Miracle-dz/UniFAR-FTUs huggingface dataset:
MEDIATING EFFECT OF SELF-EFFICACY ON THE RELATIONSHIP BETWEEN INSTRUCTION AND STUDENTS’ MATHEMATICAL REASONING

Literature is well-stocked with studies confirming that an instructional approach, self-efficacy, and mathematical reasoning skills are critical for enhancing students’ conceptual understanding and achievement in mathematics. However, there has been little emphasis on establishing whether being able to reason mathematically depends only on the instructional approach or students’ self-efficacy beliefs about mathematics also play a hidden role. A quasi-experimental study involving 301 grade 11 students from six public secondary schools in one district was carried out to investigate the mediating effect of self-efficacy on the relationship between instruction and students’ mathematical reasoning. Participants of the study were selected using the cluster random sampling method. Data were collected before and after the intervention via a mathematical reasoning test and a mathematics self-efficacy beliefs questionnaire. A Parallel Multiple Mediator Model in SPSS using the PROCESS custom dialogue version 3.4 was employed for data analysis. Findings suggest that mathematics self-efficacy and task-specific self-efficacy beliefs collectively and significantly mediate the effect of the instructional approach on students’ mathematical reasoning. The Student Teams-Achievement Division (STAD) was found to be an effective approach for enhancing students’ mathematical reasoning alongside self-efficacy beliefs. These findings provide evidence on the need to select an instructional approach that does not only focus on developing students’ cognitive abilities such as mathematical reasoning but also fosters students’ affective attributes such as maths self-efficacy beliefs.
A Flexible Approach for Automatic License Plate Recognition in Unconstrained Scenarios

Automatic License Plate Recognition is a crucial task for several applications related to Intelligent Transportation Systems, from access control to traffic monitoring. Most existing approaches are focused on a specific setup (e.g., toll control) or a single license plate (LP) region (e.g., European, US, Brazilian, Taiwanese, etc.), which limits their application. This work proposes a complete ALPR system focusing on unconstrained capture scenarios, where the LP might be considerably distorted due to oblique views. We present an Improved Warped Planar Object Detection Network (IWPOD-NET) that is able to detect the four corners of an LP in a variety of conditions, so that it can be warped to a fronto-parallel view and alleviate perspective-related distortions. Given the rectified LP, we test two different Optical Character Recognition (OCR) methods based on object detection. Our experimental results show that the proposed detector is competitive with state-of-the-art (SOTA) methods using a very limited training set. Regarding the full ALPR results, our method achieves top-scoring results for several datasets that include a variety of capture conditions and vehicle types (in particular, motorcycles).
Inhibitive Effect of Cetyltriphenylphosphonium Bromide on C- steel Corrosion in HCl Solution

Cetyltriphenylphosphonium bromide (CTPPB) was investigated as a corrosion inhibitor for C-steel in 0.5 M HCl using chemical and electrochemical techniques, as well as, surface examination by SEM. The data indicated that CTPPB gives good inhibition effect and the inhibition mechanism is based on the adsorption process forming a barrier layer film that protect the metal from acid attack. The adsorption is found to obey Langmuir model. The effect of temperature was studied. The inhibition efficiency increased by increasing inhibitor concentration and decreased with temperature. Some thermodynamic parameters for adsorption process are deduced and discussed.
Single-Fiber-Based Brillouin Optical Time Domain Analysis With Far-End Modulation

The loop configuration consisting of a lead fiber and a sensing fiber is a common approach to realize Brillouin Optical Time-Domain Analysis (BOTDA), which is however impractical to implement for some field scenarios where only a single fiber is physically available. In this paper, a novel single-fiber-based BOTDA with far-end modulation is proposed and experimentally demonstrated, in which an auxiliary light and a pulse are injected from the near-end of the fiber, and the counter-propagating probe light is generated by modulating the auxiliary light from the far-end. By dedicatedly designing the frequency and temporal profile of each lightwave, Brillouin interaction takes place only between the pulse and the probe light, while stray effects such as four wave mixing and Rayleigh beating noise are avoided, which constitutes the key advantage of the propose technique with respect to traditional single-fiber-based BOTDA techniques. A 80-km-long sensing range, 1.73 MHz frequency accuracy and 2 m spatial resolution is experimentally demonstrated with 4000 trace averages.
A Comparative Assessment of Hydrological Models in the Upper Cauvery Catchment

