Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring pre-service teachers’ reflective practice through an analysis of six-stage framework in reflective journals

ABSTRACT Reflective journal is used by the pre-service teachers (PSTs) to reflect on their learning and teaching experiences in the classroom but they lack writing it in a more critical way. This study aims to explore the common stages of reflective practice among the PSTs using a six-stage framework. The methodology utilised a case study involving a purposive sampling of a group of 20 participants in an institute of teacher education in Malaysia. The instruments were observations, reflective journals and interviews. The journals were analysed using content analysis by following the framework of reflective practice. The results showed that the PSTs reflected mostly at ‘Describing’ (36.04%), followed by ‘Analysing’ (26.00%), ‘Associating’ (21.15%), ‘Feeling’ (12.12%), ‘Assessing’ (2.78%), and ‘Developing’ (1.92%). The findings indicated that the PSTs reflected mostly at the lowest stage of ‘Describing’ compared to the highest stage of ‘Developing’. It is also interesting to note that the PSTs reflected more deeply after they were able to project their feelings and receive peer feedback. The six-stage framework used in this study not only provides the PSTs a guideline to reflect deeply, but also allows them to develop their future roles as teachers. The implication is for the policy makers and educators to include the proposed framework in the course syllabus and it is recommended for the PSTs to use it as a guideline in developing their reflective practice.
noval approach to measure the progress of pterigium from the diagnosed eyes

Pterygium is a disorder characterised by the development of fibro vascular tissue across the cornea, and it is considered to be moderately normal around the world. Careful meditation should be postponed for a period of time, since this type of ailment can manifest itself more forcefully following a medical examination. By the way, the ophthalmologist is now observing the disease incorrectly, without the support of a specific technology. This proposed system has been utilized to develop a computer technique for tracking the progression of pterygium based on previously examined eye images.
Discovering Breach Patterns on the Internet of Health Things: A Graph and Machine Learning Anomaly Analysis

Due to the rise in the Internet of Health Things (IoHT), cyber-attacks, particularly data intrusions, have become an issue for security experts. In this work, we analyze the performance of traditional statistical, machine learning, and graph-based anomaly detection approaches in response to this problem. We believe that understanding intrusion patterns can aid in the prevention of future attacks. In this work, we use the ARMA model for statistical analysis. We also use several machine learning approaches such as multinomial naive bayes, ran- dom forest, neural networks, XGBClassifier, and support vector machines (SVM). However, while our experiments show that machine learning (ML) techniques have higher precision, accuracy, and F1 score than graph-based techniques, there are aspects to a graph-based approach that could aid security experts in the discovery of certain data breaches by combining the graph-based with the statistical and ML methods. Experiments also show combining different anomaly detection techniques allows for a diverse set of intrusion patterns to be discovered. By recognizing the power of both machine learning and graph-based approaches, we analyze their precision and accuracy while explaining how existing state-of-the-art methods can detect breach patterns. Finally, by identifying the characteristics of breach patterns, we present information that security experts can use to prevent future data intrusions.
Nanosatellite attitude control using electromagnetic actuation

The paper deals with the attitude active control of a Low Earth Orbit nanosatellite, actuated with magnetorquers. The satellite is on a polar orbit and is subject to gradient gravity. Magnetic actuation is preferred for nanosatellite attitude control because of its relatively low cost, lightweight and low power consumption. The basic idea is to use the interaction between the magnetic field generated by the coils and the Earth's magnetic field. The developed control strategy is a Linear Quadratic controller for the three-axis stabilization of the satellite. This optimal controller is derived from a linear model of the system which is nonlinear and time-varying.Since magnetic actuation can lead to poor performances because only two axes can be controlled at each time instant, an hybrid actuation system with magnetorquers and reaction wheels is also investigated. Simulation results are provided in the case of a 2U CubeSat attitude stabilization to illustrate and compare the control strategy efficiency in three cases: (i) with magnetic torquers, (ii) with gyroscopic torquers and (iii) with the hybrid actuation system.
Monitoring and Evaluation of Smolt Migration in the Columbia Basin : Volume II : Evaluation of the 1996 Predictions of the Run-Timing of Wild Migrant Subyearling Chinook in the Snake River Basin using Program RealTime.

