Miracle-dz/UniFAR-FTUs huggingface dataset:
Examining Undergraduate Students’ Behavioral Intentions for M-Learning using Theoretical Framework of UTAUT

Enrolments and completion of online programs have been a concern in online learning. It has been found that learners tend to drop out of the program halfway. Consequently, it has become important to identify factors that lead to intention formation and a possible future enrolment to online platforms. The current study focuses on the intention of final year undergraduate (UG) students for m-learning using a quantitative approach, that is descriptive and cross-sectional. All the students have undergone online learning during the phase of the coronavirus pandemic; therefore, the sampling technique used is purposive sampling. Using the theory of Unified Theory of Acceptance and Use of Technology (UTAUT), the adapted questionnaire consists of items on the Likert scale. Multiple regression (MR) was used for hypotheses testing to determine which factors lead to creation of m-learning behavioral intention on a sample of 179 responses at a significance level of p<0.05. Data analysis revealed that all four predictors of UTAUT positively influence and explain 64.2% of intent for m-learning among UG students. This explanation is more in comparison to past studies. The study is crucial for educators who are developing study material. Application developers need to tailor made their applications that enable m-learning with the least effort. The decision-makers in an educational institution must provide technical staff support and the necessary infrastructure to facilitate the m-learning of the students.
Evaluation of nitrogen fertiliser in a beef production system.

This paper reports and discusses the results of 4 years of trials (1985-1988) involving 2 farmlets, one receiving 3 nitrogen applications (50 kg /ha) in autumn, winter and spring (+N) and one receiving no N fertiliser (-N). Stocking rags were 3.3 animals/ha on -N farmlet and 4.3 animals/ha on the +N farmlet for the first 3 years, with the objective being to utilise the extra N-boosted grass with extra animals/ha but not tc sacrifice individual animal performance. In the fourth year the stocking rates were kept the same on each farmlet (3.3 animals/ha) in an endeavour to utilise the extra grass grown on the +N farmlet by way of increased per head performance. In 2 of the 3 years (1985 and 1987) where the i-N farmlet supported the higher stocking rate, liveweight gain (LWG) did not differ between animals. In 1986 the extra animals on the -l-N farmlet had a lower LWG, whereas in 1988 the LWGs were similar for the 2 farmlets stocked at the same rate. The apparent DM responses (kg DM/kg N applied) ranged from 2 to 12. The rates of N fertiliser used in this trial do not appear to result in economic increases in pasture production for the beef production system reported here. Nitrogen fertiliser did not reduce the clover content of pastures rotationally grazed by beef cattle.
Facilitate the Design of Role Learning Games: The RLG Kit

This article presents a design kit for Role Learning Games (RLG) as multiplayer and multi-role learning games. They use some concepts of Role-Playing Games (RPG). Indeed, RPG are growing in education, but teachers rarely have the tools to design one themselves. Based on a RLG model, a design kit was created: the RLG Kit. Following Design-Based Research, we submitted the kit to teachers through three qualitative experiments to assess its usability. We wanted to know if users understood how to use each element of the kit, if they succeeded in creating a RLG scenario with the kit and thus to improve it.
Generic Requirements for Inclusive Healthcare Software: Supporting Older Adults and People with Intellectual and Developmental Disability

As healthcare technology advances, it is important to ensure that everyone benefits from healthcare software, regardless of age or ability. That is why our research team has been working to identify and encapsulate the unique needs of two groups when developing software systems: older adults and people with mild intellectual and developmental disabilities. Through our research, we have developed design patterns that software engineers can use to create more usable healthcare software to support these groups. These patterns identify the generic requirements for usability including accessibility, crucial for creating software that meets the needs of marginalised populations. In this paper, we describe the steps we undertook to identify and develop these requirements. We discover many overlapping needs of these two cohorts. Our research aims to benefit older adults and people with mild intellectual and developmental disabilities and improve the lives of many others facing similar challenges. As we expand our research to other cohorts, we will be able to identify additional generic requirements that make healthcare software development more inclusive and effective.
An Optimized Intrusion Detection System for Cyber-Physical System Attack Using Long Short-Term Memory

