Analysis of the Flow Field and Impact Force in High-Pressure Water Descaling

This study aims to improve the performances of the high-pressure water descaling technology used in steel hot rolling processes.In particular,a 2050 mm hot rolling line is considered,and the problem is investigated by means of a fluid–structure interaction(FSI)method by which the descaling effect produced by rolling coils with different section sizes is examined.Assuming a flat fan-shaped nozzle at the entrance of the R1R2 roughing mill,the outflow field characteristics and the velocity distribution curve on the strike line(at a target distance of 30–120 mm)are determined.It is found that the velocity in the center region of the water jet with different target distances is higher than that in the boundary region.As the target distance increases,the velocity of the water jet in the central region decreases.Through comparison with experimental results,it is shown that the simulation model can accurately predict the impact position of the high-pressure water on the impact plate,thereby providing a computational scheme that can be used to optimize the nozzle space layout and improve the slabs’descent effect for different rolling specifications.

Bioinformatics Analysis of the Biological Properties of Ewing Sarcoma

Purpose: Bioinformatics-based approach to screen and analyze differentially expressed genes associated with the biological characteristics of Ewing sarcoma. Means: The GSE17674 dataset was selected for analysis, obtained by data retrieval based on the GEO public database. The R language limma toolkit was used to screen DEmRNAs. After the data were normalized, the Metascape online analysis software and the R language clusterProfiler package were used to analyze the GO function and KEGG pathway enrichment of DEmRNAs lines, respectively. The string database was selected for PPI analysis, and the results were imported into Cytoscape software to derive the core modules and predicted core genes. The genes selected above were analyzed for tissue localization specificity. Results: Through the analysis of GSE17674, differentially expressed genes were screened out, and GO and KEGG analyses were performed on the differentially expressed genes. The GO functional enrichment analysis was mainly enriched in the process of muscle system, muscle contraction, myocyte development, contractile fibers, myogenic fibers, myofibers, myofibrillar segments, actin binding, structural composition of muscle, and actin filament binding. KEGG pathway analysis showed that the core pathways associated with the development of ES were the core genes for myocardial contraction, congestive cardiomyopathy, and hypertrophic cardiomyopathy. Five Hub genes were obtained based on Cytoscape prediction. Tissue localization specificity analysis of Hub genes was performed, and a total of 2 Hub genes with tissue specificity were screened;MYH6 was specifically expressed in cardiac cells and MYL1 was specifically expressed in skeletal muscle cells. Conclusions: The differential genes screened will help to understand the molecular mechanisms underlying the highly invasive and metastasis-prone biological characteristics of ES, as well as provide new ideas for clinical drug-targeted treatment of ES.

Target Situation Analysis and Present Situation Analysis in Planning an Oral Presentation Course

Needs analysis is an essential step in planning an oral presentation course in an ESP context.This paper illustrates two important areas in the relevant needs analysis: target situation analysis and present situation analysis.

Regional Allocation of CO_2 Intensity Reduction Targets Based on Cluster Analysis

To meet China＇s CO2 intensity target of 40%-45% reduction by 2020 based on the 2005 level, a regional allocation method based on cluster analysis is developed. Thirty Chinese provinces are classified into six groups based on economy, emissions, and reduction potential indicators. Under the equity principle, the two most developed groups axe assigned the highest reduction targets （55% and 65%, respectively）. However, their reduction potent!al is limited. Under the efficiency principle, the two groups with the highest reduction potential take the highest targets （48% and 61%, respectively）, but their economy is relatively backward. When equity and efficiency are equally weighted, the 5th group with a prominent reduction potential takes the highest target （54%）, and the 2nd and the 3rd groups with large industry scales take the second highest target （49%）. However, under all the three allocation schemes, the targets are not greater than 40% for the 4th and the 6th groups, which have a relatively low economic ability, emissions, and reduction potential. Due to inconsistency between economic and reduction potential, corresponding market mechanisms and policy instruments should be established to ensure equity and efficiency of regional target allocation.

Small Target Extraction Based on Independent Component Analysis for Hyperspectral Imagery

A small target detection approach based on independent component analysis for hyperspectral data is put forward. In this algorithm, firstly the fast independent component analysis(FICA) is used to collect target information hided in high-dimensional data and projects them into low-dimensional space.Secondly, the feature images are selected with kurtosis .At last, small targets are extracted with histogram image segmentation which has been labeled by skewness.

