Performance Model Research Articles

Waste to energy: Enhancing biogas utilization in dual-fuel engines using machine learning based prognostic analysis

Alternative fuels derived from organic matter like biomass can be a viable solution in the present scenario of increasing greenhouse gases. In the present study, waste food and animal refuse-derived biogas were tested as fuel in a diesel engine. To further enhance the waste-to-energy perspective waste cooking oil biodiesel − diesel blends were used as pilot fuel. A comprehensive set of operational settings including fuel injection timing, pressure, compression ratios, different flow rates of biogas, and engine loads were tested. Four machine learning approaches Linear Regression (LR), Decision Tree (DT), Extreme Gradient Boosting (XGBoost), and Gaussian Process Regression (GPR) were employed to develop prognostic models. The developed models for engine performance and emission were predicted with high accuracy with R2 values as high as 0.9962 for brake-specific fuel consumption and 0.9948 for brake thermal efficiency. The prediction errors were low 0.06 for BTE during model training and 0.18 during the model testing phase while it was almost negligible in the case of BSFC. All models were compared using statistical metrics and violin plots. The DT-based forecasting models were observed to be the best among all the models both based on statistical measures as well as violin plots.

A data-driven approach for predicting cash flow performance of public owners in building projects: insights from Turkish cases

PurposeThe cash flow from government agencies to contractors, called progress payment, is a critical step in public projects. The delays in progress payments significantly affect the project performance of contractors and lead to conflicts between two parties in the Turkish construction industry. Although some previous studies focused on the issues in internal cash flows (e.g. inflows and outflows) of construction companies, the context of cash flows from public agencies to contractors in public projects is still unclear. Therefore, the primary objective of this study is to develop and test diverse machine learning-based predictive models on the progress payment performance of Turkish public agencies and improve the predictive performance of these models with two different optimization algorithms (e.g. first-order and second-order). In addition, this study explored the attributes that make the most significant contribution to predicting the payment performance of Turkish public agencies.Design/methodology/approachIn total, project information of 2,319 building projects tendered by the Turkish public agencies was collected. Six different machine learning algorithms were developed and two different optimization methods were applied to achieve the best machine learning (ML) model for Turkish public agencies' cash flow performance in this study. The current research tested the effectiveness of each optimization algorithm for each ML model developed. In addition, the effect size achieved in the ML models was evaluated and ranked for each attribute, so that it is possible to observe which attributes make significant contributions to predicting the cash flow performance of Turkish public agencies.FindingsThe results show that the attributes “inflation rate” (F5; 11.2%), “consumer price index” (F6; 10.55%) and “total project duration” (T1; 10.9%) are the most significant factors affecting the progress payment performance of government agencies. While decision tree (DT) shows the best performance among ML models before optimization process, the prediction performance of models support vector machine (SVM) and genetic algorithm (GA) has been significantly improved by Broyden–Fletcher–Goldfarb–Shanno (BFGS)-based Quasi-Newton optimization algorithm by 14.3% and 18.65%, respectively, based on accuracy, AUROC (Area Under the Receiver Operating Characteristics) and F1 values.Practical implicationsThe most effective ML model can be used and integrated into proactive systems in real Turkish public construction projects, which provides management of cash flow issues from public agencies to contractors and reduces conflicts between two parties.Originality/valueThe development and comparison of various predictive ML models on the progress payment performance of Turkish public owners in construction projects will be the first empirical attempt in the body of knowledge. This study has been carried out by using a high number of project information with diverse 27 attributes, which distinguishes this study in the body of knowledge. For the optimization process, a new hyper parameter tuning strategy, the Bayesian technique, was adopted for two different optimization methods. Thus, it is available to find the best predictive model to be integrated into real proactive systems in forecasting the cash flow performance of Turkish public agencies in public works projects. This study will also make novel contributions to the body of knowledge in understanding the key parameters that have a negative impact on the payment progress of public agencies.

