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Spatial dynamics of dengue fever spreading for the coexistence of two serotypes with an application to the city of Sao Paulo, Brazil

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COMPUTER METHODS AND programS IN BIOMEDICINE 2022年 219卷 106758-106758页

作者： Pereira, F. M. M. Schimit, P. H. T. Informatics and Knowledge Management Graduate Program Universidade Nove de Julho Rua Vergueiro 235/249 São Paulo 01525-000 SP Brazil

Background and objective: Dengue fever is a disease in which individuals' spatial distribution and Aedes aegypti mosquitoes breeding places are important factors for the disease dynamics. Typically urban, dengue is a problem for least developed countries due to the ineffectiveness in controlling the vector and disorderly urbanization processes. The result is a composition of urban sanitation problems and areas with high demographic densities and intense flows of people. This paper explores the spatial distribution of vector breeding places to evaluate introducing a new dengue serotype to a population at equilibrium for a pre-existing serotype. The paper's objective is to analyze the spatial dynamics of dengue using variations of the basic reproduction ***: A model based on probabilistic cellular automata is proposed to permitting the necessary flexibility to consider some spatial distributions of vector breeding places. Then, ordinary differential equations are used as a mean-field approach of the model, and the basic reproduction number ( R 0 ) is derived considering the next-generation matrix method. A spatial approach for R 0 is also proposed, and the model is tested in a neighbourhood from the city of Sao Paulo, Brazil, to examine the potential risks of vector breeding cells ***: The results indicated that the more spread out these places, the higher are the values of R 0 . When the model is applied to a neighbourhood in Sao Paulo, residential areas may boost the infections and must be under public vigilance to combat vector breeding ***: Considering the mean-field approximation of the cellular automata model by ordinary differential equations, the basic reproduction number derived returned an estimative of the disease dynamics in the population. However, the spatial basic reproduction number was more assertive in showing areas with a higher disease incidence. Moreover, the model could be easily adapted to be used in rea

关键词： Basic reproduction number Dengue Probabilistic cellular automata Spatial basic reproduction number Spatial dynamics

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Experimentation of Integrating Roadmapping and Sci-Fi Prototyping Methods

Experimentation of Integrating Roadmapping and Sci-Fi Protot...

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Portland International Conference on management of Engineering and Technology (PICMET)

作者： Miwa Nishinaka Kunio Shirahada Yusuke Kishita Dohjin Miyamoto Hirotaka Osawa Sachiko Kiyokawa Hideaki Takeda The Graduate School of Management Kagawa University Takamatsu Kagawa Japan School of Knowledge Science Transformative Knowledge Management Research Area Japan Advanced Institute of Science and Technology (JAIST) Nomi Ishikawa Japan Department of Precision Engineering The University of Tokyo Tokyo Japan Communication in Science and Technology Education and Research Program Hokkaido University Sapporo Japan Faculty of Science and Technology Keio University Yokohama Kanagawa Japan Graduate School of Education The University of Tokyo Tokyo Japan National Institute of Informatics Tokyo Japan

ISBN: (数字)9781890843458

ISBN: (纸本)9798350369137

In 2023, we proposed a hypothetical knowledge management framework that incorporates science fiction prototyping (SFP) into the traditional roadmapping (RM) method to make RM more innovative and create a future beyond what we can currently imagine. Our main objective is to create a new organizational knowledge co-creation method for generating innovative ideas by incorporating humanistic factors utilizing SFP into RM. In the current article, we present the exploratory study we conducted to examine the hypothetical framework. Experimental workshops were held in November 2023 to compare the integrated method, in which SFP is applied to RM, with the original RM method. We followed a crossover design in the workshops and analyzed the results qualitatively and quantitatively following the perspectives proposed in the hypothetical framework. The qualitative analysis revealed successful integration regarding diverse and broader perspectives. The quantitative analysis utilizing Wilcoxon signed rank tests showed that the scores of the integrated method were significantly higher than those of RM for variables evaluating the effectiveness of the integration, while not for variables related to participants' feelings and satisfaction with the workshops, and sustainability. These findings indicate that the integration was successful in terms of its effectiveness as a method, while participants' subjective feelings toward the workshops were not different in both methods. As for sustainability, which requires a long-term perspective, no apparent differences were observed in these short-term workshops.

