Miracle-dz/UniFAR-FTUs huggingface dataset:
Connection, connectivity and choice: Learning during COVID‐19 restrictions across mainstream schools and Flexible Learning Programmes in Australia

Abstract The COVID‐19 pandemic and associated school closures may have constrained educational participation particularly for students in disadvantaged circumstances. We explore how 30 disadvantaged students in secondary school (14 mainstream/16 Flexible Learning Programme) from Queensland, New South Wales and Tasmania experienced home learning during the first wave of COVID‐19, teasing out nuances across two educational models. Drawing on semi‐structured interviews with these students, our analysis revealed three interconnected themes inflecting their learning: connection, connectivity and choice. Connection captures the desire for belonging and practices that facilitated meeting this desire during system‐wide disruptions to school routines and face‐to‐face learning. Connectivity captures the impact of digitally facilitated learning at home on students' ability to engage with curriculum content and with their learning community. Choice captures the availability of viable options to overcome barriers students encountered in their learning and possibilities to flexibly accommodate student preferences and learning needs. Students from Flexible Learning Programmes appeared generally better supported to exercise agency within the scope of their lived experience of home‐based learning. Findings indicate a need for strengthening student‐centred policy and practices aimed at leveraging the affordances of information technology, balancing self‐directed and structured learning and providing holistic support to enable meaningful student choice.
A Review of Image Classification Algorithms in IoT

With the advent of big data era and the enhancement of computing power, Deep Learning has swept the world. Based on Convolutional Neural Network (CNN) image classification technique broke the restriction of classical image classification methods, becoming the dominant algorithm of image classification. How to use CNN for image classification has turned into a hot spot. After systematically studying convolutional neural network and in-depth research of the application of CNN in computer vision, this research briefly introduces the mainstream structural models, strengths and shortcomings, time/space complexity, challenges that may be suffered during model training and associated solutions for image classification. This research also compares and analyzes the differences between different methods and their performance on commonly used data sets. Finally, the shortcomings of Deep Learning methods in image classification and possible future research directions are discussed.
The role of nursing student training status and anxiety in pediatric pain assessment

ABSTRACT This study examined how nursing students’ training status and their anxiety impacts their assessment of children’s pain. The sample included 100 psychology undergraduate students, 87 beginning nursing students, and 35 advanced nursing students. Participants watched a video of a child receive a finger stick and then rated their own state anxiety and the child’s pain via visual analog scales. Advanced nursing training was related to lower anxiety, which in turn, was related to lower pain ratings. These findings augment our understanding of how nursing training influences personal anxiety during pediatric needle procedures, which may impact pediatric pain ratings.
Predictive Maintenance Based On Machine Learning In Public Transportation Vehicles

Predictive maintenance is an approach to prevent failure in a system by estimating the time of failure before a mechanical component fails, so that the maintenance decision can be properly planned. Maintenance forecasting models are used to increase the productivity and efficiency of a hardware. In the public transport sector, whose efficiency is heavily dependent on equipment, forecasting of failures is vital. In this study, predictive maintenance work was carried out in order to minimize the problems such as stopping the voyage, delaying the journey and having an accident caused by unplanned breakdowns in public transport vehicles. Based on instant vehicle health data obtained from IoT sensors, classification techniques were run in machine learning. For maintenance planning, the probability of vehicles being normal and malfunctioning was examined with fuzzy logic and fuzzy outputs were obtained at maintenance speed. As a result of the study, almost all of the faults in the vehicles could be detected with the predictive maintenance approach applied.
TURN ON YOUR PHONES PLEASE: FROM DISTRACTION TO ENGAGEMENT WITH MOBILE LEARNING

