Miracle-dz/UniFAR-FTUs huggingface dataset:
Educational Preparedness for Virtual Teaching and Learning during COVID-19 Lockdowns: Implication for Policy Innovation

The COVID 19, global pandemic that gripped Zimbabwe throughout 2020 and beyond posed numerous challenges across sectors, including education. As a result of this disaster, several state governments devised mitigation strategies to address challenges. In light of the foregoing, the paper intends to conduct a critical review of the education sector's emergency response to the COVID19 crisis using the document analysis method. It evaluates this strategy as an alternative approach for teaching and learning, revealing its strengths, challenges, and gaps, as well as fostering recommendations for its improvement for future policy innovations. The study used a desk-review methodology, also known as systematic inquiry, to critically evaluate the foundation of education and psychology using empirically researched secondary data (Mertens 2010), which has the potential to yield important new knowledge about virtual teaching and learning where distance is not a barrier. Up to now, a number of challenges and gaps have emerged in this emergency response, necessitating the development of effective and efficient catastrophe preparedness and response policies by educational policymakers. The study findings revealed that there are gaps in Zimbabwe's disaster legislation, which has an impact on the preparedness of education for it to embark in virtual learning during COVID-19 lockdown. The research also found that the education sector is not adequately prepared for disaster risk reduction in order to reduce the sector's vulnerability. The study suggests that intensive mobilization of resource and capacity building be undertaken so as to achieve the envisioned goals in education, with no child left behind.
An empirical-likelihood-based structural-change test for INAR processes

The empirical likelihood ratio (ELR) test is proposed for uncovering a structural change in integer-valued autoregressive (INAR) processes. The limiting distribution is derived under the null hypothesis that the parameter did not change at the anticipated change points. To evaluate the finite-sample performance of the proposed ELR test, the empirical sizes and powers are investigated in a simulation study. The ELR test is also applied to real data on infectious disease and crime counts.
Curb Detection and Compensation Method for Autonomous Driving via a 3-D-LiDAR Sensor

Effective detection of curbs is crucial for the perception and localization of autonomous vehicles, but, in practical applications, complex road scenes and the occlusion of curbs by various obstacles are two difficult problems to deal with. Therefore, this article proposes a fast curb detection and compensation method. First, the multiblock RANSAC method is used for ground segmentation to distinguish between ground points and nonground points. Second, the candidate curb points are obtained by processing the ground points with the jumping-window algorithm associated with multispatial features. The jumping-window algorithm only processes the region of interest, which greatly improves the operation efficiency of the method. Third, a joint filtering method is used to refine the candidate curb points, mainly to remove obstacles and other interfering points contained in the candidate curb points. Finally, the missing part of the curb caused by the obstruction is interpolated by a piecewise cubic spline to compensate for the curb points and improve the continuity. We use the KITTI dataset to evaluate the proposed method, and the obtained precision, recall, and harmonic mean in various road scenarios are all above 80%, which is sufficient to demonstrate the robustness of our method. At the same time, the average processing time of each frame is only 47 ms, which can meet the real-time requirements of automatic driving.
Penerapan Model Concept Attainment untuk Meningkatkan Hasil Belajar pada Materi Segitiga dan Segiempat

: The main problem was students' poor learning outcomes of mathematics in the grade VII E SMP N 6 Bengkulu City The aim of this research is to know how the application of the concept attainment model improves students' learning outcomes in the grade VII E SMP N 6 Bengkulu City. This research subject was grade VII E SMP N 6 of Bengkulu in the academic year 2015/2016 which amounted to 22 students. The type of this research is classroom action research which consists of several cycles or repetitions of cycles. Each cycle consists of several steps, namely planning, implementation, observation, and reflection. Data collection techniques were observation sheets of activity and cycle tests. Based on the research, results can be concluded as the application of the concept attainment model can improve learning outcomes in which the average value of the first cycle to the third cycle, namely 55,91; 71,28; and 84,99 and classical completeness percentage of students from the first cycle to the third cycle, namely 31,82 %; 54,54% and 81,81%.
An Efficient License Plate Detection Approach Using Lightweight Deep Convolutional Neural Networks

Benefited from deep convolutional neural networks, various license plate detection methods based on deep networks have been proposed and achieved significant improvements compared with traditional methods. However, the high computational cost due to complex structures prevents these methods from being deployed in real-world applications. This paper proposes an efficient license plate detection method based on lightweight deep convolutional neural networks for improving the detection speed. To extract high-level features from input images, this paper designs a lightweight feature pyramid generation module based on a lightweight architecture and depth-wise convolutions. To further enhance feature pyramid, an efficient feature enhancement module is designed to fuse features generated by the region proposal network with backbone features. In the detection network, a light head structure based on fully connected layers is employed to further reduce the computational cost of the model. In experiments, floating point operations and detection ratio are used to evaluate the efficient of the proposed method. Experimental results on public datasets show that the proposed method achieves the best trade-off between speed and accuracy.
The spatial sodium distribution in the coma of comet Hale-Bopp (C/1995 O1)

