This article presents a mathematical model addressing a scenario involving a hybrid nanofluid flow between two infinite parallel plates.One plate remains stationary,while the other moves downward at a squeezing veloci...This article presents a mathematical model addressing a scenario involving a hybrid nanofluid flow between two infinite parallel plates.One plate remains stationary,while the other moves downward at a squeezing velocity.The space between these plates contains a Darcy-Forchheimer porous medium.A mixture of water-based fluid with gold(Au)and silicon dioxide(Si O2)nanoparticles is formulated.In contrast to the conventional Fourier's heat flux equation,this study employs the Cattaneo-Christov heat flux equation.A uniform magnetic field is applied perpendicular to the flow direction,invoking magnetohydrodynamic(MHD)effects.Further,the model accounts for Joule heating,which is the heat generated when an electric current passes through the fluid.The problem is solved via NDSolve in MATHEMATICA.Numerical and statistical analyses are conducted to provide insights into the behavior of the nanomaterials between the parallel plates with respect to the flow,energy transport,and skin friction.The findings of this study have potential applications in enhancing cooling systems and optimizing thermal management strategies.It is observed that the squeezing motion generates additional pressure gradients within the fluid,which enhances the flow rate but reduces the frictional drag.Consequently,the fluid is pushed more vigorously between the plates,increasing the flow velocity.As the fluid experiences higher flow rates due to the increased squeezing effect,it spends less time in the region between the plates.The thermal relaxation,however,abruptly changes the temperature,leading to a decrease in the temperature fluctuations.展开更多
This study numerically examines the heat and mass transfer characteristics of two ternary nanofluids via converging and diverg-ing channels.Furthermore,the study aims to assess two ternary nanofluids combinations to d...This study numerically examines the heat and mass transfer characteristics of two ternary nanofluids via converging and diverg-ing channels.Furthermore,the study aims to assess two ternary nanofluids combinations to determine which configuration can provide better heat and mass transfer and lower entropy production,while ensuring cost efficiency.This work bridges the gap be-tween academic research and industrial feasibility by incorporating cost analysis,entropy generation,and thermal efficiency.To compare the velocity,temperature,and concentration profiles,we examine two ternary nanofluids,i.e.,TiO_(2)+SiO_(2)+Al_(2)O_(3)/H_(2)O and TiO_(2)+SiO_(2)+Cu/H_(2)O,while considering the shape of nanoparticles.The velocity slip and Soret/Dufour effects are taken into consideration.Furthermore,regression analysis for Nusselt and Sherwood numbers of the model is carried out.The Runge-Kutta fourth-order method with shooting technique is employed to acquire the numerical solution of the governed system of ordinary differential equations.The flow pattern attributes of ternary nanofluids are meticulously examined and simulated with the fluc-tuation of flow-dominating parameters.Additionally,the influence of these parameters is demonstrated in the flow,temperature,and concentration fields.For variation in Eckert and Dufour numbers,TiO_(2)+SiO_(2)+Al_(2)O_(3)/H_(2)O has a higher temperature than TiO_(2)+SiO_(2)+Cu/H_(2)O.The results obtained indicate that the ternary nanofluid TiO_(2)+SiO_(2)+Al_(2)O_(3)/H_(2)O has a higher heat transfer rate,lesser entropy generation,greater mass transfer rate,and lower cost than that of TiO_(2)+SiO_(2)+Cu/H_(2)O ternary nanofluid.展开更多
The impact of different global and local variables in urban development processes requires a systematic study to fully comprehend the underlying complexities in them.The interplay between such variables is crucial for...The impact of different global and local variables in urban development processes requires a systematic study to fully comprehend the underlying complexities in them.The interplay between such variables is crucial for modelling urban growth to closely reflects reality.Despite extensive research,ambiguity remains about how variations in these input variables influence urban densification.In this study,we conduct a global sensitivity analysis(SA)using a multinomial logistic regression(MNL)model to assess the model’s explanatory and predictive power.We examine the influence of global variables,including spatial resolution,neighborhood size,and density classes,under different input combinations at a provincial scale to understand their impact on densification.Additionally,we perform a stepwise regression to identify the significant explanatory variables that are important for understanding densification in the Brussels Metropolitan Area(BMA).Our results indicate that a finer spatial resolution of 50 m and 100 m,smaller neighborhood size of 5×5 and 3×3,and specific density classes—namely 3(non-built-up,low and high built-up)and 4(non-built-up,low,medium and high built-up)—optimally explain and predict urban densification.In line with the same,the stepwise regression reveals that models with a coarser resolution of 300 m lack significant variables,reflecting a lower explanatory power for densification.This approach aids in identifying optimal and significant global variables with higher explanatory power for understanding and predicting urban densification.Furthermore,these findings are reproducible in a global urban context,offering valuable insights for planners,modelers and geographers in managing future urban growth and minimizing modelling.展开更多
In clinical research,subgroup analysis can help identify patient groups that respond better or worse to specific treatments,improve therapeutic effect and safety,and is of great significance in precision medicine.This...In clinical research,subgroup analysis can help identify patient groups that respond better or worse to specific treatments,improve therapeutic effect and safety,and is of great significance in precision medicine.This article considers subgroup analysis methods for longitudinal data containing multiple covariates and biomarkers.We divide subgroups based on whether a linear combination of these biomarkers exceeds a predetermined threshold,and assess the heterogeneity of treatment effects across subgroups using the interaction between subgroups and exposure variables.Quantile regression is used to better characterize the global distribution of the response variable and sparsity penalties are imposed to achieve variable selection of covariates and biomarkers.The effectiveness of our proposed methodology for both variable selection and parameter estimation is verified through random simulations.Finally,we demonstrate the application of this method by analyzing data from the PA.3 trial,further illustrating the practicality of the method proposed in this paper.展开更多
Damage to electrical equipment in an earthquake can lead to power outage of power systems.Seismic fragility analysis is a common method to assess the seismic reliability of electrical equipment.To further guarantee th...Damage to electrical equipment in an earthquake can lead to power outage of power systems.Seismic fragility analysis is a common method to assess the seismic reliability of electrical equipment.To further guarantee the efficiency of analysis,multi-source uncertainties including the structure itself and seismic excitation need to be considered.A method for seismic fragility analysis that reflects structural and seismic parameter uncertainty was developed in this study.The proposed method used a random sampling method based on Latin hypercube sampling(LHS)to account for the structure parameter uncertainty and the group structure characteristics of electrical equipment.Then,logistic Lasso regression(LLR)was used to find the seismic fragility surface based on double ground motion intensity measures(IM).The seismic fragility based on the finite element model of an±1000 kV main transformer(UHVMT)was analyzed using the proposed method.The results show that the seismic fragility function obtained by this method can be used to construct the relationship between the uncertainty parameters and the failure probability.The seismic fragility surface did not only provide the probabilities of seismic damage states under different IMs,but also had better stability than the fragility curve.Furthermore,the sensitivity analysis of the structural parameters revealed that the elastic module of the bushing and the height of the high-voltage bushing may have a greater influence.展开更多
According to some main assumptions in the Rouse Formula,it analyzes the applicability of Rouse distribution in the coastal region.Based on the classical Rouse Formula,the linear form of Rouse Formula and the transport...According to some main assumptions in the Rouse Formula,it analyzes the applicability of Rouse distribution in the coastal region.Based on the classical Rouse Formula,the linear form of Rouse Formula and the transport characteristics of offshore sediment were used to take lnz/h,lnc_(a),c_(a),u,lnu and z/h as the independent variables.The multiple liner regression method was used to analyze the influence of the independent variables on the vertical distribution of sediment concentration.By using the method of significance test,the factors(ln𝑢)that have less influence on sediment concentration among 6 variables were eliminated.The correlation coefficient between the calculated sediment concentration and the measured sediment concentration indicates that the adopted variables can reflect the characteristics of vertical distribution of concentration of fine sediment near shore under complex dynamic conditions.展开更多
