Miracle-dz/UniFAR-FTUs huggingface dataset:
Implementing Machine Learning in Optimizing Stock Portfolios: A review

Optimizing stock portfolios is one of the ultimate objectives of rational investors. This objective is achieved by identifying the stocks that are expected to perform well in future and then making decisions to allocate funds amongst them in order to create the optimal investment portfolio. In the past, this optimization problem was addressed with the help of different statistical tools that were static and limited. Due to technological advancements, the implementation of machine learning methods in this field has become increasingly popular, enabling researchers to solve complex and nonstationary problems. There have been many recent studies that have integrated hyperparameters tuning kernels with these algorithms and combined modified-assets-selection-models to improve portfolio optimization results. Therefore, this paper provides a focused literature review on implementing machine learning in optimizing stock portfolios for the years between 2019 and 2022, and Google scholar was used to search for the relevant studies from Q1 or Q2 journals. This review revealed that Long short-term memory model was the most applied machine learning algorithm in predicting stocks returns. Moreover, it was found that Markowitz mean-variance model (classic and modified) was the most adopted approach for portfolio optimization. Based on the literature review, future insights are highlighted, and opportunities for further research are identified.
Tuning Artificial Neural Network for Healthcare 4.0. by Sine Cosine Algorithm

From 2015 to 2022, healthcare 4.0 has made revolutionary impacts on health services. It includes machine learning (ML), internet of things (IoT), fog computing and cloud computing. The utilization of machine learning approaches supplied by IoT advances employing fog and cloud computing principles improves the performance and accuracy of healthcare models. These concepts bounded together are distinguished in their application with the researchers as they dominate alongside the best results. Inspirited by the mathematical traits of sine and cosine functions, the sine cosine algorithm (SCA) generates numerous initial random candidate solutions with the goal of fluctuation outwards or towards the ideal answer. The metaheuristic algorithm can be applied for optimization of an artificial neural network (ANN) on which the Healthcare 4.0 relies. The solution has been tested on four diverse datasets in this field as well as the results of those tests have been compared to those of other hybrid solutions with the use of same datasets as the suggested solution. The results are in the favor of the novel method, as it obtains general advantage over all tests.
Research on Key Technologies of High Precision Millimeter Wave Radar Ranging System

High precision measurement technology has a wide range of applications in industrial engineering, such as solid and liquid level measurement, detection of small defects on highly smooth metal surfaces, etc. But, accuracy of traditional measurement technology is difficult to reach the millimeter level. Based on the introduction of basic principle of FMCW radar, this paper analyzes the key technologies of high-precision radar ranging system. Related works are studied, which focuses on millimeter wave antenna, integrated circuit, and digital signal processing algorithm. At last, the future directions and outlook are prospected.
Research on Harmonic Resonance and Suppression Measures of Offshore Wind Farm

Because the capacitance effect of submarine power cables is much greater than that of overhead power transmission cables, the harmonic resonance problem caused by capacitive transmission submarine cable is very prominent. Based on the phenomenon that the 5th harmonic content of an offshore wind farm exceeds the standard, the reason of harmonic exceeding the standard will be analyzed by using modal analysis method, and the theoretical analysis results are verified by field tests. Then, according to the field conditions, a harmonic filter was designed, and combined with the Simulink simulation software, a simulation model of the offshore wind farm was built, and the harmonic current and voltage changes before and after the filter were merged were explored to test the filtering effect of the filter.
Thingspeak-Based Environmental Monitoring System Using IoT

Environmental safety is a significant feature necessary for the healthy life style. Any abnormality in the environment changes the atmospheric parameters such as humidity, temperature, smoke, level of carbon monoxide and sound. Fatal health issues such as loss or weakening of immunity & infertility occurs due to the different activities occurring in nuclear power plants such as recycling of nuclear fuel and leakage of radiation that add low levels of radiation to the atmosphere. Leakage of nuclear radiation gets mixed with fresh air in the atmosphere that results into the increase of temperature, humidity, smoke, level of carbon monoxide and sound. In this work, a wireless sensor network is used for measuring the changes in these parameters which in turn are displayed by the Thingspeak Linux based web server available freely. Monitoring of the parameters in the environment is based on Internet of Things (IoT) as radiation leakage is detected by using sensors and is transmitted to the remote location via IoT and displayed using Thingspeak web server.
Suppression of Bioelectrical Noise Signals in Motion State by Low-Cost Micropillar Hydrogel Electrode

This paper reports a micropillar hydrogel electrode to suppress bioelectrical noise signals in motion state. The hydrogel micropillar arrays with uniform spacing and same morphology are prepared through MEMS technology. The performance of micropillar hydrogel electrode is almost identical to that of the Ag/AgCl wet electrode which is regarded as "gold standard" in clinics. Moreover, compared with the dry electrode, the micropillar electrode shows lower noise signal when subject is talking or moving, making it has great potential to record bioelectrical signals in real scenes.
Management of enterprise cyber security: A review of ISO/IEC 27001:2022