Short-time maritime target detection based on polarization scattering characteristics

In this paper,a detection method combining Cameron decomposition based on polarization scattering characteristics in sea clutter background is proposed.Firstly,the Cameron decomposition is exploited to fuse the radar echoes of full polarization channels at the data level.Due to the artificial material structure on the surface of the target,it can be shown that the non-reciprocity of the target cell is stronger than that of the clutter cell.Then,based on the analysis of the decomposition results,a new feature with scattering geometry characteristics in polarization domain,denoted as Cameron polarization decomposition scattering weight(CPD-SW),is extracted as the test statistic,which can achieve more detailed descriptions of the clutter scattering characteristics utilizing the difference between their scattering types.Finally,the superiority of the proposed CPD-SW detector over traditional detectors in improving detection performance is verified by the IPIX measured dataset,which has strong stability under short-time observation in threshold detection and can also improve the separability of feature space zin anomaly detection.

Data-driven prediction of dimensionless quantities for semi-infinite target penetration by integrating machine-learning and feature selection methods

This study employs a data-driven methodology that embeds the principle of dimensional invariance into an artificial neural network to automatically identify dominant dimensionless quantities in the penetration of rod projectiles into semi-infinite metal targets from experimental measurements.The derived mathematical expressions of dimensionless quantities are simplified by the examination of the exponent matrix and coupling relationships between feature variables.As a physics-based dimension reduction methodology,this way reduces high-dimensional parameter spaces to descriptions involving only a few physically interpretable dimensionless quantities in penetrating cases.Then the relative importance of various dimensionless feature variables on the penetration efficiencies for four impacting conditions is evaluated through feature selection engineering.The results indicate that the selected critical dimensionless feature variables by this synergistic method,without referring to the complex theoretical equations and aiding in the detailed knowledge of penetration mechanics,are in accordance with those reported in the reference.Lastly,the determined dimensionless quantities can be efficiently applied to conduct semi-empirical analysis for the specific penetrating case,and the reliability of regression functions is validated.

Targeted metabolomics study of fatty-acid metabolism in lean metabolic-associated fatty liver disease patients

BACKGROUND The annual incidence of metabolic-associated fatty liver disease(MAFLD)in China has been increasing and is often overlooked owing to its insidious charac-teristics.Approximately 50%of the patients have a normal weight or are not obese.They are said to have lean-type MAFLD,and few studies of such patients are available.Because MAFLD is associated with abnormal lipid metabolism,lipid-targeted metabolomics was used in this study to provide experimental evidence for early diagnosis and pathogenesis.MAFLD and analyze metabolic pathways.UPLC-Q-Orbitrap/MS content determination was used to determine serum palmitic acid(PA),oleic acid(OA),linoleic acid(LA),and arachidonic acid(AA)levels in lean-type MAFLD patients.RESULTS Urea nitrogen and uric acid levels were higher in lean-type MAFLD patients than in healthy individuals(P<0.05).Alanine transaminase and cholinesterase levels were higher in lean-type MAFLD patients than in healthy indi-viduals(P<0.01).The expression of high-density lipoprotein and apolipoprotein A-1 were lower in lean-type MAFLD patients than in healthy individuals(P<0.05)and the expression of triglycerides and fasting blood glucose were increased(P<0.01).A total of 65 biomarkers that affected the synthesis and metabolism of fatty acids were found with P<0.05 and variable importance in projection>1.The levels of PA,OA,LA,and AA were significantly increased compared with healthy individuals.CONCLUSION The metabolic profiles of lean-type MAFLD patients and healthy participants differed significantly,yielding 65 identified biomarkers.PA,OA,LA,and AA exhibited the most significant changes,offering valuable clinical guidance for prevention and treatment of lean-type MAFLD.

Three dimensional passive underwater target motion analysis using correlated data fusion

In this paper a new method of passive underwater TMA (target motion analysis) using data fusion is presented. The findings of this research are based on an understanding that there is a powerful sonar system that consists of many types of sonar but with one own-ship, and that different target parameter measurements can be obtained simultaneously. For the analysis 3 data measurements, passive bearing, elevation and multipath time-delay, are used, which are divided into two groups: a group with estimates of two preliminary target parameter obtained by dealing with each group measurement independently, and a group where correlated estimates are sent to a fusion center where the correlation between two data groups are considered so that the passive underwater TMA is realized. Simulation results show that curves of parameter estimation errors obtained by using the data fusion have fast convergence and the estimation accuracy is noticeably improved. The TMA algorithm presented is verified and is of practical significance because it is easy to be realized in one ship.

