Combining Symbolic tools with interval analysis. An application to solve robust control problems.

Complex systems are often subjected to uncertainties that make its model difficult, if not impossible to obtain. A quantitative model may be inadequate to represent the behavior of systems which require an explicit representation of imprecision and uncertainty. Assuming that the uncertainties are structured, these models can be handled with interval models in which the values of the parameters are allowed to vary within numeric intervals. Robust control uses such mathematical models to explicitly have uncertainty into account. Solving robust control problems, like finding the robust stability or designing a robust controller, involves hard symbolic and numeric computation. When interval models are used, it also involves interval computation. The main advantage using interval analysis is that it provides guaranteed solutions, but as drawback its use requires the interaction with multiple kinds of data. We present a methodology and a framework that combines symbolic and numeric computation with interval analysis to solve robust control problems.

Static and Thermal Analysis of Aluminium (413,390,384 and 332) Piston Using Finite Element Method

The main objective of this research was to examine the suitability of aluminium alloy to design a piston of an internal combustion engine for improvement in weight and cost reduction. The piston was modelled using Autodesk Inventor 2017 software. The modelled piston was then imported into Ansys for further analysis. Static structural and thermal analysis were carried out on the pistons of the four different materials namely: Al 413 alloy, Al 384 alloy, Al 390 alloy and Al332 alloy to determine the total deformation, equivalent Von Mises stress, maximum shear stress, and the safety factor. The results of the study revealed that, aluminium 332 alloy piston deformed less compared to the deformations of aluminium 390 alloy piston, aluminium 384 alloy piston and aluminium 413 alloy piston. The induced Von Mises stresses in the pistons of the four different materials were found to be far lower than the yield strengths of all the materials. Hence, all the selected materials including the implementing material have equal properties to withstand the maximum gas load. All the selected materials were observed to have high thermal conductivity enough to be able to withstand the operating temperature in the engine cylinders.

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.

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.

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.

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.

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.

Enhancing wood efficiency through comprehensive wood flow analysis:Methodology and strategic insights

Wood,an essential natural resource in human civilization,remains widely used despite advances in technology and material substitution.The surge in greenhouse gas emissions and environmental concerns accentuates the need for optimizing wood utilization.Material flow analysis is a powerful tool for tracking material flows and stocks,aiding resource management and environmental decision-making.However,the full extent of its methodological dimensions,particularly within the context of the wood supply chain,remains relatively unexplored.In this study,we delve into the existing literature on wood flow analysis,discussing its primary objectives,materials involved,temporal and spatial scales,data sources,units,and conversion factors.Additionally,data uncertainty,data reconciliation and crucial assumptions in material flow analysis are highlighted in this paper.Key findings reveal the significance of wood cascading and substitution effects by replacing non-wood materials,where they can reduce greenhouse gas emissions more than the natural carbon sink of forests and wood products.The immediate impact of short-term wood cascading might not be as robust as the substitution effect,with energy substitution showcasing better results than material substitution.However,it's crucial to note that these conclusions could experience significant reversal from a long-term and global perspective.Strategies for improving wood efficiency involve maximizing material use,advancing construction technologies,extending product lifespans,promoting cascade use,and optimizing energy recovery processes.The study underscores the need for standardized approaches in wood flow analysis and emphasizes the potential of wood efficiency strategies in addressing environmental challenges.

New insights into developmental biology of Eimeria tenella revealed by comparative analysis of mRNA N6-methyladenosine modification between unsporulated oocysts and sporulated oocysts

Evidence showed that N6-methyladenosine(m^(6)A)modification plays a pivotal role in influencing RNA fate and is strongly associated with cell growth and developmental processes in many species.However,no information regarding m^(6)A modification in Eimeria tenella is currently available.In the present study,we surveyed the transcriptome-wide prevalence of m^(6)A in sporulated oocysts and unsporulated oocysts of E.tenella.Methylated RNA immunoprecipitation sequencing(MeRIP-seq)analysis showed that m^(6)A modification was most abundant in the coding sequences,followed by stop codon.There were 3,903 hypermethylated and 3,178 hypomethylated mRNAs in sporulated oocysts compared with unsporulated oocysts.Further joint analysis suggested that m^(6)A modification of the majority of genes was positively correlated with mRNA expression.The mRNA relative expression and m^(6)A level of the selected genes were confirmed by quantitative reverse transcription PCR(RT-qPCR)and MeRIP-qPCR.GO and KEGG analysis indicated that differentially m^(6)A methylated genes(DMMGs)with significant differences in mRNA expression were closely related to processes such as regulation of gene expression,epigenetic,microtubule,autophagy-other and TOR signaling.Moreover,a total of 96 DMMGs without significant differences in mRNA expression showed significant differences at protein level.GO and pathway enrichment analysis of the 96 genes showed that RNA methylation may be involved in cell biosynthesis and metabolism of E.tenella.We firstly present a map of RNA m^(6)A modification in E.tenella,which provides significant insights into developmental biology of E.tenella.

