Empirical Model Research Articles

A comprehensive model analysis of activity coefficients and thermodynamic properties of EAE-1DMA2P blended solvent

Solubility has an important influence on industrial applications of amine solvents. The CO2 equilibrium solubilities of EAE-1DMA2P solvents were obtained in 1:2, 1.5:1.5, and 2:1 ratios at 298–333 K and CO2 partial pressures of 8–100 kPa. Using these data, the accurate prediction of CO2 equilibrium solubility of EAE-1DMA2P solvent was achieved by constructing an activity coefficient model with an AARD of only 0.79%. In addition, by constructing the solubility prediction model of EAE-1DMA2P system based on an empirical model, KE model, Austgen model, Li-Shen model, Hu-Chakma model, Helei Liu model, and Liu et al. model the results were compared to confirm introduction of the activity coefficient has effectively improved the accuracy of the model prediction. Based on thermodynamic parameters of solubility, ΔrGm and ΔrHm, EAE-1DMA2P solvent was screened against other solvents. The results showed that EAE-1DMA2P solvent had excellent absorption, reaction rate and low desorption energy consumption, confirming its potential in CO2 capture.

Energetic particle precipitation influences global secondary ozone distribution

The secondary ozone layer is a global peak in ozone abundance in the upper mesosphere-lower thermosphere (UMLT) around 90-95 km. The effect of energetic particle precipitation (EPP) from geomagnetic processes on this UMLT ozone remains largely unexplored. In this research we investigated how the secondary ozone responds to EPP using satellite observations. In addition, the residual Mean Meridional Circulation (MMC) derived from model simulations and the atomic oxygen [O], atomic hydrogen [H], temperature measurements from satellite observations were used to characterise the residual circulation changes during EPP events. We report regions of secondary ozone enhancement or deficit across low, mid and high latitudes as a result of global circulation and transport changes induced by EPP. The results are supported by a sensitivity test using an empirical model.

Improving millimetre-wave path loss estimation using automated hyperparameter-tuned stacking ensemble regression machine learning

Path loss prediction is a crucial aspect of designing and operating wireless communication systems, especially in the millimetre-waves (mmWaves) frequency bands. However, these bands are associated with climate-related challenges: rain attenuation, and free space path loss. To address these challenges, an advanced stacking ensemble-regression machine learning (SEML) model with automated hyperparameter tuning (AHT) was proposed. The AHT-SEML model leverages multiple base regressors integrated with a meta-regressor. The model's performance was optimised using the AHT tuning technique. The AHT-SEML model's efficiency was tested using simulated path loss data from a Composite 3D Raytracing-Image-Method propagation model across four sub-Saharan cities, at mmWaves frequencies. The AHT-SEML model's performance was compared to three empirical path loss models, namely Close-In (CI), Floating Intercept (FI), and Alpha-Beta-Gamma (ABG), using evaluation metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). AHT-SEML outperformed other models in the four cities across all frequencies and scenarios with the highest Index of Agreement and lowest Bayesian information criterion. Model confidence set (MCS) analysis with CI benchmark indicates that all the models except AHT-SEML performed below the critical t-value of 2.3530 at 95% confidence level with a degree of freedom of 3, implying no significant differences in their MAEs compared to the CI. However, AHT-SEML's t-statistic values exceed this critical t-value, indicating statistically significant differences and better performance than the CI benchmark models. Similarly, F-statistics of 29.45 and 26.54 correspond to p-values of 1.91×10−14 and 2.50×10−13 for MAE and RMSE, respectively, corroborating significant differences in the AHT-SEML's performance.

Examining the daily reciprocal relations between alcohol abstinence self-efficacy and drinking among non-treatment seeking individuals with alcohol use disorder (AUD)

