Miracle-dz/UniFAR-FTUs huggingface dataset:
IMPLEMENTATION OF CHARACTER EDUCATION AT CIVIC EDUCATION IN 5.0 ERA ON FIFTH GRADE STATE ELEMENTARY EDUCATION VIDYA QASANA

The purpose of this research is to apply the character education model in the 5.0 era, find out the obstacles faced, and solutions that can be implemented in character education for fifth grade students at SDN Vidya Qasana. This research is a descriptive qualitative research case study method. The research subjects were school principals, fifth grade teachers, fifth grade students and gardeners. Sources of research data, researchers obtained from observation, interviews, and documentation. Data collection techniques through interview techniques, observation, and documentation. The validity of the data in this study were technical triangulation and peer discussion. Data analysis was carried out through the process of data reduction, data presentation and drawing conclusions. The results showed that (1) The application of character education in Civics learning in class V era 5.0 in K13 learning was carried out by the teacher by giving concrete examples to students, respecting differences, instilling discipline, disposing of waste according to its type and instilling social attitudes. (2) The results of implementing character education in Civics learning in class can be seen from the teaching and learning activities in class showing several attitudes, among others, mutual respect, submitting assignments on time, leaving class asking permission, not being noisy in class, and independent learning not depending on people other. (3) Obstacles and solutions to implementing character education at SDN Vidya Qasana. The obstacle is the lack of cooperation between the school and parents of students, students often ignore politeness, teachers do not respond to programs made by the principal who are still concerned with their personal interests. The solution to facing these obstacles is to establish communication between schools and parents of students, remind each other between students, and increase cooperation between teacher boards.
Semantics-Aware Dynamic Graph Convolutional Network for Traffic Flow Forecasting

Traffic flow forecasting is a challenging task due to its spatio-temporal nature and the stochastic features underlying complex traffic situations. Currently, Graph Convolutional Network (GCN) methods are among the most successful and promising approaches. However, most GCNs methods rely on a static graph structure, which is generally unable to extract the dynamic spatio-temporal relationships of traffic data and to interpret trip patterns or motivation behind traffic flows. In this paper, we propose a novel Semantics-aware Dynamic Graph Convolutional Network (SDGCN) for traffic flow forecasting. A sparse, state-sharing, hidden Markov model is applied to capture the patterns of traffic flows from sparse trajectory data; this way, latent states, as well as transition matrices that govern the observed trajectory, can be learned. Consequently, we can build dynamic Laplacian matrices adaptively by jointly considering the trip pattern and motivation of traffic flows. Moreover, high-order Laplacian matrices can be obtained by a newly designed forward algorithm of low time complexity. GCN is then employed to exploit spatial features, and Gated Recurrent Unit (GRU) is applied to exploit temporal features. We conduct extensive experiments on three real-world traffic datasets. Experimental results demonstrate that the prediction accuracy of SDGCN outperforms existing traffic flow forecasting methods. In addition, it provides better explanations of the generative Laplace matrices, making it suitable for traffic flow forecasting in large cities and providing insight into the causes of various phenomena such as traffic congestion.
Drugging the Undruggable Trypanosoma brucei Monothiol Glutaredoxin 1

Trypanosoma brucei is a species of kinetoplastid causing sleeping sickness in humans and nagana in cows and horses. One of the peculiarities of this species of parasites is represented by their redox metabolism. One of the proteins involved in this redox machinery is the monothiol glutaredoxin 1 (1CGrx1) which is characterized by a unique disordered N-terminal extension exclusively conserved in trypanosomatids and other organisms. This region modulates the binding profile of the glutathione/trypanothione binding site, one of the functional regions of 1CGrx1. No endogenous ligands are known to bind this protein which does not present well-shaped binding sites, making it target particularly challenging to target. With the aim of targeting this peculiar system, we carried out two different screenings: (i) a fragment-based lead discovery campaign directed to the N-terminal as well as to the canonical binding site of 1CGrx1; (ii) a structure-based virtual screening directed to the 1CGrx1 canonical binding site. Here we report a small molecule that binds at the glutathione binding site in which the binding mode of the molecule was deeply investigated by Nuclear Magnetic Resonance (NMR). This compound represents an important step in the attempt to develop a novel strategy to interfere with the peculiar Trypanosoma Brucei redox system, making it possible to shed light on the perturbation of this biochemical machinery and eventually to novel therapeutic possibilities.
Teaching science by inquiry: assessment and learning

