Model Predictions Research Articles

Spectral expansion methods for prediction uncertainty quantification in systems biology

Uncertainty is ubiquitous in biological systems. For example, since gene expression is intrinsically governed by noise, nature shows a fascinating degree of variability. If we want to use a model to predict the behaviour of such an intrinsically stochastic system, we have to cope with the fact that the model parameters are never exactly known, but vary according to some distribution. A key question is then to determine how the uncertainties in the parameters affect the model outcome. Knowing the latter uncertainties is crucial when a model is used for, e.g., experimental design, optimisation, or decision-making. To establish how parameter and model prediction uncertainties are related, Monte Carlo approaches could be used. Then, the model is evaluated for a huge number of parameters sets, drawn from the multivariate parameter distribution. However, when model solutions are computationally expensive this approach is intractable. To overcome this problem, so-called spectral expansion (SE) methods have been developed to quantify prediction uncertainty within a probabilistic framework. Such SE methods have a basis in, e.g., computational mathematics, engineering, physics, and fluid dynamics, and, to a lesser extent, systems biology. The computational costs of SE schemes mainly stem from the calculation of the expansion coefficients. Furthermore, SE effectively leads to a surrogate model which captures the dependence of the model on the uncertainty parameters, but is much simpler to execute compared to the original model. In this paper, we present an innovative scheme for the calculation of the expansion coefficients. It guarantees that the model has to be evaluated only a restricted number of times. Especially for models of high complexity this may be a huge computational advantage. By applying the scheme to a variety of examples we show its power, especially in challenging situations where solutions slowly converge due to high computational costs, bifurcations, and discontinuities.

Feasibility analysis on the debrining for compressed air energy storage salt cavern with sediment

Using the sediment void to store gas is a promising solution for the construction of compressed air energy storage (CAES) salt cavern with high impurity. However, it remains debatable whether the pressure loss of brine in the sediment can notably affect debrining. In this paper, the in-situ sediments are obtained, and their porosity, moisture content after debrining and permeability are measured. A model of debrining in salt cavern with sediment is built. The model prediction error of injection gas pressure is less than 0.34 %. A criterion for the feasibility evaluation of debrining for CAES salt cavern with sediment is proposed. The results show that the porosity of the in-situ sediment is 47.9 %. When the sediment volume reaches 98 % of the cavern volume, the pressure loss of brine in the sediment and the maximum gas pressure are less than 3.1 MPa and 17 MPa, respectively. The sediment has good permeability, it is feasible to debrine in salt cavern with sediment. It is suggested that the debrining rate and the tubing diameter increase to more than 120 m3/h and 0.14 m, respectively. This study provides valuable guidance for the debrining in salt caverns with sediment.

Piano performance evaluation dataset with multilevel perceptual features

This study aims to build a comprehensive dataset that enables the automatic evaluation of piano performances. In real-world piano performance, especially within the realm of classical piano music, we encounter a vast spectrum of performance variations. The challenge lies in how to effectively evaluate these performances. We must consider three critical aspects: (1) It is essential to gauge how performers express the music and how listeners perceive the performance, rather than focusing on the compositional characteristics of the musical piece. (2) Beyond fundamental elements like pitch and duration, we must also embrace higher-level features such as interpretation. (3) Such evaluation should be done by experts to discern the nuanced performances. Regrettably, there exists no dataset that addresses these challenging evaluation tasks. Therefore, we introduce a pioneering dataset PercePiano, annotated by music experts, with more extensive features capable of representing these nuanced aspects effectively. It encapsulates piano performance with a wide range of perceptual features that are recognized by musicians. Our evaluation benchmark includes a novel metric designed to accommodate the inherent subjectivity of perception. Furthermore, we propose an enhanced baseline framework that grounds performance on score data, aligning model predictions with human perception. Harnessing the aligned features enhances the baseline performance and proves to be adaptable to various model structures. In conclusion, our research opens new possibilities for comprehensive piano performance evaluation.

