Predictive numerical modelling of a complete pressure swing adsorption plant

BackgroundHeart failure (HF) is a leading cause of global disease burden and mortality. Accurate prognosis assessment is critical for reducing the risk of adverse events. In recent years, numerous predictive models have been developed for different HF subtypes. However, the quality of existing models varies considerably, and there remains a lack of consensus on models suitable for widespread clinical application. This study systematically reviews the current landscape of HF prediction models, analyzes their strengths and limitations, and provides guidance for future research.MethodsThis review systematically retrieved studies on prognostic prediction models for HF from databases including PubMed and Embase, with a search period spanning from the inception of each database to 19 September 2025. The risk of bias of the included studies was assessed using the prediction model risk of bias assessment tool, and the performance of the prediction models was evaluated through metrics such as the C-index and calibration.ResultsA total of 46 prediction models from 38 studies were included. According to target population classification, 14 models were developed for predicting outcomes in HF patients with reduced ejection fraction, nine models were applicable to HF patients with preserved ejection fraction, one model targeted HF patients with mildly reduced ejection fraction, and the remaining 22 were designed for all HF patients regardless of subtype. The risk of bias assessment showed that 10 models had a high risk of bias, 21 models demonstrated an unclear risk of bias, and 15 models exhibited a low risk of bias. The study systematically summarized each model's study cohort, modeling methodology, predictors, outcomes, prediction performance, presentation format, as well as strengths and limitations.ConclusionRefining the methodological processes of model construction—including optimizing study cohort selection, updating predictor screening (such as incorporating novel biomarkers, imaging indicators, and multi-omics data), improving modeling strategies, and enhancing model presentation—will contribute to the development of more accurate and clinically applicable prediction models. Such advancements hold significant potential for improving clinical outcomes in patients across all types of HF. This review provides a substantive reference for future research in this field.

Introduction: Numerous prediction models have been developed to identify high-risk individuals for lung cancer screening, aimed at improving early detection and survival rates. However, there has been no comprehensive review and meta-analysis of model performance across different sociocultural contexts. Therefore, this review aims to systematically examine the performance of lung cancer risk prediction models in Western and Asian populations. Methods: PubMed and EMBASE were searched from inception to January 2023. Studies published in English and proposed a validated model on human populations with well-defined predictive performances were included. Two reviewers independently screened titles and abstracts and the Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to appraise study quality. A random-effects meta-analysis was performed and a 95% confidence interval (CI) for model performance was provided. Between-study heterogeneity was adjusted for using the Hartung-Knapp-Sidik-Honkman test. Results: Fifty-four studies were included, with 42 from Western and 12 from Asian countries. Most Western studies were designed for ever-smokers (19/42; 45.2%) and the general population (17/42; 40.5%), and only two Asian studies developed models exclusively for never-smokers. Across both Western and Asian prediction models, the three most consistently included risk factors are age, sex, and family cancer history. In 45.2% (19/42) of Western and 50.0% (6/12) of Asian studies, models incorporated both traditional risk factors and biomarkers; 14.8% (8/54) directly compared their biomarker-based models with those incorporating only traditional risk factors, demonstrating better discrimination. Machine-learning algorithms were applied in eight Western models and two Asian models. External validation of PLCOM2012 (AUC=0.748; 95% CI: 0.719-0.777) outperformed prediction models such as Bach (AUC=0.710; 95% CI: 0.674-0.745) and Spitz models (AUC=0.698; 95% CI: 0.640-0.755). Conclusion: Despite showing promising results, the majority of Asian risk models in our study lack external validation. Our review also reveals a significant gap in prediction models for never-smokers. Future research should externally validate existing Asian models or incorporate relevant Asian risk factors in widely-used Western models (PLCOM2012) to account for their unique risk profiles and lung cancer progression patterns. Citation Format: Yah Ru Juang, Lina Ang, Wei Jie Seow. The predictive performance of risk prediction models in determining lung cancer incidence in Western and Asian countries: A systematic review and meta-analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 6210.

