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The effect of lateral growth of self-assembled GaN microdisks on UV lasing action

Nano Research 2023年第8期16卷 11096-11106页

作者： Zhiwei Si Zongliang Liu Xiaoxuan Wang Chunxiang Xu Wei Lin Xiaoxuan Luo Feng Li Xiaoming Dong Shunan Zheng Xiaodong Gao Jianfeng Wang Ke Xu School of Nano-Tech and Nano-Bionics University of Science and Technology of ChinaHefei 230026China Suzhou Institute of Nano-tech and Nano-bionics Chinese Academy of SciencesSuzhou 215123China Suzhou Nanowin Science and Technology Co Ltd.Suzhou 215123China Shenyang National Laboratory for Materials Science Jiangsu Institute of Advanced SemiconductorsSuzhou 215123China State Key Laboratory of Bioelectronics School of Biological Science and Medical EngineeringSoutheast UniversityNanjing 210096China Key Laboratory for Physical Electronics and Devices of the Ministry of Education&Shaanxi Key Lab of Information Photonic Technique School of Electronic Science and EngineeringFaculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China

There are significant differences in the extent of impurity incorporation on different crystallographic directions of GaN microstructures,and the impurity-related deep energy level behavior will have a significant impact on device ***,a comprehensive understanding of the effect of lateral growth on device performance has not been achieved due to the lack of comprehensive spatial distribution characterization of the optical behavior and impurity incorporation in GaN *** present a comprehensive study of the optical behavior and growth mechanism of self-assembled GaN microdisks using nanoscale spatially resolved cathodoluminescence(CL)*** have found a clear growth orientation-dependent optical behavior of the lateral and vertical growth sectors of self-assembled GaN *** lateral growth sector,i.e.,the{101¯1}-growth sector,forms six side facets of the microdisk and shows significant near-bandgap emission(NBE)and weak deep energy level *** effect enhanced emission was found for the first time in such a truncated hexagonal Na-flux GaN microdisk system with an ultra-smooth surface(Ra<0.7 nm)and low *** self-assembled microdisk shows significant ultraviolet(UV)lasing action(main lasing peak wavelength 370.9 nm,quality factor 1278,threshold 6×10^(4)μJ/cm^(2))under pulsed optical *** believe that the appearance of UV lasing action may be related to the light limitation on the six side facets of the lateral growth of the GaN microdisk,the high structural quality,the low content of deep energy level defects,the low surface roughness,and the low stress.

关键词： homogeneous lateral epitaxy spatially resolved cathodoluminescence deep energy level GaN microdisk lasing

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FOGO - an Optimized Fog and Edge Computing Method for VANETs 46

FOGO - an Optimized Fog and Edge Computing Method for VANETs

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46th ICT and Electronics Convention, MIPRO 2023

作者： Corluka, Renato Matos, Ivona Damjanovic, Davor Balen, Josip Josip Juraj Strossmayer University of Osijek Faculty of Electrical Engineering Computer Science and Information Technology Osijek Kneza Trpimira 2b Osijek31000 Croatia

ISBN: (纸本)9789532331042

Vehicular Ad-hoc Networks (VANETs) have many specific issues and challenges due to a heterogeneous and highly dynamic environment, and therefore traditional solutions need to be improved and adopted in order to satisfy the networking and processing requirements. Fog and edge computing principles enable VANETs to achieve a more realistic and dependable architecture by using several layers for information processing. In this paper, the developed FOGO method is improved and customized for VANETs. The proposed solution is using an enhanced infrastructure in which additional mobile fog nodes are added to the network together with the existing stationary nodes. Small and medium sized messages are processed by mobile fog nodes, which then disseminate the results across the network. Roadside units (RSUs) are fixed, highly-capable devices positioned throughout the network to assist the processing of increasing volumes of data. Additionally, the cloud is taken into account, but only when processing large amounts of data. Along with the presented method flowcharts, operating algorithms, and message structure, the proposed system architecture is described. Furthermore, potential use cases are suggested together with the metrics for the system performance evaluation. The LuST scenario was used to evaluate the proposed architecture and the results showed improved message processing efficiency. © 2023 MIPRO Croatian Society.

关键词： Fog

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Change Management in Preparing Indonesian Team to IOI 35

Change Management in Preparing Indonesian Team to IOI

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35th International Olympiad in Informatics, IOI 2023

作者： Jingga, Felix Isal, Yugo K. Cendranata, Andreas Liem, Inggriani Mulyanto, Adi Indonesia Faculty of Computer Science University of Indonesia Indonesia Bebras Indonesia National Board Organization Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Indonesia

Indonesia has been participating at the International Olympiad in Informatics (IOI) since 1995. The process of selecting the four contestants remains unchanged. All medalists in the national level competition were invited to take a series of training camps to select competitively the candidates. In the first two decades of participation in IOI, Indonesia have collected two gold medals, and these surprising achievements were merely more because of luck, and not as a result of a reliable training/selection process. We believe reliable and good training is a basis to predict and expect a stable achievement. Through careful observations and reflections, a change of course of action is needed to bring Indonesia’s achievement to the next level. This paper shares Indonesia’s experience in changing and implementing strategies during the national training camps to better prepare the four contestants to IOI. What are the changes taken by the coaching team that have been exercised that made it possible for Indonesia to achieve three gold in the last four consecutive years? © 2023 IOI, Vilnius University.

