Superlattice Engineering of Topology in Massive Dirac Fermions

We show that a superlattice potential can be employed to engineer topology in massive Dirac fermions in systems such as bilayer graphene, moir\'e graphene-boron nitride, and transition-metal dichalcogenide (TMD) monolayers and bilayers. We use symmetry analysis to analyze band inversions to determine the Chern number $\mathscr C$ for the valence band as a function of tunable potential parameters for a class of $C_4$ and $C_3$ symmetric potentials. We present a novel method to engineer Chern number $\mathscr{C}=2$ for the valence band and show that the applied potential at minimum must have a scalar together with a non-scalar periodic part. We discover that certain forms of the superlattice potential, which may be difficult to realize in naturally occurring moir\'e patterns, allow for the possibility of non-trivial topological transitions. These forms may be achievable using an external superlattice potential that can be created using contemporary experimental techniques. Our work paves the way to realize the quantum Spin Hall effect (QSHE), quantum anomalous Hall effect (QAHE), and even exotic non-Abelian anyons in the fractional quantum Hall effect (FQHE)

Machine Learning for Handwriting Recognition

With the knowledge of current data about particular subject, machine learning tries to extract hidden information that lies in the data. By applying some mathematical functions and concepts to extract hidden information, machine learning can be achieved and we can predict output for unknown data. Pattern recognition is one of the main application of ML. Patterns are usually recognized with the help of large image data-set. Handwriting recognition is an application of pattern recognition through image. By using such concepts, we can train computers to read letters and numbers belonging to any language present in an image. There exists several methods by which we can recognize hand-written characters. We will be discussing some of the methods in this paper

Monitoring Social Distancing Using OpenCv

The paper proposes a method for social separating identification based on deep understanding of how to measure the gap between people in order to mitigate the impact of the COVID-19 pandemic. By evaluating with the aid of videos as feedback, the position instrument was developed to make people aware of the importance of keeping a safe distance from one another. The input video outline from the camera has been used as details, along with a free and open source object location system based on YOLOv3. Calculation that was used to determine walker recognition. After that, the input frame outline was modified to elevated perspective for distance estimation in the 2-Dimensional plane. The RED edge and line represent the range between individuals being measured and a part of the rebellious pairing of individuals during the showcase. The proposed strategy is accepted using a pre-recorded feedback frame of people walking around the city on foot. This result demonstrates how the presented methodology can make decisions about social removing estimates for a large number of people in the input picture. As the discovery apparatus was gradually introduced, this developed technique evolved as well

Cochlear implant outcomes in patients with Meniere's disease: a large case series

OBJECTIVE: To perform a matched cohort study to assess whether patients with Meniere's Disease (MD) require more intensive auditory rehabilitation following cochlear implantation (CI) and identify factors that may affect outcomes in patients with MD. METHODS: A retrospective case review was performed. MD and control patients were matched for age, biological sex, implant manufacturer and electrode design. Outcomes measured were speech scores, number of visits to audiology department following switch-on, and post-operative active MD. RESULTS: Forty consecutive implanted MD patients were identified between May 1993 and May 2019. Patients with active MD following CI required significantly more visits to the audiology department compared to controls (P < 0.01) and patients who had inactive MD post-operatively (P < 0.01). However, in MD patients, active MD was less likely following CI (P = 0.03). In patients who continued to experience active MD post-operatively, further medical and surgical ablative intervention was required to control ongoing Meniere's attacks. CONCLUSION: We present the largest case series of performance outcomes in CI patients with MD. Although speech outcomes in MD patients are comparable to controls, patients with active MD pre-operatively are more likely to experience variation in CI performance requiring a prolonged period of auditory rehabilitation compared to inactive preoperative MD

Self‐Aligning Metallic Vertical Interconnect Access Formation through Microlensing Gas Phase Electrodeposition controlling Airgap and Morphology

Abstract This publication reports self‐aligning metallic via microlensing gas phase electrodeposition formation. Key operational parameters to fabricate vertical ruthenium and rhodium interconnects (via) with a diameter of 100 nm are discussed. Moreover, airgaps are implemented during the deposition process, which utilizes spark discharge to generate a flux of charged nanoparticles. An inert gas flow transports the nanoparticles through a reactor chamber close to the target substrate. The substrate uses a pre‐patterned resist with openings to a silicon/silicon dioxide/metal stack to direct the deposition of the nanoparticles to form localized self‐aligning vertical interconnects. Five process parameters were identified, which impact the morphology and conductance of the resulting interconnects: spark discharge power, gas flow rate, microlens via dimensions, substrate surface potential, and in situ flash lamp power. This parameter set enables a controlled adjustment of the via interconnect morphology and its minimum feature size. Gas flow rate in combination with spark discharge power contribute significantly to the morphology of the interconnect. Spark power and microlens via dimensions have the largest influence on the surface potential of the insulating resist cover, which enables a localized microlensing gas phase electrodeposition of a via with a controlled ratio between conducting diameter and airgap

Characterising within-hospitalSARS-CoV-2 transmission events using epidemiological and viral genomic data across two pandemic waves.

Hospital outbreaks of COVID19 result in considerable mortality and disruption to healthcare services and yet little is known about transmission within this setting. We characterise within hospital transmission by combining viral genomic and epidemiological data using Bayesian modelling amongst 2181 patients and healthcare workers from a large UK NHS Trust. Transmission events were compared between Wave 1 (1st March to 25th J'uly 2020) and Wave 2 (30th November 2020 to 24th January 2021). We show that staff-to-staff transmissions reduced from 31.6% to 12.9% of all infections. Patient-to-patient transmissions increased from 27.1% to 52.1%. 40%-50% of hospital-onset patient cases resulted in onward transmission compared to 4% of community-acquired cases. Control measures introduced during the pandemic likely reduced transmissions between healthcare workers but were insufficient to prevent increasing numbers of patient-to-patient transmissions. As hospital-acquired cases drive most onward transmission, earlier identification of nosocomial cases will be required to break hospital transmission chains