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Neural Regeneration Research 2025年第1期20卷 139-158页

作者： Fengjuan Jiao Lingyan Meng Kang Du Xuezhi Li School of Mental Health Jining Medical UniversityJiningShandong ProvinceChina Shandong Collaborative Innovation Center for Diagnosis Treatment and Behavioral Interventions of Mental DisordersInstitute of Mental HealthJining Medical UniversityJiningShandong ProvinceChina

Parkinson’s disease is a common neurodegenerative disease with movement disorders associated with the intracytoplasmic deposition of aggregate proteins such asα-synuclein in *** one of the major intracellular degradation pathways,the autophagy-lysosome pathway plays an important role in eliminating these *** evidence has shown that upregulation of the autophagy-lysosome pathway may contribute to the clearance ofα-synuclein aggregates and protect against degeneration of dopaminergic neurons in Parkinson’s ***,multiple genes associated with the pathogenesis of Parkinson’s disease are intimately linked to alterations in the autophagy-lysosome ***,this pathway appears to be a promising therapeutic target for treatment of Parkinson’s *** this review,we briefly introduce the machinery of ***,we provide a description of the effects of Parkinson’s disease–related genes on the autophagy-lysosome ***,we highlight the potential chemical and genetic therapeutic strategies targeting the autophagy–lysosome pathway and their applications in Parkinson’s disease.

关键词： autophagy chemical therapy gene therapy Parkinson’s disease α-synuclein

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A quantum-search-based multi-classifier for image recognition

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Science China(Physics,Mechanics & Astronomy) 2025年第1期Mechanics & Astronomy) .卷 21-31页

作者： Lu Liu Xingyu Wu Lufan Zhang Chuan Wang School of Artificial Intelligence Beijing Normal University Applied Optics Beijing Area Major Laboratory Beijing Normal University

The multi-class classification of images is a pivotal challenge within the realm of image processing. As the volume of visual data continues to expand, there is a burgeoning interest in harnessing the unique capabilities of quantum computation to augment the efficiency of classification tasks. However, many existing methods for training quantum image multi-classifiers parallel classical machine learning techniques, where the requisite circuit measurements increase linearly with the volume of training data. This work introduces a novel approach for training a quantum image multi-classifier based on the quantum search algorithm. We have meticulously conducted rigorous experiments on a handwritten digit dataset, a classic benchmark in the field. The results have been meticulously compared with previous works, and the comparative analysis not only validates the efficiency of our proposed approach, requiring only O(N/b) measurements during training, but also highlights a significant quadratic speedup of the algorithm.

关键词： quantum computation image recognition quantum search algorithm

Synergistic effect of bonding heterogeneity and phonon localization in introducing excellent thermoelectric properties in layered heteroanionic NdZnSbO material

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Journal of Energy Chemistry 2025年第1期100卷 259-272页

作者： Da Wan Shuwei Tang Shulin Bai Xiaodong Li Peng Ai Wanrong Guo Tuo Zheng College of Materials Science and Engineering Liaoning Technical University Faculty of Chemistry Northeast Normal University

Layered rare-earth metal oxides, harnessing the dual properties of oxides and two-dimensional layered materials, exhibit remarkable thermal stability and quantum confinement effects. Therefore, this work adopts the first-principles calculation combined with the Boltzmann transport theory to predict the thermoelectric properties of NdZnSbO compound. The coexistence of weak interlayer van der Waals interactions, robust intralayer ionic bonding, and partial covalent bonding leads to remarkable bonding heterogeneity, which engenders pronounced phonon scattering and imposes constraints on thermal transport along the out-of-plane direction. The weakened chemical bonds induced by the antibonding states, together with the rattling-like behavior of the Zn atom, culminate in the profound anharmonicity in the layered NdZnSbO compound. The weakening bond and heavy element contribute to the softness of phonon modes, which significantly diminishes the phonon group velocity. The redistribution-dominated four-phonon scattering process spans a large optical gap, which effectively reduces the lattice thermal conductivity. The NdZnSbO compound exhibits direct semiconductor characteristic with a bandgap of 0.73 e V by adopting the Heyd-Scuseria-Ernzerhof(HSE06) functional in combination with spin–orbit coupling(SOC) effect. The multi-valley feature of NdZnSbO compound augur favorably for band degeneracy, thus amplifying the power factor. Consequently, an optimal figure-of-merit(ZT) of 3.40 at 900 K is achieved for the n-type NdZnSbO compound. The present study delves deeply insights into the origins for the low thermal conductivity of NdZnSbO compound and proposes an optimization scheme to enhance overall thermoelectric performance.

