Water Stability and Nutrient Leaching of Different Levels of Maltose Formulated Fish Pellets

The effects of different levels of maltose on feed pellet water stability and nutrient leaching were studied. Five treatments, including control with three replicates with setup (0.0, 20, 25, 30 and 35%). Pellet leaching rates were used to indicate pellet water stability. The results show that the presence of maltose in the diets significantly improved pellet water stability (p<0.05), but the leaching rates of the feed (35% maltose) observed higher than other feeds. Increased maltose resulted in the corresponding decrease in pellet stability. The protein leaching rate of control feed and feed (20% maltose) was significantly (p < 0.05) lower than the rates of other diets The lipid leaching rate of control feed was lower than the rates of other diets, while the feed (35% maltose) was more leaching rate. It improved feeds water stability is one important reason why maltose enhances fish growth

International Price Relationship and Volatility Transmission Between Stock Index and Stock Index Futures

This study investigates the International price relationship and volatility transmissions betweenstock index and stock index futures of Malaysia, Hong Kong and Japan. Vector Autoregression(VAR) GJR-GARCH model was applied to the nine years daily price. Japanesemarkets are the main information producer to the market price changes. International marketinterdependence only affected the domestic volatility transmission of spot and futuresmarket in Hong Kong. Asymmetric effects exist in all markets and the volatility persistence ineach market is high. Finally, the overall conditional correlation estimates for spot and futuresmarkets are higher in the unrestricted model form compared to the restricted modelform

GIS and Multi-criteria Analysis for School Site Selection (Study Case: Malacca Historical City)

Abstract: A set of school suitability map would be very useful for education planners when making a complex decision within a short period of time. This study will utilize both spatial and non-spatial parameters to establish a systematic site selection process for primary schools in Melaka Tengah District. It was carried out by using Geographic Information System (GIS) and Multi-criteria Decision Analysis (MCDA). Three analysis namely demographic, safety and constrain analysis were used to identify the potential sites. Then accessibility analysis, using expertise and public opinion were used to further analyze the potential site. The resulted map showed 54.2% of the total area is highly not suitable, leaving 46% suitable for school sitting. The final safety model output was compared with field verification data from State Education Department (JPN Melaka) and Malacca Historical City Council (MBMBB).

Novel GIS based machine learning algorithms for shallow landslide susceptibility mapping

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. The main objective of this research was to introduce a novel machine learning algorithm of alternating decision tree (ADTree) based on the multiboost (MB), bagging (BA), rotation forest (RF) and random subspace (RS) ensemble algorithms under two scenarios of different sample sizes and raster resolutions for spatial prediction of shallow landslides around Bijar City, Kurdistan Province, Iran. The evaluation of modeling process was checked by some statistical measures and area under the receiver operating characteristic curve (AUROC). Results show that, for combination of sample sizes of 60%/40% and 70%/30% with a raster resolution of 10 m, the RS model, while, for 80%/20% and 90%/10% with a raster resolution of 20 m, the MB model obtained a high goodness-of-fit and prediction accuracy. The RS-ADTree and MB-ADTree ensemble models outperformed the ADTree model in two scenarios. Overall, MB-ADTree in sample size of 80%/20% with a resolution of 20 m (area under the curve (AUC) = 0.942) and sample size of 60%/40% with a resolution of 10 m (AUC = 0.845) had the highest and lowest prediction accuracy, respectively. The findings confirm that the newly proposed models are very promising alternative tools to assist planners and decision makers in the task of managing landslide prone areas

A novel ensemble artificial intelligence approach for gully erosion mapping in a semi-arid watershed (Iran)

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. In this study, we introduced a novel hybrid artificial intelligence approach of rotation forest (RF) as a Meta/ensemble classifier based on alternating decision tree (ADTree) as a base classifier called RF-ADTree in order to spatially predict gully erosion at Klocheh watershed of Kurdistan province, Iran. A total of 915 gully erosion locations along with 22 gully conditioning factors were used to construct a database. Some soft computing benchmark models (SCBM) including the ADTree, the Support Vector Machine by two kernel functions such as Polynomial and Radial Base Function (SVM-Polynomial and SVM-RBF), the Logistic Regression (LR), and the Naïve Bayes Multinomial Updatable (NBMU) models were used for comparison of the designed model. Results indicated that 19 conditioning factors were effective among which distance to river, geomorphology, land use, hydrological group, lithology and slope angle were the most remarkable factors for gully modeling process. Additionally, results of modeling concluded the RF-ADTree ensemble model could significantly improve (area under the curve (AUC) = 0.906) the prediction accuracy of the ADTree model (AUC = 0.882). The new proposed model had also the highest performance (AUC = 0.913) in comparison to the SVM-Polynomial model (AUC = 0.879), the SVM-RBF model (AUC = 0.867), the LR model (AUC = 0.75), the ADTree model (AUC = 0.861) and the NBMU model (AUC = 0.811)

Hadronic observables from SIS to SPS energies - anything strange with strangeness ?

