Parameters In Models Research Articles

Predicting atrial fibrillation risk following embolic stroke of unknown source using cardiac magnetic resonance imaging

Abstract Background Approximately 25% of embolic strokes of unknown source (ESUS) are attributed to undiagnosed atrial fibrillation (AF).1 The European Stroke Organisation (ESO) recommends implantable loop recorder (ILR) monitoring to identify ESUS patients with AF, permit the initiation of anticoagulation and reduce risk of recurrent stroke.2 However, this approach delays treatment and carries significant cost and resource implications. An alternative approach, where early anticoagulation is initiated in the absence of AF has failed to reduce recurrent stroke in four randomised controlled trials (NAVIGATE ESUS, RE-SPECT ESUS, ATTICUS and ARCADIA).3-6 An urgent need exists for new strategies to identify ESUS patients with undiagnosed AF that are at high risk of future thromboembolism. Purpose The primary objective of this study was to produce a substrate-based predictive model for AF in ESUS patients using atrial cardiac magnetic resonance imaging (CMRi). Method The Atrial CARdiac Magnetic resonance imaging in patients with embolic stroke of unknown source without documented AF (CARM-AF) Study is a prospective, multi-centre, observational study. All patients underwent CMRi and ILR insertion for AF detection within 3 months of ESUS. The main inclusion criteria were expected survival >12 months, CHA2DS2VASc≥3 and eGFR >30ml/min. Patients were allocated to the study group if AF >30 seconds was detected by ILR during the first year of follow-up and control group if no AF detected (Figure 1). Univariable analysis was used to identify clinical, echocardiographic and CMR parameters associated with AF diagnosis. Parameters with p<0.2 and no co-linearity were used to develop multivariable logistic regression models for AF prediction. Results From September 2020 to September 2022, 102 patients were enrolled and 91 were included in the final analysis. AF was detected in 17 patients during the first year of follow-up (18.6%). In univariable analysis, patients with AF were more likely to be female (p=0.02), but there were no significant differences in age, race, body mass index (BMI) or CHA2DS2VASc score (p=0.06, 0.272, 0.195, 0.211) between groups. Increased left atrial (LA) volume, surface area and reduced LA ejection fraction were associated with increased risk of AF detection at 1 year (p=0.006, 0.044, 0.008). LA fibrosis and sphericity were not associated with AF (p=0.84, 0.98). Logistic regression models for AF risk prediction were developed using patient, echocardiographic and CMR parameters. A combined model of patient and CMR parameters outperformed assessment of patient characteristics alone, achieving an AUC of 0.85 for predicting AF occurrence (p<0.005). Pseudo R2 of 0.30 (McFadden) indicated excellent model fit (Figure 2). Conclusion CARM-AF is the first study to demonstrate feasibility and utility of CMR imaging to predict AF risk following ESUS. A randomised controlled trial initiating anticoagulation using the developed model is recommended.Study DesignModel Comparison (ROC Curves)

Modeling the dissemination of privacy information in online social networks

AbstractDriven by the rapid development of information technology, online social networks (OSNs) have experienced a fast development in recent years, allowing increasingly more people to share and spread information over OSNs. The rapid rise of OSN platforms such as Facebook and Twitter is sufficient evidence of such development. As one type of information, privacy information can also be created and disseminated over an OSN, posing a severe threat to individual privacy. This article attempts to construct a model for disseminating privacy information in OSNs and to analyze the model by simulating the dissemination process of privacy information in OSNs. First, we establish network models that exhibit the main characteristics of OSNs. Second, by considering the factors related to social relationships, especially intimacy between users and the attention of users to the privacy subject, we derive the parameters for privacy information dissemination models in OSNs. Third, based on the theory of information dissemination dynamics, we construct a model for information dissemination that conforms to the properties of privacy information. We also present some experimental results based on the constructed model and analyze the characteristics of privacy information dissemination. Fourth, we study and verify the various properties of the model through a set of experiments. The proposed model provides the opportunity to better understand the dynamics of privacy information dissemination in OSNs and the effect of user behavior on dissemination.

