The nonlinear post-buckling response of functionally graded(FG)copper matrix plates enforced by graphene origami auxetic metamaterials(GOAMs)is investigated in the currentwork.The auxeticmaterial properties of the pla...The nonlinear post-buckling response of functionally graded(FG)copper matrix plates enforced by graphene origami auxetic metamaterials(GOAMs)is investigated in the currentwork.The auxeticmaterial properties of the plate are controlled by graphene content and the degree of origami folding,which are graded across the thickness of the plate.Thematerial properties of the GOAM plate are evaluated using genetic micro-mechanicalmodels.Governing nonlinear eigenvalue problems for the post-buckling response of the GOAM composite plate are derived using the virtual work principle and a four-variable nonlinear shear deformation theory.A novel differential quadrature method(DQM)algorithm is developed to solve the nonlinear eigenvalue problem.Detailed parametric studies are presented to explore the effects of graphene content,folding degree,and GO distribution patterns on the post-buckling responses of GOAM plates.Results show that high tunability in post-buckling characteristics can be achieved by using GOAM.FunctionallyGradedGraphene OrigamiAuxeticMetamaterials(FG-GOAM)plates can be used in aerospace structures to improve their structural performance and response.展开更多
This study introduces a novel mathematical model to describe the progression of cholera by integrating fractional derivatives with both singular and non-singular kernels alongside stochastic differential equations ove...This study introduces a novel mathematical model to describe the progression of cholera by integrating fractional derivatives with both singular and non-singular kernels alongside stochastic differential equations over four distinct time intervals.The model incorporates three key fractional derivatives:the Caputo-Fabrizio fractional derivative with a non-singular kernel,the Caputo proportional constant fractional derivative with a singular kernel,and the Atangana-Baleanu fractional derivative with a non-singular kernel.We analyze the stability of the core model and apply various numerical methods to approximate the proposed crossover model.To achieve this,the approximation of Caputo proportional constant fractional derivative with Grünwald-Letnikov nonstandard finite difference method is used for the deterministic model with a singular kernel,while the Toufik-Atangana method is employed for models involving a non-singular Mittag-Leffler kernel.Additionally,the integral Caputo-Fabrizio approximation and a two-step Lagrange polynomial are utilized to approximate the model with a non-singular exponential decay kernel.For the stochastic component,the Milstein method is implemented to approximate the stochastic differential equations.The stability and effectiveness of the proposed model and methodologies are validated through numerical simulations and comparisons with real-world cholera data from Yemen.The results confirm the reliability and practical applicability of the model,providing strong theoretical and empirical support for the approach.展开更多
The high thermal conductivity of the nanoparticles in hybrid nanofluids results in enhanced thermal conductivity associated with their base fluids.Enhanced heat transfer is a result of this high thermal conductivity,w...The high thermal conductivity of the nanoparticles in hybrid nanofluids results in enhanced thermal conductivity associated with their base fluids.Enhanced heat transfer is a result of this high thermal conductivity,which has significant applications in heat exchangers and engineering devices.To optimize heat transfer,a liquid film of Cu and TiO_(2)hybrid nanofluid behind a stretching sheet in a variable porous medium is being considered due to its importance.The nature of the fluid is considered time-dependent and the thickness of the liquid film is measured variable adjustable with the variable porous space and favorable for the uniform flow of the liquid film.The solution of the problem is acquired using the homotopy analysis method HAM,and the artificial neural network ANN is applied to obtain detailed information in the form of error estimation and validations using the fitting curve analysis.HAM data is utilized to train the ANN in this study,which uses Cu and TiO_(2)hybrid nanofluids in a variable porous space for unsteady thin film flow,and it is used to train the ANN.The results indicate that Cu and TiO_(2)play a greater role in boosting the rate.展开更多
A new extended distribution called the Odd Exponential Generalized Exponential-Exponential distribution(EOEGE-E)is proposed based on generalization of the odd generalized exponential family(OEGE-E).The statistical pro...A new extended distribution called the Odd Exponential Generalized Exponential-Exponential distribution(EOEGE-E)is proposed based on generalization of the odd generalized exponential family(OEGE-E).The statistical properties of the proposed distribution are derived.The study evaluates the accuracy of six estimation methods under complete samples.Estimation techniques include maximumlikelihood,ordinary least squares,weighted least squares,maximumproduct of spacing,Cramer vonMises,and Anderson-Darling methods.Twomethods of estimation for the involved parameters are considered based on progressively type Ⅱ censored data(PTⅡC).These methods are maximum likelihood and maximum product of spacing.The proposed distribution’s effectiveness was evaluated using different data sets from various fields.The proposed distribution provides a better fit for these datasets than existing probability distributions.展开更多
