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Optical spectroscopy of excitons in ReS2 monolayers grown by chemical vapor deposition

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arXiv 2024年

作者： Ojo, Solomon Onasanya, Juwon Benamara, Morad Hamad, Bothina Manasreh, M.O. Material Science and Engineering program University of Arkansas FayettevilleAR72701 United States Institute of nanotechnology University of Arkansas FayettevilleAR72701 United States Physics Department University of Arkansas FayettevilleAR72701 United States Department of Electrical Engineering and Computer Science University of Arkansas FayettevilleAR72701 United States

Monolayers of ReS2 were grown by a chemical vapor deposition technique on SiO2/Si substrates and investigated at room temperature by using μ-Raman, μ-photoluminescence (PL) and absorbance spectroscopies. The Raman scattering spectrum exhibits several phonon modes that were confirmed by the computation analysis based on the density functional theory. The ReS2 structural integrity was confirmed by using the XRD and the energy dispersion spectroscopy that was obtained by the scanning electron microscopy. Photoluminescence spectra show excitons related to interband transition in thin monolayer flakes and bulk-like structures. Additionally, a sharp PL line located at 1.2620 eV with a second harmonic peak at 2.5420 eV were observed and explained in terms of interband excitonic transitions originated within the rhenium 5d orbit. An optical absorbance spectrum with an exciton peak around 1.4660 eV was obtained for an assembly of ReS2 flakes grown on silica substrate. Copyright © 2024, The Authors. All rights reserved.

关键词： Scanning electron microscopy

ACO With Reinforcement Learning Applied to Rescues Operations on Urban Forests

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ACO With Reinforcement Learning Applied to Rescues Operation...

Congress on Evolutionary Computation

作者： Cláudio Alves Israel Mendonça Vanessa de Almeida Guimarèes Pedro Henrique González System Engineering and Computer Science Program Federal University of Rio de Janeiro Rio de Janeiro Brazil Faculty of Advanced Science and Technology Kumamoto University Kumamoto Japan COPPEAD Graduate Business School Federal University of Rio de Janeiro Rio de Janeiro Brazil

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

In the context of smart cities where green infras-tructure is incentived, besides important benefits like regulating temperatures and absorbing pollutants among others, tour by urban forests is a way to experience closer contact with nature near of big urban centers. Eventually, visitors get lost, and helping these people with velocity is important to avoid severe incidents. Normally, rescue operations mobilize firefighters, ex-pensive equipment like helicopters and public resources. Following that idea of reducing search time in rescue operations, this paper considers the Data Mule Routing Problem with Limited Autonomy (DMRP-wLA). To find high-quality solutions, this paper proposes an Ant Colony Optimization algorithm enhanced with Reinforcement Learning to create an adaptive decision-making algorithm.

关键词： Ant colony optimization Forests Smart cities Helicopters Decision making Reinforcement learning Evolutionary computation

Secure state estimation and control for cyber security of AC microgirds

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arXiv 2019年

作者： Fooladivanda, Dariush Hu, Qie Chang, Young Hwan Sauer, Peter W. Department of Mechanical and Aerospace Engineering University of California San Diego United States Department of Electrical Engineering and Computer Sciences University of California Berkeley United States Department of Biomedical Engineering and Computational Biology Program Oregon Health and Science University United States Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign United States

A timely, accurate, and secure dynamic state estimation is needed for reliable monitoring and efficient control of microgrids. The synchrophasor technology enables us to obtain synchronized measurements in real-time and to develop dynamic state estimators for real-time monitoring and control of microgrids. In this study, we consider an AC microgrid comprising several synchronous generators and inverter-interface power supplies, and focus on securely estimating the dynamic states of the microgrid from a set of corrupted data. We propose a dynamic state estimator which enables the microgrid operator to reconstruct the dynamic states of the microgrid from a set of corrupted data. Finally, we consider an AC microgrid with the same topology as the IEEE 33-bus distribution system, and numerically show that the proposed secure estimation algorithm can accurately reconstruct the attack signals. Copyright © 2019, The Authors. All rights reserved.

关键词： State estimation

A Clinical Decision Support System of Pressure Ulcers Tissue Classification 21

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A Clinical Decision Support System of Pressure Ulcers Tissue...

