Nontoxic, low-cost microcapsule phase change materials (MicroPCMs) were successfully manufactured via suspension polymerization, in which n-dodecanol was employed as the core material and crosslinked polymethyl methacrylate as the wall material. Alkylphenol polyoxyethylene ether (OP-10), polysorbate-20 (Tween-20), sodium salt of styrene-maleic anhydride polymer (SMA), sodium dodecyl sulfonate (SDS), and hexadecyltrimethylammonium chloride (1631) were employed as emulsifiers to investigate the effects of the type and amount of emulsifier on MicroPCMs. In addition, the effects of different types of crosslinking agents on the fabrication of MicroPCMs were investigated. Scanning electron microscopy was used to observe the micro-morphology of MicroPCMs. The chemical structure of the MicroPCMs was detected via Fourier transform infrared spectroscopy. The thermal properties and thermal stability of the MicroPCMs were analyzed using a differential scanning calorimeter and a thermal gravimetric analyzer, respectively. Particle size distributions of the MicroPCMs were measured using a particle size analyzer. The results demonstrate that MicroPCMs with regular morphology were prepared when the mass ratio of the SMA to the oil phase was 3%, and the latent heat and yield of the MicroPCMs were 80.29 J g−1 and 84%, respectively. Furthermore, the MicroPCMs were successfully synthesized using pentaerythritol triacrylate containing the hydroxyl group as the crosslinking agent with an average particle size of 14.18 μm and excellent thermal stability.

Satellite-based soil moisture products allow direct monitoring of agricultural drought, especially in regions with sparse ground-based observations. In this study, a soil moisture drought index only based on satellite soil moisture is developed and adopted to assess drought in Mainland Southeast Asia. We report here on an exceptionally severe Mainland Southeast Asia drought in 2016, which is believed to be strongly linked to the 2015-2016 super El Niño strongly. The event began in February 2016 and lasted until May 2016, with more than 50% of the study areas suffering from moderate drought or worse at peak period. We assess the evolution of agricultural droughts (defined as prolonged deficit in soil moisture) by placing it in the context of the forcing meteorological drought (prolonged deficit in precipitation). The drought assessment using satellite soil moisture is temporally consistent with precipitation-based metrics, but allows for better mapping of the spatial and temporal patterns of how a precipitation deficit may or may not lead to a soil moisture deficit. The specific advantages of a remote-sensing approachwide-coverage without need for spatially-dense, ground-based climatological records, and sensitivity to otherwise unmeasured irrigation inputs-suggest further opportunity for drought monitoring in ungauged regions, particularly in agricultural contexts.

Qualitative analysis of verbal data is of central importance in the learning sciences. It is labor-intensive and time-consuming, however, which limits the amount of data researchers can include in studies. This work is a step towards building a statistical machine learning (ML) method for achieving an automated support for qualitative analyses of students' writing, here specifically in score laboratory reports in introductory biology for sophistication of argumentation and reasoning. We start with a set of lab reports from an undergraduate biology course, scored by a four-level scheme that considers the complexity of argument structure, the scope of evidence, and the care and nuance of conclusions. Using this set of labeled data, we show that a popular natural language modeling processing pipeline, namely vector representation of words, a.k.a word embeddings, followed by Long Short Term Memory (LSTM) model for capturing language generation as a state-space model, is able to quantitatively capture the scoring, with a high Quadratic Weighted Kappa (QWK) prediction score, when trained in via a novel contrastive learning set-up. We show that the ML algorithm approached the inter-rater reliability of human analysis. Ultimately, we conclude, that machine learning (ML) for natural language processing (NLP) holds promise for assisting learning sciences researchers in conducting qualitative studies at much larger scales than is currently possible.

This paper is about geometric calibration of the high resolution CT (Computed Tomography) system. Geometric calibration refers to the estimation of a set of parameters that describe the geometry of the CT system. Such parameters are so important that a little error of them will degrade the reconstruction images seriously, so more accurate geometric parameters are needed in the higher-resolution CT systems. But conventional calibration methods are not accurate enough for the current high resolution CT system whose resolution can reach sub-micrometer or even tens of nanometers. In this paper, we propose a new calibration method which has higher accuracy and it is based on the optimization theory. The superiority of this method is that we build a new cost function which sets up a relationship between the geometrical parameters and the binary reconstruction image of a thin wire. When the geometrical parameters are accurate, the cost function reaches its maximum value. In the experiment, we scanned a thin wire as the calibration data and a thin bamboo stick as the validation data to verify the correctness of the proposed method. Comparing with the image reconstructed with the geometric parameters calculated by using the conventional calibration method, the image reconstructed with the parameters calculated by our method has less geometric artifacts, so it can verify that our method can get more accurate geometric calibration parameters. Although we calculated only one geometric parameter in this paper, the geometric artifacts are still eliminated significantly. And this method can be easily generalized to all the geometrical parameters calibration in fan-beam or cone-beam CT systems.

<p>The latest Coupled Model Intercomparison Project Phase 6 (CMIP6) proposes new shared pathways (SSPs) that incorporate socioeconomic development with more comprehensive and scientific experimental designs; however, few studies have been performed on the projection of future multibasin hydrological changes in China based on CMIP6 models. In this paper, we use the Equidistant Cumulative Distribution Function method (EDCDFm) to perform downscaling and bias correction in daily precipitation, daily maximum temperature, and daily minimum temperature for six CMIP6 models based on the historical gridded data from the high-resolution China Meteorological Forcing Dataset (CMFD). We use the bias-corrected precipitation, temperature, and daily mean wind speed to drive the variable infiltration capacity (VIC) hydrological model, and study the changes in multiyear average annual precipitation, annual evapotranspiration and total annual runoff depth relative to the historical baseline period (1985–2014) for the Chinese mainland, basins and grid scales in the 21st century future under the SSP2-4.5 and SSP5-8.5 scenarios. The study shows that the VIC model accurately simulates runoff in major Chinese basins; the model data accuracy improves substantially after downscaling bias correction; and the future multimodel-mean multiyear average annual precipitation, annual evapotranspiration, and total annual runoff depth for the Chinese mainland and each basin increase relative to the historical period in near future (2020–2049) and far future (2070–2099) under the SSP2-4.5 and SSP5-8.5 scenarios. The new CMIP6-based results of this paper can provide a strong reference for extreme event prevention, water resource utilization and management in China in the 21st century.</p>