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Rheological properties and regeneration mechanisms of aged bitumen recycled from two-step chemical modification of used edible oil (UEO) with high acid value
No full textThe acid value of used edible oil (UEO) affects its regenerative capacity. Modification of UEOs to reduce their acidic compound content is receiving extensive attention. However, there have been fewer investigations on the rheological performance of modified UEO reclaimed asphalt binders and their regeneration mechanism. Therefore, a two-step catalytic process was first conducted on UEO with high acid value to minimize its free fatty acid content. Apparent viscosity, oscillation, multiple stress creep recovery, linear amplitude sweep, and bending beam rheometer experiments were carried out to survey the influence of modified UEO on the rheological properties of aged binders. In addition, the chemical components, functional groups, molecular weights, and their distributions of the rejuvenator and various binders were characterized by thin-layer chromatography, gas chromatography-mass spectrometry, infrared spectroscopy, and gel chromatography analyses to reveal the regeneration mechanism of modified UEO. The results demonstrated that modified UEO can restore the rheological properties of the aged binder. The high-temperature performance of aged binders gradually decreased with the addition of modified UEO, but the rutting resistance of rejuvenated binders was acceptable. In addition, rejuvenated binders exhibited better low-temperature performance and fatigue resistance. Chemical analyses showed that modified UEO was rich in methyl esters. The modified UEO improved the homogeneity of the aged binder, restored its colloidal structure, and reduced the sulfoxide and carbonyl indices. Moreover, there was no chemical reaction during rejuvenation. Overall, using modified UEO as an asphalt rejuvenator realizes the utilization of both wastes
Microscopic morphology and adhesion performance of SBS/OMMT modified asphalt under chloride salt erosion
No full textSaline environment leads to the deterioration of asphalt adhesion behaviors, significantly impacting road surface lifespan. The objective of this study is to employ Styrene-Butadiene-Styrene (SBS) and organo-montmorillonite (OMMT) composite-modified asphalt to enhance its resistance to salt erosion. The microscopic structure, mechanical properties and nonlinear dynamical system stability of the SBS-OMMT modified asphalt before and after chloride salt erosion were assessed by atomic force microscopy (AFM). Various parameters including morphology, roughness, fractal dimension, height-height correlation function (HHCF), (Derjaguin-Muller-Toporov) DMT modulus, adhesion and Lyapunov exponent were comprehensively analyzed to assess the SBS-OMMT modified asphalt. Results indicate that with increasing OMMT content, the quantity of “bee structures” in the SBS-OMMT modified asphalt increases while surface roughness and adhesion decrease, and the DMT modulus of the bee regions increases. Post chloride salt erosion, the “bee structures” in the SBS-OMMT modified asphalt become indistinct, peak-valley depth tends toward flatness, surface roughness and bee area DMT modulus increase, and adhesion decreases. Among these, the 5 % OMMT content in the SBS-OMMT modified asphalt exhibits the least variation. Fractal dimension reflects changes in the “bee structures” due to OMMT content and chloride salt erosion. HHCF provides information on roughness variation. SBS modified asphalt chloride salt erosion into a chaotic state, and OMMT can increase its stability. Research findings suggest that OMMT to a certain extent can resist the adverse effects of chloride salt on asphalt
A Photothermal Hydrophobic Emulsified Asphalt Coating with Prussian Blue and Polytetrafluoroethylene for Improved Road Anti-Icing and Deicing
No full textIce formation on roads not only degrades pavement performance but also increases traffic safety hazards. Traditional mechanical and salt deicing methods often damage the pavement structure and pollute the environment. To address these issues, a clean method of preparing the hydrophobic coatings on the road has been developed in recent years. These coatings help reduce water accumulation on road surfaces and delay the freezing process. However, in extreme weather conditions, roads can still develop thick ice layers that are too robust to be broken or removed by vehicle traffic alone. Therefore, incorporating thermal energy becomes essential to decrease the ice accumulation and ease the deicing process. In our study, we prepared coatings by using polytetrafluoroethylene (PTFE) known for its hydrophobic properties, Prussian blue (PB) for its photothermal conversion property, and emulsified asphalt. The contact angle of the water droplet on the surface of the coating is 150.238°. When exposed to 808 nm near-infrared light, the time it took for water droplets on the coating surface to freeze extended up to 18.5 times longer than normal. During deicing test, the time required for ice removal from the coating and for the ice to melt reduced by 90.6% and 80.9%, respectively. Additionally, the inclusion of PB significantly enhanced the durability of the coating. Overall, this coating shows promising potential for application in road anti-icing and deicing processes
Mapping Michigan\u27s historical coastlines
