Search results for: correlated random damping

Authors: Janne Engblom, Elias Oikarinen

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The understanding of housing price dynamics is of importance to a great number of agents: to portfolio investors, banks, real estate brokers and construction companies as well as to policy makers and households. A panel dataset is one that follows a given sample of individuals over time, and thus provides multiple observations on each individual in the sample. Panel data models include a variety of fixed and random effects models which form a wide range of linear models. A special case of panel data models is dynamic in nature. A complication regarding a dynamic panel data model that includes the lagged dependent variable is endogeneity bias of estimates. Several approaches have been developed to account for this problem. In this paper, the panel models were estimated using the Common Correlated Effects estimator (CCE) of dynamic panel data which also accounts for cross-sectional dependence which is caused by common structures of the economy. In presence of cross-sectional dependence standard OLS gives biased estimates. In this study, U.S housing price dynamics were examined empirically using the dynamic CCE estimator with first-difference of housing price as the dependent and first-differences of per capita income, interest rate, housing stock and lagged price together with deviation of housing prices from their long-run equilibrium level as independents. These deviations were also estimated from the data. The aim of the analysis was to provide estimates with comparisons of estimates between 1980-1999 and 2000-2012. Based on data of 50 U.S cities over 1980-2012 differences of short-run housing price dynamics estimates were mostly significant when two time periods were compared. Significance tests of differences were provided by the model containing interaction terms of independents and time dummy variable. Residual analysis showed very low cross-sectional correlation of the model residuals compared with the standard OLS approach. This means a good fit of CCE estimator model. Estimates of the dynamic panel data model were in line with the theory of housing price dynamics. Results also suggest that dynamics of a housing market is evolving over time.

Keywords: dynamic model, panel data, cross-sectional dependence, interaction model

Procedia PDF Downloads 233

Authors: Ruichong Zhang

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This study proposes a recording-based approach to characterize and quantify earthquake-induced site nonlinearity, exemplified as soil nonlinearity and/or liquefaction. Alternative to Fourier spectral analysis (FSA), the paper introduces time-frequency analysis of earthquake ground motion recordings with the aid of so-called Hilbert-Huang transform (HHT), and offers justification for the HHT in addressing the nonlinear features shown in the recordings. With the use of the 2001 Nisqually earthquake recordings, this study shows that the proposed approach is effective in characterizing site nonlinearity and quantifying the influences in seismic ground responses.

Keywords: site nonlinearity, site amplification, site damping, Hilbert-Huang Transform (HHT), liquefaction, 2001 Nisqually Earthquake

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Authors: Mohammad Jalali Varnamkhasti

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The genetic algorithms have been very successful in handling difficult optimization problems. The fundamental problem in genetic algorithms is premature convergence. This paper, present a new fuzzy genetic algorithm based on chaotic values instead of the random values in genetic algorithm processes. In this algorithm, for initial population is used chaotic sequences and then a new sexual selection proposed for selection mechanism. In this technique, the population is divided such that the male and female would be selected in an alternate way. The layout of the male and female chromosomes in each generation is different. A female chromosome is selected by tournament selection size from the female group. Then, the male chromosome is selected, in order of preference based on the maximum Hamming distance between the male chromosome and the female chromosome or The highest fitness value of male chromosome (if more than one male chromosome is having the maximum Hamming distance existed), or Random selection. The selections of crossover and mutation operators are achieved by running the fuzzy logic controllers, the crossover and mutation probabilities are varied on the basis of the phenotype and genotype characteristics of the chromosome population. Computational experiments are conducted on the proposed techniques and the results are compared with some other operators, heuristic and local search algorithms commonly used for solving p-median problems published in the literature.

Keywords: genetic algorithm, fuzzy system, chaos, sexual selection

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Authors: Maria Bercea, Bernhard A. Wolf

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The thermodynamic behavior for solutions of poly (methyl methacrylate-ran-t-butyl methacrylate) of variable composition as compared with the corresponding homopolymers was investigated by light scattering measurements carried out for dilute solutions and vapor pressure measurements of concentrated solutions. The complex dependencies of the Flory Huggins interaction parameter on concentration and copolymer composition in solvents of different polarity (toluene and chloroform) can be understood by taking into account the ability of the polymers to rearrange in a response to changes in their molecular surrounding. A recent unified thermodynamic approach was used for modeling the experimental data, being able to describe the behavior of the different solutions by means of two adjustable parameters, one representing the effective number of solvent segments and another one accounting for the interactions between the components. Thus, it was investigated how the solvent quality changes with the composition of the copolymers through the Gibbs energy of mixing as a function of polymer concentration. The largest reduction of the Gibbs energy at a given composition of the system was observed for the best solvent. The present investigation proves that the new unified thermodynamic approach is a general concept applicable to homo- and copolymers, independent of the chain conformation or shape, molecular and chemical architecture of the components and of other dissimilarities, such as electrical charges.

Keywords: random copolymers, Flory Huggins interaction parameter, Gibbs energy of mixing, chemical architecture

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Authors: Newroz Nooralddin Abdulrazaq, Thuraya Mahmood Qaradaghi

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The McEliece cryptosystem is an asymmetric type of cryptography based on error correction code. The classical McEliece used irreducible binary Goppa code which considered unbreakable until now especially with parameter [1024, 524, and 101], but it is suffering from large public key matrix which leads to be difficult to be used practically. In this work Irreducible and Separable Goppa codes have been introduced. The Irreducible and Separable Goppa codes used are with flexible parameters and dynamic error vectors. A Comparison between Separable and Irreducible Goppa code in McEliece Cryptosystem has been done. For encryption stage, to get better result for comparison, two types of testing have been chosen; in the first one the random message is constant while the parameters of Goppa code have been changed. But for the second test, the parameters of Goppa code are constant (m=8 and t=10) while the random message have been changed. The results show that the time needed to calculate parity check matrix in separable are higher than the one for irreducible McEliece cryptosystem, which is considered expected results due to calculate extra parity check matrix in decryption process for g2(z) in separable type, and the time needed to execute error locator in decryption stage in separable type is better than the time needed to calculate it in irreducible type. The proposed implementation has been done by Visual studio C#.

