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Electric Energy Consumption (EEC) prediction for building operations can be performed using a Baseline Energy Model (BEM), which is vital to ensure the efficiency of the EEC estimates with its respective independent variables. However, developing the BEM to represent the relationship between independent variables can be a complex task due to the EEC variability in an educational building that differs during its operation period. The best-suited BEM must be continuously improvised to achieve good modeling with accurate and reliable predictions that capture the building operations’ current dynamics. This study aims to conduct a comparative performance assessment between deep learning, machine learning, and statistical models to develop the BEM and, therefore, predict the EEC of the building for 24, 48, 72, and 96 hours, while considering the operation of the lecture weeks and the associated number of students and staff. The hours and temperature are considered as independent variables to be tested with residual error evaluations, whilst the correlation coefficient, coefficient of determination, and training time are also taken into account. Three models with different categories involving Long Short-Term Memory (LSTM), Support Vector Regression (SVR), and AutoRegressive Integrated Moving Average with Exogenous inputs (ARIMAX) were compared, concluding that SVR was the best and can be used as a universal model in the Machine Learning Baseline Energy Model (MLBEM) studies. Accurate EEC prediction will offer a huge advantage for building operators to properly monitor, plan, and manage the EEC, hence avoiding excessive utility bills. [ABSTRACT FROM AUTHOR]

• MLm5C is a novel machine learning-based predictor of the 5-methylcytosine (m5C) modification sites of RNA sequences. • MLm5C stacks the 44 baseline (single-feature) models that combine 4 machine learning methods with 11 RNA encoding methods for highly enhanced prediction of D sites. • MLm5C outperforms state-of-the-art methods in terms of prediction performances. • The optimization of the sequence length surrounding the modification sites significantly improves the prediction performance. RNA modification serves as a pivotal component in numerous biological processes. Among the prevalent modifications, 5-methylcytosine (m5C) significantly influences mRNA export, translation efficiency and cell differentiation and are also associated with human diseases, including Alzheimer's disease, autoimmune disease, cancer, and cardiovascular diseases. Identification of m5C is critically responsible for understanding the RNA modification mechanisms and the epigenetic regulation of associated diseases. However, the large-scale experimental identification of m5C present significant challenges due to labor intensity and time requirements. Several computational tools, using machine learning, have been developed to supplement experimental methods, but identifying these sites lack accuracy and efficiency. In this study, we introduce a new predictor, MLm5C, for precise prediction of m5C sites using sequence data. Briefly, we evaluated eleven RNA sequence-derived features with four basic machine learning algorithms to generate baseline models. From these 44 models, we ranked them based on their performance and subsequently stacked the Top 20 baseline models as the best model, named MLm5C. The MLm5C outperformed the-state-of-the-art predictors. Notably, the optimization of the sequence length surrounding the modification sites significantly improved the prediction performance. MLm5C is an invaluable tool in accelerating the detection of m5C sites within the human genome, thereby facilitating in the characterization of their roles in post-transcriptional regulation. [ABSTRACT FROM AUTHOR]

Near-term iterative ecological forecasting has great potential for providing new insights into our ability to predict multiple ecological variables. However, true, out-of-sample probabilistic forecasts remain rare, and variability in forecast performance has largely been unexamined in process-based forecasts which predict multiple ecosystem variables. To explore how forecast performance varies for water temperature and dissolved oxygen, two freshwater variables important for lake ecosystem functioning, we produced probabilistic forecasts at multiple depths over two open-water seasons in Lake Sunapee, NH, USA. Our forecasting system, FLARE (Forecasting Lake And Reservoir Ecosystems), uses a 1-D coupled hydrodynamic-biogeochemical process model, which we assessed relative to both climatology and persistence null models to quantify how much information process-based FLARE forecasts provide over null models across varying environmental conditions. We found that FLARE water temperature forecasts were always more skillful than FLARE oxygen forecasts. Specifically, temperature forecasts outperformed both null models up to 11 days into the future, as compared to only two days for oxygen. Across different years, we observed variable forecast skill, with performance generally decreasing with depth for both variables. Overall, all temperature forecasts and surface oxygen, but not deep oxygen, forecasts were more skillful than at least one null model >80 % of the forecasted period, indicating that our process-based model was able to reproduce the dynamics of these two variables with greater reliability than the null models. However, process-based oxygen forecasts from deeper waters were less skillful than both null models during a majority of the forecasted period, which suggests that deep-water oxygen dynamics are dominated by autocorrelation and seasonal change, which are inherently captured by the null forecasts. Our results highlight that forecast performance varies among lake water quality metrics and that process-based forecasts can provide important information in conjunction with null models in varying environmental conditions. Altogether, these process-based forecasts can be used to develop quantitative tools which inform our understanding of future ecosystem change.

