Miracle-dz/UniFAR-FTUs huggingface dataset:
Analytical and numerical studies on the second-order asymptotic expansion method for European option pricing under two-factor stochastic volatilities

ABSTRACT The celebrated Black–Scholes model made the assumption of constant volatility but empirical studies on implied volatility and asset dynamics motivated the use of stochastic volatilities. Christoffersen in 2009 showed that multi-factor stochastic volatilities models capture the asset dynamics more realistically. Fouque in 2012 used it to price European options. In 2013, Chiarella and Ziveyi considered Christoffersen’s ideas and introduced an asset dynamics where the two volatilities of the Heston type act separately and independently on the asset price, and using Fourier transform for the asset price process and double Laplace transform for the two volatilities processes, solved a pricing problem for American options. This paper considers the Chiarella and Ziveyi model and parameterizes it so that the volatilities revert to the long-run-mean with reversion rates that mimic fast (for example daily) and slow (for example seasonal) random effects. Applying asymptotic expansion method presented by Fouque in 2012, we make an extensive and detailed derivation of the approximation prices for European options. We also present numerical studies on the behavior and accuracy of our first- and second-order asymptotic expansion formulas.
Prediksi Data Time Series Harga Penutupan Saham Menggunakan Model Box Jenkins ARIMA

The ability to predict time series data on closing market prices is critical in determining a company's stock results. The development of an efficient stock market has a positive correlation with economic growth, in a country both in the short and long term. In practice, investors tend to invest in countries that have a stable economy, low crime. The rise and fall of stock prices has made many investors develop various effective strategies in predicting stock prices in the future with the aim of making investment decisions so that investors can guarantee their profits and minimize risk.As a result, the researchers developed a model that could accurately estimate precision. Time series data models are one of the most powerful methods to render assumptions in decisions containing uncertainty. The AutoRegressive Integrated Moving Average (ARIMA) model with the Box Jenskins time series procedure is one of the most commonly used prediction models for time series results. The steps for using the Box Jenskins ARIMA model for historical details of expected stock closing prices are outlined in this paper. BBYB and YELO stock data from yahoo.finance were used as historical data. The Aikake Information Criterion (AIC), Bayesian Information Criterion (BIC) / Schawrz Bayesia Criterion (SBC), Log Probability, and Root Mean Square Error (RMSE) are used to choose an effective model, and the model chosen is ARIMA (1 , 1,2). The findings suggest that the Jenkins ARIMA box model has a lot of scope for short-term forecasting, which may help investors make better decisions. Â Keywords: prediction, the stock's current closing price, Box Jenskins ARIMA model
The Impact of Resilience on Students’ Academic Achievement: Case Study of Secondary School Students

This study was aimed at finding out the impact of resilience on students’ academic achievement. The researcher was motivated by the students’ effort to succeed despite the prevailing challenges and barriers affecting them especially in their academics. The survey research design was adopted for this study. A questionnaire was used as instrument for data collection. The simple random sampling technique was used. The questionnaire was administered to 80 students. The data were analyzed using descriptive statistics and the results presented on tables for the descriptive statistics and the chi- square for inferential statistics. Findings revealed that there is a high significant relationship between resilience and academic achievement of secondary school students in Minawao refugee camp. Recommendations were made on how to improve on their academic achievement given their prevailing circumstances and make them to be more resilient in the face of numerous challenges affecting them in any environment they find themselves
Uvjeti i efekti interesa za studij i motivacije za učenje (motivi za izbor studija i motivacijski regulacijski stilovi) kod hrvatskih i njemačkih studenata

