Miracle-dz/UniFAR-FTUs huggingface dataset:
A Pilot Study Of Teachers’ Feedback According To Carol Dweck's Mindset Theory

Psychologist Carol Dweck distinguishes fixed and growth mindset based on people’s belief that intelligence is fixed or malleable, respectively. Conducted studies show that fostering growth mindset can improve student’s motivation and educational achievement. One of the main ways in which teachers can encourage the growth mindset is a way of giving feedback. The purpose of the current study is to assess the level of teachers´ mindset according to the Mindset theory and detect whether teachers mindset is related to the way of giving feedback, particularly in response to flawless and fast student performance. Dweck Mindset Instrument (DMI) supplemented with additional questions was electronically administered to collect a set of data. The research group consisted of kindergarten, primary and secondary school teachers (N = 175). The results show that teachers most often responded to a flawless and fast performance of a pupil with a generally positive assessment, positive assessment of abilities, reactions in the spirit of growth mindset, keeping pace with other students, describing or asking questions. No relationship was found between the mindset of teachers and the way they respond to the pupil's flawless and fast performance.
Design of the Shunyi study on cardiovascular disease and age-related brain changes: a community-based, prospective, cohort study

Background Rapid economic growth and increasing lifespan have contributed to an increasing burden of chronic non-communicable diseases in China. Population-based studies focusing on cardiovascular disease and age-related brain changes, with extensive clinical, genetic, and imaging data as well as a comprehensive evaluation of brain function are lacking in China. The Shunyi cohort study aimed to investigate the determinants and consequences of cardiovascular disease and age-related brain alterations among individuals residing in a rural area of Beijing. Methods This prospective, community-based study included individuals aged 35 years and older living in five villages in Shunyi, a rural district located 20 miles from urban Beijing. A total of 1,586 individuals were enrolled between June 2013 and September 2014. Biological samples and brain magnetic resonance images were collected along with baseline clinical data through face-to-face interviews. Whole exome sequencing and quantitative assessments of cognitive and motor function were performed. Results Among the 1,586 participants included, 40% were men, and the mean age of the cohort was 56.7±10.0 years. This population had a relatively low education level. A heavy burden of vascular risk factors with a low control rate was observed in the Shunyi population. Since 2017, the cohort has been followed up annually. As of October 2019, we had failed to obtain the follow-up data of five participants. Conclusions With an extensive range of clinical, genetic, and imaging data, the Shunyi cohort study has the potential to contribute significantly towards identifying the causes and consequences of cardiovascular disease and age-related brain changes in older people in China.
Effect of Cannabidiol on the Long-Term Toxicity and Lifespan in the Preclinical Model Caenorhabditis elegans

Introduction: Despite widespread use of cannabidiol (CBD), no lifelong toxicity study has been published to date. Caenorhabditis elegans is often used in preclinical lifelong toxicity studies, due to an estimated 60–80% of their genes having a human ortholog, and their short lifespan of ∼2–3 weeks. In this study, we examined both acute and long-term exposure studies of CBD at physiologically relevant concentrations. Materials and Methods: Acute toxicity was determined by treating day 1 adults with a wide range of CBD concentrations (0.4 μM to 4 mM) and assessing mortality and motility compared to control animals. Thermotolerance was examined by treating adult animals with CBD (0.4 μM to 4 mM) and exposing them to 37°C for 4 h, and then scoring for the number of alive animals treated with CBD compared to controls. Long-term toxicity was assessed by exposing day 1 adults to 10, 40, and 100 μM CBD until all animals perished. Control animals had no active drug exposure. Results: We report both acute and long-term exposure studies of CBD to adult C. elegans at physiologically relevant concentrations. Acute toxicity results showed that no animal died when exposed to 0.4–4000 μM CBD. The thermotolerance study showed that 40 μM CBD, but not other treatment levels, significantly increased resistance to heat stress by 141% compared to the untreated controls. Notably, whole-life exposure of C. elegans to 10–100 μM CBD revealed a maximum life extension of 18% observed at 40 μM CBD. In addition, motility analysis of the same groups revealed an increase in late-stage life activity by up to 206% compared to controls. Conclusion: These results serve as the only CBD lifelong exposure data in an in vivo model to date. While further research into the lifelong use of CBD should be carried out in mammalian models, the C. elegans model indicates a lack of long-term toxicity at physiologically relevant concentrations.
The effect of polyvalent salt ions on the properties of annealed polyelectrolyte brushes

