Development Of Modeling Tools Research Articles

Modeling of Wind Power Plants and Their Impact on Economic and Environmental Development

Abstract This research addresses the environmental dynamics of wind power plants and their impact on the economy and environment. A system dynamics framework is used as a tool for model development since it accommodates relationships between complex and nonlinear variables affecting the wind power plants and their impact on the economy and environment. The scientific contribution of this research is the creation of scenario modeling that describes the interrelationships of variables and parameters affecting wind power plants and their impact. By changing the structure of the model, projections on the future of wind power plant generation can be estimated. Several scenarios being developed include adding turbines in the Sidrap and Jeneponto regencies to increase the fulfillment ratio of wind energy and scenarios to reduce CO2 emissions using solid direct air capture (S-DAC). The data and information used in this research come from the Central Statistics Agency, articles on wind energy power plants, and data from related previous studies. These models and scenarios can be applied in other regions by adjusting the parameter values of the case study model. Total wind energy depends on density, wind speed, blade cross-sectional area, and the efficiency of the Betz limit. With the addition of 25 turbines in Sidrap and 20 turbines in Jeneponto, the average fulfillment ratio is estimated to increase by around 7% due to increased production. Meanwhile, total CO2 emissions are estimated to decrease by approximately 45% due to solid direct air capture.

The optimal experiment? Influence of solution strategies on model-based optimal experimental design

Model-based optimal experimental design (OED) is a well known tool for efficient model development. However, it is not used very often. A few reasons for that are: a lack of understanding on how to work with complex OED methods and a small amount of ready-to-use tools available to directly apply OED methods. In the presented contribution OED and sampling strategies are used to categorize OED formulations as nonlinear programs. Different strategies and their combination are analyzed based on performance and robustness. Depending on the availability of measurements, control flexibility of the experimental setup, and model accuracy some strategies are more efficient than others. Based on the proposed guidelines, engineers will have a better understanding about which NLP formulation should be used for their specific task. The methods described are available to the community as a part of open-source code developed in Python.

Crop Yield Prediction using Machine Learning

Weather profoundly impacts agricultural outcomes, making accurate crop prediction vital for farmers' decision-making. This abstract presents a comprehensive overview of weather-based crop prediction, emphasizing its significance, key components, and methodologies. The process begins with the collection and analysis of historical weatherdata encompassing variables such as temperature, precipitation, humidity, and sunlight. Utilizing Python programming and data visualization libraries like Pandas and Matplotlib facilitates the exploration and visualization of this data, revealing trends and patterns. Machine learning algorithms, including regression and ensemble methods, are employedto develop predictive models. These models leverage historical weather data to forecast future crop yields accurately. Python's extensive libraries, such as Scikit-learn and TensorFlow, offer robust tools for model development and evaluation. Incorporating advanced technologies like remote sensing and satellite imagery further refines the prediction process. These tools provide real-time insights into crop health and growth, enhancing the precision of forecasts. Ultimately, weather-based crop prediction serves as a valuable decision support tool for farmers, enabling informed choices regarding planting, irrigation, and harvesting practices. By harnessing historical weather data, machine learning algorithms, and innovative technologies, stakeholders can optimize agricultural productivity, mitigate risks, and contribute to global food security

Multidimensional Model of Information Struggle with Impulse Perturbation in Terms of Levy Approximation

The focus of this research was on building a decision support system for a model that characterizes the conflict interaction of n-dimensional complex systems with non-trivial internal structures. The interpretation of the new model was focused on information warfare as the impact of rare events that quickly change certain perceptions of a large number of people. Consequently, the support for various ideas experiences stochastic jumps, a phenomenon observable through a non-classical Levy approximation scheme. The essence of our decision support system lies in its ability to navigate the complex dynamics of conflict interaction among multifaceted systems. Through the utilization of advanced modeling techniques, our aim is to illuminate the complicated interplay of factors influencing information warfare and its cascading effects on societal perceptions and behaviors. Key components of our decision support system encompass model development, simulation capabilities, data integration, and visualization tools. The significance of our work lies in its potential to inform policy formulation, conflict resolution strategies, and societal resilience in the face of information warfare. By providing decision-makers with actionable intelligence and foresight into emerging threats and opportunities, our decision support system serves as a valuable tool for navigating the complexities of modern conflict dynamics. In conclusion, developing a decision support system for modeling conflict interaction in complex systems represents an essential step toward enhancing our understanding of information warfare and its consequences. Through interdisciplinary collaboration and innovative modeling techniques, we aim to provide stakeholders with the insights and capabilities needed to navigate the developing landscape of conflict and ensure the stability and resilience of society.

