nils-herrmann/clean_scirepeval huggingface dataset:
THE IMPACT OF AVAILABLE DATA HISTORY ON THE PERFORMANCE OF PHOTOVOLTAIC GENERATION FORECASTING MODELS
Year: 2013

The continuous growth of solar power capacity raises challenges to distribution system operators regarding power quality and security of supply. Network management systems must be enhanced with short term forecasting functionalities able to predict the solar plants production in the next hours or days. The provision of individual forecasts for each solar plant on the network is often required. To that purpose, historical measurements are needed for tuning the forecasting models. The situation is challenging for new plants for which long history of measurements is not yet available. In that case, models able to provide accurate production forecasts based on few historical production data, are required. In this paper, we investigate the performance of state of theart short term PV forecasting models as a function of the historical data available for tuning. We compare the results with those obtained by a reference model whose utilization does not require more than one day of past production data. Our analysis relies on production data from a 200 kWc solar plant located in the south east of France. It shows that satisfactory performances can be expected from state of the art models, when calibrated with no more than one or two weeks of training data.
Comparative genomic analysis of Mycobacterium neoaurum MN2 and MN4 substrate and product tolerance
Venue: 3 Biotech
Year: 2017

The microbial bioconversion of sterols can afford valuable steroid precursors, such as 4 androstene 3,17 dione (AD) and androsta 1,4 diene 3,17 dione (ADD) The Mycobacterium neoaurum MN4 mutant strain can produce AD in high yield and can tolerate a higher concentration of the substrate phytosterol than the parent strain M. neoaurum MN2. In order to further investigate the mechanisms underlying the enhanced substrate and product tolerance, we performed a genomic analysis of the MN2 and MN4 strains. The genomes were sequenced using a high throughput approach and analyzed using software for genome assembly, gene prediction and functional annotation, KEGG (Kyoto Encyclopedia of Genes and Genomes) annotation, COG (cluster of orthologous) group cluster analysis, GO cluster analysis, and SNP detection and annotation. Based on comparative genomics, 184 mutations were identified in MN4, the average variant rate of 1 variant every 27,249 bases, with a TS/TV value of 0.5877 and missense mutations in one key sterol degradation genes (ChoM1) and four side chain degradation genes that encode enzymes catalysing b oxidation. The results suggest the high AD yield might be due to mutation of ChoM and genes encoding FadE, FadB and FadA b oxidation enzymes. This study provides a theoretical basis for further functional genomics analysis and heterologous production of M. neoaurum MN2 secondary metabolites.
Automated COVID 19 Detection from Chest X Ray Images A High Resolution Network (HRNet) Approach
Year: 2020

The pandemic, originated by novel coronavirus 2019 (COVID 19) continuing its devastating effect on the health, well being, and economy of the global population. A critical step to restrain this pandemic is the early detection of COVID 19 in the human body, to constraint the exposure and control the spread of the virus. Chest X Rays are one of the noninvasive tools to detect this disease as the manual PCR diagnosis process is quite tedious and time consuming. In this work, we propose an automated COVID 19 classifier, utilizing available COVID and non COVID X Ray datasets, along with High Resolution Network (HRNet) for feature extraction embedding with the UNet for segmentation purposes. To evaluate the proposed dataset, several baseline experiments have been performed employing numerous deep learning architectures. With extensive experiment, we got 99.26% accuracy, 98.53% sensitivity, and 98.82% specificity with HRNet which surpasses the performances of the existing models. Our proposed methodology ensures unbiased high accuracy, which increases the probability of incorporating X Ray images into the diagnosis of the disease.
Enhancing musical creativity through indigenous knowledge system: the case of the IGBO musicians
Year: 2016

