Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Boredom on the Academic Engagement of Students during Online Class

This research study focuses on determining the boredom and work engagement of the students in the online class setting. Boredom is a prevalent emotion of becoming uninterested in any activity. It is a common emotion for everyone and significantly affects the work engagement of the students in an online class. The researchers gathered data from 200 students from different universities. Student boredom was measured using the Utrecht Boredom Scale, which contains eight items capturing boredom's affective, cognitive, and behavioral manifestations. On the other hand, work engagement is measured using a 3-item version of the Utrecht Work Engagement Scale, which contains items measuring vigor, dedication, and absorption. The association between boredom and variables of work engagement is analyzed using the Pearson correlation test. Results revealed that students who felt bored during online learning displayed a low level of vigor and absorption. In contrast, students who showed a high level of vigor said a higher level of absorption and dedication during online learning. The result opens an opportunity to make the learning system in online classes better because it is more susceptible to boredom that lessens the work engagement of the students.
Constraint‐based multi‐agent reinforcement learning for collaborative tasks

In order to be successfully executed, collaborative tasks performed by two agents often require a cooperative strategy to be learned. In this work, we propose a constraint‐based multi‐agent reinforcement learning approach called constrained multi‐agent soft actor critic (C‐MSAC) to train control policies for simulated agents performing collaborative multi‐phase tasks. Given a task with n$$ n $$ phases, the first n−1$$ n-1 $$ phases are treated as constraints for the final task phase objective, which is addressed with a centralized training and decentralized execution approach. We highlight our framework on a tray balancing task including two phases: tray lifting and cooperative tray control for target following. We evaluate our proposed approach and compare it against its unconstrained variant (MSAC). The performed comparisons show that C‐MSAC leads to higher success rates, more robust control policies, and better generalization performance.
Wideband Low-RCS Step-Type Cavity-Backed Stacked Dielectric Resonator Antenna for Unmanned Aerial Vehicle Communications

This letter explains the design of a cavity-backed stacked dielectric resonator antenna (SDRA) with wider impedance bandwidth (IBW) and improved gain for S-band and C-band applications. The antenna mainly comprises of four dielectric resonators stacked above one another of varying size and shape. Combining the SDRA with step-type cavity and conformal radome forms the final antenna. The antenna provides a wider IBW of 103% (2.07–6.55 GHz) and dual-band axial ratio bandwidth of 27.90% (2.38–3.15 GHz) and 16% (5.01–5.88 GHz), respectively. The antenna provides a maximum gain of 9 dBi. The final cavity-backed antenna offers good radar cross-sectional reduction ranging from 2 to 8 GHz for both normal and oblique incidence angles. Hence, the proposed SDRA with the dual-layer cavity is applicable for unmanned aerial vehicle communications.
Lipid Profile Normalization Effect of Hunteria Umbellata Aqueous Fruit Extract in Wistar Rats Induced with Aloxan Monohydrate

The present study investigated the potential effect of Hunteria Umbellata fruit extract on lipid profile reduction administered at different daily doses ie 200mg/kg, 400mg/kg compared with control (1) and group5 treated at 0.3ml standard drug insulin following aloxan monohydrate induction. The lipid profile- HDL,LDL,vLDL,Triglycerides and Total cholesterol were determine using precipitant (mmol), enzymatic hydrolysis, enzymatic and point methods. Results from this study indicate that the lipid profile reduction potential of H.U fruit extract was most effective in group2 administered 200mg/kg body weight with no aloxan induction. This was closely followed by group3 treated at 400mg/kg and group5 treated with the standard drug insulin after aloxan induction compared with the control. However, goup4 induced with aloxan and treated at 200mg/kg body weight has an elevated lipid profile compared with the control and other groups studied. Findings from this study have shown that moderate dose intake of Hunteria umbellata fruit extract by subjects with hyperlipidaemia can be beneficial in the reduction of lipid profile but the rapidity of its reduction rate is dose dependent.
Uniform continuity on unbounded intervals: classroom notes

(v3.1 released September 2006)We present a teaching approach to uniform continuity on unbounded intervals which, hopefully, may help to meet the following pedagogicalobjectives:(i) To provide students with eﬃcient and simple criteria to decide whether a continuous function is also uniformly continuous;(ii) To provide students with skill to recognize graphically signiﬁcant classes of both uniformly and nonuniformly continuous functions.Assembling some well-known facts and reﬁning the resulting statement, we establish a useful asymptotic coincidence test for theuniform continuity on unbounded intervals. That test is the core of the present note and yields an easily applicable technique. Inparticular, one of its immediate consequences is the elementary fact that continuity and existence of horizontal or oblique asymptotesimply uniform continuity.Keywords: Uniform continuity; oblique asymptotes.
Ensemble physics informed neural networks: A framework to improve inverse transport modeling in heterogeneous domains

