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Centromere Landscapes Resolved from Hundreds of Human Genomes

Genomics, Proteomics & Bioinformatics 2024年第5期22卷 61-71页

作者： Shenghan Gao Yimeng Zhang Stephen J. Bush Bo Wang Xiaofei Yang Kai Ye School of Automation Science and Engineering Faculty of Electronic and Information EngineeringXi'an Jiaotong UniversityXi'an 710049China School of Computer Science and Technology Faculty of Electronic and Information EngineeringXi'an Jiaotong UniversityXi'an 710049China MOE Key Lab for Intelligent Networks&Networks Security Faculty of Electronic and Information EngineeringXi'an Jiaotong UniversityXi'an 710049China Center for Mathematical Medical The First Affiliated HospitalXi'an Jiaotong UniversityXi'an 710061China School of Life Science and Technology Xi'an Jiaotong UniversityXi'an 710049China Faculty of Science Leiden UniversityLeiden 2311 EZThe Netherlands

High-fidelity(HiFi)sequencing has facilitated the assembly and analysis of the most repetitive region of the genome,the ***,our current understanding of human centromeres is based on a relatively small number of telomere-to-telomere assemblies,which have not yet captured its full *** this study,we investigated the genomic diversity of human centromere higher order repeats(HORs)via both HiFi reads and haplotype-resolved assemblies from hundreds of samples drawn from ongoing pangenome-sequencing projects and reprocessed them via a novel HOR annotation pipeline,*** used this wealth of data to provide a global survey of the centromeric HOR landscape;in particular,we found that 23 HORs presented significant copy number variability between *** detected three centromere genotypes with unbalanced population frequencies on chromosomes 5,8,and *** inter-assembly comparison of HOR loci further revealed that while HOR array structures are diverse,they nevertheless tend to form a number of specific landscapes,each exhibiting different levels of HOR subunit expansion and possibly reflecting a cyclical evolutionary transition from homogeneous to nested structures and back.

关键词： Centromere sequence Genome evolution High-fidelity sequencing technology Higher order repeat annotation Human population

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automation of Technological Processes for Controlling Grain Moisture in the Flowu

Automation of Technological Processes for Controlling Grain ...

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2023 International Conference Automatics and Informatics, ICAI 2023

作者： Kalandarov, Palvan I. Ubaydullayeva, Shakhnoza R. Gaziyeva, Rano T. Nikolov, Nikola N. Alexandrova, Mariela I. 'Tashkent Institute of Irrigation and Agricultural Mechanization Engineers' National Research University Department 'Automation and Control of Technology Process in Production' Tashkent Uzbekistan Technical University of Varna Faculty of Computer Science and Automation Department of Automation Varna Bulgaria

ISBN: (纸本)9798350312911

The paper deals with the problems of grain wounding requires large energy costs, requiring a multi-stage process of grain processing, installation and perfect and reliable automation systems for the storage and processing of raw materials, theoretical and experimental studies and the results of the use of the microwave method in measuring the moisture content of grain and grain materials. The method under consideration is based on the principle of selective absorption of microwave radiation energy by water in materials and its derivatives. By the degree of absorption of radiation, the concentration of moisture is judged. The paper investigates the amplitude and phase methods of the passing wave in free space. The influence of the elastic properties of the material under study on the electrical characteristics of the microwave wave, suggesting fundamentally new ways of measuring the attenuation and phase shift of the electromagnetic wave, including discrete conversion of the signal of the primary converter, which does not require rigid metrological support and the use of a phase shift, is substantiated. Information on the values of the thickness of the material is analyzed in order to correct the influence of this value, consisting of two parameters: the signal of the passing wave, the attenuation and phase shift which characterizes the humidity and the additional signal - the phase wave reflected from the front surface of the materials and carrying information about its thickness. © 2023 IEEE.

关键词： automation

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Quantifying Fairness Disparities in Graph-Based Neural Network Recommender Systems for Protected Groups 19

Quantifying Fairness Disparities in Graph-Based Neural Netwo...

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19th International Conference on Web Information Systems and Technologies, WEBIST 2023

作者： Chizari, Nikzad Tajfar, Keywan Shoeibi, Niloufar Moreno-García, María N. Department of Computer Science and Automation Science Faculty University of Salamanca Plaza de los Caídos sn Salamanca37008 Spain College of Science School of Mathematics Statistics and Computer Science Department of Statistics University of Tehran Tehran Iran

ISBN: (纸本)9789897586729

The wide acceptance of Recommender Systems (RS) among users for product and service suggestions has led to the proposal of multiple recommendation methods that have contributed to solving the problems presented by these systems. However, the focus on bias problems is much more limited. Some of the most successful and recent methods, such as Graph Neural Networks (GNNs), present problems of bias amplification and unfairness that need to be detected, measured, and addressed. In this study, an analysis of RS fairness is conducted, focusing on measuring unfairness toward protected groups, including gender and age. We quantify fairness disparities within these groups and evaluate recommendation quality for item lists using a metric based on Normalized Discounted Cumulative Gain (NDCG). Most bias assessment metrics in the literature are only valid for the rating prediction approach, but RS usually provide recommendations in the form of item lists. The metric for lists enhances the understanding of fairness dynamics in GNN-based RS, providing a more comprehensive perspective on the quality and equity of recommendations among different user groups. Copyright © 2023 by SCITEPRESS - science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