The work proposes a distributed Kalman filtering(KF)algorithm to track a time-varying unknown signal process for a stochastic regression model over network systems in a cooperative way.We provide the stability analysi...The work proposes a distributed Kalman filtering(KF)algorithm to track a time-varying unknown signal process for a stochastic regression model over network systems in a cooperative way.We provide the stability analysis of the proposed distributed KF algorithm without independent and stationary signal assumptions,which implies that the theoretical results are able to be applied to stochastic feedback systems.Note that the main difficulty of stability analysis lies in analyzing the properties of the product of non-independent and non-stationary random matrices involved in the error equation.We employ analysis techniques such as stochastic Lyapunov function,stability theory of stochastic systems,and algebraic graph theory to deal with the above issue.The stochastic spatio-temporal cooperative information condition shows the cooperative property of multiple sensors that even though any local sensor cannot track the time-varying unknown signal,the distributed KF algorithm can be utilized to finish the filtering task in a cooperative way.At last,we illustrate the property of the proposed distributed KF algorithm by a simulation example.展开更多
In oil and gas exploration,elucidating the complex interdependencies among geological variables is paramount.Our study introduces the application of sophisticated regression analysis method at the forefront,aiming not...In oil and gas exploration,elucidating the complex interdependencies among geological variables is paramount.Our study introduces the application of sophisticated regression analysis method at the forefront,aiming not just at predicting geophysical logging curve values but also innovatively mitigate hydrocarbon depletion observed in geochemical logging.Through a rigorous assessment,we explore the efficacy of eight regression models,bifurcated into linear and nonlinear groups,to accommodate the multifaceted nature of geological datasets.Our linear model suite encompasses the Standard Equation,Ridge Regression,Least Absolute Shrinkage and Selection Operator,and Elastic Net,each presenting distinct advantages.The Standard Equation serves as a foundational benchmark,whereas Ridge Regression implements penalty terms to counteract overfitting,thus bolstering model robustness in the presence of multicollinearity.The Least Absolute Shrinkage and Selection Operator for variable selection functions to streamline models,enhancing their interpretability,while Elastic Net amalgamates the merits of Ridge Regression and Least Absolute Shrinkage and Selection Operator,offering a harmonized solution to model complexity and comprehensibility.On the nonlinear front,Gradient Descent,Kernel Ridge Regression,Support Vector Regression,and Piecewise Function-Fitting methods introduce innovative approaches.Gradient Descent assures computational efficiency in optimizing solutions,Kernel Ridge Regression leverages the kernel trick to navigate nonlinear patterns,and Support Vector Regression is proficient in forecasting extremities,pivotal for exploration risk assessment.The Piecewise Function-Fitting approach,tailored for geological data,facilitates adaptable modeling of variable interrelations,accommodating abrupt data trend shifts.Our analysis identifies Ridge Regression,particularly when augmented by Piecewise Function-Fitting,as superior in recouping hydrocarbon losses,and underscoring its utility in resource quantification refinement.Meanwhile,Kernel Ridge Regression emerges as a noteworthy strategy in ameliorating porosity-logging curve prediction for well A,evidencing its aptness for intricate geological structures.This research attests to the scientific ascendancy and broad-spectrum relevance of these regression techniques over conventional methods while heralding new horizons for their deployment in the oil and gas sector.The insights garnered from these advanced modeling strategies are set to transform geological and engineering practices in hydrocarbon prediction,evaluation,and recovery.展开更多
[Objectives] To analyze the influencing factors of fixed defects in patients with catheter fixation in clinical nursing work, in order to provide the best catheter fixation nursing plan for patients.[Methods] 176 inpa...[Objectives] To analyze the influencing factors of fixed defects in patients with catheter fixation in clinical nursing work, in order to provide the best catheter fixation nursing plan for patients.[Methods] 176 inpatients with indwelling catheter from surgical system of Taihe Hospital in Shiyan City from August 2022 to March 2023 were selected. Using a retrospective analysis method, the influencing factors of catheter fixation defects in the study subjects were divided into two categories based on objective characteristics: type I non modifiable influencing factors and type II modifiable influencing factors. Using the standard for catheter fixation defects, whether the patient had catheter fixation defects was determined. After classified and statistically analyzed item by item, binary Logistic multiple regression analysis was used to identify the influencing factors.[Results] The occurrence of catheter fixation defects in patients with catheter fixation was related to factors such as whether the patient was evaluated before fixation, whether the fixation method was standardized and systematic, whether there was sufficient communication between nurses and patients, and the patient s knowledge of catheter fixation. It was also influenced by factors such as the patient s age, catheterization site, catheterization number, catheterization duration, where there was a consciousness disorder, educational level, and external environmental temperature.[Conclusions] Early attention to the key factors affecting patients with catheter fixation defects can effectively prevent adverse factors and provide patients with the best catheter fixation nursing plan to improve nursing quality.展开更多
Block-flexure toppling failure is frequently encountered in interbedded anti-inclined rock(IAR)slopes,and seriously threatens the construction of hydropower infrastructure.In this study,we first investigated the Lean ...Block-flexure toppling failure is frequently encountered in interbedded anti-inclined rock(IAR)slopes,and seriously threatens the construction of hydropower infrastructure.In this study,we first investigated the Lean Reservoir area’s geological setting and the Linda landslide’s characteristics.Then,uniform design and random design were used to design 110 training datasets and 31 testing datasets,respectively.Afterwards,the toppling response was obtained by using the discrete element code.Finally,support vector regression was used to obtain the influence weights of 21 impact factors.The results show that the influence weight of the slope angle and rock formation dip angle on the toppling deformation among tertiary impact factors is 25.96%and 17.28%,respectively,which are much greater than the other 19 impact factors within the research range.For the primary impact factors,the influence weight is sorted from large to small as slope geometry parameters,joints parameters,and rock mechanics parameters.Joints parameters,especially the geometric parameters,cannot be ignored when evaluating the stability of IAR slopes.Through numerical simulation,it was qualitatively determined that failure surfaces of slopes were controlled by cross joints and that the rocks in the slope toe play a role in preventing slope deformation.展开更多
Objective To explore the clinical characteristics and methods for syndrome differentiation prediction,as well as to construct a predictive model for Qi deficiency and blood stasis syndrome in patients with acute ische...Objective To explore the clinical characteristics and methods for syndrome differentiation prediction,as well as to construct a predictive model for Qi deficiency and blood stasis syndrome in patients with acute ischemic stroke(AIS).Methods This study employed a retrospective case-control design to analyze patients with AIS who received inpatient treatment at the Neurology Department of The First Hospital of Hunan University of Chinese Medicine from January 1,2013 to December 31,2022.AIS patients meeting the diagnostic criteria for Qi deficiency and blood stasis syndrome were stratified into case group,while those without Qi deficiency and blood stasis syndrome were stratified into control group.The demographic characteristics(age and gender),clinical parameters[time from onset to admission,National Institutes of Health Stroke Scale(NIHSS)score,and blood pressure],past medical history,traditional Chinese medicine(TCM)diagnostic characteristics(tongue and pulse),neurological symptoms and signs,imaging findings[magnetic resonance imaging-diffusion weighted imaging(MRI-DWI)],and biochemical indicators of the two groups were collected and compared.The indicators with statistical difference(P<0.05)in univariate analysis were included in multivariate logistic regression analysis to evaluate their predictive value for the diagnosis of Qi deficiency and blood stasis syndrome,and the predictive model was constructed by receiver operating characteristic(ROC)curve analysis.Results The study included 1035 AIS patients,with 404 cases in case group and 631 cases in control group.Compared with control group,patients in case group were significantly older,had extended onset-to-admission time,lower diastolic blood pressure,and lower NIHSS scores(P<0.05).Case group showed lower