The demand for clean label thickening agents follows the trend in the food and biotechnology industries away from chemically derived hydrocolloids. Exopolysaccharides (EPS) from lactic acid bacteria (LAB) have the potential to meet this demand, as they are produced during fermentation and exhibit texturising functionality, making them promising candidates for applications where natural origin is valued. This study examined EPS fermentation process from Leuconostoc citreum CNTA 860 using a two-level fractional factorial design (2⁶⁻²) to assess interaction effects among six operational factors. Initial sucrose concentration was the main driver of EPS synthesis, although agitation and inoculum size also significantly influenced EPS productivity and viscosity development. Regression models reached high predictive performance (R² = 0.989; Predicted R² = 0.925 at 12 h), confirming model adequacy during early fermentation. Under optimized conditions (600 rpm, 150 g/L sucrose, C/N = 100, inoculum 10⁸ CFU/mL, 25 °C, no sucrose pulsing), EPS concentration reached 70 g/L and broth viscosity 8200 cP after 24 h, exceptionally high values for L. citreum fermentations. SEC-HPLC revealed that the presence of a 3.26–6.50 kDa glucan fraction (20–40 glucose units) strongly correlated with viscosity (r = 0.70, p<0.001). This approach provides a robust framework for scale-up and positions EPS from L. citreum CNTA 860 as promising biothickeners for food and biotechnology applications. • Time-resolved DoE linked process key variables to EPS structure and rheology • Initial sucrose and agitation jointly drove EPS concentration and viscosity • A 3.3–6.5 kDa glucan fraction (20–40 glucose units) governed viscosity development • Leuconostoc citreum CNTA 860 excelled in EPS production with thickening capacity • Optimal conditions yielded 70 g/L EPS and 8200 cP viscosity, highest for L. citreum

In this paper, we present a Monte Carlo method for estimating a nonlinear function of the mean of a multivariate normal distribution. Building on this method, we propose a parametric estimation procedure for unimodal regression models, assuming that the response variable follows a gamma distribution while some covariates are contaminated with normal measurement error. Compared to existing approaches, the proposed method accommodates multivariate covariates and features a tractable bias-corrected likelihood function, enabling faster computation and more accurate estimation when the data distribution is correctly specified. To enhance the applicability of the proposed method, we also explore various model adequacy diagnostic tools and evaluate its robustness against distributional misspecifications. Notably, we introduce a goodness-of-fit test based on a unique characterization of the gamma distribution, designed to assess the validity of the distributional assumption for the response variable. Numerical studies and real-world data applications are conducted to evaluate the finite-sample performance of the proposedmethods.

The Individual Work Performance Questionnaire (IWPQ) serves as a recognized multidimensional instrument employed for the assessment of work performance, covering task performance, contextual performance, and counterproductive work behavior. Although extensively utilized, limited research has explored its psychometric attributes within the organizational framework in China. This study aims to bridge this research void by executing a comprehensive validation investigation with a sample of 833 Chinese workers. Multiple models including the three-factor model, higher-order model, and bi-order model demonstrating strong psychometric properties. Among these, the three-factor model was chosen for more detailed examination. The initial step involved conducting confirmatory factor analysis using AMOS, evaluating factors such as normality, factor loadings, reliability, common method bias, and overall model adequacy. Subsequently, a multigroup confirmatory factor analysis was performed to investigate measurement equivalence among subgroups based on gender. Following this, structural equation modeling in SmartPLS was utilized to assess criterion-related validity by examining the correlation between overall work performance and accomplishment as gauged by Seligman’s PERMA framework. The findings indicated robust psychometric characteristics, with factor loadings surpassing 0.70, high reliability and convergent validity (CR > 0.70 and AVE > 0.50), and adequate model suitability (RMSEA < 0.05). Assessment of measurement invariance validated the stability of the tripartite structure across genders, as evidenced by RMSEA values meeting criteria for both male and female cohorts. Criterion validity assessment unveiled a substantial positive correlation between overall individual work performance and accomplishment (β = 0.511, p < 0.001), denoting a noteworthy predictive capacity. These findings establish the IWPQ as a reliable and conceptually grounded instrument suitable for assessing individual work performance in Chinese organizational contexts.