In this study, we undertake a comprehensive examination of zero-shot text classification and its associated implications. We propose the adoption of the BERT model as a method for text feature representation. Subsequently, we utilize the Pointwise Mutual Information (PMI) metric to adjust the weight values within a graph convolutional neural network, thereby facilitating the construction of a text graph. Additionally, we incorporate an attention mechanism to transform this text graph, enabling it to represent the output labels of zero-shot text classification effectively. The experimental environment is set up, and the comparison and ablation experiments of the text classification model based on BERT and graph convolutional neural network with the baseline models are carried out in several different types of datasets, and the parameter settings of λ are adjusted according to the experimental results, and the convergence of the BERT model is compared to test the robustness of the model performance and the classification effect. When λ was set to 0.60, the model achieved the best results in each dataset. When the task is set to 5-way-5-shot, the convergence rate of the model for the Snippets dataset using the penultimate layer of features can reach 74%-80% of the training accuracy at the 5,000th step. The training accuracy gradually flattens out in the first 10,000 steps, and the model achieves classification accuracy in all four learning scenarios, with good stability.

What is the role of the brain's ongoing activity for cognition? The predominant perspectives associate ongoing brain activity with resting state, the default-mode network (DMN), and internally oriented mentation. This triad is often contrasted with task states, non-DMN brain networks, and externally oriented mentation, together comprising a "dual model" of brain and cognition. In opposition to this duality, however, we propose that ongoing brain activity serves as a neuronal baseline; this builds upon Raichle's original search for the default mode of brain function that extended beyond the canonical default-mode brain regions. That entails what we refer to as the "baseline model." Akin to an internal biological clock for the rest of the organism, the ongoing brain activity may serve as an internal point of reference or standard by providing a shared neural code for the brain's rest as well as task states, including their associated cognition. Such shared neural code is manifest in the spatiotemporal organization of the brain's ongoing activity, including its global signal topography and dynamics like intrinsic neural timescales. We conclude that recent empirical evidence supports a baseline model over the dual model; the ongoing activity provides a global shared neural code that allows integrating the brain's rest and task states, its DMN and non-DMN, and internally and externally oriented cognition. [ABSTRACT FROM AUTHOR]

The Terrain Wide-swath Interferometric L-band Synthetic Aperture Radar (TwinSAR-L) mission is a spaceborne bistatic synthetic aperture radar (SAR) mission to derive a high-quality global digital elevation model (DEM). The prerequisite of the high-accuracy DEM is knowing the interferometric baseline with high precision. The challenging problem is that the baseline of the bistatic system is highly dynamic due to the fast relative motion between the two satellites. In this paper, a pixel-related baseline model based on the geometrical shift is proposed to accurately reflect the position change of satellites. The baseline error is then calibrated using height gradient information and a small number of point targets with a slight incidence angle difference, eliminating the need for low-frequency corner reflectors and avoiding the difficulty of selecting a calibration site. The proposed method has been successfully exploited during the initial Commissioning Phase of TwinSAR-L, demonstrating its effectiveness in evaluating the precise baseline and supporting the generation of high-precision DEM. [ABSTRACT FROM AUTHOR]

