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Towards Simpler Modelling Expressions for the Mechanical Characterization of Soft Materials

Micro and Nanosystems 2024年第3期16卷 172-182页

作者： Kontomaris, Stylianos-Vasileios Malamou, Anna Stylianou, Andreas Chliveros, Georgios Faculty of Engineering and Architecture Metropolitan College Athens Greece BioNanoTec LTD Nicosia Cyprus Radar Systems and Remote Sensing Lab School of Electrical & Computer Engineering National Technical University of Athens Athens Greece Department of Mechanical Engineering Faculty of Engineering School of Sciences European University Cyprus Nicosia2404 Cyprus

Aims: The aim of this paper is to develop a new, simple equation for deep spherical indentations. Background: The Hertzian theory is the most widely applied mathematical tool when testing soft materials because it provides an elementary equation that can be used to fit force-indentation data and determine the mechanical properties of the sample (i.e., its Young’s modulus). However, the Hertz equation is only valid for parabolic or spherical indenters at low indentation depths. For large indentation depths, Sneddon’s extension of the Hertzian theory offers accurate force-indentation equations, while alternative approaches have also been developed. Despite ongoing mathematical efforts to derive new accurate equations for deep spherical indentations, the Hertz equation is still commonly used in most cases due to its simplicity in data processing. Objective: The main objective of this paper is to simplify the data processing for deep spherical indentations, primarily by providing an accurate equation that can be easily fitted to force-indentation data, similar to the Hertzian equation. Methods: A simple power-law equation is derived by considering the equal work done by the indenter using the actual equation. Results: The mentioned power-law equation was tested on simulated force-indentation data created using both spherical and sphero-conical indenters. Furthermore, it was applied to experimental force-indentation data obtained from agarose gels, demonstrating remarkable accuracy. Conclusion: A new elementary power-law equation for accurately determining Young’s modulus in deep spherical indentation has been derived. © 2024 Bentham Science Publishers.

关键词： Spheres

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Generic model of metadata management system for data lakes

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International Journal of Metadata, Semantics and Ontologies 2023年第4期16卷 315-328页

作者： Elkina, Hamza Sahib, Mohamed Rida Zaki, Taher Department of Computer Engineering and Intelligent Systems Laboratory of Innovation in Mathematics and Intelligent Systems Faculty of Applied Sciences Ibnou Zohr University Agadir Morocco

Data lake metadata management is crucial for clearly describing stored data and ensuring efficient search query results, especially for semi-structured and unstructured data. Moreover, high-quality metadata provides the necessary information for decision-making. Therefore, we propose a new approach called Metadata Management for Data Lakes (MMDL) based on metadata quality and classification. Additionally, we introduce a generic model adapted to the context of a data lake by including data lake zones to identify data location during its lifecycle. The model aligns with the proposed approach and enables the handling of metrics used to evaluate metadata quality and metadata sources for provenance classification. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Data quality

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Implementing and Measuring the Performance of PB, RR and PBRR Scheduling Algorithms on ATMega32A using FreeRTOS 5

Implementing and Measuring the Performance of PB, RR and PBR...

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5th International IEEE Novel Intelligent and Leading Emerging Sciences Conference, NILES 2023

作者： Abdelhamid, Abdelrahman M. H. Zamel, Amr A. Faculty of Engineering Ece Department Egypt Zagazig University Faculty of Engineering Computer and Systems Department Egypt

ISBN: (纸本)9798350381030

This research paper discusses the Real Time Operating systems;their types, software, benefits, and drawbacks, as well as how they work, including task states and control blocks. This research goes into great detail to explain many scheduling algorithms, including Round Robin, Priority Based, Earliest Deadline First, First Come First Serve, and Non-preemptive Deadline First. It also covers the performance monitoring tools and the parameters to be monitored while measuring the performance. The problem that we shed light on is the increment of death and incidents of Liquified Petroleum Gas (LPG) burns. In 2018, The researchers found that almost 81.03% of the total burns' percentage were related to gas leaks and 2% to negligence in the kitchen. To solve this problem, we made an application using FreeRTOS. We used different scheduling algorithms such as PB and RR. Furthermore, we utilized three different time slicing intervals: (0.1 sec, 10m sec, 100μ sec). Our application was implemented on ATMega32A with internal frequency 1 MHz. The application includes two sensors and an LCD. Additionally, We added a DC motor that functions as a ventilating fan, allowing us to take appropriate actions based on the sensor readings. The results show that the best scheduling algorithm with respect to time execution was PBRR with time = 0.0656 secs, while the worst was without RTOS taking 4.581 secs. However, the memory consumption of the application without RTOS was RAM of 8%, and ROM of 18.7%, while the PBRR memory consumption was RAM of 89.9%, ROM of 31.5%. Also, We simulated the execution of the tasks using SimSo for PB scheduling algorithm. © 2023 IEEE.