U radu su priopceni rezultati empirijskog istraživanja uvjeta i efekata interesa za studij i motivacije za ucenje (motivi za izbor studija i motivacijski regulativni stilovi) kod hrvatskih i njemackih studenata. Subjekti (489) istraživanja bili su studenti razlicitih studijskih grupa Filozofskog fakulteta u Zadru i studenti (261) razlicitih studijskih grupa na Sveucilistu Bundeswehra u Munchenu. Teorijski okviri istraživanja su motivacijska teorija samoodređenja (SDT) autora Decija i Ryana i munchenska teorija interesa autora Krappa, Schiefelea, Prenzela i model licnosti (Big Five, autora Goldberga). Teorijske pretpostavke navedenih teorija posjeduju valjanost i na hrvatskim i na njemackim sveucilistima. Utvrđene su bitne razlike između dva uzorka ispitanika i glede strukture i izraženosti motivacije za ucenje i interesa za studij i glede osobitog odnosa između uvjeta (motiva izbora studija i opažene okoline za ucenje) i mogucih ucinaka (izbjegavanje napora, sklonost prekidu studija, zadovoljstvo studijem) interesa za studij i motivacije za ucenje. Utvrđene su bitne razlike u izraženosti motivacije za ucenje i interesa za studij kod hrvatskih i njemackih studenata glede spola ispitanika i godine studija. Autori situiraju rezultate istraživanja u kontekst kulturoloske valjanosti pedagosko-psiholoskih teorija interesa i intrinzicne motivacije, kao i u siri kontekst mogucnosti poboljsavanja kvalitete visokoskolske nastave na sveucilistima u Europi kao zajednickom kulturalno-pedagoskom okružju.
An Evaluation and Optimization Methodology for Efficient Power Plant Programs

Power demand side management (DSM) regulations stipulate that electric power grid enterprises of cities and provinces must complete their target of power saving through efficient power plant (EPP) programs. This investigation studies power DSM and establishes a supervision and evaluation system for the EPP program. The proposed system can be used to evaluate the performance of an EPP program such as electricity saving and investment benefit. A model of EPP is established. A cuckoo search algorithm is applied for the model optimization and its result gives an optimal decision scheme. Finally, a fuzzy comprehensive evaluation method is used to assess an EPP program. The results show that the established supervision and evaluation system and the model of an EPP program are effective and applicable for power grid enterprises.
Structural properties, LQG control and scheduling of a networked control system with bandwidth limitations and transmission delays

The problem of simultaneous LQG control and scheduling of a Networked Control System (NCS) with constant network induced delays at input and output and bandwidth limitations is investigated. Delays are considered at plant as well as controller side. Sufficient conditions for controllability, stabilizability, reconstructibility and detectability of the underlying networked control system are drawn. The proposed conditions extend previous works on structural properties of NCS by capturing both plant and controller side delays together with bandwidth limitations. A framework for computing the optimal LQG controller for the NCS with a fixed scheduling is provided. The proposed modeling approach facilitates use of LQG as well as other control methods for NCSs with delays and bandwidth limitations. In order to optimize performance, a semi-online scheduling procedure is proposed based on an offline look up table. The look up table assigns an optimal schedule with associated optimal LQG controller to initial conditions. The proposed scheme improves previous results by online deployment of schedule and LQG control with stability guarantees and very low computational overhead. A simulation example with communication delays, packet losses and bandwidth limitations in both sensor and actuator sides is included. Static optimal periodic communication sequence, Optimal Pointer Placement (OPP) approach proposed in previous works, a random access scheduling method representing contention based access policies and the proposed method are simulated and compared.
Major nutrological approaches to macronutrients in the performance and body composition of highly trained athletes: a systematic review