This study presents a dataset serving as a benchmark for the recognition of printed text in lexical lists using Chinese-IPA glossing. The paper provides an overview of the baseline model, transcription model, and PyLaia engines employed in the research. Furthermore, it elucidates the specific need for digitizing the aforementioned lexical lists, outlines the methodology employed for training the baseline model for layout analysis, and describes the training process of the transcription model using the ground truth data generated on Transkribus. This comprehensive approach encompasses both the images of the lexical list content and their corresponding transcriptions as input. Additionally, the study highlights the limitations of the model and identifies avenues for future development. By making this dataset openly accessible, it can be utilized by researchers seeking to digitize lexical lists using Chinese-IPA glossing. Moreover, since the model can recognize both Chinese characters and IPA symbols, it has the potential to contribute to linguistic analysis of languages documented in Chinese-IPA glossing. [ABSTRACT FROM AUTHOR]

This note serves as a reminder that incremental fit indices are a form of standardized effect sizes and hence, all reservations with respect to interpretations of standardized effect sizes also transfer to their interpretation. Such a realization has major implications for the interpretation and use of incremental fit indices, for the theoretical (im)possibility of default universal rules of thumb in their application, and for simulation studies mapping incremental fit indices as if their value is comparable in an absolute sense across any and all conditions. A small but illustrative working example centered around the alleged impact of model type will drive these points home. [ABSTRACT FROM AUTHOR]

A baseline model possessing both high accuracy and robust generalization capabilities is crucial for the operation evaluation and optimization of district heating (DH) substation. However, existing modeling approaches struggle to achieve a balance between accuracy and generalization. Specifically, physics-based models exhibit strong generalization but suffer from poor accuracy, while data-driven models demonstrate high accuracy but lack generalization. This paper, therefore, proposes a baseline model combining physics and data-driven approach to evaluate hourly supply temperature of DH substation. In this hybrid model, the physics-based approach is utilized for static feature learning, while the data-driven method handles dynamic feature learning of the supply temperature. Validity of this baseline model is confirmed by a practical case, which suggested that this model can effectively learned the static and dynamic features of supply temperature with a 3.49% error for daily value and 1.39% for hourly coefficient of daily profile. According to the generated baseline by this model, all hourly supply temperatures are labeled with evaluation results, with 30.8% being in normal operation state, 29.5% being too high, and 39.7% being lower than theoretical values. This baseline can also serve as an optimization target for unreasonable supply temperature settings. Furthermore, the effectiveness of the proposed baseline model is further demonstrated through a comparison with a common data-driven model. [ABSTRACT FROM AUTHOR]

This study presents a dataset serving as a benchmark for the recognition of printed text in lexical lists using Chinese-IPA glossing. The paper provides an overview of the baseline model, transcription model, and PyLaia engines employed in the research. Furthermore, it elucidates the specific need for digitizing the aforementioned lexical lists, outlines the methodology employed for training the baseline model for layout analysis, and describes the training process of the transcription model using the ground truth data generated on Transkribus. This comprehensive approach encompasses both the images of the lexical list content and their corresponding transcriptions as input. Additionally, the study highlights the limitations of the model and identifies avenues for future development. By making this dataset openly accessible, it can be utilized by researchers seeking to digitize lexical lists using Chinese-IPA glossing. Moreover, since the model can recognize both Chinese characters and IPA symbols, it has the potential to contribute to linguistic analysis of languages documented in Chinese-IPA glossing.