Selective online model updating in hybrid simulation of a full‐scale steel moment frame

AbstractThis study presents the implementation of an online model updating algorithm within a full‐scale hybrid simulation (HS) of a six story, four bay steel moment frame. The experimental substructure consists of a cruciform subassembly generating critical data on the nonlinear behavior of the first story column and two beams with reduced beam sections (RBSs) on each side. The updating algorithm focuses on the modeling parameters of plastic hinge elements representing the RBSs in the numerical model. A smooth plasticity model is utilized for beam plastic hinges with updating parameters identified from on‐line experimental data through a modified version of the unscented Kalman filter. The HS shows that the numerical beam hinges based on simple hysteretic model with updated parameters are able to capture the characteristic behavior observed in experiments. Due to fracture of beam flanges is observed in the experiments, a selective updating concept is proposed to allow for updating multiple numerical components accounting for asymmetric behavior and variability in the response. The selective updating method is validated through virtual HSs that are better able to identify and isolate the effects of fracture and other behavioral characteristics. The combination of results from physical and virtual tests highlights the benefits of model updating on the local and overall system‐level response.

Parameters optimization of solar PV cell using genetic-iterative algorithm

Accurately determining optimal parameters from current-voltage (IV) data in solar photovoltaic (PV) models is crucial for effective system simulation and control. In this study, we propose a novel approach that combines genetic algorithm and iterative techniques maximizing their strengths, and exploiting the influence of each parameter on the IV curve to categorize them into groups. This adaptable method can adjust the interval of each parameter to different scenarios during optimization. We evaluated the method across various solar cell models including both the ‘SDM’ and ‘SDM-based PMM’, achieving notable accuracy and reliability compared to other advanced meta-heuristic algorithms. The results indicate a value of 7.3870e-5 for the SDM and 9.3365e-4 for the PMM (STM6-40/36). The proposed algorithm demonstrates notable accuracy and reliability, highlighting its usefulness in accurately determining parameters in solar PV models.

Detection of outliers in survey–weighted linear regression

ABSTRACT Regression diagnostics help identify influential data points in a model. Detecting outliers in complex survey design data involving stratification, clustering, and unequal probability sampling is difficult due to the presence of masking, where one outlier makes it hard to detect others. The masking factor for survey–weighted linear regression is developed and applied to analyzing the Household Consumer Expenditure dataset of 68th round of the National Sample Survey Organization survey of India. Regression parameters are calculated before and after detection and removal of outliers. The standard error of regression parameters for survey-weighted least squares models is reduced by 2% for the intercept, 5% for variable “meat” (X 5), 4% for “served processed food” (X 9), and 4% for “packaged processed food” (X 10). Inference alters the significance of regression coefficients of the variable “served processed food” (X 9) leading to the emergence of significance. There is no change in inference for other variables.

Ganoderic acid A mitigates dopaminergic neuron ferroptosis via inhibiting NCOA4-mediated ferritinophagy in Parkinson’s disease mice

Ethnopharmacological relevanceGanoderma lucidum, a renowned tonic traditional Chinese medicine, is widely recognized for the exceptional activity in soothing nerves and nourishing the brain. It has been extensively employed to alleviate various neurological disorders, notably Parkinson's disease (PD). Aim of the studyTo appraise the antiparkinsonian effect of GAA, the main bioactive constituent of G. lucidum, and clarify the molecular mechanism through the perspective of ferritinophagy-mediated dopaminergic neuron ferroptosis. Materials and methodsPD mouse and cell models were established using 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) and 1-methyl-4-phenylpyridinium (MPP+), respectively. Cell viability, behavioral tests and immunofluorescence analysis were performed to evaluate the neurotoxicity, motor dysfunction and dopaminergic loss, respectively. Biochemical assay kits were used to determine the levels of iron, lipid reactive oxygen species (ROS), malondialdehyde (MDA), total ROS and glutathione (GSH). Western blot and immunofluorescence were applied to detect the expressions of nuclear receptor co-activator 4 (NCOA4), ferritin heavy chain 1 (FTH1), p62 and LC3B. Additionally, NCOA4-overexpressing plasmid vector was constructed to verify the inhibitory effect of GAA on the neurotoxicity and ferroptosis-related parameters in PD models. ResultsGAA significantly mitigated MPP+/MPTP-induced neurotoxicity, motor dysfunction and dopaminergic neuron loss (p＜0.01 or p＜0.05). In contrast to MPP+/MPTP treatment, GAA treatment decreased the levels of iron, MDA, lipid and total ROS, while increasing the GSH level. GAA also reduced the levels of NCOA4 and LC3B, and enhanced the expressions of FTH1 and p62 in PD models (p＜0.01 or p＜0.05). However, the protective effect of GAA against the neurotoxicity, NCOA4-mediated ferritinophagy and ferroptosis in PD model was abolished by the overexpression of NCOA4 (p＜0.01). ConclusionGAA exerted a protective effect on PD, and this effect was achieved by suppressing dopaminergic neuron ferroptosis through the inhibition of NCOA4-mediated ferritinophagy.