Creativity, which can be seen as the brainchild of imagination, draws a lot of inspirations from a number of factors. These factors may be visible or invisible; tangible or intangible; animate or inanimate, illusory or factual, mystical or mundane and so on and so forth. All these invariably constitute a very significant influence on human psyche, and which when properly harnessed produces a desired effect. Since creativity cannot operate in a vacuum, it requires human effort, time and space, which are located within a given environmental and socio cultural milieu. Musical creativity through indigenous knowledge system does not stop at the ability to compose, sing, dance and play instruments skillfully in its natural way; it extends to the critical interpretation and application of aesthetic flairs inherent in such acts. Musicians do not lose sight of their localities in their musical creations as they consider it paramount for their music to be relevant. This paper takes a critical look on musical creativity as it concerns the Igbo Musicians, discusses some of the sources of inspiration that guide their musical creation. Also discussed are the implications of indigenous knowledge application as it provides bases for musical appreciation. Through practical experiences and review of related literature, it was observed that musicians do not create outside their environmental stipulations lest their music becomes irrelevant. It is therefore recommended that musicians should always take cognizance of indigenous knowledge system in their creative output for their music to be effectively utilized and appreciated.  musical creativity, indigenous knowledge, Igbo musicians, Nigeria music
Evaluation of the Korean version of the self assessment of nursing informatics competencies scale
Venue: BMC Nursing
Year: 2019

BackgroundIn order to assess nursing students' informatics competency, we need a comprehensive Korean version scale that reflects the important advances in nursing informatics and can make up for the lack of an existing measure. This study aimed to cross culturally adapt the Self Assessment of Nursing Informatics Competencies Scale (SANICS) into Korean (K SANICS) and verify its validity and reliability with nursing students.MethodsThe design of this study was a methodological approach to translate and evaluate the Korean version tool (K SANICS) A total of 254 nursing students at four universities in Korea completed a structured questionnaire including background characteristics and the K SANICS. Reliability and validity of the 30 item K SANICS were evaluated using Cronbach's a, content validity, factor analysis, and contrasted groups approach.ResultsCronbach's a was .95. Exploratory factor analysis was performed to verify the scale's construct validity, identifying 30 items across six categories: advanced skills for clinical informatics, basic application skills, basic computer skills, roles in nursing informatics, skills for clinical applications, and attitude toward computers in nursing.ConclusionThe K SANICS may be used as a reliable assessment tool of nursing students' nursing informatics competencies. It is expected that the K SANICS will contribute to establishing, operating, and evaluating nursing informatics curricula and also can be used in a clinical setting.
Rethinking Human Machine Learning in Industry 4 0 How Does the Paradigm Shift Treat the Role of Human Learning
Year: 2018

Under the concept of "Industry 4.0" production processes will be pushed to be increasingly interconnected, information based on a real time basis and, necessarily, much more efficient. In this context, capacity optimization goes beyond the traditional aim of capacity maximization, contributing also for organization's profitability and value. Indeed, lean management and continuous improvement approaches suggest capacity optimization instead of maximization. The study of capacity optimization and costing models is an important research topic that deserves contributions from both the practical and theoretical perspectives. This paper presents and discusses a mathematical model for capacity management based on different costing models (ABC and TDABC) A generic model has been developed and it was used to analyze idle capacity and to design strategies towards the maximization of organization's value. The trade off capacity maximization vs operational efficiency is highlighted and it is shown that capacity optimization might hide operational inefficiency.  Peer review under responsibility of the scientific committee of the Manufacturing Engineering Society International Conference 2017.
Learning Dynamics and Heterogeneity of Spatial Temporal Graph Data for Traffic Forecasting
Year: 2021

