Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantifying the inherent uncertainty associated with nucleation rates estimated from induction time data measured in small volumes

Unravelling the molecular complexities of crystal nucleation from solutions is predicated on our ability to measure and interpret high quality kinetic data. This allows us to link nucleation rates to supersaturation, as well as to kinetic rate expressions and their parameters, arising from mechanistic considerations. In this context it is vital to be able to assess the reliability of measured nucleation rate data. Accordingly this contribution details a statistical approach that aims at quantifying the inherent uncertainty associated with nucleation rates obtained from induction time measurements carried out in small volumes. We investigate how uncertainties attached to nucleation rates propagate to mechanistic parameters derived from them and make recommendations for experimental protocols as well as data analysis strategies that minimize said uncertainty. The approach is applied to induction time measurements obtained for benzoic acid/toluene solutions in a wide range of supersaturations.
A novel filter feature selection method for text classification: Extensive Feature Selector

As the huge dimensionality of textual data restrains the classification accuracy, it is essential to apply feature selection (FS) methods as dimension reduction step in text classification (TC) domain. Most of the FS methods for TC contain several number of probabilities. In this study, we proposed a new FS method named as Extensive Feature Selector (EFS), which benefits from corpus-based and class-based probabilities in its calculations. The performance of EFS is compared with nine well-known FS methods, namely, Chi-Squared (CHI2), Class Discriminating Measure (CDM), Discriminative Power Measure (DPM), Odds Ratio (OR), Distinguishing Feature Selector (DFS), Comprehensively Measure Feature Selection (CMFS), Discriminative Feature Selection (DFSS), Normalised Difference Measure (NDM) and Max–Min Ratio (MMR) using Multinomial Naive Bayes (MNB), Support-Vector Machines (SVMs) and k-Nearest Neighbour (KNN) classifiers on four benchmark data sets. These data sets are Reuters-21578, 20-Newsgroup, Mini 20-Newsgroup and Polarity. The experiments were carried out for six different feature sizes which are 10, 30, 50, 100, 300 and 500. Experimental results show that the performance of EFS method is more successful than the other nine methods in most cases according to micro-F1 and macro-F1 scores.
Urban Wage Premium Revisited: Evidence from Japanese matched employer-employee data

Recent studies on the empirics of agglomeration economies have assumed a two-step channel of the urban wage premium in which agglomeration increases total factor productivity (TFP) and results in higher wages. Therefore, the present study empirically examines the validity of this two-step channel via TFP and investigates other channels of the urban wage premium by using matched employer-employee data in the Japanese manufacturing sector. The findings show that the standard wage regression approach used in the literature captures not only the TFP channel of the urban wage premium but also other effects (e.g., firm size effects on wage). Furthermore, when this TFP channel is quantified, the city size elasticity of wage becomes smaller than those in the existing literature. The implication of this study is that, by exploiting the features of the Japanese employment system, it is possible to obtain interesting results concerning the urban wage premium, especially in regard to basic pay and bonuses.
An iterative reconstruction method for CT metal artifact reduction using L1 norm data fidelity and nonlocal TV regularization

This paper deals with the so-called Metal Artifact Reduction (MAR) in CT. This problem aims at reconstructing a CT image with reduced metal induced artifact when the object contains metallic parts inside. We propose a new iterative reconstruction method to the MAR problem, which uses the L1 norm for data fidelity term and Nonlocal TV regularization. In ordinary iterative reconstruction for CT, the least-squares error || A →x - →b|| 22 Is used as data fidelity term for image reconstruction. However, it is well-known that the least-squares criterion is sensitive to the existence of abnormal (inconsistent) data in the measurement →b, such as projection data passing through the metallic parts in this work. A simple reasonable method to identify the location of metallic parts in the sinogram and exclude the corresponding projection data from the data fitting is to use the L1 norm error || A →x - →b|| 11 . Furthermore, the power of proposed method to reduce the metal artifact can be significantly improved by adding Nonlocal Total Variation (NLTV) regularization term into the cost function. Compared to existing approaches to the MAR problem, the proposed method possesses the following attractive feature. Almost every approach to MAR consists of two-step computations. The first step detects the metallic parts in the sinogram and the second step performs image reconstruction after interpolating or excluding the projection data corresponding to the identified metallic parts. On the other hand, the proposed method consists of only a single computational step, i.e. single iterative minimization of a convex cost function, leading to smartly unifying the two steps into a single step.
The Profitability of Revenue-Based Quotas under Price Negotiation