Modeling fluid flow and transport in heterogeneous systems is often challenged by unknown parameters that vary in space. In inverse modeling, measurement data are used to estimate these parameters. Due to the spatial variability of these unknown parameters in heterogeneous systems (e.g., permeability or diffusivity), the inverse problem is ill-posed and infinite solutions are possible. Physics-informed neural networks (PINN) have become a popular approach for solving inverse problems. However, in inverse problems in heterogeneous systems, PINN can be sensitive to hyperparameters and can produce unrealistic patterns. Motivated by the concept of ensemble learning and variance reduction in machine learning, we propose an ensemble PINN (ePINN) approach where an ensemble of parallel neural networks is used and each sub-network is initialized with a meaningful pattern of the unknown parameter. Subsequently, these parallel networks provide a basis that is fed into a main neural network that is trained using PINN. It is shown that an appropriately selected set of patterns can guide PINN in producing more realistic results that are relevant to the problem of interest. To assess the accuracy of this approach, inverse transport problems involving unknown heat conductivity, porous media permeability, and velocity vector fields were studied. The proposed ePINN approach was shown to increase the accuracy in inverse problems and mitigate the challenges associated with non-uniqueness.
How Board Size and Board Independence Affect Insurance Companies’ Performance

In the last two decades, corporate governance has had its “shining era of research”, especially in developed countries, where good management, transparency, and reaching the investors’ basis meant a huge deal for big corporations. Without reducing the importance of the subject, such issues have reached the developing countries a bit later in time, and investigation on the matter has been not so extensive, especially due to the lack of proper legal framework and real data. Yet corporate governance is reaching an interest lately in emerging markets, like Albania which aspires to be in the Union, and due to the “new normality” in the management style, which requires a re-adjustment of business structures. This paper attempts to determine the role that board size and board independence as corporate governance variables have in a company’s performance, measured by the board of directors’ characteristics. Even though at a very initial stage of its development, the insurance industry in Albania has applied certain corporate governance structures, mainly because insurance companies are part of international groups. As the insurance industry is integrated into the wider financial services and nowadays is getting more important in the local service-based financial market, understanding the factors affecting its profitability becomes important too. The results of this study show that there is a negative relationship between board size and corporate performance, while there is a positive relationship between board independence and corporate performance.
Lattice Boltzmann simulations of low-Reynolds-number flow past fluidized spheres: effect of Stokes number on drag force

In a fluidized bed, the drag force acts to oppose the downward force of gravity on a particle, and thus provides the main mechanism for fluidization. Drag models that are employed in large-scale simulations of fluidized beds are typically based on either fixed-particle beds or the sedimentation of particles in liquids. In low-Reynolds-number ( $Re$ ) systems, these two types of fluidized beds represent the limits of high Stokes number ( $St$ ) and low $St$ , respectively. In this work, the fluid–particle drag behaviour of these two regimes is bridged by investigating the effect of $St$ on the drag force in low- $Re$ systems. This study is conducted using fully resolved lattice Boltzmann simulations of a system composed of fluid and monodisperse spherical particles. In these simulations, the particles are free to translate and rotate based on the effects of the surrounding fluid. Through this work, three distinct regimes in the characteristics of the fluid–particle drag force are observed: low, intermediate and high $St$ . It is found that, in the low- $Re$ regime, a decrease in $St$ results in a reduction in the fluid–particle drag. Based on the simulation results, a new drag relation is proposed, which is, unlike previous models, dependent on $St$ .
Efficient Markov chain Monte Carlo sampling for electrical impedance tomography

This paper studies electrical impedance tomography (EIT) using Bayesian inference [1]. The resulting posterior distribution is sampled by Markov chain Monte Carlo (MCMC) [2]. This paper studies a toy model of EIT as the one presented in [3], and focuses on efficient MCMC sampling for this model. First, this paper analyses the computation of forward map of EIT which is the bottleneck of each MCMC update. The forward map is computed by the finite element method [4]. Here its exact computation was conducted up to five times more efficient, by updating the Cholesky factor of the stiffness matrix [5]. Since the forward map computation takes up nearly all the CPU time in each MCMC update, the overall efficiency of MCMC algorithms can be improved almost to the same amount. The forward map can also be computed approximately by local linearisation, and this approximate computation is much more efficient than the exact one. Without loss of efficiency, this approximate computation is more accurate here, after a log transformation is introduced into the local linearisation process. Later on, this improvement of accuracy will play an important role when the approximate computation of forward map will be employed for devising efficient MCMC algorithms. Second, the paper presents two novel MCMC algorithms for sampling the posterior distribution in the toy model of EIT. The two algorithms are made within the 'multiple prior update' [6] and 'the delayed-acceptance Metropolis-Hastings' [7] schemes respectively. Both of them have MCMC proposals that are made of localized updates, so that the forward map computation in each MCMC update can be made efficient by updating the Cholesky factor of the stiffness matrix. Both algorithms' performances are compared to that of the standard single-site Metropolis [8], which is considered hard to surpass [3]. The algorithm of 'multiple prior update' is found to be six times more efficient, while 'the delayed-acceptance Metropolis-Hastings' with single-site update is at least twice more efficient.
The kinocardiograph for assessment of fluid status in patients with acute decompensated heart failure

