Miracle-dz/UniFAR-FTUs huggingface dataset:
Transmutation rates determination for the study of nuclear fuel composition under irradiation

The prediction of the time evolution of the nuclear fuel composition is one of the key issues concerning the design and operation of nuclear plants. With this target in mind, a methodology, based on Monte Carlo codes calculations validated by direct measurements, was developed to estimate the transmutation rates of nuclides relevant for the study and analysis of nuclear fuel composition under irradiation, focusing on major and minor actinides build-up and burn-up and on fission product production and depletion. Material targets containing the nuclides relevant for the study and analysis of nuclear fuel composition were irradiated in a well characterized irradiation position of the TRIGA Mark II reactor of Vienna Atominstitut in order to detect the transmutation rates (production & depletion) by means of gamma-ray spectrometry technique. The experiment was then simulated using the Serpent-2 reactor model recently developed and validated as presented at the RRFM 2017 conference. Calculated and experimental results were then compared to verify the capability of Serpent-2 reactor model to predict changes in the composition of nuclear fuel under irradiation and are reported in this paper.
Continuous detonation of syngas-air mixtures in an annular flow-type cylindrical combustor

Activities aimed at developing a scientific basis for detonation engines where the fuel is continuously burned in a traveling detonation wave [1] (the scheme proposed by Academician Voitsekhovskii [2]) have been performed since the 1960s. The practical interest is focused on realization of continuous spin detonation (CSD) of fuel-air mixtures (FAMs) under conditions similar to those in air-breathing engines. CSD regimes in acetylene-air and hydrogen-air fuel-air mixtures in a flow-type annular combustor were obtained for the first time and were studied in [3]. The synthesis gas can become a promising fuel. Various technologies are used to obtain the synthesis gas: methane conversion at high pressures and temperatures in the presence of nickel catalysts (CH4 + H2O  CO + 3H2), and incomplete thermal oxidation of hydrocarbons (CnH2n+2 + 1/2nO2  nCO + (n+1)H2) [4]. In existing combustors, the synthesis gas is burned by the mechanism of conventional turbulent combustion. The present activities are aimed at obtaining and determining conditions of existence of continuous detonation of synthesis gas-air mixtures with molar fractions [СO]/[Н2]: 1/2, and 1/3.
Effect of Layer Charge Location on Potassium Exchange and Hydration of Micasl

The rate of K exchange by Ca of Al-free phlogopite (no tetrahedral ionic substitution but with octahedral vacancies) was much lower than that of pilogopite. Since some of the hydroxyls in the Al-free phlogopite are inclined like those in muscovite, it was suggested that for the rate of K exchange hydroxyl orientation is a more determinant factor than layer charge location. However, after K exchange, the hydrated layers in the former sample under moist conditions expanded to 18.64 whereas the hydrated layers in phlogopite eipanded to 14.8A. This implied that layer charge location has a greater effect on hydration behavior than on the rate of K exchange.
The Epigenetics of Anxiety Pathophysiology: A DNA Methylation and Histone Modification Focused Review

Abstract Anxiety is one of the most common psychiatric disorders diagnosed in the United States today. Like all mental illnesses, anxiety pathology includes genetic, molecular, somatic, and behavioral characteristics. Specific brain regions implicated in anxiety include the prefrontal cortex, amygdala, hippocampus, and hypothalamus. Together, these regions regulate fear-related learning and memory processes, and are innervated by neuronal projections that use glutamate and GABA as neurotransmitters. Neurotrophic factors such as brain-derived neurotrophic factor (BDNF) are also implicated in anxiety. This review discusses the neuroepigenetics of the anxiety phenotype. While studying such changes is limited to postmortem brain studies or peripheral tissue acquisition in humans, the use of animals to model anxiety phenotypes has made epigenetic research possible. In this review, we summarize and discuss a plethora of DNA methylation, histone modification, and associated gene expression differences underscoring the anxiety phenotype. The findings we outline include expression changes of various DNA methyltransferases and changes in histone modifications that affect the hypothalamic pituitary adrenal axis and stress response as well as GABA, glutamate, and BDNF signaling in the PFC, amygdala, hypothalamus, and hippocampus. Furthermore, there have been studies showing that anxiety behaviors and biological scars from stress can be reversed using histone deacetylase inhibitors, and we discuss ideas for the future of treatment. In this review, we hope that by compiling much of the data pertaining to DNA methylation and histone modifications in vivo animal studies we are able to highlight potential avenues for future research despite existing limitations.
Managerial Discretion in Accruals and Informational Efficiency