With the continuous development of deep learning, Long Short-Term Memory (LSTM) is constantly evolving. This paper presents an optimized method for building an Intrusion Detection System (IDS) for a Cyber-Physical System (CPS) using deep learning techniques. Specifically, the LSTM architecture is applied to a Recurrent Neural Network (RNN), which is trained on the Bot-IoT Dataset. This paper addresses the limitations of traditional RNNs by introducing LSTM, which effectively alleviates the problem of gradient disappearance and allows longer sequences to be used for training. The proposed method acquires high-level abstractions in data through complex architectures or combinations of nonlinear transformations, leading to high detection rates. The theoretical analysis provides insights into the algorithm's performance in terms of its computational complexity, convergence rate, and accuracy, among other important aspects. Through experiments, the optimal parameters of LSTM are determined, and the convergence speed of the neural network is optimized. This work demonstrates the potential of deep learning in IDS and facilitates the development of multi-agent systems by enhancing the agents' ability to process sequential data and temporal information.
Non‐linear prestack seismic inversion with global optimization using an edge‐preserving smoothing filter

Estimating elastic parameters from prestack seismic data remains a subject of interest for the exploration and development of hydrocarbon reservoirs. In geophysical inverse problems, data and models are in general non‐linearly related. Linearized inversion methods often have the disadvantage of strong dependence on the initial model. When the initial model is far from the global minimum, inversion iteration is likely to converge to the local minimum. This problem can be avoided by using global optimization methods.
Predicting Rice Leaf Disease Outbreaks using CNN-SVM Models: A Machine Learning Approach

The critical component in machine learning for classifying image features. Creating a deep convolutional neural network (CNN) opens the door to the possibility of significant returns and the identification of diseases affecting rice. The 5932 on-field images in this publication depict four distinct rice leaf diseases: bacterial blight, Blast, brown spot, and tungro. Additionally, the transfer learning method and the deep feature support vector machine were used to evaluate the performance of 11 CNN models (SVM). The simulation findings demonstrate that the proposed method is inferior to the deep SVM equivalent in classification performance. Convolutional neural network layers were used to develop the classification module, which offers an accuracy of up to approximately 70%. An average accurate recognition rate of 96.8%, and this study examines previous studies on picture classification using CNN. This accuracy is more significant than conventional back propagation neural network models. This work offers a fresh approach to the further investigation of various diseases.
Driver Drowsiness Detection Using Deep Learning Models Based On Different Camera Positions

Driver drowsiness is among the most critical causes of vehicular incidents. Consequently, a solution to mitigate this problem is exponentially increasing where an intelligent vehicle system is being implemented in vehicle development. In these studies, a solution is developed by implementing Convolutional Neural Network (CNN), which is tasked to identify the driver’s drowsiness behaviour. For the datasets that are needed for training and testing of CNN models, datasets are collected at VITAL Laboratories located at UiTM Shah Alam that is recorded from 3 DVR Camera that is placed in the left (CL), middle (CM), and right (CR) positions. The models proposed in these studies are MobileNet-V2, ResNet50, and InceptionV3 architecture, where the performance of each model is evaluated by classifying the behaviour based on three different camera positions. The architecture that is being chosen is InceptionV3 from camera 5, which is placed in the middle positions that successfully achieved an overall accuracy of 85.5% in classifying drowsiness behaviour.
Analyzing the Impact of BERT Embeddings on Rumor Debunking