While the significant role of technological innovation in promoting renewable energy has been extensively explored in the literature,limited attention has been paid to the impact of energy patents,particularly clean e...While the significant role of technological innovation in promoting renewable energy has been extensively explored in the literature,limited attention has been paid to the impact of energy patents,particularly clean energy patents and fossil fuel patents.This study pioneers an investigation into the effects of energy patents and energy prices on renewable energy consumption.The study utilizes data from 2000Q1 to 2023Q4 and,due to the nonlinear nature of the series,applies wavelet quantile-based methods.Specifically,it introduces the wavelet quantile cointegration approach to evaluate cointegration across different quantiles and time horizons,along with the wavelet quantile-on-quantile regression method.The results confirm cointegration across different periods and quantiles,highlighting the significant relationships between energy patents,economic factors,and renewable energy consumption.Furthermore,we found that fossil energy patents negatively affect renewable energy consumption,while clean energy patents have a similar but weaker effect,especially in the short term.In addition,higher energy prices promote renewable energy adoption while economic growth positively influences renewable energy consumption,particularly in the short term.The study formulates specific policies based on these findings.展开更多
Promoting the high penetration of renewable energies like photovoltaic(PV)systems has become an urgent issue for expanding modern power grids and has accomplished several challenges compared to existing distribution g...Promoting the high penetration of renewable energies like photovoltaic(PV)systems has become an urgent issue for expanding modern power grids and has accomplished several challenges compared to existing distribution grids.This study measures the effectiveness of the Puma optimizer(PO)algorithm in parameter estimation of PSC(perovskite solar cells)dynamic models with hysteresis consideration considering the electric field effects on operation.The models used in this study will incorporate hysteresis effects to capture the time-dependent behavior of PSCs accurately.The PO optimizes the proposed modified triple diode model(TDM)with a variable voltage capacitor and resistances(VVCARs)considering the hysteresis behavior.The suggested PO algorithm contrasts with other wellknown optimizers from the literature to demonstrate its superiority.The results emphasize that the PO realizes a lower RMSE(Root mean square errors),which proves its capability and efficacy in parameter extraction for the models.The statistical results emphasize the efficiency and supremacy of the proposed PO compared to the other well-known competing optimizers.The convergence rates show good,fast,and stable convergence rates with lower RMSE via PO compared to the other five competitive optimizers.Moreover,the lowermean realized via the PO optimizer is illustrated by the box plot for all optimizers.展开更多
License plate recognition in haze-affected images is challenging due to feature distortions such as blurring and elongation,which lead to pixel displacements.This article introduces a Displacement Region Recognition M...License plate recognition in haze-affected images is challenging due to feature distortions such as blurring and elongation,which lead to pixel displacements.This article introduces a Displacement Region Recognition Method(DR2M)to address such a problem.This method operates on displaced features compared to the training input observed throughout definite time frames.The technique focuses on detecting features that remain relatively stable under haze,using a frame-based analysis to isolate edges minimally affected by visual noise.The edge detection failures are identified using a bilateral neural network through displaced feature training.The training converges bilaterally towards the minimum edges from the maximum region.Thus,the training input and detected edges are used to identify the displacement between observed image frames to extract and differentiate the license plate region from the other vehicle regions.The proposed method maps the similarity feature between the detected and identified vehicle regions.This aids in leveraging the plate recognition precision with a high F1 score.Thus,this technique achieves a 10.27%improvement in identification precision,a 10.57%increase in F1 score,and a 9.73%reduction in false positive rate compared to baseline methods under maximum displacement conditions caused by haze.The technique attains an identification precision of 95.68%,an F1 score of 94.68%,and a false positive rate of 4.32%,indicating robust performance under haze-affected settings.展开更多
Image-based computational models have been used for vulnerable plaque progression and rupture predictions,and good results have been reported.However,mechanisms and predictions for plaque erosion are underinvestigated...Image-based computational models have been used for vulnerable plaque progression and rupture predictions,and good results have been reported.However,mechanisms and predictions for plaque erosion are underinvestigated.Patient-specific fluid-structure interaction(FSI)models based on optical coherence tomography(OCT)follow-up data from patients with plaque erosion and who received conservative antithrombotic treatment(using medication,no stenting)to identify risk factors that could be used to predict the treatment outcome.OCT and angiography datawere obtained from10 patientswho received conservative antithrombotic treatment.Five participants had worse outcomes(WOG,stenosis severity≥70%at one-year follow-up),while the other five had better outcomes(BOG,stenosis severity<70%at one-year follow-up).Patient-specific 3D FSI models were constructed to obtain morphological and biomechanical risk factor values(a total of nine risk factors)for comparison and prediction.A logistic regressionmodel was used to identify optimal predictors with the best treatment outcome prediction accuracies.Our results indicated that the combination of wall shear stress(WSS),lipid percent,and thrombus burden was the best group predictor according to the mean area under the curve(AUC)of 0.96(90%confidence interval=(0.85,1.00)).WSS was the best single predictor withmean AUC=0.70(90%confidence interval=(0.20,1.00)).Thrombus burden was the only risk factor showing statistically significant group difference,suggesting its crucial role in the outcomes of conservative anti-thrombotic therapy.This pilot study indicated that integratingmorphological and biomechanical risk factors could improve treatment outcome prediction accuracy in patients with plaque erosion compared to predictions using single predictors.Large-scale patient studies are needed to further validate our findings.展开更多
