Effects of experimental parameters on elemental analysis of coal by laser-induced breakdown spectroscopy

The purpose of this work is to improve the precision of the elemental analysis of coal using laser-induced breakdown spectroscopy (LIBS). The LIBS technique has the ability to allow simultaneous elemental analysis and on-line determination, so it could be used in the elemental analysis of coal. Organic components such as C, H, O, N and inorganic components such as Ca, Mg, Fe, Al, Si, Ti, Na, and K of coal have been identified. The precision of the LIBS technique depends strongly on the experimental conditions, and the choice of experimental parameters should be aimed at optimizing the repeatability of the measurements. The dependences of the relative standard deviation (RSD) of the LIBS measurements on the experimental parameters including the sample preparation parameters, lens-to-sample distance, sample operation mode, and ambient gas have been investigated. The results indicate that the precision of LIBS measurements for the coal sample can be improved by using the optimum experimental parameters

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Research on Validity Examination of Simulated Results of Eggplant Water Requirements with Drip Irrigation under Mulch in Sunlight Greenhouse

In order to explore the validity of the single and dual crop coefficient approaches in sunlight greenhouses, eggplant with drip irrigation was taken as the study object, and the crop water requirement was calculated via field experiments in a sunlight greenhouse. Results indicated that the results of the two model simulations are satisfactory. Model parameters of the single crop coefficient approach are perfect with a correlation index (R2) of 0.8374, a regression coefficient of 0.8281, an average mean absolute error of 0.2335 mm/day, an average standard error of 0.28 mm/day, a consistency index of relative unbiasedness of 0.9530, and a residual variance of 0.0785. For the dual crop coefficient approach, the model parameters had a correlation index (R2) of 0.8597, a regression coefficient of 0.8220, an average mean absolute error of 0.2196 mm/day, an average standard error of 0.27 mm/day, a consistency index of relative unbiasedness of 0.9543, and a residual variance of 0.0685. The results indicated that the dual crop coefficient model was better than the single crop coefficient model. Our research can provide some reference for the study of crop water requirements with drip irrigation under mulch in a sunlight greenhouse

Deficit drip irrigation improves kiwifruit quality and water productivity under rain-shelter cultivation in the humid area of South China

Comprehending crop responses to water deficit at different growth stages is crucial for developing effective irrigation strategies. Different water deficit treatments (WDTs) were applied to the kiwifruit vines to investigate the effect of water deficit during different growth stages on the fruit quality, yield, and water productivity (WP); subsequently, the technique for order preference by similarity to an ideal solution method (TOPSIS) was employed to determine optimal treatments for kiwifruit cultivation. A total of 17 irrigation treatments were applied, including one control treatment (CTL, full irrigation) and four WDTs (denoted as D15%, D25%, D35%, and D45% respectively) during the bud burst to leafing (I), flowering to fruit set (II), fruit expansion (III), and fruit maturation (IV) stages. Results showed that WDTs during I, II, III, and IV decreased evapotranspiration (ET) over the whole growth period of kiwifruit vines by 1.2–3.8, 1.5–4.4, 4.7–14.3, and 6.9–21.3% compared with CTL, respectively. WDTs during stages I and II increased fruit volume (Vf) and fruit weight (FW), while exhibiting no significant impact on yield, WP, and chemical quality of kiwifruit. WDTs during stage III improved fruit firmness (Fn), total soluble solids (TSS), and titratable acidity (TA); however, it also caused severe reduction in Vf, FW, yield, and WP. Appropriate WDTs during stage IV significantly improved Fn, TSS, TA, vitamin C (Vc), and WP without compromising Vf, FW, and yield of kiwifruit. The IV-D25% treatment was determined to be the optimal treatment for improving fruit quality and WP of kiwifruit while maintaining yield, which increased TSS, TA, Vc, and WP by 9.1, 6.1, 19.2, 4.6%, respectively; the combination of D25%, D25%, full irrigation, and D25% treatments during stages I, II, III, and IV should be a viable irrigation strategy to simultaneously achieve high yield, quality, and WP of kiwifruit

Genetic Algorithm-Optimized Extreme Learning Machine Model for Estimating Daily Reference Evapotranspiration in Southwest China

