bluuebunny/crossref_metadata_2025_split huggingface dataset:
A general model of hippocampal and dorsal striatal learning and decision making
Authors: Geerts Jesse P. , Chersi Fabian , Stachenfeld Kimberly L. , Burgess Neil 
Published: November 2020

SignificanceA central question in neuroscience concerns how humans and animals trade off multiple decision-making strategies. Another question pertains to the use of egocentric and allocentric strategies during navigation. We introduce reinforcement-learning models based on learning to predict future reward directly from states and actions or via learning to predict future “successor” states, choosing actions from either system based on the reliability of its predictions. We show that this model explains behavior on both spatial and nonspatial decision tasks, and we map the two model components onto the function of the dorsal hippocampus and the dorsolateral striatum, thereby unifying findings from the spatial-navigation and decision-making fields.
Studies in bank voles reveal strain differences between chronic wasting disease prions from Norway and North America
Authors: Nonno Romolo , Di Bari Michele A. , Pirisinu Laura , D’Agostino Claudia , Vanni Ilaria , Chiappini Barbara , Marcon Stefano , Riccardi Geraldina , Tran Linh , Vikøren Turid , Våge Jørn , Madslien Knut , Mitchell Gordon , Telling Glenn C. , Benestad Sylvie L. , Agrimi Umberto 
Published: November 2020

Significance
Chronic wasting disease (CWD) is a highly contagious disease caused by prions that affects several cervid species and is relentlessly spreading across North America. Very recently, CWD was detected for the first time in Europe. In this study, we found that Norwegian CWD strains are distinct from those causing the epidemic in North America. Moreover, we show that Norwegian reindeer and moose are affected by different CWD strains, revealing an unprecedented prion strain variation in Norwegian wild cervid populations. These findings indicate that North American CWD prions are not the proximate cause of the newly discovered Norwegian CWD cases and have implications for CWD control strategies in Europe, as well as for the safety of humans.
Green energy by recoverable triple-oxide mesostructured perovskite photovoltaics
Authors: Schneider Avi , Efrati Ariel , Alon Stav , Sohmer Maayan , Etgar Lioz 
Published: November 2020

Significance
In the progress toward a more sustainable world, wind and solar energy production are the leading renewable contenders. The emerging field of perovskite solar cells has achieved remarkable efficiencies and is gaining a steady foothold among other solar technologies. However, a worldwide deployment of perovskite solar panels poses environmental and economic problems due to degradation over time. The degrading photoactive materials are usually situated between other layers, complicating or inhibiting cell recycling. Here, we investigated a structure for perovskite solar cells composed of chemically and thermally stable oxides which includes the application of the photoactive perovskite material as a final step. This structure allows for the removal and replacement of degraded perovskite, with a full restoration of photovoltaic characteristics.
Enhanced trace element mobilization by Earth’s ice sheets
Authors: Hawkings Jon R. , Skidmore Mark L. , Wadham Jemma L. , Priscu John C. , Morton Peter L. , Hatton Jade E. , Gardner Christopher B. , Kohler Tyler J. , Stibal Marek , Bagshaw Elizabeth A. , Steigmeyer August , Barker Joel , Dore John E. , Lyons W. Berry , Tranter Martyn , Spencer Robert G. M. ,   the SALSA Science Team
Published: November 2020

Significance
Trace elements are integral to biogeochemical processes at the Earth’s surface and play an important role in the carbon cycle as micronutrients to support biological productivity. We present data from the Greenland and Antarctic ice sheets to demonstrate the importance of subglacial biogeochemical processes in mobilizing substantial quantities of these elements. Usually immobile elements are found in subglacial meltwaters at elevated concentrations compared with typical rivers, with most exhibiting distinctive size fractionation due to adsorption onto nanoparticles. Our findings suggest that ice sheets need to be included in models of global biogeochemical cycles of trace elements and studies of the fertilization of adjacent marine systems, especially the Southern Ocean, due to large export fluxes of micronutrients, most notably iron.
Triclosan leads to dysregulation of the metabolic regulator FGF21 exacerbating high fat diet-induced nonalcoholic fatty liver disease
Authors: Yueh Mei-Fei , He Feng , Chen Chen , Vu Catherine , Tripathi Anupriya , Knight Rob , Karin Michael , Chen Shujuan , Tukey Robert H. 
Published: November 2020

