Miracle-dz/UniFAR-FTUs huggingface dataset:
Artificial Pneumothorax Using the Liver-Directed Approach and Seldinger’s Technique: Technical Feasibility and Safety

Purpose This study aimed to evaluate the technical feasibility and safety of artificial pneumothorax induction for percutaneous procedures using the liver-directed approach and Seldinger’s technique. Materials and methods The data of 25 consecutive patients who underwent percutaneous procedures after inducing artificial pneumothorax were reviewed retrospectively. The liver surface was punctured with an 18-gauge indwelling needle via the intercostal space in the inferior thoracic cavity under ultrasound guidance, avoiding the lung parenchyma and leaving the catheter in place. After a deep inhalation pulled the catheter tip into the pleural cavity, a hydrophilic guidewire was inserted through the catheter. Finally, a small-diameter catheter was inserted into the pleural cavity over the guidewire to induce artificial pneumothorax. Procedure time (the time from local anesthesia to completion of the procedure), technical success (successful induction of artificial pneumothorax), clinical success (successful completion of the percutaneous procedure), and complications (categorized according to the Clavien-Dindo classification) were evaluated in this study. Results The artificial pneumothorax induction was successful in all cases. Clinical success was achieved in 23 of 25 procedures (92%). No severe complications were observed. Conclusion The liver-directed approach and Seldinger’s technique for inducing artificial pneumothorax was safe and feasible for avoiding lung injury.
Transplanting naturally regenerated tree seedlings for tropical forest restoration: a case study of Calophyllum brasiliense and Vochysia guatemalensis in Mexico

Plant species produce far more seedlings than those surviving to adulthood. It would seem reasonable to take advantage of that excess production, by relocating seedlings to desired restoration sites. There is, however, little information available on this issue. In the present study, we collected naturally regenerated seedlings of two native species, Calophyllum brasiliense (“barí”) and Vochysia guatemalensis (“corpo”), in old‐growth forest patches, and transplanted them for the enrichment of a secondary tropical forest in Veracruz, Mexico. For transplanted C. brasiliense seedlings, overall survival was 32% after 20 months, and for V. guatemalensis seedlings it was 66% after 26 months. The mean height (±standard error) of all C. brasiliense and V. guatemalensis seedlings that survived until the end of the study, was 33 ± 2 cm (from initially 12.9 ± 0.2 cm) and 52 ± 5 cm (from 4.6 ± 0.1 cm), respectively. We applied a cost–benefit model to assess the trade‐off between initial‐investment costs and the resulting survival of transplanting naturally regenerated seedlings in the current study, in comparison with direct seeding (with and without the protection of seeds and emerged seedlings) reported in a previous study for the same area and species. For C. brasiliense, the success of transplanting naturally regenerated seedlings was always inferior to direct seeding without protection, whereas for V. guatemalensis it was always superior. Comparing the transplantation of naturally regenerated seedlings with direct seeding with protection, the best propagation strategy depended on the (monetary) value that a treelet is expected to be worth after 2 years.
Unlocking the Potential of Autism Detection: Integrating Traditional Feature Selection and Machine Learning Techniques

The diagnostic process for Autism Spectrum Disorder (ASD) typically involves time-consuming assessments conducted by specialized physicians. To improve the efficiency of ASD screening, intelligent solutions based on machine learning have been proposed in the literature. However, many existing ML models lack the incorporation of medical tests and demographic features, which could potentially enhance their detection capabilities by considering affected features through traditional feature selection approaches. This study aims to address the aforementioned limitation by utilizing a real dataset containing 45 features and 983 patients. To achieve this goal, a two-phase methodology is employed. The first phase involves data preparation, including handling missing data through model-based imputation, normalizing the dataset using the Min-Max method, and selecting relevant features using traditional feature selection approaches based on affected features. In the second phase, seven ML classification techniques recommended by the literature, including Decision Trees (DT), Random Forest (RF), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), AdaBoost, Gradient Boosting (GB), and Neural Network (NN), are utilized to develop ML models. These models are then trained and tested on the prepared dataset to evaluate their performance in detecting ASD. The performance of the ML models is assessed using various metrics, such as Accuracy, Recall, Precision, F1-score, AUC, Train time, and Test time. These metrics provide insights into the models' overall accuracy, sensitivity, specificity, and the trade-off between true positive and false positive rates. The results of the study highlight the effectiveness of utilizing traditional feature selection approaches based on affected features. Specifically, the GB model outperforms the other models with an accuracy of 87%, Recall of 87%, Precision of 86%, F1-score of 86%, AUC of 95%, Train time of 21.890, and Test time of 0.173. Additionally, a benchmarking analysis against five other studies reveals that the proposed methodology achieves a perfect score across three key areas. By considering affected features through traditional feature selection approaches, the developed ML models demonstrate improved performance and have the potential to enhance ASD screening and diagnosis processes.
Improved Galactic diffuse emission model strengthens the case for a Millisecond Pulsar explanation of the Fermi GeV excess

