Sensitive Parameters Research Articles

SACR EPIDEMIC MODEL FOR THE SPREAD OF HEPATITIS B DISEASE BY CONSIDERING VERTICAL TRANSMISSION

Hepatitis B is an infectious disease that causes inflammation of the liver due to infection with the Hepatitis B virus. Hepatitis B is divided into two phases: the acute phase and the chronic phase. Hepatitis B virus (HBV) can be prevented through vaccination and treatment of susceptible and infected individuals. The spread of the virus can be modeled using mathematical modeling of epidemics. In this study, the model used consists of four classes, namely vulnerable individuals (S), acute individuals (A), chronic individuals (C), and recovered individuals (R). The purpose of this study is to explain the formation of the Hepatitis B disease epidemic model, analyze the stability of the model, perform simulations, and conduct parameter sensitivity analysis on the basic reproductive number. The result of this study is the construction of an epidemic model of the spread of hepatitis B disease in the form of a SACR model. This model takes into account the transmission that occurs not only through interactions between susceptible individuals and chronic individuals but also through the birth process, which occurs in chronic subpopulations because babies born can be chronically infected (vertical transmission from mother to baby). The model produces two equilibrium points, the disease-free equilibrium and the endemic equilibrium. Both points were analyzed for stability using the linearization method and were found to be asymptotically stable. Furthermore, the model simulation was carried out using the fourth-order Runge-Kutta method and sensitivity analysis of the basic reproduction number. From the results obtained, it can be concluded that the spread of hepatitis B disease can be minimized by reducing contact between susceptible and chronic individuals, increasing treatment of chronic individuals, and increasing the number of vaccinated individuals in susceptible populations.

Soft sensor modeling method and application based on TSECIT2FNN-LSTM

To address the issue of low accuracy in soft sensor modeling of key variables caused by multi-variable coupling and parameter sensitivity in complex processes, this paper introduces a TSK-type-based self-evolving compensatory interval type-2 fuzzy Long short-term memory (LSTM) neural network (TSECIT2FNN-LSTM) soft sensor model. The proposed TSECIT2FNN-LSTM integrates the LSTM neural network with the interval type-2 fuzzy inference system to address long-term dependencies in sequence data by utilizing the gate mechanism of the LSTM neural network. The TSECIT2FNN-LSTM structure learning algorithm uses the firing strength of the network rule antecedent to decide whether to generate new rules to improve the rationality of the network structure. TSECIT2FNN-LSTM parameter learning utilizes the gradient descent method to optimize network parameters. However, unlike other interval type-2 fuzzy neural network gradient calculation processes, the error term in the LSTM node parameter gradient of TSECIT2FNN-LSTM is propagated backwards in the time dimension. Additionally, the error term is simultaneously transferred to the upper layer network to enhance network prediction accuracy and memory capabilities. The TSECIT2FNN-LSTM soft sensor model is utilized to predict the alcohol concentration in wine and the nitrogen oxide emission in gas turbines. Experimental results demonstrate that the proposed TSECIT2FNN-LSTM soft sensing model achieves higher prediction accuracy compared to other models.

Research on laser brazing diamond coating process based on temperature field finite element simulation and machine learning

The temperature of laser brazing diamond seriously affects the quality and service life of diamond tools. In this study, a finite element model of laser brazing diamond was established to achieve digital control of the temperature field during the brazing process. It was determined that the main energy of diamond warming comes from the heat conduction of the molten pool. The maximum temperature value within the brazing layer was proposed as an important indicator of the quality of diamond coating formation. The correlation and local sensitivity of each process parameter to the maximum value of temperature were calculated by a neural network-based machine learning method, and the influence of process parameters on the temperature field was explored. The result of the theoretical analys was finally verified by the coating wear-resistance experiments, and the wear-resistance of the diamond coatings was significantly better than that of other cases when the maximum temperature value in the brazing process was in the range of 1100 °C–1130 °C.

Effects of Ionic Liquids on the Nucleofugality of Dimethyl Sulfide.

