Fidelis Barncat dataset
134430700
Photovoltaic performance prediction in Northern Nigeria using generated typical meteorological year dataset
Relevant meteorological files are needed by simulation software to assess the energy performances of buildings or efficiency of renewable energy systems. This paper adopts the Sandia method to generate typical meteorological year (TMY) using a 35 year hourly measured meteorological dataset from four stations in the northern region of Nigeria. The cumulative distribution function (CDF) for each year was compared with that of the long term composite of all the years in the period for the seven major weather indices made up of relative humidity, wind speed, minimum temperature, global solar radiation, precipitation, mean temperature and maximum temperature. The 12 typical meteorological months (TMMs) selected from the different years were used for formulation of a TMY for the zone. In addition, performance assessment of a 72 cell polycrystalline solar PV module using the generated TMY and long term (LT) values was also conducted. Two statistical indicators, the mean percentage error and the root mean square error, were adopted to evaluate the performance of each TMY with the LT mean, and also that of the PV energy system. Findings show that the TMMs are evenly spread within the data periods across the sites while closest fit between the long term mean and TMY are obtained with the global solar radiation followed by the mean temperature in all the sites especially in Bida and Minna. From the energy system analysis carried out, it was found that TMY data are able to predict the performance of the PV system to within 5% of the LT data.
Olayinka Soledayo Ohunakin Muyiwa S Adaramola Olanrewaju M Oyewola Richard Olayiwola Fagbenle Damola S Adelekan Jatinder Pal Kaur Gill Fidelis I Abam
877341
Integrated Dataset of Screening Hits against Multiple Neglected Disease Pathogens
New chemical entities are desperately needed that overcome the limitations of existing drugs for neglected diseases. Screening a diverse library of 10,000 drug like compounds against 7 neglected disease pathogens resulted in an integrated dataset of 744 hits. We discuss the prioritization of these hits for each pathogen and the strong correlation observed between compounds active against more than two pathogens and mammalian cell toxicity. Our work suggests that the efficiency of early drug discovery for neglected diseases can be enhanced through a collaborative, multi pathogen approach.
PLoS neglected tropical diseases
Solomon Nwaka Dominique Besson Bernadette Ramirez Louis Maes An Matheeussen Quentin Bickle Nuha R Mansour Fouad Yousif Simon Townson Suzanne Gokool Fidelis Cho-Ngwa Moses Samje Shailja Misra-Bhattacharya P Kalpana Murthy Foluke Fakorede J -M Paris Clive Leonard Yeates Robert G Ridley Wesley C Van Voorhis Timothy G Geary
212703760
Automatic Classification of Computational Thinking Skills in Elementary School Math Questions
This research full paper presents the design of an automatic classifier for Computational Thinking skills in math questions, based on Machine Learning and Natural Language Processing techniques. We trained and evaluated our model using a dataset of real world math questions. We obtained encouraging results that indicate the proposed approach may ease the process of assessing the level of alignment between math questions and Computational Thinking skills, which may help improve the problem solving ability in students of elementary school. These results stimulate the use of automated environments for the elaboration, classification, and resolution of math questions in conformity with Computational Thinking skills.
2019 IEEE Frontiers in Education Conference (FIE)
Erick J F Costa Claudio Elizio Calazans Campelo Livia M R Sampaio Campos
172014029
Classificacao Automatica de Questoes Problema de Matematica para Aplicacoes do Pensamento Computacional na Educacao
Recent studies indicate a greater relation of problem questions of Mathematics with Computational Thinking. However, the identification of this relation presents difficulties for a large dataset application. In this work, we present an automatic classifier of Mathematical problem questions with the aim of enabling practices involving Computational Thinking. The results show that it is possible to classify mathematical questions automatically using natural language processing and machine learning. Resumo. Estudos recentes indicam uma maior relacao de questoes problema de Matematica com o Pensamento Computacional. No entanto, a identificacao dessa relacao apresenta dificuldades de aplicacao para um grande conjunto de dados. Neste trabalho, apresentamos um classificador automatico de questoes problema de Matematica com o objetivo de possibilitar praticas envolvendo o Pensamento Computacional. Os resultados mostram que e possivel classificar questoes de Matematica de forma automatica, utilizando processamento de linguagem natural e aprendizado de maquina.
