Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Sputtering Area Ratio of Gold/Alumina Target on Microstructure and Optical Absorption Properties of Au Nanoparticles dispersed in Amorphous Alumina Dielectric films

Amorphous alumina films containing gold nanoparticles were grown by radio frequency (rf) magnetron sputtering technique, with a variation of area ratio of gold/Alumina target ( 𝑟 𝐴𝑢 𝐴𝑙 2 𝑂 3 ⁄ ). Effect of the gold content in the composite films on the microstructural and optical absorption have been investigated. From the X-ray diffraction (XRD) measurements, the texture coefficient, lattice parameter, grain size and strain were calculated and correlated with changing 𝑟 𝐴𝑢 𝐴𝑙 2 𝑂 3 ⁄ . Contrary to the melting point of free metal cluster, it is found that melting point of gold nanoparticles embedded in amorphous alumina matrix is larger than the melting point of the corresponding bulk material, decreases with increasing size of particles. The surface plasmon resonance peak wavelength, volume fraction and size of gold nanoparticles were determined from the optical absorption spectra of the samples through the Maxwell-Garnett effective model fitting, considering the size dependent mean free path limitation of free electrons. The peak wavelength of the surface plasmon resonance (SPR) was found to redshift from 500 nm to 558 nm and size increases from 2.2 nm to 5.8 nm when the 𝑟 𝐴𝑢 𝐴𝑙 2 𝑂 3 ⁄ increases from 1.3% to 2.6%, and remain at the same wavelength for larger size.
Spanish Tourist Behaviour: A Specific Objective-base Segmantation

This work uses data from the Spanish Tourism Demand Segments Survey (N=6900) conducted by the IESA-CSIC for Turismo Andaluz, SA. The objective of the paper is to develop a statistical segmentation or typology of Spanish tourists based on objective aspects of tourist behaviour measured in the survey including destinations visited, theme of the trip, lodging, transportation and travel group. Initial categorical data are reduced using multiple correspondence analysis and grouped through cluster analysis. Afterwards, identified segments are evaluated to analyse their tourist profiles with a view to examining sociological perspectives of tourist behaviour.
WAVELET FEATURE EXTRACTION BASED HUMAN IRIS RECOGNITION

Personal identification has become the most important factor in authentication processes in recent years. The various biometric identifiers such as face, iris, fingerprint, hand, voice, signature recognition are used for human identification. Among all these biometric identifiers, Iris recognition is the most reliable and accurate biometric identifier as iris cannot be forged. This paper project involves the acquisition of the image of an eye followed by the segmentation and localization of the image to obtain the image of iris. The Haar wavelet is used for feature extraction of an image. Hamming distance is measured between the image in the database and the detected image. Iris recognition is then performed by matching the iris pair with the minimum Hamming distance.
Hot Spots and Hot Moments of Nitrogen in a Riparian Corridor

We use 3‐D high‐resolution reactive transport modeling to investigate whether the spatial distribution of organic‐carbon‐rich and chemically reduced sediments located in the riparian zone and temporal variability in groundwater flow direction impact the formation and distribution of nitrogen hot spots (regions that exhibit higher reaction rates when compared to other locations nearby) and hot moments (times that exhibit high reaction rates as compared to longer intervening time periods) within the Rifle floodplain in Colorado. Groundwater flows primarily toward the Colorado River from the floodplain but changes direction at times of high river stage. The result is that oxic river water infiltrates the Rifle floodplain during these relatively short‐term events. Simulation results indicate that episodic rainfall in the summer season leads to the formation of nitrogen hot moments associated with Colorado River rise and resulting river infiltration into the floodplain. The results further demonstrate that the naturally reduced zones (NRZs) present in sediments of the Rifle floodplain have a higher potential for nitrate removal, approximately 70% greater than non‐NRZs for typical hydrological conditions. During river water infiltration, nitrate reduction capacity remains the same within the NRZs, however, these conditions impact non‐NRZs to a greater extent (approximately 95% less nitrate removal). Model simulations indicate chemolithoautotrophs are primarily responsible for the removal of nitrate in the Rifle floodplain. These nitrogen hot spots and hot moments are sustained by microbial respiration and the chemolithoautotrophic oxidation of reduced minerals in the riparian zone.
Fuel-mediated teratogenesis during early organogenesis : the effects of increased concentrations of glucose , ketones , or somatomedin inhibitor during rat embryo culture 14

