Assess Model Accuracy Research Articles

Predictive Modeling of Photovoltaic Energy Yield Using an ARIMA Approach

This paper presents a method for predicting the energy yield of a photovoltaic (PV) system based on the ARIMA algorithm. We analyze two key time series: the specific yield and the total yield of the PV system. Two ARIMA models are developed for each time series: one selected by the authors and one determined by SPSS. Model performance is evaluated through fit statistics, providing a comprehensive assessment of model accuracy. The residuals’ ACF and PACF are examined to ensure model adequacy, and confidence intervals are calculated for residuals to validate the models. A monthly forecast is then generated for both time series, complete with confidence intervals, to demonstrate the models’ predictive capabilities. The results highlight the effectiveness of ARIMA in forecasting PV energy yields, offering valuable insights for optimizing PV system performance and planning. This study contributes to the field of renewable energy forecasting by demonstrating the applicability of ARIMA models in predicting the monthly performance of photovoltaic systems.

Rapid in vivo EPID image prediction using a combination of analytically calculated attenuation and AI predicted scatter.

The electronic portal imaging device (EPID) can be used in vivo, to detect on-treatment errors by evaluating radiation exiting a patient. To detect deviations from the planning intent, image predictions need to be modeled based on the patient's anatomy and plan information. To date in vivo transit images have been predicted using Monte Carlo (MC) algorithms. A deep learning approach can make predictions faster than MC and only requires patient information for training. To test the feasibility and reliability of creating a deep-learning model with patient data for predicting in vivo EPID images for IMRT treatments. In our approach, the in vivo EPID image was separated into contributions from primary and scattered photons. A primary photon attenuation function was determined by measuring attenuation factors for various thicknesses of solid water. The scatter component ofin vivoEPID images was estimated using a convolutional neural network (CNN). The CNN input was a 3-channel image comprised of the non-transit EPID image and ray tracing projections through a pretreatment CBCT. The predicted scatter component was added to the primary attenuation component to give the full predictedin vivo EPID image. We acquired 193 IMRT fields/images from 93 patients treated on the Varian Halcyon. Model training:validation:test dataset ratios were 133:20:40 images. Additional patient plans were delivered to anthropomorphic phantoms, yielding 75 images for further validation. We assessed model accuracy by comparing model-calculated and measuredin vivoimages with a gamma comparison. Comparing the model-calculated and measured in vivo images gives a mean gamma pass rate for the training:validation:test datasets of 95.4%:94.1%:92.9% for 3%/3 mm and 98.4%:98.4%:96.8% for 5%/3mm. For images delivered to phantom data sets the average gamma pass rate was 96.4% (3%/3 mm criteria). In all data sets, the lower passing rates of some images were due to CBCT artifacts and patient motion that occurred between the time of CBCT and treatment. CONCLUSIONS: The developed deep-learning-based model can generatein vivoEPID images with a mean gamma pass rate greater than 92% (3%/3 mm criteria).This approach provides an alternative to MC prediction algorithms. Image predictions can be made in 30 ms on a standard GPU. In future work, image predictions from this model can be used to detectin vivo treatment errors and on-treatment changes in patient anatomy, providing an additional layer of patient-specific quality assurance.

Prediction of automotive response using supervised machine learning in antilock braking systems and comparison with different models for improved vehicle safety

Abstract Previous research on predicting automotive response in antilock braking systems faced several common drawbacks. Limited availability of high-quality datasets and computational constraints hindered the models' ability to generalize and perform effectively in real-time. Additionally, integrating these models into existing vehicle systems is challenging, often requiring extensive calibration and testing to ensure reliability and safety. This study focuses on enhancing automotive safety through the integration of machine learning with advanced braking systems. An antilock braking system model is developed using MATLAB, accounting for key parameters such as speed, mass, wheel radius, and moment of inertia. Various probability ranges are simulated to evaluate the system's real-time response in dynamic environments. A supervised machine learning approach is applied, with lasso regression used for model validation. Braking response was evaluated at three speeds: 25 km/h, 45 km/h, and 65 km/h. To assess model accuracy, error analysis was conducted using metrics such as mean absolute percentage error (MAPE), mean absolute error (MAE), root mean squared error (RMSE), and mean squared error (MSE). Results showed a significant improvement in prediction accuracy, with MAPE reduced to 3.2%, MAE to 0.15 seconds, RMSE to 0.25 seconds, and MSE to 0.06 seconds.A comparative analysis is done using the Python Jupyter Framework, further validating the findings. The machine learning model successfully forecasts braking response times, and validates its prediction against random datasets.

