Model Skill Research Articles

An Ensemble CNOP Method Based on a Pre-Screening Mechanism for Targeted Observations in the South China Sea

The uncertainty in the initial condition seriously affects the forecasting skill of numerical models. Targeted observations play an important role in reducing uncertainty in numerical prediction. The conditional nonlinear optimal perturbation (CNOP) method is a useful tool for studying adaptive observation. However, the traditional CNOP method highly relies on the adjoint model, and it is difficult to find the global optimal solution. In this paper, a pre-screening and ensemble CNOP hybrid method called PECNOP is proposed to identify optimal sensitive areas in targeted observations. PECNOP is an adjoint-free method that captures global CNOP with high probability, which can effectively solve the two major problems faced by traditional CNOP methods. We evaluated the performance of PECNOP by building an observation simulation system consisting of an ocean model and data assimilation. One of the assimilation experiments was dedicated to evaluating the stability and effectiveness of PECNOP in extreme events. The results show that, compared with traditional methods, PECNOP can stably capture the global CNOP. Extra observations and assimilation in the optimal sensitive areas identified by PECNOP can effectively improve forecasting by about 20% within 30 days. Therefore, PECNOP has potential to reduce the initial error of numerical models, which is important for improving forecasting.

Real needle for minimal invasive procedures training using motion sensors and optical flow

Minimally invasive percutaneous insertion procedures are widely used techniques in medicine. Their success is highly dependent on the skills of the practitioner. This paper presents a haptic simulator for training in these procedures, whose key component is a real percutaneous insertion needle with a sensory system incorporated to track its 3D location at every instant.By means of the proposed embedded vision system, the attitude (spatial orientation) and depth of insertion of a real needle are estimated. The proposal is founded on a novel depth estimation procedure based on optical flow techniques, complemented by sensory fusion techniques with the attitude calculated with data from an Inertial Measurement Unit (IMU) sensor. This procedure allows estimating the needle attitude with an accuracy of tenths of a degree and the displacement with an accuracy of millimeters. The computational algorithm runs on an embedded computer with real-time constraints for tracking the movement of a real needle. This haptic needle location data is used to reproduce the movement of a virtual needle within a simulation app.As a fundamental result, an ergonomic and realistic training simulator has been successfully constructed for healthcare professionals to acquire the mental model and motor skills necessary to practice percutaneous procedures successfully.

Review of Mathematical Modeling Research: A Descriptive Content Analysis Study

Recently, mathematical models and modeling practices have become popular in associating mathematics with real-life problems and, hence understanding this relationship. Accordingly, the mathematical modeling skill has been adopted in the standards and by researchers. Understanding the use of mathematical modeling in the learning and teaching processes of mathematics education will contribute to the future of the field. This study aimed to review the trends in mathematical modeling literature using leading research studies. This study reviewed the various types of studies indexed in the Web of Science database between 2000 and 2021 regarding how they addressed modeling. As well as mathematical modeling approaches used, the studies were reviewed in terms of basic characteristics such as publication year, sample, and research method. We evaluated studies using a form developed by the researchers, and the study revealed an increase in the number of studies over the years, and the studies were conducted mostly with pre-service teachers. In addition, we observed that research studies employed mostly small samples to closely monitor the modeling process.

ИМИТАЦИОННОЕ МОДЕЛИРОВАНИЕ БИЗНЕС-ПРОЦЕССОВ В УСЛОВИЯХ ЦИФРОВОЙ ТРАНСФОРМАЦИИ ЭКОНОМИКИ

The article is devoted to the problem of teaching skills of simulation modeling of business processes of an enterprise in the conditions of its digital transformation. An example of a training case shows the possibilities of using simulation modeling tools to visualize and quantify the results of a university admissions campaign. The direction of further research on the development of methodological materials for the development of digital twins, taking into account simulation models of business processes of the enterprise, is outlined.