Background/Objectives: Both adiposity and nutritional–inflammatory status influence glucose metabolism; however, their longitudinal associations with glycemic changes in non-diabetic populations remain unclear. We examined the independent, interactive, and joint associations of body mass index (BMI) and prognostic nutritional index (PNI) with the 3-year change in HbA1c (ΔHbA1c). PNI, a composite marker of serum albumin and peripheral lymphocyte count, reflects both protein nutritional status and systemic immune competence. We hypothesized that BMI and PNI would each independently predict ΔHbA1c and that their joint profiling would identify higher-risk subgroups. Methods: A total of 9414 non-diabetic adults from the Shanghai Suburban Adult Cohort were included. Participants with diabetes at baseline (defined as fasting plasma glucose ≥ 7.0 mmol/L, 2-h post-load glucose ≥ 11.1 mmol/L, HbA1c ≥ 6.5%, or self-reported physician diagnosis of diabetes or use of glucose-lowering medications) were excluded. BMI was measured, and PNI was calculated as serum albumin + 5 × lymphocyte count. ΔHbA1c was assessed over a 3-year period. Multivariable linear regression, interaction testing, and joint stratification were performed. Covariate selection was guided by prior biological plausibility, and model adequacy was evaluated using the Akaike Information Criterion (AIC). Results: Both BMI (β = 0.013% per kg/m2, 95% CI: 0.011–0.016, p < 0.001) and PNI (β = 0.002% per unit, 95% CI: 0.000–0.004, p = 0.019) were independently and positively associated with ΔHbA1c. No significant interaction was observed (p = 0.431). High BMI (≥24 kg/m2) was associated with glycemic worsening irrespective of PNI level (β ≈ 0.075%, p < 0.001). Among normal-weight individuals, higher PNI was associated with a modest increase in ΔHbA1c (β = 0.031%, p = 0.007). Conclusions: Although the absolute effect sizes were modest at the individual level, BMI was consistently and independently associated with glycemic deterioration therefore, even small per-unit increases may translate into meaningful risk at the population level given the high prevalence of overweight and obesity. PNI showed a small positive association, suggesting that in relatively healthy populations a higher PNI may partly capture subtle pro-glycemic factors—such as low-grade inflammation or higher protein intake—rather than representing unambiguous nutritional benefit. The absence of interaction suggests that BMI and PNI act through largely independent pathways. These findings extend prior evidence by demonstrating that PNI provides modest additional glycemic information beyond BMI in non-diabetic community-dwelling adults, particularly among those of normal weight.

Abstract Erosion along large fluvial valleys transports important sedimentary volumes across continents over geological time, modifying the loads imposed on the lithosphere and inducing isostasy‐related subsurface stresses. This work presents numerical experiments with a visco‐plastic lithosphere aimed at understanding the distribution and evolution of stresses and uplift arising from differential erosion in wide, tectonically quiescent valleys over 30 million years (Myr). We simulate valleys 30–150 km wide and a few hundred meters deep, and observe that these give rise to deviatoric stresses with maximum magnitudes ranging from 5 to 25 MPa, the distribution of which is largely controlled by the mechanical coupling between upper crust and lithospheric mantle. The upper crust in simulations with a weakly coupled lithosphere is strongly compression‐dominated beneath the valley, whereas scenarios with higher lithospheric coupling are characterized by compression and extension in similar proportions over crustal depths. Moreover, our simulations suggest that part of these stresses persists for tens of Myr after erosion rates have decreased, gradually concentrating around the valley due to viscous relaxation in the lower crust and lithospheric mantle. The adequacy of an elastic plate model in representing the topographic and stress responses of the lithosphere to fluvial incision is assessed, and examples from the Paraná, Colorado, Orange and Lachlan Rivers are discussed in light of our models. We conclude that large rivers are a factor to consider when assessing stresses in stable continental settings, especially if the valley is being actively excavated.