Developing a 3-statement operating and financial performance model for truck and rail infrastructure business: A case study of a model with automated retirement/reinvestment schedule

Developing a 3-statement operating and financial performance model for truck and rail infrastructure business: A case study of a model with automated retirement/reinvestment schedule

Decoding pain: uncovering the factors that affect the performance of neuroimaging-based pain models.

Neuroimaging-based pain biomarkers, when combined with machine learning techniques, have demonstrated potential in decoding pain intensity and diagnosing clinical pain conditions. However, a systematic evaluation of how different modeling options affect model performance remains unexplored. This study presents the results from a comprehensive literature survey and benchmark analysis. We conducted a survey of 57 previously published articles that included neuroimaging-based predictive modeling of pain, comparing classification and prediction performance based on the following modeling variables-the levels of data, spatial scales, idiographic vs population models, and sample sizes. The findings revealed a preference for population-level modeling with brain-wide features, aligning with the goal of clinical translation of neuroimaging biomarkers. However, a systematic evaluation of the influence of different modeling options was hindered by a limited number of independent test results. This prompted us to conduct benchmark analyses using a locally collected functional magnetic resonance imaging dataset (N = 124) involving an experimental thermal pain task. The results demonstrated that data levels, spatial scales, and sample sizes significantly impact model performance. Specifically, incorporating more pain-related brain regions, increasing sample sizes, and averaging less data during training and more data during testing improved performance. These findings offer useful guidance for developing neuroimaging-based biomarkers, underscoring the importance of strategic selection of modeling approaches to build better-performing neuroimaging pain biomarkers. However, the generalizability of these findings to clinical pain requires further investigation.

Poetic Performativity

The author examines the performative status of a poetic work. Following T. Różewicz, he points to non-textual and potential forms of poetry’s existence. As a counterbalance to excessive adherence to textocentrism, he proposes functionalizing the tools of the new humanities (R. Nycz) and the theory of affect (B. Massumi) in the analysis of the creative act. The author indicates ways of studying the poetic experience with reference to the performative actor model (W. Baluch). Demonstrated voice recordings of poetic performances in the context of Dramatic Theory of Literature (A. Krajewska) are examples of gaining special insight into experience and dramatic self-discovery.

Life-Cycle Performance Modeling for Sustainable and Resilient Structures under Structural Degradation: A Systematic Review

The performance of structures degrades during their service life due to deterioration and extreme events, compromising the social development and economic growth of structure and infrastructure systems. Buildings and bridges play a vital role in the socioeconomic development of the built environment. Hence, it is essential to understand existing tools and methodologies to efficiently model the performance of these structures during their life cycle. In this context, this paper aims to explore the existing literature on the life-cycle performance modeling, assessment, enhancement, and decision making of buildings and bridge infrastructure systems under deterioration and extreme events for a sustainable and resilient built environment. The main objectives are to (1) systematically review the existing literature on life-cycle performance modeling of buildings and bridges based on the PRISMA methodology, (2) provide a bibliometric analysis of the systematically assessed journal articles, (3) perform an analysis of the included articles based on the identified components of life-cycle performance modeling, and (4) provide a discussion on the utilized tools, techniques, methodologies, and frameworks for buildings and bridge infrastructure systems in the life-cycle context. The provided systematic literature review and subsequent discussions could provide an overview to the reader regarding the individual components and existing methodologies of life-cycle performance management under deterioration and extreme events.

