Multi-wheat-model ensemble responses to interannual climate variability

We compare 27 wheat models' yield responses to interannual climate variability, analyzed at locations in Argentina, Australia, India, and The Netherlands as part of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Pilot. Each model simulated 1981–2010 grain yield, and we evaluate results against the interannual variability of growing season temperature, precipitation, and solar radiation. The amount of information used for calibration has only a minor effect on most models' climate response, and even small multi-model ensembles prove beneficial. Wheat model clusters reveal common characteristics of yield response to climate; however models rarely share the same cluster at all four sites indicating substantial independence. Only a weak relationship (R² ≤ 0.24) was found between the models' sensitivities to interannual temperature variability and their response to long-term warming, suggesting that additional processes differentiate climate change impacts from observed climate variability analogs and motivating continuing analysis and model development efforts.     

Global sensitivity analysis of outputs over rice-growth process in ORYZA model

Dynamic crop models usually have a complex structure and a large number of parameters. Those parameter values usually cannot be directly measured, and they vary with crop cultivars, environmental conditions and managements. Thus, parameter estimation and model calibration are always difficult issues for crop models. Therefore, the quantification of parameter sensitivity and the identification of influential parameters are very important and useful. In this work, late-season rice was simulated with meteorological data in Nanchang, China. Furthermore, we conducted a sensitivity analysis of 20 selected parameters in ORYZA_V3 using the Extended FAST method. We presented the sensitivity results for four model outputs (LAI, WAGT, WST and WSO) at four development stages and the results for yield. Meanwhile, we compared the differences among the sensitivity results for the model outputs simulated in cold, normal and hot years. The uncertainty of output variables derived from parameter variation and weather conditions were also quantified. We found that the development rates, RGRLMN and FLV0.5 had strong effects on all model outputs in all conditions, and parameters WGRMX and SPGF had relative high effects on yield in cold year. Only LAI was sensitive to ASLA. Those influential parameters had unequal effects on different outputs, and they had different effects at four development stages. With the interaction effects of parameter variation and different weather conditions, the uncertainty of model outputs varied significantly. However, the weather conditions had negligible effects on the identification of influential parameters, although they had slight effects on the ranks of the parameters' sensitivity for outputs in the panicle-formation phase and the grain-filling phase, including yield at maturity. The results suggested that the influential parameters should be recalibrated in priority and fine-tuned with higher accuracy during model calibration.     

ACOMCD: A multiple cluster detection algorithm based on the spatial scan statistic and ant colony optimization

The spatial scan statistic (SaTScan) has become one of the most popular methods for detecting and evaluating spatial clusters. However, this method can only identify circular or elliptical clusters and is not a good fit for the detection of irregularly shaped clusters. Numerous methods have since been proposed to solve this problem. Nevertheless, if multiple clusters coexist, these methods may not identify the correct situation, because the interference between clusters can easily lead to a tree-like shaped cluster and cause confusion in the results. In this paper, we propose an Ant Colony Optimization based Multiple Cluster Detection (ACOMCD) algorithm, which combines classical SaTScan with the ant colony optimization (ACO) approach. In the initial stage, SaTScan is first used to mark the candidate cluster areas according to the significance of their maximum likelihood evaluations. Then ACO-based scan statistic is carried out separately on these candidate clusters to identify their natural shapes. The algorithm was designed for spatial regional count data only. Comparisons between ACOMCD, SaTScan, GaScan (genetic algorithm-based scan statistic), and FleXScan (flexibly shaped spatial scan statistic) on three kinds of simulated datasets show that ACOMCD performs the best in simultaneously determining the exact number of clusters and identifying multiple irregularly shaped clusters. A case study on esophageal cancer in eastern China further validates the correctness and effectiveness of ACOMCD.     

Estimating the flow velocity and discharge of ADCP unmeasured area in tidal reach

Due to the ringing and side-lobe interference, acoustic Doppler current profiler (ADCP) is unable to accurately capture the complete velocity profile in open channels near the water surface and channel bottom, which are usually called unmeasured areas. At present, the flow velocities through the unmeasured areas are most commonly estimated using the power law with the power set to be the default value. However, since the flows are unsteady and nonuniform in tidal reaches, the velocity distribution model and corresponding parameters will vary with the bathymetric, tide period, etc. Therefore, the most common estimation with the power law may not be suitable in tidal reaches. In this paper, a simple determination method of the best model is proposed. Firstly, the parameters in three classical velocity distribution models, which are called power law, logarithmic law and parabolic law models, are solved by least squares based on the ADCP measured velocity cells. Then, the corresponding root-mean-square error (RMSE) of each model is used for the quantitative indicator that the model with the minimum RMSE is chosen as the best model. At last, the flow velocity and discharge of the unmeasured area are estimated by the best model. The experiments carried out in the tidal reach of Yangtze Estuary showed that vertical flow velocity distribution various with the bathymetry and tide period, and the best models averagely improved about 2.0% of the relative standard deviation (RSD) relative to the power law method in the discharge estimation, especially at some tide period the RSD of the best model was several times better than that of power law model. For Yangtze River with an annual average discharge of 3.0×10⁴ m³/s, the improvement should not be ignored. Therefore, it will be necessary to use the best model with minimum RMSE to estimate the flow velocity in tidal reach.     

Spatial Relationships of Water Resources with Energy Consumption at Coal Mining Operations in China

Mine Water and the Environment - The spatial characterization and regional differences of water use in China’s coal mines were investigated based on a high spatial resolution mine site...     