The purpose of the study was to evaluate Jamaican early childhood pre-service teachers’attitudes towards mathematics.The study is designed according to the quantitative survey model in the descriptive type.In this st...The purpose of the study was to evaluate Jamaican early childhood pre-service teachers’attitudes towards mathematics.The study is designed according to the quantitative survey model in the descriptive type.In this study,a modified version of the Fennema-Sherman mathematics attitude scale was used to measure the mathematics attitude of 144 early childhood pre-service teachers in four different categories of the attitude scale(mathematics usefulness,confidence in learning mathematics,mathematics anxiety,and mathematics motivation).The data were collected from participants in the five teachers’colleges that offer the early childhood education program in Jamaica.The findings revealed that Jamaican early childhood pre-service teachers generally have a more positive attitude towards mathematics.A comparison among the different year groups revealed that a significantly greater percentage of the Year two group of participants possessed a more positive mathematics attitude than the other year groups.A significantly higher percentage of the Year three group indicated that they do not want to teach the subject in the future.The findings have implications for the teaching and learning of mathematics in the early childhood education program in Jamaica and,by extension,the teaching and learning of mathematics at the early childhood level of the education system.展开更多
This article is based on research on pre-service teachers＇ perspectives on their mathematics knowledge of proof in geometry. The study was framed using tile mathematical knowledge for teaching framework. This qualita...This article is based on research on pre-service teachers＇ perspectives on their mathematics knowledge of proof in geometry. The study was framed using tile mathematical knowledge for teaching framework. This qualitative study employed the use of a task-based worksheet, focus group sessions and semi-structured individual interviews. The task-based worksheet was completed by 180 pre-service mathematics teachers （second, third and fourth year mathematics education students）. Pre-service mathematics teachers are student teachers who have not yet completed their training to become teachers. After the analysis of the task-based worksheet, 20 participants were invited to participate in focus group sessions and individual interviews. The findings of the study reveal that the participants possess peripheral mathematics knowledge of proof in geometry. The study aims at assisting pre-service teachers and interested educationists to explore innovative methods of acquiring and imparting mathematics knowledge of proof in geometry. The study proposes possible changes in curriculum at school and university level.展开更多
In this paper,a new full-Newton step primal-dual interior-point algorithm for solving the special weighted linear complementarity problem is designed and analyzed.The algorithm employs a kernel function with a linear ...In this paper,a new full-Newton step primal-dual interior-point algorithm for solving the special weighted linear complementarity problem is designed and analyzed.The algorithm employs a kernel function with a linear growth term to derive the search direction,and by introducing new technical results and selecting suitable parameters,we prove that the iteration bound of the algorithm is as good as best-known polynomial complexity of interior-point methods.Furthermore,numerical results illustrate the efficiency of the proposed method.展开更多
What causes object detection in video to be less accurate than it is in still images?Because some video frames have degraded in appearance from fast movement,out-of-focus camera shots,and changes in posture.These reas...What causes object detection in video to be less accurate than it is in still images?Because some video frames have degraded in appearance from fast movement,out-of-focus camera shots,and changes in posture.These reasons have made video object detection(VID)a growing area of research in recent years.Video object detection can be used for various healthcare applications,such as detecting and tracking tumors in medical imaging,monitoring the movement of patients in hospitals and long-term care facilities,and analyzing videos of surgeries to improve technique and training.Additionally,it can be used in telemedicine to help diagnose and monitor patients remotely.Existing VID techniques are based on recurrent neural networks or optical flow for feature aggregation to produce reliable features which can be used for detection.Some of those methods aggregate features on the full-sequence level or from nearby frames.To create feature maps,existing VID techniques frequently use Convolutional Neural Networks(CNNs)as the backbone network.On the other hand,Vision Transformers have outperformed CNNs in various vision tasks,including object detection in still images and image classification.We propose in this research to use Swin-Transformer,a state-of-the-art Vision Transformer,as an alternative to CNN-based backbone networks for object detection in videos.The proposed architecture enhances the accuracy of existing VID methods.The ImageNet VID and EPIC KITCHENS datasets are used to evaluate the suggested methodology.We have demonstrated that our proposed method is efficient by achieving 84.3%mean average precision(mAP)on ImageNet VID using less memory in comparison to other leading VID techniques.The source code is available on the website https://github.com/amaharek/SwinVid.展开更多