The ISO/IEC 27001 standard is one of the most widely used and accepted standards for information security worldwide. On 25 October 2022, the third edition of the standard was published as ISO/IEC 27001:2022 to address global cybersecurity challenges and improve digital trust. This paper sought to compare and contrast the ISO/IEC 27001:2022 and ISO/IEC 27001:2013 through the lens of the National Institute of Standards and Technology (NIST)’s Cybersecurity Framework (CF) for critical infrastructure protection, version 1.1. This is because the security controls referenced in Annex A of the ISO/IEC 27001 standard are prominently referenced in each of NIST CF’s five Functions - Identify; Protect; Detect; Respond; Recover - for its implementation. The author sought to establish whether and how the ISO/IEC 27001:2022 has been improved for enterprise systems security. Descriptive statistics were utilised to determine the frequency distribution of each ISO/IEC 27001:2022 security control for each NIST CF Function. It was found that the NIST CF’s Protect Function has a higher frequency distribution of security controls than the other four Functions. Interestingly, the distribution was at 52% for both the ISO/IEC 27001:2022 and ISO/IEC 27001:2013. It was concluded that the ISO/IEC 27001:2022 is a slight improvement to the ISO/IEC 27001:2013 as it also introduced eleven new security controls one of which addresses the protection of cloud computing services, which have increasingly been adopted by many businesses.
Predisposing, Reinforcing, and Enabling Factors of Nicotine Dependence Among Young Adults in a Rural Community

Abstract Objectives The aim of this study was to determine the influence of predisposing, reinforcing, and enabling factors of nicotine dependence among young adults in a rural community. Study Design A quantitative, predictive–correlational cross-sectional study was done among 190 young adults in a rural community utilizing compact segmental sampling. Methods Data were collected from August to October 2018 utilizing a validated robotfoto, adapted determinants of smoking questionnaire, and Fagerström test to assess nicotine dependence. Stepwise multiple linear regression was used in predicting the relationship between the predictors and nicotine dependence. Results Reinforcing and enabling factors influenced nicotine dependency. The influence of other relatives (β = .179, p = .023) and radio advertisements (β = −.224, p = .008) as well as availability (access) to cigarettes (β = .228, p = .003) were significant predictors of nicotine dependency in the rural community. Conclusion Identified predictors can help form policies that can help address the availability of cigarettes and advertisements (radio) that can influence nicotine dependence among young adults in a rural community. Furthermore, the influence of other relatives (extended family) can be a factor that can increase the development of nicotine dependence.
Factors Predicting Dependence on Waterpipe Smoking Among Waterpipe Exclusive Smokers

Abstract The current study targeted a sample of waterpipe (WP) exclusive smokers to identify the levels and predictors of dependence on this smoking pattern and to examine the relationship between WP smoking (WPS) dependence and depressive symptoms. A cross-sectional research design was employed with a community sample of 270 subjects who are currently WP exclusive smokers. Assessment of WPS dependence and depressive symptoms was performed using self-report measures. About 62.6% of the subjects were WPS dependent. The unique significant predictors of WPS dependence were age, depression levels, age of starting WPS, duration of WPS, income, smoking WP daily, the belief of being attracted to WPS, and the desire to stop WPS. These predictors explained 35% of the variance in WPS dependence. WPS dependence was associated with increased depressive symptoms. Although few studies have been conducted, the WPS dependence prevalence appears to be alarmingly high among WP exclusive smokers. Factors associated with WPS dependence provide useful information that can be used to tailor WP prevention interventions.
Multipole expansion of the large-scale velocity field - Using the tensor window function

The radial components of the galaxy-peculiar velocities are related to primordial density fluctuations via a tensor window function. For a given shell of galaxies the radial velocity as a function of direction can be expanded into multipoles, the l = 1 term corresponding to the bulk motion, the l = 2 to a quadrupole anisotropy of the velocity field. The l = 0 (monopole) term must be present, if the density enhancement inside the shell differs from zero, but it would be at least partially absorbed into the local value of the Hubble constant. The distribution of these bulk velocities is derived and compared to the results obtained with the usual scalar window function. The estimated dipole velocity and the statistical scatters are significantly affected by sampling anisotropies in the galaxy selection function: the various multipoles all contribute to the apparent dipole velocity. This apparent bulk velocity is most likely to be near the galactic plane, aligned with the dipole anisotropy of the galaxy distribution. 19 refs.
Application of photogrammetric technique to astronomical telescope measurement and control