Keywords: McEliece cryptosystem, Goppa code, separable, irreducible

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Authors: Farid Khosravikia, Patricia Clayton

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The main objective of this work is to evaluate the advantages and disadvantages of various machine learning techniques in two key steps of seismic hazard and risk assessment of different types of structures. The first step is the development of ground-motion models, which are used for forecasting ground-motion intensity measures (IM) given source characteristics, source-to-site distance, and local site condition for future events. IMs such as peak ground acceleration and velocity (PGA and PGV, respectively) as well as 5% damped elastic pseudospectral accelerations at different periods (PSA), are indicators of the strength of shaking at the ground surface. Typically, linear regression-based models, with pre-defined equations and coefficients, are used in ground motion prediction. However, due to the restrictions of the linear regression methods, such models may not capture more complex nonlinear behaviors that exist in the data. Thus, this study comparatively investigates potential benefits from employing other machine learning techniques as statistical method in ground motion prediction such as Artificial Neural Network, Random Forest, and Support Vector Machine. The results indicate the algorithms satisfy some physically sound characteristics such as magnitude scaling distance dependency without requiring pre-defined equations or coefficients. Moreover, it is shown that, when sufficient data is available, all the alternative algorithms tend to provide more accurate estimates compared to the conventional linear regression-based method, and particularly, Random Forest outperforms the other algorithms. However, the conventional method is a better tool when limited data is available. Second, it is investigated how machine learning techniques could be beneficial for developing probabilistic seismic demand models (PSDMs), which provide the relationship between the structural demand responses (e.g., component deformations, accelerations, internal forces, etc.) and the ground motion IMs. In the risk framework, such models are used to develop fragility curves estimating exceeding probability of damage for pre-defined limit states, and therefore, control the reliability of the predictions in the risk assessment. In this study, machine learning algorithms like artificial neural network, random forest, and support vector machine are adopted and trained on the demand parameters to derive PSDMs for them. It is observed that such models can provide more accurate estimates of prediction in relatively shorter about of time compared to conventional methods. Moreover, they can be used for sensitivity analysis of fragility curves with respect to many modeling parameters without necessarily requiring more intense numerical response-history analysis.

Keywords: artificial neural network, machine learning, random forest, seismic risk analysis, seismic hazard analysis, support vector machine

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Authors: Showkat Ahmad Lone, Ahmadur Rahman, Ariful Islam

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The major assumption in accelerated life tests (ALT) is that the mathematical model relating the lifetime of a test unit and the stress are known or can be assumed. In some cases, such life–stress relationships are not known and cannot be assumed, i.e. ALT data cannot be extrapolated to use condition. So, in such cases, partially accelerated life test (PALT) is a more suitable test to be performed for which tested units are subjected to both normal and accelerated conditions. This study deals with estimating information about failure times of items under step-stress partially accelerated life tests using progressive failure-censored hybrid data with random removals. The life data of the units under test is considered to follow exponential life distribution. The removals from the test are assumed to have binomial distributions. The point and interval maximum likelihood estimations are obtained for unknown distribution parameters and tampering coefficient. An optimum test plan is developed using the D-optimality criterion. The performances of the resulting estimators of the developed model parameters are evaluated and investigated by using a simulation algorithm.

Keywords: binomial distribution, d-optimality, multiple censoring, optimal design, partially accelerated life testing, simulation study

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Authors: David Karasek, Veronika Kubickova, Ondrej Krystynik, Dominika Goldmannova, Lubica Cibickova, Jan Schovanek

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Introduction: Adiponectin, adipocyte-fatty acid-binding protein (A-FABP), and Wnt1 inducible signaling pathway protein-1 (WISP-1) are adipokines particularly associated with insulin resistance. The aim of the study was to compare their levels in women with gestational diabetes (GDM), type 2 diabetes mellitus (T2DM) and healthy controls and determine their relation with metabolic parameters. Methods: Fifty women with GDM, 50 women with T2DM, and 35 healthy women were included in the study. In addition to adipokines, anthropometric, lipid parameters, and markers, insulin resistance, and glucose control were assessed in all participants. Results: Compared to healthy controls only significantly lower levels of adiponectin were detected in women with GDM, whereas lower levels of adiponectin, higher levels of A-FABP and of WISP-1 were present in women with T2DM. Women with T2DM had also lower levels of adiponectin and higher levels of A-FABP compared to women with GDM. In women with GDM or T2DM adiponectin correlated negatively with body mass index (BMI), triglycerides (TG), C-peptide and positively with HDL-cholesterol; A-FABP positively correlated with BMI, TG, waist, and C-peptide. Moreover, there was a positive correlation between WISP-1 and C-peptide in women with T2DM. Conclusion: Adverse adipokines production detecting dysfunctional fat tissue is in women with GDM less presented than in women with T2DM, but more expressed compared to healthy women. Acknowledgment: Supported by AZV NV18-01-00139 and MH CZ DRO (FNOl, 00098892).