Harmful cyanobacterial bloom (HCB) is problematic for drinking water treatment, and some of its strains can produce toxins that significantly affect human health. To better control eutrophication and HCB, catchment managers need to continuously keep track of nitrogen (N) and phosphorus (P) in the water bodies. However, the high-frequency monitoring of these water quality indicators is not economical. In these cases, machine learning techniques may serve as viable alternatives since they can learn directly from the available surrogate data. In the present work, a random forest, extremely randomized trees (ET), extreme gradient boosting, k-nearest neighbors, a light gradient boosting machine, and bagging regressor-based virtual sensors were used to predict N and P in two catchments with contrasting land uses. The effect of data scaling and missing value imputation were also assessed, while the Shapley additive explanations were used to rank feature importance. A specification book, sensitivity analysis, and best practices for developing virtual sensors are discussed. Results show that ET, MinMax scaler, and a multivariate imputer were the best predictive model, scaler, and imputer, respectively. The highest predictive performance, reported in terms of R2, was 97% in the rural catchment and 82% in an urban catchment. [ABSTRACT FROM AUTHOR]

In bridge structural health monitoring systems, an accurate baseline model is particularly important for identifying subsequent structural damage. Environmental and operational loads cause nonstationarity in the strain monitoring data of urban girder bridges. Such nonstationary monitoring data can mask damage and reduce the accuracy of the established baseline model. To address this problem, a steady-state data baseline model for bridges is proposed. First, for observable effects such as ambient temperature, a directional projection decoupling method for strain monitoring data is proposed, which can reduce the nonstationary effect of ambient temperature, and the effectiveness of this method is proven using equations. Second, for unobservable effects such as traffic load, a k-means clustering method for steady state of traffic loads is proposed; using this method, which can divide the steady and nonsteady states of traffic loads and reduce the nonstationary effect of traffic loads on strain monitoring data, a steady-state baseline model is established. Finally, the effectiveness of the steady-state baseline model is verified using an actual bridge. The results show that the proposed baseline model can reduce the error caused by nonstationary effects, improve the modelling accuracy, and provide useful information for subsequent damage identification. [ABSTRACT FROM AUTHOR]

The Terrain Wide-swath Interferometric L-band Synthetic Aperture Radar (TwinSAR-L) mission is a spaceborne bistatic synthetic aperture radar (SAR) mission to derive a high-quality global digital elevation model (DEM). The prerequisite of the high-accuracy DEM is knowing the interferometric baseline with high precision. The challenging problem is that the baseline of the bistatic system is highly dynamic due to the fast relative motion between the two satellites. In this paper, a pixel-related baseline model based on the geometrical shift is proposed to accurately reflect the position change of satellites. The baseline error is then calibrated using height gradient information and a small number of point targets with a slight incidence angle difference, eliminating the need for low-frequency corner reflectors and avoiding the difficulty of selecting a calibration site. The proposed method has been successfully exploited during the initial Commissioning Phase of TwinSAR-L, demonstrating its effectiveness in evaluating the precise baseline and supporting the generation of high-precision DEM.

Summary: Despite rapid improvements in the performance of the central processing unit (CPU), the calculation cost of simulating chemically reacting flow using CFD remains infeasible in many cases. The application of the convolutional neural networks (CNNs) specialized in image processing in flow field prediction has been studied, but the need to develop a neural network design fitted for CFD has recently emerged. In this study, a neural network model introducing the finite volume method (FVM) with unique network architecture and physics‐informed loss function was developed to accelerate CFD simulations. The developed network model, considering the nature of the CFD flow field where the identical governing equations are applied to all grids, can predict the future fields with only two previous fields unlike the CNNs requiring many field images (>10 000). The performance of this baseline model was evaluated using CFD time series data from non‐reacting flow and reacting flow simulation; counterflow and hydrogen flame with 20 detailed chemistries. Consequently, we demonstrated that (a) the FVM‐based network architecture provided significantly improved accuracy of multistep time series prediction compared to the previous MLP model (b) the physic‐informed loss function prevented non‐physical overfitting problem and ultimately reduced the error in time series prediction (c) observing the calculated residuals in an unsupervised manner could monitor the network accuracy. Additionally, under the reacting flow dataset, the computational speed of this network model was measured to be about 10 times faster than that of the CFD solver. [ABSTRACT FROM AUTHOR]

