Miracle-dz/UniFAR-FTUs huggingface dataset:
Reply to RC1: 'Review of "On the role of trend and variability of hydroxyl radical (OH) in the global methane budget"'

study investigates the impact of CCM calculated on the long-term trend and interannual variability in global as inferred from inversions. concluded that accounting for the CCM simulated trend in a significantly larger increase in methane emissions in the past decades than previous estimates that did not consider changes in OH. In addition, correlations between OH variations and El Nino are found that reduce the variability in inferred emission, notably in the Tropics. view Abstract Decadal trends and interannual variations in the hydroxyl radical (OH), while poorly constrained at 30 present, are critical for understanding the observed evolution of atmospheric methane (CH 4 ). Through analyzing the OH fields simulated by the model ensemble of the Chemistry-Climate Model Initiative (CCMI), we find (1) the negative OH anomalies during the El Niño years mainly corresponding to the enhanced carbon monoxide (CO) emissions from biomass burning and (2) a positive OH trend during 1980-2010 dominated by the elevated primary production and the reduced loss of OH due to decreasing 35 CO after 2000. Both two-box model inversions and variational 4D inversions suggest that ignoring the negative anomaly of OH during the El Niño years leads to a large overestimation of the increase in global CH 4 emissions by up to 10±3Tg yr -1 to match the observed CH 4 increase over these years. Not accounting for the increasing OH trends given by the CCMI models leads to an underestimation of the CH 4 emission increase by 23±9Tg yr -1 from 1986 to 2010. The variational inversion estimated CH 4 emissions show that 40 the tropical regions contribute most to the uncertainties related to OH. This study highlights the significant impact of climate and chemical feedbacks related to OH on the top-down estimates of the global CH 4 budget.
Controllable Synthesis of Vapor-Liquid-Solid Grown GaN Nanowires for Photoelectrochemical Water Splitting Applications

Vapor-liquid-solid (VLS) grown single crystalline GaN nanowire (NW) arrays of controllable aspect ratio and density were employed as the working photoanode for photoelectrochemical water splitting (PEC-WS) applications. The NW density of GaN NWs was controlled by tuning the different VLS growth parameters, while the aspect ratio was depending on the growth time. We observed a significant increase of the photocurrent with denser GaN NWs due to the enhanced light trapping effects; however, the PEC-WS process stability was degraded with time. Further increase of the photocurrent by about 34% at 1 Volt and a better time-dependent PEC-WS stability were achieved by increasing the aspect ratio of the GaN NWs due to the enhanced surface-to-volume ratio of the photoanode. The applied bias photon-to-current efficiency of the GaN NWs revealed a great shift of the optimal potential to lower values with enhanced NW lengths, indicating an interesting modification in the photoanode performance with increasing the aspect ratio of the GaN NWs. © The Author(s) 2015. Published by ECS. This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 License (CC BY, http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse of the work in any medium, provided the original work is properly cited. [DOI: 10.1149/2.0831504jes] All rights reserved.
Composition and Antimicrobial Activity of the Leaf Essential Oil of Machilus obovatifolia From Taiwan

Abstract The leaf essential oils of Machilus obovatifolia were isolated and analyzed using either the hydrodistillation or headspace-GC methods to determine their compositions and yields. Antimicrobial activities of the leaf oils obtained were also evaluated. Ninety-three compounds were identified in the hydrodistilled leaf oil, and 74 compounds were identified by the headspace-GC and GC/MS method, respectively. The main components of the oils were β-caryophyllene (10.5%), β-phellandrene (7.8%), τ-muurolol (5.3%), α-phellandrene (5.1%) and δ-cadinene (5.0%). The leaf oils exhibited excellent antimicrobial activities.
The Effect of Magnetic Twist and Plasma Flow on the Seismology of Oscillating Flux Tubes

