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Magnetic properties and magnetocaloric effects in Eu(Ti,Nb,Mn)O_(3) perovskites

Journal of Rare Earths 2024年第8期42卷 1560-1567,I0006页

作者： Junfeng Wang Huicai Xie Quanyi Liu Zhihong Hao Zhaojun Mo Qi Fu Xinqiang Gao Jun Shen Ganjiang Innovation Academy Chinese Academy of SciencesGanzhou341119China Faculty of Materials Metallurgy and Chemistry Jiangxi University of Science and TechnologyGanzhou341000China School of Rare Earth and New Materials Engineering Gannan University of Science and TechnologyGanzhou341000China Department of Energy and Power Engineering School of Mechanical EngineeringBeijing Institute of TechnologyBeijing100081China

In perovskite EuTiO_(3),the magnetic characteristics and magnetocaloric effect(MCE) can be flexibly regulated by converting the magnetism from antiferromagnetic to *** the present work,a series of Eu(Ti,Nb,Mn)O_(3) compounds,abbreviated as ETNMO for convenience of description,was fabricated and their crystallography,magnetism together with cryogenic magnetocaloric effects were systematically *** crystallographic results demonstrate the cubic perovskite structure for all the compounds,with the space group of *** magnetic phase transitions are observed in these second-order phase transition(SOPT) *** joint substitution of elements Mn and Nb can considerably manipulate the magnetic phase transition process and magnetocaloric performance of the ETNMO *** the Mn content increases,gradually widened-ΔS_(M)-T curves are obtained,and two peaks with a broad shoulder are observed in the-ΔS_(M)-T curves for Δμ_(0)H≤0-1 *** a field change of 0-5 T,the values of maximum magnetic entropy change(-ΔS_(M)^(max)) and refrigeration capacity(RC) are evaluated to be 34.7 J/(kg·K) and 364.9 J/kg for EuTi_(0.8625)Nb_(0.0625)Mn_(0.075)O_(3), 27.8 J/(kg·K) and367.6 J/kg for EuTi_(0.8375)Nb_(0.0625)Mn_(0.1)O_(3),23.2 J/(kg·K) and 369.2 J/kg for EuTi_(0.8125)Nb_(0.0625)Mn_(0.125)O_(3),17.1 J/(kg·K) and 357.6 J/kg for EuTi_(0.7875)Nb_(0.0625)Mn_(0.15)O_(3),*** co nsiderable MCE parameters make the ETNMO compounds potential candidates for cryogenic magnetic refrigeration.

关键词： ETNMO perovskites Cryogenic refrigeration Magnetic phase transition Magnetocaloric effect Rare earths

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Fe_(3)O_(4)@SiO_(2)-Protein A-oHSV/CD63 Ab for Capturing Virus and Exosomes

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Journal of Analysis and Testing 2024年第3期8卷 335-350页

作者： Wenqing Zheng Pingyi Zheng Ran Zhao Xinyu Xu Xiao Zhang Xiaoqian Yuan Ying Xu Zichuan Liu Youxin Li Tianjin Key Laboratory for Modern Drug Delivery and High-Efficiency Collaborative Innovation Center of Chemical Science and EngineeringSchool of Pharmaceutical Science and TechnologyFaculty of MedicineTianjin UniversityTianjin300072China