关键词： Human resource management

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Nitinol Based heat Engine for Solar Energy Harvesting

Nitinol Based heat Engine for Solar Energy Harvesting

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International Conference on Perspective Technologies and Methods in MEMS Design (MEMSTECH)

作者： Shivam Nitin Satpute Sameer Ajay Sake Pavitra Praveen Sardesai Parth Sandip Sawant Apurv Vaibhav Sarode Varad Gangadharrao Sadegaonkar Marek Iwaniec Ramesh Narina Anita Dombale Nishant S. Kulkarni Mechanical Engineering Department Vishwakarma Institute of Technology Pune India Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering AGH University of Science & Technology Krakow Poland

ISBN: (数字)9798350378627

ISBN: (纸本)9798350378634

The most abundantly available renewable energy source on the earth is solar energy. Conventionally, this energy is converted into electric energy with the help of photovoltaic cells. The efficiency of this commercially available technology is around 5-25%. This article deals with the investigation of an innovative energy harvesting system that uses a Shape Memory Alloy (SMA) to utilize solar heat for generating electric power. SMA shows significant deflection when heated and this characteristic is used to develop a SMA based heat engine. This work proposes a heat engine where a SMA wire is used to convert solar radiation to perform mechanical work. Further, the mechanical work is used to drive a rotary electric generator to produce electric energy. The SMA used for this purpose is Nitinol wire, which absorbs heat from the solar radiation and transforms the same initially to mechanical motion, which later is used to produce electric power. Initially, the SMA element is curved in shape, and it shows a significant deflection when it receives the heat from solar radiation. This deflection of the SMA element is used to operate a rotary electric generator to produce electric energy. The theoretical calculations for balance of the energy flow from incident solar radiations to output electric energy is discussed. The developed prototype shows around 4.5% efficiency with an average power of 2.4 mW.

关键词： Heat engines Solar energy Electric generators Voltage Transforms Power systems Wire

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Child Tracking and Prediction of Violence on Children In Social Media Using Natural Language Processing and Machine Learning 1

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22nd International Conference on Artificial Intelligence and Soft Computing, ICAISC 2023

作者： Nallakaruppan, M.K. Srivastava, Gautam Gadekallu, Thippa Reddy Reddy, Praveen Kumar Krishnan, Sivarama Polap, Dawid School of Information Technology and Engineering Vellore Institute of Technology Vellore India Department of Mathematics and Computer Science Brandon University Brandon Canada School of Information Technology and Engineering Vellore Institute of Technology Department of Electrical and Computer Engineering Lebanese American University Lebanon Zhongda Group Zhejiang Jiaxing314312 China Faculty of Applied Mathematics Silesian University of Technology Kaszubska 23 Gliwice44-100 Poland

ISBN: (数字)9783031425059

ISBN: (纸本)9783031425042

Crimes against children are a direct threat to the world of tomorrow. The future of the world is in the hands of children who are the real wealth of mankind. The challenge for society is to address and handle challenges against children such as sexual abuse, trafficking, child labour, and harassment. Even though measures are taken by several governments globally, violence against children is an evident day-to-day process. This becomes the most challenging issue in the post-Internet era, where organized crime against children takes place around the world through a targeted group of people through virtual private networks and the dark web. What comes next is the emergence of social media, which provides an opportunity for these criminals to harm children globally through powerful social networks [15]. The existing methods focus on classifiers that do not work across multiple keywords [6] to look for sentiment analysis regarding a single classifier. There are only a few works that address the issue and these methods do not employ the multi-classification model [2]. In this paper, we propose a model which includes the Twitter API, the APIFY web scraper framework, as well as the VADER semantic analyzer in Python which possesses Decision tree analysis of sentiment prediction. The proposed work provides 99.6904% accuracy and predicts the classification of sentiment analysis. Our work has novelty in attribute selection, child trade, and violence analysis in social media and prediction of classification accuracy using the Decision Tree algorithm. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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Optimization of Cognitive Radio System Using Self-Learning Salp Swarm Algorithm

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computers, Materials & Continua 2022年第2期70卷 3821-3835页

