Modeling Approach Research Articles

Red, green, and blue model assessment and AQbD approach to HPTLC method for concomitant analysis of metformin, pioglitazone, and teneligliptin

BackgroundThe CDSCO of India has authorized a combination of metformin hydrochloride, teneligliptin hydrochloride, and pioglitazone hydrochloride for the treatment of insulin-independent diabetes. For the purpose of estimating metformin, teneligliptin, and pioglitazone combinations as well as individual commercial formulations, there are a plethora of publicly accessible chromatographic techniques. More importantly, the development of these chromatographic procedures has included the use of chemical solvents that are dangerous to both animals and the environment.ObjectivesHowever, to date, there has been no documented chromatographic technique that can concomitantly estimate various commercial formulations of drugs under study employing a uniform chromatographic condition and environmentally friendly solvents. In order to concomitantly estimate drugs under study utilizing unified chromatographic conditions, a green HPTLC method was developed.MethodThe AQbD approach was used to carry out the method development. To determine the most important method parameters and response variables, the analytical risk assessment was conducted using the risk priority number ranking and screening approach. Critical method parameters and response variables were modeled using the response surface modeling approach, which relies on the central composite design. Optimal ranges for the intended method operable design region were determined, and control strategy was framed. The chromatographic separation was carried out on preparative TLC plate precoated with silica gel G-60 F254 using 1.0%W/V ammonium acetate in ethanol: water: triethylamine (6.5:0.4:0.6, V/V) as mobile phase. The detection of the anti-diabetic drugs under study was carried out at 267 nm wavelength.ResultsThe linearity of metformin, teneligliptin, and pioglitazone was found to be 5000–25000 ng/band, 200–1000 ng/band, and 150–750 ng/band, respectively. The %RSD for robustness and precision study was found to be less than 2.0%. The %recovery of method was found to be 98–102%. The assay results were shown to be in compliance with respective labeled claims of anti-diabetic medications when the suggested method was used for concurrent analysis of several formulations and combinations of drugs under study.ConclusionThe suggested technique was evaluated utilizing red–green–blue model scoring tools. The suggested technique was determined to be precise, accurate, rapid, cost-effective, and easy to apply for the estimation of drugs under study.

Mathematical Modeling Approach and Exploration of Geometric Properties as Part of an Outdoor Activity for Primary-School Pupils in Out-of-School Learning

School-age children and being outdoors are intrinsically linked. Education and the outdoors offer unique opportunities to extend the learning potential of children at this age in a more engaging way. From the point of view of mathematics, this way of learning is very suitable and certainly motivating for the pupils since mathematics, especially geometry, is not very popular among pupils. Therefore, in this article, we describe how we used an out-of-school learning experience for second-level primary school pupils (sixth to ninth grade) to link mathematics to outdoor learning. Activities that we solved with pupils in the outdoor environment were solved in the teaching process using only modeling. Practical applications were illustrated through interesting topographic fieldwork, which we analyzed for their appropriate integration into the teaching process by means of a priori analysis. The inclusion of these practical problems was preceded by research on 781 pupils of primary schools who solved application problems related to the circle and the square. It was clear from the research that pupils have a significant problem with the geometric interpretation of simple geometric concepts, which can be improved with the use of mathematical modeling and the linking of similar problems that can be carried out in a non-school environment.

Flexible modelling of time-varying exposures and recurrent events to analyse training load effects in team sports injuries

Abstract We present a flexible modelling approach to analyse time-varying exposures and recurrent events in team sports injuries. The approach is based on the piece-wise exponential additive mixed model where the effects of past exposures (i.e. high-intensity training loads) may accumulate over time and present complex forms of association. In order to identify a relevant time window at which past exposures have an impact on the current risk, we propose a penalty approach. We conduct a simulation study to evaluate the performance of the proposed model, under different true weight functions and different levels of heterogeneity between recurrent events. Finally, we illustrate the approach with a case study application involving an elite male football team participating in the Spanish LaLiga competition. The cohort includes time-loss injuries and external training load variables tracked by Global Positioning System devices, during the seasons 2017–2018 and 2018–2019.

