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A Scalable, Web-Based Platform for Proteomics data processing, Result Storage and Analysis

JOURNAL OF PROTEOME RESEARCH 2025年第3期24卷 1241-1249页

作者： Schneider, Markus Zolg, Daniel P. Samaras, Patroklos Ben Fredj, Samia Bold, Dulguun Guevende, Agnes Hogrebe, Alexander Berger, Michelle T. Graber, Michael Sukumar, Vishal Mamisashvili, Lizi Bronsthein, Igor Eljagh, Layla Gessulat, Siegfried Seefried, Florian Schmidt, Tobias Frejno, Martin MSAID GmbH D-85748 Munich Germany MSAID GmbH D-13347 Berlin Germany

The exponential increase in proteomics data presents critical challenges for conventional processing workflows. These pipelines often consist of fragmented software packages, glued together using complex in-house scripts or error-prone manual workflows running on local hardware, which are costly to maintain and scale. The MSAID Platform offers a fully automated, managed proteomics data pipeline, consolidating formerly disjointed functions into unified, API-driven services that cover the entire process from raw data to biological insights. Backed by the cloud-native search algorithm CHIMERYS, as well as scalable cloud compute instances and data lakes, the platform facilitates efficient processing of large data sets, automation of processing via the command line, systematic result storage, analysis, and visualization. The data lake supports elastically growing storage and unified query capabilities, facilitating large-scale analyses and efficient reuse of previously processed data, such as aggregating longitudinally acquired studies. Users interact with the platform via a web interface, CLI client, or API, providing flexible, automated access. Readily available tools for accessing result data include browser-based interrogation and one-click visualizations for statistical analysis. The platform streamlines research processes, making advanced and automated proteomic workflows accessible to a broader range of scientists. The MSAID Platform is globally available via https://***.

关键词： proteomics platform pipeline CHIMERYS compute infrastructure data processing cloud AWS scalable SaaS

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A comprehensive review of data processing and target recognition methods for ground penetrating radar underground pipeline B-scan data

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DISCOVER APPLIED SCIENCES 2025年第4期7卷 1-23页

作者： Liu, Chen Li, Jue Liu, Zhengnan Tao, Sirui Li, Mingxuan Chongqing Jiaotong Univ Sch Civil Engn Chongqing Peoples R China Chongqing Jiaotong Univ Coll Traff & Transportat Chongqing Peoples R China Hunan Commun Res Inst Co Ltd Changsha Peoples R China Changsha Univ Sci & Technol Sch Traff & Transportat Engn Changsha Peoples R China

Underground pipelines hold a crucial role in modern urban infrastructure, and Ground Penetrating Radar (GPR) has been increasingly favored as a non-destructive tool for their detection and monitoring. However, the complex and varied urban underground environment, as well as the dense distribution of various pipeline materials, present significant challenges in the interpretation of GPR signals and the recognition of targets. This study provided a comprehensive review of the current state-of-the-art in data processing and target recognition methods for GPR underground pipeline B-scan data. The unique features and characteristics of GPR pipeline B-scan data were initially examined, including the impact of pipeline materials, scanning methods, and electromagnetic wave frequencies. Traditional signal processing techniques, such as filtering, wavelet transform, and empirical mode decomposition, as well as emerging machine learning and deep learning-based methods for denoising, feature extraction, and target recognition, were systematically reviewed. The advantages and limitations of these approaches in practical applications were analyzed in detail. Feasible research directions were proposed to address the current challenges and further enhance the effectiveness of GPR technology in underground pipeline detection and management. This review serves as a valuable reference for researchers and practitioners in the fields of GPR, machine learning, and underground infrastructure monitoring.

关键词： Ground penetrating radar Underground pipeline data processing Target recognition Machine learning B-scan data

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Faster and Stronger: Unleashing data processing Potential through Hardware Heterogeneity

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IEEE Internet of Things Journal 2025年第10期12卷 14559-14576页

作者： Wang, Cong Luo, Yang Du, Wenzhuo Wang, Ke Gu, Naijie Yu, Jun Huawei China University of Science and Technology of China Department of Computer Science and Technology Anhui China University of Science and Technology of China Institute of Advanced Technology Anhui China University of Science and Technology of China Department of Automation The Institute of Advanced Technology Anhui China

