Miracle-dz/UniFAR-FTUs huggingface dataset:
A new international geostationary electron model: IGE‐2006, from 1 keV to 5.2 MeV

Département Environnement Spatial, Office National d'Etudes et de Recherches Aérospatiales (ONERA) has been developing a model for the geostationary electron environment since 2003. Until now, this model was called Particle ONERA‐LANL Environment (POLE), and it is valid from 30 keV up to 5.2 MeV. POLE is based on the full complement of Los Alamos National Laboratory geostationary satellites, covers the period 1976–2005, and takes into account the solar cycle variation. Over the period 1976 to present, four different detectors were flown: charged particle analyzer (CPA), synchronous orbit particle analyzer (SOPA), energetic spectra for particles (ESP), and magnetospheric plasma analyzer (MPA). Only the first three were used to develop the POLE model. Here we extend the energy coverage of the model to low energies using MPA measurements. We further include the data from the Japanese geostationary spacecraft, Data Relay Test Satellite (DRTS). These data are now combined into an extended geostationary electron model which we call IGE‐2006.
Statistical Analysis of Field Experiments Using Neighbouring Plots

The paper describes and provides examples of four different applications of the use of neighbouring plot values in the analysis of agricultural field experiments. The topics include the use of check plots to control environmental variation in large unreplicated variety trials; spatial models, based on first differences, to accommodate fertility effects in trials that have some degree of replication; adjustment for interplot competition using a simultaneous-equations formulation; and the analysis of trials in which plot values are affected by the particular treatments on neighbouring plots.
Predicting performance times for long cycle time tasks

A long cycle time task is assumed to consist of a series of non-repetitive unique sub-tasks whose standard times average at about 1 ½ minutes. ‘Forgetting’ is therefore a consequence of a specific sub-task reappearing in the next cycle after a whole cycle time of other activities is completed. Learning behavior of long cycle tasks is therefore predicted on the learning of its constituent sub-tasks. A method for predicting the learning curve parameters for the sub-tasks (the learning constant, and execution time of the first repetition) are proposed and tested. The extent of ‘forgetting’ is empirically determined as a function of the learning constant and interruption length. Finally, a model is developed for predicting execution times for long cycle tasks.
Measurements of Coronal Rotation and Small Coronal Ejections Using the STEREO COR1 Coronagraph

Title of dissertation: Measurements of Coronal Rotation and Small Coronal Ejections Using the STEREO COR1 Coronagraphs Shaela I. Jones, Doctor of Philosophy, 2011 Dissertation directed by: Dr. Joseph M. Davila Professor Douglas C. Hamilton Department of Physics I present results of two studies conducted using the STEREO COR1 coronagraph: singleand dual-spacecraft measurements of white-light coronal rotation rates in 2007 and 2008 as a function of latitude and altitude, and trajectory characteristics of small, upward-moving coronal density enhancements. Single-spacecraft coronal rotation measurements indicate that the rotation rate of the corona in the COR1 field of view is independent of latitude and altitude, consistent with previous studies that showed rotation in the corona is very rigid compared with rotation in the photosphere. The equatorial rotation rate for this study was found to be 27.06 ± 0.08 days in 2007 and 26.97 ± 0.10 days in 2008. Using the measured rotation periods, I extract the average coronal intensity as a function of longitude at each latitude at 1.8 solar radii, and compare them to reconstructions of the coronal electron density by Kramar et al. [46]. The longitudinal structure derived from the rotation measurements is very similar to the electron density measurements, indicating that the observed rigidity of the coronal rotation does not seem to be due to projection of low-latitude features onto higher latitudes as some authors have speculated. It has been suggested that the relative rigidity of the coronal rotation may be at least in part a measurement effect, due to the selective measurement of large and/or long-lived features in coronagraph rotation studies. Following the measurements of coronal rotation in the STEREO COR1 field of view using standard coronagraph rotation measurement techniques, I present for the first time short time lag coronagraph rotation measurements, using the unique capabilities of the STEREO dual-spacecraft mission. Finally, I present results of a systematic search of nineteen days’ worth of COR1 data for small, faint, outward-moving density enhancements, sometimes referred to in the literature as plasma blobs. In the past these plasma blobs have been studied in the Large Angle Spectroscopic Coronagraph (LASCO) C2 and C3 fields of view, which extend only as low as 2−2.5R⊙, and it was believed that their origin was in pointed cusps at the top of the streamer belt. Using the COR1 coronagraph I was able to observe many such features between 1.5−2.0R⊙, a height below what would normally be expected if these features originate at the top of the streamer belt as suggested by the earlier observations. Measurements of Coronal Rotation and Small Coronal Ejections Using the STEREO COR1 Coronagraph
Lidar Scanning of Momentum Flux in and above the Atmospheric Surface Layer

