Bayesian Data-Driven Models for Irrigation Water Management

A crucial decision in the real-time management of today’s irrigation systems involves the coordination of diversions and delivery of water to croplands. Since most irrigation systems experience significant lags between when water is diverted and when it should be delivered, an important technical innovation in the next few years will involve improvements in short-term irrigation demand forecasting. The main objective of the researches presented was the development of these critically important models: (1) potential evapotranspiration forecasting; (2) hydraulic model error correction; and (3) estimation of aggregate water demands. These tools are based on statistical machine learning or data-driven modeling. These, of wide application in several areas of engineering analysis, can be used in irrigation and system management to provide improved and timely information to water managers. The development of such models is based on a Bayesian data-driven algorithm called the Relevance Vector Machine (RVM), and an extension of it, the Multivariate Relevance Vector Machine (MVRVM). The use of these types of learning machines has the advantage of avoidance of model overfitting, high robustness in the presence of unseen data, and uncertainty estimation for the results (error bars). The models were applied in an irrigation system located in the Lower Sevier River Basin near Delta, Utah. For the first model, the proposed method allows for estimation of future crop water demand values up to four days in advance. The model uses only daily air temperatures and the MVRVM as mapping algorithm. The second model minimizes the lumped error occurring in hydraulic simulation models. The RVM is applied as an error modeler, providing estimations of the occurring errors during the simulation runs. The third model provides estimation of future water releases for an entire agricultural area based on local data and satellite imagery up to two days in advance. The results obtained indicate the excellent adequacy in terms of accuracy, robustness, and stability, especially in the presence of unseen data. The comparison provided against another data-driven algorithm, of wide use in engineering, the Multilayer Perceptron, further validates the adequacy of use of the RVM and MVRVM for these types of processes

Vicarious Calibration of sUAS Microbolometer Temperature Imagery for Estimation of Radiometric Land Surface Temperature.

In recent years, the availability of lightweight microbolometer thermal cameras compatible with small unmanned aerial systems (sUAS) has allowed their use in diverse scientific and management activities that require sub-meter pixel resolution. Nevertheless, as with sensors already used in temperature remote sensing (e.g., Landsat satellites), a radiance atmospheric correction is necessary to estimate land surface temperature. This is because atmospheric conditions at any sUAS flight elevation will have an adverse impact on the image accuracy, derived calculations, and study replicability using the microbolometer technology. This study presents a vicarious calibration methodology (sUAS-specific, time-specific, flight-specific, and sensor-specific) for sUAS temperature imagery traceable back to NIST-standards and current atmospheric correction methods. For this methodology, a three-year data collection campaign with a sUAS called AggieAir , developed at Utah State University, was performed for vineyards near Lodi, California, for flights conducted at different times (early morning, Landsat overpass, and mid-afternoon ) and seasonal conditions. From the results of this study, it was found that, despite the spectral response of microbolometer cameras (7.0 to 14.0 μm), it was possible to account for the effects of atmospheric and sUAS operational conditions, regardless of time and weather, to acquire accurate surface temperature data. In addition, it was found that the main atmospheric correction parameters (transmissivity and atmospheric radiance) significantly varied over the course of a day. These parameters fluctuated the most in early morning and partially stabilized in Landsat overpass and in mid-afternoon times. In terms of accuracy, estimated atmospheric correction parameters presented adequate statistics (confidence bounds under ±0.1 for transmissivity and ±1.2 W/m²/sr/um for atmospheric radiance, with a range of RMSE below 1.0 W/m²/sr/um) for all sUAS flights. Differences in estimated temperatures between original thermal image and the vicarious calibration procedure reported here were estimated from -5 °C to 10 °C for early morning, and from 0 to 20 °C for Landsat overpass and mid-afternoon times

Oxidation-assisted alkaline precipitation : the effect of H2O2 on the size of CuO and FeOOH nanoparticles

H2O2 was demonstrated to narrow the size distribution and decrease the size of CuO and hydrous FeOOH (2-line ferrihydrite) nanoparticles under conditions of high supersaturation. We introduce oxidation-assisted alkaline precipitation (Ox-AP) and compare it to traditional alkaline precipitation (AP). While for AP, a metal salt solution (e.g., CuCl2) is mixed with an alkali (e.g., NaOH), for Ox-AP, the more reduced form of that metal salt solution (e.g., CuCl) is simultaneously mixed with that alkali and an oxidant (e.g., H2O2). The resulting precipitates were characterized with SEM, XRD, DLS and single particle ICP-MS and shown to be nanoparticles (NPs). Ox-AP CuO NPs were up to 3 times smaller than AP NPs. Ox-AP FeOOH NPs were up to 22.5% smaller than AP NPs. We discuss and propose a possible mechanism of Ox-AP through careful consideration of the known reaction chemistry of iron and copper. We propose that an increased monomer formation rate enhances the nucleation rate, which ultimately results in smaller particles with a more narrow distribution. The more distinct effect of Ox-AP on copper, was attributed to the fast formation of the stable CuO monomer, compared to AP, where the Cu(OH)(2) and/or Cu-2(OH)(3)Cl monomers are more likely formed. Although, the exact mechanism of Ox-AP needs experimental confirmation, our results nicely demonstrate the potential of using Ox-AP to produce smaller NPs with a more narrow distribution in comparison to using AP

Supersymmetric Higgs pair discovery prospects at hadron colliders

We study the potential of hadron colliders in the search for the pair production of neutral Higgs bosons in the framework of the Minimal Supersymmetric Standard Model. Using analytical expressions for the relevant amplitudes, we perform a detailed signal and background analysis, working out efficient kinematical cuts for the extraction of the signal. The important role of squark loop contributions to the signal is emphasised. If the signal is sufficiently enhanced by these contributions, it could even be observable at the next run of the upgraded Tevatron collider in the near future. At the LHC the pair production of light and heavy Higgs bosons might be detectable simultaneously.Comment: 5 pages, hep99, 6 figures; Presented at the International Europhysics Conference on High Energy Physics, Tampere, Finland, 15-21 July 199