Enhanced operation of PVWPS based on advanced soft computing optimization techniques

This study introduces three soft computing (SC) optimization algorithms aimed at enhancing the efficiency of photovoltaic water pumping systems (PVWPS). These algorithms include the Gorilla Troop Algorithm (GTO), Honey Badger Algorithm (HBA), and Snake Algorithm (SAO). The goal of the SC optimizers is to maximize the output power of the PV array (PPV) and enhance the efficiency of the DC motor (η), thereby optimizing the water flow rate (Q) of the pumping system. The analytical modeling approach proposed in this study involves forecasting the optimal duty cycle (Dop) for a buck-boost converter, taking into account variables such as solar radiation (G) and ambient temperature (T). A comparative analysis is conducted between the suggested SC optimizers and analytical modeling. MATLAB simulation is employed to explore an adaptive neuro-fuzzy inference system (ANFIS) trained for the proposed system. The objective is to assess system performance and accuracy. Findings indicate a strong convergence between the analytical model and the simulation model utilizing SC optimizers. Moreover, the neuro-fuzzy system trained offline, coupled with the proposed SC optimizers, demonstrates superior performance compared to traditional control methods like perturb and observe (P&O) and incremental conductance (IC). This superiority is evident across various metrics including motor efficiency (η), photovoltaic (PV) output power (PPV), water flow rate (Q), and time response.

Early postnatal care uptake and its associated factors following childbirth in East Africa—a Bayesian hierarchical modeling approach

BackgroundThe postnatal period is a critical period for both mothers and their newborns for their health. Lack of early postnatal care (PNC) services during a 2-day period is a life-threatening situation for both the mother and the babies. However, no data have been examined for PNCs in East Africa. Hence, using the more flexible Bayesian multilevel modeling approach, this study aims to investigate the pooled prevalence and potential factors for PNC utilization among women after delivery in East African countries.MethodsWe retrieved secondary data from the Kids Record (KR) demographic and health surveys (DHS) data from 2015 to 2022 from 10 East African countries. A total of 77,052 weighted women were included in the study. We used R 4.3.2 software for analysis. We fitted Bayesian multilevel logistic regression models. Techniques such as Rhat, effective sample size, density, time series, autocorrelation plots, widely applicable information criterion (WAIC), deviance information criterion (DIC), and Markov Chain Monte-Carlo (MCMC) simulation were used to estimate the model parameters using Hamiltonian Monte-Carlo (HMC) and its extensions, No-U-Turn Sampler (NUTS) techniques. An adjusted odds ratio (AOR) with a 95% credible interval (CrI) in the multivariable model to select variables that have a significant association with PNC was used.ResultsThe overall pooled prevalence of PNC within 48 hrs. of delivery was about 52% (95% CrI: 39, 66). A higher rate of PNC usage was observed among women aged 25–34 years (AOR = 1.21; 95% CrI: 1.15, 1.27) and 35–49-years (AOR = 1.61; 95% CrI: 1.5, 1.72) as compared to women aged 15–24 years; similarly, women who had achieved primary education (AOR = 1.96; 95% CrI: 1.88, 2.05) and secondary/higher education (AOR = 3.19; 95% CrI: 3.03, 3.36) as compared to uneducated women; divorced or widowed women (AOR = 0.83; 95% CrI: 0.77, 0.89); women who had currently working status (AOR = 0.9; 95% CrI: 0.87, 0.93); poorer women (AOR = 0.88; 95% CrI: 0.84, 0.92), middle-class women (AOR = 0.83; 95% CrI: 0.79, 0.87), richer women (AOR = 0.77; 95% CrI: 0.73, 0.81), and richest women (AOR = 0.59; 95% CrI: 0.55, 0.63) as compared to the poorest women; women who had media exposure (AOR = 1.32; 95% CrI: 1.27, 1.36), were having 3–5 children (AOR = 0.89; 95% CrI: 0.84, 0.94), had >5 children (AOR = 0.69; 95% CrI: 0.64, 0.75), had first birth at age < 20 years (AOR = 0.82; 95% CrI: 0.79, 0.84), had at least one ANC visit (AOR = 1.93; 95% CrI: 1.8, 2.08), delivered at health facilities (AOR = 2.57; 95% CrI: 2.46, 2.68), had average birth size (AOR = 0.94; 95% CrI: 0.91, 0.98) and small birth size child (AOR = 0.88; 95% CrI: 0.84, 0.92), had twin newborns (AOR = 1.15; 95% CrI: 1.02, 1.3), and fourth and above birth order (AOR = 0.88; 95% CrI: 0.82, 0.95) were individual-driven women who have been independently associated with PNC, respectively. Regarding community-level variables, rural women (AOR = 0.76; 95% CrI: 0.72, 0.79), high media exposure communities (AOR = 1.1; 95% CrI: 1.04, 1.18), communities with high wealth levels (AOR = 0.88 95% CrI: 0.83, 0.94), communities with high antenatal care (ANC) utilization (AOR = 1.13, 95% CrI: 1.07, 1.19), and long distance to health facilities (AOR = 1.5; 95% CrI: 1.38, 1.63) were among the community factors associated with PNC, respectively.ConclusionOne of the significant public health priorities in East Africa continues to be the underutilization of immediate PNC. The government ought to prioritize improving maternity and child health services, collaborating with interested parties in the area, reducing health disparities, educating mothers about child health, and other connected issues that are very beneficial.

