Miracle-dz/UniFAR-FTUs huggingface dataset:
Bayesian inference on non-stationary data

This thesis argues in favour of Bayesian techniques for the analysis of non- stationary linear time series. The main motivations are to avoid using asymptotic results and to explicitly incorporate prior beliefs, where they exist. The properties of univariate and multivariate unit root models, and the available frequentist inferential results are described. Some problems in their applications are highlighted: the discrepancies between asymptotic and finite sample properties and the role of the deterministic components in determining the reference asymptotic distributions. The advantages and disadvantages of Bayesian techniques are then examined with the recent developments in the Monte Carlo integration by Markov Chain sampling. Two case studies are conducted with the aim of providing evidence of the applicability of Bayesian techniques. The first of these cases develops a procedure to test for seasonal and/or zero frequency unit roots in quarterly series. A new parameterisation is provided and the priors implemented are discussed and justified. The analysis relies on a Gibbs sampling scheme. The inferential technique used is the evaluation of posterior odds ratios. These ratios are defined as posterior expectations of functions of the parameters, and therefore can be consistently estimated The procedure is applied to some UK variables. The results are robust with respect to different prior distributions, and conflict with some conclusions reached by using classical asymptotic unit root tests. The second case study develops a Bayesian procedure to conduct inference in cointegrated systems. Inference regards the number of cointegrating relationships and their structural interpretation, and is based on the evaluation of highest posterior density confidence intervals. The procedure is applied to three VAR systems: Danish and Finnish money demands, and UK exchange rate data. Interesting results emerge, showing significant differences with their frequentist counterparts. All these results are robust with respect to different priors.
Radionuclides Determination and Computing Radiobiological Dose for TENORM Exposure Using NaI(Tl) Gamma-ray Spectrometer

The present study is carried out to determine the natural radioactivity level and assessment of the radiological impact on El-Missikat younger granites of the Central Eastern Desert in Egypt. Regarding the distribution pattern of radionuclides associated with the abundance of naturally occurring and technically enhanced radioactive materials, 34 investigated samples were collected from predetermined locations in the El-Missikat area. The radioanalytical measurements were performed by using NaI(Tl) gamma-ray spectrometer, then PCA3 Oxford software was used to analyze the recorded spectra. The efficiency transfer of Gamma-ray was applied within EFFTRAN software, and the library of radionuclides has been designed to involve radionuclides belonging to the natural uranium-238 and thorium-232 isotopes as well potassium-40. The radiation doses were calculated utilizing activity concentrations and the conversion coefficients recommended by EPA, ICRP, and UNSCEAR. The average values of activity concentrations for U-238, Th-232, and K-40 are (9986.21, 5586.19, and 64.72 Bq/kg) respectively. The average values of calculated effective dose caused by gamma radiation of U-238, Th-232, and K-40 are (6.79E-19, 2.20E-18, and 4.30E-15 Sv/y) respectively. Most of the resulting values are significantly of a high level compared to the worldwide reference EPA, IAEA, ICRP, and UNSCEAR for similar environments. To furnish results regarding radiation protection and human safety integral of organ doses, the excess lifetime cancer risk and hazards indices were calculated to apply a perfect radiation protection methodology for the professionals.
Design of Phased Array Antenna System for LoRa Applications

The proposed work focuses on the design and fabrication of a Phased array antenna system operating in the range of Long-Range communication (LoRa) band with center frequency of 915 MHz. The phased array antenna system consisting of a 2-element patch antenna array along with tunable phase shifter is designed using Reflection Type Phase Shifter (RTPS) method. The design offers the phase shift of 11.25°, 22.5° and 45°. The cooperate-fed array feeder network is designed using two-way Wilkinson's type structure to equally split power in to two ports to enhance port impedance matching and isolation. The experimental results show the system achieves return loss better than 10 dB in the overall range of frequencies. The phased array antenna system is designed and simulated using Advance Design System (ADS). The phased array antenna system is fabricated on low-cost Epoxy glass fiber FR-4 PCB board using the chemical method. The measured results and simulated results are in good agreement with each other.
Functional approximation for the classification of smooth time series

Time series data are frequently analysed or classied by con- sidering sequences of observations directly as high-dimensional feature vectors. The presence of several hundreds or thousands of input dimen- sions can lead to practical problems. Moreover, standard algorithms are not readily applicable when the time series data is non-equidistant or the sampling rate is non-uniform. We present an approach that allows for a massive reduction of input dimensions and explicitly takes advan- tage of the functional nature of the data. Furthermore, the application of standard classication algorithms becomes possible for inhomogeneously sampled time series. The presented approach is evaluated by applying it to four publicly available time series datasets.
Visualization and Numerical Simulations of Condensing Flow in Small Diameter Channels

