Miracle-dz/UniFAR-FTUs huggingface dataset:
Bayesian Sensor Fault Detection in a Markov Jump System

In this paper, the fault detection of a latent fault in a sensor for a Markov jump system is studied. It is equivalent to detecting a change point in a coefficient vector of a measurement equation in the state space representation of a system. Indeed, the fault detection procedure is evaluated as detecting this change point and the time that the change point has occurred. To this end, first, the recursive least square (RLS) filter is proposed and under Yao's Prior setting, the Bayesian fault detection algorithm is proposed. The Smith‐Gelfand re‐sampling method is applied to approximate the posterior distribution. The performance of the Bayesian method is studied under the null and alternative hypotheses. The delay in diagnosis of the fault is measured. To study the effect of the fault time point in the performance of the Bayesian method, the sensitivity analysis is studied. The probability of the fault is studied and the Martingale approach is used to obtain the lower and upper bounds for this probability. The fault detection in integrated systems is studied and a Kalman filter, as a parallel filter, is considered to estimate the state and the effect of the unknown coefficient jump on state estimation is also studied.
A forecasting performance comparison of dynamic factor models based on static and dynamic methods

Abstract We present a comparison of the forecasting performances of three Dynamic Factor Models on a large monthly data panel of macroeconomic and financial time series for the UE economy. The first model relies on static principal-component and was introduced by Stock and Watson (2002a, b). The second is based on generalized principal components and it was introduced by Forni, Hallin, Lippi and Reichlin (2000, 2005). The last model has been recently proposed by Forni, Hallin, Lippi and Zaffaroni (2015, 2016). The data panel is split into two parts: the calibration sample, from February 1986 to December 2000, is used to select the most performing specification for each class of models in a in- sample environment, and the proper sample, from January 2001 to November 2015, is used to compare the performances of the selected models in an out-of-sample environment. The metholodogical approach is analogous to Forni, Giovannelli, Lippi and Soccorsi (2016), but also the size of the rolling window is empirically estimated in the calibration process to achieve more robustness. We find that, on the proper sample, the last model is the most performing for the Inflation. However, mixed evidencies appear over the proper sample for the Industrial Production.
A mineral feature extraction method based on virtual band simulation

ABSTRACT Remote sensing-based geological exploration is based on identifying and analyzing spectral features of minerals and rocks. Although an abundance of high quality multispectral remote sensing datasets is available, no appropriate method of full-band absorption feature extraction has been proposed for these datasets. In this work, we developed a new method, Virtual Band Continuum Removal (VBCR), for the extraction of absorption feature spectra from multispectral datasets. By setting the nodes of the continuum on virtual bands instead of the real bands, the proposed method is an improved approach for preserving the weak absorption features in multispectral datasets. VBCR was compared with a state of the art approach on both laboratory data and real image data, and the experimental results indicated the efficacy of the proposed method.
Density and abundance estimate of Antillean manatees (Trichechus manatus) within a landlocked lake in Southeastern México

Manatees are threatened along their range. In México, this species is listed as endangered. Manatee conservation strategies require density or occupancy estimates, especially in areas where species face survival risks. On turbid waters, like those found in rivers and lakes of the Southern Gulf of México coast, visual methods used to detect and count manatees underestimate actual numbers. Our goal was to estimate the density and abundance (N) of Antillean manatees in a small State Natural Protected Area, where a manatee population of unknown size inhabits. We performed line transects using a side scan sonar to detect animals and mark-recapture in the isolated population of manatees within Laguna de las Ilusiones, México, a landlocked lake that excludes transit. Using distance sampling from 14 boat trips, estimates of density and abundance were 15.5 manatees km-2 and 27 ± 5 manatees in the lake (CV ≈ 16.6 %). With MARK, from six capture events and 19 individual encounter records, the abundance estimated was 24 manatees (CV ≈ 16 to 24 %). Previous number of manatees were based only on visual surveys, which reported at least seven manatees. Density is lower than other similar studies along narrow waterways in important areas in México and other Central and South America countries. Studying this endangered subspecies is limited by cryptic habits, turbid waters, poor funding, and low densities, making density or abundance estimates difficult. However, within particular areas and established monitoring areas, these methods could be useful to generate baselines for conservation strategies.
Neural network performance enhancement for limited nuclear fusion experiment observations supported by simulations

