Incorporating empirical knowledge into data-driven variable selection for quantitative analysis of coal ash content by laser-induced breakdown spectroscopy

Laser-induced breakdown spectroscopy(LIBS)has become a widely used atomic spectroscopic technique for rapid coal analysis.However,the vast amount of spectral information in LIBS contains signal uncertainty,which can affect its quantification performance.In this work,we propose a hybrid variable selection method to improve the performance of LIBS quantification.Important variables are first identified using Pearson's correlation coefficient,mutual information,least absolute shrinkage and selection operator(LASSO)and random forest,and then filtered and combined with empirical variables related to fingerprint elements of coal ash content.Subsequently,these variables are fed into a partial least squares regression(PLSR).Additionally,in some models,certain variables unrelated to ash content are removed manually to study the impact of variable deselection on model performance.The proposed hybrid strategy was tested on three LIBS datasets for quantitative analysis of coal ash content and compared with the corresponding data-driven baseline method.It is significantly better than the variable selection only method based on empirical knowledge and in most cases outperforms the baseline method.The results showed that on all three datasets the hybrid strategy for variable selection combining empirical knowledge and data-driven algorithms achieved the lowest root mean square error of prediction(RMSEP)values of 1.605,3.478 and 1.647,respectively,which were significantly lower than those obtained from multiple linear regression using only 12 empirical variables,which are 1.959,3.718 and 2.181,respectively.The LASSO-PLSR model with empirical support and 20 selected variables exhibited a significantly improved performance after variable deselection,with RMSEP values dropping from 1.635,3.962 and 1.647 to 1.483,3.086 and 1.567,respectively.Such results demonstrate that using empirical knowledge as a support for datadriven variable selection can be a viable approach to improve the accuracy and reliability of LIBS quantification.

Evaluation of Phytochemical Analysis and Total Phenol Content of Proso Millet and Barnyard Millet

Whole grains of proso and barnyard millets were sequentially extracted using different solvents(hexane,chloroform,ethyl acetate,and methanol).Phytochemical analysis was performed qualitatively,and the total phenolic content in the extracts of proso and barnyard millets was quantified.Alkaloids and cardiac glycosides were identified in all solvent extracts of both millets.Anthraquinone and glycosides yielded negative results in all solvent extracts of both millets.Among all the solvent extracts,methanol extracts of proso and barnyard millets showed the presence of major compounds such as flavonoids,terpenoids,amino acids,tannins,and phenolics compounds.The maximum amount of phenols was found in methanolic extracts of proso and barnyard millets(0.669±0.003 and 0.625±0.003),followed by the chloroform extract of proso and barnyard millets(0.284±0.002 and 0.257±0.003).The minimum amount of phenolics was found in the acetone extract of proso and barnyard millets.The methanol extract of both millets showed the presence of major compounds with high phenolic content.

A Content Analysis Study Describing Information Regarding Female Genital Mutilation Present in US Medical Textbooks

This study examines the depth of information regarding Female Genital Mutilation (FGM)/Female Cutting (FC) present in US medical textbooks. According to Klein et al. (2018) [1], despite substantial attention regarding the effects of FGM, this practice is still ongoing all over the world. Healthcare providers are very important actors who need to understand FGM in order to provide care to girls and women subjected to it, but the small amount of existing work in this area shows a low level of knowledge and training about FGM/FC among U.S. physicians. The Center for Disease Control and Prevention (CDC) first published the estimated number of girls at risk of FGM/FC in the U.S. to be about 168,000 in 1997;that number had increased to 513,000 girls in the US by 2012 (according to Goldberg et al.) In this study, a small sample of medical textbooks were analyzed for the degree to which the textbooks include information regarding FGM/FC. The results of this study show that US medical textbooks do not include information regarding FGM/FC, suggesting that most medical providers don’t have knowledge and training about FGM/FC. The inadequate information on FGM/FC in medical textbooks reflects a culturally biased lack of recognition of the number of girls and women affected in the US. This maintains an ongoing pattern of silence that reinforces gender inequality. Considering the inadequate information covered in US medical textbooks on FGM/FC, and the concordant lack of provider training to provide advice and care to affected girls and women, incorrect and missed diagnoses may lead to harm to victims of FGM/FC. Providers would be better equipped to care for women and girls if their medical training included information regarding FGM/FC.

