Miracle-dz/UniFAR-FTUs huggingface dataset:
Visual odometry and mapping under poor visibility conditions using a stereo infrared thermal imaging system

In the past decades, developments in the field of computer vision have made both the software and hardware more capable and more easily accessible. This has enabled otherwise complex vision systems to be used in other fields, such as autonomous robotics. Although image processing systems in the visible light spectrum are commonplace in robotics today, the IR spectrum is still rarely used, although it offers certain advantages. A thermal camera can sense the temperature of objects, is independent of illumination and can actually see through heavy smoke and fog. This makes it a useful tool in particular in the field of rescue robotics, where poor vision conditions are to be expected. In this paper, the feasibility of using two thermal cameras in a stereo vision setup to map indoor scenes is to be examined. It is meant to allow an autonomous robot to perceive its indoor surroundings as a 3D space, even in poor vision conditions. The biggest challenges are the calibration of thermal cameras and the proper filtering of the raw image and the resulting disparity map. Simple and easily implemented solutions are proposed for each of these issues.
Mechanisms for the α-Adrenoceptor-Mediated Positive Inotropy in Mouse Ventricular Myocardium: Enhancing Effect of Action Potential Prolongation

Mechanisms for the α-adrenoceptor-mediated positive inotropy in neonatal mouse ventricular myocardium were studied with isolated myocardial preparations. The phenylephrine-induced positive inotropy was suppressed by prazosin, nifedipine, and chelerythrine, a protein kinase C inhibitor, but not by SEA0400, a selective Na+/Ca2+ exchanger inhibitor. Phenylephrine increased the L-type Ca2+ channel current and prolonged the action potential duration, while the voltage-dependent K+ channel current was not influenced. In the presence of cromakalim, an ATP-sensitive K+ channel opener, the phenylephrine-induced prolongation of action potential duration, as well as the positive inotropy, were smaller than in the absence of cromakalim. These results suggest that the α-adrenoceptor-mediated positive inotropy is mediated by an increase in Ca2+ influx through the L-type Ca2+ channel, and the concomitant increase in action potential duration acts as an enhancing factor.
ERROR RATES STABILITY OF THE HOMOSCEDASTIC DISCRIMINANT FUNCTION

In this study the stability of the observed error rates of the homoscedastic discriminant function relative to the number of parameters in the model using simulated data from multivariate normal populations was investigated. Three models were considered, the four, six and eight variables models, each having four values of the separator function (). Equal and unequal prior probabilities were considered for the different number of parameter and separator function configurations. The asymptotic performance of the models was considered using the cross validation error rate estimation procedure. Results indicate the six variable models as being more stable (displaying less variability in the estimated error rates) than the other models under consideration. Less deterioration was observed for the six-variable model specification as was evident in the other models and this was more pronounced for smaller values of.
Sooty Tern Optimization Algorithm-Based Deep Learning Model for Diagnosing NSCLC Tumours

Lung cancer is one of the most common causes of cancer deaths in the modern world. Screening of lung nodules is essential for early recognition to facilitate treatment that improves the rate of patient rehabilitation. An increase in accuracy during lung cancer detection is vital for sustaining the rate of patient persistence, even though several research works have been conducted in this research domain. Moreover, the classical system fails to segment cancer cells of different sizes accurately and with excellent reliability. This paper proposes a sooty tern optimization algorithm-based deep learning (DL) model for diagnosing non-small cell lung cancer (NSCLC) tumours with increased accuracy. We discuss various algorithms for diagnosing models that adopt the Otsu segmentation method to perfectly isolate the lung nodules. Then, the sooty tern optimization algorithm (SHOA) is adopted for partitioning the cancer nodules by defining the best characteristics, which aids in improving diagnostic accuracy. It further utilizes a local binary pattern (LBP) for determining appropriate feature retrieval from the lung nodules. In addition, it adopts CNN and GRU-based classifiers for identifying whether the lung nodules are malignant or non-malignant depending on the features retrieved during the diagnosing process. The experimental results of this SHOA-optimized DNN model achieved an accuracy of 98.32%, better than the baseline schemes used for comparison.
Asymptotic Expansions in Nonstationary Vector Autoregressions

