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Density Functional Theory (DFT) suffers from a strong dichotomy between accuracy and consistency. There exists a paucity of a generic linear route to the systematic improvement of computed results. Benchmark studies are often unidimensional and based on molecular/atomic properties that contour on a single Potential Energy Surface. Thus, the DFT functionals so developed and optimized to similar chemical accuracy lack a universal adherence to the unique density distribution for a fixed nuclear arrangement. We have identified this as a major source of inconsistency within the DFT framework and have explored through the cross-correlations between Structure, Energy, and Dipolar Strength. Since Range Separated Hybrid (RSH) functionals are well-known in literature to produce one of the finest molecular structures in both ground and excited states, in this communication, we have utilized CAM-B3LYP, LC- ω HPBE, and ω -B97XD to explore the above cross-correlations on well-studied test subjects comprising of Anthracene, Tetracene, Phenanthrene and Pyrene in all possible combination of these functionals. The combined responses from the functionals have revealed that major inconsistency operating between structure and energy dictates the variations in the dipolar strength and that lower RMSD and higher charge reorganization are the keys to higher dipolar strength. While LC- ω HPBE stands out, CAM-B3LYP and ω B97XD remain comparatively more like each other except in the Adiabatic Energy Difference (AED) trend. [ABSTRACT FROM AUTHOR]

The paper deals with a cosmological model containing two scalar fields which can be considered as an extension of the Brans–Dicke scalar field model. Due to highly coupled non linear field equations, Noether symmetry analysis has been imposed and as a result the Lagrangian as well as the field equations become much simple in form to have the classical solutions. The relevant cosmological parameters are analyzed graphically. Finally, quantum cosmology has been studied by constructing the Wheeler–DeWitt equation and the solution of this second order partial differential equation has been done using this symmetry analysis. [ABSTRACT FROM AUTHOR]

Highlights: This review discusses the potential of degradable metallic micromotors for a variety of biomedical and environmental applications. The design principles, fabrication techniques and degradation mechanisms of degradable metallic micromotors are reviewed in detail. Challenges and future directions for the development of degradable metallic micromotors for real-life applications are presented. Synthetic micromotor has gained substantial attention in biomedicine and environmental remediation. Metal-based degradable micromotor composed of magnesium (Mg), zinc (Zn), and iron (Fe) have promise due to their nontoxic fuel-free propulsion, favorable biocompatibility, and safe excretion of degradation products Recent advances in degradable metallic micromotor have shown their fast movement in complex biological media, efficient cargo delivery and favorable biocompatibility. A noteworthy number of degradable metal-based micromotors employ bubble propulsion, utilizing water as fuel to generate hydrogen bubbles. This novel feature has projected degradable metallic micromotors for active in vivo drug delivery applications. In addition, understanding the degradation mechanism of these micromotors is also a key parameter for their design and performance. Its propulsion efficiency and life span govern the overall performance of a degradable metallic micromotor. Here we review the design and recent advancements of metallic degradable micromotors. Furthermore, we describe the controlled degradation, efficient in vivo drug delivery, and built-in acid neutralization capabilities of degradable micromotors with versatile biomedical applications. Moreover, we discuss micromotors' efficacy in detecting and destroying environmental pollutants. Finally, we address the limitations and future research directions of degradable metallic micromotors. [ABSTRACT FROM AUTHOR]

The academic and industrial communities have proposed two-dimensional (2D) transition metal dichalcogenide (TMD) semiconductors as a future option to supplant silicon transistors at sub-10nm physical gate lengths. In this Comment, we share the recent progress in the fabrication of complementary metal-oxide-semiconductor (CMOS) devices based on stacked 2D TMD nanoribbons and specifically highlight issues that still need to be resolved by the 2D community in five crucial research areas: contacts, channel growth, gate oxide, variability, and doping. While 2D TMD transistors have great potential, more research is needed to understand the physical interactions of 2D materials at the atomic scale. 2D semiconductors have been proposed as a potential option to replace or complement silicon electronics at the nanoscale. Here, the authors discuss the recent progress and remaining challenges that need to be addressed by the academic and industrial research communities towards the commercialization of 2D transistors. [ABSTRACT FROM AUTHOR]