Global change impacts on mineral nutritional quality of cereal grains: Coordinated datasets and analyses to advance a systems-based understanding

ContextGlobal nutritional health outcomes are directly reliant on agroecosystem nutrient outputs. Appropriately, there is concern surrounding the impacts of a changing climate not only on crop yields, but also on crop nutritional quality (e.g., mineral nutrient concentrations). Quantifying the impacts of elevated CO2 concentrations, elevated temperature, drought stress, edaphic factors, and agronomic management on crop yields and mineral nutrition is critical, yet a systems-level understanding of these interactive factors is poorly developed, limiting our ability to effectively target solutions. Empirical data for climate impacts on crop nutritional quality remain scarce, with much of the research emerging from valuable, but geographically limited, Free-air CO2 Enrichment (FACE) experiments, several of which suggest that human nutrition will be adversely impacted by e[CO2]. Specific concerns center on observed declines in grain protein, iron, and zinc concentrations due to already wide-spread human nutritional deficiencies in these nutrients. ObjectivesAs global change experiments expand to pursue questions regarding interactive climate impacts on crop yields and nutritional quality, it is imperative to interrogate the measurements, data standardization, and metadata needed for unifying synthesis. The data reported for shifts in crop nutritional quality are often incomplete, precluding the generalizability and comparability of results. MethodsWe frame this review around six inter-reliant methods, tools, and practices to support maximally useful experimental datasets to inform questions of global change impacts on crop nutrition and aid in detecting genotypic differences in mineral nutrient density. The bulk of the data and discussion centers on wheat (Triticum aestivum L.) due to the central role this crop plays in human nutrition and sustained biofortification efforts. ResultsTo permit experimental comparability and synthesis, datasets should (1) clearly delineate analytical methods and standards and (2) link mean nutrient concentrations with the covariate of yield. (3) Multi-year, multi-location data is required to identify genotypes with significant deviations in nutrient concentrations, with (4) data normalized for yield within appropriate analytical frameworks. (5) Inclusion of data on soil properties, weather, and abiotic and biotic stresses as well as (6) agronomic practices and nutrient management is essential for understanding global change impacts on nutritional outcomes. ConclusionsCoordinated, multi-dimensional data will permit the syntheses and meta-analyses needed to identify and quantify climate impacts on nutrition. ImplicationsThis work is essential to effectively target nutritional solutions, to develop modeling tools to support nutritional planning, and to identify areas where agronomic management and breeding can minimize climate impacts on nutritional outcomes.

Establishing a nomogram to predict refracture after percutaneous kyphoplasty by logistic regression.

Several studies have examined the risk factors for post-percutaneous kyphoplasty (PKP) refractures and developed many clinical prognostic models. However, no prior research exists using the Random Forest (RF) model, a favored tool for model development, to predict the occurrence of new vertebral compression fractures (NVCFs). Therefore, this study aimed to investigate the risk factors for the occurrence of post-PKP fractures, compare the predictive performance of logistic regression and RF models in forecasting post-PKP fractures, and visualize the logistic regression model. We collected clinical data from 349 patients who underwent PKP treatment at our institution from January 2018 to December 2021. Lasso regression was employed to select risk factors associated with the occurrence of NVCFs. Subsequently, logistic regression and RF models were established, and their predictive capabilities were compared. Finally, a nomogram was created. The variables selected using Lasso regression, including bone density, cement distribution, vertebral fracture location, preoperative vertebral height, and vertebral height restoration rate, were included in both the logistic regression and RF models. The area under the curves of the logistic regression and RF models were 0.868 and 0.786, respectively, in the training set and 0.786 and 0.599, respectively, in the validation set. Furthermore, the calibration curve of the logistic regression model also outperformed that of the RF model. The logistic regression model provided better predictive capabilities for identifying patients at risk for post-PKP vertebral fractures than the RF model.

