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High temperature steam oxidation behavior of AlCrNbTiZr high entropy alloy coating modified by irradiation-induced atomic diffusion

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Surface and Coatings technology 2025年 511卷

作者： Yang, Zhaoming Zeng, Yuhuan Ma, Yinghao He, Hengji Gu, Ming Zeng, Guoqiang Liu, Chunhai College of Nuclear Technology and Automation Engineering Chengdu University of Technology Chengdu610059 China China Institute for Radiation Protection Taiyuan030006 China College of Materials and Chemistry & Chemical Engineering Chengdu University of Technology Chengdu610059 China School of Integrated Circuit Science and Engineering University of Electronic Science and Technology of China Chengdu610054 China Post-Doctoral Research Station of Nuclear Science and Technology Chengdu University of Technology Chengdu610059 China

High entropy alloys (HEAs) are considered as potential accident tolerant fuel (ATF) coatings due to their excellent properties. However, there is still a lack of understanding about the effects of irradiation on the steam oxidation behavior, which hinders the design of advanced ATF HEAs coatings. In the current work, the steam oxidation behavior of Au ions irradiated AlCrNbTiZr HEA coatings was investigated. It is found that irradiation changes the high-temperature steam oxidation behavior of the material primarily by enhancing elemental diffusion. Irradiation accelerated oxidation by increasing the diffusion rate and depth of oxygen (O). Irradiation optimized the phase composition of the surface oxide layer by enhancing outward diffusion of Ti, thereby releasing releases the growth stress of oxides and avoids cracking and peeling of the surface oxide layer. © 2025 Elsevier B.V.

关键词： Diffusion coatings

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Performance Evaluation of New PET/CT DigitMI 930

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IEEE Transactions on Radiation and Plasma Medical Sciences 2025年第5期9卷 578-585页

作者： Zhang, Bo Li, Bingxuan Fang, Lei Zhou, Xiaoyun Li, Ang Zhang, Xuan Liu, Yang Wang, Zhuo Kao, Chien-Min Liu, Yuqing Zhu, Xiaohua Wan, Lin Xiao, Peng Chen, Xun Iida, Hidehiro Knuuti, Juhani Xie, Qingguo Huazhong University of Science and Technology Department of Biomedical Engineering Hubei Wuhan430074 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Anhui Hefei230088 China Huazhong University of Science and Technology School of Artificial Intelligence and Automation Hubei Wuhan430074 China University of Science and Technology of China Department of Electronic Engineering and Information Science Anhui Hefei101127 China Department of Radiology University of Chicago ChicagoIL60637 United States Huazhong University of Science and Technology Tongji Medical College Department of Nuclear Medicine Tongji Hospital Hubei Wuhan430032 China Huazhong University of Science and Technology School of Software Engineering Hubei Wuhan430074 China University of Turku Turku University Hospital Turku PET Centre Turku20520 Finland

The study evaluates the performance of the DigitMI 930 positron emission tomography (PET)/CT system, featuring detector modules with an 1:1:1 coupling of the scintillation crystal, the photosensor, and the electronic readout channel, in adherence to the NEMA NU 2-2018 standard. Moreover, brain and whole-body images were used to assess image quality. The radial, tangential, and axial resolutions at a radial offset of 1 cm were 3.9, 3.9, and 3.7 mm, respectively. The average sensitivity was measured at 16.2 cps/kBq. The peak noise-equivalent count rate was calculated as 412.5 kcps at 34.5 kBq/mL. At an activity concentration of 5.3 kBq/mL, the scatter fraction was 37.5%, and the time-of-flight (TOF) resolution was 248.6 ps. The contrast recovery coefficient ranged from 70.6% to 87.7% with TOF reconstruction. Despite increased noise in shorter whole-body scans, critical lesions remained identifiable at 20-s durations per bed position. The DigitMI 930 PET/CT system demonstrates a strong overall performance, particularly noteworthy for its low spatial resolution to crystal size ratio in comparison to other clinical PET systems. Moreover, the clinical studies indicate that the DigitMI 930 PET/CT system is capable of generating high-quality clinical images with high sensitivity for detecting small lesions, even at low injection doses or short scanning times. © 2017 IEEE.