Water covers more than two-thirds of the earth's surface, but only around 0.3% is suitable for human use. Water sources are highly polluted with various chemicals, heavy metals, and agricultural wastes. The quick and precise assessment of these contaminants in the water is one of the most important aspects of environmental monitoring. Antibiotics are one of the emerging groups of persistent organic pollutants in aquatic ecosystems due to their uncontrolled release and overuse. This review highlights important optical sensing techniques and analytical tools for onsite antibiotic monitoring in water samples that are simple to use and require minimal sample preparation. The integration of nanomaterials with these optical technologies has helped with exceptional detection capability and good stability due to their unique optical properties. In particular, this review summarizes surface plasmon resonance, colorimetry, fluorescence, surface-enhanced Raman spectroscopy-based detection, and their realization toward the chip and optical fiber-based sensors development. The characteristics and advantages of various nanomaterials for antibiotic detection, their mechanism of action, and future trends are discussed in detail. The insights of this review article will be informative and guide the researchers to develop advanced optical sensors for antibiotic detection. [ABSTRACT FROM AUTHOR]

Combinatorial optimization problems arise naturally in a wide range of applications from diverse domains. Many of these problems are NP-hard and designing efficient heuristics for them requires considerable time, effort and experimentation. On the other hand, the number of optimization problems in the industry continues to grow. In recent years, machine learning techniques have been explored to address this gap. In this paper, we propose a novel framework for leveraging machine learning techniques to scale-up exact combinatorial optimization algorithms. In contrast to the existing approaches based on deep-learning, reinforcement learning and restricted Boltzmann machines that attempt to directly learn the output of the optimization problem from its input (with limited success), our framework learns the relatively simpler task of pruning the elements in order to reduce the size of the problem instances. In addition, our framework uses only interpretable learning models based on intuitive local features and thus the learning process provides deeper insights into the optimization problem and the instance class, that can be used for designing better heuristics. For the classical maximum clique enumeration problem, we show that our framework can prune a large fraction of the input graph (around 99% of nodes in case of sparse graphs) and still detect almost all of the maximum cliques. Overall, this results in several fold speedups of state-of-the-art algorithms. Furthermore, the classification model used in our framework highlights that the chi-squared value of neighborhood degree has a statistically significant correlation with the presence of a node in a maximum clique, particularly in dense graphs which constitute a significant challenge for modern solvers. We leverage this insight to design a novel heuristic we call ALTHEA for the maximum clique detection problem, outperforming the state-of-the-art for dense graphs. [ABSTRACT FROM AUTHOR]

In this present model we have discussed about the solution of the matter-dominated cosmological equations using the expression of Λ = Λ (G) . Using Noether symmetry analysis we have analyzed both the classical and quantum cosmology. We have made a suitable point transformation in order to make one variable cyclic. As a result the evolution equations become simpler to solve. Wheeler–DeWitt (WD) equation has been constructed for this cosmological model and using conserved charge we have found the solution of WD equation. Finally the solutions have been analyzed in the cosmological point of view. [ABSTRACT FROM AUTHOR]

