This study explores the potential to reach a circular economy for post-consumer Recycled Polyethylene Terephthalate (rPET) packaging and bottles by using it as a Distributed Recycling for Additive ...Manufacturing (DRAM) feedstock. Specifically, for the first time, rPET water bottle flake is processed using only an open source toolchain with Fused Particle Fabrication (FPF) or Fused Granular Fabrication (FGF) processing rather than first converting it to filament. In this study, first the impact of granulation, sifting, and heating (and their sequential combination) is quantified on the shape and size distribution of the rPET flakes. Then 3D printing tests were performed on the rPET flake with two different feed systems: an external feeder and feed tube augmented with a motorized auger screw, and an extruder-mounted hopper that enables direct 3D printing. Two Gigabot X machines were used, each with the different feed systems, and one without and the latter with extended part cooling. 3D print settings were optimized based on thermal characterization, and both systems were shown to 3D print rPET directly from shredded water bottles. Mechanical testing showed the importance of isolating rPET from moisture and that geometry was important for uniform extrusion. The mechanical strength of 3D-printed parts with FPF and inconsistent flow is lower than optimized fused filament, but adequate for a wide range of applications. Future work is needed to improve consistency and enable water bottles to be used as a widespread DRAM feedstock.
Valid Model-Free Spatial Prediction Mao, Huiying; Martin, Ryan; Reich, Brian J.
Journal of the American Statistical Association,
04/2024, Letnik:
119, Številka:
546
Journal Article
Recenzirano
Predicting the response at an unobserved location is a fundamental problem in spatial statistics. Given the difficulty in modeling spatial dependence, especially in nonstationary cases, model-based ...prediction intervals are at risk of misspecification bias that can negatively affect their validity. Here we present a new approach for model-free nonparametric spatial prediction based on the conformal prediction machinery. Our key observation is that spatial data can be treated as exactly or approximately exchangeable in a wide range of settings. In particular, under an infill asymptotic regime, we prove that the response values are, in a certain sense, locally approximately exchangeable for a broad class of spatial processes, and we develop a local spatial conformal prediction algorithm that yields valid prediction intervals without strong model assumptions like stationarity. Numerical examples with both real and simulated data confirm that the proposed conformal prediction intervals are valid and generally more efficient than existing model-based procedures for large datasets across a range of nonstationary and non-Gaussian settings.
With the advance of methods for estimating species distribution models has come an interest in how to best combine datasets to improve estimates of species distributions. This has spurred the ...development of data integration methods that simultaneously harness information from multiple datasets while dealing with the specific strengths and weaknesses of each dataset.
We outline the general principles that have guided data integration methods and review recent developments in the field. We then outline key areas that allow for a more general framework for integrating data and provide suggestions for improving sampling design and validation for integrated models.
Key to recent advances has been using point‐process thinking to combine estimators developed for different data types. Extending this framework to new data types will further improve our inferences, as well as relaxing assumptions about how parameters are jointly estimated. These along with the better use of information regarding sampling effort and spatial autocorrelation will further improve our inferences.
Recent developments form a strong foundation for implementation of data integration models. Wider adoption can improve our inferences about species distributions and the dynamic processes that lead to distributional shifts.
Past work has shown that particle material extrusion (fused particle fabrication (FPF)/fused granular fabrication (FGF)) has the potential for increasing the use of recycled polymers in 3D printing. ...This study extends this potential to high-performance (high-mechanical-strength and heat-resistant) polymers using polycarbonate (PC). Recycled PC regrind of approximately 25 mm
was 3D printed with an open-source Gigabot X and analyzed. A temperature and nozzle velocity matrix was used to find useful printing parameters, and a print test was used to maximize the output for a two-temperature stage extruder for PC. ASTM type 4 tensile test geometries as well as ASTM-approved compression tests were used to determine the mechanical properties of PC and were compared with filament printing and the bulk virgin material. The results showed the tensile strength of parts manufactured from the recycled PC particles (64.9 MPa) were comparable to that of the commercial filament printed on desktop (62.2 MPa) and large-format (66.3 MPa) 3D printers. Three case study applications were investigated: (i) using PC as a rapid molding technology for lower melting point thermoplastics, (ii) printed parts for high temperature applications, and (iii) printed parts for high-strength applications. The results show that recycled PC particle-based 3D printing can produce high-strength and heat-resistant products at low costs.
Many statisticians have had the experience of fitting a linear model with uncorrelated errors, then adding a spatially-correlated error term (random effect) and finding that the estimates of the ...fixed-effect coefficients have changed substantially. We show that adding a spatially-correlated error term to a linear model is equivalent to adding a saturated collection of canonical regressors, the coefficients of which are shrunk toward zero, where the spatial map determines both the canonical regressors and the relative extent of the coefficients' shrinkage. Adding a spatially-correlated error term can also be seen as inflating the error variances associated with specific contrasts of the data, where the spatial map determines the contrasts and the extent of error-variance inflation. We show how to avoid this spatial confounding by restricting the spatial random effect to the orthogonal complement (residual space) of the fixed effects, which we call restricted spatial regression. We consider five proposed interpretations of spatial confounding and draw implications about what, if anything, one should do about it. In doing so, we debunk the common belief that adding a spatially-correlated random effect adjusts fixed-effect estimates for spatially-structured missing covariates. This article has supplementary material online.
A goldilocks amount of H3K27me3 Reich, Tyler J; Lewis, Peter W
Nature chemical biology,
09/2023, Letnik:
19, Številka:
9
Journal Article
Recenzirano
Odprti dostop
A study of drug-resistant lymphomas with hypermorphic mutations in PRC2 has identified a ‘methylation index’ by which cancer cells maintain optimal H3K27me3 levels for survival, emphasizing the ...importance of understanding how tumors adapt to changes in chromatin and to drug-resistance mutations.
The ecology of microscopic life in household dust Barberán, Albert; Dunn, Robert R.; Reich, Brian J. ...
Proceedings - Royal Society. Biological sciences/Proceedings - Royal Society. Biological Sciences,
09/2015, Letnik:
282, Številka:
1814
Journal Article
Recenzirano
Odprti dostop
We spend the majority of our lives indoors; yet, we currently lack a comprehensive understanding of how the microbial communities found in homes vary across broad geographical regions and what ...factors are most important in shaping the types of microorganisms found inside homes. Here, we investigated the fungal and bacterial communities found in settled dust collected from inside and outside approximately 1200 homes located across the continental US, homes that represent a broad range of home designs and span many climatic zones. Indoor and outdoor dust samples harboured distinct microbial communities, but these differences were larger for bacteria than for fungi with most indoor fungi originating outside the home. Indoor fungal communities and the distribution of potential allergens varied predictably across climate and geographical regions; where you live determines what fungi live with you inside your home. By contrast, bacterial communities in indoor dust were more strongly influenced by the number and types of occupants living in the homes. In particular, the female : male ratio and whether a house had pets had a significant influence on the types of bacteria found inside our homes highlighting that who you live with determines what bacteria are found inside your home.
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