Providing predictive models for quality parameters of groundwater resources in arid areas of central Iran: A case study of kashan plain

Groundwater pollution can occur due to both anthropogenic and natural causes, leading to a decline in water quality and posing a threat to human health and the environment. The pollution of ground water resources with chemical pollutants is often considered. To manage water resources sustainably, ensuring their quality and quantity is crucial. Yet, testing groundwater can be expensive and time-consuming. So, using modeling to predict the chemical parameters of groundwater resources is considered to be an efficient and economical method. In this study, we examined three models to predict groundwater quality in dry regions by using R programming language. The random forest (RF) outperformed the other models in developing predictive models for water quality. Also, the multiple linear regression (MLR) model demonstrated strong performance, particularly in predicting total hardness (TH) in Aran Va Bidgol groundwater resources. The decision tree (DT) model did well but had lower performance than the RF model in predicting quality parameters. This approach can be efficacious in the field of effective management and protection of groundwater resources and enables the assessment of risks related to water resources.

Relation Models of Surface Parameters and Backscattering (or Radiation) Fields as a Tool for Solving Remote Sensing Problems

In this paper, an analysis of existing models for describing surfaces of various types is performed, and the possibilities of their application at the level of mathematical modeling are analyzed. Moreover, due to the large number of models and the complexity of selecting the appropriate model, e.g., when conducting a practical experiment, an algorithm for choosing a specific model depending on the initial data is proposed. According to the algorithm, a software prototype that implements this algorithm (written in Python) is proposed.

1H/17O Chemical Shift Waves in Carboxyl-Bridged Hydrogen Bond Networks in Organic Solids.

We report solid-state 1H and 17O NMR results for four 17O-labeled organic compounds each containing an extensive carboxyl-bridged hydrogen bond (CBHB) network in the crystal lattice: tetrabutylammonium hydrogen di-[17O2]salicylate (1), [17O4]quinolinic acid (2), [17O4]dinicotinic acid (3), and [17O2]Gly/[17O2]Gly·HCl cocrystal (4). The 1H isotropic chemical shifts found for protons involved in different CBHB networks are between 8.2 and 20.5 ppm, which reflect very different hydrogen-bonding environments. Similarly, the 17O isotropic chemical shifts found for the carboxylate oxygen atoms in CBHB networks, spanning a large range between 166 and 341 ppm, are also remarkably sensitive to the hydrogen-bonding environments. We introduced a simple graphical representation in which 1H and 17O chemical shifts are displayed along the H and O atomic chains that form the CBHB network. In such a depiction, because wavy patterns are often observed, we refer to these wavy patterns as 1H/17O chemical shift waves. Typical patterns of 1H/17O chemical shift waves in CBHB networks are discussed. The reported 1H and 17O NMR parameters for the CBHB network models examined in this study can serve as benchmarks to aid in spectral interpretation for CBHB networks in proteins.

Overcoming Dimensionality Constraints: A Gershgorin Circle Theorem-Based Feature Extraction for Weighted Laplacian Matrices in Computer Vision Applications.

In graph theory, the weighted Laplacian matrix is the most utilized technique to interpret the local and global properties of a complex graph structure within computer vision applications. However, with increasing graph nodes, the Laplacian matrix's dimensionality also increases accordingly. Therefore, there is always the "curse of dimensionality"; In response to this challenge, this paper introduces a new approach to reducing the dimensionality of the weighted Laplacian matrix by utilizing the Gershgorin circle theorem by transforming the weighted Laplacian matrix into a strictly diagonal domain and then estimating rough eigenvalue inclusion of a matrix. The estimated inclusions are represented as reduced features, termed GC features; The proposed Gershgorin circle feature extraction (GCFE) method was evaluated using three publicly accessible computer vision datasets, varying image patch sizes, and three different graph types. The GCFE method was compared with eight distinct studies. The GCFE demonstrated a notable positive Z-score compared to other feature extraction methods such as I-PCA, kernel PCA, and spectral embedding. Specifically, it achieved an average Z-score of 6.953 with the 2D grid graph type and 4.473 with the pairwise graph type, particularly on the E_Balanced dataset. Furthermore, it was observed that while the accuracy of most major feature extraction methods declined with smaller image patch sizes, the GCFE maintained consistent accuracy across all tested image patch sizes. When the GCFE method was applied to the E_MNSIT dataset using the K-NN graph type, the GCFE method confirmed its consistent accuracy performance, evidenced by a low standard deviation (SD) of 0.305. This performance was notably lower compared to other methods like Isomap, which had an SD of 1.665, and LLE, which had an SD of 1.325; The GCFE outperformed most feature extraction methods in terms of classification accuracy and computational efficiency. The GCFE method also requires fewer training parameters for deep-learning models than the traditional weighted Laplacian method, establishing its potential for more effective and efficient feature extraction in computer vision tasks.

