bluuebunny/crossref_metadata_2025_split huggingface dataset:
Acute caffeine supplementation improves jumping, sprinting, and change‐of‐direction performance in basketball players when ingested in the morning but not evening
Authors: Stojanović Emilija , Scanlan Aaron T. , Milanović Zoran , Fox Jordan L. , Stanković Ratko , Dalbo Vincent J. 
Published: January 2021

ABSTRACTThis study compared the effects of acute caffeine supplementation (3 mg/kg) administered in the morning and evening on performance‐related variables in basketball players. Eleven, national‐level, adolescent male basketball players underwent field‐based fitness testing on four occasions: morning (10:00) with caffeine ingestion (AMCAFF), morning (10:00) with placebo ingestion (AMPLAC), evening (21:00) with caffeine ingestion (PMCAFF), and evening (21:00) with placebo ingestion (PMPLAC). Fitness testing included of a countermovement jump without arm swing (CMJ), CMJ with arm swing (CMJAS), squat jump (SJ), Lane Agility Drill (LAD), 20‐m linear sprint, and Suicide Run with (SRD) and without dribbling (SR). Data were analysed using two‐way repeated measures analyses of variance and paired t‐tests, with effect sizes (ES) also determined for all pairwise comparisons. Follow‐up t‐test comparisons revealed that AMCAFF produced small‐moderate, significant (p<0.001), improvements in CMJ (ES = 0.51), CMJAS (ES = 0.40), SJ (ES = 0.51), and SR (ES = −0.45) compared to AMPLAC. AMCAFF also produced a moderate, significantly (p<0.001) faster LAD (ES = −0.61) compared to PMCAFF. PMPLAC demonstrated small‐moderate, significant (p<0.05) improvements in CMJ (ES = 0.43), CMJAS (ES = 0.48), and 20‐m sprint (ES = −0.63) compared to AMPLAC. In contrast, AMPLAC resulted in large, significantly (p<0.001), faster SRD (ES = −1.46) and SR (ES = −1.59) compared to PMPLAC. Given the ergogenic effects of caffeine during basketball‐specific fitness tests appear to be influenced by time of ingestion, basketball practitioners should consider administering caffeine only to players in the morning to improve vertical jump, sprinting, and change‐of‐direction performance, with no beneficial effects observed with caffeine ingestion in the evening.Highlights
The effect of caffeine supplementation on basketball‐specific performance related variables were mediated by ingestion time in elite, adolescent basketball players.
AMCAFF produced small‐moderate improvements in vertical jump, change‐of‐direction, 20‐m linear sprint, and repeated‐sprint performance compared to AMPLAC while PMCAFF produced trivial differences in each performance‐related variable compared to PMPLAC.
Comparisons between ingestion times in the placebo condition revealed vertical jump height and 20‐m sprint speed were impaired in the morning compared to the evening, but these time‐dependent differences were eliminated when caffeine was consumed in the morning.
Basketball practitioners should consider administering caffeine only to players in the morning to improve vertical jump, sprinting, and change‐of‐direction performance, with no beneficial effects observed with caffeine ingestion in the evening.
Sedentary behaviours and their relationship with body composition of athletes
Authors: Júdice Pedro B. , Hetherington‐Rauth Megan , Magalhães João P. , Correia Inês R. , Sardinha Luís B. 
Published: January 2021

