Miracle-dz/UniFAR-FTUs huggingface dataset:
Forecasting with Exponential Smoothing: The State Space Approach

I. Introduction: Basic concepts. - Getting started. II. Essentials: Linear innovations state space models. - Non-linear and heteroscedastic innovations state space models. - Estimation of innovations state space models. - Prediction distributions and intervals. - Selection of models. III. Further topics: Normalizing seasonal components. - Models with regressor variables. - Some properties of linear models. - Reduced forms and relationships with ARIMA models. - Linear innovations state space models with random seed states. - Conventional state space models. - Time series with multiple seasonal patterns. - Non-linear models for positive data. - Models for count data. - Vector exponential smoothing. IV. Applications: Inventory control application. - Conditional heteroscedasticity and finance applications. - Economic applications: the Beveridge-Nelson decomposition.
Online Learning Phenomenon: From the Perspective of Learning Facilities, Curriculum, and Character of Elementary School Students

This article aimed to describe the online learning phenomenon from the perspective of learning facilities, curriculum, and students’ characters in schools and the correlation among those three variables. This research employed a qualitative descriptive method. The data were collected through interviews, observations, and questionnaires. The research subject was the fifth-grade class of an elementary school in Palembang, which consisted of 19 students’ parents, 8 teachers, and 47 students. The findings showed that almost half of the total number of students experienced limitations in online learning facilities, most elementary school teachers could not develop curriculum and online learning models, and the online learning phenomenon triggered a decline in the students’ characters.
EVALUATION OF CUSTOMIZED POLYETHER ETHER KETONE IMPLANT IN RECONSTRUCTION OF ORBITAL FLOOR FRACTURES (CLINICAL TRIAL)”

INTRODUCTION: Orbital floor fracture is one of the most common maxillofacial fracture. Hence, many clinical methods were implemented for improvement of the techniques used in treating orbital floor fractures. Poly ether ether ketone had been widely used in reconstruction field due to its superb compatibility and proper mechanical properties. AIM OF THIS STUDY: To evaluate the clinical efficacy and radiographic performance of customized PEEK implant in the treatment of patients with orbital floor fracture. MATERIALS AND METHODS : 9 patients with recent orbital floor fracture were selected. All patients were treated using customized PEEK implants in orbital floor reconstruction. Patients were evaluated after 24-hours, one, four and six weeks for enophthalmos, diplopia, ocular motility and infraorbital nerve function in comparison with preoperative status. In addition, a radiographic investigation was performed immediately to confirm the proper placement of the implant and complete release of orbital soft tissue from the maxillary sinus. RESULT: the study was conducted on seven patients with ZMC fracture and 2 patients with blow-out fracture. None of the enrolled patients showed postoperative diplopia. Six out of the enrolled nine patients in this study reported a subjective abnormal sensation in the course of the affected infraorbital nerve at the first follow-up period. However, all patients regained normal sensation by the end of the follow-up period. The difference of the rate of postoperative ocular complications was statistically significant over the follow-up periods (p=0.036). CONCLUSION: The favorable clinical performance of the patient-specific PEEK sheet in the management of orbital floor defects makes it an exemplary reconstructive alternative with superb compatibility, great surgical precision and predictability.
Solving, Estimating and Selecting Nonlinear Dynamic Economic Models Without the Curse of Dimensionality

