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First-principles calculations of structure and elasticity of hydrous fayalite under high pressure

Chinese Physics B 2017年第12期26卷 389-396页

作者：张传瑜王绪本赵晓凤陈星润虞游田晓峰 College of Geophysics Chengdu University of Technology Chengdu 610059 China College of Optoelectronic Technology Chengdu University of Information Technology Chengdu 610225 China College of Nuclear Technology and Automation Engineering Chengdu University of Technology Chengdu 610059 China

The structures, elasticities, sound velocities, and electronic properties of anhydrous and hydrous fayalite （Fe2SiO4 and Fe1.75H0.5SiO4） under high pressure have been investigated by means of the density functional theory within the generalized gradient approximation （GGA） with the on-site Coulomb energy being taken into account （GGA＋U）. The optimized results show that H atoms prefer to substitute Fe atoms in the Fe1 site. Compared with the anhydrous fayalite Fe2SiO4, the mass density, elastic moduli, and sound velocities of Fe1.75H0.5SiO4 slightly decrease. According to our data, adding 2.3 wt% water into fayalite leads to reductions of compressional and shear wave velocities （VP and VS） by 3.4%-7.5% and 0.3%-3.4% at pressures from 0 GPa to 25 GPa, respectively, which are basically in agreement with the 2%-5% reductions of sound velocity obtained by the experimental measurement in the low velocity zones （LVZ）. Based on the electronic structure, the valence and conduction bands are slightly broader for hydrous fayalite. However, hydrous fayalite keeps the insulation characteristics under the pressures up to 30 GPa, which indicates that hydration has little effect on its electronic structure.

关键词： hydrous fayalite first-principles theory crystal structure elasticity

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DMSACNN: Deep Multiscale Attentional Convolutional Neural Network for EEG-Based Motor Decoding

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Liu, Ke Xing, Xin Yang, Tao Yu, Zhuliang Xiao, Bin Wang, Guoyin Wu, Wei Chongqing University of Posts and Telecommunications School of Computer Science and Technology Chongqing400065 China Chongqing University of Posts and Telecommunications Key Laboratory of Big Data Intelligent Computing China Chongqing University of Posts and Telecommunications Key Laboratory of Cyberspace Big Data Intelligent Security of Ministry of Education China South China University of Technology College of Automation Science and Engineering Guangzhou510641 China Chongqing Normal University National Center for Applied Mathematics in Chongqing Chongqing401331 China Shanghai Jiao Tong University School of Medicine Songjiang Hospital Songjiang Research Institute Shanghai Key Laboratory of Emotions and Affective Disorders Shanghai201600 China

Objective: Accurate decoding of electroencephalogram (EEG) signals has become more significant for the brain-computer interface (BCI). Specifically, motor imagery and motor execution (MI/ME) tasks enable the control of external devices by decoding EEG signals during imagined or real movements. However, accurately decoding MI/ME signals remains a challenge due to the limited utilization of temporal information and ineffective feature selection methods. Methods: This paper introduces DMSACNN, an end-to-end deep multiscale attention convolutional neural network for MI/ME-EEG decoding. DMSACNN incorporates a deep multiscale temporal feature extraction module to capture temporal features at various levels. These features are then processed by a spatial convolutional module to extract spatial features. Finally, a local and global feature fusion attention module is utilized to combine local and global information and extract the most discriminative spatiotemporal features. Main results: DMSACNN achieves impressive accuracies of 78.20%, 96.34% and 70.90% for hold-out analysis on the BCI-IV-2a, High Gamma and OpenBMI datasets, respectively, outperforming most of the state-of-the-art methods. Conclusion and significance: These results highlight the potential of DMSACNN in robust BCI applications. Our proposed method provides a valuable solution to improve the accuracy of the MI/ME-EEG decoding, which can pave the way for more efficient and reliable BCI systems. The source code for DMSACNN is available at https://***/xingxin-99/***. © 2025 IEEE.

