Miracle-dz/UniFAR-FTUs huggingface dataset:
Theoretical derivation of aerosol lidar ratio using Mie theory for CALIOP-CALIPSO and OPAC aerosol models

Abstract. The extinction-to-backscattering ratio, popularly known as lidar (light detection and ranging) ratio of atmospheric aerosols is an important optical property, which is essential to retrieve the extinction profiles of atmospheric aerosols. Lidar satellite observations can provide the global coverage of atmospheric aerosols along with their vertical extent. NASA's Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP) on board the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite is the only space-based platform available, so far, that provides the vertical profiles of extinction due to atmospheric aerosols. A physics-based theoretical approach is presented in the present paper that estimates lidar ratio values for CALIPSO aerosol models, which can be used as inputs to determine the extinction profiles of aerosols using CALIPSO data. The developed methodology was also qualified by comparing it with the lidar ratio values derived using AERONET (AErosol RObotic NETwork) datasets. Lidar ratio values for CALIPSO aerosol models were estimated in the range of 38.72 to 85.98 sr at 532 nm, whereas at 1064 nm lidar ratio varied between 20.11 to 71.11 sr depending upon the aerosol type and their size distributions. Aerosols are compositions of various particles; thus, the presence of water vapour in the atmosphere can affect the optical properties of the aerosols. Thus, the effect of relative humidity on lidar ratio was studied using Optical Properties of Aerosols and Clouds (OPAC) aerosol models, which are the standard aerosol models against the cluster-classified AERONET and CALIPSO aerosol models. Water-soluble particles contribute substantially in clean continental, clean marine, tropical marine and desert aerosol models and are hygroscopic in nature. Hygroscopic sulfate particles dominate the Antarctic aerosols during summertime. In the presence of relative humidity between 0 %–80 %, the lidar ratio values were observed to decrease from 53.59 to 47.13, from 53.66 to 47.15, from 53.70 to 47.16, and from 55.32 to 48.78 sr at 532 nm for clean continental, clean marine, tropical marine, and desert aerosols, respectively, whereas lidar ratio gradually increased from 47.13 to 51, from 47.15 to 51, from 47.16 to 51, and from 48.78 to 51.68 sr, respectively, for these aerosol models when relative humidity was between 80 %–99 %, due to constituent hygroscopic particles. In the case of Antarctic aerosols, the lidar ratio was observed to increase from 57.73 to 97.64 sr due to hygroscopic sulfate particles that backscattered heavily in the presence of water vapour at 532 nm. The soot particles dominate the polluted continental and polluted marine particles, causing an increase in lidar ratio over its corresponding clean counterpart. Similar results were observed at 1064 nm for OPAC aerosol models.
Signatures and mechanisms of low-dimensional neural predictive manifolds

Many of the recent advances of neural networks in sequential tasks such as natural language processing applications hinge on the use of representations obtained by predictive models. This success is usually ascribed to the emergence of neural representations that capture the low-dimensional latent structure implicit in the task. Motivated by the recent theoretical proposal that the hippocampus performs its role in sequential planning by organizing semantically related episodes in a relational network, we investigate the hypothesis that this organization results from learning a predictive representation of the world. Using an artificial recurrent neural network model trained with predictive learning on a simulated spatial navigation task, we show that network dynamics exhibit low dimensional but non-linearly transformed representations of sensory input statistics. These neural activations that are strongly reminiscent of the place-related neural activity that is experimentally observed in the hippocampus and in the entorhinal cortex. We quantify these results using measures of intrinsic dimensionality, which indeed confirm that the neural representations obtained with predictive learning reflect the low-dimensional latent structure of the spatial environment underlying the sensory input presented to the network. Moreover, the dimensionality gain of the neural representations, a measure of the discrepancy between linear and intrinsic dimensionality, allows us to follow how this process evolves as learning unfolds. Finally, we provide theoretical arguments as to how predictive learning can extract the latent manifold underlying sequential signals, and discuss how our results and methods can aid the analysis of experimental data.
Modeling Compounding for Multilingual Data Resources Thesis Proposal

