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Parameters of Sustainability in the context of Decarbonization and Circular Construction Sector 7th

Parameters of Sustainability in the Context of Decarbonizati...

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7th EAI international conference on Future Access Enablers of Ubiquitous and Intelligent Infrastructures, EAI FABULOUS 2023

作者： Mésároš, Peter Grazianová, Mária Smetanková, Jana Krajníková, Katarína Behúnová, Annamária Faculty of Civil Engineering Institute of Construction Technology Technical University of Kosice Economics and Management Vysokoskolska 4 Kosice042 00 Slovakia Faculty of Civil Engineering Experts Institute in Construction Technical University of Kosice Vysokoskolska 4 Kosice042 00 Slovakia National Infrastructure for Technology Transfer Support in Slovakia II – NITT SK II Lamačská cesta 8/A Bratislava840 05 Slovakia Faculty of Mining Ecology Process Control and Geotechnologies Institute of Earth Resources Technical University of Košice Letna 9 Košice042 00 Slovakia

ISBN: (纸本)9783031500503

Climate change is a 21st century phenomenon and is becoming one of the major challenges of environmental policy. While the manifestations of climate change are diverse across the world and across regions, its negative consequences on socio-economic and natural systems are increasingly significant and require active resolution. To this end, many organizations, associations, and governments are approaching the introduction of new measures that will help eliminate the negative effects of human activity on the environment. To this end, for example, green (energy) taxes are introduced, the amount of waste produced is monitored more and the rate of recycling and reuse of materials is supported, thus meeting the fundamental aspects of the circular economy. the contribution provides an overview and thus comparisons of selected European countries in the field of parameters supporting sustainable development, such as green, energy taxes and revenues from their collection, waste management and waste recycling rate in the construction sector. the result of the research will be the creation of a ranking of countries, based on the punctual assessment, in income from energy tax collection and in the area of waste generation, waste recycling and waste recovery rate from construction and demolition. the declared results will subsequently constitute the basis for the development of a strategic crisis plan, which can be implemented within the individual economies of European countries. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Circular construction sector Decarbonization Parameter Sustainability

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Health Technology Use in Germany Among Older Adults (Part II): Short Time Changes in Health-Related Information and mHealth Applications 1

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13th international conference on Digital Human modeling and Applications in Health, Safety, Ergonomics and Risk Management (DHM) Held as Part of the 24th international conference on Human-Computer Interaction (HCII)

作者： Schaefer, Katharina Rasche, Peter theis, Sabine Seinsch, Tobias Boddin, Maximilian Kuepper, Rebecca Broehl, Christina Wille, Matthias Zweck, Axel Brandl, Christopher Nitsch, Verena Mertens, Alexander Rhein Westfal TH Aachen Inst Ind Engn & Ergon Eilfschornsteinstr 18 D-52062 Aachen Germany Inst Sociol Future & Innovat Eilfschornsteinstr 7 D-52062 Aachen Germany Fraunhofer Inst Commun Informat Proc & Ergon FKIE Campus Blvd 55 D-52074 Aachen Germany Ruhr Univ Bochum Inst Gen Practice & Family Med Abt Allgemeinmed Univ Str 150 D-44801 Bochum Germany

ISBN: (数字)9783031060182

ISBN: (纸本)9783031060182;9783031060175

Demographic change in Germany has been discussed on a grand scale in the past years. Feared and denied at first;research was conducted;approaches were presented, and measures derived. the health care sector is an area of special interest because demographic change is affecting it twice: on the one hand, more elderly patients have to be treated while at the other hand, less medical staff is being trained. It is expected that digitization can provide a solution. But is this true? How can digitization help to relieve the burden on medical staff in particular? Are digital services and available information used by older people? Or do they reject them? Finding answers to these questions is the aim of this longitudinal study. the focus was on the usage of information and communication technology (ICT) by the elderly (older people) (60+) in Germany. this study by the interdisciplinary research team "Tech4Age" consists of two surveys from 2016/2017 (survey I) and 2018/2019 (survey II). the article presents the results of the second survey, focusing on the comparison of both surveys as well as on potential changes. the sample consisted of N = 649 participants (54.6% male and 45.4% female) with an average age of 66.11 years (SD = 5.45 years). A fundamental aspect of the surveys was to examine the use of health-related information and levels of trust, the general (smartphone and tablet) applications and mHealth applications used by older adults plus information and communication technology used by older adults. Due to the large scope of both the surveys and the results, the latter will be presented in two separate articles. this paper concentrates on the first two aspects: the use of health-related information and levels of trust as well as on general and mHealth applications used by older adults. In order to examine the use of health-related information and levels of trust, health literacy (HL) was taken into account. the focus here is on coping strategies in the context of h