关键词： Bond strength (chemical)

Engine Misfire Fault Detection Based on the Channel Attention Convolutional Model

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Computers, Materials & Continua 2025年第1期82卷 843-862页

作者： Feifei Yu Yongxian Huang Guoyan Chen Xiaoqing Yang Canyi Du Yongkang Gong School of Mechatronic Engineering Guangdong Polytechnic Normal UniversityGuangzhou510665China School of Automotive and Transportation Engineering Guangdong Polytechnic Normal UniversityGuangzhou510665China

To accurately diagnosemisfire faults in automotive engines,we propose a Channel Attention Convolutional Model,specifically the Squeeze-and-Excitation Networks(SENET),for classifying engine vibration signals and precisely pinpointing misfire *** the experiment,we established a total of 11 distinct states,encompassing the engine’s normal state,single-cylinder misfire faults,and dual-cylinder misfire faults for different *** collection was facilitated by a highly sensitive acceleration signal collector with a high sampling rate of 20,*** collected data were methodically divided into training and testing sets based on different experimental groups to ensure generalization and prevent overlap between the two *** results revealed that,with a vibration acceleration sequence of 1000 time steps(approximately 50 ms)as input,the SENET model achieved a misfire fault detection accuracy of 99.8%.For comparison,we also trained and tested several commonly used models,including Long Short-Term Memory(LSTM),Transformer,and Multi-Scale Residual Networks(MSRESNET),yielding accuracy rates of 84%,79%,and 95%,*** underscores the superior accuracy of the SENET model in detecting engine misfire faults compared to other ***,the F1 scores for each type of recognition in the SENET model surpassed 0.98,outperforming the baseline *** analysis indicated that the misclassified samples in the LSTM and Transformer models’predictions were primarily due to intra-class misidentifications between single-cylinder and dual-cylinder misfire *** delve deeper,we conducted a visual analysis of the features extracted by the LSTM and SENET models using T-distributed Stochastic Neighbor Embedding(T-SNE)*** findings revealed that,in the LSTMmodel,data points of the same type tended to cluster together with significant ***,in the SENET model,data points of various types were more widely and evenly dispersed,demonstra

关键词： Channel attention SENET model engine misfire fault fault detection

Observation of topological charge transformations in acoustic vortex using passive periodic systems

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Science China(Physics,Mechanics & Astronomy) 2025年第1期Mechanics & Astronomy) .卷 140-147页

作者： Di-Chao Chen Xie Liu Changqing Xu Da-Jian Wu Ying Cheng Xiao-Jun Liu School of Physics and Technology Nanjing Normal University Key Laboratory of Modern Acoustics School of Physics Nanjing University

Vortex transmutation utilizes a periodic system, which not only enables the alteration of the topological charge(TC) of unstable input vortices but also facilitates their transformation into stable structures. This approach is crucial for investigating the characteristics of vortices in various wave systems. Here, we achieve acoustic vortex TC transmutation by employing a welldesigned dual-structured plate external system(DPES). The interactions between the acoustic vortex and the finite-order discrete rotational symmetry lead to the transformation of its topological charge when complete rotational symmetry is broken to Nthorder discrete symmetry. Our results experimentally demonstrate the acoustic vortex TC transmutation based on passive artificial structures, enabling numerous applications in particle manipulation and information multiplexing as well as advancing our understanding and control over free-space acoustic vortices.

