Modeling Approaches Research Articles

Future directions in geological research impacting renewable energy and carbon capture: A synthesis of sustainable management techniques

Geological research plays a pivotal role in shaping the future of renewable energy and carbon capture initiatives, offering insights into sustainable management techniques. This review synthesizes the future directions in geological research that impact renewable energy and carbon capture, focusing on sustainable management techniques. Future geological research will increasingly focus on enhancing the integration of renewable energy sources into existing energy systems. This includes the development of innovative geological mapping techniques to identify and characterize renewable energy resources with greater precision, aiding in the selection of optimal sites for energy production. Additionally, there will be a growing emphasis on utilizing geological data to assess the feasibility of carbon capture and storage (CCS) projects, particularly in volcanic regions, where the unique geological characteristics offer potential for efficient carbon sequestration. Another key aspect of future geological research is the advancement of monitoring and modeling techniques to evaluate the long-term performance and environmental impact of renewable energy and CCS projects. This includes the use of advanced geophysical and geochemical methods to monitor subsurface changes associated with energy extraction and carbon storage, ensuring the effectiveness and safety of these practices. Furthermore, future research will explore the potential of geological formations, such as deep saline aquifers and depleted oil and gas reservoirs, for large-scale carbon storage. This will involve developing strategies to enhance storage capacity and mitigate the risk of CO2 leakage, contributing to the sustainable management of carbon emissions. In conclusion, future geological research will play a critical role in advancing renewable energy and carbon capture technologies, offering sustainable management techniques that are essential for addressing climate change. By focusing on innovative mapping, monitoring, and modeling approaches, researchers can pave the way for a more sustainable energy future.

Integrating Sustainable Energy Technologies into District Cooling Systems: A Review of Modelling and Optimisation Approaches

With the rising cooling demand and prevalent energy crisis, District cooling (DC) is evolving as one of the sustainable energy solution worldwide. As per ‘EHP DHC Outlook, May 2023’, the sales in DC sector reached 3 TWh, covering more than 150 European cities. The centralised nature of the DC system offer benefits such as energy efficient operations, sector coupling, peak load management. Further, the feasibility of integrating renewable technologies (eg. solar energy), free cooling sources (eg. seawater heatpumps) and thermal energy storage (eg. ice slurry) is also explored. These developments make the futuristic DC system quite complex, as it involves multiple energy transformations. In this regard, utilising computation tools for decision-making has become increasingly relevant. However, choosing the appropriate approach among the existing ones in this evolving field is a curb some task for energy practitioners. The aim of the study is to review the modelling and optimisation approaches in the context of innovative DC systems. Data collection and analysis is based on desk-review of scientific literature published in the recent past. Firstly, various possibilities of integrating sustainable energy technologies for DC applications are explored. Then, relevant modelling and simulation approaches are studied, focussing on the similarities and limitations. Finally, different optimisation methods are examined in terms of parameters such as objective functions, control variables, solving techniques. The contribution of the study involve overview of different integration pathways, identifying the strength and weakness of modelling approaches and insights into suitability of optimisation techniques. It is expected that this study will be a reference material, while performing feasibility studies as well as developing digital-twins in DC context.

A Hybrid Experimental Modelling Approach to Solar Photovoltaic Cell Temperature Prediction