Theoretical and empirical models of alcohol use and misuse indicate that abstinence self-efficacy (ASE) predicts improvements in treatment outcomes among individuals with alcohol use disorder (AUD). More recently, studies have begun examining daily fluctuations in ASE to better understand in-the-moment determinants of drinking behaviors. With the goal of assessing how ASE is implicated in maintenance (rather than changing) of hazardous drinking patterns, the current study examined daily reciprocal relations between ASE and drinking among individuals with AUD. Non-treatment seeking adults (n = 63) with AUD were recruited and completed daily surveys assessing ASE and drinking behaviors for 14 days. Data were analyzed using time-lagged multilevel modeling. Results indicated that both within- and between-person elevations in ASE predicted decreased likelihood of drinking, but only within-person ASE predicted fewer drinks consumed on drinking days. Previous-day drinking behavior was unrelated to next-day ASE; however, higher percentage of drinking days during the monitoring period (between-person) was associated with lower daily ASE. These results demonstrate that confidence in one’s ability to abstain from drinking varies considerably across days, and that fluctuations may be implicated in daily drinking decisions. The lack of effect of previous-day drinking on ASE (combined with the significant effect of average drinking frequency) may suggest that sustained periods of reduced drinking or abstinence are necessary to impact ASE. This study points to ASE’s role in the maintenance of daily drinking behavior among non-treatment-seeking individuals with AUD and reiterates the importance of self-efficacy in behavioral control and decision-making at the daily level.

Experimental investigation of quench front propagation during reflooding in 5 × 5 rod bundle channel with axial uniform power distribution

This experimental study delves into the impact of inlet injection velocity, inlet subcooling, and linear power density on the quench front propagation during reflooding in 5x5 rod bundle channel with axial uniform power distribution. The investigation utilized a film boiling test facility constructed by NUclear Safety and Operation Laboratory (NUSOL) of Xi'an Jiaotong University. The findings demonstrate a discernible sensitivity of the quench front to variations in these parameters, wherein higher linear power density, reduced flow rate, and decreased coolant subcooling lead to delayed reflooding. Moreover, the study derived the surface parameters of the heater rod use self-developed Reflooding Experimental Data Analysis code (REDA). The comprehensive experimental data and empirical models generated in this study hold significant potential for enhancing the safety analysis of nuclear reactors.

A machine learning estimator trained on synthetic data for real-time earthquake ground-shaking predictions in Southern California

After large-magnitude earthquakes, a crucial task for impact assessment is to rapidly and accurately estimate the ground shaking in the affected region. To satisfy real-time constraints, intensity measures are traditionally evaluated with empirical Ground Motion Models that can drastically limit the accuracy of the estimated values. As an alternative, here we present Machine Learning strategies trained on physics-based simulations that require similar evaluation times. We trained and validated the proposed Machine Learning-based Estimator for ground shaking maps with one of the largest existing datasets (<100M simulated seismograms) from CyberShake developed by the Southern California Earthquake Center covering the Los Angeles basin. For a well-tailored synthetic database, our predictions outperform empirical Ground Motion Models provided that the events considered are compatible with the training data. Using the proposed strategy we show significant error reductions not only for synthetic, but also for five real historical earthquakes, relative to empirical Ground Motion Models.

Formation of Si nanopillars through partial sacrificing in super passivation reactive ion etching

The vertical gate-all-around (VGAA) metal-oxide-semiconductor field-effect transistor (MOSFET) holds remarkable potential in the three-dimensional (3D) integrated circuits (ICs), primarily owing to its capacity for vertical integration. The Si nanopillar, a crucial channel in the VGAA MOSFET, is conventionally shaped via the reactive ion etching (RIE) system employing SF6/O2. Past studies have indicated that high O2 gas conditions in RIE often result in Si grasses irregular nanostructures, such as nanospikes on the bottom surface, due to over-passivation. However, this study revealed that ultrahigh O2 proportions (>70%), especially when combined with low chamber pressure, inhibit the development of Si grasses in the RIE system (termed as super passivation). Nevertheless, this scenario leads to the segmentation of the Si nanopillar. To address this issue, a proposed partial sacrificing method, achieved by sacrificing the upper segment of the nanopillar through prolonged processing time and reduced mask size, successfully yielded Si nanopillars without Si grasses. Furthermore, an empirical model was developed to elucidate how experimental parameters influence etching characteristics, encompassing etching rate and Si nanopillar shape, through a systematic examination of the RIE etching process. This research significantly contributes to the production of VGAA MOSFETs and 3D ICs.