The scaling analysis of the equilibrium behaviour of polyelectrolyte brushes immersed in water-salt solution is presented. The effect of polyvalent salt ions is investigated for the brushes with quenched and annealed charges. The nontrivial dependence of brush thickness on the ionic strength is predicted and discussed.
Sleep Stage Classification based on Two-Cycle Sleep Model Recognition using Continuous Heart Rate Variability

* Correspondence: nihb@nwpu.edu. cn; Tel. :+86-29-88431531 Abstract:Sleep stage on the whole night is not steady. Sleepers generally pass through three to five cycles. In each cycle, there are occur four typical sleep stages, such as wake stage (WS), light stage (LS), deep sleep (DS), rapid eye movement sleep stage (REM). According to the natural routine, in this paper, we investigate the stage transition and analyze the feature of stage transition using the local cluster Algorithm (LCA). Two-cycle sleep model (TCSM) is proposed to automatically classify sleep stages using over-night continuous heart rate variability (HRV) data. The generated model is based on the characteristics of the nested cycle's sleep stage distribution and the transition probabilities of sleep stages. Experiments were conducted using a public data set including 400 healthy subjects (female 239, male 161) and the model’s classification accuracy was evaluated for four sleep stages: WS, LS, DS, REM. The experimental results showed that based on the TCSM model, the segmentation classification of pure sleep is 5.2% higher than that of the traditional method, and the accuracy of segmentation classification is 11.2% higher than the traditional sleep staging accuracy. The experimental performance is promising in terms of the accuracy, sensitivity, and specificity rates compared with the ones of the state-of-the-art methods. The study contributes to improve the quality of sleep monitoring in daily life using easy-to-wear HRV sensors.
Vision-Based Obstacle Avoidance for Small Drone using Monocular Camera

This paper proposes an obstacle avoidance technique for small size drones by using a monocular camera. This method can assist the drone for doing their mission and avoid hitting any obstacle or minimise the collision as possible. For avoiding an obstacle while the drone is doing its mission, We use a computer vision technique to determines the free zone along with the obstacle that may cause a crash and send feedback control to the drone. The small size drone receives feedback about the obstacle and commands the drone to moves to a safe area then resume its trajectory.
Thrombosis of the deep dorsal penile vein and corpora cavernosa of the penis as the first symptoms of colon cancer. Case report and literature review

The case describes an 86-year-old man with thrombosis of the deep dorsal vein and corpora cavernosa of the penis who developed symptoms 4 months before hospitalisation. An accurate imaging diagnostics supplemented with a biopsy of the corpus cavernosum confirmed the initial diagnosis. Moreover, an adenocarcinoma of the hepatic flexion of the colon with numerous metastatic lesions, including those to the crus of penis were found. Diagnostic and therapeutic difficulties were described and the results obtained were discussed in the context of available literature.
Selection of the optimal stage of the group’s life cycle for its transformation into a network structure based on the plasticity coefficient

The article shows the relevance of the completed study. The goal is identified, the decomposition of it is carried out, and other components of the research logical structure are determined. There are hypothesis and methodological foundations of the study. A model presented allows choosing the optimal stage of transformation as the stage of the project’s work life cycle, and the stage of the general scheme of the project group life cycle.
Multi-objective Optimization Design for Battery Pack of Electric Vehicle Based on Neural Network of Radial Basis Function (RBF)

