Predictive Approach Research Articles

Novel optimized models to enhance performance forecasting of grid-connected PERC PV string operating under semi-arid climate conditions

This study focuses on modeling and forecasting the performance of a grid-connected photovoltaic (PV) string, aiming to enhance the accuracy of output power prediction, which is crucial for the effective management and stability of the electrical grid. While previous research has extensively examined PV modules, there is a notable lack of studies specifically targeting PV string behaviors, especially in harsh climates prevailing in semi-arid regions. This study addresses this gap by investigating the modeling and performance prediction of a Passivated Emitter Rear Cell (PERC) PV string under semi-arid climate conditions using analytical and predictive approaches based on both implicit and explicit models as Single Diode Model (SDM), Das Model (DM), Boutana et al. Model (BM) and Power Law Model (PLM). New mathematical models of DM and BM shape parameters versus temperature and irradiance along with novel analytical formulas giving DM shape parameters as a function of PV metrics have been introduced. The proposed approaches are validated using real measurements of a PERC PV string incorporating 12 JKM405M-72H PV modules operating at Green Energy Park (GEP) research facility in Morocco. The reliability of these approaches is assessed by comparing I-V curves, P-V curves and peak power generated and predicted by SDM, DM, BM and PLM to actual measurements. The comparison reveals that generated and predicted outcomes align well with measured data, with an average value of Normalized Root Mean Square Error (NRMSE) not exceeding 4.16 % for all models throughout the day. The findings indicate that the proposed approaches effectively predict the performance of the PERC PV string, accounting for climatic influences and providing insights into optimizing PV system performance, thereby contributing to improved grid stability in semi-arid regions.

Damage mechanics coupled with a transfer learning approach for the fatigue life prediction of bronze/steel diffusion welded bimetallic material

The bronze/steel diffusion welded (BSDW) bimetallic material is often applied in the rotors of piston pumps to withstand complex alternating loads under high-speed operating conditions. Although diffusion welding is a type of solid-phase welding method to achieve high-quality material connections, the fatigue problems still deserve our attention, especially the very high cycle fatigue (VHCF) and high cycle fatigue (HCF) problems. However, due to the high cost of obtaining data, it is necessary to find an efficient and high-precision fatigue life prediction method for diffusion welded materials with a small sample size. In this study, a novel method continuum damage mechanics − transfer learning method (CDM-TLM) for fatigue life prediction of BSDW material is proposed based on the transfer learning (TL) and continuum damage mechanics − finite element method (CDM-FEM). In comparison with the test results, the predicted values of BSDW material fatigue life all fall within the twice error band of the median values of the test life. The influence of frozen layers during TL and training samples in source and target models on the prediction performance is further discussed. CDM-TLM is an effective life prediction method for high-precision life prediction of BSDW material with a small sample size.

Enhancing transparency in data-driven urban pluvial flood prediction using an explainable CNN model

Mitigating severe losses caused by pluvial floods in urban areas with dense population and property requires effective flood prediction for emergency measures. Physics-based models face issues with low computational efficiency for real‐time flood prediction. An alternative approach for rapid prediction instead of physics-based models is to predict from a data-driven perspective. However, data-driven approaches for urban flood prediction are often perceived as “black box” and might raise concerns. In this study, we propose an explainable deep learning (DL) approach for rapid urban pluvial flood prediction with enhanced transparency using a convolutional neural network (CNN) and the explainable artificial intelligence (AI) framework Shapley additive explanation (SHAP). We process a systematic stepwise feature selection process and establish a CNN model considering topography, drainage networks and rainfall to predict maximum inundation depths. Then, SHAP is applied to provide trustworthy explanations for the decision making in model results. The results show that: 1) Forward selection can offer insights into selecting effective input variables for improved predictions and promote understanding of DL modelling. The spatial pattern of inundation depths predicted by the proposed CNN model shows good agreement with those predicted by the physics-based model, demonstrated by average correlation coefficient (CC) and mean absolute error (MAE) values of 0.982 and 0.021 m, respectively. 2) The CNN model substantially outperforms the physics-based model in computational speed when using the same hardware, achieving speedups of 210 times on GPU and 51 times on CPU in the case study (575167 grid cells, 14.38 km2). Particularly, it can still achieve good performance on a CPU-only standard laptop without high-performance hardware, with only a modest increase in computational time. 3) The SHAP explainable analysis confirms that the CNN model correctly captures the relationships between input variables and water depth, revealing a reasonable decision-making process, enhancing its transparency. The explainable DL approach incorporating SHAP for rapid urban pluvial flood prediction is promising to build trust among stakeholders and provide a trustworthy reference for prompt measures aiming at saving lives and protecting assets during flood emergencies. Additionally, the proposed DL approach can potentially be further expanded to analyze the causes of urban flooding events and serve as a foundation for exploring the transferability of data-driven urban flood prediction, providing benefits for better urban flood risk management.