Overview of Precious Metal Content Analysis Methods in Automotive Catalytic Converter

With the increasing awareness of environmental protection, people’s concern of pollution issues arising. Vehicles, as the most important means of transportation, its exhaust emission has received considerable attention. The catalytic converter is able to purify harmful substances in exhaust gas. The absolute content of precious metals in the catalytic converter dominates the exhaust gas purification effect. Accurate detection of precious metal content is of great significance for controlling the cost of catalysts, ensuring catalytic performance and recovering precious metals from spent catalysts. We herein summarized several instruments for precious metals content exploration, such as X-ray fluorescence spectrometer (XRF), atomic absorption spectrometer (AAS), inductively coupled plasma emission spectrometer (ICP) and spectrophotometer. In this thesis, the feasibility of using various devices for characterizing precious metal content in catalytic converters is analyzed and their strengths or weaknesses are elaborated.

QTL analysis for ascorbic acid content in strawberry fruit reveals a complex genetic architecture and association with GDP-L-galactose phosphorylase

Strawberry(Fragaria×ananassa)fruits are an excellent source of L-ascorbic acid(AsA),a powerful antioxidant for plants and humans.Identifying the genetic components underlying AsA accumulation is crucial for enhancing strawberry nutritional quality.Here,we unravel the genetic architecture of AsA accumulation using an F1 population derived from parental lines‘Candonga’and‘Senga Sengana’,adapted to distinct Southern and Northern European areas.To account for environmental effects,the F1 and parental lines were grown and phenotyped in five locations across Europe(France,Germany,Italy,Poland and Spain).Fruit AsA content displayed normal distribution typical of quantitative traits and ranged five-fold,with significant differences among genotypes and environments.AsA content in each country and the average in all of them was used in combination with 6,974 markers for quantitative trait locus(QTL)analysis.Environmentally stable QTLs for AsA content were detected in linkage group(LG)3A,LG 5A,LG 5B,LG 6B and LG 7C.Candidate genes were identified within stable QTL intervals and expression analysis in lines with contrasting AsA content suggested that GDP-L-Galactose Phosphorylase FaGGP(3A),and the chloroplast-located AsA transporter gene FaPHT4;4(7C)might be the underlying genetic factors for QTLs on LG 3A and 7C,respectively.We show that recessive alleles of FaGGP(3A)inherited from both parental lines increase fruit AsA content.Furthermore,expression of FaGGP(3A)was two-fold higher in lines with high AsA.Markers here identified represent a useful resource for efficient selection of new strawberry cultivars with increased AsA content.

Seismic Liquefaction Resistance Based on Strain Energy Concept Considering Fine Content Value Effect and Performance Parametric Sensitivity Analysis

Liquefaction is one of the most destructive phenomena caused by earthquakes,which has been studied in the issues of potential,triggering and hazard analysis.The strain energy approach is a common method to investigate liquefaction potential.In this study,two Artificial Neural Network(ANN)models were developed to estimate the liquefaction resistance of sandy soil based on the capacity strain energy concept(W)by using laboratory test data.A large database was collected from the literature.One group of the dataset was utilized for validating the process in order to prevent overtraining the presented model.To investigate the complex influence of fine content(FC)on liquefaction resistance,according to previous studies,the second database was arranged by samples with FC of less than 28%and was used to train the second ANN model.Then,two presented ANN models in this study,in addition to four extra available models,were applied to an additional 20 new samples for comparing their results to show the capability and accuracy of the presented models herein.Furthermore,a parametric sensitivity analysis was performed through Monte Carlo Simulation(MCS)to evaluate the effects of parameters and their uncertainties on the liquefaction resistance of soils.According to the results,the developed models provide a higher accuracy prediction performance than the previously publishedmodels.The sensitivity analysis illustrated that the uncertainties of grading parameters significantly affect the liquefaction resistance of soils.

