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Abstract A detailed symmetry analysis has been done for the present multi scalar-torsion gravity theory in the background of flat Friedmann Lema $$\hat{\text{ i }}$$ i ^ tre Robertson Walker (FLRW) space-time. Not only the Noether symmetry from the invariance of the Lagrangian has been determined and associated conserved quantities are evaluated, but also the conformal symmetry of the physical metric (i.e., metric of the kinetic part) has been investigated to evaluate homothetic and killing vector fields. Finally, a through study of quantum cosmology both by canonical approach and by considering quantum trajectories has been presented.

Abstract 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.

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.

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.

Abstract Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) pandemic has been considered with great importance on correct screening procedure. The detection efficiency of recent variants of concern were observed by comparing 5 commercial RT-PCR kits and a SYBR-green method developed and validated in our laboratory. The RNA was extracted from nasopharyngeal samples from suspected COVID-19 patients and RT-PCR assay was performed according to the instruction of the respective manufacturers. The specificity and sensitivity of Maccura kit was 81.8% and 82.5%, A*Star kit was 100% and 75.4%, Da An Gene kit was 100% and 68.4%, Sansure kit was 54.5% and 91.2% and TaqPath kit was 100% and 70.2% respectively. Our in house SYBR-Green method showed a consistent detection result with 90.9% specificity and 91.2% sensitivity. We also found that detection kits targeting more genes showed better accuracy which facilitates less false positive results (

Abstract In this present model we have discussed about the solution of the matter-dominated cosmological equations using the expression of $$\Lambda =\Lambda (G)$$ 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.

The goal of this study is to leverage emerging machine learning (ML) techniques to develop a framework for the global reconstruction of system variables from potentially scarce and noisy observations and to explore the epistemic uncertainty of these models. This work demonstrates the utility of exploiting the stochasticity of dropout and batch normalization schemes to infer uncertainty estimates of super-resolved field reconstruction from sparse sensor measurements. A Voronoi tessellation strategy is used to obtain a structured-grid representation from sensor observations, thus enabling the use of fully convolutional neural networks (FCNN) for global field estimation. An ensemble-based approach is developed using Monte-Carlo batch normalization (MCBN) and Monte-Carlo dropout (MCD) methods in order to perform approximate Bayesian inference over the neural network parameters, which facilitates the estimation of the epistemic uncertainty of predicted field values. We demonstrate these capabilities through numerical experiments that include sea-surface temperature, soil moisture, and incompressible near-surface flows over a wide range of parameterized flow configurations.

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.

Recently, novel applications in the space of artificial intelligence (AI) such as solving constraint optimization problems, probabilistic inferencing, contextual adaptation, and continual learning from noisy data are gaining momentum to address relevant real-world problems. A majority of these tasks are compute and/or memory intensive. While traditional deep learning has been fueled by the utilization of graphic processing units (GPUs) to accelerate algorithms primarily in the cloud, today we see a surge in the development of application/domain-specific integrated circuits and systems that aim at providing an order of magnitude improvement over traditional GPU-based approaches in terms of energy efficiency and latency. This growing branch of research taps into the realms of neuronal dynamics, collective computing using dynamical systems, harnessing stochasticity to enable probabilistic computing, and even draws inspiration from quantum computing. We envision such specialized application/domain-specific systems to perform complex tasks such as solving NP-hard optimization problems, performing reasoning and cognition in the presence of uncertainty with superior energy-efficiency (and/or area and latency improvements) compared to conventional GPU-based approaches and von Neumann computing using traditional silicon-based devices, circuits, and architectures. Of special interest is to utilize such nontraditional computing approaches to reduce the time to obtain solutions for computationally challenging problems that otherwise tend to grow exponentially with problem size. To support this vision, there needs to be fundamental advances in both nontraditional devices and circuits/architectures. Recent works have shown that novel circuit topologies and architectures involving non-Boolean, oscillatory, spiking, probabilistic, or quantum-inspired computing are more suited toward tackling applications such as solving constraint optimization problems, performing energy-based learning, performing Bayesian learning and inference, lifelong continual learning, and solving quantum-inspired applications such as Quantum Monte Carlo. A flurry of current research highlights that compared to traditional silicon-based devices, emerging nanodevices utilizing novel quantum materials such as complex oxides, ferroelectric materials, and spintronic materials can allow the realization of these novel circuits and architectures with lower foot-print area, higher energy efficiency, and lower latency.

