Miracle-dz/UniFAR-FTUs huggingface dataset:
Oscillations of the Ionosphere Caused by the 2022 Tonga Volcanic Eruption Observed With SuperDARN Radars

On 15 January 2022, the submarine volcano on the southwest Pacific island of Tonga violently erupted. Thus far, the ionospheric oscillation features caused by the volcanic eruption have not been identified. Here, observations from the Super Dual Auroral Radar Network radars and digisondes were employed to analyze ionospheric oscillations in the Northern Hemisphere caused by the volcanic eruption in Tonga. Due to the magnetic field conjugate effect, the ionospheric oscillations were observed much earlier than the arrival of surface air pressure waves, and the maximum negative line‐of‐sight (LOS) velocity of the ionospheric oscillations exceeded 100 m/s in the F layer. After the surface air pressure waves arrived, the maximum LOS velocity in the E layer approached 150 m/s. A maximum upward displacement of 100 km was observed in the ionosphere. This work provides a new perspective for understanding the strong ionospheric oscillation caused by geological hazards observed on Earth.
Miniaturized Folded-Slot CubeSat MIMO Antenna Design with Pattern Diversity

In this paper, a folded slot-based multiple-input–multiple-output (MIMO) antenna design for Cube Satellite (CubeSat) applications is presented for the ultra-high frequency (UHF) band. A unique combination of a reactively loaded meandered slot with a folded structure is presented to achieve the antenna’s miniaturization. The proposed antenna is able to operate over a wide frequency band from 430~510 MHz. Moreover, pattern diversity is achieved by the antenna’s element placement, resulting in good MIMO diversity performance. The four elements are placed on one Unit (1U) for CubeSat dimensions of 100 mm × 100 mm × 100 mm. The miniaturized antenna design with pattern diversity over a wide operating band is well suited for small satellite applications, particularly CubeSats in the UHF band.
Failure rate of a bivariate exponential distribution

It is well known that if the parent distribution has a nonnegative support and has increasing failure rate, then all the order statistics have increasing failure rate (IFR). The result is not necessarily true in the case of bivariate distributions with dependent structures. In this paper we consider a symmetric bivariate exponential distribution and show that, two marginal distributions are IFR and the distributions of the minimum and maximum are constant failure rate and IFR, respectively.
Accélération de la convergence de méthodes numériques parallèles pour résoudre des systèmes d’équations différentielles linéaires et transitoires non linéaires

La resolution des equations differentielles (EDP/EDO/EDA) est au cœur de la simulation de phenomenes physiques. L'accroissement de la taille et de la complexite des modeles necessite la mise en œuvre de methodes de resolution robustes et performantes en termes de temps de calcul. L'objectif de cette these est de proposer des methodes pour accelerer la resolution des equations differentielles par des methodes de decomposition de domaine. On considere d'abord les methodes de decomposition de domaine de Schwarz pour la resolution de grands systemes lineaires issus de la discretisation d'EDP. Afin d'accelerer la convergence de la methode de Schwarz, on propose une approximation de l'operateur de propagation d'erreur. Cette approximation respectera la structure de l'operateur exact, ce qui conduira a une reduction tres significative des temps de calcul sur le probleme des ecoulements dans les milieux poreux heterogenes. La deuxieme contribution concerne la resolution de la suite de systemes lineaires provenant de l'integration en temps de problemes non lineaires. On propose deux approches en utilisant le fait que la matrice jacobienne ne varie que peu d'un systeme a l'autre. Premierement, on applique la mise a jour de Broyden au preconditionneur RAS (Restricted Additive Schwarz) au lieu de recalculer les factorisations LU. La deuxieme approche consiste a dedier des processeurs a la mise a jour partielle et asynchrone du preconditionneur RAS. Des resultats numeriques sur le probleme de la cavite entrainee et sur un probleme de reactiondiffusion montrent qu'une acceleration super lineaire peut etre obtenue. La derniere contribution a pour objet la resolution simultanee des problemes non lineaires de pas de temps consecutifs. On etudie le cas ou la methode de Broyden est utilisee pour resoudre ces problemes non lineaires. Dans ce cas, la mise a jour de Broyden peut etre propagee d'un pas de temps a l'autre. La parallelisation a travers les pas de temps est egalement appliquee a la recherche d'une solution initiale consistante pour les equations differentielles algebriques
Numerical simulation of evaporating capillary jets