Exploring disordered packing of non-equant particles: Insights from computed tomography and Monte Carlo simulations

The influence of particle shape on disordered packing structures is investigated through a combined experimental and modeling approach, focusing on non-equant particles, i.e. particles which are either elongated or flat, or both. In an experimental campaign, particles of systematically chosen shapes are fabricated via 3D printing, then packed and imaged by X-ray Computed Tomography to extract packing properties such as the porosity, particle orientations, and tortuosities of paths through the pore space. Additionally, a Monte Carlo model is employed to simulate packing structures, allowing for broader exploration of the parameter space, and investigating properties which are challenging to measure experimentally. The model’s parameters are varied to probe the range of potential packing structures for various particle shapes, and to provide explanations for experimental results. Both model and experiments reveal a non-monotonic trend for the porosity, a monotonically decreasing trend for the average particle inclination, and a monotonically increasing trend for the tortuosity with respect to elongation and flatness. Additionally, both model and experiments show increased dependence of the packing properties on the packing method (pouring or shaking), or model parameters respectively as particle aspect ratio increases. Furthermore, the model can predict experimentally measured packing porosities with deviations of 0.03 in absolute porosity units at most for new particle shapes, once its parameters are fitted to match the physical context. This research provides insights into the effect of particle shape on packing structure, with practical implications to processes involving the packing of non-equant particles, such as crystal filtration and packed beds.

A Reduced Modeling Approach for Making Predictions with Incomplete Data Having Blockwise Missing Patterns

Incomplete data with blockwise missing patterns are commonly encountered in analytics, and solutions typically entail listwise deletion or imputation. However, as the proportion of missing values in input features increases, listwise or columnwise deletion leads to information loss, whereas imputation diminishes the integrity of the training data set. We present the blockwise reduced modeling (BRM) method for analyzing blockwise missing patterns, which adapts and improves on the notion of reduced modeling proposed by Friedman, Kohavi, and Yun in 1996 as lazy decision trees. In contrast to the original idea of reduced modeling of delaying model induction until a prediction is required, our method is significantly faster because it exploits the blockwise missing patterns to pretrain ensemble models that require minimum imputation of data. Models are pretrained over the overlapping subsets of an incomplete data set that contain only populated values. During prediction, each test instance is mapped to one of these models based on its feature-missing pattern. BRM can be applied to any supervised learning model for tabular data. We benchmark the predictive performance of BRM using simulations of blockwise missing patterns on three complete data sets from public repositories. Thereafter, we evaluate its utility on three data sets with actual blockwise missing patterns. We demonstrate that BRM is superior to most existing benchmarks in terms of predictive performance for linear and nonlinear models. It also scales well and is more reliable than existing benchmarks for making predictions with blockwise missing pattern data. Supplemental Material: The online appendix is available at https://doi.org/10.1287/ijds.2022.9016 .

