Miracle-dz/UniFAR-FTUs huggingface dataset:
The Acquisition of Knowledge by the Coaches: Can Qualitative Approach Help?

This research is to study the knowledge of four (n=4) coaches in training at two football academies in Malaysia. This study applied a qualitative methodology (interviews and observations) to explore the unique features and circumstances surrounding the football academy coaches. Subsequently, the fidings of this study revealed that i) the development of knowledge of coaching has taken place in the academy but constrained by factors involving the coach education programme at the academy level; ii) the weaknesses of the coaches in transferring important knowledge to the players. Keywords coaches’ knowledge, coaching process, coaches’ behaviour, qualitative approach
Analysis of molecular variance (AMOVA)

where xi is the frequency of the ith haplotype and δij is the fraction of nucleotides at which haplotypes i and j differ. It shouldn’t come to any surprise to you that just as there is interest in partitioning diversity within and among populations when we’re dealing with simple allelic variation, i.e., Wright’s F -statistics, there is interest in partitioning diversity within and among populations when we’re dealing with nucleotide sequence or other molecular data. The approach I’m going to describe is known as Analysis of Molecular VAriance (AMOVA) [1]. We’ll see later that AMOVA can be used very generally to partition variation when there is a distance we can use to describe how different alleles are from one another, but for now, let’s stick with nucleotide sequence data and think of δij simply as the fraction of nucleotide sites at which two sequences differ.
A Mixed-Methods Evaluation of a Gender Affirmative Education Program for Families of Trans Young People

Abstract Research evidence suggests that education can play an important role in a family’s acceptance and support of a trans(gender) young person. However, there is a lack of robust evidence around the impact of education programs for these families. This article reports the findings of a mixed-methods evaluation of an online education program developed for families of trans young people. Surveys were administered before and after participation in the education program to assess the impact of the program on several measures, including trans-related knowledge, self-reflection and insight, family problem-solving, family communication, self-efficacy, and views of gender identity. Interviews were used to explore family members’ experiences of the program, its perceived impact, and recommendations for further development. The survey findings revealed a statistically significant increase in knowledge scores from the pre-education program to post-education program. Interview participants felt the education program was accessible and easy to use and the majority reported learning about trans-related issues from the program. Fewer participants reported developing skills around self-reflection, problem-solving, and communication following participation in the program. The findings from this evaluation add to the evidence base concerning the efficacy of education for families of trans young people, with further implications and recommendations highlighted.
Institutions or human capital: which is more important for economic performance? Evidence from Brazil

We investigate the role of institutions and human capital on economic performance of Brazilian municipalities. We use instrumental variables with two-stage least squares estimators for capturing causal relationships. We found evidence that institutional quality has an important and robust effect on the income of municipalities. However, there is no robust evidence for the role of human capital. These results are similar to what Acemoglu, Galleano and Robinson (2014) documented in their research conducted with cross-country data. Human capital is likely to be a consequence of institutions.
Hairpin: a tool for predicting structural non-coding RNA genes ad initio

Background Structural RNA genes play important and various roles in gene expression and its regulation. Finding such RNA genes in a genome poses a challenge, which in most cases is solved by homology approaches. Ab intio methods for prediction exist, but are not that much explored. Results We introduce hairpin which identify potential structural RNA genes only based on the sequence. We use the algorithm to predict RNA genes in Escherichia coli K-12. When looking at very short regions of the genome, we do not get results differing very much from a random shuffling of the genome. However, at longer stretches it is a clear biological signal. It turns out that none of the regions predicted to code for RNA genes have such an annotation in literature. Conclusions Arbitrary DNA sequences seem to give rise to transcripts with secondary structures similar to real ncRNA. We therefore conclude that exclusively looking at secondary structure base-parings is in general a futile approach.
An Adaptive Density Peaks Clustering Method With Fisher Linear Discriminant

Clustering is one of the most important topics in data mining and machine learning. The density peaks clustering (DPC) algorithm is a well-known density-based clustering method that can efficiently and effectively deal with non-spherical clusters. However, the computational methods of the local density and the distance measure are simple and easily ignore the correlation and the similarity between samples, and the manual setting of parameters has a great influence on the clustering results; therefore, the clustering performance of DPC is poor on the high-dimensional datasets. To address these issues, this paper presents an adaptive DPC algorithm with Fisher linear discriminant for the clustering of complex datasets, called ADPC-FLD. First, the kernel density estimation function is introduced to calculate the local density of the sample points. Pearson correlation coefficient between samples as weight is employed to construct a weighted Euclidean distance function to measure the distance between samples. This considers both the spatial structure and the correlation of the samples. Then, a novel density estimation entropy is proposed, and based on the minimization of density estimation entropy, the density estimation parameters are adaptively selected according to the distribution characteristics of the data, which can efficiently eliminate the influence of manual setting. Third, an adaptive strategy of cluster center selection is designed to avoid the error caused by the noise data as the cluster centers and the uncertainty of manually selecting the cluster centers. Finally, Fisher linear discriminant algorithm is used to eliminate the irrelevant information and reduce the dimensionality of high-dimensional data, following on which an adaptive DPC method is implemented on six synthetic datasets, thirteen UCI datasets and seven gene expression datasets for comparing with other related algorithms. The experimental results on 26 datasets show that the proposed algorithm significantly outperforms several outstanding clustering approaches in terms of clustering accuracy and efficiency.
Nepalese School Building Stock and Implications on Seismic Vulnerability Assessment

