nils-herrmann/clean_scirepeval huggingface dataset:
High mass loading, binder free MXene anodes for high areal capacity Li ion batteries
Year: 2015

Though anodes with high Li gravimetric capacities, beyond commercial graphite, have been intensively studied, gravimetric capacity does not precisely reflect the performance of a packed cell. Li anodes with high mass loadings, which can achieve high areal capacities, are required for many commercial applications. Herein, anodes with high mass loadings were fabricated using two dimensional transition metal carbides (MXenes) Powders of the latter were cold pressed, without binders, at a pressure of 1 GPa, to create ~300 mm thick, free standing discs. When Ti3C2 was used as the anode for lithium, the initial reversible areal capacity was ~15 mAh/cm2, which decreased to 5.9 mAh/cm2 after 50 cycles, but the decrease after the first ~20 cycles was very gradual. The latter is one of the highest values ever reported to date. When Nb2C was used as the anode instead, the initial reversible capacity was ~16 mAh/cm2; this value decreased to 6.7 mAh/cm2 after 50 cycles, which is about a 14% increase compared to Ti3C2. As the research on MXenes for lithium ion batteries has just begun, there is certainly room for further improving their electrochemical performance.
A neural network aerosol typing algorithm based on lidar data
Year: 2018

Atmospheric aerosols play a crucial role in the Earth's system, but their role is not completely understood, partly because of the large variability in their properties resulting from a large number of possible aerosol sources. Recently developed lidar based techniques were able to retrieve the height distributions of optical and microphysical properties of fine mode and coarse mode particles, providing the types of the aerosols. One such technique is based on artificial neural networks (ANNs) In this article, a Neural Network Aerosol Typing Algorithm Based on Lidar Data (NATALI) was developed to estimate the most probable aerosol type from a set of multispectral lidar data. The algorithm was adjusted to run on the EARLINET 3 b 2 a 1 d profiles. The NATALI algorithm is based on the ability of specialized ANNs to resolve the overlapping values of the intensive optical parameters, calculated for each identified layer in the multiwavelength Raman lidar profiles. The ANNs were trained using synthetic data, for which a new aerosol model was developed. Two parallel typing schemes were implemented in order to accommodate data sets containing (or not) the measured linear particle depolarization ratios (LPDRs) (a) identification of 14 aerosol mixtures (high resolution typing) if the LPDR is available in the input data files, and (b) identification of five predominant aerosol types (low resolution typing) if the LPDR is not provided. For each scheme, three ANNs were run simultaneously, and a voting procedure selects the most probable aerosol type. The whole algorithm has been integrated into a Python application. The limitation of NATALI is that the results are strongly dependent on the input data, and thus the outputs should be understood accordingly. Additional applications of NATALI are feasible, e.g. testing the quality of the optical data and identifying incorrect calibration or insufficient cloud screening. Blind tests on EARLINET data samples showed the capability of NATALI to retrieve the aerosol type from a large variety of data, with different levels of quality and physical content.
A ZVS SVM Method for Grid Inverter with Extended Power Factor Angle
Venue: 2019 10th International Conference on Power Electronics and ECCE Asia (ICPE 2019 ECCE Asia)
Year: 2019

Grid inverter is required to generate reactive power to support the power system during grid faults to satisfy the grid code. Therefore the grid inverter should have the ability to operate with wide power factor range. In this paper, a Zero Voltage Switching Space Vector Modulation (ZVS SVM) scheme for three phase ZVS grid inverter with extended power factor angle is proposed. With only one switching operation of the auxiliary switch during one switching period of SVM, ZVS of all switches can be achieved. Stage operations are analyzed and ZVS condition is derived. Finally the proposed ZVS SVM method is verified by experiments.
The pyramid quantized Weisfeiler Lehman graph representation
Venue: Neurocomputing
Year: 2016

Graphs are flexible and powerful representations for non vectorial structured data. Graph kernels have been shown to enable efficient and accurate statistical learning on this important domain, but many graph kernel algorithms have high order polynomial time complexity. Efficient graph kernels rely on a discrete node labeling as a central assumption. However, many real world domains are naturally described by continuous or vector valued node labels. In this paper, we propose an efficient graph representation and comparison scheme for large graphs with continuous vector labels, the pyramid quantized Weisfeiler Lehman graph representation. Our algorithm considers statistics of subtree patterns with discrete labels based on the Weisfeiler Lehman algorithm and uses a pyramid quantization strategy to determine a logarithmic number of discrete labelings that results in a representation that guarantees a multiplicative error bound on an approximation to the optimal partial matching. As a result, we approximate a graph representation with continuous vector labels as a sequence of graphs with increasingly granular discrete labels. We evaluate our proposed algorithm on two different tasks with real datasets, on a fMRI analysis task and on the generic problem of 3D shape classification. Source code of the implementation can be downloaded from https:/web.imis.athena innovation.gr/~kgkirtzou/Projects/WLpyramid.html
Ternary superconducting cophosphorus hydrides stabilized via lithium
Venue: npj Computational Materials
Year: 2019