Whole rat embryos were explanted at head-fold, late pre-somite stage (day 9.5 of gestation) and cultured in rat sera varyingly supplemented with glucose (3, 6, 9, or 12 mg/mL), D,L sodium fl-hydroxybutyrate (2, 4, 8, or 16 mM), or both (6 mg/mL D-glucose plus 8 mM -hydroxybutyrate). During 48 h culture, increasing glucose alone or 9-hydroxybutyrate alone effected growth retardation and faulty neural and extraneural organogenesis in dose-dependent fashion. Synergistic dysmorphogenic effects occurred when minimally teratogenic concentrations ofglucose and fl-hydroxybutyrate were combined. Sera from diabetic animals containing somatomedin inhibitor bioactivity were also able to produce growth retardation and major developmental lesions in presence of amounts of glucose and ketones which of themselves were not teratogenic. Thus, aberrant fuels and fuel-related products can impair growth and organogenesis in early post-implantation embryo. Such fuel-mediated teratogenesis may be multifactorial and include possibilities for synergistic and additive interactions. Am J C/in Nuir 1986;44:986-95.
Chapter 4 Betulinic acid induces a rapid form of cell death in colon cancer stem cells

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Use of GenomiPhi DNA Amplification Kit for array CGH

Representative amplification of genomic DNA with high fidelity from a small input quantity is useful in applications involving genetic analysis, such as cancer research, genetic testing, DNA archiving, and forensics. GenomiPhi DNA Amplification Kit delivers representative amplification of genomic DNA using a simple isothermal method (1). This method employs the unique biochemical properties of Phi29 DNA polymerase, a highly processive enzyme with excellent strand displacement and proof-reading ability, to amplify DNA. GenomiPhi amplified DNA has been used successfully in several genomic applications, including PCR, SNP typing, STR genotyping, and genomic hybridization.
L.Y.R.A. – Laboratory of Inquiring in Robotics and Astronautics

In this work we present preliminary results of the L.Y. R.A. project – Laboratory of Inquiring in Robotics and Astronautics – a proposal that develops playful production interventions of robotic toys with lowcost materials and easy access. The interventions were focussed in the natural sciences, but also articulated with the arts and the Humanities by producing handmade toys by participants and discussions of STS themes. We seek to investigate whether the proposal has stimulated the interest of students for the debate and for the production of these artifacts. The name L.Y. R.A. is a reference to the brazilian Jackie Lyra, Mechanical NASA's aerospace engineer.
Representing Data Distributions with a Nonparametric Kernel Density: The Way to Estimate the Optimal Oil Contents of Palm Mesocarp at Various Periods

The most popular nonparametric density estimates is kernel density estimate. This estimate depends on the bandwidth choice which was given the optimization to kernel optimality process. We proposed Epanechnikov kernel which is the most optimal kernel in the AMISE. The resample data as replicate samples has been obtained by using bootstrap mechanism to provide the information about the sampling distribution. Then the resample data was used in Epanechnikov kernel simulation to estimate the optimal solution. This study was simulated using oil contents (%) data at various periods after pollination. The oil contents (%) were obtained by extraction of oil palm mesocarp. The result show that, Epanechnikov kernel using resamples data from bootstrap could be used for nonparametric optimization cases such as oil content (%) of oil palm mesocarp.
Monocular Image Depth Calculation Based on Convolution Neural Network

The acquisition of depth information is a very important part of scene analysis, which is mainly divided into two methods: sensor acquisition and image processing. The technology of sensor technology has very high demands on the circumference, so the image processing is a more general method. The traditional method uses binocular stereo calibration to get the depth by geometric relation, but it is still restrained because of the circumstance. Therefore, as the closest to the actual situation of the square method, monocular image depth estimator has a very large research value; For this reason, a method for depth estimation of monocular image based on DenseNet is proposed, which is based on multi-scale convolutional neural network. The DenseNet structure is added to optimize the feature acquisition process by using the characteristics of DenseNet strong feature transfer and feature reuse. The results show that the average relative error is 0.119. The root mean square error is 0.547. And the logarithmic mean space error is 0.052.
Characteristic features of knowledge workers and his place and role in the work safety and hygiene management system of manufacturing enterprise Streszczenie

The aim of the article were the presentation of the question about place and role of workers’ knowledge in the work safety and hygiene management system. In theory the characteristic features of knowledge worker were described. Particular parties of theory were adopted to practical solutions in metallurgical enterprises. As the case study was used the ArcelorMittal Poland company. On the base of its information the relations between workers training and work accidents was analysed.
ON HODRICK-PRESCOTT FILTER . A SHORT SURVEY AND APPLICATIONS

