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1. Predicting biomass comminution: Physical experiment, population balance model, and deep learning

   https://doi.org/10.1016/j.powtec.2024.119830  

   Lu, Minglei; Xia, Yidong; Bhattacharjee, Tiasha; Klinger, Jordan; Li, Zhen (May 2024, Powder Technology)

   An extended population balance model (PBM) and a deep learning-based enhanced deep neural operator (DNO+) model are introduced for predicting particle size distribution (PSD) of comminuted biomass through a large knife mill. Experimental tests using corn stalks with varied moisture contents, mill blade speeds, and discharge screen sizes are conducted to support model development. A novel mechanism in the extended PBM allows for including additional input parameters such as moisture content, which is not possible in the original PBM. The DNO+ model can include influencing factors of different data types such as moisture content and discharge screen size, which significantly extends the engineering applicability of the standard DNO model that only admits feed PSD and outcome PSD. Test results show that both models are remarkably accurate in the calibration or training parameter space and can be used as surrogate models to provide effective guidance for biomass preprocessing design. 

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2. Nuclear Norm Based Spectrum Estimation for Molecular Dynamic Simulations

   https://doi.org/10.1109/IEEECONF51394.2020.9443532  

   Li, Shuang; Becker, Stephen; Wakin, Michael B. (June 2021, The 54th Asilomar Conference on Signals, Systems, and Computers)

   null (Ed.)

   Molecular dynamic (MD) simulations are used to probe molecular systems in regimes not accessible to physical experiments. A common goal of these simulations is to compute the power spectral density (PSD) of some component of the system such as particle velocity. In certain MD simulations, only a few time locations are observed, which makes it difficult to estimate the autocorrelation and PSD. This work develops a novel nuclear norm minimization-based method for this type of sub-sampled data, based on a parametric representation of the PSD as the sum of Lorentzians. We show results on both synthetic data and a test system of methanol. 

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3. Particle Size Distribution of Kalamazoo River Sediments by FieldSed

   Ventola, A. (December 2020, Journal of geotechnical and geoenvironmental engineering)

   null (Ed.)

   The FieldSed is an inexpensive portable device for performing an image-based soil particle size analysis. The process, which includes the image analysis, is referred to as SedImaging (short for sediment imaging). The FieldSed was used for an investigation of Kalamazoo River sediments to generate over 100 particle size distributions (PSDs). Core samples taken from the river were tested in a nearby field lab. When necessary, samples were processed prior to testing in the FieldSed to remove particles greater than 2.0 mm and those finer than 0.075 mm. Doing so was necessary to ensure the efficiency of the current SedImaging method. A small number of specimens was selected for quality control testing to determine the reproducibility of SedImaging results. This testing also involved sieve analyses in ascertaining the agreement between SedImaging and sieving results. The control test results presented in this paper demonstrate that the FieldSed is a promising device that can rapidly, accurately, and repeatedly determine particle size distributions in field labs for geotechnical and geoenvironmental application 

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4. The uSed System for Soil Particle Size Distribution Determination

   Zhang, Lei; Hryciw, Roman D; Ventola, Andrea (February 2024, ASCE)

   Evans, T Matthew; Stark, Nina; Chang, Susan (Ed.)

   A new testing system for determining soil particle size distributions (PSDs) is under development. It augments existing systems generically called “SedImaging” by the addition of a water pressure transducer near the bottom of a soil sedimentation column. The original SedImaging test provides image-based PSDs for sands, while the pressure transducer will extend the PSDs into the silt range. The new test system is called the “uSed” reflecting the measurement of water pressures (u) during a SedImaging test. This paper presents the uSed theoretical equations and the results of four pilot tests on sands to verify the theory. The four specimens were coarse sand, medium-sized sand, fine sand, and a gap-graded sand. The observed pressure decay curves were qualitatively as expected and the masses of solids predicted by the uSed theory agreed with the actual specimen masses to within an error of 2%. 

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5. An Empirical Analysis of the Mutation Operator for Run-Time Adaptive Testing in Self-Adaptive Systems

   Fredericks, Erik M. (January 2018, 2018 IEEE/ACM 11th International Workshop on Search-Based Software Testing (SBST))

   A self-adaptive system (SAS) can reconfigure at run time in response to uncertainty and/or adversity to continually deliver an acceptable level of service. An SAS can experience uncertainty during execution in terms of environmental conditions for which it was not explicitly designed as well as unanticipated combinations of system parameters that result from a self-reconfiguration or misunderstood requirements. Run-time testing provides assurance that an SAS continually behaves as it was designed even as the system reconfigures and the environment changes. Moreover, introducing adaptive capabilities via lightweight evolutionary algorithms into a run-time testing framework can enable an SAS to effectively update its test cases in response to uncertainty alongside the SAS's adaptation engine while still maintaining assurance that requirements are being satisfied. However, the impact of the evolutionary parameters that configure the search process for run-time testing may have a significant impact on test results. Therefore, this paper provides an empirical study that focuses on the mutation parameter that guides online evolution as applied to a run-time testing framework, in the context of an SAS. 

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