Abstract Music offers a uniquely abstract way for the expression of human emotions and moods, wherein melodic harmony is achieved through a succinct blend of pitch, rhythm, tempo, texture, and other sonic qualities. The emerging field of “Robotic Musicianship” focuses on developing machine intelligence, in terms of algorithms and cognitive models, to capture the underlying principles of musical perception, composition, and performance. The capability of new-generation robots to manifest music in a human-like artistically expressive manner lies at the intersection of engineering, computers, music, and psychology; promising to offer new forms of creativity, sharing, and interpreting musical impulses. This manuscript explores how real-time collaborations between humans and machines might be achieved by the integration of technological and mathematical models from Synchronization and Learning, with precise configuration for the seamless generation of melody in tandem, towards the vision of human–robot symphonic orchestra. To explicitly capture the key ingredients of a good symphony—synchronization and anticipation—this work discusses a possible approach based on the joint strategy of: (i) Mapping— wherein mathematical models for oscillator coupling like Kuramoto could be used for establishing and maintaining synchronization, and (ii) Modelling—employing modern deep learning predictive models like Neural Network architectures to anticipate (or predict) future state changes in the sequence of music generation and pre-empt transitions in the coupled oscillator sequence. It is hoped that this discussion will foster new insights and research for better “real-time synchronized human-computer collaborative interfaces and interactions”.

Intent classification is an essential task for goal-oriented dialogue systems for automatically identifying customers’ goals. Although intent classification performs well in general settings, domain-specific user goals can still present a challenge for this task. To address this challenge, we automatically generate knowledge graphs for targeted data sets to capture domain-specific knowledge and leverage embeddings trained on these knowledge graphs for the intent classification task. As existing knowledge graphs might not be suitable for a targeted domain of interest, our automatic generation of knowledge graphs can extract the semantic information of any domain, which can be incorporated within the classification process. We compare our results with state-of-the-art pre-trained sentence embeddings and our evaluation of three data sets shows improvement in the intent classification task in terms of precision.

Many combinatorial optimization problems can be mapped onto the ground-state search problem of an Ising model. Exploiting the continuous-time dynamics of a network of coupled phase-transition nano-oscillators (PTNOs) allows building an Ising Hamiltonian solver for obtaining optimum or near-optimum solution with a large speed-up over discrete-time iterative digital hardware. Here, we provide insights into the continuous-time dynamics of such a PTNO-based Ising Hamiltonian solver. We highlight the formation of stable attractor states in the phase space of the coupled PTNO network using second-harmonic injection locking (SHIL) that corresponds to the minima of the Ising Hamiltonian. We show that the emergent synchronized dynamics of the PTNO network is maximized near the critical point of oscillator phase bistability beyond which the dynamics is limited by freeze-out effects. Such dynamical freeze-out severely limits the performance of the PTNO-based Ising solver from obtaining the global optimum. We highlight an improvement in the success probability of reaching the ground state by introducing an annealing scheme with linearly increasing SHIL amplitude compared with a constant SHIL. Finally, we estimate the “effective temperature” of the PTNO-based Ising solver by comparing it with the Markov chain Monte Carlo simulations. The PTNO-based Ising solver behaves like a low-temperature Ising spin system, indicating its effectiveness for optimization tasks.

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.

An efficient in vitro micropropagation protocol has been developed using nodal explants of ramie (Boehmeria nivea), with maximum shoots (42) per explant in 5 passages (passage duration: 21 days) on Murashige and Skoog medium supplemented with 2.0 mg/l 6-benzyladenine and 2.0 mg/l AgNO3. 1/2 Murashige and Skoog medium containing 40% sucrose was found to be most effective for the rooting of in vitro developed shoots. Those plantlets were acclimatized and transferred to pots for hardening under glasshouse conditions. About 91% of mericlones survived and showed no ectopic expression in respect of any morphological character in comparison with the parental stock. Furthermore, clonal fidelity of the mericlones was confirmed by using DNA markers (random amplified polymorphic DNA and inter simple sequence repeats) and by polypeptide profiling through SDS-PAGE at a genomic and protein level, respectively, which showed the true-to-type nature of the in vitro micropropagated plants. Thus the protocol developed can be used to generate safe planting material for large-scale cultivation of ramie.

