Miracle-dz/UniFAR-FTUs huggingface dataset:
Isotropic Irradiation Model from Atmospheric Discharges Using Local Atmospheric Electric Field Data

: Using a local atmospheric electric field sensor network (Field Mill), an electromagnetic link model was proposed considering a free space electric field and a theoretical electric field calculated from an atmospheric discharge. With this, it was possible to compare the theoretical value of this electric field with the experimental value of the local atmospheric electric field (estimated by the Field Mill sensor network). The results are initially analyzed considering wavelengths in the EHF spectrum ( 𝜆 ~ 10 −5 𝑚 ) and the VLF spectrum ( 𝜆 ~ 10 4 𝑚 ). As assumed before, admitting that the lightning channel acts as an isotropic irradiator and a Field Mill sensor acts as a receiving antenna for the spectrum emitted by the lightning, a irradiated power model of the electromagnetic waves reaching the
Epigenetic Dysregulations in Arsenic-Induced Carcinogenesis

Simple Summary Arsenic is a chemical element that is toxic, and long-term exposure to it causes cancers such as lung, skin, liver, and bladder cancers. Over 150 million people around the world are affected by arsenic exposure. However, the molecular mechanism of how arsenic induces carcinogenesis is still not clear. As a carcinogen, arsenic has been demonstrated not to cause point mutations. Hence, the understanding of the dysregulation of epigenetic mechanisms caused by arsenic may help to unravel the mechanisms by which arsenic induces cancers. Abstract Arsenic is a crucial environmental metalloid whose high toxicity levels negatively impact human health. It poses significant health concerns to millions of people in developed and developing countries such as the USA, Canada, Bangladesh, India, China, and Mexico by enhancing sensitivity to various types of diseases, including cancers. However, how arsenic causes changes in gene expression that results in heinous conditions remains elusive. One of the proposed essential mechanisms that still has seen limited research with regard to causing disease upon arsenic exposure is the dysregulation of epigenetic components. In this review, we have extensively summarized current discoveries in arsenic-induced epigenetic modifications in carcinogenesis and angiogenesis. Importantly, we highlight the possible mechanisms underlying epigenetic reprogramming through arsenic exposure that cause changes in cell signaling and dysfunctions of different epigenetic elements.
ASARIMA: An Adaptive Harvested Power Prediction Model for Solar Energy Harvesting Sensor Networks

Harvesting energy from solar radiation has emerged as an effective approach to prolong the lifetime of outdoor energy harvesting sensor networks. The harvested energy must be carefully managed to ensure that sufficient energy is available when solar energy is scarce. For the prediction problem of solar energy power harvesting, this paper proposes an adaptive seasonal auto-regressive integrated moving average model (ASARIMA) for solar energy harvesting prediction. A training set can be adaptively adjusted by the similarity of historical data, and then we conduct seasonal difference data fitting based on the adjusted training set to obtain the optimal prediction model parameters. Experimental results show that this ASARIMA model performs better than other existing power prediction algorithms. If the weather conditions are stable, the prediction error of the ASARIMA decreases by more than 70%. If the weather conditions change sharply, the prediction error decreases by more than 20% in comparison with those of other algorithms.
Current Trends in the Management of Temporomandibular Joint Dysfunction: A Review

The temporomandibular joint (TMJ) is a synovial bi-condylar joint with 3 degrees of freedom. One-third of the adult population reportedly suffers from temporomandibular joint dysfunction (TMD). Females are more commonly affected than males. Almost 50% of TMD patients do not require any intervention, and the symptoms are self-limiting within one year after the onset; however, 75-80% of adults suffering from TMD require medical intervention and it takes up to three years for the complete remission of the symptoms. The clinical features of TMD are clenching, clicking, and locking of the jaw, and occlusion due to faulty posture. Based on the diagnostic criteria for temporomandibular disorder (DC/TMD) criteria proposed in the year 2014, the classification of TMD is done based on axis I and axis II diagnoses. This review aims to provide an overview of TMD and examine available treatment strategies for TMD. Various conservative treatment methods have been proven to be effective, including self-care strategies, dental treatment strategies, pharmacological treatment, physical therapy modalities, manual mobilization, electrotherapy and dry needling, relaxation techniques, intra-articular injections, cognitive behavioral therapy, and surgical corrections.
Modeling cosmic ray proton induced terrestrial neutron flux: A look‐up table

