We investigated the parametric optimization on incremental sheet forming of stainless steel using Grey Relational Analysis(GRA) coupled with Principal Component Analysis(PCA). AISI 316L stainless steel sheets were use...We investigated the parametric optimization on incremental sheet forming of stainless steel using Grey Relational Analysis(GRA) coupled with Principal Component Analysis(PCA). AISI 316L stainless steel sheets were used to develop double wall angle pyramid with aid of tungsten carbide tool. GRA coupled with PCA was used to plan the experiment conditions. Control factors such as Tool Diameter(TD), Step Depth(SD), Bottom Wall Angle(BWA), Feed Rate(FR) and Spindle Speed(SS) on Top Wall Angle(TWA) and Top Wall Angle Surface Roughness(TWASR) have been studied. Wall angle increases with increasing tool diameter due to large contact area between tool and workpiece. As the step depth, feed rate and spindle speed increase,TWASR decreases with increasing tool diameter. As the step depth increasing, the hydrostatic stress is raised causing severe cracks in the deformed surface. Hence it was concluded that the proposed hybrid method was suitable for optimizing the factors and response.展开更多
Ore production is usually affected by multiple influencing inputs at open-pit mines.Nevertheless,the complex nonlinear relationships between these inputs and ore production remain unclear.This becomes even more challe...Ore production is usually affected by multiple influencing inputs at open-pit mines.Nevertheless,the complex nonlinear relationships between these inputs and ore production remain unclear.This becomes even more challenging when training data(e.g.truck haulage information and weather conditions)are massive.In machine learning(ML)algorithms,deep neural network(DNN)is a superior method for processing nonlinear and massive data by adjusting the amount of neurons and hidden layers.This study adopted DNN to forecast ore production using truck haulage information and weather conditions at open-pit mines as training data.Before the prediction models were built,principal component analysis(PCA)was employed to reduce the data dimensionality and eliminate the multicollinearity among highly correlated input variables.To verify the superiority of DNN,three ANNs containing only one hidden layer and six traditional ML models were established as benchmark models.The DNN model with multiple hidden layers performed better than the ANN models with a single hidden layer.The DNN model outperformed the extensively applied benchmark models in predicting ore production.This can provide engineers and researchers with an accurate method to forecast ore production,which helps make sound budgetary decisions and mine planning at open-pit mines.展开更多
Lightweight thin-walled structures with lattice infill are widely desired in satellite for their high stiffness-to-weight ratio and superior buckling strength resulting fromthe sandwich effect.Such structures can be f...Lightweight thin-walled structures with lattice infill are widely desired in satellite for their high stiffness-to-weight ratio and superior buckling strength resulting fromthe sandwich effect.Such structures can be fabricated bymetallic additive manufacturing technique,such as selective laser melting(SLM).However,the maximum dimensions of actual structures are usually in a sub-meter scale,which results in restrictions on their appliance in aerospace and other fields.In this work,a meter-scale thin-walled structure with lattice infill is designed for the fuel tank supporting component of the satellite by integrating a self-supporting lattice into the thickness optimization of the thin-wall.The designed structure is fabricated by SLM of AlSi10Mg and cold metal transfer welding technique.Quasi-static mechanical tests and vibration tests are both conducted to verify the mechanical strength of the designed large-scale lattice thin-walled structure.The experimental results indicate that themeter-scale thin-walled structure with lattice infill could meet the dimension and lightweight requirements of most spacecrafts.展开更多
Chinese wingnut(Pterocarya stenoptera)is a medicinally and economically important tree species within the family Juglandaceae.However,the lack of high-quality reference genome has hindered its in-depth research.In thi...Chinese wingnut(Pterocarya stenoptera)is a medicinally and economically important tree species within the family Juglandaceae.However,the lack of high-quality reference genome has hindered its in-depth research.In this study,we successfully assembled its chromosome-level genome and performed multiomics analyses to address its evolutionary history and synthesis of medicinal components.A thorough examination of genomes has uncovered a significant expansion in the Lateral Organ Boundaries Domain gene family among the winged group in Juglandaceae.This notable increase may be attributed to their frequent exposure to flood-prone environments.After further differentiation between Chinese wingnut and Cyclocarya paliurus,significant positive selection occurred on the genes of NADH dehydrogenase related to mitochondrial aerobic respiration in Chinese wingnut,enhancing its ability to cope with waterlogging stress.Comparative genomic analysis revealed Chinese wingnut evolved more unique genes related to arginine synthesis,potentially endowing it with a higher capacity to purify nutrient-rich water bodies.Expansion of terpene synthase families enables the production of increased quantities of terpenoid volatiles,potentially serving as an evolved defense mechanism against herbivorous insects.Through combined transcriptomic and metabolomic analysis,we identified the candidate genes involved in the synthesis of terpenoid volatiles.Our study offers essential genetic resources for Chinese wingnut,unveiling its evolutionary history and identifying key genes linked to the production of terpenoid volatiles.展开更多
In this study,umami taste intensity(UTI)and umami taste components in chicken breast(CB)and chickenspices blends were characterized using sensory and instrumental analysis.Our main objective was to assess the aroma-um...In this study,umami taste intensity(UTI)and umami taste components in chicken breast(CB)and chickenspices blends were characterized using sensory and instrumental analysis.Our main objective was to assess the aroma-umami taste interactions in different food matrices and reconcile the aroma-taste perception to assist future product development.The impact of key aroma,including vegetable-note"2-pentylfuran",meaty"methional",green"hexanal",and spicy-note-estragole and caryophyllene"on UTI was evaluated in monosodium glutamate and chicken extract.We found that spices significantly decreased UTI and umami taste components in CB.Interestingly,the perceptually similar odorants and tastants exhibited the potential to enhance UTI in food matrices.Methional was able to increase the UTI,whereas spicy and green-note components could reduce the UTI significantly.This information would be valuable to food engineers and formulators in aroma selection to control the UTI perceived by consumers,thus,improving the quality and acceptability of the chicken products.展开更多