Accurate traffic forecasting is critical in improving safety, stability, and efficiency of intelligent transportation systems. Despite years of studies, accurate traffic prediction still faces the following challenges, including modeling the dynamics of trafc data along both temporal and spatial dimensions, and capturing the periodicity and the spatial heterogeneity of trafc data, and the problem is more difficult for long term forecast. In this paper, we propose an Attention based Spatial Temporal Graph Neural Network (ASTGNN) for traffic forecasting. Specifically, in the temporal dimension, we design a novel self attention mechanism that is capable of utilizing the local context, which is specialized for numerical sequence representation transformation. It enables our prediction model to capture the temporal dynamics of traffic data and to enjoy global receptive elds that is beneficial for long term forecast. In the spatial dimension, we develop a dynamic graph convolution module, employing self attention to capture the spatial correlations in a dynamic manner. Furthermore, we explicitly model the periodicity and capture the spatial heterogeneity through embedding modules. Experiments on five real world traffic flow datasets demonstrate that ASTGNN outperforms the state of the art baselines.
Subjective Cash Flow and Discount Rate Expectations
Year: 2020

Why do stock prices vary? Using survey forecasts, we find that cash flow growth expectations explain most movements in the S&P 500 price dividend and price earnings ratios, accounting for at least 93% and 63% of their variation. These expectations comove strongly with price ratios, even when price ratios do not predict future cash flow growth. In comparison, return expectations have low volatility and small comovement with price ratios. Short term, rather than long term, expectations account for most price ratio variation. We propose an asset pricing model with beliefs about earnings growth reversal that accurately replicates these cash flow growth expectations and dynamics.
Explorer Discovering Graph Functional Dependencies
Year: 2018

This paper studies discovery of GFDs, a class of functional dependencies defined on graphs. We investigate the fixed parameter tractability of three fundamental problems related to GFD discovery. We show that the implication and satisfiability problems are fixed parameter tractable, but the validation problem is co W[1]hard. We introduce notions of reduced GFDs and their topological support, and formalize the discovery problem for GFDs. We develop algorithms for discovering GFDs and computing their covers. Moreover, we show that GFD discovery is feasible over large scale graphs, by providing parallel scalable algorithms for discovering GFDs that guarantee to reduce running time when more processors are used. Using real life and synthetic data, we experimentally verify the effectiveness and scalability of the algorithms.
Free group actions on varieties and the category of modular Galois Extensions for finite p groups
Year: 2014

It is known that a finite group G can only act freely on affine n space if K has positive characteristic p and G is a p group. In that case the group action is "non linear" and the ring of regular functions must be a trace surjective G algebra. Now let K be an arbitrary field of characteristic p>0 and let G be a finite p group. In this paper we study the category Ts of all finitely generated trace surjective K G algebras. In a previous paper we have shown that the objects in Ts are precisely those finitely generated K G algebras A such that the extension of A over the invariant ring A^G is a Galois extension in the sense of Chase Harrison Rosenberg. Although Ts is not an abelian category it has "s projective objects" which are analogues of projective modules, and it has (s projective) categorical generators, which we will describe explicitly. We will show that s projective objects and their rings of invariants are retracts of polynomial rings and therefore regular UFDs. The category Ts also has "weakly initial objects" which are closely related to the essential dimension of G over K. Our results yield a geometric structure theorem for free actions of finite p groups on affine K varieties. There are also close connections to open questions on retracts of polynomial rings, to embedding problems in standard modular Galois theory of p groups and, potentially, to a new constructive approach to homogeneous invariant theory.
Clustering blended learning courses by online behavior data: A case study in a Korean higher education institute
Venue: Internet High. Educ.
Year: 2016

Blended learning (BL) is recognized as one of the major trends in higher education today. To identify how BL has been actually adopted, this study employed a data driven approach instead of model driven methods. Latent Class Analysis method as a clustering approach of educational data mining was employed to extract common activity features of 612 courses in a large private university located in South Korea by using online behavior data tracked from Learning Management System and institution's course database. Four unique subtypes were identified. Approximately 50% of the courses manifested inactive utilization of LMS or immature stage of blended learning implementation, which is labeled as Type I. Other subtypes included Type C Communication or Collaboration (24.3% Type D Delivery or Discussion (18.0% and Type S Sharing or Submission (7.2% We discussed the implications of BL based on data driven decisions to provide strategic institutional initiatives.
Micro air vehicle autonomous obstacle avoidance from stereo vision
Venue: Defense Security Symposium
Year: 2014

