Miracle-dz/UniFAR-FTUs huggingface dataset:
Scale Invariance of Liquid Water Distributions in Marine Stratocumulus. Part I: Spectral Properties and Stationarity Issues

Abstract This study investigates the internal structure of marine stratocumulus (Sc) using the spatial fluctuations of liquid water content (LWC) measured along horizontal flights off the coast of southern California during the First ISCCP Regional Experiment (FIRE) in summer of 1987. The results of FIRE 87 data analyses are compared to similar ones for marine Sc probed during the Atlantic Stratocumulus Transition Experiment (ASTEX) in summer 1992 near the Azores. In this first of two parts, the authors use spectral analysis to determine the main scale-invariant regimes, defined by the ranges of scales where wavenumber spectra follow power laws; from there, they discuss stationary issues. Although crucial for obtaining meaningful spatial statistics (e.g., in climate diagnostics), the importance of establishing stationarity—statistical invariance under translation—is often overlooked. The sequel uses multifractal analysis techniques and addresses intermittency issues. By improving our understanding of both.
Effect of Magnetic Field Fluctuations in Spin‐Echo NMR Experiments

The effect of magnetic field drift and ripple on spin‐echo NMR experiments is discussed with emphasis on Carr‐Purcell spin‐echo (CPSE) experiments. It is shown that magnetic field fluctuations dephase the echo maxima with respect to the pulsed rf signal phase. This makes it difficult to use phase sensitive detection in the presence of a large field ripple even when the average field is very stable. The effect of ripple is particularly serious in CPSE experiments because the Meiboom‐Gill improvement of the CPSE sequence requires phase coherence of the echo signals. Closed equations are derived for the magnitude of the dephasing at a single echo and at the nth echo of a CPSE sequence, caused by linear magnetic field drift, sinusoidal ripple, and nonsinusoidal ripple. The results indicate that the effect of ripple is proportional to the ratio of its amplitude to its frequency, and that the effect on a CPSE sequence can be minimized by making the 180° pulse repetition rate large with respect to the ripple fre.
On “Overestimation-free Computational Version of Interval Analysis”

The transformation of interval parameters into trigonometric functions, proposed in Int. J. Comput. Meth. Eng. Sci. Mech. , vol. 13, pp. 319–328 (2012), is not motivated in comparison to the infinitely many equivalent algebraic transformations. The conclusions about the efficacy of the methodology used are based on incorrect comparisons between solutions of different problems. We show theoretically, and in the examples considered in the commented article, that changing the number of the parameters in a system of linear algebraic equations may change the initial problem, respectively, its solution set. We also correct various misunderstandings and bugs that appear in the article noted above.
An inverse optimization model for imprecise data envelopment analysis

Inverse data envelopment analysis (InDEA) is a well-known approach for short-term forecasting of a given decision-making unit (DMU). The conventional InDEA models use the production possibility set (PPS) that is composed of an evaluated DMU with current inputs and outputs. In this paper, we replace the fluctuated DMU with a modified DMU involving renewal inputs and outputs in the PPS since the DMU with current data cannot be allowed to establish the new PPS. Besides, the classical DEA models such as InDEA are assumed to consider perfect knowledge of the input and output values but in numerous situations, this assumption may not be realistic. The observed values of the data in these situations can sometimes be defined as interval numbers instead of crisp numbers. Here, we extend the InDEA model to interval data for evaluating the relative efficiency of DMUs. The proposed models determine the lower and upper bounds of the inputs of a given DMU separately when its interval outputs are changed in the performance analysis process. We aim to remain the current interval efficiency of a considered DMU and the interval efficiencies of the remaining DMUs fixed or even improve compared with the current interval efficiencies.
Tracking and higher-order correction tradeoffs for beam correction through strong turbulence

The problem of optimally pointing a laser beam is commonly referred to as the tracking problem. Accurately pointing the beam in the presence of scintillated atmospheric conditions is difficult. The objective here is to understand the limitations of how well we can point a laser beam to a target point under long path, scintillated conditions. We investigate performance for both the case of tilt-only compensation and tilt compensation used in conjunction with higher order compensation. For the scintillated conditions considered here we find that only when we considered higher order compensation in conjunction with tracking do we see benefit from tracking. This leads us to claim that the tracking problem is difficult, if not impossible, to decouple form the high order compensation problem for the scintillated conditions considered in this work.
Evaluation of horizontal gradients in sigma-coordinate shallow water models