Mathematical modeling for the production performance of cyclic multi-thermal fluid stimulation process in layered heavy oil reservoirs

Multi-thermal fluid (MTF), with a unique thermophysical characteristics, has been widely applied in the enhanced heavy oil recovery processes. Especially for the layered heavy oil reservoirs, MTF shows more attractive than the conventional saturated steam. In this study, we propose an improved mathematical model for the production performance of cyclic MTF stimulation process in layered heavy oil reservoirs. In this model, based on a three-zone assumption, we first derive a productivity model for the thermal-fluids huff-n-puff process in layered heavy oil reservoirs. Thereafter, considering the typical EOR mechanisms of non-condensable gases in MTF, the viscosity reduction effect caused by CO2 dissolution and the effect of formation energy supplement caused by N2 are mathematically described. Simultaneously, because of the nonlinear temperature spreading behavior in the formation, an MTFs flooding experiment is also performed to characterize the effect. Meanwhile, a heating radius model with the consideration of steam overlap effect is also involved in our model. Then, the calculation results are compared against the results of a commercial simulator to confirm the accuracy, while a sensitivity analysis on the reservoir and operation parameters is performed. This paper provides a quick estimation approach for the productivity of cyclic MTF stimulation process in layered heavy oil reservoirs.

Comparative analysis of hydrogel-based thermotropic glazing and fixed horizontal overhangs in building performance

Thermotropic (TT) glazing is able to achieve energy-saving capabilities similar to external shading devices by dynamically regulating solar heat gain transmitted through windows in response to ambient climatic conditions. However, the building performance of TT glazing compared to traditional shading devices remains underexplored. This study aims to bridge this knowledge gap by comparing the building performance of hydrogel-based TT glazing and fixed horizontal external shading devices in terms of building energy consumption, daylighting performance, and thermal comfort. Experimental tests were conducted over a continuous ten-day period, focusing on the indoor temperature and illumination distribution within experimental rooms equipped with either TT glazing or fixed horizontal external shading device. Building performance models were developed using EnergyPlus and validated with experimental data. Subsequently, a comparative analysis was then conducted to evaluate the impact of TT glazings and fixed horizontal external shading devices on building energy consumption, daylighting performance, and thermal comfort across five typical climate conditions in China. The findings indicated that: (1) In five climate zones, TT provided the greatest energy savings in West-facing orientations, followed by East-facing orientations. (2) TT can replace fixed horizontal external shading device with a certain length to achieve similar building energy efficiency and indoor thermal comfort performance. (3) In regard to daylighting performance, TT achieved the desired values of sUDI<100lx,50 % and sUDI300–3000lx,50 % across all four orientations in the five climates. (4) In terms of thermal comfort, TT improved thermal comfort by reducing the annual daytime average PPDoverheating across all orientations in the five climate zones.

ANN modeling and optimization of friction stir welding performance for AA6061 and AA5083 alloy joints

This research focused on the characteristics of the AA6061/AA5083 alloy joints which are fabricated by the friction stir welding (FSW) technique. The impact of FSW parameters such as tool tilt angle, pin depth, and tool geometry on the mechanical properties and fractographic characteristics were studied. Ultimate tensile strength (UTS), hardness (HBN), and specific wear rate (SWR) were examined for characterization while fractography analyses were done using scanning electron microscopy. Taguchi-Grey relational analysis (GRA) was employed to execute multicriteria optimization and recognize the best grouping of parameters. The optimized values attained from this analysis were 259.2 MPa for UTS, 94.9 HV for HBN, and 0.0819 m³/Nm for SWR. The analysis of variance (ANOVA) results derived from the GRA revealed that tool geometry exerted the most significant influence (39.79%) on output factors, followed by pin depth (24.90%) and tilt angle (24.26%). To enhance the predictive accuracy of the output responses, an artificial neural network (ANN) model was developed utilizing the Levenberg-Marquardt (LM) algorithm. The optimized ANN architecture, configured as (3–10–3), exhibited robust regression analysis outcomes, showcasing correlation coefficients ( R) of 0.99999, 0.99967, and 0.99994 for the training, validation, and test datasets. The overall R-value, computed at 0.99958, affirmed high conformity between experimental and predicted values.

