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Copyright of Journal of Graphics is the property of Journal of Graphics Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The autonomous driving simulation field lacks evaluation and forecasting systems for simulation results. The data obtained from the simulation of target algorithms and vehicle models cannot be reasonably estimated. This problem affects subsequent vehicle improvement and parameter calibration. The authors relied on the simulation results of the AEB algorithm. We selected the BP Neural Network as the basis and improved it with a genetic algorithm optimized via a roulette algorithm. The regression evaluation indicators of the prediction results show that the GA-BP neural network has better prediction accuracy and generalization ability than the original BP neural network and other optimized BP neural networks. This GA-BP neural network also fills the Gap in Evaluation and Prediction Systems. [ABSTRACT FROM AUTHOR]

Standardized catch per unit effort (CPUE) data not only yield precise and biologically meaningful abundance indices but also provide crucial insights into the spatio-temporal distribution of fisheries resources, critical for their sustainable utilization and management. In this study, we integrated chub mackerel fishery statistics from the Northwest Pacific Ocean (NPO) and marine remote sensing environmental data, and constructed CPUE standardization models for chub mackerel based on the generalized linear mixed model (GLMM) and spatio-temporal GLMM (Vector Autoregressive Spatio-Temporal, VAST) and evaluated their performance. GLMM is a statistical technique extending GLM with random effects for complex datasets with non-independent observations and correlations or hierarchies. The VAST model is a statistical model for multivariate time series data with observations at different spatial locations, displaying both temporal autocorrelation and spatial dependence. Additionally, influence analysis and simulation testing were employed to quantify the effects of explanatory variables on the differences between nominal CPUE and standardized CPUE, and to assess the estimation accuracy of different models in CPUE standardization, respectively. Finally, the distribution pattern of chub mackerel in the NPO was analyzed by estimating centers of gravity (COGs). The results indicated that: 1) The GLMM model containing all explanatory variables was considered the best model with the smallest conditional Akaike Information Criterion (CAIC), while the VAST model, with only the covariate SST, performed the best. 2) Interactions of year and spatial effects in GLMM and the spatio-temporal variable in VAST, exerted a notable influence on annual standardized CPUE and should be accounted for in the model. 3) Based on simulation testing results, the VAST model exhibited lower model error (root mean square error, RMSE) and model bias, outperforming the GLMM model in CPUE standardization. 4) From 2014 to 2018, chub mackerel exhibited high biomass density widely distributed throughout almost the entire NPO, which shifted to the central-north region from 2019 to 2021. There was no significant northing and easting shift in COGs for the population (P > 0.05). The findings of this research enhance our understanding of the variations in resource abundance indices and spatio-temporal distribution patterns of chub mackerel in the NPO.

Fisheries managers are in need of quantitative tools to inform decisions regarding selection of robust management practices, prioritising research gaps and stocks to focus on, particularly where there are limited resources or data. To support these decisions, the use of Management Strategy Evaluation (MSE), that is, closed loop simulation‐testing of management procedures, is widely regarded as best practice. However, applying MSE is time‐ and computationally intensive, and requires highly skilled expertise and processes for stakeholder input and peer review. For data‐ and capacity‐limited fisheries, MSE may be particularly challenging to implement. Yet, these are the contexts where it is most critical to test assumptions, evaluate the implications of all sources of uncertainty and identify the most informative data sources. To facilitate wider use of MSE, the Method Evaluation and Risk Assessment (MERA) framework was developed as an accessible online interface, with quick processing time, focused on generic data‐limited management procedures, but allowing progression to tailored and more data‐rich methods. The framework links a quantitative questionnaire and data input standard to a flexible operating model with optional customisation via command line access to the back‐end open‐source R libraries. Here, we illustrate a case study application of MERA for the bocinegro (Pagrus pagrus, Sparidae) fishery in the Gulf of Cadiz, where in conjunction with fishery stakeholders, a custom management procedure was developed and tested and key research gaps and data collection priorities were identified. We discuss implications for wider use of MSE in various contexts, including eco‐certification and fishery improvement projects. [ABSTRACT FROM AUTHOR]

