An expedient visible-light-promoted atom transfer radical cyclization (ATRC) reaction of unactivated alkyl iodides facilitated by earth-abundant and inexpensive manganese catalysis is described. The ...practical protocol shows a broad substrate scope and good functional-group tolerance, allowing for the preparation of synthetically valuable alkenyl iodides and diquinanes under simple and mild reaction conditions. Notably, the method provides a net redox-neutral strategy for ATRC reactions that avoids classic hydrogen atom transfer mechanism.
Resonance absorption mechanism-based metasurface absorbers can realize perfect optical absorption. Further, all-dielectric metasurface absorbers have more extensive applicability than metasurface ...absorbers that contain metal components. However, the absorption peaks of the all-dielectric metasurface absorbers reported to date are very sharp. In this work, we propose a broadband optical absorption all-dielectric metasurface, where a unit cell of this metasurface is composed of two coupled subwavelength semiconductor resonators arrayed in the direction of the wave vector and embedded in a low-index material. The results indicate that the peak absorption for more than 99% is achieved across a 60 nm bandwidth in the short-wavelength infrared region. This absorption bandwidth is three times that of a metasurface based on the conventional design scheme that consists of only a single layer of semiconductor resonators. Additionally, the coupled semiconductor resonator-based all-dielectric metasurface shows robust perfect absorption properties when the geometrical and material parameters-including the diameter, height, permittivity, and loss tangent of the resonator and the vertical and horizontal distances between the two centers of the coupled resonators-are varied over a wide range. With the convenience of use of existing semiconductor technologies in micro/nano-processing of the surface, this proposed broadband absorption all-dielectric metasurface offers a path toward realizing potential applications in numerous optical devices.
A novel copper‐catalyzed amination‐induced 1,2‐rearrangement reaction of allylic alcohols has been developed under simple and mild conditions. The commercially available N‐fluorobenzenesulfonimide ...(NFSI) is employed as an amination reagent. In this transformation, not only alkyl, but also aryl substituents can efficiently undergo 1,2‐carbon atom migration, thereby providing an efficient and powerful route to prepare a wide range of α‐quaternary Mannich bases. The reaction features a broad substrate scope, operational simplicity, and excellent practicality.
A novel copper‐catalyzed amination‐induced 1,2‐rearrangement reaction of allylic alcohols has been achieved under simple and mild conditions. This transformation exhibits a broad substrate scope and good functional group tolerance. A variety of Mannich bases containing an α‐quaternary center are readily prepared in moderate to excellent yields using the title reaction.
A one‐pot strategy for the synthesis of α‐bromo enaminones is reported. The reactions proceed via the p‐toluenesulfonic acid monohydrate (TsOH ⋅ H2O) catalyzed reactions of 3‐bromopropenals with ...anilines in dimethyl sulfoxide (DMSO) and does not require an external brominating agent. The chemoselective 1,2‐addition was accomplished by employing aniline with a sterically hindered electron‐withdrawing group attached at the ortho‐position. In addition, the reactions involving other aniline derivatives as nucleophiles have resulted in minor yields of 1,4‐addition product. The 3‐bromopropenals showed a diverse range of reactivities with aniline derivatives.
An approach to developing a blended satellite-rainfall dataset over Australia that could be suitable for operational use is presented. In this study, Global Satellite Mapping of Precipitation (GSMaP) ...satellite precipitation estimates were blended with station-based rain gauge data over Australia, using operational station data that has not been harnessed by other blended products. A two-step method was utilized. First, GSMaP satellite precipitation estimates were adjusted using rain gauge data through multiplicative ratios that were gridded using ordinary kriging. This step resulted in reducing dry biases, especially over topography. The adjusted GSMaP data was then blended with the Australian Gridded Climate Dataset (AGCD) rainfall analysis, an operational station-based gridded rain gauge dataset, using an inverse error variance weighting method to further remove biases. A validation that was performed using a 20-year range (2001 to 2020) showed the proposed approach was successful; the resulting blended dataset displayed superior performance compared to other non-gauge-based datasets with respect to stations as well as displaying more realistic patterns of rainfall than the AGCD in areas with no rain gauges. The average mean absolute error (MAE) against station data was reduced from 0.89 to 0.31. The greatest bias reductions were obtained for extreme precipitation totals and over mountainous regions, provided sufficient rain gauge availability. The newly produced dataset supported the identification of a general positive bias in the AGCD over the north-west interior of Australia.
