Miracle-dz/UniFAR-FTUs huggingface dataset:
Concentração de alfa-tocoferol no soro e colostro materno de adolescentes e adultas

OBJETIVOS: avaliar a concentracao de alfa-tocoferol no soro e colostro materno de puerperas adolescentes e adultas e verificar a adequacao nutricional de vitamina E do colostro oferecido ao lactente. METODOS: participaram do estudo 72 puerperas, sendo 25 adolescentes e 47 adultas. Foram coletados 5 mL de sangue e 2 mL de colostro em condicao de jejum para analise dos niveis de alfa-tocoferol. As amostras foram analisadas por cromatografia liquida de alta eficiencia. A adequacao nutricional do colostro para a vitamina E foi calculada pelo produto do volume estimado de ingestao de leite com a concentracao de alfa-tocoferol no colostro e por comparacao direta desse produto com o valor de referencia para ingestao do nutriente (4 mg/dia). RESULTADOS: os niveis de alfa-tocoferol no soro de puerperas adolescentes e adultas foram, respectivamente, 30,8±9,8 e 34,1±9,5 µmol/L (media±DP). No colostro, as adolescentes apresentaram concentracao de alfa-tocoferol de 32,9±15,8 µmol/L e as adultas de 30,4 ± 18,0 µmol/L, nao sendo encontrada diferenca significativa entre as concentracoes sericas, assim como no colostro dos grupos estudados. CONCLUSOES: Tanto as puerperas adolescentes quanto as adultas apresentaram um estado nutricional satisfatorio de vitamina E refletido no colostro cujos valores foram capazes de suprir o requerimento nutricional do lactente, sugerindo que a idade materna nao influencia os niveis de alfa-tocoferol do colostro humano.
Prediction models for soccer sports analytics

In recent times there has been a substantial increase in research interest of soccer due to an increase of availability of soccer statistics data. With the help of data provider firms, access to historical soccer data becomes more simple and as a result data scientists started researching in the field. In this thesis, we develop prediction models that could be applied by data scientists and other soccer stakeholders. As a case study, we run several machine learning algorithms on historical data from five major European leagues and make a com-
Comparative Study and Robustness Analysis of Quadrotor Control in Presence of Wind Disturbances

Controlling of the quadrotor has been noted for its trouble as the consequence of exceeds nonlinear system, strong coupled multivariable and external disturbances. Quadrotor position and attitude is controlled by several methodologies using feedback linearization, but when quadrotor works with unstructured inputs (e.g. wind disturbance), some limitations of this technique appear which influence flight work. Design control system with fast response, disturbance rejection, small error, and stability is the main objective of this work. So in this paper we can make use of new methods of control to design a controller of nonlinear robust with a reasonable performance to test the impact of wind disturbance in quadrotor control such as FuzzyPID controller and compared its results with the others four controllers which are PID tuned using GA, FOPID tuned using GA, ANN and ANFIS then discus which controller give the best results in the presence and absence of wind disturbance. The main objective of this paper is that performance of the designed control structure is computed by the fast response without overshoot and minim error of the position and attitude. Simulation results, shows that position and attitude control using FOPID has fast response and better steady state error and RMS error than Fuzzy-PID, ANFIS, ANN and PID tuned using GA without impact of wind disturbance but after impact of wind disturbance it was observed using Fuzzy-PID has fast response with minimum overshoot and better steady state error and RMS error than the other four controllers used in the paper and compared with most of literature reviews which didn't give the adequate results contrasted with the required position and attitude. The all controllers are tested by simulation under the same conditions using SIMULINK under MATLAB2015a.
Reconfigurable Analog Preprocessing for Efficient Asynchronous Analog-to-Digital Conversion