China’s power industry is a major energy consumer, with the carbon dioxide (CO2) generated by coal consumption making the power industry one of the key emission sectors. Therefore, it is crucial to explore energy conservation and emissions reduction strategies suitable for China’s current situation. Taking a typical cogeneration enterprise in North China as an example, this paper aims to establish a generalized regression prediction model for carbon emissions of coal-fired power plants, which will provide a reference for China to seek strategies for carbon peaking and carbon neutralization in the future. Firstly, in terms of the selection of influencing factors, this paper uses objective index screening methods, simulation means, and the eXtreme Gradient Boosting algorithm (XG-Boost) to analyze the feature importance of various influencing factors. It is concluded that the relevant influencing factors of the boiler feed water system have a strong correlation and characteristic importance with the carbon emissions results of coal-fired power plants. Therefore, this paper proposes to introduce these factors into the regression prediction model as auxiliary variables to more scientifically reflect the carbon emissions results of coal-fired power plants. Secondly, in the aspect of regression prediction model establishment, inspired by the sparrow’s foraging behavior and anti-predation behavior, this paper selects the sparrow search algorithm (SSA) with strong optimization ability and fast convergence speed to optimize the super parameters of the long short-term memory network algorithm (LSTM). It is proposed to use the SSA-LSTM algorithm to establish the carbon emissions regression prediction model of coal-fired power plants. The advantage of the SSA-LSTM algorithm is that it can effectively simplify the super parameter selection process of the LSTM algorithm, effectively solve the global optimization problem, prevent the model from falling into overfitting and local optimization, and make the carbon emissions regression prediction model of coal-fired power plants achieve a better fitting effect. By comparing the performance indicators of the model before and after the improvement, it is found that the regression prediction effect of the SSA-LSTM coal-fired power plant carbon emissions regression prediction model, which introduces boiler feed water influencing factors, has been effectively improved. Therefore, the model proposed in this paper can be used to conduct a comprehensive impact factor analysis and regression prediction analysis on the carbon emissions intensity of China’s coal-fired power plants, formulate targeted carbon emissions reduction countermeasures, and provide a theoretical basis for energy conservation and emissions reduction of China’s coal-fired power plants.

In this research, general regression neural network (GRNN), Hammerstein-wiener (HW) and non-linear autoregressive with exogenous (NARX) neural network, least square support vector machine (LSSVM) models were employed for multi-parametric (Hardness (mg/L), turbidity (Turb) (μs/cm), pH and suspended solid (SS) (mg/L)) modeling of Tamburawa water treatment plant (TWTP) at Kano, Nigeria. The weekly data from the TWTP were used and the predictive models were evaluated based on several numerical indicators. Four different non-linear ensemble techniques (GRNN-E, HW-E, NARX-E, and LSSVM-E) were subsequently employed. The comparison of the results of modeling showed that HW served as the best model for the simulation of Hardness, Turb, and SS while the NARX model demonstrated high capability in predicting pH. Yet, the HW and NARX as system identification techniques attained best overall predictive performance among the four modeling approaches. The HW model offers, therefore, a reliable and intelligent approach for predicting the treated Hardness, Turb, and SS and should be explored as a predictive tool in TWTP. Among the non-linear ensemble models, GRNN-E proved of high merit and increased the accuracy of the best single models significantly up to 30% for Hardness and Turb, 34% for pH, and 37% for SS with regards to the single models.

A hybrid control system with a discrete adaptive predictive model for a nonstationary unstable third-order dynamic plant in continuous time is synthesized and modeled. An adaptive state observer, estimating a variable parameter of the plant model with respect to the a quadratic quality criterion, was synthesized. Continuous estimation of the plant parameter for a discrete sample is used in a discrete adaptive control algorithm with a predictive model. A linear model of the control plant mimics the unstable vertical motion of plasma in a tokamak with a vertical cross-section elongated along the vertical axis compared to a given equilibrium position.

