Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling of coupled heat and mass transfer during drying of ebony wood using indirect natural convection solar dryer

Abstract This study presented the behavior of a laboratory scale solar dryer, which was designed with the objective of meeting the needs of low income carpenters of the tropical region of Africa. The following materials were used to build the solar dryer: aluminum sheets painted in black, a wooden support, and a plastic polyethylene used as a cover. Drying was carried out in the city of Yaoundé in Cameroon, and the tested boards were ebony (Diospyros crassiflora). Drying must be performed with the following conditions: slow process at a low temperature in order to maintain the proper air relative humidity. Since, no apparatus was used to force the circulation of the air, natural convection was the mode used by the dryer. The solar dryer was mathematically modeled by applying heat and mass transfer balance to the different components of the solar dryer. It is important to mention that the used heat and mass coefficients were proposed as variable with the operating conditions. The mathematical model was a reasonable description of the physical phenomena taking place in the dryer. The results show that the average relative error between numerical and experimental moisture contents was equal to 2.102%. Heat transfer convective coefficients and global mass transfer coefficients varied with drying time from 0.25 to 5.5 W/(m2K) and from 1 × 10−8 to 5.5 × 10−8 m/s, respectively. The proposed model allowed designing and dimensioning the solar dryer, to the industrial scale. It allowed following the evolution of the different temperatures and moisture content of the tested material and then optimizing the design of the dryer and the drying process conditions. Based on the model, proposing the opening of the drying chamber during the night time and the use of substances capable of retaining heat in the chamber were some proposed solutions in order to have more efficient drying process. The modeling results showed also that an improvement can be obtained at the addition of a solar collector.
Snow-darkening versus direct radiative effects of mineral dust aerosol on the Indian summer monsoon onset: role of temperature change over dust sources

Abstract. Atmospheric absorptive aerosols exert complicated effects on the climate system, two of which are through their direct radiative forcing and snow-darkening forcing. Compared to black carbon, the snow-darkening effect of dust on climate has been scarcely explored till now. When depositing in snow, dust can reduce the albedo of snow by darkening it and increasing the snowmelt. In this study, the snow-darkening effect of dust, as well as the direct radiative effect, on the Indian summer monsoon are evaluated by atmospheric general circulation model experiments. The results show that the snow-darkening and direct radiative forcing of dust both have significant impacts on the onset of the Indian monsoon, but they are distinctly opposite. The snow-darkening effect of dust weakens the Indian monsoon precipitation during May and June, opposite to black carbon. The surface temperature over central Asia and the western Tibetan Plateau becomes warmer due to the dust-induced decrease in snow cover, which leads to a local low-level cyclonic anomaly as well as an anticyclonic anomaly over the Indian subcontinent and Arabian Sea. This circulation pattern allows air currents penetrating into the Indian subcontinent more from central Asia but less from the Indian Ocean. In contrast, the direct radiative forcing of dust warms the low troposphere over the Arabian Peninsula, which intensifies moisture convergence and precipitation over the Indian monsoon region. The upper tropospheric atmospheric circulation over Asia is also sensitive to both effects. Compared to previous studies which emphasized the temperature over the Tibetan Plateau, our results further highlight an important role of surface/low tropospheric temperature changes over dust source areas, which can also significantly modify the response of summer monsoon. Thus, links between the climatic impact of dust and complicated thermal conditions over Asia are of importance and need to be clarified accurately.
Performance of a small‐scale compressed air storage (CAS)

The use of renewable energy, such as wind and solar, has significantly increased in the last decade. However, these renewable technologies have the limitation of being intermittent; thus, storing energy in the form of compressed air is a promising option. In compressed air energy storage (CAES), the electrical energy from the power network is transformed into a high‐pressure storage system through a compressor. Then, when the demand for electricity is high, the stored high‐pressure air is used to drive a turbine to generate electricity. The advantages of CAES are its high energy density and quality, and for being environmentally friendly process.
Graph-In-Graph Convolutional Networks For Brain Disease Diagnosis