A Machine Learning Augmented Data Assimilation Method for High‐Resolution Observations

AbstractThe accuracy of initial conditions is an important driver of the forecast skill of numerical weather prediction models. Increases in the quantity of available measurements, particularly high‐resolution remote sensing observational data products from satellites, are valuable inputs for improving those initial condition estimates. However, the traditional data assimilation methods for integrating observations into forecast models are computationally expensive. This makes incorporating dense observations into operational forecast systems challenging, and it is often prohibitively time‐consuming. Additionally, high‐resolution observations often have correlated observation errors which are difficult to estimate and create problems for assimilation systems. As a result, large quantities of data are discarded and not used for state initialization. Using the Lorenz‐96 system for testing, we demonstrate that a simple machine learning method can be trained to assimilate high‐resolution data. Using it to do so improves both initial conditions and forecast accuracy. Compared to using the Ensemble Kalman Filter with high‐resolution observations ignored, our augmented method has an average root‐mean‐squared error reduced by 37%. Ensemble forecasts using initial conditions generated by the augmented method are more accurate and reliable at up to 10 days of forecast lead time.

Reliable AI models can reveal key processes of heat recovery steam generator operation in air pollutant emission

Artificial intelligence (AI) algorithms have been applied to air pollution prediction modeling, but assessment of their trustworthiness is lacking. This study aims to examine whether AI-based models can identify key operational processes of a Heat Recovery Steam Generator (HRSG) facility and predict the emission of target air pollutants, including nitrogen oxides (NOx), sulfur oxides (SOx), and total suspended particles (TSP). First, key operational variables are selected based on the feature importance of the Random Forest models (that is, data-driven input selection). Secondly, Bidirectional Long Short-Term Memory-based AutoEncoder (BiLSTM-AE) and Random Forest (RF) models with the data-driven input selection are trained and evaluated using multiple predictive performance metrics. Then, the BiLSTM-AE and RF models are trained and evaluated using the operational variables selected by experts. Results demonstrate that the data-driven and expertise-based methods select five common operational variables that are key factors to predict variations of target output variables. The results from the multi-metric evaluations show a skillful prediction of BiLSTM-AE and RF models for NOx and TSP, but not SOx. For TSP prediction, RF is more sensitive to the input selection method than BiLSTM-AE. This study underscores the dependency of the prediction skill of AI algorithm-based models on the target air pollutant and input selection method, and suggests the trustworthiness of selected key operational variables by RF's feature importance.

Compilation of the Job Competencies Model of the Parent Company Specialized in the Production and Development of Atomic Energy in Iran

Objective: The aim of this study is to develop a competency model for jobs at the Bushehr Nuclear Power Plant, a subsidiary of the Atomic Energy Organization of Iran. Methodology: This mixed-methods research utilized both qualitative and quantitative approaches. Initially, qualitative data were collected through library studies and thematic analysis. Based on this data, a questionnaire was designed to gather quantitative information, which was distributed among 90 employees and managers of the Bushehr Nuclear Power Plant. The collected data were then analyzed using structural equation modeling. Findings: The results of the analysis showed that the proposed competency model had a good fit, with all structural paths being statistically significant. Both technical and managerial competencies played a crucial role in improving employee performance and safety. The model’s goodness-of-fit index was calculated at 0.413, indicating a strong fit. Conclusion: This study concluded that developing a multidimensional competency model, including technical, managerial, and communication skills, can lead to improved efficiency and safety in nuclear power plants. Implementing such models in recruitment, training, and employee evaluation processes can positively impact organizational productivity.