关键词： Positron emission tomography

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Quantitative energy-dispersive X-ray fluorescence analysis for unknown samples using full-spectrum least-squares regression

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nuclear Science and Techniques 2019年第3期30卷 149-159页

作者： Yong-Li Liu Qing-Xian Zhang Jian Zhang Hai-Tao Bai Liang-Quan Ge The College of Applied Nuclear Technology and Automation Engineering Chengdu University of Technology

The full-spectrum least-squares(FSLS) method is introduced to perform quantitative energy-dispersive X-ray fluorescence analysis for unknown solid *** on the conventional least-squares principle, this spectrum evaluation method is able to obtain the background-corrected and interference-free net peaks, which is significant for quantization analyses. A variety of analytical parameters and functions to describe the features of the fluorescence spectra of pure elements are used and established, such as the mass absorption coefficient, the Gi factor, and fundamental fluorescence formulas. The FSLS iterative program was compiled in the C language. The content of each component should reach the convergence criterion at the end of the calculations. After a basic theory analysis and experimental preparation, 13 national standard soil samples were detected using a spectrometer to test the feasibility of using the algorithm. The results show that the calculated contents of Ti, Fe, Ni, Cu, and Zn have the same changing tendency as the corresponding standard content in the 13 reference samples. Accuracies of 0.35% and 14.03% are obtained, respectively, for Fe and Ti, whose standard concentrations are 8.82% and 0.578%, respectively. However, the calculated results of trace elements (only tens of lg/g) deviate from the standard values. This may be because of measurement accuracy and mutual effects between the elements.

关键词： Energy-dispersive X-ray fluorescence analysis Full-spectrum least-squares method Effective atomic number Mass attenuation coefficient Fundamental parameter method

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Temperature-stabilized ultra-low temperature co-fired molybdate composite ceramics for superposition state generator between vortex and spherical waves

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Ceramics International 2025年

作者： Li, Fuyu Li, Yuanxun Li, Chuming Shang, Yanfeng Huang, Liyuan College of Nuclear Technology and Automation Engineering Chengdu University of Technology Sichuan Chengdu610059 China School of Electronic Science and Engineering University of Electronic Science and Technology of China Chengdu610054 China Tianfu Jiangxi Laboratory Chengdu641419 China

Interference superposition states between vortex and spherical waves can further enhance the spectrum utilization of wireless communication technologies. Here, a temperature-stabilized 0.55 Li2Mg1.94Zn0.06Mo3O12∼0.45 Li0.5Bi0.5MoO4 composite ceramic is synthesized using a solid-state reaction method. This ceramic sintered at 600 °C has excellent microwave dielectric properties (Εr = 12.5, Q × f = 22779 GHz, and τf = +5.7 ppm/°C). For the first time, this composite ceramic is used to design and demonstrate an array device for generating a superposition state between vortex and spherical waves. The superposition state is achieved by the interference between x- and y-polarized waves. The transmission amplitudes are all above 0.66 and the transmission phase covers 360°. It is revealed that the generation state can be controlled by switching the polarization state of the incident wave, including vortex wave, spherical wave and superposition state. The superposition state consists of three helical stripes rotating counterclockwise. This work promotes the development of the ULTCC field and further broadens the range of dielectric ceramic applications. © 2025 Elsevier Ltd and Techna Group S.r.l.

关键词： Ceramic products

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Optimization of Crop Yields in Sustainable Agriculture: Application of Big Data Analytics and Artificial Intelligence

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Remote Sensing in Earth Systems Sciences 2025年第2期8卷 375-386页

作者： Nimma, Divya Dhanke, Jyoti A. Murthy, G.S.N. Khandekar, Sachin Dadu Hymavathi, J. Jangir, Pradeep Singh, Mahesh Department of Computational Science University of Southern Mississippi Hattiesburg 39406 MS United States Department of Engineering Science (Mathematics) Bharati Vidyapeeth’s College of Engineering Lavale Maharashtra Pune India Department of CSE Aditya University Surampalem India Department of Civil Engineering Sinhgad College of Engineering Maharashtra Pune India Department of CSE Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan Department of Robotics and Automation Symbiosis Institute of Technology Pune Campus Symbiosis International (Deemed University) Pune India