In one month, D2L, the learning management system (LMS) at the University of Manitoba, recorded over 6,000 occurrences of students accessing the LMS from mobile devices: Students were communicating through their actions that it was time to change the way we thought about course design. This article presents results of two separate research projects designed to inform practice at the University of Manitoba with regard to the use of mobile devices in fully online courses: First, the results of a study evaluating the limitations and opportunities for BYOD (Bring Your Own Device) mobile access to courses that have not been designed for mobile devices. Next, the results of a second study examining best practices for course design to facilitate greater opportunities for learning through the LMS and mobile devices are shared. Finally, building upon the results of the previous two studies, the implications for enhancing engagement in face-to-face classes using mobile devices are discussed.
Performance Improvement of an Archimedean Spiral Antenna for 2–18 GHz Applications

Low-profile wideband spiral antennas suffer from poor impedance matching and cross polarization at lower frequencies or high variation in gain at higher frequencies. These significant differences, primarily caused by electromagnetic coupling with a conventional conductive backing, and the resulting interference are addressed in this letter by suitably designing the profile of the conductor. The proposed geometry has a maximum height of 0.08 $\lambda _{m}$ at the minimum operating frequency and has wideband circular polarization performance with a good boresight gain response over 2–18 GHz range. After numerical investigations, a prototype antenna is fabricated and characterized. An excellent impedance matching and circular polarization behavior are measured close to 2 GHz while the variation in gain is limited between 5.8 and 8.6 dBic above 10 GHz.
Subharmonic Solutions of Governed MEMS System Subjected to Parametric and External Excitations

Subharmonic periodic solutions of order ( 1 2 , 1 4 ) to a weakly second order ordinary differential equation which governed the motion of a micro-dynamical system are studied analytically. Applying the method of multiple scales, we derive the modulation equation in the amplitude and the phase of each type of periodic solutions. Determine the steady-state solutions (fixed-points of the modulation equation). Obtained the frequency-response equation (The relation between the amplitude and the detuning parameter and other parameter in the differential equation). Stability analysis of the steady-state solutions is given. Numerical study of the frequency-response equation are carried out. The results are presented in a group of Figures in which solid (dashed) curves indicated stable (unstable) preiodic solutions.
Diurnal and seasonal patterns of global urban dry islands

Urban heat islands (UHIs) are a widely studied phenomenon, while research on urban-rural differences in humidity, the so called urban dry or moisture islands (UDIs, UMIs), is less common and a large-scale quantification of the seasonal and diurnal patterns of the UDI is still lacking. However, quantification of the UDI/UMI effect is essential to understand the impacts of humidity on outdoor thermal comfort, building energy consumption, and urban ecology in cities worldwide. Here, we use a set of globally distributed air temperature and humidity measurements (1089 stations) to quantify diurnal and seasonal patterns of UHI and UDI resulting from rapid urbanization over many regions of the world. The terms ‘absolute UDI’ and ‘relative UDI’ are defined, which quantify urban–rural differences in actual and relative humidity metrics, respectively. Results show that absolute UDI is largest during daytime with the peak humidity decrease in urban areas occurring during late afternoon hours. In contrast, relative UDI is largest during night and the peak urban relative humidity (RH) decrease and vapor pressure deficit (VPD) increase occurs in the late evening hours with values of around −10% to −11% for RH and 2.9–3.6 hPa for VPD between 20–00 local time during summer. Relative and absolute UDIs are largest during the warm season, except for daytime RH UDI, which does not show any seasonal pattern. In agreement with literature, canopy air UHI is shown to be a nighttime phenomenon, which is larger during summer than winter. Relative UDI is predominantly caused by changes in actual humidity during day and UHI during nighttime.
Sparse assortment personalization in high dimensions

The data-driven conditional multinomial logit choice model with customer features performs well in the assortment personalization problem when the low-rank structure of the parameter matrix is considered. However, despite recent theoretical and algorithmic advances, parameter estimation in the choice model still poses a challenging task, especially when there are more predictors than observations. For this reason, we suggest a penalized likelihood approach based on a feature matrix to recover the sparse structure from populations and products toward the assortment. Our proposed method considers simultaneously low-rank and sparsity structures, which can further reduce model complexity and improve its estimation and prediction accuracy. A new algorithm, sparse factorial gradient descent (SFGD), was proposed to estimate the parameter matrix, which has high interpretability and efficient computing performance. As a first-order method, the SFGD works well in high-dimensional scenarios because of the absence of the Hessian matrix. Simulation studies show that the SFGD algorithm outperforms state-of-the-art methods in terms of estimation, sparsity recovery, and average regret. We also demonstrate the effectiveness of our proposed method using advertising behavior data analysis.
Comparative Assessment of Phytoconstituents, Antioxidant Activity and Chemical Analysis of Different Parts of Milk Thistle Silybum marianum L.