Introduction: Findings around nutrient timing require appropriate context because factors such as age, gender, fitness level, previous fueling status, diet status, training volume, training intensity, program design, and time before upcoming training or competition can influence the extent to which timing can play a role in the adaptive response to exercise. Thus, nutrient timing is a feeding strategy that in almost all situations can be useful for promoting recovery and training adaptations. Objective: A systematic review was carried out to elucidate the importance of macronutrient consumption by highly trained athletes on performance and body composition. Methods: The present study followed a systematic review model (PRISMA). The search strategy was carried out in PubMed, Cochrane Library, Web of Science, Scopus, and Google Scholar databases, using scientific articles from 2009 to 2021. The low quality of evidence was attributed to case reports, editorials, and brief communications, according to the GRADE instrument. The risk of bias was analyzed according to the Cochrane instrument. Results and Conclusion: After the study eligibility process, a total of 42 scientifically favorable articles were found to compose the systematic review. Biases did not compromise the scientific basis of the studies. The amount depends on the mode and intensity of exercise, the quality of protein ingested, and the individual's energy and carbohydrate status. However, it should be noted that there is preliminary evidence that consuming much higher amounts of protein (>3 g/kg/d) may confer a benefit concerning body composition. Concerns that protein intake within this range is unhealthy are unfounded in healthy, exercising individuals. One should try to consume whole foods that contain high-quality protein sources. Timing of protein intake in the period spanning the exercise session can provide several benefits, including improved recovery and greater gains in lean body mass. Essential amino acids and leucine supplements are beneficial for the exercising individual by increasing muscle protein synthesis rates, decreasing muscle protein breakdown, and possibly aiding exercise recovery.
Direct Likelihood Evaluation for the Renewal Hawkes Process

ABSTRACT An interesting extension of the widely applied Hawkes self-exiting point process, the renewal Hawkes (RHawkes) process, was recently proposed by Wheatley, Filimonov, and Sornette, which has the potential to significantly widen the application domains of the self-exciting point processes. However, they claimed that computation of the likelihood of the RHawkes process requires exponential time and therefore is practically impossible. They proposed two expectation–maximization (EM) type algorithms to compute the maximum likelihood estimator (MLE) of the model parameters. Because of the fundamental role of likelihood in statistical inference, a practically feasible method for likelihood evaluation is highly desirable. In this article, we provide an algorithm that evaluates the likelihood of the RHawkes process in quadratic time, a drastic improvement from the exponential time claimed by Wheatley, Filimonov, and Sornette. We demonstrate the superior performance of the resulting MLEs of the model relative to the EM estimators through simulations. We also present a computationally efficient procedure to calculate the Rosenblatt residuals of the process for goodness-of-fit assessment, and a simple yet efficient procedure for future event prediction. The proposed methodologies were applied on real data from seismology and finance. An R package implementing the proposed methodologies is included in the supplementary materials.
Terahertz imaging technique for cancer diagnostics using frequency conversion by gold nano-objects

ABSTRACT A technique is suggested (that includes the device's scheme and assessments of its feasibility) for imaging cancer cells in biological tissue samples in vitro and in human skin in vivo, exposing them to 4.2 THz radiation and using a THz-to-infrared (IR) converter in combination with an IR sensitive camera. The converter includes a system of gold nanoparticles embedded into a substance transparent in THz and IR, e.g., Teflon®. The heat exchange between gold nanoparticles and their embedding substance is simulated in a calculation. The convenient size of nanoparticles and their concentration in the substrate are estimated.
Comparative numerical investigation of Burgers' equation with and without Hopf-Cole transformation

Even if the numerical simulation of the unsteady viscous Burgers' equation is well documented in the literature, a detailed literature survey indicates that there is still gaps exists for comparative investigation regarding the effect of Hopf-Cole transformation on the efficiency and accuracy of schemes. In this paper, a comparative numerical investigation of Burgers' equation is presented based on two different approaches. Hopf-Cole transformation is implemented on this equation and then solved by modified Keller box scheme. We sketch a new implicit scheme with second order accuracy in space and time, which is proposed to solve Burgers' equation without using Hopf-Cole transformation. Numerical results of two test problems, which are calculated for various values of kinematic viscosity and time steps, are found to be matching with the exact solution. The new implicit box scheme is proved to be more accurate than the modified Keller box scheme with Hopf-Cole transformation based on L2 and L∞ errors.
New Algebraic Activation Function for Multi-Layered Feed Forward Neural Networks

