Miracle-dz/UniFAR-FTUs huggingface dataset:
Capture-recapture estimation of bowhead whale population size using photo-identification data

Statistical models andmaximumlikelihood methods are developed for estimating bowhead whale population size from photo-identification data. These are tested on both simulated data and actual data from 1985 and 1986 photographic studies. Initially a multinomial model that accounts for unmarked whales is used. Variance is estimated using the parametric bootstrap. In the cases considered, the variance estimators perform similarly to previously used delta method based estimators in terms of confidence interval coverage, as long as log-normal rather than symmetric confidence intervals are used for the latter. Further models are developed to account for heterogeneity in capture probabilities (highly marked whales are more likely to be captured than moderately marked) and non-random sampling caused by age segregation. These models, particularly the latter, perform better than the multinomial model on simulated data that incorporate these violations of standard capture-recapture assumptions. All three models are applied to actual bowhead whale data. The resulting estimates of the 1+ population size (animals 1 year old or older) in 1985-86 range from 4,719 (using the non-random sampling model on the small dataset in which lengths are available for all whales so that age class can be determined) to 7,331 (using the heterogeneity model on the full dataset). Standard errors are comparable to those obtained from the ice-based census in years with sub-optimal environmental conditions. All confidence intervals include the ice-based census estimates for 1985 and 1986, as well as the corresponding values of 1+ population size in the most likely trajectory from a Bayesian synthesis analysis. These most likely values – 6,649 and 6,820 – incorporate the ice-based census estimates and additional data on bowhead whale population dynamics.
Interface superconductivity: History, development and prospects

The concept of interface superconductivity was introduced over 50 years ago. Some of the greatest physicists of that time wondered whether a quasi-two-dimensional (2D) superconductor can actually exist, what are the peculiarities of 2D superconductivity, and how does the reduced dimensionality affect the critical temperature (Tc). The discovery of high-temperature superconductors, which are composed of coupled 2D superconducting layers, further increased the interest in reduced dimensionality structures. In parallel, the advances in experimental techniques made it possible to grow epitaxial 2D structures with atomically flat surfaces and interfaces, enabling some of the experiments that were proposed decades ago to be performed finally. Now we know that interface superconductivity can occur at the junction of two different materials (metals, insulators, semiconductors). This phenomenon is being explored intensely; it is also exploited as a means to increase Tc or to study quantum critical phenomena. This research may or may not produce a superconductor with a higher Tc or a useful superconducting electronic device but it will likely bring in new insights into the physics underlying high-temperature superconductivity.
BIODEGRADED MANGO PEEL BASED DIETS ALTERS LIPOGENIC GENES AND mRNA EXPRESSION IN GROWING RABBITS.

This study investigated the effect of bio fermented mango peel meal (FMPM) on lipid metabolism – related gene expression in liver tissues and abdominal adipose tissue in weaned rabbits. Twenty (20) weaned New Zealand white rabbits were randomly assigned to four dietary groups with five (5) rabbits in each group in a completely randomized design experiment. The diets had a control(G1) supplemented with unfermented mango seed meal, an experiment diet of mango peel fermented by either of P. ostreatus, (G2) and P. rivulosis (G3) and combinations of P. ostreatus plus P. rivulosis 50:50 (G4). The results showed that FMPM supplementation down regulated the mRNA expression of fatty acid synthesis (FAS) and acetyl CoA carboxylase (ACC) in liver tissues and the lipoprotein lipase (LPL) expression in abdominal fat tissues (P< 0.05). The mRNA expression of perixisomeproliferators activated receptor - alpha (PPAR-α) compared to control group, was up regulated in the livers of rabbits on fermented diets (P<0.05). However gene expressions of PPAR-γ and L-FABP liver fatty acid binding protein were not altered (P> 0.05) in rabbits fed FMPM supplemented diets. These findings indicated that FMPM regulated lipid metabolism by either increasing or decreasing the expression of lipogenic genes and altering multiple pathways in the liver. This information provided new essential information and demonstrated great potential inherent in nutrigenomics in researching nutrients added to livestock diets.
ECAPA-TDNN Based Depression Detection from Clinical Speech