To improve telemonitoring strategies in heart failure patients, there is a need for novel non‐obtrusive sensors that monitor parameters closely related to intracardiac filling pressures. This proof‐of‐concept study aims to evaluate the responsiveness of cardiac kinetic energy (KE) measured with the Kinocardiograph (KCG), consisting of a seismocardiographic (SCG) sensor and a ballistocardiographic (BCG) sensor, during treatment of patients with acute decompensated heart failure.
Hepatic Glucose Regulations by Sago (Metroxylon sagu) Resistant Starch in Diabetic Goto Kakizaki Rat

Resistant starch (RS) Sago (Metroxylon sagu) intake has been linked with the improvement in postprandial hyperglycemia and diabetes management via several modes of action including delayed glucose absorption and inhibition of carbohydrate digestion in the gastrointestinal tract. However, to our knowledge, studies on local Malaysian sago RS associated with hepatic glucose production has not been reported elsewhere. Thus, this study was done to identify the underlying mechanisms of local Malaysian RS sago native and modified known as sago RS type 2 (sago RS2) and type 4 (sago RS4) respectively in glucose regulations by analyzing the targeted genes in hepatic glucose pathways. In this study, gene expression associated with Glucose and Glycogen Metabolism Pathways analysis in the liver of spontaneously type 2 diabetic rat, Goto kakizaki treated with water (control), Hi Maize (positive control), sago RS2 and RS4 was done using Rat Glucose Metabolism RT² Profiler PCR Array which consist of 84 genes. The results showed that several genes were significantly up- and down-regulated in the diabetic rats treated with Sago. Taldo1 was significantly upregulated whereas G6PC, Sdhb and Rplp1 genes were significantly downregulated in the rat liver treated with sago RS2. In the rat liver treated with sago RS4, Idh3g gene was significantly upregulated whereas G6pc, Pdk3, Eno3, Sdhb, Galm and Tkt genes were significantly downregulated. The gene expressions identified are associated in the blood glucose homeostasis involving the regulation and enzymatic pathways of glucose and glycogen metabolisms. In conclusion, the genes identified might be useful for therapeutic targets in glucose lowering effects by reducing hepatic glucose output indicating potential of our local sago in managing diabetes.
Preparation of poly(acrylic acid)/linseed mucilage/chitosan hydrogel for ketorolac release

Studies of smart and biocompatible hydrogels have resulted in the development of efficient drug‐delivery systems controlled by external stimuli. Taking inadequate doses of ketorolac could cause health complications in humans. Therefore, it is necessary the development of a polymeric matrix for controlled drug delivery to extend the release of Ketorolac. In this work, acrylic acid was polymerized using ammonium persulfate as initiator and N,N′‐methylenebisacrylamide as a cross‐linking agent, and in the presence of chitosan (Chit) and linseed mucilage (LS) biopolymers to obtain a composite of PAAc/LS/Chit hydrogel which was used for adsorption and release of ketorolac. Hydrogel was characterized by cryo‐scanning electron microscopy (Cryo‐SEM), Fourier transform infrared spectroscopy, and thermogravimetric analysis. The effects of pH on water hydrogel swelling percentage (S), water absorption percentage (W), and ketorolac‐releasing kinetics were studied. SEM analysis showed hydrogel pore size pH‐depending, with micropore diameters ranging between 5 and 10 nm at acidic pH, while for the hydrogel swollen at pH = 9, bigger pores are observed in the range of 30 to 50 nm. It was observed that S and W increased with the pH of the medium with an S of 608% at a pH of 9 following a Fickian behavior of water diffusion into the hydrogel pore and swelling kinetics represented by a second‐order model. Ketorolac kinetic release was well described through the Korsmeyer‐Peppas mathematical model, with the release rate increasing with the pH, extending the total release time of drug to 20 h.
Parameters Sensitivity Analysis of Ant Colony Based Clustering: Application for Student Grouping in Collaborative Learning Environment