Andrias davidianus(Chinese giant salamander,CGS)is the largest and oldest extant amphibian species in the world and is a source of prospective functional food in China.However,the progress of functional peptides minin...Andrias davidianus(Chinese giant salamander,CGS)is the largest and oldest extant amphibian species in the world and is a source of prospective functional food in China.However,the progress of functional peptides mining was slow due to lack of reference genome and protein sequence data.In this study,we illustrated full-length transcriptome sequencing to interpret the proteome of CGS meat and obtain 10703 coding DNA sequences.By functional annotation and amino acid composition analysis,we have discovered various genes related to signal transduction,and 16 genes related to longevity.We have also found vast variety of functional peptides through protein coding sequence(CDS)analysis by comparing the data obtained with the functional peptide database.Val-Pro-Ile predicted by the CDS analysis was released from the CGS meat through enzymatic hydrolysis,suggesting that our approach is reliable.This study suggested that transcriptomic analysis can be used as a reference to guide polypeptide mining in CGS meat,thereby providing a powerful mining strategy for the bioresources with unknown genomic and proteomic sequences.展开更多
Background:The active components of Horcha-6 were identified using liquid chromatography with tandem mass spectrometry.Also,we investigated the potential mechanisms that explain why Horcha-6 may be effective in treati...Background:The active components of Horcha-6 were identified using liquid chromatography with tandem mass spectrometry.Also,we investigated the potential mechanisms that explain why Horcha-6 may be effective in treating migraines through the use of network pharmacology and a rat migraine model.Methods:After identifying the active components of Horcha-6,the corresponding genes of the active components’target were obtained from the Universal Protein database,and a“compound-target-disease”network was constructed using Cytoscape 3.9.0 software.For the in vivo experiments,nitroglycerin was injected intraperitoneally into rats to create a migraine model.Pre-treatment with Horcha-6 was administered orally for 14 days,and rats were subjected to migraine-related behavior tests.RNA sequencing was performed to identify the gene expression regulated by Horcha-6 in the trigeminal nerve.Results:A total of 903 chemical components of Horcha-6 have been collected in the liquid chromatography with tandem mass spectrometry.We discovered 55 of the Horcha-6 bio-active components that were evaluated based on their Percent Human Oral Absorption(≥30%)and DL values(≥0.185)on the traditional Chinese medicine systems pharmacology database.The“compound-target-disease”network contained 163 intersection targets with the migraine state.Gene Ontology analysis indicated that these components significantly regulated the immune response,vascular function,oxidative stress,etc.When Kyoto Encyclopedia of Genes and Genomes enrichment analysis was performed,we observed that most of the target genes were significantly enriched in the inflammation and neuro-related signaling pathway,toll-like receptor signaling pathway,neuroactive ligand-receptor interaction,etc.These predictions were further demonstrated via in vivo animal model experiments.The RNA sequencing results showed that 41 genes were down-regulated(P<0.05)and 86 genes were up-regulated(P<0.05)in the Horcha-6 treated group compared with the untreated group.Those genes were mainly involved in neuromodulation,vascular function,and hormone metabolism.Conclusion:The 55 bio-active components in Horcha-6 regulate inflammation,hormone metabolism,and neurotransmitters and have potential as a therapy to treat migraines.展开更多
BACKGROUND Complement components could contribute to the tumor microenvironment and the systemic immune response.Nevertheless,their role in colorectal cancer(CRC)remains a contentious subject.AIM To elucidate the rela...BACKGROUND Complement components could contribute to the tumor microenvironment and the systemic immune response.Nevertheless,their role in colorectal cancer(CRC)remains a contentious subject.AIM To elucidate the relationship between complement components and CRC risk and clinical characteristics.METHODS Searches were conducted in PubMed,the Cochrane Library,and the China National Knowledge Infrastructure database until June 1,2023.We included cohort studies encompassing participants aged≥18 years,investigating the association between complement components and CRC.The studies were of moderate quality or above,as determined by the Agency for Healthcare Research and Quality.The meta-analysis employed fixed-effects or random-effects models based on the I^(2)test,utilizing risk ratio(RR)and their corresponding 95%confidence interval(CI)for outcomes.Sensitivity and subgroup analyses were performed to validate the robustness of the collective estimates and identify the source of heterogeneity.RESULTS Data from 15 studies,comprising 1631 participants that met the inclusion criteria,were included in the meta-analysis.Our findings indicated that protein levels of cluster of differentiation 46(CD46)(RR=3.66,95%CI:1.75-7.64,P<0.001),CD59(RR=2.86,95%CI:1.36-6.01,P=0.005),and component 1(C1)(RR=5.88,95%CI:1.75-19.73,P=0.004)and serum levels of C3(standardized mean difference=1.82,95%CI:0.06-3.58,P=0.040)were significantly elevated in patients with CRC compared to healthy controls.Strong expression of CD55 or CD59 was associated with a higher incidence of lymph node metastasis,whereas strong CD46 expression correlated with a higher incidence of tumor differentiation compared to low CD46 expression(P<0.05 for all).Although specific pooled results demonstrated notable heterogeneity,subgroup analyses pointed to regional differences as the primary source of inconsistency among the studies.CONCLUSION Our analysis underscores that increased levels of specific complement components are associated with a heightened risk of CRC,emphasizing the potential significance of monitoring elevated complement component levels.展开更多
Objective: The effect of Chuanzhi Fang (ZGC) on the whole genome expression profile of RAW264.7 cells activated by lipopolysaccharide (LPS) was analyzed, and to explore the possible mechanism of action and core target...Objective: The effect of Chuanzhi Fang (ZGC) on the whole genome expression profile of RAW264.7 cells activated by lipopolysaccharide (LPS) was analyzed, and to explore the possible mechanism of action and core target of this formula on macrophage inflammatory injury at the overall level. Methods: A model of LPS-induced inflammation in RAW264.7 cells was constructed, and the effect of ZGC intervention on the genome-wide expression of inflammatory macrophages 3was examined by gene microarray technology, GO/KEGG enrichment analysis was performed for significantly differentially expressed genes among each group. Results: The results of genome-wide expression profiling microarray analysis showed that the ZGC intervention group upregulated the expression of 5 genes including C4bp and inhibited the expression of 22 genes including Mgat3, Psma6, and Siglecg relative to the LPS model group. KEGG signaling pathway analysis results showed that ZGC mainly acted through cytokine receptor interaction and the C-type lectin receptor signaling pathway. Conclusion: ZGC can interfere with the abnormal expression of 27 genes in inflammatory macrophages, and the related genes may exert corresponding anti-inflammatory effects by affecting cytokine receptor interactions, C-type lectin receptor signaling pathway, and TLR4/ NF-κB signaling pathway.展开更多
The safety and integrity requirements of aerospace composite structures necessitate real-time health monitoring throughout their service life.To this end,distributed optical fiber sensors utilizing back Rayleigh scatt...The safety and integrity requirements of aerospace composite structures necessitate real-time health monitoring throughout their service life.To this end,distributed optical fiber sensors utilizing back Rayleigh scattering have been extensively deployed in structural health monitoring due to their advantages,such as lightweight and ease of embedding.However,identifying the precise location of damage from the optical fiber signals remains a critical challenge.In this paper,a novel approach which namely Modified Sliding Window Principal Component Analysis(MSWPCA)was proposed to facilitate automatic damage identification and localization via distributed optical fiber sensors.The proposed method is able to extract signal characteristics interfered by measurement noise to improve the accuracy of damage detection.Specifically,we applied the MSWPCA method to monitor and analyze the debonding propagation process in honeycomb sandwich panel structures.Our findings demonstrate that the training model exhibits high precision in detecting the location and size of honeycomb debonding,thereby facilitating reliable and efficient online assessment of the structural health state.展开更多
