Miracle-dz/UniFAR-FTUs huggingface dataset:
In‐situ prediction of soil organic carbon by vis–NIR spectroscopy: an efficient use of limited field data

Visible–near‐infrared diffuse reflectance spectroscopy (vis–NIR DRS) has been widely used to predict soil organic carbon (SOC) in the laboratory. Predictions made directly from soil spectra measured in situ under field conditions, however, remain challenging. This study addresses the issue of incorporating in‐situ reflectance spectra efficiently into calibration data when a few field measurements only are available. We applied the synthetic minority oversampling technique (SMOTE) to generate new data with in‐situ reflectance spectra from soil profiles. Subsequently, we combined existing spectral libraries with these new synthetic data to predict SOC by partial least squares regression (PLSR). We found that models with added synthetic spectra always outperformed models based on the spectral libraries alone and in most cases also those with added in‐situ spectra only. We used the models to predict the distribution of SOC in soil profiles under five different land uses at Mount Kilimanjaro (Tanzania). Based on our results, we propose a framework for predicting SOC with a limited number of in‐situ soil spectra. This framework could effectively reduce the costs of developing in‐situ models for SOC at the local scale.
Auxiliary subunits of voltage-gated Calcium channels as modulators of ionotropic glutamate receptors

Die vorliegende Arbeit untersucht mogliche Wechselwirkungen zwischen AMPA-Rezeptoren und Hilfsuntereinheiten spannungsgesteuerter Calciumkanale. Es konnte gezeigt werden, dass die Koexpression von alpha-2-delta-Untereinheiten mit AMPA-Rezeptoren in Xenopus laevis-Oozyten die elektrophysiologischen Eigenschaften des Rezeptors verandert. Bei Koexpression in HEK-Zellen hingegen konnte keine solche Modulation festgestellt werden; daraus wurde geschlossen, dass fur die Interaktion zwischen alpha-2-delta-Untereinheiten und AMPA-Rezeptoren ein weiteres, bislang unbekanntes Adapterprotein notwendig ist, welches in Xenopus-Oozyten endigen exprimiert wird, nicht jedoch in HEK-Zellen. Diese Hypothese wird weiter unterstutzt durch den Umstand, dass durch die Markierung des AMPA-Rezeptors mit einem Fluoreszenzprotein am intrazellularen C-Terminus die Modulation durch die extrazellular lokalisierten alpha-2-delta-Untereinheiten aufgehoben wurde.
Experimental assessment of soil effects on the leakage discharge from polyethylene pipes

Leaking water from pipes depends on several factors such as pressure, pipe material, types of cracks and holes and also regime of flow through cracks. The effects of these factors on the leakage have been investigated by several researchers. However, few studies have been found considering the effects of soil around the pipes on the leakage discharge. Here, the leakage from polyethylene pipes is simulated in the laboratory, selecting several soils with different specifications. The leak discharge equation is adjusted to evaluate the effects of soil characteristics. Accordingly, grain diameter greater than 10% and 50% passing, coefficient of uniformity, coefficient of curvature, liquidity limit, plastic limit, plasticity index and hydraulic permeability have been considered to represent the soil properties. It is observed that the leakage is changed in accordance with most of the above parameters. The effects of grain diameter greater than 50% passing, plastic limit and hydraulic permeability are higher on the leakage, comparing to those of other parameters. However, no meaningful relationship is observed between the leakage and some parameters. The effects of significant characteristics are shown by the equations presented in this study.
Viscosity and Volumetric studies of some amino acids in solutions at different temperatures.