A structural equation modeling approach to leveraging the power of extant sentiment analysis tools

AbstractMachine-derived sentiment analysis has become a pervasive and useful tool to address a wide array of issues in natural language processing. Leading technology companies such as Google now provide sentiment analysis tools (SATs) as readily accessible online products. Academic researchers develop and make available SATs to support the research enterprise. One of the major challenges with SATs is the inconsistencies in results among the various SATs. Consequently, the selection of a SAT for a specific purpose may significantly impact the application. This study addresses the foregoing problem by utilizing structural equation modeling to merge the outputs of SATs to develop a combined sentiment metric without the need for a labeled training dataset. This method is applicable to a wide range of text-based problems, is data-driven, and replicable. It was tested using three publicly available datasets and compared against seven different SATs. The results indicate that as a continous measure, the proposed method outperformed other SATs in the movie reviews and SemEval datasets, and achieved a tie for first place with IBM Watson on the Sentiment 140 dataset. Also, compared to the published major alternatives, the arithmetic mean solution, this approach performed better across these three datasets.

Modeling the extra column volume of a micro simulated moving bed chromatography system: Introducing the equivalent radial flow rate distribution

Recently, the focus in chromatography model development has expanded to include the modeling of extra column volume (ECV), particularly in small- and lab-scale systems where ECV can constitute a significant portion of the total volume. Typically, ECV is modeled with 1D approaches, for example with combinations of dispersed plug flow reactors (DPFRs) and continuously stirred tank reactors (CSTRs). However, radial inhomogeneities in the ECV concentration profile necessitate higher-dimensional models for more accurate predictions. Searching for a suitable modeling approach for a micro simulated moving bed chromatography (μSMB) system, we investigated whether the 2D laminar flow model can be extended to account for additional dispersion effects, such as Dean vortices, through an equivalent radial flow rate distribution (eqFRD). For this purpose, we conducted 3D CFD simulations of the respective ECV and adapted the radial flow rate profile of a 2D simulation to match the residence time distribution observed in the CFD results. Applying the eqFRD model led to a significant improvement in prediction accuracy for isolated ECV segments, increasing from 90% to 97% compared to traditional ECV models. However, when these models were applied to the full μSMB system, the choice of ECV model had minimal impact on overall results as long as retention time within the ECV was accurately predicted. This suggests that, in the studied system, the column has a greater influence on peak shape than the ECV, allowing simpler ECV models to suffice in certain contexts. Despite these advances, significant deviations between predicted and experimental results were observed, indicating that factors such as the transition between the column and ECV, as well as detector effects, should be considered in future research. The results underscore the importance of selecting an ECV model in the context of the entire system, balancing accuracy with computational efficiency.

Spatiotemporal analysis of roadway terrains impact on large truck driver injury severity outcomes using random parameters with heterogeneity in means and variances approach