Systematic literature review on the application of machine learning for the prediction of properties of different types of concrete

Background Concrete, a fundamental construction material, stands as a significant consumer of virgin resources, including sand, gravel, crushed stone, and fresh water. It exerts an immense demand, accounting for approximately 1.6 billion metric tons of Portland and modified Portland cement annually. Moreover, addressing extreme conditions with exceptionally nonlinear behavior necessitates a laborious calibration procedure in structural analysis and design methodologies. These methods are also difficult to execute in practice. To reduce time and effort, ML might be a viable option. Material and Methods A set of keywords are designed to perform the search PubMed search engine with filters to not search the studies below the year 2015. Furthermore, using PRISMA guidelines, studies were selected and after screening, a total of 42 studies were summarized. The PRISMA guidelines provide a structured framework to ensure transparency, accuracy, and completeness in reporting the methods and results of systematic reviews and meta-analyses. The ability to methodically and accurately connect disparate parts of the literature is often lacking in review research. Some of the trickiest parts of original research include knowledge mapping, co-citation, and co-occurrence. Using this data, we were able to determine which locations were most active in researching machine learning applications for concrete, where the most influential authors were in terms of both output and citations and which articles garnered the most citations overall. Conclusion ML has become a viable prediction method for a wide variety of structural industrial applications, and hence it may serve as a potential successor for routinely used empirical model in the design of concrete structures. The non-ML structural engineering community may use this overview of ML methods, fundamental principles, access codes, ML libraries, and gathered datasets to construct their own ML models for useful uses. Structural engineering practitioners and researchers may benefit from this article’s incorporation of concrete ML studies as well as structural engineering datasets. The construction industry stands to benefit from the use of machine learning in terms of cost savings, time savings, and labor intensity. The statistical and graphical representation of contributing authors and participants in this work might facilitate future collaborations and the sharing of novel ideas and approaches among researchers and industry professionals. The limitation of this systematic review is that it is only PubMed based which means it includes studies included in the PubMed database.

Study of the microstructure evolution of alloy structural steel and inhomogeneity effect of the microscale pulsed currents during current-assisted plane strain compressions by modeling a novel cellular automata method

The current-assisted forming process is considered to be a novel process that utilizes the electroplasticity effect to reduce the deformation resistance of difficult-to-deform metals and to refine the grain size while improving the mechanical properties of the part. However, the lack of understanding of the mechanisms by which pulsed current affects grain refinement still makes it difficult to experimentally develop analytical or empirical models of the relationship between grain size, pulse current parameters, and deformation amount. It will seriously hinder the further development and application of the current-assisted forming process. To reveal the mechanism of grain refinement coupling with electroplasticity, a series of Electron Back Scattering Diffraction observation tests were carried out after current-assisted plane strain compressions. The results show that the grain orientation spread value of the pulse current condition is lower than that of the non-current condition, while the refined grain area percentage is higher than that of the non-current condition. Based on the microstructure observation results, a cellular automata model for grain refinement is proposed. This consists of a dislocation density evolution, sub-grains generation, grain fragmentation, and a grain orientation rotation algorithm. To study the electroplasticity of the grain refinement process, the cellular automata model is also embedded with a finite difference numerical algorithm for solving the microscopic current density distribution of the model. The cellular automata model simulation results show that the error of grain size accuracy of this cellular automata model under non-current and pulse current conditions is 10.48% and 8.55%, respectively. It shows that the model can accurately characterize the grain refinement behavior of the current-assisted plane strain compression. The simulation results reveal the deep mechanism of pulsed current promotion on grain refinement, i.e., an inhomogeneous microstructure leads to uneven distribution of pulse current density. The inhomogeneous current distribution will further increase the grain refinement rate in the coarse-grained regions, thus increasing the proportion of the refined grain regions in the microstructure and leading to a relatively more homogeneous microstructure under pulse current conditions. The cellular automata model accurately reveals the mechanism of electroplasticity on the grain refinement. The model will serve as a crucial theoretical reference in designing current-assisted forming process routes to ensure excellent microstructure and properties in manufactured parts. It will enhance the widespread utilization of current-assisted forming process.

Magnetopause location modeling using machine learning: inaccuracy due to solar wind parameter propagation

An intrinsic limitation of empirical models of the magnetopause location is a predefined magnetopause shape and assumed functional dependences on relevant parameters. We overcome this limitation using a machine learning approach (artificial neural networks), allowing us to incorporate general, purely data-driven dependences. For the training and testing of the developed neural network model, a data set of about 15,000 magnetopause crossings identified in the THEMIS A-E, Magion 4, Geotail, and Interball-1 satellite data in the subsolar region is used. A cylindrical symmetry around the direction of the impinging solar wind is assumed, and solar wind dynamic pressure, interplanetary magnetic field magnitude, cone angle, clock angle, tilt angle, and corrected Dst index are considered as parameters. The effect of these parameters on the magnetopause location is revealed. The performance of the developed model is compared with other empirical magnetopause models. Finally, we demonstrate and discuss the inaccuracy of magnetopause models due to the inaccurate information about the impinging solar wind parameters based on measurements near the L1 point. This inaccuracy imposes a theoretical limit on the precision of magnetopause predictions, a limit that our model closely approaches.

