Miracle-dz/UniFAR-FTUs huggingface dataset:
Acoustic correlates of perceived stress in an isolated synthetic disyllable

In production, syllables that are stressed differ from those that are unstressed on at least four well‐known acoustic parameters—duration, pitch, amplitude, and spectral reduction of the vowel. In order to determine how these parameters interact in perception of stress a disyllabic nonsense word—/bapbap/—was synthesized on the Haskins Laboratories OVEIII speech synthesizer. Four series were constructed in which one of the two syllables was systematically shortened, reduced, or lowered in steady state pitch or steady state amplitude. Linguistically sophisticated listeners were able to hear stable and reliable differences in stress only when duration was manipulated. Since duration seemed to be the critical parameter, naive subjects listened to the duration series under two conditions. In the duration judgment condition, listeners were asked to judge whether the first or second syllable sounded longer, while in the stress judgement condition they were asked whether the disyllable sounded more like PERmit or.
Hidden Hazards Beneath Cross-Validation Methods in Machine Learning-Based Sleep Apnea Detection

Obstructive sleep apnea (OSA) is a respiratory disorder highly correlated with multiple cardiovascular diseases. In the last two decades, several alternatives have been proposed to palliate the limitations of polysomnography, the current gold standard for OSA diagnosis. Such alternatives were mainly based on the heart rate variability in combination with machine learning (ML) techniques, obtaining promising results. However, the majority of these works used a cross-validation approach for the validation of the proposed methods, and rarely tested them on external sources of newly added data. Hence, some of the most common algorithms found in the state of the art have been evaluated with cross-validation and external validation in this work. The obtained results have raised important concerns on the real performance shown by the typical ML-based OSA detection methods in more realistic scenarios.
Design and Validation of a Classroom Observation Instrument to Evaluate the Quality of Mathematical Activity from a Gender Perspective

This study aimed to design and validate an instrument as a guideline for classroom observation and analysis of teaching–learning process in mathematics. The instrument considers 3 dimensions and 16 subdimensions with a gender perspective. A content validity process by eight expert judges is presented, built from a dialectical work between theory and empirical evidence from more than 100 classes observed and recorded on video from 19 educational centers. The degree of agreement between experts was determined with Fleiss Kappa and Kendall coefficients. Experts’ judgement scored each dimension from 1 to 3. Globally, an almost perfect strength of agreement was obtained in 6 of 16 dimensions with x¯>2.5, and in the other 10 dimensions, a strength of agreement was obtained between moderate and almost perfect, 2.14≤x¯≤2.5. Fleiss Kappa coefficients were highest in relevance and clarity, κ = 0.425, 95% CI [0.344, 0.506], p < 0.001 and, κ = 0.461, 95% CI [0.375, 0.548], p < 0.001, respectively. Moreover, the degree of clarity, coherence, sufficiency, and relevance was statistically moderately agreed upon in their assessments, with an overall Kendall’s W = 0.489, p < 0.001.
Damping of Coronal Loop Oscillations: Calculation of Resonantly Damped Kink Oscillations of One-dimensional Nonuniform Loops

The analytic study of coronal loop oscillations in equilibrium states with thin nonuniform boundary layers is extended by a numerical investigation for one-dimensional nonuniform equilibrium states. The frequency and the damping time of the ideal kink quasi mode are calculated in fully resistive MHD. In this numerical investigation there is no need to adopt the assumption of a thin nonuniform boundary layer, which is essential for analytic theory. An important realization is that analytical expressions for the damping rate that are equivalent for thin nonuniform layers give results differing by a factor of 2 when they are used for thick nonuniform layers. Analytical theory for thin nonuniform layers does not allow us to discriminate between these analytical expressions. The dependence of the complex frequency of the kink mode on the width of the nonuniform layer, on the length of the loop, and on the density contrast between the internal and the external region is studied and is compared with analytical theory, which is valid only for thin boundaries. Our numerical results enable us to show that there exists an analytical expression for thin nonuniform layers that might be used as a qualitative tool for extrapolation into the regime of thick nonuniform layers. However, when the width of the nonuniform layer is varied, the differences between our numerical results and the results obtained with the version of the analytical approximation that can be extended into the regime of thick nonuniform layers are still as large as 25%.
Augmentation-Assisted Robust Fringe Detection on Unseen Experimental Signals Applied to Optical Feedback Interferometry Using a Deep Network

