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On the one hand, unemployment is known to have detrimental effects on individuals' mental health. On the other hand, poor mental health reduces re‐employment chances quite drastically, creating a vicious cycle. Active labour market policies (ALMPs) such as training programs or wage subsidies have been shown to ameliorate negative effects on mental health and improve labour market integration on average for the general unemployed population. In the context of individuals with severe mental health issues, however, it is unclear whether these interventions can be expected to deliver similar positive effects. In fact, one may argue that they have the potential to worsen employment prospects of individuals by adding additional stress to their pre‐existing mental health problems. Hence, this paper estimates the long‐term causal effects of ALMPs on the labour market integration of individuals with self‐reported severe mental health issues and compares estimates to individuals without such issues using unique combined survey and administrative data. Effects are estimated using the innovative double machine learning method and show that ALMPs do not only improve labour market integration of unemployed individuals with severe mental health issues, but they do so more effectively than for other unemployed individuals. [ABSTRACT FROM AUTHOR]

Visibility observations and accurate forecasts are essential in meteorology, requiring a dense network of observation stations. This paper investigates image processing techniques for object detection and visibility determination using static cameras. It proposes a comprehensive method that includes image preprocessing, landmark identification, and visibility estimation, mirroring the observation process of professional meteorological observers. This study validates the visibility observation procedure using the k-nearest neighbors machine learning method across six locations, including four in the Czech Republic, one in the USA, and one in Germany. By comparing our results with professional observations, the paper demonstrates the suitability of the proposed method for operational application, particularly in foggy and low visibility conditions. This versatile method holds potential for adoption by meteorological services worldwide. [ABSTRACT FROM AUTHOR]

Background: Formative assessments are needed to enable monitoring how student knowledge develops throughout a unit. Constructed response items which require learners to formulate their own free‐text responses are well suited for testing their active knowledge. However, assessing such constructed responses in an automated fashion is a complex task and requires the application of natural language processing methodology. In this article, we implement and evaluate multiple machine learning models for coding energy knowledge in free‐text responses of German K‐12 students to items in formative science assessments which were conducted during synchronous online learning sessions. Dataset: The dataset we collected for this purpose consists of German constructed responses from 38 different items dealing with aspects of energy such as manifestation and transformation. The units and items were implemented with the help of project‐based pedagogy and evidence‐centered design, and the responses were coded for seven core ideas concerning the manifestation and transformation of energy. The data was collected from students in seventh, eighth and ninth grade. Methodology: We train various transformer‐ and feature‐based models and compare their ability to recognize the respective ideas in students' writing. Moreover, as domain knowledge and its development can be formally modeled through knowledge networks, we evaluate how well the detection of the ideas within responses translated into accurate co‐occurrence‐based knowledge networks. Finally, in terms of the descriptive accuracy of our models, we inspect what features played a role for which prediction outcome and if the models pick up on undesired shortcuts. In addition to this, we analyze how much the models match human coders in what evidence within responses they consider important for their coding decisions. Results: A model based on a modified GBERT‐large can achieve the overall most promising results, although descriptive accuracy varies much more than predictive accuracy for the different ideas assessed. For reasons of comparability, we also evaluate the same machine learning architecture using the SciEntsBank 3‐Way benchmark with an English RoBERTa‐large model, where it achieves state‐of‐the‐art results in two out of three evaluation categories. Lay Description: What is already known about this topic?: Formative assessments are needed to test and monitor the development of learners' knowledge throughout a unit to provide them with appropriate automated feedback.Constructed response items which require learners to formulate their own free‐text responses are well suited for testing their active knowledge.Assessing constructed responses in an automated fashion is a widely researched topic, but the problem is far from solved and most of the work focuses on predicting holistic scores or grades.To allow for a more fine‐grained and analytic assessment of learners' knowledge, systems which go beyond predicting simple grades are required.To guarantee that models are stable and make their predictions for the correct reasons, methods for explaining the models are required. What this papers adds?: A core topic in physics education is the concept of energy.We implement and evaluate multiple systems based on natural language processing technology for assessing learners' conceptual knowledge about energy physics using transformer language models as well as feature‐based approaches.The systems assess students' knowledge about various forms of energy, indicators for the same and the transformation of energy from form into another.As our systems are based on machine learning methodology, we introduce a novel German short answer dataset for training them to detect the respective knowledge elements within students' free‐text responses.We evaluate the performance of these systems using this dataset as well as the well‐established SciEntsBand‐3‐Way dataset and manage to achieve, to our best knowledge, new state‐of‐the‐art results for the latter.Moreover, we apply methodology for explaining model predictions to assess whether predictions are carried out for the correct reasons. Implications for practice and/or policy: It is indeed possible to assess constructed responses for the demonstrated knowledge about energy physics in an analytic fashion using natural language processing.Transformer language models can outperform more specialized feature‐based approaches for this task in terms of predictive and descriptive accuracy.Co‐occurrences of different concepts within the same responses can lead models to learn undesired shortcuts which make them unstable. [ABSTRACT FROM AUTHOR]