incidence of hypertension history(P<0.05).Regarding tongue and pulse characteristics,pale and dark tongue colors,white tongue coating,fine pulse,astringent pulse,and sinking pulse were more common in case group.Imaging examinations demonstrated higher proportions of centrum semiovale infarction,cerebral atrophy,and vertebral artery stenosis in case group(P<0.05).Among biochemical indicators,case group showed higher proportions of elevated fasting blood glucose and glycated hemoglobin(HbA1c),while lower proportions of elevated white blood cell count,reduced hemoglobin,and reduced high-density lipoprotein cholesterol(HDL-C)(P<0.05).Multivariate logistic regression analysis identified significant predictors for Qi deficiency and blood stasis syndrome including:fine pulse[odds ratio(OR)=4.38],astringent pulse(OR=3.67),superficial sensory abnormalities(OR=1.86),centrum semiovale infarction(OR=1.57),cerebral atrophy(OR=1.55),vertebral artery stenosis(OR=1.62),and elevated HbA1c(OR=3.52).The ROC curve analysis of the comprehensive prediction model yielded an area under the curve(AUC)of 0.878[95%confidence interval(CI)=0.855-0.900].Conclusion This study finds out that Qi deficiency and blood stasis syndrome represents one of the primary types of AIS.Fine pulse,astringent pulse,superficial sensory abnormalities,centrum semiovale infarction,cerebral atrophy,vertebral artery stenosis,elevated blood glucose,elevated HbA1c,pale and dark tongue colors,and white tongue coating are key objective diagnostic indicators for the syndrome differentiation of AIS with Qi deficiency and blood stasis syndrome.Based on these indicators,a syndrome differentiation prediction model has been developed,offering a more objective basis for clinical diagnosis,and help to rapidly identify this syndrome in clinical practice and reduce misdiagnosis and missed diagnosis.展开更多
In view of the composition analysis and identification of ancient glass products, L1 regularization, K-Means cluster analysis, elbow rule and other methods were comprehensively used to build logical regression, cluste...In view of the composition analysis and identification of ancient glass products, L1 regularization, K-Means cluster analysis, elbow rule and other methods were comprehensively used to build logical regression, cluster analysis, hyper-parameter test and other models, and SPSS, Python and other tools were used to obtain the classification rules of glass products under different fluxes, sub classification under different chemical compositions, hyper-parameter K value test and rationality analysis. Research can provide theoretical support for the protection and restoration of ancient glass relics.展开更多
Road traffic crashes are becoming thorny issues being faced worldwide.Traffic crashes are spatiotemporal events and the research on the spatiotemporal patterns and variation trends of traffic crashes has been carried ...Road traffic crashes are becoming thorny issues being faced worldwide.Traffic crashes are spatiotemporal events and the research on the spatiotemporal patterns and variation trends of traffic crashes has been carried out.However,the impact of built environment on traffic crash spatiotemporal trends has not received much attention.Moreover,the spatial non-stationarity between the variation trends of traffic crashes and their influencing factors is usually neglected.To make up for the lack of analysis of built environment factors influencing spatiotemporal hotspot trends in traffic crashes,this paper proposed a method of“ST-GWLR”for analyzing the influence of built environment factors on spatiotemporal hotspot trends of traffic crashes by combining the spatiotemporal hotspot trend analysis and Geographically Weighted Logistic Regression(GWLR)modeling methods.Firstly,the traffic crash spatiotemporal hotspot trends were explored using the space-time cube model,hotspot analysis,and Mann-Kendall trend test.Then,the GWLR was introduced to capture the spatial non-stationarity neglected by the classic Global Logistic Regression(GLR)model,to improve the accuracy of the model estimation.GWLR model is used for the first time to analyze the significant local correlation between the traffic crash spatiotemporal hotspot trends and the built environment factors,to accurately and effectively identify the built environment factors that have significant influences on the hotspot trends of traffic crashes.The performance of the GWLR models and GLR models was examined and compared sufficiently.The results showed that the proposed ST-GWLR,which captured spatial non-stationarity,performed better than the classic GLR combined with spatiotemporal analysis,and improved the prediction accuracy of the models by 14.9%,13.9%,and 15.1%,respectively.There were significant local correlations between intensifying hotspots and persistent hotspots of traffic crashes and the built environment factors.The findings of this paper have positive implications for traffic safety management and urban built environment planning.展开更多
Ignimbrites have been widely used as building materials in many historical and touristic structures in the Kayseri region of Türkiye. Their diverse colours and textures make them a popular choice for modern const...Ignimbrites have been widely used as building materials in many historical and touristic structures in the Kayseri region of Türkiye. Their diverse colours and textures make them a popular choice for modern construction as well. However, ignimbrites are particularly vulnerable to atmospheric conditions, such as freeze-thaw cycles, due to their high porosity, which is a result of their formation process. When water enters the pores of the ignimbrites, it can freeze during cold weather. As the water freezes and expands, it generates internal stress within the stone, causing micro-cracks to develop. Over time, repeated freeze-thaw (F-T) cycles lead to the growth of these micro-cracks into larger cracks, compromising the structural integrity of the ignimbrites and eventually making them unsuitable for use as building materials. The determination of the long-term F-T performance of ignimbrites can be established after long F-T experimental processes. Determining the long-term F-T performance of ignimbrites typically requires extensive experimental testing over prolonged freeze-thaw cycles. To streamline this process, developing accurate predictive equations becomes crucial. In this study, such equations were formulated using classical regression analyses and artificial neural networks (ANN) based on data obtained from these experiments, allowing for the prediction of the F-T performance of ignimbrites and other similar building stones without the need for lengthy testing. In this study, uniaxial compressive strength, ultrasonic propagation velocity, apparent porosity and mass loss of ignimbrites after long-term F-T were determined. Following the F-T cycles, the disintegration rate was evaluated using decay function approaches, while uniaxial compressive strength (UCS) values were predicted with minimal input parameters through both regression and ANN analyses. The ANN and regression models created for this purpose were first started with a single input value and then developed with two and three combinations. The predictive performance of the models was assessed by comparing them to regression models using the coefficient of determination (R2) as the evaluation criterion. As a result of the study, higher R2 values (0.87) were obtained in models built with artificial neural network. The results of the study indicate that ANN usage can produce results close to experimental outcomes in predicting the long-term F-T performance of ignimbrite samples.展开更多
Taking into account the characteristics of non-Newtonian fluids and the influence of latent heat of wax crystallization,this study establishes physical and mathematical models for the synergy of tubular heating and me...Taking into account the characteristics of non-Newtonian fluids and the influence of latent heat of wax crystallization,this study establishes physical and mathematical models for the synergy of tubular heating and mechanical stirring during the waxy crude oil heating process.Numerical calculations are conducted using the sliding grid technique and FVM.The focus of this study is on the impact of stirring rate(τ),horizontal deflection angle(θ1),vertical deflection angle(θ2),and stirring diameter(D)on the heating effect of crude oil.Our results show that asτincreases from 200 rpm to 500 rpm and D increases from 400 mm to 600 mm,there is an improvement in the average crude oil temperature and temperature uniformity.Additionally,heating efficiency increases by 0.5%and 1%,while the volume of the low-temperature region decreases by 57.01 m^(3) and 36.87 m3,respectively.Asθ1 andθ2 increase from 0°to 12°,the average crude oil temperature,temperature uniformity,and heating efficiency decrease,while the volume of the low-temperature region remains basically the same.Grey correlation analysis is used to rank the importance of stirring parameters in the following order:τ>θ1>θ2>D.Subsequently,multiple regression analysis is used to quantitatively describe the relationship between different stirring parameters and heat transfer evaluation indices through equations.Finally,based on entropy generation minimization,the stirring parameters with optimal heat transfer performance are obtained when τ=350 rpm,θ1=θ2=0°,and D=500 mm.展开更多
