Miracle-dz/UniFAR-FTUs huggingface dataset:
to Leeds Beckett Repository record

: An extension of the baseline Non-Intrusive Load Monitoring approach for energy disaggregation using temporal contextual information is presented in this paper. In detail the proposed approach uses a two-stage disaggregation methodology with appliance-specific temporal contextual information in order to capture time varying power consumption patterns in low frequency datasets. The proposed methodology was evaluated using datasets of different sampling frequency, number and type of appliances. When employing appliance-specific temporal contextual information an improvement of 1.5% up to 7.3% was observed. With the two-stage disaggregation architecture and using appliance-specific temporal contextual information the overall energy disaggregation accuracy was further improved across all evaluated datasets with the maximum observed improvement, in terms of absolute increase of accuracy, being equal to 6.8%, thus resulting in a maximum total energy disaggregation accuracy improvement equal to 10.0%.
Comparisons of Four Discrete Distributions in Count Regressions Using Elders’ Missing Teeth Data

Objectives: This present study aimed to select the best count distributions for missing teeth in elders and to investigate the relationship between missing teeth and the predictors. Materials and methods: Data were extracted from the biennial survey of 2015-2016 the U. S. National Health and Nutrition Examination Survey. Only adults aged 60 years or over who completed oral health examination and demographics interview were included. Descriptive statistics were used to demonstrate the basic information of this studied population. The performances of four different count regression models (Poisson regression, negative binomial regression with linear variance function (NB1), negative binomial regression with quadratic variance function (NB2), and zero-inflated negative binomial regression) were compared through different approaches including the values of model fit test statistics such as Akaike’s Information Criterion (AIC) and Bayesian Information Criterion (BIC), the magnitude of standard errors and a visual graph on the performances of fitted models. Results: The disparities on missing teeth existed in old adults by poverty and educational level and race/ethnicity. More missing teeth were found in participants who are Blacks (mean=13.89), with less education (<12 years) (mean: 13.11). Significance of t-test for “α” indicated that Poisson distribution is not appropriate for missing teeth due to overdispersion. NB1 is the best model with the smallest AIC and BIC and the smallest standard errors of parameter estimates compared to other three candidate models. Conclusion: The negative binomial distribution with linear variance function is the best distribution. Due to the fact of missing teeth which ranged from 0 to 28, the caution should be given when we interpreted the fitted model using NB1 as the missing teeth are close to 0 and 28. and ( ) , p i i i Var Y p μ αμ = + −∞ < < ∞ , where α is a constant [4], used to adjust the variance. Negative binomial distribution converges to Poisson distribution when α approaches to 0. In a binomial distribution, we can assume that a sequence of independent Bernoulli trials, and each trial has two possible outcomes called “success” with probability p and “failure” with probability 1-p. The random variable X is defined the number of success before a predetermined r of failures occurred, that is, X~NB(r, P). Two major forms of negative binomial distribution were known as linear (NB1) and quadratic (NB2) negative binomial distribution given by p=1 and p=2, respectively [5,6]. The outcome variable Yi given Xi is distributed as a negative binomial and its density function is denoted as:
Dissecting the Purchase-to-Pay Process: An exercise in Process Mining

The 2019 Business Process Intelligence (BPI) Challenge requires participants to analyse and understand the Purchase to Pay (PTP) process of a large multinational company operating in the area of coatings and paints within the Netherlands. The process owner aims to gain business insights through addressing a variety of business questions. We analysed the data using a variety of process mining and analytical tools. This report summarises our understanding of the event log, as well as our analytical approach, and the different techniques and steps undertaken to successfully answer the business questions. Wherever applicable, we also provided additional analysis and discussed the limitations associated with working with such data in the absence of a more comprehensive understanding of the underlying business context.
www.econstor.eu REAL TIME ECONOMETRICS

