Impact of Gate Length and Doping Variation on the DC and Analog/RF Performance of sub - 3nm Stacked Si Gate-All-Around Nanosheet FET

Silicon - The nanosheet Field Effect Transistors (FETs) are the promising device architecture for sub - 5nm technology node as per the International Roadmap for Devices and Systems (IRDS) 2020 and...     

Novel Antibacterial Targets in Protein Biogenesis Pathways

Antibiotic resistance has emerged as a global threat due to the ability of bacteria to quickly evolve in response to the selection pressure induced by anti-infective drugs. Thus, there is an urgent need to develop new antibiotics against resistant bacteria. In this review, we discuss pathways involving bacterial protein biogenesis as attractive antibacterial targets since many of them are essential for bacterial survival and virulence. We discuss the structural understanding of various components associated with bacterial protein biogenesis, which in turn can be utilized for rational antibiotic design. We highlight efforts made towards developing inhibitors of these pathways with insights into future possibilities and challenges. We also briefly discuss other potential targets related to protein biogenesis.     

Structural Characterization and Protective Properties of Spray-Deposited Titania Coating Obtained from Oxime-Modified Titanium(IV) Precursor

Protection of Metals and Physical Chemistry of Surfaces - Spray pyrolytic deposition (SPD) of titania coatings on steel substrates have been carried out using acetoxime-modified titanium(IV)...     

A dual-channel ensembled deep convolutional neural network for facial expression recognition in the wild

Facial expression recognition (FER) in the wild is an active and challenging field of research. A system for automatic FER finds use in a wide range of applications related to advanced human–computer interaction (HCI), human–robot interaction (HRI), human behavioral analysis, gaming and entertainment, etc. Since their inception, convolutional neural networks (CNNs) have attained state-of-the-art accuracy in the facial analysis task. However, recognizing facial expressions in the wild with high confidence running on a low-cost embedded device remains challenging. To this end, this study presents an efficient dual-channel ensembled deep CNN (DCE-DCNN) for FER in the wild. Initially, two DCNNs, namely the ${\text{DCNN}}_G$ and ${\text{DCNN}}_S$, are trained separately on the grayscale and Scharr-convolved vertical gradient facial images, respectively. The proposed network later integrates the two pre-trained DCNNs to obtain the dual-channel integrated DCNN (DCI-DCNN). Finally, all three neural networks, namely the ${\text{DCNN}}_G$, ${\text{DCNN}}_S$, and DCI-DCNN, are jointly fine-tuned to get a single dual-channel-multi-output model. The multi-output model produces three prediction scores for the given input facial image. The prediction scores are thus fused using the max-voting ensemble scheme to obtain the DCE-DCNN with the final classification label. On the FER2013, RAF-DB, NCAER-S, AffectNet, and CKPlus benchmark FER datasets, the proposed DCE-DCNN consistently outperforms the two individual DCNNs and numerous state-of-the-art CNNs. Moreover, the network achieves competitive recognition accuracy on all four FER in the wild datasets with reduced memory storage size and parameters. The proposed DCE-DCNN model with high throughput on resource-limited embedded devices is suitable for applications that seek real-time classification of facial expressions in the wild with high confidence.     

An optimized mix for the manufacture of sustainable aerated concrete blocks using waste rubber powder

Clean Technologies and Environmental Policy - Aerated concrete is a type of lightweight concrete in which air bubbles are incorporated into concrete using aluminium powder to make it light in...     

Stable Graphene Membranes for Selective Ion Transport and Emerging Contaminants Removal in Water

Carbon-based materials, such as graphene oxide and reduced graphene oxide membranes have been recently used to fabricate ultrathin, high-flux, and energy-efficient membranes for ionic and molecular sieving in aqueous solution. However, these membranes appeared rather unstable during long-term operation in water with a tendency to swell over time. Membranes produced from pristine, stable, layered graphene materials may overcome these limitations while providing high-level performance. In this paper, an efficient and “green” strategy is proposed to fabricate µm-thick, graphene-based laminates by liquid phase exfoliation in Cyrene and vacuum filtration on a PVDF support. The membranes appear structurally robust and mechanically stable, even after 90 days of operation in water. In ion transport studies, the membranes show size selection (>3.3 Å) and anion-selectivity via the positively charged nanochannels forming the graphene laminate. In antibiotic (tetracycline) diffusion studies under dynamic conditions, the membrane achieve rejection rates higher than 95%. Sizable antibacterial properties are demonstrated in contact method tests with Staphylococcus aureus and Escherichia coli bacteria. Overall, these “green” graphene-based membranes represent a viable option for future water management applications.     

Melt rheology of highly loaded ferrite-filled polymer composites

This paper presents new experimental data on the viscosity of ferrite-filled polymers at high levels of loading. The choice of matrix and the choice of filler and filler loading has direct relevance to commercially important systems of magnetic plastics and rubbers. The effect of shear rate, concentration, and temperature on the rheological behavior of such highly loaded systems has been discussed in detail. The differences in the relative viscosities between the filled and unfilled systems for various matrices have been explained on the basis of polymer filler affinity and filler-filler interaction.