ABSTRACTDuring the recovery period, athletes present high sedentary behaviour (SB). In non‐athletes, there is a direct relationship between SB and obesity. However, little is known about this relationship in athletes. We hypothesized that different types of SB entail different associations with body composition outcomes.We examined the associations between different types of SB and body composition outcomes in 135 athletes (70 males) aged 21.3 ± 3.9 years old. Dual‐energy X‐ray absorptiometry was performed to assess fat mass (FM), fat‐free mass (FFM) and trunk fat mass (TFM). A validated SB questionnaire (PACE) was used to estimate total SB and specific sedentary pursuits on an average day. Multiple linear regression analyses were performed, adjusting for age, sex, weekly training time, years of sport practice, and sport type.A positive association was found for total SB and total screen time with %TFM (β = 0.220, p = 0.038 and β = 0.319, p = 0.040, respectively), while an inverse association was found for %FFM (β = −0.214, p = 0.042 and β = −0.310, p = 0.026). A higher total screen time was related with a higher %FM (β = 0.283, p = 0.035). With a much stronger effect size, cell phone screen time was positively related with %FM (β = 1.447, p = 0.001).There is still debate whether high levels of physical activity can fully counteract the harms of SB. These findings suggest that SB, particularly cell phone screen time, can compromise athletes’ body composition, regardless of weekly training time. Sport federations and coaches may improve athletes’ body composition by targeting specific sedentary pursuits, i.e. total screen time and cell phone screen time, during athletes’ recovery time.Highlights
Even in athletes, SB can compromise total and regional body composition regardless of high training time.
Screen time, specifically cell phone screen time seems to be the sedentary pursuit to target in athletes.
Sport federations and coaches must control SB during athletes' recovery time.
54 W nanosecond Yb-doped all-fiber amplifier at ultra-high repetition rate (tens of MHz) based on mode-locked fiber oscillator and single-mode fiber stretcher
Authors: Yang Min , Li Pingxue , Li Shun , Xiong Wenhao , Wang Kaixuan , Yao Chuanfei , Wang Dongsheng 
Published: January 2021

Abstract
We report on a nanosecond all-fiber amplifier with repetition rate of tens of MHz. It is based on a passively mode-locked fiber oscillator at 1064 nm, which has a pulse width of 28 ps and repetition rate of 27.7 MHz. The single-mode fiber with length of 8 km is adopted as a fiber stretcher, in which new frequency components are introduced by strong nonlinear effect such as self-phase modulation, resulting in the broadening of spectral bandwidth from 0.4 nm to around 20 nm. The group-velocity dispersion of the 8 km fiber stretcher is −38 ps (nm km) −1, which stretches the pulse duration of the picosecond pulse to 1–7 ns. At the repetition rate of 27.7 MHz, an average power of 54 W is demonstrated with pulse width of 1.3 ns, and 21 W is achieved with pulse width of ∼5 ns, which have optical-to-optical conversion efficiencies of 41.2% and 30.1% respectively. This all-fiber amplifier based on the passively mode-locked fiber oscillator and 8 km single-mode fiber stretcher is capable of delivering nanosecond pulses while maintaining an ultra-high repetition rate of tens of MHz.
In-situ analyzing of air pollution by laser induced breakdown spectroscopy in laser paint removal
Authors: Deng Guoliang , Zhou Qionghua 
Published: January 2021

Abstract
Laser paint removal is widely used in industry in recent years. Compared with traditional paint removal methods, laser paint removal is considered clean and environmentally-friendly. However, particles generated in the cleaning process may cause significant air pollution and harm to the operator’s health. In this work, we demonstrated that particles with different sizes, especially those smaller than 2.5 μm, are generated in the process of laser paint removal. The chemical composition of these particles is analyzed in-situ by using laser induced breakdown spectroscopy. The results show that heavy metal elements (such as Ba, Pb and Ti) can be found in the particles. These heavy metal elements containing, small size particles pollute the air to an unacceptable level. We suggest that great attention should be taken to deal with this particulate air pollution to reduce the health risk for laser paint removal workers.
Posted prediction in social media base on Markov chain model: twitter dataset with covid-19 trends
Authors: Suryaningrat W , Munandar D , Maryati A , Abdullah A S , Ruchjana B N 
Published: January 2021