In this article,we explore the famous Selkov–Schnakenberg(SS)system of coupled nonlinear partial differential equations(PDEs)for Lie symmetry analysis,self-adjointness,and conservation laws.Moreover,miscellaneous sol...In this article,we explore the famous Selkov–Schnakenberg(SS)system of coupled nonlinear partial differential equations(PDEs)for Lie symmetry analysis,self-adjointness,and conservation laws.Moreover,miscellaneous soliton solutions like dark,bright,periodic,rational,Jacobian elliptic function,Weierstrass elliptic function,and hyperbolic solutions of the SS system will be achieved by a well-known technique called sub-ordinary differential equations.All these results are displayed graphically by 3D,2D,and contour plots.展开更多
In this paper,two crossover hybrid variable-order derivatives of the cancer model are developed.Grünwald-Letnikov approximation is used to approximate the hybrid fractional and variable-order fractional operators...In this paper,two crossover hybrid variable-order derivatives of the cancer model are developed.Grünwald-Letnikov approximation is used to approximate the hybrid fractional and variable-order fractional operators.The existence,uniqueness,and stability of the proposed model are discussed.Adams Bashfourth’s fifth-step method with a hybrid variable-order fractional operator is developed to study the proposed models.Comparative studies with generalized fifth-order Runge-Kutta method are given.Numerical examples and comparative studies to verify the applicability of the used methods and to demonstrate the simplicity of these approximations are presented.We have showcased the efficiency of the proposed method and garnered robust empirical support for our theoretical findings.展开更多
In this paper,we consider the truncated multiplicity finite range set problem of meromorphic functions on some complex disc.By using the value distribution theory of meromorphic functions,we establish a second main th...In this paper,we consider the truncated multiplicity finite range set problem of meromorphic functions on some complex disc.By using the value distribution theory of meromorphic functions,we establish a second main theorem for meromorphic functions with finite growth index which share meromorphic functions(may not be small functions).As its application,we also extend the result of a finite range set with truncated multiplicity.展开更多
An illness known as pneumonia causes inflammation in the lungs.Since there is so much information available fromvarious X-ray images,diagnosing pneumonia has typically proven challenging.To improve image quality and s...An illness known as pneumonia causes inflammation in the lungs.Since there is so much information available fromvarious X-ray images,diagnosing pneumonia has typically proven challenging.To improve image quality and speed up the diagnosis of pneumonia,numerous approaches have been devised.To date,several methods have been employed to identify pneumonia.The Convolutional Neural Network(CNN)has achieved outstanding success in identifying and diagnosing diseases in the fields of medicine and radiology.However,these methods are complex,inefficient,and imprecise to analyze a big number of datasets.In this paper,a new hybrid method for the automatic classification and identification of Pneumonia from chest X-ray images is proposed.The proposed method(ABOCNN)utilized theAfrican BuffaloOptimization(ABO)algorithmto enhanceCNNperformance and accuracy.The Weinmed filter is employed for pre-processing to eliminate unwanted noises from chest X-ray images,followed by feature extraction using the Grey Level Co-Occurrence Matrix(GLCM)approach.Relevant features are then selected from the dataset using the ABO algorithm,and ultimately,high-performance deep learning using the CNN approach is introduced for the classification and identification of Pneumonia.Experimental results on various datasets showed that,when contrasted to other approaches,the ABO-CNN outperforms them all for the classification tasks.The proposed method exhibits superior values like 96.95%,88%,86%,and 86%for accuracy,precision,recall,and F1-score,respectively.展开更多
We prove a generalization of the classical Gauss-Bonnet formula for a conical metric on a compact Riemann surface provided that the Gaussian curvature is Lebesgue integrable with respect to the area form of the metric...We prove a generalization of the classical Gauss-Bonnet formula for a conical metric on a compact Riemann surface provided that the Gaussian curvature is Lebesgue integrable with respect to the area form of the metric.We also construct explicitly some conical metrics whose curvature is not integrable.展开更多
The purpose of this work is to shed light on the effect of the pivot position on the surface pressure distribution over a 3D wing in different flight conditions.The study is intended to support the design and developm...The purpose of this work is to shed light on the effect of the pivot position on the surface pressure distribution over a 3D wing in different flight conditions.The study is intended to support the design and development of aerospace vehicles where stability analysis,performance optimization,and aircraft design are of primary importance.The following parameters are considered:Mach numbers(M)of 1.3,1.8,2.3,2.8,3.3,and 3.8,angle of incidence(θ)in the range from 5°to 25°,pivot position from h=0.2 to 1.The results of the CFD numerical simulations match available analytical data,thereby providing evidence for the reliability of the used approach.The findings provide valuable insights into the relationship between the surface pressure distribution,the Mach number and the angle of incidence.展开更多