We study how the presence of a monthly revenue-based quota impacts a retailer’s profits when prices are negotiated by a salesperson. Using transaction level data for refrigerators, we first provide reduced-form evidence that prices are impacted by the quota: the negotiated discounts are approximately 3.8% higher if the salesperson is 10% closer to reaching the quota in the final week of the month. Guided by this result, we specify and estimate a demand model that identifies the impact of the quota through two forces: the effort salespeople expend in order to sell the product and their bargaining position. Results indicate that, as salespeople get closer to reaching their quota, their effort increases regardless of the week, and their bargaining position weakens (i.e., they offer lower prices), but only in the final week of the month. We use these results to analyze the impact of the quota and find that, holding salespeoples’ total compensation fixed, eliminating quotas results in 8% lower profit for the retailer. This decrease stems primarily from the reduction in effort that outweighs any benefit from strengthening the salespeoples’ bargaining position. The change in profit is economically meaningful because eliminating both price negotiation (i.e., moving to fixed pricing) and the quota results in an up to 36% reduction in profit. This paper was accepted by Matthew Shum, marketing.
Assembly of 1H-pyrrole from cyclopentanone oxime and acetylene: a quantum-chemical model

Quantum-chemical modeling of the mechanism of 1,4,5,6-tetrahydrocyclo-penta[b]pyrrole assembly from cyclopentanone oxime and acetylene has been carried out. The kinetic and thermodynamic characteristics of all reaction stages are calculated. The computation results have revealed a fundamental possibility of 1,4,5,6-tetrahydro-cyclopenta[b]pyrrole formation via the Trofimov reaction.
Pipelines and Systems for Threshold-Avoiding Quantification of LC–MS/MS Data

The accurate processing of complex liquid chromatography coupled to tandem mass spectrometry (LC–MS/MS) data from biological samples is a major challenge for metabolomics, proteomics, and related approaches. Here, we present the pipelines and systems for threshold-avoiding quantification (PASTAQ) LC–MS/MS preprocessing toolset, which allows highly accurate quantification of data-dependent acquisition LC–MS/MS datasets. PASTAQ performs compound quantification using single-stage (MS1) data and implements novel algorithms for high-performance and accurate quantification, retention time alignment, feature detection, and linking annotations from multiple identification engines. PASTAQ offers straightforward parameterization and automatic generation of quality control plots for data and preprocessing assessment. This design results in smaller variance when analyzing replicates of proteomes mixed with known ratios and allows the detection of peptides over a larger dynamic concentration range compared to widely used proteomics preprocessing tools. The performance of the pipeline is also demonstrated in a biological human serum dataset for the identification of gender-related proteins.
Gold (III)-(Chloro)-Mono or Bis-(Triphenylphosphine) - (Naphthyl-Azo-Imidazole/ Benzimidazole/Pyridine) Complexes: Synthesis, Spectroscopic Study and Cyclic Voltammetric Study

Reaction of Gold(III) tetrachloride H[Au(Cl 4 )] with NaaiR ´ in Acetone medium following ligand ( Triphenyl phosphine, PPh 3 ) addition leads to [Au(PPh 3 ) 2 (NaaiR / )] and [Au(PPh 3 )(Cl)(NaaiR / )], [ NaaiR / = naphthyl-azo imidazole /benzimidazole /pyridine = C 10 H 4 -N=N- / C 3 H 2 -NN-1-R / , (R imidazole) / C 7 H 4 -NN-1-H (Benzimidazole),/ C 3 H 4 -N-(Pyridine), abbreviated as N,N / -chelator, where N(imidazole) and N(azo) represent N and N / , respectively; R / = H( a ), Me ( b ), PPh 3 = Triphenyl phosphine, PPh 3 ]. The 1 H NMR spectral measurements suggest the molecular structure of bis chelated complex with the protons at the aromatic region and naphthyl protons at higher ∂ value. The voltammogram also shows a good result and support the complex formation.
The BOJ's ETF Purchases and Its Effects on Nikkei 225 Stocks

