Miracle-dz/UniFAR-FTUs huggingface dataset:
Detection of H2D+: Measuring the Midplane Degree of Ionization in the Disks of DM Tauri and TW Hydrae

We report the first detection of the ground transition of the ortho-H2D+ molecule toward one disk source, DM Tau, a tentative detection toward TW Hya, and an upper limit toward LkCa 15. The three observed sources possess young gas-rich disks with large CO depletion factors. We argue that the observed ground ortho-H2D+ line originates in the outer disk midplane gas, which is cold and depleted of CO. Since H2D+ is likely the most abundant ion, the measured line intensity allows to estimate the ionization degree in the midplane gas of the outer disk. In DM Tau the electron abundance is 7 × 10-10, in TW Hya it is about 4 × 10-10, and in LkCa 15 it is less than about 2 × 10-9. This implies that the ionization in the disks is large enough for the magnetorotational instability to operate. The required ionization rate, 5 × 10-17 s-1, in TW Hya is consistent, within the associated uncertainty, with normal cosmic-ray irradiation, whereas in DM Tau it is a factor of 10 lower than the canonical value.
Growth of Au-catalyzed ordered GaAs nanowire arrays by molecular-beam epitaxy

Ordered gallium arsenide (GaAs) nanowires are grown by molecular-beam epitaxy on GaAs (111)B substrates using Au-catalyzed vapor–liquid–solid growth defined by nanochannel alumina (NCA) templates. Field-emission scanning electron microscope images show highly ordered nanowires with a growth direction perpendicular to the substrate. The size (i.e., diameter) distribution of the wires is drastically narrowed by depositing the gold catalyst through an NCA template mask; this narrows the size distribution of the gold dots and arranges them in a well-ordered array, as defined by the NCA template. The nanowire diameter distribution full width at half maximum on the masked substrate is 5.1 nm, compared with 15.7 nm on an unmasked substrate.
Object–verb order in early sixteenth-century English prose: an exploratory study

Recent work on word-order change in the history of English has shown that late Middle English prose retains object–verb order as a productive option in contexts with an auxiliary and a quantified or negated object, and also in topicalization structures. In order to determine when these limited types of object–verb order became impossible, we have examined a collection of sixteenth-century prose texts. Our findings are that the patterns attested in late Middle English in fact continue until 1550, but then appear to dwindle away. We present the relevant object–verb data, discuss the reasons for the survival of the patterns found, provide an explanation for a difference at the level of detail between the fifteenth- and sixteenth-century data, and offer some suggestions about the reasons for the eventual loss of the structures in question.
On-line relay test for automatic tuning of PI controllers for stable processes

The applicability of the basic relay tuning method is extended by a robust on-line method to tune proportional–integral controllers without breaking the closed-loop control. A relay is connected in parallel to the controller to induce self-oscillation and then simple measurements are made only on the half period of limit cycle output to obtain a first-order plus dead time model of the process dynamics. A non-iterative optimum tuning formulae is developed in order to reduce the control signal variations. The simulation and experimental studies are presented to validate the design method.
A Multicentre Italian Study on the Psychological Impact of an Inconclusive Cystic Fibrosis Diagnosis after Positive Neonatal Screening

Background: An inconclusive diagnosis of cystic fibrosis (CF) after positive newborn screening (NBS) may cause parental distress. We compared the psychological impact of CF transmembrane conductance regulator-related metabolic syndrome (CRMS)/CF screen-positive, inconclusive diagnosis (CFSPID), and clear CF diagnosis, on parents. Methods: The participants were administered the Generalized Anxiety Disorder Scale, Patient Health Questionnaire-9, and the Italian version of the Impact of Event Scale-Revised as quantitative tools and semi-structured interviews as qualitative tools. Parental experience, child representation, relationships, future information, and perception of health status were investigated. Interviews were recorded and transcribed verbatim maintaining anonymity. Results: Thirty-two families were enrolled: sixteen with CF and CRMS/CFSPID, respectively. Anxiety and depression values were high in both groups, as were the measurement of traumatic impact subscales: avoidance, intrusiveness, and hyperarousal. The children’s health was evaluated by respective parents as being nearly healthy. Conclusions: Our results highlight negative psychological impacts, including emotional and affective representations, on parents of children with inconclusive CF diagnosis compared with those with clear diagnosis.
Explaining Digital Technology: Digital Artifact Delineation and Coalescence

