Miracle-dz/UniFAR-FTUs huggingface dataset:
SECONDARY TEACHERS PERSPECTIVES ON FREE ONLINE PROGRAMS TO PROMOTE STUDENT ENGAGEMENT

"It can be challenging for teachers to keep students engaged in a physical classroom, however, in a virtual setting it is augmented further (Mobile guardian, 2020). Households can be very distracting for students and teachers are unable to walk around and cannot see if students are engaged or distracted (Farah & Barnett, 2019; McNiff, 2021). In addition, teachers can feel intimidated and overwhelmed with technology (Hertenstein, 2020; Schaffhauser, 2020). Teachers are struggling with virtual learning and have gotten little to no professional development on how to engage students in an online platform (Schwartz, 2020; Williams, 2021). This study will dive into various free online programs for virtual student engagement which will provide prospective from current teachers on the most to the least helpful program. These prospective will help provide professional development direction on which online program could be used to engage students in a virtual setting. The 32 participants included current teachers in southeast Alabama. These participants were also enrolled in a master’s of education program. The participants learned about various free online programs and were able to simultaneously implement those programs in their classroom. At the end of the semester students took an online survey asking which programs were least to most helpful for engagement, easiest to implementation, and programs they would like to know more about. Once the data was collected, descriptive statistics were used to analyze the results. The results showed the programs that were the easiest to implement were also the most helpful for engaging students. In addition, the programs they wanted to know more about were also the programs they stated were the least helpful were engaging students. A few implications of the study were at the time of the study all participants were learning and teaching virtually, this will affect the future usage as the future of virtual classroom settings is still undecided. In addition, the deep dive of each online program was limited due to the lack of time in the course. "
Can Solana be the Solution to the Blockchain Scalability Problem?

Solana is a public blockchain platform, launched in April 2018, that aims to increase scalability when compared to other blockchains without compromising decentralization and security. It supports smart contracts and the creation of decentralized applications (DApps). The study aims to collect data from the Solana blockchain and verify some of its properties such as its transactions' throughput, i.e. the rate at which valid transactions are committed into a block by the Solana blockchain during a one second interval of time (TPS). The data were collected over the period of two months (14 October - 15 December) and made public on a GitHub repository. The results of our data analysis show how the average transactions' throughput is about 2812 TPS and that the fees paid by users to have the transactions confirmed are on average much lower than the fees users pay for other blockchains that support the same functions, such as smart contract and the creation of DApps. The paper sheds light on the mechanisms of Solana blockchain that, according to their founders, promises to solve the scalability problem without sacrificing decentralization and security.
Climate Change Impacts on the Road Transport Infrastructure: A Systematic Review on Adaptation Measures

Road transport is one of the main contributors to increasing greenhouse gas (GHG) emissions, consequently aggravating global warming, but it is also one of the sectors that most suffer from climate change, which causes extreme weather events. Thus, strategies, also called adaptation measures, have been discussed to minimize the impacts of climate change on transport systems and their infrastructure; however, a knowledge gap is evident in the literature. Therefore, this article develops a systematic review with a bibliometric approach, still scarce in the literature, in renowned databases, focusing on studies developed on adaptation measures for road infrastructure. The results show that, since the development of the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC), an increasing amount of studies on the theme have been published in recognized journals such as Science of the Total Environment, Energy and Buildings and Urban Climate, analyzing climate threats such as intense precipitations and high temperatures that have led to biophysical impacts such as flooding and urban heat island. In addition, for each type of adverse weather condition, many impacts on road infrastructure can be listed, as well as ways to detect these impacts, and adaptation measures that can be used to minimize these problems.
Linearly polarized phased antenna array with an application for the automatic landing of unmanned flying vehicle

