Article
Engineering, Electrical & Electronic
Yidi Chen, Hao Liu, Jiapeng Guo, Yujie Wang, Fei Liu, Shanshan Ding, Renwen Chen
Summary: This study proposes a data collection strategy based on UAV cluster collaborative networking to address the shortcomings of wireless sensor networks in data collection. The strategy reduces overall network energy consumption and UAV flight cost while ensuring full coverage of WSN nodes, as confirmed by simulation results.
IEEE SENSORS JOURNAL
(2022)
Article
Thermodynamics
WanJun Yin, Xuan Qin
Summary: This paper proposes a method to solve the optimal scheduling problem of large-scale electric vehicles connected to the grid. By considering multiple factors and using a high-confidence wind power scenario, the collaborative optimization of coal-fired power generation, wind power generation, and electric vehicles is achieved.
Article
Automation & Control Systems
Tongjia Zheng, Qing Han, Hai Lin
Summary: This article focuses on estimating the mean-field density of large-scale systems in a distributed manner, which is important for swarm control and feedback control design. The authors propose an optimal density filter for the centralized estimation problem and a decentralized density filter for each agent to estimate the mean-field density based on local information exchange. The convergence and closeness to the centralized filter are analyzed and validated through theoretical proofs and simulation results.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2022)
Article
Multidisciplinary Sciences
Paul Stapor, Leonard Schmiester, Christoph Wierling, Simon Merkt, Dilan Pathirana, Bodo M. H. Lange, Daniel Weindl, Jan Hasenauer
Summary: This study applies mini-batch optimization methods to ODE models and benchmarks them on a large-scale cancer signaling model. The results show improved optimization performance compared to established methods and significantly reduced computation.
NATURE COMMUNICATIONS
(2022)
Article
Biochemical Research Methods
Carlos Sequeiros, Irene Otero-Muras, Carlos Vazquez, Julio R. Banga
Summary: Mechanistic dynamic models are important for understanding biomolecular networks and biological systems. Stochastic dynamic models should be used when dealing with low copy numbers and biochemical stochasticity. This article presents a novel strategy for parameter estimation in stochastic dynamic models, employing global optimization and stochastic simulation techniques.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2023)
Article
Computer Science, Interdisciplinary Applications
Ugur G. Abdulla, Roby Poteau
Summary: A numerical method for identifying parameters in large-scale systems of nonlinear ODEs in systems biology is introduced, combining optimization, sensitivity analysis, and regularization. The method demonstrates superlinear convergence in testing on canonical benchmark models, making it suitable for partial and noisy measurements. The developed software package qlopt shows advantages over popular methods/software like lsqnonlin, finincon, and nl2sol.
JOURNAL OF COMPUTATIONAL PHYSICS
(2021)
Article
Computer Science, Artificial Intelligence
Ning Ding, Yujia Qin, Guang Yang, Fuchao Wei, Zonghan Yang, Yusheng Su, Shengding Hu, Yulin Chen, Chi-Min Chan, Weize Chen, Jing Yi, Weilin Zhao, Xiaozhi Wang, Zhiyuan Liu, Hai-Tao Zheng, Jianfei Chen, Yang Liu, Jie Tang, Juanzi Li, Maosong Sun
Summary: With the rise of pre-trained language models (PLMs) and the pre-training-fine-tuning approach, it has become clear that larger models generally achieve better performance. However, scaling up PLMs leads to high costs and impracticality in terms of fine-tuning and storing all parameters. To address this, parameter-efficient adaptation of PLMs, known as delta-tuning, focuses on optimizing a subset of parameters while keeping the rest fixed, reducing computation and storage costs. This article discusses and analyzes the different approaches of delta-tuning and explores their correlations and differences, providing a unified categorization criterion. Theoretical principles underlying the effectiveness of delta-tuning are also discussed, along with an empirical study on numerous natural language processing tasks.