Abstract
The influence of social media is very attractive in disseminating information; even social media analysis is one of the focuses in the field of research in terms of data mining. In its development not only the field of social science that exists but many studies of social media that can be solved stochastically to calculate the trend of the emergence of a discussion on social media. In this paper, we investigated calculations and predictions using Markov Chains on the emergence of discussions on Twitter media related to coronavirus disease tweets or better known as covid-19. The tweet data obtained is a random sample of the tweet posts that are crawled at the specified time. The tweet data is crawled at three different observations each day for thirteen days continuously. The results of data crawling are calculated to determine the transition from one observation to the next observation. The stages of the process are; crawling tweet data with keywords coronavirus and covid-19; data cleaning process; data processing; Markov Chain modeling; n-step distribution and long-term prediction; interpretation of results. The computational results used are opportunity distribution conditions for the number of tweets. As a transition between two states, namely low (0) and high (1) relative to mean or median. The results of the opportunity distribution obtained in the next 145-time steps (0.28767, 0.71233) and (0.47368, 0.52632) in the probability distribution of the number of tweets are respectively the mean and median values. The results of the modeling show that the conversation on Twitter for 145-time steps in the next prediction is estimated to remain high along with the outbreak of coronavirus or covid-19 before this epidemic subsides.
The application of k-nearest neighbors classifier for sentiment analysis of PT PLN (Persero) twitter account service quality
Authors: Damarta R , Hidayat A , Abdullah A S 
Published: January 2021

Abstract
Social media has an important role in human life. In its implementation social media is used as a media for opinion and self-expression. One of the social media that is often used in Indonesia is Twitter. PT PLN (Persero) as a State-Owned Enterprise that is engaged in providing electricity always tries to provide optimal services. The text mining method can be used to control PT PLN (Persero) service quality by classifying Twitter data with the k-Nearest Neighbors algorithm. Text mining is used to extract information from unstructured textual data to produce useful information. Data classification is a text mining application for information retrieval. In this study the data collected will pass the preprocessing stage, using the k-Nearest Neighbors algorithm to classify data into negative, neutral, or positive classes. The data used in this study was sourced from Twitter. Data is taken from 1 December 2019 to 1 February 2020 using the Twitter API with the keyword ‘@ pln_123’. We obtained 3,000 tweet data successfully. The results are implemented in web-based applications that are built using the Python programming language. Evaluation of the k-Nearest Neighbors model produces an accuracy value of 87.41%. The classification prediction results also show that there is a tendency of the positive sentiment of 35%, the neutral sentiment of 28%, and the negative sentiment of 37%.
Invariant moment and learning vector quantization (LVQ NN) for images classification
Authors: Widagda I G A , Sastra N P , Wiharta D M , Hartati R S 
Published: January 2021

Abstract
Image classification need two main components, i.e., features and classifier. The feature commonly used for classification of images with different scale is invariant moment; its value is invariant against the spatial transformation dealing with translation, scale and rotation. The classifier that is widely used for classification is LVQ NN. It is shallow network containing only two layers, the initial value of its weight is more fixed so that its output is more stable and its algorithm is relatively simple thus both training and testing process are run fast. Based on these facts, therefore, this research proposed a combination method of invariant moment and LVQ NN (IM-LVQ). The ability of the proposed method would be compared with two other methods. Firstly, the combination method of invariant moment and Euclidean distance (IM-ED). Secondly, the combination of invariant moment and principal component analysis (IM-PCA). The performance of the three methods was evaluated quantitatively with several metrics, viz.: Confusion Matrix, Accuracy, Precision, True Positive Rate, False Positive Rate, ROC graph and training time. The evaluation of the metrics was based upon the changing (reduction) of the scale/size of training image. The results showed that IM-LVQ method outperformed the other two methods in aforementioned metrics.
Analysis of membership function in implementation of adaptive neuro fuzzy inference system (ANFIS) method for inflation prediction
Authors: Raharja M A , Darmawan I D M B A , Nilakusumawati D P E , Supriana I W 
Published: January 2021

Abstract
This research will analyze which fuzzy membership function (MF) gives the best results in the implementation of the Adaptive Neuro-fuzzy inference system (ANFIS) method. The case study conducted is to predict the growth of inflation in Bali Province with ANFIS which has the main objective of analyzing the fuzzy membership function and designing a model that can predict the value of inflation growth. Inflation can also be defined as a process of increasing general prices or decreasing the value of money continuously. Inflation growth prediction uses the ANFIS method with five input parameters in the form of regional economic indicators, and the number of pairs of initial data used is 34 annual periods. Several types of membership functions (MF) that will be tested and analyzed are triangular MF, MF trapezium, and MF gbell. The cryptic inference system used is TSK-Order One, and the learning method used is a hybrid method. Based on the research results, the analysis of the fuzzy membership function in the inflation prediction system produces the best error is 1.35E-07 with the type of triangular membership function (MF).
Detection and description generation of diabetic retinopathy using convolutional neural network and long short-term memory
Authors: Amalia R , Bustamam A , Sarwinda D 
Published: January 2021