关键词： computer operating systems

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Text Augmentation-Based Model for Emotion Recognition Using Transformers

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computers, Materials & Continua 2023年第9期76卷 3523-3547页

作者： Fida Mohammad Mukhtaj Khan Safdar Nawaz Khan Marwat Naveed Jan Neelam Gohar Muhammad Bilal Amal Al-Rasheed Department of Computer Science The University of HaripurHaripur22620Pakistan Department of Computer Systems Engineering Faculty of Electrical and Computer EngineeringUniversity of Engineering and Technology PeshawarPeshawar25120Pakistan Department of Electronics Engineering Technology University of TechnologyNowshera24100Pakistan Department of Computer Science Shaheed Benazir BhuttoWomen UniversityPeshawar25000Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Emotion Recognition in Conversations(ERC)is fundamental in creating emotionally ***-BasedNetwork(GBN)models have gained popularity in detecting conversational contexts for ERC ***,their limited ability to collect and acquire contextual information hinders their *** propose a Text Augmentation-based computational model for recognizing emotions using transformers(TA-MERT)to address *** proposed model uses the Multimodal Emotion Lines Dataset(MELD),which ensures a balanced representation for recognizing human *** used text augmentation techniques to producemore training data,improving the proposed model’s *** encoders train the deep neural network(DNN)model,especially Bidirectional Encoder(BE)representations that capture both forward and backward contextual *** integration improves the accuracy and robustness of the proposed ***,we present a method for balancing the training dataset by creating enhanced samples from the original *** balancing the dataset across all emotion categories,we can lessen the adverse effects of data imbalance on the accuracy of the proposed *** results on the MELD dataset show that TA-MERT outperforms earlier methods,achieving a weighted F1 score of 62.60%and an accuracy of 64.36%.Overall,the proposed TA-MERT model solves the GBN models’weaknesses in obtaining contextual data for ***-MERT model recognizes human emotions more accurately by employing text augmentation and transformer-based *** balanced dataset and the additional training samples also enhance its *** findings highlight the significance of transformer-based approaches for special emotion recognition in conversations.

关键词： Emotion recognition in conversation graph-based network text augmentation-basedmodel multimodal emotion lines dataset bidirectional encoder representation for transformer

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Exploring Large Language Models for Relevance Judgments in Tetun 1

Exploring Large Language Models for Relevance Judgments in T...

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1st Workshop on Large Language Models for Evaluation in Information Retrieval, LLM4Eval 2024

作者： de Jesus, Gabriel Nunes, Sérgio INESC TEC - Institute for Systems and Computer Engineering Technology and Science Portugal FEUP - Faculty of Engineering University of Porto Portugal

The Cranfield paradigm has served as a foundational approach for developing test collections, with relevance judgments typically conducted by human assessors. However, the emergence of large language models (LLMs) has introduced new possibilities for automating these tasks. This paper explores the feasibility of using LLMs to automate relevance assessments, particularly within the context of low-resource languages. In our study, LLMs are employed to automate relevance judgment tasks, by providing a series of query-document pairs in Tetun as the input text. The models are tasked with assigning relevance scores to each pair, where these scores are then compared to those from human annotators to evaluate the inter-annotator agreement levels. Our investigation reveals results that align closely with those reported in studies of high-resource languages. © 2024 CEUR-WS. All rights reserved.