关键词： Older adults Germany ICT Information-seeking behavior Health Ergonomics

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modeling and Simulation of SEPIC Converter with ANFIS Controller for DC-to-DC Power Conversion

Modeling and Simulation of SEPIC Converter with ANFIS Contro...

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international conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： Rosaiah Mudigondla Kalagotla Chenchireddy Cheruku Sidhu K Jithender Patel A Sunil Reddy D Vamshi Dept. of Electrical and Electronics Engineering Teegala Krishna Reddy Engineering College Hyderabad Telangana India

DC-DC power converters will play a crucial role in efficiently regulating voltage levels for various loads across different applications. Among the diverse types of DC-DC converters, the Single Ended Primary-Inductor Converter (SEPIC) has gained significant popularity due to its ability to step up or step down input voltage while offering galvanic isolation between the input and output. In this context, a novel approach to achieve precise voltage regulation and robust control under diverse operating conditions of integration, an Adaptive Neuro-Fuzzy Inference System (ANFIS) controller with the SEPIC converter is proposed. the outcomes of this research study have a substantial impact on advancing the control strategies for DC-DC power conversion systems, offering valuable insights into the capabilities and real-time implementation potential of the ANFIS controller.

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Information network analysis method based on two-layer complex network model

Information network analysis method based on two-layer compl...

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Computer Information Science and Application Technology (CISAT), international conference on

作者： Xianghui Meng Grainger College of Engineering university of Illinois Urbana-Champaign urbana Illinois US

ISBN: (数字)9798350375107

ISBN: (纸本)9798350375114

this paper proposes an information network analysis method based on a two-layer complex network model. the study establishes a double-layer complex network consisting of a global information network and a point-to-point information network to reflect information flow more accurately. By introducing node and edge feature vectors and applying the Bayesian logistic function, the propagation probability and delay are calculated to describe the information dissemination process. Additionally, the reaction-diffusio‘n model is utilized to investigate information flow between regions, determining the propagation probability and delay between different nodes. the paper conducts sensitivity analysis to verify the stability and applicability of the model, and coefficients are determined through computer-trained data. Finally, experiments demonstrate the model’s effectiveness in various media propagation scenarios and predict the information dissemination situation and development trends in different countries by 2050.

关键词： Analytical models Sensitivity analysis Computational modeling Complex networks Network analyzers Predictive models Prediction algorithms Stability analysis Delays context modeling

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MCC-DEM: Multi-Scale context Constraint-Based Depth Estimation Method for Monocular Camera

MCC-DEM: Multi-Scale Context Constraint-Based Depth Estimati...

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international conference on Information Systems and Computer Aided Education (ICISCAE)

作者： Cuiyun Fang Xunming Xue Guoqing Deng Chaofan Zhang Yingbo Xu Haibin Cao Yonghu Xu Fan Wang Liming Bao Anhui Institute of Optics and Fine Mechanics and Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei P. R. China University of Science and Technology of China Hefei P. R. China Anhui China Tobacco Industrial Co. LTD. Hefei P. R. China

ISBN: (数字)9798350350760

ISBN: (纸本)9798350350777

Monocular depth estimation is the task of estimating the depth value of each pixel given a monocular RGB image. For the obstacle avoidance and map building of mobile robots, the requirement of accuracy of monocular depth estimation is engaged. In this work, we propose a monocular depth estimation network based on an encoder-decoder structure, to obtain accurate depth estimation. An improved atrous spatial pyramid pooling module is used to extract the multi-scale context information, in which structural similarity error is used to optimize the loss function and better reflect the structural cues of an object. Experimental results show that the proposed method outperforms state-of-the-art methods on both a public dataset and a collected dataset in terms of overall performance.

