Miracle-dz/UniFAR-FTUs huggingface dataset:
Interferometric Measurement o{ Refractive Indices of LiNb03

An interferometric method for the determination of refractive indices is described which uses the shift in the optical path length when a parallel plate sampIe is rotated in one arm of a Michelson-type interferometer. High precision is achieved by including appropriate nurnerical fit procedures in the evaluation of the experimental results. We could prove that an accuracy of about 2· 10- 4 can be reached with the method. SampIe nonidealities (slight-wedge or lens shape) have only moderate effects on the determination of the refractive index. Measurements on LiNb03 show that not only the extraordinary index of re­ fraction varies with the Li/Nb ratio - which is in good agreement to earlier results -, but also the ordinary one.
Managerial ownership and financial distress: evidence from the Chinese stock market

PurposeThis study investigates the convergence-of-interest and entrenchment effects on the relationship between managerial ownership and financial distress using evidence from the Chinese stock market. It also analyzes whether the relationship is mediated by research and development (R&D) investment.Design/methodology/approachUsing a dataset consisting of 19,059 firm-year observations of Chinese listed companies in the Shanghai and Shenzhen Stock Exchanges between 2010 and 2020, this study employs both piecewise and curvilinear models.FindingsThe results indicate that managerial ownership has a negative association with firm financial distress in both the low (below 12%) and high (above 18%) convergence-of-interest regions of managerial ownership, suggesting that managerial ownership in this region may contribute to improve firm financial status. Meanwhile, managerial ownership has a positive association with firm financial distress in the entrenchment region (12–18%), implying that managerial ownership in the entrenchment region may contribute to impair firm financial status. Furthermore, the results show that R&D investment mediates the association between managerial ownership and financial distress.Originality/valueThis study is the first to provide evidence of a nonlinear relationship between managerial ownership and financial distress, and identify the entrenchment region in the Chinese setting.
Development and Characterization of a Dry Ear-EEG Sensor With a Generic Flexible Earpiece

While the sensors of current electroencephalography (EEG) wearables can be miniaturized such that they can be fit into an in-ear hearable device, the brain monitoring based on the state-of-art ear-EEG techniques has several drawbacks such as customization for one individual, time-consuming fabrication process, and short life cycle. To deal with this problem, a new method to design a sensor with one-channel dry electrode embedded in a generic substrate is proposed. The electrical characterizations were evaluated with 15 subjects and five EEG paradigms, in which ear-EEG and conventional scalp EEG were recorded simultaneously. Recordings from the generic dry-contact ear-EEG sensor were compared to conventional scalp EEG recordings. Reliable responses were extracted from the ear-EEG for different paradigms, albeit weaker in amplitude than from scalp EEG. The results demonstrate the feasibility of the designed generic ear-EEG sensor and its potential to achieve sustained, ease-of-use, cost-effective, and off-the-shelf ear-EEG recording use.
Comparing the effects of consumers' perceptions of companies' corporate social responsibility initiatives in emerging and developed markets

PurposeThis work proposes a framework that attempts to explain the connection between the dimensions of consumer perceived corporate social responsibility (social, environmental, economic), firm trustworthiness and firm reputation, using market level of development as a moderating factor.Design/methodology/approachMexico and Spain were selected as the emerging and developed markets; a cross-cultural study with 1173 consumers (521 from Mexico and 652 from Spain) was undertaken. In each country, participants evaluated one of two well-known companies (one making consumer products and one providing retail services). The hypotheses were tested through SEM.FindingsThe results showed that, in the emerging market, perceived environmental actions did not influence consumers' perceptions and, in the developed market, perceived social actions had no effect.Originality/valueThe study identifies two mechanisms through which consumers' perceptions of a company's CSR influence company reputation, offering evidence that the level of development of a country can have a moderating effect on how the mechanisms operate.
Sap flow as a function of variables within nested scales: ordinary least squares vs. spatial regression models