Inspired by the diverse properties of sulfur hydrides and phosphorus hydrides, we combine first principles calculations with structure prediction to search for stable structures of Li P H ternary compounds at high pressures with the aim of finding novel superconductors. It is found that phosphorus hydrides can be stabilized under pressure via additional doped lithium. Four stable stoichiometries LiPH3, LiPH4, LiPH6, and LiPH7 are uncovered in the pressure range of 100 300 GPa. Notably, we find an atomic LiPH6 with $Pm\overline 3$$Pm3 symmetry which is predicted to be a potential high temperature superconductor with a Tc value of 150 167 K at 200 GPa and the Tc decreases upon compression. All the predicted stable ternary hydrides contain the P H covalent frameworks with ionic lithium staying beside, but not for $Pm\overline 3$$Pm3 LiPH6. We proposed a possible synthesis route for ternary lithium phosphorus hydrides: LiP H2 LiPHn, which could provide helpful and clear guidance to further experimental studies. Our work may provide some advice on further investigations on ternary superconductive hydrides at high pressure.
Internal capital markets, ownership structure, and investment efficiency: Evidence from Taiwanese business groups
Year: 2020

This study uses a unique data set to thoroughly investigate how the financing provided by a business groups' internal capital markets and control enhancing ownership structure relate to investment efficiency. We find that group affiliated firms that make more intensive use of related party transactions that facilitate an internal capital market exhibit a reduced probability of under investment. We also find that pyramidal (cross) ownership improves (weakens) investment efficiency suggesting different types of control enhancing structure have strongly contrasting effects on investment efficiency. These findings reveal both the financing advantages and disadvantages of business groups.
Analysis of a Gaussian process and feed forward neural networks based filter for forecasting short rainfall time series
Venue: The 2013 International Joint Conference on Neural Networks (IJCNN)
Year: 2013

In this paper, an analysis of kernel (GP) and feedforward neural networks (FFNN) based filter to forecast short rainfall time series is presented. For the FFNN, the learning rule used to adjust the filter weights is based on the Levenberg Marquardt method and Bayesian approach by the assumption of the prior distributions. In addition, a heuristic law is used to relate the time series roughness with the tuning process. The input patterns for both NN based and kernel models are the values of rainfall time series after applying a time delay operator. Hence, the NN's outputs will tend to approximate the current value of the time series. The time lagged inputs of the GP and their covariance functions are both determined via a multicriteria genetic algorithm, called NSGA II. The optimization criteria are the quantity of inputs and the filter's performance on the known data which leads to Pareto optimal solutions. Both filters FFNN and GP Kernel are tested over a rainfall time series obtained from La Sevillana establishment. This work proposed a comparison of well known filter referenced in early work where the contribution resides in the analysis of the best horizon of the forecasted rainfall time series proposed by Bayesian adjustment. The performance attained is shown by the forecast of the next 15 months values of rainfall time series from La Sevillana establishment located in 31deg 1'22.46"S, 62deg40'9.57"O) Balnearia, Cordoba, Argentina.
Astro2020 Science White Paper: The Local Relics of of Supermassive Black Hole Seeds
Year: 2019

We have compelling evidence for stellar mass black holes (BHs) of ~5 80 M_sun that form through the death of massive stars. We also have compelling evidence for so called supermassive BHs (10^5 10^10 M_sun) that are predominantly found in the centers of galaxies. We have very good reason to believe there must be BHs with masses in the gap between these ranges: the first ~10^9 M_sun BHs are observed only hundreds of millions of years after the Big Bang, and all theoretically viable paths to making supermassive BHs require a stage of "intermediate" mass. However, no BHs have yet been reliably detected in the 100 10}^5 M_sun mass range. Uncovering these intermediate mass BHs of 10^3 10^5 M_sun is within reach in the coming decade. In this white paper we highlight the crucial role that 30 m class telescopes will play in dynamically detecting intermediate mass black holes, should they exist.
The Concept of Smart Hotels as an Innovation on the Hospitality Industry Market Case Study of Puro Hotel in Wroclaw
Year: 2016

Market observation indicates the development of a new model for running a business in hospitality industry, referred to as smart hotels. This model is specifically distinguished by the new information and communication technologies. The concept of smart hotels is relatively new and can be regarded as an innovative solution in tourism. The presented article discusses the essence of a smart hotel category and identifies the attributes of an intelligent hotel. The theoretical discussion based on the subject literature in the theory of organization and management, as well as hospitality business economics and management was also illustrated using the case study of PURO hotel in Wroclaw. The necessary information was collected from both secondary and primary sources. The most important information gathering method was interpersonal in depth interview with the aforementioned hotel's manager.
Fault tolerant FSM on FPGA using SEC DED code algorithm
Venue: 2017 International Conference on Technological Advancements in Power and Energy TAP Energy)
Year: 2017

