nils-herrmann/clean_scirepeval huggingface dataset:
Klasifikasi Penyakit Tiroid Menggunakan Algoritma C4.5 (Studi Kasus Rumah Sakit Umum Daerah (RSUD) Hasanuddin Damrah Manna)
Year: 2020

Tiroid adalah merupakan salah satu bagian tubuh yang sangat penting bagi manusia, tiroid berbentuk kelenjar dan letaknya di bawah jakun pada leher. Penyakit gondok disebabkan oleh gangguan pada kelenjar tiroid. Berdasarkan data histori penderita yang disebabkan oleh kelenjar tiroid dapat dibuat rekomendasi prediksi penyakit tiroid yang membantu tenaga kesehatan. Klasifikasi merupakansalah satu teknik dari data mining yang dapat digunakan untuk membuat prediksi. Klasifikasi dapat dilakukan dengan decision tree salah satunya dengan algoritma C4.5. Penelitian ini bertujuan membuat klasifikasi data penyakait yang disebabkan oleh gejala kelenjar tiroid dan menerapkannya dalam pembangunan sistem. Implementasi sistem menggunakan bahasa pemrograman Visual Basic 2010 dan metode penelitian yang digunakan pada penelitian ini adalah metode waterfall.metode waterfall mampu melakukan analisa kebutuhan yang digunakan untuk mengetahui dari kelemahan sistem yang lama, kemudian membuat desain dari rancangan tersebut dan dilanjutkan dengan pembuatan rancangan sistem baru. Hasil dari penelitian adalah menghasilkan rule yang dapat membantu tenaga kesehatan di RSUD Hasanuddin Damrah Manna dalam mendiagnosa penyakit yang disebabkan oleh kelenjar tiroid.
A Study on Team based Learning Based on Wechat Public Platform: The Case of Design and Implementation of Mass Media Management Course
Year: 2017

Team based learning encouraging team cooperation, characterized by active participation, focusing on open discussion is an important teaching method to stimulate students' initiative, enthusiasm and creativity, and to cultivate teamwork spirit, problem solving ability and language skills. The design and implementation of the course need to focus on decentralization, rules, mechanism and communication.
Design and application of integrated distribution network line loss analysis system
Venue: 2016 China International Conference on Electricity Distribution (CICED)
Year: 2016

Line loss calculation of distribution network is critical to energy saving and comprehensive line loss analysis is an in depth research for theoretical line loss calculation, which helps to achieve meticulous management of loss management and grid operation. This article describes various methods of line loss calculation and common algorithms. A comprehensive distribution network line loss analysis system is proposed and its feasibility is verified by practical examples.
Temperature Does Not Affect Hatch Timing in Snapping Turtles (Chelydra serpentina)
Venue: Journal of Herpetology
Year: 2019

Many oviparous species rely on hatching cues to ensure hatchlings maximize their survival, given the external environmental conditions. In nature, these cues are traditionally environmental (e.g. temperature) or social (e.g. communication between embryos) Examples of both are common throughout ectothermic taxa, particularly reptiles. In the present study, we explored the role of temperature in hatch timing in Snapping Turtles (Chelydra serpentina) We allowed embryos to incubate in wild nests for the majority of embryonic development, then isolated embryos in the lab, and maintained them at 24degC until they reached Yntema stage 25. At this developmental stage, external morphological differentiation is complete and yolk resorption begins. We then incubated embryos until pipping across a range of constant but biologically relevant temperatures (20, 23, 25, 28, or 30.5degC) To test whether thermal variance acts as a hatching cue, we also included a treatment in which temperature fluctuated diurnally around a stationary mean (25 4degC) We found that the timing of egg pipping was not related to temperature treatment, thermal fluctuation, or sex of the embryo. Thus, contrary to traditional understanding, temperatures in the range studied do not affect the duration of the final embryonic stage in C. serpentina embryos, and a definitive hatching cue in this species is yet unknown.
Music Transformer: Generating Music with Long Term Structure
Venue: ICLR
Year: 2019