Wearable medical devices, wireless sensor networks, and other energy-constrained sensing devices are often concerned with finding specific data within more-complex signals while maintaining low power consumption. Traditional analog-to-digital converters (ADCs) can capture the sensor information at a high resolution to enable a subsequent digital system to process for the desired data. However, traditional ADCs can be inefficient for applications that only require specific points of data. This work offers an alternative path to lower the energy expenditure in the quantization stage—asynchronous content-dependent sampling. This asynchronous sampling scheme is achieved by pairing a flexible analog front-end with an asynchronous successive-approximation ADC and a time-to-digital converter. The versatility and reprogrammability of this system allows a multitude of event-driven, asynchronous, or even purely data-driven quantization methods to be implemented for a variety of different applications. The system, fabricated in standard 0.5 μ m and 0.35 μ m processes, is demonstrated along with example applications with voice, EMG, and ECG signals.
Classification of Different Age Groups of People by Using Deep Learning

The P urpose of this study is to classify human images of different age groups with VggNet which is one of the Deep Learning (DL) models. Artificial intelligence, machine learning and computer vision have been carried out in recent years at very advanced level. Undoubtedly, it is a great contribution of DL in the rapid progress of these studies. Although DL foundational is based on past history, it has become popular in the imageNet competition held in 2012. This is because the top-5 error rate of 26.1% for visual object description has fallen to 15.3% for the first time with a sharp decline that year with DL. The Convolution Neural Network (CNN) is basis of DL models. It is basically composed of 4 layers. These are Convolution Layer, ReLu Layer, Pooling Layer and Full Connected Layer. DL models are designed using different numbers of these layers. In this study, people are divided into 12 classes according to age groups. These classes are man, woman, man face, woman face, old man, old woman, old man face, old woman face, boy, girl, boy face, girl face respectively. A new data set was created for people in 12 different age categories. For Each class 150 and totally 1800 images were collected. 90% of these images were used for training and the remaining 10% were used for testing. VggNet was trained with this data set. As a result of the study, it was seen that people in different age groups were estimated with 78.5% accuracy with VggNet model. DL models need to be trained with large data required. But it has been seen that training success has achieved a certain value with little data.
The Operational Consequences of Private Equity Buyouts: Evidence from the Restaurant Industry

How do private equity firms affect their portfolio companies? We document operational changes in restaurant chain buyouts using comprehensive health inspection records. Store-level operational practices improve after private equity buyout, as restaurants become cleaner, safer, and better maintained. Supporting a causal interpretation, this effect is stronger in chain-owned stores than in franchised locationsâ€”"twin" restaurants over which private equity owners have limited control. These changes are particularly apparent when private equity partners have prior industry experience. The results suggest that by bringing in industry expertise, private equity firms improve firm operations. Received October 27, 2014; accepted March 1, 2016 by Editor Francesca Cornelli.
Practical ethics for building learning analytics

Artificial intelligence and data analysis (AIDA) are increasingly entering the field of education. Within this context, the subfield of learning analytics (LA) has, since its inception, had a strong emphasis upon ethics, with numerous checklists and frameworks proposed to ensure that student privacy is respected and potential harms avoided. Here, we draw attention to some of the assumptions that underlie previous work in ethics for LA, which we frame as three tensions. These assumptions have the potential of leading to both the overcautious underuse of AIDA as administrators seek to avoid risk, or the unbridled misuse of AIDA as practitioners fail to adhere to frameworks that provide them with little guidance upon the problems that they face in building LA for institutional adoption. We use three edge cases to draw attention to these tensions, highlighting places where existing ethical frameworks fail to inform those building LA solutions. We propose a pilot open database that lists edge cases faced by LA system builders as a method for guiding ethicists working in the field towards places where support is needed to inform their practice. This would provide a middle space where technical builders of systems could more deeply interface with those concerned with policy, law and ethics and so work towards building LA that encourages human flourishing across a lifetime of learning. Practitioner NotesWhat is already known about this topic Applied ethics has a number of well‐established theoretical groundings that we can use to frame the actions of ethical agents, including, deontology, consequentialism and virtue ethics.Learning analytics has developed a number of checklists, frameworks and evaluation methodologies for supporting trusted and ethical development, but these are often not adhered to by practitioners.Laws like the General Data Protection Regulation (GDPR) apply to fields like education, but the complexity of this field can make them difficult to apply.What this paper adds Evidence of tensions and gaps in existing ethical frameworks and checklists to support the ethical development and implementation of learning analytics.A set of three edge cases that demonstrate places where existing work on the ethics of AI in education has failed to provide guidance.A "practical ethics" conceptualisation that draws on virtue ethics to support practitioners in building learning analytics systems.Implications for practice and/or policy Those using AIDA in education should collect and share example edge cases to support development of practical ethics in the field.A multiplicity of ethical approaches are likely to be useful in understanding how to develop and implement learning analytics ethically in practical contexts. [ABSTRACT FROM AUTHOR]
Complete Ensemble Empirical Mode Decomposition on FPGA for Condition Monitoring of Broken Bars in Induction Motors

