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Identification of different degrees of processed ginger using GC-IMS combined with machine learning

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Journal of Pharmaceutical Analysis 2024年第1期14卷 149-151页

作者： Shuang Liu Hongjing Dong Minmin Zhang Wei Geng xiao wang Key Laboratory for Applied Technology of Sophisticated Analytical Instruments of Shandong Province Shandong Analysis and Test CenterQilu University of Technology(Shandong Academy of Sciences)Jinan250014China Key Laboratory for Natural Active Pharmaceutical Constituents Research in Universities of Shandong Province School of Pharmaceutical SciencesQilu University of Technology(Shandong Academy of Sciences)Jinan250014China Shandong Provincial Maternal and Child Health Care Hospital Affiliated to Qingdao University Jinan250014China

Ginger,the rhizomes of Zingiber officinale Roscoe,was a wellknown edible plant species commonly used in China,which has pungent flavor[1].Ginger has numerous chemical compounds,such as phenolic constituents,volatile compounds(VOCs),and polysaccharides[2].Among them,VOCs are considered one of the effective compounds in ginger due to their functional properties,including anti-inflammatory,antioxidant,and analgesic[3].

关键词： phenolic edible rhizome

Differentially-grown cobalt regulators cooperatively involved in the tandem catalysis for high-yield production of second amines

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Nano Research 2024年第4期17卷 2444-2450页

作者： Jinhui Xu xiao wang Ying wang Fei wang Lingling Zhang Wenjie Cui Shuyan Song Hongjie Zhang State Key Laboratory of Rare Earth Resource Utilization Changchun Institute of Applied ChemistryChinese Academy of SciencesChangchun 130022China School of Applied Chemistry and Engineering University of Science and Technology of ChinaHefei 230026China Department of Chemistry Tsinghua UniversityBeijing 100084China

One-pot tandem catalysis has been regarded as one of the most atomic economic ways to produce secondary amines,the important platform molecules for chemical synthesis and pharmaceutical manufacture,but it is facing serious issues in overall efficiency.New promotional effects are highly desired for boosting the activity and regulating the selectivity of conventional tandem catalysts.In this work,we report a high-performance tandem catalyst with maximized synergistic effect among each counterpart by preciously manipulating the spatial structure,which involves the active CeO_(2)/Pt component as kernel,the densely-coated N-doped C(NC)layer as selectivity controller,and the differentially-grown Co species as catalytic performance regulators.Through comprehensive investigations,the unique growth mechanism and the promotion effect of Co regulators are clarified.Specifically,the surface-landed Co clusters(Cocs)are crucial to selectivity by altering the adsorption configuration of benzylideneaniline intermediates.Meanwhile,the inner Co particles(Cops)are essential for activity by denoting their electrons to neighboring Ptps.Benefiting from the unique promotion effect,a remarkably-improved catalytic efficiency(100%nitrobenzene conversion with 94%N-benzylaniline selectivity)is achieved at a relatively low temperature of 80℃,which is much better than that of CeO_(2)/Pt(100%nitrobenzene conversion with 12%N-benzylaniline selectivity)and CeO_(2)/Pt/NC(35%nitrobenzene conversion with 94%benzylideneaniline selectivity).

关键词： tandem catalysis CeO_(2) hierarchical structure heterogeneous catalyst dual-site materials

Influence of crystal-transforming agent on the performance and mechanism ofα-high-strength gypsum prepared from FGD gypsum

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Particuology 2024年第4期87卷 37-45页

作者： Zhuoyue Xu xiao wang Biao Jin Dawang Zhang Hui Li College of Materials Science and Engineering Xi'an University of Architecture and TechnologyXi'an710055China Schoolof Materials and Chemical Engineering Henan University of Urban ConstructionPingdingshan46736China

This paper investigates the impact of flue gas desulfurization(FGD)gypsum's crystal modifier on the characteristics and microcosmic mechanism ofα-high strength gypsum.The results demonstrate that all three crystal modifiers can convert FGD gypsum toα-high-strength gypsum.Citric acid(CA)has the most significant influence onα-high-strength gypsum,and the preparedα-high-strength gypsum is short columnar,with an aspect ratio in the range of 1-3,and has a faster setting time,a larger specific surface area,and a smaller standard consistency,higher compressive strength,greater surface hardness,and smaller crystal particle size.The initial setting time of theα-high-strength gypsum manufactured with CA crystal modifier was decreased by 36%compared to the blank sample,the final setting time was lowered by 37.5%,and the water consumption of the standard consistency was reduced by 8%.The maximum strength is 32 MPa after 2 h,the absolute dry compressive strength is up to 38 MPa,and the surface hardness is improved by 24.43%.