Silybum marianum L. is a therapeutic plant belonging to the family Asteraceae, which has exhibited silymarin, a principal component used to cure various physiochemical disorders. The study appraised the phytochemical analysis, antioxidant activity and chemical analysis of an extract from the seed, stem and leaves. Qualitative and quantitative phytochemical analysis was evaluated by the Folin–Ciocalteu reagent method and aluminum chloride colorimetric method, respectively. While the antioxidant activity was determined by using 1,1-diphenyl-2-picrylhydrazyl (DPPH) and acetate buffer in ferric chloride (FRAP) assay, respectively, the chemical profile was evaluated by Gas Chromatography-Mass Spectrometry (GC-MS) assay. The study outcomes identified that alkaloids, glycosides, flavonoids, terpenoids, steroids and catcholic tannins were present in seed, stem and leaves extracts except for saponins and Gallic tannins. Whereas, phenols were absent only in seed extract. Quantitative analysis revealed the presence of phenols and flavonoids in appreciable amounts of 21.79 (GAE/g), 129.66 (QE/g) and 17.29 (GAE/g), 114.29 (QE/g) from the leaves and stem extract, respectively. Similarly, all extracts expressed reasonable DPPH inhibition (IC50) and FRAP reducing power such as 75.98, 72.39 and 63.21% and 46.60, 51.40 and 41.30 mmol/g from the seeds, stem and leaves extract, respectively. Additionally, chemical analysis revealed the existence of 6, 8 and 9 chemical compounds from the seeds, stem and leaves extract, respectively, corresponding to 99.95, 99.96 and 98.89% of the whole extract. The chemical compound, Dibutyl phthalate was reported from all extracts while, Hexadecanoic acid, methyl ester and Silane, (1,1-dimethylethyl), dimethyl (phenylmethoxy) were reported only from the seed and leaves extract. Moreover, Methyl stearate was also a major compound reported from all extracts except for seed extract. It is demonstrable that extracts from different parts of S. marianum possess significant antioxidant activity, as well as valuable chemical compounds accountable for therapeutic effects that might be incorporated as an alternative to synthetic chemical agents.
Comparative determination of antimicrobial activity of the Balkan endemic species Stachys thracica Davidov during the process of ex situ conservation

Stachys thracica Davidov – Thracian woundwort is a Balkan endemic plant included in The Red Data Book of Bulgaria with conservational status “rare”. The plants from genus Stachys have a long history of use to treat various diseases, inflammatory conditions, coughs, ulcers, genital tumors, and infected wounds. Due to its limited distribution the information on the biological activity and chemical composition of S. thracica is rather scarce. The aim of the present research is the comparative determination of the antimicrobial activity of methanolic extracts obtained from in situ wild, in vitro cultivated and ex vitro adapted S. thracica plants. The in vitro shoot culture of the Thracian woundwort was maintained in hormone-free MS medium under controlled environmental conditions. The methanolic extracts from in situ, in vitro cultivated and ex vitro adapted S. thracica plants were active mainly against Gram-negative bacteria. All three extracts showed equal activity against Acinetobacter calcoaceticus. The establishment of in vitro shoot culture and its subsequent adaptation in ex vitro conditions was an appropriate alternative approach for the ex situ conservation of S. thracica as well as for the study of its biological activity.
The Potential of Sentinel-1A Data for Identification of Debris-Covered Alpine Glacier Based on Machine Learning Approach

