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A Comprehensive View on the Progress of Organoid Research with an Emphasis on its Relevance to Disease Characterization

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Biomedical and Environmental Sciences 2023年第10期36卷 959-971页

作者： Chandra KISHORE Vaishali Ji Saurav Mallik Ayan MUKHERJI Namrata TOMAR Soumen Kumar Pati LI Ai Min Sinthia Roy BANERJEE Soumadip GHOSH Raza Ali NAQVI University of California Irvine Medical CenterOrangeCA 92868USA Department of Botany J.D.Women’s CollegeBailey RoadPatna-800023India Harvard T.H.Chan School of Public Health BostonMA 02115USA Department of Information Technology Haldia Institute of TechnologyHaldiaWest BengalIndia Medical College of Wisconsin MilwaukeeWI 53226USA Department of Bioinformatics Maulana Abul Kalam Azad University of TechnologyNadiaWest BengalIndia Center for Precision Health School of Biomedical InformaticsThe University of Texas Health Science Center at HoustonHoustonTX 77030USA School of Computer Science and Engineering Xi’an University of TechnologyXi'an 710048ShaanxiChina Department of Information Technology Guru Nanak Institute of TechnologyKolkataWest BengalIndia Department of Computer Science and Engineering Sister Nivedita UniversityKolkataWest BengalIndia Center for Precision Health School of Biomedical InformaticsUniversity of IllinoisChicago60612USA

INTRODUCTION Organoids are primary tissue or stem cells derived cell aggregates that have the capacity for self-organization,self-renewal,and the capacity to mimic cellular and tissue level *** can overcome the shortc... 详细信息

关键词： functions. overcome shortcomings

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Simultaneous Super-Resolution and Denoising on MRI via Conditional Stochastic Normalizing Flow

Simultaneous Super-Resolution and Denoising on MRI via Condi...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Liu, Zhenhong Wang, Xingce Wu, Zhongke Zhu, Yi-Cheng Frangi, Alejandro F. Beijing Normal University School of Artificial Intelligence Beijing China Peking Union Medical College Hospital Department of Neurology Beijing China Christabel Pankhurst Institute Division of Informatics Imaging and Data Sciences School of Health Sciences The Department of Computer Science School of Engineering The University of Manchester United Kingdom Department of Cardiovascular Sciences The Department of Electrical Engineering Ku Leuven Leuven Belgium Lan Turing Institute London United Kingdom

ISBN: (纸本)9798350337488

Magnetic resonance imaging (MRI) scans often suffer from noise and low-resolution (LR), which affect the diagnosis and treatment results obtained for patients. LR images and noise come together with MRI, and the existing methods solve image super-resolution (SR) reconstruction and denoising tasks in a step-by-step manner, which influences the overall real distribution of the MRI data. In this paper, we present a simultaneous SR and denoising algorithm based on a stochastic normalizing flow (SNF), named the MR image SR and denoising model based on an SNF (SRDSNF). SRDSNF adds the encoded information of the input image as the conditional information to each reverse step of the stochastic normalizing flow, which realizes a consistent description of the spatial distribution between the reconstruction result and the input image. We introduce rangenull space decomposition and subsequence sampling strategies to enhance the consistency of the input and output data and increase the generation speed of the model. Simultaneous SR and denoising tasks experiment is carried out using the BrainWeb and NFBS datasets. The experimental results show that good SR and denoising results are obtained with fewer sampling steps, these results are consistent with the ground truths, and the structural similarity and peak signal-to-noise ratio of the results are also higher than those of the comparison methods. The proposed method demonstrates potential clinical promise. © 2023 IEEE.

关键词： Denoising Diffusion model MR image Stochastic normalizing flow Super-resolution

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Blockchain-Enhanced Energy-Efficient Architectures for Sustainable Internet of Things Ecosystems 1st

Blockchain-Enhanced Energy-Efficient Architectures for Susta...