This paper studies the statistical properties of vector autoregressions (VAR's) for quite general multiple time series which are integrated processes of order one. Functional central limit theorems are given for multivariate partial sums of weakly dependent innovations and these are applied to yield first-order asymptotics in nonstationary VAR's. Characteristic and cumulant functionals for generalized random processes are introduced as a means of developing a refinement of central limit theory on function spaces. The theory is used to find asymptotic expansions of the regression coefficients in nonstationary VAR's under very general conditions. The results are specialized to the scalar case and are related to other recent work by the author [21].
Relation between Tropical Easterly Waves, Convection, and Tropical Cyclogenesis: A Lagrangian Perspective

AbstractIn this study, a wave-following Lagrangian framework was used to examine the evolution of tropical easterly wave structure, circulation, and convection in the days leading up to and including tropical cyclogenesis in the Atlantic and east Pacific basins. After easterly waves were separated into northerly, southerly, trough, and ridge phases using the National Centers for Environmental Prediction–National Center for Atmospheric Research reanalysis 700-hPa meridional wind, waves that developed a tropical cyclone [developing waves (DWs)] and waves that never developed a cyclone [nondeveloping waves (NDWs)] were identified. Day zero (D0) was defined as the day on which a tropical depression was identified for DWs or the day the waves achieved maximum 850-hPa vorticity for NDWs. Both waves types were then traced from five days prior to D0 (D − 5) through one day after D0. Results suggest that as genesis is approached for DWs, the coverage by convection and cold cloudiness (e.g., fractional coverage by .
Evolving basis functions with dynamic receptive fields

Neural networks using radial basis functions (RBFs) are a popular representation for inducing classification schemes. However, RBF neural networks often require a large number of hidden units (basis functions) in order to adequately model the class distinctions. This is due to the static nature of each basis function. This paper uses an evolutionary program to induce dynamic basis functions whose receptive fields are dependent on the input vector. This technique requires only a single basis function per class to perform on par with RBF networks.
The Role of Gorse (Ulex parviflorus Pourr. Scrubs) in a Mediterranean Shrubland Undergoing Climate Change: Approach by Hyperspectral Measurements

The goal of this study was to observe the neighbor effect of Gorse, a plant of the Fabaceae family, on three typical species of Mediterranean shrubland: kermes oak, white Cistus and rosemary. For this purpose, a hyperspectral analysis and the application of vegetation indices (VIs) were carried out. We provide the spectral signature of Gorse, which differs mainly from that of its companion species in the band between 700 and 1350 nm. This supposed Gorse effect was tested in natural conditions and in conditions of forced drought to simulate the effects of the climate change predicted for the Mediterranean Basin. Field spectrometry demonstrated the existence of such interactions between the four species. In control stands, the presence of Gorse significantly modifies the spectral responses of kermes, white Cistus and rosemary, mainly in the near-infrared region (700–1350 nm). Both tri- and tetra-specific plant assemblages also exhibited spectral changes, suggesting an indirect effect of Gorse. Under drought conditions, one-way ANOVA followed by Fisher’s LSD test led us to identify the features involved in plants’ coexistence with Gorse. The Cistus albidus reflectance spectrum was clearly increased in the presence of Gorse in rain-exclusion conditions. The application of several VIs allowed us to extract new information on the variation of spectral signatures. Unexpectedly, nitrogen supply by Gorse was not shown, except for Cistus, as shown by the VI NDVI (N) analysis. However, this study proved that Gorse can modify the behavior of its companion species in controls, but also in drought conditions, by increasing their photosynthesis activity (NIRvP) and water content (ratio R975/R900). Gorse therefore appears as a key species in the ecosystem of the Mediterranean shrubland, but its high vulnerability to drought leaves a vacant ecological niche in plant communities. While the spectral reflectance increases linearly with the specific richness in the lack of any disturbance, by contrast, climate aridification imposes a double reciprocal profile. This clearly means that multispecific plant communities cope better with climate change. Nevertheless, knowledge of the underlying mechanisms requires further structural, chemical, and biochemical investigation.
Light Curve Modeling of Superluminous Supernovae