Solar cell temperature is critical in the determination of solar energy generated by a solar photovoltaic power plant. High temperatures are associated with a reduction in the energy generated and hence prediction of photovoltaic cell temperature is essential in temperature mitigation and solar energy forecasting especially in commercial power plants. The present study focused on the development of a machine learning based predictive model for solar photovoltaic cell temperature prediction in commercial solar photovoltaic power plants. A physical experimental set up was developed to measure solar cell temperature under different weather and other related parameters. Satellite data were also collated for those parameters difficult to measure experimentally and were used to compliment experimental data used in this study. Satellite data used in the study were statistically transformed for each parameter used to mimic experimentally measured data. Statistical approaches were adopted to analyse the influence of both the dependent and independent variables on solar cell temperature. The analysis included multicollinearity and correlation analysis with the aid of heat maps meant to establish the relationships among the independent and dependent variables. Feature selection and dimensionality reduction was also performed to reduce the input variables and maintain relevant data in the modelling process. Parameters with a strong correlation to each other had some of them eliminated in the modelling process. A solar cell temperature predictive model based on selected weather parameters was developed using a machine learning approach (Random Forests), and parameters used were selected from the statistical analysis. The prediction accuracy of the developed model was analysed using the coefficient of determination (R2) and the mean absolute percentage error (MAPE). The results indicated a higher model performance compared to generic models used in cell temperature prediction. The prediction MAPE for the developed model was 0.83 °C while an R2 value of 0.93 was obtained which was indicative of a good model. The developed model was also comparable to other contemporary models developed to predict solar photovoltaic cell temperature. Simulations were also done to determine the annual energy generated with the incorporation of the solar cell temperature prediction model. The results revealed a 3.4% difference in the annual energy generated between a simulation which considered solar cell temperature and that which ignored the solar cell temperature. The study also revealed that this difference is even larger in monetary terms when lifetime energy generation is considered. The present study demonstrated that Random Forests, a machine learning approach to predictive modelling, can handle complex models and can provide models with a higher accuracy compared to statistical modelling approaches. The study recommends the use of solar cell predictive models to improve the accuracy of energy prediction on solar photovoltaic power plants which in turn assists in energy planning and deployment.

Enhancing thermal management efficiency in robotics engineering: Exploring mechanisms, techniques, and modeling approaches

Thermal management is pivotal in robotics engineering, ensuring optimal performance and reliability of robotic systems. This comprehensive review explores various heat dissipation mechanisms, cooling techniques, and thermal modeling approaches employed in robotics engineering. Key topics include conduction-based, convection-based, and radiation-based heat transfer mechanisms, alongside liquid cooling, air cooling, and phase-change cooling techniques. Analytical thermal models, computational fluid dynamics (CFD) simulations, and finite element analysis (FEA) modeling are discussed for their roles in predicting thermal behaviors and optimizing heat dissipation strategies. By synthesizing these advancements, this review provides valuable insights into enhancing thermal management efficiency and ensuring the longevity of robotic systems.

Monitoring Land Management Practices Using Vis–NIR Spectroscopy Provides Insights into Predicting Soil Organic Carbon and Limestone Levels in Agricultural Plots

This study aimed to establish sound relationships between soil properties of agricultural land in central Spain and their spectral attributes to contribute to deriving an indicator for sustainable farm management. Sixteen plots, managed under various conditions, eight with traditional tillage and eight with other alternative managements, were selected to gather soil samples representing three predominant soil orders (Leptosols, Cambisols, and Luvisols). Soil sampling was conducted from depths ranging from 0 to 30 cm (0–50; 5–10, 10–20, and 20–30 cm), ensuring a broad spectrum of sample variability across different times and locations. The reflectance of soil 64 soil samples was measured within the range of 400 to 900 nm, and the corresponding concentrations of soil organic carbon and majoritarian minerals, calcium carbonate, quartz, phyllosilicates, K-Feldspar, and plagioclase were determined for each sample. Partial least squares analysis was employed to construct prediction models using a calibration dataset comprising 66% of randomly selected samples. The remaining 33% of samples were utilized for model validation. The prediction models for the measured soil chemical properties yielded R2 values ranging from 0.14 to 0.79. Only SOC and limestone provided accurate prediction models. These findings hold promise for developing a soil health indicator tailored for site-specific crop management. However, the complex composition of soil organic carbon and calcium carbonate in certain soils underscores the importance of careful interpretation and validation of remote sensing data, as well as the need for advanced modeling approaches that can account for the interactions between multiple soil constituents.