Microwave remote sensing is one of the main approaches to glacier monitoring. This paper provides a comparative analysis of how different types of radar information differ in identifying debris-covered alpine glaciers using machine learning algorithms. Based on Sentinel-1A data, three data suites were designed: A backscattering coefficient (BC)-based data suite, a polarization decomposition parameter (PDP)-based data suite, and an interference coherence coefficient (ICC)-based data suite. Four glaciers with very different orientations in different climatic zones of the Tibetan Plateau were selected and classified using an integrated machine learning classification approach. The results showed that: (1) The boosted trees and subspace k-nearest neighbor algorithms were optimal and robust; and (2) the PDP suite (63.41–99.57%) and BC suite (55.85–99.94%) both had good recognition accuracy for all glaciers; notably, the PDP suite exhibited better rock and debris recognition accuracy. We also analyzed the influence of the distribution of glacier surface aspect on the classification accuracy and found that the more asymmetric it was about the sensor orbital plane, the more difficult it was for the BC and PDP suites to recognize the glacier, and a large slope could further reduce the accuracy. Our results suggested that during the inventory or classification of large-scale debris-covered alpine glaciers, priority should be given to polarization decomposition features and elevation information, and it is best to divide the glaciers into multiple subregions based on the spatial relationship between glacier surface aspect and radar beams.
A Quantitative Method to Delineate the Influence Area of Work Zone Considering Route Choice Inertia and Elastic Demand

The work zone on the urban road network will affect the surrounding road traffic. To represent the influence area of the work zone, the concept of a subnetwork is proposed in this paper. Delineating a suitable subnetwork quantitatively is a challenging problem. To address this issue, the node synthesized indexes (NSI) are deployed as a variability measure that captures both the change of link flow and origin-destination (OD) demand. The inertia-based stochastic user equilibrium with the elastic demand (ISUEED) model is proposed to accurately provide the data of link flow and OD demand for the network with the work zone. Correspondingly, the data of the network without a work zone can be obtained by the stochastic user equilibrium (SUE) model. According to the value of NSI, the initial range of the subnetwork is determined. Finally, the connectivity and compactness can be guaranteed by the modified L-shell algorithm. To demonstrate the performance of the method, two case studies and sensitivity analyses are conducted based on the Braess network and the local road network in Changchun, China. The proposed method is beneficial to reduce the complexity of the traffic model by substituting the entire network with a subnetwork.
Research on a time scale algorithm based on multi-dimensional weighted average

The widespread availability of high-performance clocks has motivated interest in time scale algorithms. There are various time scale algorithms in use today, ranging in application from scientific to commercial. The weighted average algorithm has been widely used to generate an ensemble time scale. It takes one weight for each clock according to the stability or predictability to generate the ensemble time scale. However, one weight cannot reflect the long-term, mid-term and short-term stability performance of the clock simultaneously. Therefore, it cannot improve the long-term, mid-term and short-term stability at the same time. To solve this problem, a multi-dimensional weighted average algorithm is proposed in this paper, which decomposes the clock difference into three dimensions and gets weights in three dimensions to give consideration to improve long-term, mid-term and short-term stability simultaneously. The final results of simulation and experiment demonstrate that the algorithm proposed in this paper improved Allan deviations of 3.94 × 10−15 on daily and 7.86 × 10−15 on monthly averaging times with respect to those obtained from AT1 and ALGOS, respectively.
Development and fabrication of continuous carbon fiber reinforced thermoplastic porous composite structures with different infill patterns by using additive manufacturing