Dynamic Coupling Analysis of Semisubmersible Platform Float-over Method for Docking Case

In this paper,the multi-body coupled dynamic characteristics of a semisubmersible platform and an HYSY 229 barge were investigated.First,coupled hydrodynamic analysis of the HYSY 229 barge and the semisubmersible platform was performed.Relevant hydrodynamic parameters were obtained using the retardation function method of three-dimensional frequency-domain potential flow theory.The results of the hydrodynamic analysis were highly consistent with the test findings,verifying the accuracy of the multifloating hydrodynamic coupling analysis,and key hydrodynamic parameters were solved for different water depths and the coupling effect.According to the obtained results,the hydrodynamic influence was the largest in shallow waters when the coupling effect was considered.Furthermore,the coupled motion equation combined with viscous damping,fender system,and mooring system was established,and the hydrodynamics,floating body motion,and dynamic response of the fender system were analyzed.Motion analysis revealed good agreement among the surge,sway,and yaw motions of the two floating bodies.However,when the wave period reached 10 s,the motion of the two floating bodies showed severe shock,and a relative motion was also observed.Therefore,excessive constraints should be added between the two floating bodies during construction to ensure construction safety.The numerical analysis and model test results of the semisubmersible platform and HYSY 229 barge at a water depth of 42 m and sea conditions of 0°,45°,and 90° were in good agreement,and the error was less than 5%.The maximum movement of the HYSY 229 barge reached 2.61 m in the sway direction,whereas that of the semisubmersible platform was 2.11 m.During construction,excessive constraints should be added between the two floating bodies to limit their relative movement and ensure construction safety.

Brain Transcriptome Analysis Reveals Metabolic Changes Adapting to Hyperhaline or Hypohaline Environments in Spotted Scat(Scatophagus argus)

The fish brain is crucial for adjusting to environmental changes.Metabolic changes play a vital role in the adaptation to salinity change in aquatic animals.However,few studies have evaluated the responses of the fish brain to salinity changes.To evaluate the response to various salinities,spotted scat(Scatophagus argus)was cultured in water with salinity levels of 5(low salinity:LS),25(control group:Ctrl),and 35(high salinity group:HS)for 22 days.The brain transcriptome was analyzed.In total,1698 differentially expressed genes(DEGs)were identified between the HS and Ctrl groups,and 841 DEGs were identified between the LS and Ctrl groups.KEGG analysis showed that the DEGs in the HS vs.Ctrl comparison were involved in steroid biosynthesis,terpenoid backbone biosynthesis,fatty acid biosynthesis,ascorbate and aldarate metabolism,other types of O-glycan biosynthesis,and fatty acid metabolism.Glyoxylate and dicarboxylate metabolism,one carbon pool by folate,steroid biosynthesis,and cysteine and methionine metabolism were significantly enriched in the LS vs.Ctrl comparison.Additionally,the genes related to metabolism(acc,fas,hmgcr,hmgcs1,mvd,soat1,nsdhl,sqle,cel,fdft1,dnmt3a and mtr)were significantly up-regulated in the HS vs.Ctrl comparison.The genes related to metabolism(lipa,sqle,acc,fas,bhmt,mpst,dnmt3a,mtr,hao2,LOC111225351 and hmgcs1)were significantly up-regulated,while hmgcr and soat1 were significantly down-regulated in the LS vs.Ctrl compparison.These results suggest that salinity stress affects signaling pathways and genes’expressions involved in metabolic processes in the brain,and the differences in metabolism play an important role in adaptation to hyperhaline or hypohaline environments in spotted scat.This research provides a comprehensive overview of transcriptional changes in the brain under hyperhaline or hypohaline conditions,which is helpful to understand the mechanisms underlying salinity adaptation in euryhaline fishes.

