Miracle-dz/UniFAR-FTUs huggingface dataset:
Commutation Point Optimization Method for Sensorless BLdc Motor Control Using Vector Phase Difference of Back EMF and Current

Commutation error is a crucial factor that reduces the efficiency of brushless dc (BLdc) motors. This article presents a commutation point optimization method for a sensorless BLdc motor. First, the sources of commutation error under position sensorless control are analyzed, and a conclusion is drawn that the phase difference between the back electromotive force vector and phase current vector equals the total commutation error. Second, a fundamental waveform extractor is used to eliminate the influence of harmonics on the equivalent commutation error. Third, the phase difference of the fundamental waveforms is extracted by a combined phase-locked loop, and the real-time commutation error is obtained consequently. Finally, accurate commutation is achieved by compensating for the phase difference, and experimental results on a reaction flywheel prototype evaluate the presented method.
Multivariate phase type distributions - Applications and parameter estimation

In this paper we use the expectation-maximisation (EM) algorithm to estimate the parameters of Kibble’s bivariate Gamma distribution for the case where the shape parameters are integer valued. In this case, the Kibble distribution can be interpreted as a special case of a multivariate phase–type distribution allowing us to identify the estimation problem as a case of incomplete data. The EM algorithm ensures numerical stability and provides indirectly the information matrix which may be used for the calculation of confidence regions.
LITERAL TRANSLATION AS DOMINANT PROCEDURE FOUND IN "ENCANTO" MOVIE SUBTITLE

The literal translation employed in the subtitle of the movie "Encanto" was the primary focus of this study. The purpose of the study is to identify the main categories of translation procedures utilized in the movie subtitle for "Encanto" and to discuss how the literal translation procedures are applied. In this study, the qualitative descriptive approach was applied. The data for the study was derived from the movie’s subtitle, "Encanto. " The data were acquired through viewing the motion, reading the script, assessing many sorts of translation techniques, correlating the dialogue to the script, classifying and recognizing words, phrases, and clauses, then lastly selecting and noting the various categories of data. The information was seen and examined by utilizing Newmark's (1988) theory of translation procedures. There is one dominant type found in this research, such as literal translation, procedures about 24 data are found in this research. Literal procedure the subtitle translated into the closest meaning.
The Best Employee Decision Support System Using the Analytical Hierarchy Process Method at PT ASDP Indonesia Ferry (Persero)

The selection of the best employees aims to spur employee morale in improving employee performance and dedication in the company. The selection of best employees is selected based on company criteria. PT ASDP Indonesia Ferry (PERSERO), the best employee criteria applied by the company are Work Quantity, Work Quality, Attendance, Teamwork, and Initiative. Employee assessment is carried out every month by the assessment team (Vice President (VP) and Manager). The problem faced is determining the best employees with criteria and alternatives that are calculated manually. This system is a Decision Support System (DSS) built using the Analytical Hierarchy Process (AHP) method. Previously, the evaluation process for selecting the best employees had never been done. Some of the problems encountered were the absence of an employee performance appraisal process, no appropriate selection method, and a Decision Support System (DSS) was not available that could make it easier to assess the selection of the best employees.
The Adoption of Strategies to Reduce Deforestation from Tobacco Production: A Case of Smallholder Farmers in Headlands Area of Makoni District

Smallholder tobacco farmers in Zimbabwe have not yet fully adopted the strategies to reduce deforestation as recommended by tobacco contracting companies and government agencies such as the Tobacco Industry and Marketing Board (TIMB), Forestry Commission of Zimbabwe (FCZ) and Environmental Management Agency (EMA). The majority of them continue to use wood fuel for curing, leading to high rates of deforestation. This study focused on assessing the factors influencing the adoption of strategies to reduce the rate of deforestation from tobacco production, including gum plantations and coal for tobacco curing in the Makoni district's Headlands area. The target population was tobacco farmers in the study area, and the sample size was 320. Descriptive statistics and logistic regression were used to analyze the data. Based on the results, the factors that were significant in influencing farmers to adopt were the farmer's age, farming experience, agricultural training, education level and occupation. The study recommends that government agencies, including the FCZ, enforce the implementation of afforestation events like tree planting day across the country to help protect the environment from deforestation.
Low-Cost Distance Sensor Characterization of Ultrasonic and Infrared to Evaluate the “Reality Gap” in Robot System Simulation