Field-driven polyelectrolyte-polymer collisions in nanochannels.

Even though dilute (unentangled) polymer solutions cannot act as gel-like sieving media, it has been shown that they can be used to separate DNA molecules in capillary electrophoresis. The separation then comes from sporadic, independent DNA-polymer collisions. We study polymer-polymer collisions in nanochannels (i.e., channels that are smaller than the normal size of the polymers), a situation where a polyelectrolyte is forced to migrate "through" isolated uncharged molecules during electrophoresis. We use Langevin dynamics simulations to explore the nature of these collisions and their effect on the net motion of the two polymer chains. We identify several types of collisions, including some that are unique to nanochannels. When the uncharged polymer is much larger than the polyelectrolyte, the system is reminiscent of gel electrophoresis, and we propose a modified empirical reptation model to explain the data, with an orientation factor that depends on the tube diameter. We also observe that the duration of a collision is a non-monotonic function of the polymer size ratio when the two chains are of comparable size, a surprising resonance-like phenomenon, which, combined with the asymmetric nature of molecular conformations during collision, suggests possible ratchet-like mechanisms that could be used to sort polyelectrolytes in nanodevices.

Experimental characterization and numerical prediction for mechanical aging of epoxy and its carbon fiber‐reinforced composite under hydrothermal conditions

AbstractThe Aqueous environment impairs the mechanical performance of carbon fiber‐reinforced polymers (CFRPs) and decreases the durability of engineering structures made of CFRPs primarily by degrading the resin matrix. Hence, the objective of this study is to investigate and accurately predict the influence of hydrothermal aging on the epoxy and its carbon fiber reinforced polymer (CFRP). Particularly, this work developed a modeling method that combined Fick's model, empirical aging model and elastoplastic constitutive model to predict the mechanical behavior of hydrothermally aged epoxy matrix. After the experimental characterization, this epoxy modeling method demonstrated its predictive capability on the mechanical response of the deteriorated epoxy under different hydro‐thermal‐salt conditions, with the mean absolute percent error (MAPE) of approximately 4.61%. Furthermore, the transverse compressive behavior of unidirectional (UD) CFRPs could be predicted by using the representative volume element (RVE) model with the degraded matrix behavior predicted via this epoxy modeling method. Integrated with this CFRP modeling, results from mechanical tests and fracture analysis further demonstrated that the hydrothermal aging effect on the transverse compressive performance of the UD CFRPs was dominated by the deterioration of their resin matrix. Therefore, this study demonstrates the feasibility of the finite element analysis (FEA) for mechanical aging of the epoxy matrix and its CFRP under hydrothermal conditions via employing the proposed integrated modeling method.Highlights Degradation of epoxy polymer under hydro‐thermal‐salt aging conditions. Integrated model to predict mechanical response of hydrothermally aged epoxy. RVE model to predict the mechanical behavior of hydrothermally aged CFRP. Hydrothermal aging on the transverse compressive behavior of UD CFRP.

Thermospheric nitric oxide energy budget during extreme geomagnetic storms: a comparative study