Significance
While exploring triclosan-induced hepatoxicity, this study discovered that exposure to triclosan, in combination with a high-fat diet, causes nutritional imbalance, resulting in the pathogenesis of hepatic steatosis. The underlying mechanism through which triclosan imposes an impact on critical metabolic networks was delineated. The significance of this study lies in the fact that the ubiquitous presence of the environmental contaminant triclosan in conjunction with the prevalence of high consumption of dietary fat, which constitutes a good recipe for the development of fatty liver disease, are common factors encountered in everyday life. The finding that triclosan exacerbates high-fat diet-induced metabolic disorders by disrupting regulation of FGF21 expression provides a basis for a therapeutic approach in treating nonalcoholic fatty liver disease and steatohepatitis.
Oncogenic activation of PI3K-AKT-mTOR signaling suppresses ferroptosis via SREBP-mediated lipogenesis
Authors: Yi Junmei , Zhu Jiajun , Wu Jiao , Thompson Craig B. , Jiang Xuejun 
Published: November 2020

Significance
This work demonstrates that oncogenic activation of the PI3K-AKT-mTOR signaling pathway suppresses ferroptosis via the sterol regulatory element-binding protein-mediated lipogenesis, and that inhibition of this pathway potentiates the cancer therapeutic effect of ferroptosis induction. This finding unveils mechanisms for the regulation of ferroptosis and provides a potential cancer therapeutic approach for treating cancer patients bearing tumorigenic mutations in the PI3K-AKT-mTOR pathway.
Combination of the SAW and TOPSIS Method For Determining The Best Marketplace Recommendations
Authors: Frieyadie  , Sukmawati Adriana Hadi , Nurajijah  
Published: November 2020

Abstract
A marketplace is a place where sellers can sell merchandise online without the need to create a website. From the many available marketplaces, it appears that consumers’ desires in choosing a marketplace are different. Therefore a decision support system is needed to help resolve this problem. In this study, the author combines two methods, namely Simple Additive Weighting (SAW) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The SAW method is used to find normalized matrix values, and the TOPSIS method to find normalized weighted matrices up to ranking. This is because the two methods are simple, easy to understand, efficient, and can measure the relative performance of decision alternatives in a simple mathematical form. The final result of this research is to determine the best alternative of a marketplace with criteria: application appearance, features, interactivity, transaction security, and customer service.
Understanding Impact of M-banking on Individual Performance of the DeLone & McLean Method and TTF Perpective
Authors: Azizah Qudsiah Nur , Hidayat Taopik , Riana Dwiza , Dwiantoro Tino , Suhardoyo  , Fitriana Saghifa 
Published: November 2020

Abstract
Currently the public has a curiosity about e-commerce applications that are growing rapidly, such as Mobile banking (m-banking). The development of m-banking can be seen from user satisfaction, end-user interest and the success of everyone using m-banking. This study adds the Task Technology Fit (TTF) model to the DeLone & McLean model for the purpose of knowing the success of individuals using m-banking. We used 102 respondents to answer all the questions in the questionnaire. The results show that of the 14 hypotheses used there were 8 accepted hypotheses. This shows that in individual achievement, usage and user satisfaction are not important. Users consider the importance of individual performance to simplify the use of TTF. User satisfaction is influenced by system quality, information quality, and service quality.
Clustering Based Undersampling for Handling Class Imbalance in C4.5 Classification Algorithm
Authors: Nugraha Wahyu , Maulana Muhammad Sony , Sasongko Agung 
Published: November 2020

Abstract
Machine Learning is very difficult to make an effective learning model if the distribution of classes in the training data set that is used is not balanced. The problem of class imbalance is mostly found during classifications in the real world where one class is very small in number (minority class) while the other classes are very numerous (majority in class). Building a learning algorithm model without considering the problem of class imbalance causes the learning model to be flooded by majority class instances so that it ignores minority class predictions. Random undersampling and oversampling techniques have been widely used in various studies to overcome class imbalances. In this study using the undersampling strategy with clustering techniques while the classification model uses C4.5. Clustering is used to group data and the undersampling process is performed on each data group. The goal is that sample samples that are useful are not eliminated. Statistical test results from experiments using 10 imbalance datasets from KEEL-repository dan Kaggle dataset with various sample sizes indicate that clustering-based undersampling produces satisfactory performance. Improved performance can be seen from the sensitivity and AUC values that increased significantly.
Comparing Classification Algorithm With Genetic Algorithm In Public Transport Analysis
Authors: Aryanti Riska , Saryoko Andi , Junaidi Agus , Marlina Siti , Wahyudin  , Nurmalia Lia 
Published: November 2020