- Simulation of robotic systems is necessary to test for correctness and safety of these systems, but how accurate is simulation compared to the real system? In this work, we provide a methodology to characterize low-cost distance sensors (ultrasonic and infrared) and then program these sensors into our robot simulator. Our focus robotics system is a simple two-wheel robot mounted with either one of our two distance sensors, and we characterize our robot and define realistic parameters that can be simulated in our open-source software system — Centurion. The goal of this work is to characterize the similarity between our real and simulated systems, and progress to providing a confidence value of the “reality gap” of simulation over time. To do so, we provide results on how similar our real and simulated sensors are as quantified by the confidence of a Bayesian filter, and we run a simple control algorithm for object avoidance over a number of real and virtual trials to establish a measurement of simulation confidence as a function of time.
Using extreme value theory for image detection

The primary target of content based image retrieval is to return a list of images that are most similar to a query image. This is usually done by ordering the images based on a similarity score. In most state-of-the-art systems, the magnitude of this score is very different from a query to another. This prevents us from making a proper decision about the correctness of the returned images. This paper considers the applications where a confidence measurement is required, such as in copy detection or when a re-ranking stage is applied on a short-list such as geometrical verification. For this purpose, we formulate image search as an outlier detection problem, and propose a framework derived from extreme values theory. We translate the raw similarity score returned by the system into a relevance score related to the probability that a raw score deviates from the estimated model of scores of random images. The method produces a relevance score which is normalized in the sense that it is more consistent across queries. Experiments performed on several popular image retrieval benchmarks and state-of-the-art image representations show the interest of our approach.
An automatic approach to generating barrier-free qualitative schemes for color vision deficiency

ABSTRACT Hundreds of millions of people suffer from color vision deficiency, leading to confusion in the perception of maps. Barrier-free colors can reduce confusion and improve the readability of maps. However, most of these colors are manually designed by experts based on extensive experience. For most mapmakers, especially novices, creating barrier-free map colors is a challenge. In this paper, we focus on qualitative schemes, a color type that easily causes confusion for people with color vision deficiency, and propose an approach to automatically generate barrier-free colors. The proposed approach consists of two steps: 1) extracting the factors of barrier-free qualitative schemes, including color vision deficiency factors and cartographic rule factors, and characterizing them, and 2) building an optimization model using these factors to generate barrier-free qualitative schemes. The approach was tested with two experimental maps: a metro map for public use and a special-use land cover map. Twenty-two students with color vision deficiency were invited to read these maps and complete tasks. The results suggested that the map features using the generated barrier-free schemes were easy to distinguish for people with color vision deficiency. In addition, we recruited twenty-eight students with normal color vision to read the maps, and the results suggested that the generated schemes are effective for people with normal color vision as well.
Connecting the dots? Entrepreneurial ecosystems and sustainable entrepreneurship as pathways to sustainability

While the exponential growth of entrepreneurial ecosystem research has dramatically increased our understanding of the role of context in entrepreneurial outcomes, our knowledge of entrepreneurial ecosystems and environmentally sustainable entre-preneurship is still fragmented. There is ambiguity on how entrepreneurial ecosystems influence sustainable entrepreneurship and what factors facilitate or constrain sustainable entrepreneurship. We attempt to take stock of the extant scholarship on entrepreneurial ecosystems and synthesize studies examining their linkages with sustainability. We systematically reviewed 77 articles identified in the World of Science and Scopus databases to discuss the main themes. The content analysis uncovered four key themes: (a) how entrepreneurial ecosystems become more sustainable, (b) the role of entrepreneurs, (c) the role of universities, and (d) the outcomes of sustainable ecosystems. The findings reveal that interactions between different actors, including customers, suppliers, institutions, governments, and universities, can result in a sustainable entrepreneurial ecosystem. The novelty of our study arises from integrating extant studies on entrepreneurial ecosystem and sustainability in a systematic and replicable manner. We observed heightened attention to the environmental challenges in ecosystem literature and entrepreneurs' expanded roles in generating ecological and social value. Future studies can further evaluate the effectiveness of entrepreneurial ecosystems to examine whether creating an entrepreneurial ecosystem has a similar value while achieving sustainable development goals across varied contexts.
GraphMM: Graph-Based Vehicular Map Matching by Leveraging Trajectory and Road Correlations