We selected three superstorms (disturbance storm time [Dst] index less than −350 nT) of 2003–04 to study the thermospheric energy budget with a particular emphasis on the thermospheric cooling emission by nitric oxide via a wavelength of 5.3 μm. The nitric oxide radiative emission data are obtained from the Sounding of the Atmosphere by Broadband Emission Radiometry (SABER) instrument onboard the Thermosphere Ionosphere Mesosphere Energetics and Dynamics (TIMED) satellite and the thermosphere ionosphere electrodynamic general circulation model (TIEGCM) simulation. Different energy sources for the magnetospheric energy injection and the thermospheric/ionospheric dissipation processes are calculated using empirical formulations, model simulations, and space-borne and ground-based measurements. The Joule heating rates calculated from different sources showed similar variations but significant differences in the magnitude. The nitric oxide cooling power is calculated by zonally and meridionally integrating the cooling flux in the altitude range of 100–250 km. The satellite observed that cooling flux responds faster to the energy input, as compared to the modeled results. The cooling power increases by an order of magnitude during storm time with maximum radiation observed during the recovery phase. Both the satellite-observed and modeled cooling powers show a strong positive correlation with the Joule heating power during the main phase of the storm. It is found that the maximum radiative power does not occur during the strongest storm, and it strongly depends on the duration of the main phase. The model simulation predicts a higher cooling power than that predicted by the observation. During a typical superstorm, on average, a cooling power of 1.87 × 105 GW exiting the thermosphere is estimated by the TIEGCM simulation. On average, it is about 40% higher than the satellite observation.

Comparison of boosting and genetic programming techniques for prediction of tensile strain capacity of Engineered Cementitious Composites (ECC)

Plain concrete is weak against tension and has low Tensile Strain Capacity (TSC) which significantly affects its long-term performance. To overcome this issue, Engineered Cementitious Composites (ECC) were developed by incorporating polymer fibres in the cement matrix which increases ductility and provides higher TSC than plain concrete and they have emerged as a viable alternative to brittle plain concrete. This study is conducted in an attempt to develop empirical prediction models for TSC prediction of ECC without requiring extensive experimental procedures. For this purpose, two evolutionary programming techniques known as Multi Expression Programming (MEP), Gene Expression Programming (GEP) along with two boosting-based techniques: AdaBoost and Extreme Gradient Boosting (XGB) were developed using data collected from published literature. The gathered dataset had seven input parameters including water-to-binder ratio, sand, fibre content, cement, fly ash, superplasticizer, and age etc. and only one output parameter i.e., TSC. The error assessment of developed models was done using correlation coefficient, Mean Absolute Error (MAE), and Objective Function (OF) etc. and the error comparison showed that XGB has the highest accuracy having the least OF value of 0.081 as compared to 0.11 of AdaBoost, 0.13 of GEP, and 0.16 of MEP. Shapley additive analysis was conducted on the XGB model since it proved to be the most accurate, and the results highlighted that fibre content, age, and water-to-binder ratio are the most important features to predict TSC of ECC.

Semi-theoretical compression model of wet fiber web for the prediction of airflow rate in vacuum dewatering process of papermaking

This study expands the investigation of a mathematical method for predicting the airflow rate during the vacuum dewatering process of papermaking based on our previous research. By integrating poroelastic theory and initial saturation thickness, based on experimental data with a range of 20–60 kPa pressure difference and 20-200 g · m − 2 basis weight, a mathematical method with a semi-theoretical model for compression characteristics of the wet fiber web, the poroelastic porosity model (PEPM), was developed to support and expand on our previously established empirical model for porosity variations in wet fiber web, the variable porosity model (VPM). The accuracy of the mathematical method with the PEPM in predicting the airflow mass flux passing through the wet fiber web was validated using experimental data with a range of 0–60 kPa pressure difference and 20-200 g · m − 2 basis weight. The mean relative errors with the PEPM within the pressure difference range of 0-20 kPa and 0-60 kPa were 7.68% and 8.80%, respectively, compared to 17.44% and 11.67% for those with the VPM, demonstrating the improved applicability and generalization of the mathematical method, particularly in the zone of low pressure difference exceeding the range of the modeling experimental data. The PEPM effectively revealed the strain hardening behavior during the compression process and the diminishing returns of energy in the vacuum dewatering process with an increasing pressure difference. The developed mathematical method with the PEPM for the compression characteristics of the wet fiber web provides an effective and accurate way to predict the airflow rate during the vacuum dewatering process of papermaking.