Computational modeling of aging-related gene networks: a review

The aging process is a complex and multifaceted phenomenon affecting all living organisms. It involves a gradual deterioration of tissue and cellular function, leading to a higher risk of developing various age-related diseases (ARDs), including cancer, neurodegenerative, and cardiovascular diseases. The gene regulatory networks (GRNs) and their respective niches are crucial in determining the aging rate. Unveiling these GRNs holds promise for developing novel therapies and diagnostic tools to enhance healthspan and longevity. This review examines GRN modeling approaches in aging, encompassing differential equations, Boolean/fuzzy logic decision trees, Bayesian networks, mutual information, and regression clustering. These approaches provide nuanced insights into the intricate gene-protein interactions in aging, unveiling potential therapeutic targets and ARD biomarkers. Nevertheless, outstanding challenges persist, demanding more comprehensive datasets and advanced algorithms to comprehend and predict GRN behavior accurately. Despite these hurdles, identifying GRNs associated with aging bears immense potential and is poised to transform our comprehension of human health and aging. This review aspires to stimulate further research in aging, fostering the innovation of computational approaches for promoting healthspan and longevity.

Agricultural land use modeling and climate change adaptation: A reinforcement learning approach

AbstractThis paper provides a novel approach to integrate farmers' behavior in spatially explicit agricultural land use modeling to investigate climate change adaptation strategies. More specifically, we develop and apply a computationally efficient machine learning approach based on reinforcement learning to simulate the adoption of agroforestry practices. Using data from an economic experiment with crop farmers in Southeast Germany, our results show that a change in climate, market, and policy conditions shifts the spatial distribution of the uptake of agroforestry systems. Our modeling approach can be used to advance currently used models for ex ante policy analysis by upscaling existing knowledge about farmers behavioral characteristics and combine it with spatially explicit environmental and farm structural data. The approach presents a potential solution for researchers who aim to upscale information, potentially enriching and complementing existing land use modeling approaches.

3D Computational Modeling of Defective Early Endosome Distribution in Human iPSC-Based Cardiomyopathy Models

Intracellular cargo delivery via distinct transport routes relies on vesicle carriers. A key trafficking route distributes cargo taken up by clathrin-mediated endocytosis (CME) via early endosomes. The highly dynamic nature of the endosome network presents a challenge for its quantitative analysis, and theoretical modelling approaches can assist in elucidating the organization of the endosome trafficking system. Here, we introduce a new computational modelling approach for assessment of endosome distributions. We employed a model of induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) with inherited mutations causing dilated cardiomyopathy (DCM). In this model, vesicle distribution is defective due to impaired CME-dependent signaling, resulting in plasma membrane-localized early endosomes. We recapitulated this in iPSC-CMs carrying two different mutations, TPM1-L185F and TnT-R141W (MUT), using 3D confocal imaging as well as super-resolution STED microscopy. We computed scaled distance distributions of EEA1-positive vesicles based on a spherical approximation of the cell. Employing this approach, 3D spherical modelling identified a bi-modal segregation of early endosome populations in MUT iPSC-CMs, compared to WT controls. Moreover, spherical modelling confirmed reversion of the bi-modal vesicle localization in RhoA II-treated MUT iPSC-CMs. This reflects restored, homogeneous distribution of early endosomes within MUT iPSC-CMs following rescue of CME-dependent signaling via RhoA II-dependent RhoA activation. Overall, our approach enables assessment of early endosome distribution in cell-based disease models. This new method may provide further insight into the dynamics of endosome networks in different physiological scenarios.