In this paper, we examine the relation between managerial discretion in accruals and informational efficiency. We measure managerial discretion in accruals by the absolute value of abnormal accruals. Assuming that efficient prices follow a random walk, we measure informational efficiency by using stock return variance ratios. We find that the absolute value of abnormal accruals is negatively associated with the price deviation from a random walk pattern, estimated in the 12-month period subsequent to the accrual reporting; hence, future informational efficiency increases with the extent to which managers exercise discretion over accruals. The results are consistent with the view that discretionary accruals, on average, convey useful information to investors and facilitate the price convergence to its fundamental value. Our findings are robust to a battery of tests, including tests to validate both our measures of informational efficiency and our measure of managerial discretion in accruals.
Bred Vector and ENSO predictability in a hybrid coupled model for the period 1881-2000 by

In this study, we conducted a breeding analysis for a hybrid coupled ENSO (El Niño and the Southern Oscillation) model that assimilated a historic dataset of sea surface temperature (SST) for the 120 years between 1881-2000. Meanwhile, retrospective ENSO forecasts were performed for the same period. For a given initial state, fifteen bred vectors (BVs) of both SST and upper ocean heat content (HC) were derived. It was found that the average structure of the fifteen BVs was insensitive to the initial states and independent of season and ENSO phase. The average structure of the BVs shared many features already seen in both the final patterns of leading singular vectors and the ENSO BVs of other models. However individual BV patterns were quite different from case to case. The BV-rate (the average cumulative growth rate of BVs) varied seasonally, and the maximum value appeared at the time when the model ran through the boreal spring and summer. It was also sensitive to the strength of the ENSO signal (i.e., the stronger ENSO signal the smaller the BV-rate). Furthermore we explored ENSO predictability using BV analysis. Emphasis was placed on the relationship between BVs, which are able to characterize potential predictability without requiring observations, and actual prediction skills, which make use of real observations. The results showed that the relative entropy, defined using breeding vectors, was a good measure of potential predictability. A large relative entropy often leads to a good prediction skill; however, when the relative entropy was small, the prediction skill seemed much more variable. At decadal/interdecadal scales, the variations in prediction skills correlated with relative entropy.
Fluorinated surfaces: towards a universal matrix-free substrates for Laser Desorption Ionization

Matrix Assisted Laser Desorption Ionization (MALDI) is a soft ionization method that finds widespread applications in high-throughput mass spectrometric analysis. One of the main limit of this technique is that it requires the use of matrices: these molecules enable the analytes desorption and ionization (D/I) processes but also generate strong interfering signals in the low mass spectral region, preventing a suitable detection of low molecular weight compounds. The possibility to avoid their use will ease both sample preparation and mass spectrum (MS) interpretation. In recent years nanostructured surfaces have been proposed as a viable method to achieve such goal but the results are limited to specific classes of chemical compounds and the approach lacks in generality. Here we demonstrate that the fluorination of surfaces is the only step needed to activate the target and to achieve matrix-free operation with the same detection limit obtained using nanostructured chips. The results of this work suggest that D/I is a purely interfacial effect, with negligible contribution from chemical nature of the underlying substrate, provided that the substrate is conductive and able to efficiently absorb the UV laser beam.
Characterization of Microbiota Associated with Digesta and Mucosa in Different Regions of Gastrointestinal Tract of Nursery Pigs

Weaning is a crucial period when piglets have to cope with sudden dietary, social, and environmental stressors that often lead to serious intestinal dysbiosis and mortality. In this study, five mucosal and five digesta samples from each proximate jejunum, distal jejunum, and mid-colon were collected from 7- and 27-day post-weaned pigs and subjected to microbiota analysis using 16S rRNA gene profiling. Taxonomic analysis at phylum level revealed that Proteobacteria was significantly higher at 7 days (13.54%), while Bacteriodetes was higher at 27 days (30.72%) post weaning. Genera such as Campylobacter, Veillonella, Helicobacter, and Blautia that were previously reported in intestinal dysbiosis were significantly enriched in seven-day post-weaned pigs. However, microbial communities shifted as post weaning age increased with a significant increase in alpha diversity, and genera such as Moryella, Dialister, Clostridium, Streptococcus, Prevotella, and Bacteroides become significantly abundant in 27-day post-weaned pigs. Interestingly, the genus Campylobacter was significantly abundant on seven-day post-weaning in two piglets with diarrhea, implicating its role in post-weaning diarrhea. The results of this study suggest that gut microbiota in pigs with dysbiosis on 7-day post weaning undergoes significant changes toward a more normal state as the post-weaning age reaches 27 days.
Conformal Prediction Under Covariate Shift

We extend conformal prediction methodology beyond the case of exchangeable data. In particular, we show that a weighted version of conformal prediction can be used to compute distribution-free prediction intervals for problems in which the test and training covariate distributions differ, but the likelihood ratio between these two distributions is known---or, in practice, can be estimated accurately with access to a large set of unlabeled data (test covariate points). Our weighted extension of conformal prediction also applies more generally, to settings in which the data satisfies a certain weighted notion of exchangeability. We discuss other potential applications of our new conformal methodology, including latent variable and missing data problems.
Towards effective collaborations between regional climate modeling and impacts relevant modeling studies in Polar Regions

