Article
Acoustics
Jiaxin Wu, Tao Zhu, Yijun Wang, Cheng Lei, Shoune Xiao
Summary: A parameter selection method based on the Wilson-theta method and the principle of minimum response error is proposed to improve the accuracy of dynamic load identification. The method is proven to have high identification accuracy and strong robustness for the wheel-rail vertical dynamic load through simulation experiments using the SIMPACK dynamic model.
SHOCK AND VIBRATION
(2022)
Article
Automation & Control Systems
Xi Chen, Lei-ming Yuan, Guofeng Yi, Guangzao Huang, Wen Shi, Xiaojing Chen
Summary: This paper presents a novel method for identifying microplastic samples in the environment, achieving an accuracy of 0.955 when combined with FTIR technology and a new opened classifier, and automatically rejecting non-plastic samples.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
(2022)
Article
Pediatrics
Madhavi V. Ratnagiri, Lauren Ryan, Abigail Strang, Robert Heinle, Tariq Rahman, Thomas H. Shaffer
Summary: The study developed an automated approach using machine learning and ICP features to identify thoracic abdominal asynchrony, improving accuracy and consistency, reducing diagnosis time and effort. Additionally, the ICP feature helped enhance consensus among experts.
PEDIATRIC RESEARCH
(2021)
Article
Physics, Multidisciplinary
Ce Huang, Li Wang, Wei Wang, Ke Wang
Summary: A novel approach is proposed in this paper to identify the nonlinear restoring force of multistable piezoelectric energy harvesters from time-domain voltage response sensitivity analysis. Numerical simulations demonstrate the feasibility and accuracy of the proposed approach.
EUROPEAN PHYSICAL JOURNAL PLUS
(2022)
Article
Biochemistry & Molecular Biology
Anna Macioszek, Bartek Wilczynski
Summary: The article describes a new method for genome-wide data analysis called HERON, which can detect DNA regions enriched for a certain feature even in long DNA domains, demonstrating good interpretative ability for difficult data such as modified histone variants.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2021)
Article
Zoology
Michal Sulc, Anna E. Hughes, Jolyon Troscianko, Gabriela Stetkova, Petr Prochazka, Milica Pozgayova, Lubomir Pialek, Radka Pialkova, Vojtech Brlik, Marcel Honza
Summary: Individual identification is crucial for studying animal ecology and evolution. This study used an automatic analytical approach to predict the identity of bird females based on the appearance of their eggs, and focused on the common cuckoo as a model species. The results showed that individual cuckoo females lay eggs with a relatively constant appearance and that eggs laid by more genetically distant females differ more in colour. The novel method of automatic analysis outperformed human observers and can reliably assign eggs without genetic data to their mothers.
ZOOLOGICAL JOURNAL OF THE LINNEAN SOCIETY
(2022)
Article
Computer Science, Artificial Intelligence
Hai-Hui Huang, Xin-Dong Peng, Yong Liang
Summary: The study presented a novel SPLSN sparse Cox regression model, which combines self-paced learning and a log-sum absolute network-based penalty for biomarker selection in survival analysis. Results show that SPLSN can identify fewer meaningful biomarkers and achieve the best or equivalent prediction performance compared to other methods.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2021)
Article
Chemistry, Analytical
Zhuanghao Hou, Liujuan Zhan, Kaiming Cao, Moujun Luan, Xinchen Wang, Buchun Zhang, Likun Ma, Hao Yin, Zhicheng Liu, Yangzhong Liu, Guangming Huang
Summary: Direct observation of metabolites in living cells by mass spectrometry offers great potential for biological studies, but there is a challenge in identifying untargeted metabolites. In this study, a method combining stable isotope tracing and induced electrospray mass spectrometry was developed. By using specific isotopes as carbon and nitrogen sources, metabolites with labeled carbon and nitrogen atoms were synthesized in Escherichia coli. Tracking the number of labeled atoms improved the confidence in metabolite identification.
ANALYTICA CHIMICA ACTA
(2023)
Article
Environmental Sciences
Xihong Cui, Zhenxian Quan, Xuehong Chen, Zheng Zhang, Junxiong Zhou, Xinbo Liu, Jin Chen, Xin Cao, Li Guo
Summary: The study introduces a new approach to accurately extract the actual growth pattern of shrub roots using ground-penetrating radar technology, revealing spatial competition strategies of plant roots in response to changes in the soil environment and neighboring plants.