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1st International Conference on Advanced Network Technologies and Computational Intelligence, ICANTCI 2024

作者： Goyal, S.B. Rajawat, Anand Singh Verma, Chaman Illés, Zoltán Singh, Jaiteg Faculty of Information Technology City University Petaling Jaya46100 Malaysia School of Computer Sciences and Engineering Sandeep University Nashik India Department of Media and Educational Informatics Faculty of Informatics Eötvös Loránd University Budapest Hungary Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India

ISBN: (纸本)9783031860683

Novel technique are required to ensure the long-term viability and maximize the efficiency of energy consumption in the constantly changing Internet of Things (IoT) landscape. In order to accomplish these objectives, this study examines the potential use of blockchain technology into IoT architectural concepts. Our approach utilizes blockchain technology to effectively manage and enhance energy consumption in IoT devices and networks. This is achieved by capitalizing on the decentralized, secure, and transparent characteristics of blockchain. Our objective is to do this by developing a decentralized ledger system that documents all energy transactions and interactions inside an Internet of Things (IoT) environment. This strategy utilizes blockchain technology to provide accountability and real-time monitoring of energy consumption, hence facilitating improved energy allocation and consumption. We delve into the concept and execution of smart contracts that ensure transactions and automate energy-conservation procedures, thereby diminishing the necessity for centralized authority and alleviating energy wastage. In addition, we examine the possibility of blockchain technology to enable direct energy transfer between Internet of Things (IoT) devices. This feature facilitates the development of a self-sustaining Internet of Things ecosystem by enabling devices with surplus energy to vend it to those requiring it. We also tackle the difficulties associated with the integration of blockchain with IoT, such as the energy consumption resulting from blockchain activities and the ability to effectively handle large volumes of data. Furthermore, we offer resolutions to these problems. The proposed framework improves the energy efficiency of IoT ecosystems and promotes their sustainability by advocating for the use of renewable energy sources and decreasing carbon footprints. Through the utilization of simulations and real-world illustrations, we showcase the efficacy of our bl

关键词： Clean energy

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Effective solutions to prevent digital fraud by introducing electronic signature of PDF files

Effective solutions to prevent digital fraud by introducing ...

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International Conference on Control Systems and computer Science (CSCS)

作者： Ionuţ Petrişor Anghel Dragoş Rădulescu Ion Alexandru Marinescu Computer Science and Engineering Department Polytechnic University of Bucharest Bucharest Romania Digital Transformation And Governance National Institute for Research & Development in Informatics Bucharest Romania

With the exponential growth of digital transactions, the creation and verification of electronic signatures contributes decisively to the creation of a safer environment for business development and innovation, based on security factors such as confidentiality, integrity, authenticity or non-repudiation. Implementing electronic signatures in PDF files can improve the prevention of digital fraud by providing an additional level of security and authenticity. For this reason, in addition to choosing a reliable electronic signature provider, it is necessary to establish clear signing procedures in order to minimize the risk of fraud. For this purpose, a software solution intended mainly for the academic environment is presented, which facilitates the electronic signature with legal value of PDF documents using a digital certificate. This application offers the possibility of simultaneously signing of multiples files in a folder, thus streamlining the process of digital signature and avoiding the use of third-party applications.

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IoT-Enabled LoRa Wireless Sensor Network for Real-Time Air Quality Monitoring with Geographic Information System Mapping

IoT-Enabled LoRa Wireless Sensor Network for Real-Time Air Q...

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International Conference on informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Nurhuda Maulana Ian Joseph Matheus Edward Faculty of Computer Science Universitas Pembangunan Nasional Veteran Jakarta Jakarta Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia

The Internet of Things (IoT) can potentially change how humans interact with the environment. This technology is able to be applied in various fields, such as urban management, agriculture, environmental monitoring, health, and transportation. Conventional air quality monitoring systems are complex sensing equipment and they can measure various parameters of air pollutants. This system requires very high costs and maintenance. Therefore, a low-cost air quality measurement system is required and is able to measure the surrounding air pollutants. By using wireless sensor network technology as the acquisition of environmental monitoring sensor data. A system design was created in the form of geographic information capable of describing air quality in a particular area. The air parameters used are carbon monoxide, dust particulate, light intensity, temperature, and relative humidity. The wireless network uses the LoRa module connected to the gateway. The web server provides a database and geographic information system interface from the gateway through the MQTT broker. Data transmission using a radio frequency of 915 Mhz and able to send data sensors from various points. Outdoor testing at many buildings is capable of transmitting sensor data with a packet loss of 0% at a distance of fewer than 111 meters and the largest packet loss of 51.28% at a distance of 442 meters.