ABSTRACT The understanding of two-phase flow mechanisms during condensation inside small diameter channels is fundamental to design compact condensers. When dealing with small geometries, experimental investigation can be invasive and the measurement of heat transfer coefficients with low uncertainties becomes difficult. For these reasons, numerical simulations by means of the volume of fluid method are an interesting tool to study two-phase flows and they can also be used in support to experimental investigation. In the present work, first steady-state numerical simulations of R134a condensation inside horizontal channels are presented: the results are used to analyse the effect of the diameter (1 mm and 3.4 mm) on the two-phase flow and heat transfer. Since waves occur at the vapor–liquid interface and they cannot be modelled under the hypothesis of steady-state operating conditions, transient simulations (2-D axisymmetric) have also been performed to investigate the influence of waves on the condensation process. The two-phase flow has also been experimentally investigated and visualizations are compared to the numerical results.
Reverse-ChIP Techniques for Identifying Locus-Specific Proteomes: A Key Tool in Unlocking the Cancer Regulome

A phenotypic hallmark of cancer is aberrant transcriptional regulation. Transcriptional regulation is controlled by a complicated array of molecular factors, including the presence of transcription factors, the deposition of histone post-translational modifications, and long-range DNA interactions. Determining the molecular identity and function of these various factors is necessary to understand specific aspects of cancer biology and reveal potential therapeutic targets. Regulation of the genome by specific factors is typically studied using chromatin immunoprecipitation followed by sequencing (ChIP-Seq) that identifies genome-wide binding interactions through the use of factor-specific antibodies. A long-standing goal in many laboratories has been the development of a ‘reverse-ChIP’ approach to identify unknown binding partners at loci of interest. A variety of strategies have been employed to enable the selective biochemical purification of sequence-defined chromatin regions, including single-copy loci, and the subsequent analytical detection of associated proteins. This review covers mass spectrometry techniques that enable quantitative proteomics before providing a survey of approaches toward the development of strategies for the purification of sequence-specific chromatin as a ‘reverse-ChIP’ technique. A fully realized reverse-ChIP technique holds great potential for identifying cancer-specific targets and the development of personalized therapeutic regimens.
Some Approaches to Improve the Computational Efficiency of the Reliability Analysis of Complex Crack Propagation Problems

Probabilistic prediction of fatigue propagation life of complex structures necessitates considerable computational efforts. This paper presents several approaches to render the probabilistic analysis of the life under fatigue crack propagation computationally more tractable while including important issues of structural and load complexity in the modelling. To calculate the failure probability and its sensitivity with respect to the underlying random variables, we used the First Order Reliability Method (FORM). The design point search algorithm requires the gradients of the structural response. Two methods to obtain these gradients were used: the finite difference method (FDM), and a direct differentiation of the crack growth law for some of the derivatives. Within FDM, the computation of the structural responses was distributed to individual nodes of a cluster of PCs. A comparison of the results shows that the coordinates of the design points obtained using each of the two methods of gradient estimation differ only within the play allowed by the convergence limits. The model used allows for consideration of variable amplitude loading with overloads through a transformation of a complex load history to a constant-amplitude loading by the PREFFAS method. The uncertainty due to the material parameters of the model was analysed using Monte Carlo simulation, showing that the coefficient of variation of the resulting equivalent load stress is only about 8 %. A valid modelling of crack propagation requires the estimation of the crack growth law parameters from actual fatigue test results. It appears that the Paris law multiplier C and exponent m are correctly modelled using a joint normal distribution of ln C and m. It was also shown that the sensitivity of the failure probability to the correlation coefficient of the Paris law parameters is significant.
Global to Local: A Scale-Aware Network for Remote Sensing Object Detection

With the wide application of remote sensing images (RSIs) in military and civil fields, remote sensing object detection (RSOD) has gradually become a hot research direction. However, we observe two main challenges for RSOD, namely, the complicated background and the small objects issues. Given the different appearances of generic objects and remote sensing objects, the detection algorithms designed for the former usually cannot perform well for the latter. We propose a novel global-to-local scale-aware detection network (GLSANet) for RSOD, aiming to solve the abovementioned two challenges. First, we design a global semantic information interaction module (GSIIM) to excavate and reinforce the high-level semantic information in the deep feature map, which alleviates the obstacles of complex background on foreground objects. Second, we optimize the feature pyramid network to improve the performance of multiscale object detection in RSIs. Finally, a local attention pyramid (LAP) is introduced to highlight the feature representation of small objects gradually while suppressing the background and noise in the shallower feature maps. Extensive experiments on three public datasets demonstrate that the proposed method achieves superior performance compared with the state-of-the-art detectors, especially on small object detection datasets. Specifically, our algorithm reaches 94.57% mean average precision (mAP) on the NWPU VHR-10 dataset, 95.93% mAP on the RSOD dataset, and 77.9% mAP on the DIOR dataset.
Serum uric acid as a biological marker for assessment of progression of glycemic status and polyneuropathy in experimentally induced type 2 diabetic rats