Erick John Fidelis Costa Claudio E C Campelo Livia Sampaio
155581891
Global endemics area relationships of vascular plants
Abstract Endemics Area Relationships (EARs) are fundamental in theoretical and applied biogeography for understanding distribution patterns and promoting biodiversity conservation. However, calculating EARs for vascular plant species from existing data is problematic because of biased knowledge of endemic species distributions and differences between taxonomies. We aimed to overcome these challenges by developing a new standardized global dataset based on expert knowledge to produce a set of global EARs. We developed a nested circle design, with grain sizes of 104, 105, 106, 107, and 108 km2, respectively, and a global distribution of plots based on a stratified random scheme. The number of vascular plant species endemic to each circle was then estimated independently by five experts randomly chosen from a pool of 23, as both a minimum and a maximum value (lower and upper bounds of the estimation) taking into account the limitations of current knowledge and varied species concepts in existing taxonomies. This procedure resulted in a dataset of 3000 expert estimates. Based on the data, we produced three global EARs for endemic species richness using minimum, maximum and average estimates. As a validation, we used all three models to extrapolate to the entire world, producing estimates of 284,493 (minimum) 398,364 (maximum) and 312,243 (average) vascular plant species. These figures conform to the range of taxonomists' estimates. From the models, we calculated the average area needed to harbour a single endemic species as 12,875 km2 (range 9675 20,529) The global vascular plant EARs we calculated represent the first standardized, quantitative expectations of plant endemism at any given scale (sampling unit size) These EARs allow us to provide a clear answer to a long standing but elusive biogeographical question: how to assess whether any area on the surface of the Earth is rich or poor in endemics relative to the average.
Perspectives in Ecology and Conservation
Carsten Hobohm Monika Janisova Manuel J Steinbauer Sara Landi Sula E Vanderplank Carl Beierkuhnlein John-Arvid Grytnes Ole Reidar Vetaas Alessandra Fidelis Lea de Nascimento Vincent Ralph Clark Jose Maria Fernandez-Palacios Scott Franklin Riccardo Guarino Pavel V Krestov Vladimir G Onipchenko Christian Stolz Alessandro Chiarucci
19194198
OH 83: A new early modern human fossil cranium from the Ndutu beds of Olduvai Gorge, Tanzania.
OBJECTIVE Herein we introduce a newly recovered partial calvaria, OH 83, from the upper Ndutu Beds of Olduvai Gorge, Tanzania. We present the geological context of its discovery and a comparative analysis of its morphology, placing OH 83 within the context of our current understanding of the origins and evolution of Homo sapiens. MATERIALS AND METHODS We comparatively assessed the morphology of OH 83 using quantitative and qualitative data from penecontemporaneous fossils and the W.W. Howells modern human craniometric dataset. RESULTS OH 83 is geologically dated to ca. 60 32 ka. Its morphology is indicative of an early modern human, falling at the low end of the range of variation for post orbital cranial breadth, the high end of the range for bifrontal breadth, and near average in frontal length. DISCUSSION There have been numerous attempts to use cranial anatomy to define the species Homo sapiens and identify it in the fossil record. These efforts have not met wide agreement by the scientific community due, in part, to the mosaic patterns of cranial variation represented by the fossils. The variable, mosaic pattern of trait expression in the crania of Middle and Late Pleistocene fossils implies that morphological modernity did not occur at once. However, OH 83 demonstrates that by ca. 60 32 ka modern humans in Africa included individuals that are at the fairly small and gracile range of modern human cranial variation.
American journal of physical anthropology
Whitney B Reiner Fidelis Taliwawa Masao Sabrina B Sholts Agustino Venance Songita Ian G Stanistreet Harald Stollhofen R E Taylor Leslea J Hlusko
73594082
Bathymetric Survey Investigation for Lagos Lagoon Seabed Topographical Changes
Bathymetry is the study of under water depth of lake and ocean floors and accurate data for production of nautical chart is the main objective of bathymetric survey. This research work was conducted to investigate changes in the topography of the northern part of Lagos lagoon (Ikorodu route) and thereby updating the existing chart. Data acquisition was done using satellite imagery, tidal observation and reduction, depth sounding with echo sounder and GPS. Initial processing performed on observed bathymetric data includes spike removal, tidal correction on instantaneous depth and sorting with HYPACK 2008 software. Further processing was done using ArcGIS 10.0 Software. The processed depths were analyzed and presented in form of graphs and charts. A surface descriptor of sediment region for accretion and dredged region was developed using depth differential of two dataset and their sediment volumes were ascertained. From the analysis conducted, there is an indication that accreted sediment volume is greater than Dredged volume and perhaps could have being influenced by the adjoining tributaries of the lagoon water. For further studies, needs for investigating the source of accreted mass through integrated coastal management plan is recommended.