Neutrons contribute a significant radiation dose at commercial passenger airplane altitudes. With cosmic ray energies > 1 GeV, these effects could, in principle, be propagated to ground level. Under current conditions, the cosmic ray spectrum incident on the Earth is dominated by particles with energies < 1 GeV. Astrophysical shocks from events such as supernovae accelerate high‐energy cosmic rays (HECRs) well above this range. The Earth is likely episodically exposed to a greatly increased HECR flux from such events. Solar events of smaller energies are much more common and short lived but still remain a topic of interest due to the ground level enhancements they produce. The air showers produced by cosmic rays (CRs) ionize the atmosphere and produce harmful secondary particles such as muons and neutrons. Although the secondary spectra from current day terrestrial cosmic ray flux are well known, this is not true for spectra produced by many astrophysical events. This work shows the results of Monte Carlo simulations quantifying the neutron flux due to CRs at various primary energies and altitudes. We provide here look‐up tables that can be used to determine neutron fluxes from proton primaries with kinetic energies of 1 MeV–1 PeV. By convolution, one can compute the neutron flux for any arbitrary CR spectrum. This contrasts with all other similar works, which are spectrum dependent. Our results demonstrate the difficulty in deducing the nature of primaries from the spectrum of ground level neutron enhancements.
The Texture of the Lexicon. Relational Morphology and the Parallel Architecture

En este texto se reseña la última obra de Ray Jackendoff junto a Jenny Audring: The Texture of the Lexicon. Relational Morphology and the Parallel Architecture. En ella, los autores proponen una nueva teoría morfológica y organización del léxico en el marco de la conocida propuesta de Jackendoff de la Arquitectura Paralela. En esta reseña se realiza un recorrido por los distintos capítulos y secciones del libro, se enmarca el contenido dentro de la teoría lingüística y psicolingüística actual y se analizan las contribuciones de la obra a la comprensión general de la facultad del lenguaje.
Ionogels Derived from Fluorinated Ionic Liquids to Enhance Aqueous Drug Solubility for Local Drug Administration

Gelatin is a popular biopolymer for biomedical applications due to its harmless impact with a negligible inflammatory response in the host organism. Gelatin interacts with soluble molecules in aqueous media as ionic counterparts such as ionic liquids (ILs) to be used as cosolvents to generate the so-called Ionogels. The perfluorinated IL (FIL), 1-ethyl-3-methylpyridinium perfluorobutanesulfonate, has been selected as co-hydrosolvent for fish gelatin due to its low cytotoxicity and hydrophobicity aprotic polar structure to improve the drug aqueous solubility. A series of FIL/water emulsions with different FIL content and their corresponding shark gelatin/FIL Ionogel has been designed to enhance the drug solubility whilst retaining the mechanical structure and their nanostructure was probed by simultaneous SAXS/WAXS, FTIR and Raman spectroscopy, DSC and rheological experiments. Likewise, the FIL assisted the solubility of the antitumoural Doxorubicin whilst retaining the performing mechanical properties of the drug delivery system network for the drug storage as well as the local administration by a syringe. In addition, the different controlled release mechanisms of two different antitumoral such as Doxorubicin and Mithramycin from two different Ionogels formulations were compared to previous gelatin hydrogels which proved the key structure correlation required to attain specific therapeutic dosages.
Uniform dynamics of partially damped semilinear Bresse systems