关键词： Recommender systems

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Multi-View Dynamic Kernelized Evidential Clustering

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IEEE/CAA Journal of Automatica Sinica 2024年第12期11卷 2435-2450页

作者： Jinyi Xu Zuowei Zhang Ze Lin Yixiang Chen Weiping Ding the Software Engineering Institute East China Normal UniversityShanghai 200062 Shanghai Key Laboratory of Trustworthy Computing East China Normal UniversityShanghai 200062China IEEE the School of Automation Northwestern Polytechnical UniversityXi’an 710072China the School of Artificial Intelligence and Computer Science Nantong UniversityNantong 226019 the Faculty of Data Science City University of MacaoMacao 999078China

It is challenging to cluster multi-view data in which the clusters have overlapping *** multi-view clustering methods often misclassify the indistinguishable objects in overlapping areas by forcing them into single clusters,increasing clustering *** solution,the multi-view dynamic kernelized evidential clustering method(MvDKE),addresses this by assigning these objects to meta-clusters,a union of several related singleton clusters,effectively capturing the local imprecision in overlapping *** offers two main advantages:firstly,it significantly reduces computational complexity through a dynamic framework for evidential clustering,and secondly,it adeptly handles non-spherical data using kernel techniques within its objective *** on various datasets confirm MvDKE's superior ability to accurately characterize the local imprecision in multi-view non-spherical data,achieving better efficiency and outperforming existing methods in overall performance.

关键词： Evidential clustering imprecision characterizing kernel technique multi-view clustering

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Hepatocellular Carcinoma Recognition from Ultrasound Images by Fusing Convolutional Neural Networks at Decision Level 46

Hepatocellular Carcinoma Recognition from Ultrasound Images ...

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46th International Conference on Telecommunications and Signal Processing, TSP 2023

作者： Mitrea, Delia Brehar, Raluca Mocan, Cristinel Nedevschi, Sergiu Socaciu, Mihai Badea, Radu Technical University of Cluj-Napoca Faculty of Automation and Computer Science Department of Computer Science Cluj-Napoca Romania I. Hatieganu University of Medicine and Pharmacy Department of Medical Imaging Cluj-Napoca Romania

ISBN: (纸本)9798350303964

The Hepatocellular Carcinoma (HCC) is the most often met malignant tumor of the liver. It develops from cirrhosis, after a parenchyma restructuring phase, at the end of which dysplastic nodules result that can transform into HCC. Nowadays, HCC diagnosis is usually performed through needle biopsy, also through medical imaging, but most of the methods are invasive and/or expensive. Ultrasonography is the best option for screening, while computerized techniques must be developed as well to derive subtle information. In our previous research, we experimented with conventional techniques, based on advanced texture analysis methods and traditional classifiers, as well as with deep learning techniques, aiming to perform HCC recognition with maximum accuracy. We combined the CNN architectures with each other, respectively with the conventional techniques, at classifier level. In this work, we combined existing and original CNN architectures at decision level, we assessed the corresponding performance, and we compared it with our previous results. © 2023 IEEE.

关键词： Convolutional neural networks

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Method for Improving Dynamic Characteristics of Electromechanical Transducer Based on Induction Motor 5

Method for Improving Dynamic Characteristics of Electromecha...

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5th International Conference on Control Systems, Mathematical Modeling, automation and Energy Efficiency, SUMMA 2023

作者： Meshcheryakov, Viktor Yazykova, Liubov Muzyleva, Inna Valtchev, Stanimir Lipetsk State Technical University Automation and Computer Science Faculty Russia University NOVA of Lisbon UNINOVA-CTS Faculty of Sciences and Technology 2829-516 Campus Caparica Portugal

ISBN: (纸本)9798350309126

This paper proposes a universal method for improving the dynamic characteristics of induction motors. The transient processes here fluctuate greatly, which reduces the energy efficiency of the electromechanical converter. A flexible feedback on flux linkage is proposed, which makes it possible to suppress oscillations. A numerical experiment was carried out for the mode of starting an induction motor in relative variables. © 2023 IEEE.

关键词： Electric drives

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Reset-Free Reinforcement Learning via Multi-State Recovery and Failure Prevention for Autonomous Robots

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Tsinghua science and Technology 2024年第5期29卷 1481-1494页

作者： Xu Zhou Benlian Xu Zhengqiang Jiang Jun Li Brett Nener School of Mechanical Engineering Changshu Institute of TechnologyChangshu 215500China School of Automation Nanjing University of Science and TechnologyNanjing 210094China School of Electronic and Information Engineering Suzhou University of Science and TechnologySuzhou 215009China Faculty of Medicine and Health The University of SydneySydney 2006Australia Department of Electrical Electronic and Computer EngineeringThe University of Western AustraliaPerth 6009Australia

Reinforcement learning holds promise in enabling robotic tasks as it can learn optimal policies via trial and ***,the practical deployment of reinforcement learning usually requires human intervention to provide episodic resets when a failure *** manual resets are generally unavailable in autonomous robots,we propose a reset-free reinforcement learning algorithm based on multi-state recovery and failure prevention to avoid failure-induced *** multi-state recovery provides robots with the capability of recovering from failures by self-correcting its behavior in the problematic state and,more importantly,deciding which previous state is the best to return to for efficient *** failure prevention reduces potential failures by predicting and excluding possible unsafe actions in specific *** simulations and real-world experiments are used to validate our algorithm with the results showing a significant reduction in the number of resets and failures during the learning.