Abstract
Diabetic Retinopathy (DR) is one of the eye diseases suffered by diabetes patients that will cause blindness if it does not get effectively treated for a certain period of time. Early detection is needed to help patients get effective treatment based on their severity. Researchers have done copious amounts of research regarding the methods for DR detection using shallow learning and deep learning approaches. The proposed method in this paper is a combination of two deep learning architectures, namely Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). CNN is used to detect lesions on retinal fundus images, and LSTM is used for generating description sentences based on those lesions. In the training and testing process, the CNN output will be used for the input of LSTM. The training process’s target is to produce a model that can map retinal fundus images into a sentence. The results of this experiment using the MESSIDOR data set has an accuracy of around 90%.
Ethno-informatics for anthroponymy classification in Sumedang District using data mining
Authors: Abdullah A S , Rejito J , Pradana A , Zakaria M M , Permana F C , Ruchjana B N 
Published: January 2021

Abstract
Ethnoscience is used to look at culture from a scientific perspective, helping to understand how to develop different forms of knowledge and beliefs. One of the fields studied in Ethnoscience is Ethno-informatics, which is the application of informatics in culture. The methodology used is the process of knowledge discovery in databases in data mining, the process of automatically extracting knowledge from large databases, to get interesting patterns so that knowledge can be obtained. The application of anthroponymy by exploring the population database in Sumedang district, the results of the 2019 census from the Sumedang District Population Office. Then the anthroponymy application was developed in Sumedang district. The results of data processing show that the top ten names of people who are most used or most favorite in Sumedang district are: Muhammad, Muhamad, Asep, Siti, Agus, Dede, Ade, Ai, Tati, Dadang. Muhammad’s name has been a favorite since 2000 until now 2020. Meanwhile, the top ten favorite names that have just appeared in the last ten years (2010-2020) are sorted based on ranking including; Naura, Arsyla, Keyla, Raffa, Rafka, Aqila, Zahra, Keusha and Alesha. In general, the Sundanese name in Sumedang district is still widely used, but generally it is relatively down-to-earth, replaced by new names as an uptake of other cultural names. Changes in self-naming in Sumedang regency in particular, generally self-names in Sundanese culture in West Java, were caused by the rapid development of information and communication technology. This research is expected to provide input for the community, especially for the younger generation as a guide in their daily life. It takes a significant effort from the government to maintain it, so that local culture and wisdom, especially the naming of people in Sumedang district, generally Sundanese culture can be well preserved.
Implementation of audio recognition using mel frequency cepstrum coefficient and dynamic time warping in wirama praharsini
Authors: Wibawa I D G Y A , Darmawan I D M B A 
Published: January 2021

Abstract

Sekar Agung or wirama is a Balinese classic work which contains of moral values, and is usually sung during traditional or religious ceremonies. The classic nature of wirama made this art was abandoned by the younger generation who were less interested to learn or preserve it. Related to the problem, this study aimed at conducting sound matching as a medium to learn wirama praharsini based on the rule of guru and laghu. Wirama Praharsini was chosen because of the simple way of singing and pronunciation when it was compared to the other kinds of wirama. This study applied voice recognition method to identify words’ pronunciation in wirama praharsini. MFCC (Mel Frequency Cepstrum Coefficient) was used to extract the voice feature from expert and the tester. The extraction results were compared with the DTW (Dynamic Time Warping) method that was used to compare two sound features from MFCC process. The result of this study showed average accuracy value of 88.89% which indicated that the implementation of MFCC and DTW method could be done to recognize the wirama praharsini pronunciation and detect the right way to sing it based on the rules of guru and laghu in wirama.
Extreme gradient boosting (XGBoost) method in making forecasting application and analysis of USD exchange rates against rupiah
Authors: Islam S F N , Sholahuddin A , Abdullah A S 
Published: January 2021