关键词： acoustic vortex topological charge transformation artificial structure

MixerKT:A Knowledge Tracing Model Based on Pure MLP Architecture

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Computers, Materials & Continua 2025年第1期82卷 485-498页

作者： Jun Wang Mingjie Wang Zijie Li Ken Chen Jiatian Mei Shu Zhang Key Laboratory of Education Informatization for Nationalities Ministry of EducationYunnan Normal UniversityKunming650000China Yunnan Key Laboratory of Smart Education Yunnan Normal UniversityKunming650000China

In the field of intelligent education,the integration of artificial intelligence,especially deep learning technologies,has garnered significant *** tracing(KT)plays a pivotal role in this field by predicting students’future performance through the analysis of historical interaction data,thereby assisting educators in evaluating knowledgemastery and tailoring instructional *** knowledge tracingmethods,largely based on Recurrent Neural Networks(RNNs)and Transformer models,primarily focus on capturing long-term interaction patterns in sequential ***,these models may neglect crucial short-term dynamics and other relevant *** paper introduces a novel approach to knowledge tracing by leveraging a pure Multilayer Perceptron(MLP)*** proposeMixerKT,a knowledge tracing model based on theHyperMixer framework,which uniquely integrates global and localMixer feature *** architecture enables more effective extraction of both long-terminteraction trends and recent learning behaviors,addressing limitations in currentmodels thatmay overlook these key *** evaluations on twowidely-used datasets,ASSIS Tments2009 and Algebra2005,demonstrate that MixerKT consistently outperforms several state-of-the-art models,including DKT,SAKT,and Separated Self-Attentive Neural Knowledge Tracing(SAINT).Specifically,MixerKT achieves higher prediction accuracy,highlighting its effectiveness in capturing the nuances of learners’knowledge *** results indicate that our model provides a more comprehensive representation of student learning patterns,enhancing the ability to predict future performance with greater precision.

关键词： Knowledge tracing multilayer perceptron channel mixer sequence mixer

Recent development in electrode design for wide-temperature supercapacitors

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Science China Chemistry 2025年第1期68卷 96-117页

作者： Jianhong Liu Qiang Zhou Yuxiao Lin Xinsheng Zhao Guangmin Zhou Xiaoxiao Li School of Physics and Electronic Engineering Jiangsu Normal University Tsinghua-Berkeley Shenzhen Institute & Tsinghua Shenzhen International Graduate School Tsinghua University

An ever-increasing market demand has stimulated the soaring enthusiasm of researchers to develop wide-temperature supercapacitors(SCs). The active electrode is one of the most important parts of SC, which is directly related to the energy density,power transmission and long-term cyclability of the device in the wide-temperature environment. Compared with the SC electrodes aimed for room-temperature application, the SC electrodes for operating at wide-temperature scene often face greater challenges. In this review, the main challenges of SC electrodes under various temperature conditions, including low, high and cross-fade temperatures, are summarized. The relevant performance decay and failure mechanisms of wide-temperature SC electrodes are analyzed. In addition, this review deals with the recent studies and developments in robust wide-temperature SC electrodes with respect to the rational design of electrode structures and the exploitation of advanced active materials. Finally, the future directions for exploring reliable wide-temperature SCs are also proposed.

关键词： supercapacitor wide-temperature electrode design active materials

Attention Eraser and Quantitative Measures for Automated Bone Age Assessment

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Computers, Materials & Continua 2025年第1期82卷 627-644页

作者： Liuqiang Shu Lei Yu College of Computer and Information Science Chongqing Normal UniversityChongqing401331China

Bone age assessment(BAA)aims to determine whether a child’s growth and development are normal concerning their chronological *** predict bone age more accurately based on radiographs,and for the left-hand X-ray images of different races model can have better adaptability,we propose a neural network in parallel with the quantitative features from the left-hand bone measurements for *** this study,a lightweight feature extractor(LFE)is designed to obtain the featuremaps fromradiographs,and amodule called attention erasermodule(AEM)is proposed to capture the fine-grained ***,the dimensional information of the metacarpal parts in the radiographs is measured to enhance the model’s generalization capability across images fromdifferent *** is trained and validated on the RSNA,RHPE,and digital hand atlas datasets,which include images from various racial *** model achieves a mean absolute error(MAE)of 4.42 months on the RSNA dataset and 15.98 months on the RHPE *** to ResNet50,InceptionV3,and several state-of-the-art methods,our proposed method shows statistically significant improvements(p<0.05),with a reduction in MAE by 0.2±0.02 years across different racial ***,t-tests on the features also confirm the statistical significance of our approach(p<0.05).