Commercialization and industrial deployment of optical feedback interferometry or self-mixing interferometry (SMI)-based displacement instruments are held back due to inaccurate fringe detection under different optical feedback or speckle induced by operating conditions. In this work, we propose using deep neural 2-D networks, which have renowned generalization performance on unseen data. Nonetheless, training deep neural networks requires very large data which in our application would imply acquiring large datasets of experimental signals by operating such interferometers under as many operating conditions as possible. To circumvent this time- and resource-consuming process, we propose a novel data augmentation scheme that increases the amount of training data needed by a deep network for fringe detection/classification in SMI signals. Interestingly, this has enabled the trained deep network to acquire excellent generalization capability where it has learned to detect SMI fringes belonging to weak- and very strong-optical feedback regimes, even when it was only trained on moderate- and strong-feedback regime signals. Consequently, our trained model has shown robust performance for simulated weak-, moderate-, and strong-optical feedback regime SMI signals affected by additive noise. Various experimental SMI signals, acquired under different sensor and optical conditions, have also been successfully processed. We also implemented an established fringe detection method for comparison. Our work presents very good generalization capability when compared to this established method. Our novel augmentation scheme is generic and can be applied to other interferometric signals. We have released our dataset and implementation, with the hope that this will assist the community in accelerating the commercialization of optical feedback interferometry leveraging the full potential of deep learning.
Alloying effect on the magnetic properties of RFeB-type bulk magnets

Composition dependence on the magnetic properties of directly casted cylindrical RFeBM-type magnets with diameters from 0.7 mm to 1.5 mm has been studied. In order to refine the microstructure for improving the magnetic properties of bulk magnets with various sizes, the following ways were adopted in the alloys: (1) changing the boron concentration, (2) substituting different kinds of refractory elements for Fe, (3) adding a slight carbon and finally, (4) adopting multi-component refractory elements. The experimental results showed that the Nd9.5Fe72. 5Ti3B15 bulk magnets with a diameter of 0.7 mm demonstrated the optimal magnetic properties of Br = 6.5 kG, iHc = 10.3 kOe and (BH)max = 8.7 MG Oe, while the highest iHc of 16.2 kOe was achieved for the Pr9.5Fe71.5Nb4B15 magnet. Meanwhile, the diameter of the magnets could be increased to 0.9 mm preserving the attractive magnetic properties by the substitution of 0.5 at% Zr for Ti in the Nd9.5Fe72. 5Ti3B15 magnet. Finally, the directly casted Nd9.5Fe72. 5−yTi2.5Zr0.5CryB15−xCx (x = 0.25–1; y = 0–3) magnets with a larger diameter of 1.3 mm exhibited attractive magnetic properties of Br = 5.3–6.1 kG, iHc = 7.2–12.5 kOe and (BH)max = 6.5–7.2 MG Oe.
Comparison of Signal Combinations for Cardiorespiratory Sleep Staging

This work investigates the benefit of using multiple signals and preprocessing strategies for sleep staging from cardiorespiratory signals. We modified our previous Neural Network model to take different signal combinations as input. To that end, we added oxygen saturation and different respiratory signals to the electrocardiogram. We further invoked different preprocessing strategies that have been described previously for such signals, namely using downsampled signals vs. using time series of breath-to-breath intervals. We trained and tested our model variations with 4784 polysomnograms from the Sleep Heart Health Study. We found the best combination of signals to be heart rate together with a downsampled respiratory signal. The classification resulted in a k of 0.68 on hold-out test data, which outperforms our previous results and state of the art for cardiorespiratory sleep staging. We observe that combinations of cardiorespiratory signals can improve classification performance for automatic cardiorespiratory sleep staging. As there are generally more cardiorespiratory signals available and many more options for preprocessing them, we expect that further research in this area will show even more improvements.
On the numerical solution of partial differential equations defined on domains limited by curves using segmented forms of the tau-lines method

This paper discusses solving partial differential equations defined over domains limited by curves using segmented forms of the Tau-Lines Method introduced by El Misiery and Ortiz. The paper consists of two parts. In the first part discussion on how to apply the Method to a linear partial differential equation defined on a rectangular domain, with mixed boundary conditions, is given. In the second part discussion and numerical application are carried out on the Poisson equation defined: (i) on a square domain, (ii) on a domain limited by a parabolic arc. The result obtained for example (i) has been favorably compared with results reported by using two different techniques. Second and fourth order accurate Finite Difference Approximations have been used and their merits have been discussed. Four systems of equally spaced mesh-lines have been experimented and their related results have been compared. The approximate solutions, on the lines, are sought as finite expansions in terms of orthogonal polynomial b.
Modeling Spatial Heterogeneity and Historical Persistence: Nazi Concentration Camps and Contemporary Intolerance