关键词： Convolutional neural networks

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Enhancement in performance of CuMnO2 anchored over rGO for water splitting

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Journal of Physics and Chemistry of Solids 2025年 206卷

作者： Qasim, Muffarah Atta, Muhammad Shahrukh safra, Imen Makasana, Jayanti Rekha, M.M. Kumar, G. Senthil Al-Anber, Mohammed A. Das, Sankar Narayan Chaudhary, Rahul Kumar, Abhinav Oza, Ankit D. Department of Chemistry Government Graduate College Taunsa Sharif32100 Pakistan Department of Mechanical Engineering University of Engineering and Technology Taxila47080 Pakistan Department of Physics College of Science Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Marwadi University Research Centre Department of Mechanical Engineering Faculty of Engineering & Technology Marwadi University Gujarat Rajkot360003 India Karnataka Bangalore India Department of Mechanical Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai India Department of Chemistry Faculty of Sciences Applied Science Private University P.O. Box 166 Amman11931 Jordan Odisha Bhubaneswar751030 India Department of Mechanical Engineering Chandigarh Engineering College Chandigarh Group of Colleges-Jhanjeri Punjab Mohali140307 India Department of Nuclear and Renewable Energy Ural Federal University Named After the First President of Russia Boris Yeltsin Ekaterinburg620002 Russia Department of Mechanical Engineering and Renewable Energy Technical Engineering College The Islamic University Najaf Iraq University Centre for Research and Development Chandigarh University Punjab Mohali140413 India Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India

Now, the global energy demand increases and scientist are continuously trying to find renewable resources for energy production. Electrochemical water splitting known as an efficient and economical friendly method to fulfil global energy demands. In water splitting catalyst play crucial role. This study presents the synthesis of CuMnO2 electrocatalyst, that is generated via hydrothermal method. The successful fabrication of generated nanohybrid were done by XRD, BET, SEM and EDS which confirmed high crystallinity, surface area, better morphology and purity. The electrocatalytic performance of fabricated composite was checked in basic (1 M KOH) which confirmed lower Tafel slope (43 mV dec−1) and reduced overpotential (η) of 254 mV at 10 mA cm−2 current density (j). Additionally, the CuMnO2/rGO composite exhibits an elevated active surface area of 3670 cm2 and lower resistance (1.04 Ω). Moreover, rGO provide higher surface area to catch more electrons for the enhancement in specific electrical conductivity for OER. Eventually, the synergistic interaction of CuMnO2 and rGO enhances the stability and electrical conductivity of the composite by providing an electron transmission pathway and structural integrity. Therefore, the above stated results shows that synthesized composite exhibited remarkable efficacy in OER process. © 2025 Elsevier Ltd

关键词： Potassium hydroxide

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Gaussian shaper for nuclear pulses based on multilevel cascade convolution

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nuclear Science and Techniques 2022年第12期33卷 116-127页

作者： Min Wang Jian‑Bin Zhou Xiao‑Ping Ouyang Ying‑Jie Ma Xu Hong College of Nuclear Technology and Automation Engineering Chengdu University of TechnologyErxian Bridge No.1 East 3 RoadChenghua DistrictChengdu 610059China Northwest Institute of Nuclear Technology Xi’an 710024China

For nuclear measurements,it is necessary to obtain accurate information from nuclear pulses,which should be obtained by first shaping the pulses outputted by the ***,commonly used pulse-shaping algorithms have certain *** example,certain pulse-shaping algorithms have long dead-times in high-counting-rate environments or are difficult to achieve in digital *** signals are widely used in analog nuclear instruments owing to their symmetry and completeness.A Gaussian signal is usually implemented by using a multilevel S–K filter in series or in *** is difficult to construct a real-time digital Gaussian filter for the complex Gaussian filtering *** on the multilevel cascade convolution,a pulse-shaping algorithm for double exponential signals is proposed in this study,which,in addition to double exponential signals,allows more complex output signal models to be used in the new *** proposed algorithm can be used in high-counting-rate environments and has been implemented in an FPGA with fewer multipliers than those required in other traditional Gaussian pulse-shaping *** offline processing results indicated that the average peak base width of the output-shaped pulses obtained using the proposed algorithm was reduced compared with that obtained using the traditional Gaussian pulse-shaping *** results also demonstrated that signal-to-noise ratios and energy resolutions were improved,particularly for pulses with a low *** energy resolution was improved by 0.1–0.2%while improving the counting rate.