Social-ecological perspective on the suicidal behaviour factors of early adolescents in China:a network analysis

Background In early adolescence,youth are highly prone to suicidal behaviours.Identifying modifiable risk factors during this critical phase is a priority to inform effective suicide prevention strategies.Aims To explore the risk and protective factors of suicidal behaviours(ie,suicidal ideation,plans and attempts)in early adolescence in China using a social-ecological perspective.Methods Using data from the cross-sectional project‘Healthy and Risky Behaviours Among Middle School Students in Anhui Province,China',stratified random cluster sampling was used to select 5724 middle school students who had completed self-report questionnaires in November 2020.Network analysis was employed to examine the correlates of suicidal ideation,plans and attempts at four levels,namely individual(sex,academic performance,serious physical llness/disability,history of self-harm,depression,impulsivity,sleep problems,resilience),family(family economic status,relationship with mother,relationship with father,family violence,childhood abuse,parental mental illness),school(relationship with teachers,relationship with classmates,school-bullying victimisation and perpetration)and social(social support,satisfaction with society).Results In total,37.9%,19.0%and 5.5%of the students reported suicidal ideation,plans and attempts in the past 6 months,respectively.The estimated network revealed that suicidal ideation,plans and attempts were collectively associated with a history of self-harm,sleep problems,childhood abuse,school bullying and victimisation.Centrality analysis indicated that the most influential nodes in the network were history of self-harm and childhood abuse.Notably,the network also showed unique correlates of suicidal ideation(sex,weight=0.60;impulsivity,weight=0.24;family violence,weight=0.17;relationship with teachers,weight=-0.03;school-bullying perpetration,weight=0.22),suicidal plans(social support,weight=-0.15)and suicidal attempts(relationship with mother,weight=-0.10;parental mental llness,weight=0.61).Conclusions This study identified the correlates of suicidal ideation,plans and attempts,and provided practical implications for suicide prevention for young adolescents in China.Firstly,this study highlighted the importance of joint interventions across multiple departments.Secondly,the common risk factors of suicidal ideation,plans and attempts were elucidated.Thirdly,this study proposed target interventions to address the unique influencing factors of suicidal ideation,plans and attempts.

Multimodal sentiment analysis for social media contents during public emergencies

Purpose:Nowadays,public opinions during public emergencies involve not only textual contents but also contain images.However,the existing works mainly focus on textual contents and they do not provide a satisfactory accuracy of sentiment analysis,lacking the combination of multimodal contents.In this paper,we propose to combine texts and images generated in the social media to perform sentiment analysis.Design/methodology/approach:We propose a Deep Multimodal Fusion Model(DMFM),which combines textual and visual sentiment analysis.We first train word2vec model on a large-scale public emergency corpus to obtain semantic-rich word vectors as the input of textual sentiment analysis.BiLSTM is employed to generate encoded textual embeddings.To fully excavate visual information from images,a modified pretrained VGG16-based sentiment analysis network is used with the best-performed fine-tuning strategy.A multimodal fusion method is implemented to fuse textual and visual embeddings completely,producing predicted labels.Findings:We performed extensive experiments on Weibo and Twitter public emergency datasets,to evaluate the performance of our proposed model.Experimental results demonstrate that the DMFM provides higher accuracy compared with baseline models.The introduction of images can boost the performance of sentiment analysis during public emergencies.Research limitations:In the future,we will test our model in a wider dataset.We will also consider a better way to learn the multimodal fusion information.Practical implications:We build an efficient multimodal sentiment analysis model for the social media contents during public emergencies.Originality/value:We consider the images posted by online users during public emergencies on social platforms.The proposed method can present a novel scope for sentiment analysis during public emergencies and provide the decision support for the government when formulating policies in public emergencies.