Traditionally,passenger comfort in vehicles is perceived as being most influenced by acceleration and jerk.Consequently,the current research primarily focuses on developing control algorithms to limit the maximum acce...Traditionally,passenger comfort in vehicles is perceived as being most influenced by acceleration and jerk.Consequently,the current research primarily focuses on developing control algorithms to limit the maximum acceleration and jerk of the vehicle in order to improve passenger comfort.However,naturalistic driving studies demonstrate that such simple characteristics are insufficient for accurately evaluating passenger comfort.This study identifies motion complexity as a key factor of passenger comfort.A series of naturalistic driving studies are conducted,during which passenger comfort is assessed using a 5-point Likert scale.Moreover,a real-time passenger comfort measurement based on electromyography(EMG)and stepwise regression is proposed to facilitate seamless data collection.Time-series features representing motion complexity are then introduced to better describe passenger comfort.Hierarchical regression confirms that simple characteristics of motion are insufficient to explain passenger comfort,and shows that the proposed motion complexity features have a substantial effect on passenger comfort.Finally,a machine learning-based real-time passenger comfort estimation method is developed according to the foregoing findings.Experimental results show that the proposed method can accurately estimate passenger comfort in real-time using only vehicle motion information.The findings of this study suggest that vehicle motion complexity should be considered in future passenger comfort studies.展开更多
BACKGROUND The spread of the severe acute respiratory syndrome coronavirus 2 outbreak worldwide has caused concern regarding the mortality rate caused by the infection.The determinants of mortality on a global scale c...BACKGROUND The spread of the severe acute respiratory syndrome coronavirus 2 outbreak worldwide has caused concern regarding the mortality rate caused by the infection.The determinants of mortality on a global scale cannot be fully understood due to lack of information.AIM To identify key factors that may explain the variability in case lethality across countries.METHODS We identified 21 Potential risk factors for coronavirus disease 2019(COVID-19)case fatality rate for all the countries with available data.We examined univariate relationships of each variable with case fatality rate(CFR),and all independent variables to identify candidate variables for our final multiple model.Multiple regression analysis technique was used to assess the strength of relationship.RESULTS The mean of COVID-19 mortality was 1.52±1.72%.There was a statistically significant inverse correlation between health expenditure,and number of computed tomography scanners per 1 million with CFR,and significant direct correlation was found between literacy,and air pollution with CFR.This final model can predict approximately 97%of the changes in CFR.CONCLUSION The current study recommends some new predictors explaining affect mortality rate.Thus,it could help decision-makers develop health policies to fight COVID-19.展开更多
Complementary-label learning(CLL)aims at finding a classifier via samples with complementary labels.Such data is considered to contain less information than ordinary-label samples.The transition matrix between the tru...Complementary-label learning(CLL)aims at finding a classifier via samples with complementary labels.Such data is considered to contain less information than ordinary-label samples.The transition matrix between the true label and the complementary label,and some loss functions have been developed to handle this problem.In this paper,we show that CLL can be transformed into ordinary classification under some mild conditions,which indicates that the complementary labels can supply enough information in most cases.As an example,an extensive misclassification error analysis was performed for the Kernel Ridge Regression(KRR)method applied to multiple complementary-label learning(MCLL),which demonstrates its superior performance compared to existing approaches.展开更多
The SKP1 gene is an important component of the SCF(SKP1-Cullin1-F-box)complex and serves as a bridge connecting the F-box and Cullin1genes(F-box-SKP1-Cullin1).The pattern of S-RNase being ubiquitously labelled by the ...The SKP1 gene is an important component of the SCF(SKP1-Cullin1-F-box)complex and serves as a bridge connecting the F-box and Cullin1genes(F-box-SKP1-Cullin1).The pattern of S-RNase being ubiquitously labelled by the SCF complex and degraded by the 26S protease accounts for the bulk of the available self-incompatibility studies.In this study,15 ClSKP1s from the‘Xiangshui'lemon genome and ubiquitome exist in the same SKP1 conserved domain(CD)as SKP1s in other species.The q PCR results showed that SKP1-6 and SKP1-14 have tissue expression patterns specific for expression in pollen.In addition,SKP1-6 and SKP1-14 in the stigma,style and ovary were significantly upregulated after self-pollination compared to those after cross-pollination.A subcellular location showed that SKP1-6 and SKP1-14 were located in the nucleus.In addition,yeast two-hybrid(Y2H)assays,bimolecular fluorescence complementation(BiFC)and luciferase complementation imaging(LCI)assays showed that SKP1-6 interacted with F-box1,F-box33,F-box34,F-box17,F-box19,Cullin1-2 and 26S proteasome subunit 4 homolog A(26S PS4HA).SKP1-14 interacted with F-box17,F-box19,F-box35,Cullin1-2 and 26S PS4HA.The interaction of Cullin1-2 and the F-box with SKP1 as a bridge was verified by a yeast three-hybrid experiment.The ability of S3-RNase to inhibit pollen and pollen tube growth and development was assessed using in vitro pollen co-culture experiments with recombinant S3-RNase proteins.Overall,this study provides important experimental evidence and theoretical basis for understanding the mechanism of self-incompatibility in plants by revealing the key role of the SCF complex in‘Xiangshui'lemon,which is bridged by ClSKP1-6,in self-incompatibility.The results of this study are of great significance for the future indepth exploration of the molecular mechanism of the SCF complex and its wide application in the self-incompatibility of plants.展开更多
Peripheral nerve injury is a common neurological condition that often leads to severe functional limitations and disabilities.Research on the pathogenesis of peripheral nerve injury has focused on pathological changes...Peripheral nerve injury is a common neurological condition that often leads to severe functional limitations and disabilities.Research on the pathogenesis of peripheral nerve injury has focused on pathological changes at individual injury sites,neglecting multilevel pathological analysis of the overall nervous system and target organs.This has led to restrictions on current therapeutic approaches.In this paper,we first summarize the potential mechanisms of peripheral nerve injury from a holistic perspective,covering the central nervous system,peripheral nervous system,and target organs.After peripheral nerve injury,the cortical plasticity of the brain is altered due to damage to and regeneration of peripheral nerves;changes such as neuronal apoptosis and axonal demyelination occur in the spinal cord.The nerve will undergo axonal regeneration,activation of Schwann cells,inflammatory response,and vascular system regeneration at the injury site.Corresponding damage to target organs can occur,including skeletal muscle atrophy and sensory receptor disruption.We then provide a brief review of the research advances in therapeutic approaches to peripheral nerve injury.The main current treatments are conducted passively and include physical factor rehabilitation,pharmacological treatments,cell-based therapies,and physical exercise.However,most treatments only partially address the problem and cannot complete the systematic recovery of the entire central nervous system-peripheral nervous system-target organ pathway.Therefore,we should further explore multilevel treatment options that produce effective,long-lasting results,perhaps requiring a combination of passive(traditional)and active(novel)treatment methods to stimulate rehabilitation at the central-peripheral-target organ levels to achieve better functional recovery.展开更多
文摘This article presents a mathematical model addressing a scenario involving a hybrid nanofluid flow between two infinite parallel plates.One plate remains stationary,while the other moves downward at a squeezing velocity.The space between these plates contains a Darcy-Forchheimer porous medium.A mixture of water-based fluid with gold(Au)and silicon dioxide(Si O2)nanoparticles is formulated.In contrast to the conventional Fourier's heat flux equation,this study employs the Cattaneo-Christov heat flux equation.A uniform magnetic field is applied perpendicular to the flow direction,invoking magnetohydrodynamic(MHD)effects.Further,the model accounts for Joule heating,which is the heat generated when an electric current passes through the fluid.The problem is solved via NDSolve in MATHEMATICA.Numerical and statistical analyses are conducted to provide insights into the behavior of the nanomaterials between the parallel plates with respect to the flow,energy transport,and skin friction.The findings of this study have potential applications in enhancing cooling systems and optimizing thermal management strategies.It is observed that the squeezing motion generates additional pressure gradients within the fluid,which enhances the flow rate but reduces the frictional drag.Consequently,the fluid is pushed more vigorously between the plates,increasing the flow velocity.As the fluid experiences higher flow rates due to the increased squeezing effect,it spends less time in the region between the plates.The thermal relaxation,however,abruptly changes the temperature,leading to a decrease in the temperature fluctuations.