PolyGloT: A personalized and gamified eTutoring system for learning modelling and programming skills

PolyGloT: A personalized and gamified eTutoring system for learning modelling and programming skills

Relative and Unified Skill of Environmental, Edaphic, and Management Factors to Explain Crop Yield Variance Using Machine Learning

Highlights Random forests equipped with recursive feature elimination were used to predict crop yield variation using diverse predictors. The predictors belonged to either environmental, soils, or management domains. The best performing models combined predictors from all three domains and explained 75%-80% spatiotemporal yield variance. Extreme heat was most important among soils, management, and seasonal environment predictors in maize and soybean. Irrigation intensity, tile drainage, % silt, and July rain were most important at the sub-seasonal aggregation scale. Abstract. Crop yields are dictated by a complex interplay between environment, edaphic (soils), and management that are subject to change across space and time. However, to what extent each of these influences and their interactions have been important in explaining yield variance is limitedly understood. The convoluted nature of this question motivates the application of modern machine learning approaches to decipher these influences and elucidate crop yield relations with their critical drivers. Here, we used random forest modeling with recursive feature elimination (RFE) to discern the diverse drivers of historical (1981-2019) county-level maize and soybean yields in the U.S. within the realms of environment (growing and extreme degree days, precipitation, vapor pressure deficit, evaporative demand, crop water use, soil moisture), soils (sand, silt, and clay contents, bulk density, soil organic carbon, available water capacity), and management (irrigation intensity, tile drainage, nitrogen input, depth to groundwater). We found that the most effective models selected predictors from all three realms and achieved remarkable explanatory capability, accounting for 75%-80% of the spatial and temporal variance combined. Environmental predictors exhibited a non-negotiable role in determining model performance, while their combination with either soil or management predictors approached the efficacy of the best-performing model. Specific variables within each individual predictor set and their combinations were analyzed for their relative importance to the model skill. The best performing model that used soils, management, and seasonal environmental predictors evaluated extreme heat as the most influential predictor for both crops. On further inclusion of sub-seasonal environmental variables with soil and management predictors, the relative importance shifted, with irrigation intensity assuming prominence as the most influential predictor, accompanied by tile drainage, silt content, and July precipitation for both crops. RFE revealed that only eight of the most relevant predictors were sufficient to explain >70% of the yield variance, even when the total predictors included were as high as 52. that Visual representations were developed to offer insights into the functional response of crop yield to changes in critical predictors, thereby facilitating predictions across diverse agricultural production systems and over time. This research underscores the value of including soil and management indicators alongside environmental predictors to improve understanding and predictability of the intricate dynamics governing crop yield variability. Keywords: Climate variability, Drainage, Irrigation, Maize, Nitrogen, Random forest, Soils, Soybean.

Development of grade 10 students’ modelling skills on circulatory system through model-based learning

Aim: This study aims to elevate the modelling skills of Grade 10 students in understanding the circulatory system, using model-based learning, to a level where they can achieve at least a 70% passing score on a relevant unit test.Method: The study involved 23 students and implemented a model-based learning approach. Research tools included a design for model-based learning, assessments of the modelling process, a modelling ability exam, structural interviews, and student diaries. Data analysis was conducted using percentages and averages, and the action research was structured into two iterative rounds.Results: Initial findings from the first cycle revealed an average modelling ability score of 18.21 out of 24 (75.87%), with 14 students surpassing the 70% threshold. The second cycle showed marked improvement, with an average score of 19.94 out of 24, translating to an 83.09% success rate. Notably, all 23 students exceeded the 70% benchmark in this cycle.Conclusion: The implementation of model-based learning significantly enhanced the students' modelling skills in understanding the circulatory system. The method proved effective in not only achieving but surpassing the targeted 70% success threshold, demonstrating its potential as a valuable educational tool in biology.

Bridging the engineering skill gap in Nigeria: Preliminary findings and recommendations of the E4I survey

The paper presents the key findings and recommendations of an evidence-based survey report on the engineering skill gap among young engineering graduates in Nigeria. The outcome of the study highlighted the lack of technical, problem-solving, research and development, and management skills in the country’s young engineering graduates. Integrating emerging technologies like artificial intelligence, additive manufacturing, virtual reality, computer programming, computer-aided manufacturing, and computer-aided design into the curriculum was strongly recommended for the training of young engineering students in Nigerian higher education institutions. Other recommendations for skill augmentation include; enabling young graduates to develop their optimisation, modelling, simulation, design, creativity, technical, practical, machining, operation, and management skills to boost their hands-on engineering proficiencies. Strong emphasis was also on the importance of a one-year post-graduation internship program to upskill all young engineering graduates and provide a head-start in industrial settings. Lastly, adopting an outcome-based education (OBE) engineering training curriculum was recognised as crucial for addressing the skills gap.