基于新型Contention网络的滚动轴承早期故障诊断方法研究

针对滚动轴承早期故障诊断问题,为了同时建模振动信号中的高频和低频特征,实现高精度诊断,提出了一种新的模型Contention。它以一种互补的方式集成了空洞卷积和自注意力机制的优点,具有同时捕捉高频和低频信息的能力。为了验证其诊断能力,首先,在完整信息原则下将振动信号连续构造成数据集;其次,搭建Contention网络并训练,其最终测试集准确度可达100%,t-SNE显示随网络层次的深入特征被明显聚类;最后,设置对照实验,将该模型与传统RNN、CNN、Transformer模型对比,证明该模型具备突出的诊断能力。

Lagrangian coherent structure analysis on transport of Acetes chinensis along coast of Lianyungang,China

Spatial heterogeneity or“patchiness”of plankton distributions in the ocean has always been an attractive and challenging scientific issue to oceanographers.We focused on the accumulation and dynamic mechanism of the Acetes chinensis in the Lianyungang nearshore licensed fishing area.The Lagrangian frame approaches including the Lagrangian coherent structures theory,Lagrangian residual current,and Lagrangian particle-tracking model were applied to find the transport pathways and aggregation characteristics of Acetes chinensis.There exist some material transport pathways for Acetes chinensis passing through the licensed fishing area,and Acetes chinensis is easy to accumulate in the licensed fishing area.The main mechanism forming this distribution pattern is the local circulation induced by the nonlinear interaction of topography and tidal flow.Both the Lagrangian coherent structure analysis and the particle trajectory tracking indicate that Acetes chinensis in the licensed fishing area come from the nearshore estuary.This work contributed to the adjustment of licensed fishing area and the efficient utilization of fishery resources.

Accuracy analysis of iron content of galvanized coating using an X-ray fluorescence spectrometer with an L-spectrum

The accuracy(repeatability and reproducibility) of the iron content analysis of galvanized coating using an X-ray fluorescence spectrometer with an L-spectrum is not better than that of flame atomic absorption spectrometry, sometimes it exceeds the quality control limit.Influences, such as current, voltage, equipment(internal circulating water, 10%CH4+90%Ar, and vacuum) checking, instrument monitoring, sample cleaning, and oper-ators, were investigated by means of 6-sigma and lean operations to improve accuracy.

Semi-analytical solution for drained expansion analysis of a hollow cylinder of critical state soils

The expansion of a thick-walled hollow cylinder in soil is of non-self-similar nature that the stress/deformation paths are not the same for different soil material points.As a result,this problem cannot be solved by the common self-similar-based similarity techniques.This paper proposes a novel,exact solution for rigorous drained expansion analysis of a hollow cylinder of critical state soils.Considering stress-dependent elastic moduli of soils,new analytical stress and displacement solutions for the nonself-similar problem are developed taking the small strain assumption in the elastic zone.In the plastic zone,the cavity expansion response is formulated into a set of first-order partial differential equations(PDEs)with the combination use of Eulerian and Lagrangian descriptions,and a novel solution algorithm is developed to efficiently solve this complex boundary value problem.The solution is presented in a general form and thus can be useful for a wide range of soils.With the new solution,the non-self-similar nature induced by the finite outer boundary is clearly demonstrated and highlighted,which is found to be greatly different to the behaviour of cavity expansion in infinite soil mass.The present solution may serve as a benchmark for verifying the performance of advanced numerical techniques with critical state soil models and be used to capture the finite boundary effect for pressuremeter tests in small-sized calibration chambers.