Reference evapotranspiration (ET0) is an essential component in hydrological and ecological processes. The Penman–Monteith (PM) model of Food and Agriculture Organization of the United Nations (FAO) model requires a number of meteorological parameters; it is urgent to develop high-precision and computationally efficient ET0 models with fewer parameter inputs. This study proposed the genetic algorithm (GA) to optimize extreme learning machine (ELM), and evaluated the performances of ELM, GA-ELM, and empirical models for estimating daily ET0 in Southwest China. Daily meteorological data including maximum temperature (Tmax), minimum temperature (Tmin), wind speed (u2), relative humidity (RH), net radiation (Rn), and global solar radiation (Rs) during 1992–2016 from meteorological stations were used for model training and testing. The results from the FAO-56 Penman–Monteith formula were used as a control group. The results showed that GA-ELM models (with R2 ranging 0.71–0.99, RMSE ranging 0.036–0.77 mm·d−1) outperformed the standalone ELM models (with R2 ranging 0.716–0.99, RMSE ranging 0.08–0.77 mm·d−1) during training and testing, both of which were superior to empirical models (with R2 ranging 0.36–0.91, RMSE ranging 0.69–2.64 mm·d−1). ET0 prediction accuracy varies with different input combination models. The machine learning models using Tmax, Tmin, u2, RH, and Rn/Rs (GA-ELM5/GA-ELM4 and ELM5/ELM4) obtained the best ET0 estimates, with R2 ranging 0.98–0.99, RMSE ranging 0.03–0.21 mm·d−1, followed by models with Tmax, Tmin, and Rn/Rs (GA-ELM3/GA-ELM2 and ELM3/ELM2) as inputs. The machine learning models involved with Rn outperformed those with Rs when the quantity of input parameters was the same. Overall, GA-ELM5 (Tmax, Tmin, u2, RH and Rn as inputs) outperformed the other models during training and testing, and was thus recommended for daily ET0 estimation. With the estimation accuracy, computational costs, and availability of input parameters accounted, GA-ELM2 (Tmax, Tmin, and Rs as inputs) was determined to be the most effective model for estimating daily ET0 with limited meteorological data in Southwest China

Genetic Algorithm-Optimized Extreme Learning Machine Model for Estimating Daily Reference Evapotranspiration in Southwest China

Reference evapotranspiration (ET0) is an essential component in hydrological and ecological processes. The Penman–Monteith (PM) model of Food and Agriculture Organization of the United Nations (FAO) model requires a number of meteorological parameters; it is urgent to develop high-precision and computationally efficient ET0 models with fewer parameter inputs. This study proposed the genetic algorithm (GA) to optimize extreme learning machine (ELM), and evaluated the performances of ELM, GA-ELM, and empirical models for estimating daily ET0 in Southwest China. Daily meteorological data including maximum temperature (Tmax), minimum temperature (Tmin), wind speed (u2), relative humidity (RH), net radiation (Rn), and global solar radiation (Rs) during 1992–2016 from meteorological stations were used for model training and testing. The results from the FAO-56 Penman–Monteith formula were used as a control group. The results showed that GA-ELM models (with R2 ranging 0.71–0.99, RMSE ranging 0.036–0.77 mm·d−1) outperformed the standalone ELM models (with R2 ranging 0.716–0.99, RMSE ranging 0.08–0.77 mm·d−1) during training and testing, both of which were superior to empirical models (with R2 ranging 0.36–0.91, RMSE ranging 0.69–2.64 mm·d−1). ET0 prediction accuracy varies with different input combination models. The machine learning models using Tmax, Tmin, u2, RH, and Rn/Rs (GA-ELM5/GA-ELM4 and ELM5/ELM4) obtained the best ET0 estimates, with R2 ranging 0.98–0.99, RMSE ranging 0.03–0.21 mm·d−1, followed by models with Tmax, Tmin, and Rn/Rs (GA-ELM3/GA-ELM2 and ELM3/ELM2) as inputs. The machine learning models involved with Rn outperformed those with Rs when the quantity of input parameters was the same. Overall, GA-ELM5 (Tmax, Tmin, u2, RH and Rn as inputs) outperformed the other models during training and testing, and was thus recommended for daily ET0 estimation. With the estimation accuracy, computational costs, and availability of input parameters accounted, GA-ELM2 (Tmax, Tmin, and Rs as inputs) was determined to be the most effective model for estimating daily ET0 with limited meteorological data in Southwest China

Pure Rotational Raman Lidar for Temperature Measurements from 5-40 Km Over Wuhan, China

In this paper a pure rotational Raman lidar (PRR) was established for the atmospheric temperature measurements from 5 km to 40 km over Wuhan, China (30.5°N, 114.5°E). To extract the expected PRR signals and simultaneously suppress the elastically backscattered light, a high-spectral resolution polychromator for light splitting and filtering was designed. Observational results revealed that the temperature difference measured by PRR lidar and the local radiosonde below 30 km was less than 3.0 K. The good agreement validated the reliability of the PRR lidar. With the 1-h integration and 150-m spatial resolution, the statistical temperature error for PRR lidar increases from 0.4 K at 10 km up to 4 K at altitudes of about 30 km. In addition, the whole night temperature profiles were obtained for study of the long-term observation of atmospheric fluctuations

Pure Rotational Raman Lidar for Temperature Measurements from 5-40 Km Over Wuhan, China

In this paper a pure rotational Raman lidar (PRR) was established for the atmospheric temperature measurements from 5 km to 40 km over Wuhan, China (30.5°N, 114.5°E). To extract the expected PRR signals and simultaneously suppress the elastically backscattered light, a high-spectral resolution polychromator for light splitting and filtering was designed. Observational results revealed that the temperature difference measured by PRR lidar and the local radiosonde below 30 km was less than 3.0 K. The good agreement validated the reliability of the PRR lidar. With the 1-h integration and 150-m spatial resolution, the statistical temperature error for PRR lidar increases from 0.4 K at 10 km up to 4 K at altitudes of about 30 km. In addition, the whole night temperature profiles were obtained for study of the long-term observation of atmospheric fluctuations

Evapotranspiration partitioning based on underlying conductance in a complex tree-grass orchard ecosystem in the humid area of southern China

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