Physical systems governed by advection-dominated partial differential equations (PDEs) are found in applications ranging from engineering design to weather forecasting. They are known to pose severe challenges to both projection-based and non-intrusive reduced order modeling, especially when linear subspace approximations are used. In this work, we develop an advection-aware (AA) autoencoder network that can address some of these limitations by learning efficient, physics-informed, nonlinear embeddings of the high-fidelity system snapshots. A fully non-intrusive reduced order model is developed by mapping the high-fidelity snapshots to a latent space defined by an AA autoencoder, followed by learning the latent space dynamics using a long-short-term memory (LSTM) network. This framework is also extended to parametric problems by explicitly incorporating parameter information into both the high-fidelity snapshots and the encoded latent space. Numerical results obtained with parametric linear and nonlinear advection problems indicate that the proposed framework can reproduce the dominant flow features even for unseen parameter values.

The recent study of complex emotions using visual storyboards by Bhushan et al. (2020) endorses that same scenario can induce guilt/remorse or guilt/shame in people based on valence. These findings were based on behavioral data and did not consider body physiology. The present study aimed to explore the difference in the thermal signature of scenarios that elicit guilt in some and shame/remorse in others. Using storyboard depicting 13 scenarios, we analyzed the thermal changes on the forehead, eyes (left and right separately), cheek (left and right separately), nose tip, and mouth regions of the face with the objective of exploring the thermal signature of guilt, shame, and remorse. Data were collected from 31 participants using a thermal camera in a laboratory setting. We found a difference of 0.5°C or above change in temperature on the forehead, left and right cheeks, and mouth regions during guilt experience compared to shame and remorse experiences. The temperature of the right and left cheeks was high for guilt as compared to remorse for two scenarios inducing guilt/remorse, and the difference was statistically significant. For one of the scenarios inducing guilt/shame, thermal change in the right eye region was higher for shame as compared to guilt. The findings are discussed in light of the distribution of blood vessels on the face.

As the world faces the challenge of the COVID-19 pandemic, it has become an urgent need of the hour to understand how our immune system sense and respond to RNA viruses that are often life-threatening. While most vaccine strategies for these viruses are developed around a programmed antibody response, relatively less attention is paid to our innate immune defenses that can determine the outcome of a viral infection via the production of antiviral cytokines like Type I Interferons. However, it is becoming increasingly evident that the “cytokine storm” induced by aberrant activation of the innate immune response against a viral pathogen may sometimes offer replicative advantage to the virus thus promoting disease pathogenesis. Thus, it is important to fine tune the responses of the innate immune network that can be achieved via a deeper insight into the candidate molecules involved. Several pattern recognition receptors (PRRs) like the Toll like receptors (TLRs), NOD-like receptors (NLRs), and the retinoic acid inducible gene-I (RIG-I) like receptors (RLRs) recognize cytosolic RNA viruses and mount an antiviral immune response. RLRs recognize invasive viral RNA produced during infection and mediate the induction of Type I Interferons via the mitochondrial antiviral signaling (MAVS) molecule. It is an intriguing fact that the mitochondrion, one of the cell’s most vital organelle, has evolved to be a central hub in this antiviral defense. However, cytokine responses and interferon signaling via MAVS signalosome at the mitochondria must be tightly regulated to prevent overactivation of the immune responses. This review focuses on our current understanding of the innate immune sensing of the host mitochondria by the RLR-MAVS signalosome and its specificity against some of the emerging/re-emerging RNA viruses like Ebola, Zika, Influenza A virus (IAV), and severe acute respiratory syndrome-coronavirus (SARS-CoV) that may expand our understanding for novel pharmaceutical development.