Multi-scale contrast approach for stock index prediction with adaptive stock fusion

The stock index summarizes the overall movement of a group of stocks, typically calculated as a weighted average of constituent stock prices. Stock Index Prediction (SIP) aids investors in assessing economic performance and making informed decisions. Traditional SIP methods rely heavily on historical index data, neglecting valuable insights from macro-financial indicators and stock prices. However, challenges arise when incorporating these data: (1) Including numerous unfiltered macro-financial indicators introduces noise. (2) Using non-correlated stock prices can be counterproductive. (3) Supervised training on stochastic time series can lead to overfitting. To overcome these issues, we propose a multi-scale contrast approach for stock index prediction with adaptive stock fusion (MCSIP). This method identifies highly correlated macro-financial indicators and stocks, optimizing the model through self-supervised multi-scale contrastive learning. MCSIP relies on contrastive learning in the time series domain to extract robust contextual information from stock index time series, mitigating stochastic data backpropagation and ensuring reliable representation. Experiments on US and Chinese stock datasets demonstrate superior performance compared to existing methods.

Enhancing Arabidopsis thaliana ubiquitination site prediction through knowledge distillation and natural language processing

Protein ubiquitination is a critical post-translational modification (PTM) involved in diverse biological processes and plays a pivotal role in regulating physiological mechanisms and disease states. Despite various efforts to develop ubiquitination site prediction tools across species, these tools mainly rely on predefined sequence features and machine learning algorithms, with species-specific variations in ubiquitination patterns remaining poorly understood. This study introduces a novel approach for predicting Arabidopsis thaliana ubiquitination sites using a neural network model based on knowledge distillation and natural language processing (NLP) of protein sequences. Our framework employs a multi-species “Teacher model” to guide a more compact, species-specific “Student model”, with the “Teacher” generating pseudo-labels that enhance the “Student” learning and prediction robustness. Cross-validation results demonstrate that our model achieves superior performance, with an accuracy of 86.3 % and an area under the curve (AUC) of 0.926, while independent testing confirmed these results with an accuracy of 86.3 % and an AUC of 0.923. Comparative analysis with established predictors further highlights the model’s superiority, emphasizing the effectiveness of integrating knowledge distillation and NLP in ubiquitination prediction tasks. This study presents a promising and efficient approach for ubiquitination site prediction, offering valuable insights for researchers in related fields. The code and resources are available on GitHub: https://github.com/nuinvtnu/KD_ArapUbi.

Predicting creep life of CrMo pressure vessel steel using machine learning models with optimal feature subset selection

The data-driven approach for creep life prediction typically integrates numerous characteristics, including material compositions, manufacturing details, and service conditions, into machine learning models. In this study, a machine learning-based creep life prediction approach with optimal feature subset selection is established for 2.25Cr1Mo pressure vessel steel. Before model training and testing, six critical features that significantly impact the creep life of 2.25Cr1Mo steel are selected, specifically the applied stress, temperature, and chemical compositions consisting of Cr, Ni, Mn, and Mo. Various machine learning algorithms, along with the traditional L–M method, are utilized for model training and performance evaluation. Additionally, the developed models undergo validation using experimental data independent of the training and testing datasets to assess their generalization abilities. The results reveal that, among all tested models, the support vector regression (SVR) model, coupled with the optimal feature subset, demonstrates superior prediction accuracy and generalization capability. Finally, the creep life prediction model exhibiting optimal performance is deployed into a software application, leveraging the Python programming language. This predictor tool facilitates rapid and precise creep life predictions for 2.25Cr1Mo pressure vessel steel, relying solely on a limited amount of input information, and provides a clear and visual presentation of the prediction results.

