Static Modeling Approach Research Articles

Forecasting disease trajectories in critical illness: comparison of probabilistic dynamic systems to static models to predict patient status in the intensive care unit

ObjectiveConventional prediction models fail to integrate the constantly evolving nature of critical illness. Alternative modelling approaches to study dynamic changes in critical illness progression are needed. We compare static risk...

Utility of PBPK Modeling in Predicting and Characterizing Clinical Drug Interactions.

Physiologically-based pharmacokinetic (PBPK) modeling is a mechanistic dynamic modeling approach that can be used to predict or retrospectively describe changes in drug exposure due to drug-drug interactions. With advancements in commercially available PBPK software, PBPK DDI modeling has become a mainstream approach from early drug discovery through to late stage drug development and is often utilized to support regulatory packages for new drug applications. This minireview will briefly describe the approaches to predicting DDI utilizing PBPK and static modeling approaches, the basic model structures and features inherent to PBPK DDI models and key examples where PBPK DDI models have been used to describe complex DDI mechanisms. Future directions aimed at using PBPK models to characterize transporter-mediated DDI, predict DDI in special populations and assess the DDI potential of protein therapeutics will be discussed. A summary of the 209 PBPK DDI examples published to date in 2023 will be provided. Overall, current data and trends suggest a continued role for PBPK models in the characterization and prediction of DDI for therapeutic molecules. Significance Statement PBPK models have been a key tool in the characterization of various pharmacokinetic phenomenon, including drug-drug interactions. This minireview will highlight recent advancements and publications around PBPK DDI modeling, an important area of drug discovery and development research in light of the increasing prevalence of polypharmacology in clinical settings.

A modelling framework for modern heritage buildings energy simulation

The latest European polices highlight the urgent need to rehabilitate the existing building stock, responsible for 40 % of the EU's total energy consumption. In this process, a key role is played by thermal simulations, assessing the effective energy performances. However, significant discrepancies between real and simulated consumptions are frequently outlined. Inaccurate results are particularly dangerous for modern buildings, which, rarely protected, are often altered by invasive retrofitting solutions, with little regard for their heritage and cultural value. This paper introduces a comprehensive framework for the building energy simulation, ensuring the necessary model credibility. It consists of dynamic modelling, calibration and validation, enhancing the usefulness of the final results. A validated model is in fact the premise to propose a well-balanced retrofitting scenario, improving the current energy performances, reducing the operational costs, and preserving the historical values of existing buildings. As operative case-study, Chauderon administrative building in Lausanne (1969–1974), designed by the Atelier AAA in collaboration with Jean Prouvé, has been selected. Today, the complex is well-preserved in its original materiality and represents an iconic example of the modern aesthetics, with an expressed need for retrofitting. Following the proposed framework, a reliable model in WUFIplus has been created and validated according to ASHRAE 14, allowing to reliably test the efficacy of future retrofitting scenarios. The final aim of this process is to minimize the risk of inappropriate interventions. For high-quality or recognized post-World War II building stock, the use of a calibrated and validated model is justified and recommended over the static or simplified modelling approaches still commonly employed today.

Determination of the critical depths of indentations for local rolling contact fatigue initiation

Scaled twin-disc tests are conducted to study the initiation of local rolling contact fatigue (LRCF) from indentations on discs of U71MnG rail indented by sharp tap tips. Critical depths over which LRCF initiates are found for indentations of different geometries under oil lubrication. Simulating the indenting process with a static finite element modelling approach, a critical plastic strain of 170% corresponding to the critical depths is determined. Assuming this critical strain is transferrable, size-dependent critical depths are further derived for indentations on actual rails using the same modelling approach. After verification by LRCF damages in practice, a linearized relationship between the critical depth and the characteristic length is proposed for actual indentations, being 2.3–3.2 mm deep for ones with a characteristic length of 4.0–10.0 mm.