This project was initiated in 1991 in response to the Endangered Species Act (ESA) listings in the Snake River Basin of the Columbia River Basin. Primary objectives and management implications of this project include: (1)to address the need for further synthesis of historical tagging and other biological information to improve understanding and identify future research and analysis needs; (2)to assist in the development of improved monitoring capabilities, statistical methodologies and software tools to aid management in optimizing operational and fish passage strategies to maximize the protection and survival of listed threatened and endangered Snake River salmon populations and other listed and nonlisted stocks in the Columbia River Basin; (3)to design better analysis tools for evaluation programs; and (4)to provide statistical support to the Bonneville Power Administration and the Northwest fisheries community.
Life cycle assessment of electric scooters for mobility services: A green mobility solutions

The gradual rise in global warming owing to fossil fuels usage is causing massive environmental problems and global temperature rise and countries are focusing on the negative emissions technologies (NETs) to restrict the average global temperature rise to 1.5°C by 2050. As Italy instigates its stable recovery from the coronavirus emergency, the Italian government is offering Italian people financial support of up to €500 to buy a personal bicycle or e‐scooter, to reduce dependence on private cars and global warming issues and people inItaly have greeted this opportunity for new personal e‐scooters. This paper aims to employ a life cycle assessment (LCA) on the personally owned electric scooters (PEOS) that are circulating in Turin. The results are reported for five impact categories: global warming potential (GWP), acidification potential, eutrophication potential, ozone layer depletion, and human toxicity potential. We found that the environmental load associated with the usage and charging of e‐scooters is less compared with the materials and manufacturing burdens of e‐scooters. Regarding the GWP, the results of Analysis methodology of the use of the PEOS generate 21 g of CO2‐eq. per passenger‐kilometer dominated by around 50% from materials processing, 17.5% from Li‐ion battery production, 1.6% from transportation, and 30.9% from usage and charging of e‐scooter. Four scenarios are compared with the base case situation, which are the substitution of alternate materials, use of 50% recycled aluminum, transportation of e‐scooters via plan, and charging with solar power. Results from these scenarios are proved to be highly sensitive to baseline scenarios. Results also revealed that PEOS has higher (21 g CO2 eq./km) environmental consequences on the global warming potential as compared to bicycles (8 g CO2 eq./km) and lower (21 g CO2 eq./km) environmental impacts as compared with electric bicycles (40 g CO2 eq./km) and battery electric vehicles (80 g CO2 eq./km).
Best Response Computation in Multiplayer Imperfect-Information Stochastic Games

Computing a best response is a fundamental task in game theory. One of its uses is to compute the degree of approximation error of an approximation of Nash equilibrium strategies. In many game classes best responses can be computed in polynomial time, but in imperfect-information stochastic games it is equivalent to solving a POMDP and is PSPACE-complete. Prior work has developed an algorithm for computing an approximation of Nash equilibrium strategies in a 4-player imperfect-information naval strategic planning problem which is modeled as a stochastic game. A heuristic approach was developed for computing best responses to determine the approximation error of these strategies, which was shown to have limited scalability. In this paper we present approaches that utilize parallelism to significantly speed up this computation allowing us to compute best responses in significantly larger games.
Evaluation of Leaf Area Index (LAI) of Broadacre Crops Using UAS-Based LiDAR Point Clouds and Multispectral Imagery

