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Research Square 2021年

作者： Velázquez, Daniel René Tasé Helleno, André Luís Fals, Hipólito Carvajal dos Santos, Raphael Galdino Production Engineering Post-Graduation Program Methodist University of Piracicaba São Paulo Brazil Engineering School Mackenzie Presbyterian University São Paulo Brazil College of Mechanical and Industrial Engineering Oriente University Santiago de Cuba Cuba Federal Technological University of Paraná Paraná Brazil Insper - Institute for Education and Research São Paulo Brazil

Laser Metal Deposition (LMD) process is an additive manufacturing technique that has attracted the interest of the automotive and aerospace industries due to its ability to manufacture parts with complex geometries and different types of metallic materials. However, the structure of the deposited layers and the geometrical characteristics of the manufactured parts are influenced by the interaction among the deposition process parameters. In this paper, fuzzy inference (FIS) technique was used to develop two models for predicting the geometrical characteristics and, for optimizing the LMD process parameters using AISI 316 stainless steel powder and substrate. An experimental design, based on factorial analysis, was used to correlate the influence of selected deposition process parameters, laser power (Lp), powder flow (Pf) and focal length (Fl) with the process geometrical characteristics bead height (Bh), bead width (Bw), depth of penetration (Dp), dilution (d) and wetting angle (wa). The factors Lp and Fl were used with three operating levels each: Lp = 225 W, 250 W, 275 W, and Fl = 4.8 mm, 5.0 mm, 5.2 mm. The factor Pf was used with two levels, 9.40 g/s and 13.35 g/s. Analysis of variance allowed identifying that the Pf affect the Bh, Bh/Bw ratio, d and wa. The increase in Laser power (Lp) resulted in an increase of the geometric characteristics Bw and Dp. The first FIS, for predicting the bead’s geometrical characteristics, presented high adequacy (relative error up to 8.43 %) for assessing the experimental conditions. The second FIS indicated the best possible interaction, given the studied operating conditions and the variables evaluated. The maximum Output Defuzzified Index (ODI = 0.845) was obtained with the deposition process parameters Lp = 250 W, Fl = 5 mm, and Pf = 9.40 g/s. © 2021, CC BY.

关键词： Forecasting

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Micro-CT-based analysis of fibre-reinforced composites: Applications 18

Micro-CT-based analysis of fibre-reinforced composites: Appl...

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18th European Conference on Composite Materials, ECCM 2018

作者： Straumit, I. Baran, I. Gorbatikh, L. Farkas, L. Hahn, C. Ilin, K. Ivens, J. Lessard, L. Liu, Y. Nguyen, N. Matveeva, A. Mehdikhani, M. Shishkina, O. Soete, J. Takahashi, J. Vandepitte, D. Vasiukov, D. Wan, Y. Winterstein, E. Wevers, M. Lomov, S.V. Department of Materials Engineering KU Leuven Belgium Chair of Production Technology Faculty of Engineering Technology University of Twente Netherlands Siemens Industry Software NV Leuven Belgium Germany Department of Mechanical Engineering McGill University Montreal Canada Polymers and Composites Technology and Mechanical Engineering Department IMT Lille Douai France SIM M3 program Technologiepark 935 ZwijnaardeB-9052 Belgium Department of Systems Innovation University of Tokyo Japan Department of Mechanical Engineering KU Leuven Belgium

ISBN: (纸本)9781510896932

The paper presents an overview of cases in which the analysis of the internal structure and mechanical properties of fibre reinforced composites is performed based on the micro-computed X-ray tomography (micro-CT) reconstruction of the composite reinforcement geometry. In all the cases, the analysis relies on structure tensor-based algorithms for quantification of the micro-CT image, implemented in VoxTex software. © CCM 2020 - 18th European Conference on Composite Materials. All rights reserved.

关键词： Computerized tomography

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Multi-omic Analyses Shed Light on The Genetic Control of High-altitude Adaptation in Sheep

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Genomics, Proteomics & Bioinformatics 2024年第2期22卷 43-56页