This paper shows the main requirements for a system for an unmanned landing of a flying vehicle using a pseudo-conical scanning method. The research work focuses on the system sensor - a linearly polarized phased antenna array with pseudo-conical scanning, for intercepting and controlling the detected object - UAV. The design feed network uses a branch-line coupler for dephasing elements. A functional model of the considered antenna system, with nine radiation pattern beam cases, is created; its simulated and measured parameters are presented
Development and Validation of a Nomogram for the Prediction of Unfavorable Treatment Outcome Among Multi-Drug Resistant Tuberculosis Patients in North West Ethiopia: An Application of Prediction Modelling

Background Multidrug-resistant tuberculosis (MDR-TB) is a global problem and a health security threat, which makes “Ending the global TB epidemic in 2035” unachievable. Globally, the unfavourable treatment outcome remains unacceptably high. Therefore, this study aimed to develop a risk prediction model for unfavorable treatment outcomes in MDR-TB patients, which can be used by clinicians as a simple clinical tool in their decision-making. Objective The objective of this study was to develop and validate a risk prediction model for the prediction of unfavorable treatment outcomes among MDR-TB patients in North-West Ethiopia. Methods We used MDR-TB data collected from the University of Gondar and Debre Markos referral hospitals. A retrospective follow-up study was conducted and a total of 517 patients were included in the study. STATA version 16 statistical software and R version 4.0. 5 were used for the analysis. Descriptive statistics were carried out. A multivariable model was fitted using all potent predictors selected by the lasso regression method. A simplified risk prediction model (nomogram) was developed based on the binomial logit-based model, and its performance was described by assessing its discriminatory power and calibration. Finally, decision curve analysis (DCA) was done to evaluate the clinical and public health impact of the developed model. Results The developed nomogram comprised six predictors: baseline anemia, major adverse event, comorbidity, age, marital status, and treatment supporter. The model has a discriminatory power of 0.753 (95% CI: 0.708, 0.798) and calibration test of (P-value = 0.695). It was internally validated by bootstrapping method, and it has a relatively corrected discrimination performance (AUC = 0.744, 95CI: 0.699, 0.788). The optimism coefficient was found to be 0.009. The decision curve analysis showed the net benefit of the model as threshold probabilities varied. Conclusion The developed nomogram can be used for individualized prediction of unfavorable treatment outcomes in MDR-TB patients for it has a satisfactory level of accuracy and good calibration. The model is clinically interpretable and was found to have added benefits in clinical practice.
Fast and robust loop-closure detection using deep neural networks and matrix transformation for a visual SLAM system

Abstract. We investigate the loop-closure detection problem for a visual simultaneous localization and mapping (SLAM) system. Generally, a deep neural network is used to solve loop-closure detection problems. However, this method is time-consuming and occupies a large memory in practice, primarily owing to the high-dimensional nature of the data involved. Herein, we combine a pretrained convolutional neural network (CNN) model with a convolutional autoencoder to acquire a low-dimensional CNN vector as an image representation. Subsequently, the loop-closure problem is simulated by the single-image nearest-neighbor search method based on the cosine distance of the low-dimensional CNN vectors. In addition, to solve the perceptual aliasing problem in loop-closure detection, we empirically propose an efficient method for eliminating false positive loop closures by appropriately decomposing and reconstructing the similarity matrix. Furthermore, we verify through experiments that our method can effectively reduce the time and memory costs of loop-closure detection tasks while ensuring detection accuracy. Therefore, this method can provide a feasible solution to improve the real-time performance and robustness of visual SLAM systems.
Concatenator, a user-friendly program to concatenate DNA sequences, implementing graphical user interfaces for MAFFT and FastTree