关键词： Flue gas desulfurization gypsum Crystal modifier Microscopic mechanism α-high-strength gypsum

Industry-Oriented Detection Method of PCBA Defects Using Semantic Segmentation Models

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IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1438-1446页

作者： Yang Li xiao wang Zhifan He Ze wang Ke Cheng Sanchuan Ding Yijing Fan xiaotao Li Yawen Niu Shanpeng xiao Zhenqi Hao Bin Gao Huaqiang Wu Department of Internet of Things Technology and Application China Mobile Research InstituteBeijing 100053China Beijing Advanced Innovation Center for Integrated Circuits the School of Integrated CircuitTsinghua UniversityBeijing 100084China SigmaStar Technology Limited Xiamen 361199China IEEE

Automated optical inspection(AOI)is a significant process in printed circuit board assembly(PCBA)production lines which aims to detect tiny defects in PCBAs.Existing AOI equipment has several deficiencies including low throughput,large computation cost,high latency,and poor flexibility,which limits the efficiency of online PCBA inspection.In this paper,a novel PCBA defect detection method based on a lightweight deep convolution neural network is proposed.In this method,the semantic segmentation model is combined with a rule-based defect recognition algorithm to build up a defect detection frame-work.To improve the performance of the model,extensive real PCBA images are collected from production lines as datasets.Some optimization methods have been applied in the model according to production demand and enable integration in lightweight computing devices.Experiment results show that the production line using our method realizes a throughput more than three times higher than traditional methods.Our method can be integrated into a lightweight inference system and pro-mote the flexibility of AOI.The proposed method builds up a general paradigm and excellent example for model design and optimization oriented towards industrial requirements.

关键词： Automated optical inspection(AOI) deep learning defect detection printed circuit board assembly(PCBA) semantic segmentation.

Drug repurposing screening and mechanism analysis based on human colorectal cancer organoids

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Protein & Cell 2024年第4期15卷 285-304页

作者： Yunuo Mao Wei wang Jingwei Yang Xin Zhou Yongqu Lu Junpeng Gao xiao wang Lu Wen Wei Fu Fuchou Tang School of Life Sciences Biomedical Pioneering Innovation CenterDepartment of General SurgeryThird HospitalPeking UniversityBeijing 100871China Beijing Advanced Innovation Center for Genomics(ICG) Ministry of Education Key Laboratory of Cell Proliferation and DifferentiationBeijing 100871China Peking-Tsinghua Center for Life Sciences Peking UniversityBeijing 100871China Peking University Third Hospital Cancer Center Beijing 100871China The Research Center of Stem Cell and Regenerative Medicine School of Basic Medical SciencesCheeloo College of MedicineShandong UniversityJinan 250012China

Colorectal cancer(CRC)is a highly heterogeneous cancer and exploring novel therapeutic options is a pressing issue that needs to be addressed.Here,we established human CRC tumor-derived organoids that well represent both morphological and molecular heterogeneities of original tumors.To efficiently identify repurposed drugs for CRC,we developed a robust organoid-based drug screening system.By combining the repurposed drug library and computation-based drug prediction,335 drugs were tested and 34 drugs with anti-CRC effects were identified.More importantly,we conducted a detailed transcriptome analysis of drug responses and divided the drug response signatures into five representative patterns:differentiation induction,growth inhibition,metabolism inhibition,immune response promotion,and cell cycle inhibition.The anticancer activities of drug candidates were further validated in the established patient-derived organoids-based xenograft(PDOX)system in vivo.We found that fedratinib,trametinib,and bortezomib exhibited effective anticancer effects.Furthermore,the concordance and discordance of drug response signatures between organoids in vitro and pairwise PDOX in vivo were evaluated.Our study offers an innovative approach for drug discovery,and the representative transcriptome features of drug responses provide valuable resources for developing novel clinical treatments for CRC.

