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We theoretically analyze phonon-assisted tunneling transport in a quantum dot side connected to a Majorana bound state in a topological superconducting nanowire. We investigate the behavior of the current through the dot, for a range of experimentally relevant parameters, in the presence of one long-wave optical phonon mode. We consider the current-gate voltage, the current-bias voltage and the current-dot–Majorana coupling characteristics under the influence of the electron–phonon coupling. In the absence of electron–phonon interaction, the Majorana bound states suppress the current when the gate voltage matches the Fermi level, but the increase in the bias voltage counteracts this effect. In the presence of electron–phonon coupling, the current behaves similarly as a function of the renormalized gate voltage. As an added feature at large bias voltages, it presents a dip or a plateau, depending on the size of the dot–Majorana coupling. Lastly, we show that the currents are most sensitive to, and depend non-trivially on the parameters of the Majorana circuit element, in the regime of low temperatures combined with low voltages. Our results provide insights into the complex physics of quantum dot devices used to probe Majorana bound states.

We propose a model of two identical autonomous quantum refrigerators that interact through their qubits coupled to cold thermal baths. We consider the same specific disposition for each refrigerator related to a three-level system coupled to different thermal baths and, in turn, to a qubit as the object to be cooled. By coupling the two systems, we show that under certain conditions on the model parameters, the devices can operate in the regime of an independent autonomous quantum refrigerator or be subject to an adjustment of the thermodynamic performances. We further investigate the correlations induced by the interaction between the two refrigerators, and illustrate their behavior with respect to their thermodynamic performances. We have found that adjusting the different interaction constants of the model can lead to an evolution of the quantum correlations of the two machines that is inversely related to the behavior of the thermodynamic quantities of both refrigerators. However, this is not necessarily the case when the temperatures of the model are considered as the reference parameters. [ABSTRACT FROM AUTHOR]

The study presents approximate analytical solutions of the Schrödinger equation with a newly proposed potential model called Inversely Quadratic Hellmann-Kratzer potential (IQHKP). This potential is a superposition of Inversely Quadratic Hellman potential and Kratzer potential. The energy eigenvalues and corresponding wavefunction are calculated via the formula method. We applied our results to evaluate thermodynamic functions such as vibrational free energy, F, vibrational internal energy, U, vibrational entropy, S, and vibrational specific heat, C. We also reported special cases of importance.

This article presents a novel algorithm that accurately predicts market trends and identifies trading entry points for US 30‐year Treasury bonds. The proposed method employs a hybrid approach, integrating a 1‐dimensional convolutional neural network (1DCNN), long‐short term memory (LSTM), and XGBoost algorithms. The 1DCNN is used to learn local and short‐term patterns, while LSTM is employed to capture both short and long‐term dependencies. Furthermore, we have implemented an algorithm that utilizes hull moving average (HMA) and simple moving average (SMA) crossover data to detect trading entry points and major trends in the market. The combination of the SMA–HMA crossover algorithm and predictions provided by the 1DCNN‐BiLSTM‐XGBoost algorithm yields exceptional results in terms of prediction accuracy and profitability. Additionally, these integrated techniques effectively filter out noise and mitigate false breakouts, which are often observed with US 30‐year Treasury bonds. In the field of financial time series prediction, the effectiveness of 1DCNN and LSTM in identifying trading entry points and market perturbations has not been comprehensively studied. Therefore, our work fills this gap by demonstrating through experiments that the proposed 1DCNN‐BiLSTM‐XGBoost algorithm, in combination with moving average crossovers, effectively reduces noise and market perturbations. This leads to the precise identification of trading entry points and accurate recognition of trend signals for US 30‐year Treasury bonds. We demonstrate through experiments that our proposed approach achieves an average root mean squared error of 0.0001 and an R‐square value of 0.9999, highlighting its promise as a method for predicting market trends and trading entry points for US 30‐year Treasury bonds. [ABSTRACT FROM AUTHOR]

In this paper we present a high performance Deep Learning architecture based on Convolutional Neural Network (CNN). The proposed architecture is effective as it is capable of recognizing and analyzing with high accuracy different Sign language datasets. The sign language recognition is one of the most important tasks that will change the lives of deaf people by facilitating their daily life and their integration into society. Our approach was trained and tested on an American Sign Language (ASL) dataset, Irish Sign Alphabets (ISL) dataset and Arabic Sign Language Alphabet (ArASL) dataset and outperforms the state-of-the-art methods by providing a recognition rate of 99% for ASL and ISL, and 98% for ArASL.

The accumulation of plastic objects in the Earth’s environment will adversely affect wildlife, wildlife habitat, and humans. The huge amount of unrecycled plastic ends up in landfill and thrown into unregulated dump sites. In many cases, specifically in the developing countries, plastic waste is thrown into rivers, streams and oceans. In this work, we employed the power of deep learning techniques in image processing and classification to recognize plastic waste. Our work aims to identify plastic texture and plastic objects in images in order to reduce plastic waste in the oceans, and facilitate waste management. For this, we use transfer learning in two ways: in the first one, a pre-trained CNN model on ImageNet is used as a feature extractor, then an SVM classifier for classification, the second strategy is based on fine tuning the pre-trained CNN model. Our approach was trained and tested using two (02) challenging datasets one is a texture recognition dataset and the other is for object detection, and achieves very satisfactory results using two (02) deep learning strategies.

Studying qubit‐qutrit systems is crucial to harnessing their quantum advantages, especially in quantum secure technologies. In this regard, the present work consists of considering a qubit‐qutrit system under intrinsic decoherence. For this hybrid‐spin system, the relationship between negativity, local quantum uncertainty, and local quantum Fisher information is discussed. The results show that it is possible to generate and preserve the maximally correlated states by tuning the system parameters to certain fixed values. It is also demonstrated the occurrence of the sudden death/birth phenomenon by examining the behavior of quantum correlation functions. [ABSTRACT FROM AUTHOR]