Music relies heavily on repetition to build structure and meaning. Self reference occurs on multiple timescales, from motifs to phrases to reusing of entire sections of music, such as in pieces with ABA structure. The Transformer (Vaswani et al. 2017) a sequence model based on self attention, has achieved compelling results in many generation tasks that require maintaining long range coherence. This suggests that self attention might also be well suited to modeling music. In musical composition and performance, however, relative timing is critically important. Existing approaches for representing relative positional information in the Transformer modulate attention based on pairwise distance (Shaw et al. 2018) This is impractical for long sequences such as musical compositions because their memory complexity for intermediate relative information is quadratic in the sequence length. We propose an algorithm that reduces their intermediate memory requirement to linear in the sequence length. This enables us to demonstrate that a Transformer with our modified relative attention mechanism can generate minute long compositions (thousands of steps) with compelling structure, generate continuations that coherently elaborate on a given motif, and in a seq2seq setup generate accompaniments conditioned on melodies1. We evaluate the Transformer with our relative attention mechanism on two datasets, JSB Chorales and Maestro, and obtain state of the art results on the latter.
Historical changes in thermoregulatory traits of alpine butterflies reveal complex ecological and evolutionary responses to recent climate change
Venue: Climate Change Responses
Year: 2016

BackgroundTrait evolution and plasticity are expected to interactively influence responses to climate change, but rapid changes in and increased variability of temperature may limit evolutionary responses. We use historical specimens to document changes in the size and thermoregulatory traits of a montane butterfly, Colias meadii, in Colorado, USA over the past 60 years (1953 2012) We quantify forewing wing length, ventral wing melanin that increases solar absorption, and the length of thorax setae that reduces convective heat loss.ResultsThe mean of all three traits has increased during this time period despite climate warming. Phenological shifts may have extended the active season earlier at low elevations and later at high elevations, increasing exposure to cool temperatures and selecting for increases in thermoregulatory traits. Fitness increases at higher elevations due to warming could also increase thermoregulatory traits. Warmer temperatures during pupal development and later flight dates in the season are associated with decreased wing melanin, indicating a role of phenotypic plasticity in historical trait changes.ConclusionsPhenotypic shifts result from a complex interplay of ecological and evolutionary responses to climate change. Environmental variability within and across seasons can limit the evolutionary responses of populations to increasing mean temperatures during climate change.
The emergence of cyberbullying: A survey of primary school pupils' perceptions and experiences
Year: 2012

There is little research that has examined cyberbullying among children under the age of 11years. The current study explored the nature and extent of the phenomenon among primary school children aged 7 to 11 years old (N 220; 116 boys and 104 girls) and investigated their perceptions of the distress caused to victims, how victims would feel, and their recommendations to victims for coping strategies. Participants completed a modified version of Ortega, Calmaestra, and Mora Merchan's (2007) and Smith et al.'s (2008a) bullying and cyberbullying questionnaire. The results indicated that cyberbullying is used and experienced by some children in this age group, with some age and gender differences in these experiences. Cyberbullying is generally viewed negatively and children are aware that it may have a negative impact on the emotions of victims. There is some overlap between involvement in cyberbullying and traditional bullying; with children most likely to take the same role (i.e. traditional bully and cyberbully or traditional victim and cybervictim) across the two settings. The most commonly endorsed coping strategy for victims was to tell someone, which is in line with government guidance to schools. The findings are discussed in relation to research with secondary school pupils as well as addressing potential implications for interventions with this age group.
Data Integration in ETL Using TALEND
Venue: 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS)
Year: 2020

Integration is the process of combining data from different sources to support Data Analytics in organizations. The best definition of data integration is given by IBM, stating "Data Integration is the combination of technical processes and business processes used to combine data from disparate sources into valuable and meaningful information." The important terms here are "combine data. into valuable and meaningful data" where it's about making the data more organized and useful. There are various methods of combining data into an integrated view. This paper describes the various steps involved in integrating data from various sources using the ETL process The Extract, Transform and Load process, how the Talend Open Studio acting as a Data Integration and ETL tool helps in transforming heterogeneous data into homogeneous data for easy analysis and how all the integrated data is stored in a Data Warehouse to provide Business Intelligence users with suitable data for easy analysis.
Who are likely to build strong online social networks? The perspectives of relational cohesion theory and personality theory
Venue: Comput. Hum. Behav.
Year: 2018