Modeling the service life of temporary airfield operational surfaces under multi-pass aircraft trafficking

Expeditionary Airfield (EAF) surfacing systems are designed to create temporary aircraft operating surfaces. Modeling the service life of EAF surfacing systems including the matting system, aircraft, and subgrade, has historically proven difficult, exacerbated by variability between systems and the multitude of mechanisms that can constitute failure. The study presented herein outlines the development and implementation of a performance modeling approach that includes a multi-scale scheme that accounts for local characteristics of the connection points of the EAF matting system, coupled to the global characteristics of the matting array to predict cyclic passes to failure. Finite element studies were conducted for an EAF surfacing system brickwork configuration subjected to aircraft strut loads over varying California Bearing Ratio (CBR) subgrades to calibrate a transfer function to full-scale trafficking experiments. The proposed framework is then used to predict the rate of subgrade deformation for additional lay patterns, which successfully ranked the performance of each relative to full-scale trafficking experiments. An approach is proposed to couple the rate of subgrade deformation with local finite element models to capture increasing joint damage as permanent deformation accumulates, and supplemented by a variable amplitude cycle counting and damage accumulation algorithm that yields reasonable agreement with full-scale experiments while capturing the transition in failure mechanisms at higher CBR values. The results of the study presented herein captures the propensity for end connector and subgrade failure over a range of subgrade CBRs and shows promise for a broader performance framework that can be extended to other EAF surfacing systems, aircraft types, and specific matting lay patterns.

Reduction of the Emission Footprint of Gas Turbines in Future Energy System Scenarios Through Optimized Hydrogen Admixture Strategies

Abstract Due to the inherent volatility of renewable power generation technologies, dispatchable components, such as gas turbines (GT) will have to be used increasingly for residual load balancing. In addition, GTs are expected to operate fewer hours per year, more flexibly, and at lower capacities. However, fuel utilization in GT is inherently linked to emissions. As a potentially CO2-free energy carrier, hydrogen is a promising fuel for GTs and manufacturers are working on suitable combustor technologies. However, the availability of large quantities of CO2-free H2 remains unclear in the near future. In this study, a physical-based gas turbine performance model and an emission calculation tool are used to derive an optimized H2 admixture strategy for different load profiles. Characteristic load demand scenarios are derived from actual load profiles of gas power plants and the emission footprints are comprehensively evaluated by different environmental impact categories. In general, the emission footprint is increased significantly and moderately for capacity reduction and flexibility increase of GT operation. The availability of H2 in the near future is derived from forecasts for Germany, and the corresponding quantities are allocated to the partial loads according to the optimized strategy. In most scenarios, the addition of H2 is associated with a reduction in emissions compared to conventional fossil fuel operation. The greatest leverage of H2 admixture in reducing the environmental footprint is found when applied from the lowest load up, thus assisting in the start-up and shut-down process.

Optimization of Ansys CFX Input Parameters for Numerical Modeling of Pump Performance in Turbine Operation

The paper deals with the issue of determining the optimal setting of input variables in Ansys CFX for modeling pump flow in turbine operation (PAT). The pump model was created in Autodesk Inventor. The mesh for numerical simulations was created using Ansys Fluent Meshing, considering the mesh quality parameters’ skewness and aspect ratio. The Ansys CFX computational model was experimentally verified on an actual pump by measuring the performance parameters on a test circuit and using the PIV (particle image velocimetry) method. The research indicated that the most suitable setting for the model input variables was the inlet pressure and PAT flow rate combination. Another option was to adjust the pressure at the pump inlet and outlet. However, the calculation time in this case was up to 30% longer. The comparison of the model results with the experiment showed that the deviations in the numerical model performance values did not exceed 10% of the values measured on the test circuit. Only the calculated torque was 1.2 ± 0.13 Nm higher on average than the torque measured on the test circuit. This difference is most likely due to the simplification of the geometry of the computational mesh in order to reduce the computation time.