This study employs a partially temporally constrained modeling approach to examine spatiotemporal variations in driver injury severity in single-vehicle large truck crashes across different terrains in California, allowing for a nuanced understanding of how specific factors influencing injury outcomes may change over time. Utilizing crash data from January 1st, 2015, to December 31st, 2017, obtained from the Highway Safety Information System, this study categorizes terrains as flat, rolling, and mountainous terrain and employs a random parameter multinomial logit model with heterogeneity in means and variance to account for potential heterogeneity in crash injury outcomes. This approach helps understand how different terrains influence injury severities while allowing for parameter variability across observations. The analysis is further enriched by likelihood ratio tests to verify the stability and temporal transferability of the model estimates across different terrains and years. Notably, the study identifies truck overturning as the first and second event in a crash as a consistent parameter influencing injury severity across all years, emphasizing its importance regardless of terrain or time in single-vehicle large truck crashes. Furthermore, this study takes into account a wide range of variables, including driver characteristics, crash attributes, roadway characteristics, vehicle features, and environmental and temporal aspects. The findings highlight the importance of terrain-specific elements in traffic safety assessments and the need for focused measures to reduce serious injuries in truck crashes. The out-of-sample simulation revealed a significant increase in minor and severe injuries when flat terrain parameters were replaced with those from rolling or mountainous terrains. This research not only contributes to the existing literature by detailing the dynamics of injury severity in single-vehicle large truck crashes but also announces the utility of partially temporally constrained models in enhancing traffic safety management strategies.

Defect Detection and 3D Reconstruction of Complex Urban Underground Pipeline Scenes for Sewer Robots

Detecting defects in complex urban sewer scenes is crucial for urban underground structure health monitoring. However, most image-based sewer defect detection models are complex, have high resource consumption, and fail to provide detailed damage information. To increase defect detection efficiency, visualize pipelines, and enable deployment on edge devices, this paper proposes a computer vision-based robotic defect detection framework for sewers. The framework encompasses positioning, defect detection, model deployment, 3D reconstruction, and the measurement of realistic pipelines. A lightweight Sewer-YOLO-Slim model is introduced, which reconstructs the YOLOv7-tiny network by adjusting its backbone, neck, and head. Channel pruning is applied to further reduce the model’s complexity. Additionally, a multiview reconstruction technique is employed to build a 3D model of the pipeline from images captured by the sewer robot, allowing for accurate measurements. The Sewer-YOLO-Slim model achieves reductions of 60.2%, 60.0%, and 65.9% in model size, parameters, and floating-point operations (FLOPs), respectively, while improving the mean average precision (mAP) by 1.5%, reaching 93.5%. Notably, the pruned model is only 4.9 MB in size. Comprehensive comparisons and analyses are conducted with 12 mainstream detection algorithms to validate the superiority of the proposed model. The model is deployed on edge devices with the aid of TensorRT for acceleration, and the detection speed reaches 15.3 ms per image. For a real section of the pipeline, the maximum measurement error of the 3D reconstruction model is 0.57 m. These results indicate that the proposed sewer inspection framework is effective, with the detection model exhibiting advanced performance in terms of accuracy, low computational demand, and real-time capability. The 3D modeling approach offers valuable insights for underground pipeline data visualization and defect measurement.

Multimodal predictive modeling: Scalable imaging informed approaches to predict future brain health

Background:Predicting future brain health is a complex endeavor that often requires integrating diverse data sources. The neural patterns and interactions identified through neuroimaging serve as the fundamental basis and early indicators that precede the manifestation of observable behaviors or psychological states. New Method:In this work, we introduce a multimodal predictive modeling approach that leverages an imaging-informed methodology to gain insights into future behavioral outcomes. We employed three methodologies for evaluation: an assessment-only approach using support vector regression (SVR), a neuroimaging-only approach using random forest (RF), and an image-assisted method integrating the static functional network connectivity (sFNC) matrix from resting-state functional magnetic resonance imaging (rs-fMRI) alongside assessments. The image-assisted approach utilized a partially conditional variational autoencoder (PCVAE) to predict brain health constructs in future visits from the behavioral data alone. Results:Our performance evaluation indicates that the image-assisted method excels in handling conditional information to predict brain health constructs in subsequent visits and their longitudinal changes. These results suggest that during the training stage, the PCVAE model effectively captures relevant information from neuroimaging data, thereby potentially improving accuracy in making future predictions using only assessment data. Comparison with Existing Methods:The proposed image-assisted method outperforms traditional assessment-only and neuroimaging-only approaches by effectively integrating neuroimaging data with assessment factors. Conclusion:This study underscores the potential of neuroimaging-informed predictive modeling to advance our comprehension of the complex relationships between cognitive performance and neural connectivity.