Making informed economic decisions based on agricultural data is challenging without proper crop management. Data has become the single most important part of modern farming, and its rapid evolution is a major contributor to the rise of “smart farming.” Using sensors to gather objective data with the end aim of increasing productivity and sustainability has several advantages. Information that might boost productivity while decreasing resource waste and pollution levels is essential for managed farms. The cornerstone of future sustainable farming will be data-driven agriculture using robotic solutions based on AI-based techniques. This paper examines the current state of advanced farm management systems, discussing topics like data acquisition in crop fields and variable rate applications, to assist farmers in saving money, protecting the environment, and transforming food production so that it can sustainably keep up with the expected increase in population. The advent of machine learning, big data technologies, and high-performance computers has opened up new avenues for data-intensive research in the interdisciplinary subject of agro technology. These avenues of exploration have been aided by the development of more capable computers. A comprehensive literature review on the issue of the applications of machine learning in agricultural manufacturing is provided in this research. Machine learning has shown its utility in agriculture via two applications: filtering and categorizing products. The use of machine learning on sensor data is transforming conventional farm management systems into AI-enabled programs. These services provide farmers instantaneous, complete access to ideas and insights that may guide their decisions. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Artificial intelligence Big data analytics Optimize crop yields Sustainable agriculture

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Multi-modality medical image classification with ResoMergeNet for cataract, lung cancer, and breast cancer diagnosis

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Computers in Biology and Medicine 2025年 187卷 109791-109791页

作者： Ejiyi, Chukwuebuka Joseph Cai, Dongsheng Fiasam, Delali Linda Adjei-Arthur, Bonsu Obiora, Sandra Ayekai, Browne Judith Asare, Sarpong K. Jonathan, Anto Leoba Qin, Zhen College of Nuclear Technology and Automation Engineering Chengdu University of Technology Sichuan China School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan China School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan China Leeds Business School Leeds Beckett University LeedsLS1 3HB United Kingdom School of Electronic Science and Engineering University of Electronic Science and Technology of China Chengdu China

The variability in image modalities presents significant challenges in medical image classification, as traditional deep learning models often struggle to adapt to different image types, leading to suboptimal performance across diverse datasets. This is critical in the diagnosis of conditions like cataracts and cancers, where imaging data spans various modalities, including visible eye images for cataracts and histopathological images for cancers among others. Cataracts, a leading cause of blindness, and lung and breast cancers, major contributors to cancer-related deaths, require early detection for effective intervention. However, many existing models fall short in handling modality differences, limiting their performance. To address this, we propose ResoMergeNet (RMN), designed to handle multi-modal medical image classification. RMN integrates transfer learning and advanced techniques – the ResBoost framework and ConvMergeNet, enabling the model to effectively extract relevant features from both visible eye images and histopathological images. The model's architecture emphasizes both global and local feature extraction while minimizing the influence of irrelevant data, thus improving classification performance across modalities. Evaluated on the cataract dataset (binary classification), RMN achieved an accuracy of 99.17 %. For lung cancer (3-class classification), it attained 100 % accuracy, while on the BreakHis (8-class classification) dataset, RMN reached 99.24 % accuracy at 100× magnification and 98.28 % at 200× magnification. These results demonstrate RMN's robustness and adaptability to varying image modalities, highlighting its potential as a reliable diagnostic tool in medical settings. Through its versatility, RMN offers a promising solution for improving early diagnosis and healthcare outcomes in the fight against cataracts, lung, and breast cancers. © 2025 Elsevier Ltd

关键词： Lung cancer

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ATEDU-NET: An Attention-Embedded Deep Unet for multi-disease diagnosis in chest X-ray images, breast ultrasound, and retina fundus

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Computers in Biology and Medicine 2025年 186卷 109708-109708页

作者： Ejiyi, Chukwuebuka Joseph Qin, Zhen Agbesi, Victor K. Ejiyi, Makuachukwu Bennedith Chikwendu, Ijeoma A. Bamisile, Oluwatoyosi F. Onyekwere, Favour Ezinne Bamisile, Olusola O. College of Nuclear Technology and Automation Engineering Chengdu University of Technology Sichuan 610059 China Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Sichuan Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Pharmacy Department University of Nigeria Nsukka Enugu State Nsukka Nigeria School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China