Depression is a serious mood disorder that has become one of the major diseases that endanger human mental health. The automatic detection of depression using speech signals has become a promising approach for the early diagnosis of depression currently. However, there is still a performance gap between clinical practice and research, considering the lab-recorded corpus was used in most of the current studies. Therefore, we collected a Chinese clinical depression corpus, of which 131 participants with their speech during the Hamilton Rating Scale for Depression (HAMD) interview were included in this study. Furthermore, we developed a depression speech detection system based on a Time-Delay Neural Network (TDNN) model to distinguish depression i . Our approach achieves a mean F1 score of 90.8 % and an accuracy of 90.4 % by five-fold cross-validation. The result suggests that the developed TDNN-based model has a potential clinical meaning in the diagnosis of depression.
Static magnetic correlations near the insulating-superconducting phase boundary in La2-xSrxCuO4

An elastic neutron-scattering study has been performed on several single crystals of La 2 - x Sr x CuO 4 for x near a lower critical concentration x c for superconductivity. In the insulating spin-glass phase (x = 0.04 and 0.053), the previously reported one-dimensional spin modulation along the orthorhombic b axis is confirmed. Just inside the superconducting phase (x = 0.06), however, two pairs of incommensurate magnetic peaks are additionally observed corresponding to the spin modulation parallel to the tetragonal axes. These two types of spin modulations with similar incommensurabilities coexist near the boundary. The peak width K along the spin-modulation direction exhibits an anomalous maximum in the superconducting phase near x c , where the incommensurability δ increases monotonically upon doping across the phase boundary. These results are discussed in connection with the doping-induced superconducting phase transition.
Wave propagation, phase mixing and dissipation in Hall MHD

In this thesis the effect of the Hall term in the generalised Ohm’s law on Alfvén (shear) and fast wave propagation and dissipation in the ion cyclotron frequency range is investigated. The damping of an initially Gaussian field perturbation in a uniform Hall MHD plasma is treated analytically. Subsequently a 2D Lagrangian remap code (Lare2d) is used to study the damping and phase mixing of initially Gaussian field perturbations and a harmonic series of boundary-driven perturbations in a uniform field (in the presence of a transverse equilibrium density gradient). The same code is then used to study a range of initially shear and fast-wave perturbations in the vicinity of a magnetic X-type null point. The magnetic energy associated with an initially Gaussian field perturbation in a uniform resistive plasma is shown to decay algebraically at a rate that is unaffected by the Hall term to leading order in kδi where k is wavenumber and δi is ion skin depth. A similar decay law applies to whistler perturbations in the limit kδi 1. We demonstrate that in both geometries considered, the inclusion of the Hall term reduces the effectiveness of phase-mixing in plasma heating. The reduction in the damping rate in the uniform field (non-uniform density) cases, arising from dispersive effects, tends to zero in both the weak and strong phase mixing limits. In the Hall MHD X-point case, minimal reductions are seen for initially shear wave pulses, suggesting that little or no phase-mixing takes place. Nonlinear fast wave pulses which interact with the initial X-point destabilise the local field sufficiently to generate multiple null pairs; subsequent oscillatory current sheet behaviour appears unaffected by earlier differences between the MHD and Hall MHD cases.
Measurement of the nu(mu) charged-current quasielastic cross section on carbon with the ND280 detector at T2K