Protein A modified magnetic spheres(Fe_(3)O_(4)@SiO_(2)-Protein A)with 22.7 emu/g saturation magnetization were prepared and *** spheres had a diameter of 10 nm and was stable up to 229.2℃,and can bond 19.694 mg/g of Protein *** Fe_(3)O_(4)@SiO_(2)-Protein A was firstly combined with oHSV-BJ-2-A antibody to capture oHSV from disease venom,and the amount of binding virus was 1.92×10^(7)to 3.44×10^(7)PFU/***_(3)O_(4)@SiO_(2)-Protein A was modified with CD63 antibody,which was further fixed using dissuccinimide octylate(DSS)as a crosslinking *** prepared Fe_(3)O_(4)@SiO_(2)-Protein A-CD63 Ab was useful in isolating and enriching exosomes from cell *** with commercial kits,Fe_(3)O_(4)@SiO_(2)-Protein A-CD63 Ab demonstrated a better purification effect,which was successfully monitored using capillary *** total content of exosomes protein extracted using Fe_(3)O_(4)@SiO_(2)-Protein A-CD63 was 3.4 mg/g,which was obviously higher than that reported in some *** addition,Fe_(3)O_(4)@SiO_(2)-Protein A-CD63 Ab also showed its repeatability through bonding and elution of 5 cycles,which was effective in cost *** indicated Fe_(3)O_(4)@SiO_(2)-Protein A-CD63 Ab had the potential for large-scale purification of exosomes in practical applications.

关键词： Immunomagnetic spheres Antibody Exosome Herpes simplex virus

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circ2CBA: prediction of circRNA-RBP binding sites combining deep learning and attention mechanism

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Frontiers of computer science 2023年第5期17卷 217-225页

作者： Yajing GUO Xiujuan LEI Lian LIU Yi PAN School of Computer Science Shaanxi Normal UniversityXi’an 710119China Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

Circular RNAs(circRNAs)are RNAs with closed circular structure involved in many biological processes by key interactions with RNA binding proteins(RBPs).Existing methods for predicting these interactions have limitations in feature *** view of this,we propose a method named circ2CBA,which uses only sequence information of circRNAs to predict circRNA-RBP binding *** have constructed a data set which includes eight ***,circ2CBA encodes circRNA sequences using the one-hot ***,a two-layer convolutional neural network(CNN)is used to initially extract the *** CNN,circ2CBA uses a layer of bidirectional long and short-term memory network(BiLSTM)and the self-attention mechanism to learn the *** AUC value of circ2CBA reaches *** of circ2CBA with other three methods on our data set and an ablation experiment confirm that circ2CBA is an effective method to predict the binding sites between circRNAs and RBPs.

关键词： circRNAs RBPs CNN BiLSTM self-attention mechanism

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Leveraging EfficientNetB3 in a Deep Learning Framework for High-Accuracy MRI Tumor Classification

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computers, Materials & Continua 2024年第10期81卷 867-883页

作者： Mahesh Thyluru Ramakrishna Kuppusamy Pothanaicker Padma Selvaraj Surbhi Bhatia Khan Vinoth Kumar Venkatesan Saeed Alzahrani Mohammad Alojail Department of Computer Science&Engineering Faculty of Engineering and TechnologyJAIN(Deemed-to-be University)Bengaluru562112India School of Computer Science and Engineering VIT-AP UniversityAmaravati522241India Department of Computer Science&Technology Madanapalle Institute of Technology&ScienceMadanapalle517325India School of Science Engineering and EnvironmentUniversity of SalfordManchesterM54WTUK School of Computer Science Engineering&Information Systems(SCORE) Vellore Institute of Technology(VIT)Vellore632014India Management Information System Department College of Business AdministrationKing Saud UniversityRiyadh11531Saudi Arabia Adjunct Research Faculty Centre for Research Impact&OutcomeChitkara UniversityRajpuraPunjab140401India

Brain tumor is a global issue due to which several people suffer,and its early diagnosis can help in the treatment in a more efficient *** different types of brain tumors,including gliomas,meningiomas,pituitary tumors,as well as confirming the absence of tumors,poses a significant challenge using MRI *** approaches predominantly rely on traditional machine learning and basic deep learning methods for image *** methods often rely on manual feature extraction and basic convolutional neural networks(CNNs).The limitations include inadequate accuracy,poor generalization of new data,and limited ability to manage the high variability in MRI *** the EfficientNetB3 architecture,this study presents a groundbreaking approach in the computational engineering domain,enhancing MRI-based brain tumor *** approach highlights a major advancement in employing sophisticated machine learning techniques within computer science and engineering,showcasing a highly accurate framework with significant potential for healthcare *** model achieves an outstanding 99%accuracy,exhibiting balanced precision,recall,and F1-scores across all tumor types,as detailed in the classification *** successful implementation demonstrates the model’s potential as an essential tool for diagnosing and classifying brain tumors,marking a notable improvement over current *** integration of such advanced computational techniques in medical diagnostics can significantly enhance accuracy and efficiency,paving the way for wider *** research highlights the revolutionary impact of deep learning technologies in improving diagnostic processes and patient outcomes in neuro-oncology.

