Flexible Model Research Articles

Calibration and experiments of discrete element flexible model parameters for kiwifruit stalk

A method combining experimental and simulation optimization was used to calibrate parameters to enhance the accuracy of discrete element model parameters during kiwifruit stem separation. First, physical experiments were conducted to determine the intrinsic and contact parameters of kiwifruit stalk. Second, the mechanical parameters of the kiwifruit stalks were determined using three-point bending and shear tests. On this basis, simulation tests were conducted on kiwifruit stalks by combining the Hertz-Mindlin model with a bonding model, and the optimal combination of bonding parameters was confirmed using the bending strength and maximum shear force. Finally, a discrete element model of the kiwifruit was built with the determined bonding parameters and simulated, and the reliability of the model was verified through mechanical tests. The results showed that the density was 867.5 kg/m3, Poisson's ratio was 0.26, the modulus of elasticity was 3.25 × 108 Pa, the recovery coefficient between the fruit stalks and steel parts was 0.365, and the average values of the static and dynamic friction coefficients between the kiwifruit stalks and steel parts were 0.268 and 0.152, respectively. The kiwifruit stem bonding parameters were normal stiffness per unit area kn=7.201×1011 N/m3, shear stiffness per unit area kt=2.379×1011 N/m3, critical normal stress σmax=5.937×108 Pa, critical shear stress tmax = 2.354×109 Pa, and bonded disc radius Rj=0.164 mm. Compared with the results of the mechanical tests, the relative errors of the bending strength and maximum shear of the discrete element model were 2.13% and 2.84%, respectively. The results showed that the discrete element model improves the simulation of the bending and shearing processes of kiwifruit stalks and is capable of characterizing the physical properties of kiwifruit stalks. The results of this study provide a theoretical foundation for the optimal design of end effectors.

Physics-augmented neural networks for constitutive modeling of hyperelastic geometrically exact beams

We present neural network-based constitutive models for hyperelastic geometrically exact beams. The proposed models are physics-augmented, i.e., formulated to fulfill important mechanical conditions by construction, which improves accuracy and generalization. Strains and curvatures of the beam are used as input for feed-forward neural networks that represent the effective hyperelastic beam potential. Forces and moments are received as the gradients of the beam potential, ensuring thermodynamic consistency. Normalization conditions are considered via additional projection terms. Symmetry conditions are implemented by an invariant-based approach for transverse isotropy and a more flexible point symmetry constraint, which is included in transverse isotropy but poses fewer restrictions on the constitutive response. Furthermore, a data augmentation approach is proposed to improve the scaling behavior of the models for varying cross-section radii. Additionally, we introduce a parameterization with a scalar parameter to represent ring-shaped cross-sections with different ratios between the inner and outer radii. Formulating the beam potential as a neural network provides a highly flexible model. This enables efficient constitutive surrogate modeling for geometrically exact beams with nonlinear material behavior and cross-sectional deformation, which otherwise would require computationally much more expensive methods. The models are calibrated and tested with data generated for beams with circular and ring-shaped hyperelastic deformable cross-sections at varying inner and outer radii, showing excellent accuracy and generalization. The applicability of the proposed point symmetric model is further demonstrated by applying it in beam simulations. In all studied cases, the proposed model shows excellent performance.

An Evaluation of an Algorithm for the Selection of Flexible Survival Models for Cancer Immunotherapies: Pass or Fail?