Abstract Methods to measure the vertical flux of horizontal momentum using both continuous wave and pulsed Doppler lidar profilers are evaluated. The lidar measurements are compared to momentum flux observations performed with sonic anemometers over flat terrain at Hovsore, Denmark, and profile-derived vertical momentum flux observations at the Horns Rev wind farm in the North Sea. Generally, the momentum fluxes are reduced because of the finite measuring volume of the instruments, and the filtering is crudely accounted for theoretically. The essential parameter for the estimation of the reduction is the ratio of the turbulence scale to the size of the measuring volume. For the continuous wave lidar the reduction can largely be compensated by averaging Doppler spectra instead of radial velocities.
An integrated rough number-based approach to design concept evaluation under subjective environments

This study aims at improving the quality and effectiveness of concept evaluation in the early phases of new product development. New product concept evaluation has long been recognised as one of the most crucial decisions for the success of product development, because it has significant impact on the downstream development activities. However, evaluating new product concept is a process involving subjectivity and vagueness. In order to manipulate this problem, a novel decision approach for selecting more rational new product concepts is proposed. Basically, two stages of evaluation process are described: the computation of evaluation criteria weights for new product alternatives and the selection of the most appropriate concept. These stages are composed of a hybrid approach based on a rough group analytic hierarchy process (AHP) method and a rough group technique for order of preference by similarity to ideal solution (TOPSIS) method. The novel approach integrates the strength of rough number in handling vagueness, the advantage of AHP in hierarchy evaluation and the merit of TOPSIS in modelling multi-criteria decision-making. Finally, an application in mini-fridge concept evaluation is given to demonstrate the potential of the methodology.
Upcoming balloon flight of the nuclear Compton telescope

Our collaboration is developing a 2-detector prototype high resolution Compton telescope utilizing 3D imaging germanium detectors (GeDs) for a test balloon flight in Spring 2003. This instrument is a prototype for a full 12-GeD instrument, the Nuclear Compton Telescope. NCT is a balloon-borne soft gamma-ray (0.2-15 MeV) telescope designed to study astrophysical sources of nuclear line emission and polarization. The NCT program is designed to develop and test the technologies and analysis techniques crucial for the Advanced Compton Telescope, while studying gamma-ray radiation with very high spectral resolution, moderate angular resolution, and high sensitivity. NCT has a novel, ultra-compact design optimized for studying nuclear line emission in the critical 0.5-2 MeV range, and polarization in the 0.2-0.5 MeV range. This prototype flight will critically test the novel instrument technologies, analysis techniques, and background rejection procedures we have developed for high resolution Compton telescopes. We present the design and expected performance of this prototype NCT instrument.
Wavelength dependence of light-induced domain nucleation in MgO-doped congruent LiNbO3 crystal

