Miracle-dz/UniFAR-FTUs huggingface dataset:
A Burst Mode and Variable Frequency Voltage Sharing Control Mechanism for an Adjustable High Voltage Pulsed Converter

This article presents a modular adjustable high-voltage pulse (MAHVP) generation system. The proposed MAHVP generation system uses a combination of full-bridge LLC resonant converters whose input terminals are connected in parallel and whose output terminals are in series to boost the output voltage; input parallel output series configuration efficiently increases the power holding capabilities of the overall system. The MAHVP generation system uses a novel burst mode and variable frequency voltage sharing (BMVFVS) control mechanism, the BMVFVS control mechanism senses the type of load if the MAHVP generation system needs to operate at a heavy load, it uses the variable frequency voltage sharing method to divide the power equally between the modules to enhance the overall performance. For light load conditions, the MAHVP generation system operates in burst mode, and the BMVFVS control mechanism combined with the frequency modulation technique is used to control the amplitude and pulsewidth of the output voltage smoothly and efficiently. A hardware prototype of 2.4 kW is developed, and experimental results are taken to verify the feasibility and validity of the proposed system, the maximum output voltage of the developed two-module prototype is tested up to 6 kV at the high efficiency of 97%.
Characteristics of Asian American Family Caregivers of Older Adults Compared to Caregivers of Other Racial/Ethnic Groups: Behavioral Risk Factor Surveillance System 2015–2020

Asian Americans (AAs) are the fastest-growing racial/ethnic minority group in the United States. While otherwise highly heterogeneous, AAs overall value filial piety and eldercare. This study compared the health and caregiving experiences of AA caregivers of older adults to AA non-caregivers and caregivers of older adults across racial/ethnic groups. We used 2015–2020 Behavioral Risk Factor Surveillance System data for 315 AA caregivers and 3822 AA non-caregivers, plus 395 American Indian/Alaska Native, 1883 Black, 1292 Hispanic, and 20,321 non-Hispanic White caregivers. Among AAs, 4.3% were caregivers, a lower proportion than in other racial/ethnic groups. Most AA caregivers were female (59%), married (71%), in excellent/very good/good health (76%), and with at least one chronic health condition (66%). Other than relationship to care recipients, caregivers’ experiences were similar across racial/ethnic groups: most cared for <20 hours/week and provided household and personal care. Efforts to support AA caregivers should be attentive to cultural practices.
Universal critical amplitude ratios for percolation

The hypothesis of universality implies that for every scaling relation among critical exponents there exists a universal ratio among the corresponding critical amplitudes. If one writes $B{|t|}^{\ensuremath{\beta}}$, ${{A}_{F}}^{\ifmmode\pm\else\textpm\fi{}}{|t|}^{2\ensuremath{-}\ensuremath{\alpha}}$, ${C}^{\ifmmode\pm\else\textpm\fi{}}{|t|}^{\ensuremath{-}\ensuremath{\gamma}}$, and ${\ensuremath{\xi}}_{0}{|t|}^{\ensuremath{-}\ensuremath{\nu}}$ [where $t=\frac{({p}_{c}\ensuremath{-}p)}{{p}_{c}}$, $p$ being the concentration of nonzero bonds, and +(-) stands for $pl{p}_{c}$ ($pg{p}_{c}$)] for the leading singular terms in the probability to belong to the infinite cluster, the mean number of clusters, the clusters' mean-square size, and the pair connectedness correlation length, then it is shown that the ratios $\frac{{{A}_{F}}^{+}}{{{A}_{F}}^{\ensuremath{-}}}$, $\frac{{C}^{+}}{{C}^{\ensuremath{-}}}$, ${{A}_{F}}^{+}{B}^{\ensuremath{-}2}{C}^{+}$, $\frac{{\ensuremath{\xi}}_{0}^{+}}{{\ensuremath{\xi}}_{0}^{\ensuremath{-}}}$, and ${{A}_{F}}^{+}{({\ensuremath{\xi}}_{0}^{+})}^{d}$ ($d$ is the dimensionality) are universal. Similar quantities are found for the behavior at $p={p}_{c}$ (as a function of a "ghost" field). All of these universal ratios are derived from a universal scaled equation of state, which is calculated to second order in $\ensuremath{\epsilon}=6\ensuremath{-}d$. The (extrapolated) results are compared with available information in dimensionalities $d=2, 3, 4, 5$, with reasonable agreements. The amplitude relations become exact at $d=6$, when logarithmic corrections appear. Additional universal ratios are obtained for the confluent correction to scaling terms.
Molecular Gas in the Poststarburst Galactic Nucleus of NGC 4736

