Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Learning Based Vehicle Detection on Real and Synthetic Aerial Images: Training Data Composition and Statistical Influence Analysis

The performance of deep learning based algorithms is significantly influenced by the quantity and quality of the available training and test datasets. Since data acquisition is complex and expensive, especially in the field of airborne sensor data evaluation, the use of virtual simulation environments for generating synthetic data are increasingly sought. In this article, the complete process chain is evaluated regarding the use of synthetic data based on vehicle detection. Among other things, content-equivalent real and synthetic aerial images are used in the process. This includes, in the first step, the learning of models with different training data configurations and the evaluation of the resulting detection performance. Subsequently, a statistical evaluation procedure based on a classification chain with image descriptors as features is used to identify important influencing factors in this respect. The resulting findings are finally incorporated into the synthetic training data generation and in the last step, it is investigated to what extent an increase of the detection performance is possible. The overall objective of the experiments is to derive design guidelines for the generation and use of synthetic data.
Study on the Decoupled Charge Effect in Deep-Hole Cumulative Blasting of Coal Seam

Five models of cumulative blasting are established by using ANSYS/LS-DYNA to study the effect of decoupling coefficient on cumulative blasting to improve coal seam permeability. The formation and migration process of the shaped energy jets with two kinds of decoupling coefficient are compared and analyzed; also, the propagation of explosive stress waves is represented. The result showed that the air in the blast hole is the key to the formation and migration of the condensing jet. The air in the hole also could reduce the attenuation of stress wave in a certain range. However, if the decoupling coefficient is too large, the air in the hole will consume excessive explosive energy, which is also not conducive to energy transfer. Therefore, there is an optimum decoupling coefficient which can minimize the coal crushing area, increase the coal fissure area, and improve the gas extraction rate. Besides, the cumulative blasting tests were carried out in a coal seam. The test results show that decoupling charge could effectively improve coal seam permeability, and the blasting effect was better when the decoupling coefficient is between 1.67 and 2.
Deterministic and Probabilistic Global Existence Results for Nonlinear Wave Equations

This thesis presents the results of two distinct lines of research on the global existence of solutions to certain nonlinear wave equations that I have pursued over the past years. In joint work with Joachim Krieger, we prove global regularity, scattering, and a priori bounds for the energy critical Maxwell-Klein-Gordon equation for arbitrary smooth initial data satisfying a natural spatial decay assumption. The proof is based upon a novel modified Bahouri-Gérard nonlinear profile decomposition and a concentration compactness/rigidity argument by Kenig-Merle, following the method developed by Krieger-Schlag in the context of critical wave maps. In joint work with Dana Mendelson, we consider the Cauchy problem for the defocusing energy sub-critical nonlinear wave equation on R3. For initial data of super-critical regularity, the current techniques to construct local-in-time solutions to this equation break down. However, using probabilistic tools, we exhibit large families of random initial data of super-critical regularity that lead to local-in-time and even global-in-time solutions. The construction of this random initial data relies on a unit-scale decomposition of frequency space. We first present an almost sure global existence result, the proof of which is based upon a probabilistic version of Bourgain’s high-low frequency decomposition method. Using a probabilistic energy method, we then significantly improve this result regarding the regularity threshold for the initial data and the notion of uniqueness of the solutions. The key ingredient is the introduction of a modified energy functional in order to derive probabilistic growth estimates of the kinetic energy of the nonlinear components of the solutions. Moreover, we establish a probabilistic small data global existence and scattering result for the defocusing energy critical nonlinear wave equation on R3.
Estimation of Arctic Winter Snow Depth, Sea Ice Thickness and Bulk Density, and Ice Freeboard by Combining CryoSat-2, AVHRR, and AMSR Measurements