Dual phase‐field model for immiscible fluid flow through fractured unsaturated porous media

AbstractThe immiscible multiphase fluid flow in intact and fractured porous media is relevant to numerous engineering sectors, including industrial processes, geo‐engineering, and civil engineering. These encompass crucial applications that pose significant challenges in modeling, such as carbon dioxide () storage in underground reservoirs, contaminant transport, and desiccation‐induced hydraulic fracturing in unsaturated soils. In this work, a macroscopic framework is developed to describe the transport of immiscible and multiphase fluids in intact and fractured porous materials. The modeling approach utilizes the embedding of two phase‐field models within the continuum Theory of Porous Media (TPM). The case of negligible capillary pressure in the macroscopic scale is associated with many numerical challenges in solving this nonlinear DAEs system, which forms the major subject of the current work. Hence, the problem of the neglected capillary pressure is defined as a target problem. To deal with this, a thermodynamically consistent modified problem is defined, which incorporates an additional artificial macroscopic capillary pressure based on the Cahn–Hilliard phase‐field approach. To increase the robustness and stability of the numerical solution, the Newton–Raphson method is replaced by a homotopy method that allows a gradual transformation of a simpler, solvable problem into the original target problem that usually converges poorly. The sharp crack topology in the fracture regions is approximated by another phase‐field method, which forms a diffusive transition zone across the crack edges of the deformable porous material. For the solid matrix, we consider a small strain assumption with a heterogeneous distribution of the permeability parameter. The inclusion of heterogeneity allows for a more realistic modeling of crack propagation and fluid‐fluid interaction. For the numerical framework, the coupled DAE system is approximated by the finite element method (FEM), implemented in the open‐source project FEniCSx. Appropriate stabilization techniques are also discussed.

Dynamic projections of extreme sea levels for western Europe based on ocean and wind-wave modelling

Abstract. Extreme sea levels (ESLs) are a major threat for low-lying coastal zones. Climate-change-induced sea level rise (SLR) will increase the frequency of ESLs. In this study, ocean and wind-wave regional simulations are used to produce dynamic projections of ESLs along the western European coastlines. Through a consistent modelling approach, the different contributions to ESLs, such as tides, storm surges, waves, and regionalized mean SLR, as well as most of their non-linear interactions, are included. This study aims at assessing the impact of dynamically simulating future changes in ESL drivers compared to a static approach that does not consider the impact of climate change on ESL distribution. Projected changes in ESLs are analysed using non-stationary extreme value analyses over the whole 1970–2100 period under the SSP5-8.5 and SSP1-2.6 scenarios. The impact of simulating dynamic changes in extremes is found to be statistically significant in the Mediterranean Sea, with differences in the decennial return level of up to +20 % compared to the static approach. This is attributed to the refined mean SLR simulated by the regional ocean general circulation model. In other parts of our region, we observed compensating projected changes between coastal ESL drivers, along with differences in timing among these drivers. This results in future changes in ESLs being primarily driven by mean SLR from the global climate model used as boundary conditions, with coastal contributions having a second-order effect, in line with previous research.