The Charged-Current Quasi-Elastic (CCQE) interaction, νl + n → l − + p, is the dominant CC process at Eν ∼ 1 GeV and contributes to the signal in accelerator-based long-baseline neutrino oscillation experiments operating at intermediate neutrino energies. This paper reports a measurement by the T2K experiment of the νµ CCQE cross section on a carbon target with the off-axis detector based on the observed distribution of muon momentum (pµ) and angle with respect to the incident neutrino beam (θµ). The flux-integrated CCQE cross section was measured to be hσi = (0.83 ± 0.12) × 10−38 cm2 . The energy dependence of the CCQE cross section is also reported. The axial mass, MQE A , of the dipole axial form factor was extracted assuming the Smith-Moniz CCQE model with a relativistic Fermi gas nuclear model. Using the absolute (shapeonly) pµ-cosθµ distribution, the effective M QE A parameter was measured to be 1.26+0.21 −0.18 GeV/c2 (1.43+0.28 −0.22 GeV/c2 ).
An Empirical Model of the Proton Isotropic Boundary (IB)

At mid‐ and high‐latitudes, energetic protons of magnetospheric origin precipitate down to the upper atmosphere, manifested as one of the most important coupling processes between the ionosphere and magnetosphere. The equatorward boundary of the energetic proton precipitation, or called isotropic boundary (IB), is usually believed to coincide with the boundary in the magnetosphere where field line curvature scattering of protons transits from strong to weak. In this study, we statistically examine the global distribution of the IB for 39 and 115 keV ring current protons as detected by the NOAA/Polar Operational Environmental Satellites in 2013–2018 and propose an empirical model of the IB as a function of solar wind conditions, magnetic local time, and geomagnetic activity index. The IB is well ordered by SYM‐H index and interplanetary magnetic field Bz, moving to lower latitudes with more negative SYM‐H index and Bz values, and with larger AE index and solar wind pressure. Energy dispersions of the IB are also explored, consistent with the scenario of field curvature scattering in the magnetosphere. This empirical model can be used as an indirect measure of the strength of the magnetospheric stretching and may be applied in magnetosphere‐ionosphere mapping studies.
A Unified Accent Estimation Method Based on Multi-Task Learning for Japanese Text-to-Speech

We propose a unified accent estimation method for Japanese text-to-speech (TTS). Unlike the conventional two-stage meth-ods, which separately train two models for predicting accent phrase boundaries and accent nucleus positions, our method merges the two models and jointly optimizes the entire model in a multi-task learning framework. Furthermore, considering the hierarchical linguistic structure of intonation phrases (IPs), accent phrases, and accent nuclei, we generalize the proposed approach to simultaneously model the IP boundaries with accent information. Objective evaluation results reveal that the proposed method achieves an accent estimation accuracy of 80 . 4% , which is 6 . 67% higher than the conventional two-stage method. When the proposed method is incorporated into a neural TTS framework, the system achieves a 4.29 mean opinion score with respect to prosody naturalness.
Response to Letter to Editor Entitled “Arrhythmias in Patients with Hyperthyroidism”

We entitled “ The Assessment of Ventricular Arrhythmia Indicators Based on Electrocardiography in Patients With Differentiated Thyroid Cancer Followed Up With Levothyroxine Suppression. ” 2 As we mentioned in our study, ventricular arrhythmia in patients with differentiated thyroid cancer under levothyroxine suppression treatment is not well-established as atrial arrhythmias. 3 Therefore, the main objective of our study was to focus on electrocardiographic indicators, which primarily indicate ventricular arrhythmias. In this context, QT dispersion was not one of our study ’ s mainly evaluated electro-graphic parameters. When we looked at the literature, we saw that a recent study has reported levothyroxine suppression treatment affects QT dispersion in patients with differentiated thyroid cancer. 4 Evaluating QT dispersion in our study population may be a subject of new research. The other question of Yayla et al 1 was the surgical success of the patients in our study. However, our study 2 was not designed to compare presurgical and postsurgical electrocardiography. We aimed to compare electrocardiographic changes between postsurgical patients already under levothyroxine suppression treatment and healthy controls. So, we cannot comment on the surgical success of the operated patients.Thetwo topics mentioned above may be investigated in further research.
Goldstone bosons in the massless Thirring model. Witten's criterion