目的主动脉夹层疾病对主动脉血管壁各层的力学性质和微观结构的影响尚没有系统的研究。本文通过对比正常和发生A型夹层的人体升主动脉组织各层的力学性质和微观结构来探究该问题。方法从13例A型主动脉夹层患者和5例无主动脉疾病的供体...目的主动脉夹层疾病对主动脉血管壁各层的力学性质和微观结构的影响尚没有系统的研究。本文通过对比正常和发生A型夹层的人体升主动脉组织各层的力学性质和微观结构来探究该问题。方法从13例A型主动脉夹层患者和5例无主动脉疾病的供体中共采集了18个升主动脉标本。对每个升主动脉标本进一步分解以获得3个组织样本:主动脉壁全层、内膜-中膜层和外膜层。对每个组织样本进行双轴拉伸测试获得实验应力拉伸比数据,采用Fung-Type材料模型对实验数据进行拟合并计算组织硬度。采用Elastin Van Gieson染色和Masson染色来量化组织中弹性纤维和胶原纤维密度。采用统计分析以确定夹层主动脉和正常主动脉组织各层的力学和微观结构性质是否存在显著差异。结果在拉伸比为1.30时,夹层组内膜-中膜层样本的硬度在长轴方向上显著高于正常组(P=0.0068),而在其他方向或其他层组织中没有发现显著差异。尽管两组之间的弹性纤维或胶原纤维密度没有显著差异,但夹层组的所有3个组织层的弹性纤维密度通常较低,但胶原纤维密度较高。结论与正常主动脉组织相比,夹层主动脉组织中内膜-中膜层的弹性纤维密度较低,而组织硬度却较高,表明内膜-中膜层组织硬度可能是主动脉夹层的潜在生物标志物。展开更多
文摘The nonlinear post-buckling response of functionally graded(FG)copper matrix plates enforced by graphene origami auxetic metamaterials(GOAMs)is investigated in the currentwork.The auxeticmaterial properties of the plate are controlled by graphene content and the degree of origami folding,which are graded across the thickness of the plate.Thematerial properties of the GOAM plate are evaluated using genetic micro-mechanicalmodels.Governing nonlinear eigenvalue problems for the post-buckling response of the GOAM composite plate are derived using the virtual work principle and a four-variable nonlinear shear deformation theory.A novel differential quadrature method(DQM)algorithm is developed to solve the nonlinear eigenvalue problem.Detailed parametric studies are presented to explore the effects of graphene content,folding degree,and GO distribution patterns on the post-buckling responses of GOAM plates.Results show that high tunability in post-buckling characteristics can be achieved by using GOAM.FunctionallyGradedGraphene OrigamiAuxeticMetamaterials(FG-GOAM)plates can be used in aerospace structures to improve their structural performance and response.
文摘This study introduces a novel mathematical model to describe the progression of cholera by integrating fractional derivatives with both singular and non-singular kernels alongside stochastic differential equations over four distinct time intervals.The model incorporates three key fractional derivatives:the Caputo-Fabrizio fractional derivative with a non-singular kernel,the Caputo proportional constant fractional derivative with a singular kernel,and the Atangana-Baleanu fractional derivative with a non-singular kernel.We analyze the stability of the core model and apply various numerical methods to approximate the proposed crossover model.To achieve this,the approximation of Caputo proportional constant fractional derivative with Grünwald-Letnikov nonstandard finite difference method is used for the deterministic model with a singular kernel,while the Toufik-Atangana method is employed for models involving a non-singular Mittag-Leffler kernel.Additionally,the integral Caputo-Fabrizio approximation and a two-step Lagrange polynomial are utilized to approximate the model with a non-singular exponential decay kernel.For the stochastic component,the Milstein method is implemented to approximate the stochastic differential equations.The stability and effectiveness of the proposed model and methodologies are validated through numerical simulations and comparisons with real-world cholera data from Yemen.The results confirm the reliability and practical applicability of the model,providing strong theoretical and empirical support for the approach.
文摘The high thermal conductivity of the nanoparticles in hybrid nanofluids results in enhanced thermal conductivity associated with their base fluids.Enhanced heat transfer is a result of this high thermal conductivity,which has significant applications in heat exchangers and engineering devices.To optimize heat transfer,a liquid film of Cu and TiO_(2)hybrid nanofluid behind a stretching sheet in a variable porous medium is being considered due to its importance.The nature of the fluid is considered time-dependent and the thickness of the liquid film is measured variable adjustable with the variable porous space and favorable for the uniform flow of the liquid film.The solution of the problem is acquired using the homotopy analysis method HAM,and the artificial neural network ANN is applied to obtain detailed information in the form of error estimation and validations using the fitting curve analysis.HAM data is utilized to train the ANN in this study,which uses Cu and TiO_(2)hybrid nanofluids in a variable porous space for unsteady thin film flow,and it is used to train the ANN.The results indicate that Cu and TiO_(2)play a greater role in boosting the rate.
文摘A new extended distribution called the Odd Exponential Generalized Exponential-Exponential distribution(EOEGE-E)is proposed based on generalization of the odd generalized exponential family(OEGE-E).The statistical properties of the proposed distribution are derived.The study evaluates the accuracy of six estimation methods under complete samples.Estimation techniques include maximumlikelihood,ordinary least squares,weighted least squares,maximumproduct of spacing,Cramer vonMises,and Anderson-Darling methods.Twomethods of estimation for the involved parameters are considered based on progressively type Ⅱ censored data(PTⅡC).These methods are maximum likelihood and maximum product of spacing.The proposed distribution’s effectiveness was evaluated using different data sets from various fields.The proposed distribution provides a better fit for these datasets than existing probability distributions.
