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Automatic recognition of depression based on audio and video:A review

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World Journal of Psychiatry 2024年第2期14卷 225-233页

作者： Meng-Meng han Xing-Yun Li Xin-Yu yi Yun-Shao Zheng wei-Li Xia Ya-Fei Liu Qing-Xiang Wang Shandong Mental Health Center Shandong UniversityJinan 250014Shandong ProvinceChina Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center(National Supercomputer Center in Jinan)Qilu University of Technology(Shandong Academy of Sciences)Jinan 250353Shandong ProvinceChina Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and TechnologyQilu University of Technology(Shandong Academy of Sciences)Jinan 250353Shandong ProvinceChina Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer ScienceJinan 250353Shandong ProvinceChina Department of Ward Two Shandong Mental Health CenterShandong UniversityJinan 250014Shandong ProvinceChina

Depression is a common mental health *** current depression detection methods,specialized physicians often engage in conversations and physiological examinations based on standardized scales as auxiliary measures for depression ***-biological markers-typically classified as verbal or non-verbal and deemed crucial evaluation criteria for depression-have not been effectively *** physicians usually require extensive training and experience to capture changes in these *** in deep learning technology have provided technical support for capturing non-biological *** researchers have proposed automatic depression estimation(ADE)systems based on sounds and videos to assist physicians in capturing these features and conducting depression *** article summarizes commonly used public datasets and recent research on audio-and video-based ADE based on three perspectives:Datasets,deficiencies in existing research,and future development directions.

关键词： Depression recognition Deep learning Automatic depression estimation System Audio processing Image processing Feature fusion Future development

A data-driven approach to RUL prediction of tools

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Advances in Manufacturing 2024年第1期12卷 6-18页

作者： wei Li Liang-Chi Zhang Chu-han Wu Yan Wang Zhen-Xiang Cui Chao Niu Department of Mechanical Engineering University College LondonLondonWC1E 7JEUK Shenzhen Key Laboratory of Cross-Scale Manufacturing Mechanics Southern University of Science and TechnologyShenzhen518055GuangdongPeople’s Republic of China SUSTech Institute for Manufacturing Innovation Southern University of Science and TechnologyShenzhen518055GuangdongPeople’s Republic of China Department of Mechanics and Aerospace Engineering Southern University of Science and TechnologyShenzhen518055GuangdongPeople’s Republic of China School of Mechanical and Manufacturing Engineering The University of New South WalesKensingtonNSW2052Australia Baoshan Iron&Steel Co. Ltd.Shanghai200941People’s Republic of China

An effective and reliable prediction of the remaining useful life(RUL)of a tool is important to a metal forming process because it can significantly reduce unexpected maintenance,avoid machine shutdowns and increase system *** study proposes a new data-driven approach to the RUL prediction for metal forming processes under multiple contact sliding *** data-driven approach took advantage of bidirectional long short-term memory(BLSTM)and convolutional neural networks(CNN).A pre-trained lightweight CNN-based network,WearNet,was re-trained to classify the wear states of workpiece surfaces with a high accuracy,then the classification results were passed into a BLSTM-based regression model as inputs for RUL *** experimental results demonstrated that this approach was able to predict the RUL values with a small error(below 5%)and a low root mean square error(RMSE)(around 1.5),which was more superior and robust than the other state-of-the-art methods.

关键词： Remaining useful life(RUL) Bidirectional long short-term memory(BLSTM) Data-driven approach Metal forming

Low-temperature graphitization of lignin via Co-assisted electrolysis in molten salt

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Green Energy & Environment 2024年第9期9卷 1449-1458页

作者： Shijie Li wei-Li Song Xue han Qingqing Cui Yan-li Zhu Shuqiang Jiao Institute of Advanced Structure Technology Beijing Institute of Technology State Key Laboratory of Advanced Metallurgy University of Science and Technology Beijing State Key Laboratory of Explosion Science and Technology Beijing Institute of Technology

The ever-growing energy demand and environmental issues have stimulated the development of sustainable energy ***,an efficient and environmentally friendly electrochemical transformation technology was proposed to prepare highly graphitized carbon materials from an abundant natural resource-lignin （LG）.The preparation process mainly includes pyrolytic carbonization of raw LG material and electrochemical conversion of amorphous carbon ***,with the assistance of Co catalyst,the graphitization degree of the products was significantly improved,in which the mechanism was the removal of heteroatoms in LG and the rearrangement of carbon atoms into graphite ***,tunable microstructures （nanoflakes） under catalytic effects could also be observed by controlling the electrolytic *** with the products CN1 （without catalyst） and CN5 （with 10%catalyst）,the specific surface area are 158.957 and202.246 m2g-1,*** used as the electrode material for lithium-ion batteries,CN5 delivered a competitive specific capacity of～350 m Ah g-1（0.5 C） compared with commercial *** strategy proposed in this work provides an effective way to extract value-added graphite materials from lignin and can be extended to the graphitization conversion of any other amorphous carbon precursor materials.

