Miracle-dz/UniFAR-FTUs huggingface dataset:
Lower semicontinuity for polyconvex integrals without coercivity assumptions

We prove a lower semicontinuity theorem for a polyconvex functional of integral form, related to maps $u:\Omega \subset \mathbb{R}^{n}\rightarrow \mathbb{R}^{m}$ in $W^{1,n}(\Omega ;\mathbb{R}^{m})$ with $n\geq m\geq 2$, with respect to the weak $W^{1,p}$-convergence for $p>m-1$, without assuming any coercivity condition.
Investigation of wake interaction using full-scale lidar measurements and large eddy simulation: Investigation of wake interaction using full-scale lidar measurements and LES

In this paper, wake interaction resulting from two stall regulated turbines aligned with the incoming wind is studied experimentally and numerically. The experimental work is based on a full-scale remote sensing campaign involving three nacelle mounted scanning lidars. A thorough analysis and interpretation of the measurements is performed to overcome either the lack of or the poor calibration of relevant turbine operational sensors, as well as other uncertainties inherent in resolving wakes from full-scale experiments. The numerical work is based on the in-house EllipSys3D computational fluid dynamics flow solver, using large eddy simulation and fully turbulent inflow. The rotors are modelled using the actuator disc technique. A mutual validation of the computational fluid dynamics model with the measurements is conducted for a selected dataset, where wake interaction occurs. This validation is based on a comparison between wake deficit, wake generated turbulence, turbine power production and thrust force. An excellent agreement between measurement and simulation is seen in both the fixed and the meandering frame of reference. Copyright © 2015 John Wiley & Sons, Ltd.
Flow recovery from distal pressure in linearized hemodynamics: an optimal control approach

The goal of this work is to derive a reliable stable and accurate inverse problem strategy for reconstructing cardiac output blood flow entering the ascending aorta from pressure measurements at a distal site of the arterial tree, assumed here to be the descending aorta. We assume that a reduced one-dimensional model of the aorta can be linearized around its steady state, resulting in a wave system with absorbing boundary condition at the outlet. Using this model, we attempt to reconstruct the inlet flow from a pressure measurement at the distal outlet. First, we investigate the observability of the problem and prove that the inversion of the input-output operator for the flow and pressure in the space of time-periodic solutions is ill-posed of degree one. We then develop a variational approach where we minimize the discrepancy between measurements and a simulated state and penalize the error with respect to a periodic state. It is shown that the penalty strategy is convergent and provides an efficient solution for the minimization. Numerical results illustrate the robustness of our approach to noise and the potential of our method to reconstruct inlet flow from real pressure recordings during anesthesia.
The Robustness of Mark-Recapture Methods: A Case Study for the Northern Spotted Owl

A simulation study was carried out to investigate various aspects of modern methods for estimating survival probabilities from data on the recaptures of marked birds. The emphasis was on a study of the northern spotted owl in the Pacific Northwest of the United States where many captures and recaptures are of male and female nesting pairs, but the methods and many results apply generally to the study of nesting birds and denning mammals. The primary objectives of the study were to examine the bias in estimates, if any, due to temporary emigration, different capture probabilities for nesting and nonnesting birds, and nonindependent captures of males and females. Secondary objectives were to examine the properties of model selection using the quasi-Akaike information criterion (QAIC) and the power of goodness-of-fit tests for detecting temporary emigration, heterogenous capture probabilities, and dependence in capture probabilities. It was found that (i) the effects on survival estimates of the deviations from the usual assumptions for mark-recapture models are generally quite minor; (ii) the QAIC usually selects a model that is close to the correct one, although the difference may be important in terms of the conclusions from an analysis; and (iii) for the specific sample sizes considered, tests for goodness of fit have little power to detect errors in assumptions.
A METHOD FOR THE SOLUTION OF CERTAIN NON – LINEAR PROBLEMS IN LEAST SQUARES