We present 12CO (J = 1-0) observations of the poststarburst nucleus of NGC 4736. Although star in the nuclear region is currently inactive, there is a substantial amount of molecular gas, ~ 107 M®, in the central ~ 300 pc region. Comparing the observed molecular-gas surface density with the threshold gas density for star formation proposed by Kennicutt (1989, AAA 50.157.264), we find that the nuclear gas disk is stable for gravitational instability. We discuss possible scenarios for the nuclear starburst evolution of NGC 4736 in terms of either bar-driven instability fueling or minor-merger driven fueling (or triggering).
Toward Multi-Stage Phenotyping of Soybean with Multimodal UAV Sensor Data: A Comparison of Machine Learning Approaches for Leaf Area Index Estimation

Leaf Area Index (LAI) is an important parameter which can be used for crop growth monitoring and yield estimation. Many studies have been carried out to estimate LAI with remote sensing data obtained by sensors mounted on Unmanned Aerial Vehicles (UAVs) in major crops; however, most of the studies used only a single type of sensor, and the comparative study of different sensors and sensor combinations in the model construction of LAI was rarely reported, especially in soybean. In this study, three types of sensors, i.e., hyperspectral, multispectral, and LiDAR, were used to collect remote sensing data at three growth stages in soybean. Six typical machine learning algorithms, including Unary Linear Regression (ULR), Multiple Linear Regression (MLR), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Support Vector Machine (SVM) and Back Propagation (BP), were used to construct prediction models of LAI. The results indicated that the hyperspectral and LiDAR data did not significantly improve the prediction accuracy of LAI. Comparison of different sensors and sensor combinations showed that the fusion of the hyperspectral and multispectral data could significantly improve the predictive ability of the models, and among all the prediction models constructed by different algorithms, the prediction model built by XGBoost based on multimodal data showed the best performance. Comparison of the models for different growth stages showed that the XGBoost-LAI model for the flowering stage and the universal models of the XGBoost-LAI and RF-LAI for three growth stages showed the best performances. The results of this study might provide some ideas for the accurate estimation of LAI, and also provide novel insights toward high-throughput phenotyping of soybean with multi-modal remote sensing data.
Frequency Effects on the Vibrational States and Conversion of CO2 in Radio Frequency Discharges Under Martian Pressure

In recent years, with the progress of Mars exploration, the in situ utilization of CO2 resources in the Martian atmosphere has become a widely discussed subject. Nonequilibrium plasma technology as an efficient molecular activation approach has great potential for CO2 conversion. In this study, a fluid model is established to discuss driving frequency effects on the vibrational states and conversion of CO2 in radio frequency (RF) discharges at a given power density under Martian pressure. The calculated CO2 conversion and energy efficiency depending on the specific energy input are in good agreement with the experimental measurements. The simulation results found that a considerable fraction of electron energy is transferred to the asymmetric stretching modes of CO2, and the characteristic times for electron impact vibrational excitation and vibration–vibration (VV) relaxation are small, which results in a larger density of higher vibrational levels. The density of vibrational levels of CO2 increases with the driving frequency at a constant power density, which can be explained by the enhancement of vibrational excitation and VV relaxation. The computational data also show that more CO, O, and O2 are produced as the driving frequency increases at a constant power density, and the vibration-induced dissociation with $\textrm {CO}_{2}\textrm {v}_{8}$ gradually replaces the direct dissociation to be the main reaction for the production of CO and O. In addition, the increase in driving frequency causes more electron energy to be transferred to the asymmetrical stretch modes, and the vibrational energy of these vibrational states of CO2 can reduce the activation barrier of the reaction, improving the CO2 conversion and energy efficiency. This study contributes to a deeper insight into the frequency effects on plasma chemistry in RF CO2 discharges under Martian pressure and shows the optimized ways for the decomposition of CO2 on Mars.
Longitudinal Bird strike monitoring- UBC Buchanan Building