A wealth of recent research in comparative politics examines how spatial variation in historical conditions shapes modern political outcomes. In an article in the American Political Science Review, Homola, Pereira, and Tavits argue that Germans who live nearer to former Nazi concentration camps are more likely to display out-group intolerance. Clarifying the conceptual foundations of posttreatment bias and reviewing the historical record on postwar state creation in Germany, we argue that state-level differences confound the relationship between distance to camps and out-group intolerance. Using publicly available European Values Survey data and electoral results from 2017, we find no consistent evidence that distance to camps is related to contemporary values. Our findings have implications for literatures on historical persistence, causal inference with spatial data, Holocaust studies, and outgroup tolerance.
Patient migration as a response to the regulation of subnational healthcare budgets

ABSTRACT Since 2007, Italian regions with healthcare budget deficits above a specific total funding threshold are obliged to commit to a financial recovery plan (FRP). We employ a quasi-experimental strategy to assess the consequences of FRPs on access to healthcare by identifying their impact on patient migration. We find a 24–31% increase in patient migration as a result of the strictest implementation of FRPs. Our empirical analysis shows that migration is affected by the regional availability of both capital and labour inputs.
Poly-Vinylidene Fluoride (PVDF)/ Span-85 Membrane for Omega-3 PUFA Concentration: Membrane Performance and Fouling Analysis

Abstract: For increasing the efficiency of the omega-3 production, the effect of span-85 surfactant as a hydrophobic agent (0, 1, 2, 3 and 4 wt%) on the formation of a porous structure in a sub-layer PVDF membrane was studied. Also, the cross-sectional morphology of PVDF membranes was analyzed using scanning electron microscopy (SEM) and the surface property was evaluated using contact angle test. The capacity of the membranes to concentrate omega-3 was examined by the filtration of lantern fish oil. Also, the fouling of the membrane caused by oil filtration was evaluated. All filtration tests were performed in batch mode. Results of the SEM images demonstrated that the addition of span-85 resulted in the forming of a membrane with a denser and more hydrophobic structure. The addition of 2 wt% span-85 increased the omega-3 concentration, while the higher level of span-85 showed a negative effect. The best efficiency of the membrane was 42.64 wt% which was obtained by PVDF/2 wt% span-85. This efficiency was about 11% higher than the control PVDF (0 wt%). Taken together, the presence of span-85 affected the phase inversion process during the membrane formation, which improved omega-3 concentration. Moreover, the more hydrophobic surface of the membrane resulted in higher cake formation.
Chewing Efficiency Test in Subjects with Clear Aligners

The aim of this study was to evaluate the masticatory function of subjects with clear aligners and to propose a simple and repeatable method for the clinical and experimental evaluation of masticatory function. For the testing we used almonds, a natural substance that can be easily found and stored, has intermediate consistency and hardness, is insoluble in saliva, and has the ability easily lose the moisture absorbed in the mouth. Thirty-four subjects using the Invisalign® (Align Technology, Santa Clara, CA, USA) protocol were randomly selected. This was an “intercontrol test”, i.e., all subjects under the same conditions acted as controls but also as cases whilst wearing the clear aligners. Patients were asked to chew an almond for 20 s, once with aligners and once without aligners. The material was then dried, sieved, and weighted. Statistical analysis was performed to investigate any significative differences. In all our subjects, the efficiency of chewing with clear aligners was found to be comparable to the efficiency of chewing without clear aligners. In detail, the average weight after drying was 0.62 g without aligners and 0.69 g with aligners, while after sieving at 1 mm, the average weight was 0.08 g without aligners and 0.06 g with aligners. The average variation after drying was of 12%, and after sieving at 1 mm, it was 25%. In summary, there was no substantial difference between chewing with or without clear aligners. Despite some discomfort in chewing, the clear aligners were well tolerated by most subjects, who wore them without difficulty even during meals.
Maize Terpene Synthase 8 (ZmTPS8) Contributes to a Complex Blend of Fungal-Elicited Antibiotics

In maize (Zea mays), fungal-elicited immune responses include the accumulation of terpene synthase (TPS) and cytochrome P450 monooxygenases (CYP) enzymes resulting in complex antibiotic arrays of sesquiterpenoids and diterpenoids, including α/β-selinene derivatives, zealexins, kauralexins and dolabralexins. To uncover additional antibiotic families, we conducted metabolic profiling of elicited stem tissues in mapping populations, which included B73 × M162W recombinant inbred lines and the Goodman diversity panel. Five candidate sesquiterpenoids associated with a chromosome 1 locus spanning the location of ZmTPS27 and ZmTPS8. Heterologous enzyme co-expression studies of ZmTPS27 in Nicotiana benthamiana resulted in geraniol production while ZmTPS8 yielded α-copaene, δ-cadinene and sesquiterpene alcohols consistent with epi-cubebol, cubebol, copan-3-ol and copaborneol matching the association mapping efforts. ZmTPS8 is an established multiproduct α-copaene synthase; however, ZmTPS8-derived sesquiterpene alcohols are rarely encountered in maize tissues. A genome wide association study further linked an unknown sesquiterpene acid to ZmTPS8 and combined ZmTPS8-ZmCYP71Z19 heterologous enzyme co-expression studies yielded the same product. To consider defensive roles for ZmTPS8, in vitro bioassays with cubebol demonstrated significant antifungal activity against both Fusarium graminearum and Aspergillus parasiticus. As a genetically variable biochemical trait, ZmTPS8 contributes to the cocktail of terpenoid antibiotics present following complex interactions between wounding and fungal elicitation.
The Influence of Land Surface Heterogeneities on Cloud Size Development