Understanding scale-dependent influential drivers of sap flow variability can help managers and policymakers to allocate resources within a particular scale to improve forest health and resiliency against water-stress stimuli such as drought and insects, e.g. bark beetle infestations. We defined a daily measure of sap flow as a function of variables within nested scales of landscape, stand, and tree, using ordinary least squares (OLS), spatial lag and error regression models. Model covariates were elevation, latitude (Y-coordinate), longitude (X-coordinate), neighborhood tree density, tree diameter at breast height, and bark temperature for 40-surveyed Norway spruce (Picea abies) in the Czech Republic, Central Europe. Trees were spatially distributed within 19-established subplots across five plots, with distances ranging 2–9 km, at which variations in soil water potential and temperature were limited. The daily measure of sap flow within the regional scale allowed us to avoid the temporal and spatial variability of climate effects on sap flow. A relatively flat terrain across subplots also allowed us to control the effects of slope, aspect, and topography-related solar incidence angle on sap flow. Sap flow was strongly spatially autocorrelated, so OLS models failed to take spatial autocorrelation into account unless to some extent, depending on the spatial distribution of samples, by including latitude and/or longitude in the models. Among spatial regression models, spatial error models performed better than lag models, allowing to capture the effects of unmeasured independent variables. Sap flow variability for the most part (∼70%) was explained by the landscape-level variable of elevation followed by the stand-level variable of tree density, and the remaining part by variables related to tree characteristics; a nested down-scaling function, defined and visualized for the first time. Therefore, thinning forest stands and future plantations with optimum distances, based on the elevation gradients, may be required to counterbalance the allocation of resources, e.g. water, nutrients, and light, among trees, leading to enhance forest health and resiliency against water-stress stimuli.
Simulation and Optimization Design of Inductive Wear Particle Sensor

In order to monitor the diagnosis of mechanical equipment by monitoring the metal wear particles carried in large aperture lubricating oil tubes, the simulation optimization structure design was carried out based on the traditional three-coil inductance wear particle sensor. The numerical model of electromotive force induced by the wear particle sensor was established, and the coil distance and coil turns were simulated by finite element analysis software. When permalloy is covered on the surface of the excitation coil and induction coil, the background magnetic field at the air gap increases, and the induced electromotive force amplitude generated by wear particles is increased. The effect of alloy thickness on the induced voltage and magnetic field was analyzed to determine the optimum thickness, and increase the induction voltage of the alloy chamfer detection at the air gap. The optimal parameter structure was determined to improve the detection ability of the sensor. Ultimately, by comparing the extreme values of the induced voltage of various types of sensors, the simulation determined that the minimum allowable detection of the optimal sensor was 27.5 µm ferromagnetic particles.
Students’ performance in interactive environments: an intelligent model

Modern approaches in education technology, which make use of advanced resources such as electronic books, infographics, and mobile applications, are progressing to improve education quality and learning levels, especially during the spread of the coronavirus, which resulted in the closure of schools, universities, and all educational facilities. To adapt to new developments, students’ performance must be tracked in order to closely monitor all unfavorable barriers that may affect their academic progress. Educational data mining (EDM) is one of the most popular methods for predicting a student’s performance. It helps monitoring and improving students’ results. Therefore, in the current study, a model has been developed so that students can be informed about the results of the computer networks course in the middle of the second semester and 11 machine algorithms (out of five classes). A questionnaire was used to determine the effectiveness of using infographics for teaching a computer networks course, as the results proved the effectiveness of infographics as a technique for teaching computer networks. The Moodle (Modular Object-Oriented Dynamic Learning Environment) educational platform was used to present the course because of its distinctive characteristics that allow interaction between the student and the teacher, especially during the COVID-19 pandemic. In addition, the different methods of classification in data mining were used to determine the best practices used to predict students’ performance using the weka program, where the results proved the effectiveness of the true positive direction of functions, multilayer perceptron, random forest trees, random tree and supplied test set, f-measure algorithms are the best ways to categories.
Convergences for robust bilevel polynomial programmes with applications