The last catastrophic seismic events in Nepal have shown that the Nepalese school building stock is characterized by a high level of vulnerability with respect to seismic actions. According to different post-event reconnaissance reports, thousands of schools have been destroyed by the 2015 earthquakes, depriving the country of a fundamental social and economic asset. Within the SAFER (Seismic Safety and Resilience of Schools in Nepal) project, a specific working task is dedicated to the assessment of school structures in terms of probabilistic fragility functions. These curves will be included in a comprehensive seismic risk management framework for the Nepalese community. However, the derivation of fragilities has to be preceded by: (i) a meaningful subdivision of the total building inventory in different structural typologies, (ii) a detailed investigation about the recurrent seismic damage and failure modes of Nepalese constructions. These two aspects and their implications on the selection of proper structural assessment methodologies are carefully discussed and mapped in the present paper.
Retinal Stabilization Reveals Limited Influence of Extraretinal Signals on Heading Tuning in the Medial Superior Temporal Area

Heading perception in primates depends heavily on visual optic-flow cues. Yet during self-motion, heading percepts remain stable, even though smooth-pursuit eye movements often distort optic flow. According to theoretical work, self-motion can be represented accurately by compensating for these distortions in two ways: via retinal mechanisms or via extraretinal efference-copy signals, which predict the sensory consequences of movement. Psychophysical evidence strongly supports the efference-copy hypothesis, but physiological evidence remains inconclusive. Neurons that signal the true heading direction during pursuit are found in visual areas of monkey cortex, including the dorsal medial superior temporal area (MSTd). Here we measured heading tuning in MSTd using a novel stimulus paradigm, in which we stabilize the optic-flow stimulus on the retina during pursuit. This approach isolates the effects on neuronal heading preferences of extraretinal signals, which remain active while the retinal stimulus is prevented from changing. Our results from 3 female monkeys demonstrate a significant but small influence of extraretinal signals on the preferred heading directions of MSTd neurons. Under our stimulus conditions, which are rich in retinal cues, we find that retinal mechanisms dominate physiological corrections for pursuit eye movements, suggesting that extraretinal cues, such as predictive efference-copy mechanisms, have a limited role under naturalistic conditions. SIGNIFICANCE STATEMENT Sensory systems discount stimulation caused by an animal's own behavior. For example, eye movements cause irrelevant retinal signals that could interfere with motion perception. The visual system compensates for such self-generated motion, but how this happens is unclear. Two theoretical possibilities are a purely visual calculation or one using an internal signal of eye movements to compensate for their effects. The latter can be isolated by experimentally stabilizing the image on a moving retina, but this approach has never been adopted to study motion physiology. Using this method, we find that extraretinal signals have little influence on activity in visual cortex, whereas visually based corrections for ongoing eye movements have stronger effects and are likely most important under real-world conditions.
Vapor detection through dynamic process of molecule desorption from butterfly wings

Abstract This work explores an alternative vapor sensing mechanism through analyzing dynamic desorption process from butterfly wings for the differentiation of both individual and mixed vapors quantitatively. Morpho butterfly wings have been used in differentiating individual vapors, but it is challenging to use them for the differentiation of mixed vapor quantitatively. This paper demonstrates the use of Morpho butterfly wings for the sensitive and selective detection of closely related vapors in mixtures. Principal components analysis (PCA) is used to process the reflectance spectra of the wing scales during dynamic desorption of different vapors. With the desorption-based detection mechanism, individual vapors with different concentrations and mixed vapors with different mixing ratios can be differentiated using the butterfly wing based sensors. Both the original butterfly wings and butterfly wings with surface modification show the capability in distinguishing vapors in mixtures, which may offer a guideline for further improving selectivity and sensitivity of bioinspired sensors.
Mediating Effect of Audit Quality in Relationship Between Auditor Ethics and Litigation: An Empirical Study