ABSTRACT The literature pertaining to digital transformation, digital innovation, digital infrastructure, and digital platforms heavily relies on digital technology. Information Systems (IS) literature defines digital technologies as technologies that incorporate bitstrings (digital object). However, we contend that digital technologies are complex, multilayered information systems that surpass the simplicity of bitstrings. Bitstrings have existed since the inception of computers, and the contemporary use of bitstrings in IS literature is inconsistent with Computer Science and Engineering literature. We theorize artifact delineation—the capacity to provide unique interfaces and capabilities to distinct users—and coalescence—the ability to virtually integrate multiple IT architectural layers of complex information systems to conceal the details from users—to extend the core on digital technology, but more importantly to provide a better understanding of the complex nature of digital technologies for IS researchers and other domains that borrow from IS.
3-D ADI-FDTD method-unconditionally stable time-domain algorithm for solving full vector Maxwell's equations

We previously introduced the alternating direction implicit finite-difference time domain (ADI-FDTD) method for a two-dimensional TE wave. We analytically and numerically verified that the algorithm of the method is unconditionally stable and free from the Courant-Friedrich-Levy condition restraint. In this paper, we extend this approach to a full three-dimensional (3-D) wave. Numerical formulations of the 3-D ADI-FDTD method are presented and simulation results are compared to those using the conventional 3-D finite-difference time-domain (FDTD) method. We numerically verify that the 3-D ADI-FDTD method is also unconditionally stable and it is more efficient than the conventional 3-D FDTD method in terms of the central processing unit time if the size of the local minimum cell in the computational domain is much smaller than the other cells and the wavelength.
Insights into the Molecular Mechanisms Regulating Cell Behavior in Response to Magnetic Materials and Magnetic Stimulation in Stem Cell (Neurogenic) Differentiation

Magnetic materials and magnetic stimulation have gained increasing attention in tissue engineering (TE), particularly for bone and nervous tissue reconstruction. Magnetism is utilized to modulate the cell response to environmental factors and lineage specifications, which involve complex mechanisms of action. Magnetic fields and nanoparticles (MNPs) may trigger focal adhesion changes, which are further translated into the reorganization of the cytoskeleton architecture and have an impact on nuclear morphology and positioning through the activation of mechanotransduction pathways. Mechanical stress induced by magnetic stimuli translates into an elongation of cytoskeleton fibers, the activation of linker in the nucleoskeleton and cytoskeleton (LINC) complex, and nuclear envelope deformation, and finally leads to the mechanical regulation of chromatin conformational changes. As such, the internalization of MNPs with further magnetic stimulation promotes the evolution of stem cells and neurogenic differentiation, triggering significant changes in global gene expression that are mediated by histone deacetylases (e.g., HDAC 5/11), and the upregulation of noncoding RNAs (e.g., miR-106b~25). Additionally, exposure to a magnetic environment had a positive influence on neurodifferentiation through the modulation of calcium channels’ activity and cyclic AMP response element-binding protein (CREB) phosphorylation. This review presents an updated and integrated perspective on the molecular mechanisms that govern the cellular response to magnetic cues, with a special focus on neurogenic differentiation and the possible utility of nervous TE, as well as the limitations of using magnetism for these applications.
Pixel-Level Domain Adaptation for Real-to-Sim Object Pose Estimation

Transferring robotic grasping skills learned from a simulator to the real world is beneficial in reducing the cost of labeling. However, the models trained on synthetic data are brittle when being applied to real-world data due to the domain gap. In this article, we propose cycle-consistency, content-consistency, and mapping-consistency (CCM) pixel-level domain adaptation (Pixel-DA), a novel real-to-sim approach to unsupervised domain adaptation for object pose estimations, which outperforms conventional domain adaptation methods in preserving structural information, semantic information, and object pose during the transfer. The pipeline decouples domain adaptation and pose estimation, which allows the CCM Pixel-DA method to be integrated into state-of-the-art object pose estimation networks. The proposed method is further integrated into a pipeline for robot grasping. Experimental results on a real-world robot grasping system validate that the system is capable of grasping real-world objects without object pose annotations in the real-world domain.
Deep Learning Based Semantic Segmentation Technique for Anomaly Detection on Metal Surfaces Using High Calibre U- Shaped Network

Automatic detection of anomalies on the metal surface is an essential capability in industries to provide the better-quality control. To locate and identify the type of defect, it is necessary to find the Region of interest (RoI) from the captured image. Segmentation of the captured image is one among the many methods to achieve this task. Therefore, a precise and accurate segmentation method has major role to improve the metal surface anomaly detection rate in industry. As the defects are different in it’s size, shape and type, the process of semantic segmentation for metal surface is considered as a challenging task. To address this issue, a deep learning based high calibre U- shaped network is proposed. It can be considered as an automatic quality control system for industries. The proposed method is effective in predicting the presence of defects. The system is also capable to locate the position of the defect on surface without the intervention of human being. The up-sampling technique provided with the convolutional neural network in the architecture makes the system to produce high resolution outputs. The proposed system has been evaluated based on accuracy, precision, loss and IoU after training and testing the model using two different datasets called NEU metal surface defect database and Kolektor surface defect data set.
Locally Optimal Tests Against Unit Roots in Seasonal Time Series Processes