DSA Quantitative Analysis and Predictive Modeling of Obliteration in Cerebral AVM following Stereotactic Radiosurgery.

Stereotactic radiosurgery is a key treatment modality for cerebral AVMs, particularly for small lesions and those located in eloquent brain regions. Predicting obliteration remains challenging due to evolving treatment paradigms and complex AVM presentations. With digital subtraction angiography (DSA) being the gold standard for outcome evaluation, radiomic approaches offer potential for more objective and detailed analysis. We aimed to develop machine learning modeling using DSA quantitative features for post-SRS obliteration prediction. A prospective registry of patients with cerebral AVMs was screened to include patients with digital prestereotactic radiosurgery DSA. Anterior-posterior and lateral views were retrieved and manually segmented. Quantitative features were computed from the lesion ROI. Following feature selection, machine learning models were developed to predict unsuccessful 2-year total obliteration using processed radiomics features in comparison with clinical and radiosurgical features. When we evaluated through area under the receiver operating characteristic curve (AUROC), accuracy, area under the precision-recall curve F1, recall, and precision, the best performing model predictions on the test set were interpreted using the Shapley additive explanations approach. DSA images of 100 included patients were retrieved and analyzed. The best-performing clinical radiosurgical model was a gradient boosting classifier with an AUROC of 68% and a recall of 67%. When we used radiomics variables as input, the AdaBoost classifier had the best evaluation metrics with an AUROC of 79% and a recall of 75%. The most important clinico-radiosurgical features, ranked by model contribution, were lesion volume, patient age, treatment dose rate, the presence of seizure at presentation, and prior resection. The most important ranked radiomics features were the following: gray-level size zone matrix, gray-level nonuniformity, kurtosis, sphericity, skewness, and gray-level dependence matrix dependence nonuniformity. The combination of radiomics with machine learning is a promising approach for predicting cerebral AVM obliteration status following stereotactic radiosurgery. DSA could enhance prognostication of stereotactic radiosurgery-treated AVMs due to its high spatial resolution. Model interpretation is essential for building transparent models and establishing clinically valid radiomic signatures.

Do marine food subsidies predict large scale distribution of scavenging seabirds within the Bay of Biscay?

Omnivorous and opportunistic species have the potential to exploit human food subsidies. In marine ecosystem, understanding interaction between seabirds and fishery activities, particularly their scavenging behaviour on discards is crucial nowadays, in the context of EU landing obligation. The Bay of Biscay is one of the major fishing zones in Europe and a central area for the wintering of a high number of seabird species. During spring and autumn, oceanographic surveys were conducted by the R/V Thalassa with the aim to evaluate fishing resources along transects over the continental shelf, thereby providing an opportunity to study the distribution and scavenging behaviour of seabirds in this area. We investigated the influence of fishery activities (i.e. distribution of professional fishing vessels, quantity of discards) at large scale, and oceanographic conditions on the distribution of scavenging seabirds along the continental shelf. On average, large gulls (Larus spp) and northern gannets accounted for 70 % of the seabirds scavenging on discards. We compared model predictions using oceanographic variables with those using both oceanographic variables and fishery activities, in separate for spring and autumn and group of seabirds (i.e. large gulls and northern gannets). Results suggested that the distribution of scavenging seabirds was better predicted by oceanographic variables than by fishery activities. The predicted numbers of scavenging seabirds were higher in autumn than in spring, probably due to the annual cycle of seabirds (i.e. breeding versus wintering). At large scale, oceanographic variables were better predictors of suitable habitat. However, a closer evaluation of prediction differences between the two models highlighted high anomaly values for large gulls in autumn along the coast, that may indicate over predictions. These results have provided new knowledge on the ecology of scavenging seabirds, in an area with high fishery activity, especially in the context of landing obligation.

