A new fractal-theory-based criterion for hydrological model calibration

Fractality has been found in many areas and has been used to describe the internal features of time series. But is it possible to use fractal theory to improve the performance of hydrological models? This study aims at investigating the potential benefits of applying fractal theory in model calibration. A new criterion named the ratio of fractal dimensions (RD) is defined as the ratio of the fractal dimensions of simulated and observed streamflow series. To combine the advantages of fractal theory with classical criteria based on squared residuals, a multi-objective calibration strategy is designed. The selected classical criterion is the Nash–Sutcliffe efficiency (E). The E–RD strategy is tested in three study cases with different climates and geographies. The results reveal that, in most aspects, introducing RD into model calibration makes the simulation of streamflow components more reasonable. Also, pursuing a better RD during calibration leads to only a small decrease in E. We therefore recommend choosing the parameter set with the best E among the parameter sets with RD values of around 1.

Examining the Asymmetric Impact of COVID-19 Pandemic and Global Financial Crisis on Dow Jones and Oil Price Shock

COVID-19 has significantly affected the financial and commodity markets. The purpose of this investigation is to understand the impact of the COVID-19 crisis on Dow Jones and West Texas Intermediate (WTI) oil returns in relation to other crises using the Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) model. The results indicate that COVID-19 and the accompanying lockdown have adversely impacted both yields and that the impact on oil prices is more significant than on the Dow Jones index. The variance and squared residuals of oil prices and the Dow Jones reached their highest historical levels during the COVID-19 outbreak, even higher than during the global financial crisis, and especially the VaR of both markets reached their historical peak points during the COVID-19 era. The variance of WTI during COVID-19 is higher than that of DJI, as was also the case during the financial crisis. These findings confirm that COVID-19 has negatively impacted investors’ ability to determine optimal portfolios and thus the sustainability of financial and energy markets more than the global financial crisis of 2007–2009. We, therefore, suggest that policy changes are needed to maintain financial sustainability and help investors deal with future financial and other crises.

A New Algebraic Solution for Acoustic Emission Source Localization without Premeasuring Wave Velocity

The technique of acoustic emission (AE) source localization is critical for studying material failure mechanism and predicting the position of potential hazards. Most existing positioning methods heavily depend on the premeasured wave velocity and are not suitable for complex engineering practices where the wave velocity changes dynamically. To reduce the influence of measurement error of wave velocity on location accuracy, this paper proposes a new algebraic solution for AE source localization without premeasuring wave velocity. In this method, the nonlinear TDOA equations are established and linearized by introducing two intermediate variables. Then, by minimizing the sum of squared residuals of the linear TDOA equations with respect to the AE source coordinate and two intermediate variables separately, the optimal algebraic solution of the AE source coordinate in the least squares sense is obtained. A pencil-lead breaks experiment is performed to validate the positioning effectiveness of the proposed method. The results show that the new method improves the positioning accuracy by more than 40% compared with two pre-existing methods, and the minimum positioning accuracy of the proposed method can reach 1.12 mm. Moreover, simulation tests are conducted to further verify the location performance of the proposed method under different TDOA errors and the number of sensors.

A new fractal-theory-based criterion for hydrological model calibration

Fractality has been found in many areas and has been used to describe the internal features of time series. But is it possible to use fractal theory to improve the performance of hydrological models? This study aims to investigate the potential benefits of applying fractal theory in model calibration. A new criterion named ratio of fractal dimensions (RD) is defined as the ratio of fractal dimensions of simulated and observed streamflow series. To combine the advantages of fractal theory with classical criteria based on squared residuals, a multi-objective calibration strategy is designed. The selected classical criterion is Nash-Sutcliffe efficiency (E). The E-RD strategy is tested in three study cases with different climate and geography. The results of experiment reveal that, from most aspects, introducing RD into model calibration makes the simulation of streamflow components more reasonable. Besides, in calibration, only little decrease of E occurs when pursuing better RD. We therefore recommend choosing the best E among the parameter sets whose RD is around 1.