This proposal presents the progress that has been made toward finishing our dissertation. We have set out to model compounding multi-lingually; to that end, we describe a series of experiments in determining the parent words of compounds. The first experiment presents a neural model of compounds called Czech Compound Splitter , which returns the motivating words of a given compound and discriminates compounds from non-compounds. Its successor, Word Formation Analyzer for Czech, returns the motivating word(s) of any input word, and is able to discriminate compounds from both derivatives and unmotivated words. The final experiment in the series is PaReNT , which performs the same tasks, but supports 8 languages including Czech. Additionally, we propose a pipeline, partially based on already-existing tools, that endows morphologically segmented compounds with a dependency structure, modeling their internal composition.
Convergence of Nite Volume Schemes for Semilinear Convection Diiusion Equations

The topic of this work is the discretization of semilinear elliptic problems in two space dimensions by the cell centered nite volume method. Non-homogeneous Dirichlet boundary conditions are considered here. A discrete Poincar e inequality is used, and estimates on the approximate solutions are proven. The convergence of the scheme without any assumption on the regularity of the exact solution is proven using some compactness results which are shown to hold for the approximate solutions. An approximate gradient may be constructed, which tends to the exact gradient as the size of the mesh tends to 0.
Ultralow Dissipation Mechanical Resonators for Cavity-Free Quantum Control

Cavity optomechanical systems can explore the limits of quantum measurements and enact quantum control over mechanical motion. The optical cavity in these systems enhance the interaction strength between light and mechanical motion, thereby enabling access to the quantum backaction-dominated regime, where the quantum backaction of light is greater than the thermal force noise from the finite temperature environment. While optical cavities greatly enhance interaction strengths, they incur added experimental complexity and impose bandwidth restrictions that limit pulsed measurements. Over the last decade, engineered nanomechanical resonators have reached extremely low thermal decoherence rates by exploiting dissipation dilution. Dissipation diluted resonators exhibit greatly reduced mechanical dissipation due to the combination of stress and geometric nonlinearity, where the elongation of the mechanical resonator is nonlinear in the mode amplitude[1].
Virtual Internship Experiences of Library and Information Science Students During the Pandemic

This study aimed to discover the notable experiences of Library and Information Science students in a virtual internship program. It employed qualitative descriptive research design by thematically analyzing the monthly internship journal of the interns. Using Colaizzi’s method to identify themes from their experiences, the study revealed that interns expected to gain knowledge and skills on operations, services, and new information communication technologies in libraries. Orientation was found helpful in identifying the things interns needed to prepare. The interns faced issues from procrastination and technical difficulties, which they coped with employing avoidance strategies. Overall, the internship was viewed as an opportunity to learn and navigate the online digital system of the library despite the coronavirus disease 2019 pandemic. They realized their capacity and reflected on which competencies need improvement. In conclusion, students still learned the necessary knowledge and skills of a librarian; experienced the challenges faced in an actual library and were provided with various opportunities and realizations regarding the practice of librarianship. This study proposed a three-stage framework that outlines the involvement of the supervising instructors, librarians, and interns in the internship program from the preparation to the process and post-assessment.
Reducing ground subsidence involving geological CO2 storage during longwall mining operations

Underground mining causes costly subsidence damage world-wide. In the 1980s German mining research developed stowage techniques using mining wastes during longwall mining operations. These were applied successfully, but not continuously because of economic considerations at that time. A combined technique for subsidence reduction, mining waste disposal and CO2 storage is being developed within the study presented. Oedometer tests conducted on pumpable mining waste suspensions with various moisture contents reveal useful compression characteristics making them suitable for stowing materials in excavated areas. Laboratory CO2 adsorption experiments on these wastes show remarkable CO2 sorption capacities in comparison with residual coal seams. The combination of CO2 storage and subsequent stowage can lead to a cost-effective way of subsidence reduction determined by the growing prices of CO2 emission certificates and restricting regulations with regard to CO2 emissions.
Artificial tactile perception smart finger for material identification based on triboelectric sensing