This paper considers the problems facing decision-makers using econometric models in real time. It identifies the key stages involved and highlights the role of automated systems in reducing the effect of data snooping. It sets out many choices that researchers face in construction of automated systems and discusses some of the possible ways advanced in the literature for dealing with them. The role of feedbacks from the decision-maker’s actions to the data generating process is also discussed and highlighted through an example. JEL classification: C51, C52, C53.
Handling Large Structural Costs in Neural Networks with Slack Rescaling

A cost-sensitive learning objective is often important for a neural model to achieve good performance in many structured problems. An ideal objective should penalize an incorrect label by its structural discrepancy and a correct label by zero. Since this is non-convex and non-differentiable, one typically turns to a convex surrogate loss in practice ( Tsochantaridis et al. , 2004). A widely used surrogate ismargin rescalingwhich promotes the score of the true label to be greater than the loss-augmented score of the best label. However, for problems with large structural costs, this formulation is not faithful to the ideal objective: even when all structures are correctly classified, the loss may remain high. Consequently, the model wastefully updates parameters on correct instances during training. In this work, we focus on an alternative cost-sensitive objective calledslack rescaling. Unlike margin rescaling, slack rescaling is invariant to the absolute values of structural costs and ignores labels that are already well-separated. This can be seen as a tighter approximation to the ideal objective. Inference with slack rescaling is in general intractable, but we adapt recent development of an efficient inference algorithm to the deep network. We evaluate our approach on neural graph-based dependency parsing and report promising results.
Loughborough University Institutional Repository Production of liposomes using microengineered membrane and co-ow micro uidic device

Two modified ethanol injection methods have been used to produce Lipoid ® E80 and POPC (1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine) liposomes: (i) injection of the organic phase through a microengineered nickel membrane kept under controlled shear conditions and (ii) injection of the organic phase through a tapered-end glass capillary into co-flowing aqueous stream using coaxial assemblies of glass capillaries. The organic phase was composed of 20 mg ml 1 of phospholipids and 5 mg ml 1 of cholesterol dissolved in ethanol and the aqueous phase was ultra-pure water. Self-assembly of phospholipid molecules into multiple concentric bilayers via phospolipid bilayered fragments was initiated by interpenetration of the two miscible solvents. The mean vesicle size in the membrane method was 803 nm and consistent across all of the devices (stirred cell, cross-flow module and oscillating membrane system), indicating that local or temporal variations of the shear stress on the membrane surface had no effect on the vesicle size, on the condition that a maximum shear stress was kept constant. The mean vesicle size in co-flow microfludic device decreased from 131 to 73 nm when the orifice diameter in the injection capillary was reduced from 209 to 42 m at the aqueous and organic phase flow rate of 25 and 5.55 ml h -1 , respectively. The vesicle size was significantly affected by the mixing efficiency, which was controlled by the orifice size and liquid flow rates. The smallest vesicle size was obtained under conditions that promote the highest mixing rate. Computational Fluid Dynamics (CFD) simulations were performed to study the mixing process in the vicinity of the orifice.
Strategyproof Mean Estimation from Multiple-Choice Questions

Given n values possessed by n agents, we study the problem of estimating the mean by truthfully eliciting agents’ answers to multiple-choice questions about their values. We consider two natural candidates for estimation error: mean squared error (MSE) and mean absolute error (MAE). We design a randomized estimator which is asymptotically optimal for both measures in the worst case. In the case where prior distributions over the agents’ values are known, we give an optimal, polynomial-time algorithm for MSE, and show that the task of computing an optimal estimate for MAE is #P-hard. Finally, we demonstrate empirically that knowledge of prior distributions gives a significant edge.
Socket-Based Coupling of OpenFOAM and Abaqus to Simulate Vertical Water Entry Of Rigid And Deformable Structures