Oceanic Rossby wave predictability in ECMWF's subseasonal‐to‐seasonal reforecasts

AbstractIn recent years, studies have put forth various theories on the role of oceanic equatorial Rossby waves (OERW) in the subseasonal‐to‐seasonal (S2S) predictability of the Indian Ocean (IO). While much of the scientific literature uses data from in‐situ, satellite, and/or reanalysis datasets, this study focuses on reforecast fields from the European Centre for Medium‐Range Weather Forecasting's (ECMWF) S2S dataset. Evaluation of the model's predictive skill in representing OERWs and the associated variations in subsurface‐to‐surface interaction and air–sea coupling are discussed. This work provides a unique methodology to calculate and evaluate the predictability of OERWs from model forecast data. Our results indicate that the model forecasts OERWs with high skill (anomaly correlation > 0.8 out to 40 days), indicating they are a key source of oceanic subseasonal predictability at extended lead times. Analysis of the wavenumber–frequency spectra for the IO indicates a strong reduction in power throughout the model forecast time period in the oceanic equatorial Kelvin wave (OEKW) regime and modest reduction of the OERW power. Both Kelvin and Rossby waves are modulated by the subseasonal zonal wind stress anomalies and the reduction of power is impacted by biases in winds at longer forecast leads. The erroneous weakening of the OEKWs contributes to the weakening of the reflected OERWs. Previous studies have documented that ocean heat content (OHC), particularly associated with downwelling OERWs, is important to maintaining and amplifying subseasonal precipitation in the IO. The reduced OERW power results in weaker advection of enhanced OHC anomalies by the OERWs, which has numerous implications for air–sea and subsurface‐to‐surface coupling, as discussed. The atmospheric response to the waning westward transport of OHC anomalies in the western IO by OERWs is associated with a weakening of intraseasonal precipitation anomalies associated with the intraseasonal oscillation.

Efektivitas Model Project-Based Learning dalam Meningkatkan Kemampuan Literasi Numerasi Siswa melalui Praktik Lesson Study di Sekolah

Numerical literacy is an important ability for students to have in facing the challenges of dynamic educational development in the Merdeka Curriculum. The purpose of this study was to determine the effectiveness of the Project-Based Learning (PjBL) model in improving students' numeracy literacy skills through Lesson Study Practices in schools. The subjects of this research were Class 5A students at SD Negeri 35 Ampenan. The research method used is descriptive-quantitative with steps including 1) problem identification, 2) reviewing literature related to the PjBL model and numeracy literacy skills, 3) collecting and analyzing data, and 4) drawing conclusions. The instruments used in this research were implementation sheets of PjBL, student worksheets, and numerical literacy tests. The results of this study indicate that the Project Based Learning (PjBL) model is effective in increasing students' numeracy literacy skills through Lesson Study Practices. Therefore, the PjBL model can be used as a learning alternative that can develop students' literacy and numeracy skills.

Automation process in data collection for representing façades in building models as part of the renovation process

A key barrier in building-facade renovation processes is that, contrary to new designs, an initial building model where the design process is based rarely exists, and the technologies usually employed to create it (e.g., based on point cloud scanning) are costly or require modeling skills. This situation is a clear limitation, especially in early decision stages, where the level of detail required is not very high, and the analysis and studies to consider the renovation plan (e.g., simplified energy simulations and renovation potential, or estimation of the number, types, and dimensions of the prefabricated modules incorporating solar panels) highly depend on such digital models. This paper introduces a process that, based on freely available data such as open GIS sources (local Cadasters, OpenStreetMap…) and façade images, can semi-automatically generate the 3D building model of the existing conditions, and in a second step also suggests the prefabricated facades module layout for building upgrades. Additionally, no onsite visit is needed. When the upgrade is focused on the façade, a big opportunity is identified for generating the building model and a realistic representation of its envelope, only using online data sources as input. The process developed consists of a set of easy-to-use software tools that can be used independently or combined in a workflow, depending on the available data and starting conditions. Time saving is very clear and costs can be reduced.