Abstract
Congestion major cities in Indonesia caused by the proliferation of the use of private vehicles. Some people express their opinions and its opinion regarding public transport users through social media sites and other websites. This opinion can be used as a sentiment analysis material to find out whether the public transport service is positive or negative. The results of the sentiment analysis can help in the assessment and evaluation of the use of public transportation, it is also expected to improve services and facilities from public transportation so that the public tends to have a positive opinion. Based on the results of the sentiment analysis, it is expected that the community will switch to using public transportation which will certainly reduce congestion. In this study also added preprocessing stages by using the GataFramework framework to complete processes that cannot be done on RapidMiner tools. The method used in this study is sentiment analysis with the method of applying genetic algorithms for feature selection with comparative classification algorithms. Performed by testing the composition of various data. From the results of testing for the case in this study, it was found that the Support Vector Machine classification algorithm based on Genetic Algorithms had a fairly good average accuracy of 76.11% and AUC value of 0.778% with the Fair Classification diagnosis level compared to the three methods such as Naive Bayes, Support Vector Machine and Naive Bayes based on Genetic Algorithms. So that in this study Support Vector Machine classification algorithm based on Genetic Algorithm can be recommended as an algorithm classification good enough to analyze land transportation public sentiment. Based on the analysis it is expected that the public sentiment will switch to using public transport which would reduce congestion.
Stock price prediction using artificial neural network integrated moving average
Authors: Suryani I , Buani D C P 
Published: November 2020

Abstract
Stock prices are always interesting to be a research topic because stock prices always change at any time. Stock price index is a benchmark for shareholders to sell, buy or maintain it. As in this study, the data used is the closing price of ANTM’s share price which is then processed to predict future stock prices. The proposed method in this study is an integrated moving average which is used to transform data in order to improve data quality. So that it can improve the accuracy of predictions on the neural network. Based on the experiment conducted using 10 combinations of parameters on the neural network using integrated moving average, has been able to produce the RMSE value. And validation based on t-test also showed a significant difference compared to the previous model. So from the result of experiment use an integrated moving average proved to be able to improve neural network performance.
Information Technology Governance in Al Kautsar Islamic Elementary School Using COBIT 5 Framework
Authors: Haryani  , Misriati T , Hidayat R , Puspitasari D , Muthia DA , Elyana I 
Published: November 2020

Abstract
This study aims to assess the level of maturity of the use of information technology and obtain a gap value which is then analyzed to achieve the expected maturity value. Management of information technology today is very important, not only focused on profit organizations or companies. With good management of information technology, information within an institution can be achieved optimally, so that institutional goals can be achieved in accordance with the established vision and mission. With reference to COBIT 5 Enabling Process on Enterprise Goals and reality in the field after initial research conducted at Al Kautsar Islamic Elementary School, the scope of the framework for developing Information Technology management models focused on operational and staff productivity in the Balanced Scorecard included in the internal section. The focus of this research was chosen because at Al Kautsar Islamic Elementary School the Information Technology process is still in the development stage. From enterprise goals, IT-related goals can be generated, namely 8 applications, information and adequate information technology solutions and 16 competent and highly motivated IT staff. After collecting and processing data, the results of calculations for each process contained in the customer perspective that determine the achievement value of 2 of the targets set, it is expected that the management can meet these targets. Based on the analysis of audit findings using the COBIT 5 framework, for the six IT processes related to previous goals, the operator staff and the system used at Al Kautsar Islamic Elementary School are still largely in the reactive stage. This means that the application of information technology is still based on current needs, without first doing a careful IT governance planning.
Comparison of Naïve Bayes Algorithm with Genetic Algorithm and Particle Swarm Optimization as Feature Selection for Sentiment Analysis Review of Digital Learning Application
Authors: Ernawati Siti , Wati Risa , Nuris Nuzuliarini , Marita Lita Sari , Yulia Eka Rini 
Published: November 2020

Abstract
The problem examined in this study is about the user’s trust in using digital learning applications that are downloaded on playstore. Many reviews are given by the public about the application that has been downloaded on playstore. This review is very influential on their trust in using the application. The purpose of this study is to classify data according to labels and find out the best choice between the classification method and the proposed selection feature as a consideration in determining the use of digital learning applications. This study compares the classification method, the Naïve Bayes algorithm and the genetic algorithm (GA) as feature selection with the Naïve Bayes algorithm classification method and the particle swarm optimization (PSO) as feature selection to categorize the reviews in the playstore. The experimental results show that the Naïve Bayes algorithm and PSO as feature selection is the best model between the two models proposed in this study. Reviews can be classified into positive and negative labels well. The accuracy is 98.00%. The results of the classification are expected to help in making decisions when going to use digital learning application.
Generalized Reduced Gradient Approach for Solving Periodic Heterogeneous Vehicle Routing Problem with Side Constraints
Authors: Mawengkang Herman , Sutarman  
Published: November 2020