The standard method for solving least squares problems which lead to non-linear normal equations depends upon a reduction of the residuals to linear form by first order Taylor approximations taken about an initial or trial solution for the parameters.2 If the usual least squares procedure, performed with these linear approximations, yields new values for the parameters which are not sufficiently close to the initial values, the neglect of second and higher order terms may invalidate the process, and may actually give rise to a larger value of the sum of the squares of the residuals than that corresponding to the initial solution. This failure of the standard method to improve the initial solution has received some notice in statistical applications of least squares3 and has been encountered rather frequently in connection with certain engineering applications involving the approximate representation of one function by another. The purpose of this article is to show how the problem may be solved by an extension of the standard method which insures improvement of the initial solution.4 The process can also be used for solving non-linear simultaneous equations, in which case it may be considered an extension of Newton's method. Let the function to be approximated be h{x, y, z, • • • ), and let the approximating function be H{oc, y, z, • • ■ ; a, j3, y, ■ • ■ ), where a, /3, 7, • ■ ■ are the unknown parameters. Then the residuals at the points, yit zit • • • ), i = 1, 2, ■ • • , n, are
Harmonic wavelet-based statistical linearization of the Bouc-Wen hysteretic model

A harmonic wavelet-based statistical linearization method is developed for determining the Evolutionary Power Spectrum (EPS) of the response of a single-degree-of-freedom Bouc-Wen hysteretic oscillator subject to random excitation. Specifically, relying on a rigorous locally stationary wavelet-based representation of random processes, a representation of the process corresponding to a specific scale and translation level is utilized to obtain excitation-response EPS relationships. To this aim, optimal and response dependent equivalent linear elements corresponding to the specific frequency and time band are introduced. This leads to an iterative determination of the response EPS. Thus, a joint time-frequency response analysis is achieved, readily applicable even for cases of physically realistic non-separable excitation EPS. Pertinent Monte Carlo simulations demonstrate the reliability and accuracy of the method. studies (e.g. Lin et al. 1989) and for the response analysis of soil deposits (e.g. Pires, 1996). One of the reasons for the popularity of the model, besides its versatility in efficiently capturing a wide range of hysteretic behaviors, is the option of explicitly computing equivalent linear elements. Specifically, a statistical linearization method (e.g. Roberts and Spanos, 2003) was proposed by Wen (1980) where closed form expressions were derived for the equivalent linear elements of the Bouc-Wen model. The method exhibited improved accuracy in comparison to earlier attempts which involved the assumption of a narrow-band response (e.g. Wen, 1976). Further, the model was extended to account for structural degradation (Baber and Wen, 1981), pinching effects (Foliente et al. 1996), and asymmetric hysteresis (Dobson et al. 1997; Song and Der Kiureghian, 2006). A detailed presentation of the applications and the extensions of the Bouc-Wen model can be found in review papers and books, such as the ones by Wen (1986, 1989), by Ikhouane and Rodellar (2007) and by Ismail et al. (2009). Further, statistical linearization based algorithms for the analysis of the Bouc-Wen model were discussed in Faravelli et al. (1998) from a mathematically rigorous point of view. Ma et al. (2004) suggested that certain parameters of the model are insensitive, and thus a significant simplification of the model is possible. In Yang et al. (1991) the
Centripetal accelerated particle swarm optimization and its applications in machine learning

