Modeling Approaches Research Articles

Evaluating different CFD surrogate modelling approaches for fast and accurate indoor environment simulation

Indoor Environment Quality (IEQ) holds significant importance in building design and operation, and the simulation of indoor environments playing a crucial role in enhancing IEQ. Although Computational Fluid Dynamics (CFD) has been widely employed for simulating building environments, it is computationally demanding, particularly for large spaces. To tackle this challenge, we conducted a systematic evaluation of three surrogate models for accelerating CFD: Proper Orthogonal Decomposition (POD), Artificial Neural Networks (ANN), and a combined POD-ANN approach. Our evaluation criteria focused on assessing the model accuracy, the model size, computational time and extrapolation ability. A validated CFD case model and a real campus building are employed for model evaluation. The findings demonstrate that the top five modes can reconstruct the original data matrix accurately, and the POD-ANN significantly reduces model complexity and computation time by reducing the number of parameters in the neural network, the POD-ANN parameters is only 0.14 % of the ANN, and computation time is reduced by 63 %. In addition, the combination with ANN helps increase the extrapolation ability of POD significantly. In conclusion, this research proves that the POD-ANN can enhance the efficiency of CFD calculations with the advantages of both ANN and POD. By applying the POD-ANN to predict indoor temperature, we achieve faster predictions without compromising model accuracy, and an excellent extrapolation ability is achieved. This approach also reduces model complexity, highlighting its practical value for indoor environment prediction, particularly for large and complicated spaces.

Modeling the Potential Habitat Gained by Planting Sagebrush in Burned Landscapes

Many revegetation projects are intended to benefit wildlife species. Yet, there are few a priori evaluations that assess the potential efficiency of restoration actions in recovering wildlife habitats. We developed a spatial vegetation–habitat recovery model to gauge the degree to which field planting strategies could be expected to recover multi-factor habitat conditions for wildlife following wildfires. We simulated a wildfire footprint, multiple sagebrush (Artemisia spp.) planting scenarios, and tracked projected vegetation growth for 15 years post-fire. We used a vegetation transition framework to track and estimate the degree to which revegetation could accelerate habitat restoration for a Greater sage-grouse (Centrocercus) population within the Great Basin, western United States. We assessed the amount of habitat 15 years post-fire to estimate the degree to which revegetation could be expected to accelerate habitat restoration. Our results highlight a potential disconnect between the expansive areas required by wide-ranging wildlife such as sage-grouse and the relatively small areas that planting treatments have created. Habitat restorations and planting strategies that are intended to benefit sage-grouse may only speed up localized habitat restoration. This study provides an example of how linked revegetation–habitat modeling approaches can scope the expected return on restoration investment for habitat improvements and support the strategic use of limited restoration resources.

Comparison of predictive modeling approaches to estimate soil erosion under spatially heterogeneous field conditions

The accuracy of soil erosion models in agroecosystems with heterogeneous field conditions is challenging due to uncertainties from soil water fluxes and crop growth. In this study, we coupled two modeling methods (Freebairn and Rose) to represent soil erosion with a process-based crop and runoff models within the SIMPLACE framework. Their accuracy was compared to a statistical model developed using 16 erosion plots (each of 625 cm2) within the same field. Uncertainty analysis showed that runoff and slope angle were the most critical components for predicting sediment yield in both models, followed by soil erodibility in the Freebairn model and entrainment efficiency in the Rose model. However, due to plot size constraints, slope-length effects were not examined. The Freebairn model had a slightly higher accuracy (RMSE = 0.69 t ha−1 d−1) of sediment yield predictions than the Rose model (RMSE = 0.83 t ha−1 d−1). Both models are effective for predicting soil loss with appropriate parameter values.