As a carrier of EV batteries, battery pack is a key component that ensures stability, safety, and reliability of energy system for power batteries. In order to complete the requirement of light weight for EV, the battery pack shall have light weight as much as possible while meeting structural strength. This paper uses an Surrogate Models algorithm based on RBF neural network to solve the problem of multi-objective optimization for battery pack. It can be observed from LsDyna simulation results that the battery pack has declined 17.62% in mass and 30.78% in maximum deformation. Therefore, the proposed structure optimization model of battery pack hereof provides an effective design and optimization method for battery pack.
Unconventional control of excited states of a dimer molecule by a localized light field between metal nanostructures

We have made a theoretical study of the spatial interplay between the localized light field (LLF) and the electronic wavefunction of molecules. When the LLF has a nanoscale spatial variation comparable to a molecular wavefunction, this interplay is crucial to determine the optical response of molecular excited states. Such a condition can be realized in case that a molecule is lying in the vicinity of a metal nanogap. By using the calculation method applicable to arbitrary‐shaped samples, we demonstrate a drastic enhancement of the response electromagnetic field from an optical forbidden state whose magnitude is comparable to that from an allowed state. The obtained result indicates that we have a possibility to control the excited states of molecules by designing the LLF with metal nanostructures. (© 2009 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
Catalytic Synthesis of Glycerol Monoacetate Using a Continuous Expanded Bed Column Reactor Packed with Cation-Exchange Resin

The selective and continuous production of monoacetin using an expanded-bed column reactor, in which the reactive fluid flows vertically upward and the catalyst is hydrodynamically floated, was studied. Acetic acid and glycerol were used as the substrates and the proton-type cation-exchange resin Amberlyst 16 was used as the catalyst. Batch reaction experiments under various conditions were first conducted to investigate the details of the heterogeneous catalytic reaction mechanism. The esterification of acetic acid with glycerol to produce the target product monoacetin was proved to proceed predominantly over the undesired consecutive esterification reactions to diacetin and triacetin. The intraparticle and extraparticle mass-transfer resistances for the catalytic resin were negligible. The Eley−Rideal-type catalytic reaction model was successfully applied to the experimental data for the various conditions. Next, the hydrodynamic correlation between the flow rate of the reactive fluid and the correspond.
Mask Guided Pedestrian Tracking from Single Point Initialization

Pedestrian tracking has a wide range of applications, such as intelligent monitoring and unmanned driving. In pedestrian tracking system, object detection algorithms are generally used to provide the initial bounding boxes for object tracking algorithms. If the bounding box provided by detection algorithm is not accurate, it will seriously affect the performance of object tracking. To solve this problem, this paper proposes a mask guided pedestrian tracking algorithm based on Siamese network. Firstly, Mask R-CNN is introduced to acquire exemplar image and its mask. Secondly, a light-weight convolutional neural network (CNN) is applied as the backbone of proposed algorithm. Thirdly, a channel attention module is introduced to our proposed algorithm and integrated with the light-weight CNN. The feature maps of the exemplar image and its mask are adjusted by a channel attention model and fused to enhance the discrimination ability of exemplar branch. Finally, our proposed tracker is trained GOT-10k dataset and evaluated on object tracking benchmark (OTB), experimental results show the excellent performance of proposed algorithm compared with state-of-the-art trackers.
TOWARDS UNDERSTANDING THE DEVELOPMENT OF 21ST CENTURY CAPABILITIES: A SCOPING REVIEW OF CRITICAL THINKING