In this paper, we study propagating and standing magnetohydrodynamic kink waves in a magnetically twisted and flowing flux tube. The plasma flow is in the direction of the magnetic field; outside the tube, the magnetic field lines are purely longitudinal. The plasma density is constant both inside and outside the tube, and all stationary quantities vary abruptly at the tube boundary. Using the thin tube and weak twist approximations, the dispersion relation, which determines the dependence of the oscillation frequency on the wavenumber and the stationary quantities of the tube, is found. It is shown that the oscillation frequency of the propagating forward (backward) waves increases (decreases) as the flow speed increases; also, the increase in the magnetic twist increases the oscillation frequency of both forward and backward propagating waves. For very weak magnetic twist, the oscillation frequency of both fundamental mode and first overtone standing waves decreases with increasing flow speed. Our results show that if only the plasma flow or magnetic twist is present in the flux tube, the period ratio P1/P2 of the fundamental mode to the first overtone of the standing kink waves is equal to 2, but if both the plasma flow and magnetic twist are present in the flux tube, the period ratio can be smaller than 2 and support some observational cases. The asymmetry of the wave about the apex point is not affected much by the magnetic twist, but the magnetic twist causes an overestimation of both the flow speed and kink speed of the coronal loop.
Parameter Optimization Analysis of Gmapping Algorithm Based on Improved RBPF Particle Filter

Aiming at the problem of inaccurate mapping caused by the robot hardware configuration that cannot meet the needs when the Gmapping algorithm is used for indoor mapping, a method for optimizing algorithm parameters is proposed. First, the principle of the Gmapping algorithm based on improved Rao-Blackwellised particle filtering is analyzed. By using the open source robot operating system (ROS) to run the Gmapping algorithm on a mobile robot, an indoor map is constructed. Then, by configuring different parameters, the accuracy of map construction is optimized. The experimental results show that by optimizing the algorithm parameters in an indoor environment, the algorithm can better adapt to the hardware configuration and improve the quality of the map.
Darks and Stripes: Effects of Clothing on Weight Perception

In many societies, appearing slim (corresponding to a small body-mass index) is considered attractive. The fashion industry has been attempting to cater to this trend by designing outfits that can enhance the appearance of slimness. Two anecdotal rules, widespread in the world of fashion, are to choose dark clothes and avoid horizontal stripes, in order to appear slim. Thus far, empirical evidence has been unable to conclusively determine the validity of these rules, and there is consequently much controversy regarding the impact of both color and patterns on the visual perception of weight. In this paper, we aim to close this gap by presenting the results from a series of large-scale crowdsourcing studies that investigate the above two claims. We gathered a dataset of around 1000 images of people from the Web together with their ground-truth weight and height as well as clothing attributes about colors and patterns. To elicit the effects of colors and patterns, we asked crowd workers to estimate the weight in each image. For the analysis, we controlled potential confounds by matching images in pairs where the two images differ with respect to color or pattern, but are similar with respect to other relevant aspects. We created image pairs in two ways: firstly, observationally, i.e., from two real images; and secondly, experimentally, by manipulating the color or pattern of clothing in a real image via photo editing. Based on our analysis, we conclude that dark clothes indeed decrease perceived weight slightly but statistically significantly, and horizontal stripes have no discernible effect compared to solid light-colored clothes. These results contribute to advancing the debate around the effect of specific clothing colors and patterns and thus provide empirical grounds for everyday fashion decisions. Moreover, our work gives an outlook on the vast opportunities of using crowd sourcing in the modern fashion industry.
Detection of Ethnic Minority’s Symbols Based on Deep Learning