Within the wavelength range from 351 to 799 nm, the different reductions of nucleation field induced by the focused continuous laser irradiation are achieved in the 5 mol % MgO-doped congruent LiNbO3 crystals. The reduction proportion increases exponentially with decreasing irradiation wavelength and decreases exponentially with increasing irradiation wavelength. At one given wavelength, the reduction proportion increases exponentially with increasing irradiation intensity. An assumption is proposed that the reduction of nucleation field is directly related to the defect structure of crystal lattice generated by the complex coaction of incident irradiation field and external electric field. (c) 2007 American Institute of Physics.
Causal Inference out of Control: The Steerability of Consumption

Regulators and academics are increasingly interested in the causal effect that algorithmic actions of a digital platform have on consumption. We introduce a general causal inference problem we call the steerability of consumption that abstracts many settings of interest. Focusing on observational designs, we exhibit a set of assumptions for identifiability that significantly weakens the often unrealistic overlap assumptions of standard designs. The key insight behind our assumptions is to model the dynamics of consumption, viewing the platform as a controller acting on a dynamical system. From this dynamical systems perspective, we show that exogenous variation in consumption and appropriately responsive control actions are sufficient for identifying steerability of consumption. Our results illustrate the fruitful interplay of control theory and causal inference, which we corroborate with examples from econometrics, macroeconomics, and machine learning.
Guided-Motion Bicruciate-Stabilized Total Knee Arthroplasty Reproduces Native Medial Collateral Ligament Strain

Background and Objectives: Guided-motion bicruciate-stabilized (BCS) total knee arthroplasty (TKA) includes a dual cam-post mechanism with an asymmetric bearing geometry that promotes normal knee kinematics and enhances anterior-posterior stability. However, it is unclear whether the improved biomechanics after guided-motion BCS TKA reproduce soft tissue strain similar to the strain generated by native knees. The purpose of this cadaveric study was to compare medial collateral ligament (MCL) strain between native and guided-motion BCS TKA knees using a video extensometer. Materials and Methods: Eight cadaver knees were mounted onto a customized knee squatting simulator to measure MCL strain during flexion in both native and guided-motion BCS TKA knees (Journey II-BCS; Smith & Nephew, Memphis, TN, USA). MCL strain was measured using a video extensometer (Mercury® RT RealTime tracking system, Sobriety s.r.o, Kuřim, Czech Republic). MCL strain level and strain distribution during knee flexion were compared between the native and guided-motion BCS TKA conditions. Results: The mean and peak MCL strain were similar between native and guided-motion BCS TKA knees at all flexion angles (p > 0.1). MCL strain distribution was similar between native and BCS TKA knees at 8 of 9 regions of interest (ROIs), while higher MCL strain was observed after BCS TKA than in the native knee at 1 ROI in the mid portion of the MCL at early flexion angles (p < 0.05 at ≤30° of flexion). Conclusions: Guided-motion BCS TKA restored the amount and distribution of MCL strain to the values observed on native knees.
Plant Flavonoids on Oxidative Stress-Mediated Kidney Inflammation