ABSTRACT Number of attempts have been made in the past to improve the generalization performance of artificial neural networks (ANN). Internal adjustment in ANN deals with tuning of various parameters like learning rate, activation function, etc. Majority of activation functions that exist in the literature are transcendental in nature. In this paper, a novel parametric algebraic activation (PAA) function has been proposed. The function PAA is a generalized function of which Elliott activation function is a special case. PAA is a family of S-shaped curves and satisfies all the important properties of activation functions. This activation is employed on resilient propagation algorithm (RPROP) learning algorithm. Comparative performance evaluation with the widely known activation functions has been carried out on various benchmark datasets taken from University of California Irvine (UCI) machine learning repository. Comparative performance evaluation in terms of number of epochs and testing error of the proposed PAA has been made with the standard activation functions. It has been observed that decrease in number of epochs and testing error for the proposed PAA in RPROP is highly statistically significant for most of the UCI datasets when compared with standard activation functions. Thus incorporating PAA in RPROP can make it more powerful for classification.
Online Leadership Short Course for County Extension Directors

Engaging the Extension workforce in professional development is critical for learning, behavior change, and continued development. However, county Extension directors find it difficult to juggle the various demands on their time, including the need to develop the leadership competencies required to be successful in their roles. Online leadership training is one option that can be used to address this time demand faced by Extension leaders and provide an avenue for developing leadership capacity. We discuss the design and implementation of the 10-week online Leadership Short Course for county Extension directors and the reactions of participants.
Evaluation Theory and Empirical Study of Agricultural Total Factor Productivity Based on Eco-Environmental Efficiency Constraints

: In order to accurately grasp the growth of agricultural total factor productivity in Jiangxi Province and analyze the impact of agricultural development on eco-environmental efficiency in Jiangxi Province, this paper uses Malmquist-Luenberger index to introduce eco-environmental efficiency constraint into the analysis framework of agricultural total factor productivity. The agricultural total factor productivity and its decomposition components of 11 cities in Jiangxi province from 2000 to 2015 under the constraint of eco-environmental efficiency were calculated and analyzed, and the convergence analysis of agricultural total factor productivity of Jiangxi Province was conducted. The results show that: Efficiency of ecological environment in jiangxi province in 2000-2015 under the constraints of agricultural total factor productivity is not present decline during the period of stable growth and show the local features of agricultural total factor productivity of agriculture in jiangxi province output growth contribution rate is extremely low, from a technical progress and technical efficiency improvement effects on agricultural total factor productivity growth are not regular, There are regional differences in agricultural total factor productivity of jiangxi province, especially fuzhou and Ganzhou, which are not ideal. Under the constraint of eco-environmental efficiency, agricultural total factor productivity of Jiangxi Province shows the characteristics of club convergence. Based on the above analysis, relevant policy implications are proposed.
Enhanced Bottom-of-the-Atmosphere Cooling and Atmosphere Heating Efficiency by Mixed-Type Aerosols: A Classification Based on Aerosol Nonsphericity

AbstractThe current understanding of the climate effects of mixed-type aerosols is an open question. The optical and radiative properties of the anthropogenic, mixed-type, and dust aerosols were studied using simultaneous observations of a sun photometer and a depolarization lidar over the Semi-Arid Climate and Environment Observatory of Lanzhou University (SACOL), northwestern China. The aerosol radiative effect was calculated using the Santa Barbara DISORT Atmospheric Radiative Transfer (SBDART) model and was in good agreement with the Aerosol Robotic Network (AERONET) product. The anthropogenic, mixed-type, and dust aerosols were identified mainly based on the lidar-measured depolarization ratio, which was supported by the airmass back trajectories. The mixed-type aerosols exhibit lower (higher) extinctions below (above) 1.5 km above the ground, indicating anthropogenic pollution from the atmospheric boundary layer and dust aerosols above. The dust aerosols exhibit the highest absolute radiative effect.
Numerical Solutions of nonlinear evolution equation