Application of Bayesian Decision Analysis to Estimate Occupational Noise Levels from Limited Exposure Data

In recent years, Bayesian statistical modeling approaches have emerged as valuable tools in Industrial Hygiene (IH), particularly in situations with limited exposure data. This paper delves into the application of Bayesian Statistical Models, specifically Bayesian Decision Analysis (BDA), to analyze occupational noise exposure data within two Similar Exposure Groups (SEGs) of groundskeeping workers, addressing the pervasive issue of high noise levels exceeding 85 dBA. The primary aim is to estimate exposure levels based on the location of the exposure profiles for the SEGs within the AIHA® Exposure Control Categories (ECCs) 0-4, later modified and expanded to AIHA-ECC 0-5. This study employs BDA to estimate noise exposures among groundskeepers, emphasizing the scarcity of literature on BDA in noise exposure rating. Groundskeepers, facing potential risk of hearing impairment due to prolonged exposure, constitute a crucial and critical demographic, with nearly one million individuals in the United States alone. Noise data from the two SEGs was analyzed by using the Exposure Assessment Strategies Committee—EASC- AIHA® IHData Analyst for BDA modeling, complemented with the AIHA IHSTAT™ for certainty rating. The resulting posterior graphs were utilized to categorize exposure profiles into respective AIHA ECCs relative to noise OEL. These findings provide valuable insights, indicating a posterior probability of approximately 43.5% and 56.5% with 95% confidence that noise exposure levels fall within AIHA ECC 3 and AIHA ECC 4, respectively. However, while the 95% percentile decision statistics suggest exposure values lower than the OEL, the Upper Tolerance Limit (UTL) significantly surpass the OEL indicating low certainty regarding these exposure values for employees. Notably, the UTLs suggest that the SEGs are experiencing noise levels above the legally required 90 dBA for more than 5% of the time, which is an unacceptable scenario. Recommendations include implementing protective strategies such as regular training, surveillance, and monitoring, alongside engineering control strategies and mandatory use of personal protective equipment. These results underscore the critical need for refining judgments about noise exposures based on variability within and between employees, suggesting the use of additional statistical tools such as ANOVA (single-multivariate), MATLAB, and Python.

Coupled modeling and simulation of tank self-pressurization and thermal stratification

Numerical analysis of thermo-fluid dynamics for cryogenic propellant storage primarily consists of nodal and computational fluid dynamics (CFD) modeling approaches. While nodal approaches prioritize faster computation over fidelity, CFD approaches promise accuracy and fidelity at the expense of significant computational resources. This paper presents an efficient coupling methodology developed to facilitate the simulation of a long-term self-pressurization process of a cryogenic propellant tank as well as associated thermal stratification phenomenon under both normal and reduced gravity conditions. Key highlight of the methodology is the development of a coupling scheme based on a domain decomposition approach that separates the tank into two computational regions at the liquid-vapor interface. SINDA/FLUINT, the nodal code, is utilized to simulate the liquid region, while ANSYS Fluent, the CFD code, handles the vapor region. A data exchange algorithm was developed to compute required boundary conditions for each region using the local thermodynamic properties of assumed infinitely thin interface. The coupling between the nodal and CFD codes was demonstrated by simulating a self-pressurization process in a small size tank using hydrogen as the working fluid. A sensitivity study on the grid sizing and coupling time step was conducted to determine appropriate spatial and ensure a divergent-free explicit data exchange time step size. Using the coupling approach, two numerical case studies were performed to study tank self-pressurization by considering two initial hydrogen fill levels. The effectiveness and efficiency of the coupling methodology was assessed by comparing the temperature and pressure evolution results of the coupled simulation with those obtained solely from the CFD simulation. Results showed that the coupling scheme and data exchange algorithm were successfully implemented, and the coupled simulation agreed well with the CFD simulations. In addition, a stratification number was used to characterize the transient dynamic development of the thermal stratification. Lastly, the computational costs associated with both approaches were compared. Significant reductions in simulation time were achieved for both cases.

Wobble Board Performance: A Practical and Useful Quantification in Balance Assessment