The nucleofugality of dimethyl sulfide was measured in solvent mixtures containing ionic liquids. The first-order rate constants of the solvolysis of sulfonium salts were determined in mixtures containing different proportions of 1-butyl-3-methylimidazolium bis(trifluoromethanesulfonyl)imide in ethanol, representing the first report on the solvolysis of a charged species in an ionic liquid. Temperature-dependent kinetic studies allowed determination of activation parameters and rationalization of observed solvent effects in different ionic liquid mixtures. From the solvolysis data, the nucleofugality of dimethyl sulfide in different proportions of this ionic liquid in ethanol was determined. Further, the nucleofugality of dimethyl sulfide was determined in mixtures containing high proportions of each of seven other ionic liquids in ethanol. These data allowed quantification of the effects of varying both the amount of ionic liquid present and on changing the components of the ionic liquid on the nucleofugality of dimethyl sulfide. The ionic liquid mixtures were shown to affect the nucleofugality of this nucleofuge in a different manner to the previously studied monatomic charged nucleofuges, owing to different microscopic interactions in solution. This work highlighted the necessity of considering electrofuges with an appropriate range of electrofugality values along with the importance of the nucleofuge-specific sensitivity parameter.

Parametric sensitivity analysis of the modified phase-field model in polymer membrane formation by immersion precipitation method

Parametric sensitivity analysis of the modified phase-field model in polymer membrane formation by immersion precipitation method

Techno-Economic Feasibility Analysis of Post-Combustion Carbon Capture in an NGCC Power Plant in Uzbekistan

As natural gas-fired combined cycle (NGCC) power plants continue to constitute a crucial part of the global energy landscape, their carbon dioxide (CO2) emissions pose a significant challenge to climate goals. This paper evaluates the feasibility of implementing post-combustion carbon capture, storage, and utilization (CCSU) technologies in NGCC power plants for end-of-pipe decarbonization in Uzbekistan. This study simulates and models a 450 MW NGCC power plant block, a first-generation, technically proven solvent—MEA-based CO2 absorption plant—and CO2 compression and pipeline transportation to nearby oil reservoirs to evaluate the technical, economic, and environmental aspects of CCSU integration. Parametric sensitivity analysis is employed to minimize energy consumption in the regeneration process. The economic analysis evaluates the levelized cost of electricity (LCOE) on the basis of capital expenses (CAPEX) and operational expenses (OPEX). The results indicate that CCSU integration can significantly reduce CO2 emissions by more than 1.05 million tonnes annually at a 90% capture rate, although it impacts plant efficiency, which decreases from 55.8% to 46.8% because of the significant amount of low-pressure steam extraction for solvent regeneration at 3.97 GJ/tonne CO2 and multi-stage CO2 compression for pipeline transportation and subsequent storage. Moreover, the CO2 capture, compression, and transportation costs are almost 61 USD per tonne, with an equivalent LCOE increase of approximately 45% from the base case. This paper concludes that while CCSU integration offers a promising path for the decarbonization of NGCC plants in Uzbekistan in the near- and mid-term, its implementation requires massive investments due to the large scale of these plants.

On the atout ticket learning problem for neural networks and its application in securing federated learning exchanges

Artificial Neural Networks (ANNs) have become the backbone of many real-world applications, including distributed applications relying on Federated Learning (FL). However, several vulnerabilities/attacks have emerged in recent years, affecting the benefits of using ANNs in FL, such as reconstruction attacks and membership inference attacks. These attacks can have severe impacts on both the societal and professional levels. For instance, inferring the presence of a patient’s private health record in a medical study or a clinic database violates the patient’s privacy and can have legal or ethical consequences. Therefore, protecting the data and model from malicious attacks in FL systems is important. This paper introduces the Atout Ticket Learning (ATL) problem. This new problem consists of identifying sensitive parameters (atout tickets) of a neural network model, which, if modified, will increase the model’s loss by at least a given threshold ϵ. First, we formulate ATL as an ℓ0-norm minimization problem, and we derive a lower bound on the number of atout tickets needed to achieve a model degradation of ϵ. Second, we design the Atout Ticket Protocol (ATP) as an effective solution for privacy-preserving in FL systems using atout tickets, along with the benefit of noise perturbations and secure aggregation techniques. Finally, we experiment ATP against FL reconstruction attacks using new selection strategies, namely Inverting Gradients, Deep Leakage, and Improved Deep Leakage. The results show that ATP is highly robust against these attacks.