The US Chukchi Sea consists of the waters off the northwest of Alaska and is a naturally dynamic ice-driven ecosystem. The impacts from climate change are affecting the Arctic marine ecosystem as well as the coastal communities that rely on healthy marine ecosystems. In anticipation of increased ecosystem monitoring in the area, there is an opportunity to evaluate improved sampling designs for future ecological monitoring of the Chukchi Sea, an area that is sampled less comprehensively compared to other regions in Alaska. This analysis focused on standardized NOAA-NMFS-AFSC bottom trawl surveys (otter and beam trawls) and three types of survey designs: simple random, stratified random, and systematic. First, spatiotemporal distributions for 18 representative demersal fish and invertebrate taxa were fitted using standardized catch and effort data. We then simulated spatiotemporal taxon densities to replicate the three survey design types to evaluate design-based estimates of abundance and precision across a range of sampling effort. Modest increases in precision were gained from stratifying the design when compared to a simple random design with either similar or lower uncertainty and bias of the precision estimates. There were often strong tradeoffs between the precision and bias of the systematic estimates of abundance (and associated variance) across species and gear type. The stratified random design provided the most consistent, reliable, and precise estimates of abundance indices and is likely to be the most robust to changes in the survey design. This analysis provides a template for changing bottom trawl survey designs in the Chukchi Sea and potentially other survey regions in Alaska going forward and will be important when integrating new survey objectives that are more ecosystem-focused.

The autonomous driving simulation field lacks evaluation and forecasting systems for simulation results. The data obtained from the simulation of target algorithms and vehicle models cannot be reasonably estimated. This problem affects subsequent vehicle improvement and parameter calibration. The authors relied on the simulation results of the AEB algorithm. We selected the BP Neural Network as the basis and improved it with a genetic algorithm optimized via a roulette algorithm. The regression evaluation indicators of the prediction results show that the GA-BP neural network has better prediction accuracy and generalization ability than the original BP neural network and other optimized BP neural networks. This GA-BP neural network also fills the Gap in Evaluation and Prediction Systems.

The evaluation and analysis of the procedures for determining the dependability of WF wind farm equipment employed in a few publications are this article's main problems. The publications chosen for review specifically mention investigations into the dependability of WF wind farm machinery. The following topics were the authors' main areas of analysis: description and review of the techniques used to represent how technical items operate and the selection of the weight of the theoretical ideas of reliability that were used to gauge the dependability of the wind farm equipment under study. The authors of the studied works set out to address a number of significant problems pertaining to the modernization of the management of the WF equipment renewal process. The subjects of the studied works suggest that the established models of the technical object's operational process are particularly significant in both the theory and practice of the reliability of technical objects. Using Kolmogorov–Chapman equations, models of the WFD operating process that are based on the idea of Markov processes are very helpful for simulation studies. [ABSTRACT FROM AUTHOR]

Vehicle to Everything (V2X) technology is fast evolving, and it will soon transform our driving experience. Vehicles employ On-Board Units (OBUs) to interact with various V2X devices, and these data are used for calculation and detection. Safety, efficiency, and information services are among its core uses, which are currently in the testing stage. Developers gather logs during the real field test to see if the application is fair. Field testing, on the other hand, has low efficiency, coverage, controllability, and stability, as well as the inability to recreate extreme hazardous scenarios. The shortcomings of actual road testing can be compensated for by indoor testing. An HIL-based laboratory simulation test framework for V2X-related testing is built in this study, together with the relevant test cases and a test evaluation system. The framework can test common applications such as Forward Collision Warning (FCW), Intersection Collision Warning (ICW) and others, as well as more advanced features such as Cooperative Adaptive Cruise Control (CACC) testing and Global Navigation Satellite System (GNSS) injection testing. The results of the tests reveal that the framework (CarTest) has reliable output, strong repeatability, the capacity to simulate severe danger scenarios, and is highly scalable, according to this study. Meanwhile, for the benefit of researchers, this publication highlights several relevant HIL challenges and solutions. [ABSTRACT FROM AUTHOR]

