Miracle-dz/UniFAR-FTUs huggingface dataset:
On probabilistic inference approaches to stochastic optimal control

While stochastic optimal control, together with associate formulations like Reinforcement Learning, provides a formal approach to, amongst other, motor control, it remains computationally challenging for most practical problems. This thesis is concerned with the study of relations between stochastic optimal control and probabilistic inference. Such dualities – exemplified by the classical Kalman Duality between the Linear-Quadratic-Gaussian control problem and the filtering problem in Linear-Gaussian dynamical systems – make it possible to exploit advances made within the separate fields. In this context, the emphasis in this work lies with utilisation of approximate inference methods for the control problem. Rather then concentrating on special cases which yield analytical inference problems, we propose a novel interpretation of stochastic optimal control in the general case in terms of minimisation of certain Kullback-Leibler divergences. Although these minimisations remain analytically intractable, we show that natural relaxations of the exact dual lead to new practical approaches. We introduce two particular general iterative methods Ψ-Learning, which has global convergence guarantees and provides a unifying perspective on several previously proposed algorithms, and Posterior Policy Iteration, which allows direct application of inference methods. From these, practical algorithms for Reinforcement Learning, based on a Monte Carlo approximation to Ψ-Learning, and model based stochastic optimal control, using a variational approximation of posterior policy iteration, are derived. In order to overcome the inherent limitations of parametric variational approximations, we furthermore introduce a new approach for none parametric approximate stochastic optimal control based on a reproducing kernel Hilbert space embedding of the control problem. Finally, we address the general problem of temporal optimisation, i.e., joint optimisation of controls and temporal aspects, e.g., duration, of the task. Specifically, we introduce a formulation of temporal optimisation based on a generalised form of the finite horizon problem. Importantly, we show that the generalised problem has a dual finite horizon problem of the standard form, thus bringing temporal optimisation within the reach of most commonly used algorithms.
Protective Effect of Continuous Aerobic Training and Crocin on Doxorubicin-Induced Apoptosis in the Heart Tissue of Rats

Background: Doxorubicin (DOX) is widely used to treat various cancers, but its cardio-toxicity restricts its therapeutic application. Objectives: This study aimed to investigate the effect of 8 weeks of continuous aerobic training (CAT) and administration of crocin (Cr) on the apoptotic index and the expression of Bax and Bcl-2 genes on the DOX-induced cardio-toxicity of rats. Methods: In this experimental study, 50 male Wistar rats were randomly divided into 5 groups, including control, DOX, DOX + CAT, DOX + Cr, and DOX + CAT + Cr groups. Continuous aerobic training consisted of 60 minutes of running with the intensity of 40% - 60% of maximum speed (5 days/week). Apoptotic index and the expression levels of Bax and Bcl-2 genes were determined in the heart tissue. Results: Doxorubicin significantly increased Bax gene expression in the heart tissue of animals (P < 0.001). In contrast, Bcl-2 gene expression significantly decreased (P < 0.001). In all experimental groups, the Bax/Bcl-2 ratio and the percentage of the terminal deoxynucleotidal transferase-mediated biotin-deoxyuridine triphosphate nick-end labeling (TUNEL)-positive cells were significantly diminished compared to the DOX-intoxicated animals. Among experimental groups, CAT had more antiapoptotic effects against DOX-induced cardiac toxicity. Conclusions: Continuous aerobic training and Cr, alone or in combination, could attenuate DOX-induced cardiotoxicity by suppressing apoptosis in the rats.
Towards a Better Diagnosis of Prostate Cancer: Application of Machine Learning Algorithms

Introduction: Prostate cancer is one of the leading causes of death in men, and the early detection of this disease can be a significant factor in controlling and managing it. Applying data mining techniques can lead to the extraction of hidden knowledge from a huge amount of data and can help diagnose this disease by physicians. This study aims to determine the algorithm with the best performance to diagnose prostate cancer.Methods: In this study, nine data mining techniques, including Support Vector Machine, Decision Tree, Naive Bayes, K-Nearest Neighbors, Neural Network, Random Forest, Deep Learning, Auto-MLP, and Rule Induction algorithms, were used to extract hidden patterns from prostate cancer data. In this study, the data of 100 patients, which included eight characteristics, were used, and the RapidMiner Studio environment was employed for modeling. To compare the performance of the mentioned approaches used in this study to diagnose prostate cancer, accuracy, recall, precision, AUC, sensitivity, and specificity were calculated and reported for all techniques.Results: The results of this study showed that the accuracy of the applied algorithms was between 77% and 84%. Using different criteria to evaluate the techniques used showed that the two algorithms K-Nearest Neighbors and Neural Network, had better performance and accuracy (84%) than other methods. The sensitivity in these two algorithms was 80% for Neural Networks and 85% for K-Nearest Neighbors, respectively.Conclusion: The usage of different data mining techniques can lead to the discovery of hidden patterns among an enormous amount of data related to prostate cancer, and as a result, it leads to the early diagnosis of this disease and saves the subsequent costs.
In Search of Exomoons

