Miracle-dz/UniFAR-FTUs huggingface dataset:
EVALUASI PROGRAM PENERIMAAN PESERTA DIDIK BARU (PPDB) DENGAN SISTEM REAL TIME ONLINE (RTO) DI SMA NEGERI 2 BANTUL

Penelitian ini bertujuan mengetahui program PPDB dengan sistem RTO di SMA N 2 Bantul dan upaya sekolah dalam menyelesaikan hambatan yang terjadi selama pelaksanaan program. Jenis penelitian deskriptif kualitatif. Subyek penelitian kepala sekolah, wakil kepala sekolah, operator, dan panitia program. Teknik pengumpulan data menggunakan wawancara, observasi, dan studi dokumen. Uji keabsahan data dengan triangulasi dan analisis data model Miles and Huberman. Hasil penelitian menunjukkan bahwa: (1) program tersebut relevan dengan kondisi sekolah dan masyarakat. tujuan sekolah yaitu memberikan pelayanan maksimal sesuai kemampuan sekolah. Panitia yang dibutuhkan lebih sedikit dan panitia diberi sosialisasi dan pelatihan terkait program. Sarana prasarana yang mendukung pelaksanaana program tersedia cukup memadai dengan kondisi yang siap pakai dan siswa yang diterima memiliki nilai tinggi. PPDB dengan sistem RTO lebih mudah dan transparan dalam proses pendaftaran, seleksi, dan pengawasannya serta lebih efektif dan efisien dalam penyelesaian tugas. (2) Sekolah mampu menyelesaikan permasalahan sesuai dengan kewenangannya.
Growth of Harmful Dinoflagellate Margalefidinium polykrikoides in Different Nutrient Concentrations

Margalefidinium polykrikoides blooms resulted in massive fish mortality in many countries around the world. The prevalence of booms is believed to be associated with the eutrophication of the coastal area. Therefore, this study aims to determine the effects of different nitrogen (N) and phosphorus (P) concentrations on the growth of M. polykrikoides. Experiments were conducted on M. polykrikoides isolated from the affected area by exposing the cell to various N and P ratio: seawater without the addition of N and P (SW), equal N:P ratio (NP), trial ratio of N and P (TR), F/2 medium (+NP), deficient N (–N), and deficient P (–P). The experiments were carried out in triplicates for 20 d in similar environmental conditions with the determination of density, specific growth rate, and cell size during the study period. Furthermore, nutrient concentrations nitrite+nitrate (NO2 –+NO3 –), phosphate (PO4 3–), and ammonia (NH4 +) were measured initially in the medium and both nutrients and chlorophyll were determined at the end of the experiment. The results revealed that M. polykrikoides grew in all the experiments, but the growth patterns differed between the treatments. The highest specific growth rate (0.1901 ± 0.017 d–1) with the highest cell density (1709 ± 68.21 cells mL–1) with a long chain of cells was observed in TR during the exponential phase. However, cell size was significantly smaller in SW compared to other nutrient conditions during the exponential phase. This study shows that M. polykrikoides a flexible species in nutrient uptake, thus allowing the species to survive in different nutrient conditions. The understanding of this bloom mechanism is important in monitoring and management of this harmful species, particularly in Sabah coastal waters.
Anomaly detection in rotating machinery using autoencoders based on bidirectional LSTM and GRU neural networks

: A time series anomaly is a form of anomalous subsequence that indicates future faults will occur. The development of novel techniques for detecting this type of anomaly is significant for real-time system monitoring. Several algorithms have been used to classify anomalies successfully. However, the time series anomaly detection algorithm was not studied well. We use a new bidirectional LSTM and GRU neural networks-based hybrid autoencoder to detect if a machine is operating normally in this research. An autoencoder is trained on a set of 12 features taken from healthy operating data gathered promptly after a planned maintenance period using vibration sensors. The features taken from new data are then reconstructed using the trained model. If the model accurately reconstructs the features, the machine is in good working order. If the reconstruction exceeds a certain error threshold, the machine is functioning strangely and needs to be serviced.
THE MODERATING EFFECT OF CEO NARCISSISM ON PAY-PERFORMANCE SENSITIVITY