The two possible pathways toward artificial intelligence (AI)—(i) neuroscience-oriented neuromorphic computing [like spiking neural network (SNN)] and (ii) computer science driven machine learning (like deep learning) differ widely in their fundamental formalism and coding schemes (Pei et al., 2019). Deviating from traditional deep learning approach of relying on neuronal models with static nonlinearities, SNNs attempt to capture brain-like features like computation using spikes. This holds the promise of improving the energy efficiency of the computing platforms. In order to achieve a much higher areal and energy efficiency compared to today’s hardware implementation of SNN, we need to go beyond the traditional route of relying on CMOS-based digital or mixed-signal neuronal circuits and segregation of computation and memory under the von Neumann architecture. Recently, ferroelectric field-effect transistors (FeFETs) are being explored as a promising alternative for building neuromorphic hardware by utilizing their non-volatile nature and rich polarization switching dynamics. In this work, we propose an all FeFET-based SNN hardware that allows low-power spike-based information processing and co-localized memory and computing (a.k.a. in-memory computing). We experimentally demonstrate the essential neuronal and synaptic dynamics in a 28 nm high-K metal gate FeFET technology. Furthermore, drawing inspiration from the traditional machine learning approach of optimizing a cost function to adjust the synaptic weights, we implement a surrogate gradient (SG) learning algorithm on our SNN platform that allows us to perform supervised learning on MNIST dataset. As such, we provide a pathway toward building energy-efficient neuromorphic hardware that can support traditional machine learning algorithms. Finally, we undertake synergistic device-algorithm co-design by accounting for the impacts of device-level variation (stochasticity) and limited bit precision of on-chip synaptic weights (available analog states) on the classification accuracy.

This data deals with the optimization of microprojectile bombardment particles for efficient genetic transformation in an indica rice involving AmSOD gene for development of salinity tolerant transgenic lines [1]. In this study, various parameters such as effect of genotypes, helium pressure, osmoticum, explants, flight distance, particle size, particle volume, vacuum, carrier DNA and stopping screen properties have been evaluated to determine their role in transformation of indica rice involving AmSOD gene for development of salinity tolerant Pusa Basmati 1 rice variety. To perform the transformation process, plasmid vector pCAMBIA 1305.2 was used, which harbours GUS Plus™ gene, intron from the castor bean catalase gene, pBR322 ori, kanamycin resistant gene and Xho I site. The transformants have been confirmed using slot blot, polymerase chain reaction and Southern hybridization techniques. Keywords: Abiotic stress, AmSOD gene, Basmati rice, Microprojectile bombardment

Abstract 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 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 Spin waves are propagating disturbances in magnetically ordered materials, analogous to lattice waves in solid systems and are often described from a quasiparticle point of view as magnons. The attractive advantages of Joule-heat-free transmission of information, utilization of the phase of the wave as an additional degree of freedom and lower footprint area compared to conventional charge-based devices have made spin waves or magnon spintronics a promising candidate for beyond-CMOS wave-based computation. However, any practical realization of an all-magnon based computing system must undergo the essential steps of a careful selection of materials and demonstrate robustness with respect to thermal noise or variability. Here, we aim at identifying suitable materials and theoretically demonstrate the possibility of achieving error-free clocked non-volatile spin wave logic device, even in the presence of thermal noise and clock jitter or clock skew.

In this paper, an interconnect scheme based on automotion of domain walls (DWs) for all-spin logic (ASL) has been proposed. The proposed interconnect is analyzed using a comprehensive numerical model including an equivalent circuit for ASL operations, the 1-D Landau-Lifshitz-Gilbert equation for DW creation, reflection, and disappearance at the boundaries. Analytical expressions for DW transport along the wire are also presented. From the model, it is found that the reflection of the DW can be eliminated using a material with a high Gilbert damping coefficient at the end, the energy dissipation can be independent of the interconnect length, and DW displacement and energy dissipation can be further improved using a material with a low damping factor and saturation magnetization. Furthermore, the interconnect reliability is also studied by applying the thermal random noise analysis on the dynamics of DWs, and it is found that thermal fluctuations can have a significant impact on the interconnect performance; thus, the interconnect with a low Gilbert damping factor is desired to suppress the thermal noise effects.

The present work is an attempt for emergent universe scenario with modified Chaplygin gas. The universe is chosen as spatially flat FRW space-time with modified Chaplygin gas as the only cosmic substratum. It is found that emergent scenario is possible for some specific (unrealistic) choice of the parameters in the equation of state for modified Chaplygin gas.