Robust Bayesian nonparametric variable selection for linear regression

AbstractSpike‐and‐slab and horseshoe regressions are arguably the most popular Bayesian variable selection approaches for linear regression models. However, their performance can deteriorate if outliers and heteroskedasticity are present in the data, which are common features in many real‐world statistics and machine learning applications. This work proposes a Bayesian nonparametric approach to linear regression that performs variable selection while accounting for outliers and heteroskedasticity. Our proposed model is an instance of a Dirichlet process scale mixture model with the advantage that we can derive the full conditional distributions of all parameters in closed‐form, hence producing an efficient Gibbs sampler for posterior inference. Moreover, we present how to extend the model to account for heavy‐tailed response variables. The model's performance is tested against competing algorithms on synthetic and real‐world datasets.

Balancing versus modelling in weighted analysis of non-randomised studies with survival outcomes: A simulation study.

Weighting methods are widely used for causal effect estimation in non-randomised studies. In general, these methods use the propensity score (PS), the probability of receiving the treatment given the covariates, to arrive at the respective weights. All of these "modelling" methods actually optimize prediction of the respective outcome, which is, in the PS model, treatment assignment. However, this does not match with the actual aim of weighting, which is eliminating the association between covariates and treatment assignment. In the "balancing" approach, covariates are thus balanced directly by solving systems of numerical equations, explicitly without fitting a PS model. To compare modelling, balancing and hybrid approaches to weighting we performed a large simulation study for a binary treatment and a survival outcome. For maximal practical relevance all simulation parameters were selected after a systematic review of medical studies that used PS methods for analysis. We also introduce a new hybrid method that uses the idea of the covariate balancing propensity score and matching weights, thus avoiding extreme weights. In addition, we present a corrected robust variance estimator for some of the methods. Overall, our simulations results indicate that balancing approach methods work worse than expected. However, among the considered balancing methods, entropy balancing consistently outperforms the variance balancing approach. All methods estimating the average treatment effect in the overlap population perform well with very little bias and small standard errors even in settings with misspecified propensity score models. Finally, the coverage using the standard robust variance estimator was too high for all methods, with the proposed corrected robust variance estimator improving coverage in a variety of settings.

Towards exascale for wind energy simulations

We examine large-eddy-simulation modeling approaches and computational performance of two open-source computational fluid dynamics codes for the simulation of atmospheric boundary layer flows that are of direct relevance to wind energy production. The first code, NekRS, is a high-order, unstructured-grid, spectral element code. The second code, AMR-Wind, is a second-order, block-structured, finite-volume code with adaptive mesh refinement capabilities. The objective of this study is to co-develop these codes in order to improve model fidelity and performance for each. These features will be critical for running ABL-based applications such as wind farm analysis on advanced computing architectures. To this end, we investigate the performance of NekRS and AMR-Wind on the Oak Ridge Leadership Facility supercomputers Summit, using 4 to 800 nodes (24 to 4,800 NVIDIA V100 GPUs), and Crusher, the testbed for the Frontier exascale system, using 18 to 384 Graphics Compute Dies on AMD MI250X GPUs. We compare strong- and weak-scaling capabilities, linear solver performance, and time to solution. We also identify leading inhibitors to parallel scaling.

Linking explainable artificial intelligence and soil moisture dynamics in a machine learning streamflow model

ABSTRACT Machine learning algorithms are increasingly applied in hydrological studies with promising results. However, these algorithms generally lack the ability for easy interpretability of the results by users. In this study, we compare six different explainable artificial intelligence (XAI) algorithms that help understand the effect of input data on the simulation results. The methods are explored on two distinct approaches for streamflow modeling using the long short-term memory (LSTM) model: a single model approach using only meteorological forcing data and a regional approach including also static catchment attributes. To gain further insight into the internal dynamics of the LSTM models, the relationship between cell states and soil moisture is investigated. A strong correlation suggests that the LSTM models inherently capture the concept of soil moisture as a catchment-scale storage mechanism. The XAI methods are applied to derive a timestep of influence, revealing how many days of input data are relevant for the model output. All XAI methods result in similar seasonal patterns in the timestep of influence, suggesting that the methods are comparable. Setting soil moisture dynamics in context to seasonal development of the timestep of influence suggests resetting LSTM as soon as soil moisture saturation occurs.