NATURE MACHINE INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Tianle Zhang, Zhen Liu, Zhiqiang Pu, Jianqiang Yi
Summary: In this paper, a new learning method for large-scale multiagent systems is proposed, which starts from learning with a few agents and progressively increases the number of agents. An evaluation mechanism based on self-supervised learning is designed to automatically generate appropriate curricula. Moreover, a new network structure is designed to handle the dynamic size of the network input and model relational knowledge between agents and their surrounding environment.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Wu Deng, Shifan Shang, Xing Cai, Huimin Zhao, Yongquan Zhou, Huayue Chen, Wuquan Deng
Summary: The proposed HMCFQDE combines quantum evolutionary algorithm (QEA) and cooperative coevolution evolutionary algorithm (CCEA) to improve the solution efficiency and search speed. A new hybrid mutation strategy is designed to enhance convergence accuracy and stability in solving high-dimensional complex functions.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Management
Srikanth Jagabathula, Paat Rusmevichientong, Ashwin Venkataraman, Xinyi Zhao
Summary: We propose an efficient estimation method for large-scale tree logit models, utilizing a novel change-of-variables transformation to express the negative log-likelihood as a strictly convex function. Our algorithm computes a sequence of parameter estimates using simple closed-form updates, relying only on first-order information. Numerical results demonstrate that our approach outperforms state-of-the-art optimization methods, especially for large-scale tree logit models with thousands of nodes.
OPERATIONS RESEARCH
(2023)
Article
Energy & Fuels
Zhuo Wang, Daniel T. Gladwin, Matthew J. Smith, Stefan Haass
Summary: This study demonstrates how cell-level state estimation techniques can be used to achieve accurate SOC estimation on large-scale BESSs, and how parameters of DSPKF can be optimized using a genetic algorithm. The results show that using DSPKF for SOC estimation provides more accurate results compared to commercial BESS battery management systems, and when combined with TLS method, capacity estimation error can be reduced to less than 1%.
Article
Automation & Control Systems
Yao Shi, Zhiming Zhang, Pei Sun, Lei Xie, Qiming Chen, Hongye Su, Xiaoqiang Chen
Summary: This paper presents a distributed two-layer structure strategy for large-scale systems, including an upper layer known as the Steady State Target Optimizer and a lower layer with a new cooperative distributed dynamic matrix control algorithm. By considering constraints and applying the Pareto optimal principle, stable operation of production devices is ensured with reduced computational burden.
CONTROL ENGINEERING PRACTICE
(2021)
Article
Computer Science, Information Systems
Junzhuo Gao, Lei Wang
Summary: This paper proposes two communication-efficient distributed estimators for partially linear additive models with high-dimensional co-variates. B-spline basis functions are used to approximate the non-parametric functions, and a profiled communication-efficient surrogate loss function with Lasso penalty is constructed based on one local machine solving the final optimization problem. Additionally, a profiled gradient-enhanced loss estimator is derived to reduce the effect of local machines and improve algorithm stability. Theoretical convergence rates for both parametric and nonparametric components are established, and the finite-sample performance is studied through simulations and an application to appliances energy prediction data set.
INFORMATION SCIENCES
(2023)
Article
Engineering, Industrial
Haibo Jin, Xianhe Song, Hao Xia
Summary: This study develops a maintenance strategy based on the approximate dynamic programming (ADP) method for large-scale maintainable systems suffering from maintenance time uncertainties. The strategy includes an optimal schedule algorithm and utilizes the Markov decision process combined with ADP to achieve the optimal maintenance strategy for a system with a large number of states over a finite time horizon.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Automation & Control Systems
Mounira Hamdi, Lhassane Idomhgar, Samira Kamoun, Mondher Chaoui, Abdenaceur Kachouri
Summary: This paper proposes a recursive distributed parameter estimation algorithm based on the minimization of the prediction estimation error method for large-scale systems. The algorithm considers a class of large-scale systems composed of several interconnected sub-systems, each modeled by a linear discrete-time state space mathematical model with unknown parameters. Convergence analysis is achieved using the Lyapunov approach. The theoretical analysis and simulation results prove the effectiveness of the proposed algorithm.