We discuss the Ward identity and the low–energy theorem for the divergence of the axial–vector current in the massless Thirring model with fermion fields quantized in the chirally broken phase (Eur. Phys. J. C 20, 723 (2001)). The Ward identity and the low–energy theorem are analysed in connection with Witten’s criterion for Goldstone bosons (Nucl. Phys. B 145, 110 (1978)). We show that the free massless (pseudo)scalar field, bosonizing the massless Thirring model in the chirally broken phase, satisfies Witten’s criterion to interpret quanta of this field as Goldstone bosons. As has been shown in hep–th/0210104 and hep–th/0212226, Goldstone’s criterion, the non–invariance of the wave function of the ground state, is also fulfilled.
Prevalence and Risk Factors of Smoking and Vaping among Nursing Students in a Private University in Manila, Philippines

Nursing students trained to be health advocates have engaged in cigarette and electronic cigarette (EC) use. Our study aimed to determine the prevalence of cigarette and EC use among nursing students and examine how EC vaping and cigarette smoking are associated with various risk factors. This cross-sectional survey of smoking and vaping was administered to 249 nursing students in a private university in Manila, Philippines. An online-based self- assessment questionnaire (SAQ) that includes socio-demographic information, cigarette and EC usage, Perceived Stress Scale (PSS), and other risk factors was administered using on-campus and online recruitment strategies. Generalized linear models were fitted to estimate the effect of stress and other risk factors on smoking and vaping. Approximately one out of eight were exclusive vapers, one out of 25 were exclusive smokers, and one out of five were both smokers and vapers. The prevalence of smoking/vaping was 47% higher [adjusted prevalence ratio (aPR): 1.47, 95% confidence interval (95% CI): 1.04–2.07, p-value = 0.028] among students with high-stress levels than students with low to moderate stress levels. In addition, students who were sophomores (aPR: 1.69, 95% CI: 1.12–2.53, p-value = 0.012), juniors (aPR: 3.22, 95% CI: 1.91–5.42, p-value < 0.001), and seniors (aPR: 1.53, 95% CI: 0.76–3.08, p-value = 0.230) had a higher prevalence of smoking/vaping compared to freshmen students. Having a positive attitude towards vaping health impacts and a smoker/vaper peer was also associated with a higher prevalence of smoking/vaping. Therefore, effective health communication strategies and policies in universities and the community are recommended to reinforce existing smoking and vaping control efforts.
Efficient Fire Segmentation for Internet-of-Things-Assisted Intelligent Transportation Systems

Rapid developments in deep learning (DL) and the Internet-of-Things (IoT) have enabled vision-based systems to efficiently detect fires at their early stage and avoid massive disasters. Implementing such IoT-driven fire detection systems can significantly reduce the corresponding ecological, social, and economic destruction; they can also provide smart monitoring for intelligent transportation systems (ITSs). However, deploying these systems requires lightweight and cost-effective convolutional neural networks (CNNs) for real-time processing on artificial intelligence (AI)-assisted edge devices. Therefore, in this paper, we propose an efficient and lightweight CNN architecture for early fire detection and segmentation, focusing on IoT-enabled ITS environments. We effectively utilize depth-wise separable convolution, point-wise group convolution, and a channel shuffling strategy with an optimal number of convolution kernels per layer, significantly reducing the model size and computation costs. Extensive experiments on our newly developed and other benchmark fire segmentation datasets reveal the effectiveness and robustness of our approach against state-of-the-art fire segmentation methods. Further, the proposed method maintains a balanced trade-off between the model efficiency and accuracy, making our system more suitable for IoT-driven fire disaster management in ITSs.
Coping with Knowledge Inertia for Improving R&D Team Creativity by Using Structural Equation Modeling and Hierarchical Multiple Regression

