Kinematic deformation and intensity assessment of the 2021 Maduo M_(S)7.4 earthquake in Qinghai revealed by high-frequency GNSS

Rapid acquisition of the kinematic deformation field and seismic intensity distribution of large earthquakes is crucial for postseismic emergency rescue,disaster assessment,and future seismic risk research.The advancement of GNSS observation and data processing makes it play an important role in this field,especially the high-frequency GNSS.We used the differential positioning method to calculate the 1 HZ GNSS data from 98 sites within 1000 km of the M_(S)7.4 Maduo earthquake epicenter.The kinematic deformation field and the distribution of the seismic intensity by using the peak ground velocity derived from displacement waveforms were obtained.The results show that:1)Horizontal coseismic response deformation levels ranging from 25 mm to 301 mm can be observed within a 1000 km radius from the epicenter.Coseismic response deformation on the east and west sides shows bilateral asymmetry,which markedly differs from the symmetry presented by surface rupture.2)The seismic intensity obtained through high-frequency GNSS and field investigations exhibits good consistency of the scope and orientation in the high seismic intensity area,although the former is generally slightly smaller than the latter.3)There may exist obstacles on the eastern side of the seismogenic fault.The Maduo earthquake induced a certain tectonic stress loading effect on the western Kunlun Pass-Jiangcuo fault(KPJF)and Maqin-Maqu segment,resulting in higher seismic risk in the future.

Dynamic Response of Foundations during Startup of High-Frequency Tunnel Equipment

The specialized equipment utilized in long-line tunnel engineering is evolving towards large-scale,multifunctional,and complex orientations.The vibration caused by the high-frequency units during regular operation is supported by the foundation of the units,and the magnitude of vibration and the operating frequency fluctuate in different engineering contexts,leading to variations in the dynamic response of the foundation.The high-frequency units yield significantly diverse outcomes under different startup conditions and times,resulting in failure to meet operational requirements,influencing the normal function of the tunnel,and causing harm to the foundation structure,personnel,and property in severe cases.This article formulates a finite element numerical computation model for solid elements using three-dimensional elastic body theory and integrates field measurements to substantiate and ascertain the crucial parameter configurations of the finite element model.By proposing a comprehensive startup timing function for high-frequency dynamic machines under different startup conditions,simulating the frequency andmagnitude variations during the startup process,and suggesting functions for changes in frequency and magnitude,a simulated startup schedule function for high-frequency machines is created through coupling.Taking into account the selection of the transient dynamic analysis step length,the dynamic response results for the lower dynamic foundation during its fundamental frequency crossing process are obtained.The validation checks if the structural magnitude surpasses the safety threshold during the critical phase of unit startup traversing the structural resonance region.The design recommendations for high-frequency units’dynamic foundations are provided,taking into account the startup process of the machine and ensuring the safe operation of the tunnel.

Multi-omics analyses provide insights into the evolutionary history and the synthesis of medicinal components of the Chinese wingnut

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.

Effect of Planting Date on Yield and Yield Components of Grain Sorghum Hybrids

In Kansas, productivity of grain sorghum [Sorghum bicolor (L.) Moench] is affected by weather conditions at planting and during pollination. Planting date management and selection of hybrid maturity group can help to avoid severe environmental stresses during these sensitive stages. The hypothesis of the study was that late May planting improves grain sorghum yield and yield components compared with late June planting. The objectives of this research were to investigate the influence of planting dates yield and yield components of different grain sorghum hybrids, and to determine the optimal planting date and hybrid combination for maximum biomass and grains production. Three sorghum hybrids (early, medium, and late maturing) were planted in late May and late June without irrigation in Kansas at Manhattan/Ashland Bottom Research Station, and Hutchinson in 2010;and at Manhattan/North Farm and Hutchinson in 2011. Data on dry matter production, yield and yield components were collected. Grain yield and yield components were influenced by planting date depending on environmental conditions. At Manhattan (2010), greater grain yield, number of heads per plant, were obtained with late-June planting compared with late May planting, while at Hutchinson (2010) greater yield was obtained with late May planting for all hybrids. The yield component most affected at Hutchinson was the number of kernels∙panicle−1 and plant density. Late-May planting was favorable for late maturing hybrid (P84G62) in all locations. However, the yield of early maturing hybrid (DKS 28-05) and medium maturing hybrid (DKS 37-07) was less affected by delayed planting. The effects of planting dates on yield and yield components of grain sorghum hybrids were found to be variable among hybrid maturity groups and locations.

Meter-Scale Thin-Walled Structure with Lattice Infill for Fuel Tank Supporting Component of Satellite:Multiscale Design and Experimental Verification

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.

Data Component:An Innovative Framework for Information Value Metrics in the Digital Economy

The increasing dependence on data highlights the need for a detailed understanding of its behavior,encompassing the challenges involved in processing and evaluating it.However,current research lacks a comprehensive structure for measuring the worth of data elements,hindering effective navigation of the changing digital environment.This paper aims to fill this research gap by introducing the innovative concept of“data components.”It proposes a graphtheoretic representation model that presents a clear mathematical definition and demonstrates the superiority of data components over traditional processing methods.Additionally,the paper introduces an information measurement model that provides a way to calculate the information entropy of data components and establish their increased informational value.The paper also assesses the value of information,suggesting a pricing mechanism based on its significance.In conclusion,this paper establishes a robust framework for understanding and quantifying the value of implicit information in data,laying the groundwork for future research and practical applications.

Transcriptomic analysis of Andrias davidianus meat and experimental validation for exploring its bioactive components as functional foods

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.

