Article
Computer Science, Artificial Intelligence
Nathan Buskulic, Carola Doerr
Summary: This study proves that drift maximization is not optimal, as the optimal mutation strengths are usually larger than the drift-maximizing ones, implying that the optimal RLS is more risk-affine.
EVOLUTIONARY COMPUTATION
(2021)
Article
Biochemical Research Methods
Alexander Browning, Matthew Simpson
Summary: An enduring challenge in computational biology is to balance data quality and quantity with model complexity. Tools such as identifiability analysis and information criterion have been developed to harmonise this juxtaposition, yet cannot always resolve the mismatch between available data and the granularity required in mathematical models to answer important biological questions. Our study focuses on the interrelationship between the non-identifiable parameters in a complex model and the identifiable parameters in a simple surrogate model, aiming to provide additional biological insights from complex, non-identifiable models.
PLOS COMPUTATIONAL BIOLOGY
(2023)
Article
Environmental Sciences
Alba Benito-Kaesbach, Jose Manuel Amigo, Urtzi Izagirre, Nerea Garcia-Velasco, Laura Arevalo, Andreas Seifert, Kepa Castro
Summary: The presence of microplastics in the food chain is a global public concern, and its analysis presents challenges. In this study, Raman imaging was used to investigate the presence of 1 μm polystyrene microplastics in cryosections of Mytilus galloprovincialis due to its wide distribution, occurrence in the food web, and high presence in the environment. Surface imaging alone was not sufficient to confirm translocation to epithelial cells, highlighting the need for appropriate three-dimensional analytical methods.
SCIENCE OF THE TOTAL ENVIRONMENT
(2023)
Article
Automation & Control Systems
Petros G. Voulgaris, Nicola Elia
Summary: This article examines cooperative multiagent systems that aim to minimize collective social costs. The research reveals a class of social costs that can be optimized through decentralized and selfish solutions, eliminating the need for interagent communication. The results demonstrate that selfish behavior can be socially optimal in nontrivial cases.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2022)
Article
Management
Jack Brimberg, Andrea Maier, Anita Schoebel
Summary: This article discusses the concept of the distributed p-median problem, where customer demands are allocated to different facilities according to a given rule. It reveals that different properties of the distribution rule can lead to interesting results and efficient generalizations of standard p-median models.
JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY
(2021)
Article
Multidisciplinary Sciences
Elba Raimundez, Michael Fedders, Jan Hasenauer
Summary: Bayesian inference is a valuable approach for learning from data in life and natural sciences, providing information about parameter and prediction uncertainties. This study presents a method that reduces the computational complexity of generating representative samples in dynamical models. By marginalizing the posterior distribution, the proposed method is shown to be applicable to a wide range of problems, with demonstrated benefits in systems biology applications.
Article
Geosciences, Multidisciplinary
Guillaume Cinkus, Naomi Mazzilli, Herve Jourde, Andreas Wunsch, Tanja Liesch, Natasa Ravbar, Zhao Chen, Nico Goldscheider
Summary: Performance criteria are crucial in the calibration and evaluation of hydrological models. This study examines the inherent counterbalancing errors in the Kling-Gupta efficiency (KGE) and its variants. Nine performance criteria, including KGE, NSE, and modified index of agreement (d(1)), were analyzed using synthetic time series and a real case study. The results show that the KGE and some of its variants can yield higher scores by simultaneously overestimating and underestimating discharge, favoring bias and variability parameters.
HYDROLOGY AND EARTH SYSTEM SCIENCES
(2023)
Article
Management
Will Ma
Summary: This study introduces a Bayesian mechanism-design problem to analyze the maximum possible revenue earned from selling fixed-price items when the buyer's preference is private but comes from a known distribution. The study shows that assortments are generally suboptimal, but under commonly studied Bayesian priors, assortments are indeed optimal. This implies that the extensive literature on assortment optimization has greater significance than previously appreciated in terms of computing the economic limit of seller's revenue. The study provides further results, including a more general condition for optimal assortments, the inability of capturing Nested Logit choice models with the Markov Chain, and suboptimality gaps when the condition does not hold. Finally, the study demonstrates that the mechanism-design problem provides the most accurate Linear Programming relaxation for assortment optimization under the ranking distribution model.