VIRENO Study: A Novel Approach for Early Outcome Prediction after Kidney Transplantation

VIRENO Study: A Novel Approach for Early Outcome Prediction after Kidney Transplantation

Modeling group-level public sentiment in social networks through topic and role enhancement

Public sentiment within social networks exerts a profound influence on societal dynamics, underscoring the increasing demand for accurate public opinion prediction. Most existing methods predominantly measure sentiment by quantifying user sentiments individually, overlooking group-level factors that crucially contribute to public sentiment. Thus, based on our finding that public sentiment is primarily shaped by user-group interactions and their interplay with evolving topics, we innovatively model the forming process of public sentiment at the group level. In this paper, we propose the Topic and Role Enhanced Group-level Public Sentiment Prediction model (TRESP), capturing the intricate interplay among sentiment, topic, and role. Specifically, an LSTM neural network is firstly leveraged to trace the temporal correlations between topics and sentiment shifts, yielding a topic-informed content sentiment representation. Subsequently, a specially crafted hierarchical attention network captures social and role attributes, representing the overarching social group environment. Finally, we predict future public sentiment by merging the derived group sentiment representation with the group social representation, demonstrating a holistic insight into the sentiment trajectory. Extensive experiments were conducted on two real-world datasets of over 30,000 tweets collected from more than 14,000 users to validate our model. Notably, our model significantly outperforms the state-of-the-art approaches in public sentiment prediction, indicating the importance and effectiveness of encapsulating interactions both within and among user subgroups.

A Novel Deep Learning Model for Drug-drug Interactions.

Drug-drug interactions (DDIs) can lead to adverse events and compromised treatment efficacy that emphasize the need for accurate prediction and understanding of these interactions. In this paper, we propose a novel approach for DDI prediction using two separate message-passing neural network (MPNN) models, each focused on one drug in a pair. By capturing the unique characteristics of each drug and their interactions, the proposed method aims to improve the accuracy of DDI prediction. The outputs of the individual MPNN models combine to integrate the information from both drugs and their molecular features. Evaluating the proposed method on a comprehensive dataset, we demonstrate its superior performance with an accuracy of 0.90, an area under the curve (AUC) of 0.99, and an F1-score of 0.80. These results highlight the effectiveness of the proposed approach in accurately identifying potential drugdrug interactions. The use of two separate MPNN models offers a flexible framework for capturing drug characteristics and interactions, contributing to our understanding of DDIs. The findings of this study have significant implications for patient safety and personalized medicine, with the potential to optimize treatment outcomes by preventing adverse events. Further research and validation on larger datasets and real-world scenarios are necessary to explore the generalizability and practicality of this approach.

Improving heat transfer prediction across catalytic SiC interface through a multiscale framework leveraging machine learning and data fusion techniques

Accurate aerothermal prediction under hypersonic flow conditions is crucial for any thermal protection materials design and engineering. The surface catalytic effect, where dissociated atoms recombine into their molecular forms at the air-solid interface, is an exothermic reaction and plays an important role in high-enthalpy aerodynamic environment prediction. Taking SiC, a widely recognized high-temperature thermal protection material as an example, a multiscale fusion approach for aerothermal prediction is proposed. The approach utilizes reactive molecular dynamics method to construct an interface catalysis model, and the Bayesian maximum entropy to integrate experimental and simulational data into an optimized database. Subsequently, using the radial basis function neural network algorithm, a machine learning-based reaction kinetics model with precise analysis of surface catalysis is trained. The obtained catalytic recombination efficiency is used as a boundary input for computational fluid dynamics simulation, enabling rapid and accurate prediction of hypersonic thermal environment. The result shows that the predicted surface heat flux by this novel approach is consistent with the benchmark studies, but comes with significantly reduced computation time. The stagantion heat flux predictions based on the assumptions of full catalytic wall, finite rate catalytic wall (from the proposed multiscale fusion method), and non-catalytic wall are determined to be 8.65×106 W/m2, 7.51×106 W/m2, and 4.02×106 W/m2, respectively. Comparing these with the wind tunnel benchmark value of 7.48×106 W/m2, the error from the proposed multiscale fusion method is 0.30 %, indicating significant enhancement in prediction accuracy through the proposed upscaling method. The new approach could not only reveal complex exothermic reaction processes at the interface, but also enhance the prediction accuracy at much higher computational efficiency, providing an alternative for multiscale modeling of complex flow and heat transfer at the interface.