Balance is integral in ankle injury prevention and therapy, especially in high-risk sports like volleyball. For balance assessment, the recommended wobble board (WB) performance (i.e., time at equilibrium) has never been compared with the gold standard. The objective was to investigate the relationships of force-plate-derived center of pressure (CoP) with WB performance and the accuracy of WB-derived CoP estimates. Twelve high-level volleyball players completed six unipedal standing trials on a computerized WB. WB tilt angles and CoP were obtained simultaneously via tri-axis accelerometers on the WB (200 Hz) and a force plate (1000 Hz), respectively. WB performance, polynomial-transformed CoP estimates, and CoP fractal sway, sway area, and mean sway velocity were assessed via Pearson and concordance correlation, root mean square errors, and dependent t-tests. WB performance was related with CoP sway and sway area (|rlinear| = 0.714–0.842, |rnonlinear| = 0.833–0.910, p < 0.01). The strongest concordance (0.878–0.893, p < 0.001) and smallest errors (6.5–10.7%) were reported for anterior–posterior sway and sway area. Moderate to excellent relationships between the WB performance and force plate CoP variables supported the usefulness of WB performance and estimates (especially sway area) in balance assessment. Furthermore, this study presents recommendations for future analyses and modeling approaches to reflect the complexity of postural control.

How does energy modelling influence policymaking? Insights from low- and middle-income countries

Energy system models are widely used to explore, analyse and plan energy futures and sustainable transitions. These models, typically developed in high-income countries, have more recently been applied in low- and middle-income countries (LMICs). As a result, the role that models play in informing decisions in such contexts lacks adequate exploration. Drawing on 35 qualitative interviews with energy system modellers and policymakers, this research examines the experiences of developing and using energy system models that support decision-making in LMICs. The findings suggest that many conventional modelling approaches do not account for the political economy influences and developmental challenges specific to LMICs, with implications for modelling processes and outcomes. The interviews highlighted the need to understand the roles played by diverse stakeholders in shaping modelling processes, as well as the communication, interpretation and use of energy models. This indicates that, particularly in modelling projects which bring in external modellers, a good understanding of the country context is essential to design appropriate model scenarios and for their interpretation in policymaking. Finally, more in-country capacity is needed to foster local ownership of modelling projects.

Machine learning driven performance enhancement of perovskite solar cells with CNT as both hole transport layer and back contact

We propose a machine learning-based approach to enhance the performance of perovskite solar cells (PSCs) using carbon nanotubes as both hole transport layer (HTL) and back contact. Carbon-based PSCs offer low fabrication costs, long-term mechanical stability, high charge transport, and broad wavelength transparency. Our study estimates and enhances power conversion efficiency (PCE) of carbon-based PSCs through machine learning. PSC with single-walled carbon nanotube (SWCNT)-based HTL and back contact is investigated in simulations using SCAPS-1D and achieved 14.24% PCE, closely aligning with experimental data. Additionally, we explore the impact of various easily tunable fabrication parameters, such as the band gap and electron affinity of SWCNT in HTL, HTL thickness, active layer thickness, electron transport layer (ETL) thickness, HTL dopant concentration, absorber defect density, and ETL dopant concentration, on PCE. We generate a dataset of 20,480 samples to train classical and neural network-based machine learning models to predict performance parameters of PSCs, including short-circuit current density (JSC), open-circuit voltage (VOC), fill factor (FF), and PCE (η). The current density vs. voltage (J-V) characteristics curve is well-fitted by the predicted JSC, VOC, and FF, as evidenced by an average root mean squared error (RMSE) of 0.03 and a goodness of fit (R2) value of 0.99. The fully connected artificial neural network performs best, with RMSE of 0.05% and R2 of 0.99998 for predicting PCE. Genetic algorithm is used to optimize PCE, yielding a maximum PCE of 20.92%. Furthermore, an analysis of feature dimension and dataset size on prediction performance guides future modeling approaches.

Make an impact: going full circle together

ABSTRACT Sport diversification provides opportunities for individuals to develop physical literacy, establish a growth mindset, become more agile in varied environments, and develop robust strategies to improve performance. One could say the same for biomechanists, who study the control and dynamics of human movements in the context of sport. Through the lens of sport, we have focused on the ongoing interaction between the nervous system, musculoskeletal system, and the environment by using integrated experimental and modelling approaches to study well-practiced, goal-directed tasks in controlled laboratory and realistic field settings. By integrating multiple sources of information in real time to provide timely, relevant, usable, and easy to understand (TRUE) feedback during skill acquisition, we have found these resources also support learning and opportunities for self-discovery of proficiencies by coaches and athletes. Managing multimodal data acquired with emerging technological advances has also benefited from the use of FAIR data management principles, where data are findable, accessible, interoperable, and reusable. By listening, clarifying goals, and exploring together with coaches and athletes, we can bridge the gaps between what we know and what we do.