The photogrammetry technique is widely used for measuring 3-D shape in diverse industries thanks to its easy implementation and straightforward algorithm. However, most measurements done by this technique are using a single camera with multi-exposure and the results are derived after a period of time. Along with the development of the CCD and computer technology, it is now possible to use multi-camera, real-time photogrammetric measurements and the results could be derived without much time delay. A multi-camera system avoids the error caused by time-delayed exposure. So the new technique could be widely used in telescope surface or position measurement and control. In this paper, the pertinent formulations, implementation and application details are discussed before a general conclusion is drawn.
Asymptotic Bias and Optimal Convergence Rates for Semiparametric Kernel Estimators in the Regression Discontinuity Model

The regression discontinuity model has recently become a commonly applied framework for empirical work in economics. Hahn, Todd, and Van der Klaauw (2001) provide a formal development of the identification of a treatment effect in this framework and also note the potential bias problems in its estimation. This bias difficulty is the result of a particular feature of the regression discontinuity treatment effect estimation problem that distinguishes it from typical semiparametric estimation problems where smoothness is lacking. Here, the discontinuity is not simply an obstacle to overcome in estimation; instead, the size of discontinuity is itself the object of estimation interest. In this paper, I derive the optimal rate of convergence for estimation of the regression discontinuity treatment effect. The optimal rate suggests that the appropriate choice of estimator the bias difficulties are no worse than would be found in the usual nonparametric conditional mean estimation problem (at an interior point of the covariate support). Two estimators are proposed that attain the optimal rate under varying conditions. One estimator is based on Robinson's (1988) partially linear estimator. The other estimator uses local polynomial estimation and is optimal under a broader set of conditions.
Interstellar extinction law near the Galactic equator along the Camelopardalis, Perseus and Cassiopeia border

The interstellar reddening law is investigated along the Galactic equator near the Cam, Per and Cas border. We used seven-color photometry of O-B5 stars in the Vilnius photometric system in the optical range, photometry of the ANS orbiting observatory in the ultraviolet range and broad-band color indices V K in the infrared. In the optical range (345- 660 nm) the interstellar reddening law is found to be nearly normal. In the ultraviolet wavelengths shorter than the 330 nm ANS passband, the extinction is found to be slightly larger than the average. Some stars, for example HD 24432, exhibit much stronger ultraviolet extinction which is well seen already in the 345 nm passband of the Vilnius system. However, such stars are rare. The ratio R= AV=EB V is found to be 2.9, i.e., it is slightly smaller than the normal. This is confirmed by the study of the wavelength of maximum polarization of reddened stars in the area. Both the ultraviolet anomaly and the smaller R value are in good agreement with the Cardelli et al. (1988) prediction. We conclude that in the investigated area it is safe to use normal ratios of color excesses to calculate reddening-free Q-parameters for the classification of stars from photometric data in the optical spectral range.
CUTTING TEMPERATURE MEASUREMENT AND MATERIAL MACHINABILITY

Cutting temperature is very important parameter of cutting process. Around 90% of heat generated during cutting process is transferred by sawdust, and the rest is transferred to the tool and work-piece. In this research cutting temperature was measured with artificial thermocouples and metal machinability from aspect of cutting temperature was analyzed. For investigation of material machinability during turning, artificial thermocouple was placed just below the cutting top of insert, and for drilling, thermocouples were placed through screw holes on the face surface. In this way simple, reliable, economic and accurate method for investigation of machinability during cutting is obtained.
PV System with binary grey wolf optimization based MPPT for partial shading condition

This paper compares the methods utilized in MPPT under partial shading conditions (PSC) and identifies the algorithm with the best tracking speed, decreased oscillation, shorter settling time, and last but not the least, efficiency. The two dynamic aspects of a PV system are cell temperature and irradiance. Maximum power point tracking (MPPT) techniques are used to collect maximum power from PV modules during system events. There are several conventional/traditional MPPT systems that monitor the MPP under uniform conditions, however they fail to follow the maximum power from a PV module in multiple peak situations under partial shading conditions. Comparing the performance of the proposed BGWO to the performance of existing MPPT approaches, such as GWO and PSO, reveals its performance. Therefore, it has been determined that the MPPT algorithm for managing the tracking speed must be enhanced and made more efficient.
Modeling Topographic Solar Radiation Using GOES Data

Abstract In this paper the authors present an algorithm that combines solar radiation fields derived from Geostationary Operational Environmental Satellite (GOES) observations with digital elevation data to produce topographically varying insolation fields at fine grid spacing. Cloud-modulated irradiances are obtained using hourly 8-km resolution GOES observations. These irradiances are then spatially integrated to the grid spacing of the digital elevation data. The integration accounts for uncertainties in satellite navigation, the limited sensor resolution relative to the hemispheric field of view of a terrain element, and the mismatch between the instantaneous fluxes estimated by GOES observations and the time-integrated quantities typically used in distributed modeling, such as hourly fluxes. The integrated fields are partitioned into direct and diffuse components and then adjusted for the effects of elevation. Lastly, other topographic effects, such as slope orientation, shadowing, sky obstruction, a.
Surface Mesh Generation by Means of Steiner Triangulations