Tactile perception includes the direct response of tactile corpuscles to environmental stimuli and psychological parameters associated with brain recognition. To date, several artificial haptic-based sensing techniques can accurately measure physical stimuli. However, quantifying the psychological parameters of tactile perception to achieve texture and roughness identification remains challenging. Here, we developed a smart finger with surpassed human tactile perception, which enabled accurate identification of material type and roughness through the integration of triboelectric sensing and machine learning. In principle, as each material has different capabilities to gain or lose electrons, a unique triboelectric fingerprint output will be generated when the triboelectric sensor is in contact with the measured object. The construction of a triboelectric sensor array could further eliminate interference from the environment, and the accuracy rate of material identification was as high as 96.8%. The proposed smart finger provides the possibility to impart artificial tactile perception to manipulators or prosthetics.
Radiation of nonequilibrium recombining plasma flows

L’un des plus grands defis des missions prevues sur Mars est la phase d’entree atmospherique, lorsque le vaisseau spatial entre dans l’atmosphere de Mars a l’aller, ou de la Terre au retour. Les capsules spatiales entrent dans l’atmosphere a des vitesses hypersoniques ce qui comprime le gaz a l’avant de l’appareil et resulte en une augmentation de temperature jusqu’a la dissociation et l’ionisation du gaz. Ces phenomenes engendrent un rayonnement intense du gaz. Le flux radiatif arrivant sur la capsule est la principale source de chauffage de l’appareil mais de grandes incertitudes demeurent dans sa prediction. En consequence, le bouclier thermique est surdimensionne, augmentant la masse totale de l’appareil et diminuant sa charge utile. Ces incertitudes sont maximales sur l’arriere-corps de l’appareil ou le plasma l’entourant est force de se recombiner hors equilibre. Cette these presente une caracterisation experimentale de plasma hors equilibre d’air et d’azote en recombinaison, typique des plasmas d’arriere-corps. Les distributions de la population de l’azote atomique et moleculaire sont mesurees, ainsi que le rayonnement hors equilibre de ces especes. Des comparaisons avec les predictions de codes numeriques sont donnees, et de larges differences sont observees, soulignant les limites des modeles de mecanique des fluides numeriques. Ces donnees experimentales ont pour but d’etre utilisees dans la validation des modeles presents dans les codes d’entree atmospherique.
Perspective on the SAMPL and D3R Blind Prediction Challenges for Physics-Based Free Energy Methods

Solvation and binding thermodynamics of a drug-like molecule is quantified by the respective free energy (FE) change that governs physical properties like log P /log D and binding affinities as well as more complex features such as solubility or permeability. The drug discovery process benefits significantly from reliable predictions of FEs, which are hence a key area for the theoretical and modeling community. Despite the clear physical background rooted in statistical mechanics, the desired accuracy goal is hard to achieve. Current modeling methods still need to be improved in various areas related to the FE problem, such as the quality of force fields and quantum-mechanical approximations, the efficiency of sampling algorithms as well as the robustness of computational workflows. In this context, blind prediction challenges, where participants are tasked with testing their computational methods and workflows on compound property predictions without knowing the experimental data, are excellent testbeds to evaluate and improve the modeling methodology. SAMPL (Statistical Assessment of the Modeling of Proteins and Ligands) and Drug Design Data Resource-Grand Challenges (D3R-GCs) represent widely known initiatives demonstrating
The back and forth nudging algorithm applied to a shallow water model, comparison and hybridization with the 4D‐VAR