Accurately extrapolating survival beyond trial follow-up is essential in a health technology assessment where model choice often substantially impacts estimates of clinical and cost effectiveness. Evidence suggests standard parametric models often provide poor fits to long-term data from immuno-oncology trials. Palmer et al. developed an algorithm to aid the selection of more flexible survival models for these interventions. We assess the usability of the algorithm, identify areas for improvement and evaluate whether it effectively identifies models capable of accurate extrapolation. We applied the Palmer algorithm to the CheckMate-649 trial, which investigated nivolumab plus chemotherapy versus chemotherapy alone in patients with gastroesophageal adenocarcinoma. We evaluated the algorithm's performance by comparing survival estimates from identified models using the 12-month data cut to survival observed in the 48-month data cut. The Palmer algorithm offers a systematic procedure for model selection, encouraging detailed analyses and ensuring that crucial stages in the selection process are not overlooked. In our study, a range of models were identified as potentially appropriate for extrapolating survival, but only flexible parametric non-mixture cure models provided extrapolations that were plausible and accurately predicted subsequently observed survival. The algorithm could be improved with minor additions around the specification of hazard plots and setting out plausibility criteria. The Palmer algorithm provides a systematic framework for identifying suitable survival models, and for defining plausibility criteria for extrapolation validity. Using the algorithm ensures that model selection is based on explicit justification and evidence, which could reduce discordance in health technology appraisals.

Bayesian evaluation of hadron-quark phase transition models through neutron star observables in light of nuclear and astrophysics data

We investigate the role of hybrid and nucleonic equations of state (EOSs) within neutron star (NS) interiors using Bayesian inference to evaluate their alignment with recent observational data from NICER and LIGO-Virgo (LV) collaborations. We find that smooth hybrid EOSs are slightly favoured in explaining NS mass-radius relations, particularly for pulsars such as PSR J0030+0451 and PSR J0740+6620. However, this preference is not definitive, as gravitational wave (GW) data does not significantly differentiate between our hybrid and nucleonic models. Our analysis also reveals tensions between older NICER data and recent measurements for PSR J0437−4715, highlighting the need for more flexible EOS models. Through two sampling approaches — one fixing the hadronic EOS set and the other without fixing the same, we demonstrate that the hybrid EOS model can incorporate stiffer EOSs, resulting in a better agreement with NICER data but leading to higher tidal deformability, which is less consistent with GW observations. In some recent publications a parameter dc, related to the trace anomaly and its derivative, is used to indicate the presence of deconfined quark matter. We find that our hadronic model, which does not include phase transition to deconfined matter, under the influence of imposed constraints, is able to predict values below 0.2 for dc at around five times saturation density. The hybrid model goes below this threshold at lower densities under the same conditions.

Dynamic thermal simulation of a tree-shaped district heating network based on discrete event simulation

The computational complexity involved in modelling district heating (DH) networks impedes the integration of network operations into comprehensive DH system studies. We developed a flexible, accurate, and fast dynamic thermal simulation model utilising discrete event simulation (DES). This model is versatile, suitable for any tree-shaped DH network with a central heating plant and can estimate node temperatures and calculate pipe heat losses. The speed of the model is improved via using variable time steps and by incorporating two advanced techniques: lazy evaluation and a customised priority queue. To further improve the computational speed, we developed a technique to eliminate redundant sampling points. This model was tested and demonstrated excellent consistency with actual measurements. Remarkably, reducing sampling points can speed up the simulation by a factor of three without compromising the temperature accuracy. A 72-day simulation of a network with 102 pipes was completed within 0.219 s. Our findings highlight the significant potential of the DES model for large-scale dynamic network simulations and offer a promising solution for DH network simulations and system optimisation.

MPES-R: Multi-Parameter Evidence Synthesis in R for Survival Extrapolation-A Tutorial.

Survival extrapolation often plays an important role in health technology assessment (HTA), and there are a range of different approaches available. Approaches that can leverage external evidence (i.e. data or information collected outside the main data source of interest) may be helpful, given the extent of uncertainty often present when determining a suitable survival extrapolation. One of these methods is the multi-parameter evidence synthesis (MPES) approach, first proposed for use in HTA by Guyot et al., and more recently by Jackson. While MPES has potential benefits over conventional extrapolation approaches (such as simple or flexible parametric models), it is more computationally complex and requires use of specialist software. This tutorial presents an introduction to MPES for HTA, alongside a user-friendly, publicly available operationalisation of Guyot's original MPES that can be executed using the statistical software package R. Through two case studies, both Guyot's and Jackson's MPES approaches are explored, along with sensitivity analyses relevant to HTA. Finally, the discussion section of the tutorial details important considerations for analysts considering use of an MPES approach, along with potential further developments. MPES has not been used often in HTA, and so there are limited examples of how it has been used and perceived. However, this tutorial may aid future research efforts exploring the use of MPES further.

