Miracle-dz/UniFAR-FTUs huggingface dataset:
The Spatial and Temporal Variations of Turbulence in a Solar Flare

Magnetohydrodynamic plasma turbulence is believed to play a vital role in the production of energetic electrons during solar flares, and the nonthermal broadening of spectral lines is a key sign of this turbulence. Here, we determine how flare turbulence evolves in time and space using spectral profiles of Fe xxiv, Fe xxiii, and Fe xvi, observed by the Hinode/EUV Imaging Spectrometer. Maps of nonthermal velocity are created for times covering the X-ray rise, peak, and decay. For the first time, the creation of kinetic energy density maps reveal where energy is available for energization, suggesting that similar levels of energy may be available to heat and/or accelerate electrons in large regions of the flare. We find that turbulence is distributed throughout the entire flare, often greatest in the coronal loop tops, and decaying at different rates at different locations. For hotter ions (Fe xxiv and Fe xxiii), the nonthermal velocity decreases as the flare evolves and during/after the X-ray peak shows a clear spatial variation decreasing linearly from the loop apex toward the ribbon. For the cooler ion (Fe xvi), the nonthermal velocity remains relativity constant throughout the flare, but steeply increases in one region corresponding to the southern ribbon, peaking just prior to the peak in hard X-rays before declining. The results suggest turbulence has a more complex temporal and spatial structure than previously assumed, while newly introduced turbulent kinetic energy maps show the availability of the energy and identify important spatial inhomogeneities in the macroscopic plasma motions leading to turbulence.
On the Vector Choosability in Graphs

A graph G is said to be k-vector choosable over a field F if for every assignment of kdimensional subspaces of some finite-dimensional vector space over F to the vertices of G, it is possible to choose for each vertex a nonzero vector from its subspace so that adjacent vertices receive orthogonal vectors over F. The vector choice number of G over F is the smallest integer k for which G is k-vector choosable over F. This graph parameter, introduced by Haynes, Park, Schaeffer, Webster, and Mitchell (Electron. J. Comb., 2010), is inspired by well-studied variants of the chromatic number of graphs, such as the (color) choice number and the orthogonality dimension. We study the vector choice number of graphs over various fields. We first prove that the vector choice number of every graph with average degree d is at least Ω( √ d/ ln d) over any field. We then focus on bipartite graphs and consider the problem of estimating, for a given integer k, the smallest integer m for which the vector choice number of the complete bipartite graph Kk,m over a field F exceeds k. We prove upper and lower bounds on this quantity as well as for several extensions of this problem. Our results imply a substantial difference between the behavior of the choice number and that of the vector choice number. We also consider the computational aspect of the vector choice number, and show that for every k ≥ 3 it is NP-hard to decide whether the vector choice number of a given bipartite graph over F is at most k, provided that F is either the real field or any finite field.
A Game Theoretic Approach for Demand Response Allocation among Strategic Prosumers in Regulated Distribution Utilities

This paper studies an optimal allocation of demand response (DR) provisions among strategic photovoltaic (PV) prosumers, through third-party DR providers which operate within the territory of the regulated distribution utility. A game theoretic model, consisting of a sequential game coupled with a simultaneous game, is proposed to capture the competition and interaction among PV prosumers, the third-party DR providers, and the utility company. An iterative approach is proposed to solve for the Nash equilibrium of the game. With this game theoretic model, the DR provision quantities for strategic PV prosumers are optimally determined, considering the profit maximizations of the PV prosumers, the DR providers, and the utility company. Case studies on an IEEE test system verifies the proposed model and the solution approach,
Exploration of AI-Oriented Power System Transient Stability Simulations