This paper is concerned with the Bresse system that arises in the modeling of arched beams. It is given by a system of three coupled wave equations that reduces to the well-known Timoshenko model when the arch curvature is zero. In a context of nonlinear elastic foundation, we establish the existence of smooth finite-dimensional global attractors, by adding dissipation mechanism in only one of its equations. In addition, we study the uniform boundedness of longtime dynamics with respect to the curvature parameter. These results have not been considered for partially damped semilinear Bresse or Timoshenko systems.
Biodegradation of HA and β-TCP Ceramics Regulated by T-Cells

Biodegradability is one of the most important properties of implantable bone biomaterials, which is directly related to material bioactivity and the osteogenic effect. How foreign body giant cells (FBGC) involved in the biodegradation of bone biomaterials are regulated by the immune system is poorly understood. Hence, this study found that β-tricalcium phosphate (β-TCP) induced more FBGCs formation in the microenvironment (p = 0.0061) accompanied by more TNFα (p = 0.0014), IFNγ (p = 0.0024), and T-cells (p = 0.0029) than hydroxyapatite (HA), resulting in better biodegradability. The final use of T-cell depletion in mice confirmed that T-cell-mediated immune responses play a decisive role in the formation of FBGCs and promote bioceramic biodegradation. This study reveals the biological mechanism of in vivo biodegradation of implantable bone tissue engineering materials from the perspective of material-immune system interaction, which complements the mechanism of T-cells’ adaptive immunity in bone immune regulation and can be used as a theoretical basis for rational optimization of implantable material properties.
An End-to-End Deep Learning Model for Mental Arithmetic Task Classification from Multi-Channel EEG

The development of brain-computer interface (BCI) applications has recently piqued the interest of researchers because it can help physically handicapped people to communicate with their brain electroencephalogram (EEG) signal. The automatic classification of mental workload tasks from multi-channel EEG signal analysis is critical for BCI applications. In this paper, we propose an end-to-end deep learning (DL) model for the classification of the mental arithmetic task (MAT) from multi-channel EEG signals. As an end-to-end DL model, a residual-based temporal attention network (RTA-Net) is developed to achieve optimal performance for MAT classification. We have mainly considered two MAT: before mental arithmetic calculation and during mental arithmetic calculation. The RTA-Net model is validated on a freely available MAT-based EEG dataset. The results show that our proposed model yield the best performance with classification accuracy: 99.32%, F1-score: 99.20%, and Cohen’s Kappa: 98.15%, which defeat the performance of all existing methods for MAT classification. For real-world applications, our automated MAT system is ready to be tested with additional datasets.
Arithmetic of numbers of periodic points

Three problems are studied. The first is: When is a given sequence the sequence of numbers of periodic points for some map? Necessary and sufficient conditions are found for this property, and these are used to address refined versions of the question. The main results are that non-constant polynomials can never be realized in this sense, and that quadratic recurrences can only be realized under a non-trivial constraint. The second is: When does a given sequence approximate the sequence of numbers of periodic points for some map? Some tests are obtained and, inter alia, we show that the sequence (n log n) has this property if and only if s ≥ 1. In the third, the growth rate of the number of points of period n is compared with the number of points of least period n. For either quantity having a finite exponential growth rate, it is shown that the other’s growth is identical. However, for either quantity having a positive polynomial growth rate, it is found that the other cannot. Generally, proofs of statements have only been included when, to my knowledge, they are original. Exceptions to this are indicated clearly in the text. I deeply appreciate the personal support, kindness, diligence and mathematical guidance of my thesis supervisor, Thomas Ward. Thanks Tom. Cordial thanks to Graham Everest too, for his warm wisdom, for conversations about congruences, and in particular for providing an elegant proof of Lemma 3.1. I gratefully acknowledge the financial support of the E.P.S.R. C. for three years of this research.
A Study of d-DensityWave States in Strongly Correlated Electron Systems