关键词： Structured Query Language

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Towards Green AI by Reducing Training Effort of Recurrent Neural Networks Using Hyper-Parameter Optimization with Dynamic Stopping Criteria 22

Towards Green AI by Reducing Training Effort of Recurrent Ne...

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22nd IEEE International Symposium on Intelligent systems and Informatics, SISY 2024

作者： Podgorelec, Vili Fister, Iztok Vrbancic, Grega University of Maribor Intelligent Systems Laboratory Faculty of Electrical Engineering and Computer Science Slovenia

ISBN: (纸本)9798350385601

Neural networks have become a leading model in modern machine learning, able to model even the most complex data. For them to be properly trained, however, a lot of computational resources are required. With the carbon footprint of ever-growing adoption of neural networks in mind, an approach to reduce the required training resources would be very welcome. We designed a new training effort reduction method based on the calculation of area under the normalized loss curve and assessed it on the electricity consumption forecasting problem with the recurrent neural networks. The results show that the proposed method was able to considerably reduce the amount of computational resources, while maintaining the predictive performance, and thus contributing towards the Green AI. © 2024 IEEE.

关键词： Green AI hyper-parameter optimization machine learning neural networks resource optimization

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Automatic extraction of lightweight and efficient neural network architecture of heavy convolutional architectures to predict microsatellite instability from hematoxylin and eosin histology in gastric cancer

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Neural Computing and Applications 2024年第25期36卷 15295-15321页

作者： Rostami, Habib Ashkpour, Maryam Behzadi-Khormouji, Hamed Mokhtari, Maral Khayati, Armin Keshavarz, Ahmad Talatian Azad, Saeed Tabesh, Yahya Computer Engineering Department Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr75168 Iran Artificial Intelligence and Intelligent Healthcare Lab Artificial Intelligence and Data Mining Research Group Faculty of Intelligent Systems Engineering and Data Science ICT Research Institute Persian Gulf University Bushehr75168 Iran Faculty of New Science and Technologies University of Tehran Tehran Iran Department of Computer Science Imec-IDLab University of Antwerp Sint-Pietersvliet 7 Antwerp2000 Belgium Shahid Faghihi Hospital Shiraz University of Medical Sciences Shiraz Iran Department of Computer Science Engineering and IT School of Electrical and Computer Engineering Shiraz University Shiraz Iran Electrical Engineering Department Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr75168 Iran IoT and Signal Processing Research Group Faculty of Intelligent Systems Engineering and Data Science ICT Research Institute Persian Gulf University Bushehr75168 Iran Department of Mathematical Sciences Sharif University of Technology Tehran Iran

Cancers have emerged as a significant concern due to their impact on public health and society. The examination and interpretation of tissue sections stained with Hematoxylin and Eosin (H&E) play a crucial role in disease assessment, particularly in cases like gastric cancer. Microsatellite instability (MSI) is suggested to contribute to the carcinogenesis of specific gastrointestinal tumors. However, due to the nonspecific morphology observed in H&E-stained tissue sections, MSI determination often requires costly evaluations through various molecular studies and immunohistochemistry methods in specialized molecular pathology laboratories. Despite the high cost, international guidelines recommend MSI testing for gastrointestinal cancers. Thus, there is a pressing need for a new diagnostic modality with lower costs and widespread applicability for MSI detection. This study aims to detect MSI directly from H&E histology slides in gastric cancer, providing a cost-effective alternative. The performance of well-known deep convolutional neural networks (DCNNs) and a proposed architecture are compared. Medical image datasets are typically smaller than benchmark datasets like ImageNet, necessitating the use of off-the-shelf DCNN architectures developed for large datasets through techniques such as transfer learning. Designing an architecture proportional to a custom dataset can be tedious and may not yield desirable results. In this work, we propose an automatic method to extract a lightweight and efficient architecture from a given heavy architecture (e.g., well-known off-the-shelf DCNNs) proportional to a specific dataset. To predict MSI instability, we extracted the MicroNet architecture from the Xception network using the proposed method and compared its performance with other well-known architectures. The models were trained using tiles extracted from whole-slide images, and two evaluation strategies, tile-based and whole-slide image (WSI)-based, were employed and comp

关键词： Network architecture

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Enhancing Image Security with Rossler Attractor-Based Encryption 2