Nowadays, meta-heuristic optimization algorithms have been extensively applied to a variety of Machine Learning (ML) applications such as classification, recognition, prediction, data mining and web mining, combinatorial optimization and so on. The majority of them imitate the behavior of natural phenomena to find the best solution. The algorithms find promising regions in an affordable time due to exploration and exploitation ability. Although the mentioned algorithms have satisfactory results in various fields, none of them is able to present a higher performance for all applications. Therefore, searching for a new meta-heuristic algorithm is an open problem. In this study, an improved scheme of Particle Swarm Optimization (PSO) based on Newtonian’s motion laws called Centripetal Accelerated Particle Swarm Optimization (CAPSO) has been proposed to accelerate learning process and to increase accuracy in solving ML problems. A binary mode of the proposed algorithm called Binary Centripetal Accelerated Particle Swarm Optimization (BCAPSO) has been developed for discrete (binary) search space. These algorithms have been employed for problems such as non-linear benchmark functions, Multi-Layer Perceptron (MLP) learning and the 0-1 Multidimensional Knapsack Problem (MKP). The results have been compared with several well-known meta-heuristic population-based algorithms in both continuous (real) and binary search spaces. From the experiments, it could be concluded that the proposed methods show significant results in function optimization for real and binary search spaces, MLP learning for classification problems and solving MKP for binary search space.
An Interregional Survey on Critical Care Nurses’ Perceptions of Psychological and Social Factors Related to their Working Environment

Background: Critical Care Nurses (CCNs) provide care to acutely and critically ill patients and their families. Psychological and social factors at work are potential contributors to the health and wellbeing of employees such as critical care nurses working in intensive care units. Studies shows that poor working environments may lead to stress and impaired health for the CCNs, and that work factors may affect both level of competence and patient safety. Aim: The aim of this study was to investigate critical care nurses' perceptions of psychological and social factors at work. Method: A survey to investigate Critical Care Nurses' (CCNs') perceptions of psychological and social factors at work. All the CCNs (n = 240) working in 15 ICUs in four Norwegian hospitals were invited to answer the General Nordic Questionnaire for Psychological and Social Factors at Work (QPSNordic). Data was collected during three weeks in Spring 2016 and analysed with descriptive statistics, and parametric and non-parametric tests. Results: In total 103 CCN completed the questionnaire (43%). The participants responded that they sometimes or rather often faced high job demands (3,09). They very often met positive challenges at work (4,41), though they rather seldom controlled decisions about their work (2,44) or control of work pace (2,32). They rather often had a perception of mastery of work (4,12). Conclusion: The result of this study suggests that participants experienced high expectations regarding quantitative- and decision demands, but less control at work. They rather seldom or sometimes got information about the quality of the work they performed. Nevertheless, they found their work challenging in a positive way and experienced a strong sense of mastery.
The assimilation of horizontal line‐of‐sight wind information into the ECMWF data assimilation and forecasting system. Part I: The assessment of wind impact

In preparation for the Aeolus Doppler Wind Lidar satellite mission, single‐component wind information from conventional observations was assimilated into the ECMWF data assimilation system. Various Observing System Experiments have been designed and performed in order to estimate the impact of such information in numerical weather prediction. The evaluation used various adjoint diagnostic tools and traditional statistical verification methods. The importance of assimilating wind observations as either single component or full vector wind is evaluated. Comparisons between the assimilation of wind and mass observations were also made. Wind observations can lead to significant improvement in the upper troposphere, lower stratosphere and in the Tropics. Mass data are more valuable in the midlatitudes, particularly in the lower part of the atmosphere. The comparison of additional mass and wind observations in the Global Observing System is useful to understand the importance of future wind observations. The investigation also highlighted that the impact of zonal wind observations is larger than the meridional wind (Aeolus will mostly measure wind components near the zonal direction). Root mean‐squared errors of temperature and wind forecasts when the single wind component (zonal) is assimilated show around 35% degradation up to day 2 forecasts and around 20% after day 2 as compared to assimilating full vector wind. Therefore the single (zonal) wind components can provide a large fraction of the vector wind impact particularly for the medium‐range forecast, which is promising for Aeolus.
Compact, flexible triple band Sierpinski fractal antenna on a Hilbert patterned ground for W‐WBSN