Abstract Motivation Phylogenetic and phylogenomic analyses require multi-gene input files in different formats, but there are few user-friendly programs facilitating the workflow of combining, concatenating or separating, aligning and exploring multi-gene datasets. Results We present Concatenator, a user-friendly GUI-driven program that accepts single-marker and multi-marker DNA sequences in different input formats, including Fasta, Phylip and Nexus, and that outputs concatenated sequences as single-marker or multi-marker Fasta, interleaved nexus or Phylip files, including command files for downstream model selection in IQ-TREE. It includes the option to (re)align markers with MAFFT and produces exploratory trees with FastTree. Although tailored for medium-sized phylogenetic projects, Concatenator is able to process phylogenomic datasets of up to 30 000 markers. Availability and implementation Concatenator is written in Python, with C extensions for MAFFT and FastTree. Compiled stand-alone executables of Concatenator for MS Windows and Mac OS along with a detailed manual can be downloaded from www.itaxotools. org; the source code is openly available on GitHub (https://github.com/iTaxoTools/ConcatenatorGui).
Monocharged Electret Generator for Wearable Energy Harvesting Applications

Polytetrafluoroethylene (PTFE)‐based electret materials have emerged as one of the most widely used materials in underwater transducer, microphone, and transistor applications. In this paper, it is shown for the first time that a monocharged (single type, electron) PTFE electret material can be used in energy harvesting and self‐powered device applications, such as wristband and shoe insole devices. At first, a theory based on the electrostatic effect is derived for the first time for monocharged electret generator (MCEG). For a hand‐shaking movement, the MCEG‐based wristband device can generate an output voltage and current of 14 V and 0.65 µA, respectively. Furthermore, it is found that the MCEG‐based shoe insole device can generate a maximum output voltage of 178 V and a current of 2.85 µA during walking. The maximum harvested power obtained is ≈35.63 µW in shoe insole‐based devices under a resistor load. Additionally, the power generated from the shoe insole device can light up 55 light‐emitting diodes with a single step of walking. Because of the relatively large output power density, ease of fabrication, and chemical stability, this device shows great promise for wearable self‐powered device applications.
Early Experiments in Consumer Demand Theory: 1930-1970

This paper reconstructs the history of experimental research on consumer demand behavior between 1930 and 1970. The backgrounds of the experiments and their impact on the development of consumption theory are also investigated. Among other things, the paper shows that in fact many prominent economists of the period were involved in this stream of research.
A digital predistorter for wireless transmitters

A novel digital baseband predistorter with improved reduction of spectral regrowth is proposed and investigated. The improvements are achieved by using a proposed predistortion technique and extended power amplifier (PA) fundamental-frequency modeling. The digital baseband predistortion (DPD), known for its simplicity of realization, low cost and integrability; however, it suffers from poor linearizing performances. We incorporate baseband iterative injection of in-band distortion components into the baseband DPD to enhance the nonlinearity compensation. General formulas for the fundamental-frequency output of a PA with n-order nonlinearity and recursive formulas for calculating the injected components for different number of iterations are developed. The proposed iterative digital baseband predistorter is verified experimentally with a wireless PA and measured results are presented to demonstrate feasibility of the proposed concept. The spectral regrowth suppression of 20 dB is achieved for a 3.5 MHz digitally modulated signal. © 2009 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2009.
“Sometimes You're Going to Be Uncomfortable”: Pre-service Elementary Teachers’ Generative Orientations Toward Facilitating Critical Conversations Around Texts

Grounded in our commitment to support preservice teachers (PSTs) in an urban elementary teacher education program to develop anti-bias pedagogies, we engaged in a design-based study aimed at enacting critical conversations about texts that foster dialogue about power and privilege. In this analysis, we explore how PSTs engaged with children during discretionary moments within critical conversations in their field placements. We asked: What generative orientations are visible in PSTs’ analysis of and reflection on their facilitation of critical conversations with children? Findings reveal that PSTs who extended dialogue during these moments in critical conversations—rather than avoiding or ignoring children’s contributions—displayed an orientation toward seeing children as sensemakers; flexibly responded to encourage dialogue; and recognized the value of feeling uncomfortable.
Modification of the Loss Cone for Energetic Particles in the Earth's Inner Magnetosphere