A welfare analysis of a risky policy is impossible within a linear or linearized model and its certainty equivalence property. The presented algorithms are designed as a toolbox for a general model class. The computational challenges are considerable and I concentrate on the numerics and statistics for a simple model of dynamic consumption and labor choice. I calculate the optimal policy and estimate the posterior density of structural parameters and the marginal likelihood within a nonlinear state space model. My approach is even in an interpreted language twenty time faster than the only alternative compiled approach. The model is estimated on simulated data in order to test the routines against known true parameters. The policy function is approximated by Smolyak Chebyshev polynomials and the rational expectation integral by Smolyak Gaussian quadrature. The Smolyak operator is used to extend univariate approximation and integration operators to many dimensions. It reduces the curse of dimensionality from exponential to polynomial growth. The likelihood integrals are evaluated by a Gaussian quadrature and Gaussian quadrature particle filter. The bootstrap or sequential importance resampling particle filter is used as an accuracy benchmark. The posterior is estimated by the Gaussian filter and a Metropolis- Hastings algorithm. I propose a genetic extension of the standard Metropolis-Hastings algorithm by parallel random walk sequences. This improves the robustness of start values and the global maximization properties. Moreover it simplifies a cluster implementation and the random walk variances decision is reduced to only two parameters so that almost no trial sequences are needed. Finally the marginal likelihood is calculated as a criterion for nonnested and quasi-true models in order to select between the nonlinear estimates and a first order perturbation solution combined with the Kalman filter.
An Introduction to Automata Theory

Great Aunt Eugenia and other automata Sundry machines Implementing finite automata Implementation and realization Behavioural equivalence, SP partitions and reduced machines Parallel and serial composition of automata The parallel decomposition theorem The serial decomposition theorem The lattice of SP partitions Analysis of machines Concurrent systems: net theory Concurrent systems: the calculus of communicating systems. Appendix: Sets, relations and functions.
Secure public-key encryption from factorisation-related problems

Public key encryption plays a vital role in securing sensitive data in practical applications. The security of many encryption schemes relies on mathematical problems related to the difficulty of factoring large integers. In particular, subgroup problems in composite order groups are a general class of problems widely used in the construction of secure public-key encryption schemes. This thesis studies public-key encryption schemes that are provably secure based on the difficulty of subgroup or other integer factorisation related problems in the standard model. Firstly, a number of new public-key encryption schemes are presented which are secure in the sense of indistinguishability against chosen-ciphertext attack in the standard model. These schemes are obtained by instantiating the two previous paradigms for chosen-ciphertext security by Cramer and Shoup, and Kurosawa and Desmedt, with three previously studied subgroup membership problems. The resulting schemes are very efficient, and are comparable if not superior in terms of efficiency when compared to previously presented instantiations. Secondly, a new approach is presented for constructing RSA-related public key encryption schemes secure in the sense of indistinguishability against chosenciphertext attack without random oracles. This new approach requires a new set of assumptions, called the Oracle RSA-type assumptions. The motivating observation is that RSA-based encryption schemes can be viewed as tag-based encryption schemes, and as a result can be used as a building block in a previous technique for obtaining chosen-ciphertext security. Two example encryption schemes are additionally presented, each of which is of comparable efficiency to other public key schemes of similar security. Finally, the notion of self-escrowed public-key infrastructures is revisited, and a security model is defined for self-escrowed encryption schemes. The security definitions proposed consider adversarial models which reflect an attacker's ability to recover private keys corresponding to public keys of the attacker's choice. General constructions for secure self-escrowed versions of ElGamal, RSA, Cramer-Shoup and Kurosawa-Desmedt encryption schemes are also presented, and efficient instantiations are provided. In particular, one instantiation solves the 'key doubling problem' observed in all previous self-escrowed encryption schemes. Also, for another instantiation a mechanism is described for distributing key recovery amongst a number of authorities.
Evaluation of Canal Transportation and Centering Ability of Curved Canal Prepared by Two Rotary Systems

Purpose: The aim of this study was to evaluate canal transportation and centering ability ratio of 2 single Ni-Ti instruments; one shape and wave one gold using Cone Beam Computed Tomography scanning (CBCTs). Materials and Methods: Forty mesiobuccal canals of human mandibular first molars with a curvature angle that ranges from 15° to 35° were divided into 2 groups based on the type of instrument used to prepare the canal. Group I: instrumented with one shape and Group II: instrumented with Wave One Gold. Pre and post preparation, canals were scanned using CBCT scanner to evaluate the amount of canal transportation and centering ability ratio at coronal, middle, and apical parts of root canal system. Results: There was no statistical significance difference between the two tested groups (P<0.05) in canal transportation and centering ability. C onclusion: Both one shape and wave one gold systems preserving the canal’s original form.
Echocardiographic Evaluation of Aortic Elasticity Parameters in Aortic Stenosis Patients with Preserved Ejection Fractions Undergoing Transcatheter Aortic Valve Implantation