--Artificial Intelligence (AI) has made significant progress in the past 5 years and is playing a more and more important role in power system analysis and control. It is foreseeable that the future power system transient stability simulations will be deeply integrated with AI. However, the existing power system dynamic simulation tools are not AI-friendly enough. In this paper, a general design of an AI-oriented power system transient stability simulator is proposed. It is a parallel simulator with a flexible application programming interface so that the simulator has rapid simulation speed, neural network supportability, and network topology accessibility. A prototype of this design is implemented and made public based on our previously realized simulator. Tests of this AI-oriented simulator are carried out under multiple scenarios, which proves that the design and implementation of the simulator are reasonable, AI-friendly, and highly efficient.
Prominence instability and CMEs triggered by massive coronal rain in the solar atmosphere

Context. The triggering process for prominence instability and consequent coronal mass ejections (CMEs) is not fully understood. Prominences are maintained by the Lorentz force against the gravity; therefore, reduction of the prominence mass due to the coronal rain may cause the change of the force balance and hence destabilisation of the structures. Aims. We aim to study the observational evidence of the influence of coronal rain on the stability of prominence and subsequent eruption of CMEs. Methods. We used the simultaneous observations from the Atmospheric Imaging Assembly (AIA) of Solar Dynamics Observatory (SDO) and Sun Earth Connection Coronal and Heliospheric Investigation (SECHHI) of Solar Terrestrial Relations Observatory (STEREO) spacecrafts from different angles to follow the dynamics of prominence and to study the role of coronal rain in their destabilisation. Results. Three different prominences observed during the years 2011–2012 were analysed using observations acquired by SDO and STEREO. In all three cases, massive coronal rain from the prominence body led to the destabilisation of prominence and subsequently to the eruption of CMEs. The upward rising of prominences consisted of the slow and fast rise phases. The coronal rain triggered the initial slow rise of prominences, which led to the final instability (the fast rise phase) after 18–28 h in all cases. The estimated mass flux carried by coronal rain blobs showed that the prominences became unstable after 40% of mass loss. Conclusions. We suggest that the initial slow rise phase was triggered by the mass loss of prominence due to massive coronal rain, while the fast rise phase (the consequent instability of prominences) was caused by the torus instability and/or magnetic reconnection with the overlying coronal field. Therefore, the coronal rain triggered the instability of prominences and consequent CMEs. If this is the case, then the coronal rain can be used to predict the CMEs and hence to improve the space weather predictions.
Sensitivity study of fiducial-aided navigation of Unmanned Aerial Vehicles

The possible applications and benefits of autonomous Unmanned Aerial Vehicle (UAV) use in urban areas are gaining considerable attention. Before these possibilities can be realized, it is essential that UAVs be able to navigate reliably and precisely in urban environments. The most common means of determining the location of a UAV is to utilize position measurements from Global Navigation Satellite Systems (GNSS). In urban environments, however, GNSS measurements are significantly degraded due to occlusions and multipath. This research analyzes the use of camera Line-of-Sight (LOS) measurements to self-describing fiducials as a replacement for conventional GNSS measurements. An extended Kalman filter (EKF) is developed and validated for the purpose of combining continuous measurements from an Inertial Measurement Unit (IMU) with the discrete LOS measurements to accurately estimate the states of a UAV. The sensitivity of the estimation error covariance to various system parameters is assessed, including IMU grade, fiducial placement, vehicle altitude, and image processing frequency.
A Unified 3D Beam Training and Tracking Procedure for Terahertz Communication

Terahertz (THz) communication is considered as an attractive way to overcome the bandwidth bottleneck and satisfy the ever-increasing capacity demand in the future. Due to the high directivity and propagation loss of THz waves, a massive MIMO system using beamforming is envisioned as a promising technology in THz communication to realize high-gain and directional transmission. However, pilots, which are the fundamentals for many beamforming schemes, are challenging to be accurately detected in the THz band owing to the severe propagation loss. In this paper, a unified 3D beam training and tracking procedure is proposed to effectively realize the beamforming in THz communications, by considering the line-of-sight (LoS) propagation. In particular, a novel quadruple-uniform planar array (QUPA) architecture is analyzed to enlarge the signal coverage, increase the beam gain, and reduce the beam squint loss. Then, a new 3D grid-based (GB) beam training is developed with low complexity, including the design of the 3D codebook and training protocol. Finally, a simple yet effective grid-based hybrid (GBH) beam tracking is investigated to support THz beamforming in an efficient manner. The communication framework based on this procedure can dynamically trigger beam training/tracking depending on the real-time quality of service. Numerical results are presented to demonstrate the superiority of our proposed beam training and tracking over the benchmark methods.
A Robust Scheme for 3D Point Cloud Copy Detection