关键词： reinforcement learning manual reset multi-state recovery failure prediction

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A Deep Learning Approach for Faults Recognition of Dissolved Oxygen Sensor in Wastewater Treatment Plants 28

A Deep Learning Approach for Faults Recognition of Dissolved...

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28th IEEE International Conference on Emerging Technologies and Factory automation, ETFA 2023

作者： Ghinea, Liliana Maria Miron, Mihaela Ratnaweera, Harsha Electrical Engineering and Electronics Faculty of Automation Computers Department of Automatic Control and Electrical Engineering Department Galaţi Romania Computers Electrical Engineering and Electronics Faculty of Automation Department of Computer Science and Information Technology Galaţi Romania Norwegian University of Life Sciences Faculty of Science and Technology Oslo Norway

ISBN: (纸本)9798350339918

Fault diagnosis in wastewater treatment plants (WWTPs) is important to protect communities and ecosystems from toxic elements discharged into water. In this sense, fault identification of sensors plays an important role as they are the key components of the water plants control, especially because environmental legislation is very strict when referring to failures or anomalies in WWTPs. This paper analyzes the performances of two Deep Learning models, a Feedforward Neural Network (FFNN) and a 1D Convolution Neural Network (1DCNN) for identifying five operating states of the dissolved oxygen (DO) sensor: normal and faulty (bias, stuck, spike and precision degradation faults). The experiments were conducted on the Benchmark Simulator Model No 2 (BSM2) developed by the IWA Task Group. The performance of the Deep Learning (DL) classifiers was evaluated via accuracy, precision, recall, and F1-score metrics. The best overall classification accuracy was obtained by FFNN, 98.32% for training and 98.30% for testing. © 2023 IEEE.

关键词： Wastewater treatment

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Forensic Investigation of Data Storage Systems

Forensic Investigation of Data Storage Systems

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IEEE International Conference on automation, Quality and Testing, Robotics, AQTR

作者： Ioan Vasile Țicovan Gheorghe Sebestyen Faculty of Automation and Computer Science Technical University of Cluj-Napoca Satu Mare Romania Faculty of Automation and Computer Science Technical University of Cluj-Napoca Cluj-Napoca Romania

ISBN: (数字)9798350361933

ISBN: (纸本)9798350361940

Legal investigation of digital data storage systems involves the application of specific methods and procedures that assures quick retrieval of relevant information as well as veracity and security of collected data. This paper aims to explore the potential and benefits of using artificial intelligence (AI) technology in the field of computer investigation. Using a series of specific techniques, computer system investigators can enhance their efficiency in analyzing and identifying evidence. Automatic classification of data present on a storage device can increase the accuracy and reduce the effort of a human investigator. Forensic analysis of a storage device requires integration of a sequence of steps beginning with the (re)construction of the storage device’s image till the semantic interpretation of the content. The paper proposes a processing sequence that includes some AI methods for relevant data search and classification.

关键词： Accuracy Buffer storage Digital forensics Semantics Telecommunication traffic Speech recognition Solids

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A New Fuzzy Adaptive Algorithm to Classify Imbalanced Data

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computers, Materials & Continua 2022年第1期70卷 73-89页

作者： Harshita Patel Dharmendra Singh Rajput Ovidiu Petru Stan Liviu Cristian Miclea School of Information Technology&Engineering Vellore Institute of TechnologyVellore632014India Technical University of Cluj Napoca Faculty of Automation and Computer ScienceCluj Napoca400114Romania

Classification of imbalanced data is a well explored issue in the data mining and machine learning community where one class representation is overwhelmed by other *** Imbalanced distribution of data is a natural occurrence in real world datasets,so needed to be dealt with carefully to get important *** case of imbalance in data sets,traditional classifiers have to sacrifice their performances,therefore lead to *** paper suggests a weighted nearest neighbor approach in a fuzzy manner to deal with this *** have adapted the‘existing algorithm modification solution’to learn from imbalanced datasets that classify data without manipulating the natural distribution of data unlike the other popular data balancing *** K nearest neighbor is a non-parametric classification method that is mostly used in machine learning *** classification with the nearest neighbor clears the belonging of an instance to classes and optimal weights with improved nearest neighbor concept helping to correctly classify imbalanced *** proposed hybrid approach takes care of imbalance nature of data and reduces the inaccuracies appear in applications of original and traditional *** show that it performs well over the existing fuzzy nearest neighbor and weighted neighbor strategies for imbalanced learning.

关键词： Machine learning fuzzy classification nearest neighbor adaptive approach optimal weights

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