Nonlinear unsteady aerodynamic forces prediction and aeroelastic analysis of wind-induced bridge response at multiple wind speeds: A deep learning-based reduced-order model

Machine learning-based aerodynamic reduced-order models (ROMs) combine high accuracy with extremely low computational costs, making them highly effective in predicting nonlinear and unsteady bridge aerodynamic forces. Although several machine learning-based nonlinear aerodynamic models have been developed, the majority are built on a single wind speed parameter. However, in nonlinear aerodynamic prediction and aeroelastic analysis of bridges, the variability in incoming wind speed significantly influences the computed results. A ROM relying solely on a single wind speed lacks the ability to accurately forecast the intricate dynamic behaviors arising from changes in wind speed. When the incoming wind speed changes, the model's prediction accuracy significantly decreases. Usually, it is necessary to establish a new database and train a new model, which not only increases time and cost but also greatly reduces the convenience of the ROM. Addressing this challenge, this study proposes a multiple-wind-speed (MWS) nonlinear unsteady bridge aerodynamic model based on the LSTM deep neural network. Taking the Taohuayu Yellow River Bridge in the Henan Province of China as an example, the modeling process of the proposed MWS-ROM is demonstrated, along with non-linear aerodynamic predictions of the deck under various conditions and aerodynamic-elastic analysis of the deck under different wind speeds. The research results show that the trained LSTM network can accurately predict the nonlinear aerodynamic forces of bridges under single and double degrees of freedom vibration conditions. The MWS-ROM performed well in predicting convergent vibrations at low wind speeds and limits cycle oscillations at high wind speeds, aligning closely with results from the CFD full-order model. Compared to CFD, the aerodynamic ROM based on the LSTM network significantly enhances computational efficiency, consequently boosting the convenience and efficiency of bridge flutter analysis. Additionally, the methodology proposed herein can be extended for wind-induced vibration control and response prediction in other types of deck sections.

Significant statistical model of heat transfer rate in radiative Carreau tri-hybrid nanofluid with entropy analysis using response surface methodology used in solar aircraft

The recent advancement of technologies focuses on the use of solar-based heat radiation and nanotechnology. The primary source of heat is solar energy, which is obtained by absorbing sunlight. In addition, solar thermal planes, photovoltaic cells, and sun-based hybrid nanofluids all help to harness solar energy. Therefore, main concern of the study is to explored the two-dimensional Engine oil-based Carreau tri-hybrid nanofluid (SiO2 (silica dioxide), MOS2 (Molybdenum disulfide), Fe3O4(black iron oxide)) flow via a stretching sheet within solar wings. The flow phenomena of the non-Newtonian fluid enrich for the effects of radiant energy and the external heat source. By adopting similarity transfiguration, the dimensional partial main flow equations are changed into nonlinear dimensionless ordinary differential equations. The most effective and powerful tool of the semi-analytical method ADM (Adomain Decomposition Method) is utilized to solve the nanofluid flow problem. The uniqueness of the proposed study arises due to the analysis of entropy generation obtained by various irreversibility processes. The graphical illustration of outcome of various governing parameters on velocity, temperature, entropy terms and engineering quantities have been presented and theoretically analyzed. The findings reveal that The Carreau ternary hybrid nanofluids enhance the system's thermal conductivity for optimal temperature regulation in solar aircraft and better heat dissipation. The use of Carreau ternary hybrid nanofluids significantly reduces entropy generation, which implies a more reversible process, enhancing the overall efficiency of the energy conversion systems in solar aircraft. The heat flow of the system is accurately reflected by the chosen variables and their interactions, as demonstrated by the constructed model's strong statistical significance. For real-world applications, this improves the model's predictability and dependability.

Tensile properties and work hardening in Al0.3CoCrFeNi: The role of L12 precipitates and grain size

In this study, we investigate the FCC-Al0.3CoCrFeNi high entropy alloy fabricated via spark plasma sintering of atomized powders, focusing on its mechanical and work-hardening properties across three distinct microstructures: coarse-grained, fine-grained, and fine-grained with L12 nano-precipitates. Using a dislocation density-based model, we analyze the effects of grain size and L12 precipitates on these properties, achieving quantitative agreement between model predictions and experimental tensile and work-hardening behaviors. This exploration highlights the underlying deformation mechanisms at room temperature and their contributions to the strength/ductility trade-off. Significantly, our analysis reveals that twinning in HEAs manifests differently from that observed in steels. Furthermore, the incorporation of L12 precipitates emerges as a critical factor enhancing the alloy's mechanical attributes. Our findings underscore the essential roles of microstructural parameters in tailoring the mechanical properties of HEAs, offering insights that could guide the design of advanced alloys with optimized performance.