5th International Conference on Medical and Health Informatics, ICMHI 2021

作者： Chang, Dun-Hao Chu, Po-Jui Li, Yi-Jhen Ning, Chun-Kai Chien, Ting-Ying Department of Information Management Yuan Ze University Taoyuan City Taiwan Department of Plastic and Aesthetic Surgery Far Eastern Memorial Hospital Taiwan Department of Computer Science and Engineering Yuan Ze University Taoyuan City Taiwan Program in Biomedical Informatics Yuan Ze University Taoyuan City Taiwan Innovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan City Taiwan

ISBN: (纸本)9781450389846

Pressure ulcers (PUs) are a common problem associated with great morbidity and cost. Because of the lack of professional personnel in the institutions or home care system, the diagnosis and treatment of PUs were sometimes delayed. We aim to develop an automatic tissue classification and severity evaluation system of PUs using AI technology. All PU images were collected from patients in the Far Eastern Memorial Hospital (FEMH) in recent 3 years and labeled according to different tissue types. The labeled images were used to train the tissue segmentation model with U-net convolutional neural network (CNN). The percentage of the different tissue can be calculated automatically and would be compared with the manually labeled one. The output of tissue classification was transformed to a clinical decision support system (CDSS) with a rule-base formula. Our study showed that proposed system can classify each tissue automatically and reach an accuracy about 93.6%. However, the CDSS's accuracy rate was only 64% for the referral suggestion and 62% for the care recommendation. Hence, the system should be modified with more data and information. © 2021 ACM.

关键词： Tissue

FNS: An event-driven spiking neural network simulator based on the LIFL neuron model

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arXiv 2018年

作者： Susi, Gianluca Garces, Pilar Cristini, Alessandro Paracone, Emanuele Salerno, Mario Maestu, Fernando Pereda, Ernesto Laboratory of Cognitive and Computational Neuroscience Center for Biomedical Technology Technical University of Madrid and Complutense University of Madrid Spain Department of Civil Engineering and Computer Science University of Rome Tor Vergata' Italy Department of Electronic Engineering University of Rome Tor Vergata' Italy Department of Experimental Psychology Faculty of Psychology Complutense University of Madrid Cognitive Processes and Logopedy Spain Department of Industrial Engineering and IUNE University of la Laguna Spain

Limitations in processing capabilities and memory of today's computers make spiking neuron-based (human) whole-brain simulations inevitably characterized by a compromise between bio-plausibility and computational cost. It translates into brain models composed of a reduced number of neurons and a simplified neuron's mathematical model, leading to the search for new simulation strategies. Taking advantage of the sparse character of brain-like computation, the event-driven technique could represent a way to carry out efficient simulation of large-scale Spiking Neural Networks (SNN). The recent Leaky Integrate-and-Fire with Latency (LIFL) spiking neuron model is event-driven compatible and exhibits some realistic neuronal features, opening new avenues for brain modelling. In this paper we introduce FNS, the first LIFL-based spiking neural network framework, which combines spiking/synaptic neural modeling with the event-driven approach, allowing us to define heterogeneous neuron modules and multi-scale connectivity with delayed connections and plastic synapses. In order to allow multi-thread implementations a novel parallelization strategy is also introduced. This paper presents mathematical models, software implementation and simulation routines on which FNS is based. Finally, a brain subnetwork is modeled on the basis of real brain structural data, and the resulting simulated activity is compared with associated brain functional (source-space MEG) data, demonstrating a good matching between the activity of the model and that of the experimetal data. This work aims to lay the groundwork for future event-driven based personalised brain models. Copyright © 2018, The Authors. All rights reserved.

关键词： Neural networks

Lung nodules classification in CT images using Shannon and Simpson Diversity Indices and SVM

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Lung nodules classification in CT images using Shannon and S...

8th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2012

作者： Nascimento, Leonardo Barros De Paiva, Anselmo Cardoso Silva, Aristófanes Corrêa Federal University of Maranhão - UFMA Post-graduate Program in Engineering of Electricity Campus Do Bacanga Av. dos Portugueses SN Bacanga 65085-580 São Luis MA Brazil Federal University of Maranhao - UFMA Department of Computer Science Campus Do Bacanga Av. dos Portugueses SN Bacanga 65085-580 Sao Luís MA Brazil

ISBN: (纸本)9783642315367

In this work, we present the use of Shannon and Simpson Diversity Indices as texture descriptors for lung nodules in computerized Tomography (CT) images. These indices will be proposed to characterize the nodules into two classes: benign or malignant. The investigation is done using the Support Vector Machine (SVM) for classification in a dataset consisting of 73 nodules, 47 benign and 26 malignant;the results of the methodology were: sensitivity of 85.64%, specificity of 97.89% and accuracy of 92.78%. © 2012 Springer-Verlag.