A novel data-characteristic-driven modeling approach for imputing missing value in industrial statistics: A case study of China electricity statistics

As a direct reference tool to reflect the operational status and development level of national industry, industrial statistics hold significant value for numerous systematic studies. Nevertheless, it is crucial to recognize that the quality of these statistics can be compromised by the common occurrence of missing value. This issue poses a substantial challenge for analyzing and utilizing industrial statistics, impeding progress in tasks reliant upon them. Given the severity of the missing value problem in industrial statistical databases and the limitations of existing literatures on missing value imputation in terms of research objects and modeling approaches, this paper proposes a novel missing value imputation modeling approach for single-indicator panels of industrial statistics based on the idea of data-characteristic-driven (DCD). Accordingly, taking the inter-provincial “monthly power generation” data from China as an example, the imputation model was constructed and its validity was tested under different imputed objects (Jiangsu and Jilin), different missing types (continuous and discrete), and different missing rates (5%, 10% and 20%) respectively. The results indicate that the proposed DCD modeling approach in this paper exhibits excellent efficacy. The imputation model, constructed based on the data characteristic of the imputed object, demonstrates clear advantages in handling missing value with different missing types and rates. This is evident in its superior consideration of numerical accuracy, directional accuracy, and imputation stability, resulting in an outstanding comprehensive imputation effect.

New Horizons of Model Informed Drug Development in Rare Diseases Drug Development.

Model-informed approaches provide a quantitative framework to integrate all available nonclinical and clinical data, thus furnishing a totality of evidence approach to drug development and regulatory evaluation. Maximizing the use of all available data and information about the drug enables a more robust characterization of the risk-benefit profile and reduces uncertainty in both technical and regulatory success. This offers the potential to transform rare diseases drug development, where conducting large well-controlled clinical trials is impractical and/or unethical due to a small patient population, a significant portion of which could be children. Additionally, the totality of evidence generated by model-informed approaches can provide confirmatory evidence for regulatory approval without the need for additional clinical data. In the article, applications of novel quantitative approaches such as quantitative systems pharmacology, disease progression modeling, artificial intelligence, machine learning, modeling of real-world data using model-based meta-analysis and strategies such as external control and patient-reported outcomes as well as clinical trial simulations to optimize trials and sample collection are discussed. Specific case studies of these modeling approaches in rare diseases are provided to showcase applications in drug development and regulatory review. Finally, perspectives are shared on the future state of these modeling approaches in rare diseases drug development along with challenges and opportunities for incorporating such tools in the rational development of drug products.

A strategy to detect metabolic changes induced by exposure to chemicals from large sets of condition-specific metabolic models computed with enumeration techniques

BackgroundThe growing abundance of in vitro omics data, coupled with the necessity to reduce animal testing in the safety assessment of chemical compounds and even eliminate it in the evaluation of cosmetics, highlights the need for adequate computational methodologies. Data from omics technologies allow the exploration of a wide range of biological processes, therefore providing a better understanding of mechanisms of action (MoA) related to chemical exposure in biological systems. However, the analysis of these large datasets remains difficult due to the complexity of modulations spanning multiple biological processes.ResultsTo address this, we propose a strategy to reduce information overload by computing, based on transcriptomics data, a comprehensive metabolic sub-network reflecting the metabolic impact of a chemical. The proposed strategy integrates transcriptomic data to a genome scale metabolic network through enumeration of condition-specific metabolic models hence translating transcriptomics data into reaction activity probabilities. Based on these results, a graph algorithm is applied to retrieve user readable sub-networks reflecting the possible metabolic MoA (mMoA) of chemicals. This strategy has been implemented as a three-step workflow. The first step consists in building cell condition-specific models reflecting the metabolic impact of each exposure condition while taking into account the diversity of possible optimal solutions with a partial enumeration algorithm. In a second step, we address the challenge of analyzing thousands of enumerated condition-specific networks by computing differentially activated reactions (DARs) between the two sets of enumerated possible condition-specific models. Finally, in the third step, DARs are grouped into clusters of functionally interconnected metabolic reactions, representing possible mMoA, using the distance-based clustering and subnetwork extraction method. The first part of the workflow was exemplified on eight molecules selected for their known human hepatotoxic outcomes associated with specific MoAs well described in the literature and for which we retrieved primary human hepatocytes transcriptomic data in Open TG-GATEs. Then, we further applied this strategy to more precisely model and visualize associated mMoA for two of these eight molecules (amiodarone and valproic acid). The approach proved to go beyond gene-based analysis by identifying mMoA when few genes are significantly differentially expressed (2 differentially expressed genes (DEGs) for amiodarone), bringing additional information from the network topology, or when very large number of genes were differentially expressed (5709 DEGs for valproic acid). In both cases, the results of our strategy well fitted evidence from the literature regarding known MoA. Beyond these confirmations, the workflow highlighted potential other unexplored mMoA.ConclusionThe proposed strategy allows toxicology experts to decipher which part of cellular metabolism is expected to be affected by the exposition to a given chemical. The approach originality resides in the combination of different metabolic modelling approaches (constraint based and graph modelling). The application to two model molecules shows the strong potential of the approach for interpretation and visual mining of complex omics in vitro data. The presented strategy is freely available as a python module (https://pypi.org/project/manamodeller/) and jupyter notebooks (https://github.com/LouisonF/MANA).