So far, in the study of neurological brain disorder diagnosis, there are mainly two kinds of work that exploits graph via Graph Convolutional Networks (GCN). Though they have achieved remarkable success, neither of them is able to simultaneously account for the brain region level correlation and subject level correlation. To tackle this issue, we design a Graph-In-Graph Convolutional Networks(GIGCN) framework, which turns out to inherit the merits of the two kinds of existing work. Specifically, we propose a graph-in-graph data structure that consists of two types of graph, namely internal brain connective graph and external population graph. In this data structure, an internal graph is assigned to each subject whose nodes represent brain regions and edges incorporate associations between brain regions, while the external graph is a population graph generated from the demographic of all subjects where edges describe the similarity between subjects. After that, our proposed framework employs GCN to explore some deeper relationships for better disease prediction. Results on the ABIDE dataset validate the effectiveness of our proposed method.
SIMULTANEOUS EVALUATION OF ABACAVIR SULFATE AS WELL AS LAMIVUDINE IN MEDICAL FORMULATIONS BY GRADIENT REVERSED-PHASE HIGH-PERFORMANCE LIQUID CHROMATOGRAPHY TECHNIQUE

Objective: A precise, accurate, simple, and gradient reversed-phase high-performance liquid chromatography (HPLC) method was adapted for the determination of abacavir sulfate (ABV) in combination with lamivudine (LMV) having tablet formulations simultaneously. This method developed has been validated as per the guidelines of ICH.Method: Waters HPLC has been used in the method with a column named Zorbax C18 with the dimensions as 4.6 nm×150 mm, 3.5 μm. Phosphate buffer (PH - 3.9) was used as Eluent - A, Eluent - B was methanol, and water and methanol (50:50 v/v) were utilized as diluents. The rate of flow was 1.5 ml/min.Results: The wavelength of detection has been detected at about 270 nm. Linearity ranges of ABV and LMV were 88–266 μg/ml and 38–116 μg/ml, respectively. Retention times of ABV (3.66 min) and LMV (10.71 min) were determined. The values of the study of percentage recovery of ABV and LMV were determined to be within 98.3–99.2%. Conclusion: The estimation of ABV and LMV in all pharmaceutical dosage forms could be performed successfully by employing this method.
Monetary policy shocks identified using the entire yield curve: an alternative approach

ABSTRACT Conventional monetary policy (MP) shocks are typically identified as unexpected changes in the short end of the yield curve. During the zero lower-bound period, central banks attempt to provide stimulus by operating on the long end of the yield curve with new tools, such as forward guidance and large-scale asset purchase. This implies that the identification of MP shocks should be done throughout the entire yield curve. This study proposes an alternative approach to analysing the effects of MP shocks, using factor-augmented vector autoregression (FAVAR) of monthly data. We therefore used information from large datasets and made changes over the entire yield curve as shocks to the VAR, which were identified using external high-frequency instruments. Furthermore, empirical analyses using economic and financial variables showed that contractionary yield curve changes decrease the inflation rate and increase risk aversion and uncertainty, which is consistent with conventional wisdom.
Examination of all‐sky infrared radiance simulation of Himawari‐8 for global data assimilation and model verification