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Fingerprint Matching for Noisy and Distorted Patterns Using a Siamese Network With ResNet50 and Multihead Attention

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IEEE Access 2025年 13卷 88047-88064页

作者： Sawhney, Tinny Sharma, Ashu Abrol, Pawanesh Kumar Lehana, Parveen Chaahat Yadav, Vaishali Scherer, Rafal Kulakov, Oleksii Ali Alzubi, Ahmad University of Jammu DSP Laboratory Department of Electronics Jammu180006 India University of Jammu Department of Computer Science and IT Jammu180006 India Chitkara University Institute of Engineering and Technology Chitkara University Department of Computer Science and Engineering Punjab Rajpura140401 India Manipal University Jaipur Department of Computer Science & Communication Engineering Jaipur303007 India Czestochowa University of Technology Department of Artificial Intelligence Częstochowa42-200 Poland Faculty of Informatics and Software Engineering National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" Kyiv03056 Ukraine King Saud University Community College Department of Computer Science Riyadh11451 Saudi Arabia

Dermatoglyphics, the study of unique ridge patterns on fingertips, plays a crucial role in fingerprint-based identification. However, skin conditions such as psoriasis, eczema, and verruca vulgaris can distort these patterns. This distortion creates challenges for fingerprint matching systems in forensic science and biometric authentication. This paper proposes a Siamese network with a modified ResNet50 architecture and multihead attention mechanisms to improve fingerprint matching under such distortions. The Siamese network enables the system to compare fingerprint pairs effectively, even under distortions. The modified ResNet50 architecture captures intricate ridge patterns, while the multihead attention mechanisms focus on critical fingerprint regions. Overall, the proposed approach enhances the system’s ability to learn discriminative features. As a result, it can effectively differentiate between matched and unmatched fingerprint pairs, even in the presence of moderate to extreme noise. The proposed system was trained on the Sokoto coventry fingerprint dataset (SOCOFing) and a custom dataset. Experimental results demonstrate high accuracy, with the system achieving 99.47% accuracy under low noise conditions and 90% under moderate noise. However, performance declined to 55.56% under extreme distortions. A comparative analysis highlighted the superiority of the proposed system over widely used minutiae-based methods. The latter exhibited a significant drop in matching scores, falling below 15% for highly distorted fingerprints. These findings underscore the limitations of traditional fingerprint recognition techniques and highlight the effectiveness of proposed approach in handling dermatoglyphic distortions. © 2013 IEEE.

关键词： Feature Selection

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AGNOSTIC LEARNING FOR PACKING MACHINE STOPPAGE PREDICTION IN SMART FACTORIES

arXiv

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arXiv 2022年

作者： Filios, Gabriel Katsidimas, Ioannis Nikoletseas, Sotiris Panagiotou, Stefanos H. Raptis, Theofanis P. Department of Computer Engineering and Informatics University of Patras Greece and Computer Technology Institute and Press "Diophantus" Patras Greece Pisa Italy

The cyber–physical convergence is opening up new business opportunities for industrial operators. The need for deep integration of the cyber and the physical worlds establishes a rich business agenda towards consolidating new system and network engineering approaches. This revolution would not be possible without the rich and heterogeneous sources of data, as well as the ability of their intelligent exploitation, mainly due to the fact that data will serve as a fundamental resource to promote Industry 4.0. One of the most fruitful research and practice areas emerging from this data-rich, cyber-physical, smart factory environment is the data-driven process monitoring field, which applies machine learning methodologies to enable predictive maintenance applications. In this paper, we examine popular time series forecasting techniques as well as supervised machine learning algorithms in the applied context of Industry 4.0, by transforming and preprocessing the historical industrial dataset of a packing machine’s operational state recordings (real data coming from the production line of a manufacturing plant from the food and beverage domain). In our methodology, we use only a single signal concerning the machine’s operational status to make our predictions, without considering other operational variables or fault and warning signals, hence its characterization as "agnostic". In this respect, the results demonstrate that the adopted methods achieve a quite promising performance on three targeted use cases. © 2022, CC BY-NC-ND.