Social media allows us to connect and share information with anyone within seconds. Most users access social media through web-based applications on their mobile phones. The ease of information sharing through social media results in the exchange of large amounts of information among its users. This could lead to the spread of rumours, either unintentionally or intentionally. Whichever is the case, rumours can hurt concerned users and sometimes even lead to dire situations. It is impractical to manually filter out rumours from trillions of pieces of information. Thus, various research works have been going on for automatic rumour detection. In this paper, we propose a new way to look at research that has already been done to see how BERT embeddings help find rumours. The proposed model was put through 10-fold crossvalidation. Compared to the baseline implementation, subtask A got better by 3.2% and subtask B got better by 2.9%.
Effect of particle size distribution on the effective dielectric permittivity of saturated granular media

The effective permittivity of an unconsolidated porous material is shown to be not only a function of the permittivity of the individual components but also of the distribution of particle sizes present. Increasing width of particle size distribution for glass spheres and sieved quartz sand is demonstrated to reduce the effective permittivity of a mixture compared to the effective permittivity of the corresponding monosize material. The results are shown to lie between predictions made using the Maxwell‐Garnett [1904] formula for monosize spheres and a self‐similar model [Sen et al., 1981] and can be predicted by a multiple Maxwell‐Garnett‐based mixing model. The results demonstrate that the degree of sorting of a material will directly influence the effective permittivity of the mixture. This is beyond the effect of the change in permittivity caused by the significant change in porosity resulting from the mixing of different particle sizes.
A Vein Segmentation Method Based on Improved U-Net

Recently, medical research has revealed that diffusion weighted imaging (DWI) is less sensitive than susceptibility-weighted imaging (SWI) for acute ischemic stroke. Brain vein analysis in SWI is very important for personalised therapy, quantitative diagnosis, and prognosis of acute ischemic stroke. Therefore, the accurate segmentation of veins in SWI images has important significance in supplementary diagnosis and medical research. However, vein segmentation in SWI is a challenging task because different veins show distinct variability and uneven intensity and are difficult to manually split. This paper proposes an improvement method based on U-net, by using dense connection and mixing loss function, the best Dice coefficient and the ASSD were obtained by other comparison methods. The method presented in this article can be used as a potential tool to accurately measure SWI cerebral veins and help physicians in decision making.
Dual trigger and the impact on oocyte quality and embryo development: a Brazilian cohort

Objective We aimed to analyze controlled ovarian stimulation using GnRH antagonist in association with hCG (dual triggering) versus hCG alone (conventional triggering) for final oocyte maturation triggering in a population of unselected Brazilian women. Methods This was a retrospective observational study of IVF medical records between January 2019 and March 2020. Data from 335 women with infertility were included for study. All patients were divided into hCG trigger (control group; n=178) and dual trigger (n=157). Results The number of total oocytes and the number of inseminated oocytes were all significantly higher with the dual trigger protocol compared to hCG-only trigger. However, there is no significant difference in patient age, type of infertility, number of in vitro matured oocytes, trigger day, endometrial thickness, AMH concentration, the number of follicles, the number of mature oocytes and the number of fertilized oocytes. Conclusions Using the dual trigger protocol improved the number of total oocytes retrieved and the number of inseminated oocytes.
Super-twisting sliding mode control of a new muti-scroll hidden chaotic system

This report uses a control technique on the basis of a super-twisting sliding mode algorithm to stabilize the response of a new multi-scroll hidden chaotic system. The new hidden chaotic system is generated from an existing system. The system properties, i.e., Lyapunov exponents, Lyapunov dimension, dissipativity, etc., are analyzed. Compared to the existing mother chaotic system, the generated system is more chaotic and more complex. The new system also has multi-stability property. Subsequently, a second-order sliding surface and super-twisting control law are designed to achieve chattering-free sliding motion to control the newly developed system. With only one control signal(in a 3D system), the chaotic system is successfully controlled. The convergence of the chaotic states to the origin is proved mathematically as well verified from the simulation results. Moreover, the robustness of the controlled system is successfully validated in the presence of matched disturbance and parameter uncertainty.
Semantic Segmentation of Anatomical Structures in Posterior-Anterior Chest X-Ray Image Using U-Net