Regulatory competition and cross‐fertilization in bank performance in the US banking markets

AbstractThis paper examines empirically cross‐fertilization in the productivity growth of banks between a state and its neighbouring and non‐neighbouring states (i) before (i.e. 1971–1977) the interstate multibank holding company (IMBHC) deregulations and (ii) during (i.e. 1982–1995) the IMBHC deregulations, which, through cross‐border bank M&amp;As mainly among neighbouring states, could inject new blood, awaken the market for corporate control and enhance cross‐fertilization in bank performance among neighbouring states. Further, the 1978–1981 period offers a natural experiment to examine Baumol's Contestable Markets Hypothesis (CMH). The legislature of Maine made the first IMBHC deregulatory move in 1978. There was no reciprocity until New York and Alaska made their moves in 1982. Under CMH, Maine's move should inject a competitive spirit and alter bank performance for better across all—neighbouring or non‐neighbouring – banking markets during this period. Theoretically, Kane's regulatory equilibrium framework provides guidance to address these matters and Tiebout's people vote with their feet framework extends and supplements this guidance. Empirically, FDIC's annual banking data, aggregated at the state level, constitute the main input in computing the productivity growth indices for each of the 48 contiguous sample states between 1971 and 1995. Estimations of a novel spatially driven fixed effects model that uses these indices produce empirical results. The empirical model exploits the proximity of one sample state to its neighbouring states while also embracing a set of randomly chosen non‐neighbouring states as a control sample. Results show that cross‐fertilization in bank performance, observed among neighbouring states before the introduction of the IMBHC deregulations during 1971–1977, gets stronger in response to the dynamically evolving IMBHC deregulations during 1982–1995 and that improvements in banks' productivity growth during 1978–1981 support Baumol's CMH. Overall, our results demonstrate the importance and influence of cross‐fertilization, as a matter of proximity of subjects, on banks' performance and suggest promise for future research that embraces the spatial dimension of banking markets and data.

MRI-only based material mass density and relative stopping power estimation via deep learning for proton therapy: a preliminary study

Magnetic Resonance Imaging (MRI) is increasingly being used in treatment planning due to its superior soft tissue contrast, which is useful for tumor and soft tissue delineation compared to computed tomography (CT). However, MRI cannot directly provide mass density or relative stopping power (RSP) maps, which are required for calculating proton radiotherapy doses. Therefore, the integration of artificial intelligence (AI) into MRI-based treatment planning to estimate mass density and RSP directly from MRI has generated significant interest. A deep learning (DL) based framework was developed to establish a voxel-wise correlation between MR images and mass density as well as RSP. To facilitate the study, five tissue substitute phantoms were created, representing different tissues such as skin, muscle, adipose tissue, 45% hydroxyapatite (HA), and spongiosa bone. The composition of these phantoms was based on information from ICRP reports. Additionally, two animal tissue phantoms, simulating pig brain and liver, were prepared for DL training purposes. The phantom study involved the development of two DL models. The first model utilized clinical T1 and T2 MRI scans as input, while the second model incorporated zero echo time (ZTE) MRI scans. In the patient application study, two more DL models were trained: one using T1 and T2 MRI scans as input, and another model incorporating synthetic dual-energy computed tomography (sDECT) images to provide accurate bone tissue information. The DECT empirical model was used as a reference to evaluate the proposed models in both phantom and patient application studies. The DECT empirical model was selected as the reference for evaluating the proposed models in both phantom and patient application studies. In the phantom study, the DL model based on T1, and T2 MRI scans demonstrated higher accuracy in estimating mass density and RSP for skin, muscle, adipose tissue, brain, and liver. The mean absolute percentage errors (MAPE) were 0.42%, 0.14%, 0.19%, 0.78%, and 0.26% for mass density, and 0.30%, 0.11%, 0.16%, 0.61%, and 0.23% for RSP, respectively. The DL model incorporating ZTE MRI further improved the accuracy of mass density and RSP estimation for 45% HA and spongiosa bone, with MAPE values of 0.23% and 0.09% for mass density, and 0.19% and 0.07% for RSP, respectively. These results demonstrate the feasibility of using an MRI-only approach combined with DL methods for mass density and RSP estimation in proton therapy treatment planning. By employing this approach, it is possible to obtain the necessary information for proton radiotherapy directly from MRI scans, eliminating the need for additional imaging modalities.