Clustering analysis is one of the data analysis techniques that organizes items into clusters according to their degrees of similarities. In this context, bio-inspired algorithms have found success in solving clustering problems. Inspired by nature, Ant Colony based Clustering arises from ant colony behavior in organizing nests and clustering ants corpses. Accordingly, several researchers proposed different clustering algorithms that mimic the real ants behavior in forming cemeteries. However, the performance of a given algorithm depends strongly on its parameters settings. Indeed, it holds a large number of adjustable parameters that need to be instantiated by suitable values. In this paper, we study the parameters influence, more precisely the parameter <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> which is responsible for adjusting similarity between objects. In fact, we analyze the impact of <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> values on the performance of some well known Ant Colony based Clustering Algorithms applied to constructing team-works in a collaborative learning environment. After various bench tests, the choice of <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> value is determined based on the best algorithm accuracy for each learning data-set. The experimental results prove that Ant Colony algorithms performance strongly depends on <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>, especially when applied to large data-sets size. However, <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> has a negligible influence on the algorithm’s accuracy when applied to small data-sets size. Obviously, the feature selection step could be ignored since it has a negligible influence on the algorithm performance even with different values of <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>.
A Novel Neural-Based-Rainfall Newcasting System in Hong Kong

This paper describes the development of a new approach of rainfall nowcasting (very short term forecasting) by using a neural network. This approach consisted of extracting the information from radar images and evaluating past rain gauge records to provide short term rainfall forecasting. All meteorology data were provided by the Royal Observatory of Hong Kong (ROHK). Pre-processing procedures were essential for this neural network rainfall nowcasting. The forecast of rainfall every half an hour is such that a storm warning signal can be delivered to the public in advance. The network architecture was based on a recurrent Sigma-Pi network. The results are very promising and this neural based rainfall nowcasting system was capable of providing a reliable rain storm warning signal to the Hong Kong public in advance.
Experimental Evidence for Shear‐Induced Melting and Generation of Stishovite in Granite at Low (<18 GPa) Shock Pressure

Knowledge of the shock behavior of planetary materials is essential to interpret shock metamorphism documented in rocks at hypervelocity impact structures on Earth, in meteorites, and in samples retrieved in space missions. Although our understanding of shock metamorphism has improved considerably within the last decades, the effects of friction and plastic deformation on shock metamorphism of complex, polycrystalline, non‐porous rocks are poorly constrained. Here, we report on shock‐recovery experiments in which natural granite was dynamically compressed to 0.5–18 GPa by singular, hemispherically decaying shock fronts. We then combine petrographic observations of shocked samples that retained their pre‐impact stratigraphy with distributions of peak pressures, temperatures, and volumetric strain rates obtained from numerical modeling to systematically investigate progressive shock metamorphism of granite. We find that the progressive shock metamorphism of granite observed here is mainly consistent with current classification schemes. However, we also find that intense shear deformation during shock compression and release causes the formation of highly localized melt veins at peak pressures as low as 6 GPa, which is an order of magnitude lower than currently thought. We also find that melt veins formed in quartz grains compressed to >10–12 GPa contain the high‐pressure silica polymorph stishovite. Our results illustrate the significance of shear and plastic deformation during hypervelocity impact and bear on our understanding of how melt veins containing high‐pressure polymorphs form in moderately shocked terrestrial impactites or meteorites.
Fitting Human Data with Fast, Frugal, and Computable Models of Decision-Making

There is a long-standing debate regarding the extent to which human decision-making processes are “rational” in the formal sense of the word. One approach to evaluating and contrasting the psychological validity of multiple theories of decision-making is to statistically calculate the predictive validity of the algorithms and equations that instantiate those theories against actual human performance data. Previously, we presented the predictive validity results of linear regression and fuzzy logic models of decision-making data generated by participants in a military-like task (Campbell, Buff, Rhodenizer, & Dorsey, 1999). In this paper, we evaluate the predictive validity of four “fast and frugal” algorithms against these same data. While no single approach will resolve the debate regarding the nature of decision-making processes, each adds a piece to the puzzle. A better understanding of decision-making processes could improve our ability to train and provide performance support for all types of decision-makers.
Improved Estimation of Third-Order Cumulants

The (symmetric) trimmed mean estimator truncates the same number of samples at both of sides of the probability distribution. For asymmetric distributions the problem is to decide where to truncate the distribution on the left and right side. A generalized asymmetric Gaussian distribution that covers a wide range of length of tails and asymmetry is proposed and employed to approximate the distribution of this discrete data. Using this distribution, truncation points of the discrete distribution on both sides have been obtained. Starting with the trimmed mean estimator that can be used for symmetrically distributed data, an adaptive generalized trimmed mean (AGTM) estimator is developed and employed to estimate the third-order cumulants. Using these truncation parameters the AGTM of the discrete data series are computed and it is seen that the AGTM performs better in estimating third-order comulants than the mean estimator.