This paper introduces the issue of reliability simulation studies of wind farm equipment in the process of an operation. By the improvement, retrofitting and insertion of new (optimal) solutions to change the quality and terms of the use of wind farm equipment, an evaluation of their impact on reliability under real conditions can be carried out over a long period of time. Over a brief period, testing the reliability of a technical facility is only possible in a simulation. The aspect of evaluating the reliability of wind farm equipment after the application of intelligent systems, including the Wind Power Plant Expert System (WPPES), can be tested in the manner of a simulation. It was accepted in this article that the operation of the wind farm equipment is detailed based on Markov processes. The results of such research activities are the development of reliable and appropriate strategies and an exploitation policy of PE facilities. The above-mentioned issues in such a comprehensive approach have not been fully presented in the literature. The process of exploitation of complex technical objects such as PE devices is a complex random technical and technological process. [ABSTRACT FROM AUTHOR]

Rutting resistance is an important consideration for paving asphalt mixes. The cause of rutting is a shear strain in the asphalt pavement structure. Here, the rutting behavior of asphalt pavements was evaluated through laboratory simulations using the Hamburg Wheel-Tracking Test (HWTT), UK Wheel-Tracking Test (UKWTT), and Scaled Accelerated Load Simulator (SALS) test. The correlation between the three simulation tests was analyzed to determine the conversion factor (CF). Two parameters of temperature and speed of simulation testing were evaluated in this study. Subsequently, the representative frequency and dynamic modulus were calculated with consideration of viscoelasticity and time-temperature superposition. Using logical analysis, CF between the three simulation tests was validated. The results showed that the full-scale test yielded the highest stiffness modulus of 4,015 MPa, and the UKWTT yielded the lowest stiffness modulus of 1,857 MPa at 60 °C. From the HWTT results, it was found that the impact of moisture damage at 40 °C is not substantial compared to that at 50 °C. The conversion factor of HWTT at 40 °C, HWTT at 50 °C and UKWTT at 60 °C to SALS were 0.92, 0.74, and 0.63, respectively. Furthermore, the similar rutting performance among SALS, HWTT at 40 °C in dry and wet conditions, and HWTT at 50 °C in dry condition can be observed when the CF is applied. Lastly, the effectiveness of the conversion factor is diminished with increasing rutting damage. [ABSTRACT FROM AUTHOR]

The evaluation and analysis of the procedures for determining the dependability of WF wind farm equipment employed in a few publications are this article’s main problems. The publications chosen for review specifically mention investigations into the dependability of WF wind farm machinery. The following topics were the authors’ main areas of analysis: description and review of the techniques used to represent how technical items operate and the selection of the weight of the theoretical ideas of reliability that were used to gauge the dependability of the wind farm equipment under study. The authors of the studied works set out to address a number of significant problems pertaining to the modernization of the management of the WF equipment renewal process. The subjects of the studied works suggest that the established models of the technical object’s operational process are particularly significant in both the theory and practice of the reliability of technical objects. Using Kolmogorov–Chapman equations, models of the WFD operating process that are based on the idea of Markov processes are very helpful for simulation studies.

Management strategy evaluation (MSE) is a simulation approach that serves as a “light on the hill” (Smith, 1994) to test options for marine management, monitoring, and assessment against simulated ecosystem and fishery dynamics, including uncertainty in ecological and fishery processes and observations. MSE has become a key method to evaluate trade-offs between management objectives and to communicate with decision makers. Here we describe how and why MSE is continuing to grow from a single species approach to one relevant to multi-species and ecosystem-based management. In particular, different ecosystem modeling approaches can fit within the MSE process to meet particular natural resource management needs. We present four case studies that illustrate how MSE is expanding to include ecosystem considerations and ecosystem models as ‘operating models’ (i.e., virtual test worlds), to simulate monitoring, assessment, and harvest control rules, and to evaluate tradeoffs via performance metrics. We highlight United States case studies related to fisheries regulations and climate, which support NOAA’s policy goals related to the Ecosystem Based Fishery Roadmap and Climate Science Strategy but vary in the complexity of population, ecosystem, and assessment representation. We emphasize methods, tool development, and lessons learned that are relevant beyond the United States, and the additional benefits relative to single-species MSE approaches.