The Hodrick-Prescott (HP) filter is a tool commonly used in macroeconomics, and it is very usefull to extract a trend component from a time series.In this survey we point out the main results from [1] obtained until now for this type of filter and draw some lines of future research in order to obtain new results. Sakarya and de Jong [1] obtained a new representation of the transformation of the data which is implied by the HP filter. This representation is useful to analyze the properties of the HP filter without relying on ARMA based approximation, which was used in the literature before. Also, we focus on the characterization of the large T behavior of the HP filter and some conditions under where it is asimptotically equivalent to a symmetric weighted average with weights independent of sample size. Sakarya and de Jong [1] found that the cyclical component of the HP filter also has a weak dependence property when the HP filter is applied to a stationary mixing process, a linear deterministic trend process and/or a process with a unit root. The HP filter is a good tool to achieve weak dependence in time series. One of the techiques that we will pay an important role in our research is the large smoothing parameter approximation of the HP filter, which is derived in [1]. Using this approximation, authors found an alternative justification for the procedure given in 2002 in [2] (for more information, see [1]) for adjusting the smoothing parameter for the data frequency.
Correlation between Accurate Electron Density and Linear Optical Properties in Amino Acid Derivatives: l-Histidinium Hydrogen Oxalate

The accurate electron density and linear optical properties of l-histidinium hydrogen oxalate are discussed. Two high-resolution single crystal X-ray diffraction experiments were performed and compared with density functional calculations in the solid state as well as in the gas phase. The crystal packing and the hydrogen bond network are accurately investigated using topological analysis based on quantum theory of atoms in molecules, Hirshfeld surface analysis, and electrostatic potential mapping. The refractive indices are computed from couple perturbed Kohn–Sham calculations and measured experimentally. Moreover, distributed atomic polarizabilities are used to analyze the origin of the linear susceptibility in the crystal, in order to separate molecular and intermolecular causes. The optical properties are also correlated with the electron density distribution. This compound also offers the possibility to test the electron density building block approach for material science and different refinement sc.
Stochastic validation of ATM procedures by abstraction algorithms

In this paper we propose a methodology for formal reasoning based on stochastic hybrid systems theory and abstraction algorithms for stochastic dynamical systems, which provides a powerful framework to analyze stochastic models of ATM procedures. We propose the use of automatic tools for verifying probabilistic properties of ATM scenarios. In particular, we propose to use PCTL logic to define probabilistic properties of interest. We address a simple single-agent procedure of the A(Autonomous Aircraft Advanced) ConOps (Concept of Operations), describe a dynamical model for the aircraft deterministic dynamics and for the wind stochastic dynamics, and used MATLAB and PRISM tools in order to perform stochastic analysis of properties of interest of the addressed scenario.
Observation of tropical cyclones by high-resolution scatterometry

Unprecedented views of surface wind fields in tropical cyclones (hereafter TCs) are provided by the European Remote Sensing Satellite (ERS) C band scatterometer. Scatterometer measurements at C band are able to penetrate convective storms clouds, observing the surface wind fields with good accuracy. However the resolution of the measurements (50x50 km 2) limits the interpretation of the scatterometer signals in such mesoscale events. The strong gradients of the surface wind existing at scales of a few kms are smoothed in the measured features such as the intensity and location of the wind maxima, and the position of the center. Beyond the ERS systems, the scatterometers on-board the ADEOS and METOP satellites, designed by the Jet Propulsion Laboratory and by the European Space Agency, respectively, will be able to produce measurements of the backscattering coefficient at about 25x25 km 2 resolution. A few sets of ERS-1 orbits sampling TC events were produced with an experimental 25x25 km 2 resolution. Enhancing the resolution by a factor of 2 allows location of the wind maxima and minima in a TC with a much better accuracy than at 50 km resolution. In addition, a better resolution reduces the geophysical noise (variability of wind speed within the cell and effect of rain) that dominates the radiometric noise and hence improves the definition of the backscattering measurements. A comprehensive analysis of the backscattering measurements in the case of high winds and high sea states obtained within TCs is proposed in order to refine the interpretation of the wind vector derived from a backscattering model that is currently only calibrated up to moderate winds (< 20 m/s) in neutral conditions. Observations of the TOPEX-POSEIDON dual-frequency altimeter are also used for that purpose. Patterns of the surface winds in TCs are described and characteristic features concerning asymmetries in the maximum winds and in the divergence field are discussed.
IÑIGO GARMENDIA AROZTEGUI A KNOWLEDGE-DRIVEN METHOD FOR 3D RECONSTRUCTION OF TECHNICAL INSTALLATIONS IN BUILDING REHABILITA- TIONS

GARMENDIA AROZTEGUI, IÑIGO: A KNOWLEDGE-DRIVEN METHOD FOR 3D RECONSTRUCTION OF TECHNICAL INSTALLATIONS IN BUILDING REHABILITATIONS TAMPERE UNIVERSITY OF TECHNOLOGY Master of Science Thesis, 56 pages June 2018
Anthropogenic land‐use change intensifies the effect of low flows on stream fishes