MANAGEMENT SCIENCE
(2023)
Article
Computer Science, Artificial Intelligence
Shuya Nagayasu, Sumio Watanbe
Summary: In this study, by theoretical derivation, we investigate the asymptotic behaviors of the generalization loss and the free energy in Bayesian inference when there are multiple optimal probability distributions, revealing differences from conventional asymptotic analysis.
Article
Biochemical Research Methods
Rachel Mester, Alfonso Landeros, Chris Rackauckas, Kenneth Lange
Summary: Differential sensitivity analysis is crucial in parameter fitting, uncertainty understanding, and result forecasting. This paper introduces various differential sensitivity methods and evaluates their value in typical biological models. Through case studies, it demonstrates the advantages of differential sensitivity analysis over traditional methods and compares the speed, accuracy, and ease of use of different methods. The forward mode automatic differentiation is found to have the fastest computational time, while the complex perturbation method is the simplest and most generalizable.
PLOS COMPUTATIONAL BIOLOGY
(2022)
Article
Geochemistry & Geophysics
Florent Szitkar, Laurent Gernigon, Anna Lim, Marco Broenner
Summary: We used regional magnetic and local multibeam bathymetric data to investigate a prominent high in the Norwegian-Greenland Sea. The magnetization distribution and magnetic gradients suggest a basaltic environment, ruling out the possibility of an Oceanic Core Complex. Instead, we propose that this high is a basaltic hill and the depth asymmetry is likely caused by seafloor subsidence triggered by sediment accumulation from the Bear Island Fan.
Article
Mathematics, Interdisciplinary Applications
Yuan Tian, Chunxue Li, Jing Liu
Summary: Considering the complexity of the ecosystem, biological parameters in nature may be inaccurate or uncertain. This study investigates the imprecise parameters related to different species and establishes competitive models with interval-valued imprecise parameters and harvesting. The study analyzes the influence of these parameters on system behavior, discusses bio-economic equilibrium and optimal harvesting strategies, and verifies the results through numerical simulation. The findings provide guidance for the exploitation and utilization of competitive fishery resources in the presence of imprecise parameters.
CHAOS SOLITONS & FRACTALS
(2023)
Article
Engineering, Electrical & Electronic
Manel Velasco, Carlos Alfaro, Antonio Camacho, Angel Borrell, Pau Marti
Summary: This paper presents a distributed complex power sharing approach that accurately solves the problem of active and reactive power sharing in inverter-based islanded microgrids. The novel control approach uses a communication network to exchange data among all inverters, achieving accurate power sharing with an exponential convergence rate.
IEEE TRANSACTIONS ON SMART GRID
(2022)
Article
Environmental Sciences
Jenny Kupzig, Robert Reinecke, Francesca Pianosi, Martina Floerke, Thorsten Wagener
Summary: Global hydrological models (GHMs) provide important information for simulating water cycles and supporting decision-making. However, inaccuracies in GHM simulations can hinder valuable decision support. In this study, we introduce a transparent and efficient method to understand parameter control in GHMs and improve parameter estimation using global sensitivity analysis (GSA). Our findings show that traditionally neglected model parameters have a significant influence on GHM simulations, and basin attributes explain the spatial variability of parameter importance better than climate zones. Overall, our results demonstrate the effectiveness of GSA in guiding parameter estimation and improving the accuracy of GHM simulations.
ENVIRONMENTAL RESEARCH LETTERS
(2023)
Article
Food Science & Technology
Jesus Miguel Zamudio Lara, Laurent Dewasme, Hector Hernandez Escoto, Alain Vande Wouwer
Summary: Two dynamic models of beer fermentation are proposed in this study, and their parameters are estimated using experimental data. The structural identifiability of the measurement configuration and kinetic model structure is analyzed, and the predictive capability of the model is investigated. The model can be used for monitoring and controlling the beer fermentation process.