Study lepton flavor violation $B^0\rightarrow{{l_i}^{\pm}{l_j}^{\mp}}$ within the Mass Insertion Approximation

Abstract We study lepton flavor violating (LFV) decays $B^0\rightarrow{{l_i}^{\pm}{l_j}^{\mp}}$ ($B^0\rightarrow e{\mu}$, $B^0\rightarrow e{\tau}$ and $B^0\rightarrow {\mu}{\tau}$) in the $U(1)_X$SSM (SSM is acronym for the supersymmetric standard model), which is the $U(1)_X$ extension of the minimal supersymmetric standard model (MSSM). The local gauge group of $U(1)_X$SSM is $SU(3)_C\times SU(2)_L \times U(1)_Y \times U(1)_X$. These processes ($B^0\rightarrow e{\mu}$, $B^0\rightarrow e{\tau}$ and $B^0\rightarrow {\mu}{\tau}$) are strictly forbidden in the standard model (SM), but these LFV decays are a signal of new physics (NP).
We use the Mass Insertion Approximation (MIA) to find sensitive parameters that directly influence the result of the branching ratio of LFV decay $B_d\rightarrow{{l_i}^{\pm}{l_j}^{\mp}}$. Combined with the latest experimental results, we analyze the relationship between different sensitive parameters and the branching ratios of the three processes. According to the numerical analysis, these elements are very restricted from the non observation of CLFV.

A critical review on approaches to generate and validate virtual population for physiologically based pharmacokinetic models: Methodologies, case studies and way forward.

In silico modeling and simulation techniques such as physiologically based pharmacokinetic (PBPK) and physiologically based biopharmaceutics modeling (PBBM) have demonstrated various applications in drug discovery and development. Virtual bioequivalence leverages these computation tools to predict bioequivalence between reference and test formulations thereby demonstrating possibilities to reduce human studies. A pre-requisite for virtual bioequivalence is development of validated virtual population that depicts the same variability as that of observed in clinic. This development, validation and optimization of virtual population is a key attribute of virtual bioequivalence based on which conclusion of bioequivalence is made. Various strategies for optimization of virtual population based on appropriate considerations of physicochemical, physiological and disposition aspects are demonstrated with the help of six diverse case studies of immediate and modified release formulations. Once the virtual population is optimized to match in vivo variability, it can be used for various applications such as biowaivers, dissolution specification justification, f2 mismatch, establishing dissolution safe space, etc. In this review article, we attempted to describe various methodologies and approaches for optimization of virtual population using Gastroplus. Strategies based on optimization of virtual population with emphasis on specific and sensitive parameters were portrayed. We have further elucidated considerations related to study design, in vivo variability, sample size for optimization of virtual population from Gastroplus perspective. We believe that this review article provides a step-by-step process for virtual population optimization for interest of biopharmaceutics modeling scientists in order to ensure reliable and credible physiological models.

Assessment of left atrial function using two-dimensional speckle tracking echocardiography in cryptogenic stroke patients.

Cryptogenic cerebrovascular stroke can be defined as an ischemic stroke that lacks a clear cause, even after a thorough evaluation. It should be distinguished from the embolic stroke of undetermined source (ESUS), a subgroup that includes cardio-embolic sources. This study aims to assess left atrial function through two-dimensional speckle tracking echocardiography (2D-STE) to determine its potential association with cryptogenic stroke andits predictive value for subclinical atrial fibrillation (AF). Our prospective cohort study involved 62 patients with unexplained cerebrovascular stroke or TIA, regardless of gender. Following TEE assessments, 22 patients were excluded due to identified sources of cardio-embolism. The remaining 40 participants were clustered into Group I. Group II, consisted of 40 healthy individuals without significant medical history, served as a control group. Both groups underwent two-dimensional trans-thoracic echocardiography and speckle tracking echocardiography. LA dysfunction parameters exhibited significant differences between Group I and Group II. LV diastolic dysfunction, LAVI, LAEF, and LASr were notably affected in Group I. At the same time, LA diameter in the parasternal long-axis view (PLAX) displayed a significant difference with a p value of 0.001. Within Group I, 14 patients experienced AF episodes (Group Ia, 35%); while, the remaining 26 were categorized as Group Ib (65%). LV diastolic dysfunction displayed a p value < 0.011; while, LAVI, LAEF, and LASr exhibited considerable differences with p values < 0.0001. However, the LA diameter showed no significant variation between the two groups. LASr emerged as the most sensitive and specific parameter for predicting AF, with a cutoff point of ≤ 24.5% and a p value < 0.0001. LAEF showed a cutoff point of ≤ 40.5% and a p value of 0.011. Meanwhile, LAVI demonstrated the lowest sensitivity and specificity, with a mean cutoff point of ≥ 38.5ml/m2 and a p value of 0.003. 2D-STE is crucial for assessing LA dysfunction as a potential cryptogenic stroke cause after TEE and ruling out cardio-embolism sources. LASr serves as a key LA cardiopathy indicator, even preceding AF. LASr independently poses an AF risk. While LAEF and LAVI are significant LA dysfunction parameters and AF predictors, they exhibit lower sensitivity and specificity than LASr.

