Predictive Approach Research Articles

Transfer learning-based confinement strength prediction of concrete confined by FRP transverse reinforcements

Although several confinement strength models have been proposed for concrete confined by fiber-reinforced polymer (FRP) transverse reinforcements, these models show considerable scatter and discrepancies in the estimation of experimental data. Machine learning (ML) techniques may present an alternative prediction approach, but challenges arise due to insufficient experimental data. This study, therefore, develops a transfer learning-based model for predicting the confinement strength of concrete confined by FRP transverse reinforcements. Firstly, a literature review was conducted to collect a target dataset of FRP-confined concrete columns and a source dataset of steel-confined concrete columns used for knowledge transfer. Subsequently, a transfer learning algorithm was proposed to construct an ML confinement strength model for FRP-confined concrete. Finally, the performance of the proposed model was compared to that of six ML models and seven physics-based models. The model evaluation based on one-time random split indicated that the proposed model provided more accurate predictions for the confinement strength than other models considered in this study, achieving an R2 of 0.9089 on the test dataset. Shuffle split evaluation demonstrated that the proposed model exhibited superior stability and robustness compared to other models considered. Based on the proposed transfer learning-based model, the importance of the input parameters was obtained, further confirming the robustness of the proposed model.

Enhancing tuberculosis vaccine development: a deconvolution neural network approach for multi-epitope prediction

Tuberculosis (TB) a disease caused by Mycobacterium tuberculosis (Mtb) poses a significant threat to human life, and current BCG vaccinations only provide sporadic protection, therefore there is a need for developing efficient vaccines. Numerous immunoinformatic methods have been utilized previously, here for the first time a deep learning framework based on Deconvolutional Neural Networks (DCNN) and Bidirectional Long Short-Term Memory (DCNN-BiLSTM) was used to predict Mtb Multiepitope vaccine (MtbMEV) subunits against six Mtb H37Rv proteins. The trained model was used to design MEV within a few minutes against TB better than other machine learning models with 99.5% accuracy. The MEV has good antigenicity, and physiochemical properties, and is thermostable, soluble, and hydrophilic. The vaccine's BLAST search ruled out the possibility of autoimmune reactions. The secondary structure analysis revealed 87% coil, 10% beta, and 2% alpha helix, while the tertiary structure was highly upgraded after refinement. Molecular docking with TLR3 and TLR4 receptors showed good binding, indicating high immune reactions. Immune response simulation confirmed the generation of innate and adaptive responses. In-silico cloning revealed the vaccine is highly expressed in E. coli. The results can be further experimentally verified using various analyses to establish a candidate vaccine for future clinical trials.

Maize kernel damage dynamic prediction in threshing through PSO-LSTM and discrete element modelling

This study presents the development of a maize ear model and a predictive approach for kernel damage in maize threshing, integrating physical simulation and predictive analytics to understand better and forecast threshing-related damage. First, a maize ear model was developed to analyse kernel damage during threshing. Through the angle of repose experiments, the optimal number of spheres for the kernel model was established as 65. Tensile tests were conducted to evaluate the kernel-cob bond strength, revealing an average relative error in the bonding force of 8.71%. Vogel impact energy modelling was applied to the kernel threshing process to determine kernel damage. The correlation between the speed of seed grain movement and the occurrence of damage was analysed by post-processing to identify locations with frequent kernel damage in the drum. In-depth data analysis of kernel damage in the threshing drum further elucidates the inherent relationship between kernel velocity and damage extent. The study then focused on applying neural networks to predict damage rates. The comparative evaluation shows that the PSO-LSTM model has better prediction accuracy than LSTM and RNN models, with the PSO-LSTM network achieving an RMSE of 0.096, a R2 of 99.96%, and a final damage rate of 2.41% in validation tests. Threshing experiments were conducted to verify the model, showing a 1.4% discrepancy between predicted and actual damage rates. This study proposes a kernel damage prediction model and provides new insights and directions for the structural design of threshing drums.