IET CONTROL THEORY AND APPLICATIONS
(2023)
Article
Automation & Control Systems
Gemma Massonis, Julio R. Banga, Alejandro F. Villaverde
Summary: Mechanistic dynamic models of biological systems often suffer from over-parameterization, resulting in nonidentifiability and nonobservability. AutoRepar is a methodology that automatically corrects these structural deficiencies, producing reparameterized models with improved identifiability and observability. This approach increases the applicability of mechanistic models, providing reliable information about their parameters and dynamics.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Chemistry, Applied
Gerardo Gonzalez-Tejedor, Alberto Garre, Jose A. Egea, A. Aznar, Francisco Artes-Hernandez, Pablo S. Fernandez
Summary: The inactivation kinetics of Listeria monocytogenes during High Hydrostatic Pressure treatments were investigated, and the effects on quality attributes were monitored. The study found that increasing pressure intensity led to faster microbial inactivation, while mostly unaffected the quality attributes except for the reduction in vitamin C content.
FOOD SCIENCE AND TECHNOLOGY INTERNATIONAL
(2023)
Article
Biochemical Research Methods
Xabier Rey Barreiro, Alejandro F. Villaverde
Summary: In this study, we conducted a comprehensive investigation on the available computational resources for analyzing structural identifiability. We evaluated the performance of 13 different software tools developed in 7 programming languages. Our results provide insights into the strengths and weaknesses of these tools, and offer guidance for selecting the most appropriate tool for specific problems. We also identify opportunities for future developments in this field.
Article
Biochemistry & Molecular Biology
Diego Hernandez-Prieto, Pablo S. S. Fernandez, Vicente Agullo, Cristina Garcia-Viguera, Jose A. Egea
Summary: The present study examines the impact of a beverage containing citrus and maqui with various sweeteners on male and female consumers. The beverages were developed and tested as a source of polyphenols in an earlier study. Plasma samples were collected before and after two months of daily consumption, and metabolomics techniques were used to measure bioactive-compound levels. Advanced versions of ANOVA and clustering analysis were employed to determine the effects of sex and sweetener factors on these compounds. Machine learning techniques were also applied to improve the results. The findings demonstrate sex-specific regulation of certain compounds, such as caffeic acid and 3,4-dihydroxyphenylacetic acid for men, and trans ferulic acid (TFA) or naringenin glucuronide for women. Sweeteners, such as stevia for women or sucrose for men, were also observed to have an impact on the regulation of these compounds.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2023)
Article
Agriculture, Multidisciplinary
Gabriela L. Salazar-Orbea, Rocio Garcia-Villalba, Maria J. Bernal, Alberto Hernandez-Jimenez, Jose A. Egea, Francisco A. Tomas-Barberan, Luis M. Sanchez-Siles
Summary: This research examined the impact of different storage conditions on the bioactive phenolic compounds, color, and sensory attributes of strawberry and apple purees produced with various industrial technologies. The study found that storage conditions had a stronger influence on phenolic compound levels in strawberry puree, particularly anthocyanins, while the initial processing techniques of apple puree had a greater impact than storage conditions. Proanthocyanidins were the most stable phenolic group during storage, while anthocyanins were the most affected. The stability of polyphenols varied between fruits during storage, and both processing and storage could have detrimental or beneficial effects.
JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY
(2023)
Article
Environmental Sciences
Eduardo Fernandez, Hajar Mojahid, Erica Fadon, Javier Rodrigo, David A. Ruiz, Jose Egea, Mehdi Ben Mimoun, Ossama Kodad, Adnane El Yaacoubi, Mohamed Ghrab, Jose Egea, Haifa Benmoussa, Nadia Borgini, Olfa Elloumi, Eike Luedeling
Summary: To assess agroclimatic conditions for cultivating temperate trees, we studied winter chill in the Mediterranean region, and collected expert knowledge about climate change impacts and risks. Results showed significant chill losses in northern African growing regions, which likely caused irregular and delayed bloom. These regions, along with southern Europe, may lose up to 30 Chill Portions by 2050 under a moderate warming scenario. Experts foresee increasing risks of spring frost, exacerbated bloom-related problems, and more frequent heat waves.
