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•The multivariable linear regression was uses to establish composite drought indices.•Mediator and moderator variables were introduced to optimize the regression model.•MCDI-1 and ...MCDI-9 were good indicators for meteorological and agricultural drought.
Drought is one of the most frequent disasters occurring in North China and has a great influence on agriculture, ecology and economy. To monitor drought of typical dry areas in North China, Shandong Province, this paper proposed composite drought indices using multivariable linear regression (MCDIs) to integrate Tropical Rainfall Measuring Mission (TRMM) derived precipitation, Global Land Data Assimilation System Version 2.1 (GLDAS-2.1) derived soil moisture, Moderate Resolution Imaging Spectroradiometer (MODIS) derived land surface temperature (LST) and normalized difference vegetation index (NDVI) from 2013 to 2017 (March to September). Pearson correlation analyses were performed between single remote sensing drought indices and in-situ drought indices, standardized precipitation evapotranspiration index (SPEI), in different time scales to assess the capability of single indices over Shandong Province. The multivariable linear regression method was used to established MCDIs, and mediator and moderator variables were introduced to optimize the model. The correlation coefficients (r) between MCDIs and SPEIs was higher than that between each single index and SPEIs. Additionally, when we investigate the correlations of different MCDIs with both standardized precipitation index (SPI) and moisture index (MI), the highest r values with both 1-month SPI and MI were acquired by the MCDI based on 1-month SPEI (MCDI-1). This suggested MCDI-1 was suitable to monitor meteorological drought. Also, the comparison between MCDI based on 9-month SPEI (MCDI-9) and soil moisture showed MCDI-9 was a good indicator for agricultural drought. Therefore, multivariable linear regression and MCDIs were recommended to be an effective method and indices for monitoring drought across Shandong Province and similar areas.
The short-term load forecasting is crucial in the power system operation and control. However, due to its nonstationary and complicated random features, an accurate forecast of the load behavior is ...challenging. An improved short-term load forecasting method is proposed in this article. At first, the load is decomposed into different frequency components varying from the low to high levels realized by the ensemble empirical-mode decomposition algorithm. Then, the smooth and periodic low-frequency components are predicted by the multivariable linear regression method while maintaining the efficient computation capacity, while the high-frequency components with strong randomness are forecasted by the long short-term memory neural network algorithms. Thus, the actual load behavior is obtained by combining these two methods. Finally, the proposed method is validated by experiments, in which the tested data from the west area of China, Uzbekistan, and PJM Interconnection (USA) are used. The prediction of the load behavior is accurate globally along with the local details, as presented in the experiments, which verify the effectiveness of the proposed method.
The rapid, accurate, and economical estimation of crop above-ground biomass at the farm scale is crucial for precision agricultural management. The unmanned aerial vehicle (UAV) remote-sensing system ...has a great application potential with the ability to obtain remote-sensing imagery with high temporal-spatial resolution. To verify the application potential of consumer-grade UAV RGB imagery in estimating maize above-ground biomass, vegetation indices and plant height derived from UAV RGB imagery were adopted. To obtain a more accurate observation, plant height was directly derived from UAV RGB point clouds. To search the optimal estimation method, the estimation performances of the models based on vegetation indices alone, based on plant height alone, and based on both vegetation indices and plant height were compared. The results showed that plant height directly derived from UAV RGB point clouds had a high correlation with ground-truth data with an R2 value of 0.90 and an RMSE value of 0.12 m. The above-ground biomass exponential regression models based on plant height alone had higher correlations for both fresh and dry above-ground biomass with R2 values of 0.77 and 0.76, respectively, compared to the linear regression model (both R2 values were 0.59). The vegetation indices derived from UAV RGB imagery had great potential to estimate maize above-ground biomass with R2 values ranging from 0.63 to 0.73. When estimating the above-ground biomass of maize by using multivariable linear regression based on vegetation indices, a higher correlation was obtained with an R2 value of 0.82. There was no significant improvement of the estimation performance when plant height derived from UAV RGB imagery was added into the multivariable linear regression model based on vegetation indices. When estimating crop above-ground biomass based on UAV RGB remote-sensing system alone, looking for optimized vegetation indices and establishing estimation models with high performance based on advanced algorithms (e.g., machine learning technology) may be a better way.