关键词： colorectal cancer organoids drug repurposing patient-derived organoids-based xenograft mechanism of action

Detection Algorithm of Laboratory Personnel Irregularities Based on Improved YOLOv7

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Computers, Materials & Continua 2024年第2期78卷 2741-2765页

作者： Yongliang Yang Linghua Xu Maolin Luo xiao wang Min Cao School of Electrical Engineering Guizhou UniversityGuiyang550025China

Due to the complex environment of the university laboratory,personnel flow intensive,personnel irregular behavior is easy to cause security risks.Monitoring using mainstream detection algorithms suffers from low detection accuracy and slow speed.Therefore,the current management of personnel behavior mainly relies on institutional constraints,education and training,on-site supervision,etc.,which is time-consuming and ineffective.Given the above situation,this paper proposes an improved You Only Look Once version 7(YOLOv7)to achieve the purpose of quickly detecting irregular behaviors of laboratory personnel while ensuring high detection accuracy.First,to better capture the shape features of the target,deformable convolutional networks(DCN)is used in the backbone part of the model to replace the traditional convolution to improve the detection accuracy and speed.Second,to enhance the extraction of important features and suppress useless features,this paper proposes a new convolutional block attention module_efficient channel attention(CBAM_E)for embedding the neck network to improve the model’s ability to extract features from complex scenes.Finally,to reduce the influence of angle factor and bounding box regression accuracy,this paper proposes a newα-SCYLLA intersection over union(α-SIoU)instead of the complete intersection over union(CIoU),which improves the regression accuracy while increasing the convergence speed.Comparison experiments on public and homemade datasets show that the improved algorithm outperforms the original algorithm in all evaluation indexes,with an increase of 2.92%in the precision rate,4.14%in the recall rate,0.0356 in the weighted harmonic mean,3.60%in the mAP@0.5 value,and a reduction in the number of parameters and complexity.Compared with the mainstream algorithm,the improved algorithm has higher detection accuracy,faster convergence speed,and better actual recognition effect,indicating the effectiveness of the improved algorithm in this paper and its potential for practical application

关键词： University laboratory personnel behavior YOLOv7 deformable convolutional networks attention module intersection over union

Composite laminar membranes for electricity generation from water evaporation

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Nano Research 2024年第1期17卷 307-311页

作者： xiao wang Gang Yuan Han Zhou Yu Jiang Shuo wang Jiaojiao Ma Chongyang Yang Sheng Hu State Key Laboratory of Physical Chemistry of Solid Surfaces Collaborative Innovation Center of Chemistry for Energy Materials(iChEM)College of Chemistry and Chemical EngineeringXiamen UniversityXiamen 361005China Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province(IKKEM) Xiamen 361005China Pen-Tung Sah Institute of Micro-Nano Science and Technology Xiamen UniversityXiamen 361005China

Harvesting clean energy from water evaporation has been extensively investigated due to its sustainability.To achieve high efficiency,energy conversion materials should contain multiple features which are difficult to be simultaneously obtained from single-component materials.Here we use composite laminar membranes assembled by nanosheets of graphene oxide and mica,and find a sustained power density induced by water evaporation that is two orders of magnitude larger than that from membranes made by either of the components.The power output is attributed to selective proton transport driven by water evaporation through the interlayer nanochannels in the membranes.This process relies on the synergistic effects from negatively charged and hydrophilic mica surfaces that are important for proton selectivity and water transport,and the tunable electrical conductivity of graphene oxide that provides optimized internal resistance.The demonstrated composite membranes offer a strategy of enhancing power generation by combining the advantages from each of their components.