Principal Component Analysis (PCA) is a widely used technique for data analysis and dimensionality reduction, but its sensitivity to feature scale and outliers limits its applicability. Robust Principal Component Anal...Principal Component Analysis (PCA) is a widely used technique for data analysis and dimensionality reduction, but its sensitivity to feature scale and outliers limits its applicability. Robust Principal Component Analysis (RPCA) addresses these limitations by decomposing data into a low-rank matrix capturing the underlying structure and a sparse matrix identifying outliers, enhancing robustness against noise and outliers. This paper introduces a novel RPCA variant, Robust PCA Integrating Sparse and Low-rank Priors (RPCA-SL). Each prior targets a specific aspect of the data’s underlying structure and their combination allows for a more nuanced and accurate separation of the main data components from outliers and noise. Then RPCA-SL is solved by employing a proximal gradient algorithm for improved anomaly detection and data decomposition. Experimental results on simulation and real data demonstrate significant advancements.展开更多
The lightweight of high-end equipment relies on high mechanical properties magnesium(Mg) alloy structural components, because it is the best way to improve equipment service performance and reduce energy consumption. ...The lightweight of high-end equipment relies on high mechanical properties magnesium(Mg) alloy structural components, because it is the best way to improve equipment service performance and reduce energy consumption. This article summarizes the current progress and characteristics of large-scale high-performance Mg alloy components by analyzing the strengthening-toughening mechanisms, characteristics of plastic forming, and the preparation of large high mechanical properties forging blanks. Due to the lack of breakthroughs in the key technologies for forming large-scale Mg alloy components, their uniformity of mechanical properties and consistency are poor, the forming accuracy of components is low, and the production cost is high, which limit their engineering application and restrict the lightweight level of high-end equipment. In view of the above problems, the forming trends and research directions of large-scale and high mechanical properties Mg alloy components are proposed in this paper. It can provide help for the breakthrough of the key technology of large-scale Mg alloy components with high mechanical properties and expand the application of Mg alloy in high-end products.展开更多
Component-based software engineering is concerned with the develop-ment of software that can satisfy the customer prerequisites through reuse or inde-pendent development.Coupling and cohesion measurements are primaril...Component-based software engineering is concerned with the develop-ment of software that can satisfy the customer prerequisites through reuse or inde-pendent development.Coupling and cohesion measurements are primarily used to analyse the better software design quality,increase the reliability and reduced system software complexity.The complexity measurement of cohesion and coupling component to analyze the relationship between the component module.In this paper,proposed the component selection framework of Hexa-oval optimization algorithm for selecting the suitable components from the repository.It measures the interface density modules of coupling and cohesion in a modular software sys-tem.This cohesion measurement has been taken into two parameters for analyz-ing the result of complexity,with the help of low cohesion and high cohesion.In coupling measures between the component of inside parameters and outside parameters.Thefinal process of coupling and cohesion,the measured values were used for the average calculation of components parameter.This paper measures the complexity of direct and indirect interaction among the component as well as the proposed algorithm selecting the optimal component for the repository.The better result is observed for high cohesion and low coupling in compo-nent-based software engineering.展开更多
Complex systems exist widely,including medicines from natural products,functional foods,and biological samples.The biological activity of complex systems is often the result of the synergistic effect of multiple compo...Complex systems exist widely,including medicines from natural products,functional foods,and biological samples.The biological activity of complex systems is often the result of the synergistic effect of multiple components.In the quality evaluation of complex samples,multicomponent quantitative analysis(MCQA)is usually needed.To overcome the difficulty in obtaining standard products,scholars have proposed achieving MCQA through the“single standard to determine multiple components(SSDMC)”approach.This method has been used in the determination of multicomponent content in natural source drugs and the analysis of impurities in chemical drugs and has been included in the Chinese Pharmacopoeia.Depending on a convenient(ultra)high-performance liquid chromatography method,how can the repeatability and robustness of the MCQA method be improved?How can the chromatography conditions be optimized to improve the number of quantitative components?How can computer software technology be introduced to improve the efficiency of multicomponent analysis(MCA)?These are the key problems that remain to be solved in practical MCQA.First,this review article summarizes the calculation methods of relative correction factors in the SSDMC approach in the past five years,as well as the method robustness and accuracy evaluation.Second,it also summarizes methods to improve peak capacity and quantitative accuracy in MCA,including column selection and twodimensional chromatographic analysis technology.Finally,computer software technologies for predicting chromatographic conditions and analytical parameters are introduced,which provides an idea for intelligent method development in MCA.This paper aims to provide methodological ideas for the improvement of complex system analysis,especially MCQA.展开更多
An essential technology of carbon capture, utilization and storage-enhanced oil recovery (CCUS-EOR) for tight oil reservoirs is CO_(2) huff-puff followed by associated produced gas reinjection. In this paper, the effe...An essential technology of carbon capture, utilization and storage-enhanced oil recovery (CCUS-EOR) for tight oil reservoirs is CO_(2) huff-puff followed by associated produced gas reinjection. In this paper, the effects of multi-component gas on the properties and components of tight oil are studied. First, the core displacement experiments using the CH_(4)/CO_(2) multi-component gas are conducted to determine the oil displacement efficiency under different CO_(2) and CH_(4) ratios. Then, a viscometer and a liquid density balance are used to investigate the change characteristics of oil viscosity and density after multi-component gas displacement with different CO_(2) and CH_(4) ratios. In addition, a laboratory scale numerical model is established to validate the experimental results. Finally, a composition model of multi-stage fractured horizontal well in tight oil reservoir considering nano-confinement effects is established to investigate the effects of multi-component gas on the components of produced dead oil and formation crude oil. The experimental results show that the oil displacement efficiency of multi-component gas displacement is greater than that of single-component gas displacement. The CH_(4) decreases the viscosity and density of light oil, while CO_(2) decreases the viscosity but increases the density. And the numerical simulation results show that CO_(2) extracts more heavy components from the liquid phase into the vapor phase, while CH_(4) extracts more light components from the liquid phase into the vapor phase during cyclic gas injection. The multi-component gas can extract both the light components and the heavy components from oil, and the balanced production of each component can be achieved by using multi-component gas huff-puff.展开更多