The need for X-ray imaging analysis, especially on the thorax or chest area, is increasing during the pandemic. However, understanding an X-ray image requires particular expertise, Things that not everyone can understand and do. Therefore, this research was conducted to assist non-professionals in understanding the structural anatomy of the thorax via a Posterior-Anterior Chest X-ray image. This research uses a computer vision and deep learning-based approach, which has become a trend in recent years, including its use in the medical world. Using a U-Net-based neural network architecture, we perform semantic segmentation tasks to identify human organs, including the heart, bronchi, left lung, and right lung. Our proposed system trained U-Net models and achieved a satisfactory performance with 72% of mean IoU with total 850 images as data train and validation, much better than a comparative model, in carrying out the organ anatomical semantic segmentation task to assist users in medical imaging analysis.
Low-Rank Matrix Completion Based on Schattern-p Norm and Gaussian Kernel

Low-rank matrix completion has been widely used in many engineering problems, and different matrix norms have been used to approximate the rank function of the matrix to solve the difficulty of optimization. However, nonlinear relationship between matrix elements has been ignored in the matrix completion algorithms. To address this issue, we propose a novel matrix completion algorithm which takes the nonlinear relationship between matrix elements into consideration. By using the Gaussian kernel function to map the matrix to the nonlinear space, the nonlinear relationship between matrix elements is obtained. Then an objective function with Schattern-p norm, which approximates the rank function more compactly than others, is designed and the gradient descent algorithm is used to solve this optimization problem. The feasibility of the algorithm is verified on the public image data set. Compared with the existing algorithms, experimental results show that the proposed algorithm achieves better performance.
Distributed Tracking Control for High-Order Multi-Agent Systems with Parametric Uncertainties

The distributed tracking control is investigated in this paper for a class of high-order multi-agent systems with parametric uncertainties. It is hard to directly use the existing works for the high-order single-agent systems with parametric uncertainties to the multi-agent case. To complete the estimation for unknown parameters and the tracking aim for the multi-agent systems, we construct a distributed adaptive tracking controller based on the algebra graph theory and the backstepping method. It guarantees the global boundedness of all the signals and the arbitrarily small difference between the follower's and the leader's output. Finally, we make a numerical example to show the availability of the developed methods.
Column Rotation Transpose Based PV Array Reconfiguration Method for Efficient Shade Dispersion under Mismatch Conditions

Partial shading is one of the biggest hindrances to the optimal functioning of photovoltaic (PV) systems. It impacts the peak power generation by the PV systems, and if not dealt properly then can be hazardous for the PV systems. It’s therefore important to mitigate this issue by adopting some reconfiguration techniques to effectively overcome the Partial shading conditions (PSCs). In this research study, a novel reconfiguration technique has been proposed to overcome the challenges caused by the PSC. The proposed reconfiguration technique effectively disperses the shading effect and improves the efficiency of the PV system. The proposed reconfiguration technique is applicable to symmetrical PV arrays of all sizes which makes it scalable. The performance of the proposed reconfiguration technique has been analyzed for various shade cases against different configurations. The outcome of this study shows that there is a significant improvement in performance as compared to the others.
Smart Intravenous Infusion Monitoring and Alert System using IoT-based Force Sensitive Resistor and Whatabot API