Symbols are the embodiments of the ideology of ethnic minorities, the detection of ethnic minority’s symbols is an important part of the protection and inheritance of ethnic cultural heritage. Nevertheless, the ethnic symbols are miscellaneous and referred to some special combination style, it is time-consuming and inaccurate to detect them only by traditional detection methods based on machine learning. Therefore, in this paper, we propose a deep learning method based on Faster R-CNN to detect various kinds of Zhuang minority’s symbols from the Z-S dataset. Firstly, the original images of Zhuang minority’s symbols are prepocessed. Secondly, the processed images are sent to the ResNet-50 with FPN to extract feature maps. Moreover, RPN processes those feature maps to generate bounding boxes. Finally, ROI pooling layer in R-CNN converts those bounding boxes into fixed-length feature vectors, which fed into two sibling fully-connected layers for further detection tasks. Through the ablation experiments based on a certain amount of ethnic symbol images, the results indicate that the proposed method has higher detection quality (mAP), and can effectively reduce the training cost.
NiCo2S4 Nanosheets Grown on Nitrogen‐Doped Carbon Foams as an Advanced Electrode for Supercapacitors

To push the energy density limit of supercapacitors, a new class of electrode materials with favorable architectures is strongly needed. Binary metal sulfides hold great promise as an electrode material for high‐performance energy storage devices because they offer higher electrochemical activity and higher capacity than mono‐metal sulfides. Here, the rational design and fabrication of NiCo2S4 nanosheets supported on nitrogen‐doped carbon foams (NCF) is presented as a novel flexible electrode for supercapacitors. A facile two‐step method is developed for growth of NiCo2S4 nanosheets on NCF with robust adhesion, involving the growth of Ni‐Co precursor and subsequent conversion into NiCo2S4 nanosheets through sulfidation process. Benefiting from the compositional features and 3D electrode architectures, the NiCo2S4/NCF electrode exhibits greatly improved electrochemical performance with ultrahigh capacitance (877 F g−1 at 20 A g−1) and excellent cycling stability. Moreover, a binder‐free asymmetric supercapacitor device is also fabricated by using NiCo2S4/NCF as the positive electrode and ordered mesoporous carbon (OMC)/NCF as the negative electrode; this demonstrates high energy density (≈45.5 Wh kg−1 at 512 W kg−1).
A Microblog Unbalanced Data Evolution Analysis Method Based on LDA Model

As a social network platform, Microblog has a large amount of information that can be mined. Microblog differs from static texts, which is time-sensitive, and the popularity of themes is changing with time. In addition, the popularity of Microblog varies from theme to theme, and the number of Microblogs varies greatly. Therefore, it has obvious unbalance characteristics. The traditional LDA model can not mine the theme intensity evolution process, and the theme mixing phenomenon will appear in the theme of unbalanced data mining. In view of the above problems, this paper proposes a method to analyze the theme evolution law of unbalanced Microblog data by introducing time variable. The experiment shows that the problem of theme words mixing is effectively solved after the balance treatment. The introduction of time variables can mine Microblog popularity and theme intensity evolution process.
Sistem Pendukung Keputusan Rekrutmen Pada Perusahaan Outsourcing Berbasis Web

Computer technology and the internet do present many conveniences in business. Includes a web-based recruitment decision support system. In the current system, PT. X received a job application file sent to the office of PT. X, after that applicants await calls from PT. X work for the interview. Such conditions make the performance of PT. X work is considered ineffective and inefficient. Therefore we need a web-based employee recruitment system that can solve this problem. The method used in the construction of this system is the analytical method hierarchy process which is carried out in several stages, namely data collection, system requirements analysis, system analysis and design, system development, and ends with testing and revision. As a result, with a web-based recruitment system, the recruitment process is effective and efficient Keywords: Recruitment, Outsourcing, Employment, Decision Support Systems
The Sustainability Wage Gap

A large literature documents a positive correlation between a firm’s sustainability or ESG policies and firm value. However, the exact mechanism through which this relation arises remains ambiguous and it is often hard to establish the direction of causation. In this paper we propose and test the Sustainability Wage Gap channel through which firms can benefit from ESG investments by their ability to pay lower wages because of workers’ preferences for sustainable jobs. Using administrative employer-employee matched data from Sweden and a new measure that quantifies the environmental sustainability of different economic activities, we show that workers earn between 10-20% lower wages in more sustainable sectors. Motivated by survey evidence on the heterogeneity of workers’ preferences for sustainable jobs, we also show that this Sustainability Wage Gap is larger for highly talented workers and increasing over time. Providing a battery of additional tests, we argue that our evidence is difficult to reconcile with most alternative interpretations that have been suggested by previous literature.
Nondestructive Evaluation of Concrete within Nuclear Applications