By reducing electricity consumption during peak times, peak shaving could reduce the need for carbon intensive resources and defer capacity related investments. Households, where they use electricity for space or water heating, are major contributors to the winter peak demand and promising candidates for related demand response (DR) initiatives. The impact of such initiatives is determined by comparing the actual consumption during a DR event to a baseline, i.e., the estimated consumption that would have occurred in the absence of an event. This paper explores the challenges associated with modeling a baseline in the context of residential winter DR programs with individual performance-based incentives. A sample of more than a thousand residential load profiles was used in this study to provide a statistical comparison of performance metrics for different baseline load models. Arithmetic, regression based, and matching-day models were considered. Results show that adjusted arithmetic models achieve similar performances to the more complex regression model without the need for weather data. These simpler models were also found to be less sensitive to the number of events called during the season. Performing individual adjustments for each of the two daily peak periods also provides better accuracy. [ABSTRACT FROM AUTHOR]

Human activity recognition (HAR) based on wearable sensors has attracted significant research attention in recent years due to its advantages in availability, accuracy, and privacy-friendliness. HAR baseline model is essentially a general-purpose classifier trained to recognized multiple activity patterns of most user types. It provides the input for subsequent steps of model personalization. Training a good baseline model is of fundamental importance because it has significant impacts on the ultimate HAR accuracy. In practice, baseline model training in HAR is a non-trivial problem that faces two challenges: insufficient training data and biased training data. This paper proposes a novel baseline model training scheme to tackle the two challenges using Deep InfoMax (DIM)-based unsupervised feature extraction and Broad Learning System (BLS)-based incremental learning, respectively. Experimental results demonstrate that the proposed scheme outperform conventional methods in terms of overall accuracy, computational efficiency, and the ability to adapt to dynamic scenarios with changing data characteristics.

More than 80% of energy is consumed during operation phase of a building's life cycle, so energy efficiency retrofit for existing buildings is considered a promising way to reduce energy use in buildings. The investment strategies of retrofit depend on the ability to quantify energy savings by “measurement and verification” (M&V), which compares actual energy consumption to how much energy would have been used without retrofit (called the “baseline” of energy use). Although numerous models exist for predicting baseline of energy use, a critical limitation is that occupancy has not been included as a variable. However, occupancy rate is essential for energy consumption and was emphasized by previous studies. This study develops a new baseline model which is built upon the Lawrence Berkeley National Laboratory (LBNL) model but includes the use of building occupancy data. The study also proposes metrics to quantify the accuracy of prediction and the impacts of variables. However, the results show that including occupancy data does not significantly improve the accuracy of the baseline model, especially for HVAC load. The reasons are discussed further. In addition, sensitivity analysis is conducted to show the influence of parameters in baseline models. The results from this study can help us understand the influence of occupancy on energy use, improve energy baseline prediction by including the occupancy factor, reduce risks of M&V and facilitate investment strategies of energy efficiency retrofit.

More than 80% of energy is consumed during operation phase of a building's life cycle, so energy efficiency retrofit for existing buildings is considered a promising way to reduce energy use in buildings. The investment strategies of retrofit depend on the ability to quantify energy savings by “measurement and verification” (M&V), which compares actual energy consumption to how much energy would have been used without retrofit (called the “baseline” of energy use). Although numerous models exist for predicting baseline of energy use, a critical limitation is that occupancy has not been included as a variable. However, occupancy rate is essential for energy consumption and was emphasized by previous studies. This study develops a new baseline model which is built upon the Lawrence Berkeley National Laboratory (LBNL) model but includes the use of building occupancy data. The study also proposes metrics to quantify the accuracy of prediction and the impacts of variables. However, the results show that including occupancy data does not significantly improve the accuracy of the baseline model, especially for HVAC load. The reasons are discussed further. In addition, sensitivity analysis is conducted to show the influence of parameters in baseline models. The results from this study can help us understand the influence of occupancy on energy use, improve energy baseline prediction by including the occupancy factor, reduce risks of M&V and facilitate investment strategies of energy efficiency retrofit.

