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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Vengidusamy, S. Kottaimalai, R. Pallikonda Rajasekaran, M. Arunprasath, T. Govindaraj, Vishnuvarthanan Kalasalingam Academy of Research and Education Department of Electronics and Communication Engineering Tamilnadu Krishnankoil India Kalasalingam Academy of Research and Education Department of Biomedical Engineering Tamilnadu Krishnankoil India School of Computer Science and Engineering VIT Bhopal University Data Science Division Madhya Pradesh India

ISBN: (纸本)9798331530013

Neurodevelopmental disorders (NDD) are conditions that impact brain function and interfere with neurological development, resulting in difficulties with social, cognitive, and emotional skills. The most common NDD include autism spectrum disorder (ASD). It is a complicated NDD marked by ongoing difficulties in social communication and interaction, as well as restricted or repetitive behaviors. The origins of ASD are intricate, encompassing a combination of environmental, genetic, and neurobiological factors. This research delves into the latest insights regarding ASD, stressing the critical role of early diagnosis and intervention. To improve long-term outcomes. The study also examines neuroimaging methods, genetic markers, and behavioural assessments that contribute to more precise diagnoses. Furthermore, the paper addresses the variability within ASD, highlighting the need for tailored treatment approaches and the vital role of individualized educational and therapeutic strategies. Despite significant progress, there remain gaps in our comprehension of the underlying mechanisms of ASD, and this research emphasizes the necessity for continued interdisciplinary collaboration. The findings aim to enhance diagnostic accuracy, inform treatment strategies, and improve the overall and improve the overall well-being of life for individuals with ASD and their families. © 2024 IEEE.

关键词： Convolutional neural networks

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Nitrogen-Doped Amorphous Carbon Homojunction from Palmyra Sugar as a Renewable Solar Cell

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Journal of Renewable Materials 2024年第1期12卷 57-69页

作者： Budhi Priyanto Imam Khambali Irma Septi Ardiani Khoirotun Nadhiyah Anna Zakiyatul Laila M.Chasrun Hasani Bima Romadhon Retno Asih Yoyok Cahyono Triwikantoro Darminto Department of Electronic Engineering Faculty of EngineeringUniversitas Muhammadiyah MalangKota MalangIndonesia Advanced Materials Research Group Department of PhysicsFaculty of Science and Data AnalyticsInstitut Teknologi Sepuluh NopemberSurabayaIndonesia Department of Electronic Engineering Faculty of Industrials EngineeringInstitut Teknologi National MalangKota MalangIndonesia

An a-C/a-C:N junction,which used palmyra sugar as the carbon source and ammonium hydroxide(NH4OH)as the dopant source,was successfully deposited on the ITO glass substrate using the nano-spraying *** current-voltage relationship of the junction was found to be a Schottky-like contact,and therefore the junction shows the characteristic *** means the a-C and a-C:N are semiconductors with different types of ***,the samples showed an increase in current and voltage value when exposed to visible light(bright state)compared to the dark condition,thereby,indicating the creation of electron-hole pairs during the *** was also discovered that the relationship between current and voltage for the a-C/a-C:N junction sample formed a curve that satisfies the rule of the photovoltaic effect when exposed to visible light from a light *** exposure of this sample to direct sunlight at AM 1.5 conditions produced a curve that meets the rules for the emergence of the photovoltaic effect with higher characteristics for the current-voltage ***,the a-C/a-C:N junction sample is a solar cell successfully fabricated using a sample method and has a maximum efficiency of 0.0013%.

关键词： Solar cell amorphous carbon palmyra sugar

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ChainDash: An Ad-Hoc Blockchain data Analytics System 49th

ChainDash: An Ad-Hoc Blockchain Data Analytics System

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49th International Conference on Very Large data Bases, VLDB 2023

作者： Liu, Yushi Yuan, Liwei Chen, Zhihao Yu, Yekai Zhang, Zhao Jin, Cheqing Yan, Ying School of Data Science and Engineering East China Normal University China Blockchain Platform Division Ant Group China Engineering Research Center of Blockchain Data Management Ministry of Education China

The emergence of digital asset applications, driven by Web 3.0 and powered by blockchain technology, has led to a growing demand for blockchain-specific graph analytics to unearth the insights. However, current blockchain data analytics systems are unable to perform efficient ad-hoc graph analytics over both live and past time windows due to their inefficient data synchronization and slow graph snapshots retrieval capability. To address these issues, we propose ChainDash, a blockchain data analytics system that dedicates a highly-parallelized data synchronization component and a retrieval-optimized temporal graph store. By leveraging these techniques, ChainDash supports efficient ad-hoc graph analytics of smart contract activities over arbitrary time windows. In the demonstration, we showcase the interactive visualization interfaces of ChainDash, where attendees will execute customized queries for ad-hoc graph analytics of blockchain data. © 2023, VLDB Endowment. All rights reserved.