INTRODUCTION: Aortic stiffness is an important risk factor that reflects the mechanical tension and elasticity of the aorta and predicts cardiovascular mortality and morbidity. However, it has been found that aortic stiffness contributes to both symptom burden and clinical outcomes in patients with aortic stenosis (AS). In our study, we aimed to evaluate the effect of transcatheter aortic valve implantation (TAVI) on aortic elasticity parameters by echocardiography in patients with severe aortic stenosis, especially with preserved ejection fraction. METHODS: A total of 55 consecutive patients with symptomatic severe AS who underwent TAVI were included in the study. Demographic data, echocardiographic and aortic elasticity measurements of all patients were measured before and 6 months after the procedure. To evaluate the elastic properties of the aorta, aortic strain, distensibility and stiffness index were calculated. RESULTS: Left ventricular mass index (LVMI) (p <0.001) and aortic stiffness (p <0.001) were decreased, while aortic strain (p <0.001) and aortic distensibility (p <0.001) were found to be increased in the measurements performed at 6 months after TAVI. In the regression analysis, age and LVMI were found to be independent predictors for predicting improvement in aortic stiffness, on the other hand, LVMI also independently predicted the increase in aortic distensibility. CONCLUSION: Improvement in aortic elastic properties and left ventricular functions were found in patients who underwent TAVI. Age and LVMI were observed to predict the improvement in stiffness and distensibility of the aorta in patients undergoing TAVI, especially in AS patients with preserved ejection fraction.
Ultra-High-Energy Particles at the Border of Kerr Black Holes Triggered by Magnetocentrifugal Winds

The source, origin, and acceleration mechanisms of ultra-high-energy cosmic rays (UHECR) (E>1020 eV, beyond the GZK limit) remain uncertain and unclear. The main explanations are associated with particular mechanisms, such as the Fermi mechanism, in which charged particles could be accelerated by clouds of magnetized gas moving within our Galaxy, or by the magnetic reconnection of field lines at, e.g., the core of high-energy astrophysical sources, where the topology of the magnetic field is rearranged and magnetic energy is converted into kinetic energy. However, the recent observation of extragalactic neutrinos may suggest that the source of UHECRs is likely an extragalactic supermassive black hole. In the present work, we propose that charged particles can be accelerated to ultrahigh energies in marginally bound orbits near extreme rotating black holes and could be triggered by collisions of magnetocentrifugal winds; the accretion disk surrounding the black hole would provide such winds. The ultra-high-energy process is governed by the frame-dragging effects of the black hole spacetime.
Review of Top Quark Properties Measurements at the Tevatron

This review summarises the top quark physics program pursued at Fermilab’s Tevatron proton-antiproton collider, operating at a centre of mass energy of 1.96 TeV, and its two collider detectors CDF and D0. More than a decade after the discovery of the top quark at the Tevatron, it remains the only place to produce top quarks and study them directly until the Large Hadron Collider at CERN starts operation. The Tevatron’s increased luminosity and centre of mass energy offer the possibility to scrutinise the properties of the heaviest fundamental particle known to date by performing new measurements that were not feasible before, like the first evidence for electroweak production of top quarks and the resulting direct constraints on the involved couplings. In addition, the precision of prior measurements has been improved to an unprecedented level, illustrated for example by the measurement of the top quark mass with a relative precision of 0.7%, marking the most precisely measured quark mass to date and allowing the prediction of the mass of the Higgs boson that still remains to be discovered. The various measurements of top quark properties provide stringent tests on the predictions performed in the framework of the Standard Model of elementary particle physics. 1 FERMILAB-PUB-08-495-E
Cooperative Satellite-Aerial-Terrestrial Systems: A Stochastic Geometry Model