Petroleum system analysis-conjoined 3D-static reservoir modeling in a 3-way and 4-way dip closure setting: Insights into petroleum geology of fluvio-marine deposits at BED-2 Field (Western Desert, Egypt)

Imperfect determination of petroleum system processes coincidence, entrapment and charge timing, along with reservoir heterogeneity can considerably cause high risks throughout exploration and development phases of petroleum. Therefore, a complete subsurface visualization of the petroleum system nature, elements and processes, is badly required. To this end, we corroborated, in this study, static reservoir modeling with petroleum system analysis workflows, to better characterize the Cenomanian fluvio-marine reservoir, sandstones of Bahariya Formation, at Bed-2 Field, Abu Gharadig basin (Western Desert, Egypt). We used dataset of 2D seismic profiles and well logs of eight wells. The geometry and property of the reservoir were acquired performing static reservoir geocellular modeling approach. The geohistory, timing of charge, migration pathways, and accumulation sites were identified performing 1D and 2D basin modeling approaches. Combining both approaches was aimed at identifying new petroleum prospect areas, and estimating the hydrocarbon volumes, that are the need for such poorly-defined petroleum systems area. As indicated by the constructed, robust, reservoir and 1D-2D basin models, an additional hydrocarbon prospect, to the north central part of Bed-2 Field, is proposed to be drilled during the further oilfield development phases of the area. This prospect has all features that adequately lead to reliable inferences regarding the ultimate petroleum potential of the area. • Reservoir modeling delineating geometry and property, with depositional facies analysis unlocking reservoir heterogeneity • Petroleum system analysis workflow defining the hydrocarbon charge timing, confirming the hydrocarbon potential of the area • Pixel-based stochastic simulation algorithms modeling the rock property. • Hydrocarbon volumes assessment, gas initially in place (GIIP) and stock-tank oil initially in place (STOIIP), in the area • Defining new prospect areas, justifying future oilfield development plans

Evaluation of Renal Impairment Influence on Metabolic Drug Clearance using a Modelling Approach.

Chronic kidney disease (CKD) may alter drug renal elimination but is also known for interacting with hepatic metabolism via multiple uremic components. However, few global models, considering the five major cytochromes, have been published, and none specifically address the decrease in cytochrome P450 (CYP450) activity. The aim of our study was to estimate the possibility of quantifying residual cytochrome activity as a function of filtration rate, according to the data available in the literature. For each drug in the DDI-predictor database, we collected available pharmacokinetic data comparing drug exposition in the healthy patient and in various stages of CKD, before building a model capable of predicting the variation of exposure according to the degree of renal damage. We followed an In vivo Mechanistic Static Model (IMSM) approach, previously validated for predicting change in liver clearance. We estimated the remaining fraction parameters at glomerular filtration rate (GFR) = 0 and the alpha value of GFR to 50% impairment for the 5 major cytochromes using a non-linear constrained regression using Matlab software. Thirty-one compounds had usable pharmacokinetic data, with 51 AUC ratios between healthy and renal impaired patients. The remaining CYP3A4 activity was estimated to be 0.4 when CYP2D6, 2C9, 2C19 and 1A2 activity was estimated to be 0.43; 1; 0.73 and 0.7, respectively. The alpha value was estimated to be at 6.62; 25; 9.8; 1.38 and 11.04 for each cytochrome. In comparison with published data, all estimates but one were correctly predicted in the range of 0.5-2. Our approach was able to describe the impact of CKD on metabolic elimination. Modelling this process makes it possible to anticipate changes in clearance and drug exposure in CKD patients, with the advantage of greater simplicity than approaches based on physiologically-based pharmacokinetic modelling. However, a precise estimation of the impact of renal failure is not possible with an IMSM approach due to the large variability of the published data, and thus should rely on specific pharmacokinetic modelling for narrow therapeutic margin drugs.

A Novel Teaching-learning Method of Nerve Injury Using Banana and Toothpick: A Low Fidelity Static Model Approach

No abstract available

Disentangling direct and indirect effects of extreme events on coastal wetland communities.