The major assertion of this study is that teaching science by inquiry is feasible and desirable. The performance of three groups of 12th grade students (aged 16-17) was compared: Group 1 (n=22) majoring in physics who studied a conventional course; Group 2 (n=52) majoring in biology who studied an inquiry-oriented course; and Group 3 (n=50) who studied the same biology course but, in addition, studied basic concepts of scientific inquiry. Two tasks served as dependent variables; Group 1 had the lowest scores, roughly one standard deviation behind Group 2, Group 2, in turn, lagged 1 standard deviation behind Group 3. It was concluded that explicit instruction of inquiry is advantageous.
High-sensitivity Permittivity Characterization of Solid Materials Using Spoof Plasmonic Skyrmions-Based Planar Microwave Sensor

In this paper, an ultra-compact, highly sensitive broadband multimode sensing platform based on spoof plasmonic skyrmions (SP skyrmions) resonator for determining dielectric constant of dielectric materials is presented. The proposed sensor consists of an SMA coaxial feeding line and a SP skyrmions structure etched on the F4B substrate with a dielectric constant of 2.2. Due to nearly equidistant skyrmions resonance modes, wide-band multi-mode sensing can be realized. The sensitivity of the sensor is significantly improved due to the strongly localized evanescent fields on the sensor surface in a deep-subwavelength scale. Simulations show that the sensitivity of our proposed sensor can be as high as 5.2%, which at least 1.3 times higher than that of similar published sensors. These characteristics pave the way for a wide range of applications of this plasmonic skyrmions sensor in the field of dielectric material characterization.
A global implicit function theorem and its applications to functional equations

The main result of the paper is a global implicit function theorem. In the proof of this theorem, we use a variational approach and apply Mountain Pass Theorem. An assumption guarantying existence of an implicit function on the whole space is a Palais-Smale condition. Some applications to differential and integro-differential equations are given.
Race and teaching evaluations: evidence from the covid-19 pandemic

ABSTRACT While faculty of colour tend to receive lower course evaluations, it is unclear whether the lack of face-to-face interaction during the COVID-19 pandemic affected them differently. We examine this question using course-level data from a large US university. Estimates from our difference-in-differences models show that non-white faculty suffered a larger decline in ratings compared to their white peers, with the effect persisting even after controlling for English as the faculty’s second language. These findings contribute in furthering the discussion on how universities value course evaluations to measure teaching effectiveness and allocate faculty resources.
Design of a Broadband Measurement Systerm of Die-lectric Constant Based on Parallel Plate Waveguide

This paper presents a new method of measuring the complex permittivity of solid materials based on a parallel plate waveguide. Based on the analysis and improvement of the parallel plate system model, the testing system is fabricated and verified. The system is used to test the transmission and reflection coeffi-cients of a solid dielectric substrate in the 4–8 GHz frequency band. The normalized scattering parameters are obtained by time do-main gate calibration, and then the complex permittivity is de-duced by the traditional NRW (Nicolson-Ross- Weir) algorithm. Finally, the comparison between theoretical and simulation S pa-rameters of a solid dielectric material at 4–8 GHz is given. Com-pared with the traditional waveguide testing system, the system has the advantages of simple structure, low profile, fast and low cost.
Amyloid-PET and White Matter Hyperintensities Have Independent Effects on Baseline Cognitive Function and Synergistic Effects on Longitudinal Executive Function