Most existing 3D geometry copy detection research focused on 3D watermarking, which first embeds ``watermarks'' and then detects the added watermarks. However, this kind of methods is non-straightforward and may be less robust to attacks such as cropping and noise. In this paper, we focus on a fundamental and practical research problem: judging whether a point cloud is plagiarized or copied to another point cloud in the presence of several manipulations (e.g., similarity transformation, smoothing). We propose a novel method to address this critical problem. Our key idea is first to align the two point clouds and then calculate their similarity distance. We design three different measures to compute the similarity. We also introduce two strategies to speed up our method. Comprehensive experiments and comparisons demonstrate the effectiveness and robustness of our method in estimating the similarity of two given 3D point clouds.
CALIBRATION OF 122 SENSL MICROFJ-60035 SIPMS AND THE REDUCTION OF OPTICAL CROSS-TALK DUE TO COUPLED LIGHT GUIDES

Recent silicon photomultipliers (SiPMs) boast excellent production quality, eliminating the need for individual breakdown voltage calibration. We present measurements of cross-talk probability and relative gain for 122 SensL MicroFJ-60035-TSV SiPMs. These semiconductor photosensors have replaced photomultiplier tubes in many applications due to their single-photon resolution, insensitivity to magnetic fields, robustness, and cost-effective photon detection at lower voltages. Light guides expand SiPMs' small photosensitive area, and optical coupling reduces cross-talk probability compared to bare sensors at nominal bias voltage, as demonstrated in this study.
Estimation of high density lipoprotein cholesterol in the diagnosis of lepromatous leprosy.

A high incidence of increased plasma level of high density lipoprotein cholesterol (HDL-C) has been reported in cases of lepromatous leprosy. HDL-C levels were estimated in 96 (50 under treatment and 46 untreated) lepromatous leprosy patients and 84 randomly selected matched control patients suffering from other skin diseases attending skin out-patients department. HDL-C estimations were performed for the diagnosis of lepromatous leprosy in patients aged below 60 years, taking plasma HDL-C levels as 28-71 mg. /dl. in men and 34-91 mg. /dl. in women, as range of normal values. The study revealed that HDL-C levels in lepromatous leprosy group were raised and significantly different when compared with control group (t = 35.1668 and P less than 0.001). The sensitivity of the test was very high, 97.9 per cent (94/96), but specificity was low 80.95 per cent (68/84). False positive and false negative results were 19.04 per cent (16/84) and 2.08 per cent (2/96) respectively. It is opined that a negative test will be mainly useful in excluding diagnosis of lepromatous leprosy.
Optimal Power Management for Seismic Nodes

Estimating the state-of-charge of a lead-acid battery at remote seismic nodes is a key factor in managing the available power. Optimal management enables the continuous acquisition of seismic data of an area. This paper presents the management of lead-acid batteries at remote seismic nodes, using the Neural Network model's historical data to estimate the battery's state-of-charge. Powersim (PSIM) simulation tool was used to implement photovoltaic energy harvesting system with a buck mode converter and maximum power point tracking algorithm to acquire historical data. A backpropagation neural network technique for training the historical dataset of hourly points in 500 days on the Matlab platform is adopted, and a feedforward neural network is employed due to the irregularities of the input data. The neural network model's hidden layer contains the transfer function of the Tansig Function to produce the model output of state-of-charge estimations. Besides, this paper is based on the management of estimating the state-of-charge of the lead-acid battery near-realtime instead of relying on the vendor's lifecycle information. The simulated results show the simplicity and optimal estimations of state-of-charge of the lead-acid battery with RMSE of 0.023%.
Compression Resin Transfer Molding Using Inflatable Seals