基金supported by DST-FIST(Government of India)(Grant No.SR/FIST/MS-1/2017/13)and Seed Money Project(Grant No.DoRDC/733).
文摘This study numerically examines the heat and mass transfer characteristics of two ternary nanofluids via converging and diverg-ing channels.Furthermore,the study aims to assess two ternary nanofluids combinations to determine which configuration can provide better heat and mass transfer and lower entropy production,while ensuring cost efficiency.This work bridges the gap be-tween academic research and industrial feasibility by incorporating cost analysis,entropy generation,and thermal efficiency.To compare the velocity,temperature,and concentration profiles,we examine two ternary nanofluids,i.e.,TiO_(2)+SiO_(2)+Al_(2)O_(3)/H_(2)O and TiO_(2)+SiO_(2)+Cu/H_(2)O,while considering the shape of nanoparticles.The velocity slip and Soret/Dufour effects are taken into consideration.Furthermore,regression analysis for Nusselt and Sherwood numbers of the model is carried out.The Runge-Kutta fourth-order method with shooting technique is employed to acquire the numerical solution of the governed system of ordinary differential equations.The flow pattern attributes of ternary nanofluids are meticulously examined and simulated with the fluc-tuation of flow-dominating parameters.Additionally,the influence of these parameters is demonstrated in the flow,temperature,and concentration fields.For variation in Eckert and Dufour numbers,TiO_(2)+SiO_(2)+Al_(2)O_(3)/H_(2)O has a higher temperature than TiO_(2)+SiO_(2)+Cu/H_(2)O.The results obtained indicate that the ternary nanofluid TiO_(2)+SiO_(2)+Al_(2)O_(3)/H_(2)O has a higher heat transfer rate,lesser entropy generation,greater mass transfer rate,and lower cost than that of TiO_(2)+SiO_(2)+Cu/H_(2)O ternary nanofluid.
基金funded by the INTER program and cofunded by the Fond National de la Recherche,Luxembourg(FNR)and the Fund for Scientific Research-FNRS,Belgium(F.R.S-FNRS),T.0233.20-‘Sustainable Residential Densification’project(SusDens,2020–2024).
文摘The impact of different global and local variables in urban development processes requires a systematic study to fully comprehend the underlying complexities in them.The interplay between such variables is crucial for modelling urban growth to closely reflects reality.Despite extensive research,ambiguity remains about how variations in these input variables influence urban densification.In this study,we conduct a global sensitivity analysis(SA)using a multinomial logistic regression(MNL)model to assess the model’s explanatory and predictive power.We examine the influence of global variables,including spatial resolution,neighborhood size,and density classes,under different input combinations at a provincial scale to understand their impact on densification.Additionally,we perform a stepwise regression to identify the significant explanatory variables that are important for understanding densification in the Brussels Metropolitan Area(BMA).Our results indicate that a finer spatial resolution of 50 m and 100 m,smaller neighborhood size of 5×5 and 3×3,and specific density classes—namely 3(non-built-up,low and high built-up)and 4(non-built-up,low,medium and high built-up)—optimally explain and predict urban densification.In line with the same,the stepwise regression reveals that models with a coarser resolution of 300 m lack significant variables,reflecting a lower explanatory power for densification.This approach aids in identifying optimal and significant global variables with higher explanatory power for understanding and predicting urban densification.Furthermore,these findings are reproducible in a global urban context,offering valuable insights for planners,modelers and geographers in managing future urban growth and minimizing modelling.
基金Supported by the Natural Science Foundation of Fujian Province(2022J011177,2024J01903)the Key Project of Fujian Provincial Education Department(JZ230054)。
文摘In clinical research,subgroup analysis can help identify patient groups that respond better or worse to specific treatments,improve therapeutic effect and safety,and is of great significance in precision medicine.This article considers subgroup analysis methods for longitudinal data containing multiple covariates and biomarkers.We divide subgroups based on whether a linear combination of these biomarkers exceeds a predetermined threshold,and assess the heterogeneity of treatment effects across subgroups using the interaction between subgroups and exposure variables.Quantile regression is used to better characterize the global distribution of the response variable and sparsity penalties are imposed to achieve variable selection of covariates and biomarkers.The effectiveness of our proposed methodology for both variable selection and parameter estimation is verified through random simulations.Finally,we demonstrate the application of this method by analyzing data from the PA.3 trial,further illustrating the practicality of the method proposed in this paper.
基金National Key R&D Program of China under Grant Nos.2018YFC1504504 and 2018YFC0809404。
文摘Damage to electrical equipment in an earthquake can lead to power outage of power systems.Seismic fragility analysis is a common method to assess the seismic reliability of electrical equipment.To further guarantee the efficiency of analysis,multi-source uncertainties including the structure itself and seismic excitation need to be considered.A method for seismic fragility analysis that reflects structural and seismic parameter uncertainty was developed in this study.The proposed method used a random sampling method based on Latin hypercube sampling(LHS)to account for the structure parameter uncertainty and the group structure characteristics of electrical equipment.Then,logistic Lasso regression(LLR)was used to find the seismic fragility surface based on double ground motion intensity measures(IM).The seismic fragility based on the finite element model of an±1000 kV main transformer(UHVMT)was analyzed using the proposed method.The results show that the seismic fragility function obtained by this method can be used to construct the relationship between the uncertainty parameters and the failure probability.The seismic fragility surface did not only provide the probabilities of seismic damage states under different IMs,but also had better stability than the fragility curve.Furthermore,the sensitivity analysis of the structural parameters revealed that the elastic module of the bushing and the height of the high-voltage bushing may have a greater influence.