Creating an interactive database for nasopharyngeal carcinoma management: applying machine learning to evaluate metastasis and survival.

Nasopharyngeal carcinoma (NPC) patients frequently present with distant metastasis (DM), which is typically associated with poor prognosis. This study aims to develop and apply machine learning models to predict DM, overall survival (OS), and cancer-specific survival (CSS) in NPC patients to provide optimal tools for improved predictive accuracy and performance. We retrieved over 8,000 NPC patient samples with associated clinical information from the Surveillance, Epidemiology, and End Results (SEER) database. Utilizing two methods for handling missing values-imputation or deletion-we created various cohorts: DM-all, DM-slim, OS-all, OS-slim, CSS-all, and CSS-slim. Five machine learning models were deployed for the binary classification task of DM, and their performance was evaluated using the area under the curve (AUC). For the survival prediction tasks of OS and CSS, we constructed 45 combinations using nine survival machine learning algorithms. The Concordance Index (C-index), 5-year AUC, and Brier score assessed model accuracy. Patients were stratified into two risk groups for survival analysis, and the survival curves were presented. This study examines the relationships between clinical factors and survival in NPC patients. The analysis, visualized through forest plots, indicates that demographic and clinical variables like gender, marital status, tumor grade, and stage significantly affect metastatic risks and survival. Specifically, factors such as advanced stages increase metastasis and survival risks, while enhanced treatments improve survival rates. In the cohort for DM prediction, results revealed that the random forest model was the most effective, with an AUC of 0.687. In contrast, when predicting overall survival (OS), the random survival forest (RSF) model consistently showed superior performance with the highest mean C-index of 0.802, a 5-year AUC of 0.857, and a Brier score of 0.167. Similarly, for cancer-specific survival (CSS) prediction, the RSF model demonstrated a mean C-index of 0.822, a 5-year AUC of 0.884, and a Brier score of 0.165. An online Shiny server was developed to allow the models to be used freely and efficiently via http://npcml.shinyapps.io/NPCpre. This study successfully established an online tool by machine learning models for NPC metastasis and survival prediction, providing valuable references for clinicians.

Spatiotemporal functional permutation tests for comparing observed climate behavior to climate model projections

Abstract. Comparisons of observed and modeled climate behavior often focus on central tendencies, which overlook other important distributional characteristics related to quantiles and variability. We propose two permutation procedures, standard and stratified, for assessing the accuracy of climate models. Both procedures eliminate the need to model cross-correlations in the data, encouraging their application in a variety of contexts. By making only slightly stronger assumptions, the stratified procedure dramatically strengthens the ability to detect a difference in the distribution of observed and climate model data. The proposed procedures allow researchers to identify potential model deficiencies over space and time for a variety of distributional characteristics, providing a more comprehensive assessment of climate model accuracy, which will hopefully lead to further model refinements. The proposed statistical methodology is applied to temperature data generated by the state-of-the-art North American Coordinated Regional Climate Downscaling Experiment (NA-CORDEX).

Predicting diabetes in adults: identifying important features in unbalanced data over a 5-year cohort study using machine learning algorithm