Additive manufacturing is a development of fabricating 3D parts layer by layer with complex geometries and utilized in numerous engineering structural applications. Fused deposition modeling technology provides higher design flexibility with aim of low cost and simplicity of material conversion to generate the composite parts. Previous studies have mainly focused on the manufacturing and characterization of continuous carbon fiber reinforced polymer composites (CCFRPCs) structure for fully dense and solid part structures and no study has been reported for porous CCFRPC parts. In this study, CCFRPCs porous structures were manufactured using FDM technology. The porous structures were fabricated with one perimeter shell by using two different types of infill patterns (grid and triangular) at three different infill densities levels (20%, 40%, and 60%). The reasons for developing such lightweight porous composite structures with continuous carbon fiber are the reduction of mass, efficient material utilization, energy consumption, and less waste generation. This study investigated the effects on tensile and flexural properties of composite specimens under uniaxial tensile loading and flexural loading, respectively. Fracture interface of the printed porous composites were examined and explored using optical microscope after performing mechanical tests. The experimental results demonstrated that infill pattern and density levels greatly affect the mechanical properties. The specimen printed using grid infill pattern with 60% infill density exhibited highest strength level in term of mechanical test and showed maximum tensile and flexural strength of 162.9 MPa and 127.24 MPa, respectively. While triangular infill pattern with 60% infill density level revealed the maximum tensile and flexural strength of 152.62 MPa and 117.53 MPa, respectively. Hence, from the results achieved in this study showed great potential and ability to replace fully dense and solid structure by the porous structure.
The Effectiveness of Combination Therapy for Treating Methicillin-Susceptible Staphylococcus aureus Bacteremia: A Systematic Literature Review and a Meta-Analysis

Background: This meta-analysis aims to evaluate the effectiveness of combination therapy for treating MSSA bacteremia. Methods: We searched Ovid MEDLINE, EMBASE, Cochrane CENTRAL, and clinicaltrials.gov for studies including adults with MSSA bacteremia. The monotherapy group used a first-line antibiotic active against MSSA and the combination group used a first-line antibiotic plus additional antibiotic/s. The primary outcome was all-cause mortality. Secondary outcomes included persistent bacteremia, duration of bacteremia, relapse, and adverse events. Random-effects models with inverse variance weighting were used to estimate pooled risk ratios (pRR). Heterogeneity was assessed using the I2 value and the Cochrane’s Q statistic. Results: A total of 12 studies (6 randomized controlled trials [RCTs]) were included. Combination therapy did not significantly reduce 30-day mortality (pRR 0.92, 95% CI, 0.70–1.20), 90-day mortality (pRR 0.89, 95% CI, 0.74–1.06), or any-time mortality (pRR 0.91, 95% CI, 0.76–1.08). Among patients with deep-seated infections, adjunctive rifampicin may reduce 90-day mortality (3 studies with moderate-high risk of bias; pRR 0.62, 95% CI, 0.42–0.92). For secondary outcomes, combination therapy decreased the risk of relapse (pRR 0.38, 95% CI, 0.22–0.66), but this benefit was not maintained when pooling RCTs (pRR 0.54, 95% CI, 0.12–2.51). Combination therapy was associated with an increased risk of adverse events (pRR 1.74, 95% CI, 1.31–2.31). Conclusions: Combination therapy not only did not decrease mortality in patients with MSSA bacteremia, but also increased the risk of adverse events. Combination therapy may reduce the risk of relapse, but additional high-quality studies are needed.
Detecting Driver Cognition Alertness State From Visual Activities in Normal and Emergency Scenarios

Current driving behavior studies have limits in obtaining driver’s state information, and recent studies involving driver state focus on driver distraction or inattention. But it is more common that drivers operate in an intermediate subconscious state, where the drivers are neither cognitively fully focused nor distracted. There was little research to address this topic. In this study, the driver cognition alertness state information, which indicates driver subconscious alertness, is detected from eye and iris activities by non-contact computer vision methods. And a novel analysis is conducted and reveals the strong correlation between driver performance and driver cognition alertness state from experiment results. In detail, the driver cognition alertness state is quantified by the proposed metric– iris movement index, which is calculated from iris-eye relative displacements. The developed computer vision method produces high precision results in detecting faces, eyes, and irises in the experiment based on multiple deep learning networks applied in cascade, and enables displacement tracking of eye and iris targets. A filtering method is proposed and removes artifacts due to eye blinks in displacement measurements. The driving performance is estimated by a proposed performance evaluation method in a series of traffic scenarios designed with normal and emergent conditions.