Particle-hole condensates in the angular momentum $l=2$ channel, known as $d$-density wave orders, have been suggested to be realized in strongly correlated electron systems. In this dissertation, we study singlet and triplet $d$-density wave orders with a form factor of $d_{x^2-y^2}$ as well as a novel topological mixed singlet-triplet $d$-density wave with a form factor of $(i sigma d_{x^2-y^2}+d_{xy})$, and discuss the connections of these states to high-temperature superconductors and heavy-fermion materials. In Chapter 2, we discuss the spin susceptibility of the singlet $d$-density wave, triplet $d$-density wave, and spin density wave orders with hopping anisotropies. From the numerical calculation, we find nearly vertical dispersion relations for spin excitations with anisotropic incommensurability at low energies in agreement with the inelastic neutron scattering experiments in the pseudogap state of the high-temperature superconductor YBa$_{2}$Cu$_{3}$O$_{6.6}$. The vertical dispersion is a distinct feature of all three density wave states in contrast to the superconducting state, which shows an hourglass shape dispersion experimentally. In Chapter 3, we explore a mixed singlet-triplet $d$-density wave state in a two-dimensional square lattice, which is topologically nontrivial and exhibits quantum spin Hall effect. We also study the bulk-edge correspondence and Lifshitz transition in the system. In Chapter 4, we show that the skyrmions in the system carry charge $2e$ and can condense into a superconducting state. The phase transition between the density wave and superconductivity likely leads to deconfined quantum critical points. We suggest connections of this exotic state to the hidden-order state in the heavy-fermion compound URu$_{2}$Si$_{2}$. The st-DDW model is generalized to three dimensions in Chapter 5, where we propose a novel pairing mechanism in URu$_{2}$Si$_{2}$. We assume the charge $2e$ skyrmionic spin texture in the mixed singlet-triplet $d$-density wave state fractionalizes into merons and antimerons at the deconfined quantum critical point. The interaction between these fractional particles results in a spin-singlet chiral $d$-wave superconducting state consistent with experiments. The unconventional superconductivity breaks time reversal symmetry, so we expect a polar Kerr effect at the onset of the superconductivity, but not in the time-reversal-invariant hidden-order state except perhaps for magnetic impurities.
Intelligent Biped Robot Simulation Locomotion using Deep Q Network

This research suggests a deep Q-network learning approach coupled with a layered neural-based locomotion. An analog signal is produced by central pattern generation (CPG) in the higher layers and sent to motor neuron pools in the lower levels. Motor neurons produce joint angular velocity in the lower levels. The neural oscillator model in the spinal cord serves as the foundation for the central pattern generation. It reacts to the pattern of the driving model. Then, using a human musculoskeletal model, the inner state of motor neurons was constructed based on muscle activation. Additionally, the motor neuron (MN) pool is linked to the sensory neuron (SN), which transmits internal feedback from the robot. A humanoid robot simulation using the proposed concept has been used to carry various loads. Adjustments to the robot's behavior to maintain its stance.
Autenticação Contínua Usando Sensores Inerciais dos Smartphones e Aprendizagem Profunda

Muitos usuários têm optado pelo uso de dispositivos móveis como smartphones para a realização de tarefas do dia a dia. Para garantir a segurança desses dados, a maioria dos sistemas emprega soluções de autenticação estática, tais como senha, padrão em grade, chave de segurança ou sensor de impressão digital. Entretanto, em um cenário onde um usuário impostor tem acesso às senhas ou obtém acesso físico ao dispositivo desbloqueado, todos os dados acabam sendo expostos. Para lidar com esse problema, este trabalho propõe o desenvolvimento de um método de autenticação contínua para dispositivos móveis utilizando os dados de sensores inerciais. O processo de identificação do usuário genuíno ou impostor é realizado por meio de um modelo de autenticação definido a partir de uma arquitetura de rede profunda baseada em redes neurais convolucionais com camadas recorrentes. Além disso, este trabalho emprega um modelo de confiança visando evitar o bloqueio de usuários genuínos e impedir que um impostor fique muito tempo agindo sem ser detectado. Testes utilizando dados de 30 usuários mostram que o modelo proposto consegue detectar os usuários impostores em até 61 segundos.
An Efficient Numerical Scheme for Solving Multiorder Tempered Fractional Differential Equations via Operational Matrix