Probabilistic direction-dependent ionospheric calibration for LOFAR-HBA

Direction-dependent calibration and imaging is a vital part of producing radio images that are deep and have a high fidelity and highly dynamic range with a wide-field low-frequency array such as LOFAR. Currently, dedicated facet-based direction-dependent calibration algorithms rely on the assumption that the size of the isoplanatic patch is much larger than the separation between bright in-field calibrators. This assumption is often violated owing to the dynamic nature of the ionosphere, and as a result, direction-dependent errors affect image quality between calibrators. In this paper we propose a probabilistic physics-informed model for inferring ionospheric phase screens, providing a calibration for all sources in the field of view. We apply our method to a randomly selected observation from the LOFAR Two-Metre Sky Survey archive, and show that almost all direction-dependent effects between bright calibrators are corrected and that the root-mean-squared residuals around bright sources are reduced by 32% on average.

Economic crises and incidence of suicide in Iceland 1911-2017

Introduction: Suicides are number 16 as a cause of death worldwide. Causes are not always known, often associated with depression or trauma. Suicide incidence has decreased world- wide in the past three decades. The economic crisis of 2008 led to an increase in many countries. Many confounding factors make comparisons between countries difficult. This study assesses the possible impact of economic crises in Iceland on suicide incidence. Material and Methods: The work is based on suicide data from 1911 to 2017 and six economic crises from 1918 to 2008. The incidence is calculated five and ten years before and after the index year of each crisis. Possible crisis impact was assessed by applying a quasi-Poisson model to the data. Variance can be greater than model shows, so overdispersion was assessed. The evolution over time is assessed by inspection of cumulative sum of squared ­residuals (CUSUMSQ). Results: Suicide incidence increased from 1930, beginning to decline around 1990. Given a small population size there are wide upwards incidence fluctuations, within and outside the crisis ­periods. The crises of 1931 and 1948 showed an increase, wheras in the others there is no change or a decrease. The sizes of deviations from expected value are, for the whole period, in compliance with the quasi-poisson model for counts. Conclusion: There is no statistical correlation between the six economic crises and suicide incidence in the Icelandic data. The study is based on population incidence and does not preclude a negative impact of economic crises on individuals.

MENGATASI PENCILAN PADA PEMODELAN REGRESI LINEAR BERGANDA DENGAN METODE REGRESI ROBUST PENAKSIR LMS

Ordinary Least Squares (OLS) is frequent used method for estimating parameters. OLS estimator is not a robust regression procedure for the presence of outliers, so the estimate becomes inappropriate. Least Median of Squares (LMS) is one of a robust estimator for the presence of outliers and has a high breakdown value. LMS estimate parameters by minimizing the median of squared residuals. Least Median of Squares (LMS) The purpose of this study is geting a regression equation that better than the regression equation before using OLS for the data that having outlier. For the first step, checking if there is outlier at data and then searching regression equation with LMS method. In this study used data stackloss and from estimation parameter of this data, LMS estimator showed better results compared to the OLS estimator because the regression equation from LMS method have smaller value of Mean Absolute Percentage Error (MAPE).

Estudio descriptivo de lesiones de pádel: relación con el género, edad, nivel de los jugadores y localización de las lesiones