文摘According to some main assumptions in the Rouse Formula,it analyzes the applicability of Rouse distribution in the coastal region.Based on the classical Rouse Formula,the linear form of Rouse Formula and the transport characteristics of offshore sediment were used to take lnz/h,lnc_(a),c_(a),u,lnu and z/h as the independent variables.The multiple liner regression method was used to analyze the influence of the independent variables on the vertical distribution of sediment concentration.By using the method of significance test,the factors(ln𝑢)that have less influence on sediment concentration among 6 variables were eliminated.The correlation coefficient between the calculated sediment concentration and the measured sediment concentration indicates that the adopted variables can reflect the characteristics of vertical distribution of concentration of fine sediment near shore under complex dynamic conditions.
基金supported in part by Sichuan Science and Technology Program under Grant No.2025ZNSFSC151in part by the Strategic Priority Research Program of Chinese Academy of Sciences under Grant No.XDA27030201+1 种基金the Natural Science Foundation of China under Grant No.U21B6001in part by the Natural Science Foundation of Tianjin under Grant No.24JCQNJC01930.
文摘The work proposes a distributed Kalman filtering(KF)algorithm to track a time-varying unknown signal process for a stochastic regression model over network systems in a cooperative way.We provide the stability analysis of the proposed distributed KF algorithm without independent and stationary signal assumptions,which implies that the theoretical results are able to be applied to stochastic feedback systems.Note that the main difficulty of stability analysis lies in analyzing the properties of the product of non-independent and non-stationary random matrices involved in the error equation.We employ analysis techniques such as stochastic Lyapunov function,stability theory of stochastic systems,and algebraic graph theory to deal with the above issue.The stochastic spatio-temporal cooperative information condition shows the cooperative property of multiple sensors that even though any local sensor cannot track the time-varying unknown signal,the distributed KF algorithm can be utilized to finish the filtering task in a cooperative way.At last,we illustrate the property of the proposed distributed KF algorithm by a simulation example.
文摘In oil and gas exploration,elucidating the complex interdependencies among geological variables is paramount.Our study introduces the application of sophisticated regression analysis method at the forefront,aiming not just at predicting geophysical logging curve values but also innovatively mitigate hydrocarbon depletion observed in geochemical logging.Through a rigorous assessment,we explore the efficacy of eight regression models,bifurcated into linear and nonlinear groups,to accommodate the multifaceted nature of geological datasets.Our linear model suite encompasses the Standard Equation,Ridge Regression,Least Absolute Shrinkage and Selection Operator,and Elastic Net,each presenting distinct advantages.The Standard Equation serves as a foundational benchmark,whereas Ridge Regression implements penalty terms to counteract overfitting,thus bolstering model robustness in the presence of multicollinearity.The Least Absolute Shrinkage and Selection Operator for variable selection functions to streamline models,enhancing their interpretability,while Elastic Net amalgamates the merits of Ridge Regression and Least Absolute Shrinkage and Selection Operator,offering a harmonized solution to model complexity and comprehensibility.On the nonlinear front,Gradient Descent,Kernel Ridge Regression,Support Vector Regression,and Piecewise Function-Fitting methods introduce innovative approaches.Gradient Descent assures computational efficiency in optimizing solutions,Kernel Ridge Regression leverages the kernel trick to navigate nonlinear patterns,and Support Vector Regression is proficient in forecasting extremities,pivotal for exploration risk assessment.The Piecewise Function-Fitting approach,tailored for geological data,facilitates adaptable modeling of variable interrelations,accommodating abrupt data trend shifts.Our analysis identifies Ridge Regression,particularly when augmented by Piecewise Function-Fitting,as superior in recouping hydrocarbon losses,and underscoring its utility in resource quantification refinement.Meanwhile,Kernel Ridge Regression emerges as a noteworthy strategy in ameliorating porosity-logging curve prediction for well A,evidencing its aptness for intricate geological structures.This research attests to the scientific ascendancy and broad-spectrum relevance of these regression techniques over conventional methods while heralding new horizons for their deployment in the oil and gas sector.The insights garnered from these advanced modeling strategies are set to transform geological and engineering practices in hydrocarbon prediction,evaluation,and recovery.
文摘[Objectives] To analyze the influencing factors of fixed defects in patients with catheter fixation in clinical nursing work, in order to provide the best catheter fixation nursing plan for patients.[Methods] 176 inpatients with indwelling catheter from surgical system of Taihe Hospital in Shiyan City from August 2022 to March 2023 were selected. Using a retrospective analysis method, the influencing factors of catheter fixation defects in the study subjects were divided into two categories based on objective characteristics: type I non modifiable influencing factors and type II modifiable influencing factors. Using the standard for catheter fixation defects, whether the patient had catheter fixation defects was determined. After classified and statistically analyzed item by item, binary Logistic multiple regression analysis was used to identify the influencing factors.[Results] The occurrence of catheter fixation defects in patients with catheter fixation was related to factors such as whether the patient was evaluated before fixation, whether the fixation method was standardized and systematic, whether there was sufficient communication between nurses and patients, and the patient s knowledge of catheter fixation. It was also influenced by factors such as the patient s age, catheterization site, catheterization number, catheterization duration, where there was a consciousness disorder, educational level, and external environmental temperature.[Conclusions] Early attention to the key factors affecting patients with catheter fixation defects can effectively prevent adverse factors and provide patients with the best catheter fixation nursing plan to improve nursing quality.
基金supported by the National Key Scientific Instrument and Equipment Development Projects of China(No.41827808)the Major Program of the National Natural Science Foundation of China(No.42090055).
文摘Block-flexure toppling failure is frequently encountered in interbedded anti-inclined rock(IAR)slopes,and seriously threatens the construction of hydropower infrastructure.In this study,we first investigated the Lean Reservoir area’s geological setting and the Linda landslide’s characteristics.Then,uniform design and random design were used to design 110 training datasets and 31 testing datasets,respectively.Afterwards,the toppling response was obtained by using the discrete element code.Finally,support vector regression was used to obtain the influence weights of 21 impact factors.The results show that the influence weight of the slope angle and rock formation dip angle on the toppling deformation among tertiary impact factors is 25.96%and 17.28%,respectively,which are much greater than the other 19 impact factors within the research range.For the primary impact factors,the influence weight is sorted from large to small as slope geometry parameters,joints parameters,and rock mechanics parameters.Joints parameters,especially the geometric parameters,cannot be ignored when evaluating the stability of IAR slopes.Through numerical simulation,it was qualitatively determined that failure surfaces of slopes were controlled by cross joints and that the rocks in the slope toe play a role in preventing slope deformation.
基金National Natural Science Foundation of China(U22A20377)Natural Science Foundation of Hunan Province of China(23C0168).
