Structural and dynamical properties of 13-atom Cu–Co mixed clusters

In this paper, the geometric structures and the melting-like processes of the 13-atom pure copper, pure cobalt cluster and their 13-atom mixed clusters are investigated and compared by the molecular dynamics method. The calculation shows that the pure copper and cobalt clusters have the standard icosahedral structures and the mixed clusters take on the deformed icosahedral structures. The quantitative analysis shows that the deformations are slight. Moreover, an element similarity function is introduced by which the contribution of the compositions of the clusters to the deformation of the mixed clusters is analyzed and discussed. With the increase of the temperature, the migrating and recombination of the atoms on the surface of the clusters are observed, indicating the starting of the transition from solid-like to liquid-like state for the clusters. Through the calculating of the relative root-mean-squared pair separation fluctuation and monitoring the dynamical structures of the clusters, it is found that the mixed clusters experience a multi-step process in the transition.

Performance Evaluation of AquaCrop Model in Processing Tomato Biomass, Fruit Yield and Water Stress Indicator Modelling

A three-year long experiment was conducted on open-field tomato with different levels of water shortage stress. Three different water supply levels were set in 2017 and four levels for 2018 and 2019. Biomass and yield data were collected, along with leaf-temperature-based stress measurements on plants. These were used for calibration and validation of the AquaCrop model. The validation gave various results of biomass and yield simulation during the growing season. The largest errors in the prediction occurred in the middle of the growing seasons, but the simulation became more accurate at harvest in general. The prediction of final biomass and yields were good according to the model evaluation indicators. The relative root mean square error (nRMSE) was 12.1 and 13.6% for biomass and yield prediction, respectively. The modeling efficiency (EF) was 0.96 (biomass) and 0.99 (yield), and Willmott’s index of agreement (d) was 0.99 for both predicted parameters at harvest. The lowest nRMSE (4.17) was found in the simulation of final yields of 2018 (the calibration year). The best accuracy of the validation year was reached under mild stress treatment. No high correlation was found between the simulated and measured stress indicators. However, increasing and decreasing trends could be followed especially in the severely stressed treatments.

Implementasi Empirical Best Linear Unbiased Prediction Fay-Herriot dalam Menduga Rata-Rata Pengeluaran per Kapita Level Kecamatan di Provinsi Jawa Timur dengan Tambahan Informasi Cluster

Salah satu indikator ekonomi yang dapat mengukur tingkat kesejahteraan adalah kemiskinan. Penduduk tergolong miskin apabila rata-rata pengeluaran per kapita setiap bulannya dibawah garis kemiskinan. Provinsi Jawa Timur terpilih sebagai lokus penelitian dikarenakan memiliki jumlah penduduk miskin tertinggi di Indonesia selama satu dekade terakhir. Data yang digunakan berasal dari Susenas Maret 2019 dan Podes 2018 dengan 666 observasi level kecamatan. Upaya pengentasan kemiskinan memerlukan data yang akurat dan menjangkau hingga wilayah terkecil. Akan tetapi tidak semua wilayah memiliki sampel yang cukup atau bahkan tidak memiliki sampel sama sekali. Hal ini tidak memungkinkan untuk melakukan estimasi langsung. Oleh karena itu dibutuhkan metode statistik untuk dapat mengestimasi area kecil dengan baik. Metode yang dapat digunakan untuk menduga area kecil adalah Small Area Estimation (SAE). Penelitian ini menggunakan metode SAE dengan Model Empirical Best Linear Unbiased Prediction Fay-Herriot. Hasil yang diperoleh bahwa metode SAE dapat memberikan pendugaan yang lebih baik dibanding estimasi langsung yang ditunjukan dengan nilai Relative Root Mean Square Error (RRMSE) lebih kecil dibanding estimasi langsung. Estimasi pada non-sample area dilakukan dengan memanfaatkan informasi cluster.