Abstract : A detailed numerical study of evaporating capillary jets is presented. The analysis is performed through use of a Galerkin finite element method with penalty formulation for solving the equations of motion and a flux method for tracking the free surface. A parametric study is performed to analyze the temporal instability of the evaporating jet. Through varying the evaporation rate, Reynolds number, disturbance wave number, initial disturbance amplitude, and density ratio the outcomes of jet breakup are investigated. Also, pressure distribution inside the jet and multiple satellite drop formations are analyzed. Results are compared to existing analytical conclusions made from linear stability analysis. This study reveals that surface evaporation has a destabilizing effect for the low speed jets, which are considered here. That is, evaporation flux is greater at the neck than the crest, which accelerates the wave growth. Satellite drops also reduce in size as evaporation rate is increased. This reduction is seen in both the radial direction due to vapor leaving the surface and along the axis of symmetry due to decreased breakup time.
Targeted Inhibition of Matrix Metalloproteinase-8 Prevents Aortic Dissection in a Murine Model

Aortic dissection (AD) is a lethal aortic pathology without effective medical treatments since the underlying pathological mechanisms responsible for AD remain elusive. Matrix metalloproteinase-8 (MMP8) has been previously identified as a key player in atherosclerosis and arterial remodeling. However, the functional role of MMP8 in AD remains largely unknown. Here, we report that an increased level of MMP8 was observed in 3-aminopropionitrile fumarate (BAPN)-induced murine AD. AD incidence and aortic elastin fragmentation were markedly reduced in MMP8-knockout mice. Importantly, pharmacologic inhibition of MMP8 significantly reduced the AD incidence and aortic elastin fragmentation. We observed less inflammatory cell accumulation, a lower level of aortic inflammation, and decreased smooth muscle cell (SMC) apoptosis in MMP8-knockout mice. In line with our previous observation that MMP8 cleaves Ang I to generate Ang II, BAPN-treated MMP8-knockout mice had increased levels of Ang I, but decreased levels of Ang II and lower blood pressure. Additionally, we observed a decreased expression level of vascular cell adhesion molecule-1 (VCAM1) and a reduced level of reactive oxygen species (ROS) in MMP8-knockout aortas. Mechanistically, our data show that the Ang II/VCAM1 signal axis is responsible for MMP8-mediated inflammatory cell invasion and transendothelial migration, while MMP8-mediated SMC inflammation and apoptosis are attributed to Ang II/ROS signaling. Finally, we observed higher levels of aortic and serum MMP8 in patients with AD. We therefore provide new insights into the molecular mechanisms underlying AD and identify MMP8 as a potential therapeutic target for this life-threatening aortic disease.
Deep Learning Model for the Detection of Real Time Breast Cancer Images Using Improved Dilation-Based Method

Breast cancer can develop when breast cells replicate abnormally. It is now a worldwide issue that concerns people’s safety all around the world. Every day, women die from breast cancer, which is especially common in the United States. Mammography, CT, MRI, ultrasound, and biopsies may all be used to detect breast cancer. Histopathology (biopsy) is often carried out to examine the image and discover breast cancer. Breast cancer detection at an early stage saves lives. Deep and machine learning models aid in the detection of breast cancer. The aim of the research work is to encourage medical research and the development of technology by employing deep learning models to recognize cancer cells that are small in size. For histological annotation and diagnosis, the proposed technique makes use of the BreCaHAD dataset. Color divergence is caused by differences in slide scanners, staining procedures, and biopsy materials. To avoid overfitting, we used data augmentation with 19 factors, such as scale, rotation, and gamma. The proposed hybrid dilation deep learning model is of two sorts. It illustrates edges, curves, and colors, and it improves the key traits. It utilizes dilation convolution and max pooling for multi-scale information. The proposed dilated unit processes the image and sends the processed features to the Alexnet, and it can recognize minute objects and thin borders by using the dilated residual expanding kernel model. An AUC of 96.15 shows that the new strategy is better than the old one.
Nanohydrodynamic Local Compaction and Nanoplasmonic Form-Birefringence Inscription by Ultrashort Laser Pulses in Nanoporous Fused Silica

The inscription regimes and formation mechanisms of form-birefringent microstructures inside nano-porous fused silica by tightly focused 1030- and 515-nm ultrashort laser pulses of variable energy levels and pulsewidths in the sub-filamentary regime were explored. Energy-dispersion X-ray micro-spectroscopy and 3D scanning confocal Raman micro-spectroscopy revealed the micro-tracks compacted by the multi-shot laser exposure with the nanopores hydrodynamically driven on a microscale to their periphery. Nearly homogeneous polarimetrically acquired subwavelength-scale form-birefringence (refractive index modulation ~10−3) was simultaneously produced as birefringent nanogratings inside the microtracks of wavelength-, energy- and pulsewidth-dependent lengths, enabling the scaling of their total retardance for perspective phase-modulation nanophotonic applications. The observed form-birefringence was related to the hierarchical multi-scale structure of the microtracks, envisioned by cross-sectional atomic-force microscopy and numerical modeling.
Foodways of the Medieval Tibetans on the Silk Road: New evidence from the Miran Site in Xinjiang