Trustworthy savings calculations are critical to convincing investors in energy efficiency projects of the benefit and cost-effectiveness of such investments and their ability to replace or defer supply-side capital investments. However, today's methods for measurement and verification (M&V) of energy savings constitute a significant portion of the total costs of efficiency projects. They also require time-consuming manual data acquisition and often do not deliver results until years after the program period has ended. The rising availability of "smart" meters, combined with new analytical approaches to quantifying savings, has opened the door to conducting M&V more quickly and at lower cost, with comparable or improved accuracy. These meter- and software-based approaches, increasingly referred to as "M&V 2.0", are the subject of surging industry interest, particularly in the context of utility energy efficiency programs. Program administrators, evaluators, and regulators are asking how M&V 2.0 compares with more traditional methods, how proprietary software can be transparently performance tested, how these techniques can be integrated into the next generation of whole-building focused efficiency programs.This paper expands recent analyses of public-domain whole-building M&V methods, focusing on more novel M&V 2.0 modeling approaches that are used in commercial technologies, as well as approaches that are documented in the literature, and/or developed by the academic building research community. We present a testing procedure and metrics to assess the performance of whole-building M&V methods. We then illustrate the test procedure by evaluating the accuracy of ten baseline energy use models, against measured data from a large dataset of 537 buildings. The results of this study show that the already available advanced interval data baseline models hold great promise for scaling the adoption of building measured savings calculations using Advanced Metering Infrastructure (AMI) data. Median coefficient of variation of the root mean squared error (CV(RMSE)) was less than 25% for every model tested when twelve months of training data were used. With even six months of training data, median CV(RMSE) for daily energy total was under 25% for all models tested. These findings can be used to build confidence in model robustness, and the readiness of these approaches for industry uptake and adoption.

To understand how specific interventions affect a process observed over time, we need to control for the other factors that influence outcomes. Such a model that captures all factors other than the one of interest is generally known as a baseline. In our study of how different pricing schemes affect residential electricity consumption, the baseline would need to capture the impact of outdoor temperature along with many other factors. In this work, we examine a number of different data mining techniques and demonstrate Gradient Tree Boosting (GTB) to be an effective method to build the baseline. We train GTB on data prior to the introduction of new pricing schemes, and apply the known temperature following the introduction of new pricing schemes to predict electricity usage with the expected temperature correction. Our experiments and analyses show that the baseline models generated by GTB capture the core characteristics over the two years with the new pricing schemes. In contrast to the majority of regression based techniques which fail to capture the lag between the peak of daily temperature and the peak of electricity usage, the GTB generated baselines are able to correctly capture the delay between the temperature peak and the electricity peak. Furthermore, subtracting this temperature-adjusted baseline from the observed electricity usage, we find that the resulting values are more amenable to interpretation, which demonstrates that the temperature-adjusted baseline is indeed effective.

Harmful cyanobacterial bloom (HCB) is problematic for drinking water treatment, and some of its strains can produce toxins that significantly affect human health. To better control eutrophication and HCB, catchment managers need to continuously keep track of nitrogen (N) and phosphorus (P) in the water bodies. However, the high-frequency monitoring of these water quality indicators is not economical. In these cases, machine learning techniques may serve as viable alternatives since they can learn directly from the available surrogate data. In the present work, a random forest, extremely randomized trees (ET), extreme gradient boosting, k-nearest neighbors, a light gradient boosting machine, and bagging regressor-based virtual sensors were used to predict N and P in two catchments with contrasting land uses. The effect of data scaling and missing value imputation were also assessed, while the Shapley additive explanations were used to rank feature importance. A specification book, sensitivity analysis, and best practices for developing virtual sensors are discussed. Results show that ET, MinMax scaler, and a multivariate imputer were the best predictive model, scaler, and imputer, respectively. The highest predictive performance, reported in terms of R2, was 97% in the rural catchment and 82% in an urban catchment.