基于Visual Components的套筒类零件柔性制造单元的应用研究

为实现多品种中、小批量零件的智能化加工,以套筒类零件加工为研究对象,通过对零件工艺的分析,为满足单元加工、搬运、翻转等功能需求,对数控机床和工业机器人进行选型,实现柔性制造单元的设计。利用Visual Components软件进行单元搭建、仿真优化反馈以提高真实产线运行效率。利用Profinet实现制造单元真实搭建,完成单元安装调试。套筒类零件柔性制造单元的设计为生产线设计提供了新思路。

Effects of unilateral superimposed high-frequency jet ventilation on porcine hemodynamics and gas exchange during one-lung flooding

BACKGROUND Superimposed high-frequency jet ventilation(SHFJV)is suitable for respiratory motion reduction and essential for effective lung tumor ablation.Fluid filling of the target lung wing one-lung flooding(OLF)is necessary for therapeutic ultrasound applications.However,whether unilateral SHFJV allows adequate hemodynamics and gas exchange is unclear.AIM To compared SHFJV with pressure-controlled ventilation(PCV)during OLF by assessing hemodynamics and gas exchange in different animal positions.METHODS SHFJV or PCV was used alternatingly to ventilate the non-flooded lungs of the 12 anesthetized pigs during OLF.The animal positions were changed from left lateral position to supine position(SP)to right lateral position(RLP)every 30 min.In each position,ventilation was maintained for 15 min in both modalities.Hemodynamic variables and arterial blood gas levels were repeatedly measured.RESULTS Unilateral SHFJV led to lower carbon dioxide removal than PCV without abnormally elevated carbon dioxide levels.SHFJV slightly decreased oxygenation in SP and RLP compared with PCV;the lowest values of PaO_(2) and PaO_(2)/FiO_(2) ratio were found in SP[13.0;interquartile range(IQR):12.6-5.6 and 32.5(IQR:31.5-38.9)kPa].Conversely,during SHFJV,the shunt fraction was higher in all animal positions(highest in the RLP:0.30).CONCLUSION In porcine model,unilateral SHFJV may provide adequate ventilation in different animal positions during OLF.Lower oxygenation and CO_(2) removal rates compared to PCV did not lead to hypoxia or hypercapnia.SHFJV can be safely used for lung tumor ablation to minimize ventilation-induced lung motion.

Umami taste components in chicken-spices blends and potential effect of aroma on umami taste intensity

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.

Association of complement components with risk of colorectal cancer:A systematic review and meta-analysis

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.

The bio-active components of the Mongolian medicine Horcha-6 and therapeutic mechanism in the rat migraine model

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.

Therapeutic efficacy of methylprednisolone sodium succinate via diverse administration routes for mid-to high-frequency sudden sensorineural hearing loss

BACKGROUND Sudden sensorineural hearing loss(SSNHL),characterized by a rapid and unexplained loss of hearing,particularly at moderate to high frequencies,presents a significant clinical challenge.The therapeutic use of methylprednisolone sodium succinate(MPSS)via different administration routes,in combination with conventional medications,remains a topic of interest.AIM To compare the therapeutic efficacy of MPSS administered via different routes in combination with conventional drugs for the treatment of mid-to high-frequency SSNHL.METHODS The medical records of 109 patients with mid-to high-frequency SSNHL were analyzed.The patients were divided into three groups based on the route of administration:Group A[intratympanic(IT)injection of MPSS combined with mecobalamin and Ginkgo biloba leaf extract injection],Group B(intravenous injection of MPSS combined with mecobalamin and Ginkgo biloba leaf extract injection),and Group C(single IT injection of MPSS).The intervention effects were compared and analyzed.RESULTS The posttreatment auditory thresholds in Group A(21.23±3.34)were significantly lower than those in Groups B(28.52±3.36)and C(30.23±4.21;P<0.05).Group A also exhibited a significantly greater speech recognition rate(92.23±5.34)than Groups B and C.The disappearance time of tinnitus,time to hearing recovery,and disappearance time of vertigo in Group A were significantly shorter than those in Groups B and C(P<0.05).The total effective rate in Group A(97.56%)was significantly greater than that in Groups B and C(77.14%and 78.79%,χ^(2)=7.898,P=0.019).Moreover,the incidence of adverse reactions in Groups A and C was significantly lower than that in Group B(4.88%,3.03%vs 2.57%,χ^(2)=11.443,P=0.003),and the recurrence rate in Group A was significantly lower than that in Groups B and C(2.44%vs 20.00%vs 21.21%,χ^(2)=7.120,P=0.028).CONCLUSION IT injection of MPSS combined with conventional treatment demonstrates superior efficacy and safety compared to systemic administration via intravenous infusion and a single IT injection of MPSS.This approach effectively improves patients'hearing and reduces the risk of disease recurrence.

Using deep neural networks coupled with principal component analysis for ore production forecasting at open-pit mines

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.

A Hybrid Optimization Approach of Single Point Incremental Sheet Forming of AISI 316L Stainless Steel Using Grey Relation Analysis Coupled with Principal Component Analysiss

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.

Effect of Chuanzhifang component (ZGC) on macrophage inflammatory injury based on whole gene expression profile

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.

Robust Principal Component Analysis Integrating Sparse and Low-Rank Priors

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.

A Modified Principal Component Analysis Method for Honeycomb Sandwich Panel Debonding Recognition Based on Distributed Optical Fiber Sensing Signals

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.

Leveraging React Components in Business Process Management (BPM) Applications

As organizations increasingly embrace digital transformation, the integration of modern web technologies like React.js with Business Process Management (BPM) applications has become essential. React components offer flexibility, reusability, and scalability, making them ideal for enhancing user interfaces and driving user engagement within BPM environments. This article explores the benefits, challenges, and best practices of leveraging React components in BPM applications, along with real-world examples of successful implementations.

Predicting the alloying element yield in a ladle furnace using principal component analysis and deep neural network

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.