Structural defects that develop over the lifetime of nuclear power plants (NPPs) could threaten the safety and security of the concrete infrastructure. Developing a reliable nondestructive evaluation (NDE) method to evaluate the damage in NPPs and their associated structures will greatly improve the long-term operation and safety of NPPs. The purpose of the US Department of Energy Office of Nuclear Energy’s Light Water Reactor Sustainability program is to develop technologies and other solutions that can improve the reliability, sustain the safety, and extend the operating lifetimes of NPPs beyond 60 years. This report describes the progress made toward growing NDE capabilities for NPPs. It also documents expanded collaborations with the Electric Power Research Institute (EPRI) and future opportunities to collaborate with industry partners (i.e., vendors and utilities) and university partners. These collaborations will continue to stand as a framework to provide value to stakeholders by reducing uncertainties and potential delayed maintenance costs in NPPs.
Knowledge-driven SPARQL Query Construction Based on Feedback Mechanism

According to the survey, the performance of the existing knowledge graph answering system in entity linking, relation linking and SPARQL query construction in terms of execution time and accuracy cannot meet the requirements of knowledge graph answering system. For this challenge, a new feedback mechanism based Knowledge-Driven query construction method is proposed. This method takes the entity set and predicate set in the problem as input, and constructs SPARQL query statements in a knowledge-driven way to solve simple and complex problems, and further proposes heuristic ideas to deal with implicit entity problems. At the same time, the method also proposes to feed back the query results of the knowledge graph to the entity link and relationship connection steps, so that the SPARQL query statement is optimized again. The evaluation results of the LC_QuAD data show that this method outperform the existing state of the art in precision and recall rate.
Network completion via deep metric learning

Completing networks is often a necessary step when dealing with problems arising from applications in incomplete network data mining. This paper investigates the network completion problem with node attributes. We proposed a new method called DeepMetricNC by exploiting the correlation between node attributes and the underlying network structure. In DeepMetricNC, the correlation is modeled as a nonlinear mapping from node attributes to the probability of edge existence. To obtain the mapping, deep metric learning is applied with batch training and random negative sampling. DeepMetricNC has linear training time complexity and can adapt to large-scale network completion tasks. Experiments of real networks show that DeepMetricNC completes network structures better than other methods, and is more suitable when the portion of the observed part is small.
Nanostructures of PAMAM Dendrimers in Drug Delivery System for 5-Fluorouracil

In this article, we studied five noncovalent structures for adsorption of 5 fluorouracil drug (5 FL) on poly(amidoamine) G0 generation dendrimer (PAMAMG0) carrier using M06-2X and B3LYPfunctionals. We investigate the quantum molecular descriptors and the binding and solvation energies in gas phase and aqueous solution. The energetic stability of non-bonded species (PAMAMG0/5-FL1-5) was shown through evaluation of binding free energies. The solvation free energies of PAMAMG0/5-FL1-5 are negative, indicating that the solvation process is spontaneous. We considered quantum molecular descriptors such as electrophilicity power and global hardness and found reduced toxicity of 5-FL drug near PAMAMG0 carrier as well as facilitated drug release. The AIM (Atoms In Molecule) analysis for all PAMAMG0/5-FL1-5 structures demonstrated that the pseudo-hydrogen and hydrogen bonds are essential in the functionalization of PAMAMG0 with 5-FL drug. We found thatthe structure in which 5-FL drug interacts with CO functional groups of PAMAMG0 is the most stable configuration.
Hydrogen Adsorption on Palladium and Platinum Overlayers: DFT Study