关键词： Lignin Graphitic carbon Electrochemical conversion Lithium-ion batteries

Broadening environmental research in the era of accurate protein structure determination and predictions

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Frontiers of Environmental Science & Engineering 2024年第7期18卷 169-178页

作者： Mingda Zhou Tong Wang Ke Xu han Wang Zibin Li wei-xian Zhang Yayi Wang State Key Laboratory of Pollution Control and Resources Reuse Shanghai Institute of Pollution Control and Ecological SecurityCollege of Environmental Science and EngineeringTongji UniversityShanghai 200092China Translational Research Institute of Brain and Brain-Like Intelligence Shanghai Fourth People's HospitalSchool of MedicineTongji UniversityShanghai 200434China

The deep-learning protein structure prediction method AlphaFold2 has garnered enormous attention beyond the realm of structural biology,for its groundbreaking contribution to solving the"protein foiding problem"In this perspective,we explore the connection between protein structure studies and environmental research,delving into the potential for addressing specific environmental *** are promising for environmental applications because of the functional diversity endowed by their structural ***,structural studies on proteins with environmental significance remain ***,we present the opportunity to study proteins by advancing experimental determination and deep-learning prediction ***,the latest progress in environmental research via cryogenic electron microscopy is *** allows us to determine the structure of protein complexes in their native state within cells at molecular resolution,revealing environmentally-associated structural *** the remarkable advancements in computational power and experimental resolution,the study of protein structure and dynamics has reached unprecedented depth and *** advancements will undoubtedly accelerate the establishment of comprehensive environmental protein structural and functional *** opportunities for protein engineering exist to enable innovative solutions for environmental applications,such as the degradation of persistent contaminants,and the recovery of valuable metals as well as rare earth elements.

关键词： Environmental proteins Protein structure Cryogenic electron microscopy Protein structure prediction Protein engineering Artificial Intelligence

Precise Planar-Twisted Molecular Engineering to Construct Semiconducting Polymers with Balanced Absorption and Quantum yield for Efficient Phototheranostics

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Research 2024年第2期2023卷 227-238页

作者： Xiang Su Zhirong Bao wei Xie Deliang Wang Ting han Dong Wang Ben Zhong Tang Center for AIE Research Shenzhen Key Laboratory of Polymer Science and TechnologyGuangdong Research Center for Interfacial Engineering of Functional MaterialsCollege of Materials Science and EngineeringShenzhen UniversityShenzhen 518060China School of Biomedical and Pharmaceutical Sciences Guangdong University of TechnologyGuangzhou 510006China Department of Radiation and Medical Oncology Hubei Key Laboratory of Tumor Biological BehaviorsHubei Cancer Clinical Study CenterZhongnan Hospital of Wuhan UniversityWuhan 430071China Department of Materials Chemistry Huzhou UniversityHuzhou 313000China School of Science and Engineering Shenzhen Institute of Aggregate Science and TechnologyThe Chinese University of Hong KongShenzhenGuangdong 518172China

Semiconducting polymers(SPs)have shown great feasibility as candidates for near-infrared-II(NIR-Ⅱ)fluorescence imaging-navigated photothermal therapy due to their strong light-harvesting ability and flexible ***,the fluorescence signal of traditional SPs tends to quench in their aggregate states owing to the strongπ-πstacking,which can lead to the radiative decay pathway shutting *** address this issue,aggregation-induced emission effect has been used as a rational tactic to boost the aggregate-state fluorescence of NIR-Ⅱ*** this contribution,we developed a precise molecular engineering tactic based on the block copolymerizations that integrate planar and twisted segments into one conjugated polymer backbone,providing great flexibility in tuning the photophysical properties and photothermal conversion capacity of *** monomers featured with twisted and planar architectures,respectively,were tactfully incorporated via a ternary copolymerization approach to produce a series of new *** optimal copolymer(SP2)synchronously shows desirable absorption ability and good NIR-Ⅱquantum yield on the premise of maintaining typical aggregation-induced emission characteristics,resulting in balanced NIR-Ⅱfluorescence brightness and photothermal ***-dispersible nanoparticles fabricated from the optimal SP2 show efficient photothermal therapeutic effects both in vitro and in *** in vivo investigation reveals the distinguished NIR-Ⅱfluorescence imaging performance of SP2 nanoparticles and their photothermal ablation toward tumor with prominent tumor accumulation ability and excellent biocompatibility.