关键词： Impulse shaping Multilevel cascade convolution S–K filter Gaussian-like distribution Double exponential signal

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New methods to remove baseline drift in trapezoidal pulse shaping

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nuclear Science and Techniques 2015年第5期26卷 58-62页

作者：洪旭马英杰周建斌陈铁光胡云川万新峰杜鑫 College of Nuclear Technology and Automation Engineering Chengdu University of Technology Chengdu Gaotong Isotope CO. LTD. (CNNC)

Trapezoidal pulse shaping algorithm is widely applied to improve signal-to-noise ratio(SNR), throughput and energy resolution with the properties of noise suppression, pile-up pulse separation and ballistic deficit correction. The algorithm can be acquired by z transform method which is easier for derivation. However, the baseline drift of trapezoidal pulse appears because the noise superimposes on the input signal. In this paper,two new methods based on convergence analysis and noise suppression are proposed to remove the baseline drift resulting from trapezoidal pulse shaping. Simulations and experimental tests are carried out to verify the methods. The results demonstrate that the proposed methods can remove baseline drift in trapezoidal pulse shaping.

关键词：梯形脉冲基线漂移脉冲整形高信噪比噪声抑制能量分辨率收敛性分析弹道修正

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An improved SNIP denoising algorithm applied in EDXRF

An improved SNIP denoising algorithm applied in EDXRF

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2019 International Conference on applied Machine Learning and Data Science, ICAMLDS 2019

作者： Ge, Liangquan Li, Hui Tang, Chuanfeng Li, Fei Zhu, Kai Yang, Jin Applied Nuclear Technology in Geosciences Key Laboratory Sichuan Province China College of Nuclear Technology and Automation Engineering Chengdu University of Technology China

Based on the phenomenon of peak height reduction in existing denoising algorithms, this paper modifies the traditional SNIP algorithm, proposes an improved SNIP algorithm and applies it to EDXRF denoising. Two evaluation parameters RMSE and PCC are used to compare the improved SNIP algorithm with the traditional SNIP algorithm. The experimental results show that the improved SNIP algorithm has great advantages both in similarity and relevance, and the results of the improved SNIP algorithm are more consistent with the characteristics of the original data peak than the original SNIP algorithm, which is beneficial to the qualitative and quantitative analysis of the energy dispersive X-ray fluorescence spectrum. © Published under licence by IOP Publishing Ltd.

关键词： Energy dispersive spectroscopy

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Application of small sample BP neural network in quantitative analysis of EDXRF

Application of small sample BP neural network in quantitativ...

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2019 International Conference on applied Machine Learning and Data Science, ICAMLDS 2019

作者： Li, Fei Tang, Zhuoyao Ge, Liangquan Wu, Nanxing Cheng, Feng Sun, Kun Applied Nuclear Technology in Geosciences Key Laboratory Sichuan Province China College of Nuclear Technology and Automation Engineering Chengdu University of Technology China

Quantitative analysis of EDXRF is affected by matrix effect, randomness of nuclear radiation, interaction of elements, statistical fluctuation of radiation detection process, etc. Its algorithm needs to consider many factors, and the established functional relationship is often a complex non-linear function. Therefore, effective quantitative analysis method has always been the key research direction of spectral resolution technology. In this paper, we use MATLAB software to study the effect of BP neural network in quantitative analysis of EDXRF by establishing the non-linear relationship between the counting rate of each element and the content of a single element in a small sample. The experimental results show that the small sample neural network can establish a stable structure and be applied to quantitative analysis of EDXRF, but the number of samples restricts the accuracy of prediction results, most of which can only guarantee 20% to 30% relative error. © Published under licence by IOP Publishing Ltd.

关键词： MATLAB

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Research on gamma spectrum semi-quantitative analysis based on convolutional neural network

Research on gamma spectrum semi-quantitative analysis based ...