Microperimetry Sensitivity Correlates to Structural Macular Changes in Adolescents with Achromatopsia Unlike Other Visual Function Tests.

Objectives: Achromatopsia (ACHM) is a rare autosomal, recessively inherited disease that is characterized by cone dysfunction, for which several gene therapies are currently on trial. The aim of this study was to find correlations between the morphological macular changes identified using optical coherence tomography (OCT) and some visual functional parameters. Visual acuity (VA), contrast sensitivity (CS), and macular sensitivity obtained by means of microperimetry were assessed. Methods: Adolescents with ACHM underwent macular microperimetry (S-MAIA device) in mesopic condition, macular OCT, best corrected visual acuity (BCVA), low luminance visual acuity (LLVA), near vision acuity (NVA), and CS measurement. Results: Eight patients (15 eyes) with ACHM were analyzed. The mean age was 17 ± 2.7 years, and genetic variants involved the CNGA3 gene (37.5%) and CNGB3 gene (62.5%). OCT staging significantly correlated with microperimetry sensitivity parameters, namely the sensitivity of the central foveal point (p = 0.0286) and of the first and second perifoveal rings (p = 0.0008 and p = 0.0014, respectively). No correlations were found between OCT staging and VA measurements, nor with CS value. Conclusions: Among the extensive evaluated visual function tests, only microperimetry sensitivity showed a correlation with morphological macular changes identified at OCT. Microperimetry sensitivity may thus represent a useful visual function tool in natural ACHM history studies considering the upcoming research on gene therapies for the treatment of ACHM.

A Semi‐Analytical Method for Simulating Near‐Field Antiplane Wave Propagation in Layered Fluid‐Saturated Porous Media

ABSTRACTA semi‐analytical method for the near‐field antiplane wave propagation analysis in the layered fluid‐saturated porous media (FSPM) is proposed based on the Biot u–U dynamic formulation. The wave propagation equations of the FSPM are decoupled by the variable‐separating method. The thin‐layer element method (TLEM) is applied to discretize the infinite domain and construct the consistent artificial boundary condition. The finite element method (FEM) is adopted for the space discretization of the finite domain and the numerical solution of the dynamic response. The proposed method is validated by the comparison of the numerical results of this method with those in the published references and acquired from the remote artificial boundary. Subsequently, this method is applied to investigate typical near‐field antiplane wave propagation problems in the FSPM. Parametric sensitivity investigations are also executed to explore the impact of mechanical parameters, including permeability coefficients, porosity, and shear modulus of the solid phase, on the dynamic response of the FSPM. The study results confirm the efficacy and efficiency of the proposed method in the near‐field antiplane wave propagation analysis in the FSPM.

Stability and computational analysis of Influenza-A epidemic model through double time delay

Delay factors demonstration has a significant role in controlling a strain of infectious disease instead of a pharmaceutical strategy. According to the World Health Organization (WHO), 3–5 million cases are reported annually and approximately 290,000 to 650,000 respiratory deaths annually. So, in the present study, we develop a delayed mathematical model based on delay differential equations (DDEs) for the influenza epidemic using a deterministic approach by introducing double delay parameters. The four distinct sub-populations are considered susceptible, exposed, infected, and recovered. For the rigorous analysis, the fundamental properties of the model like positivity, boundedness, existence, and uniqueness, were studied. The influenza-free equilibrium (IFE) and influenza-existing equilibrium (IEE), are the two nonnegative equilibrium points that the model demonstrates. Both locally and globally, the asymptotic stability of the equilibrium points of the model is established and shown under specific situations of reproduction number. Additionally, investigated the model's parameter sensitivity and determined the relative sensitivity of each parameter. Both standard and nonstandard methods—such as Euler, Runge-Kutta, and nonstandard finite difference with a delayed sense—are presented to make computational analysis support a dynamical analysis and the best visualization of results. The stability of the non-standard finite difference scheme is thoroughly analyzed around the steady states of the model. Additionally, the results show that the nonstandard finite difference approximation is an efficient, cost-effective method, independent of time step size, to solve such highly nonlinear and complex real-world problems.