文摘While the significant role of technological innovation in promoting renewable energy has been extensively explored in the literature,limited attention has been paid to the impact of energy patents,particularly clean energy patents and fossil fuel patents.This study pioneers an investigation into the effects of energy patents and energy prices on renewable energy consumption.The study utilizes data from 2000Q1 to 2023Q4 and,due to the nonlinear nature of the series,applies wavelet quantile-based methods.Specifically,it introduces the wavelet quantile cointegration approach to evaluate cointegration across different quantiles and time horizons,along with the wavelet quantile-on-quantile regression method.The results confirm cointegration across different periods and quantiles,highlighting the significant relationships between energy patents,economic factors,and renewable energy consumption.Furthermore,we found that fossil energy patents negatively affect renewable energy consumption,while clean energy patents have a similar but weaker effect,especially in the short term.In addition,higher energy prices promote renewable energy adoption while economic growth positively influences renewable energy consumption,particularly in the short term.The study formulates specific policies based on these findings.
基金supported via funding from Prince Sattam Bin Abdulaziz University project number(PSAU/2025/R/1446).
文摘Promoting the high penetration of renewable energies like photovoltaic(PV)systems has become an urgent issue for expanding modern power grids and has accomplished several challenges compared to existing distribution grids.This study measures the effectiveness of the Puma optimizer(PO)algorithm in parameter estimation of PSC(perovskite solar cells)dynamic models with hysteresis consideration considering the electric field effects on operation.The models used in this study will incorporate hysteresis effects to capture the time-dependent behavior of PSCs accurately.The PO optimizes the proposed modified triple diode model(TDM)with a variable voltage capacitor and resistances(VVCARs)considering the hysteresis behavior.The suggested PO algorithm contrasts with other wellknown optimizers from the literature to demonstrate its superiority.The results emphasize that the PO realizes a lower RMSE(Root mean square errors),which proves its capability and efficacy in parameter extraction for the models.The statistical results emphasize the efficiency and supremacy of the proposed PO compared to the other well-known competing optimizers.The convergence rates show good,fast,and stable convergence rates with lower RMSE via PO compared to the other five competitive optimizers.Moreover,the lowermean realized via the PO optimizer is illustrated by the box plot for all optimizers.
基金supported by the Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R848)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabiathe Deanship of Scientific Research at Northern Border University,Arar,Saudi Arabia for funding this research work through the project number“NBU-FFR-2025-2932-09”.
文摘License plate recognition in haze-affected images is challenging due to feature distortions such as blurring and elongation,which lead to pixel displacements.This article introduces a Displacement Region Recognition Method(DR2M)to address such a problem.This method operates on displaced features compared to the training input observed throughout definite time frames.The technique focuses on detecting features that remain relatively stable under haze,using a frame-based analysis to isolate edges minimally affected by visual noise.The edge detection failures are identified using a bilateral neural network through displaced feature training.The training converges bilaterally towards the minimum edges from the maximum region.Thus,the training input and detected edges are used to identify the displacement between observed image frames to extract and differentiate the license plate region from the other vehicle regions.The proposed method maps the similarity feature between the detected and identified vehicle regions.This aids in leveraging the plate recognition precision with a high F1 score.Thus,this technique achieves a 10.27%improvement in identification precision,a 10.57%increase in F1 score,and a 9.73%reduction in false positive rate compared to baseline methods under maximum displacement conditions caused by haze.The technique attains an identification precision of 95.68%,an F1 score of 94.68%,and a false positive rate of 4.32%,indicating robust performance under haze-affected settings.
基金supported in part by National Sciences Foundation of China grants 11972117a Jiangsu Province Science and Technology Agency under grant number BE2016785+4 种基金support from Natural Science Foundation of China(81827806 and 62135002)support from Natural Science Foundation of China(81722025)Key R&D Project of Heilongjiang Province grant 2022ZX06C07support from the Natural Science Foundation of Shandong Province under grant number ZR2024QA110Shandong Province Medical Health Science and Technology Project(Nos.202425020256,and 202403010254).
文摘Image-based computational models have been used for vulnerable plaque progression and rupture predictions,and good results have been reported.However,mechanisms and predictions for plaque erosion are underinvestigated.Patient-specific fluid-structure interaction(FSI)models based on optical coherence tomography(OCT)follow-up data from patients with plaque erosion and who received conservative antithrombotic treatment(using medication,no stenting)to identify risk factors that could be used to predict the treatment outcome.OCT and angiography datawere obtained from10 patientswho received conservative antithrombotic treatment.Five participants had worse outcomes(WOG,stenosis severity≥70%at one-year follow-up),while the other five had better outcomes(BOG,stenosis severity<70%at one-year follow-up).Patient-specific 3D FSI models were constructed to obtain morphological and biomechanical risk factor values(a total of nine risk factors)for comparison and prediction.A logistic regressionmodel was used to identify optimal predictors with the best treatment outcome prediction accuracies.Our results indicated that the combination of wall shear stress(WSS),lipid percent,and thrombus burden was the best group predictor according to the mean area under the curve(AUC)of 0.96(90%confidence interval=(0.85,1.00)).WSS was the best single predictor withmean AUC=0.70(90%confidence interval=(0.20,1.00)).Thrombus burden was the only risk factor showing statistically significant group difference,suggesting its crucial role in the outcomes of conservative anti-thrombotic therapy.This pilot study indicated that integratingmorphological and biomechanical risk factors could improve treatment outcome prediction accuracy in patients with plaque erosion compared to predictions using single predictors.Large-scale patient studies are needed to further validate our findings.