REGIONAL ENVIRONMENTAL CHANGE
(2023)
Article
Biochemical Research Methods
Carlos Sequeiros, Irene Otero-Muras, Carlos Vazquez, Julio R. Banga
Summary: Mechanistic dynamic models are important for understanding biomolecular networks and biological systems. Stochastic dynamic models should be used when dealing with low copy numbers and biochemical stochasticity. This article presents a novel strategy for parameter estimation in stochastic dynamic models, employing global optimization and stochastic simulation techniques.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2023)
Article
Biochemical Research Methods
Alejandro F. Villaverde, Elba Raimundez, Jan Hasenauer, Julio R. Banga
Summary: Biological processes are often modelled using ordinary differential equations, and the unknown parameters of these models are estimated by optimizing the fit of model simulation and experimental data. However, accurately estimating the prediction uncertainties due to the nonlinear dependence of model characteristics on parameters is challenging. To address this, we applied four state-of-the-art methods for uncertainty quantification to four case studies of different computational complexities, revealing the trade-offs between their applicability and statistical interpretability. Our results provide guidelines for choosing the most appropriate technique for a given problem and applying it successfully.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2023)
Article
Biochemistry & Molecular Biology
Diego Hernandez-Prieto, Alberto Garre, Vicente Agullo, Cristina A. Garcia-Viguera, Jose A. Egea
Summary: Metabolic diseases have been linked to excessive consumption of high-sugar content beverages. To address this, there has been an increased demand for plant-based formulations with health-promoting properties. A longitudinal trial with 140 volunteers was conducted to assess the bioavailability of (poly)phenols in a maqui-citrus beverage, and it was discovered that the sex of the volunteer and the type of sweetener added to the beverage had an impact on the bioavailability of the (poly)phenol metabolites. Clustering analysis also revealed patterns in the distribution of metabolites based on sex and/or sweeteners. These findings highlight the potential of stevia as a bioavailability enhancer and demonstrate that sex plays a role in the metabolism of (poly)phenols.
Article
Biotechnology & Applied Microbiology
Sandra Diaz-Seoane, Elena Sellan, Alejandro F. Villaverde
Summary: Microbial communities, composed of microorganisms, are widely distributed in nature and increasingly applied in biotechnology and biomedicine. This study analyzes the structural identifiability and observability of various microbial community models and finds that some models are fully identifiable and observable, while others are structurally unidentifiable and/or unobservable under typical experimental conditions. These findings provide guidance for selecting appropriate modeling frameworks in this emerging field and avoiding inappropriate models.
BIOENGINEERING-BASEL
(2023)
Article
Biochemical Research Methods
Ahmed Taha, Mauricio Paton, David Penas, Julio Banga, Jorge Rodriguez
Summary: In this study, a method is developed to evaluate the feasibility of alternative metabolic pathways in microbes by optimizing the energy yield and driving forces of metabolic intermediates. The method uses thermodynamic principles and multi-objective optimization to consider different pathway variants. Other constraints, such as the balance of conserved components, are also taken into account. The method transforms the maximum energy yield problem into a multi-objective mixed-integer linear optimization problem and solves it using the epsilon-constraint method. The methodology is applied to analyze different pathways in propionate oxidation and CO2 fixation by microbes.
PLOS COMPUTATIONAL BIOLOGY
(2023)
Article
Agriculture, Multidisciplinary
Gabriela L. Salazar-Orbea, Rocio Garcia-Villalba, Maria J. Bernal, Alberto Hernandez-Jimenez, Jose A. Egea, Francisco A. Tomas-Barberan, Luis M. Sanchez-Siles
Summary: This research focused on how storage conditions and processing techniques affect the composition of nutrients, bioactive compounds, and sensory attributes in strawberry and apple purees. The study found that storage conditions had a stronger impact on phenolic compounds in strawberry puree, while initial processing techniques had a greater influence on apple puree. The stability of polyphenols varied between fruits during storage, and selecting the optimal storage conditions is crucial for maintaining the polyphenol content in sensitive fruits like strawberries.
JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY
(2023)
Article
Biochemical Research Methods
Gemma Massonis, Alejandro F. Villaverde, Julio R. Banga
Summary: MotivationDynamic mechanistic modelling in systems biology has been hindered by complexity and variability, as well as uncertain and sparse experimental measurements. Ensemble modelling has been introduced to mitigate these issues, but is unreliable for predicting non-observable states. In this study, the authors present a strategy to assess and improve the reliability of model ensembles, using a diversity-enforcing technique combined with identifiability and observability analysis. They demonstrate the effectiveness of their approach with models of glucose regulation, cell division, circadian oscillations, and the JAK-STAT signalling pathway.