In this paper, we extend the operational matrix method to solve the tempered fractional differential equation, via shifted Legendre polynomial. Although the operational matrix method is widely used in solving various fractional calculus problems, it is yet to apply in solving fractional differential equations defined in the tempered fractional derivatives. We first derive the analytical expression for tempered fractional derivative for x p , hence, using it to derive the new operational matrix of fractional derivative. By using a few terms of shifted Legendre polynomial and via collocation scheme, we were able to obtain a good approximation for the solution of the multiorder tempered fractional differential equation. We illustrate it using some numerical examples.
Bacteriological Profile and Resistance of Escherichia coli to Beta-lactam for Patients Consulted at Douala Laquintinie Hospital

Enterobacteriaceae are the most common causes of community-acquired and nosocomial infections. They are usually treated with beta-lactam antibiotics, i.e., penicillins, broad-spectrum cephalosporins, and carbapenems (Imipenem, meropenem, ertapenem). In order to evaluate the resistance profile of Escherichia coli (E. coli) to beta-lactam antibiotics, a 3-year retrospective study was carried out in the medical biology laboratory of the Laquintinie Hospital in Douala. The aim was to compare the behavior of Escherichia coli bacteria to penicillins, carbapenems, and cephalosporins. In order to achieve this objective, we used the results obtained from E. coli strains isolated from urine, cervico-vaginal fluid, puncture fluid, pleural fluid and pus samples. Identification was confirmed using the API 20 E Enterobacteriaceae system of biochemical testing and the VITEK 2 system. The VITEK 2 system was used to perform the antibiogram. Statistical analyses obtained from GraphPad Prim V 5.0 software allowed us to perform tests such as ANOVA, Kruskal-Wallis test, and Spearman correlations. Preliminary results show that E. coli bacteria are highly resistant to penicillins and significantly susceptible to cephalosporins of all generations if they are not ESBL (extended-spectrum beta-lactamases). Data on carbapenem behavior show less resistance and moderate susceptibility.
Forest Fire Detection using Computer Vision

Every year, approximately 70,000 wildfires occur around the world. Forest fires can have a variety of negative effects on forest cover, soil, tree development, vegetation, and general flora and fauna. Fires destroy several hectares of forest and leave behind ash, rendering it unfit for vegetation growth. Animal habitats are destroyed by the heat created by the fire. Preventing fire disasters and saving people’s lives and property requires early detection of fires before they become catastrophic. These forest fires must be detected and put out as early as possible. In this project a computer vision based deep learning model is proposed that can detect and alert in-case of forest fires. The model which is used here is called MobileNet Architecture which employs depth-wise separable convolutions and has greater advantages than using other currently existing models. The model is trained from a dataset that was scrapped from the internet. The computer vision model is built utilizing the packages in Python 3 such as NumPy, OpenCV, Pillow, Matplotlib, and TensorFlow. This method is created by training and testing the model to identify failures and establish recovery strategies. As a result, these computer vision-based systems can provide a warning at an early stage of fire, which is critical for forest fire early warning.
Privacy Computing Issues in Collecting and Using Customer Data of Mobile Devices

Merchants through smartphones and mobile applications measure and analyze users’ interests and behaviors, and identify users in applications and browsers to push services, updates, and advertisements to the users. This way of providing personalized services to smartphone users to attract potential customers has improved the user experience, created new opportunities for advertising, and changed the marketing trend of enterprises. However, the collection, processing, and analysis of user personal data has brought serious privacy issues to individuals and organizations. This paper introduces privacy risks, including user information collection and analysis of information flow between networks, analysis procedures and standards. Discussed the challenges of protecting user privacy, including privacy threats brought by user information and analysis, extraction, and exchange of private information between various advertising entities, privacy threats tracked by third parties, privacy information and related privacy issues. Next, based on these technologies, related underlying architecture, privacy mechanisms, and deployment scenarios, several technologies, and various suggestions for protecting user privacy are analyzed. Finally, the respect for people, regulations and laws, the limitations of technical solutions, ethical principles and moral dilemmas were discussed.