Handling Editor: Jani Heino Abstract 1. As ecosystems experience simultaneous disturbances, it is critical to understand how multiple stressors interact to affect ecological change. Land-use change and extreme flow events are two important stressors that could interact to affect fish populations. 2. We evaluated the individual and interactive effects of discharge and land-use change associated with oil and natural gas development on populations of two stream fishes over a 7-year period. We used repeated-state (i.e. abundance trends) and rate (i.e. colonization and persistence) responses to advance our understanding of flow-ecology relationships in a multiple-stressor framework. 3. Overall, fish abundance, colonization and persistence declined as discharge decreased. The effect of land-use change associated with oil and natural gas development differed between species, with the abundance of Mottled Sculpin declining and Mountain Sucker increasing as land-use change increased. We found both synergistic and antagonistic interactions between discharge and land-use change. Land-use change intensified the effect of low flows for Mottled Sculpin and lead to greater variability in responses to flow for Mountain Sucker. These differences between species' responses are likely due to differences in their physiological tolerances and behavioural adaptations to disturbance. 4. Synthesis and applications. Our research provides empirical evidence for the complex interactions that can arise between discharge and anthropogenic land-use change. Management efforts to reduce inputs of sediments and chemical contaminants associated with land-use change (e.g. silt fences, vegetative buffers) and promote quality refuge habitats (i.e. in-stream habitat restoration) could help mitigate the negative effects of low-flow extremes on stream fishes. Further development of flow-ecology relationships in a multiple-stressor framework will help guide management of stream fishes, and provide a better understanding of the mechanisms underlying responses of different species.
Nanoplates and their suitability for use as solar cells

The use of p-n junction nanowires as solar cells has received an increased amount of attention with results published on both individual silicon nanowires functioning as nano-solar cells and on larger surface areas covered with silicon nanowires. The results obtained using the nanowire geometry are promising and show a new direction towards a high efficiency/low cost solar cell. Here we suggest the use of a different nanostructure geometry namely nanoplates which are fabricated using group III-V materials. The key properties of the InAs nanoplates are described and results from overgrowing them with a GaAs layer shown. Different ways to implement the nanoplate geometry into a solar cell are suggested and the very first results from the characterization of a single InAs-GaAs n-p junction nanoplate solar cell presented.
Monetary Policy, Informality and Business Cycle Fluctuations in a Developing Economy Vulnerable to External Shocks

This paper develops an open economy dynamic stochastic general equilibrium (DSGE) model based on New-Keynesian micro-foundations. Alongside standard features of emerging economies, such as a combination of producer and local currency pricing for exporters, foreign capital inflow in terms of foreign direct investment and oil imports, this model also incorporates informal labor and production sectors. This customization intensifies the exposure of a developing economy to internal and external shocks in a manner consistent with the stylized facts of Business Cycle Fluctuations. We then focus on optimal monetary policy analysis by evaluating alternative interest rate rules and calibrate the model using data from Pakistan economy. The learning and determinacy analysis suggest monetary authority in developing economies to follow Taylor principle in large and to put some weight on exchange rate fluctuations even if there is relatively less inertia in the setting of policy interest rate.
Dynamic Formation Of Autonomus Agents Based On Algorithm Flocking Behavior

Approach to organizing group formations still are static, causingthe formation has not been able to adapt to the changing circumstances of both obstacle or barrier that will be encountered.This study was to model the behavior of autonomous agents formation dynamic moves past the obstacles and reshaping the initial lineup after passing obstacles based algorithm flocking behavior. There were four formations that are examined in this study i.e. column formation, a circle, a line formation and the formation of the triangle. All forms of word formation in narrow spaces and one labyrinth. The labyrinthine 121, 123 and labyrinth mazes is structuring three zigzag number of narrow spaces is bypassed by the formation of autonomous agents. The results of the error of the average position of the formation of autonomous agents in one narrow space is 0%. At 121 maze, the percentage error of the position of the movement of the formation is the largest circle formation agents namely 52.63% and the percentage error of the triangular formation is the smallest position i.e. 44.83%. In the maze of 123, the percentage error of the triangular formation is the largest position i.e. 55.42% and the smallest is the position error percentage column namely 42.42%. The percentage error of the position of the movement of the formation of the largest agencies in the maze of zigzag formation is the circle i.e. 90.69% and the smallest is the position error percentage column namely 48.488%.
Ensemble Network Architecture for Deep Reinforcement Learning

The popular deep learning algorithm is known to be instability because of the -value’s shake and overestimation action values under certain conditions. These issues tend to adversely affect their performance. In this paper, we develop the ensemble network architecture for deep reinforcement learning which is based on value function approximation. The temporal ensemble stabilizes the training process by reducing the variance of target approximation error and the ensemble of target values reduces the overestimate and makes better performance by estimating more accurate -value. Our results show that this architecture leads to statistically significant better value evaluation and more stable and better performance on several classical control tasks at OpenAI Gym environment.