Simulation study of passive target motion analysis

In this paper, the problem of underwater passive target motion analysis (TMA) in three dimensions is discussed using the measurements of passive bearings and elevation and frequency on the condition that acoustic source and observer are in different horizontal planes. Simulation results with both of the PLE (pseudo-linear estimation) and MLE (Maximum likelihood estimation) show that the TMA method is effective in oceanic environment. Its error covariance curves tend to its Cramer-Rao lower bounds.

Simultaneous Determination of Rare Earth Elements by Dual-System Iterative Target Transformation Factor Analysis

Dualcolor systems were used to reduce the collinearity of multicomponent spectra, which is described by the angles between spectra vectors. Combined with iterative target transformation factor analysis, single rare earth element was determined in its mixture. The calculated results show that the average angle between rare earth spectra in one color system(trichloroarsenazorare earths, pH 34) is 45, and that in two color systems(trichloroarsenazorare earths, pH 34, 14) is 215. This technique makes it easy to select the real number of the components in mixtures, and the determination results show dualcolor system method is an effective technique in rare earth mixture analysis.

Integrated causal inference modeling uncovers novel causal factors and potential therapeutic targets of Qingjin Yiqi granules for chronic fatigue syndrome

Objective:Chronic fatigue syndrome(CFS)is a prevalent symptom of post-coronavirus disease 2019(COVID-19)and is associated with unclear disease mechanisms.The herbal medicine Qingjin Yiqi granules(QJYQ)constitute a clinically approved formula for treating post-COVID-19;however,its potential as a drug target for treating CFS remains largely unknown.This study aimed to identify novel causal factors for CFS and elucidate the potential targets and pharmacological mechanisms of action of QJYQ in treating CFS.Methods:This prospective cohort analysis included 4,212 adults aged≥65 years who were followed up for 7 years with 435 incident CFS cases.Causal modeling and multivariate logistic regression analysis were performed to identify the potential causal determinants of CFS.A proteome-wide,two-sample Mendelian randomization(MR)analysis was employed to explore the proteins associated with the identified causal factors of CFS,which may serve as potential drug targets.Furthermore,we performed a virtual screening analysis to assess the binding affinity between the bioactive compounds in QJYQ and CFS-associated proteins.Results:Among 4,212 participants(47.5%men)with a median age of 69 years(interquartile range:69–70 years)enrolled in 2004,435 developed CFS by 2011.Causal graph analysis with multivariate logistic regression identified frequent cough(odds ratio:1.74,95%confidence interval[CI]:1.15–2.63)and insomnia(odds ratio:2.59,95%CI:1.77–3.79)as novel causal factors of CFS.Proteome-wide MR analysis revealed that the upregulation of endothelial cell-selective adhesion molecule(ESAM)was causally linked to both chronic cough(odds ratio:1.019,95%CI:1.012–1.026,P=2.75 e^(−05))and insomnia(odds ratio:1.015,95%CI:1.008–1.022,P=4.40 e^(−08))in CFS.The major bioactive compounds of QJYQ,ginsenoside Rb2(docking score:−6.03)and RG4(docking score:−6.15),bound to ESAM with high affinity based on virtual screening.Conclusions:Our integrated analytical framework combining epidemiological,genetic,and in silico data provides a novel strategy for elucidating complex disease mechanisms,such as CFS,and informing models of action of traditional Chinese medicines,such as QJYQ.Further validation in animal models is warranted to confirm the potential pharmacological effects of QJYQ on ESAM and as a treatment for CFS.

Energy Targeting for a Brewing Process Using Pinch Analysis

The rising cost of energy and environmental concerns have led the brewing industry to search for techniques of reducing energy consumption in brewery operations. In this paper, pinch analysis was applied to a typical Ugandan based brewery process to target for the energy requirements of the process. Hint software was used for the analysis. At the chosen ΔTmin of 10℃, the minimum cooling and heating utility requirements of the brewery studied were determined as being 4862.21 kW and 8294.21 kW respectively, with a pinch temperature at 68℃. It was observed that using the technique, 1806.59 kW of energy could be recovered through process to process heat exchange which presented an energy saving potential of 21.5%. It is recommended that results from this study could be used in the design or retrofit of a heat exchanger network of a brewery for improved energy efficiency. Considerations can also be made for other values of ΔTmin.

