Miracle-dz/UniFAR-FTUs huggingface dataset:
Attitudes Matter: Examining How Teaching Strategies for Attitudinal Change Help Adults Value Reflection and Calibrate Their Reflective Thinking

Developing adults’ reflective thinking habits is an aim of adult education, but the best way to do it has been overlooked. Common strategies communicate the skills and knowledge needed to reflect while providing practice opportunities. Yet research indicates that reflective habits are comprised of not only skills and knowledge but also of attitudes toward reflection. This study investigated whether attitudinal change strategies in a reflective thinking workshop induced cognitive dissonance by helping adults appreciate reflection and calibrate their reflective behavior. Participants were randomly assigned to a skills-based or skills-based plus attitudinal change workshop. Pre-post measures of learners’ need for and engagement in reflection were taken. Multivariate analysis of covariance revealed that attitudinal change strategies induced dissonance by increasing the need for reflection while decreasing perceived engagement in reflection. Exclusively skills-based strategies failed to affect the need for reflection but increased perceived engagement in reflection, creating overconfidence. Implications for research and practice are offered.
Automatic Detection of Small Sample Apple Surface Defects Using ASDINet

The appearance quality of apples directly affects their price. To realize apple grading automatically, it is necessary to find an effective method for detecting apple surface defects. Aiming at the problem of a low recognition rate in apple surface defect detection under small sample conditions, we designed an apple surface defect detection network (ASDINet) suitable for small sample learning. The self-developed apple sorting system collected RGB images of 50 apple samples for model verification, including non-defective and defective apples (rot, disease, lacerations, and mechanical damage). First, a segmentation network (AU-Net) with a stronger ability to capture small details was designed, and a Dep-conv module that could expand the feature capacity of the receptive field was inserted in its down-sampling path. Among them, the number of convolutional layers in the single-layer convolutional module was positively correlated with the network depth. Next, to achieve real-time segmentation, we replaced the flooding of feature maps with mask output in the 13th layer of the network. Finally, we designed a global decision module (GDM) with global properties, which inserted the global spatial domain attention mechanism (GSAM) and performed fast prediction on abnormal images through the input of masks. In the comparison experiment with state-of-the-art models, our network achieved an AP of 98.8%, and a 97.75% F1-score, which were higher than those of most of the state-of-the-art networks; the detection speed reached 39ms per frame, achieving accuracy-easy deployment and substantial trade-offs that are in line with actual production needs. In the data sensitivity experiment, the ASDINet achieved results that met the production needs under the training of 42 defective pictures. In addition, we also discussed the effect of the ASDINet in actual production, and the test results showed that our proposed network demonstrated excellent performance consistent with the theory in actual production.
Measuring Personality Systems in Older Adults: Age-Neutrality of the Dutch BIS/BAS Questionnaire

Introduction With a rapidly aging population, age-neutrality of psychological instruments is becoming ever more important for accurate assessment of older adults. This study aims to test the age-neutrality of the Dutch Behavioral Inhibition System/Behavioral Approach System (BIS/BAS) scales by means of Differential Item Functioning (DIF) and Differential Test Functioning (DTF) analyses. Methods DIF and DTF analyses were conducted using an odds ratio approach. Potential DIF was examined for the two primary scales and three BAS subscales in 390 Dutch-speaking participants divided over three age groups. Results Overall, the BIS-BAS scales lacked age-neutrality when comparing older adults to young adults: eight out of 20 items (ie, 40%) displayed DIF according to the adjusted Bonferroni corrected cut-offs, which exceeds the threshold of 25% for large DIF. Thus, for 40% of the test items, items were differently endorsed by young and older adults with the same position on the construct measured by the item. Therefore, the implications of the item-level DIF on the scale level were investigated across age groups. DTF analyses revealed large DTF for all BIS and BAS scales according to the adjusted Bonferroni corrected cut-offs. Discussion DIF found in items on the BIS scale and BAS-Drive and BAS-Fun Seeking scale can probably be explained by a difference in degree of expression across age groups. A solution could be the development of age-specific norms. DIF on BAS-Reward Responsiveness subscale could be partly due to measuring a different construct across age groups. Rephrasing or replacing such items with DIF may increase age-neutrality of the BIS/BAS Scales.
Segmentation of Unsound Wheat Kernels Based on Improved Mask RCNN