Bird-window collisions are the second leading cause of bird fatalities in the United States, accounting for billions of deaths each year. This study aimed to determine how many bird-window collisions occurred at the University of British Columbia's Buchanan complex, using a modified methodology created by Hager and Cosentino ( 2014). We collected data and surveyed for eight weeks and found evidence of at least thirty-three bird-window collisions. The buildings that accounted for the most collisions were Buchanan A, C and D, specifically facades twenty-five, thirteen, and ten. Contributing factors that make these facades the most problematic include the presence of vegetation. We also found that our results this year were similar to last year. However, there were critical differences in collision types and which facades accounted for collisions. Future management solutions would include applying the feather-friendly decal to Buchanan C, facade thirteen, as it was responsible for the most significant number of collisions.
Learning Physics Constrained Dynamics Using Autoencoders

We consider the problem of estimating states ( e.g., position and velocity) and physical parameters ( e.g., friction, elasticity) from a sequence of observations when provided a dynamic equation that describes the behavior of the system. The dynamic equation can arise from first principles ( e.g., Newton’s laws) and provide useful cues for learning, but its physical parameters are unknown. To address this problem, we propose a model that estimates states and physical parameters of the system using two main components. First, an autoencoder compresses a sequence of observations ( e.g., sensor measurements, pixel images) into a sequence for the state representation that is consistent with physics by including a simulation of the dynamic equation. Second, an estimator is coupled with the autoencoder to predict the values of the physical parameters. We also theoretically and empirically show that using Fourier feature mappings improves the generalization of the estimator in predicting physical parameters compared to raw state sequences when learning from high-frequency data. In our experiments on three visual and one sensor measurement tasks, our model imposes interpretability on latent states and achieves improved generalization performance for long-term prediction of system dynamics over state-of-the-art baselines.
Teachers’ perception of their self-effi cacy from the perspective of the Big Five Th eory of Personality

: In the article, on the background of theoretical considerations on the meaning of the sense of self-effi cacy in the work of a modern teacher functioning in the changing conditions of the 21st century reality and the role that personality (in terms of the Five-Factor Th eory of Personality by Paul T. Costa and Robert R. McCrae) may play in shaping this key factor, the results of empirical research conducted with the participation of Polish and American teachers are presented. Th e analyzes proved that the respondents who were characterized by a statistically signifi cantly higher level of self-effi cacy were also characterized by such an intensity of personality traits in terms of the Big Five (neuroticism, extraversion, openness to experience, agreeableness and conscientiousness), which corresponded to the pattern of teacher personality, which has been present in the pedeutological discourse over the years.
CLaP: Conditional Latent Planners for Offline Reinforcement Learning

Recent work has formulated offline reinforcement learning (RL) as a sequence modeling problem, benefiting from the simplicity and scalability of the Transformer architecture. However, sequence models struggle to model trajectories that are long-horizon or involve complicated environment dynamics. We propose CLaP (Conditional Latent Planners) to learn a simple goal-conditioned latent space from offline agent behavior, and incrementally decode good actions from a latent plan. We evaluate our method on continuous control domains from the D4RL benchmark. Compared to non-sequential models and return-conditioned sequential models, CLaP shows competitive if not better performance across continuous control tasks. It particularly does better in environments with complex transition dynamics with up to +149 . 8% performance increase. Our results suggest that decision-making is easier with simplified latent dynamics that models behavior as being goal-conditioned. 1
Indonesian Journal of Electrical Engineering and Computer Science

