The spatial organization of membrane-bound ligands is thought to regulate receptor-mediated signaling. However, direct regulation of receptor function by nanoscale distribution of ligands has not yet ...been demonstrated, to our knowledge. We developed rationally designed DNA origami nanostructures modified with ligands at well-defined positions. Using these 'nanocalipers' to present ephrin ligands, we showed that the nanoscale spacing of ephrin-A5 directs the levels of EphA2 receptor activation in human breast cancer cells. Furthermore, we found that the nanoscale distribution of ephrin-A5 regulates the invasive properties of breast cancer cells. Our ligand nanocaliper approach has the potential to provide insight into the roles of ligand nanoscale spatial distribution in membrane receptor-mediated signaling.
Structural defects such as porosity have detrimental effects on additively manufactured parts which can be reduced by choosing optimal process conditions. In this work, the relationship between ...process parameters and lack-of-fusion (LOF) porosity has been studied for the laser powder bed fusion (L-PBF) process of the Ti‐6Al‐4V alloy (Ti64). A physics-based thermo-fluid model is used to predict LOF porosity in the multilayer multitrack PBF process. To effectively map the high-dimensional processing parameters with porosity, an active learning framework has been adopted for the optimal design of experiments. Furthermore, a customized neural network-based symbolic regression tool has been utilized to identify a mechanistic relationship between processing conditions and LOF porosity. Results indicate that combining the physics-based thermo-fluid model for PBF porosity prediction with active learning and symbolic regression can find an appropriate mechanistic relationship of LOF porosity that is predictive for a wide range of processing conditions. This mechanistic relationship was further tested for other metal AM materials systems (IN718, SS316L) through non-dimensional numbers. The presented workflow effectively explores the high-dimensional process design space for different additive manufacturing materials systems.
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•A physics-based thermo-fluid model is developed and calibrated to predict the lack-of-fusion porosity of metal AM systems.•Active learning framework is proposed to obtain an optimal design of process conditions to reduce number of simulations.•A neural network based symbolic regression tool is demonstrated to predict the mechanistic process-structure relationship.
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•A mechanistic data science (MDS) framework is proposed for composite materials design.•Six steps of MDS are demonstrated to create a composite knowledge database.•An inverse problem ...is showed to predict the materials system for target properties using MDS approach.
We present a mechanistic data science (MDS) framework capable of building a composite knowledge database for composite materials design. The MDS framework systematically leverages data science to extract mechanistic knowledge from composite materials system. The composite response database is first generated for three matrix and four fiber combinations using a physics-based mechanistic reduced-order model. Next, the mechanistic features of the composites are identified by mechanistically analyzing the composites stress–strain responses. A relationship between the composite properties and the constituents’ material features are established through a mechanics constrained data science-based learning process after representing materials in latent space, following a dimension reduction technique. We demonstrate the capability of predicting a composite materials system for target properties (material elastic properties, yield strength, resilience, toughness, and density) from the MDS created knowledge database. The MDS model is predictive with reasonable accuracy, and capable of identifying the materials system along with the tuning required to achieve desired composite properties. Development of such MDS framework can be exploited for fast materials system design, creating new opportunity for performance guided materials design.
Magnetic Resonance Imaging (MRI) background magnets are made from superconducting composite wires that are a metal matrix composite (MMC). Thermal and mechanical properties of the specific wire ...matrix must be known to optimize the wire configuration for MRI magnet designs. Computational analysis techniques based on numerical homogenization may provide an accurate characterization of a multifilament MMC wire and can reduce the expenses and time required for experimental tests. Recent developments in magnesium diboride (MgB2) superconducting wire has demonstrated their feasibility in a liquid helium (LHe) free conduction-cooled MRI magnet. Computational analysis of an entire superconducting magnet design requires the elastic and thermal properties of these wires. In this work, the temperature dependent elastic modulus, Poisson’s ratio, thermal expansion coefficients, thermal conductivity, and specific heat are estimated using Finite Element Analysis (FEA) and compared to analytical approaches. The orthotropic elastic and thermal properties of four different superconducting composite wire configurations are evaluated. The experimental thermal conductivity and specific heat at room temperature agree well with its corresponding computational results. FEA based computational homogenization has been demonstrated as an acceptable method to estimate the required material properties of the MMC superconducting wire for magnet analysis of MRI manufacturing and operation.
We report the first few-mode compatible optical add/drop multiplexer (OADM) that enables add/drop functionality for mode division multiplexed (MDM) superchannels. The OADM is comprised of two ...cascaded free-space thin-film filters with 5° incident angle. The transmission of MDM superchannel CO-OFDM signals via the OADM is investigated. The experimental result shows that the OSNR penalties for add, drop and through ports are 2.6, 2.4, 0.7 dB, respectively for 3x318 Gb/s superchannels.
The study aimed to investigate the factorial structure, reliability, and validity of the Bengali version of the brief Questionnaire on Smoking Urges (QSU-Brief) tool in a sample of Bangladeshi ...smokers. The Bengali version QSU-Brief scale's reliability and validity were assessed on the basis of the data provided by 460 Bangladeshi smokers. To substantiate the data reliability, internal consistency and test-retest reliability were measured. Exploratory factor analysis (EFA), followed by confirmatory factor analysis (CFA), was conducted to validate the psychometric properties of the 10-item-QSU-Brief tool. The questionnaire showed good internal consistency (Cronbach's α = 0.94; Intraclass correlation coefficient = 0.91; p<0.001). The EFA and CFA confirmed that a two-factor solution explained 75.1% of the total variance and considered the best item structure of the Bengali version of QSU-Brief across the current study setting. The first factor reflected a strong desire to smoke, which comprised items 1, 2, 3, 5, 6, 9, and 10. While the second factor displayed expectation of relief from the negative implications, which contained items 4, 7, and 8. The study findings showed that the Bengali QSU-Brief had good reliability, validity, and factorial structure. Therefore, this tool could be an excellent candidate to evaluate smoking urges in Bangladeshi settings.