A trajectory data-driven approach for traffic risk prediction: incorporating variable interactions and pre-screening

ABSTRACT Although historical crash data and trajectory data have been widely applied to crash and risk predictions, both types of data have their own limitations. As a solution, this study investigates the impact of the traffic flow parameters and their interaction terms on risk prediction performance, employing a variable pre-screening approach (i.e. Smoothly Clipped Absolute Deviation (SCAD)). A research framework is proposed for more efficient risk prediction, and a detailed case study is further conducted using the proposed approach. In the case study, real vehicle trajectory data from HighD are processed and used, which can be aggregated to extract both traffic flow parameters and corresponding risk data during a specific time interval. As for the risk detection, Time-to-Collision (TTC) index is utilized to identify risky conditions. For different lanes (i.e. inner, middle and outer lanes), the impact of variables, including interaction terms, on risk is explored using the SCAD-logistic models. Furthermore, machine learning methods are employed to compare the risk prediction performance before and after considering interaction terms, as well as before and after variable pre-screening. Finally, the superiority of the machine learning models after SCAD-based variable pre-screening is demonstrated. Results indicate that the interaction terms between traffic flow parameters have significant impacts on the traffic risk. Besides, considering interaction terms and variable pre-screening can improve risk prediction accuracy. Furthermore, the proposed models outperform Random Forest (RF) in terms of predicting traffic risk, achieving a maximum 21.24% accuracy improvement and reducing computational time by up to 31.51%. Findings of this study are expected to contribute to the high-precision prediction of real-time risk in the future.

Freeprotmap: waiting-free prediction method for protein distance map

BackgroundProtein residue–residue distance maps are used for remote homology detection, protein information estimation, and protein structure research. However, existing prediction approaches are time-consuming, and hundreds of millions of proteins are discovered each year, necessitating the development of a rapid and reliable prediction method for protein residue–residue distances. Moreover, because many proteins lack known homologous sequences, a waiting-free and alignment-free deep learning method is needed.ResultIn this study, we propose a learning framework named FreeProtMap. In terms of protein representation processing, the proposed group pooling in FreeProtMap effectively mitigates issues arising from high-dimensional sparseness in protein representation. In terms of model structure, we have made several careful designs. Firstly, it is designed based on the locality of protein structures and triangular inequality distance constraints to improve prediction accuracy. Secondly, inference speed is improved by using additive attention and lightweight design. Besides, the generalization ability is improved by using bottlenecks and a neural network block named local microformer. As a result, FreeProtMap can predict protein residue–residue distances in tens of milliseconds and has higher precision than the best structure prediction method.ConclusionSeveral groups of comparative experiments and ablation experiments verify the effectiveness of the designs. The results demonstrate that FreeProtMap significantly outperforms other state-of-the-art methods in accurate protein residue–residue distance prediction, which is beneficial for lots of protein research works. It is worth mentioning that we could scan all proteins discovered each year based on FreeProtMap to find structurally similar proteins in a short time because the fact that the structure similarity calculation method based on distance maps is much less time-consuming than algorithms based on 3D structures.

Ensemble machine learning prediction of anaerobic co-digestion of manure and thermally pretreated harvest residues

This study aimed to clarify the statistical accuracy assessment approaches used in recent biogas prediction studies using state-of-the-art ensemble machine learning approach according to 10-fold cross-validation in 100 repetitions. Three thermally pretreated harvest residue types (maize stover, sunflower stalk and soybean straw) and manure were anaerobically co-digested, measuring biogas and methane yield alongside eight thermal preprocessing and biomass covariates. These were the inputs to an ensemble machine learning approach for biogas and methane yield prediction, employing three feature selection approaches. The Support Vector Machine prediction with the Recursive Feature Elimination resulted in the highest prediction accuracy, achieving the coefficient of determination of 0.820 and 0.823 for biogas and methane yield prediction, respectively. This study demonstrated an extreme dependency of prediction accuracy to input dataset properties, which could only be mitigated with ensemble machine learning and strongly suggested that the split-sample approach, often used in previous studies, should be avoided.