study Grounded on the view that CEO individual characteristics, such as CEO narcissism, may shed a light in why CEO compensation is decoupled from firm performance, this first paper of the thesis aims to investigate the moderating effect of CEO narcissism on the pay-performance sensitivity. Thus, fills a gap in the literature on CEO? s personality traits and executive compensation, which do not explore that executive compensation must be tied to firm performance to be viewed as a corporate governance mechanism. To test our hypothesis, we will use a sample of 3,711 U.S. firms, during the period 2002-2019, employing regression analysis (GMM-System) due to the endogenous relationship between CEO compensation and firm performance. The data will be collected through Thomson Reuters Eikon StreetEvents (CEO Narcissism), ExecuComp (CEO compensation), Compustat (accounting-based performance measures), and CSRP (market-based performance measures). Our study may contribute to shareholders and potential investors since if CEO narcissism positively moderates the relationship between CEO compensation and firm performance, it will align CEO interests for higher levels of compensation with shareholders` interests for better firm performance. Furthermore, our study could be useful for boards, as they could consider this psychological aspect when proposing compensation schemes to CEOs. Palavras-chave:
highest accuracy of each generation

Due to the popularity of the Internet, online news has become an important tool for information sharing. Predicting the popularity of online news also became a hot topic since it could help editors design better news. The prediction can be treated as a binary classification problem where “popular” is one class and “unpopular” is the other one. Researchers have built many models to solve this problem including the neural network. In this paper, experiment on using bimodal distribution removal (Slade & Gedeon, 1993) to improve the performance of a neural network classifier has been done. But bimodal distribution removal showed no improvement. After that, using the evolutional algorithm(EA) to perform feature selection was tried. The best features being selected by evolutional algorithm improved the final score by 4%. The final system received 70% accuracy on test dataset which is 3% higher than the result of Fernandes, Vinagre and Cortez’s research (2015).
Stochastic day-ahead optimal scheduling of multimicrogrids: an alternating direction method of multipliers (ADMM) approach

2022 Abstract: Multimicrogrid system is a novel notion in modern power systems as a result of developing renewable-based generation units and accordingly microgrids in distribution networks. Their energy management might be challenging due to presence of independent units. Thus, in this paper, a decentralized method for energy management of multimicrogrid systems has been proposed. Decentralized methods can enhance the privacy of users and reduce the burden of calculations. Alternating direction method of multipliers (ADMM) is selected as a decentralized approach which has the capability of breaking problems with complicating constraints in order to facilitate the solving process. Using decentralized approach not only reduces the burden of calculations, but also increases the privacy of entities. Wind turbines as renewable based generators are assumed to participate in this system. To model the uncertainties of these units, chance-constrained programming is employed. Also, due to clean output of hydrogen storage systems and fuel cells, the inclination for using these systems has expanded. Simulations on the test case study demonstrate the applicability and performance of the proposed methodology. Considering the reliability level as 0.9 results in 12 , 989$ in the test case. By considering the reliability level as 0.8, the operational cost becomes 11 , 712$ which shows a reduction of 1277$ which is achieved by jeopardizing the system reliability by 10%
Students Tele-All: Self-Efficacy and Self-Reflection as Measures of Student Success in Telepractice Training

(1) Background: With an increasing demand for telepractice services, the need for telepractice education is more important than ever. In addition to learning how to deliver these services, certain clinical and technological skills learned through telepractice apply more broadly to in-person care. Evaluating students’ abilities to master these skills is necessary to ensure clinical skill competence. (2) Methods: We utilized self-efficacy ratings and self-reflections to determine student growth after hands-on telepractice training among nursing, occupational therapy, physical therapy, and speech-language pathology students. (3) Results: Students across disciplines demonstrated growth in all measured skill areas and reported overall increased confidence. Students with less prior telepractice experience reported greater increased confidence in seven measured competency areas than students with more prior telepractice experience. The number of completed sessions at the end of student placements was correlated with increased confidence for one measured skill area. Regardless of whether or not students had prior experience when starting their placement, as the number of telepractice sessions completed increased, thus did students’ confidence levels. The results of this study support telepractice as a viable clinical education tool for student growth and the use of self-efficacy and self-reflection as valuable tools for monitoring the effectiveness of telepractice clinical learning activities.
Approximate Computation of Frequency Moments