The systematic difference between observations and simulation from weather forecast model hampers effective data assimilation and model improvement. The purpose of this study is to identify the characteristics and cause of the systematic difference or observation‐minus‐background (O − B) bias for all‐sky infrared radiances of the Himawari‐8 satellite, and propose data assimilation preprocessings and model verification. The O − B bias in cloudy scenes showed substantial negative values because of the shortage of high‐altitude clouds generated in the forecast model. Additionally, a positive bias appeared for thin ice clouds because of the excessive absorption of radiative transfer models (RTMs). These biases were traced based on a bottom‐up approach investigating individual uncertainty of RTMs, observation calibration, and the forecast model using two RTMs, reference hyperspectral sounders and synergetic measurements of CloudSat and CALIPSO. Based on these findings, data assimilation preprocessing such as quality‐control procedures excluding samples that models poorly reproduced was developed. Although the quality controls reduced the number of biased samples, non‐negligible O − B biases remained. Possible problems and treatments for the biases were discussed, including bias correction, observation error inflation, and correction of the cloud effect parameter. The O–B statistics also suggested insufficient representation of the diurnal variation in the cloud fraction in the tropics. Modified physical processes in the forecast model to increase ice clouds were tested to help improve the model bias and develop data assimilation. This trial indicated the difficulty in improving both O − B bias and variance and the necessity of adjusting the cloud effect parameters in data assimilation.
An overview on recent progress of extended state observers for uncertain systems: Methods, theory, and applications

Extended state observer (ESO) is featured with online estimating both the system's states and the total effect of external disturbance as well as nonlinear uncertain dynamics. The original ESO design, proposed for the cascade‐of‐integrator system without measurement noise, has been well studied with theoretical foundation and many successful applications. In the last years, multifarious modified ESOs have also been developed to handle many engineering systems captured by the models with general nonlinear structure, time‐delay, the noises with stochastic properties, and etc. This paper aims to comprehensively investigate the representative modified ESOs in recent years. Firstly, the ESO's design and tuning law are illustrated for general uncertain systems with multiple control inputs, multiple measured outputs and various uncertainties. The quantitative results regarding the estimation errors of ESO are also presented. Next, the extended state‐based Kalman‐typed filters to handle state estimation problem against both stochastic noises and uncertain dynamics are introduced. The consistency, stability, and asymptotic optimization of the proposed filters are rigorously shown. Furthermore, some novel designs of ESO to deal with the estimation problem of uncertain nonlinear systems with time‐delay and measurement biases are discussed. We believe this overview will be helpful for practitioners in applications of recent modified ESOs.
More Persistent Summer Compound Hot Extremes Caused by Global Urbanization

Worldwide urbanization has resulted in more hot extremes in urban areas than surrounding rural areas. Most studies focused on the contribution of urbanization to daytime hot extremes (DayHot), while the contribution to compound hot extremes (CoHot) that considers the concurrence of both daytime and nighttime heat is less understood. Here, we quantify the urbanization contributions across the world by comparing the characteristics of hot extremes between urban and rural areas from 3,401 meteorological stations classified by 30‐m resolution impervious area data. We find more significant increases in frequency, duration, and intensity of CoHot over most urban stations, where urbanization contributes to 12.5%, 15.4%, and 16.7% of the increases, respectively. Urbanization also contributes to the increase in frequency of DayHot, but has weaker impacts on the duration and intensity of Dayhot. This study implies that urbanization effects on the characteristics of hot extremes might be underestimated if only considers DayHot.
Characterizations of Polypropylene/Single-Walled Carbon Nanotube Nanocomposites Prepared by the Novel Melt Processing Technique with a Controlled Residence Time

Melt processing is considered one of the favored techniques to produce polymer nanocomposites with various inorganic fillers such as graphene and carbon nanotubes (CNTs). Due to their superior conductivity and tensile properties, among others, CNTs have been applied in broad applications. When a low filler fraction is desired, a high degree of dispersion is required in order to benefit from the intrinsic properties of CNTs. However, due to their high cohesive energy, dispersing CNTs in polymer melts is a difficult task. This study employed the melt mixing technique with a controlled residence time of 20 min to disperse single-walled carbon nanotubes (SWNTs) into a polypropylene matrix. The composites were prepared by using a corotating twin-screw extruder equipped with a back-conveying element with varying amounts of SWNTs from 0.29 to 6.56 wt. %. Mechanical, electrical, morphological, and rheological properties were evaluated. Due to the filler effect, storage, loss modulus, and complex viscosity increased with the SWNT content. Based on the van Gurp–Palmen plot, 0.29 wt. % SWNTs was the rheological percolation threshold, and the electrical property measurement revealed a 1.4 wt. % SWNT electrical percolation threshold based on the statistical percolation theory. Relatively large agglomerates were found when the SWNT content increased more than 1.28 wt. %.
Volatile Composition and Antioxidant Properties of Clove Products

