Predicting food consumption of fish populations as functions of mortality, food type, morphometrics, temperature and salinity

A large data set of relative food-consumption estimates (Q/B) of marine and freshwater fish populations (n = 108 populations, 38 species) is documented and used to derive a predictive model for Q/B, using asymptotic weight, habitat temperature, a morphological variable and food type as independent variables. Salinity is shown to have no effect on Q/B in fish well adapted to fresh or salt water (other things being equal), while mortality (Z), has a strong, positive effect on Q/B and on gross food-conversion efficiency (defined by GE = Z/(Q/B)), by affecting the ratio of small:large fish. The empirical models thus derived should be useful for parameterization of trophic models of ecosystems and similar applications.

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Informasi Akuntansi dan Harga Saham Perusahaan LQ45

Jurnal Ekonomi KIAT ◽

10.25299/kiat.2019.vol30(1).3878 ◽

2019 ◽

Vol 30 (1) ◽

pp. 32

Author(s):

Dwi Hartini Rahayu

Keyword(s):

Panel Data ◽

Stock Price ◽

Accounting Information ◽

Data Set ◽

Book Value ◽

Independent Variables ◽

Earnings Per Share ◽

Operating Cash Flow ◽

Relationship Of ◽

Positive Effect

This paper examine relationship of accounting information and stock price of LQ45 firms. Earnings per share (EPS), book-value per share (BVPS) and net operating cash flow per share (NOCFPS) are used as independent variables and stock price as dependent variable. Hypothesis was tested using balanced panel data set of 23 listed companies included in the LQ45 index during 2013-2017. The result of this study shows that EPS and NOCFPS has a positive effect on stock price, while there is no evidence that BVPS has effect on stock price.

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A multiple regression model for prediction the food consumption of Marine Fish populations

Marine and Freshwater Research ◽

10.1071/mf9890259 ◽

1989 ◽

Vol 40 (3) ◽

pp. 259 ◽

Cited By ~ 79

Author(s):

ML Palomares ◽

D Pauly

Keyword(s):

Regression Model ◽

Multiple Regression ◽

Food Consumption ◽

Size Structure ◽

Multiple Regression Model ◽

Food Type ◽

Data Set ◽

Future Extension ◽

Study Population ◽

Food Consumed

The construction of trophic (food web) models of ecosystems, as needed for both theoretical and practical purposes such as fisheries management, requires estimates of food consumption (Q) by each of the various species (groups) included in the model. These estimates are usually required on a per-biomass (B) basis, i.e. as estimates of the ratio of the food consumed to the weight of the consumers (Q/B) during a stated period. For estimates of Q/B to be most useful, they must take account of: (i) seasonal fluctuations of food intake; (ii) the age/size structure of the population; and (iii) the type of food consumed. In this study, 33 estimates of Q/B are reviewed, and an empirical multiple regression model for prediction of Q/B is presented which incorporates points (i) to (iii) above. The predictor variables are: (a) the asymptotic weight of the fish of the study population, (b) the aspect ratio of their caudal fin (as a measure of the average activity and/or metabolic levels of the fish), (c) the mean habitat temperature and (d) the food type (a dummy variable, 0 ih,carnivores and 1 in herbivores). The model explains nearly 75% of the variance in the data set used, which includes myctophids and tunas, flatfishes, rabbitfishes, and other groups from both tropical and temperate waters. The implications of this model for bioenergetics are discussed, along with its future extension, to be based on a much larger data set.

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Some statistical and CI models to predict chaotic high-frequency financial data

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189107 ◽

2020 ◽

Vol 39 (5) ◽

pp. 6419-6430

Author(s):

Dusan Marcek

Keyword(s):

Time Series Data ◽

Moving Average ◽

Methodological Approach ◽

Back Propagation ◽

Large Data ◽

Series Data ◽

Data Set ◽

Training Time ◽

Optimal Population ◽

Forecast Time

To forecast time series data, two methodological frameworks of statistical and computational intelligence modelling are considered. The statistical methodological approach is based on the theory of invertible ARIMA (Auto-Regressive Integrated Moving Average) models with Maximum Likelihood (ML) estimating method. As a competitive tool to statistical forecasting models, we use the popular classic neural network (NN) of perceptron type. To train NN, the Back-Propagation (BP) algorithm and heuristics like genetic and micro-genetic algorithm (GA and MGA) are implemented on the large data set. A comparative analysis of selected learning methods is performed and evaluated. From performed experiments we find that the optimal population size will likely be 20 with the lowest training time from all NN trained by the evolutionary algorithms, while the prediction accuracy level is lesser, but still acceptable by managers.