Association between education level and access to disease-modifying treatment in patients with multiple sclerosis in France.

We hypothesized that differences in access to disease-modifying treatments (DMTs) could explain the association between socioeconomic status and disability progression in multiple sclerosis (MS). This study aimed to analyze the association between education level and DMT use in France. All patients from OFSEP network with MS onset over 1996-2014 and aged ⩾ 25 years at onset were included. Three time-to-event outcomes were investigated using flexible parametric survival regression models: time from MS onset to first DMT (any) and to platform therapy, and time from platform therapy to switch to high-efficacy therapy. Overall, 7563 patients were included (mean follow-up 12.6 ± 5.9 years). The percentages of patients aged less than 40 years at MS onset and who initiated treatment before the age of 40 years were significantly higher in the groups with a higher education level. The time-to-event outcomes showed no major difference in DMT practices according to education level, except for women who had a significantly shorter time to DMT initiation in medium to very high education level groups versus low, at 5 years from MS clinical onset. Our results suggest that the association between education level and MS disability progression does not solely reflect different therapeutic practices, particularly in men.

Association between education level and disability progression in patients with multiple sclerosis in France.

Studies have reported an association between socioeconomic status and disability progression in multiple sclerosis (MS), but findings using the pre-MS individual socioeconomic status are missing. The objective was to investigate the association between education level and disability progression. All Observatoire Français de la Sclérose en Plaques (OFSEP) patients with MS clinical onset over 1960-2014, and aged ⩾25 years at MS onset were included. Education level was classified into four categories from low (primary/secondary school) to very high (master/doctoral degree). Time from MS onset to EDSS 4.0 was studied using flexible parametric survival models adjusted for age, period, and center, and stratified by phenotype (relapsing multiple sclerosis (RMS) and primary progressive multiple sclerosis (PPMS)) and sex. A total of 11,586 patients were included (women/men ratio = 2.5; age = 36.7 ± 8.6 years; follow-up duration 16.7 ± 9.3 years; 86.4% RMS). For women with RMS, the risk of reaching the outcome at 5 years was inversely associated with the education level (Hazard Ratio medium: 0.74 (0.63-0.87), high: 0.51 (0.43-0.62), very high: 0.39 (0.30-0.50) vs low). Results were similar for men. In PPMS, the risk was significantly different between the extreme groups (very high vs low) for women (0.45 (0.28-0.75)) and men (0.54 (0.32-0.91)), but no gradient was evident. Our study showed a strong association between education level and disability progression, regardless of sex and phenotype.

Breaking Down the Metrics: A Comparative Analysis of LLM Benchmarks

Due to the fast advancement in large language models (LLMs), the natural language processing (NLP) technology domain has witnessed a massive change. It has brought ground breaking developments in how machines understand and generate human language. However, even with these advancements, it remains hard to objectively compare and evaluate LLMs' performance because various benchmarks and metrics are used in the assessment. Such a method requires this study's writers to conduct a comparative analysis of various benchmark LLMs to explain the viability of different evaluation metrics. Analyzing the recognized standards, including GLUE, SuperGLUE, and SQuAD, reveals weaknesses and potential in the present evaluating systems. It embraces quantitative assessments of model performances and benchmarking and critical evaluations of benchmark designs and their scope. The paper discusses methodological issues in benchmarking research and explains that current measures are insufficient and lack comprehensive evaluation patterns. The conclusions of this research underline the significance of creating fully integrated and efficient reference sets that match the rate of innovation in LLM ability and inform other studies and the overall subsequent advancement of more sophisticated and flexible language models.