Keywords: adiponectin, adipocyte-fatty acid binding protein, wnt1 inducible signaling pathway protein-1, gestational diabetes, type 2 diabetes mellitus

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Authors: Aysheh Maslemani, Ruth Mayo, Greg Maio, Ariel Knafo-Noam

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Values are abstract ideals that serve as guiding principles in one's life. As inherently positive and desirable concepts, values are seen as motivators for actions and behaviors. However, research has largely ignored the possibility that values may elicit negative feelings despite being explicitly important to us. In the current study, we aim to examine this possibility. Four hundred participants over 18 years(M=41.6, SD=13.7, Female=178) from the UK completed a questionnaire in which they were asked to indicate their level of positive/negative feelings towards a comprehensive list of values and then report the importance of these values to them. The results support our argument by showing that people can have negative feelings towards their values and that people can feel both positive and negative emotions towards their values simultaneously, which means feeling ambivalence. We ran a mixed-effect model with ambivalence, value type, and their interaction as fixed effects, with by subject random intercept and by subject random slope for ambivalence. The results reveal that values that elicit less ambivalence predicted higher ratings for value importance. This research contributes to the field of values on multiple levels. Theoretically, it will uncover new insights about values, such as the existence of negative emotions towards them and the presence of ambivalence towards values. These findings may inspire future studies to explore the effects of ambivalence on people's well-being, behaviors, cognition, and their affect. We discuss the findings and consider their implications for understanding the social psychological mechanisms underpinning value ambivalence.

Keywords: emotion, social cognition, values., ambivalence

Procedia PDF Downloads 47

Authors: Aysheh Maslemani, Ruth Mayo, Greg Maio, Ariel Knafo-Noam

Abstract:

Values are abstract ideals that serve as guiding principles in one's life. As inherently positive and desirable concepts, values are seen as motivators for actions and behaviors. However, research has largely ignored the possibility that values may elicit negative feelings despite being explicitly important to us. In the current study we aim to examine this possibility. Four hundred participants over 18 years(M=41.6,SD=13.7,Female=178) from the UK completed a questionnaire in which they were asked to indicate their level of positive/negative feelings towards a comprehensive list of values and then report the importance of these values to them. The results support our argument by showing that people can have negative feelings towards their values and that people can feel both positive and negative emotions towards their values simultaneously, which means feeling ambivalence. We ran a mixed-effect model with ambivalence, value type, and their interaction as fixed effects, with by subject random intercept, and by subject random slope for ambivalence. The results reveal that values that elicit less ambivalence predicted higher ratings for value importance. This research contributes to the field of values on multiple levels. Theoretically, it will uncover new insights about values, such as the existence of negative emotions towards them, the presence of ambivalence towards values. These findings may inspire future studies to explore the effects of ambivalence on people's well-being, behaviors, cognition, and their affect. We discuss the findings and consider their implications for understanding the social psychological mechanisms underpinning value ambivalence.

Keywords: ambivalence, emotion, social cognition, values

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Authors: Z. Masoumi, B. Moaveni

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This article is about first step of structural health monitoring by identifying structural system in the presence of unknown input. In the structural system identification, identification of structural parameters such as stiffness and damping are considered. In this study, the Kalman filter (KF) design for structural systems with unknown excitation is expressed. External excitations, such as earthquakes, wind or any other forces are not measured or not available. The purpose of this filter is its strengths to estimate the state variables of the system in the presence of unknown input. Also least squares estimation (LSE) method with unknown input is studied. Estimates of parameters have been adopted. Finally, using two examples advantages and drawbacks of both methods are studied.

Keywords: Kalman filter (KF), least square estimation (LSE), structural health monitoring (SHM), structural system identification

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Authors: Behnam Zahednejad, Saeed Kosari

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Widespread deployment of Internet of Things (IoT) has raised security and privacy issues in this environment. Designing a secure two-factor authentication scheme between the user and server is still a challenging task. In this paper, we focus on Cancelable Biometric (CB) as an authentication factor in IoT. We show that previous CB-based scheme fail to provide real two-factor security, Perfect Forward Secrecy (PFS) and suffer database attacks and traceability of the user. Then we propose our improved scheme based on CB and Physically Unclonable Functions (PUF), which can provide real two-factor security, PFS, user’s unlinkability, and resistance to database attack. In addition, Key Compromise Impersonation (KCI) resilience is achieved in our scheme. We also prove the security of our proposed scheme formally using both Real-Or-Random (RoR) model and the ProVerif analysis tool. For the usability of our scheme, we conducted a performance analysis and showed that our scheme has the least communication cost compared to the previous CB-based scheme. The computational cost of our scheme is also acceptable for the IoT environment.

Keywords: IoT, two-factor security, cancelable biometric, key compromise impersonation resilience, perfect forward secrecy, database attack, real-or-random model, ProVerif

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Authors: Giovanni Incerti

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In this paper the dynamic behavior of a synchronous belt drive during start-up is analyzed and discussed. Besides considering the belt elasticity, the mathematical model here proposed also takes into consideration the electrical behaviour of the DC motor. The solution of the motion equations is obtained by means of the modal analysis in state space, which allows to obtain the decoupling of all equations of the mathematical model without introducing the hypothesis of proportional damping. The mathematical model of the transmission and the solution algorithms have been implemented within a computing software that allows the user to simulate the dynamics of the system and to evaluate the effects due to the elasticity of the belt branches and to the electromagnetic behavior of the DC motor. In order to show the details of the calculation procedure, the paper presents a case study developed with the aid of the abovementioned software.