Intravenous fluids with vitamins are given to people who are dehydrated and have an imbalance of electrolytes. When the IV bag is empty, the patient’s blood flows through the IV line toward the empty bag because their blood pressure is higher than the IV bag’s. This process, called diffusion can lead to pain and loss of blood. If the IV bag is empty and hooked up to the patient, air can get into the bloodstream. The air bubble enters the patient’s bloodstream, causing the same catastrophic effects. This paper aims to eliminate the danger by creating Smart IV Bags in light of the rising number of risks in the medical sector brought on by the reverse flow of blood in an IV bag (intravenous bag). The Smart IV bags eliminate the need for constant physical monitoring of the IV bag’s state while preventing reverse blood flow. For detection of the IV fluid state, a 0.5 “ diameter force-sensitive resistor is deployed. We integrate the system with a NodeMCU module and using Wi-Fi, it establishes communication between the smart IV bag and the person in charge at the hospital, like a nurse or a caretaker. In this study, the IV Bag is the “thing“ connected to the internet where the FSR readings are analyzed using NodeMCU script to determine if it needs to be emptied and send a WhatsApp message to the caretaker. This design establishes an IoT environment where the IV Bag automatically alerts the caretaker and eliminates the need for constant human intervention.
Design and Modeling of a Dual-Driving PMUT with Increased Round-Trip Sensitivity

Piezoelectric micromachined ultrasonic transducers (PMUTs) are fabricated with Micro-Electro-Mechanical Systems (MEMS) processes. They have advantages such as high uniformity and density, high flexibility in array configuration, and compatibility with integrated circuits. However, the transmitting/receiving sensitivity and the bandwidth of PMUTs are usually lower than those of capacitive micromachined ultrasonic transducers (CMUTs) and conventional thickness mode transducers, which are vital for acoustical imaging resolution. This work presents a novel structure of piezoelectric micromachined ultrasonic transducer (PMUT) with dual piezoelectric driving at the device’s top surface, achieving better round-trip sensitivity while keeping cell size small compared with traditional PMUTs. Due to the high transmitting performance of the PZT film and the high receiving performance of the AlN film, the designed PMUT achieves ideal round-trip sensitivity-relative bandwidth product (Rt-SBW), which is 9.68dB higher than that of the optimized PZT-PMUT and 26.36dB higher than that of the optimized AlN-PMUT, greatly improving the pulse-echo imaging quality.
Singularity Free Dynamic Control Allocation for Tilt-Rotor Multirotor Unmanned Aerial Vehicles

Tilt-rotor multi rotor unmanned aerial vehicles (UAV s) are a developing field of research due to their advantages of flexible flight capability and pose-omnidirectionality. Although numerous control allocation algorithms have been presented with relatively good reliability, some singularity configurations of tilt-rotor UAV can cause the algorithm to fail in generating a stable solution. This paper proposes a singularity-free dynamic control allocation algorithm that improves the stability of tilt-rotor multi rotor UAV s in 6 degrees of freedom (DOFs). The control allocation is formulated as a constrained optimization problem by introducing the inequality constraints derived from singular configurations. The barrier function is embedded into the objective function for constraints enforcement. The Lagrange function is then used to form the unconstrained optimization objective function. A Lyapunov-like function is proven to be negative semidefinite, time invariant and bounded. Through Barbalat's lemma, the algorithm is Uniformly Globally Stable. Simulation results obtained from trajectory tracking at singularities of the tilt-rotor multi rotor UAVs in 6 DOFs demonstrate validity of the proposed dynamic control allocation approach.
Responses of streamflow to climate and land surface change in the headwaters of the Yellow River Basin

The headwater catchments of the Yellow River Basin are of great importance for the whole basin in terms of water resources, and streamflow from these catchments has decreased in the last decades. The concept of climate elasticity was used to assess the impacts of climate and land surface change on the streamflow. Results show that for the period 1960–2000 the elasticity of streamflow in relation to precipitation and potential evapotranspiration are 2.10 and −1.04, respectively, indicating that streamflow is more sensitive to precipitation than to potential evapotranspiration. However, land use change played a more important role than climate in reducing streamflow in the 1990s. It is estimated that land use change is responsible for more than 70% of the streamflow reduction in the 1990s, while climate change contributed to less than 30% of the reduction. The precipitation elasticity appears to have an inverse relationship with the runoff coefficient but a positive relationship with the aridity index, showing that the drier the catchment, the more sensitive the streamflow with respect to precipitation change.