This report is the result of a term project for the course “Algorithmic Methods of Data Mining” at Aalto University. The goal of this project was analyze randomized algorithms for streams. In particular, the task was compute the so-called zero’th and second frequency moments of a stream, first using algorithms for exact computation and afterwards comparing these to randomized algorithms for approximate computation, which offer substantial gains in terms of space complexity in trade for result precision. “The frequency moments of a sequence containing mi elements of type i, for 1 ≤ i ≤ n, are the numbers Fk = ∑n i=1m k i ” as defined by Alon, Matias and Szegedy [3]. In this project the frequency moments of interest were F0 − the number of distinct elements in the stream − and F2 which describes the “repeat rate or Gini’s index of homogeneity needed in order to compute the surprise index of the sequence” [3],[6]. The data given to compute these frequency moments in this project was a file of approximately 8.8 GB with 911.740. 903 unprocessed words from twitter in UTF format to be used in a stream fashion. The report is structured as follows: First the exact computation of the frequency moments is discussed, afterwards the approaches to approximate computation and their results will be presented. As the topic of choosing a family of hash functions plays an important role in these randomized algorithms we will discuss this in seperate section. Afterwards we will conclude the report by summarizing our results and giving a recommendation for the setup for computing the frequency moments F0 and F2 in a similar setting. The code to run experiments for this project was implemented in C, Julia and Python. This report uses Julia [1] in combination with the Jupyter notebook [2]. With these prerequisites in place the project report can also be cloned form the following GitHub repository and run interactively: https://github.com/clement/frequency-moments. In addition the following Julia packages are needed:
Factors of Investment Decision Practices among Certified Public Accountants in Davao del Norte

: The objective of this study is to identify the determinant factors of investment decision practices and further, to develop a framework that characterizes the investment decision practices among certified public accountants in Davao Del Norte. This research is significant for investors to enhance their capacity to decide on investments and provide investment managers better ways of designing their products and services. The informants of this study were 150 CPAs using a researcher‟s modified questionnaire that was personally administered. The data were analyzed using Data Reduction Analysis, Kaiser Meyer-Olkin measure of sampling adequacy and Barlett‟s test of sphericity and Factor Rotation Method. Using the exploratory factor analysis, the study reveals that Economic Expectation, Herding Effect, Market Information, Third Party Opinion, Financial Information, News and Government Influence, Firm Perception, Over-confidence and Firm Image best influences the investment decision practices of CPAs in Davao del Norte.
Prediction of Diabetic Retinopathy and Classifiers Sensitivity Analysis Prediction of Diabetic Retinopathy

— Many eye diseases, such as Diabetic Retinopathy (DR), can lead to blindness without early clinical diagnosis, and it is extremely important to take the necessary measures before it is too late. A reliable system to detect such a disease in an early stage would be a great addition to the health care providers. In this paper, a comparative analysis of different classifiers was done for the classification of the DR dataset using different machine learning classification algorithms, such as, Naïve Bayes, J48, Random Forest (RF), Stochastic Gradient Decent (SGD), Logistic Regression (LR), Multilayer Perceptron (MP), Simple Logistic (SL) and Logistic Model Tree (LMT) classifiers, and to measure the classification accuracy, the Area Under Curve (ROC), Mean Absolute Error (MAE) and Square Root Mean Square Error (RMSE) for classifying the DR dataset. The results showed that the Logistic Regression classifier outperformed all other classifiers in the classification of the DR dataset for a classification accuracy of 74.8914%, area under curve ROC = 0.831, and RMSE = 0.4061. Then a sensitivity analysis for MP classifier was investigated in term of changing its learning rate. Also, a feature extraction method was performed on LR, MP, SL and LMT classifiers to evaluate the classification performance after selecting the relevant attributes, and the results showed that an accuracy of 72.3719% can be obtained to predict a DR case using Multilayer Perceptron by only applying a combination of up to 8 attributes instead of 19 attributes of the full dataset.
Results of total ankle replacement in 71 patients with a follow-up period 6 month to 7 years