Framework to improve software effort estimation accuracy using novel ensemble rule

This investigation focuses on refining software effort estimation (SEE) to enhance project outcomes amidst the rapid evolution of the software industry. Accurate estimation is a cornerstone of project success, crucial for avoiding budget overruns and minimizing the risk of project failures. The framework proposed in this article addresses three significant issues that are critical for accurate estimation: dealing with missing or inadequate data, selecting key features, and improving the software effort model. Our proposed framework incorporates three methods: the Novel Incomplete Value Imputation Model (NIVIM), a hybrid model using Correlation-based Feature Selection with a meta-heuristic algorithm (CFS-Meta), and the Heterogeneous Ensemble Model (HEM). The combined framework synergistically enhances the robustness and accuracy of SEE by effectively handling missing data, optimizing feature selection, and integrating diverse predictive models for superior performance across varying project scenarios. The framework significantly reduces imputation and feature selection overhead, while the ensemble approach optimizes model performance through dynamic weighting and meta-learning. This results in lower mean absolute error (MAE) and reduced computational complexity, making it more effective for diverse software datasets. NIVIM is engineered to address incomplete datasets prevalent in SEE. By integrating a synthetic data methodology through a Variational Auto-Encoder (VAE), the model incorporates both contextual relevance and intrinsic project features, significantly enhancing estimation precision. Comparative analyses reveal that NIVIM surpasses existing models such as VAE, GAIN, K-NN, and MICE, achieving statistically significant improvements across six benchmark datasets, with average RMSE improvements ranging from 11.05% to 17.72% and MAE improvements from 9.62% to 21.96%. Our proposed method, CFS-Meta, balances global optimization with local search techniques, substantially enhancing predictive capabilities. The proposed CFS-Meta model was compared to single and hybrid feature selection models to assess its efficiency, demonstrating up to a 25.61% reduction in MSE. Additionally, the proposed CFS-Meta achieves a 10% (MAE) improvement against the hybrid PSO-SA model, an 11.38% (MAE) improvement compared to the Hybrid ABC-SA model, and 12.42% and 12.703% (MAE) improvements compared to the hybrid Tabu-GA and hybrid ACO-COA models, respectively. Our third method proposes an ensemble effort estimation (EEE) model that amalgamates diverse standalone models through a Dynamic Weight Adjustment-stacked combination (DWSC) rule. Tested against international benchmarks and industry datasets, the HEM method has improved the standalone model by an average of 21.8% (Pred()) and the homogeneous ensemble model by 15% (Pred()). This comprehensive methodology underscores our model’s contributions to advancing software project management (SPM) through advanced predictive modeling, setting a new benchmark for software engineering effort estimation.

Research on dynamic performance and mechanical model of a novel magnetorheological shear thickening damper with adaptive variable damping gap

Abstract Magnetorheological shear thickening fluid (MRSTF) is an intelligent composite material, which shows considerable potential in semi-active vibration control. However, the current research is mostly limited to the study of the rheological properties of materials, and there is still a lack of damper structures that can effectively utilize the dual-control characteristics of MRSTF. In this paper, a novel damper structure with adaptive adjustable damping gap length is proposed. Its unique design integrates an adaptive internal damping gap that changes with the movement of the piston and an external gap controlled by the magnetic field, providing a relatively wide range of current-controlled damping force and reliable adaptive adjustment capabilities. Based on the M-S shear stress constitutive model and structural characteristics, the mechanical model is derived from the force balance of the micro-element, and verified by performance test and curve fitting. The effectiveness of the new damper structure proposed in this paper is verified by dynamic performance test. The results show that the damper has a wide range of current controlled damping force at low speed and reliable adaptive adjustment capability at high speed. Finally, the accuracy and universality of the modified mechanical model of damper are verified by curve fitting method. All these studies can offer an effective guidance for further application of MRSTF in the field of semi-active vibration control.

