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We present an approach for the inclusion of nonspherical constituents in high-resolutionN-body discrete element method (DEM) simulations. We use aggregates composed of bonded spheres to model nonspherical components. Though the method may be applied more generally, we detail our implementation in the existingN-body codepkdgrav. It has long been acknowledged that nonspherical grains confer additional shear strength and resistance to flow when compared with spheres. As a result, we expect that rubble-pile asteroids will also exhibit these properties and may behave differently than comparable rubble piles composed of idealized spheres. Since spherical particles avoid some significant technical challenges, most DEM gravity codes have used only spherical particles or have been confined to relatively low resolutions. We also discuss the work that has gone into improving performance with nonspherical grains, building onpkdgrav's existing leading-edge computational efficiency among DEM gravity codes. This allows for the addition of nonspherical shapes while maintaining the efficiencies afforded bypkdgrav's tree implementation and parallelization. As a test, we simulated the gravitational collapse of 25,000 nonspherical bodies in parallel. In this case, the efficiency improvements allowed for an increase in speed by nearly a factor of 3 when compared with the naive implementation. Without these enhancements, large runs with nonspherical components would remain prohibitively expensive. Finally, we present the results of several small-scale tests: spin-up due to the YORP effect, tidal encounters, and the Brazil nut effect. In all cases, we find that the inclusion of nonspherical constituents has a measurable impact on simulation outcomes.

 

The emergence of and transitions between distinct phenotypes in isogenic cells can be attributed to the intricate interplay of epigenetic marks, external signals, and gene-regulatory elements. These elements include chromatin remodelers, histone modifiers, transcription factors, and regulatory RNAs. Mathematical models known as gene-regulatory networks (GRNs) are an increasingly important tool to unravel the workings of such complex networks. In such models, epigenetic factors are usually proposed to act on the chromatin regions directly involved in the expression of relevant genes. However, it has been well-established that these factors operate globally and compete with each other for targets genome-wide. Therefore, a perturbation of the activity of a regulator can redistribute epigenetic marks across the genome and modulate the levels of competing regulators. In this paper, we propose a conceptual and mathematical modeling framework that incorporates both local and global competition effects between antagonistic epigenetic regulators, in addition to local transcription factors, and show the counterintuitive consequences of such interactions. We apply our approach to recent experimental findings on the epithelial–mesenchymal transition (EMT). We show that it can explain the puzzling experimental data, as well as provide verifiable predictions. 

Controlling nanoporosity to favorably alter multiple properties in layered crystalline inorganic thin films is a challenge. Here, we demonstrate that the thermoelectric and mechanical properties of Ca 3 Co 4 O 9 films can be engineered through nanoporosity control by annealing multiple Ca(OH) 2 /Co 3 O 4 reactant bilayers with characteristic bilayer thicknesses (b t ). Our results show that doubling b t , e.g. , from 12 to 26 nm, more than triples the average pore size from ∼120 nm to ∼400 nm and increases the pore fraction from 3% to 17.1%. The higher porosity film exhibits not only a 50% higher electrical conductivity of σ ∼ 90 S cm −1 and a high Seebeck coefficient of α ∼ 135 μV K −1 , but also a thermal conductivity as low as κ ∼ 0.87 W m −1 K −1 . The nanoporous Ca 3 Co 4 O 9 films exhibit greater mechanical compliance and resilience to bending than the bulk. These results indicate that annealing reactant multilayers with controlled thicknesses is an attractive way to engineer nanoporosity and realize mechanically flexible oxide-based thermoelectric materials. 

Oncogenic mutations within the epidermal growth factor receptor (EGFR) are found in 15 to 30% of all non–small-cell lung carcinomas. The term exon 19 deletion (ex19del) is collectively used to refer to more than 20 distinct genomic alterations within exon 19 that comprise the most common EGFR mutation subtype in lung cancer. Despite this heterogeneity, clinical treatment decisions are made irrespective of which EGFR ex19del variant is present within the tumor, and there is a paucity of information regarding how individual ex19del variants influence protein structure and function. Herein, we identified allele-specific functional differences among ex19del variants attributable to recurring sequence and structure motifs. We built all-atom structural models of 60 ex19del variants identified in patients and combined molecular dynamics simulations with biochemical and biophysical experiments to analyze three ex19del mutations (E746_A750, E746_S752 > V, and L747_A750 > P). We demonstrate that sequence variation in ex19del alters oncogenic cell growth, dimerization propensity, enzyme kinetics, and tyrosine kinase inhibitor (TKI) sensitivity. We show that in contrast to E746_A750 and E746_S752 > V, the L747_A750 > P variant forms highly active ligand-independent dimers. Enzyme kinetic analysis and TKI inhibition experiments suggest that E746_S752 > V and L747_A750 > P display reduced TKI sensitivity due to decreased adenosine 5′-triphosphate K m . Through these analyses, we propose an expanded framework for interpreting ex19del variants and considerations for therapeutic intervention. 

The development and validation of computational models to detect daily human behaviors (e.g., eating, smoking, brushing) using wearable devices requires labeled data collected from the natural field environment, with tight time synchronization of the micro-behaviors (e.g., start/end times of hand-to-mouth gestures during a smoking puff or an eating gesture) and the associated labels. Video data is increasingly being used for such label collection. Unfortunately, wearable devices and video cameras with independent (and drifting) clocks make tight time synchronization challenging. To address this issue, we present the Window Induced Shift Estimation method for Synchronization (SyncWISE) approach. We demonstrate the feasibility and effectiveness of our method by synchronizing the timestamps of a wearable camera and wearable accelerometer from 163 videos representing 45.2 hours of data from 21 participants enrolled in a real-world smoking cessation study. Our approach shows significant improvement over the state-of-the-art, even in the presence of high data loss, achieving 90% synchronization accuracy given a synchronization tolerance of 700 milliseconds. Our method also achieves state-of-the-art synchronization performance on the CMU-MMAC dataset. 

We show that an array of non-Hermitian particles can enable advanced manipulations of the scattering pattern, beyond what is possible with passive structures. Active linear elements are shown to provide zero forward scattering without sacrificing the total scattered power, and by adding more particles, it is possible to control the zero-scattering direction at will. We apply our theory to realistic implementations of scatterer arrays, using loaded dipole antennas in which we tune the load impedance and investigate the stability of these arrays based on a realistic dispersion model for the gain elements. Finally, we discuss the possibility of controlling multiple frequencies to enable broadband control of the scattering pattern.

 

The high computational complexity caused by static optimization is the key factor to hinder the development of energy management systems. Adaptive dynamic programming (ADP) is an effective dynamic optimization method to break through the bottleneck. However, the hard constraints of energy system have not been fully considered due to the non‐convexity and nonlinearity of the value function, which makes the theoretical analysis complicated and brings safe security problems in battery systems. In this article, a systematic online ADP control framework is proposed for smart buildings control to ensure hard constraints to be satisfied. The second‐order local expansion at the current state is used to replace the nonlinear value function to simplify the theoretical analysis with the error of the reminder term. Based on the local property of value function, a method for the determination of adaptive parameters is first designed. It is proven that the solution of adaptive parameters not only prevents over‐charged and over‐discharged of the battery but also limits the charging and discharging power of the battery to be less than the rated power. In addition, long‐short term memory (LSTM) neural networks, a type dynamic network with memory characteristics, are used for the implementation of the present algorithm instead of the static networks to help realize the algorithm online. Due to the hidden state of LSTM, the performance of the online algorithm is improved after running the energy system. Numerical results verify the effectiveness of the proposed online control algorithm.