No full textA recently completed study has created and documented the first comprehensive compilation of spatio-temporal shoreline change for a significant portion of Lakes Michigan, Huron and Superior with historical snapshots dating back to 1938. In total, more than 4100 km was mapped at sufficient fidelity to allow resolution at the individual property owner level, allowing property owners, communities, regional managers and planners as well as regulatory agencies to directly observe not only natural changes to shorelines, but also anthropogenic impacts associated with shoreline hardening. Products produced by this study are publicly accessible via a geospatial data portal, and include historical aerial photography mosaics, historical shoreline and bluff line positions, long-term shoreline rate of change analysis input and outputs, and an interactive web-based viewer incorporating these products with other complementary datasets. Long-term rate of change analyses found that while some areas of Lakes Michigan and Huron exhibited isolated rates of recession greater than 1 m-per-year (m/yr), the majority of the shorelines were stable over the 82 years analyzed, with Lake Superior exhibiting the most stability (85 %), followed by Lake Huron (65 %), while Lake Michigan exhibited the lowest percentage of stable shorelines (52 %). Additionally, analysis of short-term rates-of-change shows the potential to detect shoreline hardening based on the variance between a transect\u27s short-term and long-term rates-of-change
Improving Prediction of Intracranial Aneurysm Rupture Status Using Temporal Velocity-Informatics
No full textThis study uses a spatial pattern analysis of time-resolved aneurysmal velocity fields to enhance the characterization of intracranial aneurysms’ (IA) rupture status. We name this technique temporal velocity-informatics (TVI). In this study, using imaging data obtained from 112 subjects harboring IAs with known rupture status, we reconstructed 3D models to get aneurysmal velocity data by performing computational fluid dynamics (CFD) simulations and morphological information. TVI analyses were conducted for time-resolved velocity fields to quantitatively obtain spatial and temporal flow disturbance. Lastly, we employed four machine learning (ML) methods (e.g., support vector machine [SVM]) to evaluate the prediction performance of the proposed TVI. Overall, the SVM’s prediction with TVI performed the best: an area under the curve (AUC) value of 0.92 and a total accuracy of 86%. With TVI, the SVM classifier correctly identified 77 and 92% of ruptured and unruptured IAs, respectively
Noticing in the midst of building on a critical event
No full textResearch on teachers’ noticing of student mathematical thinking has typically focused on how a teacher attends to, interprets, and determines a response to an individual student contribution in isolation from the broader mathematical classroom context. This research focus is not nuanced enough, however, to fully account for the complex noticing required of a teacher engaged in responsive teaching. To support teachers in enacting responsive teaching, it is important to have a way to distinguish high-leverage student contributions from among the many contributions available to a teacher. We draw on a previously developed framework to help teachers identify such contributions, those referred to as a mathematically significant pedagogical opportunity to build on student thinking (MOST) (Leatham in Journal for Research in Mathematics Education 46:88–124, 2015). We conceptualized a productive response to a MOST—what we call building on a MOST—that involves the teacher using a coordinated combination of moves to engage the class in collaboratively developing a sense-making argument about the significant mathematics inherent in the MOST. Rather than a single act of noticing, building requires the teacher to engage in an iterative, dynamic, and relational noticing process as students contribute their ideas to the sense-making discussion. It is this ongoing noticing that allows the teacher to keep the discussion focused on making sense of the MOST. In this article, we use this responsive teaching practice and the iterative noticing it entails to unpack a nuance of teacher noticing—what we call noticing with respect to
Drivers and concerns of adopting Artificial Intelligence n managerial accounting
No full textRecent advancements in Artificial Intelligence (AI) have attracted significant attention within the managerial accounting profession. With its transformative capabilities and complexity, AI presents numerous opportunities alongside notable challenges in its adoption. This paper examines the key factors influencing AI adoption in managerial accounting and highlights common concerns faced by companies during this process. Based on interviews with representatives from 41 companies, we identified a range of factors impacting AI adoption at both institutional and individual levels. These findings offer valuable insights into AI acceptance within the field of managerial accounting
Characterization of fibrotic liver tissue microstructure for predicting shear wave speed variability: A Machine-learning-based computational study