VSCode-Antimony: a source editor for building, analyzing, and translating antimony models.

Developing biochemical models in systems biology is a complex, knowledge-intensive activity. Some modelers (especially novices) benefit from model development tools with a graphical user interface. However, as with the development of complex software, text-based representations of models provide many benefits for advanced model development. At present, the tools for text-based model development are limited, typically just a textual editor that provides features such as copy, paste, find, and replace. Since these tools are not "model aware," they do not provide features for: (i) model building such as autocompletion of species names; (ii) model analysis such as hover messages that provide information about chemical species; and (iii) model translation to convert between model representations. We refer to these as BAT features. We present VSCode-Antimony, a tool for building, analyzing, and translating models written in the Antimony modeling language, a human readable representation of Systems Biology Markup Language (SBML) models. VSCode-Antimony is a source editor, a tool with language-aware features. For example, there is autocompletion of variable names to assist with model building, hover messages that aid in model analysis, and translation between XML and Antimony representations of SBML models. These features result from making VSCode-Antimony model-aware by incorporating several sophisticated capabilities: analysis of the Antimony grammar (e.g. to identify model symbols and their types); a query system for accessing knowledge sources for chemical species and reactions; and automatic conversion between different model representations (e.g. between Antimony and SBML). VSCode-Antimony is available as an open source extension in the VSCode Marketplace https://marketplace.visualstudio.com/items?itemName=stevem.vscode-antimony. Source code can be found at https://github.com/sys-bio/vscode-antimony.

Developing of Authentic Assessment Model Learning to Appreciate Drama Texts: A Pilot Test with Senior High School Students

This research is motivated by a lack of teacher attention in managing literary appreciation learning with evidence of the lack of completeness of the assessment tools in the teacher's lesson plans. Therefore, the purpose of this study was to develop an authentic assessment model to measure the achievement of basic competence (KD) in learning the ability to appreciate literary texts (drama) for the class XI 2013 curriculum at the SMA/MA/SMK level, which focused on psychomotor and affective domains which were the focus of learning achievement on literary appreciation by first perform needs analysis, KD analysis, material analysis, and student analysis. To achieve the objectives of this study, the theory of assessment and authentic assessment was used; learning theory and development of learning device models; literary and educational theory; and learning theory of the ability to appreciate literary texts. The design implications for the development of this model refer to the 4-D model suggested by Thiagarajan, Semel and Semel (1974) which consists of 4 stages, namely define, design, develop and disseminate. The type of research is R D development research. The data of this research consists of two groups, namely qualitative data in the form of assessment model development tools, and quantitative data in the form of achievement scores of performance tasks with authentic instruments whose limited trials and dissemination were carried out in Senior High School in Padang city. The result of the research is an assessment model that is based on social cognitive learning theory, constructivism, and meaningful learning, learning syntax that focuses on students with representative assessment tools. The results of testing the assessment tool which consisted of 10 types of instruments were concluded to be able to measure the level and type of appreciation of students' literature with a "Good" qualification.

Simulating the Impact of Ore and Water Quality on Flotation Recovery during the Life of a Mine

Blending of different ore types in the concentrator feed contributes significantly to maintaining a high recovery of valuable minerals with required grades in the concentrate. It is feasible to develop an ore-blending scheme over the life of a mine already in the design phase of the plant. In addition to ore characteristics, water quality is known to impact mineral recovery. A blending plan could also be developed for the different water streams of a future concentrator. This paper describes a novel modeling and simulation approach to predict metallurgical response combining ore types and water quality. The model is based on kinetic laboratory flotation test data, and it was tested on a case study. As a result, rougher flotation grade-recovery curves dependent on ore types and water quality are presented over the predicted life of the mine. The simulation results can be exploited in project design to maximize the recovery of valuable minerals and to ensure environmentally sound and profitable mining operations. Overall, the developed modeling tool can be applied widely for minerals processed by using froth flotation and water types available for kinetic laboratory flotation tests.

