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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.

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.

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.

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.

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.

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.

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.

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.