Enhancing the accuracy of wind speed estimation model using an efficient hybrid deep learning algorithm

Estimation of wind speed holds great significance for efficient operation of wind farms and grid stability. Randomness of wind speed poses a great challenge for accurate estimation of wind speed. Hence for addressing this problem, a convolutional neural network (CNN) and long short-term memory (LSTM) based novel hybrid deep learning (DL) short-term is proposed. The prediction model is built using pre-processed wind speed dataset from Jaisalmer district, Rajasthan. For designing the proposed model, the samples of the dataset are bifurcated into four seasons such as autumn, summer, winter and spring and the prediction performance are evaluated for each season separately. The proposed hybrid algorithm (CNN-LSTM) is implemented and a comparison is made with different DL algorithms. The predictive performance are calculated by observing the statistical metrics such as root mean squared error (RMSE), mean absolute error (MAE), relative RMSE, normalized RMSE and coefficient of determination (R2). It is observed that the value of statistical metrics such as MAE, RMSE, relative RMSE and normalized RMSE of the proposed algorithm are lower than the other DL models for all the four seasons. The higher value of R2 obtained by the CNN-LSTM indicates the model is best fitted with the dataset. Conclusively, the proposed model demonstrates improved adaptability and higher prediction accuracy in comparison to the predictive skills of models utilizing the same dataset.

Systematic Literature Review: Students' Mathematical Literacy Ability Through the Problem Based Learning Model

This research aims to examine several literatures related to the Problem Based Learning learning model on mathematical literacy abilities. The research method used in this research is the SLR method. Journal searches were carried out using the keywords PBL learning model and mathematical literacy skills published in the 2019-2023 period obtained with the help of the Publish or Perish application. The results of this research show that: 1) the problem-based learning model can improve students' mathematical literacy skills; 2) research related to the PBL learning model on students' mathematical literacy abilities in the 2019-2023 period tends to be mostly carried out in 2021, at junior high school level, and is a type of quantitative research.

The Exploration of Integrating the Midjourney Artificial Intelligence Generated Content Tool into Design Systems to Direct Designers towards Future-Oriented Innovation

In an age where computing capabilities are expanding at a breathtaking pace, the advent of Artificial Intelligence-Generated Content (AIGC) technology presents unprecedented opportunities and challenges to the future of design. It is crucial for designers to investigate how to utilize this powerful tool to facilitate innovation effectively. As AIGC technology evolves, it will inevitably shift the expectations of designers, compelling them to delve deeper into the essence of design creativity, transcending traditional sketching or modeling skills. This study provides valuable insights for designers on leveraging AIGC for forward-thinking design innovation. We focus on the representative AIGC tool, “Midjourney”, to explore its integration into design systems for collaborative innovation among content creators. We introduce an AIGC-based Midjourney path for product design and present a supporting tool card set: AMP-Cards. To confirm their utility, we undertook extensive validation through advanced prototype design research, task-specific project practices, and interdisciplinary collaborative seminars. Our findings indicate that AIGC can considerably enhance designers’ efficiency during product development, especially in the “explorative product shape” phase. The technology excels in identifying design styles and quickly producing varied design solutions. Moreover, AIGC’s capacity to swiftly translate creators’ concepts into visual forms greatly aids in multidisciplinary team communication and innovation.