Keywords: belt drive, vibrations, startup, DC motor

Procedia PDF Downloads 553

Authors: Ashok Kalagura

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This paper presents a data-mining model for fault-zone identification of flexible AC transmission systems (FACTS)-based transmission line including a thyristor-controlled series compensator (TCSC) and unified power-flow controller (UPFC), using ensemble decision trees. Given the randomness in the ensemble of decision trees stacked inside the random forests model, it provides an effective decision on the fault-zone identification. Half-cycle post-fault current and voltage samples from the fault inception are used as an input vector against target output ‘1’ for the fault after TCSC/UPFC and ‘1’ for the fault before TCSC/UPFC for fault-zone identification. The algorithm is tested on simulated fault data with wide variations in operating parameters of the power system network, including noisy environment providing a reliability measure of 99% with faster response time (3/4th cycle from fault inception). The results of the presented approach using the RF model indicate the reliable identification of the fault zone in FACTS-based transmission lines.

Keywords: distance relaying, fault-zone identification, random forests, RFs, support vector machine, SVM, thyristor-controlled series compensator, TCSC, unified power-flow controller, UPFC

Procedia PDF Downloads 411

Authors: G. Shabib, Esam H. Abd-Elhameed, G. Magdy

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In this paper, a comparison of discrete time PID, PSS controllers is presented through small signal stability of power system comprising of one machine connected to infinite bus system. This comparison achieved by using a new approach of discretization which converts the S-domain model of analog controllers to a Z-domain model to enhance the damping of a single machine power system. The new method utilizes the Plant Input Mapping (PIM) algorithm. The proposed algorithm is stable for any sampling rate, as well as it takes the closed loop characteristic into consideration. On the other hand, the traditional discretization methods such as Tustin’s method is produce satisfactory results only; when the sampling period is sufficiently low.

Keywords: PSS, power system stabilizer PID, proportional-integral-derivative PIM, plant input mapping

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Authors: Bryce R. Jolley, Michael Uchic

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This study presents initial results toward the correlative characterization of inherent defects of Ti-6Al-4V additive manufacture (AM). X-Ray Computed Tomography (CT) defect data are compared and correlated with microscopic photographs obtained via automated serial sectioning. The metal AM specimen was manufactured out of Ti-6Al-4V virgin powder to specified dimensions. A post-contour was applied during the fabrication process with a speed of 1050 mm/s, power of 260 W, and a width of 140 µm. The specimen was stress relief heat-treated at 16°F for 3 hours. Microfocus CT imaging was accomplished on the specimen within a predetermined region of the build. Microfocus CT imaging was conducted with parameters optimized for Ti-6Al-4V additive manufacture. After CT imaging, a modified RoboMet. 3D version 2 was employed for serial sectioning and optical microscopy characterization of the same predetermined region. Automated montage capture with sub-micron resolution, bright-field reflection, 12-bit monochrome optical images were performed in an automated fashion. These optical images were post-processed to produce 2D and 3D data sets. This processing included thresholding and segmentation to improve visualization of defect features. The defects observed from optical imaging were compared and correlated with the defects observed from CT imaging over the same predetermined region of the specimen. Quantitative results of area fraction and equivalent pore diameters obtained via each method are presented for this correlation. It is shown that Microfocus CT imaging does not capture all inherent defects within this Ti-6Al-4V AM sample. Best practices for this correlative effort are also presented as well as the future direction of research resultant from this current study.

Keywords: additive manufacture, automated serial sectioning, computed tomography, nondestructive evaluation

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Authors: M. M. Islam, S. Sharmin, M. Shah Newaz, N. S. Juyena, M. M. Rahman, P. K. Jha, F. Y. Bari

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The aim of the present study was to select the high quality ram by measuring the scrotal biometry which has an effect on semen parameters. Ten rams were selected in the present study. Eight ejaculates were collected from each ram using artificial vagina method. Scrotal circumference was measured before and after semen collection on weekly basis using the Scrotal tape. Bio-metries of scrotum (scrotal length and scrotal volume) were calculated. Semen was evaluated for macroscopic and microscopic characteristics. The average estimated scrotal circumference (cm) and scrotal volume (cm3) in 8 different age groups were 17.16±0.05 cm and 61.30±0.70 cm3, 17.17±0.62 cm and 63.67±4.49 cm3, 17.22±0.52 cm and 64.90±4.21 cm3, 17.72±0.37 cm and 67.10±4.20 cm3, 18.41±0.35cm and 69.52±4.12cm3, 18.45±0.36cm and 77.17±3.81 cm3, 18.55±0.41 cm and 78.72±4.90 cm3, 19.10±0.30 cm and 87.35±5.45 cm3 respectively. The body weight, scrotal circumference and scrotal volume increased with the progress of age (P < 0.05). Body weight of age group 381-410 days (13.62+1.48 kg) was significantly higher than group 169-200 days (10.17±0.05 kg) and 201-230 days (10.42±1.18 kg) (p < 0.05). Scrotal circumference (SC) of age group 381-410 days (19.10±0.30 cm) was significantly higher (p < 0.05) than other groups. In age group 381-410 days, scrotal volume (SCV) (87.35±5.45 cm3) was significantly higher than other first five groups (p < 0.05). Both scrotal circumference and scrotal volume development was positively correlated with the increasing of body weight (R2= 0.51). Semen volume increased accordingly with the increasing of ages, varied from 0.35±0.00 ml to 1.15+0.26 ml. Semen volume of age group 381-410 days (1.15±0.26 ml) was significantly higher than other age groups (p < 0.05) except age group 351-380 days (p > 0.05). Mass activity of different age groups varied from 2.75 (±0.35) to 4.25 (±0.29) ml in the scale of 1-5. Sperm concentration, progressive motility (%),progressively improved according to the increasing of ages, but significant changes in these parameters were seen when the animals reaches the age 291 days or more (p < 0.05). However, normal spermatozoa (%) improved significantly from the age of 261 days or more. Mass activity (mass) was positively correlated with sperm concentration (R2=0.568) and progressive motility (%) (R2=0.616). The relationships of semen volume with body weight and scrotal measurements and sperm concentration indicate that they are useful in evaluating rams for breeding soundness and genetic improvement for fertility in indigenous ram.