Data-driven machine learning approaches for simultaneous prediction of peak particle velocity and frequency induced by rock blasting in mining

The vibrations generated by rock blasting are a serious and hazardous outcome of these activities, causing harmful effects on the surrounding environment as well as the nearby residents. Both the local ecology and human communities suffer from the consequences of these vibrations. Assessing the severity of blasting vibrations necessitates a thorough evaluation of Peak Particle Velocity (PPV) and frequency, which are essential parameters for measuring vibration velocity. Accurate prediction of vibration occurrence is critically important. Therefore, this study employs five machine learning models for predicting the PPV and frequency resulting from quarry blasting. This work compares five machine learning models (XGBoost, Catboost, Bagging, Gradient Boosting, and Random Forest Regression) to choose the most efficient performance model. The performance evaluation of each five machine learning models demonstrates each model achieved a performance of more than 0.90 during the testing phase, there was a strong correlation observed between the actual and the predicted ones. The analysis of performance metrics shows Catboost regression model demonstrate better performance prediction comparing with the other models with R2 = 0.983, MSE = 0.000078, RMSE = 0.008, NRMSE = 0.019, MAD = 0.004, MAPE= 35.197 in the PPV prediction, and R2 = 0.975, MSE = 0.000243, RMSE = 0.015, NRMSE = 0.031, MAD = 0.008, MAPE = 37.281 for the frequency prediction. This study will help mining engineers and blasting experts to select the best machine learning model and its hyperparameters in estimating ground vibration, and frequency. In the context of the mining and civil industry, the application of this study offers significant potential for enhancing safety protocols and optimizing operational efficiency. By employing machine learning models, this research aims to accurately predict and assess ground vibrations with frequency resulting from rock blasting.

A robust approach for bond strength prediction of mortar using machine learning with SHAP interpretability

Application of machine learning (ML) in predicting mortars bond strength contributes to low experimental cost and high accuracy. This study explores the performance of four ML models—Back Propagation Neural Network (BPNN), Support Vector Regression (SVR), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost)—using 304 experimental data points for training and 76 for testing. Among these, the XGBoost model demonstrated the highest predictive accuracy, with R², MAE, and RMSE values of 0.95, 0.06, and 0.13 in the training set, and 0.94, 0.08, and 0.16 in the testing set, respectively. Compared with traditional models, ML approaches, particularly XGBoost, were more effective in providing reliable predictions of bond strength. SHapley Additive exPlanation (SHAP) show that substrate type, ethylene-vinyl acetate (EVA) content and water to binder ratio W/C have the most remarkable effect on bond strength, while the type of cement and admixture shows the least effect. These findings highlight the potential of ML models in optimizing mortar formulations and improving prediction reliability.

Multiple dimensional rheology approach for oral texture prediction of yogurts

Rheological approach has been widely applied to investigate the flow behavior of fluid food for the understanding as well as prediction of oral textural sensation of such products. However, since oral texture sensation is highly dynamic and influenced by multiple contributing factors (i.e. oral temperature, oral processing time, saliva mixing, and varied shear rate), conventional rheological measurements can hardly be precise in predicting oral textural sensation and mouthfeel. Here, we propose a very different rheology approach, which synchronizes four major dimensions/variables in one set of rheological measurements: the oral residence time, the bolus temperature, the saliva ratio, and the oral shear rate. Results indicated that the multiple dimensional rheology approach improved the predictive accuracy of the in-mouth thickness of low-temperature yogurt and provided a new measure for quantifying the mouth-melting of yogurt. It appeared that apparent viscosity at a temperature of 13 °C, a saliva ratio of 20 %, and a shear rate of 1 s-1 gave the most reliable prediction of the in-mouth thickness of yogurt. This novel approach provides a much-improved accuracy of texture prediction which requires a minimal number of rheology tests. We believe that, even though this experimental approach was initially designed for low-temperature yogurt drinks, it is also applicable to the texture and mouthfeel prediction of many other food products.