The grade of wheat quality depends on the proportion of unsound kernels. Therefore, the rapid detection of unsound wheat kernels is important for wheat rating and evaluation. However, in practice, unsound kernels are hand-picked, which makes the process time-consuming and inefficient. Meanwhile, methods based on traditional image processing cannot divide adherent particles well. To solve the above problems, this paper proposed an unsound wheat kernel recognition algorithm based on an improved mask RCNN. First, we changed the feature pyramid network (FPN) to a bottom-up pyramid network to strengthen the low-level information. Then, an attention mechanism (AM) module was added between the feature extraction network and the pyramid network to improve the detection accuracy for small targets. Finally, the regional proposal network (RPN) was optimized to improve the prediction performance. Experiments showed that the improved mask RCNN algorithm could identify the unsound kernels more quickly and accurately while handling adhesion problems well. The precision and recall were 86% and 91%, respectively, and the inference time on the test set with about 200 targets for each image was 7.83 s. Additionally, we compared the improved model with other existing segmentation models, and experiments showed that our model achieved higher accuracy and performance than the other models, laying the foundation for wheat grading.
Flow regimes of large-velocity-ratio coaxial jets

An investigation of the near-field flow structure of coaxial jets with large outer to inner velocity ratio ru has been conducted. Since in all cases ru>1, the outer jet dominates the near-field flow structure. Two flow regimes are identified depending on whether ru is larger or smaller than a critical value ruc. When ru<ruc, the fast annular jet periodically pinches the central, slow jet near the end of the inner potential cone. The pinching frequency corresponds to the outer-jet mode. The length of the inner potential cone is strongly dependent on ru and behaves like A/ru, where A depends weakly on the initial conditions. When ru>ruc, the inner potential cone is truncated and is followed by an unsteady recirculation bubble with low-frequency oscillation. The transition from one regime to another is explained by a simple model whose ingredients are the turbulent entrainment rate, governed by the outer-jet mixing layers and mass conservation. This model satisfactorily predicts the dependence of the inner potential cone length on ru and the critical velocity ratio ruc. The recirculation bubble has a wake-type instability. It oscillates at a low frequency and a large amplitude compared to the Kelvin–Helmholtz mode. Angular cross-correlations in the plane parallel to the jet outlet show moreover that this oscillation displays an azimuthal precession such that the rotation time of the phase of the oscillation equals the oscillation period. These salient features are discussed in the framework of the nonlinear delayed saturation (NLDS) model.
Experiences of Non-Pharmaceutical Primary Care Interventions for Common Mental Health Disorders in Socioeconomically Disadvantaged Groups: A Systematic Review of Qualitative Studies

Common mental health disorders (CMDs) disproportionately affect people experiencing socioeconomic disadvantage. Non-pharmaceutical interventions, such as ‘social prescribing’ and new models of care and clinical practice, are becoming increasingly prevalent in primary care. However, little is known about how these interventions work and their impact on socioeconomic inequalities in health. Focusing on people experiencing socioeconomic disadvantage, this systematic review aims to: (1) explore the mechanisms by which non-pharmaceutical primary care interventions impact CMD-related health outcomes and inequalities; (2) identify the barriers to, and facilitators of, their implementation in primary care. This study is a systematic review of qualitative studies. Six bibliographic databases were searched (Medline, ASSIA, CINAHL, Embase, PsycInfo and Scopus) and additional grey literature sources were screened. The included studies were thematically analysed. Twenty-two studies were included, and three themes were identified: (1) agency; (2) social connections; (3) socioeconomic environment. The interventions were experienced as being positive for mental health when people felt a sense of agency and social connection. The barriers to effectiveness and engagement included socioeconomic deprivation and underfunding of community sector organisations. If non-pharmaceutical primary care interventions for CMDs are to avoid widening health inequalities, key socioeconomic barriers to their accessibility and implementation must be addressed.
Computers in Biology and Medicine