The paper investigates the adequacy of a simplified mathematical model of a numerical-analytical method for calculating low-power permanent magnet generators for autonomous power systems operating with a rectifier and active load. The object of study is generators with a radial arrangement of permanent magnets on the rotor surface. The model considered is based on an idealized representation of the air gap between the rotor and stator and does not take into account the tooth-and-groove structure, the actual geometric dimensions of the permanent magnets, and the nonlinear magnetic properties of ferromagnetic materials. To assess the adequacy of the model, a comparative analysis of the results of numerical-analytical calculations with the data of high-precision modeling using the finite element method in the Ansys Maxwell software package was performed. A series of FEM models of generators with varying levels of detail and complexity were was constructed. The paper presents mathematical expressions of the numerical-analytical method based on the Fourier transform, oriented towards computer implementation. The modeling was performed taking into account the electrical circuit of the generator, which includes a three-phase rectifier and an active load. The time dependencies of the magnetic field distribution and electromagnetic torque were calculated. The errors in the calculation of the electromagnetic torque obtained by the numerical-analytical method were determined in comparison with FEM models of varying degrees of detail. The results demonstrate the capabilities and limitations of the simplified mathematical model and confirm the feasibility of its use for preliminary calculations and optimization of the parameters of permanent magnet generators for autonomous renewable energy systems. References 12, figures 6, tables 3.

The work is devoted to the study of processes of conversion of low-potential thermal energy into electrical energy using a solid-state heat engine based on shape memory alloys (SMA). The relevance of the topic is due to significant losses of low-temperature heat in industrial and energy systems and the need to improve the energy efficiency of autonomous power sources. NiTi alloy springs are used as drive elements, which implement reversible austenite-martensite phase transformations and ensure the direct conversion of thermal energy into mechanical work. The aim of the work is to develop a physically based mathematical and numerical model of a heat engine, taking into account cyclic heating and cooling, thermomechanical hysteresis, the inertia of the mechanical system and electromechanical interaction with the generator, as well as to evaluate the energy characteristics of the power generation plant. A three-level approach to modelling is proposed, including an analytical quasi-static model for engineering estimates, a quasi-stationary moment balance model for determining steady states, and a complete dynamic system of differential equations that takes into account non-stationary heat transfer, phase kinetics, and inertial effects. Numerical integration was performed in Py-thon using fourth- and fifth-order Runge–Kutta methods. The simulation results confirmed the adequacy of the pro-posed model and showed the formation of self-stabilised rotation modes and power saturation. It was found that simpli-fied approaches provide a conservative estimate of energy performance, while the full dynamic model more accurately reproduces transient processes and real operating characteristics; the discrepancy between the models does not exceed 15–20%. The influence of the bilateral shape memory effect, which in the temperature range of 70–80 °C can increase electrical power by 20–30 %, was investigated. The expediency of partial immersion of SMA elements in the coolant to ensure stable cyclic heat exchange was demonstrated. The results obtained confirm the prospects of using SMA engines for the utilisation of low-potential heat and the creation of compact autonomous systems for small-scale electricity generation. References 20, figures 7.