High-resolution regional sea-ice model based on the discrete element method with boundary conditions from a large-scale model for ice drift

Abstract Understanding sea-ice dynamics at the floe scale is crucial to comprehend polar climate systems. While continuum models are commonly used to simulate large-scale sea-ice dynamics, they have limitations in accurately representing sea-ice behaviour at small scales. DEMs, on the other hand, are well-suited for modelling the behaviour of individual ice floes but face limitations due to computational constraints. To address the limitations of both approaches while combining their strengths, we explored the feasibility of using a DEM within a continuum model, where the latter provides boundary conditions for a rectangular high-resolution DEM domain. This paper presents a feasibility study where a discrete model of a 100 × 100 km2 icefield was created using high-resolution optical satellite imagery. Sea-ice dynamics were simulated in the DEM considering environmental forces and integrating large-scale ice-drift velocities as boundary conditions. Model predictions were compared with satellite observations for ice drift and deformation parameters. This numerical approach showed potential for offering accurate, high-resolution predictions of sea ice, particularly in coastal areas and near islands, and may find applications in ice navigation and climate studies. However, further development of the DEM, along with upgrades to the coupled ocean models providing data for the ice component, may be necessary. Additionally, challenges remain to develop a two-way coupling between the DEM and a continuum model, which may be needed to improve the accuracy of large-scale simulations.

Prediction of the Strength of the Concrete-Filled Tubular Steel Columns Using the Artificial Intelligence

Introduction. The machine learning algorithms are highly promising for predicting the load-bearing capacity of the building structures. The paper aims at building the predictive models for calculating the strength of the concrete-filled steel tubular (CFST) columns to enable a highly accurate prediction of the ultimate loads for the entire possible range of parameters affecting the load-bearing capacity of the eccentrically compressed columns.Materials and Methods. The article studies the eccentrically compressed short concrete-filled steel tubular (CFST) columns of circular cross-section. Model input parameters: column outer diameter, pipe wall thickness, yield strength of steel, compressive strength of concrete, relative eccentricity. Output parameters: the ultimate loads without taking into account and taking into account the random eccentricities. The models were trained on synthetic data generated based on the theoretical principles of the limit equilibrium method. Two machine learning models were built. When training the first model, the ultimate loads were determined at a given eccentricity of the longitudinal force without taking into account the additional random eccentricity. When training the second model, the additional random eccentricity was taken into account. The effect of the features on the model predictions was assessed using the Feature Importance function. The Optuna method was used to select the hyperparameters. The machine learning models were implemented in the Jupiter Notebook environment using the Gradient Boosting learning method. The total volume of the training sample was 179 025 samples.Results. The importance of the features most affecting the predictive values of the model have been determined. For both models, the outer diameter of the column and the relative eccentricity have proved to be the most important features, which is consistent with the existing experience of designing and calculating such structures. Optimisation of the hyperparameters using the Grid Search method enabled getting the improved results. The high accuracy of prediction has been ascertained by the low values of the regression metrics: MSE = 9.024; MAE = 9.250; MAPE = 0.004 — for the model built without taking into account the additional random eccentricity; MSE = 8.673; MAE = 8.673; MAPE = 0.004 — for the model built taking into account the additional random eccentricity.Discussion and Conclusion. The developed Gradient Boosting models for predicting the ultimate loads of the eccentrically compressed short concrete-filled steel tubular (CFST) columns of circular cross-section, both without taking into account and taking into account the additional random eccentricities, have demonstrated high accuracy and stability of prediction, they can be applied for assessing the strength of the columns during design and construction, which will reduce the time and resources involved in physical testing. In the future, it is planned to expand the data range by including other materials, different cross-section geometries of the columns and a slenderness parameter, which may improve the generalization ability of the model.