关键词： Blockchain

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APPROPRIATE MODE OF STORAGE OF PORCINE HEART MYOCARDIA WITHOUT TEMPERING WITH ITS PASSIVE MECHANICAL PROPERTIES 13

APPROPRIATE MODE OF STORAGE OF PORCINE HEART MYOCARDIA WITHO...

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13th South African Conference on Computational and Applied Mechanics, SACAM 2024

作者： Mabuda, I. Pandelani, T. Ngwangwa, H. Nemavhola, F. Unisa Biomedical Engineering Research Group Department of Mechanical Engineering School of Engineering College of Science Engineering and Technology University of South Africa Science Campus FL 1710 South Africa Faculty of Engineering and the Built Environment Durban University of Technology Durban South Africa

ISBN: (纸本)9780797219144

One of the important aspects of mechanics of soft tissues is perseveration during and before mechanical testing. This study addresses the critical aspect of preserving soft tissue integrity for mechanical testing, particularly focusing on the myocardium of the porcine heart. The primary objective of the study was to assess the impact of different preservation methods on the mechanical properties of this tissue. A series of experiments were conducted. Porcine heart myocardium was subjected to tensile testing using a uniaxial machine as a means of evaluating the traditional preservation methods employed in the field. To simulate real-world scenarios, harvested myocardium samples were initially preserved by immersion in either fresh water or a NaCl solution. These samples were then stored under varying temperature conditions, including freezing at -2°C, -10°C, and -20°C, or refrigeration at 2°C-3°C. The preserved myocardium underwent a thawing process before undergoing mechanical testing. The study specifically focused on the right ventricle of the porcine heart, known for its significant role in cardiac function. Notably, frozen right ventricular myocardium exhibited a slight increase in tensile strength, particularly in the circumferential direction, when compared to fresh tissue. Overall, this research sheds light on the potential impact of preservation methods and storage temperatures on myocardial mechanical properties, emphasizing the importance of considering these factors in biomechanical studies of cardiac tissues. Copyright 2024 by South African Association of Theoretical and Applied Mechanics. All rights reserved.

关键词： Tensile strength

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Assisting Visually Impaired Subjects Using Large Language Models: A Comprehensive Evaluation 6

Assisting Visually Impaired Subjects Using Large Language Mo...

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6th IEEE Novel Intelligent and Leading Emerging sciences Conference, NILES 2024

作者： Holiel, Heidi Ahmed Fawzi, Sahar Ali Al-Atabany, Walid Center for Informatics Science Nile University Medical Imaging and Image Processing Research Group Sheikh Zayed City Egypt Cairo University Systems and Biomedical Engineering Department Giza Egypt Helwan University Biomedical Engineering Department Helwan Egypt

ISBN: (纸本)9798350378511

Visual impairment significantly challenges an individual's ability to navigate and interact with their environment independently. While retinal prostheses have provided partial vision restoration, they offer limited support for complex visual tasks such as object recognition, text reading, and real-time navigation. Recent advancements in technology have introduced Large Language Models (LLMs) as promising tools to enhance these assistive technologies, potentially enhancing the capabilities of existing devices. This study explores the integration of LLMs into assistive devices for visually impaired individuals, evaluating several models, including MiniCPM-Llama3-V-2_5, PaliGemma, Blip2, Microsoft Git Large COCO, and LLaVA-Next. The models are assessed in tasks like image captioning, object detection, and optical character recognition (OCR), focusing on their effectiveness in enhancing user independence and quality of life. Our findings indicate that MiniCPM-Llama3-V-2_5 and PaliGemma excel in providing accurate, detailed, and practical outputs, crucial for real-world applications. These models demonstrate significant potential in transforming retinal prostheses from passive aids into interactive tools that enhance user engagement with their surroundings. The study concludes with discussions on the implications of these findings and suggests future research directions to further improve the integration of LLMs in assistive technologies for visually impaired individuals. © 2024 IEEE.