Evaluating the 3D structure prediction tools to identify optimal MEBPVC structure models

Multi-Epitope based Peptide Vaccines Candidates (MEBPVCs) are peptide sequences with the immunogenic epitopes interspersed with linkers, adjuvants and other components. The optimization of one of the components of MEBPVCs, epitope order, resulted in a unique epitope ordered MEBPVC showing improved vaccine potency. In addition to the above combinatorial optimization task, predicting the 3D structure of these MEBPVCs presents a significant challenge owing to the diverse modeling approaches employed by the modeling tools. Here we embarked on evaluating the performance of the threading based ab initio modeling tool: I-TASSER, Large Language Model (LLM) based tool: ESMFold and the MSA based artificial intelligence based tool: AlphaFold2. Our investigation employed RMSD, PEA, SASA, Solubility, Globularity, secondary structure analysis, Docking, MDS and MVP. I-TASSER emerged as the superior performer. The analysis revealed a strong correlation between predicted tertiary structure and secondary structure composition, especially the β-turns and random coils. It appears that accurate secondary structure identification to be a pivotal step, dictating the propagation of accuracy (or inaccuracy). This finding highlights the critical importance of secondary structure prediction/assignment algorithms for enhanced protein structure modeling accuracy especially to integrate predictions where evolutionary information is minimal and or lacking. Small sample size is attributed to the low significance of the parameters though Globularity and SASA have shown good p-values. Overall, the results support the need and utilization of the LLMs in biological research, underscoring their potential to revolutionize protein structure prediction and advance diverse biomolecular applications, accelerating Synthetic Biology research.

Human head-neck model and its application thresholds: a narrative review.

There had been many studies on human head-neck biomechanical models in the last two decades, and the associated modelling techniques were constantly evolving at the same time. Computational approaches had been widely leveraged, in parallel to conventional physical tests, to investigate biomechanics and injuries of the head-neck system in the fields like automotive industry, orthopaedic, sports medicine, etc. The purpose of this manuscript is to provide a global review of the existing knowledge related to the modelling approaches, structural and biomechanical characteristics, validation and application of the present head-neck models. This endeavor aims to support further enhancements and validations in modeling practices, particularly addressing the lack of data for model validation, as well as to prospect the future advances in terms of the topics. Seventy-four models subject to the proposed selection criteria are considered. Based on previously established and validated head-neck computational models, most of the studies performed in-depth investigations of included cases, which revolved around four specific subjects: physio-pathology, treatment evaluation, collision condition, and sports injury. Through the review of the recent 20 years research, the summarized modeling information indicated existing deficiencies and future research topics, as well as provided references for subsequent head-neck model development and application.