for the 6th ESI OpenFOAM User Conference 2018, Hamburg Germany Socket-Based Coupling of OpenFOAM and Abaqus to Simulate Vertical Water Entry Of Rigid And Deformable Structures Maximilian Müller, Matthias Haupt, Peter Horst TU Braunschweig, Institute of Aircraft Design and Lightweight Structures, Hermann-Blenk-Str. 35, D-38108 Braunschweig, maximilian.mueller@tu-braunschweig.de, +49 531 391-9919 www.tu-braunschweig.de/ifl An important element of the process of aircraft certification is the demonstration of the crashworthiness of the structure in the event of an emergency landing on water, also referred to as ditching. Novel numerical simulation methods that account for mechanical fluid-structure interaction (FSI) open up a promising way to model ditching on a high-fidelity level. High demands are placed on those simulation methods due to the strong added-mass effect during ditching [1,2,8]. This work presents a numerical simulation approach combining OpenFOAM-5.0 and the proprietary finite element structural solver ABAQUS. The coupling data is exchanged by using a socket-based communication method as part of the distributed environment ifls [4]. A Dirichlet-Neumann approach is applied along with a fixed-point coupling method adopting the simple but yet efficient and robust Aitken accelerator [5]. The socket-based communication between OpenFOAM and ifls is realized by using an OpenFOAM function object. The modular structure of the function object is shown in figure 1. The structure is based on the function object for conjugate heat transfer analysis described in [7]. Figure 1: General code structure of OpenFOAM function object for mechanical FSI Abstract for the 6th ESI OpenFOAM User Conference 2018, Hamburg Germanyfor the 6th ESI OpenFOAM User Conference 2018, Hamburg Germany The iflsCommunicatorFunctionObject class accomplishes the external communication by calling special methods of the communicator class at predefined points of the simulation. The iflsCommunicatorFunctionObject class is directly derived from the general OpenFOAM fvMeshFunctionObject class. The SocketCommunication class is used by the communicator class to realize the socket-based communication. The Interface class contains the fluid-structure interface. The calculation of the fluid forces at the fluid-structure interface as well as the application of the structural displacements to the fluid mesh are realized by the mechanicalFSI class. The developed method is applied to solve the two-dimensional water impact of rigid and deformable circular cylinders as described in [3] and [6]. The calculated results are compared with experimental data. Prospects of extending the presented method to account for both highly nonlinear structural behaviour as well as hydrodynamic effects, thus allowing the ditching simulation of full aircraft configurations, are discussed.
Demo: SLEMI: Finding Simulink Compiler Bugs through Equivalence Modulo Input (EMI)

This demo presents usage and implementation details of SLEMI. SLEMI is the first tool to automatically find compiler bugs in the widely used cyber-physical system development tool Simulink via Equivalence Modulo Input (EMI). EMI is a recent twist on differential testing that promises more eﬃciency. SLEMI implements several novel mutation techniques that deal with CPS language features that are not found in procedural languages. This demo also introduces a new EMI-based mutation strategy that has already found a new confirmed bug in Simulink version R2018a. To increase SLEMI’s eﬃciency further, this paper presents parallel generation of random, valid Simulink models. A video demo of SLEMI is available at https://www.youtube. com/watch? v=oliPgOLT6eY.
India’s Trade Liberalization and Regional Labor Market Dynamics

In this paper we study the medium and long run effects of India’s 1991 trade reforms on local labor markets. Using regional variation in the industry composition and variation in tariff reductions across industries, we compare labor market outcomes between regions facing larger and smaller tariff reductions. For this purpose we use Census data and district as a proxy for the local labor market. We find that districts that experienced greater reduction in tariffs experienced increase in employment. More importantly this effect becomes larger over time. We find evidence for a relative increase in wages in the medium term but no such effect in the long term. JEL Codes: F13, F14, F16
Memory that Drives! New Insights into Forecasting Performance of Stock Prices from SEMIFARMA-AEGAS Model