BackgroundImbalanced datasets pose significant challenges in predictive modeling, leading to biased outcomes and reduced model reliability. This study addresses data imbalance in diabetes prediction using machine learning techniques. Utilizing data from the Fasa Adult Cohort Study (FACS) with a 5-year follow-up of 10,000 participants, we developed predictive models for Type 2 diabetes.MethodsWe employed various data-level and algorithm-level interventions, including SMOTE, ADASYN, SMOTEENN, Random Over Sampling and KMeansSMOTE, paired with Random Forest, Gradient Boosting, Decision Tree and Multi-Layer Perceptron (MLP) classifier. We evaluated model performance using F1 score, AUC, and G-means—metrics chosen to provide a comprehensive assessment of model accuracy, discrimination ability, and overall balance in performance, particularly in the context of imbalanced datasets.Resultsour study uncovered key factors influencing diabetes risk and evaluated the performance of various machine learning models. Feature importance analysis revealed that the most influential predictors of diabetes differ between males and females. For females, the most important factors are triglyceride (TG), basal metabolic rate (BMR), and total cholesterol (CHOL), whereas for males, the key predictors are body Mass Index (BMI), serum glutamate Oxaloacetate Transaminase (SGOT), and Gamma-Glutamyl (GGT). Across the entire dataset, BMI remains the most important variable, followed by SGOT, BMR, and energy intake. These insights suggest that gender-specific risk profiles should be considered in diabetes prevention and management strategies. In terms of model performance, our results show that ADASYN with MLP classifier achieved an F1 score of 82.17 ± 3.38, AUC of 89.61 ± 2.09, and G-means of 89.15 ± 2.31. SMOTE with MLP followed closely with an F1 score of 79.85 ± 3.91, AUC of 89.7 ± 2.54, and G-means of 89.31 ± 2.78. The SMOTEENN with Random Forest combination achieved an F1 score of 78.27 ± 1.54, AUC of 87.18 ± 1.12, and G-means of 86.47 ± 1.28.ConclusionThese combinations effectively address class imbalance, improving the accuracy and reliability of diabetes predictions. The findings highlight the importance of using appropriate data-balancing techniques in medical data analysis.

Comparative Analysis of Linear Regression, Decision Tree, and Gradient Boosting Models for Predicting Drug Corrosion Inhibition Efficiency Using QSAR Descriptors

<table width="593" border="1" cellspacing="0" cellpadding="0"><tbody><tr><td valign="top" width="388"><p>Corrosion in industrial environments poses significant economic and safety challenges, necessitating the development of effective inhibitors. Organic compounds, particularly pharmaceuticals, have emerged as promising corrosion inhibitors due to their efficiency and environmental benefits. However, predicting these compounds' corrosion inhibition efficiency (CIE) remains complex and requires advanced computational methods. This study investigates the predictive capabilities of three machine learning (ML) models, namely linear regression, decision tree, and gradient boosting regression, using Quantitative Structure-Activity Relationship (QSAR) descriptors. A dataset containing 14 QSAR descriptors was compiled from experimental studies on various pharmaceutical-based inhibitors. The dataset was divided into training (90%) and testing (10%) subsets to evaluate model performance. The research follows the CRISP-DM methodology, a systematic framework that includes data preparation, model training, and evaluation. Key performance metrics, including Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the coefficient of determination (R²), were used to assess model accuracy. Among the models, Gradient Boosting Regression achieved the most promising results, with the lowest MSE (21.52) and the highest R² (0.21), reflecting its ability to capture non-linear relationships in the data. Despite the relatively modest R², this model demonstrates the potential for improving computational approaches to corrosion inhibition prediction. This study highlights the value of machine learning in optimizing the selection of corrosion inhibitors, potentially reducing the reliance on extensive laboratory testing and accelerating the discovery of efficient, eco-friendly solutions for industrial applications.</p></td></tr></tbody></table>

Validation of a generic finite element vehicle buck model for near-side crashes

Objective Finite element (FE) reconstructions of motor vehicle crashes using human body models are effective tools for developing a better understanding of occupant kinematics and injuries in real-world lateral crash conditions, but current near-side reconstruction methods are limited by the paucity of full-scale FE vehicle models. The objective of this study was to validate a generic vehicle model equipped with left-side airbags and intrusion capability by simulating a series of near-side crash tests for a range of vehicles and assessing model accuracy using objective evaluation methods. Methods Moving deformable barrier crash tests were reconstructed for five common vehicle classifications (compact passenger, mid-size passenger, sport utility vehicle, pickup truck, and van) using an updated version of a previously developed simplified vehicle model. Unknown vehicle and intrusion properties (pretensioner force, seatback airbag pressure, curtain airbag pressure, door panel stiffness, ratio of dynamic-to-static intrusion, intrusion velocity, and intrusion scaling factor) were estimated by parameterizing them across 224 simulations per crash test using a Latin hypercube design of experiments. Model accuracy was assessed for 13 anthropomorphic test device signals using the Correlation and Analysis (CORA) objective rating method and injury metric comparisons. Results Maximum ratings of 0.69, 0.67, 0.52, 0.52, and 0.62 were achieved for the compact passenger, midsize passenger, sport utility vehicle, pickup truck, and van classifications, respectively. On average, the abdomen displayed the most accurate behavior (0.51 ± 0.12), followed by the thorax (0.50 ± 0.10) and head (0.50 ± 0.07). The pelvis displayed the least accurate behavior (0.46 ± 0.18) of any region. Reconstructions overpraedicted injury metrics in all cases. Conclusions All vehicles achieved “fair” biofidelity ratings and the compact passenger and midsize passenger vehicles achieved “good” biofidelity ratings, validating them for kinematic evaluations with vehicle-to-vehicle nearside crash reconstructions. Regression models were developed for injuries and CORA ratings and can be used to optimize vehicle parameters in future studies.