Long-slit spectra of sodium D-line emission in the coma of comet Hale-Bopp (C/1995 O1) have been obtained on 14 March and 16 April 1997 from the Observatoire de Haute- Provence, France, and the William Herschel Telescope on La Palma, Spain. The spectra show Na spatial profiles within � 2.5 10 5 km from the nucleus along and perpendicular to the sun- comet line. In both observing periods secondary maxima are present, indicating spatial structures in the coma sodium distri- bution. A comparison with the structures seen in the cometary dust continuum is made. A Na production rate of � 31 0 24 -5 10 25 s 1 could be derived on March 14, 1997, corresponding to less than 0.3% of the normal cosmic abundance ratio Na/O.
Line Average Pooling: A Better Way to Handle Feature Maps on CNN for Skin Cancer Classification

This paper proposes a new pooling method called line average pooling (LAP), which operates between the convolution layer and the final output layer, replacing the traditional mapping method, such as Flatten and global average pooling (GAP). LAP effectively reduces the total number of parameters of the model, thereby preventing overfitting effectively while retaining more features from high-level feature maps. Additionally, it increases the fitting speed of the model. We selected the ISIC skin cancer dataset, then examined the performances of three pooling methods: LAP, GAP and Flatten, on a customized CNN model. In addition, we analyzed the fitting degree when the epoch was 100. The experimental results show that, the degree of overfitting using LAP is greatly reduced when compared with Flatten. Compared with GAP, LAP is better and faster in extracting features and fitting the training data. Both GAP and LAP demonstrate good generalization abilities, reaching 87.56% and 88.11% respectively. With proper means of additional regularization, LAP can even perform better than GAP.
Physiologically Based Pharmacokinetic Modeling to Describe the CYP2D6 Activity Score-Dependent Metabolism of Paroxetine, Atomoxetine and Risperidone

The cytochrome P450 2D6 (CYP2D6) genotype is the single most important determinant of CYP2D6 activity as well as interindividual and interpopulation variability in CYP2D6 activity. Here, the CYP2D6 activity score provides an established tool to categorize the large number of CYP2D6 alleles by activity and facilitates the process of genotype-to-phenotype translation. Compared to the broad traditional phenotype categories, the CYP2D6 activity score additionally serves as a superior scale of CYP2D6 activity due to its finer graduation. Physiologically based pharmacokinetic (PBPK) models have been successfully used to describe and predict the activity score-dependent metabolism of CYP2D6 substrates. This study aimed to describe CYP2D6 drug–gene interactions (DGIs) of important CYP2D6 substrates paroxetine, atomoxetine and risperidone by developing a substrate-independent approach to model their activity score-dependent metabolism. The models were developed in PK-Sim®, using a total of 57 plasma concentration–time profiles, and showed good performance, especially in DGI scenarios where 10/12, 5/5 and 7/7 of DGI AUClast ratios and 9/12, 5/5 and 7/7 of DGI Cmax ratios were within the prediction success limits. Finally, the models were used to predict their compound’s exposure for different CYP2D6 activity scores during steady state. Here, predicted DGI AUCss ratios were 3.4, 13.6 and 2.0 (poor metabolizers; activity score = 0) and 0.2, 0.5 and 0.95 (ultrarapid metabolizers; activity score = 3) for paroxetine, atomoxetine and risperidone active moiety (risperidone + 9-hydroxyrisperidone), respectively.
Magnetic vortex resonance in patterned ferromagnetic dots

A microwave reflection method has been used to measure the spin excitations corresponding to the translational mode of magnetic vortices in samples containing either one or two vortices. Experimental findings are complemented by micromagnetic simulations. One-vortex systems are investigated in micron-sized circular and elliptical cylinders. For ellipses, the resonance frequency can effectively be tuned by applying static magnetic fields and the field dependence of the frequency is significant for fields applied along the short axes but negligible when applied along the long axes of the ellipses. This is contrary to the circular case, where virtually no field dependence was found. This can be understood by considering the shape of the vortex potential well. Further, it is found that the resonance frequency is independent on the direction of the excitation field for the one-vortex systems. Ellipses containing two interacting vortices are also investigated. It is shown that the relative vortex core polarizations dominate the vortex translational mode and cause, in the case of opposite polarizations, a dependence on the excitation field direction. For parallel core polarizations, no dependence on the excitation field direction is found. The dependence of the resonance frequencies on applied static fields along the long and short axes are also experimentally mapped out and compared with micromagnetic simulations, where the possible eigenmodes are determined. Another section of the thesis introduces the dawning of a device based on patterned magnetic elliptical elements for the manipulation and movement of magnetic particles on a surface. The controlled movement and separation of individual particles are successfully demonstrated. Contributions to micromagnetic standard problems and simulations on magnetization switching in nanoscale particles have also been performed. The standard problems highlight some important aspects of choosing the discretization cell sizes and the finite temperature simulations show that thermal fluctuations can alter the magnetization reversal paths.
Nonlinear Thermal Responses Outweigh Water Limitation in the Attenuated Effect of Climatic Warming on Photosynthesis in Northern Ecosystems