The behavior of energetic particles in the Earth's dipole and stretched magnetic‐field models is explored with a focus on the shift of the atmospheric loss cone away from the magnetic‐field direction and on non‐adiabatic behavior occurring when the particle gyroradius becomes comparable with the gradient scale length of the local magnetic field. It is shown that the equatorial loss cone is aberrated away from the magnetic‐field direction (pitch angle of 0°) by the perpendicular drift of a charged particle (which is referred to as the “Mozer transform” described in Mozer (1966, https://doi.org/10.1029/JZ071i011p02701). It is found that the Mozer coordinate transformation in pitch‐angle/gyrophase‐angle space better organizes the behavior of bounce times, mirror altitudes, drift speeds, and first adiabatic invariant. It also describes the loss‐cone shift accurately for a certain range of values of the adiabaticity parameter ϵ, defined by the ratio of the particle gyroradius to the local radius of curvature of the magnetic field. For particles with larger ϵ, the Mozer transform (evaluated with the standard gradient‐curvature drift) breaks down and the “central trajectory” theory can be used to calculate the angular shift of the loss cone, which now includes an Earthward shift. When ϵ becomes relatively large, stochastic field line curvature (FLC) scattering occurs: we show the intimate connection between the strength of FLC scattering and the loss‐cone shift. By comparing ion orbits in the dipole and Tsyganenko (Ts89 and Ts04) magnetic fields, we conclude that the loss cone of ring‐current ions is significantly modified during geomagnetically active times.
Perceiving Fabric Immersed in Time, an Exploration of Urban Cognitive Capabilities of Neural Networks

City develops gradually with the lapse of time. Cities, as a ‘container’, are injected new urban elements along the trajectory of the times and the progress of human civilization, constructing the historical structures involved past, present and future. Thus, the cultural information of each era is preserved in the urban fabric together and urban fabric features are complex and rich, which are difficult to capture in traditional design methods. In this paper, we try to use Generative Adversarial Networks (GAN), one of the neural network algorithms, to explore the inner rules of complex urban morphological features and realize the perception of the urban fabric. Neural networks are innovatively applied to the larger and more complex city generation in this experiment. First, we collect European urban fabric as the dataset, then label data to facilitate machine training, use GAN to learn the feature of the dataset by adjusting parameters, and analyze the effect of the generated results. The automatic feature learning capability of the neural networks is used to summarize the inherent patterns and rules in urban development which is difficult for human to discover.
Comparison of the Effect of Ketamine and Dexmedetomidine Combined with Total Intravenous Anesthesia in Laparoscopic Cholecystectomy Procedures: A Prospective Randomized Controlled Study

This randomized prospective clinical study aimed to investigate the effects of dexmedetomidine or ketamine administration to total intravenous anesthesia (TIVA) on postoperative analgesia in subjects undergoing elective laparoscopic cholecystectomy procedures. 90 adults, American Society of Anesthesiologists (ASA) physical status 1 and II patients, who underwent elective laparoscopic cholecystectomy procedures were included in the study and randomized into three groups equally. Remifentanil, propofol, and rocuronium infusions were used for TIVA guided by the bispectral index. In group KETA, 10 μg/kg/min ketamine was added to TIVA before surgery, and in group DEX, 0.5 μg/kg/h dexmedetomidine was added to TIVA before surgery. Normal saline infusions were infused in the control group. Postoperative analgesia was provided with intravenous patient-controlled analgesia (PCA) morphine (1 mg bolus morphine, 5 min lockout time). Hemodynamic parameters, scores of visual analogue scale (VAS) for pain, rescue morphine requirements, and side effects such as sedation, nausea, and vomiting were recorded for 48 hours after surgery. Postoperative first analgesic requirement time was longer in group KETA (P < 0.001), and it was longer in group DEX than in the control group (P < 0.001). Pain scores were lower in group KETA and group DEX than in the control group at all corresponding times throughout the 48 h period of observation. Intravenous PCA morphine consumptions were higher in the control group than in group KETA (P < 0.001 for all followed-up times), and they were higher in group DEX than in group KETA (P < 0.001 for all followed-up times). It is concluded that the use of dexmedetomidine or ketamine infusions can be suitable as an additive for TIVA in the intraoperative period. Furthermore, the addition of both drugs to the TIVA protocol may improve postoperative pain relief and decrease opioid consumption.
ScatterHough: Automatic Lane Detection from Noisy LiDAR Data