This paper builds on the existing literature on tests of the null hypothesis of deterministic seasonality in a univariate time‐series process. Under the assumption of independent Gaussian errors, we derive the class of locally weighted mean most powerful invariant tests against unit roots at the zero and/or seasonal frequencies in a seasonally observed process. Representations for the limiting distributions of the proposed test statistics under sequences of local alternatives are derived, and the relationship with tests for corresponding moving average unit roots is explored. We also propose nonparametric modifications of these test statistics designed to have limit distributions which are free of nuisance parameters under weaker conditions on the errors. Our tests are shown to contain existing stationarity tests as special cases and to extend these tests in a number of useful directions.
The Research on Semi-Supervised Support Vector Data Description Multi-Classification Algorithm

Semi-supervised Support Vector Data Description multi-classification algorithm is presented, in order to solve less labeled data learning, difficulties in the implementation and poor results of semi-supervised multi-classification, which full use the distribution of information in of non-target samples. S3VDD-MC algorithm defines the degree of membership of non-target samples, in order to get the non-target samples’ accepted labels or refused labels, on this basis, several super-spheres constructed, a k-classification problem is transformed into k SVDDs problem. Finally, the simulation results verify the effectiveness of the algorithm.
Theoretical Study of Orientation Dependence of Piezoelectric Effects in Wurtzite Strained GaInN/GaN Heterostructures and Quantum Wells

We calculated the crystal orientation dependence of piezoelectric fields in wurtzite strained Ga0.9In0. 1N/GaN heterostructures. The highest longitudinal piezoelectric field of 0.7 MV/cm can be generated in (0001)-oriented biaxial-strained Ga0.9In0. 1N layer coherently grown on GaN. On the contrary, no longitudinal piezoelectric field is induced in strained layers grown along orientations at an off angle of 39° or 90° from (0001). The high symmetry planes with these angles are, for instance, (1124) and (1012) for 39°, and (1120) and (1010) for 90°. We also calculated the crystal orientation dependence of the transition probability in a 3-nm strained Ga0.9In0. 1N/GaN quantum well, which indicated that the transition probability with these non-(0001) orientations becomes 2.3 times larger than that with the (0001) orientation. We conclude that high-performance strained nitride-based optical devices can be obtained by control of the crystal orientation.
Simulation and control system of a power harvesting device for railroad track health monitoring

With the vastness of existing railroad infrastructure, there exist numerous road crossings which are lacking warning light systems and/or crossing gates due to their remoteness from existing electrical infrastructure. Along with lacking warning light systems, these areas also tend to lack distributed sensor networks used for railroad track health monitoring applications. With the power consumption required by these systems being minimal, extending electrical infrastructure into these areas would not be an economical use of resources. This motivated the development of an energy harvesting solution for remote railroad deployment. This paper describes a computer simulation created to validate experimental on-track results for different mechanical prototypes designed for harvesting mechanical power from passing railcar traffic. Using the Winkler model for beam deflection as its basis, the simulation determines the maximum power potential for each type of prototype for various railcar loads and speeds. Along with calculating the maximum power potential of a single device, the simulation also calculates the optimal number and position of the devices needed to power a standard railroad crossing light signal. A control system was also designed to regulate power to a battery, monitor and record power production, and make adjustments to the duty cycle of the crossing lights accordingly. On-track test results are compared and contrasted with results from the simulation, discrepancies between the two are examined and explained, and conclusions are drawn regarding suitability of the device for powering high-efficiency LED lights at railroad crossings and powering track-health sensor networks.
Flicker of extragalactic radio sources and refractive interstellar scintillation

Recent work has identified variability of flat-spectrum extragalactic radio sources at lambdaroughly-equal10 cm with rms amplitude of approx.2%--3% and time scale of days. We show that this ''flicker'' is consistent with intensity fluctuations caused by refractive scintillation in an extended interstellar medium in our Galaxy. Further observation of flicker may allow the structure of suitable sources to be partially resolved on angular scales smaller than those probed by VLBI.
Kelvin Device to Scan Large Areas for Variations in Contact Potential

Many devices depend for their operation upon the establishment of known, uniform electric fields. Unknown contact potentials between the parts of the device often impair its performance. An apparatus has been constructed to measure the magnitude as well as the variation of such contact potentials with an accuracy of ±1 mV under conditions typical of bakeable high vacuum systems.
Estimating group delay

SUMMARY It is suggested that in estimating spectra and cross spectra for a pair of stationary series the estimation of the phase may be replaced by an estimate of the group delay, which is the derivative of phase with respect to frequency. An estimate is proposed and its asymptotic properties are investigated. The results are illustrated by simulation and by application to real data. The estimation of a group delay that is constant over a nondegenerate band is also discussed.
Regularized Kalman filtering for dynamic SPECT