Study on the distribution of average wind speeds at a mountainous bridge site for structural durability design

Conventional wind speed distribution methods (e.g., Rayleigh distribution and Weibull distribution) may not adequately capture complex characteristics of wind fields in mountainous areas. To address this problem, this study proposes a semi-parametric mix method for modeling the distribution of average wind speeds based on the combination of nonparametric Kernel Density Estimation (KDE) and Generalized Pareto Distribution (GPD). In the proposed method, KDE focuses on capturing the distribution in the main part of average wind speeds, while GPD aims at performing the distribution in terms of those in the extreme part. The segment point (i.e., the threshold) between KDE and GPD distributions is determined based on the combination of conditional mean excesses criterion and empirical rule. Meanwhile, the selection of modeling parameters should ensure that the mix distribution model is continuous and differentiable at the identified threshold point. Then, the commonly-used conditional probability model is further introduced to describe the wind direction distribution. Finally, a case study based on the measured 10-min average wind speeds at a mountainous bridge site is employed to demonstrate the effectiveness of the proposed method. The results indicate that: (1) the distribution of omnidirectional average wind speeds in the mountainous bridge site exhibits an obviously single-peak characteristic, while those considering wind directionality present a certain bimodal characteristic; (2) the proposed method can effectively describe wind speed distributions with different statistical characteristics, and the fitting accuracy outperforms the frequently-employed Weibull distribution model.

Hydrodynamic slip characteristics of shear-driven water flow in nanoscale carbon slits.

This paper reports on the effects of shear rate and interface modeling parameters on the hydrodynamic slip length (LS) for water-graphite interfaces calculated using non-equilibrium molecular dynamics. Five distinct non-bonded solid-liquid interaction parameters were considered to assess their impact on LS. The interfacial force field derivations included sophisticated electronic structure calculation-informed and empirically determined parameters. All interface models exhibited a similar and bimodal LS response when varying the applied shear rate. LS in the low shear rate regime (LSR) is in good agreement with previous calculations obtained through equilibrium molecular dynamics. As the shear rate increases, LS sharply increases and asymptotes to a constant value in the high shear regime (HSR). It is noteworthy that LS in both the LSR and HSR can be characterized by the density depletion length, whereas solid-liquid adhesion metrics failed to do so. For all interface models, LHSR calculations were, on average, ∼28% greater than LLSR, and this slip jump was confirmed using the SPC/E and TIP4P/2005 water models. To address the LS transition from the LSR to the HSR, the viscosity of water and the interfacial friction coefficient were investigated. It was observed that in the LSR, the viscosity and friction coefficient decreased at a similar rate, while in the LSR-to-HSR transition, the friction coefficient decreased at a faster rate than the shear viscosity until they reached a new equilibrium, hence explaining the LS-bimodal behavior. This study provides valuable insights into the interplay between interface modeling parameters, shear rate, and rheological properties in understanding hydrodynamic slip behavior.

A geometric variation method to extract rock strength parameters via uniaxial compression testing

Numerical modelling in rock engineering and mechanics often requires inputting multiple sets of strength parameters, such as the commonly encountered tensile strength, cohesion, and friction angle. Usually, obtaining these parameters requires performing different types of rock mechanics experiments on certain rock specimens, such as tension tests and triaxial tests, which are time-consuming and labor-intensive. Uniaxial compression testing is simpler and requires less demanding equipment. This paper proposes a solution that relies solely on uniaxial compression testing to obtain multiple sets of rock strength parameters. The basic idea is to treat the process of acquiring multiple parameters as a problem of solving a set of mathematical equations. The uniaxial tests with different geometric specimens provide known quantities and the corresponding equations that link these known quantities (e.g., peak loads) with unknown quantities (e.g., strength parameters). We established three-dimensional numerical tests to represent the relationship between input strength parameters and the peak load. By transforming it into an optimization problem, numerical modelling is embedded into the objective function of the optimization algorithm, thereby achieving the automatic extraction of strength parameters. This method is verified through uniaxial compression tests using red sandstone specimens with various geometries. The results show that by changing the geometry of the rock specimens, multiple sets of strength parameters can be obtained. These parameters are successfully used to predict (extrapolate) peak loads of other geometric specimens. This method holds promise for achieving the automated extraction of material parameters for the numerical modelling of rock.