作者： Chao Li Bingchun Chen Suo Langda Peng Pu Xiaojia Zhu Shiwei Zhou Peter Kalds Ke Zhang Meenu Bhati Alexander Leonard Shuhong Huang Ran Li Awang Cuoji Xiran Wang Haolin Zhu Yujiang Wu Renqin Cuomu Ba Gui Ming Li Yutao Wang Yan Li Wenwen Fang Ting Jia Tianchun Pu Xiangyu Pan Yudong Cai Chong He Liming Wang Yu Jiang Jian-Lin Han Yulin Chen Ping Zhou Hubert Pausch Xiaolong Wang International Joint Agriculture Research Center for Animal Bio-Breeding Ministry of Agriculture and Rural Affairs/Key Laboratory of Animal GeneticsBreeding and Reproduction of Shaanxi ProvinceCollege of Animal Science and TechnologyNorthwest A&F UniversityYangling 712100China Animal Genomics ETH ZürichZürich 8092Switzerland Institute of Animal Science Tibet Academy of Agricultural and Animal Husbandry SciencesLhasa 850009China School of Biological and Pharmaceutical Engineering Lanzhou Jiaotong UniversityLanzhou 730070China College of Life Sciences Shaanxi Normal UniversityXi’an 710119China Zoology and Evolutionary Biology Department of BiologyUniversity of Konstanz78457 KonstanzGermany College of Life and Geographic Sciences Kashi UniversityKashi 844000China Beijing Key Laboratory of Captive Wildlife Technologies Beijing ZooBeijing 100044China Department of Medical Research Guangdong Provincial People’s HospitalGuangdong Academy of Medical SciencesGuangzhou 510080China Key Laboratory of Agricultural Internet of Things Ministry of Agriculture and Rural Affairs/Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent ServiceCollege of Information EngineeringNorthwest A&F UniversityYangling 712100China Institute of Animal Husbandry and Veterinary Medicine Xinjiang Academy of Agricultural and Reclamation SciencesShihezi 832000China State Key Laboratory of Sheep Genetic Improvement and Healthy Production Xinjiang Academy of Agricultural and Reclamation SciencesShihezi 832000China CAAS-ILRI Joint Laboratory on Livestock and Forage Genetic Resources Institute of Animal ScienceChinese Academy of Agricultural SciencesBeijing 100193China Livestock Genetics Program International Livestock Research InstituteNairobi 00100Kenya

Sheep were domesticated in the Fertile Crescent and then spread globally,where they have been encountering various environmental *** Tibetan sheep has adapted to high altitudes on the Qinghai-Tibet Plateau over the past 3000 *** explore genomic variants associated with high-altitude adaptation in Tibetan sheep,we analyzed Illumina short-reads of 994 whole genomes representing∼60 sheep breeds/populations at varied altitudes,PacBio High fidelity(HiFi)reads of 13 breeds,and 96 transcriptomes from 12 sheep *** testing between the inhabited altitudes and 34,298,967 variants was conducted to investigate the genetic architecture of altitude *** accurate HiFi reads were used to complement the current ovine reference assembly at the most significantly associatedβ-globin locus and to validate the presence of two haplotypes A and B among 13 sheep *** haplotype A carried two homologous gene clusters:(1)HBE1,HBE2,HBB-like,and HBBC,and(2)HBE1-like,HBE2-like,HBB-like,and HBB;while the haplotype B lacked the first *** high-altitude sheep showed highly frequent or nearly fixed haplotype A,while the low-altitude sheep dominated by haplotype *** further demonstrated that sheep with haplotype A had an increased hemoglobin–O_(2) affinity compared with those carrying haplotype *** highly associated genomic region contained the EGLN1 gene which showed varied expression between high-altitude and low-altitude *** results provide evidence that the rapid adaptive evolution of advantageous alleles play an important role in facilitating the environmental adaptation of Tibetan sheep.

关键词： Environmental adaptation High altitude Hypoxia Selection signature Sheep

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Corporate Venture Capital and Sustainability

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Journal of Open Innovation: Technology, Market, and Complexity 2022年第3期8卷

作者： Döll, Luciano Mathias Ulloa, Micaela Ines Castillo Zammar, Alexandre Prado, Guilherme Francisco do Piekarski, Cassiano Moro Post-Graduation Program of Production Engineering Federal University Technology (UTFPR) Ponta Grossa 84016-210 Brazil Department of Informatics Universidade Estadual de Ponta Grossa (UEPG) Ponta Grossa 84030-900 Brazil Coordination of Technology in Mechanical Manufacturing Instituto Federal de Santa Catarina (IFPR) Florianópolis 88020-300 Brazil