This paper presents a comparison of the predictive capability of three hydrological models, and a mean ensemble of these models, in a heavily influenced catchment in Peninsular India: GWAVA (Global Water AVailability Assessment) model, SWAT (Soil Water Assessment Tool) and VIC (Variable Infiltration Capacity) model. The performance of the three models and their ensemble were investigated in five sub-catchments in the upstream reaches of the Cauvery river catchment. Model performances for monthly streamflow simulations from 1983–2005 were analysed using Nash-Sutcliffe efficiency, Kling-Gupta efficiency and percent bias. The predictive capability for each model was compared, and the ability to accurately represent key catchment hydrological processes is discussed. This highlighted the importance of an accurate spatial representation of precipitation for input into hydrological models, and that comprehensive reservoir functionality is paramount to obtaining good results in this region. The performance of the mean ensemble was analysed to determine whether the application of a multi-model ensemble approach can be useful in overcoming the uncertainties associated with individual models. It was demonstrated that the ensemble mean has a better predictive ability in catchments with reservoirs than the individual models, with Nash-Sutcliffe values between 0.49 and 0.92. Therefore, utilising multiple models could be a suitable methodology to offset uncertainty in input data and poor reservoir operation functionality within individual models.
Modeling broadband, mobile telephone and economic growth on a macro level: Empirical evidence from G7 countries

Article history: Received: November 28, 2020 Received in revised format: December 28 2020 Accepted: January 26, 2021 Available online: January 26, 2021 Information and Communications Technology (ICT) has played overwhelming roles in the economic and social development of nations and continents in the last two decades. This study aims to explore the impact of mobile telephone and broadband use on economic growth in G7 countries using annual data covering the period of 2000-2017. We performed Pedroni cointegration, Kao cointegration, fully modified ordinary least squares (FMOLS), dynamic ordinary least squares (DOLS), and panel Granger causality tests to investigate the causal and long-run effects. The empirical findings reveal that (i) mobile telephone and broadband use contribute to economic growth in the long-run; (ii) changes in mobile telephone and broadband use significantly lead to a change in economic growth. © 2021 by the authors; licensee Growing Science, Canada
Neutrophil Gelatinase-Associated Lipocalin (NGAL) Measured at Admission is Associated With Development of Late Cardiogenic Shock and Mortality in Patients With ST-Segment Elevation Myocardial Infarction

ABSTRACT In patients with ST-elevation myocardial infarction (STEMI) the immune system is activated with an inflammatory response to follow. In STEMI patients with a severe inflammatory response, risk of development of cardiogenic shock (CS) seems increased. Neutrophil Gelatinase-Associated Lipocalin (NGAL) is a glycoprotein released from mature neutrophils and plasma concentration may increase immediately after STEMI. We therefore aimed to assess whether admission NGAL plasma concentration in patients with STEMI was associated with CS development after leaving the catheterization laboratory (late CS) and 30-day all-cause mortality. Patients and Methods: From 1,892 consecutive patients with STEMI 1,626 (86%) had plasma NGAL concentration measured upon hospital admission before angiography throughout a 1-year period at two tertiary heart centers in Denmark. Patients were stratified according to NGAL quartiles (Q1–4). To assess late CS development, we adjusted for the Observatoire Régional Breton sur l’Infarctus risk score for late CS. For mortality assessment, we adjusted for gender, age, post-PCI culprit Thrombolysis in myocardial infarction flow, left ventricular ejection fraction (LVEF), kidney dysfunction, and being comatose after cardiac arrest. Results: Increasing NGAL concentration was associated with higher age, more comorbidities, and more critical patient conditions including lower blood pressure and LVEF. When adjusted for factors associated with poor outcome, NGAL remained independently associated with both late CS development (Q4 vs. Q1–3) (OR (95% CI) 3.64 (1.79–7.41) and 30-day mortality (HR (95% CI) 3.18 (1.73–5.84)). Conclusion: Admission plasma concentration of NGAL in STEMI patients is independently associated with 30-day all-cause mortality and predictive of late CS development.
Massive open online courses (MOOCs) for professional development: meeting the needs and expectations of physical education teachers and youth sport coaches