A novel approach for the lifetime prediction and structural health monitoring of concrete sewer systems exposed to biogenic sulphide corrosion

A novel, combined experimental-modelling approach is presented for the estimation of the corrosion depth, load bearing capacity and lifetime of unreinforced concrete sewer pipes exposed to prolonged biogenic sulphide corrosion. The biogenic sulphide corrosion process is mimicked through two types of representative chemical experiments, namely: (i) long-term experiments performed under moderately controlled pH conditions, where dry-cast concrete cube samples are exposed to monthly refreshed sulphuric acid solutions with initial pH values of 3, 2 and 1 for a period of 12 months, and (ii) short-term experiments carried out under highly controlled pH conditions, in which dry-cast concrete disk samples are subjected to sulphuric acid solutions with almost constant pH values of 2, 1 and 0.5 for a period of two months. By applying X-ray diffraction, optical microscopy, scanning electron microscopy and energy dispersive X-ray spectroscopy analyses, insight into the micro-scale morphology and elemental composition of the corrosion profile is obtained. In addition, the corrosion front and depth characteristics are measured by conducting phenolphthalein tests and analyses of surface colour and texture of macro-scale samples. From the experimental results, the time course of the corrosion depth is calibrated with a model for sulphate attack under a constant pH level. The model formulation is combined with detailed finite element method results from the literature to predict the long-term load bearing capacity of a concrete sewer pipe. The model is subsequently generalised for sulphate corrosion under a varying pH level. When combined with the installation of pH measuring devices on the inside of sewer pipes, the current engineering model may serve as an excellent practical tool for continuously monitoring the structural health and predicting the lifetime of in-situ sewer systems subject to sulphate attack under random acidity fluctuations.

Breaking the Barriers: Machine-Learning-Based c-RASAR Approach for Accurate Blood-Brain Barrier Permeability Prediction.

The intricate nature of the blood-brain barrier (BBB) poses a significant challenge in predicting drug permeability, which is crucial for assessing central nervous system (CNS) drug efficacy and safety. This research utilizes an innovative approach, the classification read-across structure-activity relationship (c-RASAR) framework, that leverages machine learning (ML) to enhance the accuracy of BBB permeability predictions. The c-RASAR framework seamlessly integrates principles from both read-across and QSAR methodologies, underscoring the need to consider similarity-related aspects during the development of the c-RASAR model. It is crucial to note that the primary goal of this research is not to introduce yet another model for predicting BBB permeability but rather to showcase the refinement in predicting the BBB permeability of organic compounds through the introduction of a c-RASAR approach. This groundbreaking methodology aims to elevate the accuracy of assessing neuropharmacological implications and streamline the process of drug development. In this study, an ML-based c-RASAR linear discriminant analysis (LDA) model was developed using a dataset of 7807 compounds, encompassing both BBB-permeable and -nonpermeable substances sourced from the B3DB database (freely accessible from https://github.com/theochem/B3DB), for predicting BBB permeability in lead discovery for CNS drugs. The model's predictive capability was then validated using three external sets: one containing 276,518 natural products (NPs) from the LOTUS database (accessible from https://lotus.naturalproducts.net/download) for data gap filling, another comprising 13,002 drug-like/drug compounds from the DrugBank database (available from https://go.drugbank.com/), and a third set of 56 FDA-approved drugs to assess the model's reliability. Further diversifying the predictive arsenal, various other ML-based c-RASAR models were also developed for comparison purposes. The proposed c-RASAR framework emerged as a powerful tool for predicting BBB permeability. This research not only advances the understanding of molecular determinants influencing CNS drug permeability but also provides a versatile computational platform for the rapid assessment of diverse compounds, facilitating informed decision-making in drug development and design.

Application of a data-driven approach for maximum fatigue damage prediction of an unbonded flexible riser

The fatigue damage prediction is a challenging issue in the design and maintenance phases of unbonded flexible pipes. This arises from the necessity of conducting numerous numerical analyses, encompassing both global performance and local structural assessments, to determine the long-term fatigue damage. To handle the computational-demanding problem for fatigue damage estimation, this work applies an efficient machine learning approach known as the AK-MDAmax approach for maximum fatigue damage estimation of unbonded flexible risers. Kriging surrogate models are employed to predict the short-term distribution of fatigue damage over the sea states. The maximum long-term fatigue damage of the flexible risers is then estimated using the AK-MDAmax approach. The verification process of the AK-MDAmax model unfolds incrementally, testing its feasibility and stability across four different scenarios with varying numbers of considered locations, ranging from 4 to 512. Comparative analyses against numerical results reveal that the AK-MDAmax approach efficiently and accurately estimates maximum long-term fatigue damage across different locations. Remarkably, a 25-fold increase in efficiency is achieved with an error margin of less than 1%. This study demonstrates the substantial potential of the proposed machine learning methodology, offering designers an efficient tool for optimizing flexible risers and mitigating computational costs and time constraints.