Satellites offer a way of estimating rainfall away from rain gauges which can be utilised to overcome the limitations imposed by gauge density on traditional rain gauge analyses. In this study, ...Australian station data along with the Japan Aerospace Exploration Agency’s (JAXA) Global Satellite Mapping of Precipitation (GSMaP) and the Bureau of Meteorology’s (BOM) Australian Gridded Climate Dataset (AGCD) rainfall analysis are combined to develop an improved satellite-gauge rainfall analysis over Australia that uses the strengths of the respective data sources. We investigated a variety of correction and blending methods with the aim of identifying the optimal blended dataset. The correction methods investigated were linear corrections to totals and anomalies, in addition to quantile-to-quantile matching. The blending methods tested used weights based on the error variance to MSWEP (Multi-Source Weighted Ensemble Product), distance to the closest gauge, and the error from a triple collocation analysis to ERA5 and Soil Moisture to Rain. A trade-off between away-from- and at-station performances was found, meaning there was a complementary nature between specific correction and blending methods. The most high-performance dataset was one corrected linearly to totals and subsequently blended to AGCD using an inverse error variance technique. This dataset demonstrated improved accuracy over its previous version, largely rectifying erroneous patches of excessive rainfall. Its modular use of individual datasets leads to potential applicability in other regions of the world.
Soil moisture (SM) is critical in monitoring the time-lagged impacts of agrometeorological drought. In Australia and several south-west Pacific Small Island Developing States (SIDS), there are a ...limited number of in situ SM stations that can adequately assess soil-water availability in a near-real-time context. Satellite SM datasets provide a viable alternative for SM monitoring and agrometeorological drought provision in these regions. In this study, we investigated the performance of Soil Moisture Active Passive (SMAP), Soil Moisture and Ocean Salinity (SMOS), Soil Moisture Operational Products System (SMOPS), SM from the Advanced Microwave Scanning Radiometer 2 (AMSR-2) and SM from the Advanced Scatterometer (ASCAT) over Australia and south-west Pacific SIDS. Products were first evaluated in Australia, given the presence of several in-situ SM monitoring stations and a state-of-the-art hydrological model—the Australian Water Resources Assessment Landscape modelling system (AWRA-L). We further investigated the accuracy of SM satellite datasets in Australia and the south-west Pacific through Triple Collocation analysis with two other SM reference datasets—ERA5 reanalysis SM data and model data from the Global Land Data Assimilation System (GLDAS) dataset. All datasets have differing observation periods ranging from 1911-now, with a common period of observations between 2015–2021. Results demonstrated that ASCAT and SMOS were consistently superior in their performance. Analysis in the six south-west Pacific SIDS indicated reduced performance for all products, with ASCAT and SMOS still performing better than others for most SIDS with median R values ranging between 0.3–0.9. We conducted a case study of the 2015 El Niño and Positive Indian Ocean Dipole-induced drought in Papua New Guinea. It was shown that ASCAT is a valuable dataset indicative of agrometeorological drought for the nation, highlighting the value of using satellite SM products to provide early warning of drought in data-sparse regions in the south-west Pacific.
Automatic estimation of the poses of dairy cows over a long period can provide relevant information regarding their status and well-being in precision farming. Due to appearance similarity, cow pose ...estimation is challenging. To monitor the health of dairy cows in actual farm environments, a multicow pose estimation algorithm was proposed in this study. First, a monitoring system was established at a dairy cow breeding site, and 175 surveillance videos of 10 different cows were used as raw data to construct object detection and pose estimation data sets. To achieve the detection of multiple cows, the You Only Look Once (YOLO)v4 model based on CSPDarkNet53 was built and fine-tuned to output the bounding box for further pose estimation. On the test set of 400 images including single and multiple cows throughout the whole day, the average precision (AP) reached 94.58%. Second, the keypoint heatmaps and part affinity field (PAF) were extracted to match the keypoints of the same cow based on the real-time multiperson 2D pose detection model. To verify the performance of the algorithm, 200 single-object images and 200 dual-object images with occlusions were tested under different light conditions. The test results showed that the AP of leg keypoints was the highest, reaching 91.6%, regardless of day or night and single cows or double cows. This was followed by the AP values of the back, neck and head, sequentially. The AP of single cow pose estimation was 85% during the day and 78.1% at night, compared to double cows with occlusion, for which the values were 74.3% and 71.6%, respectively. The keypoint detection rate decreased when the occlusion was severe. However, in actual cow breeding sites, cows are seldom strongly occluded. Finally, a pose classification network was built to estimate the three typical poses (standing, walking and lying) of cows based on the extracted cow skeleton in the bounding box, achieving precision of 91.67%, 92.97% and 99.23%, respectively. The results showed that the algorithm proposed in this study exhibited a relatively high detection rate. Therefore, the proposed method can provide a theoretical reference for animal pose estimation in large-scale precision livestock farming.
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Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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