Co-occurrence of beta amyloid (Aβ) and white matter hyperintensities (WMHs) increase the risk of dementia and both are considered biomarkers of preclinical dementia. Moderation and mediation modeling were used to define the interplay between global and regional Aβ and WMHs measures in relation to executive function (EF) and memory composite scores outcomes at baseline and after approximately 2 years across a sample of 714 clinically normal participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI 2). The moderation regression analysis showed additive effects of Aβ and WMHs over baseline memory and EF scores (p = 0.401 and 0.061, respectively) and synergistic effects over follow-up EF (p < 0.05). Through mediation analysis, the data presented demonstrate that WMHs effects, mediated by global and regional amyloid burden, are responsible for baseline cognitive performance deficits in memory and EF. These findings suggest that Aβ and WMHs contribute to baseline cognition independently while WMHs volumes exert effects on baseline cognitive performance directly and through influences on Aβ accumulation.
Exact Design for Commensurate Line Low-pass Filter Using Coupled Stubs

Exact design method for a lowpass filter based on commensurate line elements is proposed, which supports equiripple in-band response and adjustable out-of-band suppression. The filter consists of a section of coupled line, three sections of connecting line, and two open-circuited stubs, all of which have the identical electrical length. The circuit can generate lowpass response with seventh-order filtering function. Each impedance value can be calculated precisely according to given specifications by using proposed design procedure.
Indicator-Aware Talking Face Generation Based on Evolutionary Multi-objective Optimization

Audio-driven talking face generation (ATFG) is typically multifaceted, requiring high-quality faces, lip movements synchronized with the audio, and plausible facial expressions. Previous works have mainly focused on minimizing a single loss function constructed on the basis of consideration of the multiple performance requirements for an ATFG model. However, as a proxy, the loss function does not always yield optimal performance when minimised. Moreover, it is unlikely that there is a single model that can be optimal in terms of all relevant quality indicators. In this paper, we formulate the training of an ATFG model as an indicator-aware multi-objective optimization problem and propose a novel approach, namely Evolutionary Multi-objective Indicator-aware audio-driven talking face Generation (EMIG), to solve this problem. EMIG explicitly uses the quality indicators in the design process of ATFG models and can be easily adapted to the preferences of users. Experimental studies demonstrate the potential advantages of evolutionary multi-objective optimization for solving the task of ATFG and the effectiveness of EMIG over five state-of-the-art ATFG methods.
AD Cancri: A Shallow Contact Solar-Type Eclipsing Binary and Evidence for a Dwarf Third Component and a 16 Year Magnetic Cycle

CCD photometric observations of AD Cancri obtained from 2000 March 7 to 2004 December 20 are presented. Variations of the light levels at the primary minimum and both maxima are found. Uniform solutions of four sets of photometric data were derived by using the Wilson-Devinney method. The solutions suggest that AD Cancri is a shallow W-type contact binary (f = 8.3% ± 1.3%) with a high mass ratio of 1/q = 0.770 ± 0.002. The long-term variation of the light curve is explained by variable dark-spot models of the more massive component star with a possible 17 yr cycle. Our 13 times of light minimum over 5 years, including others collected from the literature, have been used for the period study. The complex period changes can be sorted into a long-term period increase at rate of dP/dt = +(4.94 ± 0.16) × 10-7 days yr-1, a 16.2 yr periodic component (A3 = 0.0155 days), and a very small amplitude period oscillation (A4 = 0.0051 days, P4 = 6.6 yr). The existence of third light may indicate that there is a tertiary component in the binary system. Solving the four-band light curves of Samec & Bookmyer, it is found that the contribution of the tertiary component to the total light of the triple system increases with wavelength, which suggests that it is very cool and may be a very red main-sequence star. The small-amplitude period oscillation may be caused by the light-time effect of the cool tertiary component (M3 ~ 0.41 M☉). The 16.2 yr periodic component in the orbital period and the 17 yr cyclic activity of the dark spot on the more massive component both may reveal that the more massive component displays solar-type magnetic activity with a cycle length of about 16 yr.
Calibration of laser scanner and camera fusion system for intelligent vehicles using Nelder–Mead optimization