ADMNet: An adaptive downsampling multi-frequency multi-channel network for long-term time series forecasting

Long-term time series forecasting finds widespread applications in various domains such as energy, finance, and transportation. Decomposing time series into sub-sequences with distinct temporal relationships (periods) for analysis and modeling is an effective approach for long-term time series prediction. However, conventional decomposition techniques often rely on fixed-size parameter kernels for sliding averages, leading to inaccurate and unreasonable captures of the underlying periodicities in the time series. Additionally, it is essential to acknowledge that time series data often exhibits multi-periodic characteristics. In many time series prediction tasks, the value at a future time point is influenced by multiple periods within the time series. To accurately and flexibly capture the periodicities of time series, we propose an enhanced adaptive cyclic feature recognizer to automatically identify the periodic lengths for sliding average parameter kernel sizing. To fully exploit the multiple periodicities inherent in time series, we merge and smooth the sequences of features corresponding to the identified cycles, obtaining two frequency-blended cyclic feature fusion terms. Furthermore, we extract high-frequency random components to preserve finer details. Finally, we individually model the three cyclic feature fusion terms. In summary, we introduce the Adaptive Multi-Frequency Multi-Channel Network (ADMNet) designed to autonomously capture multi-periodic features. Experimental results show that, compared to the current state-of-the-art seven benchmark models, the proposed model achieves an average reduction in mean squared error (MSE) ranging from 6.19% to 22.75% across eight datasets, including ETT, Traffic, and Weather. This indicates that our model delivers superior predictive performance on multiple real-world datasets.

Iterative learning robust security predictive tracking control for small delay batch processes: Deception attacks on unreliable network channel

During the batch process, the need for security control is becoming increasingly urgent with the gradual penetration of network control technology. For small time delay batch processes subject to deception attacks, an iterative learning robust security predictive tracking control approach is developed. Reviewing previous studies for iterative learning control, the issue that the repetitive character of the batch process would mask the characteristics of deception attacks was not considered. Moreover, the gains of iterative learning control law are utilized offline, which makes large deviations for the system state as the operating time increases. This will lead to “excessive-input” conditions for control inputs, which means more consumption of energy and production resources. To address these challenges, we introduce robust security invariant sets to improve the robustness of the system by constraining the system state to be in a safe range. Also, the design of the iterative learning controller incorporates deception attack data for historical batches, which is continuously improved and optimized to withstand deception attacks. Meanwhile, by calculating stability conditions online, the real-time control law gain is obtained, which could make the control inputs adjusted online based on the real-time system’s dynamic characteristics, avoiding excessive inputs and improving the efficiency of energy and resource utilization. Additionally, the stability analysis proves that the system is input to state stability. Ultimately, simulation verifies the effectiveness and feasibility of the developed method.

Vehicle platoon safety considering the vehicle cut-in: A coupled reinforcement learning and model predictive control approach