Empirical mode decomposition (EMD)-based methods are powerful digital signal processing techniques because they do not need a priori information of the target signal due to their intrinsic adaptive behavior. Moreover, they can deal with non-linear and non-stationary signals. This paper presents the field programmable gate array (FPGA) implementation for the complete ensemble empirical mode decomposition (CEEMD) method, which is applied to the condition monitoring of an induction motor. The CEEMD method is chosen since it overcomes the performance of EMD and EEMD (ensemble empirical mode decomposition) methods. As a first application of the proposed FPGA-based system, the proposal is used as a processing technique for feature extraction in order to detect and classify broken rotor bar faults in induction motors. In order to obtain a complete online monitoring system, the feature extraction and classification modules are also implemented on the FPGA. Results show that an average effectiveness of 96% is obtained during the fault detection.
Female executive dan manajemen laba: Riset pada perusahaan manufaktur di Indonesia

This study aims to test the effect of female executives on earnings management. The research was conducted at the manufacturing company listed on the Indonesia Stock Exchange in the 2015-2017 period. The purposive sampling was used 75 companies as data sample. Multiple linear regression test is used to see the effect of the independent variable on the dependent variable. The result of the analysis shows that the Female Chief Executive Officer (CEO), Female Chief Financial Officer (CFO) and Female Board of Commissioner does not influence earnings management. Whereas, the growth variable affects earnings management
Air pollution and the school air environment.

There is growing concern about the association of school indoor air quality (SIAQ) with asthma, rhinitis and rhinoconjunctivitis. Students and school staff alike deserve the highest standards of school air quality possible thus ensuring a safe and productive environment for our childrens’ education. Existing studies highlighted the presence of several air pollutants present within school classrooms which have a direct association with poor health and student performance. Very little data exists about school air quality in countries situated in subtropical regions including small island nations such as Malta. The most commonly identified pollutants include CO 2 , CO, volatile organic compounds (VOC), formaldehyde, O 3 , NO 2 , and particulate matter (PM 10, PM 2.5). These have been mainly associated with an increased risk of asthma and rhinitis mainly in atopic pupils after correcting for alternative exposures. Air quality improvement is an essential measure to help prevent respiratory atopic disease in school occupants. The absence of a European SIAQ monitoring programme highlights the urgent need for more research in this field so as to issue the necessary evidence based recommendations specific to the individual countries.
What are the neuronal correlates and dynamics of gist processing, and what is its role in the interplay of two modes of visual attention?