关键词： Assistive technology

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Investigation Myocardial Perfusion Scan Parameters and Walls Motion in Patients undergoing Cardiac Surgery

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Journal of biomedical Physics and engineering 2024年第6期14卷 561-568页

作者： Movahedi, Mohammad Mehdi Arianfar, Hamidreza Atefi, Mohammad Golpaygani, Ali Tavakoli Gheisari, Farshid Mahmoudi, Tahereh Department of Medical Physics and Engineering School of Medicine Shiraz University of Medical Sciences Shiraz Iran Ionizing and Non-ioniz-ing Radiation Protection Research Center INIR-PRC Shiraz University of Medical Sciences Shiraz Iran School of Medicine Shiraz University of Medical Sciences Shiraz Iran Tehran Science and Research Branch Islamic Azad University Tehran Iran Biomedical Engineering Research Group Research Center of Technology and Engineering Standard Research Institute Karaj Iran

Background: Coronary heart disease the most prevalent form of cardiovascular disease, results from the blockage of blood flow through arteries. The Myocardial Perfusion Scan (MPS) is considered a non-invasive method to assess the heart condition and provides valuable information, such as End Diastolic Volume (EDV), End Systolic Volume (ESV), Ejection Fraction (EF), Lung to Heart Ratio (LHR), and Transient Ischemic Dilatation (TID). Objective: This study aimed to investigate changes in gated heart scan parameters to diagnose patients, who are candidates for heart surgery. Material and Methods: In this descriptive cross-sectional study, 40 patients who are candidates for heart surgery were enrolled to evaluate the relation between the parameters of the gated heart scan and the amount of ischemic area of the heart. After scanning the patients, TID, ESV, LHR, and EF and also the movements of the heart walls are examined and analyzed in these patients. Results: According to the results of the one-sample t-test showing a significant difference between the parameters, the results were within the normal range (P-value © Journal of biomedical Physics and engineering.

关键词： Cardiovascular surgery

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A machine learning method approach for designing novel high strength and plasticity metastable β titanium alloys

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Progress in Natural science:Materials International 2025年第1期35卷 156-165页

作者： Zhiduo Liu Haoyu Zhang Shuai Zhang Jun Cheng Yixuan He Ge Zhou Jiawei Liu Suping Song Lijia Chen School of Materials Science and Engineering Shenyang University of Technology Shenyang Key Laboratory of Advanced Structural Materials and Applications Shenyang University of Technology Northwest Institute for Nonferrous Metal Research Shaanxi Key Laboratory of Biomedical Metal Materials State Key Laboratory of Solidification Processing Northwestern Polytechnical University Ansteel Group Automation Co. Ltd

In order to improve efficiency and reduce costs, four machine learning models were established for the design of metastable β titanium alloys, including the Adaboost model, the LightGBM model, the Voting model, and the Stacking model. The accuracy of these models was evaluated, and all models exhibited excellent accuracy for tensile strength, yield strength, and elongation. The values of R-squared coefficients（R2） all greater than *** them, the LightGBM model showed the highest accuracy, with relatively smallest values of mean absolute error（MAE） and root mean square error（RMSE） and relatively largest value of R2. To further verify the accuracy of the model, a metastable β titanium alloy Ti-5.5Cr-5Al-4Mo-3Nb-2Zr was designed by the LightGBM model. The predicted values of the alloy's tensile strength, yield strength, and elongation under three heat treatment processes were in high agreement with the experimental values. The alloy exhibited optimal strength-plasticity matching after undergoing a solution treatment at 850°C for 0.5 h, followed by aging at 650°C for 8 h, with a tensile strength of 1317 MPa, an elongation of 11.17 %, and a strength-plasticity product of 14.711 GPa·%.

关键词： Machine learning Metastable β titanium alloy Microstructure Plasticity Strength

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Cross-cultural contextualisation for recommender systems

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Journal of Ambient Intelligence and Humanized Computing 2024年第2期15卷 1659-1670页

作者： Hong, Minsung An, Sojung Akerkar, Rajendra Camacho, David Jung, Jason J. Big Data Research Group Western Norway Research Institute Vestlandsforsking Sogndal6851 Norway Department of Computer Science and Engineering Chung-Ang University 84 Heukseok-ro Heukseok-dong Dongjak-gu Seoul Korea Republic of Information Systems Department Technical University of Madrid Madrid28031 Spain