We introduce a new approach for on board autonomous obstacle avoidance for micro air vehicles flying outdoors in close proximity to structure. Our approach uses inverse range, polar perspective stereo disparity maps for obstacle detection and representation, and deploys a closed loop RRT planner that considers flight dynamics for trajectory generation. While motion planning is executed in 3D space, we reduce collision checking to a fast z buffer like operation in disparity space, which allows for significant speed up compared to full 3d methods. Evaluations in simulation illustrate the robustness of our approach, whereas real world flights under tree canopy demonstrate the potential of the approach.
Implementasi Algoritma Convolutional Neural Network Pada Algoritma K Means Untuk Kategorisasi Data Teks
Year: 2020

Internet merupakan keberlanjutan dari pesatnya perkembangan teknologi komputer. Dalam kenyataannya pengguna internet menghasilkan banyak data, khususnya data berupa teks seperti posting pada social media dan artikel artikel. Untuk itu, diperlukan kategorisasi data untuk mengelompokkan data teks yang memiliki kategori tertentu. Bag of Words merupakan algoritma yang dapat merepresentasikan kalimat menjadi vektor. Namun Bag of Words seringkali menghasilkan dimensi data atau jumlah fitur yang sangat tinggi, sehingga memerlukan daya komputasi yang sangat tinggi. Pendekatan untuk mengatasi masalah tersebut adalah pembuatan model yang dapat merepresentasikan kalimat menjadi vektor. Model yang menggunakan algoritma Convolutional Neural Network ConvNet dapat dipakai untuk mempelajari kalimat dalam data teks untuk merepresentasikan kalimat dalam bentuk vektor. Pelatihan dan pengujian model menggunakan empat data teks yaitu SMS Spam Komentar Bully, Amazon Alexa Reviews dan Large Movie Reviews Hasil vektor kalimat menggunakan ConvNet lebih efisien dalam waktu latih dan waktu uji dibanding dengan representasi menggunakan Bag of Words Hasil pengujian vektor kalimat ConvNet dengan pengukuran Fowlkes Mallow Index untuk data teks SMS Spam adalah 0.738, untuk data teks komentar Bully adalah 0.735, untuk data teks Amazon Alexa Reviews adalah 0.908 dan untuk data teks Large Movie Reviews adalah 0.680.
Preparation of gold decorated simple and sulfur doped carbon spheres for desulfurization of fuel
Year: 2020

In the present work, because of the importance of the lowering sulfur level in fuels, the new materials have been designed for adsorptive desulfurization. In this line, carbon spheres and sulfur doped carbon spheres have been prepared from the acetylene gas and sulfur powder using chemical vapor deposition method. To increase the level of desulfurization, the prepared spheres were decorated with gold nanoparticles with different percentages (5, 10, 15 and 20% The structures and surface properties of the products were fully characterized using common methods. The study of thermal behaviors showed the sulfur doped spheres has higher thermal stabilities than the carbon spheres. The gold decorated products were employed to adsorb dibenzothiophene from n octane solution as a model fuel. The 95% adsorption efficiency and 364 (mg/g) of dibenzothiophene was achieved using 10% gold decorated sulfur doped spheres at 25 degC. The results showed that the sulfur doped product is more appropriate that the simple carbon spheres and modification of this product with gold increased its adsorption capacities.
Performance Analysis of Different Word Embedding Models on Bangla Language
Venue: 2018 International Conference on Bangla Speech and Language Processing (ICBSLP)
Year: 2018

In this paper we discuss the performance of three word embedding methods on Bangla corpus. Word embedding is a big part of natural language processing related research works. Many research works have focused on finding appropriate methods of word clustering process. Previously N gram models were used for this purpose but now with the improvement of deep learning methods, dynamic word clustering models are preferred because they reduce processing time and improve memory efficiency. In this paper we discuss the performance of three word embedding models namely, word2vec in Tensorflow, word2vec from Gensim package and FastText model. We use same dataset on all the model and analyze the outcomes. These three models are applied on a Bangla dataset containing 5,21,391 unique words to produce the clusters and we evaluate their performance in terms of accuracy and efficiency.
A Dual Axis Active Solar Tracking Device
Year: 2015

