Analysis of the Employment Situation of Non Private Enterprises in Various Regions of China

In the past 30 years, Chinese enterprises have been a hot topic of discussion and concern among the general public in terms of economic and social status, ownership structure, business mechanism, and management level. Solving the problem of employment for the people is an important prerequisite for their peaceful living and work, as well as a prerequisite and foundation for building a harmonious society. The employment situation of private enterprises has always been of great concern to the outside world, and these two major jobs have always occupied an important position in the employment field of China that cannot be ignored. With the establishment of the market economy system, individual and private enterprises have become important components of the socialist economy, making significant contributions to economic development and social progress. The rapid development of China’s economy, on the one hand, is the embodiment of the superiority of China’s socialist market economic system, and on the other hand, it is the role of the tertiary industry and private enterprises in promoting the national economy. Since the 1990s, China’s private enterprises have become a new economic growth point for local and even national countries, and are one of the important ways to arrange employment and achieve social stability. This paper studies the employment of private enterprises and individuals from the perspective of statistics, extracts relevant data from China statistical Yearbook, uses the relevant knowledge of statistics to process the data, obtains the conclusion and puts forward relevant constructive suggestions.

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.

Screening biomarkers for spinal cord injury using weighted gene co-expression network analysis and machine learning

Immune changes and inflammatory responses have been identified as central events in the pathological process of spinal co rd injury.They can greatly affect nerve regeneration and functional recovery.However,there is still limited understanding of the peripheral immune inflammato ry response in spinal cord inju ry.In this study.we obtained microRNA expression profiles from the peripheral blood of patients with spinal co rd injury using high-throughput sequencing.We also obtained the mRNA expression profile of spinal cord injury patients from the Gene Expression Omnibus(GEO)database(GSE151371).We identified 54 differentially expressed microRNAs and 1656 diffe rentially expressed genes using bioinformatics approaches.Functional enrichment analysis revealed that various common immune and inflammation-related signaling pathways,such as neutrophil extracellular trap formation pathway,T cell receptor signaling pathway,and nuclear factor-κB signal pathway,we re abnormally activated or inhibited in spinal cord inju ry patient samples.We applied an integrated strategy that combines weighted gene co-expression network analysis,LASSO logistic regression,and SVM-RFE algorithm and identified three biomarke rs associated with spinal cord injury:ANO10,BST1,and ZFP36L2.We verified the expression levels and diagnostic perfo rmance of these three genes in the original training dataset and clinical samples through the receiver operating characteristic curve.Quantitative polymerase chain reaction results showed that ANO20 and BST1 mRNA levels were increased and ZFP36L2 mRNA was decreased in the peripheral blood of spinal cord injury patients.We also constructed a small RNA-mRNA interaction network using Cytoscape.Additionally,we evaluated the proportion of 22 types of immune cells in the peripheral blood of spinal co rd injury patients using the CIBERSORT tool.The proportions of naive B cells,plasma cells,monocytes,and neutrophils were increased while the proportions of memory B cells,CD8^(+)T cells,resting natural killer cells,resting dendritic cells,and eosinophils were markedly decreased in spinal cord injury patients increased compared with healthy subjects,and ANO10,BST1 and ZFP26L2we re closely related to the proportion of certain immune cell types.The findings from this study provide new directions for the development of treatment strategies related to immune inflammation in spinal co rd inju ry and suggest that ANO10,BST2,and ZFP36L2 are potential biomarkers for spinal cord injury.The study was registe red in the Chinese Clinical Trial Registry(registration No.ChiCTR2200066985,December 12,2022).

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.