We introduce a novel Bregman projection approach applied to Kalman filter to ensure nonnegativity and spatial regularization. While we do not postulate a precise anterior information about the underlying dynamics of the physical process, we put our method into practice for the case of image reconstruction in time-dependent single photon emission computed tomography (SPECT). Classical SPECT reconstruction algorithms assume that the activity does not vary in time; this is not always the case in practice. Thus arises the need of exploring time-varying SPECT which is an ill-posed and an ill-conditioned reconstruction problem. In this paper, we will explore a Kalman reconstruction approach to estimate the dynamic activity. We formulate a linear state-space model of the problem which we solve using the optimal Kalman filter (KF) and smoother. However, Kalman output image is unidentifiable because of the presence of nonnegative activity. In addition, KF does a temporal smoothing but not a spatial regularization. We thus incorporate a projection method to ensure nonnegativity and to enforce a spatial regularization using Tikhonov and median approaches. Numerical results are provided to corroborate the effectiveness of our reconstruction method.
Stepwise heterogeneity analysis of breast tumors in perfusion DCE-MRI datasets

The signal curves in perfusion dynamic contrast enhanced MRI (DCE-MRI) of cancerous breast tissue reveal valuable information about tumor angiogenesis. Pathological studies have illustrated that breast tumors consist of different subregions, especially with more homogeneous properties during their growth. Differences should be identifiable in DCEMRI signal curves if the characteristics of these sub-regions are related to the perfusion and angiogenesis. We introduce a stepwise clustering method which in a first step uses a new similarity measure. The new similarity measure (PM) compares how parallel washout phases of two curves are. To distinguish the starting point of the washout phase, a linear regression method is partially fitted to the curves. In the next step, the minimum signal value of the washout phase is normalized to zero. Finally, PM is calculated according to maximal variation among the point wise differences during washout phases. In the second step of clustering the groups of signal curves with parallel washout are clustered using Euclidean distance. The introduced method is evaluated on 15 DCE-MRI breast datasets with different types of breast tumors. The use of our new heterogeneity analysis is feasible in single patient examination and improves breast MR diagnostics.
Using Randomized Confidence Limits to Balance Risk: An Application to Medicare Investigations

Consider a population of N payments by Medicare to a health care provider, each payment for $4000. Suppose that an unknown number M of the payments are not justified and the other N−M payments are justified. A random sample of n payments is chosen and audited to determine if money should be recouped from the provider for the unjustified payments. Medicare guidelines state that in most situations the recoupment figure be determined by the lower end of a one-sided 90% confidence interval estimate of M based on a finding of X sample payments not to be justified. An exact lower estimate L=L(X) with the property PM(L≤M)≥0.90 for all M=0, 1, …, N−1 is an integral component of the minimum sum method that has been used in setting recoupment figures in Medicare audits (Edwards et al. 2003). In this article, we show the simple construction of a randomized lower estimate LR that improves upon L in the sense that LR≥L with probability 1 with PM*(LR≤M)=0.90 for all M=0, 1, …, N−1. In addition, we report on coverage probability and expectation from a Monte Carlo study that compares LR to lower estimates that are based on normal approximation. Whereas randomized confidence intervals for the parameter π of the Binomial distribution are well-studied and discussed in the literature, this seems not to be the case with the estimation of the parameter M of the Hypergeometric distribution.
The Role of V-Set Ig Domain-Containing 4 in Chronic Kidney Disease Models

V-set Ig domain-containing 4 (VSIG4) regulates an inflammatory response and is involved in various diseases. However, the role of VSIG4 in kidney diseases is still unclear. Here, we investigated VSIG4 expression in unilateral ureteral obstruction (UUO), doxorubicin-induced kidney injury mouse, and doxorubicin-induced podocyte injury models. The levels of urinary VSIG4 protein significantly increased in the UUO mice compared with that in the control. The expression of VSIG4 mRNA and protein in the UUO mice was significantly upregulated compared with that in the control. In the doxorubicin-induced kidney injury model, the levels of urinary albumin and VSIG4 for 24 h were significantly higher than those in the control mice. Notably, a significant correlation was observed between urinary levels of VSIG4 and albumin (r = 0.912, p < 0.001). Intrarenal VSIG4 mRNA and protein expression were also significantly higher in the doxorubicin-induced mice than in the control. In cultured podocytes, VSIG4 mRNA and protein expressions were significantly higher in the doxorubicin-treated groups (1.0 and 3.0 μg/mL) than in the controls at 12 and 24 h. In conclusion, VSIG4 expression was upregulated in the UUO and doxorubicin-induced kidney injury models. VSIG4 may be involved in pathogenesis and disease progression in chronic kidney disease models.