Development and validation of the Florey Dementia Risk Score web-based tool to screen for Alzheimer's disease in primary care

It is estimated that ∼60% of people with Alzheimer's disease (AD) are undetected or undiagnosed, with higher rates of underdiagnosis in low-to middle-income areas with limited medical resources. To promote health equity, we have developed a web-based tool that utilizes easy-to-collect clinical data to enhance AD detection rate in primary care settings. This study was leveraged on the data collected from participants of the Australian Imaging, Biomarker & Lifestyle (AIBL) study and the Religious Orders Study and Memory and Aging Project (ROSMAP). The study included three phases: (1) constructing and evaluating a model on retrospective cohort data (1407 AIBL participants), (2) performing simulated trials to assess model accuracy (30 AIBL participants) and missing data tolerability (30 AIBL participants), and (3) external evaluation using a non-Australian dataset (500 ROSMAP participants). The auto-score machine learning algorithm was employed to develop the Florey Dementia Risk Score (FDRS). All the simulated trials and evaluation were performed using a web-based FDRS tool. FDRS achieved an area under the curve (AUC) of approximately 0.82 [95% CI, 0.75-0.88], with a sensitivity of 0.74 [0.60-0.86] and a specificity of 0.73 [0.70-0.79]. The accuracy of the simulated pilot trial for 30 AIBL participants with complete record was 87% (26/30 correct), while it only slightly decreased (80.0-83.3%, depending on imputation methods) for another 30 AIBL participants with one or two missing data. FDRS achieved an AUC of 0.82 [0.77-0.86] of 500 ROSMAP participants. The FDRS tool offers a potential low-cost solution to AD screening in primary care. The present study warrants future trials of FDRS for optimization and to confirm its generalizability across a more diverse population, especially people in low-income countries. National Health and Medical Research Council, Australia (GNT2007912) and Alzheimer's Association, USA (23AARF-1020292).

Supervised Machine Learning to Predict Drilling Temperature of Bone

Surgeons face a significant challenge due to the heat generated during drilling, as excessive temperatures at the bone–tool interface can lead to irreversible damage to the regenerative soft tissue and result in thermal osteonecrosis. While previous studies have explored the use of machine learning to predict the temperature rise during bone drilling, this in vitro study introduces a comprehensive approach by combining the Response Surface Methodology (RSM) with advanced machine learning techniques. The main objective lies in the comprehensive evaluation and comparison of support vector machine (SVM) and random forest (RF) models specifically for the optimization of the bone drilling parameters to prevent thermal bone necrosis. A total of 27 experiments were conducted using a multi-level factorial method, with analysis performed via the Minitab software version 19.1. Performance metrics such as the mean squared error (MSE), mean absolute percentage error (MAPE), and coefficient of determination (R2) were used to assess model accuracy. The RF model emerged as the most effective, with R2 values of 94.2% for testing and 97.3% for training data, significantly outperforming other models in predicting temperature fluctuations. This study demonstrates the superior predictive capabilities of the RF model and offers a robust framework for the optimization of surgical procedures to mitigate the risk of thermal damage.