Measurement of the absolute efficiency of the X-ARAPUCA photon detector for the DUNE Far Detector 1

The DUNE far detector has been designed to detect photons and electrons generated by the charged products of the interaction of neutrinos with a massive liquid argon (LAr) target. The photon detection system (PDS) of the first DUNE far detector (FD1) is composed of 6000 photon detection units, named X-ARAPUCA. The detection of the prompt light pulse generated by the particle energy release in LAr will complement and boost the DUNE LAr Time Projection Chamber. It will improve the non-beam events tagging and enable at low energies the trigger and the calorimetry of the supernova neutrinos. The X-ARAPUCA is an assembly of several components. Its photon detection efficiency (PDE) depends on the design of the assembly, on the grade of the individual components and on their coupling. The X-ARAPUCA PDE is one of the leading parameters for the PDS sensitivity, that in turn determines the sensitivity of the DUNE for the detection of core-collapse supernova within the galaxy and for nucleon decay searches. In this work we present the final assessment of the absolute PDE of the FD1 X-ARAPUCA baseline design, measured in two laboratories with independent methods and setups. Preliminary results were reported in Palomares (JINST 18(02):C02064, https://doi.org/10.1088/1748-0221/18/02/C02064, 2023). One hundred sixty units of these X-ARAPUCA devices have been deployed in the NP04 facility at the CERN Neutrino Platform, the 1:20 scale FD1 prototype, and will be operated during the year 2024. The assessed value of the PDE is a key parameter both in the NP04 and in the DUNE analysis and reconstruction studies.

Application of a reaction-based water quality model to the total dissolved solids concentration of the Pasig River.

With the goal to support effective water resource management, water quality models have gained popularity as tools for evaluating the distributions of pollutants and sediments. This work focuses on the application of the numerical solution of an advection-dispersion-reaction (ADR) water quality model for rivers and streams to a major Philippine waterway, the Pasig River. The water quality constituent is described by a system of reaction and advection-dispersion-reaction equations. The model and method are based on a previously used strategy where Guass-Jordan decomposition is applied to the matrix system and the resulting conservative form of the model is solved numerically using the fully implicit scheme and finite element method. The methodology is demonstrated by a case study in Pasig River involving the concentrations of total dissolved solids (TDS) obtained from the Department of Environment and Natural Resources (DENR) through the Pasig River Unified Monitoring Stations (PRUMS) report. Sensitivity analysis and parameter estimation are also applied to the model to assess which parameters influence the model output the most.

All-dielectric ellipsoidal tetramer metasurface sensor with multi-resonances from the bound states in the continuum.

We demonstrate an all-dielectric tetramer metasurface that achieves high-precision refractive index sensing through the excitation of toroidal dipole, magnetic dipole, and electric quadrupole modes, driven by bound states in the continuum (BIC). The metasurface exhibits exceptional performance, with a quality factor of 4.65 × 104, a sensitivity parameter of 649 nm/RIU, and a figure of merit of 1.51 × 104 RIU-1, achieved by optimizing symmetry and lattice perturbations in periodic silicon tetramer ellipsoidal nanodisks on a SiO2 substrate. Additionally, the sensor effectively distinguishes analytes with extinction coefficient differences as small as 0.01 through enhanced broadband absorption. This innovation demonstrates substantial potential for label-free sensing applications in microfluidic chip integration.

Quantitative analysis of construction risks in shallowly buried biased tunnel portal section

To address the prominent problem of collapse instability in shallow buried soft ground tunnels, a non-invasive stochastic finite element method was introduced. Taking Fujian Puyan Wenbishan tunnel as the background project, ABAQUS finite element software was used to analyze the tunnel excavation mechanics and parameter sensitivity. And developed the software interface program based on Python to output explicit limit state equation for the key mechanical indexes of the tunnel, so as to evaluate the tunnel reliability under different excavation methods, quantitatively. Study results show a significant improvement in efficiency and accuracy when calculating the probability of failure in tunnel excavation by the non-invasive stochastic finite element method. The maximum displacement monitoring points for the Wenbishan tunnel portal section were all vault settlement, with displacements of 33.6 mm, 30.2 mm, and 25.3 mm, respectively, using the annular retained core soil method, single sidewall guide pit and double sidewall guide pit method, with probabilities of failure of 36.11%, 28.03%, and 20.02%. It is found that the reliability of the tunnel is mainly determined by the geotechnical weight, elastic modulus and cohesion of the weak sandy soil layer, which can provide ideas for this type of engineering researches.