Little is known about the underlying mechanisms of building strong social networks and who are likely to build them. To address these issues, we develop a new theoretical framework grounded in relational cohesion theory and personality theory. We test the framework using data from a sample of 436 users of social networking sites. We use structural equation modelling for data analysis. We find that openness and agreeableness are positively, while conscientiousness and neuroticism are negatively, related to exchange frequency. In turn, exchange frequency is positively related to pleasure satisfaction and interest excitement, which are positively related to relational cohesion, which is related to seven aspects of online relationships, namely relational depth, breadth, code change, predictability, commitment, interdependence, and network convergence. Our study is the first to use the two theories to explain how personality traits affect online relationships. Our findings provide insights for information systems managers to make effective decisions on resource allocation and target markets.
Isolation of circulating tumor cells in non small cell lung cancer patients using a multi flow microfluidic channel
Venue: Microsystems nanoengineering
Year: 2019

Circulating tumor cells (CTCs) carry a wealth of information on primary and metastatic tumors critical for precise cancer detection, monitoring, and treatment. Numerous microfluidic platforms have been developed in the past few years to capture these rare cells in patient bloodstream for deciphering the critical information needed. However, the practical need for a high quality method of CTC isolation remains to be met. Herein, we demonstrate a novel multi flow microfluidic device that is able to sensitively provide high purity >87% of separation outcome without labeling. Our device is constructed and configured based on the phenomenal effect of size dependent inertial migration. The recovery rate of >93% has been achieved using spiked cancer cells at clinically relevant concentrations (10 cells per 5 mL and above) We have also successfully detected CTCs from 6 out of 8 non small cell lung cancer (NSCLC) patients, while none for 5 healthy control subjects. With these results, we envision our approach is a promising alternative for reliable CTC capture, and thus for facilitating the progress of extracting information from CTCs to personalize treatment strategies for solid tumor patients. New system to isolate circulating tumor cellsA novel multi flow microfluidic system has been devised to isolate circulating tumor cells (CTCs) in patients with non small cell lung cancer (NSCLC) NSCLC accounts for around 85% of all lung cancers. CTCs are cells that have been released from a primary tumor and are transported around the body by the blood. Those cells carry information that is critical for precise cancer detection, monitoring, and treatment; however, a high quality method for isolating CTCs has hitherto been lacking. A team headed by Ian Papautsky at the University of Illinois at Chicago developed a novel multi flow microfluidic device that affords high fidelity in separating CTCs from the blood of NSCLC patients. The authors believe that their versatile device offers considerable potential for facilitating extraction of information from CTCs in NSCLC and other forms of cancer.
Programming curvilinear paths of flat inflatables
Venue: Proceedings of the National Academy of Sciences
Year: 2019

Inflatable structures are flat and foldable when empty and both lightweight and stiff when pressurized and deployed. They are easy to manufacture by fusing 2 inextensible sheets together along a defined pattern of lines. However, the prediction of their deployed shape remains a mathematical challenge, which results from the coupling of geometrical constraints and the strongly nonlinear and asymmetric mechanical properties of their composing material: thin sheets are very stiff on extensional loads, while they easily shrink by buckling or wrinkling when compressed. We discuss the outline shape, local cross section, and state of stress of any curvilinear open path. We provide a reverse model to design any desired curved 2 dimensional shape from initially flat tubes. Inflatable structures offer a path for light deployable structures in medicine, architecture, and aerospace. In this study, we address the challenge of programming the shape of thin sheets of high stretching modulus cut and sealed along their edges. Internal pressure induces the inflation of the structure into a deployed shape that maximizes its volume. We focus on the shape and nonlinear mechanics of inflated rings and more generally, of any sealed curvilinear path. We rationalize the stress state of the sheet and infer the counterintuitive increase of curvature observed on inflation. In addition to the change of curvature, wrinkles patterns are observed in the region under compression in agreement with our minimal model. We finally develop a simple numerical tool to solve the inverse problem of programming any 2 dimensional (2D) curve on inflation and illustrate the application potential by moving an object along an intricate target path with a simple pressure input.
Categorical Data Analysis
Venue: International Encyclopedia of Statistical Science
Year: 2011