关键词： Accuracy Computational modeling Feature extraction Cameras Data mining Mobile robots Collision avoidance Streams Information systems context modeling

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Convolutional Neural Network (CNN) based Blood Vessel Segmentation from Ocular Images

Convolutional Neural Network (CNN) based Blood Vessel Segmen...

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international conference on Computing Methodologies and Communication (ICCMC)

作者： V. Sujatha Bommasamudram Sri Anitha Gullipalli Tulasi Rama Nelluri Niharika Anchula Sahithi Computer Science and Engineering Vignan's Nirula Institute of Technology and Sciences for Women Guntur India

When it comes to computer-aided diagnosis and treatment of retinal illnesses, blood vessel segmentation is one of the most essential diagnoses. Because of this particular factor, blood vessel segmentation has recently emerged as a subject that researchers are strongly encouraged to investigate. this research work has developed a model by using an upgraded Convolutional Neural Networks (CNN) model and the architecture called as Visual Geometry Group-16. A contracting route and an expanding path make up the design, and their respective functions are to gather context and to make exact localisation possible. Sigmoid and soft-max activation functions are included into the conventional VGG-16, so that the network can perform the segmentation better. the results of this segmentation have been enhanced, in particular with regard to thin blood arteries, which are notoriously difficult to identify due to the fact that they have a low contrast with the surrounding pixels. this is one of the many simple architectural designs that are put to use in the diagnosis of different retinal illnesses. this study demonstrated that such a network can be trained from start to end by using a very small number of photos and that it outperforms the approach that was previously considered the best. In addition, as compared to earlier approaches that aid in the early diagnosis of pathologies, the segmented outputs were better able to cover thinner blood arteries. this is considered as a significant advancement.

关键词： Geometry Image segmentation Visualization Computational modeling Computer architecture Retina Convolutional neural networks

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Research on predicting legal judgments by incorporating key information

Research on predicting legal judgments by incorporating key ...

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Computer Information Science and Application Technology (CISAT), international conference on

作者： Fan Xu Wei Liu Kao Guo Zihao Wang School of Computer Science & Engineering School of Artificial Intelligence Wuhan Institute of Technology Wuhan China

ISBN: (数字)9798350375107

ISBN: (纸本)9798350375114

the prediction of legal judgments is based on the description of case facts to predict the final charges. through judgment prediction technology, the judicial system can handle a large number of cases more efficiently and provide more accurate judgment results. However, in reality, the types of charges are numerous and complex. To address the issues of severe data imbalance and easily confusable charges, this paper proposes a legal judgment model that incorporates key information. Firstly, it combines the ALBERT (A Lite BERT) pre-trained model and the bidirectional Gated Recurrent Unit (GRU) model to encode cases, efficiently capturing the contextual semantic information in case descriptions. then, the concept of key information is introduced, using attention mechanisms and statistical methods to extract two distinguishable parts of key information from the cases, which are then concatenated with the case encoding for the final prediction. Additionally, category classification weights are employed to enhance the model’s ability to predict low-frequency samples. the proposed method has achieved good results on real legal judgment datasets.

关键词： Information science Law Statistical analysis Semantics Predictive models Logic gates Encoding Data models Data mining context modeling

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Sentiment Analysis Based on the BERT and Bidirectional GRU Model

Sentiment Analysis Based on the BERT and Bidirectional GRU M...