Densities ρ and viscosities η for several concentrations of amino acids (Serine, Cysteine and Threonine) at different temperatures (298.15, 303.15 and 308.15K) have been measured. On the basis of these data, the apparent molal volumes v  , partial molal volumes at infinite dilution v  , slope Sv , Gibbs free energy of activation for viscous flow of solution ∆G1,2 and Jones – Dole Bcoefficients were calculated the nature of solute-solvent and solute-solute interactions have been discussed in terms of the values of v  , v  , Sv and B-coefficents. : ةصلاخلا يي يا ز ث ييخاوس ث ي زياس تاي(يا ا ةاايا لا مايلساا يا مدام دالا تا لجللال تاةساثلا سايق تساردلا هذه جلمش تفلخخا ةرازح ثس ردب 51,ث92 ث ,1,ث92 ث ,1,ث92 يزهسظلا يل يملا مجالا بسوال جئسخ(لا هذه جادخخسأ . تقلطا v  مداملا يزهسظلا يل يملا مجالا ث v  ميملا ث Sv يالاملل لجاللا بسياو لال ةزالا سبك تقسط ∆G1,2 س يا ما سعال – لم Bباذا عي يا ز أخلا تعيبط تشقس(ا جمح . – باذال ب ذا – ميق لاخ يا باذا v  ل Sv يقل ت لجللا ماسعا تم B . Introduction : All proteins are polymers consisting of chains of amino acid chemically bound to each other. The amino acid in proteins are called α-amino acid. Each amino acid has a characteristic side chain (R group) that imparts chemical individuality to the molecule. The polarity of the R groups is an important characteristic and is the basis for classifying amino acids into four groups[1]. Amino acids are high water solubilities suggests that they exist in an ionic form (zwitter ion). In physiological media such as blood, membranes, cellular fluids, etc. , where happens to be involved in an important manner, the zwitter ionic (dipolar) character of these compounds has an important bearing on their biological functions[2]. It has been known for sometimes that viscosity measurements can provide direct evidence regarding size and state of solvation of molecules in solution[3]. The variation of relative viscosity with molarity (c) for solutions of dipolar ions or non-electrolytes is normally interpreted in term Jones-Dole equation :ηr = — = 1+ Bc + Dc 2 . (1) where η and η0 are the viscosities of the solution and the solvent respectively, B and D are empirical coefficients. B is an adjustable parameter which related to the size and shape of the solute molecules and to different solutesolvent interactions. While D coefficient is generally positive. The Dc 2 term is usually small for dilute solutions and its significance is not completely understood[4-7]. In recent years interest has been directed to study the behavior of amino acids, peptides and polypeptides in mixed aqueous solvents. Such work is important in understanding the factors that determine the stability of biopolymer[8-11]. From the literature survey it appears that the studies of viscosity and density for α-amino acid solutions are abundant[6-8, 12-14], but on those Serine, cysteine and threonine are few. With this in view, the title study has been undertaken in the light of the following aspects :η η0 *
Diferenciación de camélidos en la región Chaco-Santiagueña (Argentina) para el período Agroalfarero (350-1550 dC)

Los camelidos sudamericanos han sido uno de los recursos de mayor importancia desde la llegada de los primeros humanos al continente, registrandose su domesticacion para los Andes centro-sur alrededor de 4400-3000 aP. Las investigaciones realizadas en la region Chaco-Santiaguena para la etapa Agroalfarera (350 dC hasta la conquista espanola en el siglo dieciseis) en su mayoria han interpretado la presencia o ausencia de la especie domestica de camelidos Lama glama a partir de fuentes etnohistoricas o por consideraciones eto-ecologicas. Sin embargo, con el avance de los estudios zooarqueologicos en la region, asignamos por tecnicas osteometricas y analisis estadisticos multivariados especimenes al taxon L. glama para un sitio Agroalfarero tardio (ca. 1200-1500 dC). En este trabajo se incorporan sitios de toda la secuencia Agroalfarera y de distintas zonas geograficas de la region Chaco-Santiaguena con el fin de diferenciar especies de camelidos. A partir de los resultados se interpreta el posible uso de camelidos silvestres y domesticados en la region desde los primeros grupos sedentarios. El uso de llamas podria haber sido una herramienta importante para el establecimiento de redes de interaccion social a grandes distancias.
Characterization of the hepatitis B virus matrix domain function in virion assembly