The study reported here examined the relationship between knowledge inertia and R&D team creativity by focusing on the mediating effect of cognitive conflict with direct and moderating effect of intentional unlearning capability to find out how to improve R&D team creativity for sustainable innovation from the perspective of coping with knowledge inertia. Data from a two-wave survey of 135 R&D teams within 67 firms in China were used to conduct the analysis of structural equation modeling and hierarchical multiple regression. Results showed that as predicted, knowledge inertia can not only decrease R&D team creativity directly but also decrease it by reducing the level of cognitive conflicts among the R&D team members, causing serious damage to R&D team creativity. In addition, intentional unlearning capability can help the R&D team reduce knowledge inertia directly and moderated the negative relationship between knowledge inertia and R&D team creativity, which was when intentional unlearning capability was high, the negative relationship between knowledge inertia and R&D team creativity would be weakened, and when it was low, the relationship would be strengthened, thus further verifying the important role of intentional unlearning capability in reducing the negative impact of knowledge inertia on R&D team creativity for sustainable innovation.
Emotion Recognition of Violin Playing Based on Big Data Analysis Technologies

The rapid development of Internet technology has promoted the vigorous development of the multimedia. As one of the most classic instruments, the violin has been fully developed in its creation, education, and performance. In the face of more and more violin performances, the effective organization and retrieval of these musical works is an urgent problem to be solved, while it is common to classify and organize music based on the emotional properties of the performance. Deep learning is a model based on feature hierarchy and unsupervised feature learning, which has strong learning ability and adaptability. Based on the recurrent neural network (RNN) method, long short-term memory (LSTM) is one of the classic models of deep learning that can effectively learn the characteristics of time series data and achieve effective predictions. Therefore, based on the classical Hevner emotion classification model, this paper proposes an emotion recognition method for dynamic violin performances based on LSTM, which selects acoustic features and classifies the audio acoustic signals contained in the violin performances. In order to verify the effectiveness of this method, this paper carries out data labeled, feature selection, and model test on the actual violin music data by turn. The results show that the proposed method can greatly reduce the training time and improve the prediction accuracy, which reached 83%, higher than the existing methods. Meanwhile, the accuracy and iteration times of violin playing music of different emotional categories are also counted. Moreover, the method is robust to the genre, timbre, and noise changes, and the emotion recognition effect is superior.
The concept of modularity in supply chains : A multiple case-study in the construction industry

– The objective of this study is to assess the applicability of Fine's three‐dimensional modularity concept as a tool to describe and to analyze the alignment of product, process, and supply chain architectures. Fine claims that the degree of modularity in the final output product has a one‐to‐one correspondence with the degree of modularity in transformation processes and supply chains. , – An exploratory three‐company case study is used to investigate Fine's three‐dimensional modularity concept. , – Empirical research shows that Fine's three‐dimensional modularity concept works well for descriptive purposes. However, the concept needs refinement when it is used for analytical purposes. For process modularity, the spatial aspect can be related to the concept of the territorial economy of firms, while the time aspect can be elaborated using a product life‐cycle perspective and the concept of interface reversibility. With respect to supply chain modularity, the concept of modular production networks, which specifies combinations of different degrees of geographic, organizational, cultural, and electronic proximity, is appropriate. In the specific setting of the building industry, a methodology for developing product platform architectures would refine the dimension of product modularity. , – Starting from the above‐mentioned concepts, several strands of research are outlined for a further elaboration of product (developing a methodology for developing product platform architectures), process (taking a life‐cycle perspective on module development), and supply chain modularity (analyzing conditions for the application of modular networks). , – The application of systematic engineering design methods, interface reversibility, and interorganizational ICT allows firms to align product, process, and supply chain modularity.
Intellectual Property Rights Protection and Mergers and Acquisitions: Evidence from China

This paper examines the impact of intellectual property rights (IPR) protection on mergers and acquisitions (M&As). We document a significant increase in the probability of announcing M&A bids in provinces with well-protected IPR. That is, IPR has an impact on M&As in provinces that have more efficient IPR enforcement and better judicial protection. The result is robust to a battery of robustness checks. In addition, better IPR protection at the provincial level allows firms to initiate more cash-financed M&A bids and local bids. Our results suggest that increasing R&D expenditures and alleviating financing constraints are the possible mechanisms through which IPR protection affects the probability of M&As.