关键词： Statistical analysis Conferences knowledge management Sustainable development Creativity

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Enhancing Automated Microorganism Classification: A Study on the Influence of Compression, Non-Uniform Scaling, and Data Augmentation Using Deep Learning

SSRN

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SSRN 2024年

作者： Boukouvalas, Dimitria Theophanis Bissaco, Márcia A.S. Dellê, Humberto Deana, Alessandro M. Belan, Peterson A. de Araújo, Sidnei Alves Universidade Nove de Julho Informatics and Knowledge Management Graduate Program Rua Vergueiro 235/249 SP São Paulo01504-001 Brazil Universidade de Mogi das Cruzes Biomedical Engineering Graduate Program Av. Dr. Cândido Xavier de Almeida e Souza 200 Mogi das Cruzes SP São Paulo08780-911 Brazil Universidade Nove de Julho Medicine Graduate Program Rua Vergueiro 235/249 SP São Paulo01504-001 Brazil

With the advent of "Laboratory 4.0", a concept that represents the recent technological breakthroughs in laboratory automation, there is an increasing demand for solutions that can automate, standardize, and enhance the accuracy and speed of microorganism classification. One promising approach is the use of convolutional neural networks (CNNs), a powerful deep learning technique that has shown significant results in biomedical image analysis. However, its application in microorganism classification is still nascent mainly due to the unavailability of extensive datasets, which is a basic requirement to ensure generalization ability in learning CNNs. This study aims to fill a significant gap in the literature by exploring the influence of factors such as image compression, non-uniform scaling, and data augmentation on the performance of CNNs, specifically when using small image datasets of microorganisms. We focus on two CNN architectures, AlexNet and DenseNet-121, which have shown promising results in previous microorganism classification studies. Our experiments reveal that certain factors like the type of data compression and non-uniform scaling applied to the images can negatively impact the performance of CNNs. On the other hand, data augmentation techniques such as mirroring, rotation, and noise addition can enhance their performance. By utilizing training datasets and optimized parameters based on our findings, we were able to achieve classification accuracies of 98.61% and 99.82% with AlexNet and DenseNet-121, respectively, surpassing the results reported in current state-of-the-art studies. This research has significant implications for Laboratory 4.0, potentially reducing dataset preparation and CNN training time, lowering computational costs, and improving the consistency and accuracy of microorganism classification. It paves the way for more efficient and accurate microorganism classification methods in automated laboratories. © 2024, The Authors. All righ

关键词： Classification (of information)

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An Efficient Algorithm to Update Incremental Attributes in Morphological Trees

SSRN

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SSRN 2022年

作者： Gobber, Charles Ferreira Hashimoto, Ronaldo Fumio Alves, Wonder Alexandre Luz Informatics and Knowledge Management Graduate Program Universidade Nove de Julho São Paulo Brazil Department of Computer Science Institute of Mathematics and Statistics Universidade de São Paulo São Paulo Brazil

Attribute Filters are powerful image simplification operators that have very good contour-preservation properties. They can be efficiently computed using morphological trees. The most common strategy to compute attribute filters using morphological trees is based on three steps: (i) tree construction: the tree is built along with its attributes;(ii) filtering: we simplify the tree by removing some of its nodes based on some attribute and a filtering rule defined on a threshold value;and (iii) image reconstruction: this is the final phase where the tree is converted back to an image leading to an attribute filter. However, after filtering a few attributes in tree may change and they must be updated, since some attribute filters are computed by filtering the simplified tree again, but with a different threshold. In this paper it is presented an efficient algorithm to update incremental attributes in morphological trees with low memory consumption and fast computation. © 2022, The Authors. All rights reserved.

关键词： Image reconstruction

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Estimating Pandemic Effects in Urban Mass Transportation Systems: An Approach Based on Visibility Graphs and Network Similarity

SSRN

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SSRN 2022年

作者： Perez, Yuri Pereira, Fabio Henrique Informatics and Knowledge Management Graduate Program PPGI-UNINOVE Rua Vergueiro 235/249 - Liberdade SP São Paulo01525-000 Brazil Universidade Nove de Julho Industrial Engineering Graduate Program PPGI-UNINOVE Rua Vergueiro 235/249 - Liberdade SP São Paulo01525-000 Brazil

Urban mass transportation systems are critical elements of modern society, exerting influence in the shape, economical development and sustainability of a city. Covid-19 forced population to avoid social contact and the impacts of this scenario on public transportation in different regions of the world are still being discussed. Large metropolitan areas present heterogeneous population groups and exert different mobility roles in urban systems, so, it is important to understand how different regions of the same city may present different mobility dynamics. Here, we present a model to study how and how much different regions of a large urban system were affected by pandemics, converting time series into visibility graphs and comparing them using network similarity methods and geospatial analysis. The methods used here were able to detect an abrupt shock in demand behavior when pandemics started, quantify how different regions absorbed the shock, group lines with similar spatial location and explore if the effects on lines may be related to demand level or bus line length. © 2022, The Authors. All rights reserved.