Efficiency Maps of Shifted Inductances Axes Permanent Magnet Synchronous Motors

In this contribution, a tool developed for the study of the efficiency maps of shifted inductances axes permanent magnet synchronous motors (SIAPMSMs) is presented and made available to the readers. The research builds upon a shared foundation that has already been established in previous works focused on the power capability of synchronous machines. This contribution is an extension of previous works dedicated to the power capabilities of shifted inductances axes synchronous motors where the losses were not considered. In this new contribution, the SIAPMSM models, which include the electromagnetic losses (joule and iron losses), are discussed. The mechanical losses are not included. The classical permanent magnet synchronous machines can be considered a particular case of a SIAPMSM. They will be used to validate the developed tools. The validation study is conducted by comparing the power capabilities and the efficiency maps of SIAPMSMs and classical PMSMs obtained by the newly developed modeling tool and a previously developed modeling tool.

AnimalAccML: An open-source graphical user interface for automated behavior analytics of individual animals using triaxial accelerometers and machine learning

Automated collection of accelerometer data and subsequent machine learning modeling are prevalent combined methods for animal behavior recognition. However, there is a lack of customized tools for user-friendly machine learning model development. Meanwhile, existing models in previous research could not be directly used for behavior interpretation. The objective of this study was to design and develop a tool for customized machine learning model development and animal behavior analysis using triaxial accelerometer data. A graphical user interface was programmed with Python and saved in a public repository for open access. The interface mainly consists of pages of ‘Manage Project’, ‘Preprocess Data’, ‘Develop Models’, and ‘Analyze Behavior’. An open dataset containing triaxial accelerometer data of six beef cattle was used to test the developed interface. The main results show that users can customize appropriate machine learning models for behavior analytics through several mouse clicks on the interface. A total of 15 models can be selected and trained to determine an optimal one, and model performance can be optimized by adjusting parameters of window size, step size, and training-to-validation ratio. Data imbalance can be solved by merging minority classes into one. The newly developed model has the capacity to analyze overall behavior time budget, statistics (e.g., mean, minimum, maximum, and standard deviation) of each behavior duration, and frequency of behavior sequences. The tool is supportive for automated animal behavior analytics critical to enhancing animal welfare, housing environment, genetics selection, and flock management.

Introduction to the Special Issue on Innovation in Transportation-Enabled Urban Services, Part 1