It is essential to evaluate patient outcomes at an early stage when dealing with a pandemic to provide optimal clinical care and resource management. Many methods have been proposed to provide a roadmap against different pandemics, including the recent pandemic disease COVID-19. Due to recurrent epidemic waves of COVID-19, which have been observed in many countries, mathematical modeling and forecasting of COVID-19 are still necessary as long as the world continues to battle against the pandemic. Modeling may aid in determining which interventions to try or predict future growth patterns. In this article, we design a combined approach for analyzing any pandemic in two separate parts. In the first part of the paper, we develop a recurrent SEIRS compartmental model to predict recurrent outbreak patterns of diseases. Due to its time-varying parameters, our model is able to reflect the dynamics of infectious diseases, and to measure the effectiveness of the restrictive measures. We discuss the stable solutions of the corresponding autonomous system with frozen parameters. We focus on the regime shifts and tipping points; then we investigate tipping phenomena due to parameter drifts in our time-varying parameters model that exhibits a bifurcation in the frozen-in case. Furthermore, we propose an optimal numerical design for estimating the system’s parameters. In the second part, we introduce machine learning models to strengthen the methodology of our paper in data analysis, particularly for prediction scenarios. We use MLP, RBF, LSTM, ANFIS, and GRNN for training and evaluation of COVID-19. Then, we compare the results with the recurrent dynamical system in the fitting process and prediction scenario. We also confirm results by implementing our methods on the released data on COVID-19 by WHO for Italy, Germany, Iran, and South Africa between 1/22/2020 and 7/24/2021, when people were engaged with different variants including Alpha, Beta, Gamma, and Delta. The results of this article show that the dynamic model is adequate for long-term analysis and data fitting, as well as obtaining parameters affecting the epidemic. However, it is ineffective in providing a long-term forecast. In contrast machine learning methods effectively provide disease prediction, although they do not provide analysis such as dynamic models. Finally, some metrics, including RMSE, R-Squared, and accuracy, are used to evaluate the machine learning models. These metrics confirm that ANFIS and RBF perform better than other methods in training and testing zones.
A new approach for iron losses calculation in voltage fed time stepping finite elements

A methodology allowing one to include iron losses in voltage fed time stepping finite elements simulations is presented in this paper. Hysteresis, eddy currents and anomalous losses models are introduced in the 2D field formulation based on the magnetic vector potential. The influence of the losses on the behavior of an Epstein frame is given. Comparison between simulated and measured results shows the validity of the proposed methodology.
Higher correlation functions of chaotic dynamical systems-a graph theoretical approach

For dynamical systems conjugated to the Bernoulli shift and their higher-dimensional extensions of Kaplan-Yorke type the author calculates higher-order correlation functions by means of a graph theoretical method. The graphs relevant to this problem are forests of incomplete double binary trees. His method has similarities with the Feynman graph approach in quantum field theory: the 'free field' corresponds to a Gaussian random dynamics, the 'interacting field' to a chaotic process with non-trivial higher-order correlations. The 'coupling constant' tau 1/2 is a time scale parameter that measures how much the chaotic process differs from a Gaussian process. He develops a perturbation theory around the Gaussian limit case for sums of iterates of the fully developed logistic map with arbitrary coefficients.
Graphs in pattern recognition: successes, shortcomings, and perspectives

Abstract. Graphs in pattern recognition have been studied since the late seventies with alternating events of successes and failures. Far from wanting to propose a unique survey on the subject, we aim to present the areas in which graphs have been shown to be effective and to illustrate the problems still open in this research domain, with a focus on recent developments moving in the direction of deep learning. This work intends to arouse the interest of neophytes to this interesting domain that is the representation of the world through graphs.
Integrative Transcriptomic and Metabolomic Analysis Reveals the Molecular Mechanism of Red Maple (Acer rubrum L.) Leaf Coloring