In this paper, we consider a bilevel polynomial optimization problem, where the constraint functions of both the upper-level and lower-level problems involve uncertain parameters. We employ the deterministic robust optimization approach to examine the bilevel polynomial optimization problem under data uncertainties by providing lower bound approximations and convergences of sum-of-squares (SOS) relaxations for the robust bilevel polynomial optimization problem. More precisely, we show that under the convexity of the lower-level problem and either the boundedness of the feasible set or the coercivity of the objective function, the global optimal values of SOS relaxation problems are lower bounds of the global optimal value of the robust bilevel polynomial problem and they converge to this global optimal value when the degrees of SOS polynomials in the relaxation problems tend to infinity. Moreover, an application to an electric vehicle charging scheduling problem with renewable energy sources demonstrates that using the proposed SOS relaxation schemes, we obtain more stable optimal values than applying a direct solution approach as the SOS relaxations are capable of solving these models involving data uncertainties in dynamic charging price and weather conditions.
Analysis of the Effects of Known Sleep-Support Supplements in Relation to Life Habits, Sleep Conditions, and Sleep Problems

Sleep is a crucial component of health, and insomnia is among the most common and vexing of life-habit-related disorders. While dietary sleep-support supplements can improve sleep, choosing an effective dietary supplement can be challenging for users due to the wide variety of options available and the varying effects experienced by different individuals. In this study, to identify new criteria for estimating the effects of dietary supplements, we examined the relationships among the dietary supplements, the pre-conditions (PCs; defined as the life habits and sleep conditions before supplementation), and the sleep problems of subjects before supplementation. An open, randomized, cross-over intervention trial enrolling 160 subjects was conducted to test the efficacy of each dietary supplement (Analysis 1) and the relationships among dietary supplements, the PCs, and sleep problems (Analysis 2). To this end, l-theanine (200 mg/day), γ-aminobutyric acid (GABA) (111.1 mg/day), Apocynum venetum leaf extract (AVLE) (50 mg/day), and l-serine (300 mg/day) were administered to subjects. Before the first intervention period, life habits and sleep conditions were surveyed to identify each subject’s PCs. For each combination of supplements and sleep problems, PCs were compared between subjects whose sleep problems were improved and subjects whose sleep problems were not improved via supplementation. All the tested supplements were found to ameliorate sleep problems significantly (Analysis 1). In Analysis 2, the PCs specific to improved subjects were found to differ depending on the dietary supplements and sleep problems. In addition, subjects who consumed dairy products often showed improvement in their sleep problems with all the tested supplements. This study suggests the possibility of personalizing sleep-support supplementation based on personal life habits, sleep conditions, and sleep problems, in addition to the known efficacy of dietary supplements.
STAR FORMATION HISTORY AND DUST PRODUCTION: NGC147 AND NGC185

NGC 147 and NGC 185, paired satellites of the Andromeda galaxy, possess the same order of mass and analogous structures, but they show different star formation and different amounts of interstellar gas and dust. Therefore, we present the first reconstruction of the star formation history of NGC 147 and NGC 185. Asymptotic Giant Branch stars are highly evolved stars that are brightest in K-band. This maximum K-band magnitude is related to the birth mass of stars. As a result, we have found a 9.9 Gyr old single star formation epoch for NGC 185 followed by relatively continuous star formation. NGC 147, however, has passed through two star formation episodes; one is as old as ∼6 Gyr and the other is as recent as ∼850 Myr. Asymptotic Giant Branch stars are also important dust factories; by fitting Spectral Energy Distributions to observed near and mid infrared data for each star, we were able to measure the dust production rates of individual stars; on order of 10−5M yr −1. Hence, we estimate the total mass entering the interstellar medium to be 1.06 × 10−4M yr−1 and 2.89 × 10−4M yr−1 for NGC 147 and NGC 185.
Two alternative estimation procedures for the negative binomial cure rate model with a latent activation scheme