Keywords: breeding soundness, ram, semen quality, scrotal biometry

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Authors: Akiode Ayobami, Akiode Akinsewa, Odeku Mojisola, Salako Busola, Odutolu Omobola, Nuhu Khadija

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Purpose: Evidence exists that family planning use can contribute to reduction in infant and maternal mortality in any country. Despite these benefits, contraceptive use in Nigeria still remains very low, only 10% among married women. Understanding factors that predict contraceptive use is very important in order to improve the situation. In this paper, we analysed data from the 2013 Nigerian Demographic and Health Survey (NDHS) to better understand predictors of contraceptive use in Nigeria. The use of logistics regression and other traditional models in this type of situation is not appropriate as they do not account for social structure influence brought about by the hierarchical nature of the data on response variable. We therefore used multilevel modelling to explore the determinants of contraceptive use in order to account for the significant variation in modern contraceptive use by socio-demographic, and other proximate variables across the different Nigerian states. Method: This data has a two-level hierarchical structure. We considered the data of 26, 403 married women of reproductive age at level 1 and nested them within the 36 states and the Federal Capital Territory, Abuja at level 2. We modelled use of modern contraceptive against demographic variables, being told about FP at health facility, heard of FP on TV, Magazine or radio, husband desire for more children nested within the state. Results: Our results showed that the independent variables in the model were significant predictors of modern contraceptive use. The estimated variance component for the null model, random intercept, and random slope models were significant (p=0.00), indicating that the variation in contraceptive use across the Nigerian states is significant, and needs to be accounted for in order to accurately determine the predictors of contraceptive use, hence the data is best fitted by the multilevel model. Only being told about family planning at the health facility and religion have a significant random effect, implying that their predictability of contraceptive use varies across the states. Conclusion and Recommendation: Results showed that providing FP information at the health facility and religion needs to be considered when programming to improve contraceptive use at the state levels.

Keywords: multilevel modelling, family planning, predictors, Nigeria

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Authors: Jeremie Coullon, Robert J. Webber

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We introduce a Markov chain Monte Carlo (MCMC) sam-pler for infinite-dimensional inverse problems. Our sam-pler is based on the affine invariant ensemble sampler, which uses interacting walkers to adapt to the covariance structure of the target distribution. We extend this ensem-ble sampler for the first time to infinite-dimensional func-tion spaces, yielding a highly efficient gradient-free MCMC algorithm. Because our ensemble sampler does not require gradients or posterior covariance estimates, it is simple to implement and broadly applicable. In many Bayes-ian inverse problems, Markov chain Monte Carlo (MCMC) meth-ods are needed to approximate distributions on infinite-dimensional function spaces, for example, in groundwater flow, medical imaging, and traffic flow. Yet designing efficient MCMC methods for function spaces has proved challenging. Recent gradi-ent-based MCMC methods preconditioned MCMC methods, and SMC methods have improved the computational efficiency of functional random walk. However, these samplers require gradi-ents or posterior covariance estimates that may be challenging to obtain. Calculating gradients is difficult or impossible in many high-dimensional inverse problems involving a numerical integra-tor with a black-box code base. Additionally, accurately estimating posterior covariances can require a lengthy pilot run or adaptation period. These concerns raise the question: is there a functional sampler that outperforms functional random walk without requir-ing gradients or posterior covariance estimates? To address this question, we consider a gradient-free sampler that avoids explicit covariance estimation yet adapts naturally to the covariance struc-ture of the sampled distribution. This sampler works by consider-ing an ensemble of walkers and interpolating and extrapolating between walkers to make a proposal. This is called the affine in-variant ensemble sampler (AIES), which is easy to tune, easy to parallelize, and efficient at sampling spaces of moderate dimen-sionality (less than 20). The main contribution of this work is to propose a functional ensemble sampler (FES) that combines func-tional random walk and AIES. To apply this sampler, we first cal-culate the Karhunen–Loeve (KL) expansion for the Bayesian prior distribution, assumed to be Gaussian and trace-class. Then, we use AIES to sample the posterior distribution on the low-wavenumber KL components and use the functional random walk to sample the posterior distribution on the high-wavenumber KL components. Alternating between AIES and functional random walk updates, we obtain our functional ensemble sampler that is efficient and easy to use without requiring detailed knowledge of the target dis-tribution. In past work, several authors have proposed splitting the Bayesian posterior into low-wavenumber and high-wavenumber components and then applying enhanced sampling to the low-wavenumber components. Yet compared to these other samplers, FES is unique in its simplicity and broad applicability. FES does not require any derivatives, and the need for derivative-free sam-plers has previously been emphasized. FES also eliminates the requirement for posterior covariance estimates. Lastly, FES is more efficient than other gradient-free samplers in our tests. In two nu-merical examples, we apply FES to challenging inverse problems that involve estimating a functional parameter and one or more scalar parameters. We compare the performance of functional random walk, FES, and an alternative derivative-free sampler that explicitly estimates the posterior covariance matrix. We conclude that FES is the fastest available gradient-free sampler for these challenging and multimodal test problems.