Title: Towards effective collaborations between regional climate modeling and impacts relevant modeling studies in Polar Regions Authors: Hanna Lee,a Nadine Johnston,b Lars Nieradzik,c Andrew Orr,b Ruth H. Mottramd, Willem Jan van de Berg,e Priscilla A. Mooney,a a Division of Climate and Environment, NORCE Norwegian Research Centre, Bjerknes Centre for Climate Research, Bergen, Norway b British Antarctic Survey, Natural Environment Research Council, Cambridge, United Kingdom c Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden d National Centre for Climate Research, Danish Meteorological Institute, Copenhagen, Denmark e Institute for Marine and Atmospheric Research Utrecht, University of Utrecht, Utrecht, Netherlands Corresponding author: Hanna Lee, hanna.lee@ntnu.no Manuscript (non-LaTeX)
Dynamics arising from the impact of large‐scale afforestation on ecosystem services

Understanding how multiple ecosystem services interact, how trade‐offs emerge, and how interactions among ecosystem services may change after the Grain‐for‐Green‐Program (GFGP) and is critical for managing multiple ecosystem services. Here, we conducted a spatiotemporal approach to quantify the impact of large‐scale afforestation on four key ecosystem services (i.e., crop production, soil conservation, carbon sequestration, and water conservation). We observed that three satellite datasets (i.e., GIMMS‐LAI3g, GIMMS‐NDVI3g, and NDVI‐ESTARFM) time‐series show a slightly improved and significantly improved trends, and crop production, carbon sequestration, soil conservation, and water conservation have improved after the implementation of GFGP. Simultaneously, almost 49% of the low levels and 58% of the high levels for all four of the ecosystem services considered changed after the implementation of GFGP. The results also show that relationships among ecosystem services have shifted from a trade‐off in 1988 and 1999 to an apparent synergistic relationship in 2011.We conclude that GFGP is a successful programme for addressing trade‐offs between services. Our study provides experiential support for large‐scale restoration programme in humid area around the World.
Neural Network Based Vibration Control of Seismically Excited Civil Structures

This study proposes a neural network based vibration control system designed to attenuate structural vibrations induced by an earthquake. Classical feedback control algorithms are susceptible to parameter changes. For structures with uncertain parameters they can even cause instability problems. The proposed neural network based control system can identify the structural properties of the system and avoids the above mentioned problems. In the present study it is assumed that a full state of the structure is known, which means the at each floor horizontal displacements and rotations about the vertical axis are measured. Additionally, it is assumed the acceleration signal coming from the earthquake is also available. The proposed neural control strategy is compared with the classical linear quadratic regulator (LQR) not only in terms of displacement responses, but also required control forces. Moreover, the influence of different weighting matrices on performance of the proposed control strategy has been presented. The effectiveness of the neuro-controller has been demonstrated on two numerical examples: a simple single degree of freedom (DOF) structure and a multi-DOF structure representing a twelve story building. Both structures under consideration have been excited with El Centro acceleration signal. The results of numerical simulations on the SDOF system indicate that using neuro-controller it would be possible to obtain smaller amplitudes as compared with the LQ regulator, but it would require higher control effort.
Simulating with Rice Coalitionally Stable Burden Sharing Agreements for the Climate Change Problem

In this paper we test empirically with the Nordhaus and Yang (1996) RICE model the core property of the transfer scheme adv ocated by Germain, Toint and Tulkens (1997). This scheme is designed to sustain full cooperation in a voluntary international environmental agreement by making all countries at least as well off as they would be by joining coalitions adopting emission abatement policies that maximize their coalition payoff; under the scheme no individual country, nor any subset of countries would have an interest in leaving the international environmental agreement. The simulations show that the transfer scheme yields an allocation in the core of the carbon emission abatement game associated with the RICE model. Finally, we discuss some practical implications of the transfer scheme for current climate negotiations.
SEVA: A Systems Engineer's Virtual Assistant