Abstract
In order to transport customers’ request economically, such as, goods, meals etc it is frequently the delivery company depend on a model which is called vehicle routing problem (VRP). This model consists of a customer population with deterministic demands, and a central station which acts as the base of a set of vehicles. The purpose is to design the vehicle routes starting and terminating at the assigned central station, such that the customer demands is met, within a time windows This paper develops an optimization model for the management of periodic deliveries of a given commodity. Due to the geographic location of customers the company requires to have diverse type of vehicles with diverse capacity to serve the commodity for customers. This kind of problem is called Periodic Heterogeneous Vehicle Routing Problem (PHVRP). The purpose is to schedule periodically the deliveries according to feasible combinations of delivery days and to determine the scheduling of fleet and driver and routing policies of the vehicles. The aim is to minimize the total costs of all routes over the planning horizon. We propose a combined approach of reduced gradient and heuristic algorithm to solve the problem.
Efficiency Measurement of Operations Management of Clean Water Company using DEA
Authors: Hardiyan  , Fridayanthie Eka Wulansari , Septiani Noer Azni , Sayfulloh Asep , Kusumaningrum Aliffah , Wahyudin  
Published: November 2020

Abstract
Clean water is the most fundamental need for humans, it can be said to be the source of life. Water is an important factor in the future development process, if the management of water resources is wrong, it can cause a socio-economic development crisis for all countries. In 2017, water needs for residents of the city of Jakarta only reached 60% that served by clean water. The problem that arises is the lack of clean water supply, therefore research is needed to measure the level of efficiency, so that companies become a reference for overcoming unserved areas. Therefore DEA method is needed to measure the efficiency relative of a company. DMUs are based on annual data comparisons from 2014 to 2018 refer to homogeneous data. Efficiency measurements of clean water companies show a positive trends every year. In 2017 and 2018, the company showed extraordinary achievements with a relative level of efficiency reaching 100% or a value of 1, so that it can be a reference for the following years.
Analysis of the AMARI COVID-19 application with the Technology Acceptance Model Method
Authors: Nuryanto A , Setyawan O , Riana D , Hadianti S , Aji AMB , Pujiastuti E 
Published: November 2020

Abstract
The purpose of this research is to know the influence of the application of AMARI COVID-19 to how well the use of the application is received in the community to prevent the spread of the virus Covid-19. This type of research is explanatory research with a quantitative approach. To this end, their responses to an online questionnaire (n=113) were analyzed using IBM SPSS Statistics 21 as software. The sampling techniques used are simple random sampling. Descriptive analysis is used to analyze the research hypothesis. The test results of the five hypothesis in the study stated that the AMARI COVID-19 application has a significant effect on the prevention of the spread of Covid-19 viruses, meaning it is accepted by the wider community as one of the tools that can help prevent the spread of COVID-19 diseases.
Data mining for cryptocurrencies price prediction
Authors: Fatah Haerul , Anggraini Recha Abriana , Supriadi Deddy , Pertiwi Melisa Winda , Warnilah Ai Ilah , Ichsan Nurul 
Published: November 2020

Abstract
Electronic payments is something that is currently in high demand by investors today, but transactions are often constrained due to various problems, especially from third parties. For this reason, cryptocurrency emerged, which is one of the solutions for conducting electronic payment transactions. Some types of cryptocurrency that are most in demand by investors are bitcoin, ethereum, and ripple. The fluctuation value of cryptocurrency is very difficult to predict so that investors often experience losses when making transactions. This study aims to predict cryptocurrency prices such as bitcoin, ethereum and ripple using data mining algorithms. The data mining algorithm used in this prediction process is K-NN, Neural Network, SVM, Linear Regression, Random Forest and Decision Tree. Data mining modeling is done by dividing the dataset into each type of commodity and then analyzed using each algorithm. The results of this study indicate that the accuracy value obtained from some data mining algorithms is good enough to predict cryptocurrency prices
Improved Accuracy of Sentiment Analysis Movie Review Using Support Vector Machine Based Information Gain
Authors: Maulana Reza , Rahayuningsih Panny Agustia , Irmayani Windi , Saputra Dedi , Jayanti Wanty Eka 
Published: November 2020

Abstract
The quality of a movie can be known from the opinions or reviews of previous audiences. This classification of reviews is grouped into positive opinions and negative opinions. One of the data mining algorithms that are most frequently used in research is the Support Vector Machine because it works well as a method of classifying text but has a very sensitive deficiency in the selection of features. The Information Gain method as feature selection can solve problems faster and more stable convergence levels. After testing on two movie review datasets are Cornell and Stanford datasets. The results obtained on the Cornell dataset is the Support Vector Machine algorithm to produce an accuracy of 83.05%, while for the Support Vector Machine based on Information Gain, the accuracy value is 85.65%. Increased accuracy reached 2.6%. Then, the results obtained on the Stanford dataset is the Support Vector Machine algorithm yields a value of 86.46%, while for the Support Vector Machine based on Information Gain, the accuracy value is 86.62%. Increased accuracy reached 0.166%. Support Vector Machine based Information Gain on the problem of movie review sentiment analysis proved to provide more accurate value.