Atlantis end-to-end modeling to explore ecosystem dynamics in the Strait of Sicily, Central Mediterranean Sea

This paper describes the first application of the end-to-end model Atlantis in the Strait of Sicily (SoS). The model is designed to simulate ecosystem dynamics under the influence of fishing activities. Model performance was evaluated by comparing predicted biomass and catch of target species against observed data, utilizing multiple quantitative metrics. It reproduces accurately trophic dynamics, and biomass and catch of main target species in the SoS. Sensitivity analysis of the key model parameters identified nutrient loading and fishing pressure as the major processes influencing the ecosystem trophic spectrum and generating bottom-up and top-down effects. The results also emphasized the importance of incorporating uncertainty estimation in model predictions and robust selection of key parameters. The SoS Atlantis represents a strategic tool for the application of Ecosystem Approach to Fisheries Management (EAFM) in the Strait of Sicily and for assessing the effect of alternative management scenarios under a holistic framework.

Bayesian inference of coupled groundwater flow and radiogenic helium-4 production and transport at the catchment scale

Hydrogeological numerical models are essential for assessing radioactive waste disposal by understanding groundwater flow systems. These models typically rely on hydraulic head data, with other state variables often underutilized in model inversions. In Flanders' Neogene aquifer, where safety studies for Boom Clay are ongoing, existing models face uncertainties due to dependence on hydraulic heads alone. This study aims to: i) develop a conceptual model of radiogenic Helium-4 (4Herad) production and transport to reproduce observed concentrations; ii) evaluate the usefulness of 4Herad observations as an additional state variable for inverse conditioning; and iii) assess how including 4Herad affects model calibration and uncertainty reduction. We address these objectives by incorporating radiogenic Helium-4 (4Herad) as an unconventional state variable in model inversion to tackle parameter non-uniqueness. Utilizing a Bayesian approach, we employ two 3D models: a groundwater flow (GWF) model conditioned by hydraulic heads and a coupled GWF model with 4Herad production and transport, conditioned by both hydraulic heads and 4Herad concentrations. We hypothesize that integrating 4Herad will improve model accuracy and reduce uncertainties compared to using hydraulic head data alone. Findings indicate that major 4Herad sources include crustal fluxes (~57 %) and in situ Boom Clay production (~25 %). The coupled inference narrows the posterior distributions of parameters, especially for hydraulic conductivity (e.g., Mol, Diest, Berchem formations, and the Rauw Fault) and recharge, showing that 4Herad observations effectively reduce uncertainty beyond hydraulic head data. Including 4Herad improves model reliability and prediction accuracy, highlighting its importance for refining groundwater flow parameters and derived fluxes. To the best of our knowledge, this study marks the first-ever attempt at harnessing 4Herad quantitative insights into flow and transport parameters following a Bayesian approach at a catchment scale, setting a precedent for future research and emphasizing the innovative nature of this work. This study showcases a state-of-the-art approach in hydrogeological modelling, demonstrating the effectiveness of integrating 4Herad with hydraulic head observations to produce more reliable model predictions. By reducing uncertainties in crucial groundwater fluxes, this approach offers significant potential for improving geological disposal safety studies. This pioneering work advocates for the continued use of 4Herad in hydrogeological modelling, emphasizing its innovative contribution to future research and safety studies. Plain language summaryUnderstanding how groundwater flows is crucial for safely storing radioactive waste. Traditionally, models used to study groundwater rely mostly on measurements of water pressure, while other valuable data often go unused. In the Neogene aquifer in Flanders, where researchers are studying the Boom Clay formation for potential waste storage, relying only on water pressure has created uncertainties in the models. This study explores a new approach by using a different type of measurement: radiogenic Helium-4 (4Herad). The goals of the study are threefold: first, to build a model that accurately represents how 4Herad is produced and moves through the groundwater; second, to test whether including 4Herad as an additional measurement improves the model compared to using only water pressure data; and third, to see how using 4Herad affects the accuracy and reliability of the model. The study uses two types of models: one that relies only on water pressure data and another that includes both water pressure and 4Herad measurements. The idea is that 4Herad could provide additional insights and help reduce uncertainties in the model. The findings show that 4Herad comes mainly from two sources: the surrounding crust and the Boom Clay itself. Including 4Herad in the model helps narrow down uncertainties, particularly regarding how water moves through different layers and how much water is being replenished. This research is the first to use 4Herad in this way, using a formal statistical method to improve groundwater models. The results suggest that 4Herad is a valuable addition for making more accurate predictions and improving safety assessments for radioactive waste storage.