Vehicle to Everything (V2X) technology is fast evolving, and it will soon transform our driving experience. Vehicles employ On-Board Units (OBUs) to interact with various V2X devices, and these data are used for calculation and detection. Safety, efficiency, and information services are among its core uses, which are currently in the testing stage. Developers gather logs during the real field test to see if the application is fair. Field testing, on the other hand, has low efficiency, coverage, controllability, and stability, as well as the inability to recreate extreme hazardous scenarios. The shortcomings of actual road testing can be compensated for by indoor testing. An HIL-based laboratory simulation test framework for V2X-related testing is built in this study, together with the relevant test cases and a test evaluation system. The framework can test common applications such as Forward Collision Warning (FCW), Intersection Collision Warning (ICW) and others, as well as more advanced features such as Cooperative Adaptive Cruise Control (CACC) testing and Global Navigation Satellite System (GNSS) injection testing. The results of the tests reveal that the framework (CarTest) has reliable output, strong repeatability, the capacity to simulate severe danger scenarios, and is highly scalable, according to this study. Meanwhile, for the benefit of researchers, this publication highlights several relevant HIL challenges and solutions.

This paper introduces the issue of reliability simulation studies of wind farm equipment in the process of an operation. By the improvement, retrofitting and insertion of new (optimal) solutions to change the quality and terms of the use of wind farm equipment, an evaluation of their impact on reliability under real conditions can be carried out over a long period of time. Over a brief period, testing the reliability of a technical facility is only possible in a simulation. The aspect of evaluating the reliability of wind farm equipment after the application of intelligent systems, including the Wind Power Plant Expert System (WPPES), can be tested in the manner of a simulation. It was accepted in this article that the operation of the wind farm equipment is detailed based on Markov processes. The results of such research activities are the development of reliable and appropriate strategies and an exploitation policy of PE facilities. The above-mentioned issues in such a comprehensive approach have not been fully presented in the literature. The process of exploitation of complex technical objects such as PE devices is a complex random technical and technological process.

Background and Purpose: Educators, employers, and regulatory agencies face substantive challenges in evaluating nursing competency. Evidence on what competency is and how to measure it can mitigate the challenges. Methods: Participants (N = 67) completed three high-fidelity simulation tests. Each video-recorded test was scored by three raters using a 41-item instrument. Exploratory factor analysis was used to define the latent structure of the instrument. Results: A five-factor solution accounted for 56% of the variance, minimized negative loadings, and minimized the number of cross-loadings. The factors were minimally correlated (each r <.30). Conclusions: The factors, Vigilant Action, Role Nuances, Precision, Procedural Skills , and Risk Reduction , represent integrated dimensions of competency that can be linked to specific tasks underlying safe practice. [ABSTRACT FROM AUTHOR]

Balanced scorecard (BSC) and strategy maps (SMs) are among the most popular strategic performance management methods that have been proposed in the literature to define, analyze, and monitor performance and strategies. Despite their popularity, BSC and SMs have been criticized for their inability to simulate dynamic interrelations between performance measures and strategies over time. In this paper, a dynamic SM was developed by using system dynamics (SD) modeling based on the BSC framework. This paper describes the dynamic SM, which was conceptualized, formulated, tested, and simulated in collaboration with a construction company, as well as the experiences gained during the model development process. Simulation tests demonstrated that dynamic SMs can be used to improve strategic decision making by assessing the impact of strategies on aggregated measures of performance under different scenarios considering changes in internal processes as well as external conditions. However, the findings also revealed that SD models can have some limitations due to problems of generalization, usability, and validity. The model development process and dynamic SM structure proposed in this study can be used by other companies and researchers to develop similar models and to discover potential limitations of SD modeling. [ABSTRACT FROM AUTHOR]