关键词： Deep learning MRI brain tumor cassification EfficientNetB3 computational engineering healthcare technology artificial intelligence in medical imaging tumor segmentation neuro-oncology

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Design an optimization based ensemble machine learning framework for detecting rice leaf diseases

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Multimedia Tools and Applications 2024年第36期83卷 84401-84424页

作者： Rajasekhar, Veeramreddy Arulselvi, Gnanasekaran Babu, K. Suresh Department of Computer Science Engineering Annamalai University Tamilnadu Chidambaram India Department of Computer Science and Engineering Faculty of Engineering and Technology Annamalai University Tamil Nadu Chidambaram India International School of Technology and Sciences for Women East Gonagudem Rajanagaram A.P Rajamahendravaram India

The agriculture industry is currently dealing with serious issues with rice plants as a result of illnesses that decrease the quantity and output of the harvest. Numerous fungi and bacteria diseases harm plants that affect the growth of yield. So identification of the detection of crop diseases is essential for enhancing the productivity of crops. Many techniques have been developed to improve disease detection in crops but there are still some issues such as noise, time consumption, delay, poor segmentation, and less accuracy. To overcome these issues, this paper, designed a Spider Monkey-based Random Forest (SMbRF) model for accurate detection and classification of rice leaf diseases and the designed model was implemented in the Python system. The main aim is to develop an optimization-based random forest technique for accurate prediction of rice leaf disease by exact segmentation. Initially, preprocessing is performed to enhance the data quality and remove errors. Furthermore, separate the lesions using mean shift image segmentation and extract the shape and color features to improve the detection results of the developed model. As well, spider monkey fitness is updated in the RF classification layer for accurate classification of diseases and improves accuracy with less execution time. The experiment results show the reliability and efficiency of the designed model, it attains better performance in accuracy, sensitivity, specificity, and precision. The designed model attained 99.29% accuracy, 99.52% sensitivity, 98.76% precision, and 99% specificity proving the scalability of the designed model. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Machine learning

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Hybrid Optimization Algorithm for Handwritten Document Enhancement

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computers, Materials & Continua 2024年第3期78卷 3763-3786页

作者： Shu-Chuan Chu Xiaomeng Yang Li Zhang Václav Snášel Jeng-Shyang Pan College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China School of Information and Control Engineering Qingdao University of TechnologyQingdao266520China Faculty of Electrical Engineering and Computer Science VŠB-TU OstravaOstrava70080Czech Republic Department of Information Management Chaoyang University of TechnologyTaichung41349Taiwan

The Gannet Optimization Algorithm (GOA) and the Whale Optimization Algorithm (WOA) demonstrate strong performance;however, there remains room for improvement in convergence and practical applications. This study introduces a hybrid optimization algorithm, named the adaptive inertia weight whale optimization algorithm and gannet optimization algorithm (AIWGOA), which addresses challenges in enhancing handwritten documents. The hybrid strategy integrates the strengths of both algorithms, significantly enhancing their capabilities, whereas the adaptive parameter strategy mitigates the need for manual parameter setting. By amalgamating the hybrid strategy and parameter-adaptive approach, the Gannet Optimization Algorithm was refined to yield the AIWGOA. Through a performance analysis of the CEC2013 benchmark, the AIWGOA demonstrates notable advantages across various metrics. Subsequently, an evaluation index was employed to assess the enhanced handwritten documents and images, affirming the superior practical application of the AIWGOA compared with other algorithms.