Background: Conspiracy beliefs have become widespread throughout the COVID-19 pandemic. Previous studies have shown that endorsing conspiracy beliefs leads to lower protective guideline adherence (i.e., wearing face masks), posing a threat to public health measures. The current study expands this research across the lifespan, i.e., in a sample of adolescents with mental health problems. Here, we investigated the association between conspiracy beliefs and guideline adherence while also exploring the predictors of conspiracy beliefs. Methods: N = 93 adolescent psychiatric outpatients (57% female, mean age: 15.8) were assessed using anonymous paper–pencil questionnaires. Endorsement of generic and COVID-19 conspiracy beliefs was assessed, in addition to items measuring adherence to protective guidelines and mental health (stress, depressive symptoms, emotional/behavioral problems, and adverse childhood experiences). Multiple regressions and supervised machine learning (conditional random forests) were used for analyses. Results: Fourteen percent of our sample fully endorsed at least one COVID-19 conspiracy theory, while protective guidelines adherence was relatively high (M = 4.92, on a scale from 1 to 7). The endorsement of COVID-19 conspiracy beliefs—but not of generic conspiracy beliefs—was associated with lower guideline adherence (β = − 0.32, 95% CI − 0.53 to − 0.11, p <.001). Conditional random forests suggested that adverse childhood experiences and peer and conduct problems were relevant predictors of both conspiracy belief categories. Conclusion: While a significant proportion of our sample of adolescents in psychiatric treatment endorsed conspiracy beliefs, the majority did not. Furthermore, and to some degree, contrary to public perception, we found that adolescents show relatively good adherence to public health measures—even while experiencing a high degree of mental distress. The predictive value of adverse childhood experiences and peer/conduct problems for conspiracy beliefs might be explained by compensatory mechanisms to ensure the safety, structure, and inclusion that conspiracies provide. [ABSTRACT FROM AUTHOR]

Introduction: Artificial intelligence (AI) has the potential to transform clinical decision-making as we know it. Powered by sophisticated machine learning algorithms, clinical decision support systems (CDSS) can generate unprecedented amounts of predictive information about individuals' health. Yet, despite the potential of these systems to promote proactive decision-making and improve health outcomes, their utility and impact remain poorly understood due to their still rare application in clinical practice. Taking the example of AI-powered CDSS in stroke medicine as a case in point, this paper provides a nuanced account of stroke survivors', family members', and healthcare professionals' expectations and attitudes towards medical AI. Methods: We followed a qualitative research design informed by the sociology of expectations, which recognizes the generative role of individuals' expectations in shaping scientific and technological change. Semi-structured interviews were conducted with stroke survivors, family members, and healthcare professionals specialized in stroke based in Germany and Switzerland. Data was analyzed using a combination of inductive and deductive thematic analysis. Results: Based on the participants' deliberations, we identified four presumed roles that medical AI could play in stroke medicine, including an administrative, assistive, advisory, and autonomous role AI. While most participants held positive attitudes towards medical AI and its potential to increase accuracy, speed, and efficiency in medical decision making, they also cautioned that it is not a stand-alone solution and may even lead to new problems. Participants particularly emphasized the importance of relational aspects and raised questions regarding the impact of AI on roles and responsibilities and patients' rights to information and decision-making. These findings shed light on the potential impact of medical AI on professional identities, role perceptions, and the doctor-patient relationship. Conclusion: Our findings highlight the need for a more differentiated approach to identifying and tackling pertinent ethical and legal issues in the context of medical AI. We advocate for stakeholder and public involvement in the development of AI and AI governance to ensure that medical AI offers solutions to the most pressing challenges patients and clinicians face in clinical care. [ABSTRACT FROM AUTHOR]