Plant photosynthesis is strongly limited by temperature at high northern latitudes. The temperature sensitivity of plant photosynthesis in scenarios of future climatic warming, however, is highly uncertain. We used sun‐induced fluorescence—a satellite proxy for plant photosynthesis—to analyze the spatiotemporal response of photosynthesis to temperature at high northern latitudes. We detected a widespread decline in the sensitivity of photosynthesis to temperature during the last two decades: 3.25 versus 2.19%/°C, in the 2000s and 2010s, respectively. We used methods of machine learning and temporal composition to characterize the contribution of nonlinear thermal responses and water limitation. Both methods consistently identified the nonlinear thermal responses as the main driver of the attenuated positive warming effect. We highlighted the traditionally temperature‐limited ecosystems at high northern latitudes may be approaching the temperature tipping point, beyond which the warming effect on plant photosynthesis would transition from positive to negative.
Kinematic Tau Reconstruction and Search For The Higgs Boson in Hadronic Tau Pair Decays with the CMS Experiment

The thesis prepares a search for the Standard Model Higgs boson in the di-tau channel with the CMS experiment. Based on Monte Carlo simulations, two selections are developed for light Higgs bosons produced by the dominant processes, gluon fusion and vector-boson fusion. Both selections exclusively consider the hadronic tau decay into three charged pions. They rely on an efficient algorithm to identify the tau decay products within the hadronic environment at the LHC. A kinematic fit exploits the topology of the particular decay mode and enables the reconstruction of the entire tau momentum including the neutrino. A set of quality criteria is defined on the obtained observables, which is valid for a broad range of tau energies. This provides an efficient suppression of quark and gluon jets that fake tau decays. The Higgs boson is reconstructed from pairs of tau leptons that pass the quality requirements. The selections derive further background suppression from the event kinematics. In case of the gluon fusion, the selected sample is dominated by off-shell Z0 bosons that decay into tau pairs. The vector-boson fusion involves two additional quark jets. Their signature and the significant transversal momentum of the Higgs boson allow for a background-free selection. The significance of both selections is discussed for an integrated luminosity of 30 fb−1.
Internet of Things Based Energy Management System using Real-Time Consumption Data

Egypt is one of Africa's and the Arab world's most populous countries, with overcrowding and one of the world's fastest-growing economies. The rapid growth of the population led to a rapid rise in energy demand, placing pressure on the country's domestic energy resources in the residential sector. The home energy management system design aims to shed light on the current status of traditional home appliances in Egypt and connect them to the internet and control them remotely by using mobiles, tablets, or laptops.This paper will describe an IoT-based home energy management system for controlling and managing energy use. The system prototype is designed and utilized to handle real consumption in order to achieve minimum power consumption and ensure a whole house is under the value of a specific demand limit in order to lower peak load based on customer priority.As shown through various scenarios of home consumption, the system's accuracy reveals that this algorithm can reduce consumption by up to 55%.
Neural response based behavioral profiling of vehicle drivers to personalize alarm sequences, warn safety systems and trigger non-driver-in-loop control

Quantitative measurement of vehicle driver alertness and response readiness to on-road emergency cues could add more response time to driver assistance and safety features. Exogenous Electroencephalogram (EEG) potentials generated in Beta frequencies can imply driver’s attention to the situation, the absence of which may be associated with distraction or drowsiness. Detection of specific Alpha potentials during action-soliciting events may further indicate risk of violating response-trigger thresholds beyond which physical and vehicular response periods may become too short to avoid collision. Specific Event-Related Potentials (ERP) associated with early automatic cognitive process of consciousness to obstacles, emotional reaction to visual cues, risk evaluation, drier’s emotional intensity, anticipation and motor preparation can be identified, isolated and processed to create a model that predicts driver’s intension/capability to respond to obstacles within a derived time threshold. Isolating signals that indicate driver in-activity (extreme fatigue, sleep, visual or auditory distraction) during near-obstacle situations could further be used to preempt user-in-loop drive, in semi-autonomous vehicles, even if the driver is inhibiting normal transition to self-drive mode. Existing safety components, like airbags, could be issued pre-warnings about possible crash situations (currently they are actuated only upon impact), giving them critical additional time to get pre-actuation sequences triggered.
Observer design for nonlinear parameter‐varying systems: Application to diesel engines