关键词： Support vector machines

Gorgon and pathwalking: Macromolecular modeling tools for subnanometer resolution density maps

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Gorgon and pathwalking: Macromolecular modeling tools for su...

作者： Baker, Matthew L. Baker, Mariah R. Hryc, Corey F. Ju, Tao Chiu, Wah Verna and Marrs McLean Department of Biochemistry and Molecular Biology National Center for Macromolecular Imaging Baylor College of Medicine Houston TX 77030 United States Program in Structural and Computational Biology and Molecular Biophysics Baylor College of Medicine Houston TX 77030 United States Department of Computer Science and Engineering Washington University in St. Louis St. Louis MO 63130 United States

The complex interplay of proteins and other molecules, often in the form of large transitory assemblies, are critical to cellular function. Today, X-ray crystallography and electron cryo-microscopy (cryo-EM) are routinely used to image these macromolecular complexes, though often at limited resolutions. Despite the rapidly growing number of macromolecular structures, few tools exist for modeling and annotating structures in the range of 3-10 Å resolution. To address this need, we have developed a number of utilities specifically targeting subnanometer resolution density maps. As part of the 2010 Cryo-EM Modeling Challenge, we demonstrated two of our latest de novo modeling tools, Pathwalking and Gorgon, as well as a tool for secondary structure identification (SSEHunter) and a new rigid-body/flexible fitting tool in Gorgon. In total, we submitted 30 structural models from ten different subnanometer resolution data sets in four of the six challenge categories. Each of our utlities produced accurate structural models and annotations across the various density maps. In the end, the utilities that we present here offer users a robust toolkit for analyzing and modeling protein structure in macromolecular assemblies at non-atomic resolutions. © 2012 Wiley Periodicals, Inc.

关键词： X ray crystallography

Empowering Machines to Think Like Chemists: Unveiling Molecular Structure-Polarity Relationships with Hierarchical Symbolic Regression

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arXiv 2024年

作者： Lou, Siyu Liu, Chengchun Chen, Yuntian Mo, Fanyang Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China Ningbo Institute of Digital Twin Eastern Institute of Technology Ningbo315200 China School of Materials Science and Engineering Peking University Beijing100871 China -Preferred Program Peking University Shenzhen Graduate School Shenzhen518500 China

Thin-layer chromatography (TLC) is a crucial technique in molecular polarity analysis. Despite its importance, the interpretability of predictive models for TLC, especially those driven by artificial intelligence, remains a challenge. Current approaches, utilizing either high-dimensional molecular fingerprints or domain-knowledge-driven feature engineering, often face a dilemma between expressiveness and interpretability. To bridge this gap, we introduce Unsupervised Hierarchical Symbolic Regression (UHiSR), combining hierarchical neural networks and symbolic regression. UHiSR automatically distills chemical-intuitive polarity indices, and discovers interpretable equations that link molecular structure to chromatographic behavior. Copyright © 2024, The Authors. All rights reserved.

关键词： Thin layer chromatography

M2FN: Multi-step modality fusion for advertisement image assessment

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arXiv 2021年

作者： Park, Kyung-Wha Ha, Jung-Woo Lee, JungHoon Kwon, Sunyoung Kim, Kyung-Min Zhang, Byoung-Tak Interdisciplinary Program in Neuroscience Seoul National University Korea Republic of NAVER AI LAB NAVER CLOVA Statistics and Actuarial Science Soongsil University Korea Republic of School of Biomedical Convergence Engineering Pusan National University Korea Republic of Department of Computer Science and Engineering Seoul National University Korea Republic of Surromind Robotics

Assessing advertisements, specifically on the basis of user preferences and ad quality, is crucial to the marketing industry. Although recent studies have attempted to use deep neural networks for this purpose, these studies have not utilized image-related auxiliary attributes, which include embedded text frequently found in ad images. We, therefore, investigated the influence of these attributes on ad image preferences. First, we analyzed large-scale real-world ad log data and, based on our findings, proposed a novel multi-step modality fusion network (M2FN) that determines advertising images likely to appeal to user preferences. Our method utilizes auxiliary attributes through multiple steps in the network, which include conditional batch normalization-based low-level fusion and attention-based high-level fusion. We verified M2FN on the AVA dataset, which is widely used for aesthetic image assessment, and then demonstrated that M2FN can achieve state-of-the-art performance in preference prediction using a real-world ad dataset with rich auxiliary attributes. © 2021, CC BY-NC-SA.

关键词： Marketing