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international conference on Information Systems and Computer Aided Education (ICISCAE)

作者： Siyu Yao Jianlong Bi Ocean University of China Qingdao China Northwest Normal University Lanzhou China

ISBN: (数字)9798350350760

ISBN: (纸本)9798350350777

As the Internet continues to develop, a vast amount of user-generated textual data has emerged on various online platforms. Sentiment analysis has become a crucial tool for understanding user attitudes and emotions to analyze the specific emotional tendencies expressed in this data accurately. In this context, this paper employs a sentiment analysis model constructed using a BERT-based deep learning approach combined with a bidirectional GRU neural network. Initially, the BERT model is utilized to extract high-quality semantic features from the input textual data. these embedding representations extracted by the BERT model are then passed to the bidirectional GRU layer to capture sequential information further. After the GRU layer processes the features extracted by BERT, it ultimately determines the sentiment tendency. Experimental results on the IMDB movie review dataset demonstrate that the BERT-BiGRU prediction model significantly improves accuracy and F1 score compared to other models, providing an effective method and reference for the field of sentiment analysis.

关键词： Analytical models Sentiment analysis Accuracy Reviews Computational modeling Bidirectional control Predictive models Feature extraction Motion pictures Encoding

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FF-UNET: An Enhanced Framework for Improved Segmentation of Brain Tumors in MRI Images

FF-UNET: An Enhanced Framework for Improved Segmentation of ...

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Pattern Recognition and Artificial Intelligence (PRAI), international conference on

作者： Jinru Liu Uzair Aslam Bhatti Mengxing Huang Hao Tang Yuanyuan Wu Siling Feng Yu Zhang School of Information and Communication Engineering Hainan University Haikou China

ISBN: (数字)9798350350890

ISBN: (纸本)9798350350906

With the development of medical imaging technology, accurate and effective tumor segmentation is crucial. In this study, we investigated the potential of deep learning based brain tumor MRI image segmentation technology. We propose an enhancement method called FF-UNET, which utilizes feature fusion and combines the capabilities of UNet and CNN models to improve segmentation accuracy. using preprocessing techniques to improve tumor visibility, and then using a customized layered UNet model for segmentation. To reduce overfitting, a discard layer is introduced after each convolutional block stack. In addition, the CNN process utilizes the context of brain tumor MRI images to further enhance the segmentation performance of the model. In two datasets, our method achieved an accuracy of over 98%, with accuracy and Jaccard coefficients exceeding 90%. the experimental results indicate that our proposed framework outperforms state-of-the-art models in distinguishing brain tissue. the FF-UNET model has great potential as a feasible diagnostic tool, enabling radiologists to accurately segment brain tumor images and improve patient care.

关键词： Image segmentation Accuracy Magnetic resonance imaging Shape measurement Brain tumors Radiology Brain modeling Data models Convolutional neural networks Testing

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Deep Learning in ChatGPT - A Survey

Deep Learning in ChatGPT - A Survey

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international conference on Trends in Electronics and Informatics (ICEI)

作者： A.M. Jasmine Hashana P. Brundha Mohamed Uvaze Ahamed Ayoobkhan Fazila S Computer Science and Engineering Francis Xavier Engineering College Tirunelveli India Software engineering New Uzbekistan University Tashkent Uzbekistan

Abstract-As a subset of machine learning, deep learning makes use of multiple-layer neural networks to learn with available data and make decisions or predictions. A large language model called ChatGPT is based on deep learning, specifically a type of neural network called a transformer. ChatGPT's transformer architecture uses attention mechanisms to focus on the most important parts of the input, allowing it to process and comprehend a large amount of text data. In order for the model to comprehend the context and meaning of natural language text, it is trained on a huge database of text, including articles and books. One of the main importance of using deep learning in ChatGPT is its intelligence to understand relationships and patterns from the input text and generate or predict new text that is homogeneous to the input/training data. Because of this, ChatGPT is able to respond to questions and prompts in a manner that is comparable to that of a human, making it useful for a wide scope of natural language processing missions like translating languages, summarizing texts, and responding to questions. It's worth noting that, while deep learning has been highly effective in ChatGPT, it is not without its limitations. To train, deep learning models can be very complex and require a lot of data and computing power.

关键词： Deep learning Training Adaptation models Computational modeling Computer architecture Chatbots Transformers

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