Corporate Venture Capital (CVC) has been receiving increasingly more attention all over the world as a special way for accessing new ideas and innovative opportunities through minor-share investing in established companies. The purpose of CVC investments may either be purely financial or to pursue strategic goals. Organisations often seek to take actions that impact positively on sustainability by assembling related knowledge and technologies. These resources may come from invested startups through the use of a CVC programme. This research aims to measure and analyse the Corporate Venture Capital programmes of companies listed in the ISE B3 Corporate Sustainability Index. To this end, a three-step methodology was conducted. First, a systematic review of the literature took place, followed by a review of companies based on secondary sources such as their websites. Finally, a survey was developed and was opted to survey companies through their Investor Relationship (IR) public channels. Results show that 27 of the companies listed in the ISE B3 Index have CVC programmes that contemplate organizational initiatives besides investment. In this sense, one can claim that at least 70% of the ISE B3 Index companies are somehow involved in CVC operations. The results contribute to the literature on corporate venture capital and sustainability by showing that companies spend from 10% to 15% of their capital in sustainable businesses in order to remain competitive. © 2022 by the authors.

关键词： corporate venture capital open innovation startups sustainability

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A Hybrid Model With Error Correction for Wind Speed Forecasting

A Hybrid Model With Error Correction for Wind Speed Forecast...

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Latin America Congress on Computational Intelligence (LA-CCI)

作者： Tathiana M. Barchi Lucas F. P. Costa Erickson Puchta Marcella S. R. Martins Mauren Louise Sguario Coelho de Andrade Paulo S. G. de Mattos Neto Hugo Valadares Siqueira Graduate Program in Computer Sciences (PPGCC) Federal University of Technology - Paraná (UTFPR) Ponta Grossa Brazil Graduate Program in Production Engineering (PPGEP) Federal University of Technology - Paraná (UTFPR) Ponta Grossa Brazil Federal University of Technology - Paraná (UTFPR) Ponta Grossa Brazil Centro de Informática Universidade Federal de Pernambuco Recife Brazil

ISBN: (纸本)9781728188652

In recent times wind energy generation has stood out due its integration with traditional electricity grids. Many investigations addressed wind speed forecasting since it presents high volatile and intermittent behavior. Due to this, such a source shows accuracy challenges in relation to its prediction. In this work, a hybrid model based on error correction is proposed, combining the linear Autoregressive and Moving average (ARMA) model and the Multilayer Perceptron (MLP). The approaches was applied in two databases referring to the Brazilian northeast -a prominent region in wind energy. The results reveal that the proposed hybrid model showed good results in comparison to linear and neural-based methods.

关键词： Analytical models Wind energy Databases Wind speed Computational modeling Artificial neural networks Predictive models

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Complexity measurement in two supply chains with different competitive priorities

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IFAC-PapersOnLine 2019年第13期52卷 1699-1704页

作者： Sellitto, Miguel A. Lolli, Francesco Rimini, Bianca Balugani, Elia Production and Systems Engineering Graduate Program Universidade do Vale do Rio dos Sinos São LeopoldoRS 93022750 Brazil Department of Sciences and Methods for Engineering University of Modena and Reggio Emilia Via Amendola 2 Padiglione Morselli Reggio Emilia42122 Italy

Complexity measurement based on the Shannon information entropy is widely used to evaluate variety and uncertainty in supply chains. However, how to use a complexity measurement to support control actions is still an open issue. This article presents a method to calculate the relative complexity, i.e., the relationship between the current and the maximum possible complexity in a Supply Chain. The method relies on unexpected information requirements to mitigate uncertainty. The article studies two real-world Supply Chains of the footwear industry, one competing by cost and quality, the other by flexibility, dependability, and innovation. The second is twice as complex as the first, showing that competitive priorities influence the complexity of the system and that lower complexity does not ensure competitivity. © 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Uncertainty analysis Adaptive systems Reliability Shoe manufacture Supply chains Complex adaptive systems Complexity Dependability Flexibility Footwear industry Uncertainty Variety

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Solving integer multi-objective optimization problems using TOPSIS, Differential Evolution and Tabu Search

arXiv

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arXiv 2022年

作者： Schneider, Erick R.F.A. Krohling, Renato A. PPGI UFES- Federal University of Espírito Santo Av. Fernando Ferrari 514 ES Espírito Santo VitóriaCEP 29075-910 Brazil Department of Production Engineering Graduate Program in Computer Science PPGI UFES Federal University of Espírito Santo Av. Fernando Ferrari 514 ES Espírito Santo VitóriaCEP 29075-910 Brazil

This paper presents a method to solve non-linear integer multiobjective optimization problems. First the problem is formulated using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Next, the Differential Evolution (DE) algorithm in its three versions (standard DE, DE best and DEGL) are used as optimizer. Since the solutions found by the DE algorithms are continuous, the Tabu Search (TS) algorithm is employed to find integer solutions during the optimization process. Experimental results show the effectiveness of the proposed method. © 2022, CC BY.