Automatic area estimation of algal blooms in water bodies from UAV images using texture analysis

Algal blooms,the spread of algae on the surface of water bodies,have adverse effects not only on aquatic ecosystems but also on human life.The adverse effects of harmful algal blooms(HABs)necessitate a convenient solution for detection and monitoring.Unmanned aerial vehicles(UAVs)have recently emerged as a tool for algal bloom detection,efficiently providing on-demand images at high spatiotemporal resolutions.This study developed an image processing method for algal bloom area estimation from the aerial images(obtained from the internet)captured using UAVs.As a remote sensing method of HAB detection,analysis,and monitoring,a combination of histogram and texture analyses was used to efficiently estimate the area of HABs.Statistical features like entropy(using the Kullback-Leibler method)were emphasized with the aid of a gray-level co-occurrence matrix.The results showed that the orthogonal images demonstrated fewer errors,and the morphological filter best detected algal blooms in real time,with a precision of 80%.This study provided efficient image processing approaches using on-board UAVs for HAB monitoring.

Advances in microfluidic-based DNA methylation analysis

DNA methylation has been extensively investigated in recent years,not least because of its known relationship with various diseases.Progress in analytical methods can greatly increase the relevance of DNA methylation studies to both clinical medicine and scientific research.Microflu-idic chips are excellent carriers for molecular analysis,and their use can provide improvements from multiple aspects.On-chip molecular analysis has received extensive attention owing to its advantages of portability,high throughput,low cost,and high efficiency.In recent years,the use of novel microfluidic chips for DNA methylation analysis has been widely reported and has shown obvious superiority to conventional methods.In this review,wefirst focus on DNA methylation and its applications.Then,we discuss advanced microfluidic-based methods for DNA methylation analysis and describe the great progress that has been made in recent years.Finally,we summarize the advantages that microfluidic technology brings to DNA methylation analysis and describe several challenges and perspectives for on-chip DNA methylation analysis.This review should help researchers improve their understanding and make progress in developing microfluidic-based methods for DNA methylation analysis.

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.

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.

Evaluation of the seismic behavior of reinforced concrete structures with flat slab-column gravity frame and shear walls through nonlinear analysis methods

This paper presents an investigation of the seismic behavior of reinforced concrete(RC)structures in which shear walls are the main lateral load-resisting elements and the participation of flat slab floor systems is not considered in the seismic design procedure.In this regard,the behavior of six prototype structures(with different heights and plan layouts)is investigated through nonlinear static and time history analyses,implemented in the OpenSees platform.The results of the analyses are presented in terms of the behavior of the slab-column connections and their mode of failure at different loading stages.Moreover,the global response of the buildings is discussed in terms of some parameters,such as lateral overstrength due to the gravity flat slab-column frames.According to the nonlinear static analyses,in structures in which the slab-column connections were designed only for gravity loads,the slab-column connections exhibited a punching mode of failure even in the early stages of loading.However,the punching failure was eliminated in structures in which a minimum transverse reinforcement recommended in ACI 318(2019)was provided in the slabs at joint regions.Furthermore,despite neglecting the contribution of gravity flat slab-column frames in the lateral load resistance of the structures,a relatively significant overstrength was imposed on the structures by the gravity frames.

Molecular cloning,characterization and promoter analysis of LbgCWIN1 and its expression profiles in response to exogenous sucrose during in vitro bulblet initiation in lily

Lily(Lilium spp.) is an important ornamental flower, which is mainly propagated by bulbs. Cell wall invertases(CWINs), which catalyze the irreversibly conversion of sucrose into glucose and fructose in the extracellular space, are key enzymes participating in sucrose allocation in higher plants. Previous studies have shown that CWINs play an essential role in bulblet initiation process in bulbous crops, but the underlying molecular mechanism remains unclear. Here, a CWIN gene of Lilium brownii var. giganteum(Lbg) was identified and amplified from genomic DNA. Quantitative RT-PCR assays revealed that the expression level of LbgCWIN1 was highly upregulated exactly when the endogenous starch degraded in non-sucrose medium during in vitro bulblet initiation in Lbg. Phylogenetic relationship, motif, and domain analysis of LbgCWIN1 protein and CWINs in other plant species showed that all sequences of these CWIN proteins were highly conserved. The promoter sequence of LbgCWIN1 possessed a number of alpha-amylase-, phytohormone-, light-and stress-responsive cis-elements. Meanwhile, β-glucuronidase(GUS) assay showed that the 459 bp upstream fragment from the translational start site displayed maximal promoter activity. These results revealed that LbgCWIN1 might function in the process of in vitro bulblet initiation and be in the response to degradation of endogenous starch.