By reducing electricity consumption during peak times, peak shaving could reduce the need for carbon intensive resources and defer capacity related investments. Households, where they use electricity for space or water heating, are major contributors to the winter peak demand and promising candidates for related demand response (DR) initiatives. The impact of such initiatives is determined by comparing the actual consumption during a DR event to a baseline, i.e., the estimated consumption that would have occurred in the absence of an event. This paper explores the challenges associated with modeling a baseline in the context of residential winter DR programs with individual performance-based incentives. A sample of more than a thousand residential load profiles was used in this study to provide a statistical comparison of performance metrics for different baseline load models. Arithmetic, regression based, and matching-day models were considered. Results show that adjusted arithmetic models achieve similar performances to the more complex regression model without the need for weather data. These simpler models were also found to be less sensitive to the number of events called during the season. Performing individual adjustments for each of the two daily peak periods also provides better accuracy.

This paper documents the development and application of a general statistical methodology to assess the accuracy of baseline energy models, focusing on its application to Measurement and Verification (M&V) of whole-building energy savings. The methodology complements the principles addressed in resources such as ASHRAE Guideline 14 and the International Performance Measurement and Verification Protocol. It requires fitting a baseline model to data from a training period and using the model to predict total electricity consumption during a subsequent prediction period.We illustrate the methodology by evaluating five baseline models using data from 29 buildings. The training period and prediction period were varied, and model predictions of daily, weekly, and monthly energy consumption were compared to meter data to determine model accuracy. Several metrics were used to characterize the accuracy of the predictions, and in some cases the best performing model as judged by one metric was not the best performer when judged by another metric.

Classical artificial potential approach of motion planning is extended for emulating human driving behaviour in two dimensions. Different stimulus parameters including type of ego‐vehicle, type of obstacles, relative velocity, relative acceleration, and lane offset are used. All the surrounding vehicles are considered to influence drivers' decisions. No emphasis is laid on vehicle control; instead, an ego vehicle is assumed to reach the desired state. The study is on human‐like driving behaviour modelling. The developed motion planning algorithm formulates repulsive and attractive potentials in a data‐driven way in contrast to the classical arbitrary formulation. Interaction between the stimulus parameters is explicitly considered by using multivariate cumulative distribution functions. Comparison of two‐dimensional (lateral and longitudinal) performance indicators with a baseline model and generative adversarial networks indicate the effectiveness and suitability of the developed motion planning algorithm in the mixed traffic environment. [ABSTRACT FROM AUTHOR]

Modern commercial aircraft are usually configured with aircraft condition monitoring system to collect the operating data of subsystems and components, which can be used for airborne system health monitoring and predictive maintenance. This paper presents a baseline model based aircraft auxiliary power unit performance assessment and remaining useful life prediction method using aircraft condition monitoring system reports data, which can facilitate a cost-effective management of auxiliary power units of aircraft fleet. Firstly, the performance baseline model for auxiliary power unit is established using random forest method. Then a health index characterizing the performance degradation of in-service auxiliary power units is obtained based on the performance baseline model. Finally, the performance degradation trend is predicted using Bayesian dynamic linear model. To improve the prediction accuracy, four performance baseline models are established from the data of auxiliary power units under different operating conditions, among which an optimal model is determined. This data-driven baseline model can be used to quantify the performance degradation of auxiliary power units in service, and can be further used to evaluate the remaining useful life of auxiliary power unit using a Bayesian dynamic model. The developed approach is applied on a real data set from 22 auxiliary power units of a commercial aircraft fleet. The results show that the computed health index can effectively characterize the auxiliary power units performance degradation and the remaining useful life relative prediction errors are less than 4% when auxiliary power unit enters the rapid degradation stage. This would allow operators to accurately assess the performance degradation for the auxiliary power units and further proactively plan future maintenance events based on remaining useful life prediction. [ABSTRACT FROM AUTHOR]

