Building a map of restriction sites from double-digest gel data can be a complex and frustrating task, especially when many DNA fragments are detected or when the gel results are ambiguous. 'Double Digester' is an interactive, graphical computer program which helps researchers understand and resolve such data. It explicitly represents the experimental data, the associated uncertainties, the researcher's hypotheses and possible map interpretations. Alternative solutions are frequently possible, and the differences between them may help determine which additional experiments might resolve ambiguities. Initial use has confirmed the benefits of this approach, and has suggested ways in which it can be refined and extended. Double Digester meets the need for a practical tool to help build restriction maps, and also illustrates how a computer-based tool can confront experimental uncertainty in an integrated fashion.
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http://dx.doi.org/10.1093/bioinformatics/10.4.435 | DOI Listing |
Sci Rep
January 2025
CAAC Academy, Civil Aviation Flight University of China, Chengdu, 618307, China.
In practical supply chain operations, efficient order allocation significantly enhances the overall efficiency of the supply chain. Real production environments are plagued by numerous uncertainties, such as unpredictable customer orders, which greatly amplify the complexity of solving practical allocation problems. This study focuses on the problem of allocating orders to parallel machines with varying efficiencies under uncertain and high-dimensional conditions.
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January 2025
School of Economics and Management, Shandong Agricultural University, Taian, China.
Agriculture is a major contributor to global greenhouse gas emissions, highlighting the urgent need for effective carbon reduction strategies. This study presents an innovative integrated model that employs Fermatean Neutrosophic Set in conjunction with the Weighted Influence Nonlinear Gauge System and the Analytic Hierarchy Process combined with the Entropy Weight Method to assess key factors influencing agricultural carbon reduction. Our study delineates the hierarchical importance of factors influencing carbon emissions, with carbon emission reduction policy (τ4) emerging as the paramount factor, attributed a value of 0.
View Article and Find Full Text PDFJ Chem Theory Comput
January 2025
Department of Electrical & Computer Engineering, Stony Brook University, Stony Brook, New York 11794, United States.
In this work, we develop a novel Bayesian approach to study the adsorption and desorption of CO onto a Pd(111) surface, a process of great importance in natural sciences. The motivation for this work comes from the recent availability of time-resolved infrared spectroscopy data and the need for model interpretability and uncertainty quantification in chemical processes. The objective is to learn the relevant parameters that characterize the process: coverage with time, rate constants, activation energies, and pre-exponential factors.
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December 2024
Aschaffenburg University of Applied Sciences, Faculty of Engineering, Aschaffenburg, 63743, Germany.
Design of experiments (DOE) is an established method to allocate resources for efficient parameter space exploration. Model based active learning (AL) data sampling strategies have shown potential for further optimization. This paper introduces a workflow for conducting DOE comparative studies using automated machine learning.
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December 2024
Beijing Engineering Research Center of Precision Measurement Technology and Instruments, Beijing University of Technology, Beijing 100124, China. Electronic address:
Dual-impulse behaviors of rolling bearings have been widely researched for quantitative diagnosis. However, it is challenging to accurately extract entry and exit moments of the fault from noise-contaminated raw signals. To address this issue, a novel quantitative diagnosis method based on digital twin model is proposed to assess the fault severity from the original signal waveform.
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