Shear strength model of FRP-reinforced concrete beams without shear reinforcement

The current design codes for evaluating the shear strength of concrete beams reinforced with fiber-reinforced polymer (FRP) bars predominantly rely on a semi-empirical approach through test data calibration without a robust theoretical background. This approach may lead to either overestimation or underestimation, resulting in significant scattering in strength predictions. This study proposed a modified approach to enhance the existing unified shear-strength model developed for steel-reinforced concrete (RC) beams based on the compression zone failure mechanism, for predicting the shear strength of FRP-RC beams without shear reinforcement. The proposed model integrates strain and stress approaches, accounting for the distinctive characteristics associated with FRP reinforcement. Additionally, the overall shear resistance capacity of the FRP-RC beam is considered as the sum of contributions from the intact concrete of the compression zone and the aggregate interlock mechanism. The reliability of the proposed model is verified by comparing its predictions with a comprehensive database comprising 264 FRP-RC beam specimens without shear reinforcement reported by 41 research groups. Furthermore, the accuracy of the proposed model was assessed through a comparative analysis with existing evaluation methods, encompassing machine learning-based models, empirical models, theoretical models, and design codes. The results indicate that the proposed model satisfactorily predicts the shear strength of FRP-RC beams within a wide range of design parameters. In comparison with machine learning-based models, the proposed theoretical model exhibits a similar level of accuracy in terms of the mean shear strength ratio and coefficient of determination (R2) values but with less scattering, indicated by lower coefficient of variation (COV) and root mean square error (RMSE) values. In addition, the proposed model outperforms existing empirical and theoretical models, as well as current design codes, by significantly enhancing prediction accuracy, demonstrated by lower COV, RMSE, and higher R2 values. Finally, a parametric study was conducted to investigate the effects of the key parameters on the shear strength of FRP-RC beams using both the proposed model and representative existing evaluation methods.

An assessment of anticipated future changes in water erosion dynamics under climate and land use change scenarios in South Asia

Water erosion has emerged as a significant global environmental challenge, particularly in South Asia, recognized as one of the regions hardest to hit globally by climate change. This study aims to identify the potential erosion hotspots in South Asia and to assess areas under risk in the future due to climate and land use change scenarios. We utilized the Revised Universal Soil Loss Equation (RUSLE), an empirical erosion model, to estimate erosion for both historical and future periods, considering two distinct Shared Socio-economic Pathways (SSP126 and SSP585). The baseline model predicts a mean water erosion rate of 10.6 t. ha−1. year−1 in South Asia, with more than 30 t. ha−1. year−1 recorded for 6.3 % of the total land area. Notably, Bangladesh and Nepal exhibit significantly higher erosion rates than other South Asian countries. Further, our projection indicates an + 10 % increase in erosion rates during the near future (2021–2040) under SSP585 compared to the historical estimates. A substantial rise of approximately + 31 % and + 39 % in water erosion rates in South Asia is anticipated during the far future periods of 2061–2080 and 2081–2100 under SSP585. At the country level, Bangladesh (+42 %), Bhutan (+108 %), and Nepal (+70 %) are expected to experience substantial increases in erosion rates during the 2081–2100 period, with India projecting a + 38 % increase. Additionally, a significant + 35 % expansion in the spatial extent of high erosion areas (>30 t. ha−1. year−1) is foreseen in 2081–2100 under SSP585. Furthermore, under the low climate change scenario (SSP126), the impact on the erosion rates is minimal, while under SSP585, a substantial increase in erosion rates is anticipated. Regions such as Southern India, the arid and semi-arid areas near the India-Pakistan border, and the Himalayan foothills are identified as hotspots for significant changes in erosion rates. Acknowledging a certain level of uncertainty, our study provides valuable insights into erosion risk areas in South Asia and their plausible future impacts due to climate and land use changes. This information can assist policymakers in South Asian countries in developing national-level strategies for soil conservation and climate resilience.

Dynamics of measles immunity from birth and following vaccination.

Measles remains a major threat to human health despite widespread vaccination. While we know that maternal antibodies can impair vaccine-induced immunity, the relative contributions of pre-existing immunity levels, maternal and infant characteristics on vaccine responses remain unclear, hampering evidence-based vaccination policy development. Here we combine serological data from 1,505 individuals (aged 0-12 years) in a mother-infant cohort and in a child cohort with empirical models to reconstruct antibody trajectories from birth. We show that while highly heterogeneous across a population, measles antibody evolution is strongly predictive from birth at the individual level, including following vaccination. Further, we find that caesarean section births were linked with 2.56 (95% confidence interval: 1.06-6.37) increased odds of primary vaccine failure, highlighting the long-term immunological consequences of birth route. Finally, we use our new understanding of antibody evolution to critically assess the population-level consequences of different vaccination schedules, the results of which will allow country-level evaluations of vaccine policy.