Abstract Origins of superluminous supernovae (SLSNe) discovered by recent SN surveys are still not known well. One idea to explain the huge luminosity is the collision of dense CSM and SN ejecta. If SN ejecta is surrounded by dense CSM, the kinetic energy of SN ejecta is efficiently converted to radiation energy, making them very bright. To see how well this idea works quantitatively, we performed numerical simulations of collisions of SN ejecta and dense CSM by using one-dimensional radiation hydrodynamics code STELLA and obtained light curves (LCs) resulting from the collision. First, we show the results of our LC modeling of SLSN 2006gy. We find that physical parameters of dense CSM estimated by using the idea of shock breakout in dense CSM (e.g., Chevalier & Irwin 2011, Moriya & Tominaga 2012) can explain the LC properties of SN 2006gy well. The dense CSM's radius is about 1016 cm and its mass about 15 M⊙. It should be ejected within a few decades before the explosion of the progenitor. We also discuss how LCs change with different CSM and SN ejecta properties and origins of the diversity of H-rich SLSNe. This can potentially be a probe to see diversities in mass-loss properties of the progenitors. Finally, we also discuss a possible signature of SN ejecta-CSM interaction which can be found in H-poor SLSN.
Tuning the Wavelength: Manipulation of Light Signaling to Control Plant Defense

The growth–defense trade-off in plants is a phenomenon whereby plants must balance the allocation of their resources between developmental growth and defense against attack by pests and pathogens. Consequently, there are a series of points where growth signaling can negatively regulate defenses and where defense signaling can inhibit growth. Light perception by various photoreceptors has a major role in the control of growth and thus many points where it can influence defense. Plant pathogens secrete effector proteins to manipulate defense signaling in their hosts. Evidence is emerging that some of these effectors target light signaling pathways. Several effectors from different kingdoms of life have converged on key chloroplast processes to take advantage of regulatory crosstalk. Moreover, plant pathogens also perceive and react to light in complex ways to regulate their own growth, development, and virulence. Recent work has shown that varying light wavelengths may provide a novel way of controlling or preventing disease outbreaks in plants.
Gust Front Characteristics and the Kinematics Associated with Interacting Thunderstorm Outflows

Abstract The morphology, kinematic and thermodynamic characteristics of 30 gust fronts were examined with single and dual-Doppler radar and surface mesonet data collected in eastern Colorado during the summers of 1982 and 1984. The majority of gust fronts examined exhibited the general shape of laboratory-produced gravity currents, including the elevated head, body and turbulent wake region. The average head depth was 1.3 km, only 0.1 km above the average body depth. Small-scale features in the vertical and horizontal vorticity fields were also observed. The passage of the fronts was marked, in order of event, by a pressure rise, wind direction and velocity change, and temperature drop at the surface. The average propagation speed and maximum surface wind within the outflows were 8.6 and 14.5 m s−1, respectively. The average maximum temperature drop at the surface was 3.5°C and the average hydrostatic pressure rise was 0.06 kPa. Dual-Doppler analyses of colliding gust fronts revealed strong circulations a.
Assessment of Project Management Maturity Models Strengths and Weaknesses