With the rapid advancement of AI technology, there has been a substantial surge in the need for computational resources. Particularly in deep learning, machine learning, and large-scale data analysis, the processing of extensive datasets necessitates exceptionally high levels of computational efficacy and speed. Conventional homogeneous computing platforms, predominantly reliant on Central processing Units (CPU), have encountered challenges in meeting the escalating demands for high-performance computing. Consequently, this study advocates for heterogeneous hardware acceleration technology, strategically migrating data operations from CPU to varied hardware components (e.g. GPU, NPU) to enhance processing efficiency and computational performance during the data preprocessing phase. We conducted experiments to evaluate the impact of utilizing hardware heterogeneous acceleration technologies on data processing speed under various workloads and system hardware configurations. By adjusting parameters like batch size and CPU utilization rates, we compared the performance of frameworks that support hardware heterogeneity with popular deep learning frameworks (e.g. PyTorch and TensorFlow) across various hardware configurations and neural network models. Empirical findings demonstrate that the system framework optimized through heterogeneous hardware acceleration technology (the preprocessing speed is improved in all the given experimental environment tests) exhibits commendable universality and superiority in performance. Codes are available at https://***/mindspore-ai/mindspore. © 2014 IEEE.

关键词： data processing Training Artificial intelligence data preprocessing Computational modeling Hardware acceleration data models Graphics processing units Central processing Unit Tensors

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A hierarchical Wi-Fi log data processing framework for human mobility analysis in multiple real-world communities

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TRAVEL BEHAVIOUR AND SOCIETY 2025年 39卷

作者： Yuan, Yuqiu Zhu, Lei Joshi, Mohini Univ North Carolina William States Lee Coll Engn Dept Ind & Syst Engn Charlotte NC 28223 USA Univ North Carolina Sch Data Sci Charlotte NC 28223 USA

Wi-Fi log data, including communication actions between clients and Access Points (APs), can be used to infer human movement and travel activity and thus would serve as a reliable data source for human mobility analysis. As more and more modern cities or communities provide public free Wi-Fi services, a vast amount of public Wi-Fi log data will be collected and have the potential to be used to characterize human travel patterns and thus develop more effective urban transportation management strategies. However, Wi-Fi log data processing is not trivial. Wi-Fi networks established by various internet equipment manufacturers and devices have different network settings and log file formats. Additionally, the complexity of Wi-Fi log data, along with the ping-pong phenomenon and invalid messages, can result in analysis bias and errors. Though previous studies have processed the specific Wi-Fi log data individually in different ways, a common framework that can address public Wi-Fi data from different locations is needed to improve data processing efficiency and analysis effectiveness. This study proposed a hierarchical and general Wi-Fi data processing and analysis framework to extract client travel activities from Wi-Fi log data. Wi-Fi log data collected from three communities in North Carolina- one university campus, the city of Wilson, and the town of Holly Springs, were processed and analyzed. Based on that, travel activities across different communities with specific Wi-Fi networks could be compared and analyzed to provide community human mobility and travel activity insights and the correlation between human travel and Wi-Fi network features.

关键词： Wi-Fi log data Human mobility analysis Generalized hierarchical framework data processing

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Revolutionizing multi-omics analysis with artificial intelligence and data processing

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QUANTITATIVE BIOLOGY 2025年第3期13卷 e70002-e70002页

作者： Yetgin, Ali Toros Agri Ind & Trade Co Inc Res & Dev Ctr Mersin Turkiye Cukurova Univ Inst Nat & Appl Sci Dept Biotechnol Adana Turkiye

Our understanding of intricate biological systems has been completely transformed by the development of multi-omics approaches, which entail the simultaneous study of several different molecular data types. However, there are many obstacles to overcome when analyzing multi-omics data, including the requirement for sophisticated data processing and analysis tools. The integration of multi-omics research with artificial intelligence (AI) has the potential to fundamentally alter our understanding of biological systems. AI has emerged as an effective tool for evaluating complicated data sets. The application of AI and data processing techniques in multi-omics analysis is explored in this study. The present study articulates the diverse categories of information generated by multi-omics methodologies and the intricacies involved in managing and merging these datasets. Additionally, it looks at the various AI techniques-such as machine learning, deep learning, and neural networks-that have been created for multi-omics analysis. The assessment comes to the conclusion that multi-omics analysis has a lot of potential to change with the integration of AI and data processing techniques. AI can speed up the discovery of new biomarkers and therapeutic targets as well as the advancement of personalized medicine strategies by enabling the integration and analysis of massive and complicated data sets. The necessity for high-quality data sets and the creation of useful algorithms and models are some of the difficulties that come with using AI in multi-omics study. In order to fully exploit the promise of AI in multi-omics analysis, more study in this area is required.