This paper deals with the problem of designing a nonlinear observer for diesel engines. The goal is to estimate the masses entering the cylinders and the inlet and exhaust pressures. To achieve this, the considered model subject to disturbances contains linear parameter‐varying part and nonlinear part having a large Lipschitz constant. The H ∞ criterion is used to reject the disturbances. Besides, the modified mean value theorem is applied to express the nonlinear error dynamics as a convex combination of known matrices with time‐varying coefficients. The sufficient conditions are derived and given in terms of linear matrix inequality. The advantage of the proposed method is that it can be applied to a wider class of nonlinear systems, particulary our case: diesel engines. The proposed approach is tested and evaluated using an advanced diesel engine professional simulator AMEsim (LMS International, Leuven, Belgium). Copyright © 2014 John Wiley & Sons, Ltd.
Estimation of the Minimum Backflashover Current and Backflashover Rate of a 150 kV Overhead Transmission Line: Frequency and Current-Dependent Effects of Grounding Systems

Frequency- and current-dependent effects dominate the response of grounding systems. This work investigates the influence of tower grounding system modeling on estimating the minimum backflashover current and backflashover rate (BFR) of a typical 150 kV overhead transmission line. The minimum backflashover current is determined through ATP-EMTP simulations. Four grounding system modeling approaches are evaluated, namely: (i) a constant ground resistance, (ii) the frequency-dependent (FD) response of the grounding system with constant electrical properties of soil, (iii) the FD response with FD soil properties, and (iv) a soil ionization model. Actual concentrated and extended tower grounding systems used in practice for the investigated 150 kV line are considered. The FD response of these grounding systems is obtained using a hybrid code based on electromagnetic field analysis and circuit theory. Results show that simulating the FD response is important for the extended grounding system. For the concentrated grounding system, the minimum backflashover current and BFR are affected considerably by including soil ionization in simulations; FD effects influence BFR results primarily for low soil resistivity values.
Stabilisation of Viral Membrane Fusion Proteins in Prefusion Conformation by Structure-Based Design for Structure Determination and Vaccine Development

The membrane surface of enveloped viruses contains dedicated proteins enabling the fusion of the viral with the host cell membrane. Working with these proteins is almost always challenging because they are membrane-embedded and naturally metastable. Fortunately, based on a range of different examples, researchers now have several possibilities to tame membrane fusion proteins, making them amenable for structure determination and immunogen generation. This review describes the structural and functional similarities of the different membrane fusion proteins and ways to exploit these features to stabilise them by targeted mutational approaches. The recent determination of two herpesvirus membrane fusion proteins in prefusion conformation holds the potential to apply similar methods to this group of viral fusogens. In addition to a better understanding of the herpesviral fusion mechanism, the structural insights gained will help to find ways to further stabilise these proteins using the methods described to obtain stable immunogens that will form the basis for the development of the next generation of vaccines and antiviral drugs.
Embedded Machine Learning: Towards a Low-Cost Intelligent IoT edge

Deploying Machine Learning algorithms at the network edge is an ongoing research goal for both industry and academic researchers. Owing to the ubiquitous nature of Internet of Things devices and smart environments in various domains, the availability of Machine learning and deep learning capabilities on edge devices is rapidly becoming a necessity to achieve full utilization of the large amounts of data produced by these devices. However, resource constrained low-cost embedded processors like microcontrollers are typically used as the edge devices, consequently limiting their computing capabilities and memory capacity, thereby making the implementation of typical Machine Learning algorithms that are generally computationally expensive on these constrained devices extremely challenging. Therefore, in this paper we adopt a proof-of-concept approach to demonstrate the deployment procedure of an anomaly detection algorithm on low-cost and low-power embedded devices for potential application in the healthcare and wellness domain.
Influence of High-Pressure Die Casting Parameters on the Cooling Rate and the Structure of EN-AC 46000 Alloy

The paper presents the results of the research on the impact of process parameters of high-pressure, cold-chamber die casting of an industrial casting made of aluminium alloy on the casting properties assessed macroscopically by measuring the casting average density and microscopically through the characteristics of the casting microstructure. The analysis covers the influence of three selected velocity settings of the pressing plunger, which determine the filling time, and three values of the compression pressure setting characteristic of the third phase of the casting process. The cooling and solidification simulations of the casting were performed using the ProCAST software. During the simulation tests, the impact of the filling rate of the alloy into the die cavity on the cooling rate and the alloy solidification path at selected points were determined. The conducted research allowed linking the process parameters with the parameters of the casting structure with different wall thicknesses. Metallographic examinations of the castings were carried out using a light microscopy, SEM, and EDS analysis. The fraction of the phases α(Al), the size of dendritic cells, and the size of silicon particles, in the cross-sections of the castings with wall thickness of 3, 6, and 11 mm, respectively, were determined.