The ability for humans to extract information from their environment with no more than brief glimpse is well-establish in vision science. This is known as ‘gist extraction’, and is the product of the first feed-forward sweep of information from the eye, through the visual cortex, and into higher-order cognitive control areas in the frontal lobes. This process results in ‘sparse’ attention, which contains multiple elements including gist. The first aim of this thesis was to more clearly define gist in terms of its neuronal locations and time signatures, including observing the effects of multiple gists destructively colliding (in which the presence of more than one relevant image causes participants to be unable to identify the presence of a cued target). The second aim of the thesis was to explore the relationship between sparse attention and focused attention. As established by the data, sparse attention represents a broad ‘vision at a glance’ understanding of the visual environment. Focused attention represents a narrow mode of attention. The following novel findings were identified in this thesis: 1. Processing of target category gist can be observed neuronally using fMRI techniques in category-selective cortex; these selective areas show different % BOLD signal responses to their preferred targets. Place-selective areas show a greater % BOLD signal change in a perceptually-driven manner, and face- selective regions show a greater BOLD change in a manner requiring both the actual presence and the subjective perception of the target. 2. Destructive interference is a decisional, not a perceptual process; it is associated with the N300 and D220 ERPs, which are indicative of greater resources being allocated to a difficult task, conflict resolution, and decision-making. 3. Sparse and focused attention operate in a serial manner, with the first feed- forward sweep (including gist) producing the conscious percept of the world.
Do female commissioners have higher social responsibility? Research in manufacturing companies in Indonesia

This study aims to test the slack resources and feminism of the board on the quality of corporate social responsibility disclosure. The population of this study is all companies listed on the Indonesian Stock Exchange during the 2014-2016 period. The data are collected by using the official IDX website. From this research, there were 539 companies listed in 2016, 525 companies listed in 2015 and 498 companies listed in 2014. This research consists of 75 companies that published a sustainability report. The purposive sampling technique used for selecting the sample. The results of this study found that the slack resources and the feminism of the director has no significant effect on the quality of CSR disclosure while the feminism of commissioners has a negative impact on the quality of CSR disclosure.
SMS Spam Detection Based on Long Short-Term Memory and Gated Recurrent Unit

—An SMS spam is the message that hackers develop and send to people via mobile devices targeting to get their important information. For people who are ignorant, if they follow the instruction in the message and fill their important information, such as internet banking account in a faked website or application, the hacker may get the information. This may lead to loss their wealth. The efficient spam detection is an important tool in order to help people to classify whether it is a spam SMS or not. In this research, we propose a novel SMS spam detection based on the case study of the SMS spams in English language using Natural Language Process and Deep Learning techniques. To prepare the data for our model development process, we use word tokenization, padding data, truncating data and word embedding to make more dimension in data. Then, this data is used to develop the model based on Long Short-Term Memory and Gated Recurrent Unit algorithms. The performance of the proposed models is compared to the models based on machine learning algorithms including Support Vector Machine and Naïve Bayes. The experimental results show that the model built from the Long Short-Term Memory technique provides the best overall accuracy as high as 98.18%. On accurately screening spam messages, this model shows the ability that it can detect spam messages with the 90.96% accuracy rate, while the error percentage that it misclassifies a normal message as a spam message is only 0.74%.
Business analytics capability, organisational value and competitive advantage

ABSTRACT Business Analytics makes the assumption that given a sufficient set of analytics capabilities exist within an organisation, the existence of these capabilities will result in the generation of organisational value and/or competitive advantage. Taken further, do enhanced capability levels lead to enhanced impact for organisations? Capability in this study is grounded in the four pillars of Governance, Culture, Technology and People from the Cosic, Shanks and Maynard capability framework. We set out to undertake the first empirical investigation to measure if there is a positive relationship between Business Analytics capability levels as defined by Cosic, Shanks and Maynard, and the generation of value and competitive advantage for organisations, and do enhanced capability levels lead to enhanced impact. Data gathered from a survey of 64 senior analytics professionals from 17 sectors provides evidence to support that a strong and statistically significant correlation exists between higher capability levels and the ability to generate enhanced organisational value and competitive advantage. Additionally, a revised definition of Business Analytics is proposed, given that Business Analytics should give rise to organisational value and/or competitive advantage and that for this to occur the necessary capabilities must be in place.
Gray-box Network Fuzzing using Genetic Algorithms and Code Coverage