文摘The purpose of the study was to evaluate Jamaican early childhood pre-service teachers’attitudes towards mathematics.The study is designed according to the quantitative survey model in the descriptive type.In this study,a modified version of the Fennema-Sherman mathematics attitude scale was used to measure the mathematics attitude of 144 early childhood pre-service teachers in four different categories of the attitude scale(mathematics usefulness,confidence in learning mathematics,mathematics anxiety,and mathematics motivation).The data were collected from participants in the five teachers’colleges that offer the early childhood education program in Jamaica.The findings revealed that Jamaican early childhood pre-service teachers generally have a more positive attitude towards mathematics.A comparison among the different year groups revealed that a significantly greater percentage of the Year two group of participants possessed a more positive mathematics attitude than the other year groups.A significantly higher percentage of the Year three group indicated that they do not want to teach the subject in the future.The findings have implications for the teaching and learning of mathematics in the early childhood education program in Jamaica and,by extension,the teaching and learning of mathematics at the early childhood level of the education system.
文摘This article is based on research on pre-service teachers＇ perspectives on their mathematics knowledge of proof in geometry. The study was framed using tile mathematical knowledge for teaching framework. This qualitative study employed the use of a task-based worksheet, focus group sessions and semi-structured individual interviews. The task-based worksheet was completed by 180 pre-service mathematics teachers （second, third and fourth year mathematics education students）. Pre-service mathematics teachers are student teachers who have not yet completed their training to become teachers. After the analysis of the task-based worksheet, 20 participants were invited to participate in focus group sessions and individual interviews. The findings of the study reveal that the participants possess peripheral mathematics knowledge of proof in geometry. The study aims at assisting pre-service teachers and interested educationists to explore innovative methods of acquiring and imparting mathematics knowledge of proof in geometry. The study proposes possible changes in curriculum at school and university level.
基金Supported by University Science Research Project of Anhui Province(2023AH052921)Outstanding Youth Talent Project of Anhui Province(gxyq2021254)。
文摘In this paper,a new full-Newton step primal-dual interior-point algorithm for solving the special weighted linear complementarity problem is designed and analyzed.The algorithm employs a kernel function with a linear growth term to derive the search direction,and by introducing new technical results and selecting suitable parameters,we prove that the iteration bound of the algorithm is as good as best-known polynomial complexity of interior-point methods.Furthermore,numerical results illustrate the efficiency of the proposed method.
文摘What causes object detection in video to be less accurate than it is in still images?Because some video frames have degraded in appearance from fast movement,out-of-focus camera shots,and changes in posture.These reasons have made video object detection(VID)a growing area of research in recent years.Video object detection can be used for various healthcare applications,such as detecting and tracking tumors in medical imaging,monitoring the movement of patients in hospitals and long-term care facilities,and analyzing videos of surgeries to improve technique and training.Additionally,it can be used in telemedicine to help diagnose and monitor patients remotely.Existing VID techniques are based on recurrent neural networks or optical flow for feature aggregation to produce reliable features which can be used for detection.Some of those methods aggregate features on the full-sequence level or from nearby frames.To create feature maps,existing VID techniques frequently use Convolutional Neural Networks(CNNs)as the backbone network.On the other hand,Vision Transformers have outperformed CNNs in various vision tasks,including object detection in still images and image classification.We propose in this research to use Swin-Transformer,a state-of-the-art Vision Transformer,as an alternative to CNN-based backbone networks for object detection in videos.The proposed architecture enhances the accuracy of existing VID methods.The ImageNet VID and EPIC KITCHENS datasets are used to evaluate the suggested methodology.We have demonstrated that our proposed method is efficient by achieving 84.3%mean average precision(mAP)on ImageNet VID using less memory in comparison to other leading VID techniques.The source code is available on the website https://github.com/amaharek/SwinVid.
文摘In this article,we explore the famous Selkov–Schnakenberg(SS)system of coupled nonlinear partial differential equations(PDEs)for Lie symmetry analysis,self-adjointness,and conservation laws.Moreover,miscellaneous soliton solutions like dark,bright,periodic,rational,Jacobian elliptic function,Weierstrass elliptic function,and hyperbolic solutions of the SS system will be achieved by a well-known technique called sub-ordinary differential equations.All these results are displayed graphically by 3D,2D,and contour plots.