文摘Objective To explore the clinical characteristics and methods for syndrome differentiation prediction,as well as to construct a predictive model for Qi deficiency and blood stasis syndrome in patients with acute ischemic stroke(AIS).Methods This study employed a retrospective case-control design to analyze patients with AIS who received inpatient treatment at the Neurology Department of The First Hospital of Hunan University of Chinese Medicine from January 1,2013 to December 31,2022.AIS patients meeting the diagnostic criteria for Qi deficiency and blood stasis syndrome were stratified into case group,while those without Qi deficiency and blood stasis syndrome were stratified into control group.The demographic characteristics(age and gender),clinical parameters[time from onset to admission,National Institutes of Health Stroke Scale(NIHSS)score,and blood pressure],past medical history,traditional Chinese medicine(TCM)diagnostic characteristics(tongue and pulse),neurological symptoms and signs,imaging findings[magnetic resonance imaging-diffusion weighted imaging(MRI-DWI)],and biochemical indicators of the two groups were collected and compared.The indicators with statistical difference(P<0.05)in univariate analysis were included in multivariate logistic regression analysis to evaluate their predictive value for the diagnosis of Qi deficiency and blood stasis syndrome,and the predictive model was constructed by receiver operating characteristic(ROC)curve analysis.Results The study included 1035 AIS patients,with 404 cases in case group and 631 cases in control group.Compared with control group,patients in case group were significantly older,had extended onset-to-admission time,lower diastolic blood pressure,and lower NIHSS scores(P<0.05).Case group showed lower incidence of hypertension history(P<0.05).Regarding tongue and pulse characteristics,pale and dark tongue colors,white tongue coating,fine pulse,astringent pulse,and sinking pulse were more common in case group.Imaging examinations demonstrated higher proportions of centrum semiovale infarction,cerebral atrophy,and vertebral artery stenosis in case group(P<0.05).Among biochemical indicators,case group showed higher proportions of elevated fasting blood glucose and glycated hemoglobin(HbA1c),while lower proportions of elevated white blood cell count,reduced hemoglobin,and reduced high-density lipoprotein cholesterol(HDL-C)(P<0.05).Multivariate logistic regression analysis identified significant predictors for Qi deficiency and blood stasis syndrome including:fine pulse[odds ratio(OR)=4.38],astringent pulse(OR=3.67),superficial sensory abnormalities(OR=1.86),centrum semiovale infarction(OR=1.57),cerebral atrophy(OR=1.55),vertebral artery stenosis(OR=1.62),and elevated HbA1c(OR=3.52).The ROC curve analysis of the comprehensive prediction model yielded an area under the curve(AUC)of 0.878[95%confidence interval(CI)=0.855-0.900].Conclusion This study finds out that Qi deficiency and blood stasis syndrome represents one of the primary types of AIS.Fine pulse,astringent pulse,superficial sensory abnormalities,centrum semiovale infarction,cerebral atrophy,vertebral artery stenosis,elevated blood glucose,elevated HbA1c,pale and dark tongue colors,and white tongue coating are key objective diagnostic indicators for the syndrome differentiation of AIS with Qi deficiency and blood stasis syndrome.Based on these indicators,a syndrome differentiation prediction model has been developed,offering a more objective basis for clinical diagnosis,and help to rapidly identify this syndrome in clinical practice and reduce misdiagnosis and missed diagnosis.
文摘In view of the composition analysis and identification of ancient glass products, L1 regularization, K-Means cluster analysis, elbow rule and other methods were comprehensively used to build logical regression, cluster analysis, hyper-parameter test and other models, and SPSS, Python and other tools were used to obtain the classification rules of glass products under different fluxes, sub classification under different chemical compositions, hyper-parameter K value test and rationality analysis. Research can provide theoretical support for the protection and restoration of ancient glass relics.
基金supported by the National Natural Science Foundation of China[grant numbers 42101449,42090012 and 61825103]the Natural Science Foundation of Hubei Province,China[grant numbers 2022CFB773 and 2020CFA001]+2 种基金the Key Research and Development Program of Hubei Province,China[grant number 2022BAA048]the Chutian Scholar Program of Hubei Provincethe Yellow Crane Talent Scheme.
文摘Road traffic crashes are becoming thorny issues being faced worldwide.Traffic crashes are spatiotemporal events and the research on the spatiotemporal patterns and variation trends of traffic crashes has been carried out.However,the impact of built environment on traffic crash spatiotemporal trends has not received much attention.Moreover,the spatial non-stationarity between the variation trends of traffic crashes and their influencing factors is usually neglected.To make up for the lack of analysis of built environment factors influencing spatiotemporal hotspot trends in traffic crashes,this paper proposed a method of“ST-GWLR”for analyzing the influence of built environment factors on spatiotemporal hotspot trends of traffic crashes by combining the spatiotemporal hotspot trend analysis and Geographically Weighted Logistic Regression(GWLR)modeling methods.Firstly,the traffic crash spatiotemporal hotspot trends were explored using the space-time cube model,hotspot analysis,and Mann-Kendall trend test.Then,the GWLR was introduced to capture the spatial non-stationarity neglected by the classic Global Logistic Regression(GLR)model,to improve the accuracy of the model estimation.GWLR model is used for the first time to analyze the significant local correlation between the traffic crash spatiotemporal hotspot trends and the built environment factors,to accurately and effectively identify the built environment factors that have significant influences on the hotspot trends of traffic crashes.The performance of the GWLR models and GLR models was examined and compared sufficiently.The results showed that the proposed ST-GWLR,which captured spatial non-stationarity,performed better than the classic GLR combined with spatiotemporal analysis,and improved the prediction accuracy of the models by 14.9%,13.9%,and 15.1%,respectively.There were significant local correlations between intensifying hotspots and persistent hotspots of traffic crashes and the built environment factors.The findings of this paper have positive implications for traffic safety management and urban built environment planning.
文摘Ignimbrites have been widely used as building materials in many historical and touristic structures in the Kayseri region of Türkiye. Their diverse colours and textures make them a popular choice for modern construction as well. However, ignimbrites are particularly vulnerable to atmospheric conditions, such as freeze-thaw cycles, due to their high porosity, which is a result of their formation process. When water enters the pores of the ignimbrites, it can freeze during cold weather. As the water freezes and expands, it generates internal stress within the stone, causing micro-cracks to develop. Over time, repeated freeze-thaw (F-T) cycles lead to the growth of these micro-cracks into larger cracks, compromising the structural integrity of the ignimbrites and eventually making them unsuitable for use as building materials. The determination of the long-term F-T performance of ignimbrites can be established after long F-T experimental processes. Determining the long-term F-T performance of ignimbrites typically requires extensive experimental testing over prolonged freeze-thaw cycles. To streamline this process, developing accurate predictive equations becomes crucial. In this study, such equations were formulated using classical regression analyses and artificial neural networks (ANN) based on data obtained from these experiments, allowing for the prediction of the F-T performance of ignimbrites and other similar building stones without the need for lengthy testing. In this study, uniaxial compressive strength, ultrasonic propagation velocity, apparent porosity and mass loss of ignimbrites after long-term F-T were determined. Following the F-T cycles, the disintegration rate was evaluated using decay function approaches, while uniaxial compressive strength (UCS) values were predicted with minimal input parameters through both regression and ANN analyses. The ANN and regression models created for this purpose were first started with a single input value and then developed with two and three combinations. The predictive performance of the models was assessed by comparing them to regression models using the coefficient of determination (R2) as the evaluation criterion. As a result of the study, higher R2 values (0.87) were obtained in models built with artificial neural network. The results of the study indicate that ANN usage can produce results close to experimental outcomes in predicting the long-term F-T performance of ignimbrite samples.
基金supported by the National Natural Science Foundation of China(Grant no.52304065)China Postdoctoral Science Foundation(Grant no.2022MD723759).
文摘Taking into account the characteristics of non-Newtonian fluids and the influence of latent heat of wax crystallization,this study establishes physical and mathematical models for the synergy of tubular heating and mechanical stirring during the waxy crude oil heating process.Numerical calculations are conducted using the sliding grid technique and FVM.The focus of this study is on the impact of stirring rate(τ),horizontal deflection angle(θ1),vertical deflection angle(θ2),and stirring diameter(D)on the heating effect of crude oil.Our results show that asτincreases from 200 rpm to 500 rpm and D increases from 400 mm to 600 mm,there is an improvement in the average crude oil temperature and temperature uniformity.Additionally,heating efficiency increases by 0.5%and 1%,while the volume of the low-temperature region decreases by 57.01 m^(3) and 36.87 m3,respectively.Asθ1 andθ2 increase from 0°to 12°,the average crude oil temperature,temperature uniformity,and heating efficiency decrease,while the volume of the low-temperature region remains basically the same.Grey correlation analysis is used to rank the importance of stirring parameters in the following order:τ>θ1>θ2>D.Subsequently,multiple regression analysis is used to quantitatively describe the relationship between different stirring parameters and heat transfer evaluation indices through equations.Finally,based on entropy generation minimization,the stirring parameters with optimal heat transfer performance are obtained when τ=350 rpm,θ1=θ2=0°,and D=500 mm.