Received May 25, 2022 Revised Nov 18, 2022 Accepted Nov 24, 2022 Sentiment analysis on views and opinions expressed in Indian regional languages has become the current focus of research. But, compared to a globally accepted language like English, research on sentiment analysis in Indian regional languages like Malayalam are very low. One of the major hindrances is the lack of publicly available Malayalam datasets. This work focuses on building a Malayalam dataset for facilitating sentiment analysis on Malayalam texts and studying the efficiency of a pre-trained deep learning model in analyzing the sentiments latent in Malayalam texts. In this work, a Malayalam dataset has been created by extracting 2,000 tweets from Twitter. The bidirectional encoder representations from transformers (BERT) is a pretrained model that has been used for various natural language processing tasks. This work employs a transformer-based BERT model for Malayalam sentiment analysis. The efficacy of BERT in analyzing the sentiments latent in Malayalam texts has been studied by comparing the performance of BERT with various machine learning models as well as deep learning models. By analyzing the results, it is found that a substantial increase in accuracy of 5% for BERT when compared with that of Bi-GRU, which is the next bestperforming model.
Content Analysis and Its Application with Dynamic Online Content

Content analysis is a well-established and widely used research method. In its early form, it was used extensively in the quantitative analysis of newspapers, and its applications later evolved to include electronic media such as radio and television. It has recently been applied to digital media, including the Internet. However, the use of content analysis in analyzing online content has been chiefly applied to static content, such as ‘static’ websites, in the early days of the Internet. Studies that involve its use in analyzing dynamic Internet content—for example, content that resides behind databases—are relatively much less common. This article is not written as a research paper per se. This article will instead discuss reflections on the efficacy of content analysis as a research method when applied to dynamic content such as DRs by using a previous study, which has applied content analysis to the dynamic content of digital repositories (DRs), as a case study. The previous study used as the basis for this article had applied content analysis to several DRs using manual counting by the researcher. In the process, several idiosyncrasies in terms of the way institutions populate their DRs with digital objects and the user metadata to facilitate discoverability of those digital objects were encountered that have introduced some ‘complication. ’ This article will focus on how content analysis, as a research method, can be adapted to account for those idiosyncrasies to produce better results. This article will also identify the limitations and challenges of content analysis in dynamic online environments and offer some suggested approaches.
of chicken IFI6

3 4 Interferon-alpha inducible protein 6 (IFI6) is an interferon-stimulated gene (ISG), belonging to the 5 FAM14 family of proteins and is localized in the mitochondrial membrane, where it plays a role in 6 apoptosis. Transcriptional regulation of this gene is poorly understood in the context of inflammation 7 by intracellular nucleic acid-sensing receptors and pathological conditions caused by viral 8 infection. In this study, chicken IFI6 (ch IFI6 ) was identified and studied for its molecular features and 9 transcriptional regulation in chicken cells and tissues, i.e., lungs, spleens, and tracheas from highly 10 pathogenic avian influenza virus (HPAIV)-infected chickens. The ch IFI6 -coding sequences contained 11 1638 nucleotides encoding 107 amino acids in three exons, whereas the duck IFI6 -coding sequences 12 contained 495 nucleotides encoding 107 amino acids. IFI6 proteins from chickens, ducks, and quail 13 contain an interferon-alpha inducible protein IF6/IF27-like superfamily domain. Expression of ch IFI6 14 was higher in HPAIV-infected White Leghorn chicken lungs, spleens, and tracheas than in mock- 15 infected controls. TLR3 signals regulate the transcription of ch IFI6 in chicken DF-1 cells via the NF- 16 κB and JNK signaling pathways, indicating that multiple signaling pathways differentially contribute 17 to the transcription of ch IFI6 . Further research is needed to unravel the molecular mechanisms 18 underlying IFI6 transcription, as well as the involvement of ch IFI6 in the pathogenesis of HPAIV in 19 chickens. 20
AJEB-03-2021-0038_proof 234..254

