Miracle-dz/UniFAR-FTUs huggingface dataset:
A comparison of headnet electrode arrays for electrical impedance tomography of the human head

Three types of commercially available headnet electrode arrays, designed for use in EEG, and conventional EEG Ag/AgCl cup electrodes were tested on human subjects, and a realistic, saline-filled head-shaped tank was prepared with vegetable skin to simulate human skin in order to determine the optimum electrode system for electrical impedance tomography (EIT) of the human head. Impedance changes during EIT acquisition were produced in healthy volunteers during a finger–thumb apposition task and in tanks by the insertion of a Perspex rod. Signal-to-baseline noise, measured from raw EIT data, was 2.3 ± 0.3 and 2.3 ± 0.2 for the human and tank data, respectively. In both the human and tank experiments, a commercial hydrogel elasticated electrode headnet produced the least amount of baseline noise, and was the only headnet in the human data with noise levels acceptable for EIT imaging. Image quality measured in the tank was similar for most of the headnets tested, except that the EEG electrodes produced a higher positional error and electrodes in a geodesic elasticated net produced images with worse subjective image quality. Overall, the hydrogel elasticated headnet was judged to be the most suitable for human neuroimaging with EIT.
Toric Fano varieties and convex polytopes

In this thesis we study toric Fano varieties. Toric varieties are a particular class of algebraic variety which can be described in terms of combinatorial data. Toric Fano varieties correspond to certain convex lattice polytopes whose boundary lattice points are dictated by the singularities involved. Terminal toric Fano varieties correspond to convex lattice polytopes which contain only the origin as an internal lattice point, and whose boundary lattice points are precisely the vertices of the polytope. The situation is similar for canonical toric Fano varieties, with the exception that the condition on boundary lattice points is relaxed. We call these polytopes terminal (or canonical) Fano polytopes. The heart of this thesis is the development of an approach to classifying Fano polytopes, and hence the associated varieties. This is achieved by ordering the polytopes with respect to inclusion. There exists a finite collection of polytopes which are minimal with respect to this ordering. It is then possible to “grow” these minimal polytopes in order to obtain a complete classification. Critical to this method is the ability to find the minimal polytopes. Their description is inductive, requiring an understanding of the lower-dimensional minimal polytopes. A generalisation of weighted projective space plays a crucial role – the associated simplices form the building blocks of the minimal polytopes. A significant part of this thesis is dedicated to attempting to understand these building blocks. A classification of all toric Fano threefolds with at worst terminal singularities is given. The three-dimensional minimal canonical polytopes are also found, making a complete classification possible.
Importance Sampling Simulation and Multiple-Hyperplane Realization of the Bayesian Decision Feedback Equaliser

For the class of equalisers that employs a symbol-decision finite-memory structure with decision feedback, the optimal solution is known to be the Bayesian decision feedback equaliser (DFE). The complexity of the optimal Bayesian DFE however increases exponentially with the length of the channel impulse response (CIR). It has been noted that, when the signal to noise ratio (SNR) tends to infinity, the decision boundary of the Bayesian DFE is asymptotically piecewise linear and consists of several hyperplanes. This asymptotic property can be exploited for efficient simulation and implementation of the Bayesian DFE. An importance sampling (IS) simulation technique is presented based on this asymptotic property for evaluating the lower-bound bit error rate (BER) of the Bayesian DFE under the assumption of correct decisions being fed back. A design procedure is developed, which chooses appropriate bias vectors for the simulation density to ensure asymptotic efficiency of the IS simulation. As the set of hyperplanes that form the asymptotic Bayesian decision boundary can easily be found, they can be used to partition the observation space. The resulting multiple-hyperplane detector can closely approximate the optimal Bayesian detector, at an advantage of considerably reduced decision omplexity.
High-field Domain Formation Conditions in Semiconductor Multiple Quantum Well Sequential Resonant Tunneling Structures

We investigated the tunneling current-voltage ( I–V ) characteristics of Al0.3Ga0. 7As/GaAs multiple quantum well (MQW) diodes with various electron densities, N s, and scattering rates, Γ. Clear periodic negative differential resistances (NDRs) due to the formation of high-field domains were observed for the samples with low N s and Γ, while such NDRs were completely absent in the first plateau region of the I–V characteristics of the samples with high N s and Γ. It was found that such formation/destruction of the high-field domains in MQW diodes is controlled by the interplay of the tunneling rate and the electron supply functions between adjacent coupled quantum wells. The condition for the high-field domain formation is clarified as a function of N s and Γ.
Kinetics of surfactant adsorption at fluid/fluid interfaces: non-ionic surfactants