The green energy also called renewable energy has gained much attention nowadays. Some renewable energy types are solar energy, hydro potential energy, wind energy, biomass energy and terrestrial heat. Among these, solar energy is effective that can be used. Nowadays, the solar panels are popular and are often used to convert solar energy into electricity. Mostly, solar panel that exists and used now is static, it cannot generates higher power. The solar panel should always be parallel to the sun in order to get more sunlight. This project attempts to generate more power from sunlight using solar panel tracking. If the solar panel is parallel to the sun, that the power generated from the solar panels will be maximum. Therefore, this project is concerned with the position of the solar panel. This system consists of hardware, electrical and software development. The stand was created and combine the stepper motor with the stand. A photoresistor have been used as a sensor to detect a sunlight. Then, the stepper motor and sensor circuit are connected with the microcontroller. Lastly, develop a program to control the sensor and stepper motor and burn the program in Arduino. The goal of this project is to develop a system of automatic sun tracking position. The system will move the solar panel angle to keep in parallel with the sun and generate maximum power at all times. In this project, a dual axis active solar tracking device is presented. A dual axis active solar tracking system changes both azimuth (horizontal) and altitude (vertical) angle of solar panel. It is believe that, A dual axis solar tracker system are more efficient and generates more power than fixed solar panels. There are a few recommendations for the future work of this project. The newly developed sensor can be reduced in size. Smaller sensor is useful if the sensor is going to be produced in higher quantity. The overall design can be improved for better and attractive appearance for the user.
The effects of diltiazem on growth, reproduction, energy reserves, and calcium dependent physiology in Daphnia magna.
Venue: Chemosphere
Year: 2019

With the growth of both the pharmaceutical industry and the human population and longevity, more drugs are used and processed each day. Inevitably, these pharmaceuticals enter wastewater through human excretion and improper disposal of leftovers. One such medication, diltiazem, a calcium channel blocker, is of importance due to its widespread consumption, and prevalence in aquatic environments. To study the sub lethal effects of diltiazem on aquatic animals, we investigated its impacts no feeding behaviour, heart rate, respiration, growth, and reproduction of a bioindicator species, Daphnia magna. When exposed to environmentally relevant concentrations, D. magna increased their heart rate by 12% and oxygen consumption by 48% However, exposure did not have any effects on thoracic limb movement frequency or peristalsis (i.e. feeding behaviour) Individuals exposed to diltiazem for a longer duration (16 days) showed a 44% decrease in lipid reserves and produced between 17 and 28% fewer neonates which were 10 12% larger. Our study demonstrated that exposure to diltiazem creates an energy imbalance in D. magna which could, in the long run, influence their populations.
COMPASSION FATIGUE, BURNOUT, AND SELF CARE: WHAT SOCIAL WORK STUDENTS NEED TO KNOW
Year: 2015

iii ACKNOWLEDGEMENTS .iv LIST OF TABLES viii CHAPTER   Self Care 7 Compassion Fatigue 9 Burnout 10 Theories Guiding Conceptualization 14 Definition of Terms 14 Self Care 15 Compassion Fatigue 17 Burnout 18   21 Research Design 21
PLC controlled stepper motor drive for NC positioning system
Year: 2014

In this paper, an electric drive system for precision control of numerically controlled positioning system is designed, assembled and experimentally tested. The proposed drive system is based on PLC controlled stepper motor. The PLC is used as an indexer to perform full stepping, half stepping and microstepping modes of operation of stepper motor. The drive system has been implemented in two axes NC positioning system. The linear displacement for each machine axis has been evaluated and compared for the three stepping modes of stepper motor. Experimental and calculation results show that the response per pulse (resolution) for full stepping mode is 8.35000 m