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Abstract Recently, doping guest materials such as quantum dots (QDs) into liquid crystals (LCs) has been of great interest since their addition substantially enhances the properties of LC and opens new avenues for scientific advancement. Here, we report the induction of homeotropic alignment in cells without alignment layers of the negative dielectric nematic liquid crystal, N-(4-Methoxybenzylidene)-4-butylaniline (MBBA) by doping with carbon dots (CDs ∼2.8 ± 0.72 nm). The CDs-MBBA composites (CDs concentration: 0.002, 0.01, 0.03, 0.1 and 0.3 wt%) were investigated using optical polarising microscopy, electro-optical and dielectric techniques. Polarizing optical micrographs and voltage dependent optical transmission revealed the induced homeotropic alignment for all the composites under investigation. Interestingly, the least concentrated sample, 0.002 wt% exhibited partial homeotropic alignment. However, due to light leakage, the optical transmission value below threshold voltage was relatively higher than the rest of the composites. MBBA is a negative dielectric material, hence the application of a voltage across the cell was able to switch the alignment from a dark to a bright state for all composites. However, above a certain voltage (>threshold voltage), the bright state produced some instabilities. The value of dielectric permittivity was observed to decrease with increasing concentration, confirming the effect of CDs in producing homeotropic alignment in MBBA. Measurements as a function of temperature were conducted to examine the thermal stability of the induced alignment. The alignment was found to be stable throughout the nematic phase of MBBA. The induction of such alignment without conventional alignment (i.e., rubbing of polyimides) technique can be helpful in addressing the evolving display demands by making liquid crystal displays (LCDs) and other display devices cost effective. 

Machine learning is an important tool in the study of the phase behavior from molecular simulations. In this work, we use un-supervised machine learning methods to study the phase behavior of two off-lattice models, a binary Lennard-Jones (LJ) mixture and the Widom–Rowlinson (WR) non-additive hard-sphere mixture. The majority of previous work has focused on lattice models, such as the 2D Ising model, where the values of the spins are used as the feature vector that is input into the machine learning algorithm, with considerable success. For these two off-lattice models, we find that the choice of the feature vector is crucial to the ability of the algorithm to predict a phase transition, and this depends on the particular model system being studied. We consider two feature vectors, one where the elements are distances of the particles of a given species from a probe (distance-based feature) and one where the elements are +1 if there is an excess of particles of the same species within a cut-off distance and −1 otherwise (affinity-based feature). We use principal component analysis and t-distributed stochastic neighbor embedding to investigate the phase behavior at a critical composition. We find that the choice of the feature vector is the key to the success of the unsupervised machine learning algorithm in predicting the phase behavior, and the sophistication of the machine learning algorithm is of secondary importance. In the case of the LJ mixture, both feature vectors are adequate to accurately predict the critical point, but in the case of the WR mixture, the affinity-based feature vector provides accurate estimates of the critical point, but the distance-based feature vector does not provide a clear signature of the phase transition. The study suggests that physical insight into the choice of input features is an important aspect for implementing machine learning methods. 

This paper measures excess labor supply in equilibrium. We induce hiring shocks—which employ 24 percent of the labor force in external month-long jobs—in Indian local labor markets. In peak months, wages increase instantaneously and local aggregate employment declines. In lean months, consistent with severe labor rationing, wages and aggregate employment are unchanged, with positive employment spillovers on remaining workers, indicating that over a quarter of labor supply is rationed. At least 24 percent of lean self-employment among casual workers occurs because they cannot find jobs. Consequently, traditional survey approaches mismeasure labor market slack. Rationing has broad implications for labor market analysis. (JEL E24, J22, J23, J31, J64, O15, R23) 

Polyelectrolyte solutions are of considerable scientific and practical importance. One of the most widely studied polymer is polystyrene sulfonate (PSS), which has a hydrophobic backbone with pendant charged groups. A polycation with similar chemical structure is poly(vinyl benzyltri methyl) ammonium (PVBTMA). In this work, we develop coarse-grained (CG) models for PSS and PVBTMA with explicit CG water and with sodium and chloride counterions, respectively. We benchmark the CG models via a comparison with atomistic simulations for single chains. We find that the choice of the topology and the partial charge distribution of the CG model, both play a crucial role in the ability of the CG model to reproduce results from atomistic simulations. There are dramatic consequences, e.g., collapse of polyions, with injudicious choices of the local charge distribution. The polyanions and polycations exhibit a similar conformational and dynamical behavior, suggesting that the sign of the polyion charge does not play a significant role. 

Detecting and locating damage information from waves reflected off damage is a common practice in non-destructive structural health monitoring systems. Yet, the transmitted ultrasonic guided waves are affected by the physical and material properties of the structure and are often complicated to model mathematically. This calls for data-driven approaches to model the behaviour of waves, where patterns in wave data due to damage can be learned and distinguished from non-damage data. Recent works have used a popular dictionary learning algorithm, K-SVD, to learn an overcomplete dictionary for waves propagating in a metal plate. However, the domain knowledge is not utilized. This may lead to fruitless results in the case where there are strong patterns in the data that are not of interest to the domain. In this work, instead of treating the K-SVD algorithm as a black box, we create a novel modification by enforcing domain knowledge. In particular, we look at how regularizing the K-SVD algorithm with the one-dimensional wave equation affects the dictionary learned in the simple case of vibrating string. By adding additional non-wave patterns (noise) to the data, we demonstrate that the “wave-informed K-SVD” does not learn patterns which do not obey the wave equation hence learning patterns from data and not the noise. 

This work discusses an optimization framework to embed dictionary learning frameworks with the wave equation as a strategy for incorporating prior scientific knowledge into a machine learning algorithm. We modify dictionary learning to study ultrasonic guided wave-based defect detection for non-destructive structural health monitoring systems. Specifically, this work involves altering the popular-SVD algorithm for dictionary learning by enforcing prior knowledge about the ultrasonic guided wave problem through a physics-based regularization derived from the wave equation. We confer it the name “wave-informed K-SVD.” Training dictionary on data simulated from a fixed string added with noise using both K-SVD and wave-informed K-SVD, we show an improved physical consistency of columns of dictionary matrix with the known modal behavior of different one-dimensional wave simulations is observed. 

Abstract Mutations inFARS2, the gene encoding the mitochondrial phenylalanine‐tRNA synthetase (mtPheRS), have been linked to a range of phenotypes including epileptic encephalopathy, developmental delay, and motor dysfunction. We report a 9‐year‐old boy with novel compound heterozygous variants ofFARS2, presenting with a pure spastic paraplegia syndrome associated with bilateral signal abnormalities in the dentate nuclei. Exome sequencing identified a paternal nonsense variant (Q216X) lacking the catalytic core and anticodon‐binding regions, and a maternal missense variant (P136H) possessing partial enzymatic activity. This case confirms and expands the phenotype related toFARS2mutations with regards to clinical presentation and neuroimaging findings.