Keywords: Bayesian inverse problems, Markov chain Monte Carlo, infinite-dimensional inverse problems, dimensionality reduction

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Authors: Abigail Vallance

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Current research shows a correlation between declining mental health in the United States and the effect of COVID-19 on young adults and adolescents. Anxiety and depression are the two most common psychiatric illnesses, which are also the leading impediments to academic success. Spending six hours a day or more using computers is associated with higher risks of depression, with this time constraint pervasive even in present-day academia. Along with many hours on the computer, common issues COVID-19 had on students’ academic performance during online school included technical difficulties, poor support services, and difficulty adapting to online learning. Given the volume of requirements with unrealistic deadlines, and despite experiencing COVID-19, students showed an increase in their levels of anxiety. Besides the prevalent effect of COVID-19 on mental health, many studies show a correlation between mental health, COVID-19, academia, and sports performance. Academic research showed that negative self-talk, in relation to one’s self-efficacy, correlated with negative academic performance. Research showed that students who reported negative self-efficacy when test-taking led to negative test results. Furthermore, in sports performance, negative effects were found when athletes engage in negative self-talk. Overall, motivational self-talk, by oneself and through teammates and coaches, correlated with better performance than regular self-talk in sports. In relation to sports performance, the COVID-19 pandemic canceled complete sports seasons for millions of adolescents across the country. Many student-athletes use their sport to release emotions and escape from their mental health, but this was taken away. The purpose of this study is to address the current increase in mental health diagnoses in adolescents, including suicide rates after the COVID-19 pandemic began in 2020.This literature analysis is actively being studied.

Keywords: self-talk, COVID-19, mental health, adolescents

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Authors: Orkide Donma, Mustafa M. Donma

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Obesity, a disease associated with a low-grade inflammation, is a risk factor for the development of metabolic syndrome (MetS). So far, MetS risk factors such as parameters related to glucose and lipid metabolisms as well as blood pressure were considered for the evaluation of this disease. There are still some ambiguities related to the characteristic features of MetS observed particularly in pediatric population. Hormonal imbalance is also important, and quite a lot information exists about the behaviour of some hormones in adults. However, the hormonal profiles in pediatric metabolism have not been cleared yet. The aim of this study is to investigate the profiles of cortisol, insulin, and thyroid hormones in children with MetS. The study population was composed of morbid obese (MO) children without (Group 1) and with (Group 2) MetS components. WHO BMI-for age and sex percentiles were used for the classification of obesity. The values above 99 percentile were defined as morbid obesity. Components of MetS (central obesity, glucose intolerance, high blood pressure, high triacylglycerol levels, low levels of high density lipoprotein cholesterol) were determined. Anthropometric measurements were performed. Ratios as well as obesity indices were calculated. Insulin, cortisol, thyroid stimulating hormone (TSH), free T₃ and free T₄ analyses were performed by electrochemiluminescence immunoassay. Data were evaluated by statistical package for social sciences program. p<0.05 was accepted as the degree for statistical significance. The mean ages±SD values of Group 1 and Group 2 were 9.9±3.1 years and 10.8±3.2 years, respectively. Body mass index (BMI) values were calculated as 27.4±5.9 kg/m² and 30.6±8.1 kg/m², successively. There were no statistically significant differences between the ages and BMI values of the groups. Insulin levels were statistically significantly increased in MetS in comparison with the levels measured in MO children. There was not any difference between MO children and those with MetS in terms of cortisol, T₃, T₄ and TSH. However, T₄ levels were positively correlated with cortisol and negatively correlated with insulin. None of these correlations were observed in MO children. Cortisol levels in both MO as well as MetS group were significantly correlated. Cortisol, insulin, and thyroid hormones are essential for life. Cortisol, called the control system for hormones, orchestrates the performance of other key hormones. It seems to establish a connection between hormone imbalance and inflammation. During an inflammatory state, more cortisol is produced to fight inflammation. High cortisol levels prevent the conversion of the inactive form of the thyroid hormone T₄ into active form T₃. Insulin is reduced due to low thyroid hormone. T₃, which is essential for blood sugar control- requires cortisol levels within the normal range. Positive association of T₄ with cortisol and negative association of it with insulin are the indicators of such a delicate balance among these hormones also in children with MetS.

Keywords: children, cortisol, insulin, metabolic syndrome, thyroid hormones

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Authors: Burak Yildirim, Muhsin Tunay Gencoglu

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This publication shows the effects of Microgrid (MG) integration on the power systems oscillating stability. Generated MG model power systems were applied to the IEEE 14 bus test system which is widely used in stability studies. Stability studies were carried out with the help of eigenvalue analysis over linearized system models. In addition, Hopf bifurcation point detection was performed to show the effect of MGs on the system loadability margin. In the study results, it is seen that MGs affect system stability positively by increasing system loadability margin and has a damper effect on the critical modes of the system and the electromechanical local modes, but they make the damping amount of the electromechanical interarea modes reduce.