Evaluation of hydration behavior of cement-stabilized macadam via electrochemical impedance spectroscopy

Electrochemical Impedance Spectroscopy (EIS) was an important method for continuous and non-destructive monitoring of cement hydration process. The EIS, porosity and compressive strength of cement-stabilized macadam with different structures and cement content at different hydration ages were tested. By fitting the quasi-Randles equivalent circuit model, the electrochemical parameters were obtained to analyze the hydration degree and microstructure changes of materials during the hydration process. The mathematical models of electrochemical parameters and hydration parameters in skeleton dense structure, suspension dense structure and skeleton pore structure were established. The results show that the electrochemical parameters of the quist-Randles equivalent circuits model had a fine correlation with the hydration parameters of cement-stabilized macadam materials. The impedance curves of different cement-stabilized macadam structures had the same variation laws under different cement contents. As the hydration age increased, more hydration products formed and filled the internal pores of the structure, leading to the gradual formation of a dense structure in the material, further reducing the porosity and increasing the pore solution resistance RS. Meanwhile, the increase in cement content also led to a gradual increase in the compressive strength of the sample. The research results show that Rs was inversely proportional to porosity and had a good logarithmic relationship with compressive strength.

ML-SAFT: A machine learning framework for PCP-SAFT parameter prediction

The Perturbed Chain Polar Statistical Associating Fluid Theory (PCP-SAFT) equation of state (EoS) is widely used to predict fluid-phase thermodynamics, but parameterization of PCP-SAFT for individual molecules is often challenging. We propose a machine learning framework called ML-SAFT that can turn experimental data in predictive models of PCP-SAFT parameters. We demonstrate methods for automated large scale regression of PCP-SAFT parameters and thus create a large PCP-SAFT parameter dataset in the literature. We then evaluate several machine learning architectures for predicting PCP-SAFT parameters. We find that our best model provides accurate predictions for a wider range of molecules than existing predictive methods with 40 % average absolute deviation (% AAD) in vapor pressure predictions and 8 % AAD in density predictions.

Modeling and Reliability Analysis of MEMS Gyroscope Rotor Parameters under Vibrational Stress.

Vibrational environments can cause drift or changes in Micro-Electro-Mechanical System (MEMS) gyroscope rotor parameters, potentially impacting their performance. To improve the effective use of MEMS gyroscopes, this study introduced a method for evaluating the reliability of parameter degradation under vibration. We analyzed the working principle of MEMS gyroscope rotors and investigated how vibration affects their parameters. Focusing on zero bias and scale factor as key performance indicators, we developed an accelerated degradation model using the distributional assumption method. We then collected degradation data for these parameters under various vibration conditions. Using the Copula function, we established a reliability assessment approach to evaluate the degradation of the MEMS gyroscope rotor's zero bias and scale factor under vibration, enabling the determination of reliability for these parameters. Experimental findings confirmed that increasing stress levels lead to reduced failure times and increased failure rates for MEMS gyroscope rotors, with significant changes observed in the zero bias parameter. Our evaluation method effectively characterizes changes in the reliability of the MEMS gyroscope rotor's scale factor and zero bias over time, providing valuable information for practical applications of MEMS gyroscopes.

Mechanical strength recognition and classification of thermal protective fabric images after thermal aging based on deep learning

Objectives. Currently, numerous studies have focused on testing or modeling to evaluate the safe service life of thermal protective clothing after thermal aging, reducing the risk to occupational personnel. However, testing will render the garment unsuitable for subsequent use and a series of input parameters for modeling are not readily available. In this study, a novel image recognition strategy was proposed to discriminate the mechanical strength of thermal protective fabric after thermal aging based on transfer learning. Methods. Data augmentation was used to overcome the shortcoming of insufficient training samples. Four pre-trained models were used to explore their performance in three sample classification modes. Results. The experimental results show that the VGG-19 model achieves the best performance in the three-classification mode (accuracy = 91%). The model was more accurate in identifying fabric samples in the early and late stages of strength decline. For fabric samples in the middle stage of strength decline, the three-classification mode was better than the four-classification and six-classification modes. Conclusions. The findings provide novel insights into the image-based mechanical strength evaluation of thermal protective fabrics after aging.

