Latent Uncertainties Research Articles

Design complexity based flexible order dispatching for additive manufacturing production

Understanding design complexity for additive manufacturing (AM) is essential in AM production planning since conventional make-to-order production for individual AM orders of complex designs can amplify operational uncertainty in an entire AM production system. As a response, this study aims not only to demonstrate the impact of design complexity on AM production but also to propose a novel order dispatching approach based on design complexity that mitigates operational uncertainty in an AM production system. First, a design complexity measure was developed using an information theoretic approach. Next, a discrete-event simulation model to represent an AM production system consisting of parallel AM machines for jet-engine bracket designs was built to identify the impact of design complexity on average order lead time and total production cost through regressions. Finally, a flexible order dispatching rule that reflects operational attitudes toward design complexity was proposed to determine part-processing priorities by tracking both part- and system-level design complexity states in a centralized queue for AM production. The proposed dispatching rule was compared with relevant static dispatching rules to assess its performance in operational efficiency under varied attitudes toward design complexity. The findings from this study clearly showed the negative impact of design complexity on operational performance for AM production. Moreover, the proposed dispatching rule resulted in lead time reduction and balanced lead time performance in AM production against alternative static dispatching strategies. This study demonstrates the importance of design complexity-based flexible operations to properly handle latent uncertainties in an AM production system.

Hedging Against Uncertainty in Biomass Processing Network Design Using a Data-Driven Approach

Sustainability has become a key concern in process systems engineering. Renewable energy, such as biofuels, and renewable materials, such as bioproducts, could replace their non-renewable, petroleum-based counterparts. However, there remain many challenges in producing biofuels and bioproducts economically and efficiently. There are many different biomass feedstocks, processes to convert them, and many different possible biofuels and bioproducts to produce. Furthermore, prices and demands of biofuels and bioproducts are uncertain. The variation of price or demand of one bioproduct could influence price or demand of another, further complicating the problem. An approach that can identify economical, efficient, and sustainable biofuel and bioproduct production processes from the myriad possible options while also considering correlated and uncorrelated price and demand uncertainties of the final bioproducts is required. In this work, a data-driven decision-making framework is proposed for biomass processing network design that directly integrates machine learning with robust optimization. Principal component analysis (PCA) is used to identify latent uncertainties behind observed uncertainty data. A kernel density estimation approach captures probability distributions of the projected uncertainty data extracted from PCA. This uncertainty data analysis approach is applied to a bioconversion product and process network to identify cost-effective and environmentally-friendly biofuels and bioproducts production pathways. Our approach identifies a total annualized cost of $ 18.3M/y, 6 % lower than the cost found with conventional adaptive robust optimization.

Data-driven decision making under uncertainty integrating robust optimization with principal component analysis and kernel smoothing methods

This paper proposes a novel data-driven robust optimization framework that leverages the power of machine learning and big data analytics for decision-making under uncertainty. By applying principal component analysis to uncertainty data, correlations between uncertain parameters are effectively captured, and latent uncertainty sources are identified. These data are then projected onto each principal component to facilitate extracting distributional information of latent uncertainties using kernel density estimation techniques. To explicitly account for asymmetric distributions, we introduce forward and backward deviation vectors into the data-driven uncertainty set, which are further incorporated into novel data-driven static and adaptive robust optimization models. The proposed framework not only significantly ameliorates the conservatism of robust optimization, but also enjoys computational efficiency and wide-ranging applicability. Three applications on optimization under uncertainty, including model predictive control, batch production scheduling, and process network planning, are presented to demonstrate the applicability of the proposed framework.

