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ArticleEnhancing image quality in circular-view photoacoustic tomography using randomized detection points(IOP Publishing Ltd, 2024-12-16)Circular-view (circular scan) photoacoustic computed tomography (PACT) with low-density detection points (DPs) is an efficient, high-speed, and inexpensive modality with numerous (pre-) clinical applications. However, as the number of DPs decreases (decrease in A-lines), some unwanted streak artifacts appear in the reconstructed PACT image. Here, we present an approach to address the mentioned challenge and enhance image contrast. In this method, several low-resolution-images (LRIs) are reconstructed by employing a few DPs' data with randomized locations. These LRIs are used in computing an artifact score matrix (ASM) to identify the location of artifacts. Three numerical (two vasculatures and human brain), two experimental (triangle and complex leaf), and an in vivo (a rat brain) studies were conducted to evaluate the efficacy of the proposed method (applying the computed ASM to the final reconstructed PA image). Our findings show that the proposed method outperforms conventional methods and offers better image quality. The signal-to-noise ratio and structural similarity index values of the proposed method are quantitatively 20 dB and 25% better than the conventional method, respectively. Furthermore, compared to the conventional method, the proposed method has an artifact standard deviation that is 50 times lower.
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PreprintPredicting Performance of Microfluidic-Based Alginate Microfibers with Feature-Supplemented Deep Neural Networks( 2024-12-11)Selection of solution concentrations and flow rates for the fabrication of microfibers using a microfluidic device is a largely empirical endeavor of trial-and-error, largely due to the difficulty of modeling such a multiphysics process. Machine learning, including deep neural networks, provides the potential for allowing the determination of flow rates and solution characteristics by using past fabrication data to train and validate a model. Unfortunately, microfluidics suffers from low amounts of data, which can lead to inaccuracies and overtraining. To reduce the errors inherent with developing predictive and design models using a deep neural network, two approaches are investigated: dataset expansion using the statistical properties of available samples and model enhancement through introduction of physics-related parameters, specifically dimensionless numbers such as the Reynolds, capillary, Weber, and Peclet numbers. Results show that introduction of these parameters provides enhanced predictive capability leading to increased accuracy, while no such improvements are yet observed for design parameter selection.
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ArticleLife-cycle environmental assessment of ultra-high-performance concrete with sustainable materials and fiber substitutions(Elsevier Ltd, 2024-12-10)Ultra-high-performance concrete (UHPC) stands at the forefront of cementitious materials used for construction, owing to its unparalleled strength and durability. However, the high cement content and excessive use of steel fibers in the conventional UHPC pose significant carbon dioxide emission and environmental challenges, necessitating the exploration of sustainable alternatives. This study asseses the potential environmental impact reduction achieved by incorporating sustainable materials in UHPC mixtures. The research focuses on replacing conventional UHPC materials with Portland limestone cement (PLC), recycled glass powder, and polyester fibers as lower-impact alternatives. The UHPC mixtures are chosen in a way that falls under the same compressive strength class and offers similar performance characteristics. A life cycle assessment (LCA) methodology is employed to evaluate the environmental performance of different UHPC mixtures. The LCA follows a cradle-to-gate approach, considering key factors such as global warming potential (GWP), energy use, raw material extraction, transportation, and production processes. The results show that substituting ordinary Portland cement (OPC) with PLC and incorporating recycled glass powder reduced the GWP of UHPC mixtures by up to 17%. Moreover, partially replacing steel fibers with polyester fibers further reduced the GWP by 29%. These findings highlight the potential for significant environmental impact reductions in UHPC production through material optimization, contributing to more sustainable construction practices without compromising mechanical performance.
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ArticleFrom concept to classroom: Developing instructor dashboards through human centered design(Elsevier Ltd, 2024-12-09)Dashboards visually provide automated feedback and communicate classroom data that instructors cannot otherwise recall. To date, dashboards have been designed more from a technical than a pedagogical perspective. Although researchers have called for a human-centered approach to dashboard design, research documenting design processes has been limited in the literature. In the present study, a human-centered approach was employed to design an instructor dashboard. The design processes and illustrative prototypes are presented, along with the features participant instructors perceived to be the most and least helpful. The integration of a human-centered approach to guide the processes involved in designing actionable instructor dashboards is also discussed. Three components of a human centered approach are recommended: (a) designing with multiple stakeholders, (b) aligning pedagogy with analytics, and (c) training on data use. This research contributes to the field by demonstrating how human-centered design can bridge the gap between technological potentials and pedagogical features in instructor dashboards, ultimately leading to effective integration of such tools in higher education classrooms.
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ArticleDFT investigation of the impact of inner-sphere water molecules on RE nitrate binding to internal pore and external surface of MCM-22(Royal Society of Chemistry, 2024-12-02)The impact of inner-sphere water molecules on the binding of rare earth (RE) nitrates to MCM-22 aluminosilicates is analyzed. We used cluster models of MCM-22 to investigate the binding phenomena through localized-basis density functional theory (DFT) calculations. We also conducted plane-wave DFT calculations for a few selected binding configurations using the entire periodic MCM-22 unit cell to check for consistency. Two different MCM-22 cluster models are developed to represent an internal pore and an external surface. Starting with pure silica MCM-22, we substituted one Si with Al and added a H atom on the O bridging the Si and Al to create a Brønsted acid site (BAS), [triple bond, length as m-dash]Si–{OH}–Al[triple bond, length as m-dash]. Specifically, we investigated the binding of two RE nitrate aqua complexes, [X(NO3)3(H2O)n] where n = 4 (3) for X = Nd (Yb) via the reaction X(NO3)3(H2O)n + [triple bond, length as m-dash]Si–{OH}–Al[triple bond, length as m-dash] → [triple bond, length as m-dash]Si–{OX(NO3)2(H2O)n}–Al[triple bond, length as m-dash] + HNO3 at BASs, and via an analogous reaction at silanol sites, [triple bond, length as m-dash]Si–{OH}. The above analysis just includes the inner coordination sphere H2O. Actually, for the Nd (Yb) complex, after binding at the T1 and T2 sites (T1 site) within the internal pore, one of the H2O molecules leaves this inner sphere. The binding strength at BASs and silanol sites is calculated from the energy change during the above reactions. One finds that Nd complexes prefer binding at the internal pore, while Yb complexes have a comparable binding preference both at the internal pore and external surface. The cluster calculations show good agreement with periodic calculations, implying that the cluster models are suitable for binding studies. Compared to the binding of non-hydrated RE nitrates, the explicit H2O molecules have a minimal impact on overall binding energy trends, but they do increase individual binding energy values. This study also demonstrated the stronger binding affinity of BASs over silanol sites.