Model reduction of fractional impedance spectra for time–frequency analysis of batteries, fuel cells, and supercapacitors

Joint time–frequency analysis is an emerging method for interpreting the underlying physics in fuel cells,batteries,and supercapacitors.To increase the reliability of time–frequency analysis,a theoretical correlation between frequency-domain stationary analysis and time-domain transient analysis is urgently required.The present work formularizes a thorough model reduction of fractional impedance spectra for electrochemical energy devices involving not only the model reduction from fractional-order models to integer-order models and from high-to low-order RC circuits but also insight into the evolution of the characteristic time constants during the whole reduction process.The following work has been carried out:(i)the model-reduction theory is addressed for typical Warburg elements and RC circuits based on the continued fraction expansion theory and the response error minimization technique,respectively;(ii)the order effect on the model reduction of typical Warburg elements is quantitatively evaluated by time–frequency analysis;(iii)the results of time–frequency analysis are confirmed to be useful to determine the reduction order in terms of the kinetic information needed to be captured;and(iv)the results of time–frequency analysis are validated for the model reduction of fractional impedance spectra for lithium-ion batteries,supercapacitors,and solid oxide fuel cells.In turn,the numerical validation has demonstrated the powerful function of the joint time–frequency analysis.The thorough model reduction of fractional impedance spectra addressed in the present work not only clarifies the relationship between time-domain transient analysis and frequency-domain stationary analysis but also enhances the reliability of the joint time–frequency analysis for electrochemical energy devices.

Transcriptome-based analysis of key genes and pathways affecting the linoleic acid content in chickens

Linoleic acid is an essential polyunsaturated fatty acid that cannot be synthesized by humans or animals themselves and can only be obtained externally.The amount of linoleic acid present has an impact on the quality and flavour of meat and indirectly affects consumer preference.However,the molecular mechanisms influencing the deposition of linoleic acid in organisms are not clear.As the molecular mechanisms of linoleic acid deposition are not well understood,to investigate the main effector genes affecting the linoleic acid content,this study aimed to screen for hub genes in slow-type yellow-feathered chickens by transcriptome sequencing(RNA-Seq)and weighted gene coexpression network analysis(WGCNA).We screened for candidate genes associated with the linoleic acid content in slow-type yellow-feathered broilers.A total of 399 Tiannong partridge chickens were slaughtered at 126 days of age,fatty acid levels were measured in pectoral muscle,and pectoral muscle tissue was collected for transcriptome sequencing.Transcriptome sequencing results were combined with phenotypes for WGCNA to screen for candidate genes.KEGG enrichment analysis was also performed on the genes that were significantly enriched in the modules with the highest correlation.A total of 13310 genes were identified after quality control of transcriptomic data from 399 pectoral muscle tissues.WGCNA was performed,and a total of 26 modules were obtained,eight of which were highly correlated with the linoleic acid content.Four key genes,namely,MDH2,ATP5B,RPL7A and PDGFRA,were screened according to the criteria|GS|>0.2 and|MM|>0.8.The functional enrichment results showed that the genes within the target modules were mainly enriched in metabolic pathways.In this study,a large-sample-size transcriptome analysis revealed that metabolic pathways play an important role in the regulation of the linoleic acid content in Tiannong partridge chickens,and MDH2,ATP5B,RPL7A and PDGFRA were screened as important candidate genes affecting the linoleic acid content.The results of this study provide a theoretical basis for selecting molecular markers and comprehensively understanding the molecular mechanism affecting the linoleic acid content in muscle,providing an important reference for the breeding of slow-type yellowfeathered broiler chickens.

Application of Isogeometric Analysis Method in Three-Dimensional Gear Contact Analysis

Gears are pivotal in mechanical drives,and gear contact analysis is a typically difficult problem to solve.Emerging isogeometric analysis(IGA)methods have developed new ideas to solve this problem.In this paper,a threedimensional body parametric gear model of IGA is established,and a theoretical formula is derived to realize single-tooth contact analysis.Results were benchmarked against those obtained from commercial software utilizing the finite element analysis(FEA)method to validate the accuracy of our approach.Our findings indicate that the IGA-based contact algorithmsuccessfullymet theHertz contact test.When juxtaposed with the FEA approach,the IGAmethod demonstrated fewer node degrees of freedomand reduced computational units,all whilemaintaining comparable accuracy.Notably,the IGA method appeared to exhibit consistency in analysis accuracy irrespective of computational unit density,and also significantlymitigated non-physical oscillations in contact stress across the tooth width.This underscores the prowess of IGA in contact analysis.In conclusion,IGA emerges as a potent tool for addressing contact analysis challenges and holds significant promise for 3D gear modeling,simulation,and optimization of various mechanical components.

