In this paper, a space-air-ground quantum (SPARQ) network is developed as a means for providing a seamless on-demand entanglement distribution. The node mobility in SPARQ poses significant challenges to entanglement routing. Existing quantum routing algorithms focus on stationary ground nodes and utilize link distance as an optimality metric, which is unrealistic for dynamic systems like SPARQ. Moreover, in contrast to the prior art that assumes homogeneous nodes, SPARQ encompasses heterogeneous nodes with different functionalities further complicates the entanglement distribution. To solve the entanglement routing problem, a deep reinforcement learning (RL) framework is proposed and trained using deep Q-network (DQN) on multiple graphs of SPARQ to account for the network dynamics. Subsequently, an entanglement distribution policy, third-party entanglement distribution (TPED), is proposed to establish entanglement between communication parties. A realistic quantum network simulator is designed for performance evaluation. Simulation results show that the TPED policy improves entanglement fidelity by 3% and reduces memory consumption by 50% compared with benchmark. The results also show that the proposed DQN algorithm improves the number of resolved teleportation requests by 39% compared with shortest path baseline and the entanglement fidelity by 2% compared with an RL algorithm that is based on long short-term memory (LSTM). It also improved entanglement fidelity by 6% and 9% compared with two state-of-the-art benchmarks. Moreover, the entanglement fidelity is improved by 15% compared with DQN trained on a snapshot of SPARQ. Additionally, SPARQ enhances the average entanglement fidelity by 23.5% compared with existing networks spanning only space and ground layers.
This work is performed to study the effect of adsorption of various first row adatoms (such as Be, C, F, Li and O) on (8, 0) zigzag boron nitride nanotubes (BNNTs) on their structural, electronic and magnetic properties. These calculations are based on density functional theory using pseudopotentials technique. For this purpose, five different sites namely axial, hexagonal, zigzag, on top of N and/or B (which are the most preferred available sites for adatoms on (8, 0) BNNTs) were utilized. The energetically stable sites for each of the first-row adatoms are found to be different because of their different electronic configurations caused by the charge transfer/ rearrangements between s-p or p-p orbitals. The binding energies of all adatoms on (8, 0) BNNTs have been calculated through structural optimization process after adsorbing these five adatoms at the above said sites on the BNNTs and are found to be in the energy range from -2.04 to 2.96 eV. It is further elaborated that F, Be and C adatoms on (8, 0) BNNTs show strong induced magnetization at specific localized sites depending upon the nature of adatom, whereas weak magnetization is noticed for Li and O adatoms on the BNNTs. Such localized induced magnetization could be associated with the hybridization of s-p or p-p orbitals of adatoms and B and/or N atoms.
Abstract Arm rehabilitation activities need to be monitored continuously in terms of analysis by experts within sufficient information to discover arm dysfunction and disorders such as stroke early. Although there are numerous previous researches about the home-based rehabilitation procedures and arm performance, some drawbacks still exist. For example, current rehabilitation devices are too complicated and required supervision by qualified therapists rather than their high prices. Moreover, data from these devices take much time to be sent to doctors for monitoring purpose. Therefore, this paper proposes a home-based online multisensory arm rehabilitation monitoring system. This system is designed through three main stages which are data acquisition, data processing, and data logging respectively. Data acquisition is by three different types of sensors; flex sensor (for arm’s bending), force-sensitive resistors (for hand fingers’ forces) and accelerometer (for arm movement directions). In data processing, Arduino Mega Controller and ESP Wi-Fi shield are used to design Internet of Things (IoT) web-based system (ThingSpeak) for data logging that allows doctors to diagnose stroke recovery and give feedbacks for patients. Overall, this project showed a robust system for arm rehabilitation using portable, user-friendly, and low power consumption devices at a low cost.
In the present study, a molecular dynamics simulation employing embedded atom method potential is performed to investigate the formation and characterization of CuZr bulk metallic glasses (BMGs). To elucidate the effect of component concentration of three samples of BMGs including Cu 25 Zr 75 , Cu 50 Zr 50 , and Cu 75 Zr 25 that are formed by melt quenching. The local structure of BMGs is analyzed by means of radial distribution function and local atomic number density, ρ. The mechanical behavior of three compositions is investigated using uniaxial compressive loading at a constant strain rate. It is revealed from the results that yield strength increases with increasing Cu concentration. Thermal expansion of CuZr BMGs is examined and variation in length and volume is measured. The analysis revealed that Cu 25 Zr 75 , and Cu 50 Zr 50 exhibited the typical expansion behavior while Cu 75 Zr 25 showed an anomalous behavior.