And Unto Dust Shalt Thou Return” Death and the Semiotics of Remembrance in an Ethiopian Orthodox Christian Village

This ethnographic article discusses funerary practice, Orthodox Christian ideas of body and spirit, and the ways in which people make memorials for each other on the Zege Peninsula in northwest Ethiopia. I pay special attention to gravestones because, here as in many other places, physical memorials to the dead become locations where latent uncertainties and conflicts about the relationship between spirit and matter, body and soul, and this world and the next, tend to crystallize. I show that material memorials highlight ambiguities in Orthodox attitudes to human embodiment and challenge priestly monopolies over relations between the living and the dead. Because of material chains of mediation and memorialization, the disaggregating practices of Orthodox funerary ritual can never fully untangle the deceased from their worldly social entanglements.

Latent uncertainties of the precalculated track Monte Carlo method.

While significant progress has been made in speeding up Monte Carlo (MC) dose calculation methods, they remain too time-consuming for the purpose of inverse planning. To achieve clinically usable calculation speeds, a precalculated Monte Carlo (PMC) algorithm for proton and electron transport was developed to run on graphics processing units (GPUs). The algorithm utilizes pregenerated particle track data from conventional MC codes for different materials such as water, bone, and lung to produce dose distributions in voxelized phantoms. While PMC methods have been described in the past, an explicit quantification of the latent uncertainty arising from the limited number of unique tracks in the pregenerated track bank is missing from the paper. With a proper uncertainty analysis, an optimal number of tracks in the pregenerated track bank can be selected for a desired dose calculation uncertainty. Particle tracks were pregenerated for electrons and protons using EGSnrc and geant4 and saved in a database. The PMC algorithm for track selection, rotation, and transport was implemented on the Compute Unified Device Architecture (cuda) 4.0 programming framework. PMC dose distributions were calculated in a variety of media and compared to benchmark dose distributions simulated from the corresponding general-purpose MC codes in the same conditions. A latent uncertainty metric was defined and analysis was performed by varying the pregenerated track bank size and the number of simulated primary particle histories and comparing dose values to a "ground truth" benchmark dose distribution calculated to 0.04% average uncertainty in voxels with dose greater than 20% of Dmax. Efficiency metrics were calculated against benchmark MC codes on a single CPU core with no variance reduction. Dose distributions generated using PMC and benchmark MC codes were compared and found to be within 2% of each other in voxels with dose values greater than 20% of the maximum dose. In proton calculations, a small (≤ 1 mm) distance-to-agreement error was observed at the Bragg peak. Latent uncertainty was characterized for electrons and found to follow a Poisson distribution with the number of unique tracks per energy. A track bank of 12 energies and 60000 unique tracks per pregenerated energy in water had a size of 2.4 GB and achieved a latent uncertainty of approximately 1% at an optimal efficiency gain over DOSXYZnrc. Larger track banks produced a lower latent uncertainty at the cost of increased memory consumption. Using an NVIDIA GTX 590, efficiency analysis showed a 807 × efficiency increase over DOSXYZnrc for 16 MeV electrons in water and 508 × for 16 MeV electrons in bone. The PMC method can calculate dose distributions for electrons and protons to a statistical uncertainty of 1% with a large efficiency gain over conventional MC codes. Before performing clinical dose calculations, models to calculate dose contributions from uncharged particles must be implemented. Following the successful implementation of these models, the PMC method will be evaluated as a candidate for inverse planning of modulated electron radiation therapy and scanned proton beams.

TH‐A‐19A‐04: Latent Uncertainties and Performance of a GPU‐Implemented Pre‐Calculated Track Monte Carlo Method