ABSTRACT Background Professional development (PD) in a range of occupations has become increasingly digitised. Numerous digital courses are available, with evidence that social media, blogs and apps are increasingly being used for PD. Yet despite clear benefits, there is little robust evidence on the characteristics of digital PD that impact positively on learning and practice, particularly for physical education (PE) teachers and youth sport coaches. This paper provides new insights into the characteristics of effective PD in the context of a complex digital landscape. Purpose While advocated as an innovative genre from which to optimise learners’ proclivity for sharing, curiosity and discovery, little is known about how professional learners respond to massive open online courses (MOOCs) courses to inform their practice. The purpose of this study was to understand how participants responded to the learning design of two MOOCs (n = 13,104 from 155 countries) in the fields of physical education and youth sport coaching. Methods Drawing from a mixed methodology, data were generated from semi-structured interviews (n = 27) and online survey methods (n = 66) with participants across both MOOCs. Findings New data offer insights into the features of course design that practitioners found positive in promoting engagement. It was apparent in the data, for example, that four features were influential: establishing relevance, facilitating bridging, designing for personalisation, and building community. Constructed themes reflect how participants organised and negotiated MOOC experiences, and illuminate the ways in which they navigated and used course content. Evidence from this study provides insights into the ways in which digital genre for PD might be structured to facilitate engagement and presents broader challenges to the ways in which pedagogy is conceptualised and practiced online. Conclusions The refined focus on digital genre as a form of social action in this study seeks to ensure that learners needs can be met in a complex and ever-changing PD digital landscape. In this regard, a more nuanced approach is required that helps explicate the cognitive tools that participants engage as they organise their learning experiences on digital platforms, and how this aligns with their expectations and needs of online PD.
Intelligent Attendance System with Face Recognition using the Deep Convolutional Neural Network Method

Recording student attendance in lectures can be done in several ways, namely giving initials on the attendance sheet or by the lecturer calling each student and then giving a checkmark on the attendance sheet or attendance recording system. This method is inefficient because it is done repeatedly at every meeting, resulting in reduced lecturing time. Some researchers are trying to develop various ways to overcome this, such as using fingerprints, Internet of Things devices, cards with RFID technology, QR codes, and smartphones. However, these technologies require many devices, and they may be costly. The purpose of this research is to develop an intelligent attendance system with facial recognition technology that can identify many people simultaneously without having to make direct contact using the Deep Convolutional Neural Network method. The system is then tested and analyzed for its accuracy in identifying and recording student attendance. The results of research conducted on 16 students in a lecture show that the system can be used to record student attendance with an accuracy of 81.25% in the condition that the student facing forward, 75.00% in the student condition facing sideways, and 43.75% in the student condition facing down.
Power Data Preprocessing Method of Mountain Wind Farm Based on POT-DBSCAN

Due to the frequent changes of wind speed and wind direction, the accuracy of wind turbine (WT) power prediction using traditional data preprocessing method is low. This paper proposes a data preprocessing method which com-bines POT with DBSCAN (POT-DBSCAN) to improve the prediction ef ﬁ ciency of wind power prediction model. Firstly, according to the data of WT in the normal operation condition, the power prediction model of WT is estab-lished based on the Particle Swarm Optimization (PSO) Arithmetic which is combined with the BP Neural Network (PSO-BP). Secondly, the wind-power data obtained from the supervisory control and data acquisition (SCADA) system is preprocessed by the POT-DBSCAN method. Then, the power prediction of the preprocessed data is carried out by PSO-BP model. Finally, the necessity of preprocessing is veri ﬁ ed by the indexes. This case analysis shows that the prediction result of POT-DBSCAN preprocessing is better than that of the Quartile method. There-fore, the accuracy of data and prediction model can be improved by using this method.
Iterative Switching Time Optimization for Mixed-integer Optimal Control Problems