In this paper two alternative estimation procedures based on the EM algorithm are proposed forthe flexible negative binomial cure rate model with a latent activation scheme. The Weibull modelas well as the log-normal and gamma distributions are also considered for the time-to-event datafor the non-destroyed cells. Simulation studies show the satisfactory performance of the proposedmethodology. The impact of misspecifying the survival function on both components of the model(cured and susceptible) is also evaluated. The use of the new methodology is illustrated with areal data set related to a clinical trial on Phase III cutaneous melanoma patients.
Short-Term Wind Power Forecasting and Uncertainty Analysis Based on Hybrid Temporal Convolutional Network

The integration of large-scale wind power into power grids has made accurate short-term wind power forecasting a key technology for the safe and economical operation of power grids. A novel method based on variational mode decomposition (VMD), temporal convolutional network (TCN), and Gaussian mixture model (GMM) was proposed for accurate short-term wind power forecasting and uncertainty analysis. First, the wind speed information was decomposed into different mode components via VMD. Second, TCN was employed to capture accurately the time-series dependence of data by training and forecasting different mode component data. On this basis, GMM was used to calculate the distribution characteristics of short-term wind power forecasting errors and quantify the confidence interval of wind power forecasting. Results demonstrated that the root mean square error (RMSE) value of the VMD-TCN model for wind power forecasting for 4 h during winter is 4.69%, 3.13%, 2.48%, 1.21%, and 0.7% lower than the RMSE values of wavelet neural network, BP neural network, PSO-BP hybrid model, long short-term memory model, and TCN model, respectively. The proposed method has a certain promoting effect on improving the accuracy of short-term wind power forecasting.
Support Vector Machine Based K-type Kernel Function

A new K-type kernel is proposed,and then a K-type support vector machine(KSVM) is obtained in this study.It is firstly proved that the K-type kernel function satisfies the Mercer's condition and is a translation invariant kernel.The properties of the K-type kernel are analyzed theoretically,showing that it is characterized by its small amount of calculation and time-calculating cost,high precision and wide applicability.Besides,computer experiments are made to explain its advantages over others.
The Effect of Project Names on Escalation of Commitment in Information Systems Projects

This study explores how project names may influence the tendency to escalate commitment through two experiments. Our findings from Experiment 1 show that a positive project name evokes positive affective reactions to the project. These, in turn, are associated with a greater willingness to continue a failing project. Results from Experiment 2 show that a technological project name can similarly evoke more positive affective reactions and a greater willingness to continue but only for decision makers with high technology readiness. For decision makers with low technology readiness the effect was reversed.
TCU-Net: Transformer Embedded in Convolutional U-Shaped Network for Retinal Vessel Segmentation

Optical coherence tomography angiography (OCTA) provides a detailed visualization of the vascular system to aid in the detection and diagnosis of ophthalmic disease. However, accurately extracting microvascular details from OCTA images remains a challenging task due to the limitations of pure convolutional networks. We propose a novel end-to-end transformer-based network architecture called TCU-Net for OCTA retinal vessel segmentation tasks. To address the loss of vascular features of convolutional operations, an efficient cross-fusion transformer module is introduced to replace the original skip connection of U-Net. The transformer module interacts with the encoder’s multiscale vascular features to enrich vascular information and achieve linear computational complexity. Additionally, we design an efficient channel-wise cross attention module to fuse the multiscale features and fine-grained details from the decoding stages, resolving the semantic bias between them and enhancing effective vascular information. This model has been evaluated on the dedicated Retinal OCTA Segmentation (ROSE) dataset. The accuracy values of TCU-Net tested on the ROSE-1 dataset with SVC, DVC, and SVC+DVC are 0.9230, 0.9912, and 0.9042, respectively, and the corresponding AUC values are 0.9512, 0.9823, and 0.9170. For the ROSE-2 dataset, the accuracy and AUC are 0.9454 and 0.8623, respectively. The experiments demonstrate that TCU-Net outperforms state-of-the-art approaches regarding vessel segmentation performance and robustness.
A COVID-19 Infection Model Considering the Factors of Environmental Vectors and Re-Positives and Its Application to Data Fitting in Japan and Italy