Unified Machine Learning Techniques for High-Dimensional Survival Data Analysis

Survival analysis is a statistical method used to analyze time-to-event data. The existence of states where event results become invisible after a given time is one of the primary difficulties with this regulation. This is considered censorship. This kind of data may be found in a variety of applications, such as mechanical system failure rates, patient mortality rates during clinical trials, and the length of unemployment in a community. Assessing the so-called survival and hazard functions is one of the primary goals of survival analysis. Many machine-learning algorithms have also been developed to tackle censored data and other difficult problems with real-world data. Machine-learning techniques enhance survival analysis by incorporating flexible modeling approaches, handling high-dimensional data, capturing nonlinear relationships, and accounting for censorship. They provide powerful tools to extract meaningful insights and make accurate predictions in time-to-event analysis across various healthcare, finance, and engineering domains. The standard statistical approaches and machine learning techniques advanced for survival analysis are structured in this unified framework, as is the implementation of some machine learning techniques applying to the GBSG2 survival analysis dataset.

Implication of ICWFPSO as Optimization Neural Network Algorithm on Sales Forecasting System

Predictive systems play a crucial role in a company's operations and strategy by aiding in more informed and data-driven decision-making and more effective planning and budgeting. It is possible to develop an intelligent system to perform forecasting. Neural networks offer significant advantages in forecasting systems due to their flexible modeling capabilities. However, this algorithm's fundamental weakness is the slow convergence rate and being trapped in a local minimum. To overcome it, this research is conducted to optimize the NN algorithm using the ICWFPSO to produce a forecasting algorithm with high accuracy and faster execution time using real e-commerce sales data for the past 7 years. Algorithm performance testing tests the Mean Absolute Error (MAE) value of the forecasting system using three scenarios: the NN forecasting algorithm, the NN optimized with ICWFPSO on the weight value, and the same scheme. Still, the optimized value is the hyperparameter value. ICWPSO has been shown to enhance the performance of PSO by tuning the inertia weight dynamically, which helps balance exploration and exploitation during the optimization process. The best prediction result is obtained when optimizing the hyperparameters using the ICWFPSO optimization technique compared to using traditional NN or optimizing weight value with ICWFPSO with the MAE value of 245.32958984375, and the best performance is obtained at iterations below 100. Further, gradient-based optimization methods might be generally preferred for their efficiency and effectiveness in handling large-scale neural network training.

Feasibility of an Obesity Prevention Program for Latino Families from First Trimester of Pregnancy to Child Age 18 Months and Predictors of Program Attendance.

Background: The high prevalence of obesity in Latino families with low income necessitates prevention beginning in pregnancy and continuing through infancy. Due to systemic inequities, adverse social determinants of health (SDoH) and mental health symptoms may limit program efficacy by presenting barriers to attendance. We sought to assess: (1) the feasibility of the Starting Early Program (StEP) Prenatal, a 17-session intervention beginning early in pregnancy and continuing to 18 months postpartum; and (2) the effects of adverse SDoH (material hardship, low social support) and mental health symptoms (depression, anxiety, stress) on program attendance. Methods: We conducted a single-arm feasibility trial of StEP Prenatal, enrolling from December 2018 to February 2020 (n = 231). We assessed feasibility (recruitment, retention, fidelity, attendance) and direct and interactive effects of adverse SDoH and mental health symptoms on attendance. We used zero-inflated Poisson regression, adjusting for maternal age, marital status, nativity, education, and pandemic timing. Results: We recruited 57% of eligible participants, with 213 remaining eligible to receive the full program. Retention was 75%. Median fidelity for group format was 64%; median attendance per session was 69%; median number of program sessions attended was 13. Baseline material hardship and high perceived stress predicted approximately one additional session attended. Similar effects were seen for low social support in the absence of anxiety symptoms. Conclusion: Despite pandemic disruptions, StEP Prenatal was feasible to deliver and participants with adverse SDoH at baseline were particularly motivated to attend. Futures studies should tailor programs to baseline SDoH and test flexible implementation models.