Information on snow depth on sea ice and bulk sea ice density is required to convert CryoSat-2 radar freeboard (<inline-formula> <tex-math notation="LaTeX">$F_{r}$ </tex-math></inline-formula>) into sea ice thickness (SIT). It is difficult to obtain their information on an Arctic basin scale; therefore, most CryoSat-2 SIT products largely rely on the distributions of snow depth and bulk sea ice density derived from parameterizations, which are based on sea ice type and climatological values. Several observational studies have found that the distributions of parameterized variables are inaccurate compared to the actual distributions. This study aims to develop a new type of retrieval algorithm for snow depth, SIT and bulk density, and ice freeboard in the Arctic winter by synergizing active CryoSat-2 with passive microwave and infrared measurements. Two parameterizations for the snow–ice thickness ratio and bulk sea ice density were combined with the hydrostatic balance and radar wave speed correction equations. Consequently, solutions for the four target variables were obtained and applied to different CryoSat-<inline-formula> <tex-math notation="LaTeX">$2~F_{r}$ </tex-math></inline-formula>, derived from empirical and waveform-fitting (WF) retracker algorithms. The retrieved thickness-related parameters based on <inline-formula> <tex-math notation="LaTeX">$F_{r}$ </tex-math></inline-formula> from the Lognormal WF retracker algorithm showed good agreement with the airborne snow depth, total freeboard, and mooring ice draft measurements. The retrieved multiyear sea ice bulk density was significantly higher than the value of 882 kg <inline-formula> <tex-math notation="LaTeX">$\cdot ~\text{m}^{-3}$ </tex-math></inline-formula>, which was used in the previous density parameterization, showing a higher agreement with values from in situ measurements. The spatial and interannual variabilities of SIT increased when the results from this study were compared with those based on previous parameterizations.
Analogous study of cumulative biogas production by anaerobic digestion of sewage treatment plant sludge, the proposal of universal dimensionless models

The current global concern is how to replace polluting fossil fuels with sustainable green fuels. On this basis, in this study, a characterization protocol for sewage sludge in Algeria was carried out. Their objective was to study the process of anaerobic digestion for biogas production by analogy to experiments that have already been made in the literature. The performance of the proposed models has been validated with experimental data from the scientific literature. Biodegradable organic matter levels are high (160–500 mg L−1) giving an acceptable yield for biogas production. The modeling of cumulative biogas production was carried out by Gompertz and models were proposed for different products. It has been observed that there is a better agreement of the models proposed with the experimental data with a maximum value of r2 = 0.9996. The modeling of the degradation of organic matter was carried out by the first order‐model, and dimensionless models were proposed. The latter gave a good agreement with the experimental data better than the model in the literature, with a maximum value of r2 = 0.9985. The proposed models to study the anaerobic digestion process of organic matter, as well as those related to the process of cumulative biogas production, are universal (without units of measurement), which will help producers in this field to improve production, as well as reduce the investment cost through preliminary simulations.
Emotional Dysfunction and Interoceptive Challenges in Adults with Autism Spectrum Disorders

People with autism spectrum disorder (ASD) frequently show impaired sensory processing in different senses, including the interoceptive system. Recent findings suggest that interoception is a fundamental component of emotional experience and that impaired interoception is associated with alexithymia. This study aims to explore the association and interrelation between interoceptive confusion, alexithymia, and the capacity for emotional regulation among a sample of 33 adults with ASD compared to a control group of 35 adults with neurotypical development and its mutual impact. The participants answered a series of questionnaires addressing these three variables. The results showed (1) significant differences between the groups in all dimensions, with dysfunctional emotional regulation, impaired interoception, and alexithymia in the ASD group, (2) significant correlations between interoceptive confusion, emotional clarity, and alexithymia in the ASD group but only positive correlations between interoceptive confusion and alexithymia in the CG, and (3) that emotional clarity, alexithymia, and autism explain 61% of the variance in interoceptive confusion. These results are in line with previous studies and suggest that training interoceptive ability may enhance emotional clarity and reduce alexithymia among those diagnosed with ASD, with significant implications in the planning of treatment.
Comparison of the Administration Route of Stem Cell Therapy for Ischemic Stroke: A Systematic Review and Meta-Analysis of the Clinical Outcomes and Safety

Stem cell treatment is emerging as an appealing alternative for stroke patients, but there still needs to be an agreement on the protocols in place, including the route of administration. This systematic review aimed to assess the efficacy and safety of the administration routes of stem cell treatment for ischemic stroke. A systematic review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. A comprehensive literature search was undertaken using the PubMed, Scopus, and Cochrane databases. A total of 21 publications on stem cell therapy for ischemic stroke were included. Efficacy outcomes were measured using the National Institutes of Health Stroke Scale (NIHSS), the modified Rankin Scale (mRS), and the Barthel index (BI). Intracerebral administration showed a better outcome than other routes, but a greater number of adverse events followed due to its invasiveness. Adverse events were shown to be related to the natural history of stroke not to the treatment. However, further investigation is required, since studies have yet to compare the different administration methods directly.
The Representational Deficit of Latinxs in the U.S. House of Representatives