Aim of the study Accurate forecasting of groundwater levels is essential for sustainable water-resource management in data-limited settings. This study develops an operational, reproducible workflow to forecast a regional shallow-groundwater index using only historical monitoring records. Monthly groundwater-level observations from six automatic monitoring wells (P1-P6) in the southeastern coastal plain of Nghe An province (Vietnam) were obtained from the Nghe An Environmental Monitoring Center. For each month, the regional series was calculated as the arithmetic mean of the six well levels, providing a single representative indicator for the study area. Material and methods Monthly groundwater level data from May 2013 to April 2025 were analyzed using three forecasting approaches: Seasonal Naïve (SNaïve), Seasonal Autoregressive Integrated Moving Average (SARIMA), and Exponential Smoothing State Space (ETS). The dataset was divided into a training period (May 2013–April 2024) and a testing period (May 2024–April 2025). Model performance was assessed using RMSE, MAE, and MAPE, supported by residual diagnostics and the corrected Akaike Information Criterion (AICc) to ensure model adequacy and parsimony. Results and conclusions The ETS model produced the lowest forecast errors and generated residuals closest to white noise, outperforming both SNaïve and SARIMA. These results demonstrate that ETS offers a robust and reliable framework for one-year-ahead groundwater forecasting. The model’s performance provides valuable support for irrigation planning, drought preparedness, and sustainable aquifer management in regions characterized by strong seasonal dynamics.

Outliers in medical datasets can substantially distort regression models, leading to biased estimates and unreliable clinical inferences. This study evaluated residual-based diagnostic methods-including standardized residuals, Cook's Distance, DFFITS, DFBETAS, Covariance Ratio, and standardized scores-for detecting influential observations in a simple linear regression model. Using age as the predictor and heart rate as the outcome, data from 30 individuals were analyzed. The initial model (R² = 0.22) revealed one highly influential outlier (heart rate = 290 at age 42), consistently identified across multiple diagnostics. Removal of this case improved model adequacy, yielding a more stable regression (R² = 0.10) with no further significant outliers detected. Findings highlight that even a single outlier can markedly distort regression-based conclusions, underscoring the importance of employing complementary diagnostic tools to ensure valid statistical inference in medical research.

Perinatal asphyxia accounts for nearly one-quarter of neonatal complications worldwide, positioning as a primary driver of neonatal death and long-term health issues. For neonates identified with perinatal asphyxia, the probability of adverse outcomes fluctuates throughout their hospitalization, with distinct patient characteristics and medical variables impacting their recovery path differently at various points post-admission. Survival-based approaches that use duration of stay as the time scale are therefore essential for characterizing this time-varying risk in resourcelimited settings. A study was conducted on neonates admitted to the Neonatal Intensive Care Unit (NICU) and Special Newborn Care Units (SNCU) at Dr. Ram Manohar Institute of Medical Sciences (RMLIMS) in Lucknow, Uttar Pradesh, from January 1, 2024, to December 31, 2024, covering the period from admission to discharge. The event of interest was on perinatal asphyxia morbidity in neonates. Neonates who were discharged alive were treated as censored observations. We used semi-parametric Cox proportional hazards regression and parametric Weibull, Exponential, log-logistic, and Gompertz survival models to identify key predictors of perinatal asphyxia. Among these applied models, the Weibull proportional hazards model showed the best fit. The model's adequacy was confirmed with the Cox-Snell residual plot. Key significant predictors of perinatal asphyxia included the absence of cry at birth, a low five-minute APGAR score (Appearance, Pulse, Grimace, Activity, and Respiration), preterm gestational age, prolonged labor, fetal distress, and the need for resuscitation at birth. Weibull parametric survival models offer a solid framework for understanding time-dependent perinatal asphyxia risk in resource-limited NICU settings. The identified predictors define a clinically actionable risk profile spanning intrapartum events, neonatal condition at birth, and gestational maturity.