Keywords: Eigenvalue analysis, microgrid, Hopf bifurcation, oscillatory stability

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Authors: Anjali Chouksey, Riya Nimje, Jahanvi Saraf

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Online transactions have increased dramatically in this new ‘social-distancing’ era. With online transactions, Fraud in online payments has also increased significantly. Frauds are a significant problem in various industries like insurance companies, baking, etc. These frauds include leaking sensitive information related to the credit card, which can be easily misused. Due to the government also pushing online transactions, E-commerce is on a boom. But due to increasing frauds in online payments, these E-commerce industries are suffering a great loss of trust from their customers. These companies are finding credit card fraud to be a big problem. People have started using online payment options and thus are becoming easy targets of credit card fraud. In this research paper, we will be discussing machine learning algorithms. We have used a decision tree, XGBOOST, k-nearest neighbour, logistic-regression, random forest, and SVM on a dataset in which there are transactions done online mode using credit cards. We will test all these algorithms for detecting fraud cases using the confusion matrix, F1 score, and calculating the accuracy score for each model to identify which algorithm can be used in detecting frauds.

Keywords: machine learning, fraud detection, artificial intelligence, decision tree, k nearest neighbour, random forest, XGBOOST, logistic regression, support vector machine

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Authors: Piyarat Parklug, Busaba Supawattanaobodee, Penpat Pinyowattanasilp

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The radioactive iodine thyroid uptake (RAIU) has been widely used to differentiate the cause of thyrotoxicosis and treatment. Twenty-four hours RAIU is routinely used to calculate the dose of radioactive iodine (RAI) therapy; however, 2 days protocol is required. This study aims to evaluate the modification of Penpat Pinyowattanasilp equation application by the exclusion of outlier data, 3 hours RAIU less than 20% and more than 80%, to improve prediction of 24-hour uptake. The equation is predicted 24 hours RAIU (P24RAIU) = 32.5+0.702 (3 hours RAIU). Then calculating separation first and second therapeutic doses in Graves’ disease patients. Methods; This study was a retrospective study at Faculty of Medicine Vajira Hospital in Bangkok, Thailand. Inclusion were Graves’ disease patients who visited RAI clinic between January 2014-March 2019. We divided subjects into 2 groups according to first and second therapeutic doses. Results; Our study had a total of 151 patients. The study was done in 115 patients with first RAI dose and 36 patients with second RAI dose. The P24RAIU are highly correlated with actual 24-hour RAIU in first and second therapeutic doses (r = 0.913, 95% CI = 0.876 to 0.939 and r = 0.806, 95% CI = 0.649 to 0.897). Bland-Altman plot shows that mean differences between predictive and actual 24 hours RAI in the first dose and second dose were 2.14% (95%CI 0.83-3.46) and 1.37% (95%CI -1.41-4.14). The mean first actual and predictive therapeutic doses are 8.33 ± 4.93 and 7.38 ± 3.43 milliCuries (mCi) respectively. The mean second actual and predictive therapeutic doses are 6.51 ± 3.96 and 6.01 ± 3.11 mCi respectively. The predictive therapeutic doses are highly correlated with the actual dose in first and second therapeutic doses (r = 0.907, 95% CI = 0.868 to 0.935 and r = 0.953, 95% CI = 0.909 to 0.976). Bland-Altman plot shows that mean difference between predictive and actual P24RAIU in the first dose and second dose were less than 1 mCi (-0.94 and -0.5 mCi). This modification equation application is simply used in clinical practice especially patient with 3 hours RAIU in range of 20-80% in a Thai population. Before use, this equation for other population should be tested for the correlation.

Keywords: equation, Graves’disease, prediction, 24-hour uptake

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Authors: Habib Khodaverdiloo, Fatemeh Afrasiabi, Farrokh Asadzadeh, Martinus Th. Van Genuchten

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The soil particle-size distribution (PSD) is known to affect a broad range of soil physical, mechanical and hydraulic properties. Complete descriptions of a PSD curve should provide more information about these properties as opposed to having only information about soil textural class or the soil sand, silt and clay (SSC) fractions. We compared the accuracy of 19 different models of the cumulative PSD in terms of fitting observed data from a large number of Iranian soils. Parameters of the six most promising models were correlated with measured values of the field saturated hydraulic conductivity (Kfs), the mean weight diameter of soil aggregates (MWD), bulk density (ρb), and porosity (∅). These same soil properties were correlated also with conventional PSD parameters (SSC fractions), selected geometric PSD parameters (notably the mean diameter dg and its standard deviation σg), and several other PSD parameters (D50 and D60). The objective was to find the best predictions of several soil physical quality indices and the soil hydraulic properties. Neither SSC nor dg, σg, D50 and D60 were found to have a significant correlation with both Kfs or logKfs, However, the parameters of several cumulative PSD models showed statistically significant correlation with Kfs and/or logKfs (|r| = 0.42 to 0.65; p ≤ 0.05). The correlation between MWD and the model parameters was generally also higher than either with SSC fraction and dg, or with D50 and D60. Porosity (∅) and the bulk density (ρb) also showed significant correlation with several PSD model parameters, with ρb additionally correlating significantly with various geometric (dg), mechanical (D50 and D60), and agronomic (clay and sand) representations of the PSD. The fitted parameters of selected PSD models furthermore showed statistically significant correlations with Kfs,, MWD and soil porosity, which may be viewed as soil quality indices. Results of this study are promising for developing more accurate pedotransfer functions.