One of the primary ways in which climate change will impact coastal freshwater wetlands is through changes in the frequency, intensity, timing and distribution of extreme weather events. Disentangling the direct and indirect mechanisms of population- and community-level responses to extreme events is vital to predicting how species composition of coastal wetlands will change under future conditions. We extended static structural equation modelling approaches to incorporate system dynamics in a multi-year multispecies occupancy model to quantify the effects of extreme weather events on a coastal freshwater wetland system. We used data from an 8-year study (2009-2016) on St. Marks National Wildlife Refuge in Florida, USA, to quantify species-specific and community-level changes in amphibian and fish occupancy associated with two flooding events in 2012 and 2013. We examine how physical changes to the landscape, including potential changes in salinity and increased wetland connectivity, may have contributed to or exacerbated the effects of these extreme weather events on the biota of isolated coastal wetlands. We provide evidence that the primary effects of flooding on the amphibian community were through indirect mechanisms via changes in the composition of the sympatric fish community that may have had lethal (i.e. through direct predation) or non-lethal (i.e. through direct or indirect competitive interactions) effects. In addition, we have shown that amphibian species differed in their sensitivity to direct flooding effects and indirect changes in the fish community and wetland-specific conductance, which led to variable responses across the community. These effects led to the overall decline in amphibian species richness from 2009 to 2016, suggesting that wetland-breeding amphibian communities on St. Marks National Wildlife Refuge may not be resilient to predicted changes in coastal disturbance regimes because of climate change. Understanding both direct and indirect effects, as well as species interactions, is important for predicting the effects of a changing climate on individual species, communities and ecosystems.

The quantum dynamics of H2 on Cu(111) at a surface temperature of 925K: Comparing state-of-the-art theory to state-of-the-art experiments.

We present results of our recently expanded static corrugation model (SCM) approach that included the relevant surface temperature effects, applied to the dissociative chemisorption reaction of H2 on a Cu(111) surface. The reaction and rovibrationally elastic scattering probabilities that we obtain at a quantum dynamical (QD) level, as an average of many statically distorted surface configurations, show great agreement with those of a dynamic surface model, which reinforces the validity of the sudden approximation inherent to the SCM. We further investigate several simple methods of binning the final rovibrational state of quasi-classical dynamics simulations, to find those best suited to reproduce the QD results for our system. Finally, we show that the SCM obtained results reproduce experimental dissociation curves very well, when the uncertainty in experimental saturation values is taken into account. Some indication of a slow channel, so far only observed in experiment, can also be found at low incidence energies, although more rigorous QD simulations are required to reduce the noise inherent to our propagation methods.

A static modeling approach to the Brown Limestone carbonate reservoir, Geisum Oilfield, Gulf of Suez, Egypt

The Gulf of Suez in Egypt contains more than 80 conventional oil fields with reservoirs from Precambrian up to Quaternary age. To date, these fields have all been conventional resources. This abstract will take part of the Gulf of Suez sequence within the fields of Petrogulf Misr Company and present a work process for unconventional resource assessment of the Brown Limestone formation within one of these areas. The Brown Limestone formation is a Late Cretaceous Pre-rift mega sequence succession and plays an important role in the conventional system of Gulf of Suez, Brown Limestone formation is not only as one of the important source rocks, but also a fractured carbonate reservoir in multiple fields especially is the southern Geisum oil field. However, this formation is characterized by uncertainty due to the complexity of reservoir architecture, various lithologies, lateral facies variations, and heterogeneous reservoir quality. These reservoir challenges, in turn, affect the effectiveness of further exploitation of this reservoir along the Gulf of Suez Basin. In this work, we conduct an integrated study using multidisciplinary datasets and techniques to determine the precise structural, petrophysical, and facies characteristics of the Brown Limestone Formation and predict their complex geometry in 3D space. The Brown Limestone formation is considered to be as a reservoir in the study area. The value of water saturation ranges from 15 to 45%, where the value of Effective Porosity ranges from 11 to 15% for the selected potential intervals in Brown Limestone due to the highly structural setting in the study area, so Reservoir thickness was used as the proxy for reservoir effectiveness where thicker reservoir had a higher chance of containing multiple intervals for good potential intervals.