AbstractThis study analyzes the effects of land surface heterogeneities at various horizontal scales on the transition from shallow to deep convection and on the cloud size distribution. An idealized case of midlatitude summertime convection is simulated by means of large-eddy simulations coupled to an interactive land surface. The transition is accelerated over heterogeneous surfaces. The simulation with an intermediate patch size of 12.8 km exhibits the fastest transition with a transition time two-thirds that over a homogeneous surface. A similar timing is observed for the precipitation onset whereas the total accumulated rainfall tends to increase with patch size. The cloud size distribution can be approximated by a power law with a scale break. The exponent of the power law is independent of the heterogeneity scale, implying a similar cloud cover between the simulations. In contrast, the scale break varies with patch size. The size of the largest clouds does not scale with the boundary layer height, .
Source-Free Cross-Domain State of Charge Estimation of Lithium-Ion Batteries at Different Ambient Temperatures

Machine learning methods for state of charge (SOC) estimation of lithium-ion batteries (LiBs) face the problem of domain shift. Varying conditions, such as different ambient temperatures, can cause performance degradation of the estimators due to data distribution discrepancy. Some transfer learning methods have been utilized to tackle the problem. At real-time transfer, the source model is supposed to keep updating itself online. In the process, source domain data are usually absent because the storage and acquisition of all historical running data can involve violating the privacy of users. However, existing methods require coexistence of source and target samples. In this article, we discuss a more difficult yet more practical source-free setting where there are only the models pretrained in source domain and limited target data can be available. To address the challenges of the absence of source data and distribution discrepancy in cross-domain SOC estimation, we propose a novel source-free temperature transfer network (SFTTN), which can mitigate domain shift adaptively. In this article, cross-domain SOC estimation under source-free transfer setting is discussed for the first time. To this end, we define an effective approach named minimum estimation discrepancy (MED), which attempts to align domain distributions by minimizing the estimation discrepancy of target samples. Extensive transfer experiments and online testing at fixed and changing ambient temperatures are performed to verify the effectiveness of SFTTN. The experiment results indicate that SFTTN can achieve robust and accurate SOC estimation at different ambient temperatures under source-free scenario.
Quantitative Evaluation of Stem-like Markers of Human Glioblastoma Using Single-Cell RNA Sequencing Datasets

Simple Summary A common issue in glioblastoma stem cells (GSCs) studies is the need to efficiently and precisely target GSCs using reliable biomedical markers. Using single-cell RNA sequencing datasets, we quantitatively evaluated an extensive number of GSCs markers with multiple parameters that dictate the feasibility of various laboratory and therapeutic applications. We present promising marker candidates with their scores on the corresponding parameters and apply sequential selection based on these parameters. Both previously approved and novel markers are proposed according to the evaluation. We demonstrate the possibility of choosing a biomedical marker in a nonarbitrary way and provide quantitative references for potential GSCs markers. Abstract Targeting glioblastoma (GBM) stem-like cells (GSCs) is a common interest in both the laboratory investigation and clinical treatment of GBM. Most of the currently applied GBM stem-like markers lack validation and comparison with common standards regarding their efficiency and feasibility in various targeting methods. Using single-cell RNA sequencing datasets from 37 GBM patients, we obtained a large pool of 2173 GBM stem-like marker candidates. To evaluate and select these candidates quantitatively, we characterized the efficiency of the candidate markers in targeting the GBM stem-like cells by their frequencies and significance of being the stem-like cluster markers. This was followed by further selection based on either their differential expression in GBM stem-like cells compared with normal brain cells or their relative expression level compared with other expressed genes. The cellular location of the translated protein was also considered. Different combinations of selection criteria highlight different markers for different application scenarios. By comparing the commonly used GSCs marker CD133 (PROM1) with markers selected by our method regarding their universality, significance, and abundance, we revealed the limitations of CD133 as a GBM stem-like marker. Overall, we propose BCAN, PTPRZ1, SOX4, etc. for laboratory-based assays with samples free of normal cells. For in vivo targeting applications that require high efficiency in targeting the stem-like subtype, the ability to distinguish GSCs from normal brain cells, and a high expression level, we recommend the intracellular marker TUBB3 and the surface markers PTPRS and GPR56.