Evaluation of Plotting Position Formulae for Pearson Type 3 Distribution in Three Hydrological Stations on the Niger River

In this study, eight unbiased plotting position formulae recommended for Pearson Type 3 distribution were evaluated by comparing the simulated series of each formula with the annual maximum series (AMS) of River Niger at Baro, Koroussa and Shintaku hydrological stations, each having data length of 51years, 53 years and 58 years respectively. The parameters of Pearson Type 3 distribution were computed by the method of moments with corrections for skewness. While the fitting of Pearson Type 3 distribution proceeds with the development of flood – return period (Q-T) relationship, followed by application of the derived Q- T relation to compute simulated discharges for comparison with AMS of the study stations. The plotting position formulae were evaluated on the basis of optimum values of the statistically goodness-of-fit of probability plot correlation coefficient (PPCC), relative root mean square error (RRMSE), percent bias (PBIAS), mean absolute error (MAE) and Nash-sutcliffe efficiency (NSE), across the stations. The plotting position formulae were ranked on scale of 1 to 8. Thus a plotting formula that best simulates the empirical observations using the goodness-of-measures was scored “1” and so on. The individual scores per plotting position were summed across the gof tests to obtain the total score. The study show that Chegodayev is the best plotting position formula for Baro, Weibull is the best plotting position Formula for Kourassou and Shintaku hydrological stations. The overall performances of the eight plotting position formulae across the hydrological stations show that weibull distribution is the overall best having scored 27, seconded by Chegodayev with 30 and thirdly, Beard with 38. The Pearson Type 3 distribution had been found one of the best probability distribution model of flood flow in Nigeria and this study was conducted to gain in-depth knowledge of the distribution. Finally, this study recommends extension of the studies to Log-Pearson Type 3 distribution.

Talking with Strangers: Improving Serianthes Transplant Quality with Interspecific Companions

Mixtures of species in natural or agricultural systems can increase the performance of individuals or groups relative to monocultures, often through facilitative mechanisms. Mechanisms include root communication by which plants can interrogate the identity of adjacent plants and respond negatively or positively. Alternatively, mixtures of species can ameliorate the harmful effects of soil biota that are pronounced in monocultures, thereby improving plant productivity. Limited investments into roots by shade-grown Serianthes plants in nurseries have been correlated with reduced survival after transplantation to forested habitats. We used companion container cultures in two studies to determine if heterospecific neighbor, or “stranger” roots could experimentally increase the root growth of Serianthes grandiflora plants used as surrogates for the critically endangered Serianthes nelsonii. In one study, native sympatric eudicot and pteridophyte companions increased relative root growth and conspecific companions decreased root growth in comparison to control plants that were grown with no companions. In a second study, the phylogeny of companion plants elicited different root growth responses following the order of congeneric < eudicot = monocot < gymnosperm < pteridophyte. We propose the use of stranger roots that are experimentally maintained in production containers as a passive protocol to improve relative and absolute root growth, leading to improved post-transplant growth and survival of container-grown Serianthes plants.

Coniferous Plantations Growing Stock Volume Estimation Using Advanced Remote Sensing Algorithms and Various Fused Data

Spatial distribution prediction of growing stock volume (GSV) for supporting the sustainable management of forest ecosystems, is one of the most widespread applications of remote sensing. For this purpose, remote sensing data were used as predictor variables in combination with ground data obtained from field sample plots. However, with the increase in forest GSV values, the spectral reflectance of remote sensing imagery is often saturated or less sensitive to the GSV changes, making accurate estimation difficult. To improve this, we examined the GSV estimation performance and data saturation of four optical remote sensing image datasets (Landsat 8, Sentinel-2, ZiYuan-3, and GaoFen-2) in the subtropical region of Central South China. First, various feature variables were extracted and three optimization methods were used to select optimal feature variable combinations. Subsequently, k-nearest-neighbor (kNN), random forest regression, and categorical boosting algorithms were employed to build the GSV estimation models, and evaluate the GSV estimation accuracy and saturation. Second, Gram Schmidt (GS) and NNDiffuse pan sharpening (NND) methods were employed to fuse the optimal multispectral images and explore various image fusion schemes suitable for GSV estimation. We proposed an adaptive stacking (AdaStacking) model ensemble algorithm to further improve GSV estimation performance. The results indicated that Sentinel-2 had the highest GSV estimation accuracy exhibiting a minimum relative root mean square error of 20.06% and saturation of 434 m3/ha, followed by GaoFen-2 with a minimum relative root mean square error of 22.16% and a saturation of 409 m3/ha. Among the four fusion images, the NND-B2 image—obtained by fusing the GaoFen-2 green band and Sentinel-2 multispectral image with the NND method—had the best estimation accuracy. The estimated optimal RMSEs of NND-B2 were 24.4% and 16.5% lower than those of GaoFen-2 and Sentinel-2, respectively. Therefore, the fused image data based on GF-2 and Sentinel-2 can effectively couple the advantages of the two images and significantly improve the GSV estimation performance. Moreover, the proposed adaptive stacking model is more effective in GSV estimation than a single model. The GSV estimation saturation value of the AdaStacking model based on NND-B2 was 5.4% higher than that of the KNN-Maha model. The GSV distribution map estimated by AdaStacking model used the NND-B2 dataset corresponded accurately with the field observations. This study provides some insights into the optical image fusion scheme, feature selection, and adaptive modeling algorithm in GSV estimation for coniferous forest.