关键词： Metaheuristic algorithm gannet optimization algorithm hybrid algorithm handwritten document enhancement

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Multi-Task Learning for Alzheimer's Disease Diagnosis and Mini-Mental State Examination Score Prediction

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Big Data Mining and Analytics 2024年第3期7卷 828-842页

作者： Jin Liu Xu Tian Hanhe Lin Hong-Dong Li Yi Pan Hunan Provincial Key Lab on Bioinformatics School of Computer Science and EngineeringCentral South UniversityChangsha 410083China Xinjiang Engineering Research Center of Big Data and Intelligent Software School of SoftwareXinjiang UniversityUrumqi 830091China School of Science and Engineering University of DundeeDundeeDD14HNUK Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

Accurately diagnosing Alzheimer's disease is essential for improving elderly ***,accurate prediction of the mini-mental state examination score also can measure cognition impairment and track the progression of Alzheimer's ***,most of the existing methods perform Alzheimer's disease diagnosis and mini-mental state examination score prediction separately and ignore the relation between these two *** address this challenging problem,we propose a novel multi-task learning method,which uses feature interaction to explore the relationship between Alzheimer's disease diagnosis and minimental state examination score *** our proposed method,features from each task branch are firstly decoupled into candidate and non-candidate parts for ***,we propose feature sharing module to obtain shared features from candidate features and return shared features to task branches,which can promote the learning of each *** validate the effectiveness of our proposed method on multiple *** Alzheimer's disease neuroimaging initiative 1 dataset,the accuracy in diagnosis task and the root mean squared error in prediction task of our proposed method is 87.86%and 2.5,*** results show that our proposed method outperforms most state-of-the-art *** proposed method enables accurate Alzheimer's disease diagnosis and mini-mental state examination score ***,it can be used as a reference for the clinical diagnosis of Alzheimer's disease,and can also help doctors and patients track disease progression in a timely manner.

关键词： multi-task learning Alzheimer's disease diagnosis mini-mental state examination score prediction

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A hybrid CNN-SVM model optimized with PSO for accurate and non-invasive brain tumor classification

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Neural Computing and Applications 2025年 1-30页

作者： Semwal, Tanay Jain, Sania Mohanta, Agradeep Jain, Ankur School of Computer Science and Engineering VIT Bhopal University Madhya Pradesh Kothri Kalan India Faculty of Science The Maharaja Sayajirao University of Baroda Gujarat Vadodara India

Brain and central nervous system (CNS) cancers are the leading cause of cancer-related mortality, presenting significant diagnostic challenges due to their aggressive nature and diverse manifestations. While biopsies are the gold standard for diagnosis, their invasiveness and associated risks necessitate the development of noninvasive, automated tools for accurate and rapid tumor classification. This research presents a hybrid approach that combines a convolutional neural network (CNN) for feature extraction with a support vector machine (SVM) classifier, optimized by particle swarm optimization (PSO). The proposed model achieves multiclass classification across four tumor categories: glioma, meningioma, pituitary, and no tumor. The model incorporates data augmentation, dropout layers, pooling, batch-normalization, and hyperparameter tuning via PSO, to enhance generalization and minimize overfitting. The experimental analysis on the brain tumor MRI dataset highlights the model's strong performance, with an overall accuracy of 84.77%, a macro-average F1-score of 0.80, and a macro-average precision of 0.91. The performance of the PSO-optimized CNN-SVM model was benchmarked against traditional machine learning (ML) and deep learning (DL) methods, which demonstrated mean accuracies between 48 and 72%. The proposed hybrid model significantly surpasses these conventional approaches, establishing its superiority in classification accuracy and reliability. The results highlight the proposed model's reliability as a noninvasive diagnostic solution, offering considerable upside for enhancing early brain tumor detection and aiding in treatment planning. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Convolutional neural networks

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Meaningful image encryption algorithm based on compressive sensing and integer wavelet transform

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Frontiers of computer science 2023年第3期17卷 161-175页