Hybrid modeling approach for the tether of an airborne wind energy system

The design of controllers for the automatic operation of airborne vehicles is challenging. Especially in the emerging research field of airborne wind energy, where tethered aircraft convert energy from wind at higher altitudes into electricity, the development of robust and safe control systems will be a critical success factor. Typically, these systems consist of one or more controlled aircraft, a ground station, and one or more tethers that transfer aerodynamic forces acting on the wings to the ground. To understand these systems and control them for real-time simulations, suitable tether models must extend the classical flight mechanical models. In this paper, a hybrid approach is presented, combining an elastic spring model with a catenary model of the tether. In particular, for real-time simulations in a model-in-the-loop environment where a static modeling approach is sufficient, this hybrid model is intended to provide an alternative to the commonly used spring models. Furthermore, such a static tether model neglects high-frequency dynamic oscillations that can occur with dynamic lumped mass models. As consequence, the focus is on primary influences such as stiffness and sag due to external loads. The developed hybrid model is analyzed and compared with the spring model for a simulated tethered flight. It can be concluded that this hybrid static model, although subject to strong assumptions such as symmetrical tether sag, represents the tether more accurately. For example, it allows a more precise design of the flight controller before conducting any flight tests. This model can be used as a reasonable alternative to the simple spring model whenever a static modeling approach of the tether is adequate.

A Static Modeling Approach for Thin-Walled Soft Robotic Arms Considering Geometric and Material Nonlinearity

The current design based on air chambers makes thin wall a typical feature of soft robotic arms. Considering this characteristic, a new static modeling method that integrates the principle of virtual work and a spring-fluid equivalent modeling idea is proposed in this work. The method is able to consider both geometric and material nonlinearity of the soft robotic arm. A general physical model of the thin-walled soft robotic arm and the static modeling process are presented. Then, an experimental prototype of the thin-walled soft robotic arm is fabricated and the experimental verification of the proposed method is carried out. The comparisons between the theoretical prediction and experimental results under different load conditions illustrate that the proposed static modeling method can be used to describe the static deformation of soft robotic arms with high accuracy. The proposed static modeling method provides a new way to model the soft robotic arm and has guiding significance for the design of soft robotic arms.

COMPONENT BASED AGGREGATE LOAD MODELLING OF MODERN LOADS

Modelling of the different power system components is very essential for the adequate operation and planning of the network. Load modelling is one of the areas in power system engineering that is often overlooked but has a considerable impact on power system dynamics. Load modelling still needs further investigation to develop an adequate and precise load model. Consumer's demand depends on their behaviour, seasonal factor, time factor and other exogenous factors. It is a challenging task to maintain the power quality, stability and adequacy of the system with a changing load behaviour. This paper focuses on the mathematical representation of residential and commercial load characteristics of a distribution feeder from bottom-up approach. A composite static load modelling approach is presented in the paper, which is classified into three major sections i.e. load inventory survey, laboratory experiment and parameter estimation. Consumer's load behaviour and appliances have been determined from the survey. Similarly, voltage-power correlation of load from laboratory experiments assist to classify the consumer's load and parameter estimation is based on the load composition of a certain time duration. Load composition here incorporates the duty cycle and weekly factor along with other related consumer data.

Effects of a negative interest rate policy in bank profitability and risk taking: Evidence from European banks

This paper analyses the effect of a negative interest rate policy (NIRP) on profitability and risk taking in the European banking sector and whether this effect is differentiated according to the bank business model. Using a dataset of 2596 banks from 29 European countries over the period 2011–2019 and applying a static modelling approach, we conclude that the implementation of NIRPs lowers the net interest margin and the return on assets of a representative bank by 14.5 basis points and 18.5 basis points, respectively. We also conclude that a decrease in the short-term interest rate lowers the net interest margin when interest rates are already negative. In an environment of negative interest rates, we do not find that European banks take on more risk and conclude that the effects of the implementation of NIRPs influence banks’ profitability and risk taking differently, depending on the business model adopted.