Purpose: This paper attempts to empirically examine the effect of auditor’s ethics on litigation of audit results by using mediating variables of auditor quality. Design/methodology/approach: This study considers a sample of 220 auditors working in public accounting firms using random survey methods. The main technique for analyzing data is Structural Equation Modeling. Findings: The test results show that the auditor ethics has a positive and significant effect on audit quality, while auditor quality has a negative and significant effect on auditor’s litigation. Practical implications: The findings emphasize the importance of the ethical aspects of auditors’ in conducting their tasks. Originality/value: Audit quality in this study is able to strengthen the influence of auditor’s ethics on auditor’s litigation.
Ensino de programação em robótica com Arduino para alunos do ensino fundamental: relato de experiência

O artigo relata as práticas vivenciadas de ensino de programação para a plataforma Arduino, por meio de um curso elaborado para alunos do Ensino Fundamental II pertencentes a 9 (nove) escolas públicas do município de Curitiba, abrangendo ao todo 117 alunos do 5º ao 9º ano. Estes alunos fazem parte de grupos que participam de competições de robótica de forma regular, entretanto sem possuir familiaridade com a plataforma Arduino, de maneira geral. No decorrer do curso, os alunos eram observados e constatou-se a capacidade de aprendizado dos alunos em um tempo bastante breve, assim como as dificuldades na assimilação dos conceitos básicos de programação, os quais eram apresentados de forma incremental e tendo foco na proposição de desafios a partir de noções mais simples. O uso do simulador para as tarefas de programação facilitou a transição da forma de programação visual, da qual os alunos já possuíam certo conhecimento, para uma programação mais textual. Busca-se mostrar ainda como o aprendizado de programação pode auxiliar na constituição do pensamento formal a partir do concreto. Ficou evidenciado o caráter motivador que as atividades de robótica proporcionam ao processo de aprendizagem e que podem, por sua vez, servir de facilitador para a introdução de conceitos mais complexos relativos à linguagens de programação.
In Support of a Rationally Managed Fishery: Age and Growth in Patagonian Toothfish ( Dissostichus eleginoides )

IN SUPPORT OF A RATIONALLY MANAGED FISHERY: AGE AND GROWTH IN PATAGONIAN TOOTHFISH (Dissostichus eleginoides). Julian R. Ashford Old Dominion University, 2001 Director: Dr. Cynthia Jones Patagonian toothfish (Dissostichus eleginoides) occur on the continental shelves and shelf breaks of southern South America and the Southern Ocean. Stock structure, critical to good fisheries management, can be inferred from growth differences between areas, but available growth data are compromised by inconsistencies in age estimation methods, sampling and sample sizes, and techniques used to derive estimates. I asked the scientific question: how is growth in Patagonian toothfish structured spatially within the Southern Ocean? I developed a multi-stage randomized design to sample fish caught by commercial longline, and an age estimation methodology. Because toothfish are difficult to age, I developed an ANOVA model for estimating precision and accuracy o f age data relative to a standard, as the basis for a protocol for quality control of age data. The methodology was used to obtain age data from toothfish taken from the Falkland Islands and South Georgia in the South Atlantic, and the Kerguelen Islands and Heard Island in the southern Indian Ocean. I estimated von Bertalanffy growth parameters for each area, constructed models to describe rival hypotheses of stock mixing and separation between areas, and selected between the models using normal likelihood methods. The abundance of the captured population varied at a scale o f c500 m (76% of variance), and between fishing days (24%). Most variation in length composition was captured at scales less than 500 m (79%). I calculated that sampling 16 10-coil lengths of line/day on 36 days of a voyage of 60 days hauling would be the optimal sampling strategy. Significant bias in age estimation was found between readers and between readings by one reader but, once accounted for, precision of age estimation remained similar between sexes; however, a validation test of the accuracy of the age estimation methodology was inconclusive. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Growth data supported the hypothesis of stock separation between the Falkland Islands and South Georgia, but not between South Georgia and Kerguelen. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
A Neural Network Based Prediction System of Distributed Generation for the Management of Microgrids

In the modern scenario of smart-grids, the concept of virtual power plant (VPP) is undoubtedly a cornerstone for the smooth integration of renewable energy sources into existing energy systems with a high penetration level. A VPP is the aggregation of decentralized medium-scale power sources, including photovoltaic and wind power plants, combined heat and power units, as well as demand-responsive loads and storage systems, with a twofold objective. On one hand, VPP relieves the stability and dispatchability problems on the external smart grid since it can be operated on an individual basis, appearing as a single system on the whole. On the other hand, VPP improves flexibility coming from all the networked units and enable traders to enhance forecasting and trading programs of renewable energies. This paper proposes a novel distributed decentralized prediction method for the management of VPPs. The novelty of the proposed technique is to effectively combine the concepts of neural networks and machine learning with a distributed architecture that is suitable for the aggregation purposes of the VPP.
Multi-Scale Residual Reconstruction Neural Network With Non-Local Constraint