Wormhole Geometry Modelling on Carbonate Matrix Acidizing: A Literature Review

Matrix acidizing is a critical well-stimulation technique used to enhance the permeability of carbonate reservoirs by creating channels or "wormholes" through the dissolution of the rock matrix. The efficiency of this process is significantly influenced by the geometry of the wormholes formed. This review paper provides a comprehensive analysis of current research on wormhole geometry modeling in carbonate matrix acidizing, synthesizing findings from experimental studies, analytical models, and numerical simulations. Key factors affecting wormhole geometry, such as acid concentration, injection rate, and rock properties, are discussed. The review highlights the contributions of various modeling approaches in predicting wormhole formation and propagation, emphasizing the importance of accurately capturing the coupled effects of fluid flow, chemical reactions, and rock dissolution. The implications for optimizing acidizing treatments and enhancing hydrocarbon recovery are explored, alongside recommendations for future research. These include the need for field validation, advanced modeling techniques, real-time monitoring, interdisciplinary collaboration, and sustainability considerations. This synthesis aims to provide a foundation for improving the design and execution of matrix acidizing operations in carbonate reservoirs.

Modeling of Future Streamflow Hazards in Interior Alaska River Systems and Implications for Applied Planning

There is a growing need for proactive planning for natural hazards in a changing climate. Computational modeling of climate hazards provides an opportunity to inform planning, particularly in areas approaching ecosystem state changes, such as Interior Alaska, where future hazards are expected to differ significantly from historical events in frequency and severity. This paper considers improved modeling approaches from a physical process perspective and contextualizes the results within the complexities and limitations of hazard planning efforts and management concerns. Therefore, the aim is not only to improve the understanding of potential climate impacts on streamflow within this region but also to further explore the steps needed to evaluate local-scale hazards from global drivers and the potential challenges that may be present. This study used dynamically downscaled climate forcing data from ERA-Interim reanalysis datasets and projected climate scenarios from two General Circulation Models under a single Representative Concentration Pathway (RCP 8.5) to simulate an observational gage-calibrated WRF-Hydro model to assess shifts in streamflow and flooding potential in three Interior Alaska rivers over a historical period (2008–2017) and two future periods (2038–2047 and 2068–2077). Outputs were assessed for seasonality, streamflow, extreme events, and the comparison between existing flood control infrastructure in the region. The results indicate that streamflow in this region is likely to experience increases in seasonal length and baseflow, while the potential for extreme events and variable short-term streamflow behavior is likely to see greater uncertainty, based on the divergence between the models.

Numerical simulation of groundwater in hyporheic zone with coupled parameter stochastic scheme

Groundwater numerical modeling is a crucial scientific tool for understanding groundwater circulation and supporting regional water resource planning and management. The effectiveness of these models depends largely on the accuracy of hydrogeological parameters within aquifers, which are often spatially heterogeneous and randomly distributed due to complex geological and tectonic factors. Traditional modeling approaches frequently overlook this randomness, compromising the precision and resolution of groundwater simulations. This study focuses on a section of the Qingshui River in the Huaihe River Basin. Using field and laboratory data, probability distribution functions for key parameters like hydraulic conductivity, specific yield, and specific storage were developed. These functions were integrated into the groundwater model to reflect the inherent stochastic nature of aquifer properties. This integration significantly enhanced model accuracy, reducing the root mean square error of simulated water levels from 0.47–1.43 m to 0.13–0.16 m and improving the Nash-Sutcliffe efficiency coefficients (NSE) from −2.96–0.73 to 0.94–0.98. Additionally, the model facilitated analysis of the interactions between river and groundwater, particularly in the hyporheic zone, under various scenarios. It identified spatial and temporal variations in groundwater recharge dynamics and delay effects at different distances from the river channel. For instance, recharge rates at 50 m and 150 m from the river were 0.295 m/day and 0.015 m/day, respectively, indicating stronger recharge closer to the river. The study also assessed the impact of varying river flows, riverbed permeability, and irrigation practices on water exchanges between the river and groundwater. These factors were found to significantly influence the intensity of water exchange, seepage, and groundwater reserves. This research provides valuable insights for managing river-groundwater interactions and analyzing the ecological environment of surrounding groundwater systems, underscoring the importance of incorporating stochastic characteristics into groundwater modeling.

Multivariate analysis of metabolic state vulnerabilities across diverse cancer contexts reveals synthetically lethal associations.