It is the exceptionally rare case one can directly and with little uncertainty measure fish absolute abundance through many stock generations in all areas of a stock's range. Instead, we often seek "gold standard" stock assessments—models that use catch, abundance indices and biological compositions to produce precise and unbiased indicators of stock status for management use. Unfortunately, data and resource limitations affect our ability to collect all the desired information and apply methods with low uncertainty in the results. To confront this challenge of poorly informative data and low resource situations, a host of analytical approaches have been developed to engage the power of fisheries science to inform management decisions despite limitations. These methods are numerous and often challenging to understand and navigate, despite being simplified (though not simple) approaches. It is important to understand where these methods come from, how they can be used, and how to evaluate them. Often they are presented as alchemically providing golden outputs despite heavy assumptions and impure inputs. Here I aim to provide both scientific context of and guidance in organizing and applying so-called data and resource limited stock assessments. I offer a list of best practices by presenting fundamental principles of modelling and highlighting leading edge tools for organizing and conducting analyses under a variety of constraining conditions, offering a conceptualization of stock assessment expressing the interconnectedness of each method and how those can be largely unified under a common modeling framework. The concept of a stock assessment continuum is described, along with discrete examples in the form of a decision tree outlining the major modelling groups for a large variety of data availability scenarios. The basic approach to applied fisheries science and management is presented as interpreting uncertain model outputs (i.e, indicators) using reference points that can then be linked to management decisions via control rules that should express risk tolerance to meeting management objectives in light of uncertain outcomes. The role of simulation testing of management procedures is highlighted in order to evaluate robustness to uncertainty. While more and better data should be a focus of any management system, there is no excuse to wait for golden outputs. We have the tools and theory ready to help direct management of data and resource limited stocks now. [ABSTRACT FROM AUTHOR]

Multispecies stock assessment models require predator diet data, e.g. stomach samples. Diet data can be unavailable, sparse, of small sample size, or very noisy. It is unclear if multispecies interactions can be estimated without bias when interactions are weak. Research is needed about how model performance is affected by the availability or quality of diet data and by the method for fitting it. We developed seven age-structured operating models that simulate trophic interactions for two fish species and different scenarios of diet data availability or quality. The simulated data sets were fitted using four statistical catch-at-age models that estimated fishing, predation and residual natural mortality and differed in the way the diet data was fitted. Fitting the models to diet data averaged over time should be avoided since it resulted in estimation bias. Fitting annual diet composition per stomach produced bias estimates due to the occurrence of zeros in the observed proportions and the statistical assumptions for the diet model. Fitting to annual stomach proportions averaged across stomachs led to unbiased results even if the number of stomachs was small, the interactions were weak or some sampled years and ages were missing. These methods should be preferred when fitting multispecies models. [ABSTRACT FROM AUTHOR]

• VAST model outperforms GLMM in standardizing CPUE for chub mackerel, with higher conditional R 2 and lower CAIC. • Year × spatial interaction (GLM/GLMM) and spatio-temporal variable (VAST) strongly impact standardized CPUE. • Highly influential explanatory variables in CPUE standardization models may not impact nominal and standardized CPUE differences. • There was no significant shift in COGs for the population (P > 0.05). • First step for sustainable conservation and utilization of chub mackerel. Standardized catch per unit effort (CPUE) data not only yield precise and biologically meaningful abundance indices but also provide crucial insights into the spatio-temporal distribution of fisheries resources, critical for their sustainable utilization and management. In this study, we integrated chub mackerel fishery statistics from the Northwest Pacific Ocean (NPO) and marine remote sensing environmental data, and constructed CPUE standardization models for chub mackerel based on the generalized linear mixed model (GLMM) and spatio-temporal GLMM (Vector Autoregressive Spatio-Temporal, VAST) and evaluated their performance. GLMM is a statistical technique extending GLM with random effects for complex datasets with non-independent observations and correlations or hierarchies. The VAST model is a statistical model for multivariate time series data with observations at different spatial locations, displaying both temporal autocorrelation and spatial dependence. Additionally, influence analysis and simulation testing were employed to quantify the effects of explanatory variables on the differences between nominal CPUE and standardized CPUE, and to assess the estimation accuracy of different models in CPUE standardization, respectively. Finally, the distribution pattern of chub mackerel in the NPO was analyzed by estimating centers of gravity (COGs). The results indicated that: 1) The GLMM model containing all explanatory variables was considered the best model with the smallest conditional Akaike Information Criterion (CAIC), while the VAST model, with only the covariate SST , performed the best. 2) Interactions of year and spatial effects in GLMM and the spatio-temporal variable in VAST, exerted a notable influence on annual standardized CPUE and should be accounted for in the model. 3) Based on simulation testing results, the VAST model exhibited lower model error (root mean square error, RMSE) and model bias, outperforming the GLMM model in CPUE standardization. 4) From 2014 to 2018, chub mackerel exhibited high biomass density widely distributed throughout almost the entire NPO, which shifted to the central-north region from 2019 to 2021. There was no significant northing and easting shift in COGs for the population (P > 0.05). The findings of this research enhance our understanding of the variations in resource abundance indices and spatio-temporal distribution patterns of chub mackerel in the NPO. [ABSTRACT FROM AUTHOR]