Free AccessAboutSectionsView PDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked InEmail Go to SectionFree Access HomeService ScienceVol. 15, No. 1 Introduction to the Special Issue on Innovation in Transportation-Enabled Urban Services, Part 1Guest Editors: Niels Agatz, Soo-Haeng Cho, Hai Wang, Editor-in-Chief: Saif BenjaafarGuest Editors: Niels Agatz, Soo-Haeng Cho, Hai Wang, Editor-in-Chief: Saif BenjaafarPublished Online:13 Mar 2023https://doi.org/10.1287/serv.2023.0317Rapid developments in city infrastructure and technologies are creating numerous opportunities and inspiring innovative and emerging urban services. Among these innovations, complex systems of urban transportation and logistics have embraced advances and have been reshaped significantly. They enable innovative new urban services, which are now booming and changing everyday life for urban residents.This special issue of Service Science explores perspectives on innovation in transportation-enabled urban services. We hope that the special issue will enhance the understanding of the planning, operation, and management of such services. Contributions are expected to demonstrate rigorous model development, economic/econometric analysis, and decision-making tools based on optimization and/or data-driven approaches. Research in this special issue addresses the novel challenges and opportunities that arise in these transportation/logistics systems and relevant transportation-enabled urban services.The articles in the special issue represent an exciting diversity in research settings, methodologies, and applications. The articles are composed into two parts. Part 1 of the special issue includes four papers. In what follows, we provide a brief description of the papers in Part 1. The introduction of papers in Part 2 is rendered to the journal issue scheduled for September 2023.Yan et al. (2023) study an incentive mechanism for private parking-sharing programs in an imperfect information setting. They propose a matching-and-pricing mechanism for a driver’s demand-reporting problem in parking-sharing programs in which owners share their private parking slots with other drivers. They generate a driver-slot matching solution and show that, under the assumption that drivers do not know with certainty whether other drivers will show up to compete for the parking slots, the mechanism proposed induces drivers to truthfully report their private information about the travel plans and guarantees some desirable properties.Schwamberger et al. (2023) study the dynamic customer contacting for e-fulfillment in times of crisis. Specifically, the outbreak of the coronavirus disease 2019 pandemic increased demand for online grocery orders for both click & collect (C&C) and attended home delivery (AHD) and outstripped delivery capacity by a wide margin. They investigate a new demand management concept of proactively contacting customers and develop a decision policy for the C&C fulfillment method to address the problem of when to contact customers. They also extend this approach to the AHD setting and propose a three-step procedure to solve this problem.van der Gaast and Arslan (2023) explore the last-mile logistics operations of instant grocery delivery services. They introduce an instant delivery problem to replicate and examine two widely adopted strategies in the rapid delivery market: the personal shopper system (PSS) and the inventory owned delivery (IOD) system. They propose a tailored rolling horizon framework that utilizes column generation to browse updated delivery plans for arriving customer orders. Computational studies show that the PSS is a highly competitive strategy compared with IOD, particularly when dealing with small-sized customer orders.Ke and Mo (2023) study a one-way car-sharing system with both station-based and free-floating service types, which apply the two types in different parts of the service area. They establish a mixed-integer linear-programming model to maximize the operator profit, considering both strategic decisions of service type and operational decisions of relocation and trip selection. They propose a heuristic algorithm to effectively solve the model. Computational results demonstrate that the integrated system achieves a larger profit and higher demand fulfillment rate compared with the single station-based or free-floating system.ReferencesKe H, Mo Y (2023) Station-based or free-floating? An integrated carsharing system with dual service types. Service Sci. 15(1):58–76.Google ScholarSchwamberger J, Fleischmann M, Strauss A (2023) Feeding the nation—dynamic customer contacting for e-fulfillment in times of crisis. Service Sci. 15(1):22–40.Google Scholarvan der Gaast JP, Arslan AM (2023) Personal shopper systems in last-mile logistics. Service Sci. 15(1):41–57.Google ScholarYan P, Cai X, Chu F, Ni D, He H (2023) An incentive mechanism for private parking-sharing programs in an imperfect information setting. Service Sci. 15(1):3–21.Google Scholar Back to Top Next FiguresReferencesRelatedInformation Volume 15, Issue 1March 2023Pages 1-78, C3 Article Information Metrics Information Published Online:March 13, 2023 Copyright © 2023, INFORMSCite asGuest Editors: Niels Agatz, Soo-Haeng Cho, Hai Wang, Editor-in-Chief: Saif Benjaafar (2023) Introduction to the Special Issue on Innovation in Transportation-Enabled Urban Services, Part 1. Service Science 15(1):1-2. https://doi.org/10.1287/serv.2023.0317 PDF download

Progress in and Prospects of Genome Editing Tools for Human Disease Model Development and Therapeutic Applications.

Programmable nucleases, such as zinc finger nucleases (ZFNs), transcription activator-like effector nucleases (TALENs), and clustered regularly interspaced short palindromic repeats (CRISPR)/Cas, are widely accepted because of their diversity and enormous potential for targeted genomic modifications in eukaryotes and other animals. Moreover, rapid advances in genome editing tools have accelerated the ability to produce various genetically modified animal models for studying human diseases. Given the advances in gene editing tools, these animal models are gradually evolving toward mimicking human diseases through the introduction of human pathogenic mutations in their genome rather than the conventional gene knockout. In the present review, we summarize the current progress in and discuss the prospects for developing mouse models of human diseases and their therapeutic applications based on advances in the study of programmable nucleases.

Exploring quantitative structure-property relationship models for environmental fate assessment of petroleum hydrocarbons.