Purpose – This study aims at investigating the impact of the disclosure and the Shariah governance on the financial performance in MENASA (Middle East, North Africa and Southeast Asia) Islamic banks. Design/methodology/approach –We use the Generalized Least Squares (GLS) regression models to check the interdependence relationship between the disclosure, the Shariah governance and the financial performance of 47 Islamic banks (IBs) from ten countries operating in MENASA region. The sample period is from 2012 to 2019. In these regressions models, Return on Assets (ROA) and Return on Equity (ROE) are the dependent variables. The disclosure and the Shariah governance indicators are the independent factors. To measure the Shariah governance, we use the three sub-indices, which are the Board of Directors (BOD), the Audit Committee (AC) and the Shariah Supervisory Board (SSB). Size, Leverage and Age of the bank are used as control variables. We also used The Generalized Method of Moments (GMM) and the three-stage least squares (3SLS) estimations for robustness check. Findings –Result shows a negative relationship between the disclosure and the two performancemeasures in IBs. Furthermore, as far as the governance indicators are concerned, we found that the BOD andAC, as well as the BOD and SSB, have a positive and significant impact on the ROA and ROE, respectively. This reveals that good governance had a significant association with higher performance in MENASA IBs. Originality/value – The paper considers both IBs that adopt mandatory as well as voluntary AAOIFI standards and the GLSmethod to investigate the impact of the AAOIFI disclosure and the Shariah governance on ROA and ROE. Also, it uses the GMM and the 3SLS estimations for robustness check. It is relevant for researchers, policymakers and stakeholders concerned with IBs’ performance.
Accelerating vaccine development through a deep probabilistic programming approach to protein structure prediction

I n this thesis, three manuscripts will be presented that focus on utilizing a novel approach to protein structure prediction, subsequently allowing the acceleration of vaccine development. The first manuscript presents a deep, probabilistic, and generative model of local protein structure. The proposed model represents a means of evaluating the possible conformations that small protein fragments adopt. The model produces fragment libraries at a quality on-par with state-of-the-art models, at a fraction of the run time, without the need for external information and third-party tools to guide the library construction. In manuscript 2 I use this model of local protein structure to accelerate the vaccine design process. Vaccines typically induce an immune response through the combination of structural B cell epitopes and small linear T cell epitopes. In manuscript 2, I present an approach that uses the local protein structure model to modify the coronavirus spike protein through peptide grafting. We show that the model can adapt the spike protein of SARS-CoV-2, in a manner that preserves the important B-cell epitopes needed to induce an antibody response, while enriching for T cell epitopes that can boost this response. I show that vaccine constructs designed using this model express at a higher level than those designed with a naive approach allowing only small modifications of the spike protein. The vaccine constructs are able to induce an antibody response against the wildtype in immunized mice, indicating proper folding of the modified protein construct. The model presented in manuscript 1 focuses only on inferring backbone dihedral angles. This focus on internal coordinates limit the ability to model fragments that are larger than 9 amino acids. The third and final manuscript presents a means to alleviate this problem by introducing a novel multi-scale approach employing likelihoods over both internal coordinates as well as reconstructed 3D-coordinates. I show that this change improves the models performance on short fragments while allowing modelling of longer protein fragments as well.
Choice of observation type affects Bayesian calibration of ice sheet model projections

Determining reliable probability distributions for ice sheet mass change over the coming century is critical to im-proving uncertainties in sea-level rise projections. Bayesian calibration, a method for constraining projection uncertainty using observations, has been previously applied to ice sheet projections but the impact of the chosen observation type on the calibrated posterior probability distributions has not been quantiﬁed. Here, we perform three separate Bayesian calibrations to constrain uncertainty in Greenland Ice Sheet projections using observations of velocity change, dynamic thickness change, and 5 mass change. Comparing the posterior probability distributions shows that the maximum a posteriori ice sheet mass change can differ by 130% for the particular model ensemble that we used, depending on the observation type used in the calibration. More importantly for risk-averse sea level planning, posterior probabilities of high-end mass change scenarios are highly sensitive to the observation selected for calibration. Finally, we show that using mass change observations alone may result in projections that overestimate ﬂow acceleration and underestimate dynamic thinning around the margin of the ice sheet. 10