The volatile chemical composition and antioxidant properties of whole and ground clove buds have been revisited together with those from essential oils. The major bioactive compounds showed to be eugenol, eugenyl acetate and β-caryophyllene; their different quantitative contribution justifies the unequal aroma and antioxidant properties of clove products. Eugenol was identified as the most abundant chemical from whole clove buds and essential oils, while β-caryophyllene is the dominant compound of ground clove, the preferred product in the food industry. This research enables the clarification of the role of clove products in culinary, food industry and in dieting respecting chemical composition and properties.
Single Transition Metal Atom-Doped Graphene Supported on a Nickel Substrate: Enhanced Oxygen Reduction Reactions Modulated by Electron Coupling

Single transition metal (TM) atom-doped graphene has been experimentally proved to be effective for catalyzing electrochemical reactions. However, oxygen reduction reactions (ORR) exhibit poor catalytic performance due to the high binding energy between the active site and the adsorbed species. Here, we propose a feasible strategy to modulate the binding strength of the oxygenated species on the graphitic nanosheet by introducing metal substrate gating. Density functional theory calculation results reveal the remarkable enhancement of ORR performance with the confinement of the Ni(111) surface, and the overpotentials for six different transition metal (TM) atom-doped pentagon|octagon|pentagon (5|8|5) graphene (TM@5|8|5G) systems on the Ni(111) surface can be significantly reduced, particularly for the case of Co@5|8|5G (from 0.98 to 0.33 V). This is mainly attributed to the electron coupling between the metal substrate and TM@5|8|5G. Moreover, the catalytic activity can be well modulated by adjusting d-band centers of TM atoms, leading to an ideal energy level of the d-band center for TM@5|8|5G on the Ni substrate (−1.48 eV), at which ORR can achieve the highest performance.
A two-stage method for improving discrimination and variable selection in DEA models

One of the main challenges when applying data envelopment analysis (DEA) is the selection of appropriate input and output variables. This paper addresses this important problem using a novel two-stage method. In the first stage, we use entropy theory to generate a comprehensive efficiency score (CES) of each decision-making unit. In the second stage, we select input and output variables using the Bayesian information criterion, when CES is treated as a dependent variable and the input and output variables are used as explanatory variables. We use stochastic data to demonstrate that our proposed method can improve the discrimination power of DEA and determine the important input and output variables. Finally, we compare the proposed method with principal component analysis using datasets on carbon emissions in China. This comparison demonstrates the practical value of our proposed method.
Machine learning is a powerful tool to study the effect of cancer on species and ecosystems

Cancer is an understudied but important process in wildlife. Cancerous cells are proposed to have had significant effect on the evolution of metazoan species due to their negative effect on host fitness. However, gaining knowledge on the impact of cancer on species and ecosystems is currently relatively slow as it requires expertise in both ecology and oncology. The field can greatly benefit from automation to reduce the need of excessive manpower and analyse complex ecological datasets. In this commentary, we examine how machine learning has been used to gain knowledge on oncogenic processes in wildlife. Using a landscape ecology approach, we explore spatial scales ranging from the size of a molecule up to whole ecosystems and detail, for each level, how machine learning has been used, or could contribute to obtain insights on cancer in wildlife populations and ecosystems. We illustrate how machine learning is a powerful toolbox to conduct studies at the interface of ecology and oncology. We provide guidance for the readers of both fields on how to implement machine learning tools in their research and identify directions to move the field forward using this promising technology. We demonstrate how applying machine learning to complex ecological datasets will (a) contribute to quantitating the effect of cancer at different life stages in wildlife; (b) allow the mining of long‐term datasets to understand the spatiotemporal variability of cancer risk factors and (c) contribute to mitigating cancer risk factors and the conservation of endangered species. With this study, we aim to facilitate the use of machine learning to wildlife species and to encourage discussion between the scientists of the fields of oncology and ecology. We highlight the importance of international and pluridisciplinary collaborations to collect high‐quality datasets on which efficient machine learning algorithms can be trained.
The Winter Heat Budget of Sea Ice in Kotzebue Sound: Residual Ocean Heat and the Seasonal Roles of River Outflow