Long short-term memory (LSTM) neural networks for predicting dynamic responses and application in piezoelectric energy harvesting

Abstract Long Short-Time Memory (LSTM) deep neural networks are capable of learning order dependence in sequence problems and capturing long-term, non-linear temporal dependencies between the input and out of a system. With the long-term vision to model dynamical systems to which analytical or numerical methods are impossible or difficult to apply, this paper presents a study of modeling system dynamics and predicting responses using the LSTM networks, which have demonstrated excellent capability in predicting single-mode responses in a prior study. However, the LSTM network exhibits difficulties in modeling and predicting multi-mode responses accurately. To resolve the multi-mode issue, this paper presents an approach that obtains an equivalent network consisting of a set of sub-networks learned on isolated modes, and demonstrates its effectiveness on a simulated 2-degree-of-freedom (2DOF) mass-spring-damper system of nonlinear Duffing springs. The second part of the paper is focused on the application of the proposed approach in piezoelectric energy harvesting. Experiments are conducted on a harvester subjected to random base-motion excitation and exhibiting nonlinearity in its multi-mode response. Both the direct and mode-separation LSTM modeling approaches are applied to predict the output voltage given a random base-motion excitation. The mode-separation approach outperforms the direct approach significantly, and yields an excellent match between the actual and predicted responses. Specifically, for a test electrical voltage response of RMS value 0.2241 V, the difference between the actual test and predicted responses by using the mode-separation approach has an RMS value of 0.0504 V, compared to 0.1645 V obtained by using the direct LSTM approach. It is also much lower than the RMS value of 0.1835 V obtained by using the attention-based LSTM network, another comparison method. Leveraging a deep learning strategy, the validated approach opens up opportunities for accurately modeling energy harvesting systems of high complexities and/or strong nonlinearities.

Technologically advanced running shoes reduce oxygen cost and cumulative tibial loading per kilometer in recreational female and male runners

Technologically advanced running shoes (TARS) improve performance compared to classical running shoes (CRS). Improved race performance has been attributed to metabolic savings in male runners, but it remains unclear if these same benefits are experienced among females and in recreational runners. The mechanisms behind these benefits are still not fully understood despite the need for optimisation, and their influence on injury mechanisms has not been explored. Here we combined biomechanical, physiological, and modelling approaches to analyse joint mechanics, oxygen uptake, and tibial load in nineteen male and female recreational runners running with CRS and TARS at their individual lactate threshold speed (12.4 ± 1.9 km/h). Oxygen uptake was 3.0 ± 1.5% lower in TARS than in CRS. Ankle dorsiflexion, joint moment and joint power were reduced in TARS compared to CRS at various phases of stance including midstance, while knee joint mechanics were mostly similar throughout. There were no significant differences for tibial bending moment during the stance phase but cumulative tibial damage per kilometre was 12 ± 9% lower in TARS compared to CRS. Our results suggest that running with TARS reduces oxygen cost in recreational female and male runners, which may partly be explained by differences in lower limb joint mechanics. The lower cumulative tibial bone load with TARS may allow runners to run longer distances in this type of shoe compared to CRS.

Enhancing flow rate prediction of the Chao Phraya River Basin using SWAT–LSTM model coupling

Study regionChao Phraya River Basin—a major river with unique characteristics located in Thailand. Study focusThis study sought to simulate the flow rates in the Chao Phraya River Basin, which is a tidal river that poses challenges to traditional modeling approaches. The soil and water assessment tool (SWAT) is a hydrological model extensively employed for simulating flow rates. However, limitations arise in applying the SWAT model to the Chao Phraya River Basin due to its tidal nature, resulting in an unsatisfactory model performance. To address this, a long short-term memory (LSTM) model, i.e., the SWAT–LSTM model, was introduced to complement the SWAT model. New hydrological insights for the RegionThe collaborative coupling of hydrological information derived from the SWAT and LSTM notably enhanced the model performance. This improvement was assessed using the Nash-Sutcliffe efficiency (NSE), demonstrating an increase from 0.13 to 0.72. The incorporation of topographic static data in the coupling model was also investigated to provide the basic characteristics of the basin to the model. The results yielded an NSE exceeding 0.79. The shoreline water level was identified as a crucial input feature for indicating tidal patterns. The findings highlight the effectiveness of coupling the SWAT with LSTM for predicting tidal river flow rates, implying their applicability in similar scenarios across different basins.