Leaf area index (LAI) is an established structural variable that reflects the three-dimensional (3-D) leaf layering of vegetation in response to environmental inputs. In this context, unmanned aerial system (UAS) based methods present a new approach to such plant-to field-scale LAI assessment for precision agriculture applications. This article used UAS-based light detection and ranging (LiDAR) data and multispectral imagery (MSI) as two modalities to evaluate the LAI of a snap bean field, toward eventual yield modeling and disease risk assessment. LiDAR-derived and MSI-derived metrics were fed to multiple biophysical-based and regression models. The correlation between the derived LAI and field-measured LAI was significant. Six LiDAR-derived metrics were fit in eight models to predict LAI, among which the square root of the laser penetration index achieved the most accurate prediction result ( ${R^2}$= 0.61, nRMSE = 19%). The MSI-derived models, which contained both structural features and spectral signatures, provided similar predicting effectiveness, with predicted ${R^2}$≈0.5 and nRMSE≈22%. We furthermore observed variation in model effectiveness for different cultivars, different cultivar groups, and different UAS flight altitudes, for both the LiDAR and MSI approaches. For data collected at a consistent flight altitude, MSI-derived models could even exceed LiDAR-derived models, in terms of accuracy. This finding could support the possibility of replacing LiDAR with more cost-effective MSI-based approaches. However, LiDAR remains a viable modality, since a LiDAR-derived 3-D model only required a single predictor variable, while an MSI-derived model relied on multiple independent variables in our case.
Electrohydrodynamic jet printing via low-solute concentration inks for enhanced positioning accuracy

ABSTRACT Electrohydrodynamic 3D printing (EHD-3DP) employing the solution and melting liquids as inks, receives extensive applications in the manufacturing of different materials. However, the jet lagging effect associated with positioning accuracy has yet to be solved. Here, we report a new technique of non-tailing jet printing to deposit fibres precisely. By applying poly(ε-caprolactone) (PCL) solution at an ultra-low concentration of 5 wt. %, a straight-line jet profile perpendicular to the collector plane was obtained at the high motion speeds of 30–90 mm/s. This enabled the moving jet-to-collector point kept up with the projection of nozzle tip on collector plane, leading to improved positioning accuracy of PCL fibres. The optimal protocol on material and processing parameters avoided the structural staccatos of printed fibres. This work provides insights into how the printed fibres from low-concentration inks are influenced by different parameters. The technique was applicable for the complex linear and non-linear pattern printing.
Performance assessment for fiber tracking of unidirectional carbon fiber reinforced plastic by digital image correlation of X-ray computed-tomography

Cross-sectional X-ray computed tomography of unidirectional carbon fiber reinforced plastic (CFRP) composites was performed at a synchrotron radiation facility. Fiber locations in each tomogram were identified and connected longitudinally via updated digital image correlation, from which a CFRP numerical model was automatically developed. Fiber-tracking accuracy was important for obtaining exact duplicates; thus, the performance was experimentally analyzed with two types of unidirectional CFRPs formed with circular or kidney-shaped cross-sectional carbon fibers. The accuracy improved and then converged to a certain value with decreasing distance between each tomogram. Better accuracy was obtained for the circular fibers. Average error in the estimated fiber locations was less than in the fiber diameter. However, errors caused overlapped fibers. The experimental results thus indicated that fiber-tracking accuracy had to be improved to develop duplicates without overlapped fibers.
“If You Don’t Fight Like Hell, You’re Not Going to Have a Country”: An Intersectional Settler Colonial Analysis of Trump’s “Save America” Speech and Other Messages of (Non)belonging