The Impact of Technological Value Recognition on Job Performance Under Digital Transformation

This study focuses on two key factors influencing job performance, technological value recognition (TVR) and emotional labor, that inform the digital labor job performance model. In this study, we argue that digital enterprises enhancing digital workers' TVR and emotional labor can enhance employees' job performance. By analyzing data from 571 digital preschool teachers working in 27 cities in the Yangtze River Delta region of China, we came to the following conclusions: TVR has a positive impact on job performance, emotional labor has a positive impact on job performance, and emotional labor mediates the relationship between TVR and job performance. Our findings also indicate that enhancing the digital resource support employers offered and providing emotional training to employees are two important actions companies can take to improve job performance and innovation capabilities in the digital transformation era.

Theoretical modelling and experimental evaluation of thermal performance of a combined earth-to-air heat exchanger and return air hybrid system

To both alleviate thermal saturation in surrounding soil and avoid indoor air pollution caused by closed-loop earth-to-air heat exchanger (EAHE) systems, this study proposes a hybrid mode that combines EAHE and return air (RA), i.e., the EAHE-RA system. A theoretical model was proposed to predict the thermal performance of EAHE-RA systems considering both sensible and latent heat transfer inside the EAHE. Full-scale experiments on both open-loop EAHE system and EAHE-RA system were carried out. The results indicated that the average cooling capacity of EAHE in EAHE-RA system was decreased by 1202.8 W (43.82 %) in summer compared to open-loop EAHE system, while the average cooling capacity recovery of return air was 645.4 W. In winter, the average heating capacity of the EAHE of EAHE-RA system was decreased by 394.1 W (48.91 %), while the average heating capacity recovery of the return air was 1021.7 W. Theoretical predictions of EAHE outlet and indoor air temperature agree well with the measured ones in both summer and winter. Theoretical results indicated that the EAHE-RA system has lower/higher pipe wall temperatures in summer/winter than the open-loop EAHE does, indicating a lower thermal interference on surrounding soils and thus better durability.

Predictive model for college students’ performance in higher mathematics

This study investigated the influence of psychological factors (attitude, learning engagement, motivation, and self-efficacy) and instructional factors (teacher-related factors and learning environment) on college students' performance in higher mathematics. It developed a predictive model for higher math performance. This utilized a cross-sectional research design using a survey to gather data at a university in Cebu province, Philippines, with 391 students from Bachelor of Science in Industrial Engineering and Bachelor of Secondary Education Major in Mathematics programs selected through purposive sampling, resulting in an 87% response rate. Data were analyzed using descriptive statistics, Pearson correlation, and multiple regression. Findings revealed that students generally held positive attitudes towards math, demonstrated average learning engagement, were highly motivated, and reported average self-efficacy. Both teacher-related factors and the learning environment were perceived very positively. While both psychological and instructional factors showed significant positive correlations with mathematics performance, multiple regression analysis indicated that only attitude was not a significant predictor. Learning engagement emerged as the strongest predictor, followed by learning environment and self-efficacy. The main contribution of this study is the creation of a predictive model, Higher Mathematics Performance = 1.82 + 0.008 (attitude) + 0.257 (learning engagement) + 0.039 (motivation) + 0.098 (self-efficacy) + 0.084 (teacher-related factors) + 0.155 (learning environment) + e, which accounted for 82.7% of the variance in higher mathematics performance. Hence, the predictive model developed in this study can serve as a valuable tool to design interventions and effective strategies for improving mathematics performance and overall academic achievement in higher education. This leads to a broad picture of the fact that these factors are correlated and how the various correlations can describe strategies for proposing relevant programs necessary for the improvement of performance in higher mathematics.