Two decades ago, astronomers began detecting planets orbiting stars other than our Sun, so-called exoplanets. Since that time, the rate of detections and the sensitivity to ever-smaller planets has improved dramatically with several Earth-sized planets now known. As our sensitivity dives into the terrestrial regime, increasingly the community has wondered if the moons of exoplanets may also be detectable, so-called "exomoons". Their detection represents an outstanding challenge in modern astronomy and would provide deep insights into the uniqueness of our Solar System and perhaps even expand the definition of habitability. Here, I will briefly review theoretical studies exploring the formation and evolution of exomoons, which serve to guide observational searches and provide testable hypotheses. Next, I will outline the different methods which have been proposed to accomplish this challenging feat and their respective merits. Finally, initial results from observational efforts will be summarized with a view to future prospects as well.
Developing a Multilingual Stemmer for the Requirement of Text Categorization and Information Retrieval

: Information retrieval IR is the process of finding information (generally documents) that matches the needs of the user. One way to improve the search effectiveness, as well as the quality of text categorization is to build an effective stemmer that helps to match users’ queries with relevant documents in IR and reduce the space of textual representation in TC. This has been always an interesting research topic in IR and TC. We can define stemming as the process of reducing inflected and derived words to their reduced forms (stems or roots). Many stemmers have been developed for different languages, but there is always many weaknesses and problems. In the present work, we have developed a multilingual stemming approach, based on the extraction of the word root and that exploits the technique of n-grams of characters. Our experiments have been done on three languages which are: Arabic, English, and French.
Bayesian Analysis of Treatment Effect Models

This chapter reviews Bayesian approaches and Markov chain Monte Carlo (MCMC) methods for estimating treatment-response models. We begin by reviewing the standard continuous outcome / continuous treatment specification under normality and then move on to discuss procedures for handling limited dependent treatment variables and outcomes within this framework. We also discuss methods for relaxing the standard “Gaussian” assumptions commonly made in textbook treatments of this class of problems and commonly seen in empirical applications. In so doing, we discuss issues of model comparison in finite mixture models and conclude with references to some recent work on this topic, including instrument imperfection.
Effect of Generations and Feather Color Groups on Productive and Reproductive Performance of Female Quail

The research was carried out in the poultry unit fields / Nineveh Research Department, to identify the effect of four generations (1st , 2nd, 3rd and 4th) and feather color groups (black, brown, desert and white) of female quail on their productive performance. The results showed a significant improvement (P <0.05) through generations from 1st to 4th on egg production percentage and weight of eggs produced / kg, a significant increase (P <0.05) for feed consumption / kg as well as a significant decrease (P <0.05) for abnormal color percentage, while the four generations had no significant effect on Feed conversion ratio (feed kg / egg kg), age at 5% eggs production / day, hatchability percentage, fertility percentage, and sexual ratio. On the other hand, the desert color group feathers excelled significantly (P <0.05) in eggs weight / kg. , Feed consumed / kg and the food conversion ratio / kg eggs compared to the black group only, and the lowest significant percentage (P <0.05) for colors anomalous was of the desert color group, there was no significant difference between the color groups of the rest of the other traits.
Mach Reflection of Detonation Waves

The diffraction of a nominally planar gaseous detonation at a wedge was investigated to determine the critical wedge angle for transition from regular to Mach reflection. Experiments were conducted in a square 83-mm cross-section detonation tube using stoichiometric mixtures of hydrogen-oxygen at 0.2 bars. Experimental results for the triple-point trajectory angle produced during Mach reflection were obtained using the smoke foil technique and are compared with analytic calculations made using three-shock theory and the oblique detonation polars. Measurements of the cell size behind the overdriven Mach stem are also reported. Both analytic and experimental results are compared with work from previous investigations to address apparent discrepancies in the existing literature. Introduction The motivation for this study is to resolve discrepancies in previous investigations regarding Mach reflection of a detonation wave. Whereas the diffraction of a shock wave by a wedge in a nonreactive gas has been the topic of considerable investigation, the reactive (detonation) case has received only limited attention in studies by Ong, Gvozdeva and Predvoditeleva, Manzhalei and Subbotin, Gavrilenko et al., Gavrilenko and Prokhorov > and Edwards et al. In each of these investigations, the detonation is given similar treatment as a nonreactive shock and standard inviscid twoand three-shock theories applied to predict the triple-point trajectory and the critical angle for transition from regular to Mach reflection. Although detonations can be simplistically analyzed as shock waves followed by a thin chemical reaction zone, there are notable differences between the dynamics of nonreactive shocks and detonations. Copyright © 1991 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved. * Graduate Student. Department of Aeronautical Engineering. * Associate Professor. Department of Mechanical Engineering.
In vitro and in vivo antimicrobial activity of an active plant-based quadrocomplex for skin hygiene