Stock price forecasting, a popular growth-enhancing exercise for investors, is inherently complex – thanks to the interplay of financial economic drivers which determine both the magnitude of memory and the extent of non-linearity within a system. In this paper, we accommodate both features within a single estimation framework to forecast stock prices and identify the nature of market efficiency commensurate with the proposed model. We combine a class of semiparametric autoregressive fractionally integrated moving average (SEMIFARMA) model with asymmetric exponential generalized autoregressive score (AEGAS) errors to design a SEMIRFARMA-AEGAS framework based on which predictive performance of this model is tested against competing methods. Our conditional variance includes leverage effects, jumps and fat tail-skewness distribution, each of which affects magnitude of memory in a stock price system. A true forecast function is built and new insights into stock price forecasting are presented. We estimate several models using the Skewed Student-t maximum likelihood and find that the informational shocks have permanent effects on returns and the SEMIFARMA-AEGAS is appropriate for capturing volatility clustering for both negative (long Value-at-Risk) and positive returns (short Value-at-Risk). We show that this model has better predictive performance over competing models for both long and/or some short time horizons. The predictions from SEMIRFARMA-AEGAS model beats comfortably the random walk model. Our results have implications for marketefficiency: the weak efficiency assumption of financial markets stands violated for all stock price returns studied over a long period.
Fluid structure interaction during the Resin Transfer Molding (RTM) manufacturing process for continuous fiber reinforced composites

Structural sandwich components made of continuous fiber reinforced composites are increasingly demanded by the automotive industry. A fast, robust and highly automated process is needed to produce those parts economically. In the Resin Transfer Molding (RTM) manufacturing process, a liquid polymer resin infiltrates a dry fibrous preform, which behaves like anisotropic porous media. Additionally, polymer foam cores can be embedded between the fibrous layers before resin infiltration to manufacture sandwich parts. To avoid infiltration or crushing of the core material during the following injection, the fluid pressure inside the mold must not exceed a specific limit [1]. Mold filling simulations can help to understand the complex interaction of fluid pressure, preform deformation and foam core deformation, but a fluid structure interaction (FSI) between all components is mandatory for this purpose. The resin flow characteristics are based on the permeability of the preform, which depends on the fiber volume fraction and thus on the preform compaction, which changes during the infiltration. Furthermore, the viscosity of the polymer resin is dependent on temperature and cure degree of the ongoing polymerization. Those two parameters strongly influence the pressure inside the mold and therefore also the deformation of the foam core. By using the computational fluid dynamics (CFD) tool OpenFOAM, conventional RTM mold filling simulations can be conducted as a compressible two-phase flow through porous media. The finite volume (FV) based method was implemented and successfully validated in previous works of the authors [2, 3]. To simulate the mold filling with a deforming foam core, an FSI coupling between OpenFOAM and CalculiX is implemented using the preCICE coupling library [4]. An application example of mold filling simulations with a foam core shows the implementation of the FSI coupling. By a variation of process and material parameters like e.g. injection velocity, temperature or fiber volume fraction, the influence on the foam core deformation can be evaluated. In the future, this FSI coupling allows simulating the mold filling process with any deforming core material. This enables a sophisticated optimization of the manufacturing process regarding process time as well as part quality and therefore can increase the use of continuous fiber reinforced composites in the automotive industry.
Foreign Direct Investment, Markups, Prices and Productivity: Evidence from India

This paper uses a rich panel data set of Indian manufacturing firms to analyze the effects of horizontal and vertical foreign direct investment on various outcomes of domestic firms. We apply recent methodological advances in the estimation of production functions together with detailed product-level information on prices and quantities to estimate physical productivity, markups, marginal costs and proxies for product quality. We also employ instrumental variables based on initial allocations of foreign ownership across industries and exploit cross-industry and time-series variation in FDI reforms. Our preliminary results highlight the importance of price adjustments in identifying the productivity effects of FDI. JEL codes: F61, F23, G34, L25, D22, D24
Electroencephalogram (EEG) Sensor for Teleoperation of Domotics Applications via Virtual Environments