Article
Engineering, Multidisciplinary
Alexandre Cortiella, Kwang-Chun Park, Alireza Doostan
Summary: This work introduces an iterative sparse-regularized regression method for recovering governing equations of nonlinear dynamical systems with improved accuracy and robustness in the presence of state measurement noise. By utilizing a reweighted l(1) regularization approach, the method demonstrates viability for a wide range of potential applications through empirical examples of well-known nonlinear dynamical systems.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2021)
Article
Energy & Fuels
Omar Arahbi, Benoit Huard, Jean-Denis Gabano, Thierry Poinot
Summary: This paper discusses the identification of battery impedance parameters using Electrochemical Impedance Spectroscopy (EIS) measurements and fractional modeling in the frequency domain. It proposes an automatic initialization method for a Complex Nonlinear Least Squares algorithm based on fractional modeling and EIS measurements to accurately estimate impedance parameters for different diffusion scenarios.
JOURNAL OF ENERGY STORAGE
(2023)
Article
Engineering, Aerospace
Rui Zhu, Dong Jiang, Stefano Marchesiello, Dario Anastasio, Dahai Zhang, Qingguo Fei
Summary: To address the uncertainty and false mode or omission phenomena caused by traditional sequence determination methods, an automatic nonlinear subspace identification method is proposed. This method estimates the calculation range of system modal order through eigenvalue decomposition of constructed Hankel matrix, and introduces similarity coefficient and distance function for clustering the modal results to remove the poles of false modes and obtain the cluster stabilization diagram to determine the best system order. Then, the modal parameters and nonlinear coefficients are obtained, and the effectiveness and robustness of the proposed method are verified through simulation and experimental studies.
Article
Engineering, Electrical & Electronic
Guoyi Xu, Pragya Sharma, David Lee Hysell, Edwin Chihchuan Kan
Summary: Indoor device-free object sensing is an important technology with various applications. RFID provides a low-cost solution to satisfy the mathematical requirement for arbitrary indoor layouts. The combination of received signal strength indicator and carrier phase allows for accurate object localization.
IEEE SENSORS JOURNAL
(2022)
Article
Mathematics, Applied
Christian Aarset, Martin Holler, Tram Thi Ngoc Nguyen
Summary: This study introduces and analyzes a learning-informed parameter identification method for partial differential equations (PDEs) in an innovative framework. The nonlinearity is approximated using a neural network, with its parameters being learned from measurement data. The unknown state is assumed to be observed with noise, and both the state and physical parameters are identified together with the neural network's parameters. By treating the state as an additional variable, this all-at-once setting avoids explicitly constructing the parameter-to-state map. The practical feasibility of this method is confirmed through experiments using two different algorithmic settings.
APPLIED MATHEMATICS AND OPTIMIZATION
(2023)
Article
Radiology, Nuclear Medicine & Medical Imaging
Silvia Pradella, Lorenzo Nicola Mazzoni, Mayla Letteriello, Paolo Tortoli, Silvia Bettarini, Cristian De Amicis, Giulia Grazzini, Simone Busoni, Pierpaolo Palumbo, Giacomo Belli, Vittorio Miele
Summary: This study developed an automatic software FLORA that can identify, classify, and quantify ischemic and non-ischemic myocardial lesions, improving the observer's performance and the consistency of evaluations.
Article
Engineering, Chemical
Lorenz Fleitmann, Christoph Gertig, Jan Scheffczyk, Johannes Schilling, Kai Leonhard, Andre Bardow
Summary: This paper integrates computer-aided molecular design of solvents with heat-integrated process design to select and design optimal solvents for maximum process performance. The results of two case studies show that designed solvents improve process performance compared to previous heuristics, highlighting the importance of integrating molecular and process design.