关键词： property conducting twisted

Facet effects on bimetallic ZnSn hydroxide microcrystals for selective electrochemical CO2 reduction

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Green Energy & Environment 2024年第8期9卷 1314-1320页

作者： Liu han Cheng-wei Wang Shan-shan Luo ying-tang Zhou Bing Li Ming Liu School of Chemistry and Chemical Engineering Harbin Institute of Technology National Engineering Research Center for Marine Aquaculture Marine Science and Technology CollegeZhejiang Ocean University

Employing crystal facets to regulate the catalytic properties in electrocatalytic carbon dioxide reduction reaction（eCO2RR） has been well demonstrated on electrocatalysts containing single metals but rarely explored for bimetallic systems. Here, we synthesize Zn Sn（OH）6（ZSO）microcrystals（MCs） with distinct facets and investigate the facet effects in eCO2RR. Electrochemical studies and in situ Fourier Transform Infrared Spectroscopy（in situ-FTIR） reveal that ZSO MCs produce mainly C1 products of HCOOH and CO. The {111} facet of the ZSO MCS exhibits higher selectivity and faradaic efficiency（FE） than that of the {100} facet over a wide range of potentials（-0.9 V ～-1.3 V versus RHE）. Density Functional Theory（DFT） calculations elucidate that the {111} facet is favorable to the adsorption/activation of CO2molecules,the formation of intermediate in the rate-determining step, and the desorption of C1 products of CO and HCOOH molecules.

关键词： ZnSn(OH) 6 Crystal facets Electrochemical carbon dioxide reduction C1 product Density functional theory

Crystal phase control and ignition properties of HNS/CL-20 composite microspheres prepared by microfluidics combined with emulsification techniques

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Particuology 2024年第2期85卷 241-251页

作者： yi Liu Yunyan Guo Rui Zhu Jinqiang Zhou Zhengxu Lin Kai han Chongwei An Jingyu Wang Bidong Wu School of Environment and Safety Engineering North University of ChinaTaiyuan030051China School of Engineering Beijing Institute of TechnologyBeijing100081China Shanxi Province Ultrafine Powder Engineering Technology Research Center Taiyuan030051China

Improved controllability and energy density of ignition agents are of great significance for the devel-opment of energetic composite *** this study,droplet microfluidics and emulsification tech-niques were combined to prepare HNS/CL-20 composite microspheres with polyglycidyl azide polymer(GAP)as the *** influence of binder content on the morphology of microspheres was investi-gated,and the microspheres were characterized and tested for particle size,crystal structure,thermal decomposition,dispersibility,mechanical sensitivity,combustion behavior and detonation *** results showed that microspheres prepared with a binder content of 3%had higher sphericity and particle size *** microspheres retained the crystal structure of both HNS and CL-20(ε-type).Compared with raw HNS,the microspheres had higher apparent activation energy,better safety per-formance,and good *** ignition experiments and detonation performance tests show that HNS/CL-20 composite microspheres have excellent ignition performance,obvious combustion flame,and significant energy release effects,which are expected to achieve high energy and high-speed response of the igniter,thus improving the ignition reliability in special environments or systems.

关键词： Droplet microfluidics Emulsification technology Energetic materials Composite microspheres Crystal phase control Ignition

Macroscopic polarization enhancement boosting piezo-photocatalytic performance via Nb-doping on B-site of Bi_(4)Ti_(3)O_(12)nanosheets

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Journal of Advanced Ceramics 2024年第4期13卷 437-446页

作者： Hongjian Yu Xindong wei Min Wang Yan Zhang Zheng Wu Fan Guo Jie han School of Chemistry and Chemical Engineering Yangzhou UniversityYangzhou 225002China Engineering Research Center of Ministry of Education for Geological Carbon Storage and Low Carbon Utilization of Resources Beijing Key Laboratory of Materials Utilization of Nonmetallic Minerals and Solid WastesNational Laboratory of Mineral MaterialsSchool of Material Sciences and TechnologyChina University of Geosciences(Beijing)Beijing 100083China School of Environmental and Chemical Engineering Xi’an Key Laboratory of Textile Chemical Engineering AuxiliariesXi'an Polytechnic UniversityXi'an 710048China