A comparative analysis of transformed indolent lymphomas and de novo diffuse large B-cell lymphoma: a population-based cohort study

Histologic transformation (HT) of indolent non-Hodgkin lymphoma (iNHL) to diffuse large B-cell lymphoma (DLBCL) carries a poor prognosis. Using the Surveillance, Epidemiology, and End Results-17 database, we conducted a population-based study of adult patients with transformed follicular lymphoma (t-FL), marginal zone lymphoma (t-MZL), lymphoplasmacytic lymphoma/Waldenström macroglobulinemia (t-LPL/WM), and de novo DLBCL. Primary outcome was relative survival (RS), and secondary outcomes included overall survival (OS) and lymphoma-specific survival (LSS). Outcomes were modeled using flexible parametric survival models, while multivariable modeling was used to compare RS, OS, and LSS. The incidence of HT was highest in splenic MZL (SMZL, 6.78%) and lowest in extranodal MZL (EMZL, 1.62%). Median follow-up times were similar for patients with de novo DLBCL and transformed indolent lymphomas. The 5-year RS and OS were longer in de novo DLBCL compared to all other transformed iNHL subtypes (68 versus 59%, respectively). For t-FL, early transformation (within 2 years of diagnosis, Hazard ratio [HR] = 1.34) and prior treatment (HR = 1.89) were associated with inferior survival. This association was not observed in other transformed lymphoma subtypes. This is the first comparative study to show that the outcomes of t-LPL/WM were inferior compared to de novo DLBCL and highlights the need to incorporate early experimental therapies in patients with t-FL with early transformation and receipt of prior chemotherapy.

Vertical vibration characteristics of wheelset-gearbox-bogie frame system in different track irregularity conditions: simulation and full-scale test

ABSTRACT The vibration characteristics of a wheelset-gearbox-bogie frame system are studied under wheel-roller roughness and varying track irregularities utilizing a full-scale rolling test rig. A corresponding dynamic model is developed based on the discrete time transfer matrix method, which integrates a modular flexible wheelset model. The results reveal that, (1) low-frequency vibrations (0–50 Hz): dominated by track irregularities below 200 km/h but by wheel-roller rotation above 350 km/h; (2) mid-frequency vibrations (50–500 Hz): prominent shock effects appear at discrete frequencies above 350 km/h, related to wheel-roller roughness; (3) high-frequency vibrations (500–1000 Hz): great differences under various irregularities and distinguishing structural natural frequencies becomes difficult. Amplitude contributions at speeds below 200 km/h are mid (57–62%), low (26–32%), and high (11–14%). Above 350 km/h, mid-frequency increases while low-frequency decreases by 15%, respectively. To improve vibration performance at superspeed (e.g., 500 km/h), stricter wheel-rail roughness control becomes imperative.

Strategies to Strengthen Hospital Response for Chemical, Biological, Radiological, and Nuclear Incident: A Multisite Study.

In responding to a Chemical, Biological, Radiological, and Nuclear explosive (CBRNe) disaster, clinical leaders have important decision-making responsibilities which include implementing hospital disaster protocols or incident command systems, managing staffing, and allocating resources. Despite emergency care clinical leaders' integral role, there is minimal literature regarding the strategies they may use during CBRNe disasters. The aim of this study was to explore emergency care clinical leaders' strategies related to managing patients following a CBRNe disaster. Focus groups across 5 tertiary hospitals and 1 rural hospital in Queensland, Australia. Thirty-six hospital clinical leaders from the 6 study sites crucial to hospital disaster response participated in 6 focus groups undertaken between February and May 2021 that explored strategies and decision making to optimize patient care following a CBRNe disaster. Analysis revealed the use of rehearsals, adopting new models of care, enacting current surge management processes, and applying organization lessons were facilitating strategies. Barriers to management were identified, including resource constraints and sites operating over capacity. Enhanced education and training of clinical leaders, flexible models of care, and existing established processes and tested frameworks could strengthen a hospital's response when managing patients following a CBRNe disaster.