After more than a decade since its discovery, the Galactic center gamma-ray excess - discovered with the Fermi Large Area Telescope - remains puzzling. While the spectrum of the signal can be explained by either dark matter or an unresolved population of millisecond pulsars, the spatial morphology of this excess seems to hold the key to separate the two theories. In this contribution, we present the results of a recent study in which we use bleeding edge models for interstellar gas, inverse Compton emission, and stellar mass models to reanalyze the Galactic center excess. We find that the spatial morphology of the excess is highly correlated with stellar matter in the Galactic bulge, providing strong support for the millisecond pulsar hypothesis.
HIGHER ORDER KORTEWEG-DE VRIES MODELS FOR INTERNAL WAVES

By using asymptotic methods, evolution equation is derived for the internal waves in density stratiﬁed ﬂuid. This evolution equation arise as a solvability condition. A higher-order extension of the familiar Korteweg-de Vries equation is produced for internal waves in a density stratiﬁed ﬂow with a free surface. All coeﬃcients of this extended Korteweg-de Vries equation are expressed via integrals of the modal function for the linear theory of long internal waves.
Carbynes connected to polycyclic aromatic hydrocarbons as potential carriers of diffuse interstellar bands

Di ﬀ use insterstellar bands (DIBs) are absorption features in the spectra of reddened stars, caused by the absorption of light by the interstellar medium. Organic molecules based on polycyclic aromatic hydrocarbons (PAHs), revealed by infrared emission bands, are present in the interstellar medium and are considered to be possibly responsible for DIBs. However, the speciﬁc carbon-based nanostructures are still unidentiﬁed, with the notable exception of C + 60 (Campbell et al. 2015, Nature, 523, 322). In the present work, using state-of-the-art time-dependent density functional theory (TDDFT) and many-body perturbation theory within the GW approximation, we predict that carbon chains (carbynes) connected to PAH groups exhibit absorption spectra that can be tuned in the energy window of the unexplained DIB spectrum. Our theoretical results reveal electronic transitions in both the visible and near-infrared range depending on the length of the carbyne chain and the nature of the connected PAHs, thus providing new insights into the possible carbon-based species
Capacity Enhancement in D2D 5G Emerging Networks: A Survey

Several efforts are being made to improve the capacity of 5G networks using emerging technologies of interest. One of the indispensable technologies to fulfill the need is device-to-device (D2D) communication with its untapped associated benefits. Interference is introduced at the base station due to massive traffic congestion. The purpose of this research is to expand the knowledge of interference mitigation in D2D using stochastic geometrical tools which will result in capacity enhancement. This study uses a literature review method based on 5G and other already existing literature on D2D communication. More than one hundred and twenty papers on D2D communications in cellular networks exist but no precise survey paper on interference management to enhance capacity using stochastic geometrical tools exists. The contribution of this survey to theory is that apart from already existing capacity enhancement methods, interference mitigation using stochastic geometrical tools is another technique that can also be used for capacity enhancement in D2D communications.
A novel adaptive Gaussian-weighted mean filter for removal of high-density salt-and-pepper noise from images

Salt-and-pepper (SAP) noise is one of the common impulse noises. It is generated mostly during the process of image capture and storage, due to false locations in memory and damaged image sensors. SAP noise seriously degrades the quality of images and affects the performance of subsequent image processing, such as edge detection, image segmentation and object recognition. Thus, it is quite necessary to remove SAP noise from corrupted images efficiently. In this paper, we propose a fast-adaptive Gaussian-weighted mean filter (FAGWMF) for removing salt-and-pepper noises. Our denoising filter consists of four stages. At the first stage, we preprocess the image by enlarging and flipping the image. Then, we detect noisy pixels by comparing the pixel value with the maximum (255) and minimum (0) value. One pixel is regarded as noise if its value is equal to the maximum or minimum value. Otherwise, it is regarded as noisefree pixel. At the third stage, we determine the working window size by enlarging the filter window continuously until the quantity of noise-free pixels it includes reaches to the predetermined threshold or the window radius reaches to the predetermined maximum value. At the last stage, we replace each noise candidate by its Gaussian-weighted mean value of the noise-free pixels in window. Using Gaussian-weighted template, the central candidates will get larger weights than those on edges, which helps to preserve the edge information efficiently. Simulation results show that compared to some state-of-the-art algorithms, our proposed filter has faster execution speed and better restoration quality.
Phase-shift De-ghosting