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2019 International Conference on applied Machine Learning and Data Science, ICAMLDS 2019

作者： Li, Fei Wang, Jing Ge, Liangquan Hu, Fangyan Cheng, Feng Sun, Kun Applied Nuclear Technology in Geosciences Key Laboratory Sichuan Province China College of Nuclear Technology and Automation Engineering Chengdu University of Technology China

In the field of practical application of nuclear technology, one of the vital steps is the interpretation of gamma energy spectrum, which can obtain the type and content of radionuclides and achieve further analyzation. Convolutional neural network is one the most basic and effective algorithm structures in deep learning because it has local receptive field, weight sharing and down sampling structure, it solves the problem of parameter expansion caused by full connection and reduces the number of weights. That can be used to study the resolution of gamma-ray spectra of convolutional neural networks. This paper introduces the spectral forming principle of gamma spectra and the convolutional neural network, and USES the convolutional neural network to study the spectral decomposition of gamma spectra. In this paper, a multi-layer convolution neural network model is built based on C# software. The convolution neural network is applied to gamma-ray spectrum decomposition, and the U, Th, K nuclides are identified and semi-quantitatively calculated. By identifying and analysing different energy spectrum data, it is shown that the modified model structure can be applied to the spectral decomposition of gamma spectrum. © Published under licence by IOP Publishing Ltd.

关键词： Convolution

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Shallowly discussing the humanity design of urban public facilities

Shallowly discussing the humanity design of urban public fac...

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2nd International Conference on Advances in Energy and Environmental Science, ICAEES 2014

作者： Hu, Chuan-Mei College of Applied Nuclear Technology and Automation Engineering Chengdu University of Technology Chengdu Sichuan province 610059 China

ISBN: (纸本)9783038352099

The influence factors of humanity design of public facilities contain four aspects: natural environment, human environment, regional culture and human factors. The principles that humanity design of public facilities should be obey to meet the needs of people's activities, people's psychology, the organic unity of the man and materialized, to achieve the artistic beauty and meet the cultural identity. Humanity design can cause the user's thoughts resonate with the designer, and it can promote the sustainable development of urban public facilities because of its mutual integration with the environment. © (2014) Trans Tech Publications, Switzerland.

关键词： Sustainable development

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Flow Rate Estimation Based on Magnetic Particle Detection Using a Miniatured High-Sensitivity OPM

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Liu, Ying Huang, Binyue Li, Jiajie Li, Renjie Zhai, Yueyang Beihang University School of Instrumentation and Optoelectronic Engineering Beijing100191 China Hangzhou Innovation Institute Beihang University Zhejiang Provincial Key Laboratory of Ultra-Weak Magnetic-Field Space and Applied Technology Hangzhou310051 China Hefei National Laboratory Hefei230088 China Beihang University School of Instrumentation and Optoelectronic Engineering Shen Yuan Honors College Beijing100191 China Beijing Institute of Technology School of Automation Beijing100081 China

The efficacy of magnetic particles (MPs) in cancer treatments prompts the investigation into MP detection methods. However, due to their microsize, MPs produce weak magnetic field signals that necessitate highly sensitive measurements to extract useful information. In this study, we introduce a technique for detecting individual MPs using a compact optically pumped magnetometer (OPM). A noninvasive and radiation-free flow rate estimation method based on the OPM is further proposed, demonstrating its clinical potential. A miniaturized dual-beam spin-exchange relaxation-free (SERF) OPM is fabricated for the system, achieving a remarkable sensitivity of 8.6 fT/Hz{1/2} in a compact volume of 7.7 cm3. The magnetometer accurately measures the dynamic magnetic fields, enabling the detection of translational and rotational motions of MPs in fluid flow. The particle rotation frequencies are extracted from magnetometer responses using the continuous wavelet transform (CWT), revealing a positive correlation between the flow rate and rotation frequency. Besides, CWT effectively mitigates the cardiac magnetic interference that may arise during in vivo measurements, showcasing its high applicability in processing MP signals. Finally, 90.2% of the flow rates are correctly predicted by a regression tree trained with mean and standard deviation as predictors. Overall, this highly sensitive system facilitates noninvasive and rapid flow estimation, validating promising potential for biomedical research and clinical practice. © 1963-2012 IEEE.

关键词： Magnetic field measurement

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