23 papers · ranked by Valyu relevance
Chao Li, Jinjin Tang, Jun Zhang, Qingqing Zhao + 3 more
'Jian Li' 'Yong-Hong Kuo'] Urban rail transit train operation plan is a comprehensive production plan encompassing line planning, timetabling, and rolling stock scheduling. In order to solve the problem of infeasibility of the line plan and timetable because the number of rolling stocks could be only precisely…
David A. Liñán, Luis A. Ricardez-Sandoval
Mixed integer nonlinear programming (MINLP) in chemical engineering originated as a tool for solving optimal process synthesis and design problems. Since then, the application of MINLP has expanded to encompass control and operational decisions that are in line with the arising challenges faced by the industry, e.g.…
Damien van de Berg, Nilay Shah, Ehecatl Antonio del Río-Chanona
Planning, scheduling, and control typically constitute separate decision-making units within chemical companies. Traditionally, their integration is modelled sequentially, but recent efforts prioritize lower-level feasibility and optimality, leading to large-scale, potentially multi-level, hierarchical formulations.…
Authors not listed
This comprehensive review examines the evolution of autonomous materials synthesis laboratories that integrate artificial intelligence with advanced robotics to accelerate discovery. Traditional materials development pipelines typically require 10-20 years, but self-driving laboratories (SDLs) and Materials…
Xiaobao Yu, Wenjing Zhao, Sani Isah Abba
Output instability is one of the important constraints limiting the large-scale application of renewable energy. The development of comprehensive energy systems can effectively improve energy utilization efficiency, but there is still a problem of randomness in renewable energy output. The paper conducts research on…
Liming Wei, Guoqing An
Renewable energy generation has become the general trend with increasing environmental problems. However, the instability of renewable energy generation and the diversification of user demand are highlighted and the optimization of energy scheduling has become the key to solve the problem. This study introduces an…
Charlotte Merzbacher, Oisin Mac Aodha, Diego A. Oyarzún
Recent advances in synthetic biology have enabled the construction of molecular circuits that operate across multiple scales of cellular organization, such as gene regulation, signalling pathways and cellular metabolism. Computational optimization can effectively aid the design process, but current methods are…
Carlos Lopera, Madelem Giraldo, Natalia Herrera
Microalgae and cyanobacteria have emerged as promising resources for sustainable agriculture; however, integrated methodologies for the rational design of crop-specific agricultural formulations remain scarce. This study proposes an integrated framework that combines biomass production, species characterization…
Florian Gisperg, Robert Klausser, Mohamed Elshazly, Julian Kopp + 2 more
'Eva Přáda Brichtová' 'Oliver Spadiut'] Title: ABSTRACT Bayesian optimization is a stochastic, global black-box optimization algorithm. By combining Machine Learning with decision-making, the algorithm can optimally utilize information gained during experimentation to plan further experiments-while balancing…
Authors not listed
This paper introduces Optical Fiber Chemistry (OFC) as a fourth-generation catalytic paradigm, distinguished not by incremental improvements in catalyst materials but by a fundamental reconfiguration of the catalytic reaction platform. By employing optical fibers as active photonic control elements, OFC achieves gen-…
Shijie Deng, Brandon Jolly, James Wilkes, Yu Mu + 5 more
Integrated catalysis is an emerging methodology that can streamline the multistep synthesis of complicated products in a single reaction vessel, achieving a high degree of control and reducing the waste and cost of the overall process. Integrated catalysis utilizes spatial and temporal control to couple different…
Rimnoma S. Ouedraogo, Peter McCloskey, Beatrice Mwaipopo, Bipana Paudel Timilsena + 4 more
Banana and plantain (Musa spp.) production in Sub-Saharan Africa is severely constrained by multiple diseases, with Banana bunchy top virus (BBTV) representing the most devastating viral threat. Inadequate diagnostic infrastructure limits effective management, particularly for asymptomatic infections disseminated…