Preliminary safety analysis for heavy water-moderated molten salt reactor

The heavy water-moderated molten salt reactor(HWMSR)is a newly proposed reactor concept,in which heavy water is adopted as the moderator and molten salt dissolved with fissile and fertile elements is used as the fuel.Issues arising from graphite in traditional molten salt reactors,including the positive temperature coefficient and management of highly radio-active spent graphite waste,can be addressed using the HWMSR.Until now,research on the HWMSR has been centered on the core design and nuclear fuel cycle to explore the viability of the HWMSR and its advantages in fuel utilization.However,the core safety of the HWMSR has not been extensively studied.Therefore,we evaluate typical accidents in a small modular HWMSR,including fuel salt inlet temperature overcooling and overheating accidents,fuel salt inlet flow rate decrease,heavy water inlet temperature overcooling accidents,and heavy water inlet mass flow rate decrease accidents,based on a neutronics and thermal-hydraulics coupled code.The results demonstrated that the core maintained safety during the investigated accidents.

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.

Stability Analysis of Inverse Lax-Wendroff Procedure for a High order Compact Finite Difference Schemes

This paper considers the finite difference(FD)approximations of diffusion operators and the boundary treatments for different boundary conditions.The proposed schemes have the compact form and could achieve arbitrary even order of accuracy.The main idea is to make use of the lower order compact schemes recursively,so as to obtain the high order compact schemes formally.Moreover,the schemes can be implemented efficiently by solving a series of tridiagonal systems recursively or the fast Fourier transform(FFT).With mathematical induction,the eigenvalues of the proposed differencing operators are shown to be bounded away from zero,which indicates the positive definiteness of the operators.To obtain numerical boundary conditions for the high order schemes,the simplified inverse Lax-Wendroff(SILW)procedure is adopted and the stability analysis is performed by the Godunov-Ryabenkii method and the eigenvalue spectrum visualization method.Various numerical experiments are provided to demonstrate the effectiveness and robustness of our algorithms.

Trends and hotspots in gastrointestinal neoplasms risk assessment: A bibliometric analysis from 1984 to 2022

BACKGROUND Gastrointestinal neoplasm(GN)significantly impact the global cancer burden and mortality,necessitating early detection and treatment.Understanding the evolution and current state of research in this field is vital.AIM To conducts a comprehensive bibliometric analysis of publications from 1984 to 2022 to elucidate the trends and hotspots in the GN risk assessment research,focusing on key contributors,institutions,and thematic evolution.METHODS This study conducted a bibliometric analysis of data from the Web of Science Core Collection database using the"bibliometrix"R package,VOSviewer,and CiteSpace.The analysis focused on the distribution of publications,contributions by institutions and countries,and trends in keywords.The methods included data synthesis,network analysis,and visualization of international collaboration networks.RESULTS This analysis of 1371 articles on GN risk assessment revealed a notable evolution in terms of research focus and collaboration.It highlights the United States'critical role in advancing this field,with significant contributions from institutions such as Brigham and Women's Hospital and the National Cancer Institute.The last five years,substantial advancements have been made,representing nearly 45%of the examined literature.Publication rates have dramatically increased,from 20 articles in 2002 to 112 in 2022,reflecting intensified research efforts.This study underscores a growing trend toward interdisciplinary and international collaboration,with the Journal of Clinical Oncology standing out as a key publication outlet.This shift toward more comprehensive and collaborative research methods marks a significant step in addressing GN risks.CONCLUSION This study underscores advancements in GN risk assessment through genetic analyses and machine learning and reveals significant geographical disparities in research emphasis.This calls for enhanced global collaboration and integration of artificial intelligence to improve cancer prevention and treatment accuracy,ultimately enhancing worldwide patient care.