Many real-world mission-critical applications require continual online learning from noisy data and real-time decision making with a defined confidence level. Brain-inspired probabilistic models of neural network can explicitly handle the uncertainty in data and allow adaptive learning on the fly. However, their implementation in a compact, low-power hardware remains a challenge. In this work, we introduce a novel hardware fabric that can implement a new class of stochastic neural network called Neural Sampling Machine (NSM) by exploiting the stochasticity in the synaptic connections for approximate Bayesian inference. We experimentally demonstrate an in silico hybrid stochastic synapse by pairing a ferroelectric field-effect transistor (FeFET)-based analog weight cell with a two-terminal stochastic selector element. We show that the stochastic switching characteristic of the selector between the insulator and the metallic states resembles the multiplicative synaptic noise of the NSM. We perform network-level simulations to highlight the salient features offered by the stochastic NSM such as performing autonomous weight normalization for continual online learning and Bayesian inferencing. We show that the stochastic NSM can not only perform highly accurate image classification with 98.25% accuracy on standard MNIST dataset, but also estimate the uncertainty in prediction (measured in terms of the entropy of prediction) when the digits of the MNIST dataset are rotated. Building such a probabilistic hardware platform that can support neuroscience inspired models can enhance the learning and inference capability of the current artificial intelligence (AI). Neural sampling machines make use of noise to perform learning. Here, Dutta et al. present a hybrid stochastic synapse composed out of a ferroelectric transistor combined with a stochastic selector exhibiting multiplicative synaptic noise required for implementing a neural sample machine. [ABSTRACT FROM AUTHOR]

We explored the observed similarity/dissimilarity among guilt, shame, and remorse (GSR) by conducting an empirical study in three phases—(i) generation of scenarios, (ii) development of illustrations and determination of item (scenario) equivalence, and (iii) rating of GSR induced by the scenarios. Ten young adults wrote their life experiences of guilt, shame and remorse, respectively, yielding a total of 34 scenarios. The scenarios were presented to 138 participants, and they were instructed to reflect upon the emotion (GSR) that best suited the scenarios. Principal component analysis indicated that the same scenario induced guilt as well as shame or remorse depending on the valence. Scenarios with positive factor load assessed guilt, whereas shame and remorse had negative factor load. Following an iterative process, the scenarios were converted into illustrations where each scenario was depicted in a storyboard comprising a set of three illustrations. Generalized procrustes analysis was performed to assess item equivalence between the narrations and the corresponding storyboards. Fourteen participants first rated the narratives and then the storyboards after a gap of 24 h. The findings suggest no significant difference between the illustrated scenarios and the narrative, thus leading to 13 scenarios. Multidimensional scaling suggests that the perception of shame and remorse overlaps and this is distinct from guilt. [ABSTRACT FROM AUTHOR]

Automated construction of knowledge hierarchies from huge data corpora is gaining increasing attention in recent years, in order to tackle the infeasibility of manually extracting and semantically linking millions of concepts. As a knowledge hierarchy evolves with these automated techniques, there is a need for measures to assess its temporal evolution, quantifying the similarities between different versions and identifying the relative growth of different subgraphs in the knowledge hierarchy. In this paper, we focus on measures that leverage structural properties of the knowledge hierarchy graph to assess the temporal changes. We propose a principled and scalable similarity measure, based on Katz similarity between concept nodes, for comparing different versions of a knowledge hierarchy, modeled as a generic directed acyclic graph. We present theoretical analysis to depict that the proposed measure accurately captures the salient properties of taxonomic hierarchies, assesses changes in the ordering of nodes, along with the logical subsumption of relationships among concepts. We also present a linear time variant of the measure, and show that our measures, unlike previous approaches, are tunable to cater to diverse application needs. We further show that our measure provides interpretability, thereby identifying the key structural and logical difference in the hierarchies. Experiments on a real DBpedia and biological knowledge hierarchy showcase that our measures accurately capture structural similarity, while providing enhanced scalability and tunability. Also, we demonstrate that the temporal evolution of different subgraphs in this knowledge hierarchy, as captured purely by our structural measure, corresponds well with the known disruptions in the related subject areas. [ABSTRACT FROM AUTHOR]

The present work deals with scalar field cosmology in the framework of a quantum gravity modified Einstein–Hilbert Lagrangian with variable G and Λ . Using renormalization group, variable G behaves as a minimally coupled filed (not the scalar–tensor theory) and variable Λ can be interpreted as a potential function. The point Lagrangian for this model in the background of homogeneous and isotropic flat FLRW space-time model experiences point-like Noether symmetry and equivalent potential function Λ (G) is determined. Using a point transformation in the 3D augmented space is found that one of the variable become cyclic and as a consequence there is considerable simplification to the physical system. Lastly, the constants of motion can be written in compact form and it is possible to have analytic cosmological solutions in the present context. [ABSTRACT FROM AUTHOR]