Das Hepatitis B Virus (HBV) stellt weltweit ein groses Gesundheitsproblem dar, da die uber 350 Millionen persistent infizierten Trager ein hohes Risiko haben, an Leberzirrhose oder –krebs zu erkranken. Das Entwickeln benotigter neuer Behandlungsmethoden setzt ein fundiertes Wissen uber die Molekularbiologie des Virus voraus. In dieser Arbeit wurde die Rolle der Matrix-Domane (MD) des grosen Hullproteins im Assemblierungsprozess charakterisiert. Eine Reihe von Mutationen wurde in MD erzeugt, und deren Auswirkung auf die Sekretion von Viren mittels eines neu etablierten Nachweissystems getestet. Wahrend einige Positionen variabel waren, waren andere Reste hoch konserviert und invariabel. Diese spielen eine spezifische Rolle bei der Virenbildung und eignen sich als Target fur eine antivirale Therapie. Aufgrund dieser Ergebnisse wurde ein Model fur den Prozess der Kapsidumhullung vorgeschlagen.
Valorization of acid isolated high yield lignin nanoparticles as innovative antioxidant/antimicrobial organic materials

In this study, dissolution of pristine alkali lignin into ethylene glycol, followed by addition of different acidic conditions (HCl, H2SO4, and H3PO4 at different pH) has been considered as a simple method to prepare high yield lignin nanoparticles (LNP). Field emission scanning electron microscopy (FESEM), Zeta potential, gel permeation chromatography (GPC), and thermogravimetric analysis (TGA) have been utilized to determine the influence of the precipitation procedures on particle size, Zeta potential, molecular weight, and thermal stability of final obtained LNP. Fourier transform infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS), and nuclear magnetic resonance (NMR) were also considered to investigate the influence of lignin chemical structures and composition on its antioxidative and antimicrobial behaviors. Results from DPPH (1,1-diphenyl-2-picryl-hydrazyl) activity revealed the antioxidant response of LNP aqueous solutions, whereas results from antimicrobial tests confirmed LNP .
UJI MUTAGENISITAS DAN ANTI KANKER EKSTRAK ASETON DAN N-HEKSANA DARI KULIT BATANG SESOOT (Garcinia picrorrhiza Miq.)

Mutagenicity and anti-mutagenicity of the acetone and n-hexane extracts of Garcinia picrorrhiza Miq.bark have been studied by Ames method using Salmonella typhimurium TA 97, TA 98 TA 100, TA 102, dan Escherichia coli WP2. The results showed that the two extracts had a positive effect. It can be concluded that the acetone and n-hexane extracts of Garcinia picrorrhiza Miq. bark have anticancer activity.
Determinants and Career Consequences of Early Audit Partner Rotations

We examine how often and why some audit partners rotate off client engagements before the end of the maximum five-year cycle period. Specifically, we investigate whether audit quality issues play a role for engagement partners and clients to separate prematurely. For a sample of about 4,000 within-audit firm partner rotations for Big 6 clients over the 2008 to 2014 period, we find that client characteristics such as financial leverage or performance have little explanatory power. In contrast, severe audit quality issues such as financial restatements or PCAOB inspection findings are associated with early partner rotations. These associations are more pronounced for early rotations that are not explained by scheduled retirements, promotions, or temporary leaves as well as for large clients and when partners are less experienced. We also find that female partners have a higher likelihood of early rotation for audit quality reasons. Early rotations have career consequences. Partners are assigned to fewer SEC issuer clients, manage fewer audit hours, receive lower partner ratings, and are more likely to be internally inspected after being rotated early. Our results suggest that audit quality concerns are an important factor for early partner rotations with ensuing negative career consequences for partners’ client assignments and management responsibilities.
Team versus Individual: Evidence from Financial Analysts during COVID-19 Pandemic

Whether the performance of teams is better than that of individuals is still an open question. In this paper I look at one important team in the financial market, the financial analyst team, and take the advantage of the exogenous shock introduced by stay-at-home orders in the United States during the COVID-19 pandemic, to investigate whether analyst teams can issue more timely forecasts compared to individual analysts. The main findings are: (i) after the stay-at-home orders, both team and individual analysts issue fewer timely forecasts;(ii) compared to individual analysts, team analysts can issue more timely forecasts after the stay-at-home orders;and (iii) teams with female leaders and higher diversity perform better. In summary, this paper provides new evidence on the performance difference between teams and individuals especially during tough times.
Wage Risk and Portfolio Choice: The Role of Correlated Returns