A longitudinal platoon control method based on Twin Delayed Deep Deterministic Policy Gradient (TD3) and Model Predictive Control (MPC) is proposed to solve the problems of low following efficiency and system instability in longitudinal platoon control. Firstly, Dynamic Bayesian Network (DBN) and Long Short-Term Memory (LSTM) network are introduced to identify the driving behavior of bystanders and derive the MPC objective constraint function containing three indicators of following, comfort and fuel consumption according to the platoon dynamics equation. Secondly, the system prediction model and cost function are introduced into the action-critic network of TD3 to solve the problem of no model training in the traditional TD3 algorithm and to speed up the training speed and accuracy of the network. On this basis, a Bellman equation is proposed to calculate the time-domain difference error and the expected loss function to solve the TD3 network overestimation problem. Finally, the joint simulation platform is built to simulate the platoon driving conditions and compare with the DDPG optimization algorithm and the traditional MPC algorithm respectively. The results show that the improved TD3-MPC algorithm satisfies the constraints and the spacing error is controlled within 0.3 m, and the vehicle speed changes more smoothly in the scenario of speed fluctuation in the front vehicle, and the ride comfort of the platoon is improved, and it has better robustness in the scenario of vehicle cut-in. The experimental results show that when the vehicle speeds are 20, 40, and 60 km/h, compared to MPC, the average spacing errors are reduced by 27.56%, 25.64%, and 28.04%, respectively, and compared to DDPG-MPC, the average spacing errors are reduced by 6.59%, 8.77%, and 7.86%, respectively.

Identification of Potential Feature Genes in CRSwNP Using Bioinformatics Analysis and Machine Learning Strategies.

The pathogenesis of CRSwNP is complex and not yet fully explored, so we aimed to identify the pivotal hub genes and associated pathways of CRSwNP, to facilitate the detection of novel diagnostic or therapeutic targets. Utilizing two CRSwNP sequencing datasets from GEO, differential expression gene analysis, WGCNA, and three machine learning methods (LASSO, RF and SVM-RFE) were applied to screen for hub genes. A diagnostic model was then formulated utilizing hub genes, and the AUC was generated to evaluate the performance of the prognostic model and candidate genes. Hub genes were validated through the validation set and qPCR performed on normal mice and CRSwNP mouse model. Lastly, the ssGSEA algorithm was employed to assess the differences in immune infiltration levels. A total of 239 DEGs were identified, with 170 upregulated and 69 downregulated in CRSwNP. Enrichment analysis revealed that these DEGs were primarily enriched in pathways related to nucleocytoplasmic transport and HIF-1 signaling pathway. Data yielded by WGCNA analysis contained 183 DEGs. The application of three machine learning algorithms identified 11hub genes. Following concurrent validation analysis with the validation set and qPCR performed after establishing the mouse model confirmed the overexpression of BTBD10, ERAP1, GIPC1, and PEX6 in CRSwNP. The examination of immune cell infiltration suggested that the infiltration rate of type 2 T helper cell and memory B cell experienced a decline in the CRSwNP group. Conversely, the infiltration rates of Immature dendritic cell and Effector memory CD8 T cell were positive correlation. This study successfully identified and validated BTBD10, ERAP1, GIPC1, and PEX6 as potential novel diagnostic or therapeutic targets for CRSwNP, which offers a fresh perspective and a theoretical foundation for the diagnostic prediction and therapeutic approach to CRSwNP.

Enhancing Surface Wind Speed and Temperature Prediction Using Surface-Layer Emulator and Transfer Learning

Abstract Accurate prediction of surface wind speed and temperature is crucial for many sectors. Physical schemes in numerical weather prediction (NWP) and data-driven correction approaches have limitations due to uncertainties in parameterization and lack of robustness, respectively. This study introduces a physics-informed data model, PhyCorNet, which combines a deep learning–based physics emulator (PhyNet) and a subsequent correction network (CorNet). PhyNet imitates the revised surface-layer parameterization scheme [default option of the Weather Research and Forecasting (WRF) Model]. CorNet refines predictions by mitigating the difference between PhyNet and observations. PhyCorNet enables gridded forecasts despite the training data being point based. Therefore, PhyCorNet can be regarded as a rediagnostic scheme for surface wind speed at 10 m (WS10) and temperature at 2 m (T2). Compared to WRF, it reduced diagnostic root-mean-square errors in the 24-h forecasts for WS10 and T2 by 40% and 36%, respectively, across China, with unbiased forecasts at almost all sites. PhyCorNet addresses the oversmoothing prediction issue of other deep learning models by providing the ability to represent fine-scale features and perform well in statistically extreme samples. In grid cells without observations for training, PhyCorNet performed much better than WRF, demonstrating the zero-shot learning capability. This study implies that the emulator plus bias correction provided by PhyCorNet could be used as a simple but effective approach to improve the performance of other diagnostic quantities in NWP. Significance Statement We developed a new model called PhyCorNet to improve the surface wind speed and temperature forecasts. Existing weather forecast models have limitations in accurately predicting these variables. PhyCorNet first uses a neural network (PhyNet) to mimic an existing physics-based wind and temperature calculation method. Another neural network [correction network (CorNet)] improves the PhyNet forecasts by comparing them with observations. Across China, PhyCorNet reduced 24-h forecast errors for wind speed by 40% and temperature by 36% compared to a conventional model. It performed well under diverse conditions and overcame the oversmoothing issues faced by other machine learning models. Importantly, PhyCorNet outperformed traditional models in areas without historical observations. We suggest that this new framework can also be used to enhance the forecasts of other atmospheric variables.