Located in the Tarim Basin in southern Xinjiang, the Miran site is well known for its so-called “winged angel” frescos and diverse cultural artifacts dating from the Han Dynasty through Tubo Kingdom (~200 BC–900 AD) periods. This essay presents the first integrated archeobotanical and isotopic investigation on the archeobotanical remains at Miran. Our findings suggest that, despite implementing mixed-crop cultivation, Tibetans at Miran became increasingly invested in drought-resistant millet-based agriculture. New seed δ13C data recovered from naked barley and wheat at Miran provide additional suggestions that medieval Tubo people likely employed improved irrigation techniques at this Silk Road settlement.
Heterogeneous mission planning for a single unmanned aerial vehicle (UAV) with attention-based deep reinforcement learning

Large-scale and complex mission environments require unmanned aerial vehicles (UAVs) to deal with various types of missions while considering their operational and dynamic constraints. This article proposes a deep learning-based heterogeneous mission planning algorithm for a single UAV. We first formulate a heterogeneous mission planning problem as a vehicle routing problem (VRP). Then, we solve this by using an attention-based deep reinforcement learning approach. Attention-based neural networks are utilized as they have powerful computational efficiency in processing the sequence data for the VRP. For the input to the attention-based neural networks, the unified feature representation on heterogeneous missions is introduced, which encodes different types of missions into the same-sized vectors. In addition, a masking strategy is introduced to be able to consider the resource constraint (e.g., flight time) of the UAV. Simulation results show that the proposed approach has significantly faster computation time than that of other baseline algorithms while maintaining a relatively good performance.
Developing self-efficacy through the social constructivist mentorship

ABSTRACT In this article, I connect self-efficacy and social constructivism and how this shaped me as a researcher and academic, as well as how connections to my advisor, Dr. Terry Wood, gave me the self-efficacy to persevere during the personal and scholarly challenges of my doctoral program. I discuss, using Bandura’s social cognitive sources of self-efficacy, how my development from graduate student, to researcher, to professor was influenced by verbal persuasions, mastery experiences, vicarious experiences and emotional/affective states. I also take up how social cognitive theory informed the lens I use in my current research. My interactions with Dr. Wood as a graduate student also showed me how to apply theory to my ideas and how it looks different in different settings. From her, I learned how to formulate studies that inform mathematics education through a situated learning perspective and be confident and efficacious while doing so.
Pulmonary Function in Paediatric Patients with Inflammatory Bowel Disease

Background: Among the extraintestinal manifestations of inflammatory bowel disease (IBD), those involving the lungs are relatively rare and often overlooked. There are only scarce data on the prevalence of IBD-associated lung involvement in children. Objectives: The aim of our study was to assess pulmonary function in IBD children by different methods and to evaluate the influence of immunosuppressive therapy on disease severity. Methods: Seventy-two children with IBD (mean age of 14.45 ± 2.27 years) and 40 age-matched healthy controls (mean age of 14.17 ± 2.82) were included in the study. Pulmonary function tests (PFTs) were carried out by means of spirometry, oscillometry (IOS) and fractional exhaled nitric oxide (FeNO) to assess the pulmonary involvement. Results: Certain differences were observed between the study group and the control group, regarding the spirometric and oscillometry parameters. The fractions of exhaled nitric oxide did not differ between the group with IBD patients and the control group with regards to disease activity, the duration of illness and the administered immunosuppressive treatment. Conclusions: The mean spirometry results were significantly different in the study group compared to the controls, although they were still within the normal limits. The pulmonary function abnormalities did not depend on either the disease activity or the immunosuppressive therapy. Oscillometry could be a supplementary method to assess pulmonary resistance. In turn, FeNO does not appear to be useful either in screening IBD children for pulmonary involvement or for the evaluation of disease activity. It appears then that only general screening of asymptomatic patients is a suitable method and a necessary recommendation in this population, prompting a revision of the current diagnostic approach.
Optimal control design for linear time-varying systems by interpolated variational iteration method

Optimal control of linear time-varying (LTV) systems is one of the most common problems in engineering, economics, biology, etc. Solving this problem is not always possible, and this motivates us to improve the existing approximate solution methods. In this paper, the optimality conditions are first obtained, and then the well-known variational iteration method, interpolated by B-spline functions, is applied to solve these conditions. The method is also accelerated through the elimination of redundant calculations. Our method provides a feedback controller for LTV optimal control problems and does not need to solve any system of equations or optimization problems at each iteration. The convergence of the proposed method is proved, and the algorithm’s efficiency is illustrated through several examples.
Estimation of TAR Models