The purpose of this article is to analyze the most popular maturity models in order to identify their strengths and weaknesses. Research conducted by international project management communities such as Software Engineering Institute (SEI), Project Management Institute (PMI), International Project Management Association (IPMA), Office of Government Commerce (OGC) and International Organization for Standardization (ISO) showed that organizations with high managerial maturity are more likely to achieve their planned project goals than those that do not identify and standardize their best management practices. This circumstance has encouraged scientists from all over the world to start developing various models that can measure and evaluate managerial maturity in projects. Nowadays, the variety of models created has led to considerable difficulty in understanding the strengths and weaknesses of each model. To solve this problem, the article authors conducted a critical analysis to identify the strengths and weaknesses of the most popular project management maturity models. The results obtained will be of interest to project managers, members of project teams, heads of organizations, project offices and everyone involved in the development of project activities. Based on the analysis, it was found that the most developed maturity models are based on international codes of knowledge of project management. Most maturity models ignore the presence of structural and infrastructural elements, such as a workplace, the necessary equipment and software, the availability of professional standards, instructions, regulations, etc. It was also revealed that there are no processes for assessing the effectiveness and efficiency of using the best practices in the maturity models.
Modeling and Suppression of Reverberation Components

We propose a maximum likelihood type approach for estimating the arrival times of signals which have propagated via a continuum of paths, i.e. temporally spread channels. The channel spreading is included in the model by using a discrete prolate spheroidal sequence (DPSS) to represent the channel impulse response of given duration, but unknown shape. The unknown parameters are estimated using an iterative methodology which decomposes the original data into its constituent components and then estimates the parameters of the individual components through a sequence of one dimensional searches. Computer simulation examples indicate that the method performs well.
Experimental observation of the rotation properties of atomic multipoles

The resonant interaction of optical radiation with atomic vapors can excite the atoms into anisotropic states with long lifetimes. The conventional description of these states uses an expansion in a basis of multipole moments, which are represented by irreducible tensor operators. We demonstrate an experimental method that permits the excitation of these atomic multipole moments in specific, experimentally controllable spatial orientations. Complementary optical methods permit the observation of multipole moments of arbitrary order and a direct measurement of their spatial orientation. We demonstrate these procedures in the ground state of atomic sodium, using optical pumping to polarize the angular-momentum substates. We show how the experimental results can be used to separate signal contributions that originate from different multipole moments.
Correlation of HMGB1, TLR2 and TLR4 with left ventricular diastolic dysfunction in sepsis patients

Left ventricular diastolic dysfunction (LVDD) is a common consequence of sepsis due to dysregulated inflammatory responses. Here we aim to investigate high mobility group box 1 (HMGB1), toll‐like receptor 2 (TLR2) and toll‐like receptor 4 (TLR4) as serum biomarkers to assess LVDD risk of patients with sepsis. We recruited 120 patients with sepsis, among which 52 had ultrasonically confirmed LVDD and 68 were without LVDD. Blood samples were collected, and enzyme‐linked immunosorbent assay (ELISA) was used to analyse levels of HMGB1, TLR2 and TLR4 in serum. Multivariate analysis was performed to assess the odds ratio of the serum biomarkers. Spearman's correlation analysis was conducted to evaluate the correlation between the serum biomarkers to B‐type natriuretic peptide (BNP) and cardiac troponin I (cTnl) levels and the ratios of early diastolic mitral inflow velocity to early diastolic mitral annulus velocity (E/e′ ratios) in ultrasound. Receiver operating curve was used to measure the sensitivity and specificity of HMGB1, TLR2 and TLR4 individually and in combination as diagnostic markers. Elevated HMGB1, TLR2 and TLR4 had significant values in predicting LVDD suggested by high odds ratio (all P < .05). A significant correlation was found between these values and cTnl, the current gold standard for LVDD analysis. HMGB1, TLR2 and TLR4 also showed a high diagnostic sensitivity and specificity in ROC analysis. HMGB1, TLR2 and TLR4 are potentially valuable in predicting LVDD risk among patients with sepsis, providing additional tools with the capability of potentially assisting the clinical management of patients with sepsis.
Analysis of photon density distribution in three section DBR tunable laser diode