关键词： Industry 4.0

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Transref: Multi-Scale Reference Embedding Transformer for Reference-Guided Image Inpainting

SSRN

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SSRN 2024年

作者： Liu, Taorong Liao, Liang Chen, Delin Xiao, Jing Wang, Zheng Lin, Chia-Wen Satoh, Shin'ichi School of Computer Science Wuhan University Hubei Wuhan430072 China School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore Department of Electrical Engineering The Institute of Communications Engineering National Tsing Hua University Hsinchu30013 Taiwan Digital Content and Media Sciences Research Division National Institute of Informatics Tokyo101-8430 Japan

Image inpainting for completing complicated semantic environments and diverse hole patterns of corrupted images is challenging even for state-of-the-art learning-based inpainting methods trained on large-scale data. A reference image capturing the same scene of a corrupted image offers informative guidance for completing the corrupted image as it shares similar texture and structure priors to that of the holes of the corrupted image. In this work, we propose a Transformer-based encoder-decoder network for Reference-guided image inpainting, named TransRef. Specifically, the guidance is conducted progressively through a reference embedding procedure, in which the referencing features are subsequently aligned and fused with the features of the corrupted image. For precise utilization of the reference features for guidance, a reference-patch alignment (Ref-PA) module is proposed to align the patch features of the reference and corrupted images and harmonize their style differences, while a reference-patch transformer (Ref-PT) module is proposed to refine the embedded reference feature. Moreover, to facilitate the research of reference-guided image restoration tasks, we construct a publicly accessible benchmark dataset containing 50K pairs of input and reference images. Both quantitative and qualitative evaluations demonstrate the efficacy of the reference information and the proposed method over the state-of-the-art methods in completing complex holes. Code and dataset can be accessed at: https://***/Cameltr/TransRef. © 2024, The Authors. All rights reserved.

关键词： Embeddings

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USEFUSE: UTILE STRIDE FOR ENHANCED PERFORMANCE IN FUSED LAYER ARCHITECTURE OF DEEP NEURAL NETWORKS

arXiv

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arXiv 2024年

作者： Ibrahim, Muhammad Sohail Usman, Muhammad Lee, Jeong-A Department of Mechanical Systems Engineering Kumoh National Institute of Technology Gumi-Si Korea Republic of Faculty of Informatics and Data Science University of Regensburg Regensburg Germany Department of Computer Engineering Chosun University Gwangju Korea Republic of

Convolutional Neural Networks (CNNs) are crucial in various applications, but their deployment on resource-constrained edge devices poses challenges. This study presents the Sum-of-Products (SOP) units for convolution, which utilize low-latency left-to-right bit-serial arithmetic to minimize response time and enhance overall performance. The study proposes a methodology for fusing multiple convolution layers to reduce off-chip memory communication and increase overall performance. An effective mechanism detects and skips inefficient convolutions after ReLU layers, minimizing power consumption without compromising accuracy. Furthermore, efficient tile movement guarantees uniform access to the fusion pyramid. An analysis demonstrates the utile stride strategy improves operational intensity. Two designs cater to varied demands: one focuses on minimal response time for mission-critical applications, and another focuses on resource-constrained devices with comparable latency. This approach notably reduced redundant computations, improving the efficiency of CNN deployment on edge devices. © 2024, CC BY.

关键词： Convolutional neural networks

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Correction to: On some novel bright, dark and optical solitons to the cubic-quintic nonlinear non-paraxial pulse propagation model

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Optical and Quantum Electronics 2024年第4期56卷 1-3页

作者： Tariqa, Kalim U. Khater, Mostafa M. A. Inc, Mustafa Department of Mathematics Mirpur University of Science and Technology Mirpur Pakistan School of Medical Informatics and Engineering Xuzhou Medical University Xuzhou People’s Republic of China Department of Basic Science Obour High Institute for Engineering and Technology Cairo Egypt Department of Computer Engineering Biruni University Istanbul Turkey

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