The pressure wavefield recorded by a horizontal or slanted streamer in the presence of a calm sea surface can be directly de-ghosted in a spectral domain. Horizontal and slanted de-ghosting algorithms are therefore computationally efficient, and in case of mild variations this restrictive assumption on streamer shape can be accommodated for by optimizing the ghost model parameters in time-space windows.
Directional Adaptive MUSIC-like Algorithm under {\alpha}-Stable Distributed Noise

An algorithm called MUSIC-like algorithm was originally proposed as an alternative method to the MUltiple SIgnal Classification (MUSIC) algorithm for direction-of-arrival (DOA) estimation. Without requiring explicit model order estimation, it was shown to have robust performance particularly in low signal-to-noise ratio (SNR) scenarios. In this letter, the working principle of a relaxation parameter {\beta}, a parameter which was introduced into the formulation of the MUSIC-like algorithm, is provided based on geometrical interpretation. To illustrate its robustness, the algorithm will be examined under symmetric {\alpha}-stable distributed noise environment. An adaptive framework is then developed and proposed in this letter to further optimize the algorithm. The proposed adaptive framework is compared with the original MUSIC-like, MUSIC, FLOM-MUSIC, and SSCM-MUSIC algorithms. A notable improvement in terms of targets resolvability of the proposed method is observed under different impulse noise scenarios as well as different SNR levels.
A Novel Software Reliability Growth Model Based on Generalized Imperfect Debugging NHPP Framework

Non-Homogeneous Poisson Process (NHPP) is a standard framework in the field of software reliability analysis. The core of NHPP consists in determining the Mean Value Function (MVF) of cumulative error number at a specific time slot. However, practice shows the difficulty in finding a general model to fit all sorts of fault data. A certain model is only sensitive to the specific object(s). Modeling failure MVF for NHPP still faces a number of challenges such as making reasonable explanation of assumption, determining fault detection rate per error, fault modification efficiency, error introduction rate, etc. In this research, we propose a novel Software Reliability Growth Model (SRGM) by leveraging generalized imperfect debugging NHPP framework. We first provide physical explanations for assumptions on error modification, error introduction and fault detection rate per error. Meanwhile, we generate a typical constraint relationship between the total error introduction rate and change rate of generalized residual errors. We also describe the fault detection rate per error with the form of exponential decay function, and use error reduction factor to form the new model. Furthermore, we make extensive discussions based on our proposed model. The experimental results confirm that our proposed model is effective on fault fitting and prediction, especially excellent on short-term prediction.
Advanced Imaging in the Current Era of Acute Reperfusion Therapies

Reperfusion of the ischemic brain parenchyma with intravenous thrombolysis and/or mechanical thrombectomy is the cornerstone of acute ischemic stroke treatment. A paradigm shift from “time is brain” to a more precision medicine approach now called “imaging is brain”, has taken place during the last decade. This transformation has been fueled by the progress in neuroimaging. Advanced Neuroimaging incorporates perfusion imaging in order to depict real-time cerebral perfusion disturbances and provide maps of the penumbra and ischemic core that will inform individualized clinical decisions. Advance neuroimaging has now a fundamental role in triaging patients that will receive reperfusion treatments beyond the conventional time windows of 4.5 hours for intravenous thrombolysis and 6 hours for mechanical thrombectomy. We provide a narrative review of all the pivotal observational studies and randomized-controlled clinical trials that supported the use of advance neuroimaging, as well as technical issues and pitfalls that may be useful for its implementation in routine clinical practice.
Hybrid high-order methods for interface problems

The purpose of this Ph.D. thesis is to design and analyse Hybrid High-Order (HHO) methods on some interface problems. By interface, we mean (i) diffuse interface, and (ii) interface as an immersed boundary. The first half of this manuscrit is dedicated to diffuse interface, more precisely we consider the so called Cahn–Hilliard problem that models the process of phase separation, by which the two components of a binary fluid spontaneously separate and form domains pure in each component. In the second half, we deal with the interface as an immersed boundary and consider a hybrid dimensional model for the simulation of Darcy flows and passive transport in fractured porous media, in which the fracture is considered as an hyperplane that crosses our domain of interest.
Mechanical Properties and Deformation Mechanisms of Nanocrystalline U-10Mo Alloys by Molecular Dynamics Simulation

U-Mo alloys were considered to be the most promising candidates for high-density nuclear fuel. The uniaxial tensile behavior of nanocrystalline U-10Mo alloys with average grain sizes of 8–23 nm was systematically studied by molecular dynamics (MD) simulation, mainly focusing on the influence of average grain size on the mechanical properties and deformation mechanisms. The results show that Young’s modulus, yield strength and ultimate tensile strength follow as average grain size increases. During the deformation process, localized phase transitions were observed in samples. Grain boundary sliding and grain rotation, as well as twinning, dominated the deformation in the smaller and larger grain sizes samples, respectively. Increased grain size led to greater localized shear deformation, resulting in greater stress drop. Additionally, we elucidated the effects of temperature and strain rate on tensile behavior and found that lower temperatures and higher strain rates not only facilitated the twinning tendency but also favored the occurrence of phase transitions in samples. Results from this research could provide guidance for the design and optimization of U-10Mo alloys materials.
Environmental Detection for Autonomous Vehicles Based on Multi-View Image and 3D LiDAR Point Cloud Map