Assessment of IRI2020 model accuracy in predicting ionospheric parameters: Insights from multiple ionosonde stations

This research assesses the performance of the IRI2020 model in forecasting the ionospheric critical frequency of the F2 layer (foF2) and Maximum Usable Frequency (MUF) of the ionosphere from 2020 to 2023 across multiple ionosonde stations. The primary objective is to analyse the characteristics of foF2 and MUF across various longitudinal sectors during the solar minimum to ascending phase of solar cycle 25 and to evaluate the IRI2020 model’s performance in replicating observed data. Additionally, the study explores the influence of various space weather parameters on the variability of these ionospheric parameters. The findings reveal that the IRI2020 model overestimates the daily mean values of both foF2 and MUF(3000)F2 across the stations over the three years. While exhibiting good performance in capturing the pattern of hourly predictions, the model tends to overestimate foF2 more than MUF(3000)F2, with better predictions observed for MUF(3000)F2. However, hourly estimations display a mix of underestimation and overestimation, predominantly overestimating values. Correlations ranged from 0.58 to 0.92 for foF2 and 0.72 to 0.91 for MUF(3000)F2, showing variability across stations. As solar activity increases, seasonal variations become more evident, especially in stations located in the northern hemisphere.In contrast, stations in the southern hemisphere exhibit less pronounced seasonal variations, suggesting a notable hemispheric asymmetry. Although the IRI2020 better captured the general trend of foF2 and MUF(3000)F2 in our investigation, the percentage deviation for MUF(3000)F2 ranges from 1 % to 15.1 %, which can result in a significant range reduction for practical applications like HF communications. Stations closer to the geomagnetic equatorial line exhibit stronger seasonal asymmetry and higher deviations. Some key space weather parameters, including sunspot number R, F10.7 and R/F10.7 ratio, were found to significantly influence ionospheric variability, with sunspot number R and R/F10.7 showing a stronger correlation with foF2 and MUF(3000)F2. These suggest that the variability of the solar active region parameters primarily controls ionospheric foF2 and MUF(3000)F2.

Adjusting primary-care funding by deprivation: a cross-sectional study of lower-super-output-areas in England.

Previous research has called for General Practice (GP) funding to be adjusted by deprivation data. However, there is no evidence that this would better meet clinical need. We assessed 1. how accurately the capitation formula (Carr Hill), and total GP funding predicts clinical need and 2. whether adjusting by the Index of Multiple Deprivation score (IMD), improves accuracy. Cross-sectional analysis of 32 844 Lower-Super-Output-Areas in England in 2021-2022. Sensitivity analysis used data from 2015-2019. Weighted average Carr-Hill Index (CHI), total GP funding and five measures of clinical need were calculated for each LSOA. For both CHI and total funding, four sets of generalised linear models were calculated for each outcome measure: unadjusted; Age-adjusted; IMD-adjusted; and age and IMD adjusted. Adjusted R2 assessed model accuracy. In unadjusted models, CHI was a better predictor than total-funding of Combined Morbidity Index (CMI) (R2=49.81%,29.31% respectively), combined diagnosed and undiagnosed morbidity (R2=43.52%,21.9%), emergency admissions (R2=32.75%,16.95%). Total-funding was a better predictor than capitation of GP appointments per patient (R2=28.5%, 22.5% respectively) and age and sex standardised mortality rates (R2=0.42%,0.37%).. Adjusting for age and IMD improved all ten models (R2=62.2%,53.2%,48.6%,38.5%,40.5%, 32.8%, 29.1%,34.6%, 25.2%,25.2% respectively). All age and IMD adjusted models significantly outperformed age-adjusted models (p<0.001). Sensitivity analysis confirmed findings. Adjusting capitation or total-funding by IMD would increase funding efficiency, especially for long term outcomes, such as mortality. However, adjusting for IMD without age could have unwanted consequences.

Towards improved biofilm models.

Biofilms are complex microbial communities that have a critical function in many natural ecosystems, industrial settings as well as in recurrent and chronic infections. Biofilms are highly heterogeneous and dynamic assemblages that display complex responses to varying environmental factors, and those properties present substantial challenges for their study and control. In recent years, there has been a growing interest in developing improved biofilm models to offer more precise and comprehensive representations of these intricate systems. However, an objective assessment for ascertaining the ability of biofilms in model systems to recapitulate those in natural environments has been lacking. In this Perspective, we focus on medical biofilms to delve into the current state-of-the-art in biofilm modelling, emphasizing the advantages and limitations of different approaches and addressing the key challenges and opportunities for future research. We outline a framework for quantitatively assessing model accuracy. Ultimately, this Perspective aims to provide a comprehensive and critical overview of medically focused biofilm models, with the intent of inspiring future research aimed at enhancing the biological relevance of biofilm models.