关键词： artificial intelligence data processing deep learning machine learning multi-omics neural networks

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data processing method for evaluating pipe wall thinning in nuclear secondary systems using SVM regression algorithm

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NUCLEAR ENGINEERING AND TECHNOLOGY 2025年第7期57卷

作者： Mun, Seongbin Oh, Young-jin Lee, Sanghoon KEPCO Engn & Construct Co Ltd Smart Convergence Res Dept Naju Si South Korea

Nuclear power plants have been experiencing continuous pipe wall thinning in carbon steel materials used in the secondary system, potentially leading to pipe rupture. To prevent the sudden pipe rupture caused by this thinning, pipe thinning management programs have been conducted which include periodic thickness measurement and remaining life assessment for sampled pipes and fittings. However, periodic thickness measurement data from ultrasonic testing (UT) have a significant range of uncertainty, which can significantly affect the assessed thinning values. Moreover, the uncertainty of the evaluated thinning value is intensified because the amount of thinning of a certain pipe or fitting is defined by its maximum thinning value. Therefore, a data processing method to minimize the effect of thickness measurement uncertainty is crucial to determine more reliable thinning values. In this study, a data processing method based on the support vector machine regression algorithm was proposed, which was adjusted and modified considering the general characteristics of pipe thinning phenomena. Using datasets of thickness measurements constructed by assumed wall thickness shapes and measurement uncertainty, it was confirmed that the proposed method reduces the uncertainty and bias of evaluated thinning values.

关键词： Pipe wall thickness Pipe wall thinning Measurement error data processing Kernel regression Support vector machine regression

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data processing and Pattern Recognition for Electronic Nose

Data Processing and Pattern Recognition for Electronic Nose

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： David Raymond Shivnath Chaudhri Kahkasha Shaikh Department of Computer Science and Medical Engineering Faculty of Enfineering and Technology Datta Meghe Institute of Higher Education and Research Wardha India Depertment of Computer Science and Design Faculty of Enfineering and Technology Datta Meghe Institute of Higher Education and Research Wardha India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

Electronic nose (e-nose) technology has become a powerful tool for identifying and evaluating complex scents in a variety of contexts, such as environmental monitoring, medical diagnostics, and food quality control. This review study looks at how important pattern recognition and data processing are to improving e-nose system performance. This study covered challenges, including data complexity, cross-sensitivity and interference, sensor drift, and a lack of standardization. The article covers several future directions, such as the use of artificial intelligence (AI) and machine learning to advance pattern recognition and predictive analytics, integration with the Internet of Things (IoT) and big data for real-time monitoring, and advancements in sensor technology for improved sensitivity and *** research work is underway towards the development of low power flexible sensors, improved multi sensor fusion Methodologies, smart adaptive algorithms, Application for personalized health monitoring and diagnostics as well to make it green technologies. To move forward with emerging technologies and for e-nose technology to be applicable, it will have to overcome these weaknesses.

关键词： Machine learning Quality control Interference Electronic noses data processing Pattern recognition Complexity theory Medical diagnosis Internet of Things Environmental monitoring

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Research on data processing technology based on virtual neutron signal generator

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JOURNAL OF INSTRUMENTATION 2025年第1期20卷

作者： Sun, Shangqing Huang, Qichang Wan, Bo Yang, Daibo Zhao, Yang Wang, Miao Xiong, Bangping Xi, Ge Luo, Yin Xia, Yuan Wei, Wenbin Miao, Yunxin Nucl Power Inst China Natl Key Lab Nucl Reactor Technol Chengdu 610213 Peoples R China

Neutron measurement systems play a crucial role in many fields. However, testing these systems requires neutron sources, which pose radiation hazards. As a safer alternative, developers often opt for simulated neutron signal generators. The goal of this study is to develop a virtual neutron signal generator that meets the development needs of neutron measurement system in pulse mode, campbell mode, and current mode, providing a non-nuclear auxiliary design tool. Inside the Loongson CPU, three digital channels are used to generate pulse signal, campbell signal current signal. Each digital channel consists of the amplitude module, trigger module and waveform module. The amplitude module uses either measured spectrum or simulated spectrum generated GEANT4 to obtain physically meaningful neutron pulse amplitudes. The trigger module generates high-speed triggered clock sequence based on the exponentially distributed time intervals between adjacent neutron pulses. The waveform module generates the waveform of neutron signals. neutron signals generated inside the Loongson CPU are transmitted to the FPGA via USB3.0. Then, the FPGA outputs virtual neutron analog signals through DAC. Experimental results show that virtual neutron signal generator can accurately produce virtual neutron analog signals, making suitable as a testing tool for neutron measurement systems.