Abstract Evaluating horizontal gradients in three‐dimensional shallow water models that use bottom‐following sigma coordinates can lead to large errors near steep bathymetry. A technique that has been proposed to minimize this problem involves computing horizontal gradients in cartesian coordinates, while treating all other terms in a sigma coordinate framework. We study this technique through both truncation error analysis and numerical experimentation, and compare it to the direct application of sigma coordinates. While the Cartesian coordinate method has better convergence properties and generally smaller truncation errors when horizontal gradients are zero, the sigma coordinate method can be more accurate in other physically relevant situations. Also, the Cartesian coordinate method introduces significant numerical diffusion of variable sign near the bottom (where physical diffusion is particularly small), thus potentially leading to instabilities. Overall, we consider the sigma coordinates to be the .
A Bayesian Approach for Fitting a Random Effect Differential Item Functioning Across Group Units

This study proposed a multilevel logistic regression model to evaluate variation of differential item functioning (DIF). The model accounts for the three level nested structure of the data and combines results of logistic regression analyses to investigate the variation of DIF across level-3 units. A simulation study is presented to assess the adequacy of the proposed model. The parameters of the proposed model were estimated by using a Bayesian approach implemented by the WinBUGS 1.4.
Micro- and Macroscopic Road Traffic Analysis using Drone Image Data

The current development in the drone technology, alongside with machine learning based image processing, open new possibilities for various applications. Thus, the market volume is expected to grow rapidly over the next years. The goal of this paper is to demonstrate the capabilities and limitations of drone based image data processing for the purpose of road traffic analysis. In the first part a method for generating microscopic traffic data is proposed. More precisely, the state of vehicles and the resulting trajectories are estimated. The method is validated by conducting experiments with reference sensors and proofs to achieve precise vehicle state estimation results. It is also shown, how the computational effort can be reduced by incorporating the tracking information into a neural network. A discussion on current limitations supplements the findings. By collecting a large number of vehicle trajectories, macroscopic statistics, such as traffic flow and density can be obtained from the data. In the second part, a publicly available drone based data set is analyzed to evaluate the suitability for macroscopic traffic modeling. The results show that the method is well suited for gaining detailed information about macroscopic statistics, such as traffic flow dependent time headway or lane change occurrences. In conclusion, this paper presents methods to exploit the remarkable opportunities of drone based image processing for joint macro-and microscopic traffic analysis.
Nanomechanical biosensing with immunomagnetic separation

We report a biosensing method that combines immunomagnetic separation and nanomechanical detection. In this method, same magnetic beads that are used to “fish” biomolecules from complex mixtures enable deflection of a cantilever structure upon excitation by an oscillating magnetic field. Biotin-coated magnetic beads were used to capture and separate streptavidin from serum. Streptavidin loaded magnetic beads were exposed to a differential cantilever system whose sensing arm was functionalized with biotin. The magnetic force applied on the streptavidin-beads resulted in differential cantilever deflections that could be detected down to 0.26 Arms in air.
Investigation of buffer traps in an AlGaN/GaN/Si high electron mobility transistor by backgating current deep level transient spectroscopy

The influence of a substrate voltage on the dc characteristics of an AlGaN/GaN high electron mobility transistor (HEMT) on silicon (111) substrate is profited to investigate traps that are located between the substrate and the two-dimensional electron gas channel. The transient of the drain current after applying a negative substrate voltage is evaluated in the temperature range from 30 to 100 °C. With this method, known as backgating current deep level transient spectroscopy, majority carrier traps with activation energy of 200 meV as well as minority carrier traps at 370 meV are identified. The experiments are performed on completed HEMTs, allowing the investigation of the influence of device fabrication technology.
Network Community Detection Based on Co-Neighbor Modularity Matrix with Spectral Clustering

The problem of community detection is one of the outstanding issues in the study of network systems. This paper presents co-neighbor modularity matrix to measure the quality of community detection. The problem of community detection is projected into clustering of eigenvectors in Euclidean space. Network community structure is detected with spectral clustering algorithm which is free from the noise of initial mean point in K-mean algorithm. The experimental results suggest that the method is efficient in finding the structure of community.
Development of a fiber-less fNIRS system and its application to hair-covered head

While most commercially available functional near-infrared spectroscopy (fNIRS) systems employ optical fibers for both the measurement optode and the transmission cable for optical signals, their material inflexibility presents some problems in stable optode fixation to the head surface and adequate cable lining to the main system. In practice, mechanical fluctuations of optical fibers in fNIRS measurement often lead to motion artifacts in the signals. A few fiberless fNIRS systems are available and equipped with light sources and detectors that directly adhere to the scalp surface. However, their shapes and detection sensitivities are not suitable for usage on a hair-covered head. Based on the commercial fiber-less fNIRS system OEG-16 (Spectratech Inc., Japan), we developed a new source-detector unit that was designed with LEDs for enhanced illumination, avalanche photodiodes instead of photodiodes, and a new holder system. The electrical circuits of the system were modified after the design. By simultaneous implementation of multidistance fNIRS measurement and hemodynamic modality separation on conventional fNIRS data at the bilateral parietal area during single-sided motor tasks, significant functional signals were observed only at the position contralateral to the side of movement. This is the first report describing a fiber-less fNIRS system that can detect functional signals on a hair-covered head. We believe this fiber-less system will improve the utility of fNIRS, particularly in less restraining conditions.
Multivariate methods in pharmaceutical applications