We present a model treating the kinetics of adsorption of soluble surface-active molecules at the interface between an aqueous solution and another fluid phase. The model accounts for both the diffusive transport inside the solution and the kinetics taking place at the interface using a free-energy formulation. In addition, it offers a general method of calculating dynamic surface tensions. Non-ionic surfactants are shown, in general, to undergo a diffusion-limited adsorption, in accord with experimental findings.
An Empirical Investigation of the Accounting Information Systems Quality

The organization relies heavily on information system in order always be competitive. Accounting information system implementation success would encourage generates of quality accounting information. Personality characteristic is a concept that is identified as a factor driving the successful implementation of accounting information system. This research aims to examine and analyze the influence of personality characteristics on the quality of accounting information system and the implication on quality of accounting information by disseminating questionnaire on 67 banking organization in Indonesia and 254 people respondents participate in filling the questionnaire. The result showed that the accounting information system has not qualified because the organization has not been fullest regard for characteristics of the personality of the user system to conform with the objectives of the company in anticipation of a complex situation. Causes of accounting information are not yet qualified because of the application of accounting information system is not yet fully accessible whenever and wherever in order to match the needs of banking organizations.
A Study on Issues, Challenges and Application in Data Science

Data science, also known as data-driven science, is an interdisciplinary field about scientific methods, processes, and systems to extract knowledge or insights from data in various forms, either structured or unstructured, similar to data mining. Data science is about dealing with large quality of data for the purpose of extracting meaningful and logical results/conclusions/patterns. It’s a newly emerging field that encompasses a number of activities, such as data mining and data analysis. It employs techniques ranging from mathematics, statistics, and information technology, computer programming, data engineering, pattern recognition and learning, visualization, and high performance computing. This paper gives a clear idea about the different data science technologies used in Big data Analytics. Data science is a "concept to unify statistics, data analysis and their related methods" in order to "understand and analyze actual phenomena" with data. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science, in particular from the subdomains of machine learning, classification, cluster analysis, data mining, databases, and visualization. Data Science is much more than simply analysing data. There are many people who enjoy analysing data who could happily spend all day looking at histograms and averages, but for those who prefer other activities, data science offers a range of roles and requires a range of skills. Data science includes data analysis as an important component of the skill set required for many jobs in the area, but is not the only skill. In this paper the authors effort will concentrated on to explore the different issues, implementation and challenges in Data science.
Effect of intermediate capacitance on slow-scale instability of Cuk power factor correction converter operating in discontinuous capacitor voltage mode

Due to features of near-unity power factor, soft turn-off switching and low switch current stress, Cuk power factor correction (PFC) converter operating in discontinuous capacitor voltage mode (DCVM) has attracted much attention. However, it is found that with the increase of intermediate capacitance, slow-scale instability occurs in voltage mode control (VMC) DCVM Cuk PFC converter. In this study, a simplified model for VMC DCVM Cuk PFC converter is established, upon which the effect of intermediate capacitance on the slow-scale instability and the corresponding bifurcation behaviours are studied by using harmonic balance method and Floquet theory. Furthermore, the stability boundaries in terms of the intermediate capacitance under different load resistance and input voltage are given, which is helpful for parameter design of VMC DCVM Cuk PFC converter. Finally, numerical simulations and experimental results are provided to verify the analysis results.
A machine learning–based framework for analyzing car brand styling

To avoid the requirement of expert knowledge in conventional methods for car styling analysis, this article proposes a machine learning–based method which requires no expert-engineered features for car frontal styling analysis. In this article, we aim to identify the group behaviors in car styling such as the degree of brand styling consistency among different automakers and car styling patterns. The brand styling consistency is considered as a group behavior in this article and is formulated as a brand classification problem. This classification problem is then solved by a machine learning method based on the PCANet for automatic feature encoding and the support vector machine for feature-based classification. The brand styling consistency can thus be measured based on the classification accuracy. To perform the analysis, a car frontal styling database with 23 brands is first built. To present discovered brand styling patterns in classification, a decoding method is proposed to map salient features for brand classification to original images for revelation of salient styling regions. To provide a direct perception in brand styling characteristics, frontal styling representatives of several brands are present as well. This study contributes to efficient identification of brand styling consistency and visualization of brand styling patterns without relying on expert experience.
Computer Simulation of Core Structure of Screw Dislocations in C60 Crystals Using Girifalco Potential