关键词： Complex networks

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An approach based on genetic algorithm and variable neighborhood search for solving the capacitated vehicle routing problem 7

An approach based on genetic algorithm and variable neighbor...

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7th International Conference on Information Systems, Logistics and Supply Chain, ILS 2018

作者： De Araujo Lima, Stanley Jefferson De Araújo, Sidnei Alves Nove de Julho University Informatics and Knowledge Management Graduate Program São Paulo Brazil

This work presents a hybrid approach for solving the Capacitated Vehicle Routing Problem (CVRP) that employs Genetic Algorithm (GA), Variable Neighborhood Search (VNS) and Gillett & Miller (GM) heuristics. The GM heuristic is employed to provide feasible solutions which are included in the initial population of GA, while the VNS algorithm is used to refine the solutions provided by GA, after a certain number of generations without improvements. In addition, we present an alternative binary encoding scheme for GA which generates the populations based on a seed called permutation matrix. Conducted experiments demonstrated that the proposed approach provided good solutions for CVRP with low computational cost and allowed to evidence that the use of GM and VNS helped the GA to converge on promising points on the search space in a reduced number of generations. © ILS 2018 - Information Systems, Logistics and Supply Chain, Proceedings. All rights reserved.

关键词： Genetic algorithms

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Co-Creation in the Game Industry:A Case Study in a Brazilian Developer

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Social Networking 2018年第1期7卷 63-78页

作者： Marcos Vinicius Cardoso Huoston Rodrigues Batista Ernani Marques da Silva Marcos Antonio Gaspar Ivanir Costa Fábio Luís Falchi de Magalhaes Universidade Anhembi Morumbi Sao PauloBrazil Graduate Program in Informatics and Knowledge Management Universidade Nove de Julho(UNINOVE)Sao PauloBrazil

The objective of this work is to understand how the process of co-creation occurs within the creative industry of video games. For this, concepts of the creative industry, co-creation, information and communication technologies (ICTs) and innovation were studied. Since this is an exploratory and qualitative research, a single case study was chosen as a method. The analysis was done in a Brazilian company that develops games for mobile devices. The results show that the consumer (gamer) participates in value creation in the company’s products, mainly in the initial production stages. In the later phases, after the launch and other steps of the life cycle, although there is a relationship with the users, the joint creation of value is little explored by the organization. Also, it was verified that the co-creation process is supported by different information and communication tools, which provide agility to the process.

关键词： Co-Creation Empirical Studies Gaming Communities Information and Communication Technologies Innovation

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Dynamics of multiplayer games on complex networks using territorial interactions

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Physical Review E 2019年第3期99卷 032306-032306页

作者： Pedro H. T. Schimit Karan Pattni Mark Broom Informatics and Knowledge Management Graduate Program Universidade Nove de Julho Rua Vergueiro 235/249 CEP 01504-000 São Paulo São Paulo Brazil Department of Mathematical Sciences University of Liverpool Mathematical Sciences Building Liverpool L69 7ZL United Kingdom Department of Mathematics City University of London Northampton Square London EC1V 0HB United Kingdom

The modeling of evolution in structured populations has been significantly advanced by evolutionary graph theory, which incorporates pairwise relationships between individuals on a network. More recently, a new framework has been developed to allow for multiplayer interactions of variable size in more flexible and potentially changing population structures. While the theory within this framework has been developed and simple structures considered, there has been no systematic consideration of a large range of different population structures, which is the subject of this paper. We consider a large range of underlying graphical structures for the territorial raider model, the most commonly used model in the new structure, and consider a variety of important properties of our structures with the aim of finding factors that determine the fixation probability of mutants. We find that the graphical temperature and the average group size, as previously defined, are strong predictors of fixation probability, while all other properties considered are poor predictors, although the clustering coefficient is a useful secondary predictor when combined with either temperature or group size. The relationship between temperature or average group size and fixation probability is sometimes, however, nonmonotonic, with a directional reverse occurring around the temperature associated with what we term “completely mixed” populations in the case of the hawk-dove game, but not the public goods game.