This review covers material published within the field of pharmacy in the last five years. Articles concerning experimental design, optimization and applications of multivariate techniques have been published, from factorial designs to multivariate data analysis, and the combination of the two in multivariate design. The number of publications on this topic testifies to the good results obtained in the studies. Much of the published material highlights the usefulness of experimental design, with many articles dealing with optimization, where much effort is spent on getting useful results. Examples of multivariate data analysis are comparatively few, but these methods are gaining in use. The employment of multivariate techniques in different applications has been reviewed. The examples in this review represent just a few of the possible applications with different aims within pharmaceutical applications. A number of companies are using experimental design as a standard tool in preformulation and in combination with response surface modeling. The properties of e.g. a tablet can be optimized to fulfill a well‐specified aim such as a specific release profile, hardness, disintegration time etc. However, none of the companies apply multivariate methods in all steps of the drug development process. As this is still very much a growing field, it is only a question of time before experimental design, optimization and multivariate data analysis are implemented throughout the entire formulation process, from performulation to multivariate process control. Copyright © 2002 John Wiley & Sons, Ltd.
Assessing Model Fit in Latent Class Analysis When Asymptotics Do Not Hold

The application of latent class (LC) analysis involves evaluating the LC model using goodness-of-fit statistics. To assess the misfit of a specified model, say with the Pearson chi-squared statistic, a p-value can be obtained using an asymptotic reference distribution. However, asymptotic p-values are not valid when the sample size is not large and/or the analyzed contingency table is sparse. Another problem is that for various other conceivable global and local fit measures, asymptotic distributions are not readily available. An alternative way to obtain the p-value for the statistic of interest is by constructing its empirical reference distribution using resampling techniques such as the parametric bootstrap or the posterior predictive check (PPC). In the current paper, we show how to apply the parametric bootstrap and two versions of the PPC to obtain empirical p-values for a number of commonly used global and local fit statistics within the context of LC analysis. The main difference between the PPC .
Development of a 3-D Rehabilitation System for Upper Limbs Using ER Actuators in a Nedo Project

New training methods and exercises for upper limbs rehabilitation are made possible by application of robotics and virtual reality technology. The technologies can also make quantitative evaluations and enhance the qualitative effect of training. We have joined a project managed by NEDO (New Energy and Industrial Technology Development Organization as a semi–governmental organization under the Ministry of Economy, Trade and Industry of Japan) 5-year Project, "Rehabilitation System for the Upper Limbs and Lower Limbs", and developed a 3–DOF exercise machine for upper limbs (EMUL) using ER actuators. In this paper, we also present the development of software for motion exercise trainings and some results of clinical evaluation. Moreover, it is discussed how ER actuators ensure the mechanical safety.
MoO3 as combined hole injection layer and tapered spacer in combinatorial multicolor microcavity organic light emitting diodes

Multicolor microcavity (μC) organic light-emitting diode (OLED) arrays were fabricated simply by controlling the hole injection and spacer MoO3 layer thickness. The normal emission was tunable from ∼490 to 640 nm and can be further expanded. A compact, integrated spectrometer with two-dimensional combinatorial arrays of μC OLEDs was realized. The MoO3 yields more efficient and stable devices, revealing a new breakdown mechanism. The pixel current density reaches ∼4 A/cm2 and a maximal normal brightness ∼140 000 Cd/m2, which improves photoluminescence-based sensing and absorption measurements.
Evaluation of thermal exposure on absorbing objects with digital holographic interferometry method

The digital holographic interferometry methods that are used in this work nowadays is widely applied to research of temporal dynamics of objects transformation in different processes [1, 2]. Authors use developed stand and software that are used for detecting and controlling of phase changes of transparent objects with time resolution not less than 100 ms for a long time. Samples of recording polymer material for volume holography «Difphen», solid solution of organic dye phenanthrenequinone (PQ) in polymethylmethacrylate (PMMA), were used as an object of research. Samples were prepared in the shape of plane-parallel disks, 40 mm in diameter and thickness (2÷4) mm [3]. The using radiation with wavelength λ=473 nm is located in the region of absorption of PQ and presents by itself a beam of radiation of solid state DPSS laser which is (2.5÷3.0) mm in diameter and its power is of about 50 mWt. The part of the sample that was exposed by the radiation, absorbing energy, is bleaching and heating up. The bleaching process takes place just in localized area (exposed area), while increase of temperature from exposed area to unexposed areas of the sample is spread by heat transfer. For observation of the process of transfer of heat in the quality of probe radiation we use radiation with wavelength λ = 532 nm in spectral area of light-insensitivity the sample. The probe area was 20x20 mm, which allowed us to evaluate thermal effects in object’s area, located out of reach of laser beam 473nm.
Gain-distribution measurements along an ultralong erbium-doped fiber amplifier using optical-time-domain reflectometry