关键词： graphene oxide mica water evaporation electrical double layer surface charge ion selectivity

S1-supported Pd@CeO_(2) quasi-core@shell materials as advanced catalysts for selective hydrogenation of furfural

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Chinese Chemical Letters 2024年第3期35卷 493-496页

作者： Xiang Chu Lingling Zhang Ke wang Rui Zhang xiao wang Shuyan Song Hongjie Zhang State Key Laboratory of Rare Earth Resource Utilization Changchun Institute of Applied ChemistryChinese Academy of SciencesChangchun 130022China School of Applied Chemistry and Engineering University of Science and Technology of ChinaHefei 230026China

Bare Pd metal nanoparticles invariably suffer from poor selectivity in furfural hydrogenation by forming flat configurations,with the aromatic ring of the substrate molecules parallel to the metal surface.Herein,we put forward a promising solution by using CeO_(2)as promoters to modify Pd nanoparticles for modulating the adsorption behaviors of furfural molecules.To achieve the highly-desired ultra-small Pd@CeO_(2)core@shell nanostructure,a“constrained auto-redox”synthesis is developed,in which silicalite-1 supports play the key role of providing their surface as the landing place of PdO_(x)precursors for inhibiting the overgrowth and the deformation.To the best of our knowledge,this is one of the smallest core@shell materials obtained from aqueous synthesis.When evaluated as catalysts,Pd@CeO_(2)/S-1 gives 98.9%conversion of furfural with 94.3%selectivity for furfural alcohol in 15 h,which is much better than that of Pd/S-1(88.6%conversion with 44.3%selectively).The DFT simulation reveals a strong interaction between the defects of CeO_(2)and the oxygen atom of the–CHO group in furfural molecules,which benefits the selective hydrogenation occurred in the–CHO group rather than the furan ring.

关键词： Core@shell Palladium Ceria Auto-redox synthesis Hydrogenation

Microscopic defects formation and dynamic mechanical response analysis of Q345 steel plate subjected to explosive load

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Defence Technology（防务技术） 2024年第2期32卷 430-442页

作者： Zhengqing Zhou Zechen Du Yulong Zhang Guili Yang Ruixiang wang Yuzhe Liu Peize Zhang Yaxin Zhang xiao wang Research Institute of Macro-Safety Science University of Science and TechnologyBeijing100083China School of Civil and Resource Engineering University of Science and TechnologyBeijing100083China China North Industry Advanced Technology Generalization Institute Beijing100089China College of Field Engineering PLA Army Engineering UniversityNanjing210007China School of Mechanical Engineering Nantong UniversityNantong226019China

As the basic protective element, steel plate had attracted world-wide attention because of frequent threats of explosive loads. This paper reports the relationships between microscopic defects of Q345 steel plate under the explosive load and its macroscopic dynamics simulation. Firstly, the defect characteristics of the steel plate were investigated by stereoscopic microscope(SM) and scanning electron microscope(SEM). At the macroscopic level, the defect was the formation of cave which was concentrated in the range of 0-3.0 cm from the explosion center, while at the microscopic level, the cavity and void formation were the typical damage characteristics. It also explains that the difference in defect morphology at different positions was the combining results of high temperature and high pressure. Secondly, the variation rules of mechanical properties of steel plate under explosive load were studied. The Arbitrary Lagrange-Euler(ALE) algorithm and multi-material fluid-structure coupling method were used to simulate the explosion process of steel plate. The accuracy of the method was verified by comparing the deformation of the simulation results with the experimental results, the pressure and stress at different positions on the surface of the steel plate were obtained. The simulation results indicated that the critical pressure causing the plate defects may be approximately 2.01 GPa. On this basis, it was found that the variation rules of surface pressure and microscopic defect area of the Q345 steel plate were strikingly similar, and the corresponding mathematical relationship between them was established. Compared with Monomolecular growth fitting models(MGFM) and Logistic fitting models(LFM), the relationship can be better expressed by cubic polynomial fitting model(CPFM). This paper illustrated that the explosive defect characteristics of metal plate at the microscopic level can be explored by analyzing its macroscopic dynamic mechanical response.

关键词： Explosive load Q345 steel Micro defect Finite element simulation Dynamic response Data fitting