Full text linkObjective: This study aimed to establish a link between the microstructure of simulated fibrotic liver tissues and the measured shear wave speed variability using a machine-learning-based approach. Approach: Fibrotic liver tissues were simulated using biphasic random fields. The underlying microstructure of the simulated fibrotic liver pathology (sFLP) was characterized using spatial pattern distribution analysis.A machine learning (ML) technique was implemented to identify top-rated spatial characteristic (SC) features and provide context for shear wave speed (SWS) variability, ultimately enabling us to use the SWS variability to infer its underlying tissue microstructure. Different combinations of top-three features were tested to understand the sensitivity of our parameter selection. Main Results: Even though volume fraction and the SWS estimates were highly correlated, percent inclusion by itself, as a single predictive factor was not an accurate indicator of the SWS estimates. For the sFLP tissue models developed for the current study, none of the individual SC features were able to predict the SWS estimates. Regardless of the top features identified, the model prediction correlation remained constant for each prediction iteration. However, even though the top three features across the five ML-based prediction iterations had different specific names, the features were all highly correlated. Significance: The findings from our current study suggest that while the percent inclusion rate was highly correlated to the Mean SWS and SWS-STD, the percent inclusion rate alone cannot predict Mean SWS or SWS-STD. Mean SWS and SWS-STD provide unique information regarding the sFLP tissue microstructure, and both SWS estimates should be considered when analyzing fibrotic liver tissue. This computational study provides a theoretical basis and insight into the potential utility of SWS variability as a means for characterizing liver fibrosis in vivo, laying a foundation for follow-up studies
Current Experimental, Statistical, and Mechanistic Approaches to Optimizing Biomolecule Separations in Aqueous Two-phase Systems
No full textAqueous two-phase systems (ATPS) have been used to purify a range of biomolecules, including small molecules, monoclonal antibodies, viruses, and whole cells. They are known for selective separations, creating a stabilizing, low-shear environment, and high yields. Recently, as biomanufacturing attempts to adopt continuous processing, attention has shifted to ATPS for its ability to operate fully continuously while incurring lower costs than many chromatographic methods. But despite 60 years of exploration and development, the complex network of interlinked driving forces controlling these separations has prevented robust development of process understanding, and most ATPS separations are still optimized using slow and costly manual screening methods. As a result, industry has been unwilling to adopt ATPS. Fortunately, a growing body of literature is developing statistical and mechanistic models of ATPS to predict liquid-liquid equilibria and separations with reduced experimental burden. This review surveys the application of these models to ATPS, comparing their progress and potential to promote rapid development of bioseparations in the near and long term. The discussion evaluates the adaptability of statistical tools, like response surface methodology and artificial neural networks, and contrasts it with the process understanding generated through application of semi-empirical thermodynamic models. Strategies are explored to automate optimization of separations for new biomolecules using these models to create artificial data. By understanding the landscape of models applied to ATPS, this review will start a discussion about bringing this technology closer to commercialization and enabling continuous processing on a broader scale
Wood Decomposition in Poorly-drained Forested Wetland Soils: How Important are Termites?
No full textTermites are important wood decomposers in tropical and sub-tropical forest ecosystems, but less is known about termite activity in temperate forests, especially poorly-drained wetlands. Therefore, we carried out a 5-year study to assess the effects of a seasonally-variable water table on wood decomposition by termites. The study was carried out on three forested wetland sites on the Atlantic Coastal Plain of the US where the water table was: 1) only in the mineral soil, 2) at the soil surface for 3.7 months/year, and 3) at the soil surface for 5.7 months/year, and on an adjacent drier upland forest. We used wood stakes of aspen (Populus tremuloides Michx.), red maple (Acer rubrum L.) and loblolly pine (Pinus taeda L.), which were placed on the surface litter, at the litter-mineral soil interface, and in mineral soil, and sampled annually. Stake mass loss was used to measure the effect of termites on wood decomposition.
Wood decomposition and termite activity on the three wetland sites was controlled by soil depth to the water table, being greatest where the water table was rarely above the mineral soil surface, and least where the litter layer was inundated the longest. At the end of 5 years the effect of termites on wood stake mass loss was highest at the litter surface (51%), and lowest in the mineral soil (30%) where microbial decay dominated. However, small differences in surface elevation can affect termite activity and wood mass loss in the mineral soil. Termites likely fed on stakes during drier summer months, often only on the stake surface, and favored aspen, less for red maple, and least for pine. Termites dominated decomposition in the upland. Stake mass loss was lowest on the litter surface but exceeded 85% at all three soil locations. In contrast to the wetlands, termites showed little difference in wood stake feeding preference. This effect of soil water content on termite feeding preference has not been observed elsewhere.
Our study suggests that altered precipitation amounts projected in future climate scenarios could have a greater effect than temperature on wood decomposition by termites in poorly-drained wetland ecosystems. Much more information is needed on termite activities in mineral soil across different climate regimes, and what factors control termite preference for wood species in soils with different water regimes