Simple Summary Increased stress is often observed in patients with kidney diseases, contributing to renal injury progression. Flavonoids are naturally occurring plant compounds with known health benefits, including antiapoptotic, anti-inflammatory, and antioxidant properties. Flavonoids can protect the kidney by improving antioxidant status, ameliorating excess reactive oxygen species levels, and acting as Nrf2-mediators in generating antioxidant responses in the body. Flavonoids also modulate inflammatory markers, exert anti-inflammatory effects, and protect the cells from apoptotic cell death in the kidney. Interestingly, few clinical trials have reported a direct correlation between a flavonoid-rich diet and better kidney disease prognosis. However, flavonoids have a low bioavailability in the body, making it essential to understand better their molecular mechanism of action. We suggest that a flavonoid-rich diet could have promising nephroprotective effects and beneficial outcomes in treating patients with kidney diseases. Abstract The kidney is susceptible to reactive oxygen species-mediated cellular injury resulting in glomerulosclerosis, tubulointerstitial fibrosis, tubular cell apoptosis, and senescence, leading to renal failure, and is a significant cause of death worldwide. Oxidative stress-mediated inflammation is a key player in the pathophysiology of various renal injuries and diseases. Recently, flavonoids’ role in alleviating kidney diseases has been reported with an inverse correlation between dietary flavonoids and kidney injuries. Flavonoids are plant polyphenols possessing several health benefits and are distributed in plants from roots to leaves, flowers, and fruits. Dietary flavonoids have potent antioxidant and free-radical scavenging properties and play essential roles in disease prevention. Flavonoids exert a nephroprotective effect by improving antioxidant status, ameliorating excessive reactive oxygen species (ROS) levels, and reducing oxidative stress, by acting as Nrf2 antioxidant response mediators. Moreover, flavonoids play essential roles in reducing chemical toxicity. Several studies have demonstrated the effects of flavonoids in reducing oxidative stress, preventing DNA damage, reducing inflammatory cytokines, and inhibiting apoptosis-mediated cell death, thereby preventing or improving kidney injuries/diseases. This review covers the recent nephroprotective effects of flavonoids against oxidative stress-mediated inflammation in the kidney and their clinical advancements in renal therapy.
Morphology Features of Ferroelectric Submicron Domains Written by E-Beam under a Metal Film in LiNbO3

The practical significance of ferroelectric domains and various domain boundaries has been growing steadily in recent years. In this work, various domain structures were written with an electron beam through a thin aluminum film on a −Z cut of bulk lithium niobate. The use of relatively low accelerating voltages (5 and 10 kV) and the grounding of the surface metallization made it possible to write periodic structures (1D and 2D) on large areas with domain sizes ≤1 μm. Selective domain etching and AFM in contact mode were used to observe various domain shapes obtained in the experiments. An unusual feature of the submicron-sized domains was needle-like vertices. Importantly, the vertices of the domains were deepened relative to the irradiated surface. It was found that the size and proximity of the irradiated regions to each other in the patterns used can significantly change the upper part of the domains. The experimental data were analyzed and discussed taking into account the computer simulation of the spatial field distribution of injected electron beam charges. The obtained results contribute to the development of controlled writing of submicron-sized domain structures using an electron beam.
Dark Energy and Modified Gravity: A Dynamical Systems Approach

The mystery of dark energy has pervaded the field of cosmology for decades, and a satisfactory theoretical framework for describing its origins remains elusive. Models that aim to describe dark energy are relatively easy to propose, and split roughly into two categories: theories that introduce some field content, and theories that modify the gravitational interactions of the Universe. Once such a theory is formulated, its validity is sometimes far harder to comprehend, due to the relative complexity of the resulting field equations. The dynamical systems approach aims to alleviate this difficulty, by reformulating the field equations as a set of simpler differential equations, which permits the classification of the entire cosmic history within a given dark energy model. We will firstly examine models with added field content using dynamical systems tools; this will allow us to confirm the viability of each model without too many complicated calculations. We then make the natural progression towards theories of modified gravity, again with the hope of ruling out unfeasible models. We will also briefly make contact with observational restrictions on each theory of dark energy, which again is a task made easier in the dynamical systems framework. We thus conclude that the methods of dynamical systems, when applied to dark energy and cosmology more generally, are a powerful way of describing the Universe, and can be applied effectively across the entire field of theoretical cosmology.
Humour Conversation Generation: Exploiting Incongruity-Based Features and Deep Learning Methods

—This paper introduces the methods to detect and generate humorous conversation based on conversational data gathered from different resources. In order to gather and filter the humorous conversation data, we combined Incongruity-Based Features and a neural-based method to filter the data. To find humorous conversation data, our method is useful. In the later part of our paper, we implemented three main types of text generation models to compare with our finetuned GPT-J model; our finetuned model outperforms the traditional methods like LSTM and Seq2Seq models; It only requires a few datasets compared to methods, but it performs better. It can generate humorous replies according to the context question, just like a human.
Citrus Bergamia and Cynara Cardunculus Reduce Serum Uric Acid in Individuals with Non-Alcoholic Fatty Liver Disease