and Ankle Online Journal 13 (4): 1 The purpose of this study was to analyse the results of treatment in patients with arthritis of the ankle joint (AJ) based on analysis after surgery involving total ankle arthroplasty. We evaluated the efficiency of AJ replacement (71 patients). ​ All patients were divided into two groups: prospective (6, 12 and 24 months) and retrospective (3, 5, and 7 years). The results were evaluated with the help of a visual analogue scale (VAS) and the 100-point AOFAS scale; we also performed X-ray examinations. With regard to AJ replacement, we identified a significant risk factor for the most frequent complication, which was aseptic instability of the implant components. Total ankle replacement (TAR) provides good or satisfactory treatment results in the vast majority of patients examined in the absence of complications: 100% on the VAS and 96% on the AOFAS scale after 2 years; 100% on both scales after 3 years; 92.3% on both scales after 5 years; and 85.7% on both scales after 7 years. At the same time, the dynamics of the various indicators studied were
Eﬃcient Detection of Parkinson Disease Using Multiple Machine Learning Techniques

In the recent years there has been a signiﬁcant surge in the diagnosis and monitoring of several diseases, out of which Parkinson disease (PD) is one of them which is commonly found in the elderly population. In this project we aim at developing a simpliﬁed and eﬃcient screening method to diagnose PD beneﬁting medical practitioners who can screen the patients remotely. Currently, a series of spiral drawings are used as standard tests, diﬀerent approaches are proposed that proves to provide better results compared to spiral drawings using the advanced Deep Neural Network models (Resnet 34, Resnet 50, Vgg19)in this project. Vgg19 outperforms Residual network models for spiral, wave and fusion based data. It is also observed that wave drawings are more insightful compared to spiral and fusion based data.
In vitro inhibitory potential of Amaranthus lividus L. against therapeutic target enzymes

vitro ABSTRACT Background and Aims: The search for new enzyme inhibitors in plants is attractive because they can be used as drugs in the treatment of various diseases. Amaranthus spp. (Amaranthaceae) includes about 70 different species, some of which are edible and some of which are used in traditional medicine to treat various ailments. Amaranthus lividus L. is a vegetable whose stems and leaves are used for human consumption in Turkey. Methods: In this study, the in vitro enzyme inhibition potential of A. lividus on α -amylase, α -glucosidase, acetylcholinester- ase (AChE), elastase, lipase, neuraminidase and tyrosinase was investigated for the first time. For this purpose, water extract of A. lividus was prepared. The tests of enzyme inhibitory activity were carried out by spectrophotometric and fluorometric methods. Results: The water extract showed significant α -glucosidase and α -amylase inhibitory activities. Also, it displayed good elas- tase, lipase and tyrosinase inhibitory activities. However, it exhibited low inhibitory activity on AChE and neuraminidase. Conclusion: The plant and its active constituents may be used as an antidiabetic enzyme inhibitor with future phytochemical constituent analysis.
Twitter account classification using account metadata: organizationvs. individual