The rate and extent of biodegradation of petroleum hydrocarbons in the different aquatic environments is an important element to address. The major avenue for removing petroleum hydrocarbons from the environment is thought to be biodegradation. The present study involves the development of predictive quantitative structure-property relationship (QSPR) models for the primary biodegradation half-life of petroleum hydrocarbons that may be used to forecast the biodegradation half-life of untested petroleum hydrocarbons within the established models' applicability domain. These models use easily computable two-dimensional (2D) descriptors to investigate important structural characteristics needed for the biodegradation of petroleum hydrocarbons in freshwater (dataset 1), temperate seawater (dataset 2), and arctic seawater (dataset 3). All the developed models follow OECD guidelines. We have used double cross-validation, best subset selection, and partial least squares tools for model development. In addition, the small dataset modeler tool has been successfully used for the dataset with very few compounds (dataset 3 with 17 compounds), where dataset division was not possible. The resultant models are robust, predictive, and mechanisticallyinterpretable based on both internal and external validation metrics (R2 range of 0.605-0.959. Q2(Loo) range of 0.509-0.904, and Q2F1 range of 0.526-0.959). The intelligent consensus predictor tool has been used for theimprovement of the prediction quality for test set compounds which provided superior outcomes to those from individual partial least squares models based on several metrics (Q2F1 = 0.808 and Q2F2 = 0.805 for dataset 1 in freshwater). Molecular size and hydrophilic factor for freshwater, frequency of two carbon atoms at topological distance 4 for temperate seawater, and electronegative atom count relative to size for arctic seawater were found to be the most significant descriptors responsible for the regulation of biodegradation half-life of petroleum hydrocarbons.

3–4D soil model as challenge for future soil research: Quantitative soil modeling based on the solid phase

AbstractA 3–4D soil model represents a logical step forward from one‐dimensional soil columns (1D), two‐dimensional soil maps (2D), and three‐dimensional soil volumes (3D) toward dynamic soil models (4D), with time as the fourth dimension. The challenge is to develop modeling tools that account for the states of soil properties, including the spatial structure of solids and pores, as well as their dynamics, including soil mass and solute transfers in landscapes. Our envisioned 3–4D soil model approach aims at improving the capability to predict fundamental soil functions (e.g., plant growth, storage, matter fluxes) that provide ecosystem services in the socioeconomic context.This study provides a structured overview on current soil models, challenges, open questions, and urgent research needs for developing a 3–4D soil model. A 3–4D soil model should provide an inventory of spatially distributed and temporally variable soil properties. As basis for this, we propose a mass balance model for the solid phase, which needs to be supplemented by a model describing its structure. This should eventually provide adequate 3D parameter sets for the numerical modeling of soil functions (e.g., flow and transport). The target resolution is decameters in the horizontal plane and centimeters to decimeters in the vertical direction to represent characteristic soil properties and soil horizons. The actual state of soils and their properties can be estimated from spatial data that represent the soil forming factors, with the use of machine learning tools. Improved modeling of the dynamics of soil bulk density, biological processes, and the pore structure are required to relate the solid mass balance to matter fluxes.A 3–4D soil model can be built from several types of modeling approaches. We distinguish between (1) process models that simulate mass balances, fluxes and soil structure dynamics, (2) statistical pedometric models using machine learning and geostatistics to estimate the soil inventory within landscapes, and (3) pedotransfer functions to link observable attributes to specific model parameters required to simulate soil functions including water and matter fluxes. This should provide the prerequisites to predict the spatial distribution of soil functions and their changes in response to external forcing.This endeavor can draw upon many already established models and techniques, yet combining them into a newly created 3–4D soil model is a truly an ambitious, but promising task. The core of such a model is the bookkeeping of the solid mass together with soil structure, while accounting for biogeochemical and mechanical processes. The presented concepts are ambitious in context for research avenues toward the improvement of soil modeling by conjoining methods from a wide range of disciplines, including geological, geophysical, pedological, and remote sensing and visualization applications. The paper reviews and outlines research tools and needs for the 3‐D, spatially continuous representation of relevant soil properties and the modeling to represent the dynamics of soil properties and soil functions.