Donald Trump turned the presidency into a live-action reality television series comprising a chaotic blend of televised and tweeted intrigue. The nation’s collective anxiety coalesced in a nationalistic, authoritarian denouement on January 6, 2021. During Trump’s speech at the Save American Rally, he returned to familiar themes, telling the story of a ravaged America and his role as its victimized hero. As Trump concluded his speech urging supporters to “fight like hell,” rioters assailed the U.S. capitol revealing the violence of colonial invasion. To understand and, thus, respond to the insurrection, we must first recognize the dominant structures that create the conditions of its perpetration and excusal. This intersectional analysis addresses the colonial impulse for violent exclusion through the interpretive framework of settler colonialism. Examining four interlocking themes, we incorporate historical and contemporary popular culture rhetorical devices to triangulate our findings. We illustrate how social identity and settler colonialism occur in popular culture at the intersections of American, Christian nationalism, racialization, hetero-genderism, and ableism. Overall, our analysis explores how Trump prepared his base through years of cultural manipulation, promoting a populist, White vision of the world with Trump symbolic of its savior. To subvert these exploitations, we must participate in their deconstruction to destabilize the power of colonial institutions.
Steel surface defect detection algorithm combined with attention mechanism

To solve the problem of low detection accuracy of steel surface defects due to background interference and various target shapes, a steel surface defect detection algorithm with attention mechanism is proposed to improve detection accuracy. In view of the small proportion of the target defect area in the overall image and background interference, a two-way attention module (TWA-Block) is proposed to establish the long-distance dependence of the spatial domain and channel domain features. It enhances the contour and texture features of defect area in shallow features, and suppresses the background to a certain extent. The experimental results show that the average accuracy (MAP) of the YOLOv3 model fused with the attention mechanism on the NEU-DET dataset reaches 79.5%, which is 14.4% higher than the YOLOv3 algorithm. Compared with the standard steel surface defect detection methods, the algorithm effectively improves the detection accuracy.
Software Orchestrated and Hardware Accelerated Artificial Intelligence: Toward Low Latency Edge Computing

Driven by the expeditious wireless evolution and growing complexity of Internet of Things systems, edge intelligence has been widely recognized as a novel paradigm to enable ubiquitous smart industry and consumer applications, which uses mobile edge computing (MEC) to push artificial intelligence (AI) related computing to the network edge near mobile terminals and data sources for low-latency data processing. However, design and deployment of edge intelligence remain extremely challenging due to resource-constrained edge computing environments and latency-related considerations. These motivate us to explore edge intelligence from different perspectives, particularly software orchestration (e.g., AI model design, model optimization, and resource management) and hardware acceleration methods (e.g., AI chip customization). However, the performance improvement of edge intelligence using just one single perspective is extremely limited, especially when its advantages are exhausted, which seriously hinders further performance development. In this article, we propose an edge computing framework with co-acceleration of software orchestration and hardware to break through the bottleneck of a single acceleration perspective on performance improvement, enabling high-quality edge intelligence service deployment. Simulation results show that the proposed edge computing framework has dramatically improved system performance in facilitating low-latency edge intelligence.
Computational Techniques and their Usage to Optimize Car Design for Better Performance

This paper describes the use of different computational fluid dynamics (CFD) models and other techniques that can be used to get low coefficient of drag. The upper body of a car is designed using surface modeling in CATIA. It is analyzed using K-epsilon realizable model in ANSYS Fluent. Changes in design are made based on the high coefficient of drag at high speeds. Then, K-epsilon realizable model is used for analysis. To get better accuracy, a refined mesh for the wake region is used. Afterwards, k-omega standard model is used. At the end, the upper body of the passenger car and its tyres are examined to get the final results of the coefficient of drag. The same design with the same meshing conditions is also analyzed in Star CCM+ to verify the results. INDEX TERMS: ANSYS Fluent, computational fluid dynamics (CFD), coefficient of drag, Star CCM+
Adaptation and Validation of a Sinhala version of the Radbound Oral Motor Inventory (ROMP) for Parkinson's disease