This paper proposes an iterative method to solve Mixed-Integer Optimal Control Problems arising from systems with switched dynamics. The so-called relaxed problem plays a central role within this context. Through a numerical example, it is shown why relying on the relaxed problem can lead the solution astray. As an alternative, an iterative Switching Time optimization method is proposed. The method consists of two components that iteratively interact: a Switching Time optimization (STO) problem and a sequence optimization. Each component is explained in detail, and the numerical example is resolved, the results of which shows the efficiency of the proposed algorithm. Finally, the advantages and disadvantages of the method are discussed and future lines of research are sketched.
Hybrid Spine Simulator Prototype for X-ray Free Pedicle Screws Fixation Training

Simulation for surgical training is increasingly being considered a valuable addition to traditional teaching methods. 3D-printed physical simulators can be used for preoperative planning and rehearsal in spine surgery to improve surgical workflows and postoperative patient outcomes. This paper proposes an innovative strategy to build a hybrid simulation platform for training of pedicle screws fixation: the proposed method combines 3D-printed patient-specific spine models with augmented reality functionalities and virtual X-ray visualization, thus avoiding any exposure to harmful radiation during the simulation. Software functionalities are implemented by using a low-cost tracking strategy based on fiducial marker detection. Quantitative tests demonstrate the accuracy of the method to track the vertebral model and surgical tools, and to coherently visualize them in either the augmented reality or virtual fluoroscopic modalities. The obtained results encourage further research and clinical validation towards the use of the simulator as an effective tool for training in pedicle screws insertion in lumbar vertebrae.
Prognostic value and association of sarcopenic obesity and systemic inflammatory indexes in patients with hepatocellular carcinoma following hepatectomy and the establishment of novel predictive nomograms.

Background The specific impacts of sarcopenic obesity (SO) on hepatocellular carcinoma (HCC) and the association between SO and systemic inflammation remain unclear. This study aimed to investigate the prognostic value and association of SO and systemic inflammation with outcomes after hepatectomy for HCC and develop novel nomograms based on SO and inflammatory indexes for survival prediction. Methods We retrospectively enrolled 452 patients with HCC who underwent radical hepatectomy between January 2012 and March 2015 in Fujian Provincial Hospital as the training cohort. In addition, 275 patients during the same period were enrolled as the external validation cohort. Patients were classified into different groups according to the presence of sarcopenia and obesity. Different inflammation indexes were evaluated to select the best predictor of overall survival (OS) and recurrence-free survival (RFS). Univariate and multivariate logistic regression were performed to investigate the associations between inflammatory indexes and SO. The inflammatory indexes with the highest predictive values and SO were selected for subgroup analyses to establish a novel classification system: the SOLMR grade. SOLMR grades identified in the multivariate Cox analysis were selected to construct novel nomograms for OS and RFS. Results SO (P<0.001) was an independent risk factor for OS and RFS. The lymphocyte-monocyte ratio (LMR) had the highest areas under the receiver operating characteristic (ROC) curves (AUCs) for OS (P<0.001) and RFS (P<0.001) and was identified as an independent factor of SO (P=0.001). SO and the LMR were selected to establish the SOLMR grade. Multivariate Cox analysis revealed that SOLMR grade was a significant independent predictor of OS (P<0.001) and RFS (P<0.001). Nomograms based on SOLMR grades were generated and accurately predicted 1-, 3- and 5-year OS and RFS in HCC patients. The C-index of the novel nomograms was higher than those of the other conventional staging systems (P<0.001). Conclusions Both SO and the LMR were independent risk factors for OS and RFS in HCC patients after hepatectomy. The LMR was an independent factor of SO. The novel nomograms developed from the SOLMR grading system combining SO with the LMR provide good prognostic estimates of the outcomes of HCC patients.
Study on Performance of Anaerobic Digestion for Treating Municipal Solid Waste at Different Temperature