A New Approach for Prediction of Foliar Dust in a Coal Mining Region and Its Impacts on Vegetation Physiological Processes Using Multi‐Source Satellite Data Sets

AbstractEstimating foliar dust (FD) is essential in understanding the complex interaction between FD, vegetation, and the environment. The elevated FD has a significant impacts on vegetation physiological processes. The present study aims to explore the potential of multi‐sensor optical satellite data sets (e.g., Landsat‐8, 9; Sentinel‐2B, and PlanetScope) in conjunction with in situ data sets for FD estimation over the Jharsuguda coal mining region in Eastern India. The efficacy of different spectral bands and various radiometric indices (RIs) was tested using linear regression models for FD estimation. Furthermore, the study attempts to quantify the impacts of FD on vegetation's physiological processes (e.g., carbon uptake, transpiration, water use efficiency, leaf temperature) through proxy data sets. The key findings of the study uncovered sensor‐specific and common trends in vegetation spectral profiles under varying FD concentrations. A saturation threshold was observed around 50 g/m2 of FD concentration, beyond which additional FD concentration exhibited limited impact on spectral reflectance. On the other hand, the assessment of FD estimation models revealed distinct performances and shared trends across various satellite sensors. Notably, near‐infrared and shortwave infrared‐1 bands, along with certain RIs, such as the Global Environmental Monitoring Index and the Non‐Linear Index, emerged as pivotal for accurate FD estimation. Besides, the study results revealed that vegetation‐associated carbon uptake experienced a ∼2 to 3 gC reduction for every additional gram of FD per square meter. Moreover, the vegetation transpiration reduction per unit of FD ranged from approximately 0.0005 to 0.0006 mm/m2/day, highlighting a moderate impact on transpiration levels. These findings aid a significant evidence base to our understanding of FD's impact on vegetation physiological processes.

Analysis of health recommendations using longitudinal quality of life data: QoL@TbA - A transformer-based approach.

Objective: Health recommendation systems suggest behavioral modifications to improve quality of life. However, current approaches do not facilitate the generation or examination of such recommendations considering the multifeature longitudinal evolution of behaviors. This paper proposes the use of a deep learning transformer-based model that allows the analysis of recommendations for behavior changes. Methods: We adapted a prediction approach, namely Behavior Sequence Transformer (BST), which analyzes temporal human routines and patterns, generating inductive outcomes. The evaluation relied on a case study that employed the behavioral history and profile of the English Longitudinal Study of Ageing (ELSA) participants (n = 2682), predicting their psychological mood (normal, pre-depressed, depressed) according to input recommendations for behavioral changes. Root mean squared error (RMSE) and learning curves were used to track the recommendation accuracy evolution and possible overfitting problems. Results: Experiments demonstrated lower RMSE values for the multifeature model (0.28/0.03) when compared to its single-feature versions (marital status, 0.59/0.001), (high pressure, 0.357/0.04), (diabetes, 0.36/0.01), (sleep quality, 0.57/0.02), (level of physical activity, 0.57/0.01). Conclusions: The results demonstrate the architecture's capability to analyze multifeatured longitudinal data, supporting the generation of suggestions for concurrent modifications across multiple input features. Moreover, these suggestions align with findings in specialized literature.