Hydrogen adsorption on twenty different palladium and platinum overlayer surfaces with (111) crystallographic orientation was studied by means of periodic DFT calculations on the GGA-PBE level. Palladium and platinum overlayers here denote either the Pd and Pt mono- and bilayers deposited over (111) crystallographic plane of Pd, Pt, Cu, and Au monocrystals or the (111) crystallographic plane of Pd and Pt monocrystals with inserted one-atom-thick surface underlayer of Pd, Pt, Cu, and Au. The attention was focused on the bond lengths, hydrogen adsorption energetics, mobility of adsorbed hydrogen, and surface reactivity toward hydrogen electrode reactions. Both the ligand and strain effects were considered, found to lead to a significant modification of the electronic structure of Pd and Pt overlayers, described through the position of the d-band center, and tuning of the hydrogen adsorption energy in the range that covers approximately 120 kJmol−1. Mobility of hydrogen adsorbed on studied overlayers was found to be determined by hydrogen-metal binding energy. Obtained results regarding Pd layers on Pt(111) and Au(111) surfaces, in conjunction with some of the recent experimental data, were used to explain its electrocatalytic activity towards hydrogen evolution reaction.
It Ain’t Just What Funds Disclose (It's The Way That They Do It)

The finance and accounting literature document how operating company disclosure tone and textual attributes affect firm performance, earnings persistence, stock volatility, capital costs, and retail investor behavior. In this Article, we explore the applicability of these theories to mutual fund disclosure, examining the extent to which disclosure tone influences fund risk and performance. Following (Loughran and McDonald 2011), we first develop customized dictionaries (word lists) specific to mutual fund disclosure language. We also introduce a novel sentiment scoring framework that generates a transparent sentence and disclosure-level score for our sample of 132,000 mutual fund summary prospectuses (497k) from 2010-2018. Our results demonstrate that investment strategy (IS) sections differ in tone and function from principal risk (PR) sections, results that we explore in conjunction with Tucker and Xia (2020) examination of disclosure readability. In particular, we find IS sections are more negative than PR sections—a trend that only increases over time. We also note differences in sentiment scores among CRSP categories, suggesting that asset class drives disclosure tone. A fixed effect regression analysis explores the effect of disclosure tone on fund performance and risk.
Scalable Optimization Approach of Optimal PV Sizing Problem for Minimizing Network Loss and Inverter Loss

The use of photovoltaics (PV) in electric power networks has increased because of advantages such as power loss reduction, environmental friendliness, voltage proﬁle improvement, and postponement of system upgrades. However, using PVs of an inappropriate size leads to greater power losses due to variations in PV outputs and demand loads. This paper formulates the optimal sizing PV problem (OPSP) for minimizing inverter losses and network losses. The OPSP is a large-scale and non-convex optimization that is diﬃcult to solve. To resolve this computational issue, this paper proposes a new approach that consists of two steps. The ﬁrst step is to relax the original non-convex problem into a convex one and to prove that the convexiﬁcation is exact provided that over-satisfaction of the load is allowed. The second step is to decompose the original large-scale optimization into small-scale sub-problems. Moreover, the decomposed sub-problems can be computed in parallel. Numerical simulations implementing parallel processing verify the eﬀectiveness of our approach.
Research on the Classification of Aviation Safety Reports Based on Text and Knowledge Graph

The existing automatic classification methods of aviation safety reports are mainly traditional machine learning classification algorithms, which have the disadvantages of relying on manual feature extraction and selection, unable to consider domain information and dealing with complex dependencies, which limits the improvement of classification accuracy. Therefore, this paper proposes an automatic classification algorithm for aviation safety reports that combines text and knowledge graph. The algorithm uses the knowledge triples with rich relationships as the background knowledge in the aviation safety field into the word2vec word vector training process so that the words can not only learn context information in training but also learn the complex semantics between words in the aviation field information, so that the trained word vectors can express richer semantic features, thereby improving the classification effect. Through a series of comparative experiments, the results show that the algorithm has the highest F1-score value and has a classification accuracy rate of up to 91.4%.