The agricultural sector is highly vulnerable to climate change, particularly in drought-prone environments. An understanding of perceptions, adaptation strategies, and their determinants including a ...gender analysis can benefit vulnerable farmers and policy makers. Using a survey of 360 farming household heads and their spouses, this study identified the intra-household perceptions and their determinants, the major strategies adopted by the farmers to adapt to climate change, and the factors that affect their adaptation decision and choice of strategies including the role of intra-household decision making in a drought prone environment of Bangladesh. The adaptation methods identified include short-duration and drought-tolerant rice varieties, supplementary irrigation for crop production, non-rice winter and horticultural crops, and improved channels for irrigation and water harvesting. Discrete choice model results indicate that age, household size, membership in any organization, access to credit, drought severity, amount of cultivated land, and agricultural subsidy significantly influence farmers’ adaptation decision and choices. Results reveal that climate change perceptions of husbands and spouses within the same households differ significantly and intra-household decision making plays a significant role in adaptation decision and selection of alternative adaptation strategies. The results would improve our understanding of farms and farming households and their climate change perceptions and adaptation choices by location and gender, thereby enabling us to outline better strategies to adapt to the changing climate.
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This paper presents a statistical physics-based machine learning model for predicting defects, such as surface roughness and lack-of-fusion porosity, in the laser powder bed fusion of metals ...(PBF-LB/M) additive manufacturing process. The statistical physics-based model is calibrated and validated against controlled single-track experiments and used for statistical prediction for multi-layer and multi-track cases for PBF-LB/M defects. A mechanistic reduced-order-based stochastic calibration process is introduced to capture the stochastic nature of the melt pool. The calibrated physics-based digital shadow model is demonstrated for predicting the surface roughness of the National Institute of Standards and Technology (NIST) overhang part X4, with a difference of 9.3% compared to the experimental results. By leveraging data obtained from both the physics-based model and experiments, a machine learning model has been trained for fast predictions (inference time of 0.4 ms) with high accuracy (error bound of 6.7%). This model can predict melt pool geometries under various processing conditions, offering a control strategy for the PBF-LB/M process. Further, the trained machine learning model is showcased to demonstrate a control application of melt pool geometries (width and depth) for specific processing parameters. These developed models (physics-based and machine learning) serve as a digital shadow of the PBF-LB/M process, offering predictive capabilities to build a digital twin model for process control, optimization, and online monitoring.
•Propose two parameterized digital shadows: PPB-DS and PPB-ML-DS for LPBF.•PPB-DS predicts melt pool geometries and diagnoses defects.•Stochastic calibration of PPB-DS offers statistical predictions for LPBF.•PPB-ML-DS enables rapid predictions for real-time monitoring and control.
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•Coupling Loss Induced Quench protection has been proposed for MgB2 MRI magnet design.•Numeric simulations calculate magnet’s temperature distribution, stress and strain.•Peak stress and strain ...values are below MgB2 wire’s prescribed limits.
Mechanical analysis of the stress and strains developed in the coils were calculated for a ten coil 1.5 T MRI magnet design with magnesium diboride (MgB2) wire protected with Coupling Loss Induced Quench (CLIQ). The temperature distribution inside the coils was first simulated in MATLAB to solve the governing heat and circuit equations. Simulations were performed on the magnet, in which each coil was divided into two subsections, with two CLIQ units while the capacitor ranged from 5 to 20 mF and the initial charging voltage ranged from 2.6 kV to 1.3 kV in order to keep the total stored energy in the CLIQ system constant. The wire’s filamentary twist pitch remained constant at 5 cm for all simulations. The exported temperature distribution was expanded to form a representative unit cell (RUC) representing the wire composite and then imported into ANSYS to calculate the 1st principle strain in the MgB2 filament and shear stress across the epoxy for the coils. A peak temperature of 191 K occurred inside the coil with the initial quench when the CLIQ unit had a 20 mF capacitor charged to 1.3 kV. According to the mechanical simulations, the largest resulting peak strain in the wire was 0.034%, and peak shear stress was 44 MPa.
In this report, we use first-principles calculations to study the methylammonium lead iodide (MAPI) perovskite interfaces with titanium dioxide (TiO
2
), zinc oxide (ZnO), and tin oxide (SnO
2
). Our ...study suggests that the binding energy of MAPI on SnO
2
is weak compared to TiO
2
and ZnO. However, we show that the strong binding to TiO
2
and ZnO deprotonates CH
3
NH
3
molecules which influences the decomposition process. Among the three interfaces studied in this report, TiO
2
-based interfaces showed the highest charge transfer followed by the interfaces formed on SnO
2
. We report a possible interfacial recombination mechanism inside MAPI/TiO
2
and MAPI/SnO
2
devices. Our study concludes that MAPI/SnO
2
interfaces provide advantages due to their improved stability for perovskite solar cells.
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