Nowadays, satellite and aerial platforms are playing an important role in realizing global seamless wireless coverage. In this paper, a cooperative satellite-aerial-terrestrial network (SATN) is considered, in which two kinds of relaying links, satellite and aerial relaying links, are used to assist a group of aerial terminals to forward their information to a remote terrestrial destination (D). Specifically, we model these aerial platforms sharing the same frequency band as a Matérn hard-core point process type-II. Also, a group of aerial jammers at D’s side is modeled as a Poisson point process. To demonstrate the end-to-end (e2e) performance of the two relaying links, the statistical characteristics of the received signal-to-interference are characterized and then a closed-form expression for the outage probability (OP) over the uplink from the aerial source to the satellite/the aerial relay, the downlink from the satellite/the aerial relay to D, and the inter-aerial relay link are derived. Numerical results are presented to verify the proposed analysis models and compare the outage performance of the considered cooperative SATN with the two relay links under numerous scenarios.
Special number field sieve

Integer factorization is a problem not yet solved for arbitrary integers. Huge integers are therefore widely used for encrypting, e.g. in the RSA encryption scheme. The special number field sieve holds the current factorization record for factoring the number 2^(1039)+1. The algorithm depends on arithmetic in an algebraic number field and is a further development from the quadratic sieve factoring algorithm. We therefor present the quadratic sieve as an introduction to the ideas behind the special number field sieve first. Then the special number field is described. The key concepts is evaluated one bye one. Everything is illustrated with the corresponding parts of an example factorization. The running time of the special number field sieve is then evaluated and compared against that of the quadratic sieve. The special number field sieve only applies to integers of a special form, but a generalization has been made, the general number field sieve. It is slower but all estimates suggests it is asymptotically faster than all other existing general purpose algorithms.
Properties of the Generalized Zig-Zag Product of Graphs

The operation of zig-zag products of graphs is the analogue of the semidirect product of groups. Using this observation, we present a categorical description of zig-zag products in order to generalize the construction for the category of simple graphs. Also, we examine the covering properties of zig-zag products and we utilize these results to estimate their spectral invariants in general. In addition, we provide specific spectral analysis for some such products.
Multi-Abnormal ECG Signal Classification using Dispersion Entropy and Statistic Feature

ABSTRAKElektrokardiogram (EKG) adalah salah satu perangkat medis yang paling banyak digunakan untuk mendiagnosis masalah jantung. Sinyal abnorma EKG mempunyai variasi dan beberapa mirip antara yang satu dengan lainnya. Oleh karena itu, pada penelitian ini diusulkan metode klasifikasi kelainan jantung berdasarkan EKG menggunakan fitur statistik orde satu dan Dispersion Entropy (DisEn) untuk tahap ekstraksi ciri. Sedangkan untuk tahap klasifikas sinyal EKG multi-abnormal, kami membandingkan metode Support Vector Machine (SVM) dan K-Nearest Neighbor (KNN). Pada penelitian ini diklasifikasikan tujuh kelas EKG, yaitu Normal, Atrial Fibrillation (AFIB), Atrial Flutter (AFL), Atrial Premature Beats (APB), Begiminy, Left Bundle Branch Block (LBBB), dan Premature Ventricular Contraction (PVC). Dari simulasi ini, sistem dapat mendeteksi sinyal normal dan abnormal dengan akurasi 85,1% menggunakan K-NN. Sementara itu, pada simulasi klasifikasi tujuh kelas sinyal EKG menghasilkan akurasi hingga 75.1%. Kata kunci: EKG, klasifikasi, Dispersion Entropy, statistik ABSTRACTElectrocardiogram (ECG) is one of the most widely used medical devices to diagnose heart disease. Abnormal ECG signals have variations and some are similar to another. Therefore, in this study, proposed a method for classifying cardiac abnormalities based on ECG using first-order statistical features and Dispersion Entropy (DisEn) for feature extraction. Meanwhile, for the multiabnormal ECG signal classification stage, we compared the Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) methods. In this study, seven ECG classes were classified, namely Normal, Atrial Fibrillation (AFIB), Atrial Flutter (AFL), Atrial Premature Beats (APB), Begiminy, Left Bundle Branch Block (LBBB), and Premature Ventricular Contraction (PVC). From this simulation, the system can detect normal and abnormal signals with an accuracy of 85.1% using K-NN. Meanwhile, the classification simulation of seven classes of ECG signals produces an accuracy of up to 75.1%. Keywords: ECG, classification, Dispersion Entropy, statistics
A machine learning-based approach for sentiment analysis on distance learning from Arabic Tweets