Compression resin transfer molding using inflatable seals is a new variant of LCM (“Liquid composite molding”) processes, which uses the inflatable seals to compress the fiber reinforcements and drive the resin to impregnate the fabric preform, resulting to fill the entire mold cavity. During resin injection, the preform is relaxed. Consequently, the resin enters easily and quickly into the mold cavity. After, the necessary resin is injected into the mold cavity, the compression stage takes place, in a stepwise manner, by swelling the inflatable seals. The objective of this paper is to present this new process and study the effect of the number of inflatable seals on the filling time.
Charting Shifts in Saccharomyces cerevisiae Gene Expression across Asynchronous Time Trajectories with Diffusion Maps

In this work, Saccharomyces cerevisiae gene expression was used as a biosensor to capture differences across and between fermentations of Pinot noir grapes from 15 unique sites representing eight American Viticultural Areas. This required development of a novel analysis method, DMap-DE, for investigation of asynchronous gene expression data. ABSTRACT During fermentation, Saccharomyces cerevisiae metabolizes sugars and other nutrients to obtain energy for growth and survival, while also modulating these activities in response to cell-environment interactions. Here, differences in S. cerevisiae gene expression were explored over a time course of fermentation and used to differentiate fermentations, using Pinot noir grapes from 15 unique sites. Data analysis was complicated by the fact that the fermentations proceeded at different rates, making a direct comparison of time series gene expression data difficult with conventional differential expression tools. This led to the development of a novel approach combining diffusion mapping with continuous differential expression analysis (termed DMap-DE). Using this method, site-specific deviations in gene expression were identified, including changes in gene expression correlated with the non-Saccharomyces yeast Hanseniaspora uvarum, as well as initial nitrogen concentrations in grape musts. These results highlight novel relationships between site-specific variables and Saccharomyces cerevisiae gene expression that are linked to repeated fermentation outcomes. It was also demonstrated that DMap-DE can extract biologically relevant gene expression patterns from other contexts (e.g., hypoxic response of Saccharomyces cerevisiae) and offers advantages over other data dimensionality reduction approaches, indicating that DMap-DE offers a robust method for investigating asynchronous time series gene expression data. IMPORTANCE In this work, Saccharomyces cerevisiae gene expression was used as a biosensor to capture differences across and between fermentations of Pinot noir grapes from 15 unique sites representing eight American Viticultural Areas. This required development of a novel analysis method, DMap-DE, for investigation of asynchronous gene expression data. It was demonstrated that DMap-DE reveals biologically relevant shifts in gene expression related to cell-environment interactions in the context of hypoxia and fermentation. Using these data, it was discovered that gene expression by non-Saccharomyces yeasts and initial nitrogen content in grape musts are correlated with differences in gene expression among fermentations. These findings highlight important relationships between site-specific variables and gene expression that may be used to understand why foods and beverages, including wine, possess sensory characteristics associated with or derived from their place of origin.
The Effect of Quantum Confinement on Optical Properties of CdSe Quantum Dots at Room Temperature

CdSe quantum dots possess a tuning energy gap which can control gap values according to the size of the quantum dots, this is made the material able to absorb the wavelengths within visible light. A simple model is provided for the absorption coefficient, optical properties, and optical constants for CdSe quantum dots from the size 10nm to 1nm with the range of visible region between (300-730) nm at room temperature. It turns out that there is an absorption threshold for each wavelength, CdSe quantum dots begin to absorb the visible spectrum of 1.4 nm at room temperature for a wavelength of 300 nm. It has been noted that; when the wavelength is increased, the absorption threshold also increases. This applies to the optical properties and optical constants, where their values start to change from the threshold at 1.4 nm. The obtained results indicate that the range of the absorption coefficient can cover the ultraviolet, visible and to the infrared region when the quantum sizes are relatively large ( the size  9 nm), while the small sizes give small ranges of it, as only the ultraviolet region (the size = 1.4 nm) or part of the visible region ( the size > 1.4 nm ). What resulted from this difference in the results of the absorption coefficient, had a significant impact on the optical properties. Although the material has high transmittance ( reach more 75%), it is considered to have low absorbance ( less than 0.01%), at the same time the reflectivity had been valued between ( 14% to 22%) according to of size dot. The optical conductivity is proportional to quantum dot size, where an increase of it depends on the increasing of quantum dot size. It was also found that the real part of the dielectric constant is much greater than the imaginary part values, this is an indication that; the numbers of polarized charges towards the electric field were much greater than the polarized charges opposite to the direction of the field. It is worth noting that the behaviour of the refractive index is similar to the real part, while the extinction index resembles that of the imaginary part.
Iatrogenic Acute Pseudo-Pericardial Tamponade in a Dusky Pygmy Rattlesnake (Sistrurus miliarius barbouri)