Kuwait is a country with great potential for concentrated solar power (CSP) due to its high solar resource. The current work analyzes the potential of the application of CSP technology in the country and its particularities. Specifically, the current work examines a CSP plant with parabolic trough collector (PTC) technology and how this technology is affected in its performance depending on the location (inland or coastal) and the refrigeration system (wet, dry, or seawater cooled). The modeling methodology is based on a two-step approach: first, the Rankine steam power cycle is modeled in detail in a state-of-the-art steady-state simulation environment, obtaining the performance of the power cycle for each cooling technology; second, the power cycle performance maps are integrated into a detailed transient system-level simulation model of the complete CSP plant in order to perform annual yield simulations for the different representative locations of the climate of Kuwait. These results provide valuable information on both the potential of this technology for its more immediate application such as electricity generation and other future applications such as the desalination of seawater where the location must necessarily be coastal.

The nonhydrostatic multiscale model on the B grid (NMMB) was employed to forecast an episode of intense local Kosava wind in northeast Serbia. Kosava, a vigorously turbulent local wind, originates from the east or southeast near the Danube's "Iron Gate," moves westward over Belgrade, and then extends northward into the regions of Romania and Hungary. Typically attributed to a jet-effect wind within the narrow gorge of the "Iron Gate," it can reach maximum speeds exceeding 30 m/s. The NMMB model, with a horizontal resolution of 1.2 km, was utilized for the 2019 Kosava episode forecast. The high resolution, that surpasses the typical standards in numerical prediction models used by national meteorological services and other centers, can be crucial for accurately predicting strong wind gusts and capturing the specific dynamics and characteristics of the wind associated with the narrow gorge. The NMMB model results are compared with measured wind data and the results from models with lower resolutions.

Aseismic ability estimation of school building using predictive data mining models

Applications of predictive modelling early in the COVID-19 epidemic

In this study, the surface prediction numerical model for micro-endmilling, including the non-dynamic errors of micro-endmilling such as tool geometric and set-up errors, was proposed. For verifying the proposed prediction numerical model, the inspection method of tool non-dynamic errors was proposed and applied to the verification experiments. In micro-endmilling, the geometry and manufacture error of micro tool and tool set-up error affect the quality of micro cutting process seriously compared to macro endmilling because the specific cutting energy and feed per tooth to tool radius are very large and commented errors works in combination. Therefore, for the numerical model for predicting the micro-endmilling surface, error variables such as tool dish, tool eccentricity, tilted edge part, and tool runout were applied to the proposed numerical model. Besides, it is very important that those errors are measured accurately. Finally, the numerical prediction model was verified by the micro cutting experiments applied the inspection method of tool non-dynamic errors.

China has highly emphasized the research and operational application of numerical weather prediction. This paper determines the objective function parameters, such as CAPE and SRH, to apply an ensemble numerical prediction model in weather forecasting. Preprocessing and evaluating rainfall data is necessary to construct the WRF-ARW numerical weather prediction model. The WRF-ARW model is applied to simulate the weather forecasts in Henan Province, and the difficulties and challenges faced in the efficient implementation of the parameterized scheme are outlined. The WRFARW model’s prediction errors for the maximum rainfall and total rainfall in Henan Province range from 1.78%-13.51% and 0.16%-3.78%, respectively, which are significantly less than 15%, and the model is more predictive than the others. The raw data test set’s credibility ranges from 0.957 to 0.997, which is close to 1, indicating that the raw data collected in this paper are highly credible. The WRF-ARW model’s qualification rates for forecasting maximum rainfall and total rainfall are 86.7% and 93.3%, respectively, and its overall accuracy is grade B and grade A, respectively. The pass rates for the peak occurrence time of maximum rainfall and total rainfall were 93.3% and 86.7%, respectively, and the overall prediction accuracy was Grade A and Grade B, respectively. The WRF-ARW model is effective in weather forecasting throughout Henan Province. In summary, the WRF-ARW model is very effective in improving the efficiency of ensemble numerical weather prediction and parameterization schemes in Henan Province.