Targeting the distinct metabolic needs of tumor cells has recently emerged as a promising strategy for cancer therapy. The heterogeneous, context-dependent nature of cancer cell metabolism, however, poses challenges in identifying effective therapeutic interventions. Here, we utilize various unsupervised and supervised multivariate modeling approaches to systematically pinpoint recurrent metabolic states within hundreds of cancer cell lines, elucidate their association with tumor lineage and growth environments, and uncover vulnerabilities linked to their metabolic states across diverse genetic and tissue contexts. We validate key findings via analysis of data from patient-derived tumors and pharmacological screens, and by performing new genetic and pharmacological experiments. Our analysis uncovers new synthetically lethal associations between the tumor metabolic state (e.g., oxidative phosphorylation), driver mutations (e.g., loss of tumor suppressor PTEN), and actionable biological targets (e.g., mitochondrial electron transport chain). Investigating the mechanisms underlying these relationships can inform the development of more precise and context-specific, metabolism-targeted cancer therapies.

Dynamics of Saltwater Intrusion in a Heterogeneous Coastal Environment: Experimental, DC Resistivity, and Numerical Modeling Approaches

Saltwater intrusion (SWI) is a critical concern affecting coastal groundwater sources due to natural and anthropogenic activities. The health of coastal aquifers is deteriorated by excessive SWI, mainly caused by the disturbance of the freshwater–saltwater equilibrium due to the escalating population, climate change, and the rising demand for freshwater resources for human activities. Therefore, gaining insight into the dynamics of SWI is crucial, particularly concerning the various factors that influence the intrusion mechanism. The present study focuses on the experimental simulation of saltwater in freshwater aquifers, considering boundary conditions and density-dependent effects. Two geological scenarios within coastal environments were investigated: First, a uniform, homogeneous case consisting of only sand, and second, a heterogeneous case in which layers of sand, clay, and sand mixed with pebbles are used. During the experiment, DC resistivity sounding data, as part of a widely recognized geophysical method, were collected and subsequently inverted to determine the depth of the freshwater–saltwater interface (FSWI). A finite element analysis was employed to generate numerical models based on experimental feedback. Further, for validation purposes, electrical resistivity tomography (ERT) data were collected from two distinct locations: near the seacoast and an aquaculture area. The ERT results show the presence of salinity intrusion in the study area, attributed mainly to groundwater overpumping and fish farming practices. The experimental findings indicate that the advancement of saltwater is affected by the geological properties of the media they traverse. The porosity (ϕ) and permeability (k) of the geological layer play a crucial role during the passage of saltwater flux into freshwater aquifers. The FSWI deviated along the clay boundary and hindered the easy passage of saltwater into surrounding layers. The alignment of experimental, numerical, and geophysical data suggests that this integrated approach could be valuable for studying SWI and can be applied in different geological settings, including tidal flats and alluvial plains.

Random forest with feature selection and K-fold cross validation for predicting the electrical and thermal efficiencies of air based photovoltaic-thermal systems

The recent global energy transition policies emphasize the utilization of renewable sources as a primary energy solution. Solar energy systems, in particular, offer significant advantages in terms of cost-effectiveness and environmental friendliness. This work focuses on the modeling of a photovoltaic-thermal (PV/T) air-based system using machine learning (ML) techniques. The primary contribution lies in estimating the electrical and thermal efficiencies of the PV/T system through the application of the random forest (RF) technique and K-fold cross-validation. The results obtained are notably impressive when compared to the most commonly used techniques reported in the literature: Artificial Neural Networks (ANN), Support Vector Machine (SVM), and linear regression (LR). The assessment demonstrates that Random Forest outperforms these techniques in predicting both Electrical efficiency and Thermal efficiency with R² values of 99.9996 % and 81.2 % respectively, and MAE of 0.0034 and 2.64 respectively. Furthermore, selecting the most important features for electrical efficiency not only reduced the processing time but also improved the performance. The Random Forest model exhibited robust stability and demonstrated strong generalization capabilities, as evidenced by its consistent and promising electrical efficiency performance across a 10-fold cross-validation assessment. The proposed method could be a key solution to accurately model the electrical and thermal performances and serve as an alternative to traditional physical modeling approaches.