Unraveling Chain Branching in Cool Flames.

In cool flames, autoxidation of organic compounds forms alkyl hydroperoxides and ketohydroperoxides, and this controls the critical rate of chain branching, but there have been large uncertainties in the decomposition rate constants. We synthesized a series of hydroperoxides and measured their decomposition rate constants in pyrolysis experiments by spray-vaporization jet-stirred-reactor synchrotron vacuum ultraviolet photoionization mass spectrometry. Structural variation of the hydroperoxides, including alkyl, cycloalkyl, aromatic, and heterocyclic functionalities, has only a slight effect on their decomposition rate constants. Calculated rate constants are in good agreement with the experiment. The rate constant of ketohydroperoxide decomposition was obtained by theoretical calculation of 3-hydroperoxy butanal and tested by the pyrolysis of synthesized 3-hydroperoxy-3-phenylpropionate. The rate constant of ketohydroperoxide decomposition is close to that of alkyl hydroperoxides. The new chain-branching rate constants improves the cool-flame kinetic model, which is essential for removing discrepancies in model predictions and for the design of high-efficiency and low-emission engines.

PBPK modeling: What is the role of CYP3A4 expression in the gastrointestinal tract to accurately predict first-pass metabolism?

Gastrointestinal first-pass metabolism plays an important role in bioavailability and in drug-drug interactions. Physiologically-based pharmacokinetic (PBPK) modeling is a powerful tool to integrate these processes mechanistically. However, a correct bottom-up prediction of GI first-pass metabolism is challenging and depends on various model parameters like the level of enzyme expression and the basolateral intestinal mucosa permeability (Pmucosa). This work aimed to investigate if cytochrome P450 (CYP) 3A4 expression could help predict the first-pass effect using PBPK modeling or whether additional factors like Pmucosa do play additional roles using PBPK modeling. To this end, a systematic review of the absolute CYP3A expression in the human gastrointestinal tract and liver was conducted. The resulting CYP3A4 expression profile and two previously published profiles were applied to PBPK models of seven CYP3A4 substrates (alfentanil, alprazolam, felodipine, midazolam, sildenafil, triazolam, and verapamil) built-in PK-Sim®. For each compound, it was assessed whether first-pass metabolism could be adequately predicted based on the integrated CYP3A4 expression profile alone or whether an optimization of Pmucosa was required. Evaluation criteria were the precision of the predicted interstudy bioavailabilities and area under the concentration-time curves. It was found that none of the expression profiles provided upfront an adequate description of the extent of GI metabolism and that optimization of Pmucosa as a compound-specific parameter improved the prediction of most models. Our findings indicate that a pure bottom-up prediction of gastrointestinal first-pass metabolism is currently not possible and that compound-specific features like Pmucosa must be considered as well.