A multi-stage lithium-ion battery aging dataset using various experimental design methodologies

This dataset encompasses a comprehensive investigation of combined calendar and cycle aging in commercially available lithium-ion battery cells (Samsung INR21700-50E). A total of 279 cells were subjected to 71 distinct aging conditions across two stages. Stage 1 is based on a non-model-based design of experiments (DoE), including full-factorial and Latin hypercube experimental designs, to determine the degradation behavior. Stage 2 employed model-based parameter individual optimal experimental design (pi-OED) to refine specific dependencies, along with a second non-model-based approach for fair comparison of DoE methodologies. While the primary aim was to validate the benefits of optimal experimental design in lithium-ion battery aging studies, this dataset offers extensive utility for various applications. They include training of machine learning models for battery life prediction, calibrating of physics-based or (semi-)empirical models for battery performance and degradation, and numerous other investigations in battery research. Additionally, the dataset has the potential to uncover hidden dependencies and correlations in battery aging mechanisms that were not evident in previous studies, which often relied on pre-existing assumptions and limited experimental designs.

Model estimation of eastern oyster larval performance from food quantity and quality measures in western Mississippi Sound

Oyster Crassostrea virginica population recovery is critical in degraded estuarine systems, such as Mississippi Sound, USA, where repeated mass mortality events have depleted local oyster stocks. Owing to multiple recent die-offs, the western Mississippi Sound oyster population is recruitment-limited; population growth is constrained by the entry of new individuals into the extant population. Therefore, oyster recovery requires an adequate supply of larvae capable of timely development, growth, and successful metamorphosis. Larval performance and settlement potential are influenced by ambient temperature, salinity, and food supply. Food quantity is important to larvae, but so is food quality, as larvae require a balanced diet of lipids, proteins, and carbohydrates to develop and survive through metamorphosis. In this study, in situ environmental and food conditions during the 2021 and 2022 spawning seasons from 7 oyster reefs in western Mississippi Sound were integrated into an established biochemically based larval performance model to estimate periods facilitative of successful metamorphosis. In 2021, model-estimated larval survivorship was suppressed through much of the spawning season by prolonged, extremely low salinity (&lt;5 ppt) and inadequately balanced food supply. Higher seasonal salinity and more balanced food composition increased model-estimated larval survivorship in 2022, despite lower total food content, suggesting larval performance was primarily governed by the quality of available food. Model-estimated settlement windows were compared to settlement windows derived from concomitant field observations of recruitment. Strong agreement between model-estimated and observed settlement windows validates the effectiveness of the model and informs on the underlying causes of recruitment limitation in western Mississippi Sound.

Prediction of individual performance and verbal intelligence scores from resting-state fMRI in children and adolescents.

The neuroimaging basis of intelligence remains elusive; however, there is a growing body of research employing connectome-based predictive modeling to estimate individual intelligence scores, aiming to identify the optimal set of neuroimaging features for accurately predicting an individual's cognitive abilities. Compared to adults, the disparities in cognitive performance among children and adolescents are more likely to captivate individuals' interest and attention. Limited research has been dedicated to exploring neuroimaging markers of intelligence specifically in the pediatric population. In this study, we utilized resting-state functional magnetic resonance imaging (fMRI) and intelligence quotient (IQ) scores of 170 healthy children and adolescents obtained from a public database to identify brain functional connectivity markers associated with individual intellectual behavior. Initially, we extracted and summarized relevant resting-state features from whole-brain or functional network connectivity that were most pertinent to IQ scores. Subsequently, these features were employed to establish prediction models for both performance and verbal IQ scores. Within a 10-fold cross-validation framework, our findings revealed that prediction models based on whole-brain functional connectivity effectively predicted performance IQ scores( ) but not verbal IQ scores( ). Results of prediction models based on brain functional network connectivity further demonstrated the exceptional predictive ability of the default mode network (DMN) and fronto-parietal task control network (FTPN) for performance IQ scores ( ). The above findings have also been validated using an independent dataset. Our findings suggest that the performance IQ of children and adolescents primarily relies on the connectivity of brain regions associated with DMN and FTPN. Moreover, variations in intellectual performance during childhood and adolescences are closely linked to alterations in brain functional network connectivity.