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The Determinants of Capital Adequacy Ratio: The Case of the Vietnamese Banking System in the Period 2011-2015

VNU Journal of Science Economics and Business ◽

10.25073/2588-1108/vnueab.4070 ◽

2017 ◽

Vol 33 (2) ◽

Author(s):

Ngoc Anh Nguyen

Keyword(s):

Banking System ◽

Precious Metals ◽

Capital Adequacy ◽

The Other ◽

Total Asset ◽

Data Set ◽

Hand Size ◽

Net Interest Margin ◽

Capital Adequacy Ratio ◽

Positive Effect

The analysis of a data set of observation for Vietnamese banks in period from 2011 - 2015 shows how Capital Adequacy Ratio (CAR) is influenced by selected factors: asset of the bank SIZE, loans in total asset LOA, leverage LEV, net interest margin NIM, loans lost reserve LLR, Cash and Precious Metals in total asset LIQ. Results indicate based on data that NIM, LIQ have significant effect on CAR. On the other hand, SIZE and LEV do not appear to have significant effect on CAR. Variables NIM, LIQ have positive effect on CAR, while variables LLR and LOA are negatively related with CAR.

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Pengaruh Etika Bisnis Islam Terhadap Kesejahteraan Pedagang (Studi Kasus Pedagang Pusat Pasar Medan)

AT-TAWASSUTH: Jurnal Ekonomi Islam ◽

10.30821/ajei.v4i1.4092 ◽

2019 ◽

Vol 4 (1) ◽

pp. 185

Author(s):

Ya’ti Ikhwani Nasution

Keyword(s):

Free Will ◽

Quantitative Research ◽

Statistical Tools ◽

Independent Variables ◽

Islamic Business Ethics ◽

Negative Effect ◽

Data Collection Technique ◽

Questionnaire Method ◽

Market Center ◽

Positive Effect

The purpose of this study is to find out whether there is an influence of Islamic business ethics with the variables of unity, equilibrium, free will, responsibility, benevolence and the welfare of traders in the Pusat Pasar Medan. This research is a quantitative research and the analysis used is multiple regression analysis. The data collection technique used is the questionnaire method obtained directly from the respondent, namely the Pusat Pasar Medan Trader. Analyzed using statistical tools, namely SPSS Version 22. Based on the results of data processing has shown that there is a significant influence as partially and simultaneously among the unity, equilibrium, free will, responsibility and benovelence towards the welfare of traders in the Medan Market Center. For unity, free will, responsibility and benovelence have a positive effect on the welfare of traders in Medan Market Center. While the equilibrium variable has a negative effect on the welfare of Medan Market Center traders. The adjusted R square value is 0.345. This means that 34.5% increase in welfare can be explained by independent variables, namely the variables of unity, equilibrium, free will, responsibility and kindness. While 65.5% is explained by other factors.

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In silico Prediction of Inhibitory Constant of Thrombin Inhibitors Using Machine Learning

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207322666181220130232 ◽

2019 ◽

Vol 21 (9) ◽

pp. 662-669 ◽

Cited By ~ 1

Author(s):

Junnan Zhao ◽

Lu Zhu ◽

Weineng Zhou ◽

Lingfeng Yin ◽

Yuchen Wang ◽

...

Keyword(s):

Machine Learning ◽

Prediction Models ◽

Regression Tree ◽

Large Data ◽

Thrombin Inhibitors ◽

Coagulation Cascade ◽

Gradient Boosting ◽

Support Vector ◽

Data Set ◽

Descriptor Selection

Background: Thrombin is the central protease of the vertebrate blood coagulation cascade, which is closely related to cardiovascular diseases. The inhibitory constant Ki is the most significant property of thrombin inhibitors. Method: This study was carried out to predict Ki values of thrombin inhibitors based on a large data set by using machine learning methods. Taking advantage of finding non-intuitive regularities on high-dimensional datasets, machine learning can be used to build effective predictive models. A total of 6554 descriptors for each compound were collected and an efficient descriptor selection method was chosen to find the appropriate descriptors. Four different methods including multiple linear regression (MLR), K Nearest Neighbors (KNN), Gradient Boosting Regression Tree (GBRT) and Support Vector Machine (SVM) were implemented to build prediction models with these selected descriptors. Results: The SVM model was the best one among these methods with R2=0.84, MSE=0.55 for the training set and R2=0.83, MSE=0.56 for the test set. Several validation methods such as yrandomization test and applicability domain evaluation, were adopted to assess the robustness and generalization ability of the model. The final model shows excellent stability and predictive ability and can be employed for rapid estimation of the inhibitory constant, which is full of help for designing novel thrombin inhibitors.