Keywords: particle size distribution, soil texture, hydraulic conductivity, pedotransfer functions

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Authors: Mandira Mishra, Mark Allen

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This meta-analysis explored whether rejection sensitivity relates to facets of romantic relationships. A comprehensive literature search identified 60 studies (147 effect sizes; 16,955 participants) that met inclusion criteria. Data were analysed using inverse-variance weighted random effects meta-analysis. Mean effect sizes from 21 meta-analyses provided evidence that more rejection sensitive individuals report lower levels of relationship satisfaction and relationship closeness, lower levels of perceived partner satisfaction, a greater likelihood of intimate partner violence (perpetration and victimization), higher levels of relationship concerns and relationship conflict, and higher levels of jealousy and self-silencing behaviours. There was also some evidence that rejection sensitive individuals are more likely to engage in risky sexual behaviour and are more prone to sexual compulsivity. There was no evidence of publication bias and various levels of heterogeneity in computed averages. Random effects meta-regression identified participant age and sex as important moderators of pooled mean effects. These findings provide a foundation for the theoretical development of rejection sensitivity in romantic relationships and should be of interest to relationship and marriage counsellors and other relationship professionals.

Keywords: intimate partner violence, relationship satisfaction, commitment, sexual orientation, risky sexual behaviour

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Authors: Rikson Gultom

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Hate Speech and Abusive language on social media is difficult to detect, usually, it is detected after it becomes viral in cyberspace, of course, it is too late for prevention. An early detection system that has a fairly good accuracy is needed so that it can reduce conflicts that occur in society caused by postings on social media that attack individuals, groups, and governments in Indonesia. The purpose of this study is to find an early detection model on Twitter social media using machine learning that has high accuracy from several machine learning methods studied. In this study, the support vector machine (SVM), Naïve Bayes (NB), and Random Forest Decision Tree (RFDT) methods were compared with the Support Vector machine with genetic algorithm (SVM-GA), Nave Bayes with genetic algorithm (NB-GA), and Random Forest Decision Tree with Genetic Algorithm (RFDT-GA). The study produced a comparison table for the accuracy of the hate speech and abusive language detection model, and presented it in the form of a graph of the accuracy of the six algorithms developed based on the Indonesian-language Twitter dataset, and concluded the best model with the highest accuracy.

Keywords: abusive language, hate speech, machine learning, optimization, social media

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Authors: Anumol Jose, Sajana Nazar, M. R. Vishnu, M. Anilkumar

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Water yam is an underutilized tropical tuber crop and a rich source of polyphenol compounds and acylated anthocyanins. There is an inverse relationship between the risk of chronic human diseases and the consumption of polyphenolic rich diet. Dioscorea alata is a plant species with several undocumented landraces. In this study, several landraces of water yam with distinct morphological features were collected from all over kerala. Distinct variation in morphological feature among landraces was tuber colour and only those landraces which expressed consistent morphological characters for constitutively two growing seasons were included in the study. Plants were categorized according to the L*a*b* colour attributes of tuber extracts. There were five categories, red, pink, orange, yellow and white. Total phenol, flavanoid and anthocyanin content of the tuber extracts were measured spectroscopically and correlated with antioxidant properties determined by 2,2-diphenyl-1-picryl-hydrazyl-hydrate free radical method and ferric reducing antioxidant power assay. Landraces showed statistically significant difference in all the parameters studied and strong correlation were observed between total phenol and antioxidant activity. Out of the five categories orange coloured tubers showed relatively high phenol and flavanoid content.Colour variations of tuber extracts correlated with anthocyanin quantity and polymeric nature of anthocyanins. This study helps to identify and categorize landraces of D.alata with potential health benefits and commercial applications. Distinct colour characteristics of tuber could be useful in the field of natural colorants. This study also aimed to document and preserve landraces of water yams for further study and research in this area.

Keywords: the antioxidant property, anthocyanins, polyphenols, water yam

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Authors: Gerald Todd

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Recent advances in random epistemologies and ubiquitous theory have paved the way for web services. Given the current status of linear-time communication, cyberinformaticians compellingly desire the exploration of link-level acknowledgements. In order to realize this purpose, we concentrate our efforts on disconfirming that DHTs and model checking are mostly incompatible.

Keywords: LAN, cyberinformatics, model checking, communication

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Authors: Yu-Chieh Huang, Pei-Shih Chen, Tao-Hsin Tung

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Background: Urinary tract infection is the most common bacterial infection to our best knowledge. Objective: This study is to investigate whether cranberry ingesting could improve the urinary tract infection. Methods: We searched the PubMed and Cochrane Library for relevant randomized controlled trials without language limitations between 9 March 1994 and June 30, 2017, with a priori defined inclusion and exclusion criteria. The search terms included (cranberry OR Vaccinium macrocarpon OR Vaccinium oxy-coccus OR Vaccinium microcarpum OR Vaccinium erythrocarpum OR Vaccinium) AND (urinary tract infection OR bacteriuria OR pyuria) AND (effect OR effective-ness OR efficacy) AND (random OR randomized). Results: There were 26 studies met the selection criteria included among 4709 eligible participants. We analyzed all trials in meta-analysis. The random-effects pooled risk ratio (RR) for the group using cranberry versus using placebo was 0.75; 95%CI[0.63, 0.880]; p-value=0.0002) and heterogeneity was 56%. Furthermore, we divided the subjects into different subgroup to analysis. Ingesting cranberry seemed to be more effective in some subgroups, including the patients with recurrent UTI (RR, 0.71; 95%CI[0.54,0.93]; p-value=0.002) (I²= 65%) and female population (RR, 0.73, 95%CI[0.58,0.92]; p-value=0.002) (I²= 59%). The prevention effect was not different between cranberry and trimethoprim (RR, 1.25, 95%CI[0.67, 2.33]; p-value=0.49) (I²= 68%). No matter the forms of cranberry were capsules or juice, the efficacy was useful. Conclusions: It is showed that cranberry ingesting is usefully associated with prevention UTI. There are more effective in prevention of UTI in some groups.

Keywords: cranberry, effectiveness, prevention, urinary tract infect

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