Enhancing pneumonia prognosis in the emergency department: a novel machine learning approach using complete blood count and differential leukocyte count combined with CURB-65 score

BackgroundPneumonia poses a major global health challenge, necessitating accurate severity assessment tools. However, conventional scoring systems such as CURB-65 have inherent limitations. Machine learning (ML) offers a promising approach for prediction. We previously introduced the Blood Culture Prediction Index (BCPI) model, leveraging solely on complete blood count (CBC) and differential leukocyte count (DC), demonstrating its effectiveness in predicting bacteremia. Nevertheless, its potential in assessing pneumonia remains unexplored. Therefore, this study aims to compare the effectiveness of BCPI and CURB-65 in assessing pneumonia severity in an emergency department (ED) setting and develop an integrated ML model to enhance efficiency.MethodsThis retrospective study was conducted at a 3400-bed tertiary medical center in Taiwan. Data from 9,352 patients with pneumonia in the ED between 2019 and 2021 were analyzed in this study. We utilized the BCPI model, which was trained on CBC/DC data, and computed CURB-65 scores for each patient to compare their prognosis prediction capabilities. Subsequently, we developed a novel Cox regression model to predict in-hospital mortality, integrating the BCPI model and CURB-65 scores, aiming to assess whether this integration enhances predictive performance.ResultsThe predictive performance of the BCPI model and CURB-65 score for the 30-day mortality rate in ED patients and the in-hospital mortality rate among admitted patients was comparable across all risk categories. However, the Cox regression model demonstrated an improved area under the ROC curve (AUC) of 0.713 than that of CURB-65 (0.668) for in-hospital mortality (p<0.001). In the lowest risk group (CURB-65=0), the Cox regression model outperformed CURB-65, with a significantly lower mortality rate (2.9% vs. 7.7%, p<0.001).ConclusionsThe BCPI model, constructed using CBC/DC data and ML techniques, performs comparably to the widely utilized CURB-65 in predicting outcomes for patients with pneumonia in the ED. Furthermore, by integrating the CURB-65 score and BCPI model into a Cox regression model, we demonstrated improved prediction capabilities, particularly for low-risk patients. Given its simple parameters and easy training process, the Cox regression model may be a more effective prediction tool for classifying patients with pneumonia in the emergency room.

Constructing personalized characterizations of structural brain aberrations in patients with dementia using explainable artificial intelligence

Deep learning approaches for clinical predictions based on magnetic resonance imaging data have shown great promise as a translational technology for diagnosis and prognosis in neurological disorders, but its clinical impact has been limited. This is partially attributed to the opaqueness of deep learning models, causing insufficient understanding of what underlies their decisions. To overcome this, we trained convolutional neural networks on structural brain scans to differentiate dementia patients from healthy controls, and applied layerwise relevance propagation to procure individual-level explanations of the model predictions. Through extensive validations we demonstrate that deviations recognized by the model corroborate existing knowledge of structural brain aberrations in dementia. By employing the explainable dementia classifier in a longitudinal dataset of patients with mild cognitive impairment, we show that the spatially rich explanations complement the model prediction when forecasting transition to dementia and help characterize the biological manifestation of disease in the individual brain. Overall, our work exemplifies the clinical potential of explainable artificial intelligence in precision medicine.

Deep ensemble learning for material removal rate prediction in chemical mechanical planarization with pad surface topography