From standard portfolio-choice theory it is well-understood that background risk, overwhelmingly due to wage risk, is one of the central determinants of individuals’ portfolio composition: higher background risk reduces risky investments. However, if background risk is negatively correlated with financial market risk, higher background risk implies more risky investment. We quantify the influence of wage risk on German investors’ financial portfolio shares and find that an increase of the residual variance of wages by one standard deviation implies a reduction of the financial portfolio share by 3 percentage points. We do not find that the correlation of wage risk with financial market risk has a significant impact on portfolio choice and provide evidence that this may be due to a lack of salience.
How do MNEs and Domestic Firms Respond Locally to a Global Demand Shock? Evidence from a Pandemic

Global shocks bring unanticipated changes in the business environment of foreign multinational enterprises (MNEs) and rival domestic firms. We examine whether there is a difference between how MNEs and domestic firms react in heterogeneous local or subnational markets to a global demand shock. Leveraging the 2009-10 H1N1 influenza pandemic as a source of exogenous variation in global demand for influenza vaccines, we investigate the role of subnational heterogeneity in economic resources, industry infrastructure, and political alignment within an emerging economy on the behavior of incumbent MNEs and rival domestic firms. We find that following the pandemic, MNE market share in the influenza vaccine market relative to the non-influenza vaccine markets declines more in regions with lower government health spending per capita, and also in regions unaligned with the federal government. Additional analyses suggest that these changes in market share are not caused by a reduction in MNE revenues. Rather, they are caused by domestic firms that were already present in non-influenza vaccine markets diversifying by entering the highly-related influenza vaccine market. Finally, a granular examination of the differential responses reveals that such responses are not related to pre-shock differences in regional coverage of MNEs and domestic firms. This study contributes to the extant literature by suggesting that the direct costs, or opportunity costs, of new market and region entry are relatively greater for MNEs than for domestic firms, particularly in regions that have inadequate health infrastructure and are politically not aligned.
Tunable Full-Color Solid-State Fluorescent Carbon Dots for Light Emitting Diodes

Carbon dots (CDs) as a class of new fluorescent materials have attracted numerous interests. However, there remains a significant challenge to prepare CDs with full-color solid-state photoluminescence (PL). In this work, CDs with tunable full-color solid-state PL are prepared via one-step microwave method using phloroglucinol and urea as sources by regulating reactant ratio and microwave power. The maximum emission of CDs gradually shifts from 445 to 643 nm, and quantum yield of five typical CDs with blue (B), green (G), yellow (Y), orange (O) and red (R) emission reaches up 48.2%, 26.0%, 18.5%, 13.7% and 5.7%, respectively. It is the first time, as far as we know, to realize full-color solid-state CDs without additional disperse matrices. The G-, Y-, O-, R-CDs show aggregation-induced red-shift behavior, while B-CDs exhibit unique crystallization-induced emission enhancement property. Finally, CDs are utilized as phosphors combing with ultraviolet chip to fabricate multicolor and white light emitting diodes (LED). The obtained white LED exhibits an adjustable correlated temperature (3937−8588 K) and high color rendering index (80−88). This study provides a novel guidance for developing self-quenching resistant CDs with multicolor emission.
Embedding a Heavy-Ball type of Momentum into the Estimating Sequences

We present a new accelerated gradient-based method for solving smooth unconstrained optimization problems. The goal is to embed a heavy-ball type of momentum into the Fast Gradient Method (FGM). For this purpose, we construct a generalization of the estimating sequences, which allows for encoding any form of information about the cost function that can aid in further accelerating the minimization process. In the black box framework, we propose a construction for the generalized estimating sequences, which is obtained by exploiting the history of the previously constructed estimating functions. From the viewpoint of efficiency estimates, we prove that the lower bound on the number of iterations for the proposed method is O (√ κ 2 ) . Our theoretical results are further corroborated by extensive numerical experiments on various types of optimization problems, often dealt with in signal processing. Both synthetic and real-world datasets are utilized to demonstrate the efficiency of our proposed method in terms of decreasing the distance to the optimal solution as well as in terms of decreasing the norm of the gradient.
HYPER: Learned Hybrid Trajectory Prediction via Factored Inference and Adaptive Sampling