Abstract An approximately 7-yr-old male dusky pygmy rattlesnake (Sistrurus miliarius barbouri) developed marked depression in mentation shortly after restraint in an acrylic tube during a preventative health evaluation. Radiographs taken during the examination demonstrated a constrictive lesion due to the restraint tube at the level of the heart. A presumptive diagnosis of iatrogenic pseudo-pericardial tamponade was made, and the animal was immediately removed from the restraint tube. The snake's mentation returned to normal within a few minutes. Plasma collected during the examination was tested for cardiac troponin I (cTnI), which measured 0.06 ng/ml. Re-evaluation of the snake 6 wk later revealed the snake had continued to eat and behave normally. During the reassessment, the snake's plasma cTnI was found to have decreased to 0.00 ng/ml. The snake has continued to do well for the last year, before writing this case report. This is the first description of pseudo-pericardial tamponade in a venomous snake due to restraint in a tube. This is also the first description of elevated plasma cTnI associated with a cardiogenic disorder in a reptile.
A Survey on Deep Learning for Human Activity Recognition

Human activity recognition is a key to a lot of applications such as healthcare and smart home. In this study, we provide a comprehensive survey on recent advances and challenges in human activity recognition (HAR) with deep learning. Although there are many surveys on HAR, they focused mainly on the taxonomy of HAR and reviewed the state-of-the-art HAR systems implemented with conventional machine learning methods. Recently, several works have also been done on reviewing studies that use deep models for HAR, whereas these works cover few deep models and their variants. There is still a need for a comprehensive and in-depth survey on HAR with recently developed deep learning methods.
Mansard Roofline Model: Reinforcing the Accuracy of the Roofs

Continuous enhancements and diversity in modern multi-core hardware, such as wider and deeper core pipelines and memory subsystems, bring to practice a set of hard-to-solve challenges when modeling their upper-bound capabilities and identifying the main application bottlenecks. Insightful roofline models are widely used for this purpose, but the existing approaches overly abstract the micro-architecture complexity, thus providing unrealistic performance bounds that lead to a misleading characterization of real-world applications. To address this problem, the Mansard Roofline Model (MaRM), proposed in this work, uncovers a minimum set of architectural features that must be considered to provide insightful, but yet accurate and realistic, modeling of performance upper bounds for modern processors. By encapsulating the retirement constraints due to the amount of retirement slots, Reorder-Buffer and Physical Register File sizes, the proposed model accurately models the capabilities of a real platform (average rRMSE of 5.4%) and characterizes 12 application kernels from standard benchmark suites. By following a herein proposed MaRM interpretation methodology and guidelines, speed-ups of up to 5× are obtained when optimizing real-world bioinformatic application, as well as a super-linear speedup of 18.5× when parallelized.
Pleura and Peritoneum

Pressurized intraperitoneal aerosol chemotherapy (PIPAC) is a new drug delivery method for intraabdominal cavity chemotherapy. It combines the benefits of aminimally invasive approach (lowmorbidity andeasy to repeat)with the pharmacokinetic advantages of intraperitoneal administration and tolerance seems excellent. We would like to report one case of a serious adverse event, acute respiratory distress syndrome, which is likely related to oxaliplatin administration; all signs disappeared within a few days.