关键词： Ecology & evolution Evolutionary dynamics Network evolution Network structure

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Developing and validating a machine-learning algorithm to predict opioid overdose in Medicaid beneficiaries in two US states: a prognostic modelling study

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The Lancet Digital Health 2022年第6期4卷 e455-e465页

作者： Lo-Ciganic, Wei-Hsuan Donohue, Julie M Yang, Qingnan Huang, James L Chang, Ching-Yuan Weiss, Jeremy C Guo, Jingchuan Zhang, Hao H Cochran, Gerald Gordon, Adam J Malone, Daniel C Kwoh, Chian K Wilson, Debbie L Kuza, Courtney C Gellad, Walid F Department of Pharmaceutical Outcomes and Policy College of Pharmacy University of Florida Gainesville FL United States Center for Drug Evaluation and Safety College of Pharmacy University of Florida Gainesville FL United States Department of Health Policy and Management Graduate School of Public Health University of Pittsburgh Pittsburgh PA United States Center for Pharmaceutical Policy and Prescribing Health Policy Institute University of Pittsburgh Pittsburgh PA United States Division of General Internal Medicine School of Medicine University of Pittsburgh Pittsburgh PA United States Heinz College of Information Systems and Public Policy Carnegie Mellon University Pittsburgh PA United States Department of Mathematics University of Arizona Tucson AZ United States Division of Rheumatology Department of Medicine University of Arizona Arthritis Center University of Arizona Tucson AZ United States Program for Addiction Research Clinical Care Knowledge and Advocacy Division of Epidemiology Department of Internal Medicine University of Utah Salt Lake City UT United States Department of Pharmacotherapy College of Pharmacy University of Utah Salt Lake City UT United States Informatics Decision-Enhancement and Analytic Sciences Center Veterans Affairs Salt Lake City Health Care System Salt Lake City UT United States Center for Health Equity Research Promotion Veterans Affairs Pittsburgh Healthcare System Pittsburgh PA United States

Background: Little is known about whether machine-learning algorithms developed to predict opioid overdose using earlier years and from a single state will perform as well when applied to other populations. We aimed to develop a machine-learning algorithm to predict 3-month risk of opioid overdose using Pennsylvania Medicaid data and externally validated it in two data sources (ie, later years of Pennsylvania Medicaid data and data from a different state). Methods: This prognostic modelling study developed and validated a machine-learning algorithm to predict overdose in Medicaid beneficiaries with one or more opioid prescription in Pennsylvania and Arizona, USA. To predict risk of hospital or emergency department visits for overdose in the subsequent 3 months, we measured 284 potential predictors from pharmaceutical and health-care encounter claims data in 3-month periods, starting 3 months before the first opioid prescription and continuing until loss to follow-up or study end. We developed and internally validated a gradient-boosting machine algorithm to predict overdose using 2013–16 Pennsylvania Medicaid data (n=639 693). We externally validated the model using (1) 2017–18 Pennsylvania Medicaid data (n=318 585) and (2) 2015–17 Arizona Medicaid data (n=391 959). We reported several prediction performance metrics (eg, C-statistic, positive predictive value). Beneficiaries were stratified into risk-score subgroups to support clinical use. Findings: A total of 8641 (1·35%) 2013–16 Pennsylvania Medicaid beneficiaries, 2705 (0·85%) 2017–18 Pennsylvania Medicaid beneficiaries, and 2410 (0·61%) 2015–17 Arizona beneficiaries had one or more overdose during the study period. C-statistics for the algorithm predicting 3-month overdoses developed from the 2013–16 Pennsylvania training dataset and validated on the 2013–16 Pennsylvania internal validation dataset, 2017–18 Pennsylvania external validation dataset, and 2015–17 Arizona external validation dataset were 0·841 (95%

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Study of initial guess influence on the quality of solutions on binary genetic algorithm in job shop scheduling problem 14

Study of initial guess influence on the quality of solutions...

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14th International Symposium on Operational Research, SOR 2017

作者： Ferreira da Cruz, Valmir Pereira, Fabio Henrique Universidade Nove de Julho Industrial Engineering Graduate Program Francisco Matarazzo Av. 612 São Paulo Brazil Universidade Nove de Julho Informatics and knowledge management Graduate Program Francisco Matarazzo Av. 612 São Paulo Brazil

ISBN: (纸本)9789616165501

The purpose of this study is to evaluate the influence of the initial guess to generate the Genetic Algorithm population of solutions of scheduling problems in relation to the quality and feasibility of the solutions. The scheduling problem is defined as to find the sequence of operations on the machines that optimize some performance measure as, for example, the use of resources and the total processing time (makespan). It is common to treat such problems with the use of metaheuristics as genetic algorithm mainly due to its computational complexity. This work carried out experiments with a set of literature instances, varying the sequencing rule used in the generation of initial solutions. Usually, rules from the literature have been tested and identified a hybrid rule that generates a smaller number of non-feasible solutions and the number of instances that have reached the optimal makespan. © 2017 Slovenian Society Informatika. All Rights Reserved.

关键词： Genetic algorithms

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