作者： Nitin Mittal Harbinder Singh Vikas Mittal Shubham Mahajan Amit Kant Pandit Mehedi Masud Mohammed Baz Mohamed Abouhawwash Department of Electronics&Communication Engineering Chandigarh UniversityMohali140413India School of VLSI Design and Embedded Systems National Institute of TechnologyKurukshetra136119India School of Electronics&Communication Engineering Shri Mata Vaishno Devi UniversityKatra182320India Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Engineering College of Computer and Information TechnologyTaif UniversityTaif21994Saudi Arabia Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

CognitiveRadio(CR)has been developed as an enabling technology that allows the unused or underused spectrum to be used dynamically to increase spectral *** improve the overall performance of the CR systemit is extremely important to adapt or reconfigure the *** Decision Engine is a major module in the CR-based system that not only includes radio monitoring and cognition functions but also responsible for parameter *** meta-heuristic algorithms offer numerous advantages compared to traditional mathematical approaches,the performance of these algorithms is investigated in order to design an efficient CR system that is able to adapt the transmitting parameters to effectively reduce power consumption,bit error rate and adjacent interference of the channel,while maximized secondary user ***-Learning Salp Swarm Algorithm(SLSSA)is a recent meta-heuristic algorithm that is the enhanced version of SSA inspired by the swarming behavior of *** this work,the parametric adaption of CR system is performed by SLSSA and the simulation results show that SLSSA has high accuracy,stability and outperforms other competitive algorithms formaximizing the throughput of secondary *** results obtained with SLSSA are also shown to be extremely satisfactory and need fewer iterations to converge compared to the competitive methods.

关键词： Cognitive radio meta-heuristic algorithm cognitive decision engine salp swarm algorithm

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Evaluating importance of AI in Robotics and Automation in Healthcare for Different age Groups 1

Evaluating importance of AI in Robotics and Automation in He...

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1st International Conference on Healthcare Innovations, Software and engineering Technologies, HISET 2024

作者： Abu Eid, Aws Ismail Pethkar, Rajendra Huda, Bintul Patil, Jaykar Bhaskar Gatla, Ranjith Kumar Kumbhar, Ramchandra Arab Open University Faculty of Computing Studies Amman Jordan Jaywant College of Engineering and Polytechnic Department of Mechanical Engineering Karad India Sharda University Department of Computer Science & Technology Srinagar India Jaywant Institute of Management Karad India Institute of Aeronautical Engineering Department of Electrical and Electronics Engineering Hyderabad India Department of Radio-Diagnosis Karad India

ISBN: (纸本)9798350360707

Nowadays robotics is mostly used in the world. There are many industries which used robots for reducing time and to have high precision of work. The use of artificial intelligence is now increasing which helps in automations and processing of machines. Thus, the focus of study is to find the different applications of automation of robotics (i.e. robotic process automation) in the field of healthcare and its impact on the employment of medical field. Four major categories can be used to group AI applications in healthcare: expressive, analytical, prognostic, and prescriptive. The only way to close the skills gap in the healthcare industry is to increase the application of artificial intelligence. Hospitals are developing mental commit robots to treat their patients. Through physical contact, these robots can have an impact on humans' psyche, physiology, and social interactions. Thus, in coming time most of the healthcare industry will be with the robots which will reduces the human interventions with the patients through one of the host servers. © 2024 IEEE.

关键词： medical robotics

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Comprehensive Overview of Multi-agent Systems for Controlling Smart Grids

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CSEE Journal of Power and Energy Systems 2022年第1期8卷 115-131页

作者： Om Prakash Mahela Mahdi Khosravy Neeraj Gupta Baseem Khan Hassan Haes Alhelou Rajendra Mahla Nilesh Patel Pierluigi Siano Power System Planning Division Rajasthan Rajya Vidyut Prasaran Nigam Ltd.Jaipur-302005India Media Integrated Communication Laboratory Graduate School of EngineeringOsaka UniversityOsakaJapan Computer Science and Engineering Department Oakland UniversityRochesterUSA Department of Electrical and Computer Engineering Hawassa UniversityHawassa 1530Ethiopia Faculty of Mechanical and Electrical Engineering Tishreen UniversityLattakiaSyria Department of Electrical Engineering National Institute of Technology Kurukshetra Department of Management&Innovation Systems University of Salerno84084 SalernoItaly

Agents are intelligent entities that act flexibly and autonomously and make wise decisions based on their intelligence and experience.A multi-agent system(MAS)contains multiple,intelligent,and interconnected collaborating agents for solving a problem beyond the ability of a single agent.A smart grid(SG)combines advanced intelligent systems,control techniques,and sensing methods with an existing utility power *** controlling smart grids,various control systems with different architectures have already been ***-based control of power system operations has been shown to overcome the limitations of time required for analysis,relaying,and protection;transmission switching;communication protocols;and management of plant *** systems provide an alternative for fast and accurate power network *** paper provides a comprehensive overview of MASs used for the control of smart *** paper provides a wide-spectrum view of the status of smart grids,MAS-based control techniques and their implementation for the control of smart *** of MASs in the control of various aspects of smart grids-including the management of energy,marketing energy,pricing,scheduling energy,reliability,network security,fault handling capability,communication between agents,SG-electrical vehicles,SG-building energy systems,and soft grids—have been critically *** than a hundred publications on the topic of MAS-based control of smart grids have been critically examined,classified,and arranged for fast reference.