Abstract
Economic conditions in Indonesia are still unstable, causing the US dollar exchange rate to increase. This is because most international transactions in Indonesia use US dollars. Prediction or forecasting is chosen as one of the important things in choosing a market to invest in buying and selling. This research will focus on making forecasting applications and analyzing the exchange rate of USD against rupiah based on time series data or temporal datasets from the Investing.com site using machine learning methods, namely Extreme Gradient Boosting (XGBoost). Applications created using the python programming language and streamlit framework. Modeling is carried out using the Knowledge Discovery in Database (KDD) methodology with the stages of dividing the dataset with a 50:50 percentage share into test and train data. The modeling uses hyperparameter tuning values, namely n_estimators = 1000, max_depth = 1, x_colsample_bytree = 0.9894, x_gamma = 0.9989, x_min_child = 1.0, x_reg_lamda = 0.2381, and x_subsample = 0.7063 with best loss or RMSE 451.4151. The values of Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) when making the model were 6.61374% and 3.95485%. Meanwhile, when testing the model, the RMSE is 0.23577% and MAPE is 0.11643%.
Stability analysis for yellow virus disease mathematical model of red chili plants
Authors: Amelia R , Anggriani N , Istifadah N , Supriatna A K 
Published: January 2021

Abstract
One of the main obstacles in the cultivation of red chili plants (Capsicum annuum) is the yellow virus disease caused by the Gemini virus. The virus is transmitted through insect vectors which can be controlled using insect pathogenic fungi (Verticillium lecanii). To understand the dynamics of the spread of the yellow virus in Capsicum annuum (C. annuum), we have formulated and analyzed a mathematical model of plant disease. From this model, the value of the Basic Reproduction Number (ℜ0) is presented, which is an essential quantity in an epidemiological model. The obtaining results indicate that when the value of ℜ0 is greater than one, the endemic equilibrium point is asymptomatically stable. However, if the value of ℜ0 is smaller than one, then the endemic equilibrium point is unstable. Also, we provide numerical simulations to illustrate the results.
Quadratic embedding constants of squid graph and kite graph
Authors: Purwaningsih M , Sugeng K A 
Published: January 2021

AbstractThe connected graphG= (V,E) is classified as a Quadratic Embedding (QE) class based on the distance matrixDwhich is conditionally definite negative and if the quadratic embedding constant (QEC) of the graphGis non-positive. In this study, QEC be calculated for Squid Graph containingC3,C4,C5, and kite graph containingC4. All of these graphs are included in the QE class, withQEC= 0 for squid graphs and kite graphs containingC4,QEC(Sq(3,m)) < 0 for squid graphs containingC3, and also for squid graphs containingC5.
Some characterizatsion of coprime graph of dihedral group D
2n
Authors: Syarifudin A G , Nurhabibah  , Malik D P , Wardhana I G A W 
Published: January 2021

Abstract
The Coprime graph of group G denoted Γ
G
is a graph with vertices is an element of G, and two distinct vertices are adjacent when its order relative prime. In 2020, Gazir et al. give some characterizations of Γ
D

2n
for n a prime power. The method that uses in this paper is deductive proof by taking some example of a coprime graph of D
2n
, then generalized the characterization of example. This paper gives some characteristics of the coprime graph of a dihedral group for more general cases. One of the result, Γ
D

2n
is a multipartite graph with girth 3, radius 1, and diameter 2.
On Ramsey (Cn, H)-minimal graphs
Authors: Nabila M , Baskoro E T 
Published: January 2021

Abstract
Let F, G and H be given simple graphs. The notation F → (G, H) means that for any red-blue coloring of all edges on F there is either a red copy of G or a blue copy of H as a subgraph of F. A graph F is called as a Ramsey (G, H)-minimal if it satisfies the following two conditions: (i) F → (G, H), and (ii) F − e ↛ (G, H) for any e ∈ F . The set of all Ramsey (G, H)-minimal graphs is denoted by R(G, H). In this paper, we study on the graphs in R(G, H) if G is a cycle Cn
and H is a path P
3. In particular, we construct some classes of such Ramsey minimal graphs.