Wearable Wireless Body Sensor Networks (W‐WBSNs) are the best substitute for traditional healthcare systems by effectively monitoring patients within or remotely from the hospitals. Quite a few tiny sensors linked with one another on the body can efficiently observe the functional status of the human body. The paper introduces a new triple‐band step‐shaped Sierpinski fractal wearable antenna with a Hilbert fractal slot on the partial ground for W‐WBSN applications. It comprises the step‐shaped Sierpinski fractal radiator, situated on the upper side, and a partial ground placed on the lower side of the substrate. A partial ground with a Hilbert fractal slot is used to achieve a compact wearable antenna with improved bandwidth and electromagnetic exposure rate characteristics. The antenna is constructed using a flexible Jeans substrate (permittivity 1.7, loss tangent 0.025) of size 60 × 60 mm2 with a thickness of 2.5 mm as it is a potential candidate. It demonstrates an impedance bandwidth of 6%, 7%, and 10.1%, a gain of 2.57 dBi, 3.608 dBi, and 4.19 dBi, the radiation efficiency of 54.19%, 52.5%, and 54.8% at 3.3 GHz, 5.2 GHz, and 5.8 GHz correspondingly in free space. Also, the peak SAR value of 0.64 W/kg, 0.46 W/kg, and 1.17 W/kg at 3.3 GHz, 5.2 GHz, and 5.8 GHz correspondingly, for 10 g of an average human tissue. The compact, flexible low SAR makes the anticipated antenna a superior contender for wireless wearable network applications.
Which Potential Evapotranspiration Formula to Use in Hydrological Modeling World‐Wide?

Although many potential evapotranspiration (PET) formulas are available, there is still a lack of knowledge on when and where to use them in catchment modeling world‐wide. Here we experimented with three different formulas in a global hydrological model (the World‐wide HYPE), using 15 years of observations from 5,338 streamflow gauges and global evapotranspiration from Earth‐observations (MOD16). We tested model performance in a multi‐process approach to select the best formula for catchments covering the global landmass. From comparing the results with land‐cover, climate classification, water‐energy limitations, we found that climate is the main driver behind the spatial patterns in model performance. Hargreaves was the best PET formula in 50% of the catchments, most of them located in the Amazonas, central Europe, and Oceania; Jensen‐Haise was better for catchments in northern latitudes (36%). Finally, Priestley‐Taylor was the best formula for India and latitudes above 65° North. The selection of a PET formula seems to be more critical in tropical regions close to the equator, where the differences in performance are above 50%. This is also where PET is highest. We found a strong connection between the five main Köppen regions and the PET formulas, further supported by landcover analysis. Hence, the PET formulas differed in their capacity to provide useful input to the water balance modeling, with complex formulas only giving improved predictions in temperate and polar regions; however, for the rest of the globe simpler formulas were better. We thus recommend to apply different PET formulas based on climatic regions world‐wide.
Feasibility and Acceptability of a Remotely Delivered Executive Function Intervention That Combines Computerized Cognitive Training and Metacognitive Strategy Training in Chronic Stroke

Executive dysfunction after stroke is associated with limitations in daily activities and disability. Existing interventions for executive dysfunction show inconsistent transfer to everyday activities and require frequent clinic visits that can be difficult for patients with chronic mobility challenges to access. To address this barrier, we developed a telehealth-based executive function intervention that combines computerized cognitive training and metacognitive strategy. The goal of this study was to describe intervention development and to provide preliminary evidence of feasibility and acceptability in three individuals who completed the treatment protocol. The three study participants were living in the community and had experienced a stroke >6 months prior. We assessed satisfaction (Client Satisfaction Questionnaire-8 [CSQ-8]), credibility (Credibility and Expectancy Questionnaire), and feasibility (percent of sessions completed). All three subjects rated the treatment in the highest satisfaction category on the CSQ-8, found the treatment to be credible, and expected improvement. Participants completed a median of 96% of computerized cognitive training sessions and 100% of telehealth-delivered metacognitive strategy training sessions. Individuals with chronic stroke may find a remotely delivered intervention that combines computerized cognitive training and metacognitive strategy training to be feasible and acceptable. Further evaluation with larger samples in controlled trials is warranted.
DFT simulation of the Raman spectrum of single layer graphene