Article
Cell Biology
Emmanuel Lujan, Liliana N. Guerra, Alejandro Soba, Nicolas Visacovsky, Daniel Gandia, Juan C. Calvo, Cecilia Suarez
INTEGRATIVE BIOLOGY
(2016)
Article
Biochemistry & Molecular Biology
Ezequiel Goldberg, Cecilia Suarez, Mauricio Alfonso, Juan Marchese, Alejandro Soba, Guillermo Marshall
BIOELECTROCHEMISTRY
(2018)
Article
Cell Biology
Emmanuel Lujan, Daniela Soto, Maria S. Rosito, Alejandro Soba, Liliana N. Guerra, Juan C. Calvo, Guillermo Marshall, Cecilia Suarez
INTEGRATIVE BIOLOGY
(2018)
Article
Computer Science, Interdisciplinary Applications
Alejandro Soba, Cecilia Suarez, Maraelys Morales Gonzalez, Luis Enrique Bergues Cabrales, Ana Elisa Bergues Pupo, Juan Bory Reyes, Jose Pablo Martinez Tasse
MATHEMATICS AND COMPUTERS IN SIMULATION
(2018)
Article
Biochemical Research Methods
Diego Fernandez Slezak, Mariano Sigman, Guillermo A. Cecchi
PLOS COMPUTATIONAL BIOLOGY
(2018)
Article
Biochemistry & Molecular Biology
N. Olaiz, E. Signori, F. Maglietti, A. Soba, C. Suarez, P. Turjanski, S. Michinski, G. Marshall
BIOELECTROCHEMISTRY
(2014)
Article
Oncology
L. N. Guerra, C. Suarez, D. Soto, A. Schiappacasse, D. Sapochnik, P. Sacca, G. Piwien-Pilipuk, B. Peral, J. C. Calvo
CLINICAL & TRANSLATIONAL ONCOLOGY
(2015)
Article
Multidisciplinary Sciences
Cecilia Suarez, Alejandro Soba, Felipe Maglietti, Nahuel Olaiz, Guillermo Marshall
Article
Multidisciplinary Sciences
Gustavo Landfried, Diego Fernandez Slezak, Esteban Mocskos
Article
Computer Science, Interdisciplinary Applications
E. Lujan, M. S. Rosito, A. Soba, C. Suarez
COMPUTER PHYSICS COMMUNICATIONS
(2019)
Article
Biochemistry & Molecular Biology
Ezequiel Goldberg, Alejandro Soba, Daniel Gandia, Maria Laura Fernandez, Cecilia Suarez
Summary: Electrochemotherapy (ECT) is a well-established technique used to increase cellular uptake of cytotoxic agents in certain cancer treatments. Understanding the mechanisms involved in this complex process is important for improving treatment strategies. The study results support the hypothesis that pore aperture is favored at cell poles by electric field and mechanical stress forces.
BIOELECTROCHEMISTRY
(2021)
Article
Clinical Neurology
Hernan Chaves, Francisco Dorr, Martin Elias Costa, Maria Mercedes Serra, Diego Fernandez Slezak, Mauricio F. Farez, Gustavo Sevlever, Paulina Yanez, Claudia Cejas
Summary: The study compared the robustness of a CNN-based software Entelai Pic with traditional software for brain volume estimation in healthy controls, showing better correlation between Entelai Pic and FreeSurfer in whole-brain volume estimation. Additionally, Entelai Pic provided similarly robust brain volume segmentations on the same and different scanners compared to FreeSurfer.
JOURNAL OF NEURORADIOLOGY
(2021)
Article
Neurosciences
Veronica Nin, Hernan Delgado, Andrea Paula Goldin, Diego Fernandez-Slezak, Laouen Belloli, Alejandra Carboni
Summary: Executive functions play a crucial role in reasoning, planning, and self-regulatory skills, and their importance for educational success is supported by evidence. Computerized activities have been shown to improve executive functions, although most studies have been conducted in controlled laboratory settings. The present study examined the effectiveness of a short training program aimed at stimulating executive functions in kindergarten classrooms of different socioeconomic backgrounds. The findings showed improvements in working memory span and performance in a fluid intelligence task for all children in the training group, but gains in inhibitory control and cognitive flexibility were observed only in children from low socioeconomic schools. No evidence of improved performance in a planning task or classroom behaviors associated with executive functions was found.
JOURNAL OF COGNITIVE ENHANCEMENT
(2023)
Article
Psychology, Mathematical
Martin A. Miguel, Pablo Riera, Diego Fernandez Slezak
Summary: Measuring human capabilities related to time synchronization and response time often requires precise experimental setups. This paper introduces a cost-effective experimental setup with easy-to-use code for analyzing response times efficiently.
BEHAVIOR RESEARCH METHODS
(2022)
Article
Electrochemistry
M. Marino, N. Olaiz, E. Signori, F. Maglietti, C. Suarez, S. Michinski, G. Marshall
ELECTROCHIMICA ACTA
(2017)