关键词： Coordinated control multi-agent systems renewable energy sources smart energy infrastructure smart grid

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An Ensemble Deep Learning Approach for Accurate Urinary Sediment Detection Using YOLOv9e and KD-YOLOX-ViT

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IEEE Access 2025年 13卷 101934-101954页

作者： Mansura Naznine Abdus Salam Muhammad Mohsin Khan Sadia Farhana Nobi Muhammad E. H. Chowdhury Department of Electrical Engineering Qatar University Doha Qatar Department of Computer Science and Engineering Rajshahi University of Engineering and Technology Rajshahi Bangladesh Department of Electrical and Computer Engineering Rajshahi University of Engineering and Technology Rajshahi Bangladesh Department of Neurology Hamad Medical Corporation Doha Qatar Department of Public Health and Informatics BSMMU Dhaka Bangladesh

Urinary Tract Infections (UTIs) are common medical diseases that occur from bacterial infections which can impact any area of the urinary system. Detecting urine particles is crucial for diagnosing UTIs and other renal abnormalities, as the presence of red blood cells (RBCs), white blood cells (WBCs), casts, fungi, and bacteria in urine can indicate disorders such as hematuria, kidney stones, and urinary tract malignancies. This study introduces an advanced deep learning method that utilizes ensemble of YOLOv9e and KD-YOLOX-ViT models. YOLOv9e model is built upon the Generalized Efficient Layer Aggregation Network (GELAN) and Programmable Gradient Information (PGI). The KD-YOLOX-ViT model integrates knowledge distillation and Vision Transformer (ViT) modules within the YOLOX framework to improve performance. This ensemble framework combines the strengths of YOLOv9e and KD-YOLOX-ViT models to automate the detection and classification of seven distinct urine sediment particles, effectively addressing challenges related to class imbalance, image resolution, and domain adaptation. The YOLOv9e model exhibited exceptional performance, obtaining precision, recall, and mean average precision (mAP50) scores of 88.5%, 88.1%, and 92.2%, respectively. The KD-YOLOX-ViT model also performed well, achieving precision, recall, mAP50, and mAP50-95 scores of 86%, 88%, 86.7%, and 53.3%, respectively. By ensembling YOLOv9e and KD-YOLOX-ViT using Weighted Box Fusion (WBF), the model achieved a final mAP50 of 94.18%. The model’s adaptability was further validated through rigorous external validation on a novel dataset, yielding in a mAP50 of 94.64%. Comparative analysis with state-of-the-art methods confirms the model’s real-time analytical capabilities. Moreover, the integration of eXplainable AI (XAI) enhances interpretability, offering valuable insights for confident clinical diagnosis. This comprehensive approach shows significant promise in advancing diagnostic accuracy and enabling

关键词： Sediments Accuracy Adaptation models Kidney Deep learning Microscopy White blood cells medical diagnostic imaging Feature extraction Bacteria

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Gastrointestinal Disease Prediction Using Transfer Learning

Gastrointestinal Disease Prediction Using Transfer Learning

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International Conference on computer Vision and Internet of Things 2023, ICCVIoT 2023

作者： Sujatha, R. Helen, D. Hemamalini, U. Divya, V. Dharshini, A. Priya School of Information Technology & Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Computer Applications Faculty of Science and Humanities SRM Institute of Science and Technology Kattankulathur Chennai India Department of Information Technology VELS Institute of Science Technology and Advanced Studies Pallavaram Chennai India

ISBN: (纸本)9781839539848

According to the Global Cancer Observatory's 2018 estimations, gastrointestinal cancer is the third most common cause of cancer deaths and the fifth most common cause of being diagnosed with cancer worldwide. Diagnosis is crucial, and gastroscopy is used to detect stomach cancer early to enhance patient survival. This study proposed a Deep Learning based computer-Aided Diagnostics (CADx) method to identify gastroscopy disease. The dataset is trained with an adversarial training technique. The proposed approach uses the deep convolution neural network based on VGG16, VGG19, and InceptionV3. The dataset is trained with an adversarial training technique. The Inception V3 has the best accuracy among them, with an accuracy in the training of 94.44% and a validation accuracy of 84.75%, with a minor loss compared to VGG16 and VGG19 models. © The Institution of engineering & technology 2023.

关键词： Deep learning

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