In this paper, 3D point cloud and depth map fusion system for 3D environment detection is proposed. This algorithm uses the depth map generated by virtual perspective mapping technology to assist LiDAR point cloud map information matching, and distinguishes objects according to distance and color to facilitate observation. At the same time, the depth map is used to assist in the generation of two-dimensional point cloud information to repair the missing problems to achieve the effect of restoring the point cloud information. The method solves the problems of sparse point cloud information of distant objects, reflections caused by the material of cars, and light penetration caused by glass, so that the point cloud map information can be more accurate. After a series of experiments, the accuracy rate of the proposed method can reach 91.27%, the precision of matching completeness is 91.61%, the F1-measure is 92.37%, and the recall rate can reach 93.14%. Compared with most methods, our proposed method has better performance. The proposed method successfully solves the problems that LiDAR point cloud map data will be missing and sparse due to environmental factors to achieve high-precision point cloud map.
Level of Technological Implementation in Science Instruction to Enhance National Achievement Test (NAT) Performance in Butuan City Division, Philippines

The study probed the integration of technology in Science instruction in relation to the students’ performance in National Achievement Test (NAT) results. It utilized a researcher-made questionnaire to obtain the data. The respondents of the study were the upper and lower 30% performing schools in the National Achievement Test (NAT) for the aforementioned school years. Results of the study revealed that the available technological resources for use in Science instruction in Butuan City Division for the upper 30% were printer, desktop computer, and Internet connection while for the lower 30% were desktop computer, printer, and speaker; that the level of technological skills of both the 30% school-respondents were rated expert in creating a presentation, using social networks, and their knowledge to operate computer. For the level of implementation in Technology-Assisted Science instruction the respondents were rated occasionally. On the other hand, data revealed that the relationship between the level of technological skills of the teachers, school’s implementation in Technology-Assisted Science instruction, and the performance in Science NAT between the upper and lower 30% school-respondents was significant. In conclusion, school-participants considered lack of computer sets as the major challenge they faced upon the implementation of Technology-Assisted Science instruction.
VALIDATION OF PHOTOVOLTAIC MODEL FOR APPLICATION IN A DISTRIBUTED ENERGY SOURCE USING WEATHER DATA

This work presents a photovoltaic model for a simulation of the electric energy production of a distributed energy system. The model will be validated through measured weather data and energy produced by an 88.04 kWp power plant installed on the Federal University of Espírito Santo (UFES) in Vitória. The validation consists of comparing the real power data acquired by the photovoltaic plant and the power data calculated from the model using weather data, specifications of the panels and inverters and the constructive characteristics of the system. The proposed model is based on the paper “Management of an island and grid-connected microgrid using hybrid economic model predictive control with weather data”, Applied Energy, v. 278, 2020, https://doi.org/10.1016/j.apenergy. 2020.115581. In addition, the model has been improved with the inclusion of new intrinsic parameters of the material, the calculation of losses and the adjustments for different tilt angles and azimuth orientations. Results of this method reveal that the model was able to reproduce the behavior of the real photovoltaic plant, presenting a mean absolute percentage error of 8.8% between real and simulated data.
Comet C/2011 W3 (Lovejoy) between 2 and 10 Solar Radii: Physical Parameters of the Comet and the Corona

Comet C/2011 W3 (Lovejoy) is the first sungrazing comet in many years to survive perihelion passage. We report ultraviolet observations with the Ultraviolet Coronagraph Spectrometer (UVCS) spectrometer aboard the Solar and Heliospheric Observatory satellite at five heights as the comet approached the Sun. The brightest line, Lyα, shows dramatic variations in intensity, velocity centroid, and width during the observation at each height. We derive the outgassing rates and the abundances of N, O, and Si relative to H, and we estimate the effective diameter of the nucleus to be several hundred meters. We consider the effects of the large outgassing rate on the interaction between the cometary gas and the solar corona and find good qualitative agreement with the picture of a bow shock resulting from mass loading by cometary neutrals. We obtain estimates of the solar wind density, temperature, and speed, and compare them with predictions of a global magnetohydrodynamic simulation, finding qualitative agreement within our uncertainties. We also determine the sublimation rate of silicate dust in the comet’s tail by comparing the visible brightness from the Large Angle Spectroscopic Coronagraphs with the Si iii intensity from UVCS. The sublimation rates lie between the predicted rates for olivines and pyroxenes, suggesting that the grains are composed of a mixture of those minerals.