Preliminary electromagnetic analysis of the COOL blanket for CFETR

The supercritical CO_(2)cOoled Lithium-Lead(COOL)blanket has been designed as one advanced blanket candidate for the Chinese Fusion Engineering Test Reactor(CFETR).This work focuses on the electromagnetic(EM)loads(Maxwell force and Lorentz force)acting on the COOL blanket,which are important mechanical loads in further structural analysis of the COOL blanket.A 3D electromagnetic analysis is performed using the ANSYS finite element method to obtain EM loads on the COOL blanket in this study.At first,the magnetic scalar potential(MSP)method is used to obtain the magnetic field and the Maxwell force on the COOL blanket.Then,the magnetic vector potential(MVP)method is performed during a plasma disruption event to get the eddy current distribution.At last,a multi-step method is adopted for the calculation of the Lorentz force and the torque.The maximum Lorentz forces of inboard and outboard blanket structural components are 5624 kN and 2360 kN respectively.

SFGA-CPA: A Novel Screening Correlation Power Analysis Framework Based on Genetic Algorithm

Correlation power analysis(CPA)combined with genetic algorithms(GA)now achieves greater attack efficiency and can recover all subkeys simultaneously.However,two issues in GA-based CPA still need to be addressed:key degeneration and slow evolution within populations.These challenges significantly hinder key recovery efforts.This paper proposes a screening correlation power analysis framework combined with a genetic algorithm,named SFGA-CPA,to address these issues.SFGA-CPA introduces three operations designed to exploit CPA characteris-tics:propagative operation,constrained crossover,and constrained mutation.Firstly,the propagative operation accelerates population evolution by maximizing the number of correct bytes in each individual.Secondly,the constrained crossover and mutation operations effectively address key degeneration by preventing the compromise of correct bytes.Finally,an intelligent search method is proposed to identify optimal parameters,further improving attack efficiency.Experiments were conducted on both simulated environments and real power traces collected from the SAKURA-G platform.In the case of simulation,SFGA-CPA reduces the number of traces by 27.3%and 60%compared to CPA based on multiple screening methods(MS-CPA)and CPA based on simple GA method(SGA-CPA)when the success rate reaches 90%.Moreover,real experimental results on the SAKURA-G platform demonstrate that our approach outperforms other methods.

A Survey of Digital Content Required for STD Prevention Education Taught by School Nurses

Purpose: In recent years, there has been concern in Japan about the increase in STDs among adolescents aged 15 - 19. In order to prevent STDs and guide adolescents toward desirable sexual behavior, this study’s goal was to examine the actual state of STD prevention education taught by school nurses as part of the school curriculum, as well as the kinds of digital content they wish to have for teaching on the subject. Method: An anonymous, self-administered questionnaire was distributed by email and leaflet. Targeting the 100 valid responses received from the surveyed school nurses, descriptive statistics were made for each survey item and comparisons were made between the early- and mid-career groups based on years of experience. Results: 70.0% of respondents reported teaching about STDs in Health & Physical Education classes. School nurses in the early-career group used ready-made materials, while the mid-career group used both ready-made and self-made materials. 95% of respondents reported that they had little or no knowledge of STDs, while 84% reported that they were “good” or “fairly good” at teaching classes on sexuality. Both groups reported difficulty with the topics of “phimosis” and “nocturnal emissions” in the physiological category, “sexual behavior” in the “sex-adjacent” category, and “sexual and reproductive issues” in the psychosocial category. Respondents expressed a need for digital content covering the topics of “sexual violence”, “sexual abuse”, “sexually transmitted diseases”, and “how to turn down sex”. The mid-career group desired digital content for more items than the early-career group, with significant differences in the pathogens involved in sexually transmitted diseases and sex-adjacent items. 32.0% of respondents answered that they had done self-study for sex education classes in the past three years, and both groups desired self-study on “sexually transmitted diseases”, with the mid-career group significantly higher than the early-career group on “cervical cancer & HPV”, “emergency contraceptives”, and “media literacy”. Conclusion: Health & Physical Education is the main subject that covers STDs, and pre-existing materials are commonly used. It is expected that this is due to nurses’ busy schedules and the fact that only one school nurse is assigned to each school. The data collected suggested that nurse teachers want categorized digital content that can be used in the classes they are responsible for, rather than educational content on topics they are not comfortable with. In addition, given the changing environment surrounding sex, it was clear that they wish to understand the realities and needs of the high school students they teach, and desire self-study opportunities to improve their teaching methods.