This paper examines the impacts of the Bank of Japan's (BOJ) exchange-traded funds (ETFs) purchasing program that has been conducted since December 2010. The program is a part of the BOJ's unconventional monetary policy and has accelerated since the introduction of the Quantitative and Qualitative Easing in April 2013. In this study, the influence of underlying stocks is assessed by comparing the performance of the stocks (those included in the Nikkei 225 and others) using a difference-in-difference analysis. We also separate morning and afternoon returns to control for the fact that the BOJ tends to purchase ETFs when performance of the stock market is weak, in the morning session. We find that the Nikkei 225 component stocks' afternoon returns are significantly higher than those of non-Nikkei 225 stocks when the BOJ purchases ETFs. However, the subsample analysis demonstrates that the impact on Nikkei 225 stock returns becomes smaller over time despite the growing purchase amounts. Overall, our results indicate that the cumulative treatment effects on the Nikkei 225 are around 20% as of October 2017.
Study on the Integral Compensator Using Supercapacitor for Energy Harvesting in Low-Power Sections of Solar Energy

The risk of environmental pollution is a consequence of every kind of energy, including fossil fuels, nuclear power plants, and thermoelectric power plants. For the purpose of reducing the use ratio of such energy, research on eco-friendly energy is being actively carried out, and has shown that among all kinds of energy, solar energy has an advantage: it can supply us with inexhaustible clean energy. However, since solar energy depends on sunlight, the output may be unstable as it is influenced by weather or surrounding structures. In this paper, there is presented a control system which transmits power to a storage device, in a specific state, after the energy of the low-illumination section is charged in a supercapacitor using the accumulation-type controller by use of a supercapacitor. Feedback from the power output of photovoltaic panels (PVs) demonstrates that the power of the low-illumination section can be charged without being discarded. The charging rate was compared with other solar controllers being sold on the market, and the comparison was made through state of charge (SOC) measurements after the battery had been charged by photovoltaic panels for a whole day. It was confirmed that the solar controller, by use of supercapacitor integrator proposed in this paper, stored higher levels of energy than the existing solar controllers over the same hours and under the same conditions.
Numerical and physical modeling of extreme waves at Wave Hub

ABSTRACT Ransley, E. , Hann, M. , Greaves, D. , Raby, A. and Simmonds, D. , 2013. Numerical and physical modelling of extreme waves at Wave Hub With a history of international failures, the survivability of coupled systems of wave energy devices and their moorings, particularly those to be installed at development sites like Wave Hub, is surrounded by uncertainty. Potential design solutions require a better understanding of the hydrodynamics and structural loading experienced during extreme events, like rogue wave impact, in order to mitigate the risk of device and mooring failure. Rogue waves are waves with amplitudes far greater than those expected, given the surrounding sea conditions. Intense study into these events stems from their potential for catastrophic impact on ocean engineering structures. However, little is known about their physical origins and, currently, there is no consensus on their definition or explanation of the mechanism which drives them. This paper concerns the numerical modeling and experimental validation of extreme rogue wave examples at the Wave Hub site. Using hindcast data, the 100 year extreme wave at the Wave Hub site is determined. This extreme wave is replicated in Plymouth University's new COAST Lab using a NewWave, dispersive focusing input. To simulate and analyse these events, we duplicate these conditions in a numerical wave tank (NWT), solving the fully nonlinear Navier-Stokes equations, with a free surface, using the Volume of Fluid (VoF) method and open source CFD library OpenFOAM®. The comparison shows that the CFD software is capable of simulating focused waves similar to those produced in the physical tank but tends to overestimate the crest heights. It is also noted that nonlinear effects are important when considering the shape and location of focused wave events.
International Research Conference on Food , Nutrition , and Cancer The Chemopreventive Bioflavonoid Apigenin Modulates Signal Transduction Pathways in Keratinocyte and Colon Carcinoma Cell Lines 1 , 2