A deductive approach of 3D complex fault modeling and application

A deductive approach of 3D complex fault modeling and application

A Wavelet Multi-Scale Takagi-Sugeno Fuzzy Approach for Financial Time Series Modeling

Fuzzy logic has been introduced as a modeler suitable for many situations where the data may be uncertain, and difficult to be described via the existing exact and analytic tools. However, although fuzzy models have succeeded to fit many situations, they fail in many others especially nonlinear, non-stationary, volatile, fuzzy, and fluctuated data such as financial time series. In this context, the need has emerged for more effective models to describe the data while preserving the fuzzy model as a basic descriptor of data fuzziness. In the present work, we develop a hybrid approach combining the Takagi-Sugeno fuzzy model with the wavelet decomposition to investigate financial time series as complex systems. The new approach showed effectively a high performance compared to existing methods via error estimates and Lyapunov theory of stability. The model is applied empirically to the Saudi Arabia Tadawul market traded over the period January 01, 2011, to December 31, 2022, a period characterized by many critical movements and phenomena such as the Arab Spring, Qatar embargo, Yemen war, NEOM project, 2030 KSA vision and the last COVID-19 pandemic, which makes its study of great importance to understand markets situations and also for policymakers, managers and investors.

Seismic performance of inclined discontinuous broken-back block-type gravity quay walls: Numerical investigation and GEP-based modeling

This study explores the seismic performance of discontinuous broken-back block-type gravity quay walls with varying configurations using the finite element method (FEM). For this purpose, plane strain FE meshes are constructed by integrating adaptive meshing and error-based adaptivity techniques. To simulate discontinuities along the wall height and the interaction between the wall and the neighboring medium, interface elements are comprehensively applied between the concrete blocks and between the wall and the surrounding soils. The developed FE models are initially validated against observations from corresponding 1g shaking table tests available in the literature. The primary objective is to evaluate the effectiveness of different inclination angles of the wall body, its base block, and the underlying seabed soil layer in mitigating seismic deformations and lateral earth pressures. FE results reveal that simultaneously inclining the wall body, base block, and seabed layer by 9° significantly reduces permanent horizontal displacement, base sliding, and settlement of the wall. Under a worst-case seismic scenario with a PGA of 0.9g and a frequency of 3 Hz, these deformation parameters decrease by 37.26%, 14.47%, and 48.88%, respectively, compared to the non-inclined quay wall. The suggested mitigation approach improves the overall stability and serviceability of ports and their inland infrastructures during seismic and post-seismic events. This study also develops a predictive polynomial model utilizing Gene Expression Programming (GEP) based on 630 data points derived from validated FE models. The GEP model aims to predict the horizontal displacement of both inclined and non-inclined broken-back block-type quay walls. The novelty of the current research lies in the investigation of inclined configurations as a remediation technique to mitigate deformations and enhance the seismic resilience and performance of broken-back block-type quay walls, utilizing an advanced FE modeling approach. Additionally, a practical machine learning-based model is proposed to estimate the wall's seismic deformations.

Comparative effectiveness of digital variance and subtraction angiography in lower limb angiography: A Monte Carlo modelling approach

ObjectiveBy modelling patient exposures of interventional procedures, this study compares the reduction of radiation detriment between Digital Variance Angiography (DVA) and Digital Subtraction Angiography (DSA). MethodsThe paper presents a retrospective risk assessment using an in-house developed tool on 107 patient exposures from a clinical trial of DVA used to diagnose peripheral arterial disease (PAD). DICOM exposure parameters were used to initiate the PENELOPE (PENetration and Energy LOss of Positrons and Electrons) Monte Carlo simulation, radiation quality and quantity, and irradiation geometry. The effective dose and the lifetime attributable risk (LAR) for cancer incidence and mortality are calculated based on the International Commission on Radiation Protection’s (ICRP) 103 recommendations and the Committee on the Biological Effects of Ionising Radiations’ latest (BEIR VII) report, respectively. ResultsThe study found that procedures conducted using DVA significantly reduce the radiation exposure of patients, compared to DSA. The collective effective dose for the DVA group was 58% lower than that for the DSA group. Correspondingly, the LAR of different organs showed a substantial decrease for cancer incidence (25–75%) and mortality (51–84%). ConclusionDVA demonstrates a considerable reduction in physical dosimetric quantities and consequently effective dose and cancer risk, suggesting its potential as a safer alternative to DSA in interventional radiology. The use of DVA supports the optimisation of patient radiation protection and aligns with the principles of ALARA (as low as reasonably achievable).