Implementing RIS3 (Research and Innovation Strategies for Smart Specialization) to unlock Iran’s regional potential

Centralized and top-down policymaking, resource allocation, and infrastructure concentration have long shaped Iran's industrial landscape. Rooted in its historical governance model and resource-based economic structure, this approach has inadvertently marginalized regions with untapped potential. A regional approach to industrial development is essential to address the disparities among Iranian provinces. While Research and Innovation Strategies for Smart Specialization (RIS3) has gained prominence for promoting specialization within regions and fostering inter-regional synergy, its adoption remains limited in large developing countries with diverse innovation portfolios. This article proposes a mathematical model for RIS3 that extends beyond high-tech industries to encompass medium – and low-tech sectors, acknowledging the full spectrum of innovation capabilities. The output of model's implementation for Iran’s provinces aligns relatively well with regional spatial planning documents, underscoring its reliability. The model's flexibility also allows adaptation to other contexts, making it a valuable tool for policymakers and practitioners seeking tailored strategies.

Flexible modelling of time-varying exposures and recurrent events to analyse training load effects in team sports injuries

Abstract We present a flexible modelling approach to analyse time-varying exposures and recurrent events in team sports injuries. The approach is based on the piece-wise exponential additive mixed model where the effects of past exposures (i.e. high-intensity training loads) may accumulate over time and present complex forms of association. In order to identify a relevant time window at which past exposures have an impact on the current risk, we propose a penalty approach. We conduct a simulation study to evaluate the performance of the proposed model, under different true weight functions and different levels of heterogeneity between recurrent events. Finally, we illustrate the approach with a case study application involving an elite male football team participating in the Spanish LaLiga competition. The cohort includes time-loss injuries and external training load variables tracked by Global Positioning System devices, during the seasons 2017–2018 and 2018–2019.

Kalkayotl 2.0. Bayesian phase-space modelling of star-forming regions, stellar associations, and open clusters

Star-forming regions, stellar associations, and open clusters are fundamental stellar systems where predictions from star-formation theories can be robustly contrasted with observations. We aim to provide the astrophysical community with a free and open-source code to infer the phase-space (i.e. positions and velocities) parameters of stellar systems with lesssim 1000 stars based on Gaia astrometry and possibly observed radial velocities. We upgrade an existing Bayesian hierarchical model and extend it to model 3D (positions) and 6D (positions and velocities) stellar coordinates and system parameters with a flexible variety of statistical models, including a linear velocity field. This velocity field allows for the inference of internal kinematics, including expansion, contraction, and rotation. We extensively validated our statistical models using realistic simulations that mimic the properties of the Gaia Data Release 3. We applied Kalkayotl to beta -Pictoris, the Hyades, and Praesepe, recovering parameter values compatible with those from the literature. In particular, we found an expansion age of $19.1 Myr for beta -Pictoris and rotational signal of $32\! $ for the Hyades and that Praesepe's rotation reported in the literature comes from its periphery. The robust and flexible Bayesian hierarchical model that we make publicly available here represents a step forward in the statistical modelling of stellar systems. The products it delivers, such as expansion, contraction, rotation, and velocity dispersions, can be directly contrasted with predictions from star-formation theories.