3D Integrated Structural, Facies and Petrophysical Static Modeling Approach for Complex Sandstone Reservoirs: A Case Study from the Coniacian\u2013Santonian Matulla Formation, July Oilfield, Gulf of Suez, Egypt

The Coniacian–Santonian Matulla Formation is one of the important reservoirs in the July oilfield, Gulf of Suez Basin. However, this formation is characterized by uncertainty due to the complexity of reservoir architecture, various lithologies, lateral facies variations and heterogeneous reservoir quality. These reservoir challenges, in turn, affect the effectiveness of further exploitation of this reservoir along the Gulf of Suez Basin. In this work, we conduct an integrated study using multidisciplinary datasets and techniques to determine the precise structural, petrophysical, and facies characteristics of the Matulla Formation and predict their complex geometry in 3D space. To complete this study, 30 2D seismic sections, five digital well logs, and core samples of 75 ft (ft = 0.3048 m) length were used to build 3D models for the Matulla reservoir. The 3D structural model shows strong lateral variation in thickness of the Matulla Formation with NW–SE, NE–SW and N–S fault directions. According to the 3D facies model, shale beds dominate the Matulla Formation, followed by sandstone, carbonate, and siltstone beds. The petrophysical model demonstrates the Matulla reservoir's ability to store and produce oil; its upper and lower zones have good quality reservoir, whereas its middle zone is a poor quality reservoir. The most promising areas for hydrocarbon accumulation and production via the Matulla reservoir are located in the central, southeast, and southwest sectors of the oilfield. In this approach, we combined multiple datasets and used the most likely parameters calibrated by core measurements to improve the reservoir modeling of the complex Matulla reservoir. In addition, we reduced many of the common uncertainties associated with the static modeling process, which can be applied elsewhere to gain better understanding of a complex reservoir.

Static Modeling of the Fiber-Reinforced Soft Pneumatic Actuators Including Inner Compression: Bending in Free Space, Block Force, and Deflection upon Block Force.

Fiber-reinforced soft pneumatic actuators (FR-SPAs) are among the most successful soft actuators in the soft robotics community considering their structural strength, motion range, and force output. Inspired by the pneumatic artificial muscle, the bending-type tubular SPAs have also been applied with fiber winding for body reinforcement and then utilized in many applications. Due to their superior utility and popularity, FR-SPAs have been extensively modeled using different methods. However, very little attention was given to the compression on the inner wall of the FR-SPAs by the compressed air. Furthermore, there is no unified modeling principle for bending, force output, and deflection of the FR-SPA. In this article, we take into account the inner compression and propose a static modeling approach based on the moment equilibrium for three important and frequently studied cases: free bending, block force, and deflection upon block force of the FR-SPA. Stress analysis of the material inside the fiber wall is conducted, which reveals the influence of radial compression by the input air. Then, the new stress expression is used in the moment equilibrium and results in the SPA models for the three aforementioned cases. The models are experimentally verified using SPAs featuring two profile designs and three different silicone rubbers. The results show that the placement of the fiber wall plays an important role in the SPA behavior. More importantly, the models successfully differentiate the two profiles and produce fairly accurate predictions of the bending angles, block forces, and deflections. The understanding of the compression effect offers a new variable in the FR-SPA design process, which can be used to tune the SPA properties for different applications.

Modelling the habitat of the endangered Carpentarian Grasswren (Amytornis dorotheae): The importance of spatio-temporal habitat availability in a fire prone landscape

Species distribution modelling (SDM), a tool increasingly adopted to quantify geographic range size, often predicts species’ distributions as static. However, habitat availability may exhibit spatial and temporal variation when dynamic processes, such as fire, determine suitability. Static SDM approaches may not satisfactorily represent this dynamic process. We investigated the potential use of SDM to quantify dynamic habitat availability by applying the MaxEnt SDM technique to model the habitat of the Carpentarian Grasswren (Amytornis dorotheae), an endangered Australian passerine dependent on long unburnt vegetation in a fire prone system. By adjusting a typical SDM approach to incorporate the dynamic nature of fire, we modelled the spatio-temporal variation of suitable habitat over 12 years and compared it to a static modelling approach. Incorporating fire as a dynamic process increased the importance of the fire variable to models (from <18% to >35% permutation importance) and improved model performance, as evaluated by the AUC using cross-validation. Our dynamic model revealed sizeable temporal variation in the area and spatial arrangement of suitable habitat that was not apparent in the static model. This result may partly solve the mystery of why the species occurs as widely separated populations despite the presence of seemingly suitable intervening habitat. In areas where the species is no longer found, habitat availability was less consistent due to frequent fire, and fire refugia was more limited and isolated, when compared to sites with recent records. These results demonstrate that, when compared to a static approach, a dynamic SDM approach can lead to improved understanding of dynamic ecological processes, and their impact on a species.