[Objectives]To regulate the quality of Wanjinxiang Shushuang Ointment through simultaneously quantifying menthol,camphor,1,8-cineole,linalool,borneol and caryophyllene oxide by QAMS(quantitative analysis of multi-comp...[Objectives]To regulate the quality of Wanjinxiang Shushuang Ointment through simultaneously quantifying menthol,camphor,1,8-cineole,linalool,borneol and caryophyllene oxide by QAMS(quantitative analysis of multi-components by single-marker).[Methods]The method was performed using an Agilent DB-WAX(30 m×0.32 mm,0.25μm)polyethylene glycol chromatographic column;with nitrogen employed as a carrier gas.The constant pressure was 4.73 psi;and the injection temperature was 240℃,with a shunt ratio:of 10:1;The hydrogen flame ion detector with a detector temperature of 240℃;the injection volume was 0.3μL.To verify the accuracy and applicability of QAMS,the results were compared with those obtained using the internal standard method(naphthalene).[Results]In Wanjinxiang Shushuang Ointment,menthol,camphor,1,8-cineole,linalool,borneol,caryophyllene oxide and naphthalene were well separated by the same chromatography with good linearity in their respective ranges(R≥0.9992).The average recoveries were 99.66%,101.03%,98.07%,98.24%,101.39%,and 103.39%with RSD s of 0.69%,1.52%,1.25%,1.94%,1.44%,and 2.74%,respectively.The QAMS is similar to the internal standard method.[Conclusions]This simple,accurate method with high precision,separation and reproducibility can serve as a reference for the quality control of Wanjinxiang Shushuang Ointment.展开更多
The composition control of molten steel is one of the main functions in the ladle furnace(LF)refining process.In this study,a feasible model was established to predict the alloying element yield using principal compon...The composition control of molten steel is one of the main functions in the ladle furnace(LF)refining process.In this study,a feasible model was established to predict the alloying element yield using principal component analysis(PCA)and deep neural network(DNN).The PCA was used to eliminate collinearity and reduce the dimension of the input variables,and then the data processed by PCA were used to establish the DNN model.The prediction hit ratios for the Si element yield in the error ranges of±1%,±3%,and±5%are 54.0%,93.8%,and98.8%,respectively,whereas those of the Mn element yield in the error ranges of±1%,±2%,and±3%are 77.0%,96.3%,and 99.5%,respectively,in the PCA-DNN model.The results demonstrate that the PCA-DNN model performs better than the known models,such as the reference heat method,multiple linear regression,modified backpropagation,and DNN model.Meanwhile,the accurate prediction of the alloying element yield can greatly contribute to realizing a“narrow window”control of composition in molten steel.The construction of the prediction model for the element yield can also provide a reference for the development of an alloying control model in LF intelligent refining in the modern iron and steel industry.展开更多
The large blast furnace is essential equipment in the process of iron and steel manufacturing. Due to the complex operation process and frequent fluctuations of variables, conventional monitoring methods often bring f...The large blast furnace is essential equipment in the process of iron and steel manufacturing. Due to the complex operation process and frequent fluctuations of variables, conventional monitoring methods often bring false alarms. To address the above problem, an ensemble of greedy dynamic principal component analysis-Gaussian mixture model(EGDPCA-GMM) is proposed in this paper. First, PCA-GMM is introduced to deal with the collinearity and the non-Gaussian distribution of blast furnace data.Second, in order to explain the dynamics of data, the greedy algorithm is used to determine the extended variables and their corresponding time lags, so as to avoid introducing unnecessary noise. Then the bagging ensemble is adopted to cooperate with greedy extension to eliminate the randomness brought by the greedy algorithm and further reduce the false alarm rate(FAR) of monitoring results. Finally, the algorithm is applied to the blast furnace of a large iron and steel group in South China to verify performance.Compared with the basic algorithms, the proposed method achieves lowest FAR, while keeping missed alarm rate(MAR) remain stable.展开更多
In general,malachite is recovered via sulfidization–xanthate flotation,although many unsatisfactory flotation indexes are frequently obtained as a result of the presence of associated calcite.This phenomenon occurs b...In general,malachite is recovered via sulfidization–xanthate flotation,although many unsatisfactory flotation indexes are frequently obtained as a result of the presence of associated calcite.This phenomenon occurs because the dissolved components of malachite and calcite affect the flotation behavior of both minerals.In this study,the effect of the dissolved components derived from malachite and calcite on the flotation behavior and surface characteristics of both minerals was investigated.Flotation tests indicated that malachite recovery decreased when the calcite supernatant was introduced,while the presence of the malachite supernatant increased the recovery of calcite.Dissolution and adsorption tests,along with zeta potential measurements,X-ray photoelectron spectroscopy,Fourier transform infrared spectrometry,and timeof-flight secondary ion mass spectrometry demonstrated that the Ca species in the calcite supernatant were adsorbed on the malachite surface,which hindered the interaction of Na2S with malachite,thereby resulting in the insufficient adsorption of sodium isoamyl xanthate(NaIX)on the surface of malachite.By contrast,the Cu species in the malachite supernatant were adsorbed on the calcite surface,and they provided active sites for the subsequent adsorption of Na_(2)S and NaIX.展开更多
文摘We investigated the parametric optimization on incremental sheet forming of stainless steel using Grey Relational Analysis(GRA) coupled with Principal Component Analysis(PCA). AISI 316L stainless steel sheets were used to develop double wall angle pyramid with aid of tungsten carbide tool. GRA coupled with PCA was used to plan the experiment conditions. Control factors such as Tool Diameter(TD), Step Depth(SD), Bottom Wall Angle(BWA), Feed Rate(FR) and Spindle Speed(SS) on Top Wall Angle(TWA) and Top Wall Angle Surface Roughness(TWASR) have been studied. Wall angle increases with increasing tool diameter due to large contact area between tool and workpiece. As the step depth, feed rate and spindle speed increase,TWASR decreases with increasing tool diameter. As the step depth increasing, the hydrostatic stress is raised causing severe cracks in the deformed surface. Hence it was concluded that the proposed hybrid method was suitable for optimizing the factors and response.