We study in this paper a new data assimilation algorithm, called the back and forth nudging (BFN). This scheme has been very recently introduced for simplicity reasons, as it does not require any linearization, or adjoint equation, or minimization process in comparison with variational schemes, but nevertheless it provides a new estimation of the initial condition at each iteration. We study its convergence properties as well as efficiency on a 2D shallow water model. All along the numerical experiments, comparisons with the standard variational algorithm (called 4D‐VAR) are performed. Finally, a hybrid method is introduced, by considering a few iterations of the BFN algorithm as a preprocessing tool for the 4D‐VAR algorithm. We show that the BFN algorithm is extremely powerful in the very first iterations and also that the hybrid method can both improve notably the quality of the identified initial condition by the 4D‐VAR scheme and reduce the number of iterations needed to achieve convergence. Copyright © 2008 John Wiley & Sons, Ltd.
Promotion of empathy, self-concept and basic values: an intervention in a female prison

Incarceration of women is permeated by countless structural problems and inefficiency in guaranteeing fundamental rights and psychological assistance to women. In this article, the effects of intervention workshops aimed to promote self-concept, empathy, and basic values for incarcerated women are reviewed. The study comprised 56 women aged between 18 and 63 years, incarcerated in a Public Prison ( M = 32.23; SD = 9.25). Fifteen of these women participated in the intervention program, while the remaining sample comprised the control group. Both groups were evaluated in the pre-and post-intervention period, using the Self-Concept Scale, the Interpersonal Reactivity Index and the Basic Values Questionnaire. During the meetings field notes and participant observations were used to describe meaningful experiences. No statistically significant variations were found in the scores of the scales used, which may be related to the need to adapt these instruments to groups with low levels of education. On the other hand, the study found that the use of workshops enabled the construction of a pedagogical space, with very different contingencies compared to the prison environment, in which women were encouraged to experience their emotions and feelings.
Brain Dynamic States Analysis based 3D Convolutional Neural Network

— Drowsiness driving is one major factor of traffic accident. Monitoring the changes of brain signals provides an effective and direct way for drowsiness detection. One 3D convolutional neural network (3D CNN)-based forecasting system has been proposed to monitor electroencephalography (EEG) signals and predict fatigue level during driving. The limited weight sharing and channel-wise convolution were both applied to extract the significant phenomenon in various frequency bands of brain signals and the spatial information of EEG channel location, respectively. The proposed 3D CNN with limited weight sharing and channel-wise convolution has been demonstrated to predict reaction time (RT) of driving with low root mean square error (RMSE) through the brain dynamics. This proposed approach outperforms with the state-of-the-art algorithms, such as traditional CNN, Neural Network (NN), and support vector regression (SVR). Compared with traditional CNN and Artificial Neural Network, the RMSE of 3D CNN-based RT prediction has been improved 9.5% (RMSE from 0.6322 to 0.5720) and 8% (RMSE from 0.6217 to 0.5720), respectively. We envision that this study might open a new branch between deep learning application in neuro-cognitive analysis and real world application.
Genomic prediction of agronomic traits in sugarcane using machine learning and quantitative genetics

To improve the efficiency of sugarcane breeding, introduction of genomic selection (GS) into breeding programs is anticipated. For sugarcane cultivars with highly heterozygous polyploid genomes, non-additive genetic effects must be considered in genomic prediction models. In this context, the use of machine learning techniques is an effective approach to model nonadditive genetic effects. In addition, pedigree information that tracks sugarcane breeding lineages can be used to calculate the degree of association among genotypes and as input for predictive models. In the present study, we used the breeding population used in a KARC/NARO sugarcane breeding program in Japan as experimental material and verified the following attributes. Specifically, the prediction accuracy of BLUP was validated based on genomic and pedigree information. In addition, a new machine learning method that can explicitly process the interaction between whole-genome markers and genetic background was developed, and comparative verification was performed using the conventional random forest method. Pedigree information showed high predictive accuracy, and when columns of the numerator relationship matrices were used as input for the machine learning models, their accuracy was identical or superior to that of BLUP depending on traits. Finally, we confirmed the effectiveness of the proposed machine learning method using interactions among markers as a model. Overall, these GS methods are expected to improve sugarcane breeding efficiency.
Effects of cholecalciferol and omega-3 fatty acids on hepcidin levels in 5/6 nephrectomy rats