Fast predesign methodology of centrifugal compressor for PEMFCs combining a physics-based loss model and an interpretable machine learning method

For boosted hydrogen fuel cells, the centrifugal compressor greatly affects the system's efficiency, stack power and costs. However, the design of the centrifugal compressor is a lengthy and high-cost process. To tackle this issue, a new method combining a physics-based loss model and a machine learning method was proposed to achieve a fast and more accurate preliminary design step. A high-precision physics-based loss model was developed and validated for data generation. Furthermore, two gradient boosting decision tree (GBDT) models with Bayesian hyperparameter turning were established to construct mapping relationships between geometric parameters and aerodynamic performance. Then, the Sobol sensitivity analysis and Shapley Additive Explanations (SHAP)-based interpretability were used to explore the impact of the geometric parameters on the aerodynamic performances. Results showed that the outlet blade angle, impeller outlet diameter, impeller outlet width, inducer shroud diameter, and inducer blade angle (at the shroud) were the five most sensitive parameters. Additionally, some practical design recommendations were extracted using SHAP-based interpretability. The isentropic efficiencies of two redesign cases with optimal geometric parameters were increased by 2.59% and 1.85% compared with the baseline impellers. The performance prediction was completed within a time of 6 s using the proposed new predesign method. This study represents a significant new attempt to advance the preliminary design methodology of centrifugal compressors.

Robust and stochastic sparse subspace clustering

Sparse subspace clustering (SSC) has been widely employed in machine learning and pattern recognition, but it still faces scalability challenges when dealing with large-scale datasets. Recently, stochastic SSC (SSSC) has emerged as an effective solution by leveraging the dropout technique. However, SSSC cannot robustly handle noise, especially non-Gaussian noise, leading to unsatisfactory clustering performance. To address the above issues, we propose a novel robust and stochastic method called stochastic sparse subspace clustering with the Huber function (S3CH). The key idea is to introduce the Huber surrogate to measure the loss of the stochastic self-expression framework, thus S3CH inherits the advantage of the stochastic framework for large-scale problems while mitigating sensitivity to non-Gaussian noise. In algorithms, an efficient proximal alternating minimization (PAM)-based optimization scheme is developed. In theory, the convergence of the generated sequence is rigorously proved. Extensive numerical experiments on synthetic and six real datasets validate the advantages of the proposed method in clustering accuracy, noise robustness, parameter sensitivity, post-hoc analysis, and model stability.

Vascular Alterations in Uterine and Ovarian Hemodynamics and Hormonal Analysis throughout Pregnancy Loss in Cows under Heat Stress.

This current study examined the ovarian (OA) and middle uterine arteries (MUA) blood flow under heat stress conditions regarding hormonal status (progesterone (P4), estradiol (E2), and nitric oxide metabolites (NOMs) assays). Eighteen pluriparous cows were examined, with twelve only being subjected to the natural mating as the other six animals were not bred. Pregnancy diagnosis was confirmed at day 30 by embryonic heartbeat and CL graviditatis (n = 6; pregnant), but some animals (n = 6) showed early embryonic death (EED), with the presence of control cows (n = 6). In the pregnant group, luteal diameter (cm) increased after mating, while in the pregnancy loss group it increased (p < 0.05) until reaching day 38 (1.41 ± 0.03), then decreased again. Luteal vascularity was declined in cows with EED after day 36 (p < 0.05) and reached its lowest level at day 50. P4 levels elevated in cows with EED until day 36 (13.64 ± 0.11) then declined. Both ipsilateral OA and MUA Doppler indices were declined in both groups except in cows who suffered from EED; both were elevated from day 38 until day 50 after mating. Ipsilateral peak systolic point (PSV cm/sec) of OA and MUA was elevated in both groups (p < 0.05), but in cows with EED this parameter was declined. E2 and NOMs levels were declined in cows with EED from day 40 and day 38 after mating (p < 0.05), respectively. This study provided novel data on the relations among the luteal diameter, E2, P4, and NOM levels, and luteal hemodynamics that predicts the amount of blood supply, which acts as a sensitive parameter to detect the alterations in luteal function during the first 50 days after mating.