基金This work was supported by the Pilot Seed Grant(Grant No.RES0049944)the Collaborative Research Project(Grant No.RES0043251)from the University of Alberta.
文摘Ore production is usually affected by multiple influencing inputs at open-pit mines.Nevertheless,the complex nonlinear relationships between these inputs and ore production remain unclear.This becomes even more challenging when training data(e.g.truck haulage information and weather conditions)are massive.In machine learning(ML)algorithms,deep neural network(DNN)is a superior method for processing nonlinear and massive data by adjusting the amount of neurons and hidden layers.This study adopted DNN to forecast ore production using truck haulage information and weather conditions at open-pit mines as training data.Before the prediction models were built,principal component analysis(PCA)was employed to reduce the data dimensionality and eliminate the multicollinearity among highly correlated input variables.To verify the superiority of DNN,three ANNs containing only one hidden layer and six traditional ML models were established as benchmark models.The DNN model with multiple hidden layers performed better than the ANN models with a single hidden layer.The DNN model outperformed the extensively applied benchmark models in predicting ore production.This can provide engineers and researchers with an accurate method to forecast ore production,which helps make sound budgetary decisions and mine planning at open-pit mines.
基金The authors are grateful for the support by National Key Research and Development Program of China(2021YFF0500300,2020YFB1708300)the National Natural Science Foundation of China(52205280,12172041).
文摘Lightweight thin-walled structures with lattice infill are widely desired in satellite for their high stiffness-to-weight ratio and superior buckling strength resulting fromthe sandwich effect.Such structures can be fabricated bymetallic additive manufacturing technique,such as selective laser melting(SLM).However,the maximum dimensions of actual structures are usually in a sub-meter scale,which results in restrictions on their appliance in aerospace and other fields.In this work,a meter-scale thin-walled structure with lattice infill is designed for the fuel tank supporting component of the satellite by integrating a self-supporting lattice into the thickness optimization of the thin-wall.The designed structure is fabricated by SLM of AlSi10Mg and cold metal transfer welding technique.Quasi-static mechanical tests and vibration tests are both conducted to verify the mechanical strength of the designed large-scale lattice thin-walled structure.The experimental results indicate that themeter-scale thin-walled structure with lattice infill could meet the dimension and lightweight requirements of most spacecrafts.
基金supported by National Natural Science Foundation of China(32360307)the Natural Science Foundation of Inner Mongolia(2023MS03031)+1 种基金Inner Mongolia Grassland Talents Project(3211002406)the Open Fund of State Key Laboratory of Tree Genetics and Breeding(Chinese Academy of Forestry)(Grant No.TGB2021004).
文摘Chinese wingnut(Pterocarya stenoptera)is a medicinally and economically important tree species within the family Juglandaceae.However,the lack of high-quality reference genome has hindered its in-depth research.In this study,we successfully assembled its chromosome-level genome and performed multiomics analyses to address its evolutionary history and synthesis of medicinal components.A thorough examination of genomes has uncovered a significant expansion in the Lateral Organ Boundaries Domain gene family among the winged group in Juglandaceae.This notable increase may be attributed to their frequent exposure to flood-prone environments.After further differentiation between Chinese wingnut and Cyclocarya paliurus,significant positive selection occurred on the genes of NADH dehydrogenase related to mitochondrial aerobic respiration in Chinese wingnut,enhancing its ability to cope with waterlogging stress.Comparative genomic analysis revealed Chinese wingnut evolved more unique genes related to arginine synthesis,potentially endowing it with a higher capacity to purify nutrient-rich water bodies.Expansion of terpene synthase families enables the production of increased quantities of terpenoid volatiles,potentially serving as an evolved defense mechanism against herbivorous insects.Through combined transcriptomic and metabolomic analysis,we identified the candidate genes involved in the synthesis of terpenoid volatiles.Our study offers essential genetic resources for Chinese wingnut,unveiling its evolutionary history and identifying key genes linked to the production of terpenoid volatiles.
基金supported by the National Natural Science Foundation of China (31622042)。
文摘In this study,umami taste intensity(UTI)and umami taste components in chicken breast(CB)and chickenspices blends were characterized using sensory and instrumental analysis.Our main objective was to assess the aroma-umami taste interactions in different food matrices and reconcile the aroma-taste perception to assist future product development.The impact of key aroma,including vegetable-note"2-pentylfuran",meaty"methional",green"hexanal",and spicy-note-estragole and caryophyllene"on UTI was evaluated in monosodium glutamate and chicken extract.We found that spices significantly decreased UTI and umami taste components in CB.Interestingly,the perceptually similar odorants and tastants exhibited the potential to enhance UTI in food matrices.Methional was able to increase the UTI,whereas spicy and green-note components could reduce the UTI significantly.This information would be valuable to food engineers and formulators in aroma selection to control the UTI perceived by consumers,thus,improving the quality and acceptability of the chicken products.