Action of circulating and infiltrating B cells in the immune microenvironment of colorectal cancer by single-cell sequencing analysis

BACKGROUND The complexity of the immune microenvironment has an impact on the treatment of colorectal cancer(CRC),one of the most prevalent malignancies worldwide.In this study,multi-omics and single-cell sequencing techniques were used to investigate the mechanism of action of circulating and infiltrating B cells in CRC.By revealing the heterogeneity and functional differences of B cells in cancer immunity,we aim to deepen our understanding of immune regulation and provide a scientific basis for the development of more effective cancer treatment strategies.AIM To explore the role of circulating and infiltrating B cell subsets in the immune microenvironment of CRC,explore the potential driving mechanism of B cell development,analyze the interaction between B cells and other immune cells in the immune microenvironment and the functions of communication molecules,and search for possible regulatory pathways to promote the anti-tumor effects of B cells.METHODS A total of 69 paracancer(normal),tumor and peripheral blood samples were collected from 23 patients with CRC from The Cancer Genome Atlas database(https://portal.gdc.cancer.gov/).After the immune cells were sorted by multicolor flow cytometry,the single cell transcriptome and B cell receptor group library were sequenced using the 10X Genomics platform,and the data were analyzed using bioinformatics tools such as Seurat.The differences in the number and function of B cell infiltration between tumor and normal tissue,the interaction between B cell subsets and T cells and myeloid cell subsets,and the transcription factor regulatory network of B cell subsets were explored and analyzed.RESULTS Compared with normal tissue,the infiltrating number of CD20+B cell subsets in tumor tissue increased significantly.Among them,germinal center B cells(GCB)played the most prominent role,with positive clone expansion and heavy chain mutation level increasing,and the trend of differentiation into memory B cells increased.However,the number of plasma cells in the tumor microenvironment decreased significantly,and the plasma cells secreting IgA antibodies decreased most obviously.In addition,compared with the immune microenvironment of normal tissues,GCB cells in tumor tissues became more closely connected with other immune cells such as T cells,and communication molecules that positively regulate immune function were significantly enriched.CONCLUSION The role of GCB in CRC tumor microenvironment is greatly enhanced,and its affinity to tumor antigen is enhanced by its significantly increased heavy chain mutation level.Meanwhile,GCB has enhanced its association with immune cells in the microenvironment,which plays a positive anti-tumor effect.

Diagnostic Accuracy of Computerized Bowel Sound Analysis with Non-Invasive Devices for Irritable Bowel Syndrome:A Systematic Review and Meta-Analysis

Objective To assess the diagnostic accuracy of bowel sound analysis for irritable bowel syndrome(IBS)with a systematic review and meta-analysis.Methods We searched MEDLINE,Embase,the Cochrane Library,Web of Science,and IEEE Xplore databases until September 2023.Cross-sectional and case-control studies on diagnostic accuracy of bowel sound analysis for IBS were identified.We estimated the pooled sensitivity,specificity,positive likelihood ratio,negative likeli-hood ratio,and diagnostic odds ratio with a 95% confidence interval(CI),and plotted a summary receiver operat-ing characteristic curve and evaluated the area under the curve.Results Four studies were included.The pooled diagnostic sensitivity,specificity,positive likelihood ratio,nega-tive likelihood ratio,and diagnostic odds ratio were 0.94(95%CI,0.87‒0.97),0.89(95%CI,0.81‒0.94),8.43(95%CI,4.81‒14.78),0.07(95%CI,0.03‒0.15),and 118.86(95%CI,44.18‒319.75),respectively,with an area under the curve of 0.97(95%CI,0.95‒0.98).Conclusions Computerized bowel sound analysis is a promising tool for IBS.However,limited high-quality data make the results'validity and applicability questionable.There is a need for more diagnostic test accuracy studies and better wearable devices for monitoring and analysis of IBS.