The most frequent, noticeable, and frequent natural calamity in the karakoram region is landslides. Extreme landslides have occurred frequently along Karakoram highway, particularly during the monsoon, causing a major loss of life and property. Therefore, it was necessary to look for a solution to increase growth and vigilance in order to lessen losses related to landslides caused by natural disasters. By utilizing contemporary technologies, an early warning system might be developed. Artificial neural networks (ANNs) are widely used nowadays across many industries. This paper's major goal is to provide new integrative models for assessing landslide susceptibility in a prone area of north of Pakistan. To do this, the training of an artificial neural network (ANN) is supervised using metaheuristic and Bayesian techniques: particle swarm optimization algorithm (PSO), Genetic algorithm (GA), Bayesian optimization Gaussian process (BO_GP), and Bayesian optimization Gaussian process (BO_TPE). 304 previous landslides and the eight most prevalent conditioning elements combine to form a geographical database. The models are hyper-parameter optimized, and the best ones are employed to generate the susceptibility maps. The area under the receiving operating characteristic curve (AUROC) accuracy index found demonstrated that the maps produced by both Bayesian and metaheuristic algorithms are highly accurate. The effectiveness and efficiency of applying artificial neural networks (ANNs) for landslide mapping, susceptibility analysis, and forecasting are studied in this research it’s observed from experimentation that the performance differences for GA, BO_GP, and PSO compared to BO_TPE are relatively small, ranging from 0.3166% to 1.8399%. This suggests that these techniques achieved comparable performance to BO_TPE in terms of AUC. However, it's important to note that the significance of these differences can vary depending on the specific context and requirements of the ML task. Additionally in this study, we explore eight feature selection algorithms to determine the geospatial variable importance for landslide susceptibility mapping along the KKH. The algorithms considered include Information Gain, Gain Ratio, OneR Classifier, Subset Evaluators, Principal Components, Relief Attribute Evaluator, Correlation, and Symmetrical Uncertainty. These algorithms enable us to evaluate the relevance and significance of different geospatial variables in predicting landslide susceptibility. By applying these feature selection algorithms, we aim to identify the most influential geospatial variables that contribute to landslide occurrences along the KKH. The algorithms encompass a diverse range of techniques, such as measuring entropy reduction, accounting for attribute bias, generating single rules, evaluating feature subsets, reducing dimensionality, and assessing correlation and information sharing. The findings of this study will provide valuable insights into the critical geospatial variables associated with landslide susceptibility along the KKH. These insights can aid in the development of effective landslide mitigation strategies, infrastructure planning, and targeted hazard management efforts. Additionally, the study contributes to the field of geospatial analysis by showcasing the applicability and effectiveness of various feature selection algorithms in the context of landslide susceptibility mapping.
Ochratoxin A (OTA) is a naturally occurring mycotoxin that contaminates animal feed and human food. OTA is nephrotoxic, hepatotoxic, immunosuppressive and a potent renal carcinogen in rodents. In the present study, we evaluated the genotoxicity of OTA in L5178Y tk+/− (3.7.2C) mouse lymphoma cells using the microwell version of the mouse lymphoma gene mutation assay (MLA) and the comet assay modified to detect oxidative DNA damage. Cells were treated for 4 hours with 0, 5, 10, 25, 50 or 100 µM of OTA in the presence and absence of exogenous metabolic activation (S9). Benzo[a]pyrene (1 µg/mL) and 4-nitroquinoline-1-oxide (0.1 µg/mL) were used as positive control with and without S9, respectively. OTA treatment produced dose-dependent increases in cytotoxicity and tk mutant frequency, with significant increases in mutant frequency detected at concentrations ≥25 µM with and without S9. Similarly treated cells were used for the comet assay conducted with and without formamidopyrimidine-DNA glycosylase for the determination of oxidative DNA damage. OTA exposure resulted in a significant increase in both direct and oxidative DNA damage, with induction of oxidative damage being greater. The results indicate that OTA is mutagenic in mouse lymphoma assay; and that OTA-generated oxidative DNA damage is, at least partially, responsible for its mutagenicity in the assay.
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