Chemical mechanical planarization (CMP) stands as a critical process in semiconductor manufacturing, necessitating precise prediction of material removal rate (MRR) to achieve optimal global planarization of material surface step heights. A notable hurdle in MRR prediction lies in the nonlinear pad-wafer interaction stemming from variations in the polishing pad surface. Despite numerous extant studies examining the asperity of the pad surface and the wafer-pad contact, aspects such as slurry lubrication and micro-topographical structure of the pad surface remain unaddressed. This study firstly proposes a deep ensemble learning-based approach for MRR prediction, leveraging the intricate interactions between the overall features of the pad surface and the polishing pressure. We experimentally generated diverse pad surfaces by controlling the process variables governing pad surface topography. The Multi-TabNet model, constructed by independently training multiple TabNet instances with distinct initialization and hyperparameters, furnishes a framework for MRR prediction across a broad spectrum of pad surface features, encompassing pores and cores. The results demonstrate a root mean square error of 80 Å/min, with the error margin reduced by up to 70 % compared to predictions utilizing a single parameter of the pad surface. Furthermore, it exhibits an error improvement exceeding 17 % relative to the existing method solely considering asperity. This study not only imparts insights for addressing MRR variations contingent on pad condition but also facilitates dependable material removal even on variable pad surfaces due to conditioner wear.

A deep learning-aided multi-objective optimization of a downstream process for production of monoclonal antibody products

Monoclonal antibodies (mAbs) have emerged as the dominant class of therapeutic moieties due to their superior therapeutic potential and safety profile. Downstream processing plays a pivotal role in ensuring the purity, yield, and quality of these therapeutic products. The biopharmaceutical industry generates a significant amount of data, making it an attractive target for creating machine learning (ML) and deep learning (DL) applications. This paper reports a DL-based 2D-convolutional neural network (2D-CNN) to predict: (i) Protein A mAb elute concentration, (ii) Cation exchange (CEX) mAb elute concentration, (iii)% Acidic variant, (iv) Aggregate (%), and (v)% Basic variant from process data that is routinely collected during biopharma manufacturing, including Protein A elution UV, CEX elution UV, CEX elution conductivity, and post viral inactivation pH profiles. Further, interpretation of predicted outputs using the SHAPLEY technique and optimization through multi-objective Bayesian optimization methods have been attempted so as to match the biosimilar to the reference molecule with respect to the critical quality attributes. It is observed that the proposed model outperforms other published approaches in prediction. The optimum results were experimentally validated by performing three consecutive runs, and a mean percentage deviation of less than 3% was reported. The proposed approach demonstrates the feasibility of real-time prediction and optimization in biopharmaceutical product manufacturing using CNN-aided modeling and multi-objective weighted sum optimization.

Predicting skin cancer risk from facial images with an explainable artificial intelligence (XAI) based approach: a proof-of-concept study

Efficient identification of individuals at high risk of skin cancer is crucial for implementing personalized screening strategies and subsequent care. While Artificial Intelligence holds promising potential for predictive analysis using image data, its application for skin cancer risk prediction utilizing facial images remains unexplored. We present a neural network-based explainable artificial intelligence (XAI) approach for skin cancer risk prediction based on 2D facial images and compare its efficacy to 18 established skin cancer risk factors using data from the Rotterdam Study. The study employed data from the Rotterdam population-based study in which both skin cancer risk factors and 2D facial images and the occurrence of skin cancer were collected from 2010 to 2018. We conducted a deep-learning survival analysis based on 2D facial images using our developed XAI approach. We subsequently compared these results with survival analysis based on skin cancer risk factors using cox proportional hazard regression. Among the 2810 participants (mean Age=68.5±9.3 years, average Follow-up=5.0 years), 228 participants were diagnosed with skin cancer after photo acquisition. Our XAI approach achieved superior predictive accuracy based on 2D facial images (c-index=0.72, 95% CI: 0.70-0.74), outperforming that of the known risk factors (c-index=0.59, 95% CI 0.57-0.61). This proof-of-concept study underscores the high potential of harnessing facial images and a tailored XAI approach as an easily accessible alternative over known risk factors for identifying individuals at high risk of skin cancer. The Rotterdam Study is funded through unrestricted research grants from Erasmus Medical Center and Erasmus University, Rotterdam, Netherlands Organization for the Health Research and Development (ZonMw), the Research Institute for Diseases in the Elderly (RIDE), the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the European Commission (DG XII), and the Municipality of Rotterdam. G.V. Roshchupkin is supported by the ZonMw Veni grant (Veni, 549 1936320).