Soil moisture, soil organic carbon, and nitrogen content prediction are considered significant fields of study as they are directly related to plant health and food production. Direct estimation of these soil properties with traditional methods, for example, the oven-drying technique and chemical analysis, is a time and resource-consuming approach and can predict only smaller areas. With the significant development of remote sensing and hyperspectral (HS) imaging technologies, soil moisture, carbon, and nitrogen can be estimated over vast areas. This paper presents a generalized approach to predicting three different essential soil contents using a comprehensive study of various machine learning (ML) models by considering the dimensional reduction in feature spaces. In this study, we have used three popular benchmark HS datasets captured in Germany and Sweden. The efficacy of different ML algorithms is evaluated to predict soil content, and significant improvement is obtained when a specific range of bands is selected. The performance of ML models is further improved by applying principal component analysis (PCA), a dimensional reduction method that works with an unsupervised learning method. The effect of soil temperature on soil moisture prediction is evaluated in this study, and the results show that when the soil temperature is considered with the HS band, the soil moisture prediction accuracy does not improve. However, the combined effect of band selection and feature transformation using PCA significantly enhances the prediction accuracy for soil moisture, carbon, and nitrogen content. This study represents a comprehensive analysis of a wide range of established ML regression models using data preprocessing, effective band selection, and data dimension reduction and attempt to understand which feature combinations provide the best accuracy. The outcomes of several ML models are verified with validation techniques and the best- and worst-case scenarios in terms of soil content are noted. The proposed approach outperforms existing estimation techniques. [ABSTRACT FROM AUTHOR]

During the lockdown of universities and the COVID-Pandemic most students were restricted to their homes. Novel and instigating teaching methods were required to improve the learning experience and so recent implementations of the annual PhysioNet/Computing in Cardiology (CinC) Challenges posed as a reference. For over 20 years, the challenges have proven repeatedly to be of immense educational value, besides leading to technological advances for specific problems. In this paper, we report results from the class ‘Artificial Intelligence in Medicine Challenge’, which was implemented as an online project seminar at Technical University Darmstadt, Germany, and which was heavily inspired by the PhysioNet/CinC Challenge 2017 ‘AF Classification from a Short Single Lead ECG Recording’. Atrial fibrillation is a common cardiac disease and often remains undetected. Therefore, we selected the two most promising models of the course and give an insight into the Transformer-based DualNet architecture as well as into the CNN-LSTM-based model and finally a detailed analysis for both. In particular, we show the model performance results of our internal scoring process for all submitted models and the near state-of-the-art model performance for the two named models on the official 2017 challenge test set. Several teams were able to achieve F1 scores above/close to 90% on a hidden test-set of Holter recordings. We highlight themes commonly observed among participants, and report the results from the self-assessed student evaluation. Finally, the self-assessment of the students reported a notable increase in machine learning knowledge. [ABSTRACT FROM AUTHOR]

The paper investigates potentials and challenges during the interpretation of prehistoric settlement dynamics based on large archaeological datasets. Exemplarily, this is carried out using a database of 1365 Neolithic sites in the Weiße Elster river catchment in Central Germany located between the southernmost part of the Northern German Plain and the Central Uplands. The recorded sites are systematically pre-processed with regard to their chronology, functional interpretation and spatial delineation. The quality of the dataset is reviewed by analyzing site distributions with respect to field surveys and modern land use. The Random Forests machine learning algorithm is used to examine the impact of terrain covariates on the depth of sites and pottery preservation. Neolithic settlement dynamics are studied using Site Exploitation Territories, and site frequencies per century are used to compare the intensity of land use with adjacent landscapes. The results show that the main trends of the Neolithic settlement dynamics can be derived from the dataset. However, Random Forests analyses indicate poor pottery preservation in the Central Uplands and a superimposition of Neolithic sites in the southernmost part of the Northern German Plain. Throughout the Neolithic the margins between soils on loess and the Weiße Elster floodplain were continuously settled, whereas only Early and Late Neolithic land use also extended into the Central Uplands. These settlement patterns are reflected in the results of the Site Exploitation Territories analyses and explained with environmental economic factors. Similar with adjacent landscapes the Middle Neolithic site frequency is lower compared to earlier and later periods. [ABSTRACT FROM AUTHOR]