A Systems Engineer’s Virtual Assistant (SEVA) is a novel attempt to bridge the gap between Natural Language Processing (NLP), Knowledge Base (KB) Construction research, and NASA’s Systems Engineering domain. In this work, we pro-pose the design of an explainable, human-in-the-loop, and interactive personal assistant system. The assistant will help a Systems Engineer in their daily work environment through complex information management and high-level question-answering to augment their problem-solving abilities. We describe the fundamental characteristics of the assistant by understanding operational, functional, and system requirements from Systems Engineers and NASA’s Systems Engineering Handbook. The assistant is designed to act as a workbench to manage dynamic information about projects and analyze hypothetical scenarios. It is also designed to make logical inferences and perform temporal reasoning by handling domain information and information related to schedule and resources. In addition, the system learns new information over time by interacting with its user and can perform case-based reasoning from previous experiences. The knowledge base design describes a novel hybrid approach to build a domain-independent common-sense framework with which domain-speciﬁc engineers can attune it and build their projects. Using these speciﬁc objectives and constraints, the architecture of a personal assistant is proposed. Main contributions of this design paper are Systems Engineering (SE) domain analysis, a survey of existing research, preliminary experiments using the state-of-the-art systems to explore the feasibility, a proposal of a complete architecture with component level detail, and identiﬁcation of areas that require further research and development.
A Multigrid Method using Explicit Approximate Inverses for the Numerical Solution of Two-Dimensional Time-Dependent Problems

During the recent decades, multigrid methods have been extensively used in order to solve large scale linear systems derived from the discretization of partial differential equations using the finite difference method. The purpose of this work is the derivation of hybrid heterogeneous schemes based on time implicit approximating schemes, namely backward differences and Crank–Nicolson method, with the multigrid method in conjunction with explicit approximate inverses for the efficient iterative solution of unsymmetric linear systems. Thus, a new class of smoothers, based on approximate inverses, has been derived. Moreover, a dynamic relaxation scheme is proposed based on the dynamic over - under relaxation (DOUR) algorithm. In addition, a new class of Krylov subspace iterative methods in conjunction with multigrid preconditioning based on explicit approximate inverses is used for solving initial value problems. Finally, the applicability of the new proposed hybrid heterogeneous implicit time - explicit approximate inverses combined with multigrid methods for parabolic PDEs is discussed by solving characteristic time-dependent problems in two dimensions and the numerical results are given.
Predicted band-gap pressure coefficients of all diamond and zinc-blende semiconductors: Chemical trends

We have studied systematically the chemical trends of the band-gap pressure coefficients of all group IV, III-V, and II-VI semiconductors using first-principles band-structure method. We have also calculated the individual {open_quotes}absolute{close_quotes} deformation potentials of the valence-band maximum (VBM) and conduction-band minimum (CBM). We find that (1) the volume deformation potentials of the {Gamma}{sub 6c} CBM are usually large and always negative, while (2) the volume deformation potentials of the {Gamma}{sub 8v} VBM state are usually small and negative for compounds containing occupied valence {ital d} state but positive for compounds without occupied valence {ital d} orbitals. Regarding the chemical trends of the band-gap pressure coefficients, we find that (3) a{sub p}{sup {Gamma}{minus}{Gamma}} decreases as the ionicity increases (e.g., from Ge{r_arrow}GaAs{r_arrow}ZnSe), (4) a{sub p}{sup {Gamma}{minus}{Gamma}} increases significantly as anion atomic number increases (e.g., from GaN{r_arrow}GaP{r_arrow}GaAs{r_arrow}GaSb), (5) a{sub p}{sup {Gamma}{minus}{Gamma}} decreases slightly as cation atomic number increases (e.g., from AlAs{r_arrow}GaAs{r_arrow}InAs), (6) the variation of a{sub p}{sup {Gamma}{minus}L} are relatively small and follow similar trends as a{sub p}{sup {Gamma}{minus}{Gamma}}, and (7) the magnitude of a{sub p}{sup {Gamma}{minus}X} are small and usually negative, but are sometimes slightly positive for compounds containing first-row elements. Our calculated chemical trends are explained in terms of the energymore » levels of the atomic valence orbitals and coupling between these orbital. In light of the above, we suggest that {open_quotes}empirical rule{close_quotes} of the pressure coefficients should be modified. {copyright} {ital 1999} {ital The American Physical Society}« less
Optimal Energy Routing Design in Energy Internet with Multiple Energy Routing Centers Using Artificial Neural Network-Based Reinforcement Learning Method

In order to cope with the energy crisis, the concept of an energy internet (EI) has been proposed as a novel energy structure with high efficiency which allows full play to the advantages of multi-energy coupling. In order to adapt to the multi-energy coupled energy structure and achieve flexible conversion and interaction of multi-energy, the concept of energy routing centers (ERCs) is proposed. A two-layered structure of an ERC is established. Multi-energy conversion devices and connection ports with monitoring functions are integrated in the physical layer which allows multi-energy flow with high flexibility. As for the EI with several ERCs connected to each other, energy flows among them are managed by an energy routing controller located in the information layer. In order to improve the efficiency and reduce the operating cost and environmental cost of the proposed EI, an optimal multi-energy management-based energy routing design problem is researched. Specifically, the voltages of the ERC ports are managed to regulate the power flow on the connection lines and are restricted on account of security operations. An artificial neural network (ANN)-based reinforcement learning algorithm was proposed to manage the optimal energy routing path. Simulations were done to verify the effectiveness of the proposed method.