关键词： data processing Digital electronic circuits

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Value of blockchain for scope 3 carbon disclosure: The moderating role of data processing technologies

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INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 2025年 279卷

作者： Chen, Yuan Feng, Yunting Lai, Kee-Hung Zhu, Qinghua Dalian Univ Technol Sch Econ & Management 2 Linggong Rd Dalian 116024 Peoples R China Donghua Univ Glorious Sun Sch Business & Management 1882 West Yanan Rd Shanghai 200051 Peoples R China Hong Kong Polytech Univ Dept Logist & Maritime Studies Hung Hom Hong Kong Peoples R China Shanghai Jiao Tong Univ Antai Coll Econ & Management 1954 Huashan Rd Shanghai 200030 Peoples R China

Disclosing carbon emissions in Scope 3 is essential for mitigating pollution and the associated environmental damage, and blockchain can enhance the disclosure. However, the effect of blockchain on Scope 3 carbon disclosure remains unclear due to a lack of empirical evidence. This paper investigates the value of blockchain for Scope 3 carbon disclosure and examines whether this value can be strengthened by integrating data processing technologies, including artificial intelligence (AI), cloud computing, and big data analytics (BDA). Drawing upon the coordination theory, we posit that blockchain as a recording and tracing technology can improve the coordination among supply chain members on collecting carbon emissions data, thereby facilitating firms' Scope 3 carbon disclosure. Furthermore, data processing technologies enable efficient utilization and management of the collected data, potentially coordinating with blockchain to enhance Scope 3 carbon disclosure. We test these relationships using regression analysis based on a sample of 422 observations for Chinese listed firms during 2021 and 2022. The results show that blockchain adoption is positively associated with a firm's Scope 3 carbon disclosure. In addition, adopting each of the three data processing technologies-AI, cloud computing, and BDA-further strengthens the positive relationship. This study contributes to academic knowledge and evidence on blockchain and sustainable supply chain management with practical suggestions for managing carbon emissions at the supply chain level through the combined adoption of blockchain and data processing technologies.

关键词： Carbon disclosure Scope 3 Blockchain Digital technology data processing

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Automated data processing and feature engineering for deep learning and big data applications: A survey

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Journal of Information and Intelligence 2025年第2期3卷 113-153页

作者： Alhassan Mumuni Fuseini Mumuni Department of Electrical and Electronic Engineering Cape Coast Technical University Cape Coast P. O. Box DL 50 Ghana Department of Electrical and Electronic Engineering University of Mines and Technology Tarkwa P. O. Box 237 Ghana

Modern approach to artificial intelligence (AI) aims to design algorithms that learn directly from data. This approach has achieved impressive results and has contributed significantly to the progress of AI, particularly in the sphere of supervised deep learning. It has also simplified the design of machine learning systems as the learning process is highly automated. However, not all data processing tasks in conventional deep learning pipelines have been automated. In most cases data has to be manually collected, preprocessed and further extended through data augmentation before they can be effective for training. Recently, special techniques for automating these tasks have emerged. The automation of data processing tasks is driven by the need to utilize large volumes of complex, heterogeneous data for machine learning and big data applications. Today, end-to-end automated data processing systems based on automated machine learning (AutoML) techniques are capable of taking raw data and transforming them into useful features for big data tasks by automating all intermediate processing stages. In this work, we present a thorough review of approaches for automating data processing tasks in deep learning pipelines, including automated data preprocessing – e.g., data cleaning, labeling, missing data imputation, and categorical data encoding – as well as data augmentation (including synthetic data generation using generative AI methods) and feature engineering – specifically, automated feature extraction, feature construction and feature selection. In addition to automating specific data processing tasks, we discuss the use of AutoML methods and tools to simultaneously optimize all stages of the machine learning pipeline.

关键词： AutoML Automated data preprocessing data processing Automated feature engineering Generative artificial intelligence Big data

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