Thesis describes the creation of a fuzzer which uses a genetic algorithm and runtime code coverage metrics to get better results. Research is carried out to show that the new fuzzer performs better than other fuzzers. Additional research is performed on the impact of different code coverage metrics on the genetic algorithm and thus the peformance of the fuzzer.
Topics in sparse functional data analysis

This dissertation consists of three research papers that address different problems in modeling sparse functional data. The first paper (Chapter 2) focuses on the statistical inference for Analysis of Covariance (ANCOVA) models on sparse functional data. In an analysis of covariance model for sparse functional data, the treatment effects, after adjusting for the effects of subject specific covariates, are represented by functions of time. We apply the seemingly unrelated kernel estimator, which takes the within subject correlation into account, to estimate the nonparametric components of the model, and test treatment effects using a generalized quasi-likelihood ratio test. We derived the asymptotic distribution of the test statistics under both the null and some local alternative hypothesis, and show that the proposed test enjoys the Wilks property and is minimax most powerful when the within-subject correlation structure is correctly specified. The second paper (Chapter 3) develops an algorithm to impute missing values in spatiotemporal satellite images based on sparse functional data analysis methods. We model the satellite images as functional data which is both sparse in temporal domain and spatial domain and assume they are repeated measurements of a latent spatiotemporal process. We assume the latent spatiotemporal process is composed of fixed mean function, random temporal effect and random spatial effect. We propose an algorithm to estimate each component using functional principle component analysis (FPCA) techniques. The proposed imputation algorithm is validated on real data and shows great performance in all aspects compared with its competitors. The third paper (Chapter 4) proposes a hierarchical multiresolution imputation (HMRI) algorithm for imputation of high-resolution spatiotemporal satellite images, which is an extension of the second paper. HMRI is demonstrated by using the Moderate Resolution Imaging Spectrophotometer (MODIS) daily land surfact temperature (LST) data and shows satisfactory imputation results. HMRI shows large improvement in prediction accuracy compared with other existing methods.
Proposed pedagogies for teaching and learning chemical bonding in secondary education

Abstract In a preceding publication (Tsaparlis, G., Pappa, E. T., & Byers, B. (2018). Teaching and learning chemical bonding: Research-based evidence for misconceptions and conceptual difficulties experienced by students in upper secondary schools and the effect of an enriched text. Chemistry Education Research and Practice, 19(4), 1253–1269), we reviewed previous studies on students’ misconceptions and conceptual difficulties with the topic of chemical bonding and tested the knowledge of tenth-grade Greek students on certain key aspects of bonding. In addition, we presented an enriched teaching text on this topic for the tenth grade and examined its effectiveness with regard to the same aspects of bonding. In the present study, we review earlier studies, which made proposals concerning the teaching of this topic, and provide some proposals of our own, based on the findings of our previous study. We recommend that a spiral curriculum spanning all three upper-secondary grades should be adopted. A learning progressions approach, employing lower and upper anchors of relevant scientific knowledge is considered, and a proposed list of potential core concepts, lever concepts, and stepping-stones are presented. Finally, the pros and cons of a modern qualitative quantum mechanical approach to bonding are considered.
Modeling and simulation of transfemoral amputee gait

ii TABLE OF CONTENTS.iv LIST OF FIGURES.vi LIST OF TABLES.xv CHAPTER 1INTRODUCTION.1 1.1 OBJECTIVES AND AIMS.6 CHAPTER 2 – LITERATURE REVIEW.8 2.1 BACKGROUND.8 2.2 STATE-OF-ART OF PROSTHESIS.8 2.3 PREVIOUS WORK . 15 2.4 STATE-OF-ART IN MUSCULOSKELETAL MODELING.17 2.5 SUMMARY.21 CHAPTER 3–METHODS.23 3.