Modeling multi-modal high-level intent is important for ensuring diversity in trajectory prediction. Existing approaches explore the discrete nature of human intent before predicting continuous trajectories, to improve accuracy and support explainability. However, these approaches often assume the intent to remain fixed over the prediction horizon, which is problematic in practice, especially over longer horizons. To overcome this limitation, we introduce HYPER, a general and expressive hybrid prediction framework that models evolving human intent. By modeling traffic agents as a hybrid discrete-continuous system, our approach is capable of predicting discrete intent changes over time. We learn the probabilistic hybrid model via a maximum likelihood estimation problem and leverage neural proposal distributions to sample adaptively from the exponentially growing discrete space. The overall approach affords a better trade-off between accuracy and coverage. We train and validate our model on the Argoverse dataset, and demonstrate its effectiveness through comprehensive ablation studies and comparisons with state-of-the-art models.
FADNet++: Real-Time and Accurate Disparity Estimation with Configurable Networks

Deep neural networks (DNNs) have achieved great success in the area of computer vision. The disparity estimation problem tends to be addressed by DNNs which achieve much better prediction accuracy than traditional hand-crafted feature-based methods. However, the existing DNNs hardly serve both efficient computation and rich expression capability, which makes them difficult for deployment in real-time and high-quality applications, especially on mobile devices. To this end, we propose an efficient, accurate, and configurable deep network for disparity estimation named FADNet++. Leveraging several liberal network design and training techniques, FADNet++ can boost its accuracy with a fast model inference speed for real-time applications. Besides, it enables users to easily configure different sizes of models for balancing accuracy and inference efficiency. We conduct extensive experiments to demonstrate the effectiveness of FADNet++ on both synthetic and realistic datasets among six GPU devices varying from server to mobile platforms. Experimental results show that FADNet++ and its variants achieve state-of-the-art prediction accuracy, and run at a significant order of magnitude faster speed than existing 3D models. With the constraint of running at above 15 frames per second (FPS) on a mobile GPU, FADNet++ achieves a new state-of-the-art result for the SceneFlow dataset.
New insights into price drivers of crude oil futures markets: Evidence from quantile ARDL approach

This paper investigates the cointegration between possible determinants of crude oil futures prices during the COVID-19 pandemic period. We perform comparative analysis of WTI and newly-launched Shanghai crude oil futures (SC) via the Autoregressive Distributed Lag (ARDL) model and Quantile Autoregressive Distributed Lag (QARDL) model. The empirical results confirm that economic policy uncertainty, stock markets, interest rates and coronavirus panic are important drivers of WTI futures prices. Our findings also suggest that the US and China's stock markets play vital roles in movements of SC futures prices. Meanwhile, CSI300 stock index has a significant positive short-run impact on SC futures prices while S\&P500 prices possess a positive nexus with SC futures prices both in long-run and short-run. Overall, these empirical evidences provide practical implications for investors and policymakers.
Networked Time Series Prediction with Incomplete Data

A networked time series (NETS) is a family of time series on a given graph, one for each node. It has a wide range of applications from intelligent transportation, environment monitoring to smart grid management. An important task in such applications is to predict the future values of a NETS based on its historical values and the underlying graph. Most existing methods require complete data for training. However, in real-world scenarios, it is not uncommon to have missing data due to sensor malfunction, incomplete sensing coverage, etc. In this paper, we study the problem of NETS prediction with incomplete data . We propose NETS-ImpGAN, a novel deep learning framework that can be trained on incomplete data with missing values in both history and future. Furthermore, we propose Graph Temporal Attention Networks , which incorporate the attention mechanism to capture both inter-time series and temporal correlations. We conduct extensive experiments on four real-world datasets under different missing patterns and missing rates. The experimental results show that NETS-ImpGAN outperforms existing methods, reducing the MAE by up to 25%.