Identifying the best common factor model via exploratory eactor analysis

Currently,there is no solid criterion for judging the quality of the estimators in factor analysis.This paper presents a new evaluation method for exploratory factor analysis that pinpoints an appropriate number of factors along with the best method for factor extraction.The proposed technique consists of two steps:testing the normality of the residuals from the fitted model via the Shapiro-Wilk test and using an empirical quantified index to judge the quality of the factor model.Examples are presented to demonstrate how the method is implemented and to verify its effectiveness.

Construction Activity Analysis of Workers Based on Human Posture Estimation Information

Identifying workers’construction activities or behaviors can enable managers to better monitor labor efficiency and construction progress.However,current activity analysis methods for construction workers rely solely on manual observations and recordings,which consumes considerable time and has high labor costs.Researchers have focused on monitoring on-site construction activities of workers.However,when multiple workers are working together,current research cannot accu rately and automatically identify the construction activity.This research proposes a deep learning framework for the automated analysis of the construction activities of multiple workers.In this framework,multiple deep neural network models are designed and used to complete worker key point extraction,worker tracking,and worker construction activity analysis.The designed framework was tested at an actual construction site,and activity recognition for multiple workers was performed,indicating the feasibility of the framework for the automated monitoring of work efficiency.

Periodic signal extraction of GNSS height time series based on adaptive singular spectrum analysis

Singular spectrum analysis is widely used in geodetic time series analysis.However,when extracting time-varying periodic signals from a large number of Global Navigation Satellite System(GNSS)time series,the selection of appropriate embedding window size and principal components makes this method cumbersome and inefficient.To improve the efficiency and accuracy of singular spectrum analysis,this paper proposes an adaptive singular spectrum analysis method by combining spectrum analysis with a new trace matrix.The running time and correlation analysis indicate that the proposed method can adaptively set the embedding window size to extract the time-varying periodic signals from GNSS time series,and the extraction efficiency of a single time series is six times that of singular spectrum analysis.The method is also accurate and more suitable for time-varying periodic signal analysis of global GNSS sites.

Systematic Security Guideline Framework through Intelligently Automated Vulnerability Analysis

This research aims to propose a practical framework designed for the automatic analysis of a product’s comprehensive functionality and security vulnerabilities,generating applicable guidelines based on real-world software.The existing analysis of software security vulnerabilities often focuses on specific features or modules.This partial and arbitrary analysis of the security vulnerabilities makes it challenging to comprehend the overall security vulnerabilities of the software.The key novelty lies in overcoming the constraints of partial approaches.The proposed framework utilizes data from various sources to create a comprehensive functionality profile,facilitating the derivation of real-world security guidelines.Security guidelines are dynamically generated by associating functional security vulnerabilities with the latest Common Vulnerabilities and Exposure(CVE)and Common Vulnerability Scoring System(CVSS)scores,resulting in automated guidelines tailored to each product.These guidelines are not only practical but also applicable in real-world software,allowing for prioritized security responses.The proposed framework is applied to virtual private network(VPN)software,wherein a validated Level 2 data flow diagram is generated using the Spoofing,Tampering,Repudiation,Information Disclosure,Denial of Service,and Elevation of privilege(STRIDE)technique with references to various papers and examples from related software.The analysis resulted in the identification of a total of 121 vulnerabilities.The successful implementation and validation demonstrate the framework’s efficacy in generating customized guidelines for entire systems,subsystems,and selected modules.

Uncertainty quantification of inverse analysis for geomaterials using probabilistic programming

Uncertainty is an essentially challenging for safe construction and long-term stability of geotechnical engineering.The inverse analysis is commonly utilized to determine the physico-mechanical parameters.However,conventional inverse analysis cannot deal with uncertainty in geotechnical and geological systems.In this study,a framework was developed to evaluate and quantify uncertainty in inverse analysis based on the reduced-order model(ROM)and probabilistic programming.The ROM was utilized to capture the mechanical and deformation properties of surrounding rock mass in geomechanical problems.Probabilistic programming was employed to evaluate uncertainty during construction in geotechnical engineering.A circular tunnel was then used to illustrate the proposed framework using analytical and numerical solution.The results show that the geomechanical parameters and associated uncertainty can be properly obtained and the proposed framework can capture the mechanical behaviors under uncertainty.Then,a slope case was employed to demonstrate the performance of the developed framework.The results prove that the proposed framework provides a scientific,feasible,and effective tool to characterize the properties and physical mechanism of geomaterials under uncertainty in geotechnical engineering problems.