关键词： Multiobjective optimization

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Correction to: Measuring the Adoption of Circular Economy in Manufacturing Companies: the Proposal of the Overall Circularity Effectiveness (OCE) Index

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Circular Economy and Sustainability 2022年第3期3卷 1723-1723页

作者： Baumer-Cardoso, Marina I. Ashton, Weslynne S. Campos, Lucila M. S. Graduate Program in Production Engineering Universidade Federal de Santa Catarina Florianópolis Brazil Illinois Institute of Technology Stuart School of Business Chicago USA

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Rational Design of Safer Inorganic Nanoparticles via Mechanistic Modeling-Informed Machine Learning

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ACS Nano 2025年第23期19卷 21538-21555页

作者： Cave, Joseph Christiono, Anne Schiavone, Carmine Pownall, Henry J. Cristini, Vittorio Staquicini, Daniela I. Pasqualini, Renata Arap, Wadih Brinker, C. Jeffrey Campen, Matthew Wang, Zhihui Van Nguyen, Hien Noureddine, Achraf Dogra, Prashant Mathematics in Medicine Program Department of Medicine Houston Methodist Research Institute Houston 77030 TX United States Physiology Biophysics and Systems Biology Program Graduate School of Medical Sciences Weill Cornell Medicine New York 10065 NY United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge 02139 MA United States Department of Chemical Materials and Industrial Production Engineering University of Naples Federico II Naples 80138 Italy Department of Medicine Houston Methodist Houston 77030 TX United States Department of Medicine Weill Cornell Medicine New York 10065 NY United States Neal Cancer Center Houston Methodist Research Institute Houston 77030 TX United States Department of Imaging Physics University of Texas M.D. Anderson Cancer Center Houston 77030 TX United States Rutgers Cancer Institute Newark 08901 NJ United States Division of Cancer Biology Department of Radiation Oncology Rutgers New Jersey Medical School Newark 08901 NJ United States Division of Hematology/Oncology Department of Medicine Rutgers New Jersey Medical School Newark 08901 NJ United States Department of Chemical and Biological Engineering University of New Mexico Albuquerque 87106 NM United States College of Pharmacy University of New Mexico Albuquerque 87106 NM United States Department of Physiology and Biophysics Weill Cornell Medical College New York 10065 NY United States Department of Electrical and Computer Engineering University of Houston Houston 77204 TX United States

The safety of inorganic nanoparticles (NPs) remains a critical challenge for their clinical translation. To address this, we developed a machine learning (ML) framework that predicts NP toxicity both in vitro and in vivo, leveraging physicochemical properties and experimental conditions. A curated in vitro cytotoxicity dataset was used to train and validate binary classification models, with top-performing models undergoing explainability analysis to identify key determinants of toxicity and establish structure-toxicity relationships. External testing with diverse inorganic NPs validated the predictive accuracy of the framework for in vitro settings. To enable organ-specific toxicity predictions in vivo, we integrated a physiologically based pharmacokinetic (PBPK) model into the ML pipeline to quantify NP exposure across organs. Retraining the ML models with PBPK-derived exposure metrics yielded robust predictions of organ-specific nanotoxicity, further validating the framework. This PBPK-informed ML approach can thus serve as a potential alternative approach to streamline NP safety assessment, enabling the rational design of safer NPs and expediting their clinical translation. © 2025 The Authors. Published by American Chemical Society.

关键词： artificial intelligence cytotoxicity machine learning mathematical modeling nanoparticle nanotoxicity PBPK

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The Bioremediation of Industrial Effluents byPhanerochaete chrysosporium: The COD, TOC Removal Efficiency and Biochemical Assessment forDreissena polymorpha

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ChemistrySelect 2024年第14期9卷

作者： Assoc. Prof. Dr. Osman Serdar Prof. Dr. Numan Yıldırım Assoc. Prof. Dr. Gokhan Onder Erguven Zozan Ketenalp Prof. Dr. Nuran Cikcikoglu Yildirim Dr. Barbaros Durmus Basic Sciences of Aquatic Products Munzur University Turkey Department of Plant and Animal Production Munzur University Turkey Department of Urbanization and Environmental Issues Munzur University Turkey Department of Environmental Engineering Munzur University Turkey Department of Veterinary Medicine Laboratory and Veterinarian Health Program Munzur University Turkey Department of Environmental Engineering Firat University Turkey

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