An investigation was undertaken at the Instructional Farm, Barrister Thakur Chhedilal College of Agriculture and Research Station, Bilaspur, Chhattisgarh, India, a constituent college of Indira Gandhi Krishi Vishwavidyalaya, Raipur, Chhattisgarh, India to elucidate the nature of gene action for yield and its component traits in sesame (Sesamum indicum L.) through generation mean analysis in five crosses, namely Thilarani × TKG 55, Thilarani × Phule til, Thilarani × PKDS 12, Thilarani × JTS 08 and Thilothamma × JTS 08. The adequacy of the additive-dominance model was tested using A, B, C and D scaling tests. Significant scale effects for most traits indicated the presence of non-allelic gene interactions, demonstrating that simple additive-dominance inheritance was insufficient to explain trait expression. Pronounced epistasis was observed for days to 50 % flowering and days to maturity particularly in Thilarani × TKG 55, Thilarani × PKDS 12 and Thilarani × JTS 08, while seed yield per plant showed strong gene interaction in all crosses except Thilothamma × JTS 08. Generation mean analysis using the six-parameter model (m, d, h, i, j, l) revealed that both additive and non-additive gene effects governed the inheritance of the studied characters. Dominance effects were generally higher than additive effects for traits such as number of capsules per plant, biological yield and seed yield, especially in Thilarani × PKDS 12 and Thilarani × JTS 08. Duplicate type epistasis predominated in most crosses, indicating dispersion of favorrable alleles, whereas complementary epistasis was observed in specific combinations such as Thilarani × Phule til and Thilothamma × JTS 08. Traits like capsule length, number of seeds per capsule, thousand seed weight and oil content exhibited appreciable additive effects in certain crosses, suggesting the presence of fixable genetic variance. Overall, the study indicates that yield and its components in sesame are governed by complex inheritance involving additive, dominance and epistatic gene effects, necessitating appropriate breeding strategies for the development of superior genotypes.

Marine-derived endophytic fungi have long been recognised as valuable sources of therapeutic metabolites. In the present study, the endophyte Penicillium rubens strain PrubVJ was studied for optimised production of bioactive compounds using Response Surface Methodology (RSM). Experimental data predicted that incubation time was identified as the most influential variable, with maximum protein yield (190mg/L) observed at day 20, antioxidant activity (74%) at 10-15 days and total phenolics (1,724 ± 19µg GAE g⁻¹ DW) at day 25. The optimal fermentation conditions (25°C, pH 3.6, 4g/L salinity, 10 days incubation) with a desirability of 0.92, yielding 158 ± 1.325mg/L protein, 1,798 ± 18µg GAE g⁻¹ DW phenolics, and 84.9% antioxidant activity were predicted from the RSM model. Experimental validation under these conditions achieved protein yield of 190mg/L, phenolics of 1,724µg GAE g⁻¹ DW, and 74% antioxidant activity, confirming the reliability and robustness of the model. The developed quadratic models exhibited strong statistical reliability with stochastic parameters such as F-value and p-value, and coefficients of determination (R², adjusted R² and predicted R²) above 80% for all responses, validating model adequacy. Collectively, these findings establish P. rubens strain PrubVJ as a promising microbial platform, providing a reliable framework for optimising fungal fermentation. This study recommends the optimized strategy for large-scale pharmaceutical bioprocessing of fungal metabolites, to reduce production costs and accelerate the discovery of novel bioactive metabolites with pharmaceutical relevance, particularly for anticancer, antimicrobial, and antioxidant applications.

This study investigates a range of parameter estimation methods for the Half-Logistic Inverse Rayleigh Distribution (HLIRD) under two distinct sampling frameworks: ranked set sampling (RSS) and simple random sampling (SRS). The estimation techniques considered include maximum likelihood estimation, ordinary and weighted least squares, and the maximum and minimum product of spacings methods. Model adequacy is evaluated using five goodness-of-fit criteria: the Anderson–Darling (AD) statistic, its right- and left-tail variants, the second-order left-tail AD statistic, and the Cramér–von Mises statistic. An extensive simulation study is conducted to thoroughly evaluate and compare the performance of the proposed estimators while maintaining a fixed total number of observations across both sampling schemes. The practical relevance of the proposed methods is further illustrated through an application to a real dataset consisting of 69 carbon fiber specimens, with tensile strength measurements (in GPa) recorded at a gauge length of 20 mm. The numerical results demonstrate that estimators based on RSS consistently outperform their SRS counterparts across all considered performance measures, including mean squared error, bias, and mean absolute relative error. Overall, the findings highlight the advantages of employing RSS for parameter estimation of the HLIRD, particularly due to its superior efficiency in small-sample scenarios.