COVID-19, which broke out globally in 2019, is an infectious disease caused by a novel strain of coronavirus, and its spread is highly contagious and concealed. Environmental vectors play an important role in viral infection and transmission, which brings new difficulties and challenges to disease prevention and control. In this paper, a type of differential equation model is constructed according to the spreading functions and characteristics of exposed individuals and environmental vectors during the virus infection process. In the proposed model, five compartments were considered, namely, susceptible individuals, exposed individuals, infected individuals, recovered individuals, and environmental vectors (contaminated with free virus particles). In particular, the re-positive factor was taken into account (i.e., recovered individuals who have lost sufficient immune protection may still return to the exposed class). With the basic reproduction number R0 of the model, the global stability of the disease-free equilibrium and uniform persistence of the model were completely analyzed. Furthermore, sufficient conditions for the global stability of the endemic equilibrium of the model were also given. Finally, the effective predictability of the model was tested by fitting COVID-19 data from Japan and Italy.
"Shame on You": Father Parenting Impacts Guilt, Morality, and Stress During Shaming

This paper explores a connection between father parenting traits, guilt and shame proneness, moral identity, and physiological responses to moral shame. In this study, participants answered questionnaires and responded to Kohlberg's (1981) moral dilemma. The researcher read scripted feedback that morally shamed the participants, and cortisol levels were measured following this process. The results were analyzed using multiple linear regression. The father's chaos parenting trait was positively related to increases in cortisol during shaming, and the father's coercion parenting trait was negatively related to increases in cortisol during shaming. The father's structure parenting trait was positively related to guilt-negative behavior evaluations and warmth was negatively related to guilt repair. Finally, the father's warmth was negatively related to moral integrity while chaos was positively related. This research provides insight into the complex process of reacting to moral shame.
Exploring the Enteric Virome of Cats with Acute Gastroenteritis

Simple Summary Acute gastroenteritis (AGE) is a very common illness and a significant cause of morbidity and mortality in cats less than one year of age. Along with known viruses historically recognized as primary agents of AGE, in the last years deep sequencing technologies have identified a variety of novel viruses. In this study, we explored the feline enteric virome from cats with or without diarrhoea, unveiling a wide diversity of viruses. Abstract Viruses are a major cause of acute gastroenteritis (AGE) in cats, chiefly in younger animals. Enteric specimens collected from 29 cats with acute enteritis and 33 non-diarrhoeic cats were screened in PCRs and reverse transcription (RT) PCR for a large panel of enteric viruses, including also orphan viruses of recent identification. At least one viral species, including feline panleukopenia virus (FPV), feline enteric coronavirus (FCoV), feline chaphamaparvovirus, calicivirus (vesivirus and novovirus), feline kobuvirus, feline sakobuvirus A and Lyon IARC polyomaviruses, was detected in 66.1% of the samples. Co-infections were mainly accounted for by FPV and FCoV and were detected in 24.2% of the samples. The virome composition was further assessed in eight diarrhoeic samples, through the construction of sequencing libraries using a sequence-independent single-primer amplification (SISPA) protocol. The libraries were sequenced on Oxford Nanopore Technologies sequencing platform. A total of 41 contigs (>100 nt) were detected from seven viral families infecting mammals, included Parvoviridae, Caliciviridae, Picornaviridae, Polyomaviridae, Anelloviridae, Papillomaviridae and Paramyxoviridae, revealing a broad variety in the composition of the feline enteric virome.