Microplastics (MPs), a growing class of emerging pollutants in the environment, have attracted widespread attention due to their adsorption properties. Recent research on MPs has mainly concentrated ...on seawater, and little work has been conducted on freshwater. Investigating and predicting the adsorption behavior of organic pollutants by MPs are necessary in freshwater. In this study, the adsorption behavior of 13 organic chemicals by polyethylene (PE) and chlorinated polyethylene (CPE) MPs was determined under freshwater conditions. Results shows the majority of the organic chemicals exhibit no distinctive differences in their adsorption on two MPs. However, the adsorption of polycyclic aromatic hydrocarbons and chlorobenzene on CPE is obviously stronger than that on PE, and the result is a counter for two pesticides. Quantitative structure activity relationship (QSAR) analysis was performed for the prediction of adsorption capacity. A QSAR model with acceptable performance (R2 = 0.8586) was built to predict the adsorptive affinity (expressed as logKd) of organic compounds on the PE MPs via multivariable linear regression (MLR) on forty-nine determined and collected data. The octanol/water partition coefficient (logKow) and excess molar refractive index (E) play dominant roles in the model. A QSAR model with satisfactory performance (R2 = 0.9302) was also established for logKd values from CPE MPs in freshwater by using 13 adsorption data determined. The logKow and most negative charge on Cl atom (Q-max,cl) play decisive roles in the adsorption. The findings can provide a scientific basis for the risk assessment of waters contaminated by MPs and organic pollutants.
•The adsorption of organics on microplastics was investigated in freshwater.•The adsorption affinity of 13 organic compounds on PE and CPE was compared.•QSAR models were established to predict the adsorption affinity.•The structural factors of compounds ascribing to adsorption were discussed.
•The multicollinearity among traditional input variables of cooling load predictions is proven.•PCA is applicable to avoid multicollinearity and improve prediction accuracy.•Including CEHT as an ...input variable is an effective way to improve prediction accuracy.•Dynamic two-step correction is proposed and is proven to be an effective way to improve prediction accuracy.•Dynamic model building is proven to be an effective measure to improve prediction accuracy.
The cooling load prediction of heating, ventilating and air-conditioning (HVAC) systems in office buildings is fundamental work for optimizing the operation of HVAC systems. In this paper, an improved multivariable linear regression model is proposed to predict the daily mean cooling load of office buildings in which three main measures, including the principal component analysis (PCA) of meteorological factors, cumulative effect of high temperature (CEHT) and dynamic two-step correction, are used to improve prediction accuracy. The site measured cooling load of two office buildings in Tianjin is used to validate the model and evaluate the prediction accuracy. Meanwhile, four contrast models with one or two of the three measures are also built. A comparison among the models proves that a combination of the three measures could effectively improve the prediction accuracy. The predicted load of the proposed model has acceptable agreement with actual load, where the mean absolute relative error is less than 8%.
•Relationships of green leaf VNS-1/SWIR-2 spectra developed with reference databases.•Noisy SWIR-2 modeled using statistics/mathematics of VNS-1/SWIR-2 relationships.•Comprehensive green leaf ...spectral reference database improves SWIR-2 reconstruction.
Leaf spectra (reflectance and transmittance) are commonly measured using a portable spectroradiometer and an integrating sphere or contact probe with an artificial light source. However, spectral data may be obscured due to water vapor and low signal-to-noise ratios, especially in the shortwave infrared-2 region (SWIR-2, 2001–2500 nm). Therefore, we proposed a spectral reconstruction approach to retrieve noise-free SWIR-2 fresh green leaf spectra by referring to the previously published quality-controlled fresh green leaf spectral reference databases. We processed 896 pairs of fresh tea (Camellia sinensis var. sinensis) leaf reflectance/transmittance from Alishan in central Taiwan. We selected a subset (500–1900 nm) of the spectra in the visible, near-infrared, and SWIR-1 regions (VNS-1) that were relatively insensitive to atmospheric conditions. We matched those spectra with publicly available reference green leaf spectral databases, and selected the one that was most similar to each Alishan VNS-1 spectrum. We then used multivariable linear regression, linear parameter multiplication and spectral reversion to reconstruct SWIR-2 spectra. Finally, we used another set of green leaf spectral databases to assess the performance of the proposed method. The performance of the reconstruction approach was satisfactory, with mean (± standard deviation) root-mean-square errors (RMSEs) of 0.0041 ± 0.0019 (reflectance) and 0.0054 ± 0.0027 (transmittance) for each spectrum and RMSEs of 0.0058 ± 0.0027 (reflectance) and 0.0055 ± 0.0043 (transmittance) for each SWIR-2 band. The proposed approach successfully modeled SWIR-2, which could be further improved with the availability of a more comprehensive set of green leaf reference spectral databases.
Sequencing cell-free DNA in maternal plasma is an effective noninvasive prenatal testing technique that has been used in fetal aneuploidy screening worldwide. However, its clinical application is ...limited by the low fetal fraction (<4%) of cell-free DNA in many singleton pregnancies, which usually results in screen failures or no calls. In addition, dizygotic twin contributions of cell-free DNA into the maternal circulation can vary by 2-fold, complicating the quantitative diagnosis of fetal aneuploidy.
We performed semiconductor sequencing of shorter fragments (107–145 bp) of circulating cell-free DNA to improve the fetal DNA fraction at lower uniquely mapped reads (1–8.5 MB) to reduce the probability of no calls.
