Drought Event Analysis and Projection of Future Precipitation Scenario in Abaya Chamo Sub-Basin, Ethiopia

Monthly observed and future precipitation magnitudes were subjected to statistical trend analysis to examine possible time series behavior. Future precipitation was downscaled from large-scale output through statistical downscaling. The observed and downscaled future precipitation was analyzed for drought events using the Standardized Precipitation Index (SPI) method. In the Abaya Chamo sub-basin, Ethiopia precipitation is explained by below average magnitudes in most of the low land area, characterized by moderate to extreme drought episodes. Nine drought events were discerned during the period of 1988 to 2015, i.e. once in three years, resulting in harvest failure and subsequent food insecurity. The NCEP-NCAR and CanESM2 model predictors were used to statistically downscale the precipitation data. The monthly observed and downscaled precipitation magnitudes were in good agreement. The RCP-2.6, RCP-4.5 and RCP-8.5 long-term future scenarios were computed to evaluate future drought patterns. The mean annual precipitation scenario decreased by 0.2% to 13.7%, 0.5% to 6.4% and 0.1% to 1.3% for the period from 2016 to 2040, 2050s and 2080s respectively. The increase in mean precipitation was projected to be 0.7% to 12.2%, 0.2% to 11.7% and 0.1% to 17.8% for the period from 2016 to 2040, 2050s and 2080s respectively. The present analysis may provide useful information associated to drought events to decision makers and can be used as a basis for future research in this area

SpidersRUs: Automated development of vertical search engines in different domains and languages

In this paper we discuss the architecture of a tool designed to help users develop vertical search engines in different domains and different languages. The design of the tool is presented and an evaluation study was conducted, showing that the system is easier to use than other existing tools. Categories and Subject Descriptor

APPLICATION OF SOFT COMPUTING TECHNIQUES FOR PREDICTING COOLING TIME REQUIRED DROPPING INITIAL TEMPERATURE OF MASS CONCRETE

Minimizing the thermal cracks in mass concrete at an early age can be achieved by removing the hydration heat as quickly as possible within initial cooling period before the next lift is placed. Recognizing the time needed to remove hydration heat within initial cooling period helps to take an effective and efficient decision on temperature control plan in advance. Thermal properties of concrete, water cooling parameters and construction parameter are the most influencing factors involved in the process and the relationship between these parameters are non-linear in a pattern, complicated and not understood well. Some attempts had been made to understand and formulate the relationship taking account of thermal properties of concrete and cooling water parameters. Thus, in this study, an effort have been made to formulate the relationship for the same taking account of thermal properties of concrete, water cooling parameters and construction parameter, with the help of two soft computing techniques namely: Genetic programming (GP) software “Eureqa” and Artificial Neural Network (ANN). Relationships were developed from the data available from recently constructed high concrete double curvature arch dam. The value of R for the relationship between the predicted and real cooling time from GP and ANN model is 0.8822 and 0.9146 respectively. Relative impact on target parameter due to input parameters was evaluated through sensitivity analysis and the results reveal that, construction parameter influence the target parameter significantly. Furthermore, during the testing phase of proposed models with an independent set of data, the absolute and relative errors were significantly low, which indicates the prediction power of the employed soft computing techniques deemed satisfactory as compared to the measured data

APPLICATION OF SOFT COMPUTING TECHNIQUES FOR PREDICTING COOLING TIME REQUIRED DROPPING INITIAL TEMPERATURE OF MASS CONCRETE

Minimizing the thermal cracks in mass concrete at an early age can be achieved by removing the hydration heat as quickly as possible within initial cooling period before the next lift is placed. Recognizing the time needed to remove hydration heat within initial cooling period helps to take an effective and efficient decision on temperature control plan in advance. Thermal properties of concrete, water cooling parameters and construction parameter are the most influencing factors involved in the process and the relationship between these parameters are non-linear in a pattern, complicated and not understood well. Some attempts had been made to understand and formulate the relationship taking account of thermal properties of concrete and cooling water parameters. Thus, in this study, an effort have been made to formulate the relationship for the same taking account of thermal properties of concrete, water cooling parameters and construction parameter, with the help of two soft computing techniques namely: Genetic programming (GP) software “Eureqa” and Artificial Neural Network (ANN). Relationships were developed from the data available from recently constructed high concrete double curvature arch dam. The value of R for the relationship between the predicted and real cooling time from GP and ANN model is 0.8822 and 0.9146 respectively. Relative impact on target parameter due to input parameters was evaluated through sensitivity analysis and the results reveal that, construction parameter influence the target parameter significantly. Furthermore, during the testing phase of proposed models with an independent set of data, the absolute and relative errors were significantly low, which indicates the prediction power of the employed soft computing techniques deemed satisfactory as compared to the measured data