Fast and simultaneous prediction of inner quality parameters on intact mangos by near infrared spectroscopy: Impact of spectra pre-processing on prediction accuracy

Near infrared spectroscopy or known as NIRS has been widely employed in many fields including agriculture, especially for sorting and grading of agricultural products. Spectra pre-processing is one of the main factors affecting model accuracy and prediction capabilities of NIRS. The objective of the present study was to study the impact of different spectra corrections namely mean centering (MC), mean normalization (MN), de-trending (DT), multiplicative scatter correction (MSC), standard normal variate (SNV) and orthogonal signal correction (OSC), to the prediction accuracy of quality parameters: titratable acidity (TA) and soluble solids content (SSC) in intact mango. A total of 91 mango samples (cv. Kent) were used as dataset for calibration and external prediction which was separated by means of systematic sampling based on a property (SSBP) approach. Diffuse reflectance spectra (log1/R) were acquired and recorded in wavelength range of 1000 – 2500 nm by Antaris Fourier transform NIR instrument. Judging from calibration and prediction performance, MSC found to be the best spectra pre-processing method prior to prediction model development with R2 prediction are 0.72 for TA and 0.76 for SSC. Although MSC increase the prediction performances based on R2, RMSE, RPD and RER metrics compared to the baseline, the achieved RPD, 1.9 for TA and 1.8 for SSC of this findings are still poor and need improvements to achieve even higher levels of accuracy and reliability necessitates for real-time applications.

Utilizing machine learning algorithm and Carrera unified formulation for vibration analysis of nanocomposite-enhanced bridge structures rested on an innovative elastic foundation: verification of the results via nondestructive testing

This paper presents an advanced vibration analysis of Al2O3 nanocomposite-reinforced concrete bridge structures resting on an innovative elastic foundation using the Carrera Unified Formulation (CUF). The primary objective is to investigate the dynamic response of these bridges under various loading conditions, accounting for the reinforcing effects of Al2O3 nanocomposites within the concrete matrix. The formulation incorporates a novel elastic foundation model designed to more accurately simulate realistic boundary conditions and soil-structure interaction. The accuracy and reliability of the CUF-based vibration analysis are further validated using nondestructive testing (NDT) techniques, which enable the detection of potential damage and anomalies in the bridge structures. Moreover, a machine learning (ML) algorithm is employed to predict the vibrational characteristics, facilitating an efficient comparison with the results obtained from CUF and NDT. The combination of theoretical modeling, experimental verification, and ML predictions highlights the robustness of the proposed method. The results demonstrate the effectiveness of using Al2O3 nanocomposites to enhance the mechanical properties of bridge structures, improving their vibrational performance, stability, and longevity. This study provides a comprehensive framework for future applications in bridge engineering, combining high-fidelity numerical methods with state-of-the-art testing and computational techniques.

Machine learning derived model for the prediction of bleeding in dual antiplatelet therapy patients.

This study aimed to develop a predictive model for assessing bleeding risk in dual antiplatelet therapy (DAPT) patients. A total of 18,408 DAPT patients were included. Data on patients' demographics, clinical features, underlying diseases, past history, and laboratory examinations were collected from Affiliated Dongyang Hospital of Wenzhou Medical University. The patients were randomly divided into two groups in a proportion of 7:3, with the most used for model development and the remaining for internal validation. LASSO regression, multivariate logistic regression, and six machine learning models, including random forest (RF), k-nearest neighbor imputing (KNN), decision tree (DT), extreme gradient boosting (XGBoost), light gradient boosting machine (LGBM), and Support Vector Machine (SVM), were used to develop prediction models. Model prediction performance was evaluated using area under the curve (AUC), calibration curves, decision curve analysis (DCA), clinical impact curve (CIC), and net reduction curve (NRC). The XGBoost model demonstrated the highest AUC. The model features were comprised of seven clinical variables, including: HGB, PLT, previous bleeding, cerebral infarction, sex, Surgical history, and hypertension. A nomogram was developed based on seven variables. The AUC of the model was 0.861 (95% CI 0.847-0.875) in the development cohort and 0.877 (95% CI 0.856-0.898) in the validation cohort, indicating that the model had good differential performance. The results of calibration curve analysis showed that the calibration curve of this nomogram model was close to the ideal curve. The clinical decision curve also showed good clinical net benefit of the nomogram model. This study successfully developed a predictive model for estimating bleeding risk in DAPT patients. It has the potential to optimize treatment planning, improve patient outcomes, and enhance resource utilization.

