Integration of phase-change materials(PCMs)created a unique opportunity to implement reconfigurable photonics devices that their performance can be tuned depending on the target *** PCMs such as Ge-Sb-Te(GST)and Ge-Sb...
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Integration of phase-change materials(PCMs)created a unique opportunity to implement reconfigurable photonics devices that their performance can be tuned depending on the target *** PCMs such as Ge-Sb-Te(GST)and Ge-Sb-Se-Te(GSST)rely on melt-quench and high temperature annealing processes to change the organization of the molecules in the materials’*** a reorganization leads to different optical,electrical,and thermal properties which can be exploited to implement photonic memory cells that are able to store the data at different resistance or optical transmission *** the great promise of conventional PCMs for realizing reconfigurable photonic memories,their slow and extremely power-hungry thermal mechanisms make scaling the systems based on such devices *** addition,such materials do not offer a stable multi-level response over a long period of *** address these shortcomings,the research carried out in this study shows the proof of concept to implement next-generation photonic memory cells based on two-dimensional(2D)birefringence PCMs such as SnSe,which offer anisotropic optical properties that can be switched *** demonstrate that by leveraging the ultrafast and low-power crystallographic direction change of the material,the optical polarization state of the input optical signal can be *** enables the implementation of next-generation high-speed polarization-encodable photonic memory cells for future photonic computing *** to the conventional PCMs,the proposed SnSe-based photonic memory cells offer an ultrafast switching and low-loss optical response relying on ferroelectric property of SnSe to encode the data on the polarization state of the input optical *** a polarization encoding scheme also reduces memory read-out errors and alleviates the scalability limitations due to the optical insertion loss often seen in optical transmission encoding.
Effective management of electricity consumption (EC) in smart buildings (SBs) is crucial for optimizing operational efficiency, cost savings, and ensuring sustainable resource utilization. Accurate EC prediction enabl...
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The Nong Han Chaloem Phrakiat Lotus Park is a tourist attraction and a source of learning regarding lotus ***,as a training area,it lacks appeal and learning motivation due to its conventional presentation of informat...
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The Nong Han Chaloem Phrakiat Lotus Park is a tourist attraction and a source of learning regarding lotus ***,as a training area,it lacks appeal and learning motivation due to its conventional presentation of information regarding lotus *** current study introduced the concept of smart learning in this setting to increase interest and motivation for *** neural networks(CNNs)were used for the classification of lotus plant species,for use in the development of a mobile application to display details about each *** scope of the study was to classify 11 species of lotus plants using the proposed CNN model based on different techniques(augmentation,dropout,and L2)and hyper parameters(dropout and epoch number).The expected outcome was to obtain a high-performance CNN model with reduced total parameters compared to using three different pre-trained CNN models(Inception V3,VGG16,and VGG19)as *** performance of the model was presented in terms of accuracy,F1-score,precision,and recall *** results showed that the CNN model with the augmentation,dropout,and L2 techniques at a dropout value of 0.4 and an epoch number of 30 provided the highest testing accuracy of *** best proposed model was more accurate than the pre-trained CNN models,especially compared to Inception *** addition,the number of total parameters was reduced by approximately 1.80–2.19 *** findings demonstrated that the proposed model with a small number of total parameters had a satisfactory degree of classification accuracy.
Alzheimer’s disease(AD)is a significant challenge in modern healthcare,with early detection and accurate staging remaining critical priorities for effective *** Deep Learning(DL)approaches have shown promise in AD di...
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Alzheimer’s disease(AD)is a significant challenge in modern healthcare,with early detection and accurate staging remaining critical priorities for effective *** Deep Learning(DL)approaches have shown promise in AD diagnosis,existing methods often struggle with the issues of precision,interpretability,and class *** study presents a novel framework that integrates DL with several eXplainable Artificial Intelligence(XAI)techniques,in particular attention mechanisms,Gradient-Weighted Class Activation Mapping(Grad-CAM),and Local Interpretable Model-Agnostic Explanations(LIME),to improve bothmodel interpretability and feature *** study evaluates four different DL architectures(ResMLP,VGG16,Xception,and Convolutional Neural Network(CNN)with attention mechanism)on a balanced dataset of 3714 MRI brain scans from patients aged 70 and *** proposed CNN with attention model achieved superior performance,demonstrating 99.18%accuracy on the primary dataset and 96.64% accuracy on the ADNI dataset,significantly advancing the state-of-the-art in AD *** ability of the framework to provide comprehensive,interpretable results through multiple visualization techniques while maintaining high classification accuracy represents a significant advancement in the computational diagnosis of AD,potentially enabling more accurate and earlier intervention in clinical settings.
This article introduces a novel Multi-agent path planning scheme based on Conflict Based Search (CBS) for heterogeneous holonomic and non-holonomic agents, designated as Heterogeneous CBS (HCBS). The proposed methodol...
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I had the privilege and the pleasure to work closely with Stephen J. Pennycook for about twenty years, having a group of post-docs and Vanderbilt-University graduate students embedded in his electron microscopy group ...
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I had the privilege and the pleasure to work closely with Stephen J. Pennycook for about twenty years, having a group of post-docs and Vanderbilt-University graduate students embedded in his electron microscopy group at Oak Ridge National Laboratory, spending on average a day per week there. We combined atomic-resolution imaging of materials,electron-energy-loss spectroscopy, and density-functional-theory calculations to explore and elucidate diverse materials phenomena, often resolving long-standing issues. This paper is a personal perspective of that journey, highlighting a few examples to illustrate the power of combining theory and microscopy and closing with an assessment of future prospects.
The steady-state security region(SSR)offers ro-bust support for the security assessment and control of new power systems with high uncertainty and ***,accurately solving the steady-state security region boundary(SS-RB...
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The steady-state security region(SSR)offers ro-bust support for the security assessment and control of new power systems with high uncertainty and ***,accurately solving the steady-state security region boundary(SS-RB),which is high-dimensional,non-convex,and non-linear,presents a significant *** address this problem,this paper proposes a method for approximating the SSRB in power systems using the feature non-linear converter and improved oblique decision ***,to better characterize the SSRB,boundary samples are generated using the proposed sampling *** samples are distributed within a limited distance near the ***,to handle the high-dimensionality,non-convexity and non-linearity of the SSRB,boundary samples are converted from the original power injection space to a new fea-ture space using the designed feature non-linear ***-sequently,in this feature space,boundary samples are linearly separated using the proposed information gain rate based weighted oblique decision ***,the effectiveness and generality of the proposed sampling method are verified on the WECC 3-machine 9-bus system and IEEE 118-bus system.
In light of the escalating privacy risks in the big data era, this paper introduces an innovative model for the anonymization of big data streams, leveraging in-memory processing within the Spark framework. The approa...
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In the era of big data, with the increase in volume and complexity of data, the main challenge is how to use big data while preserving the privacy of users. This study was conducted with the aim of finding a solution ...
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This paper presents a framework for producing robotic fabrics using square lattice formations of interlinked Kilobot modules. The framework supports: (i) fabrics of arbitrary size and shape;(ii) different types of def...
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