Aiming at the problem that the fuzzy neural network (FNN) technique itself does not provide the input matrix to the FNN prediction model, we present a prediction modeling methodology which combines the computation and analysis of condition number with FNN, and design the computation and analysis of analog deviation for the input matrix to choose samples close correlated with predictand as training samples, thus effectively reducing the scale of network and evidently enhancing the prediction ability of the FNN prediction model. Using the same CMA T213 and Japanese numerical prediction product (NPP) data, we performed the contrast experiments and analyses of the FNN prediction model for daily regional mean precipitation based on condition number and analog deviation against the condition number-FNN prediction model and the traditional stepwise regression prediction model, and results show that under the condition of the same number of selected predictors, the prediction accuracy of the FNN prediction model based on condition number and analog deviation is 12.6% higher than that of the stepwise regression model in the experiment of independent samples of 49 days.

The human immune system is an ensemble of complex interactions between different cell types. The focus of this dissertation lies on T cells. Each T cell carries multiple copies of a receptor in their membrane: the T cell receptor or TCR. These receptors can recognize small amino acid sequences that are presented by cells. In the case of cancer cells, these epitopes might be tumor specific and thus provide a way for T cells to specifically kill cancerous cells. To understand and monitor the immune responses in cancer patients, it is crucial to identify T cells targeting tumor-specific epitopes. The goal of this dissertation is to improve this identification process by providing new computational strategies. More specifically, we introduce prediction models to directly identify epitope-specific TCRs within repertoire data. We will start by explaining how these computational models were trained and how they can be used to monitor and improve our understanding of TCR repertoires. Hereafter, we demonstrate how the prediction models can reveal new insights in the T cell compartment when monitoring the response of cancer patients during immunotherapy. The main finding of this study is that our prediction framework can reveal patterns in the TCR repertoire that are linked with clinical response. This opens the door for computational predictions in the evaluation and design of immunotherapy. During the course of this thesis, numerous other prediction models were published, each trying to improve the overall performance. We noticed that most studies focused more on model design instead of data quality. Hence, at the end of this dissertation, we go back to the origin of the prediction models, the training data, to study the aspects of epitope-specific TCR repertoires that might influence model performance. Using both published and simulated TCR data, we investigated the structure of epitope-specific TCR repertoires. Interestingly, while T cells specific for one epitope can be grouped into clusters of similar TCR sequences, these TCR clusters can have greatly different TCR patterns. As a result, TCR clusters recognizing different epitopes can share more similarities than those targeting the same epitope. This highly complicates the prediction problem. In addition, we show how model performance is impacted by the diversity of the training data, stressing the need to understand the training data before interpreting the prediction results. Altogether, we introduce epitope-TCR prediction models to understand the human TCR repertoire, in particular in relation to cancer immunotherapy.

Ice accretion for ships and offshore structures. Part 1 - State of the art review

Steam power turbine-based power plant approximately contributes 90% of the total electricity produced in the United States. Mainly steam turbine consists of multiple types of turbine, boiler, attemperator, reheater, etc. Power is produced through the steam with high pressure and temperature that is conducted by the turbines. The dynamics of the power plant are highly nonlinear considering all these elements in the model. We proposed to capture the dynamics of the power plant through Simulink modeling where data are generated, and a subsequent data-driven predictive modeling approach is used to detect the power generation from these turbines by the multivariate Gaussian process (MGP). The modeling approach is considered to predict the power generation from these turbines which can capture the cross-correlations between the turbines. Also, the sensitivity analysis of the input parameters is constructed for each turbine to find out the most important factors.