This study employed a combination of ultraviolet spectrophotometry, LC-ESI-MS/MS system, and RNA-sequencing technology; the extracts and isolation of total RNA from the red and yellow leaf strains of red maple (Acer rubrum L.) at different developmental stages were subjected to an intercomparison of the dynamic content of chlorophyll and total anthocyanin, flavonoid metabolite fingerprinting, and gene expression. The metabonomic results indicated that one hundred and ninety-two flavonoids were identified, which could be classified into eight categories in the red maple leaves. Among them, 39% and 19% were flavones and flavonols, respectively. The metabolomic analysis identified 23, 32, 24, 24, 38, and 41 DAMs in the AR1018r vs. AR1031r comparison, the AR1018r vs. AR1119r comparison, the AR1031r vs. AR1119r comparison, the AR1018y vs. AR1031y comparison, the AR1018y vs. AR1119y comparison, and the AR1031y vs. AR1119y comparison, respectively. In total, 6003 and 8888 DEGs were identified in AR1018r vs. AR1031r comparison and in the AR1018y vs. AR1031y comparison, respectively. The GO and KEGG analyses showed that the DEGs were mainly involved in plant hormone signal transduction, flavonoid biosynthesis, and other metabolite metabolic processes. The comprehensive analysis revealed that caffeoyl-CoA 3-O-methyltransferase (Cluster-28704.45358 and Cluster-28704.50421) was up-regulated in the red strain but down-regulated in the yellow strain, while Peonidin 3-O-glucoside chloride and Pelargonidin 3-O-beta-D-glucoside were up-regulated in both the red and yellow strains. By successfully integrating the analyses on the behavior of pigment accumulation, dynamics of flavonoids, and differentially expressed genes with omics tools, the regulation mechanisms underlying leaf coloring in red maple at the transcriptomic and metabolomic levels were demonstrated, and the results provide valuable information for further research on gene function in red maple.
Remarques sur la décomposition en éléments simples des fonctions doublement périodiques

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Catching the light curve of flaring GRBs: The opportunity offered by scanning telescopes

We discuss the project of a wide field optical telescope equipped with panoramic detectors in order to catch an optical transient (OT) simultaneously with Gamma–Ray Burst (GRB). This telescope scans the gamma–ray telescope field of view, and with 1 m mirror for 1 s of integration time would detect OTs up to 16 mag; the probability of covering the GRB location in a field of 20° is 12%. The detection limit could be raised and the probability of catching OTs could be over 90% using a system of special telescopes with wide aperture and large collecting area.
A hand-held, high frequency ultrasound scanner

A new real-time high frequency ultrasound scanner has been developed to easily image superficial structures and estimate blood flow, with several major improvements. A great limitation of previous high frequency systems has been the requirement to water-couple the mechanically-scanned transducer to the object to be imaged, and slowly scan the transducer using a fixed motion system. In this system, we have integrated a high-speed actuator used in conjunction with a low mass transducer enclosed in a hand-held scanhead, which allows direct placement on the eyelid or skin. The design of this system is optimized for high frame rates and clinical versatility. Implemented on this system are B-scan, M-mode, and swept-scan imaging modes, with velocity estimation in the latter two. A series of experiments were performed to characterize system performance. The system provides a frame rate of 10 fps, which is partially limited by the execution time of the software. Phantom images from the scanner have 8-bit resolution with a SNR in excess of 35 dB, providing sharp, high-resolution images.
Silicon-Induced Morphological, Biochemical and Molecular Regulation in Phoenix dactylifera L. under Low-Temperature Stress