Modeling and inferential procedures (estimation and precision assessment) are discussed for a single proportion, thereafter in the context of cross classified data, i.e. contingency tables, is discussed. First, the situation of two by two tables is given some treatment and then the more general context of R by C tables is considered. Particular emphasis is put on testing the null hypothesis of no association (independence) between the row and column classification. Next, the setting of multiple contingency tables is considered. Attention is given to the log linear model and to the multinomial regression model. When the covariate is continuous, these methods naturally extend to logistic regression, a method at the intersection of generalized linear modeling and categorical data analysis. When the outcome is a count, one often uses Poisson regression. Finally, correlated categorical data settings are briefly discussed.
The p AAA algorithm for data driven modeling of parametric dynamical systems
Venue: ArXiv
Year: 2020

The AAA algorithm has become a popular tool for data driven rational approximation of single variable functions, such as transfer functions of a linear dynamical system. In the setting of parametric dynamical systems appearing in many prominent applications, the underlying (transfer) function to be modeled is a multivariate function. With this in mind, we develop the AAA framework for approximating multivariate functions where the approximant is constructed in the multivariate Barycentric form. The method is data driven, in the sense that it does not require access to full state space data and requires only function evaluations. We discuss an extension to the case of matrix valued functions, i.e. multi input/multi output dynamical systems, and provide a connection to the tangential interpolation theory. Several numerical examples illustrate the effectiveness of the proposed approach.
Chemical Alterations of Pine Wood Lignin during Heat Sterilization
Year: 2016

This study investigated alterations in the lignin of pinewood (Pinus sylvestris L. during heat sterilization. The Klason lignin content was determined, and the dioxane lignin was isolated. Changes in the dioxane lignin were evaluated by size exclusion chromatography (SEC) as well as Fourier transform infrared (FTIR) spectroscopy. The analyses illustrated an increase in the percentage of extractives and the amount of dioxane lignin yields, while a decrease in the acid insoluble lignin content was also observed. Changes in the macromolecular characteristics of the dioxane lignin (molecular weight and polydispersity) were not significant (p 0.05) however, they indicated that simultaneous degradation and condensation reactions had occurred. The FTIR spectra of the dioxane lignins showed an increase at 3420 cm 1 (O H stretching vibration) indicating the cleavage of ether bonds. The band at 1715 cm 1 (unconjugated C=O stretching) increased after an initial decrease due to the cleavage of the b alkyl aryl ether bonds. Also, the band at 1660 cm 1 (conjugated C=O groups) increased, which was caused by the cleavage of the a alkyl aryl ether bonds.
Deep convolutional neural networks for classifying head and neck cancer using hyperspectral imaging
Venue: Journal of biomedical optics
Year: 2017

Surgical cancer resection requires an accurate and timely diagnosis of the cancer margins in order to achieve successful patient remission. Hyperspectral imaging (HSI) has emerged as a useful, noncontact technique for acquiring spectral and optical properties of tissue. A convolutional neural network (CNN) classifier is developed to classify excised, squamous cell carcinoma, thyroid cancer, and normal head and neck tissue samples using HSI. The CNN classification was validated by the manual annotation of a pathologist specialized in head and neck cancer. The preliminary results of 50 patients indicate the potential of HSI and deep learning for automatic tissue labeling of surgical specimens of head and neck patients.
The Spectrum Approach
Year: 2006