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Correlation between the structure and skin permeability of compounds

Scientific Reports ◽

10.1038/s41598-021-89587-5 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Ruolan Zeng ◽

Jiyong Deng ◽

Limin Dang ◽

Xinliang Yu

Keyword(s):

Large Data ◽

Qsar Model ◽

Coefficient Of Determination ◽

Support Vector ◽

Skin Permeability ◽

Data Set ◽

Test Set ◽

Svm Algorithm ◽

Svm Model ◽

Toxicity Relationship

AbstractA three-descriptor quantitative structure–activity/toxicity relationship (QSAR/QSTR) model was developed for the skin permeability of a sufficiently large data set consisting of 274 compounds, by applying support vector machine (SVM) together with genetic algorithm. The optimal SVM model possesses the coefficient of determination R2 of 0.946 and root mean square (rms) error of 0.253 for the training set of 139 compounds; and a R2 of 0.872 and rms of 0.302 for the test set of 135 compounds. Compared with other models reported in the literature, our SVM model shows better statistical performance in a model that deals with more samples in the test set. Therefore, applying a SVM algorithm to develop a nonlinear QSAR model for skin permeability was achieved.

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Galaxy spin direction distribution in HST and SDSS show similar large-scale asymmetry

Publications of the Astronomical Society of Australia ◽

10.1017/pasa.2020.46 ◽

2020 ◽

Vol 37 ◽

Author(s):

Lior Shamir

Keyword(s):

Large Scale ◽

Spiral Galaxies ◽

Hubble Space Telescope ◽

Gravitational Interaction ◽

Large Data ◽

Sloan Digital Sky Survey ◽

Data Sets ◽

Dipole Axis ◽

Data Set ◽

The Asymmetry

Abstract Several recent observations using large data sets of galaxies showed non-random distribution of the spin directions of spiral galaxies, even when the galaxies are too far from each other to have gravitational interaction. Here, a data set of $\sim8.7\cdot10^3$ spiral galaxies imaged by Hubble Space Telescope (HST) is used to test and profile a possible asymmetry between galaxy spin directions. The asymmetry between galaxies with opposite spin directions is compared to the asymmetry of galaxies from the Sloan Digital Sky Survey. The two data sets contain different galaxies at different redshift ranges, and each data set was annotated using a different annotation method. The results show that both data sets show a similar asymmetry in the COSMOS field, which is covered by both telescopes. Fitting the asymmetry of the galaxies to cosine dependence shows a dipole axis with probabilities of $\sim2.8\sigma$ and $\sim7.38\sigma$ in HST and SDSS, respectively. The most likely dipole axis identified in the HST galaxies is at $(\alpha=78^{\rm o},\delta=47^{\rm o})$ and is well within the $1\sigma$ error range compared to the location of the most likely dipole axis in the SDSS galaxies with $z>0.15$ , identified at $(\alpha=71^{\rm o},\delta=61^{\rm o})$ .

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The Complete DNA Sequence of the Mitochondrial Genome of a “Living Fossil,” the Coelacanth (Latimeria chalumnae)

Genetics ◽

10.1093/genetics/146.3.995 ◽

1997 ◽

Vol 146 (3) ◽

pp. 995-1010 ◽

Cited By ~ 1

Author(s):

Rafael Zardoya ◽

Axel Meyer

Keyword(s):

Mitochondrial Genome ◽

Tandem Repeats ◽

Phylogenetic Analyses ◽

Large Data ◽

Molecular Data ◽

Phylogenetic Position ◽

Data Set ◽

Living Fossil ◽

Latimeria Chalumnae ◽

Relationship Of

The complete nucleotide sequence of the 16,407-bp mitochondrial genome of the coelacanth (Latimeria chalumnae) was determined. The coelacanth mitochondrial genome order is identical to the consensus vertebrate gene order which is also found in all ray-finned fishes, the lungfish, and most tetrapods. Base composition and codon usage also conform to typical vertebrate patterns. The entire mitochondrial genome was PCR-amplified with 24 sets of primers that are expected to amplify homologous regions in other related vertebrate species. Analyses of the control region of the coelacanth mitochondrial genome revealed the existence of four 22-bp tandem repeats close to its 3′ end. The phylogenetic analyses of a large data set combining genes coding for rRNAs, tRNA, and proteins (16,140 characters) confirmed the phylogenetic position of the coelacanth as a lobe-finned fish; it is more closely related to tetrapods than to ray-finned fishes. However, different phylogenetic methods applied to this largest available molecular data set were unable to resolve unambiguously the relationship of the coelacanth to the two other groups of extant lobe-finned fishes, the lungfishes and the tetrapods. Maximum parsimony favored a lungfish/coelacanth or a lungfish/tetrapod sistergroup relationship depending on which transversion:transition weighting is assumed. Neighbor-joining and maximum likelihood supported a lungfish/tetrapod sistergroup relationship.

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