"All-versus-nothing" proof of tripartite quantum steering and genuine entanglement certification in the two-sided device-independent scenario

We consider the task of certification of genuine entanglement of tripartite states. We first present an "all-versus-nothing" proof of tripartite Einstein-Podolsky-Rosen (EPR) steering by demonstrating the non-existence of a local hidden state (LHS) model in the tripartite network as a motivation to our main result. A full logical contradiction of the predictions of the LHS model with quantum mechanical outcome statistics for any three-qubit generalized Greenberger-Horne-Zeilinger (GGHZ) states and pure W-class states is shown, using which, one can distinguish between the GGHZ and W-class states in the two-sided device-independent (2SDI) steering scenario. We next formulate a 2SDI fine-grained steering inequality for the tripartite scenario. We show that the maximum quantum violation of this FGI can be used to certify genuine entanglement of three-qubit pure states.Comment: Analysis and results strengthened (sharp logical contradiction proofs of W-class states added), comments are welcom

A bacterial foraging optimization and learning automata based feature selection for motor imagery EEG classification

Selection of relevant features is an open problem in Brain-computer interfacing (BCI) research. Sometimes, features extracted from brain signals are high dimensional which in turn affects the accuracy of the classifier. Selection of the most relevant features improves the performance of the classifier and reduces the computational cost of the system. In this study, we have used a combination of Bacterial Foraging Optimization and Learning Automata to determine the best subset of features from a given motor imagery electroencephalography (EEG) based BCI dataset. Here, we have employed Discrete Wavelet Transform to obtain a high dimensional feature set and classified it by Distance Likelihood Ratio Test. Our proposed feature selector produced an accuracy of 80.291% in 216 seconds

Active Power Control of Retrofit LED Tube Lamps for Achieving Entitled Energy Savings in View of the EU Ban on Mercury

The performance of commercially available retrofit LED tubes intended for the replacement of linear fluorescent lamps is measured and analyzed with respect to “real-world” installed electronic ballasts, as ascertained through field recovery from installed bases, such as office buildings, parking garages, and industrial installations in western Europe from 2018 to 2020. Results show a wide variation in lamp power draw, which not only thwarts the energy-saving and climate protection aspects of the LED retrofit solution but also poses potential safety risks. Given the EU’s goals under the Restriction of Hazardous Substances (RoHS) directive to phase out mercury-containing fluorescent lighting, starting from September 2023, the situation is alarming. We show that this lamp power spread is due to the fundamental differences in impedance between fluorescent lamps and LEDs, in combination with the passive nature of the driver electronics that are currently employed in commercially available LED tube lamps. In response to this disparity, a novel driver topology including active power control (APC) is introduced, which shows that the power-spread problem can be avoided, and we offer a manufacturable solution. A prototype retrofit LED tube lamp incorporating this APC driver technology is shown to deliver safe and predictable energy savings, outlining a path toward guaranteeing the expected return-on-investment and positive environmental impact of the solid-state lighting replacement of mercury-containing linear fluorescent lamps

Graphene-Based Nanomaterials for Theranostic Applications

Graphene and graphene-based nanomaterials such as graphene oxide (GO), reduced graphene oxide (rGO) and graphene quantum dots (GQDs) have gained a lot of attention from diverse scientific fields for applications in sensing, catalysis, nanoelectronics, material engineering, energy storage and biomedicine due to its unique structural, optical, electrical and mechanical properties. Graphene-based nanomaterials emerge as a novel class of nanomedicine for cancer therapy for several reasons. Firstly, its structural properties like high surface area and aromaticity enables easy loading of hydrophobic drugs. Secondly, presence of oxygen containing functional groups improve its physiological stability and also act as site for biofunctionalization. Thirdly, its optical absorption in the NIR region enable them to act as photoagents for photothermal and photodynamic therapies of cancer, both in vitro and in vivo. Finally, its intrinsic fluorescence property helps in bioimaging of cancer cells. Overall, graphene-based nanomaterials can act as agents for developing multifunctional theranostic platforms for carrying out more efficient detection and treatment of cancers. This review provides a detailed summary of the different applications of graphene-based nanomaterials in drug delivery, nucleic acid delivery, phototherapy, bioimaging and theranostics

Two Dimensional Error-Correcting Codes using Finite Field Fourier Transform

We construct 2D codewords by using 2D finite field Fourier transform. The components of the codeword in the transformed domain are from an extension field GF(2(m)) and are linked by a conjugacy constraint. We provide encoding and decoding schemes in the transformed domain

Two-Dimensional Algebraic Codes for Multiple Burst Error Correction

We construct native binary 2D binary cyclic codes capable of correcting multiple occurrences of multiple non-overlapping <italic>pre-defined</italic> 2D error shapes using frequency domain techniques. The starting location of each error shape is determined using a novel decoding algorithm based on a careful selection of the common zero set. The proposed code construction is generic and offers <italic>superior</italic> coding rates compared to the native 2D BCH code with the same error correction ability

Cubic Gold Nanorattles with a Solid Octahedral Core and Porous Shell as Efficient Catalyst: Immobilization and Kinetic Analysis

Plasmonic nanostructures having porous morphology have attracted a great deal of attention in catalysis because of high surface-to-volume ratio, better surface reactivity, and availability of various structural features. We report the synthesis, immobilization, and kinetic analysis of cubic gold nanorattles (AuNRTs) comprising a solid octahedral core surrounded by a thin porous cube-shaped gold shell toward the reduction of <i>p</i>-nitrophenol (an environmental pollutant) and degradation of organic dyes (Congo red and methylene blue) as model systems. Kinetic investigation of our study showed that AuNRTs are an excellent catalyst compared with solid silver nanocube containing an octahedral gold core (AuOCT@Ag) and gold nanospheres (AuNSs), which could be attributed to the porous structure of nanorattles with three available surfaces: outer and inner walls and inner core for catalysis. A detailed analysis of the different kinetic and thermodynamic parameters revealed that AuNRTs have the highest reaction rate constant, lowest activation energy, pre-exponential factors, and entropy of activation. Furthermore, the immobilized AuNRTs into calcium alginate beads could retain their catalytic efficiency up to 15 cycles, demonstrating high stability and reproducibility. The present system shows the ability to efficiently degrade pollutants and thus can be used for potential environmental remediation application