This paper explores the extent to which Latinxs were substantively represented in the 112th U.S. House of Representatives (2011–2013). We make use of a large national sample of Americans to tap into the congruence of the attitudes of constituents with actual roll call votes taken by their legislators in office. In doing so, we are able to make comparisons between constituent attitudes and legislative behavior for Latinx versus non-Latinx constituents. Using a more refined measure than previous studies of constituent-legislator dyads across congressional districts, we find that Latinx respondents face a representational deficit relative to non-Latinx whites and explore the various factors, individual- and contextual-level, that explain variation in that relationship. One such factor is the size of the Latinx population in a district. We find that larger Latinx populations are associated with decreased representation for Latinx respondents and, further, that this deficit is largely rooted in anti-Latinx attitudes and behavior on the part of non-Latinx whites in those districts. On the whole, the findings here are consistent with the backlash hypothesis.
The spectral element method: An efficient tool to simulate the seismic response of 2D and 3D geological structures

We present the spectral element method to simulate elastic-wave propagation in realistic geological structures involving complieated free-surface topography and material interfaces for two- and three-dimensional geometries. The spectral element method introduced here is a high-order variational method for the spatial approximation of elastic-wave equations. The mass matrix is diagonal by construction in this method, which drastically reduces the computational cost and allows an efficient parallel implementation. Absorbing boundary conditions are introduced in variational form to simulate unbounded physical domains. The time discretization is based on an energy-momentum conserving scheme that can be put into a classical explicit-implicit predictor/multi-corrector format. Long-term energy conservation and stability properties are illustrated as well as the efficiency of the absorbing conditions. The associated Courant condition behaves as ΔtC < O (nel−1/ndN−2), with nel the number of elements, nd the spatial dimension, and N the polynomial order. In practice, a spatial sampling of approximately 5 points per wavelength is found to be very accurate when working with a polynomial degree of N = 8. The accuracy of the method is shown by comparing the spectral element solution to analytical solutions of the classical two-dimensional (2D) problems of Lamb and Garvin. The flexibility of the method is then illustrated by studying more realistic 2D models involving realistic geometries and complex free-boundary conditions. Very accurate modeling of Rayleigh-wave propagation, surface diffraction, and Rayleigh-to-body-wave mode conversion associated with the free-surface curvature are obtained at low computational cost. The method is shown to provide an efficient tool to study the diffraction of elastic waves by three-dimensional (3D) surface topographies and the associated local effects on strong ground motion. Complex amplification patterns, both in space and time, are shown to occur even for a gentle hill topography. Extension to a heterogeneous hill structure is considered. The efficient implementation on parallel distributed memory architectures will allow to perform real-time visualization and interactive physical investigations of 3D amplification phenomena for seismic risk assessment.
Ergodicity of age structure in populations with Markovian vital rates, III: Finite-state moments and growth rate; an illustration

Leslie (1945) models the evolution in discrete time of a closed, single-sex population with discrete age groups by multiplying a vector describing the age structure by a matrix containing the birth and death rates. We suppose that successive matrices are chosen according to a Markov chain from a finite set of matrices. We find exactly the long-run rate of growth and expected age structure. We give two approximations to the variance in age structure and total population size. A numerical example illustrates the ergodic features of the model using Monte Carlo simulation, finds the invariant distribution of age structure from a linear integral equation, and calculates the moments derived here.
2D LiDAR Based Reinforcement Learning for Multi-Target Path Planning in Unknown Environment

Global path planning techniques have been widely employed in solving path planning problems, however they have been found to be unsuitable for unknown environments. Contrarily, the traditional Q-learning method, which is a common reinforcement learning approach for local path planning, is unable to complete the task for multiple targets. To address these limitations, this paper proposes a modified Q-learning method, called Vector Field Histogram based Q-learning (VFH-QL) utilized the VFH information in state space representation and reward function, based on a 2D LiDAR sensor. We compared the performance of our proposed method with the classical Q-learning method (CQL) through training experiments that were conducted in a simulated environment with a size of 400 square pixels, representing a 20-meter square map. The environment contained static obstacles and a single mobile robot. Two experiments were conducted: experiment A involved path planning for a single target, while experiment B involved path planning for multiple targets. The results of experiment A showed that VFH-QL method had 87.06% less training time and 99.98% better obstacle avoidance compared to CQL. In experiment B, VFH-QL method was found to have an average training time that was 95.69% less than that of the CQL method and 83.99% better path quality. The VFH-QL method was then evaluated using a benchmark dataset. The results indicated that the VFH-QL exhibited superior path quality, with efficiency of 94.89% and improvements of 96.91% and 96.69% over CQL and SARSA in the task of path planning for multiple targets in unknown environments.
Three Men Make a Tiger: The Effect of Consensus Testimony on Chinese and U.S. Children’s Judgments about Possibility