This article compares the analytical results from two models, based on the theory of hereditary creep strain, with experimental results on the rheological properties of lightweight sintered aggregate concrete under cyclically varying loads. In a previous article, the authors analyzed the adequacy of standard models for the same test results. Because the use of standard models is very complex and does not improve the approximation of test results without additional calibration, the authors suggest reconsidering the use of hereditary models for LWAC. The application of four such long-term models was analyzed. Among these models, the Arutiunian theory of hereditary creep with aging and the modified hereditary theory with Bažant aging function yielded quantitatively and qualitatively correct results. The application of hereditary creep theory allowed for the formulation of the total strain as a superposition of strain increments, obtained by an integral equation. This equation was applied to a series of constant stress increments and decrements, as in the case of cyclic loading, and it was mathematically described in segmented form. Knowledge of the properties of LWAC and useful long-term models is essential for the design of prestressed structures made of lightweight aggregate concrete subjected to time-varying loads.

Teacher performance is a critical determinant of school effectiveness and educational quality. However, leadership practices in many private schools tend to rely on a single approach, limiting their adaptability to contextual demands. This study addresses a gap in the literature by examining the integrated effect of transformational–instructional leadership and work motivation on teacher performance. This study employed a quantitative cross-sectional survey design involving 64 teachers from private senior high schools in Manado City, Indonesia, selected through proportional random sampling. Data were collected using validated Likert-scale questionnaires and analyzed using multiple linear regression. Prior to hypothesis testing, classical assumption tests (normality, multicollinearity, and heteroscedasticity) were conducted to ensure model adequacy. The findings reveal that both transformational–instructional leadership and work motivation have significant positive effects on teacher performance. Transformational–instructional leadership emerged as the strongest predictor (β = 0.758; p 0.001), followed by work motivation (β = 0.230; p = 0.001). Simultaneously, both variables explain a substantial proportion of variance in teacher performance (Adjusted R² = 0.814), indicating a strong model fit. The results suggest that the integration of transformational and instructional leadership creates a synergistic effect that enhances teacher performance by addressing both motivational and instructional dimensions. However, the cross-sectional design and reliance on self-reported data limit causal interpretation and may introduce response bias. This study highlights the importance of adopting a hybrid leadership approach supported by strong teacher motivation to improve performance in private school contexts. Future research should employ longitudinal designs and more robust analytical models to validate these findings.

Self-compassion and psychological resilience are pivotal in navigating the complexities of digital age stressors, notably nomophobia-fear of being without one's mobile phone. The primary objective of this study was to examine the association among psychological resilience, self-compassion and nomophobia. Moreover, the study employed structural equation modelling (SEM) to investigate the mediating effect of self-compassion in the relationship between psychological resilience and nomophobia among nursing students in Türkiye. A cross-sectional design was employed. We collected data from 387 nursing students through an online survey. Personal Information Form, Nomophobia Questionnaire, Self-compassion Scale and the Brief Psychological Resilience Scale were employed to collect the data. The results indicated medium levels of nomophobia, psychological resilience and self-compassion. Gender analysis showed higher levels of nomophobia in females, while males reported greater self-compassion and psychological resilience. The results of the correlation analyses indicated that there was a positive correlation between psychological resilience and self-compassion, and a negative correlation between both of these variables and nomophobia. Path analysis validated the hypotheses, including self-compassion's full mediating effect between resilience and nomophobia. The SEM's fit indices confirmed the model's adequacy. Our research underscores the importance of self-compassion as a mediator in the relationship between psychological resilience and nomophobia. It suggests that enhancing self-compassion could be crucial for reducing nomophobia levels, offering a new direction for interventions aimed at improving the mental well-being of nursing students.