OPTIMIZATION OF THE TEMPERATURE CONTROL SCHEME FOR ROLLER COMPACTED CONCRETE DAMS BASED ON FINITE ELEMENT AND SENSITIVITY ANALYSIS METHODS

Achieving an effective combination of various temperature control measures is critical for temperature control and crack prevention of concrete dams. This paper presents a procedure for optimizing the temperature control scheme of roller compacted concrete (RCC) dams that couples the finite element method (FEM) with a sensitivity analysis method. In this study, seven temperature control schemes are defined according to variations in three temperature control measures: concrete placement temperature, water-pipe cooling time, and thermal insulation layer thickness. FEM is employed to simulate the equivalent temperature field and temperature stress field obtained under each of the seven designed temperature control schemes for a typical overflow dam monolith based on the actual characteristics of a RCC dam located in southwestern China. A sensitivity analysis is subsequently conducted to investigate the degree of influence each of the three temperature control measures has on the temperature field and temperature tensile stress field of the dam. Results show that the placement temperature has a substantial influence on the maximum temperature and tensile stress of the dam, and that the placement temperature cannot exceed 15 °C. The water-pipe cooling time and thermal insulation layer thickness have little influence on the maximum temperature, but both demonstrate a substantial influence on the maximum tensile stress of the dam. The thermal insulation thickness is significant for reducing the probability of cracking as a result of high thermal stress, and the maximum tensile stress can be controlled under the specification limit with a thermal insulation layer thickness of 10 cm. Finally, an optimized temperature control scheme for crack prevention is obtained based on the analysis results

Experimental and Numerical Study of the Influence of Solar Radiation on the Surface Temperature Field of Low-Heat Concrete in a Pouring Block

With the influence of intense solar radiation heat and the greater temperature difference between day and night, surface concrete with a drastic temperature change can easily experience a great nonlinear temperature difference, which increases the risk of early-age concrete cracking. In this study, a distributed optical fiber temperature sensing (DTS) system is used to monitor the surface temperature gradient of concrete in real time, and a solar radiation heat monitoring test is also carried out based on the Baihetan project. Based on this, a solar radiation loading model and a finite element model of a typical pouring block considering solar radiation are established. Combined with the measured temperature data and different calculation conditions, the surface temperature changes of medium-heat and low-heat concrete experiencing solar radiation are analyzed, and the temperature control effect of surface concrete with different surface insulation measures is further analyzed. The results show that the temperature variation of medium-heat concrete at the same depth is more obvious than that of low-heat concrete. Additionally, the temperature variation of low-heat concrete is noticeable within 20 cm of the top surface. In addition, in an intense solar radiation environment, covering the concrete with a 4- or 5-centimeter-thick polyethylene coil can effectively control the surface temperature gradient and maximum daily amplitude of low-heat concrete, and surface concrete cured by running water has a significant temperature control effect. Therefore, it is suggested that 22–24 °C water temperatures be used for water curing during periods of intense solar radiation during the day and a 4-centimeter-thick polyethylene coil be used for coverage at night. These study results have been employed in the Baihetan project to optimize the temperature control scheme of the pouring blocks

Contributions of glume and awn to photosynthesis, 14C assimilates and grain weight in wheat ears under drought stress