: Organizations present their existence on social media to gain followers and reach out to the crowds. Social media-related tasks and applications, such as social media graph construction, sentiment analysis, and bot detection, are required to identify the entities’ account types. Some applications focus on personal accounts, whereas others only need nonpersonal accounts. This paper addresses the account classification problem using only minimum amount of data, which is the metadata of the account’s profile. The proposed approach classifies accounts either as organization or individual , in a language-independent manner, without collecting the accounts’ tweet content. The model uses a long short term memory (LSTM) network for processing the textual properties and a fully-connected neural network for processing the numerical features. We apply our solution to a collection of Twitter accounts, as it is one of the most widely used social networks. Our classifier, based solely on the account metadata, achieves an average of 97 . 4% accuracy under 7-fold cross-validation. The experiments show that the account metadata is a qualified resource for accurately estimating the account types.
Effect of Physical Parameters on the Growth and Sporulation of the Alternaria Alternate in Coriander

in on Abstract Coriander is edible spices in India and also used for medicine purpose Out of five different solid media tested, Potato Dextrose Agar (PDA) proved to be the best for mycelial growth (88.35 mm) of the fungus, followed by Corn meal medium (63.17 mm) and Minimum mycelial growth was observed on Martin’s medium. Temperature is most important physical environmental factor for regulating the growth and reproduction of fungi. Ultra low or high temperature adversely affects the germination and growth. Maximum mycelial growth of A. alternata was recorded at 25 0 C (89.00 mm). Good mycelial growth of Alternaria alternata was observed at 100 per cent RH followed by 90 per cent. the help of sterilized cork borer and incubated at 5 different levels of temperature viz . , 15, 20, 25, 30 and 35 0 C for 7 days. Observations on mycelial growth was recorded after 7 days of incubation. on different levels of relative revealed that 100 per cent relative humidity supported maximum mycelial growth of Alternaria alternata while minimum mycelial growth at 50 per cent relative humidity. The results are in close conformity with the
Chapter 13: Virtual Schools: Reflections on Key Issues

A VLE can be understood as a computer-accessible cognitive context where students interact with mediated representations rather than their experiential equivalent. For students of school age, an emergent variation is the Virtual School, where the internet is used to deliver some or all of the courses that would traditionally be offered in a “bricks-and-mortar” school in face-to-face mode with a teacher. Increasingly, Virtual Schools are an available alternative to conventional schools. Unlike their physical counterparts, Virtual Schools are, for the most part, not restricted by the restraints of timetables, bells, uniforms, or other organizational characteristics of face-to-face teaching. Virtual Schools have shown considerable growth in recent years, particularly in the U.S.A. (Clark, 2001) and Canada (SAEE, 2002). They are seen as having identifiable advantages. Mittleman (2001) refers to a Virtual School in Israel as “breaking barriers of time and place” (p. 84), while Florida High School in the U.S.A. uses the motto “any time, any place, any path, any place” (Florida Virtual School, 2003). Virtual Schools have been described in quite glowing terms. Berman (1999) sees virtual learning in the following way:
Full-Wave Simulation of an Optofluidic Transmission-Mode Biosensor

We present a study of an optofluidic biosensor. The sensor operates in a transmission mode wherein detection is based on a shift in the transmission spectrum caused by the contrast in refractive index between the carrier fluid and the target biomaterial. We study the behavior of the sensor using 2D full-wave electromagnetic analysis, and perform parametric studies of sensitivity as a function of key device parameters.
Macroeconomic Now- and Forecasting Based on the Factor Error Correction Model Using Targeted Mixed Frequency Indicators

Since the influential paper of Stock and Watson (2002), the dynamic factor model (DFM) has been widely used for forecasting macroeconomic key variables such as GDP. However, the DFM has some weaknesses. For nowcasting, the dynamic factor model is modified by using the mixed data sampling technique. Other improvements are also studied mostly in two directions: a pre-selection is used to optimally choose a small number of indicators from a large number of indicators. The error correction mechanism takes into account the co-integrating relationship between the key variables and factors and, hence, captures the long-run dynamics of the non-stationary macroeconomic variables. This papers proposes the factor error correction model using targeted mixedfrequency indicators, which combines the three refinements for the dynamic factor model, namely the mixed data sampling technique, pre-selection methods, and the error correction mechanism. The empirical results based on euro-area data show that the now- and forecasting performance of our new model is superior to that of the subset models.