Accurate forecasting of dengue incidence requires statistical models that explicitly accommodate overdispersion, temporal dependence, and delayed environmental forcing. We develop a Bayesian negative binomial dynamic regression model to generate monthly forecasts of dengue hemorrhagic fever (DHF) incidence in Kanchanaburi Province, Thailand. Transmission persistence is captured through lagged dengue incidence, while delayed climatic effects are represented using locally observed maximum temperature and relative humidity. Model adequacy and predictive performance are assessed using posterior predictive checks and leave-one-out cross-validation (LOO-CV). The negative binomial specification consistently outperforms Poisson-based alternatives under substantial overdispersion. Importantly, forecasting performance is not determined solely by the strength of marginal climate-dengue associations. Instead, it depends critically on the spatial representativeness of climatic inputs relative to the population at risk. Models informed by climatically representative observations yield more stable and robust out-of-sample forecasts, even when marginal associations are comparatively weaker. These findings underscore the distinction between explanatory association and predictive utility in climate-driven infectious disease models and provide practical guidance for the development of climate-informed dengue early warning systems in endemic settings.

Hydrogen-centric multi-energy systems for high-renewables grids: Technologies, flexibility services, and deployment pathways - A critical review

Ants, Formicidae, are a group of small social insects that inhabit nearly all terrestrial environments. Three competing hypotheses of ant relationships have been proposed, differing in the placement of Martialinae, a subfamily of cryptic, endogean ants. We used BUSCO genes to investigate the signals in individual and concatenated gene datasets. We found that gene trees support all three hypotheses. After concatenation, the three signals persist but their relative strength is model-dependent. The CAT-posterior mean site frequencies approach (which our model adequacy tests show best explains the across-site compositional heterogeneity of the data) finds Martialinae as the sister of all ants but Leptanillinae. We tested the effect of across-lineage compositional heterogeneity using data-recoding and excluding highly heterogeneous taxa. These tests did not lead to the emergence of significant support for alternative tree topologies. However, we identified strong gene- and site-discordance in the data and evidence that signals representing incongruent evolutionary processes exist in ant genomes supporting all three hypotheses. Incomplete lineage sorting and/or introgression seem to have significantly affected early ant evolution, which might make it impossible to establish whether Leptanillinae, Leptanillinae plus Martialinae, or Martialinae represents the sister of all the other ants.

Serplulimab is a fully humanized anti-PD-1 monoclonal antibody approved for small-cell lung cancer and other malignancies. The initial dosing strategy was based on body weight (WT-based); however, flat-dosing offers greater convenience and reduced variability. This study characterized the population pharmacokinetics (PopPK) of serplulimab using data from 11 clinical trials and quantitatively evaluated the appropriateness of transitioning from WT-based to flat-dose regimens. Serplulimab concentration-time data from 2110 patients were analyzed using nonlinear mixed-effects modeling. A previously developed two-compartment model with time-varying clearance best described the pharmacokinetic (PK) profile. A stepwise forward-addition and backward-elimination procedure was used to evaluate covariate effects on PK parameters. Model adequacy was confirmed by diagnostic plots, prediction-corrected visual predictive checks (pcVPCs), and bootstrap analysis. Simulations compared exposures between 3 mg/kg every 2 weeks (Q2W), 4.5 mg/kg every 3 weeks (Q3W), 200 mg Q2W, and 300 mg Q3W regimens. Body weight and albumin were the main predictors of exposure. Although statistically significant, covariate effects were modest (≤ 20%) and not clinically meaningful, supporting a unified dosing strategy. Simulations showed that flat-dose regimens achieved exposure comparable to WT-based dosing, with < 25% differences in median Cavg and Cmax across body-weight ranges. Exposure-response (E-R) analyses revealed no meaningful association between exposure and outcomes. The PopPK model adequately described serplulimab PK and supports transitioning from WT-based to flat-dosing, demonstrating comparable exposure, efficacy, and safety across regimens.