Apigenin is a nonmutagenic chemopreventive agent found in fruits and green vegetables. In this study, we used two different epithelial cell lines (308 mouse keratinocytes and HCT116 colon carcinoma cells) to determine the effect of apigenin on the mitogen-activated protein kinase (MAPK) cascade. Apigenin induced a dose-dependent phosphorylation of both extracellular signal-regulated protein kinase (ERK) and p38 kinase but had little effect on the phosphorylation of c-jun amino terminal kinase (JNK). We used immunoprecipitation-coupled kinase assays to show that apigenin increased the kinase activity of ERK and p38 but not JNK. Consistent with these results, we found that apigenin induced a 7.4-fold induction in the phosphorylation of Elk, the downstream phosphorylation target of ERK kinase. Similarly, apigenin induced a 3.2-fold induction in the phosphorylation of activating transcription factor-2, the downstream phosphorylation target of p38 kinase. Little change was observed in the phosphorylation of c-jun, the phosphorylation target of JNK. These data suggest that part of the chemopreventive activity of apigenin may be mediated by its ability to modulate the MAPK cascade. J. Nutr. 133: 3800S–3804S, 2003.
Single stage ultrafiltration for enhanced reverse selectivity in a binary protein system

ABSTRACT In single stage operation, high quality separation of similar size proteins was performed via batch ultrafiltration. Two permeable proteins, hemoglobin and ovalbumin, were separated using a 100 kDa polyethersulfone membrane for a volume concentration factor of 3.5. At low ionic strength, hemoglobin and ovalbumin showed their enhanced reverse selectivity of 11 and selectivity of 14 at pH 9 and 3.5, respectively. Protein adsorption on the membrane during ultrafiltration was carefully controlled by fine adjustment of pH and ionic strength of feed. The high separation factor was achieved as a result of pore constriction by protein adsorption and the electrostatic interaction between proteins and the membrane.
Improvement in Classification Algorithms through Model Stacking with the Consideration of their Correlation

In this research we analyzed the performance of some well-known classification algorithms in terms of their accuracy and proposed a methodology for model stacking on the basis of their correlation which improves the accuracy of these algorithms. We selected; Support Vector Machines (svm), Naive Bayes (nb), k-Nearest Neighbors (knn), Generalized Linear Model (glm), Latent Discriminant Analysis (lda), gbm, Recursive Partitioning and Regression Trees (rpart), rda, Neural Networks (nnet) and Conditional Inference Trees (ctree) in our research and preformed analyses on three textual datasets of different sizes; Scopus 50,000 instances, IMDB Movie Reviews having 10,000 instances, Amazon Products Reviews having 1000 instances and Yelp dataset having 1000 instances. We used R-Studio for performing experiments. Results show that the performance of all algorithms increased at Meta level. Neural Networks achieved the best results with more than 25% improvement at Meta-Level and outperformed the other evaluated methods with an accuracy of 95.66%, and altogether our model gives far better results than individual algorithms’ performance.
Effects of R&D Investments and Market Signals on International Acquisitions: Evidence from IPO Firms

We investigate how intangible assets in the form of R&D influence firms’ hazards of engaging in international acquisitions. On the one hand, previous research has noted that the tacit and redeployable nature of R&D investments may prompt firms to expand their operations overseas and create value from international acquisitions. On the other hand, it is difficult for other firms to evaluate the quality and prospects of an acquirer’s intangible resources, thereby hampering its ability to finance and execute international M&A deals. In the context of international acquisitions undertaken by firms just completing their initial public offerings (IPOs), we argue and find that the IPO firm’s engagement in post-IPO international acquisition activity is generally negatively related to its R&D intensity. This effect contrasts previous arguments on the internalization advantages possessed by R&D-intensive firms. We also argue that firms able to convey their resources and prospects through such signals as previous international alliances and foreign sales can mitigate information problems presented by their intangibles, and thus carry out and benefit from cross-border acquisitions. We therefore identify an unexamined tradeoff that R&D investments present in the international M&A context and discuss how international signals can facilitate cross-border transactions subject to various market frictions.
Face Manipulation with Generative Adversarial Network