Stochastic Analysis and Modeling of Velocity Observations in Turbulent Flows

Highly turbulent water flows,often encountered near human constructions like bridge piers,spillways,and weirs,display intricate dynamics characterized by the formation of eddies and vortices.These formations,varying in sizes and lifespans,significantly influence the distribution of fluid velocities within the flow.Subsequently,the rapid velocity fluctuations in highly turbulent flows lead to elevated shear and normal stress levels.For this reason,to meticulously study these dynamics,more often than not,physical modeling is employed for studying the impact of turbulent flows on the stability and longevity of nearby structures.Despite the effectiveness of physical modeling,various monitoring challenges arise,including flow disruption,the necessity for concurrent gauging at multiple locations,and the duration of measurements.Addressing these challenges,image velocimetry emerges as an ideal method in fluid mechanics,particularly for studying turbulent flows.To account for measurement duration,a probabilistic approach utilizing a probability density function(PDF)is suggested to mitigate uncertainty in estimated average and maximum values.However,it becomes evident that deriving the PDF is not straightforward for all turbulence-induced stresses.In response,this study proposes a novel approach by combining image velocimetry with a stochastic model to provide a generic yet accurate description of flow dynamics in such applications.This integration enables an approach based on the probability of failure,facilitating a more comprehensive analysis of turbulent flows.Such an approach is essential for estimating both short-and long-term stresses on hydraulic constructions under assessment.

Harnessing the Market Potential of the Bamboo Industry in Central Luzon, Philippines: An Analysis of the Internal and External Environment

The bamboo industry in Central Luzon holds significant promise for economic development and environmental sustainability. This study aims to analyze the internal and external factors influencing the bamboo industry in the region through SWOT and PESTLE analyses. Based on a focus group discussion involving key industry players, the study explores the industry’s strengths, weaknesses, opportunities, and threats, as well as political, economic, social, technological, legal, and environmental factors. Findings reveal the importance of comprehensive strategies that address political stability, economic growth, consumer awareness, technological advancement, legal compliance, and environmental sustainability. Recommendations include capacity-building for production and marketing, the establishment of bamboo treatment facilities, and advocacy for supportive policies. By addressing these factors, the bamboo industry in Central Luzon can realize its potential for socio-economic development and environmental stewardship.

User Profile & Attitude Analysis Based on Unstructured Social Media and Online Activity

As social media and online activity continue to pervade all age groups, it serves as a crucial platform for sharing personal experiences and opinions as well as information about attitudes and preferences for certain interests or purchases. This generates a wealth of behavioral data, which, while invaluable to businesses, researchers, policymakers, and the cybersecurity sector, presents significant challenges due to its unstructured nature. Existing tools for analyzing this data often lack the capability to effectively retrieve and process it comprehensively. This paper addresses the need for an advanced analytical tool that ethically and legally collects and analyzes social media data and online activity logs, constructing detailed and structured user profiles. It reviews current solutions, highlights their limitations, and introduces a new approach, the Advanced Social Analyzer (ASAN), that bridges these gaps. The proposed solutions technical aspects, implementation, and evaluation are discussed, with results compared to existing methodologies. The paper concludes by suggesting future research directions to further enhance the utility and effectiveness of social media data analysis.

R-Factor Analysis of Data Based on Population Models Comprising R- and Q-Factors Leads to Biased Loading Estimates

Effects of performing an R-factor analysis of observed variables based on population models comprising R- and Q-factors were investigated. Although R-factor analysis of data based on a population model comprising R- and Q-factors is possible, this may lead to model error. Accordingly, loading estimates resulting from R-factor analysis of sample data drawn from a population based on a combination of R- and Q-factors will be biased. It was shown in a simulation study that a large amount of Q-factor variance induces an increase in the variation of R-factor loading estimates beyond the chance level. Tests of the multivariate kurtosis of observed variables are proposed as an indicator of possible Q-factor variance in observed variables as a prerequisite for R-factor analysis.