Predicting and 3D modeling of karst zones using seismic facies analysis in Ordovician carbonates of the Tahe oilfield, China

Karsted carbonates of the Ordovician Yingshan Formation represent significant hydrocarbon reservoirs in the Tarim Basin, China. Due to the geologic complexity of the formation, realistically predicting and modeling karst zones and rock properties is challenging. This drives the need to apply diverse techniques for building a suitable geologic model. We have developed a static model approach that uses fully automated seismic facies classification processes for predicting and modeling patterns associated with karst elements. Our method uses a seismic attribute and well logs as input data. We initially processed a seismic facies volume using the hierarchical clustering technique. This is based on seismic attribute values that take into account an optimal number of classes. The outcome reveals various patterns illustrated with low amplitudes highlighting the geomorphology of paleokarst elements. Simultaneously, a seismic traces map of the karsted interval was processed using the hybrid clustering technique conducted on seismic trace shape. In this case, the karst facies was extracted from the output and used as secondary input data in trend analysis of the model. Both outputs obtained from clustering techniques are processed in a volume of the most probable facies, which delineate the karst patterns. The results of the modeling process are visualized in various time slices and cross sections, appropriately recognizing the relationship of estimated patterns with karst zones. We have evaluated the karstification thickness and porosity map obtained from the 3D model that detail a reasonable connectivity between karst elements. This is based on the paleogeographic location and type of filling, as well as the dissolution development along the main striking faults. Finally, our method outputs a logical model of karst zones located within the host rock, which reduces the uncertainty and identify nonperforated segments.

Comparative Analysis of Bioenergy Crop Impacts on Water Quality Using Static and Dynamic Land Use Change Modeling Approach

Researchers and federal and state agency officials have long been interested in evaluating location-specific impact of bioenergy energy crops on water quality for developing policy interventions. This modeling study examines long-term impact of giant miscanthus and switchgrass on water quality in the Cache River Watershed (CRW) in Arkansas, United States. The bioenergy crops were simulated on marginal lands using two variants of a Soil and Watershed Assessment Tool (SWAT) model. The first SWAT variant was developed using a static (single) land-use layer (regular-SWAT) and for the second, a dynamic land-use change feature was used with multiple land use layers (location-SWAT). Results indicated that the regular-SWAT predicted larger losses for sediment, total phosphorus and total nitrogen when compared to location-SWAT at the watershed outlet. The lower predicted losses from location-SWAT were attributed to its ability to vary marginal land area between 3% and 11% during the 20-year modeling period as opposed to the regular-SWAT that used a fixed percentage of marginal land area (8%) throughout the same period. Overall, this study demonstrates that environmental impacts of bioenergy crops were better assessed using the dynamic land-use representation approach, which would eliminate any unintended prediction bias in the model due to the use of a single land use layer.

Machine Learning Strategy That Leverages Large Data sets to Boost Statistical Power in Small-Scale Experiments.

Machine learning methods have proven invaluable for increasing the sensitivity of peptide detection in proteomics experiments. Most modern tools, such as Percolator and PeptideProphet, use semi-supervised algorithms to learn models directly from the datasets that they analyze. Although these methods are effective for many proteomics experiments, we suspected that they may be suboptimal for experiments of smaller scale. In this work, we found that the power and consistency of Percolator results was reduced as the size of the experiment was decreased. As an alternative, we propose a different operating mode for Percolator: learn a model with Percolator from a large dataset and use the learned model to evaluate the small-scale experiment. We call this a “static modeling” approach, in contrast to Percolator’s usual “dynamic model” that is trained anew for each dataset. We applied this static modeling approach to two settings: small, gel-based experiments and single-cell proteomics. In both cases, static models increased the yield of detected peptides and eliminated the model-induced variability of the standard dynamic approach. These results suggest that static models are a powerful tool for bringing the full benefits of Percolator and other semi-supervised algorithms to small-scale experiments.