Purpose:Assessing the performance and uncertainty of a pre‐calculated Monte Carlo (PMC) algorithm for proton and electron transport running on graphics processing units (GPU). While PMC methods have been described in the past, an explicit quantification of the latent uncertainty arising from recycling a limited number of tracks in the pre‐generated track bank is missing from the literature. With a proper uncertainty analysis, an optimal pre‐generated track bank size can be selected for a desired dose calculation uncertainty.Methods:Particle tracks were pre‐generated for electrons and protons using EGSnrc and GEANT4, respectively. The PMC algorithm for track transport was implemented on the CUDA programming framework. GPU‐PMC dose distributions were compared to benchmark dose distributions simulated using general‐purpose MC codes in the same conditions. A latent uncertainty analysis was performed by comparing GPUPMC dose values to a “ground truth” benchmark while varying the track bank size and primary particle histories.Results:GPU‐PMC dose distributions and benchmark doses were within 1% of each other in voxels with dose greater than 50% of Dmax. In proton calculations, a submillimeter distance‐to‐agreement error was observed at the Bragg Peak. Latent uncertainty followed a Poisson distribution with the number of tracks per energy (TPE) and a track bank of 20,000 TPE produced a latent uncertainty of approximately 1%. Efficiency analysis showed a 937× and 508× gain over a single processor core running DOSXYZnrc for 16 MeV electrons in water and bone, respectively.Conclusion:The GPU‐PMC method can calculate dose distributions for electrons and protons to a statistical uncertainty below 1%. The track bank size necessary to achieve an optimal efficiency can be tuned based on the desired uncertainty. Coupled with a model to calculate dose contributions from uncharged particles, GPU‐PMC is a candidate for inverse planning of modulated electron radiotherapy and scanned proton beams.This work was supported in part by FRSQ‐MSSS (Grant No. 22090), NSERC RG (Grant No. 432290) and CIHR MOP (Grant No. MOP‐211360).

Citizenship in the Netherlands: locally produced, nationally contested

The Netherlands is often considered an extreme example of individualism and multiculturalism, two factors that many politicians and social scientists consider to be the main causes for the alleged decline in citizenship. In this paper, we examine Dutch citizens' conceptions of citizenship to test these negative expectations. We found the fear that a modern, individualistic, and diverse citizenry only care for their own rights to be misplaced; citizens were willing to exert effort to uphold the society they live in. Their efforts, however, were conditional upon returns in terms of a responsive government and in improvements to their individual lives. Communitarian, local, and rather submissive notions of citizenship were deeply shared – with a liberal twist among many migrants. We also found that ‘nationalist’ republican notions of citizenship awaken latent uncertainties and divisions among citizens rather than creating ‘new’ unity. This imagination of citizenship leaves Dutch society wanting for the deliberative, political elements of citizenship.

TH-C-T-617-01: A Proposed Alternative to Phase-Space Recycling Using Adaptive Kernel Density Estimator

Purpose: To investigate the adaptive kernel density estimation (AKDE) method as an alternative to recycling phase-space (PS) files or histogram binning during MC accelerator simulation. The AKDE approach has the potential to mitigate statistical “noise” issues (due to latent variance) in the PS data, without the need for an overwhelmingly large number of particles. Method and Materials: We have implemented a nonparametric density estimation technique, the AKDE method, to generate additional PS variables in the vicinity of simulated PS points in MC accelerator simulation. The method involves the placement of kernels at simulated PS points, and the window-width is allowed to vary based on the density of the PS points. After successfully testing the method for sampling 1-D and 2-D exponentials we sampled PS files generated from accelerator simulations. The original PS (x,y,u,v,E) was reduced to a rotationally invariant PS (r,θα,E) assuming azimuthal symmetry above the collimating jaws. The new PS point (r',θ',α',E') is generated by sampling in the vicinity of (r,θ,α,E) To test the method we simulated dose profiles for a 10×10cm2 field using 1×106 particles in the PS file as input. Results: Results indicate that a minimum number of PS points are needed to allow accurate density estimation. Preliminary calculations using a global window-width for each PS variable produce relatively smooth profiles even with as little as 10 million particles in the PS file. However, profiles with the Epanechnikov kernel appear to be smoother and in better agreement with the original PS versus either Gaussian or uniform kernels. Conclusion: By generating unbiased PS samples “on-the-fly” in the neighborhood of simulated PS points AKDE has been shown to be a promising alternative to PS recycling or histogram-binning (source modeling), where binning effects and propagation of systematic latent uncertainties may pose potential problems.