A comprehensive review of deep learning in EEG-based emotion recognition: classifications, trends, and practical implications

Emotion recognition utilizing EEG signals has emerged as a pivotal component of human–computer interaction. In recent years, with the relentless advancement of deep learning techniques, using deep learning for analyzing EEG signals has assumed a prominent role in emotion recognition. Applying deep learning in the context of EEG-based emotion recognition carries profound practical implications. Although many model approaches and some review articles have scrutinized this domain, they have yet to undergo a comprehensive and precise classification and summarization process. The existing classifications are somewhat coarse, with insufficient attention given to the potential applications within this domain. Therefore, this article systematically classifies recent developments in EEG-based emotion recognition, providing researchers with a lucid understanding of this field’s various trajectories and methodologies. Additionally, it elucidates why distinct directions necessitate distinct modeling approaches. In conclusion, this article synthesizes and dissects the practical significance of EEG signals in emotion recognition, emphasizing its promising avenues for future application.

Physics to system-level modeling of silicon-organic-hybrid nanophotonic devices

The continuous growth in data volume has sparked interest in silicon-organic-hybrid (SOH) nanophotonic devices integrated into silicon photonic integrated circuits (PICs). SOH devices offer improved speed and energy efficiency compared to silicon photonics devices. However, a comprehensive and accurate modeling methodology of SOH devices, such as modulators corroborating experimental results, is lacking. While some preliminary modeling approaches for SOH devices exist, their reliance on theoretical and numerical methodologies, along with a lack of compatibility with electronic design automation (EDA), hinders their seamless and rapid integration with silicon PICs. Here, we develop a phenomenological, building-block-based SOH PICs simulation methodology that spans from the physics to the system level, offering high accuracy, comprehensiveness, and EDA-style compatibility. Our model is also readily integrable and scalable, lending itself to the design of large-scale silicon PICs. Our proposed modeling methodology is agnostic and compatible with any photonics-electronics co-simulation software. We validate this methodology by comparing the characteristics of experimentally demonstrated SOH microring modulators (MRMs) and Mach Zehnder modulators with those obtained through simulation, demonstrating its ability to model various modulator topologies. We also show our methodology's ease and speed in modeling large-scale systems. As an illustrative example, we use our methodology to design and study a 3-channel SOH MRM-based wavelength-division (de)multiplexer, a widely used component in various applications, including neuromorphic computing, data center interconnects, communications, sensing, and switching networks. Our modeling approach is also compatible with other materials exhibiting the Pockels and Kerr effects. To our knowledge, this represents the first comprehensive physics-to-system-level EDA-compatible simulation methodology for SOH modulators.

Fragment-based virtual screening identifies novel leads against Plasmepsin IX (PlmIX) of Plasmodium falciparum: Homology modeling, molecular docking, and simulation approaches