Ear photosynthesis plays a key role in wheat photosynthesis during the grain filling stage, particularly under drought stress. Thus, dissecting the responsibilities of the glume and awn in photosynthetic carbon fixation and assimilates transportation during the grain filling stage in spikes is imperative. In this study, the detachment of the glume (DG) and awn (DA) of a wheat variety (Pubing143) was used to estimate their influences on ear photosynthesis and dry matter distribution. Radioactive carbon-14 (14C) isotope was detected by a multifunctional liquid scintillation counting system. The accumulation of 14C assimilates and their contributions to grain weight were then calculated. Under well-watered conditions, ear photosynthesis was reduced by 16.8 % and 46.2 % 25 d after anthesis (DAA) in the de-glumed control (DGC) and de-awned control (DAC) treatments, respectively, compared with the intact ear control (IEC). Under drought stress, ear photosynthesis was reduced by 46 % and 74.2 % at 25 DAA after removing the glume and awn, respectively. Under normal conditions, the number of 14C assimilates of DGC, and DAC was reduced by 14.6 % and 20.9 % in grains at 25 DAA, respectively, compared with the IEC. Compared with IED, the 14C assimilates of DGD, and DAD declined by 17.2 % and 27 %, respectively, in grains at 25 DAA under drought conditions. Under well-watered conditions, the grain weight per pot was reduced by 11.2 % and 25.4 % in the de-glumed control (DGC) and de-awned control (DAC) treatments, respectively, compared with the intact ear control (IEC). The grain weight per pot was further reduced after removing the glume and awn (16 % and 32.2 %, respectively) under drought stress. Furthermore, the awn contribution to grain weight was twice that of the glume. Our results suggest that the glume and awn of ears play prominent roles during grain filling in wheat, especially under drought stress, and that the awn is more crucial than the glume

Identification of immunity-related genes in Ostrinia furnacalis against entomopathogenic fungi by RNA-seq analysis.

BACKGROUND: The Asian corn borer (Ostrinia furnacalis (Guenée)) is one of the most serious corn pests in Asia. Control of this pest with entomopathogenic fungus Beauveria bassiana has been proposed. However, the molecular mechanisms involved in the interactions between O. furnacalis and B. bassiana are unclear, especially under the conditions that the genomic information of O. furnacalis is currently unavailable. So we sequenced and characterized the transcriptome of O. furnacalis larvae infected by B. bassiana with special emphasis on immunity-related genes. METHODOLOGY/PRINCIPAL FINDINGS: Illumina Hiseq2000 was used to sequence 4.64 and 4.72 Gb of the transcriptome from water-injected and B. bassiana-injected O. furnacalis larvae, respectively. De novo assembly generated 62,382 unigenes with mean length of 729 nt. All unigenes were searched against Nt, Nr, Swiss-Prot, COG, and KEGG databases for annotations using BLASTN or BLASTX algorithm with an E-value cut-off of 10(-5). A total of 35,700 (57.2%) unigenes were annotated to at least one database. Pairwise comparisons resulted in 13,890 differentially expressed genes, with 5,843 up-regulated and 8,047 down-regulated. Based on sequence similarity to homologs known to participate in immune responses, we totally identified 190 potential immunity-related unigenes. They encode 45 pattern recognition proteins, 33 modulation proteins involved in the prophenoloxidase activation cascade, 46 signal transduction molecules, and 66 immune responsive effectors, respectively. The obtained transcriptome contains putative orthologs for nearly all components of the Toll, Imd, and JAK/STAT pathways. We randomly selected 24 immunity-related unigenes and investigated their expression profiles using quantitative RT-PCR assay. The results revealed variant expression patterns in response to the infection of B. bassiana. CONCLUSIONS/SIGNIFICANCE: This study provides the comprehensive sequence resource and expression profiles of the immunity-related genes of O. furnacalis. The obtained data gives an insight into better understanding the molecular mechanisms of innate immune processes in O. furnacalis larvae against B. bassiana

Thermal Parameters Inversion Method for Concrete Dam Based on Optimal Temperature Measuring Point Selecting

Concrete thermal parameters in a natural pouring environment are essential inputs for simulating the temperature field of a concrete dam. This paper proposes a two-stage thermal parameters inversion method for a concrete dam based on optimal temperature measuring point selection to improve the accuracy of parameters. Firstly, a selection method of optimal measuring point for thermal parameters inversion is presented and the temperature response sensitivity of measuring points when the parameters disturb is taken as the critical evaluation index. And then, an inversion model is established based on support vector regression (SVR) and particle swarm optimization (PSO). Finally, the proposed method is applied to the thermal parameter inversion of a concrete dam. The results show that the proposed method is effective for improving the inversion accuracy and obtaining accurate parameters. The average error of the inversion results based on the SVR-PSO model is 28.54% lower than that of the genetic algorithm optimization using a back propagation neural network (BPNN-GA). Besides that, the average error of the inversion results based on the optimal measurement points is 35.57% lower than that of the nonoptimized ones