It has recently been shown that artificial neural networks (NNs) are able to establish nontrivial connections between the heat fluxes and the magnetic topology at the edge of Wendelstein 7-X (W7-X) (Böckenhoff et al 2018 Nucl. Fusion 58 056009), a first step in the direction of real-time control of heat fluxes in this device. We report here on progress on improving the performance of these NNs. A particular challenge here is that of generating a suitable training set for the NN. At present, experimental data are sparse, and simulated data, which are much more abundant, do not match the experimental data closely. It is found that the NNs show significantly improved performance on experimental data when experimental and simulated data are combined into a common training set, relative to training performed on only one of the two data sets. It is also found that appropriate pre-processing of the data improves performance. The architecture of the NN is also discussed. Overall a significant improvement in NN performance was seen—the normalized error reduced by more than a factor of three over the previous results. These results are important since heat flux control in a W7-X, as well as in a future fusion power plant, is likely a key issue, and must start with a very limited set of experimental training data, complemented by a larger, but not necessarily fully realistic, set of simulated data.
Investigation of the aerodynamic performance and flow physics on cross sections of Dragonfly wing on flapping and pitching motion in low Reynolds number

In this research, the flow physics and aerodynamic performance of dragonfly cross sections, used in Micro Aerial Vehicles (MAVs), in low Reynolds are investigated. The main objective of the research is to study the performance of dragonfly wing cross-sections flapping motion in Reynolds 5000 and 10,000. Pitching motion is one of the most important mechanisms in force lifting generation, and the effects of Reynolds number and mean angle of attack on aerodynamic coefficients have been extensively investigated for the pitching motion. In the present study, the geometry of two cross sections of dragonfly was extracted. Incompressible, two-dimensional and unsteady Navier–Stokes equations have been used to simulate the flow. k − ɛ RNG model was used for turbulence modeling. To simulate the wing pitching motion, the dynamic mesh method was used. The results showed that in flapping motion, pitching-up rotation has caused a rapid increase in lift coefficient. Furthermore, it was found that the absence of stall does not increase the lift and drag coefficients, while formation of new strong vorticity layers have caused an increase in lift coefficient. On the other hand, corrugations on the cross sections of the dragonfly in the pitching motion cause the delay of separation and increasing the lift coefficient. In flapping motion and the pitching motion, the lift coefficients of three cross sections were increased due to stronger vorticity layers by reducing the Reynolds number. Due to the existence of corrugations, the first and the second cross sections have good aerodynamic performance, compared to the flat plate. The comparison carried out in the current research showed that the second cross section is a proper replacement for the flat plate in MAVs.
The definition of syntactic types: The generation, analysis, and sorting of universes of superblock designs

Syntactic types are defined as sets of designs with particular properties. We are specifically interested in the definition of deformed grids, a particular type of street network internal to superblocks. The superblocks under study are surrounded by arterial streets and have traversing local main streets and internal street infills. The argument takes advantage of three kinds of rules or algorithms: generative rules that produce a universe of designs, analytical algorithms that can be applied to the description of properties of interest for each member of the universe, and query or sorting rules that allow us to identify those members of the universe that have particular ranges and combinations of properties of interest. The iterative application of analytical and sorting algorithms assists the transition from an intuitive to formalized definitions of type.
Numerical study of the natural transition of water boundary layers over heated/cooled flat plates