The striking similarity between biological locomotion gaits and the evolution of phase patterns in coupled oscillatory network can be traced to the role of central pattern generator located in the spinal cord. Bio-inspired robotics aim at harnessing this control approach for generation of rhythmic patterns for synchronized limb movement. Here, we utilize the phenomenon of synchronization and emergent spatiotemporal pattern from the interaction among coupled oscillators to generate a range of locomotion gait patterns. We experimentally demonstrate a central pattern generator network using capacitively coupled Vanadium Dioxide nano-oscillators. The coupled oscillators exhibit stable limit-cycle oscillations and tunable natural frequencies for real-time programmability of phase-pattern. The ultra-compact 1 Transistor-1 Resistor implementation of oscillator and bidirectional capacitive coupling allow small footprint area and low operating power. Compared to biomimetic CMOS based neuron and synapse models, our design simplifies on-chip implementation and real-time tunability by reducing the number of control parameters. Designing alternative paradigms for bio-inspired analog computing that harnesses collective dynamics remains a challenge. Here, the authors exploit the synchronization dynamics of coupled vanadium dioxide-based insulator-to-metal phase-transition nano-oscillators for adaptive locomotion control. [ABSTRACT FROM AUTHOR]

The present work deals with evolution of the fractal model of the universe in the background of homogeneous and isotropic FLRW space-time geometry. The cosmic substrum is taken as perfect fluid with barotropic equation of state. A general prescription for the deceleration parameter is determined and it is examined whether the deceleration parameter may have more than one transition during the evolution of the fractal universe for monomial form of the fractal function as a function of the scale factor. Finally, the model has been examined by making comparison with the observed data. [ABSTRACT FROM AUTHOR]

The paper deals with generalized Rastall theory of gravity and its cosmological consequences in the background of homogeneous and isotropic flat FLRW model with perfect fluid as the matter context. The model shows a non singular era (emergent scenario) at the early phase of expansion for a particular choice of the Rastall parameter. Also the model finds to be equivalent to the particle creation mechanism in Einstein gravity in the framework of non-equilibrium thermodynamics. Universal thermodynamics is briefly presented and it is found that the entropy function in Rastall theory is the usual Bekenstein entropy and there is no correction to it. Finally, a complete cosmic history starting from inflation to late time acceleration is presented for suitable choices of the Rastall parameter. [ABSTRACT FROM AUTHOR]

Recently, a new stability criterion has been introduced for general cylindrically symmetric thin-shell wormholes. The present work deals with thin-shell wormholes in the background of static cylindrically symmetric space-time i) with a cosmological constant and ii) with or without a magnetic field in Brans-Dicke gravity and the stability criterion is examined in the perspective of this newly introduced stability analysis. Finally, the stability conditions have been analyzed geometrically. [ABSTRACT FROM AUTHOR]

The possibility of using spin waves for information transmission and processing has been an area of active research due to the unique ability to manipulate the amplitude and phase of the spin waves for building complex logic circuits with less physical resources and low power consumption. Previous proposals on spin wave logic circuits have suggested the idea of utilizing the magneto-electric effect for spin wave amplification and amplitude- or phase-dependent switching of magneto-electric cells. Here, we propose a comprehensive scheme for building a clocked non-volatile spin wave device by introducing a charge-to-spin converter that translates information from electrical domain to spin domain, magneto-electric spin wave repeaters that operate in three different regimes - spin wave transmitter, non-volatile memory and spin wave detector, and a novel clocking scheme that ensures sequential transmission of information and non-reciprocity. The proposed device satisfies the five essential requirements for logic application: nonlinearity, amplification, concatenability, feedback prevention, and complete set of Boolean operations. [ABSTRACT FROM AUTHOR]