With the development of the neural network, some novel reconstructed networks are proposed to solve the problem of compressive sensing (CS) reconstruction. Compare with the traditional reconstruction algorithms, they can reconstruct the original images from the compressed measurement quickly and accurately with a low sampling rate. However, the CS reconstruction algorithms based on neural network ignore the image non-local similarity that is important prior information for the reconstruction. We propose a multi-scale residual reconstruction neural network with non-local constraint (NL_MRN). First, it considers the prior image non-local similarity and adds a non-local operation into the reconstruction network. Then, different scale residual reconstruction modules that have different convolution kernel size are combined to obtain the final output. Finally, the loss function of the whole network is defined as a weighted sum of the loss function of different scale reconstruction modules. What is more, the training efficiency of the network is improved by the proposed segmental training method. The theoretical analysis and the experimental results show that the proposed NL_MRN achieve better reconstruction compared with other reconstruction algorithms, especially at a low sampling rate.
A Golden Section Search Algorithm Based Loss Minimization for Induction Motor Drive

Abstract: The present manuscript proposes a loss model controller (LMC) based on the Golden Section Search (GSS) algorithm for efficiency optimization of induction motor (IM) drives. The vector controlled or field-orientated induction motor (IM) drive system operates at rated flux in all the speed range. Even at low load, where the flux demand for IM operation is low, the drive is operated at rated flux and thus, unnecessarily energy is wasted. Therefore, to overcome this problem, the proposed algorithm searches for the optimal reference value of flux component of current ( ds i ) for which the electrical input power of the system should be minimal. The search algorithm is very fast and the algorithm has no effect on parameter variation and also no additional hardware machinery for hardware implementation is required. In addition, it does not require the knowledge of speed and torque in the searching process, and is insensitive to motor parameter variations. The simulation results for various speed patterns and operating conditions are presented in this paper. Stability study of the whole drive system is also carried out utilizing the optimizing scheme. The simulation results demonstrate the effectiveness of the proposed algorithm and also the experimental validation of same on dSPACE-1104 based laboratory prototype has confirmed the robustness of the proposed algorithm in terms of optimal efficiency.
IoT for Real Time Data Logger and pH Controller

Acidity in wastewater is the critical problem in developing country. The absence of efficient wastewater management has caused serious environmental problems and cost issues. Therefore, in this paper IoT-based data logger and pH controller is proposed to reduce the inefficiency. IoT is a concept whereby objects around us can interact and exchange information with each other without human intermediaries through the Internet. One of the implementation of IoT is to monitor the level of liquid acidity through smartphones. It needs additional tools such as sensors, microcontrollers, and other devices that are then connected to the internet. Android-based mobile phone is used to interact with sensors, microcontroller, and other tools through the internet wherever the user is. From the testing, there is a successful communication between the components of the device, sensors, and Android devices. It is possible to adjust the acidity of the liquid automatically by activating the pump in accordance with the results of the pH reading.
Use of GeneXpert and the role of an expert panel in improving clinical diagnosis of smear-negative tuberculosis cases

Setting A high proportion of notified tuberculosis cases in the Philippines are clinically diagnosed (63%) as opposed to bacteriologically confirmed. Better understanding of this phenomenon is required to improve tuberculosis control. Objectives To determine the percentage of smear negative presumptive tuberculosis patients that would be diagnosed by GeneXpert; compare clinical characteristics of patients diagnosed as tuberculosis cases; and review the impact that the current single government physician and a reconstituted Tuberculosis Diagnostic committee (expert panel) may have on tuberculosis over-diagnosis. Design This a cross-sectional study of 152 patients 15–85 years old with two negative Direct Sputum Smear Microscopy results, with abnormal chest X-ray who underwent GeneXpert testing and review by an expert panel. Results Thirty-two percent (48/152) of the sample were Xpert positive and 93% (97/104) of GeneXpert negatives were clinically diagnosed by a single physician. Typical symptoms and X-ray findings were higher in bacteriologically confirmed tuberculosis. When compared to the GeneXpert results the Expert panel’s sensitivity for active tuberculosis was high (97.5%, 39/40), specificity was low (40.2%, 35/87). Conclusion Using the GeneXpert would increase the level of bacteriologically confirmed tuberculosis substantially among presumptive tuberculosis. An expert panel will greatly reduce over-diagnosis usually seen when a decision is made by a single physician.
Synergy of wide-field infrared survey explorer (WISE) and the Sloan Digital Sky Survey in Stripe 82

xii CHAPTER .