The influence of different temperature on performance of anaerobic fermentation was studied. The parameters adopted for evaluation were biogas production、fermentation starting time and the proportions between solid/water during the digestion process under different temperature. The evaluation of sudden change of temperature affecting the fermentation process were studied too. The result indicated that the optimum temperature for MSW anaerobic fermentation was 55℃. The organic solid waste were degraded very well and the methane concentration in biogas were comparatively high during 55℃.
On Multiple-Resonator-based Implementation of IEC/IEEE Standard P-Class Compliant PMUs

This article deals with the implementation of the P-Class PMU compliant with IEC/IEEE Standard 60255-118-1:2018 by usage of a multiple-resonator (MR)-based approach for harmonic analysis having been proposed recently. In previously published articles, it has been shown that a trade-off between opposite requirements is possible by shifting a measurement time stamp along the filter window. Positioning the time stamp in a proximity of the time window center assures flat-top frequency responses. In this article, through simulation tests carried out under various conditions, it is shown that requirements of the IEC/IEEE Standard 60255-118-1:2018 can be satisfied by the second and third order MR structure for particular conditions of the time stamp location.
Association between metabolic indicators and clinical endometritis during the transition period in Brown Swiss cows

The objective of the current study was to evaluate the relationship between the serum macromineral, metabolite profiles, and the clinical endometritis during the transition period in Brown Swiss dairy cows. Sixty Brown Swiss dairy cows were used in the present study. Blood samples collected at d 10 (± 4) antepartum and 3, 10 and 30 (± 4) days in milk (DIM) to determine calcium (Ca), phosphorus, glucose, cholesterol, non-esterified fatty acid (NEFA) levels. Beta-hydroxybutyric acid (BHB) concentration measured during the postpartum period. Receiver operating characteristics (ROC) curves were used to determine the cow-level thresholds for the subsequent development of clinical endometritis. In addition, pairwise comparisons were made of the area under the curve (AUC) of ROC curves for the thresholds of NEFA, Ca, and glucose predicting clinical endometritis. The mean Ca concentration at 3 DIM was 8.85 ± 0.20 mg/dL in healthy cows compared to 8.30 ± 0.22 mg/dL in cows that subsequently developed endometritis (P < 0.05). NEFA concentrations at 10 DIM and BHB concentration at 10 and 30 ± 4 DIM were higher (P < 0.05) in cows that subsequently developed endometritis. Serum NEFA concentration at 10 days postpartum is the best predictor for diagnosis of clinical endometritis with the AUC values of 0.741. The cows with clinical endometritis also had significantly higher values of glucose at 3 DIM and lower BCS at 10 DIM (P < 0.05). While 58.6% of the cows that developed clinical endometritis were cyclic, 64.5% were cyclic in healthy cows at 30 ± 4 DIM. Serum NEFA concentration was the only risk factor for clinical endometritis at 10 DIM. In addition, a decrease in serum Ca at 3 DIM and increase in NEFA and BHB concentrations at 10 and 30 ± 4 DIM may be associated with clinical endometritis and delayed resumption activity on the ovaries.
Water Jacket and Slot Optimization of a Water-Cooling Permanent Magnet Synchronous In-Wheel Motor

This article investigates the water jacket and slot optimization of a water-cooling permanent magnet synchronous in-wheel motor. The optimization consists of the improvements of the water jacket heat dissipation and slot heat dissipation. In order to reduce the temperature difference between the water and water jacket wall, the impact of the water jacket configuration parameters and shapes are theoretically studied. According to the researches, the method for the water jacket optimization is proposed. A slot thermal model is established to optimize the thermal behavior in slots. The variations of the thermal performances with impregnation materials and slot configuration parameters are analyzed. On the basis of the analysis, this article presents a method for the thermal optimization. The validity of the method is supported by the finite element analysis, computational fluid dynamics and experiments.