The natural transition of water boundary layers on heated/cooled flat plates is investigated using numerical methods. The influences of wall temperature, oncoming flow velocity, and onco ming flow temperature on the transition are studied. Laminar basic flows are obtained by solving the Blasius equations, while water temperature is taken into consideration. Linear stability analyses of the basic flows are performed, and the transition positions are predicted using the eN method. The spectra of wall pressure fluctuations in the laminar region are also predicted. The results show that the heated wall produces a fuller velocity profile and a thinner boundary layer for the laminar flow and that a cooled wall causes the opposite effects. Consequently, the heated wall delays transition, while the cooled wall advances transition. As wall temperature increases, the effect of transition delay becomes stronger until the maximal delay effect occurs at an optimal wall temperature. Wall temperatures higher than the optimal value weaken the transition delay effect. This phenomenon of the heated wall is called transition reversal. A heated wall suppresses the wall pressure fluctuation, and a cooled wall has the opposite effect. The oncoming flow velocity has little influence on the non-dimensional transition position, while the oncoming flow temperature has a strong influence on it. At the same wall temperature, a higher oncoming flow temperature leads to a thicker boundary layer, stronger instability, and an earlier transition. A lower oncoming flow temperature leads to stronger sensitivity of the transition to the wall temperature.
Ameliorative Effects of 5-7, Dihydroxy Flavone (Chrysin) on Hippocampus of Wistar Rats with Doxorubicin-induced Cognitive Impairment

Concern is increasing regarding the effect of chemotherapy induced cognitive impairment on oncology patients. This sought to investigate the effect of 5-7, dihydroxyflavone on the hippocampus of wistar rats with Doxorubicin induced cognitive impairment. 30 male Wistar rats were procured and acclimatized for 14 days with feed and water, they were divided into six (6) experimental groups of five (5) animals each. Group 1 served as normal control. Group 2 was induced with 2mg/kg of DOX and was untreated. Group 3 received 150mg/kg of Chrysin. Groups 4, 5 and 6 were induced with 2mg/kg of doxorubicin and treated with Chrysin at doses of 50mg/kg, 100mg/kg, and 150mg/kg respectively. The study lasted for 21 days. The body weight of the animals were recorded three (3) times before and after induction and then recorded again at the end of the 21 days treatment. At the end of the experiment, there were significantly increase in body weight. The increase was more pronounce in group 1 and group 2. In the oxidative stress analysis and ELISA analysis on the levels of inflammatory cytokines (IL-1 & IL-6), there was a significant increase in the levels compared to the control group 1. In the neurobehavioural test, group 6 had a high spontaneous alternation percentage compared to other groups. The treatment with Chrysin significantly had an ameliorative effect on the treated animal groups and in group 3. The result from this work suggest that Chrysin extract had an ameliorative effect on cognitive impairment of the hippocampus.
Existence of multiple stable stationary patterns to some reaction-diffusion equation in heterogeneous environments (Theory of Biomathematics and its Applications VI)

$\partial 0^{l}/\partial n=0,$ $(x\in\partial\Omega)$ . Asymptotic behaviour of the unique solution $u=u_{d}$ is alos well-known as $darrow 0$ . In this paper we consider two reaction-diffusion model with weak Allee effect, which appears as a populationa growth model, and show existence of multiple stable stationary patterns under certain heterogeneous evnvironments. Throughout this paper, we say that a solution is stable, which is called weakly stable sometimes, if it is a local minimizers of the associated energy functional. There are several works on the existence of stable staionary patterns for the rectiondiffusion model with strong Allee effect and environment factor $a(x)$ :
From wind to loads: wind turbine site-specific load estimation using databases with high-fidelity load simulations