The equilibrium molecular configuration around a 1/2[101] screw dislocation in C60 crystals in a fcc phase was calculated by means of molecular dynamics. The molecules were assumed to be rigid and the spherical intermolecular potential derived by Girifalco was utilized. The dislocation was found to dissociate into two Shockley partials. The partial dislocation had a spreadout core and the width of the Burgers vector density at the half-peak height reached 13 times the magnitude of the Burgers vector.
STUDI TENTANG REKRUITMEN, SELEKSI DAN ALOKASI KEGIATAN TENAGA KEPERAWATAN DI DAERAH TERPENCIL DI JATIM DAN NTT

The criteria of remote areas as difficult geographic areas to access that causes difficulty to reach out health centres including nearest hospitals and as consequences the health manpower development would be different from other areas. The study aimed to determine recruitment, selection and job allocation of nursing staffs at health centres in remote areas in the Provinces of East Java and East Nusa Tenggara. It was conducted in Sumenep Regency in East Java Province which consisted of 6 (six) health centres in remote islands, and South Central Timor Regency in East Nusa Tenggara Province consisted 11 (eleven) health centres in remote mountaineous. There were 34 nursing staff as respondents for each regency. Data collected by depth interview, questionaires, secondary data and focus group discussion. The data were analyzed discriptively. Results showed that the distribution of nursing staffs were relatively inequity in Sumenep Regency whereas they were located evenly in South Central Timor Regency. Recruitment of the nursing respondents were in accordance to other non remote areas that increasing by civil servant candidate (Calon Pegawai Negeri Sipil/CPNS), yet the recruiment of CPNS in remote areas were not a priority. The rotation nursing staffs were not so high and most of them felt that their work did not comply with their expectation and competencies. They were just, 20,6%-23,5%, had continuing education in Diplomas (D3). They worked for treatment, mother and child health cares, in-patient cares and health promotion. The reward system was not good and financial incentives for remote areas was none anymore. It concludes that the recruitmentof nursing staffs at health centers in remote areas did not have a specific pattern, not prioritizing local citizen and althoughthe distribution was good but continuing education was still lack. Key words: recruitment, job allocation, nursing staf, performance appraisal
Influence of window size in small-window colour measurement, particularly of teeth

Tooth colour is often measured with a small window for illumination and measurement. This causes edge loss of the light, resulting in systematic errors in colour coordinates. The authors give a quantification of the edge losses, and explains their cause. They measured reflectance spectra for 27 Formalin fixated extracted incisors using a small-window reflectance spectrophotometer equipped with external diaphragms of 3, 4, and 5 mm diameter, and using a spectroradiometer. They calculated the colour coordinates L*a*b* from these spectra. Finally, 16 randomly chosen teeth were illuminated with a pencil beam ( lambda =543 nm, and lambda =633 nm) while the emerging light was measured as a function of distance from the illuminated spot using a CCD detector. These data were used to calculate small-window edge losses, and thus to predict the small-window reflectance factors relative to spectroradiometrically determined reflectance, at both 543 nm and 633 nm. In all instruments the same spot on the tooth was illuminated and measured, and the teeth were always wet. Colour coordinates for the small-window colour measurements deviate significantly from those determined using the spectroradiometer. These deviations can be explained from the wavelength-dependant edge loss that arises in small-window colour measurement.
X-ray Reflectivity of Curved Alpha-Quartz Crystals

X-ray reflectivity curves at 20 keV photon energy of alpha-quartz spherically curved crystals have been measured using a micrometric synchrotron beam at the ESRF BM5. Six thin quartz crystals samples with curvature radii of 150 and 250 mm have been studied. Experimental results are compared with diffraction profile calculations using a lamellar model and a good agreement is found.
Models of the contribution of blazars to the anisotropy of the extragalactic diffuse gamma-ray background

We study the relation between the measured anisotropies in the extragalactic diffuse gamma-ray background (DGRB) and the DGRB spectral intensity, and their potential origin from the unresolved blazar population. Using a physical-evolution model for blazars with a luminosity dependent density evolution (LDDE) and an observationally-determined luminosity-dependent blazar spectral energy distribution (SED), we find that blazars can account for the observed anisotropy of the DGRB consistent with their observed source-count distribution, but are in turn constrained in contributing significantly to the observed DGRB intensity. For the best-fit LDDE model accounting for the DGRB anisotropy and source-count distribution, blazars only contribute 5.9+2.1−1.0% (68% CL) of the DGRB intensity above 1 GeV. Requiring a higher fraction of the DGRB intensity contribution by blazars overproduces the DGRB anisotropy, and therefore blazars in the LDDE+SED-sequence model cannot simultaneously account for the DGRB intensity as well as anisotropy. We discuss the limitations of LDDE models. However, these models do not require the many unjustified and observationally-inconsistent simplifying assumptions — including a single power law for all blazar spectra and a simple broken power-law model for their source-count distribution — that are present in much previous work.
The race between stars and quasars in reionizing cosmic hydrogen