Differentially biased sampling strategies reveal the non-stationarity of species distribution models for Indian small felids

Species Distribution Models (SDMs) have been used extensively to understand species-habitat relationships and design conservation strategies. The ability to train these models using a wide variety of datasets and modelling algorithms has led to their wide applicability across systems. However, the ease of modelling also leads to their use as off-the-shelf models without a detailed investigation of the data and their suitable end-use application. The effect of various modelling parameters on inferences has been explored, however, their interaction with training data type is limited. We used country-wide data for four sympatric Indian small cat species to understand the sensitivity of SDMs to data types, sampling extents and their interaction. Our results reveal the non-stationarity of models with varying modelling parameters. The extent of the training dataset had major implications on the inferences and interacted strongly with the type of dataset used. The divergent distribution of the target species revealed that the effect of sampling extent was more pronounced for species that have limited distribution within the predictive extent. Lastly, our results highlight the significance of sampled environmental space in explaining the non-stationarity of the model outputs.

Thermo-magnetic radiative flow in porous enclosure with deep-learning parameter estimation

This study numerically investigates convective heat transfer, fluid flow, and entropy generation within an inverted T-shaped porous enclosure filled with a hybrid nanofluid. The interplay between thermal radiation and a magnetic field is considered, as these factors significantly influence thermal acquisition processes in renewable and thermal engineering applications, particularly solar power collectors with the selected physical domain. By analyzing various combinations of flow parameters, this work aims to identify favorable conditions for maximizing heat transfer efficiency in such systems. A Darcy–Forchheimer–Brinkman-based mathematical model has been incorporated and numerically simulated using the penalty finite element method (FEM). Moreover, the variation of fluid flow, isotherms, and entropy generation is thoroughly analyzed for the variation of non-dimensional permeability parameter (Darcy number, Da), buoyancy force (Rayleigh number, Ra), magnetic field (Hartmann number, Ha), porosity value (ϵ), and thermal radiation parameter (Rd). The comprehensive result demonstrates that increasing values of Ra, Da, ϵ, and Rd intensify entropy generation, convective heat, and fluid flow phenomena, whereas the increasing value of Ha substantially weakens these phenomena. The thermal efficiency of the model attends at the lower buoyancy forces (Ra≤105) for each flow parameter (except for Ha). However, the increasing magnetic forces (Ha) substantially improve the thermal efficiency of the developed model. Additionally, this study employs a feed-forward neural network (FNN) based deep learning (DL) approach to predict the parametric influence on convective heat transport rate (Num), total entropy generation rate (Stot), and total Bejan number due to heat transport irreversibility (Beθ,tot) based on the minimally supplied experimental data for FNN training process. The trained FNN model predicts the results very quickly with 98.6% accuracy compared to FEM results, demonstrating its efficiency in approximating the parametric influence of complex mathematical model results. Moreover, this approach significantly reduces computational costs and efforts compared to traditional numerical methods, making it a valuable tool for forecasting the influence of an unknown set of flow parameters in complex mathematical models.

Answering the key stakeholder questions about the impact of offshore wind farms on marine life using hypothesis testing to inform targeted monitoring

Abstract Stakeholders need scientific advice on the environmental impacts of offshore wind (OW) before the facilities are installed. The utility of conventional environmental monitoring methods as a basis for forecasting OW impacts is limited because they do not explain the causes of the observed effects. We propose a multistep approach, based on process-oriented hypothesis testing, targeted monitoring and numerical modeling, to answer key stakeholder questions about planning an OW facility: Q1—Where do we place future OW farms so that impacts on the ecosystem are minimized? Q2—Which species and ecosystem processes will be impacted and to what degree? Q3—Can we mitigate impacts and, if so, how? and Q4—What are the risks of placing an OW facility in one location vs. another? Hypothesis testing can be used to assess impacts of OW facilities on target species-ecological process. This knowledge is transferable and is broadly applicable, a priori, to assess suitable locations for OW (Q1). Hypothesis testing can be combined with monitoring methods to guide targeted monitoring. The knowledge generated can identify the species/habitats at risk (Q2), help selecting/developing mitigation measures (Q3), and be used as input parameters for models to forecast OW impacts at a large spatial scale (Q1; Q4).