Geometric prior guided hybrid deep neural network for facial beauty analysis

Facial beauty analysis is an important topic in human society.It may be used as a guidance for face beautification applications such as cosmetic surgery.Deep neural networks(DNNs)have recently been adopted for facial beauty analysis and have achieved remarkable performance.However,most existing DNN-based models regard facial beauty analysis as a normal classification task.They ignore important prior knowledge in traditional machine learning models which illustrate the significant contribution of the geometric features in facial beauty analysis.To be specific,landmarks of the whole face and facial organs are introduced to extract geometric features to make the decision.Inspired by this,we introduce a novel dual-branch network for facial beauty analysis:one branch takes the Swin Transformer as the backbone to model the full face and global patterns,and another branch focuses on the masked facial organs with the residual network to model the local patterns of certain facial parts.Additionally,the designed multi-scale feature fusion module can further facilitate our network to learn complementary semantic information between the two branches.In model optimisation,we propose a hybrid loss function,where especially geometric regulation is introduced by regressing the facial landmarks and it can force the extracted features to convey facial geometric features.Experiments performed on the SCUT-FBP5500 dataset and the SCUT-FBP dataset demonstrate that our model outperforms the state-of-the-art convolutional neural networks models,which proves the effectiveness of the proposed geometric regularisation and dual-branch structure with the hybrid network.To the best of our knowledge,this is the first study to introduce a Vision Transformer into the facial beauty analysis task.

Quantitative characterizations of anisotropic dynamic properties in organic-rich shale with different kerogen content

Understanding the quantitative responses of anisotropic dynamic properties in organic-rich shale with different kerogen content(KC)is of great significance in hydrocarbon exploration and development.Conducting controlled experiments with a single variable is challenging for natural shales due to their high variations in components,diagenesis conditions,or pore fluid.We employed the hot-pressing technique to construct 11 well-controlled artificial shale with varying KC.These artificial shale samples were successive machined into prismatic shape for ultrasonic measurements along different directions.Observations revealed bedding perpendicular P-wave velocities are more sensitive to the increasing KC than bedding paralleling velocities due to the preferential alignments of kerogen.All elastic stiffnesses except C_(13)are generally decreasing with the increasing KC,the variation of C_(1) and C_(33)on kerogen content are more sensitive than those of C_(44)and C_(66).Apparent dynamic mechanical parameters(v and E)were found to have linear correlation with the true ones from complete anisotropic equations independent of KC,which hold value towards the interpretation of well logs consistently across formations,Anisotropic mechanical parameters(ΔE and brittlenessΔB)tend to decrease with the reducing KC,withΔB showing great sensitivity to KC variations.In the range of low KC(<10%),the V_(P)/V_(S) ratio demonstrated a linearly negative correlation with KC,and the V_(P)/V_(S) ratio magnitude of less than 1.75may serve as a significant characterization for highly organic-rich(>10%)shale,compilation of data from natural organic rich-shales globally verified the similar systematic relationships that can be empirically used to predict the fraction of KC in shales.