Abstract. We define and demonstrate a procedure for quick assessment of site-specific lifetime fatigue loads, using surrogate models calibrated by means of a database with high-fidelity load simulations. The performance of six surrogate models is assessed by comparing site-specific lifetime fatigue load predictions at ten sites. The surrogate methods include polynomial-chaos expansion, quadratic response surface, universal Kriging, importance sampling, and nearest-neighbor interpolation. Practical bounds for the database and calibration are defined via nine environmental variables, and their relative effects on the fatigue loads are evaluated by means of Sobol sensitivity indices. Of the surrogate-model methods, polynomial-chaos expansion provided an accurate and robust performance in prediction of the different site-specific loads. Although the Kriging approach showed slightly better accuracy, it also demanded more computational resources. Taking into account other useful properties of the polynomial chaos expansion method within the performance comparisons, we consider it to generally be the most useful for quick assessment of site-specific loads.
A Linear Discriminant Analysis based Modulation Recognition Method for Linear Modulations

In this paper, a new method for signal modulation classification based on Linear Discriminant Analysis for multi-class cases is introduced and its results are compared with two other methods of automatic modulation classification. Higher-order complex cumulants are used as feature vectors in the first place. Then, linear Fisher discriminant analysis maps these vectors to another space to separate different classes efficiently. One versus all support vector machine classifier and kernel Fisher discriminant analysis methods with radial basis function kernel is used. The achieved results in comparison with two research papers show that the proposed method classifies the classes in a shorter time with equal or better accuracy.
Two-axis Gimbal System Stabilization Using Adaptive Feedback Linearization

This paper is devoted to adaptive feedback linearization based stabilization of a two-axis gimbal platform. A Lyapunov based adaptation mechanism is proposed for stabilization of a two axes gimbal system through a feedback linearized control structure. The uncertainty of the products of inertia is incorporated in the design procedure. The performed computer simulations demonstrate the robust performance of the proposed two-axis gimbal stabilizer considering the cross-coupling effects between inner and outer gimbals. The proposed control structure could be utilized in guided missiles, spacecraft, and navigation systems.
Discretization error cancellation in electronic structure calculation: toward a quantitative study

It is often claimed that error cancellation plays an essential role in quantum chemistry and first-principle simulation for condensed matter physics and materials science. Indeed, while the energy of a large, or even medium-size, molecular system cannot be estimated numerically within chemical accuracy (typically 1 kcal/mol or 1 mHa), it is considered that the energy difference between two configurations of the same system can be computed in practice within the desired accuracy. The purpose of this paper is to provide a quantitative study of discretization error cancellation. The latter is the error component due to the fact that the model used in the calculation (e.g. Kohn-Sham LDA) must be discretized in a finite basis set to be solved by a computer. We first report comprehensive numerical simulations performed with Abinit [1,2] on two simple chemical systems, the hydrogen molecule on the one hand, and a system consisting of two oxygen atoms and four hydrogen atoms on the other hand. We observe that errors on energy differences are indeed significantly smaller than errors on energies, but that these two quantities asymptotically converge at the same rate when the energy cutoff goes to infinity. We then analyze a simple one-dimensional periodic Schrodinger equation with Dirac potentials, for which analytic solutions are available. This allows us to explain the discretization error cancellation phenomenon on this test case with quantitative mathematical arguments.
Pilot-Partitioning Protocol and Anti-Jamming Methods in Distributed Massive MIMO Systems

Accurate channel state information (CSI) is crucial for effective beamforming in wireless communication systems. Unfortunately, the estimation of CSI might not be accurate due to pilot contamination, which happens when the transmission of pilots from the users to access points (APs) is interfered. In systems with a large number of users, non-orthogonal pilot training can increase pilot contamination resulting in incorrect CSI estimation. To address this issue, we propose a pilot-partitioning protocol for distributed massive multiple-input multiple-output (MIMO) systems. The protocol decomposes long-length orthogonal pilots into short-length sub-pilots, transmits them in consecutive slots, and uses the channel correlation among consecutive slots to regenerate orthogonal pilots. In addition, we consider the impact of adversarial transmissions, such as jamming attacks, on CSI estimation. To protect the system against jamming attacks, we propose an AP selection framework to eliminate ineffective APs where the estimated CSI is strongly distorted by jammers. Furthermore, we develop power control strategies for achieving the net throughput maximization and user fairness. Our numerical results show that the proposed designs improve system performance by up to 6.47 times compared to non-optimized scenarios.
Recent Advances in Deep Learning and Medical Imaging for Head and Neck Cancer Treatment: MRI, CT, and PET Scans