Arctic sea ice impacts the lives of people around the globe, from those who consider it home to those who have never seen it. It is a habitat for the hunted, a garden for the hungry, a bridge between places, a buffer against raging seas (Fang et al., 2018; Gearheard et al., 2013). It is a reflective cap on the top of the world that helps keep oceans cooling and jet streams moving (Francis & Vavrus, 2015; Screen & Simmonds, 2010). And as sea ice cover shrinks, it becomes ever more important to understand the intricacies of interactions between the atmosphere, the ice, and the water below. In this study, we present measurements of the processes affecting the formation and melt of coastal sea ice under the influence of a river outflow, made collaboratively by scientists and Indigenous Elders from the community of Kotzebue, Alaska. In doing so, we weave together potential implications of a rapidly warming Arctic at multiple scales, from livelihoods Abstract The winters of 2017/18 and 2018/19 saw unprecedented sea ice minima in the Bering and Chukchi Seas. Kotzebue Sound, located in the southeastern corner of the Chukchi Sea, remained largely ice-free throughout the winter in a stark departure from both recorded history and Indigenous oral history. The only sea ice that persisted throughout the winter was landfast ice and most of this occupied a small corner of the Sound near the outflow of the Noatak and Kobuk Rivers. We present oceanographic and atmospheric time series from a heavily instrumented “ice-tethered observatory” located on this landfast ice above the river outflow channel. This observing station was deployed as part of the Ikaaġvik Sikukun (Iñupiaq for “Ice Bridges”) project, in which hypotheses and subsequent observational programs were co-produced in partnership with an Indigenous Elder Advisory Council in Kotzebue. The measurements allow us to quantify the heat budget of the ice above the outflow channel, and identify the ocean as the primary source of heat contributing to thinning of the ice, which began in early February and accelerated rapidly before the recovery of the station in April of 2019. In concert with the ice-tethered observations, we use 2 years of oceanographic measurements from the mouth of Kotzebue Sound to characterize the influence of river outflow on the heat budget of the landfast ice, revealing a seasonal pattern in which the rivers promote ice formation and growth in the fall/winter, but help to drive melting of the ice in spring and early summer.
Comparison of a Neutral Density Model With the SET HASDM Density Database

The EXospheric TEMperatures on a PoLyhedrAl gRid (EXTEMPLAR) method predicts the neutral densities in the thermosphere. The performance of this model has been evaluated through a comparison with the Air Force High Accuracy Satellite Drag Model (HASDM). The Space Environment Technologies (SET) HASDM database that was used for this test spans the 20 years 2000 through 2019, containing densities at 3 hr time intervals at 25 km altitude steps, and a spatial resolution of 10° latitude by 15° longitude. The upgraded EXTEMPLAR that was tested uses the newer Naval Research Laboratory MSIS 2.0 model to convert global exospheric temperature values to neutral density as a function of altitude. The revision also incorporated time delays that varied as a function of location, between the total Poynting flux in the polar regions and the exospheric temperature response. The density values from both models were integrated on spherical shells at altitudes ranging from 200 to 800 km. These sums were compared as a function of time. The results show an excellent agreement at temporal scales ranging from hours to years. The EXTEMPLAR model performs best at altitudes of 400 km and above, where geomagnetic storms produce the largest relative changes in neutral density. In addition to providing an effective method to compare models that have very different spatial resolutions, the use of density totals at various altitudes presents a useful illustration of how the thermosphere behaves at different altitudes, on time scales ranging from hours to complete solar cycles.