Nonlinear evolution of acoustic disturbance such as a sound wave is governed by Burgers equation.This presentation will focus on the evolution of wave motion is governed by Burgers equation, comparing solutions in limited time ranges, such as those are obtained by numerical method known asparabolic method, with exact solutions obtained using the method of matched asymptotic coordinateexpansions
Linear and weakly nonlinear analyses of cylindrical Couette flow with axial and radial flows

Extending previous linear stability analyses of the instabilities developing in permeable Taylor–Couette–Poiseuille flows where axial and radial throughflows are superimposed on the usual Taylor–Couette flow, we further examine the linear behaviour and expand the analysis to consider the weakly nonlinear behaviour of convective-type instabilities by means of the derivation of the fifth-order amplitude equation together with direct numerical simulations. Special attention is paid to the influence of the radius ratio $\unicode[STIX]{x1D702}=r_{in}/r_{out}$ , and particularly to wide gaps (small $\unicode[STIX]{x1D702}$ ) and how they magnify the effects of the radial flow. The instabilities take the form of pairs of counter-rotating toroidal vortices superseded by helical ones as the axial flow is increased. Increasing the radial inflow draws these vortices near the inner cylinder, where they shrink relative to the annular gap, when this gap is wide. Strong axial and radial flows in a narrow annular gap lead to a very large azimuthal wavenumber with steeply sloped helical vortices. Strong radial outflow in a wide annular gap results in very large helical vortices. The analytical and numerical saturated vortices match quite well. In addition, radial inflows or outflows can turn the usually supercritical bifurcation from laminar to vortical flow into a subcritical one. The radial flow above which this change occurs decreases as the radius ratio $\unicode[STIX]{x1D702}$ decreases. A practical motivation for this weakly nonlinear analysis is found in modelling dynamic filtration devices, which rely on vortical instabilities to reduce the processes of accumulation on their membranes.
The analytical description and detection of two types of dark matter: the WIMPs and axion

Contemporarily, the dark matter is considered as the jewel of the modern physics and whether it is existence or not still puzzling scholars. With the development of the detection techniques, the searching range and detection accuracy have been updated dramatically. In this paper, the basic principle and analytical descriptions for dark matter is introduced. Subsequently, the state-of-art detection approaches for the two well-known dark matter types (i.e., WIMPs and axion) are demonstrated. These results shed light on the further development of dark matter searching.
Evaluation of a method to quantify the number concentrations of submicron water-insoluble aerosol particles based on filter sampling and complex forward-scattering amplitude measurements

Abstract Water-insoluble aerosol particles (WIAPs), such as black carbon (BC), mineral dust, and primary biological aerosol particles (PBAPs), affect climate through their interaction with radiation and clouds. However, with the exception of BC, methods to identify WIAP types and quantify their number concentrations are limited. Here, we evaluated a method that has been recently developed to measure the number concentrations of submicron WIAPs based on atmospheric aerosol measurements at an urban site in Nagoya, Japan. In this method, atmospheric aerosol particles are collected on a filter and dispersed in water. Then, the complex forward-scattering amplitudes of individual particles are measured. This complex parameter reflects the complex refractive index, volume, and shape of each measured particle, enabling the characterization of these physical properties from the signals. The WIAPs were classified as BC-like, dust-like, and PBAP-like particles based on their complex amplitude data. The number concentrations of BC-like particles were strongly correlated with those of refractory BC particles measured by a Single Particle Soot Photometer. BC-like and dust-like particles dominated the population of the submicron WIAPs, which was also confirmed using electron microscopy and Wideband Integrated Bioaerosol Sensor observations. Under the observed atmospheric conditions, the number concentrations of WIAPs were measured with their dispersion efficiency from a filter to water of approximately 50%. These results indicate that our method based on filter sampling and complex forward-scattering amplitude measurements has the potential to become a new technique for quantifying the spatio-temporal distributions of WIAPs.