Understanding the characteristics of individual growth is a critical component of population assessment. Most fisheries stock assessments assume constant values for each life history parameter, despite growing evidence that growth is variable at individual, temporal, and spatial scales. Otoliths contain important information pertaining to age and growth, among other things, and otolith increment data correlate with climate indices on a decadal scale. We expand on this concept to include individual- and year-level variation, and develop a nonlinear mixed-effects model to analyze otolith increment data. We then fit the model to otolith increment data for splitnose rockfish, and simulation-test the ability to precisely estimate year effects without bias. Generally, given a sample size of at least 50 otoliths, the model performs well at estimating year effects. With this method, species-specific indices of growth can be extracted from otolith increment data, and potentially be used in stock assessments to detect the effects of climate change on fish growth. [ABSTRACT FROM AUTHOR]

Copyright of Der Ophthalmologe is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Tailoring cloud support for each autonomous-driving application would require maintaining multiple infrastructures, potentially resulting in low resource utilization, low performance, and high management overhead. To address this problem, the authors present a unified cloud infrastructure with Spark for distributed computing, Alluxio for distributed storage, and OpenCL to exploit heterogeneous computing resources for enhanced performance and energy efficiency. [ABSTRACT FROM PUBLISHER]

Background and Purpose: There is growing evidence that simulation testing is appropriate for assessing nursing competence. We compiled evidence on the validity and reliability of the Nursing Performance Profile (NPP) method for assessing competence. Methods: Participants (N = 67) each completed 3 high-fidelity simulation tests; raters (N = 31) scored the videotaped tests using a 41-item competency rating instrument. Results: The test identified areas of practice breakdown and distinguished among subgroups differing in age, education, and simulation experience. Supervisor assessments were positively correlated, r = .31. Self-assessments were uncorrelated, r = .07. Interrater agreement ranged from 93% to 100%. Test--retest reliability ranged from r = .57 to .69. Conclusions: The NPP can be used to assess competence and make decisions supporting public safety. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Lack-of-fit in a stock assessment model can be related to both data weighting and the treatment of process error. Although these contributing factors have been studied separately, interactions between them are potentially problematic. In this study we set up a simple simulation intended to provide general guidance to analysts on the performance of an age-structured model under differing assignments of compositional data weight and process variance. We compared cases where the true sample size is under-, ‘right-’ or over-weighted, and the degree of process variance (in this case temporal variability in selectivity) is under, correctly, or overestimated. Each case was evaluated with regard to estimation of spawning biomass, and MSY-related quantities. We also explored the effects of the estimation of natural mortality, steepness, as well as incorrectly specifying process error in selectivity when there is none. Results showed that right-weighted estimation models assuming the correct degree of process error performed best in estimating all quantities. Underweighting produced larger relative errors in spawning biomass, particularly when too much process error was allowed. Conversely, overweighting produced larger errors mainly when the degree of process error was underestimated. MSY-related quantities were sensitive to both the estimation of natural mortality, and particularly steepness. We suggest that data weighting and the treatment of process error should not be considered independently: estimation is most likely to be robust when process error is allowed (even if overestimated) and when compositional data are not excessively down-weighted. [ABSTRACT FROM AUTHOR]