Two short reports outlining the Spectrum Appraisal Approach developed by Hugh Barton Marcus Grant (2006) The aim of this technique is to enable decision makers to take a quick but holistic overview of policy from the sustainability and human health standpoint. It acts as a summary of the strengths and weaknesses of a policy. Spectrum appraisal is then discussed alongside the strengths and weaknesses of EIA and HIA.
Graphene oxide Fe3O4 nanocomposite magnetic solid phase extraction followed by UHPLC MS/MS for highly sensitive determination of eight psychoactive drugs in urine samples.
Venue: Talanta
Year: 2020

Graphene oxide Fe3O4 (GO Fe3O4) nanocomposite was synthesized by a facile chemical co precipitation method. The GO Fe3O4 was used as magnetic sorbent to extract the eight psychoactive drugs from urine samples. The analytes are morphine (MOR) 6 monoacetylmorphine (6 MAM) amphetamine (AMP) methamphetamine (MAMP) codeine, cocaine, dolantin and benzoylecgonine (BZE) which were determined by ultrahigh performance liquid chromatography tandem mass spectrometry (UHPLC MS/MS) This method has high selectivity for the target analytes. The limit of detection (LOD) and limit of quantification (LOQ) were 0.02 0.2 mg L 1 and 0.05 0.5 mg L 1, respectively. The Mandel's fitting test revealed good linearity within all linear ranges. The linear ranges were calculated as 0.05 1000 mg L 1 for AMP, MAMP, cocaine and dolantin; 0.1 1000 mg L 1 for 6 MAM and codein; and 0.5 1000 mg L 1 for MOR and BZE. The recoveries ranged in 80.4 105.5% The intra day and inter day RSDs are in the range of 2.7 13.1% and 3.9 13.7% respectively. Magnetic solid phase extraction (MSPE) with GO Fe3O4 provides a convenient, rapid and green sample pretreatment method for extracting the target psychoactive drugs from urine. This methodology can be used for simultaneous or individual detection of eight major psychoactive drugs with high sensitivity. This method has high potential in clinical and forensic areas for psychoactive drugs analysis.
Black swan events in China's stock markets: Intraday price behaviors on days of volatility
Year: 2019

China's stock market crash on August 24, 2015 affected global stock markets, indicating a possible black swan event. This study looked at trading days when sudden rises and drops occurred in 2015 in its stock markets to examine the intraday fluctuation behaviors of stock prices in order to answer several questions: (a) How did the market resume stability after volatility in stock prices occurred? (b) Did the corrections after days of sudden rises differ from those after days when sudden drops occurred? (c) Which of the investigated trading days could be classified as black swan events? Five trading days with the highest and lowest daily returns in 2015 in the Shanghai and Shenzhen stock markets were selected as the days of volatility. The quantile autoregression unit root test was used to test whether stock price indices converged or dispersed on days of high volatility as time passed. The results reveal that a black swan event was identifiable only for the sudden drop on May 28. On other trading days, China's stock markets exhibited notable corrections of mean reversion, and the speed of market recovery increased with the extent of price volatility. In addition, wave patterns of slow rises and rapid falls in intraday stock price fluctuations and corrections were observable after occurrences of stock price volatility, showing that overreactions and underreactions happened consecutively.
Enhanced Tweet Hybrid Recommender System Using Unsupervised Topic Modeling and Matrix Factorization Based Neural Network
Venue: Unsupervised and Semi Supervised Learning
Year: 2019

Recommender systems (RS) were created to recommend interesting items to users. There are two recommendation techniques: content based filtering (CBF) and collaborative filtering (CF) CBF makes recommendations based on a user's past behavior, whereas CF makes a recommendation using past neighbors' opinions who have similar behavior to the target user. Nowadays, there are many data on social networks, including Tweets on Twitter. Thus, many researchers have studied RS based on Tweets using latent Dirichlet allocation (LDA) to extract latent data from observed data. Nevertheless, those researchers use either CBF or CF with LDA only. However, CBF provides recommendations that are too specific, whereas CF has sparsity and a cold start problem. Therefore, this research proposes a new method of recommending Tweets based on hybrid RS with LDA (unsupervised topic modeling) and generalized matrix factorization (supervised learning based neural network) From experimental results, the proposed method outperforms on mean absolute error and coverage.