• Soil reflectance composites were generated with Sentinel-2 L2A data. • A multi-channel CNN learning framework for soil organic carbon prediction is proposed. • A dual input deep learning architecture improves the predictions. • The framework produced results that can be interpreted. The Copernicus Sentinel-2 multispectral imagery data may be aggregated to extract large-scale, bare soil, reflectance composites, which enable soil mapping applications. In this paper, this approach was tested in the German federal state of Bavaria, to provide estimations for soil organic carbon (SOC). Different temporal ranges were considered for the generation of the composites, including multi-annual and seasonal ranges. A novel multi-channel convolutional neural network (CNN) is proposed. By leveraging the advantages of deep learning techniques, it utilizes complementary information from different spectral pre-treatment techniques. The SOC predictions indicated little dissimilarity amongst the different composites, with the best performance attained for the six-year composite containing only spring months (RMSE = 12.03 g C · kg−1, R2 = 0.64, RPIQ = 0.89). It has been demonstrated that these outcomes outperform other well-known machine learning techniques. An ablation analysis was accordingly performed to evaluate the interplay of the CNN's different components to disentangle the advantages of each aspect of the proposed framework. Finally, a DUal inPut deep LearnIng architecture, named DUPLICITE, is proposed, which concatenates deep spectral features derived from the CNN mentioned earlier, as well as topographical and environmental covariates through an artificial neural network (ANN) to exploit their complementarity. The proposed approach was demonstrated to provide an improvement in the overall prediction performance (RMSE = 11.60 gC · kg−1, R2 = 0.67, RPIQ = 0.92). [ABSTRACT FROM AUTHOR]

Weather forecast models are essential for sustainable energy systems. However, forecast accuracy may not be the best metric for developing forecast models. A more or less conservative forecast may be preferred over pure accuracy. For example, forecasting accurately in times of energy-deprived situations may be more important than in times of excess wind power generation. This is not accounted for when learning a wind power forecast without system knowledge. Wind power forecasts directly impact system energy schedules. Therefore, to optimise system costs, our paper proposes a neural network structure for wind power forecasts directly considering varying energy system conditions. To train this neural network optimally, this paper models the system dispatch and their costs as an optimisation problem. Then, this paper connects the neural network with the optimisation model. In this connection, the implicit function theorem and the Karush–Kuhn–Tucker (KKT) conditions provide the system cost gradients during the training of the neurons. A case study using onshore and offshore weather data from Germany and The Netherlands showed forecast errors of system costs reduced by up to 10 % with high wind capacity. Wind curtailment was reduced by more than 20 % for individual cases but increased overall. Modified cases of the optimal power flow (OPF) with tighter line constraints led to a higher advantage of the proposed method. The approach led to more consistent forecast performance and reduced error variance by up to 70 %. • Neural networks for wind power forecasts considering varying energy system conditions • System dispatch and costs as an optimisation during the neural network training • The implicit function theorem and KKT conditions provide the system cost gradients • German and Dutch studies confirmed trained forecasts reduced system costs up to 10 % • More consistent forecast performance with reduced error variance by up to 70 %. [ABSTRACT FROM AUTHOR]

• The experiment obtained the crack data of the completely life cycle of crossing nose. • A new algorithm combining four feature selection algorithms, which screens out the 8 crack features most relevant to rolling fatigue. • The supervised learning of support vector machine regression (SVR) has achieved the best results compared to other methods in the regression fitting of the crack feature data. • The results represent a simple system to evaluate the life cycle of crossing nose and the early prediction of fatigue. • The system finds the location of cracks appearance that can create dangerous defects in the crossing nose surface. It is essential to assess the rolling contact fatigue (RCF) of turnouts and maintain them in advance. It saves a lot of money while protecting the safety of railway operations. In Germany, the damage on rails, especially crossing noses, mainly depends on the subjective judgment of experts. There are no objective and comprehensive evaluation criteria. This paper presents the application of image processing and supervised machine learning algorithms to crossing nose fatigue judgment. The fatigue characteristics of the crossing nose rolling contact surface along the life cycle of the crossing nose are analyzed. The study used crack information from magnetic particle inspection (MPI) images of crossing nose surfaces. It uses basic image processing methods to collect physical information about features of fatigue cracks in images. Existing feature selection methods are used to exclude irrelevant features and retain valuable features. And we select the best feature selection method through the regression results. Statistically significant crack features and combinations that depict the surface fatigue state are found. In this paper, by comparing several usually machine learning regression algorithms, it is found that the supervised learning of support vector machine regression (SVR) has achieved the best results in the regression fitting of the crack feature data in this paper. The regression results form a simple system to evaluate the life cycle of crossing nose. The system finds the location of cracks that can create dangerous defects in the crossing nose surface. The research result consists of the early prediction of rail contact fatigue. [ABSTRACT FROM AUTHOR]