Zoran Jakšić, Swagata Devi, Olga Jakšić, Koushik Guha + 3 more
The application of artificial intelligence in everyday life is becoming all-pervasive and unavoidable. Within that vast field, a special place belongs to biomimetic/bio-inspired algorithms for multiparameter optimization, which find their use in a large number of areas. Novel methods and advances are being published at…
Juliette Gamot, Mathieu Balesdent, Arnault Tremolet, Romain Wuilbercq + 2 more
'Romain Wuilbercq' 'Nouredine Melab' 'El‐Ghazali Talbi'] The optimal layout of a complex system such as aerospace vehicles consists in placing a given number of components in a container in order to minimize one or several objectives under some geometrical or functional constraints. This paper presents an extended…
Eneko Osaba, Josu Díaz-de-Arcaya, Juncal Alonso, Jesús L. Lobo + 2 more
'Gorka Benguria' 'Iñaki Etxaniz'] Multiobjective optimization is a hot topic in the artificial intelligence and operations research communities. The design and development of multiobjective methods is a frequent task for researchers and practitioners. As a result of this vibrant activity, a myriad of techniques have…
Máté Mohácsi, Márk Patrik Török, Sára Sáray, Luca Tar + 1 more
Finding optimal parameters for detailed neuronal models is a ubiquitous challenge in neuroscientific research. Recently, manual model tuning has been replaced by automated parameter search using a variety of different tools and methods. However, using most of these software tools and choosing the most appropriate…
Serena Landers, Sahil Pontula, Shiekh Zia Uddin, Sachin Vaidya + 2 more
We introduce the CLUSTER algorithm (\textbf{c}oordinate-\textbf{l}evel \textbf{u}pdate \textbf{s}trategy for \textbf{t}rust-region step \textbf{e}valuation \textbf{r}efinement) for local derivative-free optimization problems where there is a cost to changing each parameter (or clusters of parameters). For example, this…
Richa Verma, Dinesh Kumar, Kazuma Kobayashi, Syed Bahauddin Alam
Robust optimization is a method for optimization under uncertainties in engineering systems and designs for applications ranging from aeronautics to nuclear. In a robust design process, parameter variability (or uncertainty) is incorporated into the engineering systems' optimization process to assure the systems'…
Pablo Quijano Velasco, Kedar Hippalgaonkar, Balamurugan Ramalingam
The discovery of optimal conditions of chemical reactions is a labor-intensive, time-consuming task that requires exploring a high-dimensional parametric space. Historically the optimization of chemical reactions has been performed by manual experimentation guided by human intuition and Design of Experiments where one…
Stephan Grein, David R. Penas, Daniel Weindl, Polina Lakrisenko + 2 more
Dynamic models are central to the computational life sciences but typically contain unknown parameters that must be inferred from experimental data. High-throughput measurements have made this task increasingly challenging, yielding high-dimensional search spaces and non-convex objectives with many local optima. This…
Qi Zhao, Bai Yan, Taiwei Hu, Xiang‐Long Chen + 1 more
—Metaheuristics are prominent gradient-free optimizers for solving hard problems that do not meet the rigorous mathematical assumptions of analytical solvers. The canonical manual optimizer design could be laborious, untraceable and error-prone, let alone human experts are not always available. This arises increasing…
Authors not listed
Experimental design plays an important role in efficiently acquiring informative data for system characterization and deriving robust conclusions under resource limitations. Recent advancements in high-throughput experimentation coupled with machine learning have notably improved experimental procedures. While Bayesian…
Giannis Poulopoulos, Hercules Avramopoulos, Cosimo Trono
Highlights What are the main findings?1. Optical sensing in industry and smart manufacturing is not a single platform choice. Distributed fiber sensing, FBG sensors, integrated photonic sensors, and nanophotonic/plasmonic devices address different combinations of spatial coverage, sensing volume, measurand type, and…