 0.71 and 0.93 are obtained for turbidity and Chl-a, respectively. We conclude that airborne drone data has major potential to be embedded in operational monitoring programmes and can form useful links between satellite and in situ observations

Extraction Efficiency of N-13 (t1/2 = 9.96 Min) Atoms From a Graphite Target - Comparison Between Off-line and Online Obtained Results

An off-line release study N-13 (T1/2 = 9.96 min) produced by a proton induced reaction on a graphite target (POCO-graphite EDM3, density = 1.84 g/cm3, grain size approximately 3-mu-m) has been performed. The activation energy for the diffusion process is determined to be 6.15(16)x10(5) J/Mol. With this acitivation energy, extraction efficiences for N-13 are obtained at different temperatures and are compared to on-line measured extraction efficiencies

THE INFLUENCE OF SHAPE CHANGES ON THE ALPHA-DECAY IN THE BI-191(M,G)-]TL-187-]AU-183 CHAIN

The 191Bi(m,g) --> 187Tl --> 183Au alpha-decay chain has been studied using mass-separated sources. The alpha-branching ratios have been measured. Out of the reduced alpha-decay widths, it is clear that the pi-S1/2 --> pi-S1/3 and pi-h9/2 --> pi-h9/2 transitions are not hindered although they involve strong shape changes