In this study, we ask whether consensus testimony affects children’s judgments of the possibility of improbable and impossible events. Fifty-six U.S. and Chinese 8-year-olds made possibility judgments before and after hearing three speakers affirm or deny the possibility of improbable and impossible events. Results indicated that whereas both U.S. and Chinese children altered their judgments in the direction of the consensus testimony, this effect was stronger for Chinese children. U.S. children were particularly receptive to consensus for improbable events and when the consensus provided correct information, whereas Chinese children were similarly willing to change their judgment regardless of event type and the validity of the testimony. We propose that the extent of the influence of testimony on possibility judgments varies based on cultural setting. Our findings have potential applications for domains that require evaluation of counterintuitive claims, like religious and scientific education.
Microscopic accident prediction models for signalized intersections

The main objective of this thesis is to develop microscopic accident prediction models for estimating the safety potential of 4-leg signalised intersections in the City of Vancouver, B.C. and describes the applications of these models in traffic safety analysis. The aim, therefore, is to examine the traffic variables that appear to underlie the occurrence of accidents of these intersections and explain, in a statistical sense, the generation of accidents as a function of these variables. Generalised linear regression was employed to develop the models because of its superiority over conventional linear regression in modelling accident occurrence. The statistical software package GLIM4 was used to accomplish this task. The study made use of a sample of 8466 accidents that occurred at 170 4-leg signalised intersections during the years of 1994-1996. The data on accident frequencies and traffic volumes were obtained from the City of Vancouver. Several models that have different applications in the field of traffic safety were developed in this study for the 4-leg signalised intersections of the City of Vancouver. Different error structures that can be utilised to model the relationship between accidents and traffic flows are reviewed. Microscopic models for different accident types were developed in conjunction with macroscopic models for Total, Severe and Property Damage Only accidents. The microscopic models are presented in conjunction with the three macroscopic models which all resulted in statistical significance. Several model applications are discussed. Examples of how to obtain location-specific safety estimates, how to identify accident prone locations, how to rank the accident prone locations, and how to conduct a before and after safety evaluation are presented. Microscopic and macroscopic models are used simultaneously to determine which intersections should be regarded as accident
Predicting the Behavioral Intention of Greek University Faculty Members to Use Moodle

An emerging area of research is the study of factors that influence the use of e-learning systems in higher education. Previous studies have mainly focused on the factors that influence the adoption of learning management systems (LMS) from the student’s point of view, and rarely from the point of view of the university lecturers. Moodle is an open-source LMS that has been used increasingly by the higher education community worldwide in the past few years. The purpose of this study is to investigate the factors that explain the acceptance of Moodle by academic personnel (faculty members) in the Greek higher education system. The convenience sample consists of 85 lecturers from different universities in Greece. All of them reported having used Moodle. Using the technology acceptance model, the correlations between the six latent variables (perceived ease of use, perceived usefulness, behavioral intention to use, perceived self-efficacy, subjective norms, and technology complexity) were examined. Five of the eight hypotheses were supported by variance base structural equation modelling. The total explained variance of the faculty members’ behavioral intention to use Moodle was estimated to be 68.3%. Perceived usefulness and perceived ease of use had a high overall effect. Subjective norms, self-efficacy, and technological complexity influenced the teachers’ intentions to adopt Moodle. This study recommends training as well as technical support for academic personnel. In addition, stakeholders should address these factors to increase usability, awareness of new opportunities for the educational community, accessibility, and the general dissemination of the benefits of learning management systems in education.
Lung Inflammation Induced by Inactivated SARS-CoV-2 in C57BL/6 Female Mice Is Controlled by Intranasal Instillation of Vitamin D

The COVID-19 pandemic was triggered by the coronavirus SARS-CoV-2, whose peak occurred in the years 2020 and 2021. The main target of this virus is the lung, and the infection is associated with an accentuated inflammatory process involving mainly the innate arm of the immune system. Here, we described the induction of a pulmonary inflammatory process triggered by the intranasal (IN) instillation of UV-inactivated SARS-CoV-2 in C57BL/6 female mice, and then the evaluation of the ability of vitamin D (VitD) to control this process. The assays used to estimate the severity of lung involvement included the total and differential number of cells in the bronchoalveolar lavage fluid (BALF), histopathological analysis, quantification of T cell subsets, and inflammatory mediators by RT-PCR, cytokine quantification in lung homogenates, and flow cytometric analysis of cells recovered from lung parenchyma. The IN instillation of inactivated SARS-CoV-2 triggered a pulmonary inflammatory process, consisting of various cell types and mediators, resembling the typical inflammation found in transgenic mice infected with SARS-CoV-2. This inflammatory process was significantly decreased by the IN delivery of VitD, but not by its IP administration, suggesting that this hormone could have a therapeutic potential in COVID-19 if locally applied. To our knowledge, the local delivery of VitD to downmodulate lung inflammation in COVID-19 is an original proposition.