Social media platforms such as Twitter, YouTube, Instagram and Facebook are leading sources of large datasets nowadays. Twitter’s data is one of the most reliable due to its privacy policy. Tweets have been used for sentiment analysis and to identify meaningful information within the dataset. Our study focused on the distance learning domain in Saudi Arabia by analyzing Arabic tweets about distance learning. This work proposes a model for analyzing people’s feedback using a Twitter dataset in the distance learning domain. The proposed model is based on the Apache Spark product to manage the large dataset. The proposed model uses the Twitter API to get the tweets as raw data. These tweets were stored in the Apache Spark server. A regex-based technique for preprocessing removed retweets, links, hashtags, English words and numbers, usernames, and emojis from the dataset. After that, a Logistic-based Regression model was trained on the pre-processed data. This Logistic Regression model, from the field of machine learning, was used to predict the sentiment inside the tweets. Finally, a Flask application was built for sentiment analysis of the Arabic tweets. The proposed model gives better results when compared to various applied techniques. The proposed model is evaluated on test data to calculate Accuracy, F1 Score, Precision, and Recall, obtaining scores of 91%, 90%, 90%, and 89%, respectively.
Evaluation of Pregnancies with Pre-existing Hypertension and Diabetes

The complications in pregnancy can happen due to diabetes and hypertension which may affect the mother or the fetus. Objective: To evaluate pregnancy in hypertensive and diabetic pregnant women to detect associated risk factors and complications. Methods:The research was a cross-sectional method of study, conducted over 4 months from December 2021 to March 2022 in a private hospital in Gujranwala, Pakistan. A sample size of 50 was considered as per convenience. The data was analyzed using SPSS V20 software. The age of patients considered was minimum 18 years to maximum 50 years . The scan was done using greyscale type 2D Mindary ultrasound equipment. A written consent form was also taken from patients. Results: The results showed that 15(30%) patients were presented with diabetes mellitus, 32(64%) has hypertension and 3(6%) has both hypertension and diabetes mellitus. The complications included low lying placenta and placental abruption in 1(2%), polyhydramnios in 3(6%) and oligohydramnios in 1(2%). Microcephaly was found in 1(2%) and hydrocephalus was seen in 2(4%). Fetal abortion was found in 7(14%) and only 3(6%) have chance of having a fetus with abnormalities due to hypertension and diabetes whereas most of them have normal birth 43(86%) with no complications. Conclusion: In conclusion, the majority of women with diabetes mellitus and hypertension can have a normal pregnancy and birth, but some may face difficulties such as intrauterine fetal mortality, fetal congenital abnormalities and defects and still birth
Lie elements in pre-Lie algebras, trees and cohomology operations

We give a simple characterization of Lie elements in free pre-Lie algebras as elements of the kernel of a map between spaces of trees. We explain how this result is related to natural operations on the Chevalley-Eilenberg complex of a Lie algebra. We also indicate a possible relation to Loday's theory of triplettes.
Localization of an ultra wide band wireless endoscopy capsule inside the human body using received signal strength and centroid algorithm

Wireless capsule endoscopy (WCE) is used for imaging and diagnosing diseases in the gastrointestinal (GI) system. The location of the disease detected by WCE is still an important problem. Location information is very important for the surgical or drug treatment of the detected disease. In this study, RSS-based centroid algorithm has been used in order to accurately predict the capsule position on a sample data set. The effect of different parameters such as number of sensors used on the proposed mathematical model, location of sensors on positioning is analyzed in detail. The results show that a precise position detection is possible with fewer sensors positioned correctly. As a result, the positioning error with the correctly selected sensors is reduced by approximately 55%. In addition, the performance of the proposed method was compared with the classical centroid algorithm and more than 50% improvement was achieved.