Cultural Heritage (CH) domain is rapidly moving from traditional heritage sites into smart cultural heritage environment through various technologies. As one of the important technologies in the smart space, Recommender Systems (RSs) have been widely utilised to personalised services and matching visitors’ goals and behaviours. Whereas, cultural difference is often considered a barrier to technology transfer or adoption. However, few studies focus on how the cultural factor influences recommendation despite cultural difference largely affects user preferences in the RSs. Furthermore, existing researches have mainly analysed evaluation results of their recommendation to reveal cultural differences, rather than utilising the cross-cultural factors into RSs. In this paper, we propose a novel concept of cross-cultural contextualisation and a model to compute the cross-cultural factor affecting users (countries or cultures) preferences by using matrix factorisation and clustering techniques. In addition, we discuss how to apply the model to RSs in CH domain through cross-domain recommendation techniques. Note that the two computational techniques were used to analyse cross-cultural factors which impact to user preferences, rather than to recommend items. In other words, the proposed model and computing results capable of utilisation into the other RSs as well as various research fields. Results of experiments with a real-world dataset showed effectiveness of the proposed model and supported that there is cultural difference influencing users’ rating behaviours. Furthermore, a systematic analysis of dataset and the experimental results presented that individual users could be considered as country-wise groups for the model to analyse the cross-cultural factors. © Springer-Verlag GmbH Germany, part of Springer Nature 2019.

关键词： Recommender systems

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Utilizing Slope Entropy as an Effective Index for Wearable EEG-Based Depth of Anesthesia Monitoring 46

Utilizing Slope Entropy as an Effective Index for Wearable E...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Shahbakhti, Mohammad Beiramvand, Matin Far, Somayeh Mohammadi Solé-Casals, Jordi Lipping, Tarmo Augustyniak, Piotr Kaunas University of Technology Biomedical Engineering Institute Kaunas Lithuania Tampere University Faculty of Information Technology and Communication Tampere Finland Agh University of Krakow Department of Biocybernetics and Biomedical Engineering Krakow Poland University of Vic-Central University of Catalonia Data and Signal Processing Research Group Vic Spain

ISBN: (纸本)9798350371499

Based on prior research indicating a decrease in the spectral slope of electroencephalogram (EEG) during anesthesia induction and an increase during recovery, we propose Slope Entropy (SlopEn), which uniquely emphasizes variations in signal slope, as a new index for monitoring the depth of anesthesia (DoA). The performance of SlopEn is investigated on just a single frontal EEG channel and is compared against other well-known entropy metrics utilized in the field. After filtering the EEG signal, four types of entropy, including SlopEn, are derived from all EEG sub-bands and separately inputted to a regressor for estimating DoA index values. Comparing the results obtained using SlopEn with those from the Sample entropy demonstrates the superiority of the former, achieving a higher correlation coefficient (0.75 vs. 0.63) and a lower median absolute error (4.2 vs. 6.2) between the estimated and reference DoA index values. These findings establish that the SlopEn has the potential to become a valuable index for DoA monitoring using single frontal channel EEG systems. © 2024 IEEE.

关键词： Electroencephalography

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Conceptual Design of a Device for Online Calibration of Spirometer Based on Neural Network

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Journal of biomedical Physics and engineering 2023年第3期13卷 291-296页

作者： Nafisi, Vahid Reza Eghbal, Manouchehr Torbati, Nasim Biomedical Engineering group Department of Electrical & Information Technology Iranian Research Organization for Science and Technology Tehran Iran Food and Drug Admin-istration Ministry of Health and Medical Education Tehran Iran

Daily calibration of spirometry devices plays an important role in promoting the accuracy of pulmonary diagnostic results. It is needed to have more precise and adequate instru-ments for calibrating spirometry during the clinical use. In this work, a device was de-signed and developed based on a calibrated-volume syringe and an electrical circuit was also built to measure the air flux. Some colored tapes with specific size and order covered the syringe piston. When the piston moved in front of the color sensor, the input air flow was calculated according to the width of the strips and transferred to the computer. A Radial Basis Function (RBF) neural network estimator used new data to modify the previous estimation function for increasing the accuracy and the reliability. The simulation showed that the root mean square of the error improved from 13.7±0.37% to 4.2±0.22%, i.e. the calibration curve has improved about 70%. © 2023, Shiraz University of Medical sciences. All rights reserved.

关键词： Artificial Intelligence Calibration Color Sensor Neural Network Model Spirometry

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