Generative adversarial network has appeared as an effective image manipulation tool in recent years and has been widely used. The GAN-based manipulation of face images is also possible and tools including DeepFake are already misused. In this paper, we discuss the pros and cons of face manipulation with generative adversarial network. We find that this technique can be very useful for recovering masked face and further improving face recognition accuracy.
Detection of vortex coherent structures in superfluid turbulence

Filamentary regions of high vorticity irregularly form and disappear in the turbulent flows of classical fluids. We report an experimental comparative study of these so-called “coherent structures” in a classical vs. quantum fluid, using liquid helium with a superfluid fraction varied from 0% up to 83%. The low-pressure core of the vorticity filaments is detected by pressure probes located on the sidewall of a 78-cm-diameter von Kármán cell driven up to record turbulent intensity . The statistics of occurrence, magnitude and relative distribution of the filaments in a classical fluid are found indistinguishable from their superfluid counterpart, namely the bundles of quantized vortex lines. This suggests that the internal structure of vortex filaments, as well as their dissipative properties have a negligible impact on their macroscopic dynamics, such as lifetime and intermittent properties.
Generalized Kramers–Wannier duality for spin systems with non-commutative symmetry

In 1941 H Kramers and G Wannier discovered a special symmetry which relates low-temperature and high-temperature phases in the planar Ising model. The corresponding transformation, the Kramers–Wannier transform, is a special non-local substitution in the partition function. The existence of such transformations is a general property of lattice spin systems. Generalization of the KW transform to spin systems with non-Abelian symmetry is essential for many problems in statistical physics and field theory. This problem is very difficult and cannot be carried out by classical methods (like Fourier transform in the commutative case). We present new results which solve this problem for finite non-Abelian groups.
Development of the Acceptance towards Lesbian, Gay, Bisexual, and Transgender Scale

This study describes the stepwise development of the Acceptance Towards Lesbian, Gay, Bisexual, and Transgender Scale in the Indian context. Indian society has a strong focus on collectivism, differentiating it from the western culture of individualism. An open-ended survey, followed by focused group discussions were conducted to understand the attitude, behaviour, and opinions towards the LGBT community. The results of the qualitative study helped in the formulation of a close-ended survey. Further, exploratory factor analysis was deployed on the results of the close-ended survey. This yielded a scale with an 18-item distribution among three acceptance measuring parameters: knowledge and awareness; perception of human rights; and inclusion in daily life, which demonstrate multidimensional aspects of acceptance in India. The three parameters were strongly correlated with each other implying strong interdependence of these parameters.
Geoinformation laboratory for determining objects by unmanned aerial vehicles

The paper considers the problem of positioning unmanned aerial vehicles (UAVs) without using satellite navigation systems. The application of positioning occurs through the visual channel of flight control by means of an on-Board video camera on the basis of the terrain plan or satellite photos. The aim of the work is to improve the accuracy of the positioning method in accordance with several flight control positions, predetermined landmarks and closed route flight (both open or obstructed ways), for implementation in control computer systems or mobile applications. The obtained results will improve the effectiveness of UAV technology in applications for environmental monitoring, construction monitoring, agriculture, maritime industry, forestry, law enforcement, urban and municipal economy.
Range‐wide population viability analyses reveal high sensitivity to wildflower harvesting in extreme environments

1. The ecological effects of harvesting from wild populations are often uncertain, es pecially since the sensitivity of populations to harvesting can vary across species’ geographical ranges. In the Cape Floristic Region (CFR, South Africa) biodiversity hotspot, wildflower harvesting is widespread and economically important, provid ing an income to many rural communities. However, with very few species studied to date, and without considering range-