Resumen Objetivo: Analizar la relación existente según el género, la edad y el nivel de los jugadores de pádel con la cantidad de lesiones descritas por ellos mismos. Método: Participaron 148 jugadores (75 hombres, 73 mujeres, 33.95 ± 9.87 años). Se registraron un total de 151 lesiones clasificadas en función del tipo de lesión, localización anatómica y situación (entrenamiento o competición). Estudio descriptivo retrospectivo utilizando una adaptación de un cuestionario de lesiones deportivas autocompletado por los jugadores. Se utilizó la prueba de Chi-Cuadrado, los Residuos y la V de Cramer para conocer el grado de asociación entre las variables. Resultados: Los resultados mostraron: i) predominio de lesiones musculares en los miembros inferiores; ii) alto número de lesiones en el tronco en hombres y de los miembros superiores en mujeres (p = 0.046); iii) mayor índice de lesiones musculares en mayores de 35 años y tendinosas en menores de 35 años (p = 0.009); iv) mayor número de lesiones en jugadores de menor nivel, especialmente en tendones y hombro (p = 0.031). Conclusiones: Las lesiones más frecuentes en pádel son las musculares, localizadas en los miembros inferiores. El género masculino presenta más lesiones en el tronco mientras que el número de lesiones en mujeres se centra en el miembro superior. Los jugadores mayores de 35 años presentaron más lesiones musculares mientras que se registró un mayor número de lesiones tendinosas en menores de 35 años. Finalmente, el nivel de los jugadores está relacionado inversamente con el número de lesiones (a mayor nivel, menor número de lesiones). Abstract Objective: To analyze the relationship according to gender, age and level of padel players with the number of self-reported injury history. Method: A total of 148 players (75 men, 73 women, 33.95 ± 9.87 years old) take part in this study, from whom 151 injuries were registered and classified regarding the type, location and injury situation (training or competition). A retrospective, descriptive study was set out, using an adapted version of a self-reported sports injury questionnaire. Chi-Squared, Residuals and Cramer’s V were calculated to study the strength of association between variables. Results: Findings revealed: i) overall predominance of muscle injuries in the lower limbs; ii) greater number of trunk injuries in men and upper limbs in women (p = 0.046); iii) higher rate of muscular lesions in players older than 35 years and tendinosis in younger than 35 years (p = 0.009); iv) higher rate of injuries in lower level players, especially in tendons and shoulder (p = 0.031). Conclusions: Muscular accounted for the majority of injuries in padel, particularly on the lower limbs. Males reported more trunk injuries, whilst females suffered more on the upper limbs. Players older than 35 years informed more muscular injuries; by contrast, tendinous injuries prevailed in younger than 35 years. Finally, the level of players revealed an inverse relationship in the number of injuries (the higher the level, the lower the injury rate).

Incorporating Case-Based Reasoning for Radiation Therapy Knowledge Modeling: A Pelvic Case Study

Knowledge models in radiotherapy capture the relation between patient anatomy and dosimetry to provide treatment planning guidance. When treatment schemes evolve, existing models struggle to predict accurately. We propose a case-based reasoning framework designed to handle novel anatomies that are of same type but vary beyond original training samples. A total of 105 pelvic intensity-modulated radiotherapy cases were analyzed. Eighty cases were prostate cases while the other 25 were prostate-plus-lymph-node cases. We simulated 4 scenarios: Scarce scenario, Semiscarce scenario, Semiample scenario, and Ample scenario. For the Scarce scenario, a multiple stepwise regression model was trained using 85 cases (80 prostate, 5 prostate-plus-lymph-node). The proposed workflow started with evaluating the feature novelty of new cases against 5 training prostate-plus-lymph-node cases using leverage statistic. The case database was composed of a 5-case dose atlas. Case-based dose prediction was compared against the regression model prediction using sum of squared residual. Mean sum of squared residual of case-based and regression predictions for the bladder of 13 identified outliers were 0.174 ± 0.166 and 0.459 ± 0.508, respectively ( P = .0326). For the rectum, the respective mean sum of squared residuals were 0.103 ± 0.120 and 0.150 ± 0.171 for case-based and regression prediction ( P = .1972). By retaining novel cases, under the Ample scenario, significant statistical improvement was observed over the Scarce scenario ( P = .0398) for the bladder model. We expect that the incorporation of case-based reasoning that judiciously applies appropriate predictive models could improve overall prediction accuracy and robustness in clinical practice.

Adjusting weight growth curve of male quails Coturnix japonica reared in the semi-arid region of the State of Pernambuco

This study adjusted different regression models to describe the growth pattern of meat quails from birth to 42 days of age. Data of 300 male quails were used. Weight and height information of all quails were collected weekly from the 1st to the 42nd day of age. Body weight of poultry was subjected to the polynomial, logistic, Gompertz, Weibull, and log-normal regression models. The criteria used to choose the best model to explain the growth curve of quails were the coefficient of determination of the model, Akaike’s information criterion, sum of squared residuals and Willmott’s index. For all the models used, the variables age and height were significant to explain the weight of quails. The polynomial (R² = 99.99%, AIC = 24.68, SSR = 27.5, d = 0.9999) and log-normal (R² = 99.60%, AIC = -17.5, SSR = 107.15, d = 0.9989) models presented the best fit criteria and were recommended to explain the growth of quails.