This article describes the development of a virtual environment used to simulate a domotic environment controlled by the acquisition of electroencephalographic signals, captured by the Emotive Epoc sensor. The base modules of this sensor are used for the identification of control actions oriented to the control of the actuation signals in the environment, validating the functionality of the tool through five test users, obtaining 87% accuracy in the execution of actions remote command of virtual environment.
Long noncoding RNA MACC1-AS1 is a potential sponge of microRNA-34a in cervical squamous cell carcinoma and upregulates cyclin-dependent kinase 6

The present study aimed to investigate the role of long noncoding RNA MACC1-AS1 in cervical squamous cell carcinoma (CSCC). In the present study MACC1-AS1 expression as analyzed using reverse transcription-quantitative PCR. The interactions between MACC1-AS1 and miR-34a was analyzed via overexpression experiments. Cell cycle and proliferation analyses were performed to analyze the roles of MACC1-AS1 in regulating cancer cell cycle progression and cell proliferation. It was observed that MACC1-AS1 was upregulated in CSCC, and its expression levels were elevated with the increase in clinical stage. Bioinformatics analysis revealed that MACC1-AS1 may be a sponge of miR-34a, which can target cyclin-dependent kinase 6 (CDK6). In CSCC cells, MACC1-AS1 overexpression led to upregulation of CDK6, while miR-34a overexpression had the opposite effect and reduced the effects of MACC1-AS1 overexpression in co-transfected cells. Cell cycle and proliferation analyses demonstrated that MACC1-AS1 and CDK6 promoted cell cycle progression and cell proliferation. By contrast, miR-34a had the opposite effect on cell cycle proliferation and cell proliferation, reducing the effects induced by MACC1-AS1 overexpression. Therefore, the lncRNA MACC1-AS1 may serve as a sponge of miR-34a to upregulate CDK6, thereby promoting cell cycle progression and cell proliferation.
www.econstor.eu Do methodical traps lead to wrong development strategies for welfare? A multilevel approach considering heterogeneity across industrialized and developing countries

Subjective well-being (SWB) is becoming increasingly important as welfare concept in both scientific research and politics, as it comprises additional welfare aspects compared to the GDP per capita. Consequently, it becomes important to explicitly identify its driving forces and clarify still ambivalent findings of the literature. For this purpose, with a multilevel model we investigate the extent to which individual-level and national variables together influence subjective well-being. Moreover, we expect that life satisfaction of people in developing countries is determined differently than life satisfaction of people in industrialized countries. The database used includes both individual and national variables and is split into two subsamples of 40 industrialized countries and 41 developing countries. The results show that the national environment is highly important for a person’s SWB. Thus, neglecting this national level would generate biased estimates. Moreover, the split into industrialized and developing countries shows that statistically significant and substantial differences in the effects on life satisfaction exist. Important differences are found for example regarding the income variables. We identify a saturation effect of income on the individual level, whose level is however different depending on the development status of the countries. Moreover, on the aggregated level a significant impact of GDP per capita is found for the developing but not for the industrialized countries. Thus, this study indicates that multilevel modelling approaches are necessary to obtain robust results and that the impact of macroeconomic variables diverges in dependence of the country’s development status.
A CONSTITUTIVE MODEL AND DATA FOR METALS SUBJECTED TO LARGE STRAINS, HIGH STRAIN RATES AND HIGH TEMPERATURES

This paper presents a constitutive model and data for materials subjected td large strains, high strain rates and high temperatures. The basic form of the model is well suited for computations because it uses variables which are readily available in most of the applicable computer codes. The materials considered are OFHC copper, Cartridge brass, Nickel 200, Armco iron, Carpenter electrical iron, 1006 steel, 2024-T351 aluminum, 7039 aluminum, 4340 steel, S-7 tool steel, Tungsten alloy and DU*. 75Ti (depleted uranium). The data for the material constants are obtained from torsion tests over a wide range of strain rates, static tensile tests, dynamic Hopkinson bar tensile tests and Hopkinson bar teats at elevated temperatures. The model and data are evaluated by comparing computational results with data from cylinder impact tests.