Tree demographic drivers across temperate rain forests, after accounting for site-, species-, and stem-level attributes.

Diverse drivers such as climate, soil fertility, neighborhood competition, and functional traits all contribute to variation in tree stem demographic rates. However, these demographic drivers operate at different scales, making it difficult to compare the relative importance of each driver on tree demography. Using c. 20,000 stem records from New Zealand's temperate rain forests, we analyzed the growth, recruitment, and mortality rates of 48 tree species and determined the relative importance of demographic drivers in a multilevel modeling approach. Tree species' maximum height emerged as the one most strongly associated with all demographic rates, with a positive association with growth rate and negative associations with recruitment and mortality rates. Climate, soil properties, neighborhood competition, stem size, and other functional traits also played significant roles in shaping demographic rates. Forest structure and functional composition were linked to climate and soil, with warm, dry climates and fertile soil associated with higher growth and recruitment rates. Neighborhood competition affected demographic rates depending on stem size, with smaller stems experiencing stronger negative effects, suggesting asymmetric competition where larger trees exert greater competitive effects on smaller trees. Our study emphasizes the importance of considering multiple drivers of demographic rates to better understand forest tree dynamics.

Coupling Pre- and Postnatal Infant Exposures with Physiologically Based Pharmacokinetic Modeling to Predict Cumulative Maternal Levetiracetam Exposure During Breastfeeding.

Although breastfeeding ensures optimal infant development and maternal health, mothers taking medications may abandon breastfeeding because of uncertainties regarding toxicity to infants. Current methods in predicting infant risk to maternal medication exposure do not account for breastfeeding-related variability or in utero exposure via the umbilical cord (UC). Previously, our workflow integrated variability in infant anatomy and physiology, breast milk intake volume, and drug concentrations in breast milk using physiologically based pharmacokinetic (PBPK) modeling. The upper area under the curve ratio (UAR) was then calculated to assess infant risk from maternal drug. Herein, we enhanced this workflow by coupling pre- and postnatal exposures to predict the overall levetiracetam exposure in breastfeeding infants. A published pediatric PBPK model of levetiracetam was used to simulate an infant population (n = 100). Daily infant doses were simulated using a weight-normalized milk intake model to calculate volumes ingested across age groups, alongside literature-derived or simulated milk concentrations across maternal doses to predict infant concentrations. Published UC concentrations were used to develop a cord-coupled neonatal model (CCM), which was integrated with the PBPK and milk intake models and evaluated by comparing observed and simulated infant blood concentrations using a 90% prediction interval (PI). UC concentration data from 14 mothers were used to develop the CCM. A total of 16 paired (known milk concentrations) and two unpaired (unknown milk concentrations) individual infant concentrations were identified for evaluating the model along with population values of 64 infants from two age groups (2-4 and 7-31days). The CCM improved the predictions overall compared with the original workflow, largely due to improvements for the youngest age group evaluated. Overall, 83% (10 of 12) of the individual infant plasma concentrations were successfully captured within the 90% PI for the paired, quantifiable (i.e. above the limit of quantification) evaluation datasets. After administration of a maternal dose of levetiracetam 2000mg, the calculated UAR ranged from 0.13 to 0.27 for the 95th percentile infants. To our knowledge, this is the first report to combine prenatal levetiracetam exposures from the UC and postnatal exposures from breastfeeding to predict overall infant drug exposure. The results indicate that infant exposure in infants aged 0-7 days may approach therapeutic levels of levetiracetam in the highest-risk infants (i.e. 95th percentile), with a low likelihood of adverse effects based on published clinical studies. This integrated modeling approach provides a more holistic analysis of neonatal exposures. It can be applied in future studies to derive the UAR of drugs administered during breastfeeding to identify infants at risk of potential toxicity.