Background: Screening for laboratory indicators and modifiable risk factors for diabetic polyneuropathy (DPN) is crucial for early detection and development of novel treatments. This study investigated the possible correlation of serum uric acid to the progression of glycemic status and polyneuropathy in experimentally induced type 2 diabetic rats. Methods: Sixty rats were divided into non-diabetic, diabetic 4-week non-treated, diabetic 8-week non-treated, diabetic 4-week metformin-treated, diabetic 8-week metformin-treated. Rats were evaluated for glycemic state, serum uric acid, lipid profile, inflammatory and oxidative stress parameters. Hot plate test, sciatic nerve conduction speed, and sciatic nerve histopathological changes were assessed. Results: Four weeks after high-fat diet (HFD) and low dose of streptozotocin (STZ) injection, rats showed significant elevation in blood glucose, HbA1c and HOMA-IR, uric acid, malondialdehyde, TNF-α, IL-6, thermal hyperalgesia and significant reduction of nerve conduction speed and total antioxidant capacity associated with significant changes in lipid profile and histopathological structure of sciatic nerve. These changes were more prominent after 8 weeks of STZ injection than in 4-week diabetic-non-treated group. Metformin treatment significantly improved all parameters, meanwhile the improvement was more prominent in 8-week than 4-week group. Conclusion: Serum uric acid can be taken as a useful biological marker for assessment of progression of diabetic status and polyneuropathy in type 2 diabetes.
Mesh Bias for the Eigenerosion Approach

In fracture mechanics, having the right crack path is a necessary condition for achieving reliable results. On the other hand, a discretized continuum is unable to represent a random crack path perfectly. Thus, challenges lie ahead on the accuracy of the numerical representation of this crack. Albeit the eigenfracture scheme has been well established in the recent years, different simulation results have shown some dependencies of the crack path on the mesh orientation, particularly when dealing with unstructured meshes. An r‐adaptive mesh refinement technique is introduced in this work, which circumvents possible mesh bias. In this regard, eroded finite elements are adaptively aligned in the direction of configurational forces calculated at the crack tip. Investigating the bounds of the eigenerosion scheme related to meshing and the introduction of possible improvements contribute to avoiding mesh induced anisotropy and further consolidate the approach.
The effect of uniform electric field on the cross-stream migration of a drop in plane Poiseuille flow

The effect of a uniform electric field on the motion of a drop in an unbounded plane Poiseuille flow is studied analytically. The drop and suspending media are considered to be Newtonian and leaky dielectric. We solve for the two-way coupled electric and flow fields analytically by using a double asymptotic expansion for small charge convection and small shape deformation. We obtain two important mechanisms of cross-stream migration of the drop: (i) shape deformation and (ii) charge convection. The second one is a new source of cross-stream migration of the drop in plane Poiseuille flow which is due to an asymmetric charge distribution on the drop surface. Our study reveals that charge convection can cause a spherical non-deformable drop to migrate in the cross-stream direction. The combined effect of charge convection and shape deformation significantly alters the drop velocity, drop trajectory and steady state transverse position of the drop. We predict that, depending on the orientation of the applied uniform electric field and the relevant drop/medium electrohydrodynamic parameters, the drop may migrate either towards the centreline of the flow or away from it. We obtain that the final steady state transverse position of the drop is independent of its initial transverse position in the flow field. Most interestingly, we show that the drop can settle in an off-centreline steady state transverse position. Two-dimensional numerical simulations are also performed to study the drop motion in the combined presence of plane Poiseuille flow and a tilted electric field. The drop trajectory and steady state transverse position of the drop obtained from numerical simulations are in qualitative agreement with the analytical results.
Skill Fusion in Hybrid Robotic Framework for Visual Object Goal Navigation

In recent years, Embodied AI has become one of the main topics in robotics. For the agent to operate in human-centric environments, it needs the ability to explore previously unseen areas and to navigate to objects that humans want the agent to interact with. This task, which can be formulated as ObjectGoal Navigation (ObjectNav), is the main focus of this work. To solve this challenging problem, we suggest a hybrid framework consisting of both not-learnable and learnable modules and a switcher between them—SkillFusion. The former are more accurate, while the latter are more robust to sensors’ noise. To mitigate the sim-to-real gap, which often arises with learnable methods, we suggest training them in such a way that they are less environment-dependent. As a result, our method showed top results in both the Habitat simulator and during the evaluations on a real robot.
NUMERICAL STUDY OF A SLENDER-SHIP THEORY OF WAVE RESISTANCE,