Understanding Trust and Reliance Development in AI Advice: Assessing Model Accuracy, Model Explanations, and Experiences from Previous Interactions.

Understanding Trust and Reliance Development in AI Advice: Assessing Model Accuracy, Model Explanations, and Experiences from Previous Interactions.

Management of Exchange Rate Forecasting Through Vanilla Long Short-Term Memory Networks (LSTM) and Auto-Regressive Integrated Moving Average (ARIMA)

Predicting foreign exchange rates presents a formidable challenge within financial forecasting, given its pivotal role in influencing a country's economic trajectory. To address this challenge, numerous forecasting models are employed with the aim of anticipating future exchange rate movements. This study aims to determine the efficacy of two prominent machine learning models, namely Vanilla Long Short-Term Memory Networks (LSTM) and Auto-Regressive Integrated Moving Average (ARIMA), in forecasting the exchange rate between the Malaysian Ringgit (MYR) and the United States Dollar (USD). Employing Python's robust statistical packages for time series forecasting, both Vanilla LSTM and ARIMA models undergo rigorous training on the dataset. Leveraging Python's programming capabilities enables in-depth analysis, essential for model refinement and accuracy assessment. Upon comparing the error measures of both models, it becomes evident that the Vanilla LSTM model outperforms ARIMA, exhibiting lower Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) values. Specifically, the MSE and RMSE for Vanilla LSTM stand at 0.0102 and 0.1011, respectively, surpassing ARIMA's 0.0113 and 0.1062. Thus, affirming Vanilla LSTM's superiority in exchange rate forecasting. Consequently, the study concludes that Vanilla LSTM emerges as the most accurate model for exchange rate prediction, with a projected exchange rate of RM4.22 for July 2022.

Integrating threshold models with Markov chains for information diffusion in social networks

In this paper, I introduce an agent-based approach that leverages Markov models to formalize and understand information propagation within social networks. This methodology aims to explain the internal information states of agents and employs probabilistic model checking to analyze these models. Initially, I provide a comprehensive overview of the current research on information diffusion in social networks. Following this, I delve into the fundamentals of model checking and illustrate how this technique can be used to assess model accuracy, particularly in managing large networks with high precision. To demonstrate the effectiveness of this approach, I conduct a series of experiments. The results show that this paper provides a robust and effective method for analyzing complex information diffusion and network behavior.

Deep learning with improved hybrid neuro-turning for predictive control of flux based on experimental DCMD module design of water desalination system

This study explores two scenarios for optimizing the predictive control of flux in water desalination systems: experimental design of direct contact membrane distillation (DCMD) and artificial intelligence (AI) models. Deep learning networks, specifically Long Short-Term Memory (LSTM), and standalone AI models, including hybrid Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN), were evaluated. Evaluation criteria, along with 2D graphical visualization, were used to assess model accuracy. ANFIS-C4 emerged as the standout model, achieving perfect predictive skills with 100 % accuracy and low RMSE of 0.0522 in the training phase, and maintaining high performance in testing with 99.73 % accuracy and RMSE of 0.7121. Similarly, ANN-C4 demonstrated near-perfect accuracy with 100 % accuracy and RMSE of 0.0643 in the training phase and also maintained excellent performance in the testing phase. Despite requiring multiple inputs, ANFIS and ANN show remarkably high capability in predictive accuracy. Among LSTM models, LSTM-C3 achieved the highest training DC of 91.35 %, indicating robust performance in capturing training data variability. LSTM-C2 showed superior generalization with a testing DC of 0.8539, despite using only two input parameters. The narrow distribution of predicted values around the median in violin plots for ANFIS-C4 and ANN-C4, along with their low RMSE, validates their superior performance. The exceptional results of ANFIS-C4 and ANN-C4 highlight their potential in water desalination applications, supporting Sustainable Development Goal 6 (SDGs-6) and Environmental Protection Agency (EPA) objectives for sustainable water management. This research underscores the importance of advanced computational models in enhancing the efficiency and reliability of water desalination systems, contributing to global efforts in sustainable water resource management.