关键词： Bone age assessment attention eraser quantitative feature metacarpal bones

Discovery of a new family member of arsenic–vanadium polyoxometalates with high catalytic performance for one-pot coupling of CO2, epoxides and amines

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Tungsten 2025年第1期7卷 80-87页

作者： Fang-Qian Wang Yin Zhang Wei-Wei Wang Mei-Qian Fu Hong-Bing Mo Wei-Dong Yu Chao Liu Jun Yan College of Chemistry and Chemical Engineering Central South University Junior Education Department Changsha Normal University School of Science Hunan University of Technology and Business

Arsenic-vanadium polyoxometalates（POMs） represent a family of tremendous potential catalysts owing to their peculiar physical and chemical *** in CO2conversion,using POMs to catalyze the conversion of CO2and epoxide into chemical intermediates is the research ***,A new family number of arsenic-vanadium POMs,[N（CH3）4]4[As8V12O38（H2O）]·2H2O({As8V12O38}),was reported and characterized by single-crystal X-ray diffraction,fourier transform infrared spectroscopy,X-ray photoelectron spectroscopy,*** from other allotropicity,{As8V12O38}shows a different structure pattern with an I-43d *** obtained {As8V12O38} was employed as a catalyst for the *** initial catalytic reaction confirmed that {As8V12O38} can catalyze the formation of epoxy carbonate by using 1.01 kPa CO2and epoxide at 70℃.Furtherly,when added amine in the catalytic system,oxazolidinone could be obtained by high yields,which provides an efficient way to convert CO2into profitable ***,a possible catalytic reaction mechanism was proposed by using different characterization analyses.{As8V12O38} first catalyzes the epoxide and CO2to form an epoxy carbonate and then catalyzes epoxy carbonate and benzylamine into oxazolidinone.

关键词： Arsenic CO2 conversion Oxazolidinone Polyoxometalates Vanadium

Transforming waste particles into valuable adsorbents via amyloid-mediated molecular engineering

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Science China Chemistry 2025年第1期68卷 317-328页

作者： Qingmin Yang Jian Zhao Yujia Zhang Xingyu Zhou Hao Ren Bowen Hu Zhongli Lei Lixin Chen Peng Yang School of Material and Chemical Engineering Bengbu University Anhui Provincial Engineering Laboratory of Silicon-Based Materials School of Chemistry and Chemical Engineering Northwestern Polytechnical University Key Laboratory of Applied Surface and Colloid Chemistry Ministry of Education School of Chemistry and Chemical EngineeringShaanxi Normal University Xi'an Key Laboratory of Polymeric Soft Matter School of Chemistry and Chemical Engineering Shaanxi Normal University International Joint Research Center on Functional Fiber and Soft Smart Textile School of Chemistry and Chemical EngineeringShaanxi Normal University

The high-value utilization of industrial solid waste using a facile and eco-friendly process is of great interest and significance in reducing environmental pollution and developing a green circular economy. Herein, we propose an amyloid-mediated molecular engineering strategy to transform particulate waste into valuable adsorbents for metal ions. Our method has the advantage of aqueous solution fabrication under mild conditions without the use of high-temperature hydrothermal methods and toxic chemical reagents. Amyloid-mediated molecular engineering manipulates the phase transition of bovine serum albumin(BSA)on particulate waste surfaces, resulting in a remarkable ～3.1 times improvement in the adsorption capacity of fly ash, a typical industrial solid waste for gold ions after modification with the phase-transitioned BSA(PTB). The resultant adsorption ability was 69–1,980 times higher than those of conventional and emerging adsorbent materials such as ion exchange resins, activated carbon(AC), covalent organic frameworks(COFs), and metal-organic frameworks(MOFs). We further demonstrated the application of our PTB-modified materials in the recovery of precious metals from low-grade gold ore and electronic waste leachates. Consequently, this strategy could increase the value of waste materials nearly 27 times. In addition, this method is generally extendable to other conventional industrial adsorbents such as resin, clay, and Al2O3, and enhances their adsorption capabilities at least twofold. Overall, this work provides a simple and green approach for improving the adsorption performance of solid particles, and is expected to develop into a universal strategy for transforming waste particles into high-value-added products.

关键词： amyloid-like protein aggregates surface engineering fly ash solid waste adsorption