CHEMIE INGENIEUR TECHNIK
(2023)
Article
Energy & Fuels
Lorenz Fleitmann, Philipp Ackermann, Johannes Schilling, Johanna Kleinekorte, Jan G. Rittig, Florian vom Lehn, Artur M. Schweidtmann, Heinz Pitsch, Kai Leonhard, Alexander Mitsos, Andre Bardow, Manuel Dahmen
Summary: Co-design of alternative fuels and future SI engines achieves high engine efficiencies. Computer-aided molecular design (CAMD) of renewable fuels has gained attention for tailoring molecular structure to the needs of SI engines. An optimization-based fuel design method targeting SI engine efficiency is proposed, integrating automated prediction of various fuel properties and considering their combined impact.
Article
Energy & Fuels
Marten Lache, Christoph Kappelhoff, Jan Seiler, Andre Bardow
Summary: Adsorption chillers can avoid greenhouse gas emissions by utilizing waste heat and a natural refrigerant. To overcome the limitation of water's freezing point, a boiling antifreeze using ethanol is proposed. Ethanol can prevent freezing at -5 degrees C and extend the operating range of the adsorption chiller, while retaining some of the favorable properties of water.
Article
Chemistry, Physical
Philipp Rehner, Johannes Schilling, Andre Bardow
Summary: Integrated molecular and process design optimizes process variables together with molecules as an additional degree of freedom. This work proposes a graph-based molecular representation approach that encodes the full structure of the molecule during optimization, enabling the integration of advanced property models. The framework is applied to the design of the working fluid for an organic Rankine cycle, demonstrating its efficiency in molecular design.
MOLECULAR SYSTEMS DESIGN & ENGINEERING
(2023)
Article
Computer Science, Interdisciplinary Applications
Andreas Kaemper, Roman Delorme, Ludger Leenders, Andre Bardow
Summary: The operation of multi-energy systems requires repeated optimization to respond to changing energy prices. However, solving operational optimization problems in a reliably short time is challenging due to complex time-coupling constraints. This study presents a decomposition method that efficiently solves the operational optimization by utilizing artificial neural networks.
COMPUTERS & CHEMICAL ENGINEERING
(2023)
Article
Computer Science, Interdisciplinary Applications
Florian Joseph Baader, Philipp Althaus, Andre Bardow, Manuel Dahmen
Summary: Volatile electricity prices make demand response attractive for processes that can modulate their production rate. However, scheduling optimization problems often cannot be solved in real time when nonlinear dynamic processes must be scheduled simultaneously with their local multi-energy system. This work extends dynamic ramping constraints to flat multi-input multi-output processes by a coordinate transformation, allowing for a mixed-integer linear formulation that guarantees feasible operation.
COMPUTERS & CHEMICAL ENGINEERING
(2023)
Article
Thermodynamics
Benedikt Winter, Clemens Winter, Timm Esper, Johannes Schilling, Andre Bardow
Summary: The availability of property data is a major obstacle in chemical development. Predictive models, such as group contribution methods and machine learning, have been developed to overcome this bottleneck. However, integrating physical constraints into machine learning models remains a challenge.
FLUID PHASE EQUILIBRIA
(2023)
Article
Thermodynamics
Marvin Kasterke, Julia Thien, Carsten Flake, Thorsten Brands, Leo Bahr, Andre Bardow, Hans-Juergen Koss
Summary: Raman spectroscopy is an effective tool for determining liquid-liquid equilibria (LLE) in parallel microfluidic flows. However, plug flows hinder the establishment of a stable flow regime, making it difficult to collect sufficient Raman signal for quantification. To address this issue, a measurement setup is developed to analyze LLE in microfluidic plug flows. The setup successfully automates the entire measurement process and provides accurate results for industrially relevant mixtures.
FLUID PHASE EQUILIBRIA
(2023)
Article
Green & Sustainable Science & Technology
David Yang Shu, Sarah Deutz, Benedikt Alexander Winter, Nils Baumgaertner, Ludger Leenders, Andre Bardow
Summary: Carbon capture and storage can reduce greenhouse gas emissions and provide negative emissions for the transition to a net-zero society. Combining energy system modeling with life-cycle assessment, this study analyzes the economic and environmental impacts of carbon dioxide storage on the transition to net-zero emissions. The results show that increasing carbon dioxide storage beyond the minimum requirement can significantly lower costs and environmental impacts, offering economic and environmental benefits in the transition to net-zero energy systems.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2023)
Article
Multidisciplinary Sciences
Wan Ru Leow, Simon Voelker, Raoul Meys, Jianan Erick Huang, Shaffiq A. Jaffer, Andre Bardow, Edward H. Sargent
Summary: The production of hydrogen and hydrocarbon refining contribute significantly to CO2 emissions in the chemicals industry. Coupled electrification can cut emissions by up to 39%, even with the current electricity mix. Chemicals manufacturing is a major greenhouse gas emitter, with over half of the emissions coming from ammonia and oxygenates. By using electrolyzer systems that convert hydrocarbons to oxygenates and generate H-2 from water, emissions from fossil-based ammonia and oxygenates can be reduced by up to 88%. Low-carbon electricity is not necessary for a substantial reduction in global chemical industry emissions, as a 39% reduction can be achieved with the electricity carbon footprint available in the US or China today. Researchers interested in this area are provided with considerations and recommendations.