Map matching of sparse vehicle trajectories is a fundamental problem in location-based services, such as traffic flow analysis and vehicle routing. Existing literature mainly relies on sequence-to-sequence (Seq2Seq) models to capture the intra-trajectory correlation of an input trajectory and to sequentially predict the matched road segments. Due to the limited expressive capability of sequential models, these methods fall short of extracting inter-trajectory and trajectory-road correlations as well as correlation between road segments. We present GraphMM, a graph-based approach that explicitly utilizes all aforementioned correlations. Our model exploits the graph nature of map matching and incorporates graph neural networks and conditional models to leverage both road and trajectory graph topology, while manages to align road segments and trajectories in latent space. We formally analyze the expressive power of our model in capturing various correlations and propose efficient algorithms for model training and inference. In particular, our optimization techniques dramatically reduce the computational complexity, making our model feasible on datasets with thousands of road segments. Extensive experiments show that our model significantly enhances prediction accuracy, while improving training and inference efficiency by up to an order of magnitude over both the industrial implementation of the hidden Markov model and state-of-the-art Seq2Seq-based methods.
Bridging Scale Methods for Computational Nanotechnology

The rapid advances in nanotechnology, nanomaterials and nanomechanics offer huge potentials in national defense, homeland security, and private industry. An emphasis on nanoscale entities will make our manufacturing technologies and infrastructure more sustainable in terms of reduced energy usage and environmental pollution. Advances in the synthesis of nanoscale materials have stimulated ever-broader research activities in science and engineering devoted entirely to these materials and their applications. This is due in large part to the combination of their expected structural perfection, small size, low density, high stiffness, high strength and excellent electronic properties. As a result, nanoscale materials may find use in a wide range of applications in material reinforcement, field emission panel display, chemical sensing, drug delivery, nanoelectronics and tailor-designed materials. Nanoscale devices have great potential as sensors and as medical diagnostic and delivery systems.
Induction of haploid plants for speed-up breedıng in sunflower (Helianthus annuus L.) by pollen irradiation

The creation of homozygous plants is essential for both genetic studies and the production of hybrid seeds, particularly in plants that were pollinated by an alien species. Dihaploid (DH) technology is an important application in obtaining homozygous lines used to plant speed breeding programs. The generation of haploid embryos by pollen irradiation and pollination of female flowers with irradiated pollen has been successfully applied. The influence of irradiated pollen on the induction of haploids in sunflower (Helianthus annuus L.) has been evaluated on 16 different sunflower lines from Turkey’s National Sunflower Breeding Program. In this study, pollen grains were irradiated with varying dosages of Gamma rays (500-1000 Gy) to generate parthenogenetic haploid embryos in oilseed sunflowers. At a period of 12–20 days following the process of pollination, embryos were plated into modified MS culture media under a 16/8 h photoperiod and a temperature of 25◦C. Parthenogenetic 18457 embryos at different developmental stages were rescued in vitro and 650 were germinated. The ploidy analysis of regenerants was made with chromosome count and flow cytometry. Except for 500 Gy, all dosages were effective for inducing embryos and haploid plants. Obtained results indicate that parthenogenetic haploid embryos can be produced in K3AD SN:8, IMI 069, and IMI 044 sunflower genotypes by selecting the appropriate Gamma ray dose and embryonic developmental stage. This study was focused to recover sunflower doubled haploids to accelerate the production of non-segregating lines for breeding.
Critical analysis of angle modulated particle swarm optimisers

This dissertation presents an analysis of the angle modulated particle swarm optimisation (AMPSO) algorithm. AMPSO is a technique that enables one to solve binary optimisation problems with particle swarm optimisation (PSO), without any modifications to the PSO algorithm. While AMPSO has been successfully applied to a range of optimisation problems, there is little to no understanding of how and why the algorithm might fail. The work presented here includes in-depth theoretical and emprical analyses of the AMPSO algorithm in an attempt to understand it better. Where problems are identified, they are supported by theoretical and/or empirical evidence. Furthermore, suggestions are made as to how the identified issues could be overcome. In particular, the generating function is identified as the main cause for concern. The generating function in AMPSO is responsible for generating binary solutions. However, it is shown that the increasing frequency of the generating function hinders the algorithm’s ability to effectively exploit the search space. The problem is addressed by introducing methods to construct different generating functions, and to quantify the quality of arbitrary generating functions. In addition to this, a number of other problems are identified and addressed in various ways. The work concludes with an empirical analysis that aims to identify which of the various suggestions made throughout this dissertatioin hold substantial promise for further research.
Contribution to Statistical Techniques for Identifying Differentially Expressed Genes in Microarray Data