Improving climate model skill over High Mountain Asia by adapting snow cover parameterization to complex-topography areas

Abstract. This study investigates the impact of topography on five snow cover fraction (SCF) parameterizations developed for global climate models (GCMs), including two novel ones. The parameterization skill is first assessed with the High Mountain Asia Snow Reanalysis (HMASR), and three of them are implemented in the ORCHIDEE land surface model (LSM) and tested in global land–atmosphere coupled simulations. HMASR includes snow depth (SD) uncertainties, which may be due to the elevation differences between in situ stations and HMASR grid cells. Nevertheless, the SCF–SD relationship varies greatly between mountainous and flat areas in HMASR, especially during the snow-melting period. The new parameterizations that include a dependency on the subgrid topography allow a significant SCF bias reduction, reaching 5 % to 10 % on average in the global simulations over mountainous areas, which in turn leads to a reduction of the surface cold bias from −1.8 ∘C to about −1 ∘C in High Mountain Asia (HMA). Furthermore, the seasonal hysteresis between SCF and SD found in HMASR is better captured in the parameterizations that split the accumulation and the depletion curves or that include a dependency on the snow density. The deep-learning SCF parameterization is promising but exhibits more resolution-dependent and region-dependent features. Persistent snow cover biases remain in global land–atmosphere experiments. This suggests that other model biases may be intertwined with the snow biases and points out the need to continue improving snow models and their calibration. Increasing the model resolution does not consistently reduce the simulated SCF biases, although biases get narrower around mountain areas. This study highlights the complexity of calibrating SCF parameterizations since they affect various land–atmosphere feedbacks. In summary, this research spots the importance of considering topography in SCF parameterizations and the challenges in accurately representing snow cover in mountainous regions. It calls for further efforts to improve the representation of subgrid-scale processes affecting snowpack in climate models.

Measurements and Model Improvement: Insight into NWP Model Error Using Doppler Lidar and Other WFIP2 Measurement Systems

Abstract Doppler-lidar wind-profile measurements at three sites were used to evaluate NWP model errors from two versions of NOAA’s 3-km-grid HRRR model, to see whether updates in the latest version 4 reduced errors when compared against the original version 1. Nested (750-m grid) versions of each were also tested to see how grid spacing affected forecast skill. The measurements were part of the field phase of the Second Wind Forecasting Improvement Project (WFIP2), an 18-month deployment into central Oregon–Washington, a major wind-energy-producing region. This study focuses on errors in simulating marine intrusions, a summertime, 600–800-m-deep, regional sea-breeze flow found to generate large errors. HRRR errors proved to be complex and site dependent. The most prominent error resulted from a premature drop in modeled marine-intrusion wind speeds after local midnight, when lidar-measured winds of greater than 8 m s−1 persisted through the next morning. These large negative errors were offset at low levels by positive errors due to excessive mixing, complicating the interpretation of model “improvement,” such that the updates to the full-scale versions produced mixed results, sometimes enhancing but sometimes degrading model skill. Nesting consistently improved model performance, with version 1’s nest producing the smallest errors overall. HRRR’s ability to represent the stages of sea-breeze forcing was evaluated using radiation budget, surface-energy balance, and near-surface temperature measurements available during WFIP2. The significant site-to-site differences in model error and the complex nature of these errors mean that field-measurement campaigns having dense arrays of profiling sensors are necessary to properly diagnose and characterize model errors, as part of a systematic approach to NWP model improvement. Significance Statement Dramatic increases in NWP model skill will be required over the coming decades. This paper describes the role of major deployments of accurate profiling sensors in achieving that goal and presents an example from the Second Wind Forecast Improvement Program (WFIP2). Wind-profile data from scanning Doppler lidars were used to evaluate two versions of HRRR, the original and an updated version, and nested versions of each. This study focuses on the ability of updated HRRR versions to improve upon predicting a regional sea-breeze flow, which was found to generate large errors by the original HRRR. Updates to the full-scale HRRR versions produced mixed results, but the finer-mesh versions consistently reduced model errors.

Evaluation of CMIP6 GCMs for Simulating Atmospheric Rivers: Relating The Model Skill For Key AR Variables to the Skill for Winds and Water Vapor

Evaluation of CMIP6 GCMs for Simulating Atmospheric Rivers: Relating The Model Skill For Key AR Variables to the Skill for Winds and Water Vapor