Despite continuous efforts to develop safer and efficient medications, malaria remains a major threat posing great challenges for new drug discovery. The emerging drug resistance, increased toxicities, and impoverished pharmacokinetic profiles exhibited by conventional drugs have hindered the search for new entities. Plasmepsins, a group of Plasmodium-specific, aspartic acid protease enzymes, are involved in many key aspects of parasite biology, and this makes them interesting targets for antimalarial chemotherapy. Among different isoforms, PlmIX serves as an unexplored antimalarial drug target that plays a crucial role along with PlmV and X in the parasite’s survival by digesting hemoglobin in the host’s erythrocytes. In this study, fragment-based virtual screening was performed by modeling the three-dimensional structure of PlmIX and predicting its ligand-binding pocket by using the Sitemap tool. Screening identified the fragments with the XP docking score ≤ −3 kcal/mol from the OTAVA General Fragment Library (≈16,397 fragments), and the selected fragments were chosen for ligand breeding. The resulting ligands (≈69,858 ligands) were subsequently subjected to filtering based on the QikProp properties along with carcinogenicity testing performed using CarcinoPred-EL and then docked in the SP (≈14,078 ligands) as well as XP mode (≈3,104 ligands), and compared with that of control ligands 49C and I0L. The top-ranked ligands were taken further for the calculation of the free energy of binding using Prime MM–GBSA. Overall, a total of six complexes were taken further for MD simulation studies performed at 100 ns to attain a better understanding of the binding mechanisms, and compounds 3 and 4 were found to be the most efficient ones in silico. The analysis of compound 3 revealed that the carbonyl group present in position 1 on the isoindoline moiety (Arg554) was responsible for inhibitory activity against PlmIX. However, the analysis of compound 4 revealed that the amide linkage sandwiched between the phenyl ring and isoquinoline moiety (Lys555 and Ser226) as well as carbonyl oxygen of the carbamoyl group present at position 2 of the pyrazole ring (Gln222) were responsible for PlmIX inhibitory activity, owing to their crucial interactions with key amino acid residues.

Modeling of Heat and Mass Transfer in Cement-Based Materials during Cement Hydration—A Review

Cement-based materials encompass a broad spectrum of construction materials that utilize cement as the primary binding agent. Among these materials, concrete stands out as the most commonly employed. The cement, which is the principal constituent of these materials, undergoes a hydration reaction with water, playing a crucial role in the formation of the hardened composite. However, the exothermic nature of this reaction leads to significant temperature rise within the concrete elements, particularly during the early stages of hardening and in structures of substantial thickness. This temperature rise underscores the critical importance of predictive modeling in this domain. This paper presents a review of modeling approaches designed to predict temperature and accompanying moisture fields during concrete hardening, examining different levels of modeling accuracy and essential input parameters. While modern commercial finite element method (FEM) software programs are available for simulating thermal and moisture fields in concrete, they are accompanied by inherent limitations that engineers must know. The authors further evaluate effective commercial software tools tailored for predicting these effects, intending to provide construction engineers and stakeholders with guidance on managing temperature and moisture impacts in early-age concrete.

Development of a SARS-CoV-2 viral dynamic model for patients with COVID-19 based on the amount of viral RNA and viral titer.

The target-cell limited model, which is one of the mathematical modeling approaches providing a quantitative understanding of viral dynamics, has been applied to describe viral RNA profiles of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in previous studies. However, these models have been developed mainly using patient data from the early phase of the pandemic. Furthermore, no reports focused on the profiles of the viral titer. In this study, the dynamics of both viral RNA and viral titer were characterized using data reflecting the current clinical situation in which the Omicron variant has become epidemic and vaccines for SARS-CoV-2 have become available. Consecutive data for 5212 viral RNA levels and 5216 viral titers were obtained from 720 patients with coronavirus disease 2019 (COVID-19) in a phase II/III study for ensitrelvir. Our model assumed that productively infected cells would produce only infectious viruses, which could be transformed into non-infectious viruses, and has been used to describe the dynamics of both viral RNA levels and viral titer. The time from infection to symptom onset (tinf) of unvaccinated patients was estimated to be 3.0 days, which was shorter than that of the vaccinated patients. The immune-related parameter as a power function for the vaccinated patients was 1.1 times stronger than that for the unvaccinated patients. Our model allows the prediction of the viral dynamics in patients with COVID-19 from the time of infection to symptom onset. Vaccination status was identified as a factor influencing tinf and the immune function.