基金funded by Shenzhen Science and Technology Innovation Commission(KCXFZ20201221173207022)。
文摘Andrias davidianus(Chinese giant salamander,CGS)is the largest and oldest extant amphibian species in the world and is a source of prospective functional food in China.However,the progress of functional peptides mining was slow due to lack of reference genome and protein sequence data.In this study,we illustrated full-length transcriptome sequencing to interpret the proteome of CGS meat and obtain 10703 coding DNA sequences.By functional annotation and amino acid composition analysis,we have discovered various genes related to signal transduction,and 16 genes related to longevity.We have also found vast variety of functional peptides through protein coding sequence(CDS)analysis by comparing the data obtained with the functional peptide database.Val-Pro-Ile predicted by the CDS analysis was released from the CGS meat through enzymatic hydrolysis,suggesting that our approach is reliable.This study suggested that transcriptomic analysis can be used as a reference to guide polypeptide mining in CGS meat,thereby providing a powerful mining strategy for the bioresources with unknown genomic and proteomic sequences.
基金supported by grants The Natural Science Foundation of Inner Mongolia(2019MS08104)The Natural Science Foundation of Inner Mongolia(2022ZD09)The Central Government Guiding Special Funds for Development of Local Science and Technology(2020ZY0020).
文摘Background:The active components of Horcha-6 were identified using liquid chromatography with tandem mass spectrometry.Also,we investigated the potential mechanisms that explain why Horcha-6 may be effective in treating migraines through the use of network pharmacology and a rat migraine model.Methods:After identifying the active components of Horcha-6,the corresponding genes of the active components’target were obtained from the Universal Protein database,and a“compound-target-disease”network was constructed using Cytoscape 3.9.0 software.For the in vivo experiments,nitroglycerin was injected intraperitoneally into rats to create a migraine model.Pre-treatment with Horcha-6 was administered orally for 14 days,and rats were subjected to migraine-related behavior tests.RNA sequencing was performed to identify the gene expression regulated by Horcha-6 in the trigeminal nerve.Results:A total of 903 chemical components of Horcha-6 have been collected in the liquid chromatography with tandem mass spectrometry.We discovered 55 of the Horcha-6 bio-active components that were evaluated based on their Percent Human Oral Absorption(≥30%)and DL values(≥0.185)on the traditional Chinese medicine systems pharmacology database.The“compound-target-disease”network contained 163 intersection targets with the migraine state.Gene Ontology analysis indicated that these components significantly regulated the immune response,vascular function,oxidative stress,etc.When Kyoto Encyclopedia of Genes and Genomes enrichment analysis was performed,we observed that most of the target genes were significantly enriched in the inflammation and neuro-related signaling pathway,toll-like receptor signaling pathway,neuroactive ligand-receptor interaction,etc.These predictions were further demonstrated via in vivo animal model experiments.The RNA sequencing results showed that 41 genes were down-regulated(P<0.05)and 86 genes were up-regulated(P<0.05)in the Horcha-6 treated group compared with the untreated group.Those genes were mainly involved in neuromodulation,vascular function,and hormone metabolism.Conclusion:The 55 bio-active components in Horcha-6 regulate inflammation,hormone metabolism,and neurotransmitters and have potential as a therapy to treat migraines.
文摘BACKGROUND Complement components could contribute to the tumor microenvironment and the systemic immune response.Nevertheless,their role in colorectal cancer(CRC)remains a contentious subject.AIM To elucidate the relationship between complement components and CRC risk and clinical characteristics.METHODS Searches were conducted in PubMed,the Cochrane Library,and the China National Knowledge Infrastructure database until June 1,2023.We included cohort studies encompassing participants aged≥18 years,investigating the association between complement components and CRC.The studies were of moderate quality or above,as determined by the Agency for Healthcare Research and Quality.The meta-analysis employed fixed-effects or random-effects models based on the I^(2)test,utilizing risk ratio(RR)and their corresponding 95%confidence interval(CI)for outcomes.Sensitivity and subgroup analyses were performed to validate the robustness of the collective estimates and identify the source of heterogeneity.RESULTS Data from 15 studies,comprising 1631 participants that met the inclusion criteria,were included in the meta-analysis.Our findings indicated that protein levels of cluster of differentiation 46(CD46)(RR=3.66,95%CI:1.75-7.64,P<0.001),CD59(RR=2.86,95%CI:1.36-6.01,P=0.005),and component 1(C1)(RR=5.88,95%CI:1.75-19.73,P=0.004)and serum levels of C3(standardized mean difference=1.82,95%CI:0.06-3.58,P=0.040)were significantly elevated in patients with CRC compared to healthy controls.Strong expression of CD55 or CD59 was associated with a higher incidence of lymph node metastasis,whereas strong CD46 expression correlated with a higher incidence of tumor differentiation compared to low CD46 expression(P<0.05 for all).Although specific pooled results demonstrated notable heterogeneity,subgroup analyses pointed to regional differences as the primary source of inconsistency among the studies.CONCLUSION Our analysis underscores that increased levels of specific complement components are associated with a heightened risk of CRC,emphasizing the potential significance of monitoring elevated complement component levels.
基金Chinese Academy of Traditional Chinese Medicine Autonomous Topic Selection Project(No.ZZ2018017)Research Development Fund Project of the Medical Experimental Center of the Chinese Academy of Traditional Chinese Medicine(No.FZ2023003)。
文摘Objective: The effect of Chuanzhi Fang (ZGC) on the whole genome expression profile of RAW264.7 cells activated by lipopolysaccharide (LPS) was analyzed, and to explore the possible mechanism of action and core target of this formula on macrophage inflammatory injury at the overall level. Methods: A model of LPS-induced inflammation in RAW264.7 cells was constructed, and the effect of ZGC intervention on the genome-wide expression of inflammatory macrophages 3was examined by gene microarray technology, GO/KEGG enrichment analysis was performed for significantly differentially expressed genes among each group. Results: The results of genome-wide expression profiling microarray analysis showed that the ZGC intervention group upregulated the expression of 5 genes including C4bp and inhibited the expression of 22 genes including Mgat3, Psma6, and Siglecg relative to the LPS model group. KEGG signaling pathway analysis results showed that ZGC mainly acted through cytokine receptor interaction and the C-type lectin receptor signaling pathway. Conclusion: ZGC can interfere with the abnormal expression of 27 genes in inflammatory macrophages, and the related genes may exert corresponding anti-inflammatory effects by affecting cytokine receptor interactions, C-type lectin receptor signaling pathway, and TLR4/ NF-κB signaling pathway.