Validation of a Natural Language Processing Algorithm using National Reporting Data to Improve Identification of Anesthesia-related ADVerse evENTs: The “ADVENTURE” Study

BackgroundReporting and analysis of adverse events (AE) is associated with improved healthcare learning, quality outcomes, and patient safety. Manual text analysis is time-consuming, costly, and prone to human errors. We aimed to demonstrate the feasibility of machine learning and natural language processing (NLP) approaches for early predictions of adverse events and provide input to direct quality improvement and patient safety initiatives. MethodsWe used machine learning to analyze 9,559 continuously reported AE by clinicians and healthcare systems to the French National Health accreditor (HAS) between January 1, 2009, and December 31, 2020, for a total of 135,000 unique de-identified AE reports. We validated the labeling and determined the associations between different root causes and patient consequences. The model was validated by independent expert anesthesiologists. ResultsThe machine learning and Artificial Intelligence (AI) model trained on 9,559 AE datasets accurately categorized 8800 (88%) of reported AE. The three most frequent AE types were “difficult orotracheal intubation” (16.9% of AE reports), “medication error” (10.5%), and “post-induction hypotension” (6.9%). The accuracy of the AI model reached 70.9% sensitivity, 96.6% specificity for “difficult intubation”, 43.2% sensitivity, and 98.9% specificity for “medication error.” ConclusionsThis unsupervised method provides an accurate, automated, AI-supported search algorithm that ranks and helps to understand complex patterns of risky patient situations and has greater speed, precision, and clarity when compared to manual human data extraction. Machine learning (ML) and natural language processing models can effectively be used to process natural language AE reports and augment expert clinician input. This model can support clinical applications and methodological standards of implementations and be used to better inform and enhance decision-making for improved risk management and patient safety. Trial RegistrationThe study was approved by the ethics committee of the French Society of Anesthesiology (IRB 00010254-2020-20) and the CNIL (CNIL: 118 58 95) and the study was registered with ClinicalTrials.gov (NCT: NCT05185479).

Evaluating the efficacy of univariate LSTM approach for COVID-19 data prediction in Indonesia

The coronavirus disease 2019 (COVID-19) pandemic, originating in 2020, has emerged as a critical global issue due to its rapid and widespread transmission. Indonesia, among the affected nations, has taken measures to address the situation, including the development of a deep learning model for predicting future COVID-19 infection and spread. This predictive tool serves as a valuable reference for the government and stakeholders, aiding them in making informed decisions and implementing appropriate measures to contain the virus. The deep learning model employs the long short-term memory (LSTM) algorithm, chosen for its ability to recognize temporal patterns in the country’s COVID-19 data. The model creation process involves data collection, preprocessing, model architecture planning, modeling, training, and evaluation. Two LSTM models were developed: a univariate and a multivariate model. Following thorough training and evaluation, the univariate model emerged as the superior choice, boasting evaluation metrics of 16.72 for mean absolute percentage error (MAPE) and 66.36 for root mean squared error (RMSE). This model was then deployed on a publicly accessible website, presenting visualizations of past COVID-19 data and predictions of future cases through line graphs. This user-friendly platform enables the public to access and analyze the data easily.

A graph-based approach for traffic prediction using similarity and causal relations between nodes

Accurate traffic prediction is crucial for the development of intelligent transportation systems (ITS) and various smart applications. To achieve this, effectively capturing the complex interplay between spatial and temporal dependencies across traffic nodes is essential. However, existing research often focuses solely on fixed spatial dependencies between neighboring nodes, neglecting the potential influence of distant nodes. This paper addresses these limitations by exploring two key types of relationships among traffic nodes. First, we examine indirect relationships based on similarity, where nodes exhibit similar traffic patterns irrespective of their physical proximity. Second, we investigate direct causal relationships, where traffic conditions at one node are directly influenced by other nodes. Based on these findings, we propose a novel approach named TPSC (Traffic Prediction using nodes Similarity and Causal relations). TPSC incorporates a classifier that utilizes the K-Prototype algorithm to group nodes based on their traffic similarity and nearby points of interest (POIs). Separate models are then trained for each cluster. Our model leverages a spatial module that employs graph convolutional network representation learning alongside transfer entropy to capture causal relationships and dynamic spatial dependencies. Additionally, a temporal module is introduced to capture periodic temporal dependencies across three components: recent patterns, daily patterns, and weekly patterns. Extensive experiments are conducted to evaluate proposed model on four large-scale traffic datasets for multiple traffic characteristics including speed, flow, and travel time. The experimental results demonstrate that TPSC surpasses the performance of the compared baselines, highlighting its superior predictive capabilities in traffic prediction.