Contribution to Statistical Techniques for Identifying Differentially Expressed Genes in Microarray Data Ahmed Hossain Doctor of Philosophy Graduate Department of Dalla Lana School of Public Health University of Toronto 2011 With the development of DNA microarray technology, scientists can now measure the expression levels of thousands of genes (features or genomic biomarkers) simultaneously in one single experiment. Robust and accurate gene selection methods are required to identify differentially expressed genes across different samples for disease diagnosis or prognosis. The problem of identifying significantly differentially expressed genes can be stated as follows: Given gene expression measurements from an experiment of two (or more) conditions, find a subset of all genes having significantly different expression levels across these two (or more) conditions. Analysis of genomic data is challenging due to high dimensionality of data and low sample size. Currently several mathematical and statistical methods exist to identify significantly differentially expressed genes. The methods typically focus on gene by gene analysis within a parametric hypothesis testing framework. In this study, we propose three flexible procedures for analyzing microarray data. In the first method we propose a parametric method which is based on a flexible distribution, Generalized Logistic Distribution of Type II (GLDII), and an approximate likelihood ratio test (ALRT) is developed. Though the method considers gene-by-gene analysis, the ALRT method with distributional assumption GLDII appears to provide a
Mining User Behavior Patterns in Online Social Networks: A Review of Recent Advances

This paper reviews the recent advances in mining user behavior patterns in online social networks, focusing on the integration of machine learning, deep learning, Natural Language Processing (NLP), and sentiment analysis. The proliferation of social media platforms has led to an explosion in the amount of user-generated content, creating both opportunities and challenges for understanding and predicting user behaviors. Recent advancements in machine learning and deep learning have paved the way for sophisticated techniques to extract and analyze this information, revealing valuable insights into user behavior. A major part of this review discusses how NLP techniques, combined with machine learning algorithms, have been effectively used for sentiment analysis to interpret and gauge user sentiments, fostering an understanding of trends, attitudes, and opinions in social networks. Moreover, we examine how these advanced methods have improved the accuracy of user behavior prediction, enabling more personalized and engaging experiences on these platforms. This comprehensive review not only provides a synthesized understanding of the current state-of-the-art methodologies but also identifies promising directions for future research in the mining of user behavior patterns in online social networks.
Einsteinian gravity from BRST quantization of a topological action

The curvature-squared model of gravity, in the affine form proposed by Weyl and Yang, is deduced from a topological action in 4D. More specifically, we start from the Pontrjagin (or Euler) invariant. Using the BRST antifield formalism with a double duality gauge fixing, we obtain a consistent quantization in spaces of double dual curvature as classical instanton type background. However, exact vacuum solutions with double duality properties exhibit a `vacuum degeneracy'. By modifying the duality via a scale breaking term, we demonstrate that only Einstein's equations with an induced cosmological constant emerge for the topology of the macroscopic background. This may have repercussions on the problem of `dark energy' as well as `dark matter' modeled by a torsion induced quintaxion.
Development, validity, and reliability of a questionnaire on psychological factors of organic food consumption among Malaysian adults

People believe organic food is safer and healthier since it limits the use of synthetic chemicals as agricultural inputs, which has increased global demand for it. However, it has been reported that Malaysians' actual consumption of organic food is low, despite their great intention to do so. Much previous research has proposed trust in organic food labeling and certification as a potential cause of the intention-behavior gap. However, empirical data to support this view is still lacking. This study aims to develop and validate a questionnaire to assess Malaysians' intention and actual consumption of organic food, along with its factors, using the Theory of Planned Behavior (TPB) with trust as an additional construct. Before the study, the questionnaire was pre-tested for face validity by five expert panels. This cross-sectional study gathered 119 usable self-administered questionnaires completed by Malaysian adults aged 18 to 64. The construct validity was evaluated using exploratory factor analysis (EFA) with principal component analysis (PCA), and varimax rotation. There was a three-factor solution for the attitude construct namely health, food safety, and environmental concern, while only one-factor solution for subjective norm, perceived behavioral control, trust, intention, and actual consumption of organic food. Each construct had a minimum Cronbach's alpha of . 70. The questionnaire is a valid and reliable instrument that can be used in larger study settings to further understand the psychological factors of organic food consumption among Malaysian adults to promote more sustainable food consumption in this nation.
A Numerical Framework for Operational Solar Wind Prediction

We report on the development of numerical components for an operational solar wind prediction framework. In particular, we discuss the coronal magnetic field component based on the potential field source surface and Schatten current sheet models, and the solar wind speed component which uses the empirical Wang-Sheeley-Arge relation between solar wind speed and open magnetic field lines. Using synoptic maps of photospheric fields in a stand-alone operational mode, the components provide the configuration of the global coronal magnetic field and prediction of the solar wind speed at 1AU. A good agreement between numerical prediction and solar wind speed observations has been found.