基金Supported by National Natural Science Foundation of China(Grant No.52221005)Tsinghua-Toyota Joint Research Fund.
文摘Traditionally,passenger comfort in vehicles is perceived as being most influenced by acceleration and jerk.Consequently,the current research primarily focuses on developing control algorithms to limit the maximum acceleration and jerk of the vehicle in order to improve passenger comfort.However,naturalistic driving studies demonstrate that such simple characteristics are insufficient for accurately evaluating passenger comfort.This study identifies motion complexity as a key factor of passenger comfort.A series of naturalistic driving studies are conducted,during which passenger comfort is assessed using a 5-point Likert scale.Moreover,a real-time passenger comfort measurement based on electromyography(EMG)and stepwise regression is proposed to facilitate seamless data collection.Time-series features representing motion complexity are then introduced to better describe passenger comfort.Hierarchical regression confirms that simple characteristics of motion are insufficient to explain passenger comfort,and shows that the proposed motion complexity features have a substantial effect on passenger comfort.Finally,a machine learning-based real-time passenger comfort estimation method is developed according to the foregoing findings.Experimental results show that the proposed method can accurately estimate passenger comfort in real-time using only vehicle motion information.The findings of this study suggest that vehicle motion complexity should be considered in future passenger comfort studies.
文摘BACKGROUND The spread of the severe acute respiratory syndrome coronavirus 2 outbreak worldwide has caused concern regarding the mortality rate caused by the infection.The determinants of mortality on a global scale cannot be fully understood due to lack of information.AIM To identify key factors that may explain the variability in case lethality across countries.METHODS We identified 21 Potential risk factors for coronavirus disease 2019(COVID-19)case fatality rate for all the countries with available data.We examined univariate relationships of each variable with case fatality rate(CFR),and all independent variables to identify candidate variables for our final multiple model.Multiple regression analysis technique was used to assess the strength of relationship.RESULTS The mean of COVID-19 mortality was 1.52±1.72%.There was a statistically significant inverse correlation between health expenditure,and number of computed tomography scanners per 1 million with CFR,and significant direct correlation was found between literacy,and air pollution with CFR.This final model can predict approximately 97%of the changes in CFR.CONCLUSION The current study recommends some new predictors explaining affect mortality rate.Thus,it could help decision-makers develop health policies to fight COVID-19.
基金Supported by the Indigenous Innovation’s Capability Development Program of Huizhou University(HZU202003,HZU202020)Natural Science Foundation of Guangdong Province(2022A1515011463)+2 种基金the Project of Educational Commission of Guangdong Province(2023ZDZX1025)National Natural Science Foundation of China(12271473)Guangdong Province’s 2023 Education Science Planning Project(Higher Education Special Project)(2023GXJK505)。
文摘Complementary-label learning(CLL)aims at finding a classifier via samples with complementary labels.Such data is considered to contain less information than ordinary-label samples.The transition matrix between the true label and the complementary label,and some loss functions have been developed to handle this problem.In this paper,we show that CLL can be transformed into ordinary classification under some mild conditions,which indicates that the complementary labels can supply enough information in most cases.As an example,an extensive misclassification error analysis was performed for the Kernel Ridge Regression(KRR)method applied to multiple complementary-label learning(MCLL),which demonstrates its superior performance compared to existing approaches.
基金supported by grants from the National Natural Science Foundation of China(Grant No.31960585)Science and Technology Major Project of Guangxi(Grant No.Guike AA22068092)+1 种基金Guangxi Science and Technology Vanguard Special Action Project(Grant No.202204)State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources(Grant Nos.SKLCUSA-a201906,SKLCU-SA-c201901)。
文摘The SKP1 gene is an important component of the SCF(SKP1-Cullin1-F-box)complex and serves as a bridge connecting the F-box and Cullin1genes(F-box-SKP1-Cullin1).The pattern of S-RNase being ubiquitously labelled by the SCF complex and degraded by the 26S protease accounts for the bulk of the available self-incompatibility studies.In this study,15 ClSKP1s from the‘Xiangshui'lemon genome and ubiquitome exist in the same SKP1 conserved domain(CD)as SKP1s in other species.The q PCR results showed that SKP1-6 and SKP1-14 have tissue expression patterns specific for expression in pollen.In addition,SKP1-6 and SKP1-14 in the stigma,style and ovary were significantly upregulated after self-pollination compared to those after cross-pollination.A subcellular location showed that SKP1-6 and SKP1-14 were located in the nucleus.In addition,yeast two-hybrid(Y2H)assays,bimolecular fluorescence complementation(BiFC)and luciferase complementation imaging(LCI)assays showed that SKP1-6 interacted with F-box1,F-box33,F-box34,F-box17,F-box19,Cullin1-2 and 26S proteasome subunit 4 homolog A(26S PS4HA).SKP1-14 interacted with F-box17,F-box19,F-box35,Cullin1-2 and 26S PS4HA.The interaction of Cullin1-2 and the F-box with SKP1 as a bridge was verified by a yeast three-hybrid experiment.The ability of S3-RNase to inhibit pollen and pollen tube growth and development was assessed using in vitro pollen co-culture experiments with recombinant S3-RNase proteins.Overall,this study provides important experimental evidence and theoretical basis for understanding the mechanism of self-incompatibility in plants by revealing the key role of the SCF complex in‘Xiangshui'lemon,which is bridged by ClSKP1-6,in self-incompatibility.The results of this study are of great significance for the future indepth exploration of the molecular mechanism of the SCF complex and its wide application in the self-incompatibility of plants.
基金supported by grants from the Natural Science Foundation of Tianjin(General Program),Nos.23JCYBJC01390(to RL),22JCYBJC00220(to XC),and 22JCYBJC00210(to QL).
文摘Peripheral nerve injury is a common neurological condition that often leads to severe functional limitations and disabilities.Research on the pathogenesis of peripheral nerve injury has focused on pathological changes at individual injury sites,neglecting multilevel pathological analysis of the overall nervous system and target organs.This has led to restrictions on current therapeutic approaches.In this paper,we first summarize the potential mechanisms of peripheral nerve injury from a holistic perspective,covering the central nervous system,peripheral nervous system,and target organs.After peripheral nerve injury,the cortical plasticity of the brain is altered due to damage to and regeneration of peripheral nerves;changes such as neuronal apoptosis and axonal demyelination occur in the spinal cord.The nerve will undergo axonal regeneration,activation of Schwann cells,inflammatory response,and vascular system regeneration at the injury site.Corresponding damage to target organs can occur,including skeletal muscle atrophy and sensory receptor disruption.We then provide a brief review of the research advances in therapeutic approaches to peripheral nerve injury.The main current treatments are conducted passively and include physical factor rehabilitation,pharmacological treatments,cell-based therapies,and physical exercise.However,most treatments only partially address the problem and cannot complete the systematic recovery of the entire central nervous system-peripheral nervous system-target organ pathway.Therefore,we should further explore multilevel treatment options that produce effective,long-lasting results,perhaps requiring a combination of passive(traditional)and active(novel)treatment methods to stimulate rehabilitation at the central-peripheral-target organ levels to achieve better functional recovery.