In image segmentation for medical image analysis, effective upsampling is crucial for recovering spatial information lost during downsampling. This challenge becomes more pronounced when dealing with diverse medical image modalities, which can significantly impact model performance. Plain and standard skip connections, widely used in most models, often fall short of maintaining high segmentation accuracy across different modalities, because essential spatial information transferred from the encoder to the decoder is lost. Inspired by these limitations, the Attention-Embedded Deep UNet (ATEDU-Net) is presented here, an innovative framework designed and tested on diverse medical image modalities, including X-ray, breast ultrasound, and retinal fundus images. ATEDU-Net features a unique architecture that combines progressive context refinement modules (PCRM) and global context modules (GCM) within a U-shaped network structure with Convgroup blocks which facilitate the integration of convolutional operations. This allows ATEDU-Net to autonomously capture rich spatial details and crucial contextual information from input images. The GCM captures essential contextual information for informed decision-making, while the PCRM enhances feature attention, resulting in precise and robust segmentation. The experimentation and analysis demonstrate that ATEDU-Net promises to be a powerful tool for medical professionals, aiding in the early detection of chest-related diseases, accurate localization of breast tumors, and early identification of eye diseases. This, in turn, contributes significantly to the formulation of optimal therapeutic strategies, enhancing patient care and outcomes. The versatility of ATEDU-Net is further highlighted by its ability to analyze medical images across various modalities, making it well-suited for the complex task of medical image analysis in diverse clinical scenarios. © 2025 Elsevier Ltd

关键词： Image segmentation

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A Comprehensive Methodology of Field-Oriented Control Design With Parameter Variation Analysis for Interior Permanent Magnet Synchronous Machine Drives

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IEEE Access 2025年 13卷 89524-89541页

作者： Tiago Davi Curi Busarello Abdullah Bubshait Oruganti V. S. R. Varaprasad Abdulhakeem Alsaleem Marcelo Godoy Simões Department of Control Automation and Computation Engineering Federal University of Santa Catarina Florianopolis Brazil Department of Electrical Engineering College of Engineering King Faisal University Al Ahsa Saudi Arabia Smart Transportation Electrification and Energy Research (STEER) Group Department of Electrical Computer and Software Engineering Faculty of Engineering and Applied Science Ontario Tech University Oshawa ON Canada Department of Electrical Engineering College of Engineering Qassim University Buraydah Saudi Arabia School of Technology and Innovations University of Vaasa Vaasa Finland

This paper proposes a comprehensive methodology for Field-Oriented Control (FOC) with parameter variation analysis for Interior Permanent Magnet Synchronous Machines (IPMSM). The modeling approach for an IPMSM is first presented, followed by a step-by-step procedure for designing a vector-controlled strategy. The formulation is based on a dq-rotating reference frame aligned with the rotor shaft position. A key distinction of this methodology from traditional approaches is that the current controller is designed in the time domain based on desired time constants, while the speed control is formulated within a frequency response domain framework. The proposed hybrid approach enables accurate tuning of the Proportional-Integrator (PI) controllers for both current and speed control loops. Additionally, a parameter variation analysis is conducted to enhance the proposed methodology. The validity region for the design procedure is presented, ensuring that for any wide speed variation of the machine, loop gains are properly tuned. One of the main advantages of the proposed methodology is that it provides a fast, reliable, and accurate technique for implementing IPMSM drive systems. Results from a Controller Hardware-in-the-Loop (C-HIL) setup with an external microcontroller are presented. The comprehensive design approach is validated under two different IPMSM parameter sets, demonstrating its effectiveness.

关键词： PI control Stators Tuning Mathematical models Velocity control Rotors Vectors Stator windings Robustness Microcontrollers

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Advancing cancer diagnosis and prognostication through deep learning mastery in breast, colon, and lung histopathology with ResoMergeNet

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Computers in Biology and Medicine 2025年 185卷 109494-109494页

作者： Ejiyi, Chukwuebuka Joseph Qin, Zhen Agbesi, Victor K. Yi, Ding Atwereboannah, Abena A. Chikwendu, Ijeoma A. Bamisile, Oluwatoyosi F. Bakanina Kissanga, Grace-Mercure Bamisile, Olusola O. College of Nuclear Technology and Automation Engineering & Sichuan Industrial Internet Intelligent Monitoring and Application Engineering Research Center Chengdu University of Technology Sichuan Chengdu China Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Sichuan Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China School of Life Science University of Electronic Science and Technology of China Chengdu China