The development of a high-performance ferroelectric piezo-photocatalyst is an efficient strategy for advancing sustainability within the environmental and energy ***,a major challenge lies in the creation of a strong polarized electric field that can effectively hinder charge recombination,both within the bulk and on the surface of ***,we synthesize a series of Nb-doped Bi_(4)Ti_(3)O_(12)nanosheets via a facile one-pot hydrothermal method to achieve synergistically enhanced piezo-photocatalytic performance in CO_(2)reduction and pollutant *** optimized doped Bi_(4)Ti_(3)O_(12)demonstrates remarkable efficiency in the conversion of CO_(2)into CO,with a high production rate of 72.7μmol∙g−1∙h−1 without using co-catalysts or any sacrificial agent,surpassing the performance of unmodified Bi_(4)Ti_(3)O_(12)by up to 4.69 ***,our catalyst demonstrates ultra-fast piezo-photocatalytic degradation of organic pollutant Rhodamine B(RhB)at low concentrations and exceptional piezo-photocatalytic activity at high concentrations,outperforming most previously reported state-of-the-art *** systematic corroboration of catalyst characterization and experimental analysis reveals that the synergistic effect arises from the amplified macroscopic polarization induced by lattice distortion caused by the larger Nb ions,thereby improving piezo-photocatalytic *** research thus offers valuable insights into the direct design and fabrication of versatile catalytic systems,with applications spanning CO_(2)valorization and beyond.

关键词： CO_(2)reduction dye degradation piezo-photocatalysis Nb-doped Bi_(4)Ti_(3)O_(12) macroscopic polarization regulation

TSC prediction and dynamic control of BOF steelmaking with state-of-the-art machine learning and deep learning methods

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Journal of Iron and Steel Research International 2024年第1期31卷 174-194页

作者： Tian-yi Xie Cai-dong Zhang Quan-lin Zhou Zhi-qiang Tian Shuai Liu han-jie Guo Material Technology Research Institute Hesteel GroupShijiazhuang050023HebeiChina Tangsteel Company Hesteel GroupTangshan063611HebeiChina School of Artificial Intelligence Beijing Technology and Business UniversityBeijing100048China School of Metallurgical and Ecological Engineering University of Science and Technology BeijingBeijing100083China

Mathematical(data-driven)models based on state-of-the-art(SOTA)machine learning and deep learning models and data collected from 12,786 heats were established to predict the values of temperature,sample,and carbon(TSC)test,including temperature of molten steel(TSC-Temp),carbon content(TSC-C)and phosphorus content(TSC-P),which made prepa-ration for eliminating the TSC *** maximize the prediction accuracy of the proposed approach,various models with different inputs were implemented and compared,and the best models were applied to the production process of a Hesteel Group steelmaking plant in China in the *** number of tabular features(hot metal information,scrap,additives,blowing practices,and preset values)was expanded,and time series(off-gas profiles and blowing practice curves)that could reflect the entire steelmaking process were introduced as ***,the latest machine learning models(LightGBM,CatBoost,TabNet,and NODE)were used to make predictions with tabular features,and the best coefficient of determination R^(2)values obtained for TSC-P,TSC-C and TSC-Temp predictions were 0.435(LightGBM),0.857(Cat-Boost)and 0.678(LightGBM),respectively,which were higher than those of classic models(backpropagation and support vector machine).Then,making predictions was performed by using SOTA time series regression models(SCINet,DLinear,Informer,and MLSTM-FCN)with original time series,SOTA image regression models(NesT,CaiT,ResNeXt,and GoogLeNet)with resized time series,and the proposed Concatenate-Model and Parallel-Model with both tabular features and time *** optimization and comparisons,it was finally determined that the Concatenate-Model with MLSTM-FCN,SCINet and Informer as feature extractors performed the best,and its R^(2)values for predicting TSC-P,TSC-C and TSC-Temp reached 0.470,0.858 and 0.710,*** field test accuracies for TSC-P,TSC-C and TSC-Temp were 0.459,0.850 and 0.685,respectively.A related importance analysis was carri

关键词： BOF steelmaking In-blow prediction TSC test Machine learning Deep learning Field application