Context: The current epidemiological situation causes a new surge of interest to perspective antimicrobial formulations for proper skin hygiene. Aims: To evaluate in vitro and in vivo the antimicrobial activity of a novel active quadrocomplex (QC) for skin hygiene based on Melaleuca alternifolia essential oil, eucalyptol, (-)-α-bisabolol and silver citrate. In addition, to analyze the phytochemical constituents by gas chromatography-mass spectrometry (GC-MS) and to assess the skin irritant potential after regular washing. Methods: The phytochemical analysis was performed using GC-MS. The Minimum Inhibitory Concentrations (MICs) were assessed using a colony-counting method with resazurin. The type of pharmacological interaction was investigated using a modern checkerboard assay. Results: The chemical composition exhibited 18 resolved phytochemicals with the highest concentrations for (-)-α-bisabolol (32.2%) and terpinen-4-ol (31.6%) through the GC-MS analysis. QC agents showed antimicrobial activity against Gram-positive and Gram-negative strains, with MIC values ranging from 1.25 to 40.00 mg/mL. The checkerboard assay demonstrated reduced MIC values for the combinations of QC agents against all reference strains. QC showed significant inhibition of Staphylococcus aureus growth with an average efficiency of 99.91% and Candida albicans 99.94 %. In vivo, the investigation of QC showed higher immediate and prolonged efficiency compared to base formulation (p<0.05). Dermatology evaluation indicated that QC added to the soap base did not affect the skin of hands during regular hand-washing and did not cause any negative effects on the skin. Conclusions: QC exhibits a balanced performance between antimicrobial activity and biological safety and can be considered a promising bioactive composition for regular hand hygiene application. Therefore, additional investigations are needed to study the mechanism of antimicrobial activity.
A study of the Chamaeleon I dark cloud and T-association. II. High-resolution IRAS maps around HD 97048 and 97300.

Les cartes IRAS a haute resolution et la photometrie IR basee au sol de deux regions du nuage sombre Chamaeleon I contenant les nebuleuses reflechissantes Ced 111 et Ced 112, d'etoiles centrales HD 97048 et 97300 respectivement, sont presentees. 15 sources IR, dont trois non-repertoriees dans le catalogue IRAS, sont detectees dans ces regions. Les densites de flux de ces sources sont estimees. Les distributions d'energie spectrale et de luminosite IR des etoiles pre-sequence principale sont determinees. Ces deux regions apparaissent comme centres de formation stellaire a faible masse.
Fast and accurate three-dimensional reconstruction from cone-beam projection data using algebraic methods

Cone-beam computed tomography (CT) is an emerging imaging technology, as it provides all projections needed for three-dimensional (3D) reconstruction in a single spin of the X-ray source-detector pair. This facilitates fast, low-dose data acquisition as required for imaging fast moving objects, such as the heart, and intra-operative CT applications. Current cone-beam reconstruction algorithms mainly employ the Filtered-Backprojection (FBP) approach. In this dissertation, a different class of reconstruction algorithms is studied: the algebraic reconstruction methods. Algebraic reconstruction starts from an initial guess for the reconstructed object and then performs a sequence of iterative grid projections and correction backprojections until the reconstruction has converged. Algebraic methods have many advantages over FBP, such as better noise tolerance and better handling of sparse and non-uniformly distributed projection datasets. So far, the main repellant for using algebraic methods in routine clinical situations was their slow speed. Apart from providing solutions for this pressing problem, we will also apply, for the first time, algebraic methods in the context of general low-contrast cone-beam tomography. This new context poses several challenges, both for reconstruction quality and speed. To eliminate the strong aliasing effects that occur when standard algebraic methods are applied for cone angles exceeding 20$\sp\circ$, we introduce the new concept of depth-adaptive basis function kernels. Then, a comprehensive study is conducted on how various parameters, such as grid initialization, relaxation coefficient, the number of iterations, and correction method (ART or SART) influence cone-beam reconstruction quality and speed. Finally, a new algorithm, the Weighted Distance Scheme, is proposed that optimally arranges the order in which the grid projections are performed, which reduces the number of iterations and promotes reconstruction quality. We find that three iterations are sufficient to obtain good reconstruction quality in the general case. A cost function is then developed that relates the computational effort of FBP with that of algebraic methods. In an attempt to match the cost of algebraic methods and FBP, a new accurate and efficient projection algorithm is proposed that reaches its goal by caching projection computations for reuse in the subsequent backprojection. Finally, we propose a new hardware-accelerated scheme for algebraic methods that utilizes readily available off-the-shelf graphics hardware and enables a reconstruction to be performed in less than 2 minutes at good quality.
Heat energy consumption forecast analysis: model-oriented multi-objective approach