This paper focuses on the design of an automatic error detection and correction of FSMs for soft errors. Single Event Upsets (SEUs) due to radiation impose an increasing problem to the reliable operation of FPGA used in communication, mile aero, automotive and industrial designs. By developing safe case sequential logic and fault tolerant state machines with error mitigation circuitry logic, the FPGA design can be protected from SEUs. When a soft error occurs, these method ensures safe design operation by returning the design to a known safe or reset state of operation. This logic provides reliable system operation. The objective of this work is to implement automatic error detection and correction of FSMs for single event upsets is presented. We also analyze how bit errors within same clock cycle can be removed by the addition of error checking bits using SEC DED code algorithm. The SEC DED is commonly used as error detection and correction since it is more efficient t han p arity c hecker. T he S EC DED c ode c an be used as the error detection and correction circuitry behind the FSMs to improve the reliable operation. The proposed architecture is simulated in VCS. This structure is synthesized in Xilinx Virtex 7 for implementing on FPGA. The result of the proposed design is analyzed and compared to determine its performance in terms of reliability.
A Decision Support Tool for Video Retinal Angiography 2018
Year: 2019

Fluorescein angiogram (FA) is a medical procedure that helps the ophthalmologists to monitor the status of the retinal blood vessels and to diagnose proper treatment. This research is motivated by the necessity of blood vessel segmentation of the retina. Retinal vessel segmentation has been a major challenge and has long drawn the attention of researchers for decades due to the presence of complex blood vessels with varying size, shape, angles and branching pattern of vessels, and non uniform illumination and huge anatomical variability between subjects. In this thesis, we introduce a new computational tool that combines deep learning based machine learning algorithm and a signal processing based video magnification method to support physicians in analyzing and diagnosing retinal angiogram videos for the first time in the literature. The proposed approach has a pipeline based architecture containing three phases image registration for large motion removal from video angiogram, retinal vessel segmentation and video magnification based on the segmented vessels. In image registration phase, we align distorted frames in the FA video using rigid registration approaches. In the next phase, we use baseline capsule based neural networks for retinal vessel segmentation in comparison with the state of the art methods. We move away from traditional convolutional network approaches to capsule networks in this work. This is because, despite being widely used in different computer vision applications, convolutional neural networks suffer from learning ability to understand the object part relationships, have high computational times due to additive nature of neurons and, loose information in the pooling layer. Although having these drawbacks, we use deep learning methods like U Net and Tiramisu to measure the performance and accuracy of SegCaps. Lastly, we apply Eulerian video magnification to magnify the subtle changes in the retinal video. In this phase, magnification is applied to segmented videos to visualize the flow of blood in the retinal vessels.
Challenges in the atmospheric characterization for the retrieval of spectrally resolved fluorescence and PRI region dynamics from space
Year: 2021

In the coming years, Earth Observation missions like the FLuorescence EXplorer (FLEX) will acquire the radiance signal from the visible to the near infrared at a very high spectral resolution, enabling exciting prospects for new insights in satellite based photosynthetic studies. In this context, the process of de coupling atmospheric and vegetation related spectral signatures will become essential to guarantee a reliable estimation of the vegetation photosynthetic activity from space. Dynamic changes related to the vegetation photosynthetic status result in subtle contributions to the top of atmosphere radiance signal, e.g. due to the emission of the solar induced chlorophyll fluorescence 650 800 nm) or due to changes in surface reflectance spectra (500 600 nm) indicating variations in the vegetation photoprotection and light use efficiency. Conversely, atmospheric effects (molecular and aerosol absorption and scattering) dominate the spectral interval of interest for vegetation studies. This article presents a comprehensive overview of the atmospheric radiative effects caused by aerosols, ozone (O3) water vapor (H2O) oxygen (O2) and atmospheric pressure and temperature changes within the visible and near infrared spectral interval, and paying special attention to the co occurring vegetation related spectral changes associated with the fluorescence emission and the activation of the photoprotection mechanisms. Since the largest uncertainties in the atmospheric correction process are associated with the characterization of the aerosol radiative effects, this work largely concentrates on the satellite retrieval related implications under different aerosol absorbing and scattering scenarios on a global scale. Through a simulation exercise, it is evaluated to what extent aerosol climatology could influence the accuracy of satellite derived surface apparent reflectance spectra impacting; therefore, any vegetation related satellite product on a seasonal and global scale.
Extending MARTE to Support the Specification and the Generation of Data Intensive Applications for Massively Parallel SoC
Venue: 2012 15th Euromicro Conference on Digital System Design
Year: 2012