Efficient operation of district heating networks requires a precise forecasting of the thermal loads and an optimised dispatch strategy for the available generation and storage portfolio. This paper presents a holistic modelling and optimisation approach: first, detailed process modelling and optimisation of power plants and thermal storages; second, a numerical model for dispatch optimisation; and third, machine-learning-based load forecasting. The work is based on operating data from the district heating network of the city of Ulm in Germany. The paper presents the modelling, validation and simulation results of stationary and instationary process simulation for a biomass-fired combined heat and power plant. The analysis identifies a potential to integrate additional renewable power by "power-to-heat" technologies into different parts of the process. The economic benefit is quantified by mixed-integer linear programming optimisation applied to the district heating network. In order to allow for real-time dispatch optimisation, a machine-learning-based thermal load forecasting method was developed and evaluated, based on a 72-h forecast horizon. In addition, the economic impact of prediction uncertainties is analysed with the numerical dispatch optimisation tool. • Holistic optimisation helps reducing installation and operating costs as well as emissions in district heating. • Process simulation allows the optimized integration of renewable electricity into biomass combined heat and power plants. • Machine learning significantly reduces thermal load forecasting errors, also for long-term forecasts up to 72 h in advance. • Dispatch optimisation allows to quantify impacts of inaccurate load forecasts on peaking boiler operation. [ABSTRACT FROM AUTHOR]

Weather is a pivotal factor for crop production as it is highly volatile and can hardly be controlled by farm management practices. Since there is a tendency towards increased weather extremes in the future, understanding weather-related yield factors becomes increasingly important not only for yield prediction, but also for the design of insurance products. Although insurance products mitigate financial losses for farmers, they suffer from considerable basis risk, i.e., a discrepancy between losses and the indemnity payment. The objective of this paper was to explore the potential of machine learning for estimating the relationship between crop yield and weather conditions at the farm level and to use it as a tool for reducing basis risk in index insurance applications. An artificial neural network was set up and calibrated to a rich set of farm-level yield data in Germany, covering the period from 2003 to 2018. A nonlinear regression model, which uses rainfall, temperature, and soil moisture as explanatory variables for yield deviations, served as a benchmark. The empirical application revealed that compared with traditional estimation approaches, the gain in forecasting precision by using machine learning techniques was substantial. Moreover, the use of regionalized models and disaggregated high-resolution weather data improved the performance of artificial neural networks. A considerable part of yield variability at the farm level, however, could not be captured by statistical methods which solely use "big weather data". Our findings have important implications for the design of weather-index based insurance because they document that a rather high level of basis risk remains if insurance products are based on an estimation of the weather-yield relationship. [Display omitted] • Due to increasing weather extremes in the future, understanding weather-related yield factors becomes more important. • This paper explores the potential of machine learning for estimating farm-level yield losses and basis risk reduction. • Use of high-resolution weather data improves the performance of artificial neural networks for estimating yield losses. • More homogenous insurance zones can reduce the basis risk and improve estimation precision. • The findings have implications for the design of weather-index based insurances because a rather high level of basis risk remains. [ABSTRACT FROM AUTHOR]

Energy-economic simulation models with high levels of detail, high time resolutions, or large populations (e.g., distribution networks, households, electric vehicles, energy communities) are often limited due to their computational complexity. This paper introduces a novel methodology, combining cluster-based time series aggregation and sampling methods, to efficiently emulate simulation models using machine learning and significantly reduce both simulation and training time. Machine learning-based emulation models require sufficient and high-quality data to generalize the dataset. Since simulations are computationally complex, their maximum number is limited. Sampling methods come into play when selecting the best parameters for a limited number of simulations ex ante. This paper introduces and compares multiple sampling methods on three energy-economic datasets and shows their advantage over a simple random sampling for small sample-sizes. The results show that a k-means cluster sampling approach (based on unsupervised learning) and adaptive sampling (based on supervised learning) achieve the best results especially for small sample sizes. While a k-means cluster sampling is simple to implement, it is challenging to increase the sample sizes if the emulation model does not achieve sufficient accuracy. The iterative adaptive sampling is more complex during implementation, but can be re-applied until a certain accuracy threshold is met. Emulation is then applied on a case study, emulating an energy-economic simulation framework for peer-to-peer pricing models in Germany. The evaluated pricing models are the "supply and demand ratio" (SDR) and "mid-market rate pricing" (MMR). A time series aggregation can reduce time series data of municipalities by 99.4% with less than 5% error for 98.2% (load) and 95.5% (generation) of all municipalities and hence decrease the simulation time needed to create sufficient training data. This paper combines time series aggregation and emulation in a novel approach and shows significant acceleration by up to 88.9% of the model's initial runtime for the simulation of the entire population of around 12,000 municipalities. The time for re-calculating the population (e.g., for different scenarios or sensitivity analysis) can be increased by a factor of 1100 while still retaining high accuracy. The analysis of the simulation time shows that time series aggregation and emulation, considered individually, only bring minor improvements in the runtime but can, however, be combined effectively. This can significantly speed up both the simulation itself and the training of the emulation model and allows for flexible use, depending on the capabilities of the models and the practitioners. The results of the peer-to-peer pricing for approximately 12,000 German municipalities show great potential for energy communities. The mechanisms offer good incentives for the addition of necessary flexibility. [ABSTRACT FROM AUTHOR]