Raman spectroscopy is one of the most suitable tools for studying few-layer graphene. The position of the G band and the defect-induced D and D' bands in the spectra of perfect single-layer graphene with sp2-hybridized carbon atoms and hydrogenated graphene with 27.7% sp3-hybridized carbon atoms are simulated using the Density Functional Theory (DFT) method with Perdew-Burke-Ernzerhof (PBE) functional. In the case of perfect graphene, the Raman G band is predicted at 1612 cm-1. In the case of the hydrogenated structure, a new feature appears. Namely, along with the G band, now shifted to 1591 cm-1, an additional feature, located at 1703 cm-1, is clearly seen. The latter is due to oscillations, involving six atomic benzene rings, containing two sp3-hybridized C atoms. According to our results, the presence of defects, related to sp3 hybridized carbon, gives rise to the appearance of the defect D' band in the Raman spectrum of defective graphene. This study shows that it is possible to simulate Raman spectra using the DFT method, with the results qualitatively matching the experimental data.
Identification of Airport Similar Days Based on K-prototype Cluster and Grey Correlation Analysis

Considering similar air traffic control techniques for the present based on close historical dates is a good approach due to the unpredictability of weather and air traffic, as well as to increase controller efficiency. A K-prototype clustering technique and grey correlation analysis are proposed to discover similar days to address the problem of similar identification. Firstly, the weather and air traffic datasets are used to create a set of features broken down into numerical and categorical attributes. Secondly, the historical data are clustered using the K-prototype clustering, which is then paired with grey correlation analysis to identify days similar to the reference day and examine the traffic management initiatives employed on that day. Finally, the research uses actual weather information and aircraft schedules from Nanjing Lukou International Airport as examples. The outcomes demonstrate that the similar days picked by the model are representative and can serve as a foundation for airport controllers' decision-making.
Correlation filter tracking of channel response constraints

To solve the problem of moving object loss and inaccurate localization caused by the rapid change of object appearance, partial occlusion, and scale change in the tracking process, the channel response constraints correlation filter was proposed, i.e., CRCCF. Firstly, the channel self-optimizing regularization is introduced on the background-aware correlation filter (BACF), reducing the interference of redundant channel information to adapt to the complex environment. Then, the different response error regularization was introduced, the work used differences of adjacent frames response figures to learn the object appearance changes, which can smooth the volatility of the response figure to alleviate the drift problem model. And the Alternating Direction Method of Multipliers (ADMM) is used to optimize the model. Finally, a large number of experiments were carried out on OTB50, and OTB100 datasets to verify that the proposed algorithm has better performance under the conditions of fast motion, background clutter, and motion blur.
Application Research of Power Battery System Fault Identification Method Based on Multi-Classifier Fusion

In this paper, a module fault identification method for a power battery system based on multi-classifier fusion is designed. Taking the inverter single tube open circuit fault of the power battery system as an example, the deep learning method of deep neural network and convolutional neural network is used to build the primary diagnosis layer. The fusion method of evidential reasoning rules is used to fuse the primary diagnosis results. The fault data are obtained through simulation of the simulation model, which verifies that the scheme avoids the disadvantage of unstable diagnosis results of a single classifier, and obtains more stable diagnostic results with higher diagnostic accuracy.
Optimal Dispatch of Multi-energy Microgrid Considering Flexible Resources Aggregation

Distributing energy has facilitated the interaction between electricity, heating, and gas systems. Aggregating flexible resources in the optimal scheduling process of multi-energy microgrids can promote the complementary coordination of multi-energy systems and reduce useless energy waste. In this paper, flexible resource aggregation analysis is added to the optimal scheduling problem of multi-energy microgrids. Firstly, a resource aggregation model based on Mincowski addition is proposed to fully characterize the schedulable potential of flexible resources. Then, a multi-energy microgrid collaborative optimal scheduling model is established to fully exploit the operational potential of flexible resources. Finally, a numerical example is simulated to verify the validity of the aggregation model and control algorithm above-mentioned, which improves the efficiency and accuracy of optimal control of multi-energy microgrids.