A novel method is presented here for the calibration of a sensor fusion system for intelligent vehicles. In this example, the sensors are a camera and a laser scanner which observe the same scene from different viewpoints. The method employs the Nelder–Mead direct search algorithm to minimize the sum of squared errors between the image coordinates and the re-projected laser data by iteratively adjusting and improving the calibration parameters. The method is applied to a real set of data collected from a test vehicle. Using only 11 well-spaced target points observable by each sensor, 12 intrinsic and extrinsic parameters indicating the position relationship between the sensors can be estimated to give an accurate projection. Experiments show that the method can project the laser points onto the image plane with an average error of 1.01 pixels (1.51 pixels worst case).
Adaptive Neural Network Fixed-Time Control for Robotic Manipulators with Input Quantization

An adaptive neural network fixed-time control method is proposed for an uncertain robotic manipulator in this paper. To deal with input quantization and improve the stability, fixed-time control is added to the controller design. In addition, adaptive neural network is applied to approximate the uncertainties of the model. Compared with existing results, fixedtime convergence is used to enhance the learning rate for neural networks and to improve the system accuracy. The validity and stability of the proposed control is proved by the simulation.
A Fast Transient Response Capacitor-Less LDO Achieving -88 dB PSR at 10 kHz

A capacitor-less LDO with fast response achieving - 88dB PSR at 10kHz in $0.18\upmu\mathrm{m}$ BCD process is proposed. A Frequency Compensation and Fast Transient response Enhancement Circuit (FC&FTEC) and a Negative Capacitance Circuit (NCC) are applied, in order to enhance the PSR and response. The post simulation results show that the LDO achieves -88 dB PSR at 10kHz. When the load current changes from 300 μA to $50 mA$ or from 50mA to $300uA$, the rise time and the fall time are 100 ns respectively, the recovery time is less than 2.02 $\upmu\mathrm{s}$, the quiescent current is 126 $\upmu\mathrm{A}$. The undershoot and overshoot are only 98mV and 185mV respectively.
Duration of Syllable Nuclei in English

This study deals with the influence of preceding and following consonants on the duration of stressed vowels and diphthongs in American English. A set of 1263 CNC words, pronounced in an identical frame by the same speaker, was analyzed spectrographically, and the influences of various classes of consonants on the duration of the nucleus were determined. The residual durational differences are analyzed as intrinsic durational characteristics, associated with each syllable nucleus. The theory is tested with a set of 30 minimal pairs of CNC words, uttered by five different speakers.
Annotation-Free Deep Learning-Based Prediction of Thyroid Molecular Cancer Biomarker BRAF (V600E) from Cytological Slides

Thyroid cancer is the most common endocrine cancer. Papillary thyroid cancer (PTC) is the most prevalent form of malignancy among all thyroid cancers arising from follicular cells. Fine needle aspiration cytology (FNAC) is a non-invasive method regarded as the most cost-effective and accurate diagnostic method of choice in diagnosing PTC. Identification of BRAF (V600E) mutation in thyroid neoplasia may be beneficial because it is specific for malignancy, implies a worse prognosis, and is the target for selective BRAF inhibitors. To the authors’ best knowledge, this is the first automated precision oncology framework effectively predict BRAF (V600E) immunostaining result in thyroidectomy specimen directly from Papanicolaou-stained thyroid fine-needle aspiration cytology and ThinPrep cytological slides, which is helpful for novel targeted therapies and prognosis prediction. The proposed deep learning (DL) framework is evaluated on a dataset of 118 whole slide images. The results show that the proposed DL-based technique achieves an accuracy of 87%, a precision of 94%, a sensitivity of 91%, a specificity of 71% and a mean of sensitivity and specificity at 81% and outperformed three state-of-the-art deep learning approaches. This study demonstrates the feasibility of DL-based prediction of critical molecular features in cytological slides, which not only aid in accurate diagnosis but also provide useful information in guiding clinical decision-making in patients with thyroid cancer. With the accumulation of data and the continuous advancement of technology, the performance of DL systems is expected to be improved in the near future. Therefore, we expect that DL can provide a cost-effective and time-effective alternative tool for patients in the era of precision oncology.
Multi secret image color visual cryptography schemes for general access structures