Comparison of statistical indices for the evaluation of crop models performance

This study presents a comparison of the usual statistical methods used for crop model assessment. A case study was conducted using a data set from observations of the total dry weight in diploid potato crop, and six simulated data sets derived from the observationsaimed to predict the measured data. Statistical indices such as the coefficient of determination, the root mean squared error, the relative root mean squared error, mean error, index of agreement, modified index of agreement, revised index of agreement, modeling efficiency, and revised modeling efficiency were compared. The results showed that the coefficient of determination is not a useful statistical index for model evaluation. The root mean squared error together with the relative root mean squared error offer an excellent notion of how deviated the simulations are in the same unit of the variable and percentage terms, and they leave no doubt when evaluating the quality of the simulations of a model.

An Automatic and Simplified Approach to Muscle Path Modelling

The current paper aims at proposing an automatic method to design and adjust simplified muscle paths of a musculoskeletal model. These muscle paths are composed of a limited set of via points and an optimization routine is developed to place these via points on the model in order to fit moment arms and musculotendon lengths input data. The method has been applied to a forearm musculoskeletal model extracted from the literature, using theoretical input data as an example. Results showed that for $75\%$ of the muscle set, the relative root mean square error was under $29.23\%$ for moment arms and of $1.09\%$ for musculotendon lengths with regard to the input data. These results confirm the ability of the method to automatically generate computationally efficient muscle paths for musculoskeletal simulations. Using only via points lowers computational expense compared to paths exhibiting wrapping objects. A proper balance between computational time and anatomical realism should be found to help those models being interpreted by practitioners.

Growing Stock Volume Retrieval from Single and Multi-Frequency Radar Backscatter

While products generated at global levels provide easy access to information on forest growing stock volume (GSV), their use at regional to national levels is limited by temporal frequency, spatial resolution, or unknown local errors that may be overcome through locally calibrated products. This study assessed the need, and utility, of developing locally calibrated GSV products for the Romanian forests. To this end, we used national forest inventory (NFI) permanent sampling plots with largely concurrent SAR datasets acquired at C- and L-bands to train and validate a machine learning algorithm. Different configurations of independent variables were evaluated to assess potential synergies between C- and L-band. The results show that GSV estimation errors at C- and L-band were rather similar, relative root mean squared errors (RelRMSE) around 55% for forests averaging over 450 m3 ha−1, while synergies between the two wavelengths were limited. Locally calibrated models improved GSV estimation by 14% when compared to values obtained from global datasets. However, even the locally calibrated models showed particularly large errors over low GSV intervals. Aggregating the results over larger areas considerably reduced (down to 25%) the relative estimation errors.

Calibration-Based Estimators using Different Distance Measures under Two Auxiliary Variables: A Comparative Study

This article introduces calibration estimators under different distance measures based on two auxiliary variables in stratified sampling. The theory of the calibration estimator is presented. The calibrated weights based on different distance functions are also derived. A simulation study has been carried out to judge the performance of the proposed estimators based on the minimum relative root mean squared error criterion. A real-life data set is also used to confirm the supremacy of the proposed method.