LHC di-lepton searches for Z′ bosons which explain measurements of b → sl+l− transitions

Several current measurements of b→sl+l− processes are in tension with Standard Model predictions, whereas others (e.g. RK and RK⁎) are in reasonable agreement with them. We examine some recent Z′ models that fit the data as a whole appreciably better than does the Standard Model, confronting the models with ATLAS resonance searches in the e+e− and μ+μ− channels. The models are constrained by both channels; we find that the searches rule out less than one fifth of the allowed range of MZ′. We estimate that the HL-LHC can roughly double the current sensitivity in MZ′, but to cover the whole of the parameter space would take a more powerful machine, such as a 100 TeV pp collider.

Statistical Evaluation of Flotation Behavior of Chalcopyrite in the Presence of SIPX and Acetoacetic Acid n-Octyl Ester as a Novel Collector Blend: A Sustainable Approach

The chalcopyrite deposit in Malanjkhand, India, is the largest copper ore producer in the country. However, its flotation performance has room for improvement. This study used standard flotation experiments using a mechanical flotation cell using a conventional collecting reagent and a collector blend consisting of xanthate and ester. A three-factor, three-level Box-Behnken design was used to statistically evaluate the experimental design. The obtained data were analyzed using an ANOVA, cubic plots, and response surface methods. The goal was to evaluate the effects and interactions of three key process parameters in chalcopyrite flotation: the dosages of sodium silicate (depressant), sodium isopropyl xanthate (collector), and acetoacetic acid n-Octyl ester (co-collector/modifier). The results implied that introducing acetoacetic acid n-Octyl ester along with minor tweaks in the dosages of sodium isopropyl xanthate helped increase copper grades by at least 15%, with good recovery percentiles. Among the three parameters tested, the copper’s grade and recovery were considerably positively influenced by the AoE dosage in the collector blend. Employing 0.006 kg/t sodium silicate and 0.0065 kg/t sodium isopropyl xanthate with 0.005 kg/t of acetoacetic acid n-Octyl ester, an optimum copper recovery of 88.87% could be achieved. However, with a sodium silicate dosage of 0.0048 kg/t, a SIPX dosage of 0.008 kg/t, and an AoE dosage of 0.005 kg/t, optimized copper grade (1.55%) could be achieved, which is a 78.1% increase from the feed sample grade. To validate the expected results, verification experiments were carried out, and the experimental findings were found to be on at par with the statistical model predictions. Furthermore, the calculated SPI value of 0.0000147cap Kg−1 for a global index per resident lies between 0 and 1.

A regional ocean–atmosphere coupled model using CMA-TRAMS and LICOM: preliminary results for tropical cyclone gale prediction over the northern South China Sea

This paper provides a comparative analysis of the performance of a high-resolution regional ocean–atmosphere coupled model in predicting tropical cyclone (TC) gales over the northern South China Sea. The atmosphere and ocean components of the coupled system are represented by the China Meteorological Administration's Tropical Regional Atmosphere Model for the South China Sea (CMA-TRAMS) and the LASG/IAP Climate system Ocean Model (LICOM), respectively. The Ocean Atmosphere Sea Ice Soil Version 3 (OASIS3) software has been utilized for the exchange of momentum, heat and freshwater fluxes between these two components. An assessment of the coupled model's three-day predictions for five TCs’ gales was conducted. Preliminary findings indicate that the predicted TC tracks show less sensitivity to oceanic influences than the predicted TC intensities. Significant improvement in predicting the surface TC gales has been achieved through coupling the ocean model. This improvement is attributed to the impact of the warmer ocean's effect on TC intensification, counteracting the cooling effect of the cold wake. In summary, coupling has enhanced the model's predictive capabilities for TC gales. A detailed assessment of the coupled model's performance in predicting other tropical weather phenomena is forthcoming.