基金supported by the National Key Research and Development Program of China(No.2018YFA0702800)the National Natural Science Foundation of China(No.12072056)supported by National Defense Fundamental Scientific Research Project(XXXX2018204BXXX).
文摘The safety and integrity requirements of aerospace composite structures necessitate real-time health monitoring throughout their service life.To this end,distributed optical fiber sensors utilizing back Rayleigh scattering have been extensively deployed in structural health monitoring due to their advantages,such as lightweight and ease of embedding.However,identifying the precise location of damage from the optical fiber signals remains a critical challenge.In this paper,a novel approach which namely Modified Sliding Window Principal Component Analysis(MSWPCA)was proposed to facilitate automatic damage identification and localization via distributed optical fiber sensors.The proposed method is able to extract signal characteristics interfered by measurement noise to improve the accuracy of damage detection.Specifically,we applied the MSWPCA method to monitor and analyze the debonding propagation process in honeycomb sandwich panel structures.Our findings demonstrate that the training model exhibits high precision in detecting the location and size of honeycomb debonding,thereby facilitating reliable and efficient online assessment of the structural health state.
文摘Principal Component Analysis (PCA) is a widely used technique for data analysis and dimensionality reduction, but its sensitivity to feature scale and outliers limits its applicability. Robust Principal Component Analysis (RPCA) addresses these limitations by decomposing data into a low-rank matrix capturing the underlying structure and a sparse matrix identifying outliers, enhancing robustness against noise and outliers. This paper introduces a novel RPCA variant, Robust PCA Integrating Sparse and Low-rank Priors (RPCA-SL). Each prior targets a specific aspect of the data’s underlying structure and their combination allows for a more nuanced and accurate separation of the main data components from outliers and noise. Then RPCA-SL is solved by employing a proximal gradient algorithm for improved anomaly detection and data decomposition. Experimental results on simulation and real data demonstrate significant advancements.
基金The financial support from Joint Funds of the National Natural Science Foundation of China (No.U20A20230)the Natural Science Foundation of China (No.52075501)+1 种基金Young Elite Scientists Sponsorship Program by CAST (2022QNRC001)the Key R&D program of Shanxi Province (No.2020XXX015)。
文摘The lightweight of high-end equipment relies on high mechanical properties magnesium(Mg) alloy structural components, because it is the best way to improve equipment service performance and reduce energy consumption. This article summarizes the current progress and characteristics of large-scale high-performance Mg alloy components by analyzing the strengthening-toughening mechanisms, characteristics of plastic forming, and the preparation of large high mechanical properties forging blanks. Due to the lack of breakthroughs in the key technologies for forming large-scale Mg alloy components, their uniformity of mechanical properties and consistency are poor, the forming accuracy of components is low, and the production cost is high, which limit their engineering application and restrict the lightweight level of high-end equipment. In view of the above problems, the forming trends and research directions of large-scale and high mechanical properties Mg alloy components are proposed in this paper. It can provide help for the breakthrough of the key technology of large-scale Mg alloy components with high mechanical properties and expand the application of Mg alloy in high-end products.
基金We deeply acknowledge Taif University for Supporting this research through Taif University Researchers Supporting Project number(TURSP-2020/231),Taif University,Taif,Saudi Arabia.
文摘Component-based software engineering is concerned with the develop-ment of software that can satisfy the customer prerequisites through reuse or inde-pendent development.Coupling and cohesion measurements are primarily used to analyse the better software design quality,increase the reliability and reduced system software complexity.The complexity measurement of cohesion and coupling component to analyze the relationship between the component module.In this paper,proposed the component selection framework of Hexa-oval optimization algorithm for selecting the suitable components from the repository.It measures the interface density modules of coupling and cohesion in a modular software sys-tem.This cohesion measurement has been taken into two parameters for analyz-ing the result of complexity,with the help of low cohesion and high cohesion.In coupling measures between the component of inside parameters and outside parameters.Thefinal process of coupling and cohesion,the measured values were used for the average calculation of components parameter.This paper measures the complexity of direct and indirect interaction among the component as well as the proposed algorithm selecting the optimal component for the repository.The better result is observed for high cohesion and low coupling in compo-nent-based software engineering.
基金the National Natural Science Foundation of China(Grant No.:81803734)National S&T Major Special Project for New Innovative Drugs Sponsored(Grant No.:2019ZX09201005).
文摘Complex systems exist widely,including medicines from natural products,functional foods,and biological samples.The biological activity of complex systems is often the result of the synergistic effect of multiple components.In the quality evaluation of complex samples,multicomponent quantitative analysis(MCQA)is usually needed.To overcome the difficulty in obtaining standard products,scholars have proposed achieving MCQA through the“single standard to determine multiple components(SSDMC)”approach.This method has been used in the determination of multicomponent content in natural source drugs and the analysis of impurities in chemical drugs and has been included in the Chinese Pharmacopoeia.Depending on a convenient(ultra)high-performance liquid chromatography method,how can the repeatability and robustness of the MCQA method be improved?How can the chromatography conditions be optimized to improve the number of quantitative components?How can computer software technology be introduced to improve the efficiency of multicomponent analysis(MCA)?These are the key problems that remain to be solved in practical MCQA.First,this review article summarizes the calculation methods of relative correction factors in the SSDMC approach in the past five years,as well as the method robustness and accuracy evaluation.Second,it also summarizes methods to improve peak capacity and quantitative accuracy in MCA,including column selection and twodimensional chromatographic analysis technology.Finally,computer software technologies for predicting chromatographic conditions and analytical parameters are introduced,which provides an idea for intelligent method development in MCA.This paper aims to provide methodological ideas for the improvement of complex system analysis,especially MCQA.
基金supported by the National Natural Science Foundation of China(No.52174038 and No.52004307)China Petroleum Science and Technology Project-major project-Research on tight oil-shale oil reservoir engineering methods and key technologies in Ordos Basin(ZLZX2020-02-04)Science Foundation of China University of Petroleum,Beijing(No.2462018YJRC015).