In memoriam of Jordi Mancebo

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Multispectral Remote Sensing for Yield Estimation Using High-Resolution Imagery From an Unmanned Aerial Vehicle

Satellites and autonomous unmanned aerial vehicles (UAVs) are two major platforms for acquiring remotely-sensed information of the earth’s surface. Due to the limitations of satellite-based imagery, such as coarse spatial resolution and fixed schedules, applications of UAVs as low-cost remote sensing systems are rapidly expanding in many research areas, particularly precision agriculture. UAVs can provide imagery with high spatial resolution (finer than 1 meter) and acquire information in visible, near infrared, and even thermal bands. In agriculture, vegetation characteristics such as health, water stress, and the amount of biomass, can be estimated using UAV imagery. In this study, three sets of high-resolution aerial imagery have been used for yield estimation based on vegetation indices. These images were captured by the Utah State University AggieAir™ UAV system flown in June 2017, August 2017, and October 2017 over a field experiment pasture site located in northern Utah. The pasture study area is primarily tall fescue. The field experiment includes 20 50 x 20-m plots, with 4 replications of 5 irrigation levels. Approximately 60 yield samples were harvested after each flight. Sample locations were recorded with high-accuracy real-time kinematic (RTK) GPS. In addition, the leaf area index (LAI) for each sample plot was measured using an optical sensor (LAI2200C) before harvesting. The relationship of yield for each sample versus vegetation indices (VIs) was explored. The VIs include the normalized difference vegetation index (NDVI), calculated using AggieAir imagery, and LAI measured using a ground-based sensor. The results of this study reveal the correlation between vegetation indices and the amount of biomass

Spatial and Temporal Analysis of Precipitation and Effective Rainfall Using Gauge Observations, Satellite, and Gridded Climate Data for Agricultural Water Management in the Upper Colorado River Basin

Accurate spatial and temporal precipitation estimates are important for hydrological studies of irrigation depletion, net irrigation requirement, natural recharge, and hydrological water balances in defined areas. This analysis supports the verification of water savings (reduced depletion)from deficit irrigation of pastures in the Upper Colorado River Basin. The study area has diverse topography with scattered fields and few precipitation gauges that are not representative of the basin.Gridded precipitation products from TRMM-3B42, PRISM, Daymet, and gauge observations were evaluated on two case studies located in Colorado and Wyoming during the 2014–2016 irrigation seasons. First, the resolution at the farm level is discussed. Next, bias occurrence at different timescales (daily to monthly) is evaluated and addressed. Then, the coverage area of the gauge station, along with the impact of the dominant wind direction on the shape of the coverage area, is evaluated. Ultimately, available actual ET maps derived from the METRIC model are used to estimate spatial effective rainfall. The results show that the spatial resolutions of TRMM and PRISM are not adequate at the farm level, while Daymet is a better fit but lacks the adequate latency versus TRMM andPRISM. When compared against local weather station records, all three spatial datasets were found to have a bias that decreases at coarser temporal intervals. However, the performance of Daymet andPRISM at the monthly time step is acceptable, and they can be used for water resource management at the farm level. The adequacy of an existing gauge station for a given farm location depends on the willingness to accept the risk of the bias associated with a non-persistent, non-symmetric gauge coverage area that is highly correlated with the dominant wind direction. Among all goodness off it statistics considered in the study, the interpretation of the summation of error makes more sense for quantifying the rainfall bias and risk for the user. Finally, based on the USDA-SCS model and actual spatial ET, overall, seasonal effective rainfall tends to be less than 60% of total rainfall for agricultural lands

ASUHAN KEPERAWATAN PADA NY. E. A. U & NY. E. B YANG MENGALAMI ABORTUS INKOMPLIT DENGAN NYERI AKUT DI RUANG CEMPAKA RSUD MGR. GABRIEL MANEK, SVD ATAMBUA

Latar Belakang:    Abortus Inkomplit adalah pengeluaran hasil konsepsi pada kehamilan sebelum 20 minggu dengan masih adanya sisa yang tertinggal di dalam uterus. Masalah yang sering muncul di abortus inkomplit adalah nyeri. Nyeri adalah perasaan yang tidak nyaman yang sangat subyektif dan hanya orang yang mengalaminya yang dapat menjelaskan dan mengevaluasi perasaan tersebut. Tujuan:     Tujuan penelitian  ini adalah untuk melaksanakan asuhan keperawatan pada klien yang mengalami abortus inkomplit dengan nyeri akut di RSUD Mgr Gabriel Manek,SVD Atambua. Metode:  jenis metode  penelitian yang digunakan adalah deskriptif dengan rancangan penelitian studi kasus. Hasil:   Hasil penelitian mendapatkan data bahwa ada perbedaan nyeri yaitu  pada rentang respon nyeri dimana klien 1 skala nyeri 6 (sedang) dan klien 2 skala nyeri 3 (nyeri ringan) sehingga dilakukan intervensi dan implementasi mengajarkan teknik relaksasi napas dalam. Setelah diberikan teknik relaksasi klien 1 nyeri tidak berkurang sedangkan klien 2 nyeri berkurang. Kesimpulan:  Berdasarkan hasil penelitian studi kasus, didapatkan perbedaan yaitu pada tindakan teknik relaksasi napas dalam pada klien 1 nyeri tidak berkurang sedangkan pada klien 2 nyeri berkurang