Thermal microstress measurements in Al2O3/SiC nanocomposites by Cr3+ fluorescence microscopy

Alumina/SiC ‘nanocomposites’ show significant property improvements compared with pure alumina. The improvements are thought to stem at least in part from the microstresses caused by the thermal expansion mismatch between alumina and SiC. These microstresses have been measured previously by neutron and X-Ray diffraction. This paper reports stress measurements using Cr³⁺ fluorescence microscopy of the alumina matrix. The results show that although fluorescence microscopy is less powerful than the diffraction techniques in terms of the range of information provided, it does provide an alternative method of measuring subsurface microstresses in these materials which is quicker, cheaper and higher in spatial resolution.     

A fuzzy-based genetic approach to the diagnosis of manufacturing systems

This paper describes the development of a hybrid approach that integrates graph theory, fuzzy sets and genetic algorithms for the diagnosis of manufacturing systems. The approach enables the modelling of causal relations of system components in manufacturing systems. Based on the model thus established, a worst-first search technique has been proposed and developed for the identification of probable fault-propagation paths. As manufacturing diagnosis often involves the interpretation of uncertainty, fuzzy-set theory is employed for this purpose. Unlike conventional diagnostic systems which assume that all the system components or nodes of a manufacturing system model are measurable, the genetic-algorithm-based search engine developed in this work is able to deal with nodes that cannot be, or are not, measured. Details of the hybrid approach, the worst-first search technique and the genetic-algorithms-based search engine are discussed. The framework of a prototype fuzzy-based genetic diagnostic system is described. Details of the system validation are also presented.     

New method in synthesis of nano uranyl(VI) Schiff base complexes: Characterization and electrochemical studies

This study presents a new method in synthesis of nano uranyl Schiff base complexes. In this method slow addition of dilute uranyl(VI) acetate solution to dilute Schiff base solution following the reflux for about 24 h, yields nano uranyl(VI) Schiff base complexes. Characterization of Schiff base ligands and nano uranyl complexes has been done using ¹H NMR, IR, UV–vis spectroscopy, elemental analysis. Schiff base ligands were synthesized by the condensation of one mole 3,4-diaminobenzophenone and two moles salicylaldehyde or substituted salicylaldehyde (3-OMe, 4-OMe, 5-OMe, 5-Br, 5-Cl). The electrochemical properties of the uranyl(IV) complexes were investigated by cyclic voltammetry. A good correlation was observed between the oxidation potentials and the electron withdrawing character of the substituents on the Schiff base ligands, according to the following trend: 5-MeO < H < 5-Br ≈ 5-Cl. Also the effect of the position of the substituted groups of Schiff base on the anodic potentials is as follows: 5-OMe < 3-OMe < 4-OMe.     

Spatial prediction of landslide susceptibility using data mining-based kernel logistic regression,naive Bayes and RBFNetwork models for the Long County area (China)

The main goal of this study is to assess and compare three advanced machine learning techniques, namely, kernel logistic regression (KLR), naïve Bayes (NB), and radial basis function network (RBFNetwork) models for landslide susceptibility modeling in Long County, China. First, a total of 171 landslide locations were identified within the study area using historical reports, aerial photographs, and extensive field surveys. All the landslides were randomly separated into two parts with a ratio of 70/30 for training and validation purposes. Second, 12 landslide conditioning factors were prepared for landslide susceptibility modeling, including slope aspect, slope angle, plan curvature, profile curvature, elevation, distance to faults, distance to rivers, distance to roads, lithology, NDVI (normalized difference vegetation index), land use, and rainfall. Third, the correlations between the conditioning factors and the occurrence of landslides were analyzed using normalized frequency ratios. A multicollinearity analysis of the landslide conditioning factors was carried out using tolerances and variance inflation factor (VIF) methods. Feature selection was performed using the chi-squared statistic with a 10-fold cross-validation technique to assess the predictive capabilities of the landslide conditioning factors. Then, the landslide conditioning factors with null predictive ability were excluded in order to optimize the landslide models. Finally, the trained KLR, NB, and RBFNetwork models were used to construct landslide susceptibility maps. The receiver operating characteristics (ROC) curve, the area under the curve (AUC), and several statistical measures, such as accuracy (ACC), F-measure, mean absolute error (MAE), and root mean squared error (RMSE), were used for the assessment, validation, and comparison of the resulting models in order to choose the best model in this study. The validation results show that all three models exhibit reasonably good performance, and the KLR model exhibits the most stable and best performance. The KLR model, which has a success rate of 0.847 and a prediction rate of 0.749, is a promising technique for landslide susceptibility mapping. Given the outcomes of the study, all three models could be used efficiently for landslide susceptibility analysis.

     

A new perspective on the temporal pattern of human activities in cities: The case of Shanghai

Time is a fundamental characteristic for understanding human activities. When analysing temporal pattern of a group of activities, most researchers tend to utilise one temporal attribute when representing time use of activities. Thus, temporal pattern of activities is usually visualised and understood as a profile of various observations listed sequentially over time. This paper aims to investigate the temporal pattern of activities in urban areas from a new perspective. Temporal pattern is visualised and analysed as the distribution of activity points in a two-dimensional temporal plane defined by the start and end time of activities as x and y axes. Kernel density estimation is used as a typical method to observe the temporal pattern of activities in Shanghai based on a one-week smart card dataset generated in the Shanghai's metro system. The results show that the proposed perspective can reveal considerably more information regarding the temporal pattern than a conventional one can.