The learning and teaching of so-called general capabilities – including critical and creative thinking, problem solving, and ethical reasoning – is widely considered as problematic because of their detachment from a particular context of application. At the same time, an increasing employer demand across industries for such 21st century capabilities, specifically critical thinking, seemingly belies the lack of clarity around what is critical thinking and its relevance to a wide range of contexts, while articulating how it might develop and be assessed in distinct contexts. This begs the question: how might educators recognise, in generative ways, the learning and development of 21st century capabilities in their practice? This paper reports on a scoping review exploring the nature, application and influences of critical thinking in diverse professional and disciplinary practices. Key characteristics of critical thinking in action were distilled and are discussed here in relation to how they shape the design of resources and research probes that both support teachers and students in developing critical thinking in a range of contexts, and enable them to investigate how that development might be recognised, understood and tracked over time. Here, we explore the implications for developing critical thinking capabilities as applied in complex problem-solving situations. To do that, we contextualise our discussion within an innovative school-university research partnership designing transdisciplinary challenge projects to pilot how these 21st century capabilities can be developed and actioned for future practical contexts.
Reconsidering the Use of the Passive Voice in Scientific Writing

Abstract I briefly review the use of the passive and active voices in scientific writing and formulate an argument in favor of the active voice's use. I provide fictitious examples of each narrative type and make a side-by-side comparison. Each style has advantages and disadvantages. Papers written in the active voice are concise and clear, although they are also considered colloquial and unsophisticated. Those written in the passive voice, in turn, are centered on the actions – rather than on the individuals – and are perceived as more objective but can also be ambiguous and pompous. I conclude this essay by noting that the active voice – with its greater economy, simplicity, and precision – is now pervasive in scientific writing, and I urge educators to greatly reduce teaching and enforcing the use of the passive voice.
Potential of Hibiscus sabdariffa Linn. in managing FGF21 resistance in diet‐induced‐obesity rats via miR‐34a regulation

Abstract Background Obesity is a cause of FGF21 resistance, which affects the browning and thermogenesis process of the adipose tissue. Decreased receptor expression is influenced by miR‐34a, whose expression is increased in obesity. While FGF21‐based therapies have been widely investigated, the potential activity of Hibiscus sabdariffa Linn. extract (HSE) against FGF21 resistance is unknown. Objective This study aims to determine the effects of HSE on the expression of miR‐34a and FGF21 receptors in white adipose tissue. Methods This experimental study used 24 male Sprague‐Dawley rats and divided into four groups: Control (N); diet‐induced‐obesity rats (DIO); DIO rats with HSE 200 mg/kgBW/day and DIO rats with HSE 400 mg/kgBW/day. Rats were fed a high‐fat diet for 17 weeks. HSE was administered daily for 5 weeks. The administration of HSE 400 mg/kgBW/day resulted in the equivalent expression of miR‐34a to that of the control (p > 0.05). Results FGFR1 receptor expression was also similar to controls (p > 0.05). Beta‐klotho expression was significantly lower than that of control (p < 0.05) but equivalent to that of DIO rats (p < 0.05). Conclusions H. sabdariffa has the potential to reduce FGF21 resistance in DIO rats through the suppression of miR‐34a expression and an increase in the number of FGFR1 and beta‐klotho receptors in adipose tissue.
Partner with a Third-Party Delivery Service or Not? -- a Prediction-and-Decision Tool for Restaurants Facing Takeout Demand Surges During a Pandemic

Amidst the COVID-19 pandemic, restaurants become more reliant on no-contact pick-up or delivery ways for serving customers. As a result, they need to make tactical planning decisions, including whether to partner with third-party service platforms that charge subscription fees or partial revenue from food sales, to form their own delivery team, or both. In this paper, we develop an integrated prediction-and-decision model to analyze the profit of combining the two approaches and to decide the needed number of drivers under stochastic demand. We first employ the Susceptible-Infected-Recovered (SIR) model to forecast future infected cases in a given region and then construct an autoregressive-moving-average (ARMA) regression model to predict food-ordering demand. Using samples of the predicted demand, we formulate a stochastic integer program to optimize food delivery plans. We conduct numerical studies using COVID-19 data and food-ordering demand data collected from local restaurants in Nuevo Leon, Mexico, from April to October 2020, to demonstrate the results of our approach for helping restaurants build contingency plans under rapid environmental changes. Our approach can be used for tactical decision making under unexpected demand surges and our computational results indicate that for restaurants having larger variances of the number of daily takeout orders and farther average delivery distances, they can gain more benefits from partnering with third-party delivery platforms.
Why is All Covid-19 News Bad News?