NATURE COMMUNICATIONS
(2023)
Article
Chemistry, Multidisciplinary
Marvin Bachmann, Simon Voelker, Johanna Kleinekorte, Andre Bardow
Summary: Syngas is a crucial chemical used in the production of chemicals and fuels. Its market volume is expected to grow, but its current production process emits significant amounts of greenhouse gases. Here, we provide a comprehensive environmental assessment of alternative syngas pathways, showing that bio-based and mill gas-based syngas are the most effective in reducing greenhouse gas emissions. However, the effectiveness depends on the availability and current use of conventional feedstocks.
ACS SUSTAINABLE CHEMISTRY & ENGINEERING
(2023)
Article
Chemistry, Multidisciplinary
Johanna Kleinekorte, Jonas Kleppich, Lorenz Fleitmann, Verena Beckert, Luise Blodau, Andrei Bardow
Summary: A sustainable chemical industry needs to quantify its emissions and resource consumption by life cycle assessment (LCA), but detailed mass and energy balances are usually not available at early process development stages. To address this issue, a fully automated, predictive LCA framework (APPROPRIATE) based on Gaussian Process Regression is introduced, which is applicable at Technology Readiness Level 2. By employing an encoder-decoder network combined with transfer learning, the framework achieves a condensed molecular descriptor as a latent representation to overcome limited LCA data availability. The proposed framework also integrates process descriptors, such as the stoichiometric sum of the reactants' impacts, to distinguish between process alternatives and incorporate changes in the background systems. Compared to state-of-the-art predictive LCA approaches, the APPROPRIATE framework shows increased prediction accuracy, especially in terms of the global warming impact.
ACS SUSTAINABLE CHEMISTRY & ENGINEERING
(2023)
Article
Chemistry, Multidisciplinary
Tim Langhorst, Benedikt Winter, Dennis Roskosch, Andre Bardow
Summary: Current chemical process development aims to improve sustainability. Decision-making thus needs to assess potential environmental benefits. Reliable life cycle inventory data is often unavailable at early design stages. Stoichiometry-based estimation methods that employ proxies are compared and benchmarked to a new database of industrially validated processes. Most estimation methods underestimate global warming impact. Combining yield range assumption and average process energy demand as a proxy improves predictions of inventory data and overall global warming impact.
ACS SUSTAINABLE CHEMISTRY & ENGINEERING
(2023)
Article
Energy & Fuels
Lisa Neumaier, Dennis Roskosch, Johannes Schilling, Gernot Bauer, Joachim Gross, Andre Bardow
Summary: The selection of improved refrigerants for heat pumps is becoming more important due to increasingly stringent regulations and new applications. The chosen refrigerants should be environmentally friendly and maximize the heat pump process performance. It has been found that the isentropic efficiency of the compressor may vary significantly depending on the refrigerant, suggesting the need for a refrigerant-dependent compressor model for refrigerant selection.
Article
Chemistry, Physical
D. Freire Ordonez, C. Ganzer, T. Halfdanarson, A. Gonzalez Garay, P. Patrizio, A. Bardow, G. Guillen-Gosalbez, N. Shah, N. Mac Dowell
Summary: The current energy crisis has led to unprecedented natural gas prices worldwide, impacting the cost of food and fertilisers. In this context, green hydrogen is gaining popularity with the projected reduction in renewables and electrolyser costs. This study evaluates the current and future costs of reliable green hydrogen production, taking into account the variability of renewable energy sources.