The cosmological background of ionizing radiation has been dominated by quasars once the Universe aged by ∼ 2 billion years. At earlier times (redshifts z ≳ 3), the observed abundance of bright quasars declines sharply, implying that cosmic hydrogen was reionized by stars instead. Here, we explain the physical origin of the transition between the dominance of stars and quasars as a generic feature of structure formation in the concordance ΛCDM cosmology. At early times, the fraction of baryons in galaxies grows faster than the maximum (Eddington-limited) growth rate possible for quasars. As a result, quasars were not able to catch up with the rapid early growth of stellar mass in their host galaxies.
Observing the shadow of Einstein-Maxwell-Dilaton-Axion black hole

In this paper, the shadows cast by Einstein-Maxwell-Dilaton-Axion black hole and naked singularity are studied. The shadow of a rotating black hole is found to be a dark zone covered by a deformed circle. For a fixed value of the spin a, the size of the shadow decreases with the dilaton parameter b. The distortion of the shadow monotonically increases with b and takes its maximal when the black hole approaches to the extremal case. Due to the optical properties, the area of the black hole shadow is supposed to equal to the high-energy absorption cross section. Based on this assumption, the energy emission rate is investigated. For a naked singularity, the shadow has a dark arc and a dark spot or straight, and the corresponding observables are obtained. These results show that there is a significant effect of the spin a and dilaton parameter b on these shadows. Moreover, we examine the observables of the shadow cast by the supermassive black hole at the center of the Milky Way, which is very useful for us to probe the nature of the black hole through the astronomical observations in the near future.
Phasor measurement unit based data acquisition and transfer system for the solution of the economic dispatch problem using the real time digital simulator

: The electric power utilities play a vital role in the generation, transmission and distribution of the electrical power to the end users. The power utilities generally face two major issues – firstly, power systems are expected to operate close to the maximum capacity, and secondly, there is a need for accurate monitoring and control of the power system network using the modern technological advances together with their associated configuration tools. These two issues are interconnected as better monitoring allows for better control of the power system. The development of the new standard-based power system technologies contribute to concept of building of a smarter grid. The challenge is that this process requires the development of new control and operation architectures and methods for data acquisition, data transfer, and control computation. These methods require data for the dynamic state of the entire power system in real-time, which allows for the introduction of synchrophasor-based monitoring and control of the power system. This paper describes the research work for integration of the newer existing power system technologies to build fully automated systems for the real-time solution of power system energy management problem, incorporating data measurement and acquisition, data transfer and distribution through a communication network, and data storage and retrieval in one complete system. The paper further details the developed methods, algorithms, procedures, software and hardware tools for implementation of a lab-scale prototype of the power system and the acquisition and transfer of the data to the control center in order to allow for the solution of the optimal power dispatch problem in real-time using real-time data. The developed prototype systems are tested for the five-bus power system model, and the system operation test results are presented. The model is developed within the RSCAD software environment and the output signals are fed to the software-based PMU. The data from the GTNET-PMU is transferred to the Phasor Data Concentrator (PDC) using the Ethernet. The developed data acquisition, data transfer, data retrieval and data storage system algorithms and software programs can be expanded for use in the power grid energy management system for the economic dispatch solution in regional or national control centers, smart grid applications, educational courses and postgraduate research at universities.
In vivo cerebrovascular measurement combining diffuse near-infrared absorption and correlation spectroscopies

We combine two near-infrared diffuse optical techniques to study variations of blood flow, haemoglobin concentration, and blood oxygen saturation in the functioning rat brain. Diffuse correlation spectroscopy (or flowmetry) monitors changes in the cerebral blood flow, without the use of the principles of tracer clearance, by measuring the optical phase-shifts caused by moving blood cells. Near-infrared absorption spectroscopy concurrently measures tissue absorption at two wavelengths to determine haemoglobin concentration and blood oxygen saturation in this same tissue volume. This optical probe is non-invasive and was employed through the intact skull. The utility of the technique is demonstrated in vivo by measuring the temporal changes in the regional vascular dynamics of rat brain during hypercapnia. Temporal and spatial variations of cerebral blood flow, haemoglobin concentration and blood oxygen saturation during hypercapnia are compared with other measurements in the literature, and a quantitative analysis demonstrating the self-consistency of our combined observations of vascular response is presented.