作者： Xiaoling HUANG Youxia DONG Guodong YE Yang SHI Faculty of Mathematics and Computer Science Guangdong Ocean UniversityZhanjiang 524088China School of Software Engineering Tongji UniversityShanghai 200092China

A new meaningful image encryption algorithm based on compressive sensing(CS)and integer wavelet transformation(IWT)is proposed in this *** of all,the initial values of chaotic system are encrypted by RSA algorithm,and then they are open as public *** make the chaotic sequence more random,a mathematical model is constructed to improve the random ***,the plain image is compressed and encrypted to obtain the secret ***,the secret image is inserted with numbers zero to extend its size same to the plain *** applying IWT to the carrier image and discrete wavelet transformation(DWT)to the inserted image,the secret image is embedded into the carrier ***,a meaningful carrier image embedded with secret plain image can be obtained by inverse ***,the measurement matrix is built by both chaotic system and Hadamard matrix,which not only retains the characteristics of Hadamard matrix,but also has the property of control and synchronization of chaotic ***,information entropy of the plain image is employed to produce the initial conditions of chaotic *** a result,the proposed algorithm can resist known-plaintext attack(KPA)and chosen-plaintext attack(CPA).By the help of asymmetric cipher algorithm RSA,no extra transmission is needed in the *** simulations show that the normalized correlation(NC)values between the host image and the cipher image are *** is to say,the proposed encryption algorithm is imperceptible and has good hiding effect.

关键词： image encryption algorithm compressive sensing integer wavelet transform Hadamard matrix

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Comprehensive multimodal approach for Parkinson’s disease classification using artificial intelligence: insights and model explainability

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Soft Computing 2025年第3期29卷 1845-1877页

作者： Balaha, Hossam Magdy Hassan, Asmaa El-Sayed Ahmed, Rawan Ayman Balaha, Magdy Hassan Bioengineering Department J.B. Speed School of Engineering University of Louisville LouisvilleKY United States Computer Science and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt Mathematics and Engineering Physics Department Faculty of Engineering Mansoura University Mansoura Egypt Department of Obstetrics and Gynecology Faculty of Medicine Tanta University Tanta Egypt

Parkinson’s disease (PD) is a debilitating neurodegenerative disorder affecting millions worldwide. Early detection is vital for effective management, yet remains challenging. In this study, we investigated four distinct datasets for PD detection. Through comprehensive experimentation employing ensemble methods and feature selection, we achieved high classification accuracies across the datasets. For the Oxford Parkinson’s Disease Detection Dataset, an accuracy of 95.67%, precision of 97.59%, recall of 84.5%, specificity of 99.32%, and F1-score of 90.57% were achieved. For the Alzheimer Parkinson Diseases 3 Class Dataset, the "Stacking" approach surpasses individual models, reaching an accuracy of 99.85%, precision of 99.81%, recall of 99.81%, specificity of 99.86%, and F1 of 99.81%. For the NewHandPD dataset, Regarding the Spiral category, The "Base-P32-384" model surpasses others with an accuracy of 97.35%, precision of 96.50%, recall of 98.57%, and F1-score of 97.53%. The collective "Stacking" approach proves highly effective regarding the Circle category, achieving 100% across all performance metrics. Regarding the Meander category, the "Base-P16-224" model achieves an accuracy of 97.35%, precision of 99.26%, recall of 95.71%, specificity of 99.19%, and F1 of 97.45%. The Mobile Device Voice Recordings at King’s College London (MDVR-KCL) dataset contains two datasets. Regarding the "SpontaneousDialogue" dataset, accuracy, BAC, precision, recall, specificity, and F1-score were computed, resulting in values of 94.03%, 92.83%, 90.78%, 100.0%, and 85.67%, respectively. Regarding the "ReadText" dataset, accuracy, BAC, precision, recall, specificity, and F1-score were computed, resulting in values of 91.89%, 90.62%, 87.5%, 100.0%, and 81.25%, respectively. Our findings highlight the efficacy of leveraging diverse data sources and advanced machine learning techniques to enhance PD detection accuracy. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germa

关键词： Neurodegenerative diseases

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