A distribution theory is developed for least squares estimates of the threshold in threshold autoregressive (TAR) models. We find that if we let the threshold effect (the difference in slopes between the two regimes) get small as the sample size increases, then the asymptotic distribution of the threshold estimator is free of nuisance parameters (up to scale). Similarly, the likelihood ratio statistic for testing hypotheses concerning the unknown threshold is asymptotically free of nuisance parameters. These asymptotic distributions are non-standard, but are available in closed form so critical values are readily available. To illustrate this theory, we report applications of these methods to TAR models fit to the U.S. unemployment rate and to the U.S. 3-month Treasury Bill rate. We find statistically significant threshold effects.
On Performance of Shrinkage Methods – A Monte Carlo Study

Multicollinearity has been a serious problem in regression analysis, Ordinary Least Squares (OLS) regression may result in high variability in the estimates of the regression coefficients in the presence of multicollinearity. Least Absolute Shrinkage and Selection Operator (LASSO) methods is a well established method that reduces the variability of the estimates by shrinking the coefficients and at the same time produces interpretable models by shrinking some coefficients to exactly zero. We present the performance of LASSO -type estimators in the presence of multicollinearity using Monte Carlo approach. The performance of LASSO, Adaptive LASSO, Elastic Net, Fused LASSO and Ridge Regression (RR) in the presence of multicollinearity in simulated data sets are compared Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) criteria. A Monte Carlo experiment of 1000 trials was carried out at different sample sizes n (50, 100 and 150) with different levels of multicollinearity among the exogenous variables (ρ = 0.3, 0.6, and 0.9). The overall performance of Lasso appears to be the best but Elastic net tends to be more accurate when the sample size is large.
Identifying mothers experiencing emotional distress in the neonatal intensive care unit. Application of PPTSD questionnaire in a Greek NICU population

The birth of a high-risk infant such as an extremely premature infant can represent an important traumatic experience for mothers. Perinatal Post Traumatic Stress Disorder Questionnaire (PPTSDQ) explores retrospectively maternal post-traumatic stress reaction. This shelf-rating questionnaire explores the potential for experiencing posttraumatic symptoms related to childbirth and the ensuing post-natal period. The PTSD questionnaire was originally developed by DeMier and Hynan and their colleagues at the University of Wisconsin and has been widely used in research and in clinical practice for identifying mothers experiencing significant emotional distress during the post-natal period, so they may be referred for mental health services. The present study aims to introduce this tool in perinatal settings as an early intervention. It has been widely used with other measures of post-traumatic stress and depression, such as the Openness Scale from the NEO-PR, the self-report measure of depression BDI-II, the IES (Impact Event Scale) and the EPDS (Edinburgh Postnatal Depression Scale). Although already a useful clinical instrument the current study used the revised version. This modification refines the response options from dichotomous choices to a Likert scale format by Callahan Borja and Hynan. Numerous qualitative and quantitative studies state that premature delivery is a highly stressful event and document the full range of post-traumatic sequelae, such as intrusive recollections, behavioral avoidance, and hyperarousal, as well as attachment difficulties following childbirth. Furthermore, the severity of neonatal complications and gestational age have been found to be predictive of PTSD symptomatology in parents as measured by the PPQ. For this reason, the current study aims to give increased focus to mothers having a premature birth and often expecting their children to die. The sample comprises 25 mothers of prematurely born infants hospitalized in the NICU and 25 mothers of full-term infants born in the maternity ward of the same Greek hospital who responded to the Perinatal PTSD Questionnaire and equally the PERI a postnatal complication rating inventory and the clinical interview for parents CLIP. Mothers of high-risk infants present post-traumatic stress reactions related to prematurity. The Perinatal PTSD Questionnaire identifies postnatal maternal distress but should not substitute a clinical interview, yet findings indicate that equally identifies pre-existing distress symptoms associated with maternal personality traits that emerged with the traumatic event of the unexpected birth. Due to the consistency of the population of the experimental group, who come mostly from the provinces, the possibility of a follow- up of the cases is quite limited.
Security and privacy preservation using constructive hierarchical data-sharing approach in cloud environment

ABSTRACT With the vast development in the cloud computing environment, many cloud users intend to outsource the data to a remote location and share the data with multiple users. The hierarchical model is an extensively used data organization process. It is highly complex to guarantee integrity, privacy, and confidentiality of the data and the structure of the model when the sensitive data is held in this manner. This research attempts to give a solution to hierarchically ensure the security and privacy of the information while data sharing occurs. A constructive hierarchical data sharing (CHDS) method is proposed to adopt symmetric encryption over the rooted hierarchical graph structure. The hierarchical graph model deals with incoming data features to establish the model’s privacy and authenticity. Based on this model, the proposed CHDS is known to be transparent, secure, and confident in the public environment. Here, performance metrics like computational complexity, key generation, prediction accuracy, and execution time are evaluated. The outcomes provide the security of the multi-party environment without forfeiting sensible resources when the hierarchical model grows to a more significant number of siblings, edges, and vertices. The proposed CHDS gives a better trade-off when compared with various existing approaches.