Simple Summary Deep learning techniques have significant potential in head and neck cancer imaging, particularly in tumor detection, segmentation, and outcome prediction using magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET) scans. Advanced deep learning methods, such as convolutional autoencoders, generative adversarial networks (GANs), and transformer models, have further enhanced imaging capabilities. Comparing deep learning and traditional techniques and the advantages and limits of each reveals their complementary roles in cancer management. Integrating radiogenomics with deep learning models promises further advancements in personalized care. However, challenges such as standardization, data quality, model overfitting, and computational requirements persist. Addressing these issues, integrating multimodal and temporal information, enhancing explainability, and conducting clinical validation are crucial for implementing deep learning models in head and neck cancer diagnosis and treatment. Overcoming these obstacles will pave the way for improved patient outcomes and personalized treatment strategies in head and neck cancer management. Abstract Deep learning techniques have been developed for analyzing head and neck cancer imaging. This review covers deep learning applications in cancer imaging, emphasizing tumor detection, segmentation, classification, and response prediction. In particular, advanced deep learning techniques, such as convolutional autoencoders, generative adversarial networks (GANs), and transformer models, as well as the limitations of traditional imaging and the complementary roles of deep learning and traditional techniques in cancer management are discussed. Integration of radiomics, radiogenomics, and deep learning enables predictive models that aid in clinical decision-making. Challenges include standardization, algorithm interpretability, and clinical validation. Key gaps and controversies involve model generalizability across different imaging modalities and tumor types and the role of human expertise in the AI era. This review seeks to encourage advancements in deep learning applications for head and neck cancer management, ultimately enhancing patient care and outcomes.
Ensemble sensitivity analysis of Greenland blocking in medium‐range forecasts

The North Atlantic Oscillation (NAO) is the leading mode of variability in the large‐scale circulation over the North Atlantic in winter, and strongly influences the weather and climate of Europe. On synoptic time‐scales, the negative phase of the NAO often corresponds to the occurrence of a blocking episode over Greenland. Hence, the dynamics and predictability of these blocking events is of interest for the prediction of the NAO and its related impacts over a wide region.
Intégration de l'optimisation par colonies de fourmis dans CP Optimizer

Nous presentons un algorithme generique-hybride pour la resolution de problemes d'optimisation combinatoire. Cet algorithme est le fruit de la combinaison de l'algorithme d'optimisation par colonie de fourmis (ACO: Ant Colony Optimization) et IBM ILOG CP Optimizer (produit commercialise dedie a la resolution de problemes combinatoires). Le probleme a resoudre est modelise avec le langage de modelisation de CP Optimizer et il est resolu par l'algorithme que nous proposons. Cet algorithme fonctionne en deux phases sequentielles. La premiere phase sert a echantillonner l'espace des solutions et a construire des traces de pheromones par ACO. Durant la deuxieme phase, CP Optimizer effectue une recherche arborescente exhaustive guidee par les traces de pheromones accumulees lors de la premiere phase. Nous avons teste l'algorithme propose sur, principalement, trois problemes a savoir: le probleme du sac a dos, le probleme d'affectation quadratique et le probleme de l'ensemble stable maximum. Les premiers resultats sur ces trois problemes montrent que ce nouvel algorithme ameliore les performances de CP Optimizer.
Interferometry for rough surface

Summary form only. Interferometry is suitable for the inspection of smooth surfaces. However, the fringe pattern is not observable for a rough surface, such as a machined surface, because of the light scattering. A technique which enables one to observe the interference fringes for a rough surface is proposed in this paper.