The presented work proposes a MARTE (Modeling and Analysis of Real Time and Embedded systems) extension for the specification of data parallel applications designed to be executed on mppSoC, a massively parallel System on Chip. These applications can be clearly specified and generated using our transformation chain, which is automated and is a combination of contributions in different domains such as Model Driven Engineering, MARTE modeling and automatic code generation. The modeling methodology as well as the generation process are validated by an image processing application example.
Robust task space motion control of a mobile manipulator using a nonlinear control with an uncertainty estimator
Venue: Comput. Electr. Eng.
Year: 2018

In this study, a robust nonlinear control method with an uncertainty estimator is proposed and applied to a kinematically redundant mobile manipulator (MM) position tracking in its task space. The proposed MM has four degrees of freedom (dof) serial manipulator mounted on three of mobile bases. The proposed method incorporates a feed forward control term to reinforce the control action with extravagance from the desired acceleration vector; a disturbance estimator to reimburse for the unknown effects such as parametric uncertainties, external disturbances, unmodeled dynamics and a decentralized PID (proportional integral derivative) controller as a feedback loop to strengthen the stability of the system. The main strengths of the proposed scheme are its high robustness against parameter uncertainties and external disturbances, simplicity in design and ease of implementation. The effectiveness, feasibility, and robustness of the proposed method are illustrated using computer based simulations with and without uncertainty estimator.
Improved Algorithm for Automatic Elevated Body Temperature Screening using Short Wavelength Technology
Venue: 2021 International Conference on Emerging Smart Computing and Informatics (ESCI)
Year: 2021

Although temperature cameras cannot be used to detect infection or the existence of viruses, temperature cameras can be used to detect elevated body temperatures. The FLIR temperature scanner has long been used in public places, such as airports, train terminals, business establishments, factories, and skin surface measurements, as an effective tool and Elevated Body Temperature (EBT) Is helpful in identifying individuals. Affected by the global outbreak of Corona virus disease (COVID 19) it has been officially identified as an epidemic. Society is deeply concerned about the spread of infection, and is eventually looking for tools to stop the spread of the virus. The FLIR camera can be used as a screening tool to detect the increased skin temperature in the highest traffic public places through rapid individual screening. If the skin temperature is found above the average temperature in major body locations, such as the corner of the eye, and forehead, then the individual may be selected for additional screening. It is necessary to identify individuals with EBT, who should then be sent to a virus specific diagnostic testing center for examination. This may help in reducing the spread of viruses, and infections. All the methods available to measure body temperature are all based on edge detection. These methods are very sensitive to noise, and in these the system of noise reduction is quite complex. Therefore, an automated method is being introduced to measure the temperature of the corner of the Eye and Forehead using the "salt and pepper" and "colormap" method, which is less complex, and still has high accuracy.
Nonlinear analysis of radially functionally graded hyperelastic cylindrical shells with axially varying thickness and non uniform pressure loads based on perturbation theory
Year: 2019

In this study, nonlinear analysis for thick cylindrical pressure vessels with arbitrary variable thickness made of hyperelastic functionally graded material properties in nearly incompressible state and clamped boundary conditions under non uniform pressure loading is presented. Thickness and pressure of the shell are considered in axial direction by arbitrary nonlinear profiles. The FG material properties of nearly incompressible hyperelastic shell are graded in the radial direction with a power law distribution. Effective combination of shear deformation theory and match asymptotic expansion of perturbation theory are used to derived and solve the nonlinear governing equations, respectively. A numerical modelling based on finite element method is presented to validate the results of the current analytical solution. The effect of material constants, non homogeneity index, geometry and pressure profiles on displacements, stresses and hydrostatic pressure distributions are illustrated for different hyperelastic material properties and case studies. This approach enables insight to the nature of the deformation and stress distribution through the thickness of rubber vessels and may offer the potential to study the mechanical functionality of blood vessels such as artificial or natural arteries in physiological pressure range.
Affective Balance, Team Prosocial Efficacy and Team Trust: A Multilevel Analysis of Prosocial Behavior in Small Groups
Venue: PloS one
Year: 2015

Little research has focused on how individual and team level characteristics jointly influence, via interaction, how prosocially individuals behave in teams and few studies have considered the potential influence of team context on prosocial behavior. Using a multilevel perspective, we examined the relationships between individual (affective balance) and group (team prosocial efficacy and team trust) level variables and prosocial behavior towards team members. The participants were 123 students nested in 45 small teams. A series of multilevel random models was estimated using hierarchical linear and nonlinear modeling. Individuals were more likely to behave prosocially towards in group members when they were feeling good. Furthermore, the relationship between positive affective balance and prosocial behavior was stronger in teams with higher team prosocial efficacy levels as well as in teams with higher team trust levels. Finally, the relevance of team trust had a stronger influence on behavior than team prosocial efficacy.