Three-Dimensional Discrete Observability Analysis for Air-to-Air Missile Target Tracking

The interception information of infrared（ IR）-guided air-to-air missiles（ AAM） is mainly estimated only using the basic bearing measurements. In order to intercept highly maneuverable targets,it is essential to study the system observability to improve the target tracking system performance.The uniqueness of this paper is that the observability analysis is derived based on a discrete three-dimensional （3D） system model. During the maneuvering scenario,the system is approximated by a segment-by-segment system. The relationship between missile-target motion and observability is given by direct and dual approaches. Meanwhile sufficient observability conditions are derived. Moreover,a numerical simulation is conducted and an alternate method is provided to reinforce the proposed observability analysis results.

Reliability Analysis of Heterogeneous Sensor-Cloud Systems against Targeted Attacks

Based on the wide application of cloud computing and wireless sensor networks in various fields,the Sensor-Cloud System(SCS)plays an indispensable role between the physical world and the network world.However,due to the close connection and interdependence between the physical resource network and computing resource network,there are security problems such as cascading failures between systems in the SCS.In this paper,we propose a model with two interdependent networks to represent a sensor-cloud system.Besides,based on the percolation theory,we have carried out a formulaic theoretical analysis of the whole process of cascading failure.When the system’s subnetwork presents a steady state where there is no further collapse,we can obtain the largest remaining connected subgroup components and the penetration threshold.Theoretically,this result is the critical maximum that the coupled SCS can withstand.To verify the correctness of the theoretical results,we further carried out actual simulation experiments.The results show that a scale-free network priority attack’s percolation threshold is always less than that of ER network which is priority attacked.Similarly,when the scale-free network is attacked first,adding the power law exponentλcan be more intuitive and more effective to improve the network’s reliability.

Kernel-based fisher discriminant analysis for hyperspectral target detection

A new method based on kernel Fisher discriminant analysis （KFDA） is proposed for target detection of hyperspectral images. The KFDA combines kernel mapping derived from support vector machine and the classical linear Fisher discriminant analysis （LFDA）, and it possesses good ability to process nonlinear data such as hyperspectral images. According to the Fisher rule that the ratio of the between-class and within-class scatters is maximized, the KFDA is used to obtain a set of optimal discriminant basis vectors in high dimensional feature space, All pixels in the hyperspectral images are projected onto the discriminant basis vectors and the target detection is performed according to the projection result. The numerical experiments are performed on hyperspectral data with 126 bands collected by Airborne Visible/Infrared Imaging Spectrometer （AVIRIS）, Tbe experimental results show the effectiveness of the proposed detection method and prove that this method has good ability to overcome small sample size and spectral variability in the hyperspectral target detection.

A Model for Cross-Domain Opinion Target Extraction in Sentiment Analysis

Opinion target extraction is one of the core tasks in sentiment analysison text data. In recent years, dependency parser–based approaches have beencommonly studied for opinion target extraction. However, dependency parsersare limited by language and grammatical constraints. Therefore, in this work, asequential pattern-based rule mining model, which does not have such constraints,is proposed for cross-domain opinion target extraction from product reviews inunknown domains. Thus, knowing the domain of reviews while extracting opinion targets becomes no longer a requirement. The proposed model also revealsthe difference between the concepts of opinion target and aspect, which are commonly confused in the literature. The model consists of two stages. In the firststage, the aspects of reviews are extracted from the target domain using the rulesautomatically generated from source domains. The aspects are also transferredfrom the source domains to a target domain. Moreover, aspect pruning is appliedto further improve the performance of aspect extraction. In the second stage, theopinion target is extracted among the aspects extracted at the former stage usingthe rules automatically generated for opinion target extraction. The proposedmodel was evaluated on several benchmark datasets in different domains andcompared against the literature. The experimental results revealed that the opiniontargets of the reviews in unknown domains can be extracted with higher accuracythan those of the previous works.