Background: Anemia is a common complication of chronic kidney disease (CKD). In patients with CKD-related anemia, an inverse relationship between vitamin D and hepcidin levels has been observed. Hepcidin is a key regulator of iron homeostasis, mediated via binding to ferroportin. The aim of this study was to investigate the effects of cholecalciferol and omega-3 fatty acids (FA) on hepcidin levels using 5/6 nephrectomized (Nx) rats. Methods: Male Sprague-Dawley rats were divided into five groups: sham control, 5/6 Nx, 5/6 Nx treated with cholecalciferol, 5/6 Nx treated with omega-3 FA, and 5/6 Nx treated with both cholecalciferol and omega-3 FA. We measured the hepcidin and ferroportin levels in the kidney and liver by enzyme-linked immunosorbent assays and Western blots. We evaluated hepcidin expression in the kidney by immunohistochemical staining. Results: Among the five groups, 5/6 Nx rats exhibited the worst kidney function. Compared with the sham controls, 5/6 Nx rats showed significantly increased serum hepcidin levels and decreased vitamin D levels. Supplementation with either omega-3 FA or cholecalciferol decreased hepcidin and increased vitamin D levels, with a concurrent improvement of anemia. Furthermore, 5/6 Nx rats treated with omega-3 FA/cholecalciferol showed decreased ferroportin and ferritin levels, while iron and total iron-binding capacity levels increased. Conclusions: Treatment with a combination of cholecalciferol and omega-3 FA may improve anemia in a CKD rat model by decreasing hepcidin levels.
Soil erosion by surface water flow on a stony, semiarid hillslope

Soil erosion on hillslopes occurs by processes of soil splash from raindrop impacts and sediment entrainment by surface water flows. This study investigates the process of soil erosion by surface water flow on a stony soil in a semiarid environment. A field experimental method was developed whereby erosion by concentrated flow could be measured in predefined flow areas without disturbing the soil surface. The method allowed for measurements in this study of flow erosion at a much wider range of slopes (2·6 to 30·1 per cent) and unit discharge rates (0·0007 to 0·007 m2 s−1) than have been previously feasible. Flow velocities were correlated to discharge and hydraulic radius, but not to slope. The lack of correlation between velocity and slope might have been due to the greater rock cover on the steeper slopes which caused the surface to be hydraulically rougher and thus counteract the expected effect of slope on flow velocity. The detachment data illustrated limitations in applying a linear hydraulic shear stress model over the entire range of the data collected. Flow detachment rates were better correlated to a power function of either shear stress (r2 = 0·51) or stream power (r2 = 0·59). Published in 1999 by John Wiley & Sons, Ltd.
An Efficient Method for the In Vitro Production of Azol(in)e-Based Cyclic Peptides

Heterocycle-containing cyclic peptides are promising scaffolds for the pharmaceutical industry but their chemical synthesis is very challenging. A new universal method has been devised to prepare these compounds by using a set of engineered marine-derived enzymes and substrates obtained from a family of ribosomally produced and post-translationally modified peptides called the cyanobactins. The substrate precursor peptide is engineered to have a non-native protease cleavage site that can be rapidly cleaved. The other enzymes used are heterocyclases that convert Cys or Cys/Ser/Thr into their corresponding azolines. A macrocycle is formed using a macrocyclase enzyme, followed by oxidation of the azolines to azoles with a specific oxidase. The work is exemplified by the production of 17 macrocycles containing 6–9 residues representing 11 out of the 20 canonical amino acids.