A method is presented for the efficient and automatic generation of unstructured grids on surfaces. The overall procedure uses a Steiner triangulation where sites are added in arbitrary order and hence the tracking of fronts is not needed in the algorithm. The connectivity of the new points is initially generated by direct subdivision and further improved by using local reconnection. The Lawson's method is used to obtain a Delaunay tessellation but other reconnection criteria may be used with minor modifications (e.g. minimise the maximum angle). The mesh generation procedure uses an approximate physical space grid to define the surface during the grid generation process. At the end the mapped space coordinates are transformed back to the actual surface. The field point distribution is controlled by an element size function obtained from the solution of a Laplace equation whose boundary conditions are derived from the boundary node information. Additional control is possible by prescribing dummy boundaries. The combination of grid smoothing and the elimination of nodes of degree 3, 4, pairs of nodes of degree 5 and nodes of degree higher than 7, as a postprocessing step has proved to be an efficient tool to improve the grid quality. Results are presented which demonstrate that high-quality unstructured grids can be efficiently generated on surfaces using the present method.
On minimizing the ruin probability by investment and reinsurance

We consider a classical risk model and allow investment into a risky asset modelled as a Black-Scholes model as well as (proportional) reinsurance. Via the Hamilton-Jacobi-Bellman approach we find a candidate for the optimal strategy and develop a numerical procedure to solve the HJB equation. We prove a verification theorem in order to show that any increasing solution to the HJB equation is bounded and solves the optimisation problem. We prove that an increasing solution to the HJB equation exists. Finally two numerical examples are discussed.
Fibonacci and Lucas Numbers With Applications

Preface. List of Symbols. Leonardo Fibonacci. The Rabbit Problem. Fibonacci Numbers in Nature. Fibonacci Numbers: Additional Occurrances. Fibonacci and Lucas Identities. Geometric Paradoxes. Generalized Fibonacci Numbers. Additional Fibonacci and Lucas Formulas. The Euclidean Algorithm. Solving Recurrence Relations. Completeness Theorems. Pascal's Triangle. Pascal-Like Triangles. Additional Pascal-Like Triangles. Hosoya's Triangle. Divisibility Properties. Generalized Fibonacci Numbers Revisited. Generating Functions. Generating Functions Revisited. The Golden Ratio. The Golden Ratio Revisited. Golden Triangles. Golden Rectangles. Fibonacci Geometry. Regular Pentagons. The Golden Ellipse and Hyperbola. Continued Fractions. Weighted Fibonacci and Lucas Sums. Weighted Fibonacci and Lucas Sums Revisited. The Knapsack Problem. Fibonacci Magic Squares. Fibonacci Matrices. Fibonacci Determinants. Fibonacci and Lucas Congruences. Fibonacci and Lucas Periodicity. Fibonacci and Lucas Series. Fibonacci Polynomials. Lucas Polynomials. Jacobsthal Polynomials. Zeros of Fibonacci and Lucas Polynomials. Morgan-Voyce Polynomials. Fibonometry. Fibonacci and Lucas Subscripts. Gaussian Fibonacci and Lucas Numbers. Analytic Extensions. Tribonacci Numbers. Tribonacci Polynomials. Appendix 1: Fundamentals. Appendix 2: The First 100 Fibonacci and Lucas Numbers. Appendix 3: The First 100 Fibonacci Numbers and Their Prime Factorizations. Appendix 4: The First 100 Lucas Numbers and Their Prime Factorizations. References. Solutions to Odd-Numbered Exercises. Index.
Total sleep deprivation selectively impairs motor preparation sub-stages in visual search task: Evidence from lateralized readiness potentials

Introduction Many studies have provided evidence of a damage effect triggered by total sleep deprivation (TSD). However, it remains unclear whether the motor preparation processing is affected by TSD. Methods In the current study, 23 volunteers performed a stimulus-response compatibility visual search task before and after TSD while undergoing spontaneous electroencephalography (EEG). Results Repeated-measures analysis of variance revealed that: Compared with that at baseline, the visual search task’s accuracy decreased after TSD, while the response time variance increased significantly. The peak amplitude of the stimulus-locked lateralized readiness potential (LRP) induced by a compatible stimulus was significantly more negative than that induced by an incompatible stimulus before TSD, whereas this difference was not significant after TSD. However, when taking sleep status into consideration, there were no significant main or interaction effects on response-locked LRPs. Discussion Our findings suggest that TSD damages visual search behavior, selectively impairs the earlier sub-stages of motor preparation (sensory integration). These findings will provide a new perspective for understanding the effects of sleep loss.