• A baseline closure concept is applied for model development and validation. • The baseline model is updated regarding bubble-induced turbulence. • The improvement is demonstrated for a wide range of bubbly flow conditions. • Two-equation RANS model with additional BIT source terms was shown to be effective. The baseline model for the CFD-simulation of turbulent bubbly flows in the Euler-Euler framework was proposed several years ago. It defines a fixed set of closures including all constants to avoid case by case tuning and to have a basis for sustainable model development. The present paper extends the baseline model by improving the modelling of bubble-induced turbulence. The closure terms in the transport equations of the k-ω SST model are revisited and replaced with the model recently proposed by Ma et al. (Ma et al., 2017. Physical Review Fluids 2, 034301) which is based on an analysis of the turbulent kinetic energy budget obtained from direct numerical simulation data. Detailed validation results for various flow configurations with a wide range of gas and liquid volumetric fluxes are presented. In case of vertical pipe flow significant improvements in the predicted gas volume fraction and velocity profiles are obtained, especially in high gas volume fraction cases where bubble-induced turbulence is dominant. Simulations of other configurations, such as uniform and non-uniform bubble columns, show that the model of Ma et al. (2017) results in an also for these cases overall improvement. Therefore, the baseline model is now updated to include the new model for bubble-induced turbulence. [ABSTRACT FROM AUTHOR]

• A baseline closure concept is presented for simulation of bubbly flow with phase change. • A new correlation for computation of inter-phase heat transfer coefficient is presented. • The new correlation is tested for evaporating and condensing bubbly flows. • The simulations are carried out by applying the best practice guidelines for CFD. • A general improvement is evidenced in comparison to the Ranz-Marshall correlation. In line with the best practice guidelines for computational fluid dynamics in nuclear reactor safety, Helmholtz-Zentrum Dresden – Rossendorf proposed an Euler-Euler baseline closure concept some years ago. The consideration is that simulations with a fixed set of closures instead of case by case tuning may help to identify model inadequacy and facilitate further improvement. Currently, the baseline model concerns interphase momentum and turbulence exchange as well as bubble coalescence and breakup. It has been tested for a wide range of isothermal applications with different geometrical configurations and material systems. In the present work, the baseline model is extended to non-isothermal flows by including a mechanistic model for the computation of overall interphase heat transfer coefficient. The extended baseline model is validated for both bubble-growing in superheated liquid and -condensing in sub-cooled liquid. The baseline model proposal is independent on the use of a certain CFD code. The presented simulation is carried out with the commercial software ANSYS CFX by employing the best practice guidelines. The simulated liquid temperature, gas volume fraction, vapor bubble size and velocity are compared with the measured ones. The effectivity of the model is demonstrated by a general good agreement. [ABSTRACT FROM AUTHOR]

• A two-stage construction defect identification method is proposed. • No baseline is required for sealant defect identification. • The proposed identification method is validated by numerical and on-site tests. Accidental defects occur often during the construction of the structural sealant in a glass curtain wall structure. However, there are tens of thousands of glass panel units in a building, and the baseline models of different panel units are different from each other. In this paper, a two-stage construction defect identification method is proposed to identify a few units with structural sealant defect from a large number of units. In the first stage, the average driving-point accelerance (DA) was used as statistical baseline DA to calculate the accumulative difference of driving-point accelerance (ADDA). Then, the abnormal panel units are identified by the quantile estimation method. In the second stage, structural sealant construction defects are detected further by comparing the peak frequency difference between the statistical baseline DA and the abnormal DA. Several numerical examples and on-site experiments indicated that the probability density function of ADDA is agreement with the Weibull distribution, and the proposed method can effectively detect the structural sealant construction defects from many units without baseline model. [ABSTRACT FROM AUTHOR]