We identified 2903 plasma samples from pregnant women, including 86 dizygotic twin pregnancy, that were collected at a single prenatal diagnostic center between October 2015 and July 2018. Size-selection noninvasive prenatal testing for fetal aneuploidy was applied to 2817 plasma samples (1409 male and 1408 female fetuses) and 86 dizygotic twins using noninvasive prenatal testing with and without size selection. Shorter fragment size was the key factor affecting fetal fraction in multivariable linear regression models as well as to validate the accuracy of the size selection for noninvasive prenatal testing.
Analysis of 1409 male fetuses by multivariable linear regression showed that maternal age, body mass index, number of pregnancies, average cell-free DNA size, maternal plasma cell-free DNA concentration, library concentration, and multiple gestation were negatively correlated with fetal fraction. Conversely, gestational age and uniquely mapped reads were positively correlated with fetal fraction. Compared with ≤120 bp cell-free DNA fragments, mean fetal fraction differences were –3.57% (95% confidence interval, –5.95% to –1.19%), for 121–130 bp, –9.52% (95% confidence interval, –11.89% to –7.14%) for 131–140 bp, and –14.47% (95% confidence interval, –18.37% to –10.58%) for ≥141 bp (Ptrend < .0001). These results were statistically significant after multivariable adjustments in models for fetal fraction. Meanwhile, results from 86 dizygotic twins showed that the size selection increased the fetal fraction by ∼3.2-fold, with 98.8% of samples reaching a fetal fraction >10%. Improved detection accuracy was also achieved.
Sequencing shorter cell-free DNA fragments is a reasonable strategy to reduce the probability of no calls results because of low fetal fraction and should be recommended to pregnant subjects.
OBJECTIVES:The objective of this study was to identify parameters which are related to speech recognition in quiet and in noise of cochlear implant (CI) users. These parameters may be important to ...improve current fitting practices.
DESIGN:Adult CI users who visited the Amsterdam UMC, location VUmc, for their annual follow-up between January 2015 and December 2017 were retrospectively identified. After applying inclusion criteria, the final study population consisted of 138 postlingually deaf adult Cochlear CI users. Prediction models were built with speech recognition in quiet and in noise as the outcome measures, and aided sound field thresholds, and parameters related to fitting (i.e., T and C levels, dynamic range DR), evoked compound action potential thresholds and impedances as the independent variables. A total of 33 parameters were considered. Separate analyses were performed for postlingually deafened CI users with late onset (LO) and CI users with early onset (EO) of severe hearing impairment.
RESULTS:Speech recognition in quiet was not significantly different between the LO and EO groups. Speech recognition in noise was better for the LO group compared with the EO group. For CI users in the LO group, mean aided thresholds, mean electrical DR, and measures to express the impedance profile across the electrode array were identified as predictors of speech recognition in quiet and in noise. For CI users in the EO group, the mean T level appeared to be a significant predictor in the models for speech recognition in quiet and in noise, such that CI users with elevated T levels had worse speech recognition in quiet and in noise.
CONCLUSIONS:Significant parameters related to speech recognition in quiet and in noise were identifiedaided thresholds, electrical DR, T levels, and impedance profiles. The results of this study are consistent with previous study findings and may guide audiologists in their fitting practices to improve the performance of CI users. The best performance was found for CI users with aided thresholds around the target level of 25 dB HL, and an electrical DR between 40 and 60 CL. However, adjustments of T and/or C levels to obtain aided thresholds around the target level and the preferred DR may not always be acceptable for individual CI users. Finally, clinicians should pay attention to profiles of impedances other than a flat profile with mild variations.
Understanding the complex mechanisms of climate change and its environmental consequences requires the collection and subsequent analysis of geospatial data from observations and numerical modeling. ...Multivariable linear regression and mixed-effects models were used to estimate daily surface fine particulate matter (PM2.5) levels in the megacity of Pakistan. The main parameters for the multivariable linear regression model were the 10-km-resolution satellite aerosol optical depth (AOD) and daily averaged meteorological parameters from ground monitoring (temperature, dew point, relative humidity, wind speed, wind direction, and planetary boundary layer height). Ground-based PM2.5 was measured in two stations in the city, Korangi (industrial/residential) and Tibet Center (commercial/residential). The initial linear regression model was modified using a stepwise selection procedure and adding interaction parameters. Finally, the modified model showed a strong correlation between the PM2.5–satellite AOD and other meteorological parameters (R2 = 0.88–0.92 and p-value = 10−7 depending on the season and station). The mixed-effect technique improved the model performance by increasing the R2 values to 0.99 and 0.93 for the Korangi and Tibet Center sites, respectively. Cross-validation methods were used to confirm the reliability of the model to predict PM2.5 after 10 years.
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