Construction of canal surfaces based on a specified flat curvature line

The research relevance is predefined by the widespread use of elements and structures that have the shape of canal surfaces in engineering practice and the possibilities of reproducing the surface through kinematics. The research aims to develop new means of modeling canal surfaces referred to as a grid of curvature lines by introducing elements with special properties into the structural model that simplify the solution of differential equations and reduce the amount of computation. To achieve the research methods, the synthetic geometric method, methods of linear algebra, the theory of differential equations and differential geometry, as well as methods of computer geometric modeling and visualization of three-dimensional objects were used. Studies on modeling and studying the properties of channel surfaces are analyzed. The research on the problem of the surfaces and lines of curvature is considered in more detail and the conditions under which it is possible to simplify the solution of the differential equation are identified. It was proved that the condition of contact between the canal surface and the plane along a given plane curve is sufficient for this curve to be one of the curvature lines of the family of orthogonal to the generating circles. This allowed to reduce the solution of the differential equation to two quadratures. The expressions of the corresponding integrals and an algorithm for modeling the canal surface with the possibility of referring to a grid of curvature lines were obtained. The expressions that define the desired surface include the parametric equations of a given plane line; a function that determines the radii of the spheres of the family depending on the parameter of this line. A specific example of modeling a surface based on a defined formula was also considered, and images of this surface with visualization of the coordinate grid were presented. The research’s practical values are defined by the possibility of using the developed modeling tools in the design and computer-aided design of the geometry of real products containing surfaces of a smooth transition of variable radius

Open Knee(s): A Free and Open Source Library of Specimen-Specific Models and Related Digital Assets for Finite Element Analysis of the Knee Joint

There is a growing interest in the use of virtual representations of the knee for musculoskeletal research and clinical decision making, and to generate digital evidence for design and regulation of implants. Accessibility to previously developed models and related digital assets can dramatically reduce barriers to entry to conduct simulation-based studies of the knee joint and therefore help accelerate scientific discovery and clinical innovations. Development of models for finite element analysis is a demanding process that is both time consuming and resource intensive. It necessitates expertise to transform raw data to reliable virtual representations. Modeling and simulation workflow has many processes such as image segmentation, surface geometry generation, mesh generation and finally, creation of a finite element representation with relevant loading and boundary conditions. The outcome of the workflow is not only the end-point knee model but also many other digital by-products. When all of these data, derivate assets, and tools are freely and openly accessible, researchers can bypass some or all the steps required to build models and focus on using them to address their research goals. With provenance to specimen-specific anatomical and mechanical data and traceability of digital assets throughout the whole lifecycle of the model, reproducibility and credibility of the modeling practice can be established. The objective of this study is to disseminate Open Knee(s), a cohort of eight knee models (and relevant digital assets) for finite element analysis, that are based on comprehensive specimen-specific imaging data. In addition, the models and by-products of modeling workflows are described along with model development strategies and tools. Passive flexion served as a test simulation case, demonstrating an end-user application. Potential roadmaps for reuse of Open Knee(s) are also discussed.

Spatial scale evaluation of forecast flood inundation maps

Flood inundation forecast maps provide an essential tool to disaster management teams for planning and preparation ahead of a flood event in order to mitigate the impacts of flooding on the community. Evaluating the accuracy of forecast flood maps is essential for model development and improving future flood predictions. Conventional, quantitative binary verification measures typically provide a domain-averaged score, at grid level, of forecast skill. This score is dependent on the magnitude of the flood and the spatial scale of the flood map. Binary scores have limited physical meaning and do not indicate location-specific variations in forecast skill that enable targeted model improvements to be made. A new, scale-selective approach is presented here to evaluate forecast flood inundation maps against remotely observed flood extents. A neighbourhood approach based on the Fraction Skill Score is applied to assess the spatial scale at which the forecast becomes skilful at capturing the observed flood. This skilful scale varies with location and when combined with a contingency map creates a novel categorical scale map, a valuable visual tool for model evaluation and development. The impact of model improvements on forecast flood map accuracy skill scores are often masked by large areas of correctly predicted flooded/unflooded cells. To address this, the accuracy of the flood-edge location is evaluated. The flood-edge location accuracy proves to be more sensitive to variations in forecast skill and spatial scale compared to the accuracy of the entire flood extent. Additionally, the resulting skilful scale of the flood-edge provides a physically meaningful verification measure of the forecast flood-edge discrepancy. The methods are illustrated by application to a case study flood event (with an estimated return period of 120 to 550 years) of the River Wye and River Lugg (UK) in February 2020. Representation errors are introduced where remote sensing observations capture flood extent at different spatial resolutions in comparison with the model. The sensitivity of the verified skilful scale to the resolution of the observations is investigated. Re-scaling and interpolating observations leads to a small reduction in skill score compared with the observation flood map derived at the model resolution. The domain-averaged skilful scale remains the same with slight location-specific variations in skilful scale evident on the categorical scale map. Overall, our novel emphasis on scale, rather than domain-average score, means that comparisons can be made across different flooding scenarios and forecast systems and between forecasts at different spatial scales. • A novel spatial scale-selective approach to evaluate forecast flood maps against Synthetic Aperture Radar data. • Validation of the flood edge gives a physically meaningful measure of prediction accuracy. • Conventional contingency flood maps are improved by including a location specific skilful spatial scale.