Njokanma Fidelis Olusegun Temitope Badejo
52078615
BioTIME: A database of biodiversity time series for the Anthropocene
Motivation The BioTIME database contains raw data on species identities and abundances in ecological assemblages through time. These data enable users to calculate temporal trends in biodiversity within and amongst assemblages using a broad range of metrics. BioTIME is being developed as a community led open source database of biodiversity time series. Our goal is to accelerate and facilitate quantitative analysis of temporal patterns of biodiversity in the Anthropocene. Main types of variables included The database contains 8,777,413 species abundance records, from assemblages consistently sampled for a minimum of 2 years, which need not necessarily be consecutive. In addition, the database contains metadata relating to sampling methodology and contextual information about each record. Spatial location and grain BioTIME is a global database of 547,161 unique sampling locations spanning the marine, freshwater and terrestrial realms. Grain size varies across datasets from 0.0000000158 km2 (158 cm2) to 100 km2 (1,000,000,000,000 cm2) Time period and grain BioTIME records span from 1874 to 2016. The minimal temporal grain across all datasets in BioTIME is a year. Major taxa and level of measurement BioTIME includes data from 44,440 species across the plant and animal kingdoms, ranging from plants, plankton and terrestrial invertebrates to small and large vertebrates. Software format .csv and .SQL.
Global ecology and biogeography a journal of macroecology
Maria A Dornelas Laura H Antao Faye Moyes Amanda E Bates Anne E Magurran Dusan Adam Asem A Akhmetzhanova Ward Appeltans Jose Manuel Arcos Haley Arnold Narayanan Ayyappan Gal Badihi Andrew H Baird Miguel Barbosa Tiago Egydio Barreto Claus Bassler Alecia Bellgrove Jonathan Belmaker Lisandro Benedetti-Cecchi Brian J Bett Anne D Bjorkman Magdalena Blazewicz Shane A Blowes Christopher P Bloch Timothy C Bonebrake Susan Boyd Matt Bradford Andrew J Brooks James H Brown Helge Bruelheide Phaedra Budy Fernando Carvalho Edward Castaneda-Moya Chaolun Allen Chen John F Chamblee Tory J Chase Laura Siegwart Collier Sharon K Collinge Richard S Condit Elisabeth J Cooper Johannes H C Cornelissen Unai Cotano Shannan Kyle Crow Gabriella Damasceno Claire H Davies Robert A Davis Frank P Day Steven Degraer Tim S Doherty Timothy E Dunn Giselda Durigan J Emmett Duffy Dor Edelist Graham J Edgar Robin Elahi Sarah C Elmendorf Anders Enemar S K Morgan Ernest Ruben Escribano Marc Estiarte Brian S Evans Tung-Yung Fan Fabiano Turini Farah Luiz F Loureiro Fernandes Fabio Z Farneda Alessandra Fidelis Robert N L Fitt Anna Maria Fosaa Geraldo Antonio Daher Correa Franco Grace E Frank William R Fraser Hernando Garcia Roberto Cazzolla Gatti Or Givan Elizabeth Gorgone-Barbosa William A Gould Corinna Gries Gary D Grossman Julio R Gutierrez Stephen S Hale Mark E Harmon John Harte Gary N Haskins Don Henshaw Luise Hermanutz Pamela Hidalgo Pedro Higuchi Andrew S Hoey Gert Van Hoey Annika Hofgaard Kristen T Holeck Robert D Hollister Richard T Holmes Mia O Hoogenboom Chih-hao Hsieh Stephen P Hubbell Falk Huettmann Christine L Huffard Allen H Hurlbert Natalia Macedo Ivanauskas David Janik Ute Jandt Anna M Jazdzewska Tore Johannessen Jill Johnstone Julia A Jones Faith A M Jones Jung Hak Kang Tasrif Kartawijaya Erin Keeley Douglas A Kelt Rebecca Kinnear Kari Klanderud Halvor Knutsen Christopher C Koenig Alessandra Rocha Kortz Kamil Kral Linda A Kuhnz Chao-Yang Kuo David J Kushner Claire Laguionie-Marchais Lesley T Lancaster Cheol Min Lee Jonathan S Lefcheck Esther Levesque David C Lightfoot Francisco Lloret John D Lloyd Adria Lopez-Baucells Maite Louzao Joshua S Madin Borgthor Magnusson Shahar Malamud I Matthews Kent P McFarland Brian J McGill Diane M McKnight William O Mclarney Jason Meador Peter L Meserve Daniel J Metcalfe Christoph F J Meyer Anders Michelsen Nataliya A Milchakova Tom Moens Even Moland Jon Moore Carolina Mathias Moreira Jorg Muller Grace E P Murphy Isla H Myers-Smith Randall W Myster Andrew D Naumov Francis C Neat James A Nelson Michael Paul Nelson Stephen Newton Natalia Norden Jeffrey C Oliver Esben Moland Olsen Vladimir G Onipchenko Krzysztof Pabis Robert Pabst Alain Paquette Sinta Pardede David M Paterson Raphael Pelissier Josep Penuelas Alejandro Perez-Matus Oscar Pizarro Francesco Pomati Eric Post Herbert H T Prins John C