Cancer, a global health threat, demands effective diagnostic solutions to combat its impact on public health, particularly for breast, colon, and lung cancers. Early and accurate diagnosis is essential for successful treatment, prompting the rise of Computer-Aided Diagnosis Systems as reliable and cost-effective tools. Histopathology, renowned for its precision in cancer imaging, has become pivotal in the diagnostic landscape of breast, colon, and lung cancers. However, while deep learning models have been widely explored in this domain, they often face challenges in generalizing to diverse clinical settings and in efficiently capturing both local and global feature representations, particularly for multi-class tasks. This underscores the need for models that can reduce biases, improve diagnostic accuracy, and minimize error susceptibility in cancer classification tasks. To this end, we introduce ResoMergeNet (RMN), an advanced deep-learning model designed for both multi-class and binary cancer classification using histopathological images of breast, colon, and lung. ResoMergeNet integrates the Resboost mechanism which enhances feature representation, and the ConvmergeNet mechanism which optimizes feature extraction, leading to improved diagnostic accuracy. Comparative evaluations against state-of-the-art models show ResoMergeNet's superior performance. Validated on the LC-25000 and BreakHis (400× and 40× magnifications) datasets, ResoMergeNet demonstrates outstanding performance, achieving perfect scores of 100 % in accuracy, sensitivity, precision, and F1 score for binary classification. For multi-class classification with five classes from the LC25000 dataset, it maintains an impressive 99.96 % across all performance metrics. When applied to the BreakHis dataset, ResoMergeNet achieved 99.87 % accuracy, 99.75 % sensitivity, 99.78 % precision, and 99.77 % F1 score at 400× magnification. At 40× magnification, it still delivered robust results with 98.85 % accuracy,

关键词： Lung cancer

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Targeted-tuning competitive acidic CO2RR via metalloid antagonism sites

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Materials Today 2025年 83卷 54-63页

作者： Sheng, Beibei Cao, Dengfeng Qi, Zhenghang Shou, Hongwei Xia, Yujian Su, Xiaozhi Chen, Shuangming Wu, Chuanqiang Liu, Hengjie Chimtali, Peter Joseph Chu, Yongheng Liu, Chongjing Wu, Xiaojun Song, Li National Synchrotron Radiation Laboratory CAS Center for Excellence in Nanoscience Key Laboratory of Precision and Intelligent Chemistry School of Nuclear Science and Technology University of Science and Technology of China Hefei230029 China SINOPEC Shanghai Research Institute of Petrochemical Technology Co. Ltd. Shanghai201208 China Engineering Laboratory of Chemical Resources Utilization in South Xinjiang of Xinjiang Production and Construction Corps College of Chemistry and Chemical Engineering Tarim University Alar843300 China School of Chemistry and Materials Science CAS Center for Excellence in Nanoscience and Synergetic Innovation of Quantum Information & Quantum Technology Hefei National Laboratory for Physical Sciences at the Microscale University of Science and Technology of China Hefei230026 China Advanced Light Source Lawrence Berkeley National Laboratory Berkeley CA94720 United States Shanghai Synchrotron Radiation Facility Shanghai Institute of Applied Physics Chinese Academy of Sciences Shanghai201204 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Institutes of Physical Science and Information Technology Anhui University Hefei230601 China

To mitigate the high separation costs associated with conventional neutral/alkaline electrocatalysis for CO2 reduction reactions (CO2RR), acidic CO2RR offers economic advantages and improved efficiency in CO2 utilization. However, it typically involves the cleavage of M−H bonds at a relatively negative potential, leading to the predominant formation of H2 and poor HCOOH selectivity. Herein, we develop a facile solid-phase thermal diffusion approach to controllably synthesize a novel metalloid-metal single atom alloys (m-SAAs) electrocatalyst Te1Bi with unique metalloid antagonistic sites, thus enabling high-efficient acidic CO2-to-HCOOH conversion. Electrochemical test and operando synchrotron radiation multi-techniques (SRMS) characterization reveal that metalloid Te sites bring steric hindrance effect and blocks *H coupling. Furthermore, it actively adsorbs OH− species as a proton source, allowing for effective separation of protons and electrons in space. Thus, leading to enhanced hydrogenation in acidic CO2RR to produce HCOOH. The flow cell test results demonstrate that the carefully designed Te1Bi catalyst exhibits a milder reaction potential, along with higher HCOOH Faraday efficiency (∼94.5 %) and single-pass carbon efficiency (SPCE, ∼40 %) in acidic media. This work significantly expands the family of SAAs and offers a novel perspective to analyze the regulation of competitive reactions through site-specific modifications for industrial acidic CO2RR. © 2024 Elsevier Ltd

关键词： Synchrotron radiation

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