Climate changes abruptly affect optimum growth temperatures, leading to a negative influence on plant physiology and productivity. The present study aimed to investigate the extent of low-temperature stress effects on date palm growth and physiological indicators under the exogenous application of silicon (Si). Date palm seedlings were treated with Si (1.0 mM) and exposed to different temperature regimes (5, 15, and 30 °C). It was observed that the application of Si markedly improved fresh and dry biomass, photosynthetic pigments (chlorophyll and carotenoids), plant morphology, and relative water content by ameliorating low-temperature-induced oxidative stress. Low-temperature stress (5 and 15 °C), led to a substantial upregulation of ABA-signaling-related genes (NCED-1 and PyL-4) in non Si treated plants, while Si treated plants revealed an antagonistic trend. However, jasmonic acid and salicylic acid accumulation were markedly elevated in Si treated plants under stress conditions (5 and 15 °C) in comparison with non Si treated plants. Interestingly, the upregulation of low temperature stress related plant plasma membrane ATPase (PPMA3 and PPMA4) and short-chain dehydrogenases/reductases (SDR), responsible for cellular physiology, stomatal conductance and nutrient translocation under silicon applications, was observed in Si plants under stress conditions in comparison with non Si treated plants. Furthermore, a significant expression of LSi-2 was detected in Si plants under stress, leading to the significant accumulation of Si in roots and shoots. In contrast, non Si plants demonstrated a low expression of LSi-2 under stress conditions, and thereby, reduced level of Si accumulation were observed. Less accumulation of oxidative stress was evident from the expression of superoxide dismutase (SOD) and catalase (CAT). Additionally, Si plants revealed a significant exudation of organic acids (succinic acid and citric acid) and nutrient accumulation (K and Mg) in roots and shoots. Furthermore, the application of Si led to substantial upregulation of the low temperature stress related soybean cold regulated gene (SRC-2) and ICE-1 (inducer of CBF expression 1), involved in the expression of CBF/DREB (C-repeat binding factor/dehydration responsive element binding factor) gene family under stress conditions in comparison with non Si plants. The current research findings are crucial for exploring the impact on morpho-physio-biochemical attributes of date palms under low temperature and Si supplementation, which may provide an efficient strategy for growing plants in low-temperature fields.
Molecular alterations and therapeutic targets in pancreatic neuroendocrine tumors

Human pancreatic neuroendocrine tumors (PanNETs) are a rare, deadly tumor type that is sporadic or arises in the background of a hereditary syndrome. A critical genetic event in sporadic tumors is inactivation of the gene menin 1 (MEN1) on chromosome 11, and indeed, PanNETs occur in patients with the hereditary syndrome multiple endocrine neoplasia type 1 (MEN1) due to germline mutations in the gene. Here, we review the recent progress in the field of molecular genetics and therapeutic targets of PanNETs. The key genomic alterations, including MEN1, ATRX/DAXX, mammalian target of rapamycin (mTOR), DNA damage and repair associated genes, vascular endothelial growth factor receptor (VEGFR) and SSTRs, and epigenetic aberrations in PanNETs are discussed. In addition, the commonly used preclinical models for PanNETs are enumerated.
Study of pentyl-cyanobiphenyl nematic doped with gold nanoparticles

We studied the dispersion of up to 20 wt% gold nanoparticles (GNP) in a room temperature nematic: cyanobiphenyl homologue 4′-pentyl-4-biphenylcarbonitrile (PBPCN or 5CB). At 15% GNP, the threshold voltage decreases to 1.2 times its value for the pure nematic and the switching-off time decreases by a factor of three. We show that this decrease of display parameters is caused by a 1.8 times decrease in dielectric anisotropy, a 1.3 times change in the average elastic parameter, and a change of about 4.6 times the rotational viscosity. The doped GNP did not affect the thermal stability of the material's nematic phase and the material parameters behaviour versus gold concentration was different from the reported earlier analogical behaviour for another cyanobiphenyl homologue, 4′-hexyl-4-biphenylcarbonitrile (HBPCN or 6CB). We propose that these observed differences between the properties of two homologues doped with gold are due to differences in the number of carbon atoms in the alkyl fragment of liquid crystal molecules.