文摘An essential technology of carbon capture, utilization and storage-enhanced oil recovery (CCUS-EOR) for tight oil reservoirs is CO_(2) huff-puff followed by associated produced gas reinjection. In this paper, the effects of multi-component gas on the properties and components of tight oil are studied. First, the core displacement experiments using the CH_(4)/CO_(2) multi-component gas are conducted to determine the oil displacement efficiency under different CO_(2) and CH_(4) ratios. Then, a viscometer and a liquid density balance are used to investigate the change characteristics of oil viscosity and density after multi-component gas displacement with different CO_(2) and CH_(4) ratios. In addition, a laboratory scale numerical model is established to validate the experimental results. Finally, a composition model of multi-stage fractured horizontal well in tight oil reservoir considering nano-confinement effects is established to investigate the effects of multi-component gas on the components of produced dead oil and formation crude oil. The experimental results show that the oil displacement efficiency of multi-component gas displacement is greater than that of single-component gas displacement. The CH_(4) decreases the viscosity and density of light oil, while CO_(2) decreases the viscosity but increases the density. And the numerical simulation results show that CO_(2) extracts more heavy components from the liquid phase into the vapor phase, while CH_(4) extracts more light components from the liquid phase into the vapor phase during cyclic gas injection. The multi-component gas can extract both the light components and the heavy components from oil, and the balanced production of each component can be achieved by using multi-component gas huff-puff.
基金Supported by Projects of Guizhou Provincial Department of Education-Growth Program of Young Scientific and Technological Talents in Guizhou Ordinary Higher Education Institutions(QJHKY[2022]264)Rolling Support for Provincial University Research Platform Team Projects(QJJ[2022]010).
文摘[Objectives]To regulate the quality of Wanjinxiang Shushuang Ointment through simultaneously quantifying menthol,camphor,1,8-cineole,linalool,borneol and caryophyllene oxide by QAMS(quantitative analysis of multi-components by single-marker).[Methods]The method was performed using an Agilent DB-WAX(30 m×0.32 mm,0.25μm)polyethylene glycol chromatographic column;with nitrogen employed as a carrier gas.The constant pressure was 4.73 psi;and the injection temperature was 240℃,with a shunt ratio:of 10:1;The hydrogen flame ion detector with a detector temperature of 240℃;the injection volume was 0.3μL.To verify the accuracy and applicability of QAMS,the results were compared with those obtained using the internal standard method(naphthalene).[Results]In Wanjinxiang Shushuang Ointment,menthol,camphor,1,8-cineole,linalool,borneol,caryophyllene oxide and naphthalene were well separated by the same chromatography with good linearity in their respective ranges(R≥0.9992).The average recoveries were 99.66%,101.03%,98.07%,98.24%,101.39%,and 103.39%with RSD s of 0.69%,1.52%,1.25%,1.94%,1.44%,and 2.74%,respectively.The QAMS is similar to the internal standard method.[Conclusions]This simple,accurate method with high precision,separation and reproducibility can serve as a reference for the quality control of Wanjinxiang Shushuang Ointment.
基金supported by the National Natural Science Foundation of China(No.51974023)State Key Laboratory of Advanced Metallurgy,University of Science and Technology Beijing(No.41621005)。
文摘The composition control of molten steel is one of the main functions in the ladle furnace(LF)refining process.In this study,a feasible model was established to predict the alloying element yield using principal component analysis(PCA)and deep neural network(DNN).The PCA was used to eliminate collinearity and reduce the dimension of the input variables,and then the data processed by PCA were used to establish the DNN model.The prediction hit ratios for the Si element yield in the error ranges of±1%,±3%,and±5%are 54.0%,93.8%,and98.8%,respectively,whereas those of the Mn element yield in the error ranges of±1%,±2%,and±3%are 77.0%,96.3%,and 99.5%,respectively,in the PCA-DNN model.The results demonstrate that the PCA-DNN model performs better than the known models,such as the reference heat method,multiple linear regression,modified backpropagation,and DNN model.Meanwhile,the accurate prediction of the alloying element yield can greatly contribute to realizing a“narrow window”control of composition in molten steel.The construction of the prediction model for the element yield can also provide a reference for the development of an alloying control model in LF intelligent refining in the modern iron and steel industry.
基金supported by the National Natural Science Foundation of China (61903326, 61933015)。
文摘The large blast furnace is essential equipment in the process of iron and steel manufacturing. Due to the complex operation process and frequent fluctuations of variables, conventional monitoring methods often bring false alarms. To address the above problem, an ensemble of greedy dynamic principal component analysis-Gaussian mixture model(EGDPCA-GMM) is proposed in this paper. First, PCA-GMM is introduced to deal with the collinearity and the non-Gaussian distribution of blast furnace data.Second, in order to explain the dynamics of data, the greedy algorithm is used to determine the extended variables and their corresponding time lags, so as to avoid introducing unnecessary noise. Then the bagging ensemble is adopted to cooperate with greedy extension to eliminate the randomness brought by the greedy algorithm and further reduce the false alarm rate(FAR) of monitoring results. Finally, the algorithm is applied to the blast furnace of a large iron and steel group in South China to verify performance.Compared with the basic algorithms, the proposed method achieves lowest FAR, while keeping missed alarm rate(MAR) remain stable.
基金financially supported by Yunnan Fundamental Research Projects (No.202101BE070001-009)National Natural Science Foundation of China (No.51464029)。
文摘In general,malachite is recovered via sulfidization–xanthate flotation,although many unsatisfactory flotation indexes are frequently obtained as a result of the presence of associated calcite.This phenomenon occurs because the dissolved components of malachite and calcite affect the flotation behavior of both minerals.In this study,the effect of the dissolved components derived from malachite and calcite on the flotation behavior and surface characteristics of both minerals was investigated.Flotation tests indicated that malachite recovery decreased when the calcite supernatant was introduced,while the presence of the malachite supernatant increased the recovery of calcite.Dissolution and adsorption tests,along with zeta potential measurements,X-ray photoelectron spectroscopy,Fourier transform infrared spectrometry,and timeof-flight secondary ion mass spectrometry demonstrated that the Ca species in the calcite supernatant were adsorbed on the malachite surface,which hindered the interaction of Na2S with malachite,thereby resulting in the insufficient adsorption of sodium isoamyl xanthate(NaIX)on the surface of malachite.By contrast,the Cu species in the malachite supernatant were adsorbed on the calcite surface,and they provided active sites for the subsequent adsorption of Na_(2)S and NaIX.