A model based on the transfer matrix method (TMM), for the analysis of photon density distribution in three section distributed Bragg reflector (DBR) Tunable Laser Diode, is presented. The key feature of the model is the use of modified oscillation condition. This model provides longitudinal distribution of photon density in each section of laser for different values of grating and phase current. It is shown that both grating and phase current caused to a downward shift of photon density distribution profile. Simulation shows that, photon density distribution has peak at the active-passive interface.
Analysis of a tubular linear motor with soft magnetic composites for reciprocating compressors

This paper presents the analysis of a single-phase tubular linear motor formed with soft magnetic composites for direct driving reciprocating compressors. This direct-drive design can solve the potential cylinder wearing problem caused by the crank side force in conventional reciprocating compressors. The overall size may also be reduced without transmission mechanism. To produce sufficient thrust for the cooling cycles, the designed motor features a structure of three-dimensional flux distributions that allows an increase of slot-fill factor to enhance the thrust density. Moreover, the motor makes use of the inherent cogging force which contributes to the overall thrust. Finite element analysis is employed to verify the performance. The results show that the motor is capable of producing high thrust with a compact size compared to other types of motors.
A structure-based model for turbulent-boundary-layer wall pressures

Practical prediction of structural vibrations due to a turbulent boundary layer currently depends on empirical representations of the unsteady wall pressures. Improvements in these representations would be greatly facilitated if a simple, physically based model were available to test ad hoc assumptions and provide rigorous interpolation of experimental data. A possible candidate is the attached-eddy model, developed from Townsend's initial ideas by Perry and co-workers in the context of turbulence velocity spectra. This approach employs the superposition of contributions from individual ‘eddies’, of varying size, to yield its predictions. It is shown here that the same methodology can be applied for wall pressures, once the field due to an eddy has been obtained via solution of the governing Poisson equation. Comparisons with large-eddy simulation and experimental data, spanning a two-decade Reynolds number range, show remarkably good agreement, given the simplicity of the model. It is concluded that this approach has the potential to provide useful physical insight and, subject to its extension to a time-resolved form, improvements to existing empirical formulations.
ENTANGLEMENT IN OPEN QUANTUM SYSTEMS

In the framework of the theory of open systems based on quantum dynamical semigroups, we solve the master equation for two independent bosonic oscillators interacting with an environment in the asymptotic long-time regime. We give a description of the continuous-variable entanglement in terms of the covariance matrix of the quantum states of the considered system for an arbitrary Gaussian input state. Using the Peres-Simon necessary and sufficient condition for separability of two-mode Gaussian states, we show that the two non-interacting systems immersed in a common environment and evolving under a Markovian, completely positive dynamics become asymptotically entangled for certain environments, so that their non-local quantum correlations exist in the long-time regime.
Impact of COVID-19 on anxiety and depression - biopsychosocial factors.

Anxiety and depression are prevalent mental disorders around the world. The etiology of both diseases is multifactorial, involving biological and psychological issues. The COVID-19 pandemic settled in 2020 and culminated in several changes in the routine of individuals around the world, affecting mental health. People infected with COVID-19 are at greater risk of developing anxiety and depression, and individuals previously affected by these disorders have worsened the condition. In addition, individuals diagnosed with anxiety or depression before being affected by COVID-19 developed the severe illness at higher rates than individuals without mental disorders. This harmful cycle involves several mechanisms, including systemic hyper-inflammation and neuroinflammation. Furthermore, the context of the pandemic and some previous psychosocial factors can aggravate or trigger anxiety and depression. Disorders are also risks for a more severe picture of COVID-19. This review discusses research on a scientific basis, which brings evidence on biopsychosocial factors from COVID-19 and the context of the pandemic involved in anxiety and depression disorders.
Oxygen isotopic abundances in evolved stars. I - Six barium stars

Mesures des rapports isotopiques 16 O/ 17 O 16 O/ 18 O pour quatre etoiles Ba II: HD 121447, HD 101013, HD 178717 et HR 774 et pour deux etoiles Ba peu rigoureuses: ο Vir et 16 Ser