Background: First line (1L) TP-Ex-like regimen followed by 2nd-line (2L) immunotherapy represents one of the standards of care in platinum-sensitive recurrent and/or metastatic squamous cell carcinoma of head and neck (R/M SCCHN). We report our experience from 2 tertiary care institutions of India. Methods: This is a retrospective analysis of consecutive patients of platinum-sensitive R/M SCCHN treated with 1L weekly paclitaxel, carboplatin, and cetuximab (PCC) regimen followed by cetuximab maintenance (if non-progressive) or 2L nivolumab or oral metronomic chemotherapy (OMCT) on progression. Overall response rates (ORR), progression-free survival after 1L and 2L (PFS-1 & PFS-2), overall survival (OS), and safety were evaluated. Results: The study included 54 patients; median age 56.5 years; 89% men; 11% had cardiac dysfunction; 13% had renal dysfunction. After 1L PCC, ORR was 59.3%; median PFS-1 was 7.031 months; 61% had progression; 35% were treated with nivolumab and 18% with OMCT. The ORR was 26.3% (nivolumab) and 10% (OMCT). Median PFS-2 was 6.5 months (nivolumab) and 2 months (OMCT). The median OS was 15.01 months (entire cohort), 20.6 months (nivolumab), and 7 months (OMCT). Grade III/IV adverse events on PCC included neutropenia (31.4%), anaemia (35.1%), thrombocytopenia (7.4%), febrile neutropenia (11.1%), and skin reaction (16.6%); no Grade-III/IV treatment-related toxicities on 2L. Conclusions: 1L weekly PCC is an effective regimen for palliative therapy of platinum- sensitive R/MSCCHN with an acceptable toxicity profile. The addition of 2L nivolumab on progression further improves the outcomes.

The multichannel quantum defect theory (MQDT) in combination with the frame transformation (FT) approach is applied to model the Fano–Feshbach resonances measured for ^7 Li ^87 Rb and ^6 Li ^87 Rb Marzok et al (2009 Phys. Rev. A http://dx.doi.org/10.1103/PhysRevA.79.012717 79 http://dx.doi.org/10.1103/PhysRevA.79.012717 ). The MQDT results show a level of accuracy comparable to that of previous models based on direct, fully numerical solutions of the the coupled channel Schrödinger equations. Here, energy levels deduced from 2-photon photoassociation (PA) spectra for ^7 Li ^85 Rb are assigned by applying the MQDT approach, obtaining the bound state energies for the coupled channel problem. Our results confirm that MQDT yields a compact description of PA observables as well as the Fano–Feshbach resonance positions and widths.

Background: First line (1L) TP-Ex-like regimen followed by 2nd-line (2L) immunotherapy represents one of the standards of care in platinum-sensitive recurrent and/or metastatic squamous cell carcinoma of head and neck (R/M SCCHN). We report our experience from 2 tertiary care institutions of India. Methods: This is a retrospective analysis of consecutive patients of platinum-sensitive R/M SCCHN treated with 1L weekly paclitaxel, carboplatin, and cetuximab (PCC) regimen followed by cetuximab maintenance (if non-progressive) or 2L nivolumab or oral metronomic chemotherapy (OMCT) on progression. Overall response rates (ORR), progression-free survival after 1L and 2L (PFS-1 & PFS-2), overall survival (OS), and safety were evaluated. Results: The study included 54 patients; median age 56.5 years; 89% men; 11% had cardiac dysfunction; 13% had renal dysfunction. After 1L PCC, ORR was 59.3%; median PFS-1 was 7.031 months; 61% had progression; 35% were treated with nivolumab and 18% with OMCT. The ORR was 26.3% (nivolumab) and 10% (OMCT). Median PFS-2 was 6.5 months (nivolumab) and 2 months (OMCT). The median OS was 15.01 months (entire cohort), 20.6 months (nivolumab), and 7 months (OMCT). Grade III/IV adverse events on PCC included neutropenia (31.4%), anaemia (35.1%), thrombocytopenia (7.4%), febrile neutropenia (11.1%), and skin reaction (16.6%); no Grade-III/IV treatment-related toxicities on 2L. Conclusions: 1L weekly PCC is an effective regimen for palliative therapy of platinum- sensitive R/MSCCHN with an acceptable toxicity profile. The addition of 2L nivolumab on progression further improves the outcomes.