Background: The Radboud Oral Motor Inventory for Parkinson's disease (ROMP) is a patient-rated assessment measuring patients' perceptions of speech, swallowing, and saliva control among patients with idiopathic Parkinson's disease (IPD). Objective: The present study was carried out to adapt and validate the Sinhala version of the ROMP questionnaire in a Sinhala-speaking patient cohort diagnosed with IPD. Materials and Methods: The study population consisted of patients diagnosed with IPD attending a tertiary care neurology clinic at the National Hospital of Sri Lanka. ROMP was translated from English to Sinhala, and an expert committee verified its content. Construct validity was assessed by correlating the Sinhala ROMP scores with the subscales in speech, salivation, and swallowing of the Unified Parkinson's Disease Rating Scale and with five-point Likert-type scale to assess dysarthria, dysphagia, and drooling by a speech and language therapist. Test–retest reproducibility was assessed by repeating the questionnaire in 2 weeks. Results: A cohort of 21 patients was evaluated (male to female ratio = 2.5:1, mean age was 58.8 [±8.3] years). The Spearman's correlations between ROMP and the Likert-type scale assessment, that is, speech r = 0.85 (P < 0.01), swallowing r = 0.86 (P < 0.01), and drooling r = 0.88 (P < 0.01), and subscales of the UPDRS were statistically significant, that is, speech r = 0.75 (P < 0.01), swallowing r = 0.96 (P < 0.01), and salivation r = 0.94 (P < 0.01). Reproducibility of the three domains and total intraclass correlation coefficients indicated a high level of agreement in test–retest reproducibility (range: 0.98–0.99). The three subdomains of the instrument also had excellent internal consistency (total Cronbach's α = 0.99). Conclusion: The Sinhala version of ROMP has proved to be a good assessment tool for dysphagia, dysarthria, and drooling in the early stage of IPD patients.
Facial Recognition using Dilated Sobel Edge Detection (DSED) Technique

- Facial Recognition is an intricate process in identifying the exact match for a face among several images. Recognition can be done with the mapping method called edge detection. Edge detection is an infrequent method for facial recognition though it produces better results in accuracy. Now the challenging part of recognition is that the time span and distinct illumination, pose and facial expressions tends to vary the accuracy. Sobel Edge Detection uses sobel operator to identify the edges and maps the features of the face in order to identify the face accurately. By keeping the base idea of Sobel Edge Detection, the Dilated Sobel Edge Detection technique is proposed. In order to attain better recognition rate, essential features alone are extracted by dilation. This paper compares the proposed method with the existing methods.
Quasi-static partial element equivalent circuit models of magneto-dielectric materials

This study aims to present the formulation of the partial element equivalent circuit (PEEC) for dispersive and lossy linear magneto-dielectric materials under the quasi-static hypothesis. The magnetic permeability and dielectric permittivity are usually available as tabulated data at different frequency samples. They are incorporated in the time-domain PEEC formulation by resorting to a rational approximation of their frequency-dependent constitutive relations. Thus, a set of ordinary differential equations is obtained besides those obtained by enforcing Kirchoff voltage and current laws to the PEEC equivalent circuit. The resulting augmented model preserves the circuit structure of the usual PEEC model and, hence, can be run in Spice-like circuit solvers. The robustness and accuracy of the proposed time-domain approach is tested by comparison with its frequency-domain counterpart despite the larger number of unknowns.
Detection and quantification of lateral, illicit connections and infiltration in sewers with Infra-Red camera: conclusions after a wide range of experiments

The most known problem of separate sewer systems is the existence of illicit connections and, hence, the deposition of untreated sewage to the receiving waters or the occurrence of urban flooding due to overloading of wastewater systems with storm water. Although there are several known and relative expensive techniques for inspection of storm sewer systems (e.g. smoke or sound based technologies), most of them concentrate on the structural or operational reliability of the inspected pipe and ignore the conditions that prevail beneath the surface of the water, detecting only some defects. An infrared camera (Flir A35) was used for a more comprehensive approach towards the inspection of a storm sewer pipe, by identifying abnormal thermal finger prints (due to lateral connections) along a flume. The illicit connections were simulated by discharging warm or cold water through several types of lateral connections (variation of diameter, intrusion, depth, etc.). Data analysis revealed that the detection and quantification of illicit connections is possible, albeit under certain conditions.