Background and Objectives: Hyperuricemia and liver steatosis are risk factors for cardiovascular diseases and mortality. The use of natural compounds could be a safe and effective alternative to drugs for the treatment of fatty liver and hyperuricemia. Polyphenolic fraction of Citrus Bergamia in association with the extract of Cynara Cardunculus, as nutraceutical, is able to reduce body weight, hepatic steatosis and markers of oxidative stress. Then, we performed a secondary analysis of a double-blind placebo-controlled trial to examine the effects of this nutraceutical on serum uric acid levels in adults with fatty liver. Materials and Methods: The study included 94 individuals with hepatic steatosis. For six weeks, the intervention group was given a nutraceutical (300 mg/day) comprising a Bergamot polyphenol fraction and Cynara Cardunculus extract. The control group received a daily pill of placebo. Serum uric acid, lipids, glucose and anthropometric parameters were assessed at baseline and after 6 weeks. Results: We found a greater reduction in serum uric acid in the participants taking the nutraceutical rather than placebo (−0.1 ± 0.7 mg/dL vs. 0.3 ± 0.7 mg/dL, p = 0.004), and especially in those with moderate/severe hepatic steatosis also after adjustment for confounding variables. In addition, we analysed the two groups according to tertiles of uric acid concentration. Among participants taking the nutraceutical, we found in those with the highest baseline serum uric acid (>5.4 mg/dL) the greater reduction compared to the lowest baseline uric acid (−7.8% vs. +4.9%; adjusted p = 0.04). The stepwise multivariable analysis confirmed the association between the absolute serum uric acid change and nutraceutical treatment (B = −0.43; p = 0.004). Conclusions: A nutraceutical containing bioactive components from bergamot and wild cardoon reduced serum uric acid during 6 weeks in adults with fatty liver. Future investigations are needed to evaluate the efficacy of this nutraceutical in the treatment of hyperuricaemia.
On the Implicit Geometry of Cross-Entropy Parameterizations for Label-Imbalanced Data

Various logit-adjusted parameterizations of the cross-entropy (CE) loss have been proposed as alternatives to weighted CE for training large models on label-imbalanced data far beyond the zero train error regime. The driving force behind those designs has been the theory of implicit bias, which for linear(ized) models, explains why they successfully induce bias on the optimization path towards solutions that favor minorities. Aiming to extend this theory to non-linear models, we investigate the implicit geometry of classifiers and embeddings that are learned by different CE parameterizations. Our main result characterizes the global minimizers of a non-convex cost-sensitive SVM classifier for the unconstrained features model, which serves as an abstraction of deep nets. We derive closed-form formulas for the angles and norms of classifiers and embeddings as a function of the number of classes, the imbalance and the minority ratios, and the loss hyperparameters. Using these, we show that logit-adjusted parameterizations can be appropriately tuned to learn symmetric geometries irrespective of the imbalance ratio. We complement our analysis with experiments and an empirical study of convergence accuracy in deep-nets.
Conditional Variational Autoencoders for Emotionally-aware Chatbot Based on Transformer

—Rising demand for artiﬁcial intelligence-powered chatbots with sentiment analysis is creating new growth opportunities for numerous areas. Thus, building empathetic natural language processing agents becomes an interdisciplinary ﬁeld of natural language processing. Some researchers presented the seq2seq model to make responses more emotional, while others tried the generative model for more variation. However, it is arduous to control the emotion and sentiment of generated sentences. In this paper, we focus on the need for the variational empathetic chatbot. The model combines the plain Transformer chatbot model and Conditional Variational Autoencoders (CVAE). With the help of neural emotional classiﬁers and pre-trained weights from RoBERTa, our model achieves the best score in automatic and human evaluation. Experimentally, we show in the quantitative and qualitative analyses that the proposed models can successfully generate high-quality abstractive conversation responses following designated emotions.