Polishing the black box: flexible model-based partitioning surrogate models for interpretable machine learning model

Polishing the black box: flexible model-based partitioning surrogate models for interpretable machine learning model

Early postnatal care uptake and its associated factors following childbirth in East Africa—a Bayesian hierarchical modeling approach

BackgroundThe postnatal period is a critical period for both mothers and their newborns for their health. Lack of early postnatal care (PNC) services during a 2-day period is a life-threatening situation for both the mother and the babies. However, no data have been examined for PNCs in East Africa. Hence, using the more flexible Bayesian multilevel modeling approach, this study aims to investigate the pooled prevalence and potential factors for PNC utilization among women after delivery in East African countries.MethodsWe retrieved secondary data from the Kids Record (KR) demographic and health surveys (DHS) data from 2015 to 2022 from 10 East African countries. A total of 77,052 weighted women were included in the study. We used R 4.3.2 software for analysis. We fitted Bayesian multilevel logistic regression models. Techniques such as Rhat, effective sample size, density, time series, autocorrelation plots, widely applicable information criterion (WAIC), deviance information criterion (DIC), and Markov Chain Monte-Carlo (MCMC) simulation were used to estimate the model parameters using Hamiltonian Monte-Carlo (HMC) and its extensions, No-U-Turn Sampler (NUTS) techniques. An adjusted odds ratio (AOR) with a 95% credible interval (CrI) in the multivariable model to select variables that have a significant association with PNC was used.ResultsThe overall pooled prevalence of PNC within 48 hrs. of delivery was about 52% (95% CrI: 39, 66). A higher rate of PNC usage was observed among women aged 25–34 years (AOR = 1.21; 95% CrI: 1.15, 1.27) and 35–49-years (AOR = 1.61; 95% CrI: 1.5, 1.72) as compared to women aged 15–24 years; similarly, women who had achieved primary education (AOR = 1.96; 95% CrI: 1.88, 2.05) and secondary/higher education (AOR = 3.19; 95% CrI: 3.03, 3.36) as compared to uneducated women; divorced or widowed women (AOR = 0.83; 95% CrI: 0.77, 0.89); women who had currently working status (AOR = 0.9; 95% CrI: 0.87, 0.93); poorer women (AOR = 0.88; 95% CrI: 0.84, 0.92), middle-class women (AOR = 0.83; 95% CrI: 0.79, 0.87), richer women (AOR = 0.77; 95% CrI: 0.73, 0.81), and richest women (AOR = 0.59; 95% CrI: 0.55, 0.63) as compared to the poorest women; women who had media exposure (AOR = 1.32; 95% CrI: 1.27, 1.36), were having 3–5 children (AOR = 0.89; 95% CrI: 0.84, 0.94), had >5 children (AOR = 0.69; 95% CrI: 0.64, 0.75), had first birth at age < 20 years (AOR = 0.82; 95% CrI: 0.79, 0.84), had at least one ANC visit (AOR = 1.93; 95% CrI: 1.8, 2.08), delivered at health facilities (AOR = 2.57; 95% CrI: 2.46, 2.68), had average birth size (AOR = 0.94; 95% CrI: 0.91, 0.98) and small birth size child (AOR = 0.88; 95% CrI: 0.84, 0.92), had twin newborns (AOR = 1.15; 95% CrI: 1.02, 1.3), and fourth and above birth order (AOR = 0.88; 95% CrI: 0.82, 0.95) were individual-driven women who have been independently associated with PNC, respectively. Regarding community-level variables, rural women (AOR = 0.76; 95% CrI: 0.72, 0.79), high media exposure communities (AOR = 1.1; 95% CrI: 1.04, 1.18), communities with high wealth levels (AOR = 0.88 95% CrI: 0.83, 0.94), communities with high antenatal care (ANC) utilization (AOR = 1.13, 95% CrI: 1.07, 1.19), and long distance to health facilities (AOR = 1.5; 95% CrI: 1.38, 1.63) were among the community factors associated with PNC, respectively.ConclusionOne of the significant public health priorities in East Africa continues to be the underutilization of immediate PNC. The government ought to prioritize improving maternity and child health services, collaborating with interested parties in the area, reducing health disparities, educating mothers about child health, and other connected issues that are very beneficial.