We analyze the tone of COVID-19 related English-language news articles written since January 1, 2020. Ninety one percent of stories by U.S. major media outlets are negative in tone versus fifty four percent for non-U.S. major sources and sixty five percent for scientific journals. The negativity of the U.S. major media is notable even in areas with positive scientific developments including school re-openings and vaccine trials. Media negativity is unresponsive to changing trends in new COVID-19 cases or the political leanings of the audience. U.S. major media readers strongly prefer negative stories about COVID-19, and negative stories in general. Stories of increasing COVID-19 cases outnumber stories of decreasing cases by a factor of 5.5 even during periods when new cases are declining. Among U.S. major media outlets, stories discussing President Donald Trump and hydroxychloroquine are more numerous than all stories combined that cover companies and individual researchers working on COVID-19 vaccines.
A Novel Sample-Enhanced Dataset based on MFF for Large-Angle Face Recognition

Large-angle face recognition has always been a huge challenge due to the scarcity of large-angle dataset. In this paper, a novel sample-enhanced dataset is constructed, which is composed of various angle face picture samples from -90° to 90° relative to the front face. The constructed dataset is obtained by enhancing large-angle face samples of the CASIA-WebFace dataset. The large-angle face samples are generated from small-angle face samples of the CASIA-WebFace dataset, which is based on the multi-task feature framework (MFF). By employing these sample datasets, four trained FaceNets are achieved for face recognition. Finally, to test the effectiveness of the four face recognition networks for the large-angle face, 300 large-angle face pictures of different basketball players are selected as the samples of the experiment. The results demonstrate that the accuracy of large-angle face recognition has been greatly improved when utilizing the FaceNet that is trained by the novle enhanced dataset.
Identifying Evidences of Computer Programming Skills Through Automatic Source Code Evaluation

This research is contextualized in the teaching of computer programming. Continuous assessment of source codes produced by students on time is a challenging task for teachers. The literature presents different methods for automatic evaluation of source code, mostly focusing on technical aspects. This research presents the A-Learn EvId method, having as the main differential the evaluation of high-level skills instead of technical aspects. The following results are highlighted: updating the state of the art through systematic mapping; a set of 37 skills identifiable through 9 automatic source code evaluation strategies; construction of datasets totaling 8651 source codes. Resumo. Esta pesquisa é contextualizada no ensino de programação de computadores. Avaliar os códigos fonte produzidos pelos alunos de forma contı́nua e em tempo hábil é uma tarefa desafiadora para os professores. A literatura apresenta diferentes métodos para avaliação automática de código fonte, em sua maioria focando em aspectos técnicos. Esta pesquisa apresenta o método A-Learn EvId, tendo como principal diferencial a avaliação de habilidades de alto nı́vel ao invés de aspectos técnicos. Destacam-se os seguintes resultados: mapeamento sistemático para atualização do estado da arte; um conjunto de 37 habilidades identificáveis por meio de 9 estratégias automáticas de avaliação de código fonte; construção de bases de dados totalizando 8651 códigos fonte.
Single-Photon Decay Spectroscopy. II. The Pile-up Problem

When Rutherford and Geiger tested the independence of simultaneous α particle emissions their results showed only general agreement with expectation. The main failure in retrospect appears to be the neglect of dead time, the processing time of the ocular system for counting flashes, which we find from their results to be about 0.5 s. This paper deals with pile-up in photon counting and related fields. We deal with multichannel scaling and the measurement of time-dependent fluorescence processes for sources of various characteristics. Both mathematical and electronic methods of dealing with the problem are discussed.