Priscu Pieter Provoost Kathleen L Prudic Erkki Pulliainen Bramasamdura Rangana Ramesh Olivia Mendivil Ramos Andrew Rassweiler Jose Eduardo Rebelo Daniel C Reed Peter B Reich Suzanne M Remillard Anthony J Richardson J Paul Richardson Itai van Rijn Ricardo Rocha Victor H Rivera-Monroy Christian Rixen Kevin P Robinson Ricardo Ribeiro Rodrigues Denise de Cerqueira Rossa-Feres Lars G Rudstam Henry A Ruhl Catalina S Ruz Erica M Sampaio Nancy B Rybicki Andrew L Rypel Sofia Sal Beatriz Salgado F A M dos Santos Ana Paula Savassi-Coutinho Sara E Scanga Jochen Schmidt Robert Lee Schooley Fakhrizal Setiawan Kwang-Tsao Shao Gaius R Shaver Sally Sherman Thomas W Sherry Jacek Sicinski Caya Sievers Ana Carolina da Silva Fernando Rodrigues da Silva Fabio Lang da Silveira Jasper A Slingsby Tracey Irene Smart Sara Snell Nadejda A Soudzilovskaia Gabriel B G Souza Flaviana Maluf Souza Vinicius Castro Souza Christopher D Stallings Rowan Stanforth Emily H Stanley Jose Mauro Sterza Maarten Stevens Rick D Stuart-Smith Yzel Rondon Suarez Sarah R Supp Jorge Yoshio Tamashiro S A Tarigan Gary P Thiede Simon Thorn Anne Tolvanen Maria Teresa Zugliani Toniato Orjan Totland Robert R Twilley Gediminas Vaitkus Nelson Valdivia Martha Isabel Vallejo Thomas J Valone Carl Van Colen Jan Vanaverbeke Fabio Venturoli Hans M Verheye Marcelo Vianna Rui P Vieira Tomas Vrska Con Quang Vu Lien Van Vu Robert B Waide Conor Waldock David J Watts Sara Lynn Webb Tomasz Emanuel Wesolowski Ethan P White Claire E Widdicombe Dustin J Wilgers Richard Williams Stefan B Williams M H Williamson Michael R Willig Trevor J Willis Sonja Wipf Kerry D Woods Eric J Woehler Kyle J A Zawada Michael L Zettler Thomas Hickler
134165752
Evaluation and application of alternative rainfall data sources for forcing hydrologic models in the Mara Basin
Accurate and spatially distributed rainfall data are crucial for a realistic simulation of the hydrological processes in a watershed. However, limited availability of observed hydro meteorological data often challenges the rainfall runoff modelling efforts. The main goal of this study is to evaluate the Climate Forecast System Reanalysis (CFSR) and Water and Global Change (WATCH) rainfall by comparing them with gauge observations for different rainfall regimes in the Mara Basin (Kenya/Tanzania) Additionally, the skill of these rainfall datasets to simulate the observed streamflow is assessed using the Soil and Water Assessment Tool (SWAT) The daily CFSR and WATCH rainfall show a poor performance (up to 52% bias and less than 0.3 correlation) when compared with gauge rainfall at grid and basin scale, regardless of the rainfall regime. However, the correlations for both CFSR and WATCH substantially improve at monthly scale. The 95% prediction uncertainty (95PPU) of the simulated daily streamflow, as forced by CFSR and WATCH rainfall, bracketed more than 60% of the observed streamflows. We however note high uncertainty for the high flow regime. Yet, the monthly and annual aggregated CFSR and WATCH rainfall can be a useful surrogate for gauge rainfall data for hydrologic application in the study area.
Tadesse Alemayehu Fidelis Kilonzo Ann Van Griensven Willy Bauwens
55865913
Evolutionary Model for Virus Propagation on Networks
The significant research activity into the logarithmic analysis of complex networks will yield engines that will minimize virus propagation over networks. This task of virus propagation is a recurring subject and design of complex models will yield solutions used in a number of events not limited to and include its propagation, network immunization, resource management, capacity service distribution, dataflow, adoption of viral marketing amongst others. Machine learning, stochastic models are successfully employed to predict virus propagation and its effects on networks. This study employs SI models for independent cascade and the dynamic models with Enron dataset (of e mail addresses) and presents comparative result using varied machine models. It samples 25,000 e mails of Enron dataset with Entropy and Information Gain computed to address issues of blocking, targeting and extent of virus spread on graphs. Study addressed the problem of the expected spread immunization and the expected epidemic spread minimization; but not the epidemic threshold (for space constraint)
A Ojugo Fidelis Obukowho Aghware R Yoro M O Yerokun A Eboka Christiana Nneamaka Anujeonye Fidelia Ngozi Efozia