Study of industrial interactive design system based on virtual reality teaching technology in industrial robot

Abstract Across numerous disciplines, virtual reality (VR) had been used to aid decision-making in training, design, and evaluation processes. Both the educational and industrial groups have contributed to a vast knowledge based on a variety of VR topics during the last two decades. VR has been expanded to industry in recent years, but the majority of its applications do not involve industrial robots. To study the application of VR technology in industrial design, it is better to combine the design activities with computer-integrated manufacturing system and bring new opportunities for the innovation of industrial design. Therefore, in this article, an application of industrial interactive design system based on VR technology in the education domain is explored. First, the function and scheme design of industrial robot assembly and adjustment system are designed, and the model is established. Finally, SolidWorks and 3DsMAX are selected as three-dimensional model development tools. Unity 3D is used as the VR development engine; HTC VIVE is used as VR equipment. The study shows that the design of the machine motion instruction interpreter is effective, and the specific steps of the system to realize real-time control are also given. The feasibility of the system is verified through the analysis of typical applications of industrial robots.

Classifying Operational Events in Cable Yarding by a Machine Learning Application to GNSS-Collected Data: A Case Study on Gravity-Assisted Downhill Yarding

Cable yarding remains an important option in steep terrain timber harvesting, a reason for which new or improved operational efficiency models are required to support science and practice. Developed traditionally, these models are known to require many resources, a reason for which new approaches to the problem were researched lately, mainly by the use of Global Navigation Satellite System (GNSS) data, and spatial and statistical inference systems. This study evaluates the possibility of using GNSS data and machine learning techniques to classify important cable yarding events in the time domain. Three classes were assumed by the study as being relevant for cable yarding operational setup, namely carriage moving in the uphill (MU) and downhill (MD) directions, as well as carriage stopped (S). Data collected by a consumer-grade GNSS unit was processed to extract some differential parameters which were coupled with GNSS motorial and geometric features to feed a Multi-Layer Perceptron Neural Network with Back propagation (MLPNNB) in a pre-evaluation phase which aimed at mining the data structure as a strategy to develop the best MLPNNB configuration for training and testing. Leg distance, difference in elevation, speed of the carriage, and difference in heading were used together and interchangeably in this phase, based on logical assumptions. As a result of pre-evaluation, a MLPNNB using all these datasets was found to be the best scenario. Based on this outcome, the data was split into a training (70%) and a testing (30%) subset, then the MLPNNB was used to learn and generalize on these subsets. The main results indicate that the MLPNNB had an excellent performance, with a classification accuracy of 98.7, 98.4, and 98.8% in the pre-evaluation, training, and testing phases, respectively. Log-loss errors were also found to be very low (5, 5.9, and 4.1%, respectively), indicating a high generalization capability of the MLPNNB model. Based on the results, the main conclusion of the study is that original and derived GNSS data coupled with machine learning techniques could prove to be an important tool for operational monitoring and cable yarding efficiency model development, mainly due to the possibility of working with large amounts of data.