We calculate $p, \pi^\pm, K^\pm$ and $\Lambda$(+$\Sigma^0$) rapidity distributions and compare to experimental data from SIS to SPS energies within the UrQMD and HSD transport approaches that are both based on string, quark, diquark ($q, \bar{q}, qq, \bar{q}\bar{q}$) and hadronic degrees of freedom. The two transport models do not include any explicit phase transition to a quark-gluon plasma (QGP). It is found that both approaches agree rather well with each other and with the experimental rapidity distributions for protons, $\Lambda$'s, $\pi^\pm$ and $K^\pm$. Inspite of this apparent agreement both transport models fail to reproduce the maximum in the excitation function for the ratio $K^+/\pi^+$ found experimentally between 11 and 40 A$\cdot$GeV. A comparison to the various experimental data shows that this 'failure' is dominantly due to an insufficient description of pion rapidity distributions rather than missing 'strangeness'. The modest differences in the transport model results -- on the other hand -- can be attributed to different implementations of string formation and fragmentation, that are not sufficiently controlled by experimental data for the 'elementary' reactions in vacuum.Comment: 46 pages, including 15 eps figures, to be published in Phys. Rev.

Shallow Landslide Prediction Using a Novel Hybrid Functional Machine Learning Algorithm

Coastal wetland mapping plays an essential role in monitoring climate change, the hydrological cycle, and water resources. In this study, a novel classification framework based on the gravitational optimized multilayer perceptron classifier and extended multi-attribute profiles (EMAPs) is presented for coastal wetland mapping using Sentinel-2 multispectral instrument (MSI) imagery. In the proposed method, the morphological attribute profiles (APs) are firstly extracted using four attribute filters based on the characteristics of wetlands in each band from Sentinel-2 imagery. These APs form a set of EMAPs which comprehensively represent the irregular wetland objects in multiscale and multilevel. The EMAPs and original spectral features are then classified with a new multilayer perceptron (MLP) classifier whose parameters are optimized by a stability-constrained adaptive alpha for a gravitational search algorithm. The performance of the proposed method was investigated using Sentinel-2 MSI images of two coastal wetlands, i.e., the Jiaozhou Bay and the Yellow River Delta in Shandong province of eastern China. Comparisons with four other classifiers through visual inspection and quantitative evaluation verified the superiority of the proposed method. Furthermore, the effectiveness of different APs in EMAPs were also validated. By combining the developed EMAPs features and novel MLP classifier, complicated wetland types with high within-class variability and low between-class disparity were effectively discriminated. The superior performance of the proposed framework makes it available and preferable for the mapping of complicated coastal wetlands using Sentinel-2 data and other similar optical imagery

Constraints on the χ_(c1) versus χ_(c2) polarizations in proton-proton collisions at √s = 8 TeV

The polarizations of promptly produced χ_(c1) and χ_(c2) mesons are studied using data collected by the CMS experiment at the LHC, in proton-proton collisions at √s=8  TeV. The χ_c states are reconstructed via their radiative decays χ_c → J/ψγ, with the photons being measured through conversions to e⁺e⁻, which allows the two states to be well resolved. The polarizations are measured in the helicity frame, through the analysis of the χ_(c2) to χ_(c1) yield ratio as a function of the polar or azimuthal angle of the positive muon emitted in the J/ψ → μ⁺μ⁻ decay, in three bins of J/ψ transverse momentum. While no differences are seen between the two states in terms of azimuthal decay angle distributions, they are observed to have significantly different polar anisotropies. The measurement favors a scenario where at least one of the two states is strongly polarized along the helicity quantization axis, in agreement with nonrelativistic quantum chromodynamics predictions. This is the first measurement of significantly polarized quarkonia produced at high transverse momentum