Optical gain changes as a function of distance along a long distributed erbium-doped fiber amplifier were measured, using optical-time-domain reflectometry. Two dispersion-shifted erbium-doped fibers with fiber lengths of 9-19 km and doping concentrations of 0.5 (0.5 x 10(-4) wt %) and 0.1 (0.1 x 10(-4) Wt %) parts in 10(6) have been tested, using 1.48-microm InGaAsP laser diode and 0.98-microm Ti:sapphire laser pump sources. It is shown how local gain and loss change with an increase in pump power.
Retrieval of the penetration of light and horizontal visibility in coastal waters from ocean color remote sensing data

We present two algorithms for the retrieval of 1) vertical light penetration and 2) horizontal visibility in coastal waters using ocean color data. The Secchi depth, a proxy to vertical visibility or water transparency, is related to the former two properties and can be retrieved from two irradiance reflectances. The algorithm development includes the use of classical approximations in marine optics, sensitivity analyses based on radiative transfer calculations, and the use of an extensive in situ optical data set. Finally, match-ups between in situ measurements of the Secchi depth and ocean color data were used for the parameterization of the operational algorithm. Maps of Secchi depths are presented over different coastal regions.
Bifurcation phenomena of mean-field coupled self-sustained oscillators with two different time scales under the influence of external white noise

We report stochastic phenomena of inter-cluster synchronization in ensembles of coupled self-sustained oscillators with different time scales. A mean-field model of two interconnected clusters of coupled limit cycle oscillators having same trajectories with two native frequencies is considered to analytically derive the time evolution of the order parameters in the thermodynamic limit. A set of the order parameters obtained for the system exhibits noise induced bifurcations, including those from torus type attractors to fixed point ones for the case with two different heat baths in accordance with the introduction of the time scale.
The cascade EM algorithm

The estimate-maximize (EM) algorithm is an iterative method for finding maximum-likelihood parameter estimates from incomplete data. The authors develop an extension of the EM algorithm that may be useful in accelerating the algorithm and in simplifying the computations involved. The extension works with an intermediate complete data specification, and performs intermediate steps at some iterations. The authors consider the problem of parameter identification of a continuous-time linear dynamic system given discrete-time observations, and show that the proposed algorithm accelerates the convergence of the EM algorithm and simplifies the computations involved. >
Degradation of (AlGa)As DH lasers due to facet oxidation

The facet deterioration of (AlGa)As DH lasers by aging was analyzed by Auger electron spectroscopy in combination with Ar sputter etching. It was found that oxide was formed on the laser facet during cw operation, and the oxidation degraded lasers not only in long‐term operation but also even at an early stage of operation.
On the heavy-traffic limit theorem for GI/G/∞ queues

A revealing alternate proof is provided for the Iglehart (1965), (1973)–Borovkov (1967) heavy-traffic limit theorem for GI/G/s queues. This kind of heavy traffic is obtained by considering a sequence of GI/G/s systems with the numbers of servers and the arrival rates going to ∞ while the service-time distributions are held fixed. The theorem establishes convergence to a Gaussian process, which in general is not Markov, for an appropriate normalization of the sequence of stochastic processes representing the number of customers in service at arbitrary times. The key idea in the new proof is to consider service-time distributions that are randomly stopped sums of exponential phases, and then work with the discrete-time vector-valued Markov chain representing the number of customers in each phase of service at arrival epochs. It is then easy to show that this sequence of Markov chains converges to a multivariate O–U (Ornstein–Uhlenbeck) diffusion process by applying simple criteria in Stroock and Varadhan (1979). The Iglehart–Borovkov limit for these special service-time distributions is the sum of the components of this multivariate O–U process. Heavy-traffic convergence is also established for the steady-state distributions of GI/M/s queues under the same conditions by exploiting stochastic-order properties.
Rejection of α-particle background for neutrinoless double beta decay search with pixel detectors

Experiments searching for the neutrinoless double beta decay require a very low level of background. Most experiments aim for a background rate of about 10−4 to 10−3events/kg ·keV ·yr. α-particles can be an important part of the background since they occur as decay products of many isotopes in the natural decay chains. They are emitted with kinetic energies of a few MeV and therefore produce background at the Q-value of most double beta isotopes. In this publication we demonstrate experimentally that α-particle background can be reduced by about six orders of magnitude exploiting its topology of triggered pixels which is different from neutrinoless double beta decay of 116Cd.