Forecasting the expected quantity of heat energy consumption by multiple residential buildings can be a time consuming task, if done manually. The energy consumption in a building can be represented as a data parameter that changes over a period of time. For forecasting purposes autoregressive models are most often used to analyse time series data. One solution of the problem is to perform a hyperparameter optimization and derive the best results according to a selected metric. However, the grid search approach is often a time-consuming and resource-intensive task requiring additional human interaction for the final assessment of the most suitable parameters. The purpose of this study is to explore the possibility of developing a fully automated software algorithm that will provide sufficient parameters for the prediction of heat energy using the seasonal autoregressive integrated moving average with exogenous variables model (SARIMAX).
The winds of change: the role of community engagement and benefit-sharing in wind farm developments

This article reviews the sustainability dilemma raised by the need for increased renewable (low-carbon) energy generation – replacing fossil-based sources – to accommodate the desired climate targets while at the same time enhancing the social acceptance by local communities. A “triple challenge” of climate change, renewable energy production, and the social license are at the center of such a dilemma. Even though a low-carbon or ultimately a net-zero emissions development pattern is hypothetically conceivable – the social acceptance of “social license to build and operate” renewable energy developments (e.g., wind parks) remains a key to successfully achieving the climate targets. The drivers of a social license to operate onshore wind farms are diverse. This paper looks at benefit sharing and meaningful community participation schemes as mechanisms to attain the social license to operate new renewable energy projects. We identified challenges that affect the efficacy of these mechanisms. In the absence of certain conditions, these mechanisms might not achieve the expected social license. The challenges can be grouped into two categories: those under the scope of the regulators or planning authorities; and those related to the developers. The need for and the advantages of community engagement and benefit-sharing initiatives to achieve the social license to operate are illustrated with examples from wind development projects (in the UK and Spain).
Generation of heralded single photons in pure quantum states

Single photons - discrete wavepackets of light - are one of the most fundamental entities in physics. In recent years, the ability to consistently create and manipulate both single photons and pairs of photons has facilitated everything from tests of quantum theory to the implementation of quantum-enhanced precision measurements. These activities all fall within the scope of the rapidly-growing field of quantum information - the exploitation of the properties of quantum states (and specifically their capability to exist in superpositions) to accomplish tasks that would not be possible with classical objects. One stated goal of research in quantum information is to build a device consisting of a network of quantum logic gates that can evaluate quantum algorithms. The photonic implementation of individual logic gates has already been demonstrated. However, partly due to standard methods of preparing single photons, current schemes have severe limitations in terms of scaling up from a single logic gate to multiple concatenated operations. Until now it has not been proven that single photons can be generated in pure and indistinguishable quantum states, something upon which the successful operation of optical quantum logic gates relies. This thesis presents an experimental demonstration of simultaneous generation of almost identical single photons in highly pure states from two independent sources based on parametric downconversion. This is a process of photon pair generation during the passage of a light beam through a nonlinear crystal; one photon from the resulting pair is detected to herald the other. The work herein describes, refines, and implements a technique that minimises the strong quantum correlations usually present within each pair by spectral engineering of the source. This allows the heralded single photons to be in pure states, a property that is confirmed by observing a high-visibility two-photon interference effect without spectral filtering.
RSSI study of wireless Internet of Things technologies

The Internet of Things (IoT) idea now entails a dispersed collection of different sensor networks with several functions that collect data, which is then analysed and used in applications such as smart cities. These networks are capable of transmitting massive volumes of data in a relatively efficient, yet unsecure wireless environment. These applications will only succeed if is developed a dependable a low-cost real-time method for pinpointing accurate location. Power consumption is another consideration for indoor localisation. Recent wireless technologies like ZigBee, Bluetooth Low Energy (BLE) and Long Range (LoRa), use less resources, making them ideal for interior positioning. When IoT devices are utilised, these technologies are compared in terms of precision of localisation and power consumption. Tracking the Received Signal Strength Indicator (RSSI) values can be used to locate mobile sensor nodes for low-power IoT networks. The RSSI values of sensor nodes in the BLE, ZigBee and LoRa networks for IoT were explore in this study.
Comparative criteria for objective evaluation of the efficiency of RF power amplifiers

Objective comparison of radio frequency amplifiers, which are infinitely diverse in their parameters and purpose is a difficult engineering task. For its successful solution it is necessary to define objective criteria that allow to quantify qualitatively and quantitatively the scheme and mode of operation of the amplifier, without this assessment depending on the absolute values of current, voltage, power and parameters of active and passive elements in the electronic circuit.