Lane detection plays an essential role in autonomous driving. Using LiDAR data instead of RGB images makes lane detection a simple straight line, and curve fitting problem works for realtime applications even under poor weather or lighting conditions. Handling scatter distributed noisy data is a crucial step to reduce lane detection error from LiDAR data. Classic Hough Transform (HT) only allows points in a straight line to vote on the corresponding parameters, which is not suitable for data in scatter form. In this paper, a Scatter Hough algorithm is proposed for better lane detection on scatter data. Two additional operations, ρ neighbor voting and ρ neighbor vote-reduction, are introduced to HT to make points in the same curve vote and consider their neighbors’ voting result as well. The evaluation of the proposed method shows that this method can adaptively fit both straight lines and curves with high accuracy, compared with benchmark and state-of-the-art methods.
FT-IR Method Limitations for β-Glucan Analysis

β-glucans are known as biological response modifiers. However, different sources can result in structural differences and as a result differences in their biological activity. The hot water extraction method allows to obtain, high molecular weight β-glucans without altering their structure by using strong chemicals, such as alkalis or acids. Analysis of β-glucans by FT-IR and NMR spectroscopy in solid state is superior to analysis in solution as it allows researchers to study the preserved structure of the extracted polysaccharides. FT-IR spectroscopy was used in this study to make side-by-side comparison analysis of hot water extracted β-glucans from different yeast sources. NMR spectroscopy was used to confirm findings made by FT-IR spectroscopy. Extracted β-glucans exhibit characteristic structure of β-1,3/1,6-linked glucans with noticeable levels of proteins, possibly in a form of oligopeptides, chitin and other impurities. β-glucans obtained from C. guilliermondii, P. pastoris and S. pastorianus exhibited higher protein content. Differences in mannan, chitin and α-glucan content were also observed; however, the species-specific structure of obtained β-glucans could not be confirmed without additional studies. Structural analysis of high molecular weight β-glucans in solid state by FT-IR spectroscopy is difficult or limited due to band intensity changes and overlapping originating from different molecules.
Distinct PKA Signaling in Cytosolic and Mitochondrial Compartments in Electrically Paced Atrial Myocytes

Protein kinase A (PKA) is a key nodal signaling molecule that regulates a wide range of cellular functions in the cytosol and mitochondria. The distribution of A-kinase anchoring proteins that tether PKA, the local interaction with degradation molecules, and regulation by Ca2+, may lead to distinct spatiotemporal cAMP/PKA signaling in these compartments. In this work, FRET-based sensors were used to investigate PKA signaling in the cytosol, outer mitochondrial membrane (OMM), and mitochondrial matrix (MM) and its crosstalk with Ca2+ in response to electrical stimulation of cultured rabbit atrial cells. A gradual decrease in PKA activity eliminating the ability of the atrial cells to respond to physiological electrical stimulation, was observed upon treatment of cells with H-89. Chelation of intracellular Ca2+ by BAPTA reduced PKA activity and diminished its response to forskolin, an AC stimulator. Under basal conditions, PKA activity in response to forskolin was lower in the OMM compared to the cytosol and MM. In response to electrical stimulation in the presence of ISO, distinct compartmentalization of PKA activity was observed, with higher activity in the cytosol and MM than in the OMM. Thus, distinct Ca2+-dependent spatiotemporal cAMP/PKA signaling exists in atrial cells, likely mediating its excitation and mitochondrial function.