文摘In this paper,two crossover hybrid variable-order derivatives of the cancer model are developed.Grünwald-Letnikov approximation is used to approximate the hybrid fractional and variable-order fractional operators.The existence,uniqueness,and stability of the proposed model are discussed.Adams Bashfourth’s fifth-step method with a hybrid variable-order fractional operator is developed to study the proposed models.Comparative studies with generalized fifth-order Runge-Kutta method are given.Numerical examples and comparative studies to verify the applicability of the used methods and to demonstrate the simplicity of these approximations are presented.We have showcased the efficiency of the proposed method and garnered robust empirical support for our theoretical findings.
基金Supported by National Natural Science Foundation of China(12061041)Jiangxi Provincial Natural Science Foundation(20232BAB201003).
文摘In this paper,we consider the truncated multiplicity finite range set problem of meromorphic functions on some complex disc.By using the value distribution theory of meromorphic functions,we establish a second main theorem for meromorphic functions with finite growth index which share meromorphic functions(may not be small functions).As its application,we also extend the result of a finite range set with truncated multiplicity.
基金the Researchers Supporting Project Number(RSP2023 R157),King Saud University,Riyadh,Saudi Arabia.
文摘An illness known as pneumonia causes inflammation in the lungs.Since there is so much information available fromvarious X-ray images,diagnosing pneumonia has typically proven challenging.To improve image quality and speed up the diagnosis of pneumonia,numerous approaches have been devised.To date,several methods have been employed to identify pneumonia.The Convolutional Neural Network(CNN)has achieved outstanding success in identifying and diagnosing diseases in the fields of medicine and radiology.However,these methods are complex,inefficient,and imprecise to analyze a big number of datasets.In this paper,a new hybrid method for the automatic classification and identification of Pneumonia from chest X-ray images is proposed.The proposed method(ABOCNN)utilized theAfrican BuffaloOptimization(ABO)algorithmto enhanceCNNperformance and accuracy.The Weinmed filter is employed for pre-processing to eliminate unwanted noises from chest X-ray images,followed by feature extraction using the Grey Level Co-Occurrence Matrix(GLCM)approach.Relevant features are then selected from the dataset using the ABO algorithm,and ultimately,high-performance deep learning using the CNN approach is introduced for the classification and identification of Pneumonia.Experimental results on various datasets showed that,when contrasted to other approaches,the ABO-CNN outperforms them all for the classification tasks.The proposed method exhibits superior values like 96.95%,88%,86%,and 86%for accuracy,precision,recall,and F1-score,respectively.
基金Support by the Project of Stable Support for Youth Team in Basic Research Field,CAS(Grant No.YSBR-001)NSFC(Grant Nos.12271495,11971450 and 12071449).
文摘We prove a generalization of the classical Gauss-Bonnet formula for a conical metric on a compact Riemann surface provided that the Gaussian curvature is Lebesgue integrable with respect to the area form of the metric.We also construct explicitly some conical metrics whose curvature is not integrable.
文摘The purpose of this work is to shed light on the effect of the pivot position on the surface pressure distribution over a 3D wing in different flight conditions.The study is intended to support the design and development of aerospace vehicles where stability analysis,performance optimization,and aircraft design are of primary importance.The following parameters are considered:Mach numbers(M)of 1.3,1.8,2.3,2.8,3.3,and 3.8,angle of incidence(θ)in the range from 5°to 25°,pivot position from h=0.2 to 1.The results of the CFD numerical simulations match available analytical data,thereby providing evidence for the reliability of the used approach.The findings provide valuable insights into the relationship between the surface pressure distribution,the Mach number and the angle of incidence.
文摘目的主动脉夹层疾病对主动脉血管壁各层的力学性质和微观结构的影响尚没有系统的研究。本文通过对比正常和发生A型夹层的人体升主动脉组织各层的力学性质和微观结构来探究该问题。方法从13例A型主动脉夹层患者和5例无主动脉疾病的供体中共采集了18个升主动脉标本。对每个升主动脉标本进一步分解以获得3个组织样本:主动脉壁全层、内膜-中膜层和外膜层。对每个组织样本进行双轴拉伸测试获得实验应力拉伸比数据,采用Fung-Type材料模型对实验数据进行拟合并计算组织硬度。采用Elastin Van Gieson染色和Masson染色来量化组织中弹性纤维和胶原纤维密度。采用统计分析以确定夹层主动脉和正常主动脉组织各层的力学和微观结构性质是否存在显著差异。结果在拉伸比为1.30时,夹层组内膜-中膜层样本的硬度在长轴方向上显著高于正常组(P=0.0068),而在其他方向或其他层组织中没有发现显著差异。尽管两组之间的弹性纤维或胶原纤维密度没有显著差异,但夹层组的所有3个组织层的弹性纤维密度通常较低,但胶原纤维密度较高。结论与正常主动脉组织相比,夹层主动脉组织中内膜-中膜层的弹性纤维密度较低,而组织硬度却较高,表明内膜-中膜层组织硬度可能是主动脉夹层的潜在生物标志物。