Abstract In the proposed visual cryptography schemes for general access structures, the single secret image black-and-white visual cryptography schemes with meaningful shares have been constructed, in which the shares are innocent looking images. The meaningful shares have not been realized in single secret image color schemes; neither have the multi secret images color schemes. In this paper, the multi secret images color visual cryptography schemes for general access structures with meaningful shares are constructed by the method of matrix concatenation, the pixel expansion is obtained, and the validity of the scheme is proven. In our scheme, the different combination of meaningful color shares can be used to recover distinct color secret images. The multi secret images black-and-white visual cryptography scheme is a special case of our color scheme. *Supported by National Natural Science Foundation of China (Grant No. 90304014) and the Major State Basic Research Development Program of China (Grant No. .
Genome-Wide Analysis and Expression of Cyclic Nucleotide–Gated Ion Channel (CNGC) Family Genes under Cold Stress in Mango (Mangifera indica)

The ‘king of fruits’ mango (Mangifera indica) is widely cultivated in tropical areas and has been threatened by frequent extreme cold weather. Cyclic nucleotide–gated ion channel (CNGC) genes have an important function in the calcium-mediated development and cold response of plants. However, few CNGC-related studies are reported in mango, regardless of the mango cold stress response. In this study, we identified 43 CNGC genes in mango showing tissue-specific expression patterns. Five MiCNGCs display more than 3-fold gene expression induction in the fruit peel and leaf under cold stress. Among these, MiCNGC9 and MiCNGC13 are significantly upregulated below 6 °C, suggesting their candidate functions under cold stress. Furthermore, cell membrane integrity was damaged at 2 °C in the mango leaf, as shown by the content of malondialdehyde (MDA), and eight MiCNGCs are positively correlated with MDA contents. The high correlation between MiCNGCs and MDA implies MiCNGCs might regulate cell membrane integrity by regulating MDA content. Together, these findings provide a valuable guideline for the functional characterization of CNGC genes and will benefit future studies related to cold stress and calcium transport in mango.
Flow cytometer system for single-shot biosensing based on whispering gallery modes of fluorescent microspheres

We report an innovative label-free biosensor based on statistical analysis of several whispering gallery modes spectral shifts in polystyrene fluorescent microspheres using a custom microflow cytometer. Whispering gallery modes analysis enables detection of nanometer-sized analytes showing promising possibilities for virus, bacteria and molecular detection. To demonstrate this, fluorophore-doped microspheres of the appropriate size parameter are mixed in an aqueous solution. Then, a syringe pump pushes the solution through a fiber optic flow cell where a laser beam illuminates the analysis area to excite the microspheres and their fluorescence is collected. This device provides a low-cost and user friendly solution that could enhance spectrum acquisition rates up to 5 spectra per second thanks to the considerable amount of microspheres flowing through the excitation area per unit time. Finally, the fluorescence spectra are statistically investigated using an instantaneous measurement of apparent refractive index algorithm to determine a reliable value for the refractive index of the environment since the exact radius of the microsphere scanned is unknown. This refractive index becomes an effective value for the local perturbation caused by inhomogeneities on the microsphere surface and hence, determines whether or not inhomogeneities, such as bacteria, are adsorbed by comparing to a control sample. Combining a flow cell with our detection algorithm, we reduce the period of a 50 microspheres experiment from 161 minutes to 14 minutes when the flow rate is 2000 µl/h and the microsphere concentration is 5 µsphere/µl.
Design and calibration of the JET neutral particle analyzer

The neutral particle analyzer with energy and mass selection capability built for JET is described. The analyzer is able to detect in the same shot both H atoms from 0.3 to 320 keV and D atoms from 0.15 to 160 keV. In order to optimize the ion optics of the analyzer an oblique entrance in the magnet has been chosen. An ion trajectory code shows that in this way a good mass separation and a high efficiency can be achieved. The problems inherent in the analyzer calibration for a wide energy range are discussed, and the calibration procedure is described. In conclusion, the calibration results obtained with five analyzers are presented, together with theoretical consideration on ion optics and atomic processes, which have enabled us to write a simple interpolation formula for the experimental data.