Fast Buffet-Onset Prediction and Optimization Method Based on Pretrained Flowfield Prediction Model

A transonic buffet is a detrimental phenomenon that occurs on supercritical airfoils and limits the aircraft operating envelope. Traditional methods for predicting buffet onset rely on multiple computational fluid dynamics simulations to assess a series of airfoil flowfields and then apply criteria to them, which is slow and hinders optimization efforts. This study introduces an innovative approach for rapid buffet-onset prediction. A machine-learning flowfield prediction model was pretrained on a large database and then deployed offline to replace the simulations in the buffet prediction process for new airfoil designs. Unlike using a model to directly predict buffet onset, the proposed technique offers better visualization capabilities by providing users with intuitive flowfield outputs. It also demonstrates superior generalization ability, as evidenced by a 32.5% reduction in the average buffet-onset prediction error on the testing dataset. This method was used to optimize the buffet performance of 11 distinct airfoils within and outside the training dataset. The optimization results were verified with simulations and proved to yield improved samples across all cases. It was affirmed that the pretrained flowfield prediction model can be applied to accelerate aerodynamic shape optimization, but further work is still needed to raise its reliability for this safety-critical task.

Adaptive SARIMA modeling for continuous chamber temperature tracking in ultra-low temperature freezers

Ultra-low temperature (ULT) freezers are vital for storing perishable biological materials, requiring continuous monitoring of chamber thermal conditions to ensure sample integrity. A reliable dynamic data-driven model is key for smart proactive surveillance of the ULT freezers through real-time status tracking. However, the dynamic nature of ULT freezers, influenced by external disturbances, demands adaptive models to sustain performance. Complex models thus face challenges due to time-consuming parameter updates. To ensure model prediction performance while minimising model updating time, this study proposes a Seasonal Autoregressive Integrated Moving Average (SARIMA) model-based approach for short-term ULT freezer chamber temperature prediction. This method solely relies on historical chamber temperature data, eliminating the need for additional measurements. Moreover, two adaptive duty cycle length (DCL) selection strategies and a one-step ahead DCL estimation method are developed to address auto-correlation issues caused by recurring duty cycles. Comparative analyses with reference models demonstrate that the SARIMA model, incorporating future DCL effects, excels in one-step prediction and multi-step forecast under stable and variant DCL periods. Moreover, it outperforms the reference models in auto-correlation representation, affirming its reliability in predicting future states. The proposed modelling approach promises an effective method for freezing chamber temperature prediction, enabling continuous status tracking and early fault detection. Moreover, it holds great potential for modelling other critical temperatures in refrigeration systems and facilitating flexible energy operations.

Dynamic spatial-temporal model for carbon emission forecasting

Addressing the urgent need for accurate carbon emissions forecasting to support global emissions reduction goals, this paper introduces a novel approach for carbon emissions prediction that dynamically considers the spatial–temporal correlation of carbon emissions across forecasting targets. Traditional statistical and machine learning models have limitations, such as oversimplification and inefficiency in capturing evolving dynamics among multiple forecasting targets, alongside a focus on longer forecasting horizons like monthly or yearly. To tackle these, we propose an innovative Dynamic Spatial-Temporal Graph Convolutional Recurrent Network (DSTGCRN), a blend of graph convolutional and recurrent neural network structures customized for up-to-date multi-regional spatial–temporal predictions at a daily level. Comprehensive empirical analyses on datasets from China, the US, and the EU confirm DSTGCRN’s superior performance and robustness across diverse geographical contexts. It outperforms the second-best among 10 baseline models, achieving 40.6%, 24.6%, and 38.5% improvements in MAE, RMSE, and MAPE, respectively, across various horizons. The incorporation of environmental data, including temperature and Air Quality Index, as supplementary predictors in the DSTGCRN model, has demonstrated its efficacy in the context of the China dataset. The DSTGCRN model’s improved accuracy and daily multi-regional forecasting deepen emission dynamics understanding, supporting the development of informed and region-specific environmental policies that can respond promptly to changes.