Maxent Predictive Species Distribution Models and Model Accuracy Assessment for Two Species of Psilochalcis Kieffer (Hymenoptera: Chalcididae) Occurring in the Eastern Great Basin of Utah, USA

Two species of Psilochalcis wasps (P. minuta and P. quadratis) were recently described from Utah’s eastern Great Basin. The extent of their known distributions is extremely limited, based on few data points. We developed species distribution models (SDMs) using Maxent modeling software for each Psilochalcis species to identify areas of probable suitable habitat for targeted collecting to improve our knowledge of their distributions. We used six occurrence data points for P. minuta and eight occurrence data points for P. quadratis, along with ten environmental variables as inputs into the Maxent modeling software. Model-predicted areas with a potential suitable habitat value greater than 0.69 were mapped using ArcGIS Pro to help select locations for model accuracy assessment. Employing Malaise traps, eighteen sites were sampled to evaluate each SDM’s ability to predict the occurrence of Psilochalcis species. Psilochalcis minuta occurred at eight of nine juniper-dominated sample sites that were predicted as having high suitability by the model for this species. Likewise, P. quadratis occurred at two of four cheatgrass-dominated sample sites predicted by the model. Psilochalcis minuta occurred at three of nine sampled sites that were not predicted by the model, and P. quadratis occurred at seven of fourteen non-predicted sites. The Maxent SDM results yielded an AUC value of 0.70 and p-value of 0.02 for P. minuta and 0.68 and 0.02. for P. quadratis. These results were reflected in our model accuracy assessment. Of the selected environmental variables, aspect, historic fire disturbance, and elevation yielded the greatest percent contributions to both species’ models. Sympatric distributions were observed for P. minuta and P. quadratis. Elevation, vegetation type, NDVI, and soil type are the most important environmental variables in differentiating areas of optimal suitable habitat for the two species.

Coupling cellular automata and What If? models for residential expansion simulation: A case study of Southwest Sydney, Australia

AbstractThe impact of urban expansion on achieving sustainable development goals (SDGs) has become a significant research topic in the field of geographic information science. In this article, we describe a coupled cellular automata (CA)—‐What If? model to explore SDG11 “Sustainable cities and communities.” The model calculates overall residential land use demand based on historical data archives using the What If? planning support system (PSS), and then allocates it using a CA model that incorporates variables related to SDG11.2.1 and 11.7.1. Historical datasets for years 2016 and 2021 from Southwest Sydney, Australia were used to assess model accuracy, after which two residential expansion scenarios (years 2021 and 2026) were generated. Based on the modeling results, the SDG‐related spatial variables can improve the overall accuracy of CA sub‐models using an XGBoost machine learning training methodology. The simulation results of these scenarios confirm the effectiveness of the coupled CA‐What If? model, which has the potential to generate more reliable scenario results than the standalone What If? PSS for modeling urban growth of cities across Australia and internationally.

Challenges in Rotor Aerodynamic Modeling for Non-Uniform Inflow Conditions

Within an international collaboration framework, the accuracy of rotor aerodynamic models used for design load calculations of wind turbines is being assessed. Where the use of high-fidelity computation fluid dynamics (CFD) and mid-fidelity free-vortex wake (FVW) models has become commonplace within the wind energy community, these still fail to meet the requirements in terms of execution time and computational cost needed for design load calculations. The fast but engineering fidelity blade-element/momentum (BEM) method can therefore still be considered the industry workhorse for design load simulations.At the same time, upscaling of wind turbine rotors makes inflow non-uniformities (e.g. shear, veer, turbulence) more important. The objective of this work is to assess model accuracy in non-uniform inflow conditions, which violate several BEM assumptions. Thereto a comparison in turbulent inflow has been executed including a wide variety of codes, focusing on the DanAero field measurements, where a 2.3-MW turbine was equipped with, among other sensors, a pressure measurement apparatus. The results indicate that, although average load patterns are in good agreement, this does not hold for the unsteady loads that drive fatigue damage and aero-elastic stability. A simplified comparison round in vertical shear was initiated to investigate the observed differences in a more controlled manner. A consistent offset in load amplitude was observed between CFD and free-vortex codes on the one hand and BEM-type codes on the other hand. To shed more light on the observations, dedicated efforts are ongoing to pinpoint the cause for these differences, in the end leading to guidelines for an improved BEM implementation.