Performance Analysis of Magnetic Nanoparticles during Targeted Drug Delivery:Application of OHAM

In recent years,the emergence of nanotechnology experienced incredible development in the field of medical sciences.During the past decade,investigating the characteristics of nanoparticles during fluid flow has been one of the intriguing issues.Nanoparticle distribution and uniformity have emerged as substantial criteria in both medical and engineering applications.Adverse effects of chemotherapy on healthy tissues are known to be a significant concern during cancer therapy.A novel treatment method of magnetic drug targeting(MDT)has emerged as a promising topical cancer treatment along with some attractive advantages of improving efficacy,fewer side effects,and reduce drug dose.During magnetic drug targeting,the appropriate movement of nanoparticles(magnetic)as carriers is essential for the therapeutic process in the blood clot removal,infection treatment,and tumor cell treatment.In this study,we have numerically investigated the behavior of an unsteady blood flowinfused with magnetic nanoparticles during MDT under the influence of a uniform external magnetic field in a microtube.An optimal homotopy asymptotic method(OHAM)is employed to compute the governing equation for unsteady electromagnetohydrodynamics flow.The influence of Hartmann number(Ha),particle mass parameter(G),particle concentration parameter(R),and electro-osmotic parameter(k)is investigated on the velocity of magnetic nanoparticles and blood flow.Results obtained show that the electro-osmotic parameter,along with Hartmann’s number,dramatically affects the velocity of magnetic nanoparticles,blood flow velocity,and flow rate.Moreover,results also reveal that at a higher Hartman number,homogeneity in nanoparticles distribution improved considerably.The particle concentration andmass parameters effectively influence the capturing effect on nanoparticles in the blood flow using a micro-tube for magnetic drug targeting.Lastly,investigation also indicates that the OHAM analysis is efficient and quick to handle the system of nonlinear equations.

Depolarization Degree to Determine Dihedral Attribute of Radar Target

This paper investigates the ability of the depolarization degree, derived from the characteristic polarization states at the resonant frequency set, to identify corner or swept, i.e. dihedral, changes in same-class targets by a metallic wire example. A well-estimated depolarization degree requires a robust extraction of the fundamental target resonance set in two orthogonal sets of fully co-polarized and cross-polarized polarization channels, then finding the null polarization states using the Lagrangian method. Such depolarization degree per resonance mode has the potential to form a robust feature set because it is relatively less sensitive to onset ambiguity, invariant to rotation, and could create a compact, recognizable, and separable distribution in the proposed feature space. The study was limited to two targets with two swept changes of fifteen degrees within normal incidence;under a supervised learning approach, the results showed that the identification rate converging to upper-bound (100%) for a signal-to-noise ratio above 20 dB and lower-bound around (50%) below −10 dB.

Application of molybdenum target X-ray photography in imaging analysis of caudal intervertebral disc degeneration in rats

BACKGROUND Conventional plain X-ray images of rats,the most common animals used as degeneration models,exhibit unclear vertebral structure and blurry intervertebral disc spaces due to their small size,slender vertebral bodies.AIM To apply molybdenum target X-ray photography in the evaluation of caudal intervertebral disc(IVD)degeneration in rat models.METHODS Two types of rat caudal IVD degeneration models(needle-punctured model and endplate-destructed model)were established,and their effectiveness was verified using nuclear magnetic resonance imaging.Molybdenum target inspection and routine plain X-ray were then performed on these models.Additionally,four observers were assigned to measure the intervertebral height of degenerated segments on molybdenum target plain X-ray images and routine plain X-ray images,respectively.The degeneration was evaluated and statistical analysis was subsequently conducted.RESULTS Nine rats in the needle-punctured model and 10 rats in the endplate-destructed model were effective.Compared with routine plain X-ray images,molybdenum target plain X-ray images showed higher clarity,stronger contrast,as well as clearer and more accurate structural development.The McNemar test confirmed that the difference was statistically significant(P=0.031).In the two models,the reliability of the intervertebral height measured by the four observers on routine plain X-ray images was poor(ICC<0.4),while the data obtained from the molybdenum target plain X-ray images were more reliable.CONCLUSIONMolybdenum target inspection can obtain clearer images and display fine calcification in the imaging evaluation of caudal IVD degeneration in rats,thus ensuring a more accurate evaluation of degeneration.