L'article present est une suite de l'etude numerique, par Chen et Noblesse, citee en reference, de la theorie de la resistance d'onde d'un navire mince presentee par Noblesse dans une publication citee en reference. On rappelle la formule de Havelock qui exprime la resistance generatrice d'onde en fonction de la fonction energie d'onde de Kochin dont on donne l'expression en fonction du potentiel des vitesses calcule a l'aide d'une fonction de Green. On presente et discute les resultats numeriques relatifs a trois coques en forme de poutres creuses. Ces resultats comportent des courbes representatives du spectre de la fonction de Kochin et de la variation de la resistance d'onde avec le nombre de Froude
A machine-learning-based Intrusion detection for IIoT infrastructure

Industrial Internet of Things (IIoT) is rapidly expanding in the industrial sector, and it is critical to ensure the security of IIoT systems against cyber threats. This paper proposes an intrusion detection system for IIoT based on machine-learning algorithms. The proposed system uses a Decision Tree, Random Forest, and Naive Bayes algorithms to classify the incoming network traffic as normal or malicious. The dataset used in this research consists of 500,000 samples, including 250,000 attack samples and 250,000 normal samples. The dataset was preprocessed and features were extracted to train and test the proposed intrusion detection system. To evaluate the performance of the proposed system, accuracy, recall, precision, and F1-score, were used for evaluation. The results of the experiments show that the proposed system achieved high accuracy, recall, and precision in detecting malicious traffic in the IIoT network. The Decision Tree algorithm achieved the highest accuracy of 99.73%, followed by Random Forest with 99.70% accuracy and naive Bayes with 99.69% accuracy. The proposed intrusion detection system is expected to enhance the security of IIoT systems and mitigate the risk of cyber attacks in the industrial sector. It is recommended to integrate the proposed system with existing IIoT security systems to improve their effectiveness and provide a security solution.
Mechanical fault diagnosis based on deep transfer learning: a review

Mechanical fault diagnosis is an important method to accurately identify the health condition of mechanical equipment and ensure its safe operation. With the advent of the era of ‘big data’, it is an inevitable trend to choose deep learning for mechanical fault diagnosis. At the same time, to improve the generalization ability of deep learning applications in different scenarios of fault diagnosis, mechanical diagnosis based on transfer learning has also been proposed and become an important branch in the field of mechanical fault diagnosis. This paper introduces the principle of transfer learning, summarizes the research and application of transfer learning in the field of fault diagnosis, discusses the shortcomings of transfer learning in the field of fault diagnosis, and discusses the future research direction of transfer learning in the field of fault diagnosis.
Crystal Design, Antitumor Activity and Molecular Docking of Novel Palladium(II) and Gold(III) Complexes with a Thiosemicarbazone Ligand

The current research describes the synthesis and characterization of 2-acetylpyridine N(4)-cyclohexyl-thiosemicarbazone ligand (HL) and their two metal complexes, [Au(L)Cl][AuCl2] (1) and [Pd(L)Cl]·DMF (2). The molecular structures of the compounds were determined by physicochemical and spectroscopic methods. Single crystal X-ray diffraction was employed in the structural elucidation of the new complexes. The complexes showed a square planar geometry to the metal center Au(III) and Pd(II), coordinated with a thiosemicarbazone molecule by the NNS-donor system and a chloride ion. Complex (1) also shows the [AuCl2]− counter-ion in the asymmetric unit, and complex (2) has one DMF solvent molecule. These molecules play a key role in the formation of supramolecular structures due to different interactions. Noncovalent interactions were investigated through the 3D Hirshfeld surface by the dnorm function and the 2D fingerprint plots. The biological activity of the compounds was evaluated in vitro against the human glioma U251 cells. The cytotoxicity results revealed great antitumor activity in complex (1) compared with complex (2) and the free ligand. Molecular docking simulations were used to predict interactions and properties with selected proteins and DNA of the synthesized compounds.
Smooth Lyapunov functions for homogeneous differential inclusions

This paper provides a construction method of a smooth homogeneous Lyapunov function associated with a discontinuous homogeneous system, which is locally and asymptotically stable. First, we analyze two similar converse Lyapunov theorems for differential inclusions and unify them into a simple theorem. Next, we propose a new definition of homogeneous differential inclusion. Then, we construct a smooth, homogeneous Lyapunov function associated with the homogeneous differential inclusion. Finally, we show that the order of homogeneity of a homogeneous system indicates the speed of convergence.
A Comparative Study of Morphological Gradient and Its Improved Form for CT Saturation Detection

Magnetization of a Current Transformer (CT) is a non-linear characteristic, which can cause distortion in its secondary waveform, causing malfunction of protective relay. In this paper, two algorithms based on mathematical morphology, morphological gradient and improved morphological gradient have been compared for detection of CT saturation. The improved morphological gradient is effective in removing noise and separating transient information from the signal. Starting and end time of saturation can be detected using this algorithm.