Enhancing Image Security with Rossler Attractor-Based Encryp...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Al-Najjar, Hazem Al-Rousan, Nadia University Of Petra Faculty Of Information Technology Department Of Computer Science Amman Jordan German Jordanian University Faculty Of Electrical Engineering And Information Systems Department Of Computer Engineering Jordan

ISBN: (纸本)9798350394962

we introduced image encryption algorithms with high sensitivity, such that even a single alteration in a plain-text image would result in a complete transformation of the ciphered image. The first algorithm employed pixel values in a substitution approach, whereas the second algorithm utilized pixel values to generate chaotic function keys. Additionally, we employed a Rossler chaotic function with pixel data to generate chaotic values for the shuffling and substitution processes. After subjecting these algorithms to rigorous testing using standard metrics, our experiments revealed the efficiency of the algorithms in image encryption. Notably, even a minute change in the original image resulted in a vastly different ciphered image. These proposed algorithms underscored the effectiveness of integrating chaotic functions with pixel information, significantly enhancing image security. © 2024 IEEE.

关键词： Cryptography

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Fuzzy clustering for electric field characterization and its application to thunderstorm interpretability

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Digital Communications and Networks 2025年第2期11卷 299-307页

作者： Xu Yang Hongyan Xing Xinyuan Ji Wei Xu Witold Pedrycz School of Electronics and Information Engineering Nanjing University of Information Science and TechnologyNanjing 210044China Department of Electrical and Computer Engineering University of AlbertaEdmonton AB T6G 2R3Canada Systems Research Institute Polish Academy of Sciences00-901 WarsawPoland Department of Computer Engineering Faculty of Engineering and Natural SciencesIstinye University34010 IstanbulTurkey

Changes in the Atmospheric Electric Field Signal(AEFS)are highly correlated with weather changes,especially with thunderstorm ***,little attention has been paid to the ambiguous weather information implicit in AEFS *** this paper,a Fuzzy C-Means(FCM)clustering method is used for the first time to develop an innovative approach to characterize the weather attributes carried by ***,a time series dataset is created in the time domain using AEFS *** AEFS-based weather is evaluated according to the time-series Membership Degree(MD)changes obtained by inputting this dataset into the ***,thunderstorm intensities are reflected by the change in distance from a thunderstorm cloud point charge to an AEF ***,a matching relationship is established between the normalized distance and the thunderstorm dominant MD in the space ***,the rationality and reliability of the proposed method are verified by combining radar charts and expert *** results confirm that this method accurately characterizes the weather attributes and changes in the AEFS,and a negative distance-MD correlation is obtained for the first *** detection of thunderstorm activity by AEF from the perspective of fuzzy set technology provides a meaningful guidance for interpretable thunderstorms.

关键词： Atmospheric electric field(AEF) Thunderstorm Fuzzy C-means(FCM) Attribute

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Performance Modelling of IoT in Smart Agriculture 1

Performance Modelling of IoT in Smart Agriculture

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1st IEEE International Conference on Advances in Electronics, Communication, Computing and Intelligent Information systems, ICAECIS 2023

作者： Gamal, Abdelrahman S. Mohamed, Hoda K. Ain Shams University Faculty of Engineering Computer and Systems Engineering Department Cairo Egypt

ISBN: (纸本)9798350348057

Nowadays, IoT is being used in several applications, such as smart cities, health care and innovating agriculture and other applications. Moreover, the evolution of IoT technologies such as LoRaWAN, SIGFOX, ZigBee, and others drives the industry to convert their traditional systems to IoT systems due to the need to achieve better performance. One of the most challenging applications in an IoT system is smart agriculture due to the need for high energy and water consumption to reach the desired crop yield. Several related works in different countries tried to solve this challenge for a better water consumption in irrigation process to get the maximum crop yield production. In this paper, we used artificial intelligence and deep learning to predict the crop yield and the needed parameters of the soil to achieve the best performance with minimum power consumption. Also, we used convolutional neural networks to detect the crop deficit needed for real-time monitoring. The performed simulation has shown that the proposed model outperforms other related works and will positively increase both saving resources and the performance. © 2023 IEEE.

关键词： Crops

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