• This paper explores probabilistic extensions to Deep Learning algorithms and their application on energy time series forecasting. • The methods tested are concrete Dropouts, deep Ensembles, bayesian neural Networks, deep gaussian Processes, and functional neural processes. • The methods are evaluated in terms of calibration, sharpness, and how well they indicate a lack of knowledge (Epistemic Uncertainty). • Good epistemic uncertainty estimates were given using functional neural Processes, deep Ensembles, and deep gaussian processes methods, while concrete dropouts and bayesian neural networks needed tunning for better performance. • The application of the methods was feasible for energy time series forecasting, and they produced good estimates of their confidence in a forecast. Deep Learning methods are widely successful and continue to be applied in new fields, including forecasting for energy time series. However, they lack an indication of their confidence in predictions, which helps evaluate, interpret, and improve the forecasting. This paper explores probabilistic extensions to Deep Learning algorithms and their application on energy time series forecasting. The methods tested are Concrete Dropouts, Deep Ensembles, Bayesian Neural Networks, Deep Gaussian Processes, and Functional Neural Processes. For evaluation, two load forecasting scenarios are considered: 1-hour-ahead and 24-hour-ahead load forecasting. The methods are evaluated in terms of calibration, sharpness, and how well they indicate a lack of knowledge (Epistemic Uncertainty). As a reference, simple Neural Network and Quantile Regression models are used. Also, the ARIMA model and Persistence model are implemented to compare the results. Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and Continuous Ranked Probability Score (CRPS) are computed to measure the error performance. Based on endogenous load time series in Germany, the results showed that Concrete Dropout, Deep Ensembles, and Bayesian Neural Networks performed similarly well and better than the reference methods. They also indicated that the simpler Concrete Dropout and Deep Ensembles have an advantage in both speed and convergence time. Good Epistemic Uncertainty estimates were given using Functional Neural Processes, Deep Ensembles, and Deep Gaussian Processes methods, while Concrete dropouts and Bayesian Neural Networks needed tunning for better performance. The application of the methods was feasible for energy time series forecasting, and they produced good estimates of their confidence in a forecast. [ABSTRACT FROM AUTHOR]

Due to highly uncertain underground conditions, most estimations of drilling and geological parameters must be performed using numerical modeling, thus by fitting empirical models to measurement data or by using an approach based on data analysis. In this paper, a combination of these two solutions is proposed for a realistic replication of historical drilling data in a drilling software simulator environment e.g., the DrillSIM:600 at Drilling Simulator Celle (Clausthal University of Technology). The whole process ranging from the modeling over the model evaluation and selection to the integration in the simulator environment is integrated within the presented framework. For this study several empirical models and five machine learning algorithms were applied to benchmark the performance on predicting the drilling parameter Rate of Penetration (ROP) using data from a single geothermal well located in northern Germany. The results of the comparison between the classical and the data driven models prove that the use of data analysis methods is a very valuable alternative to explore, describe, diagnose, and predict the underground conditions and drilling parameters. Those can be used to optimize the entire drilling process by replicating critical situations, recommending better practices or emphasizing the proper performance of a whole drilling project. Thus, by turning raw historical data in valuable insights, the further education of decision makers and rig crews can be significantly enhanced. [Display omitted] • Methodology for automated generation of real-time models from field data. • Enhanced training and optimization by realistic real-time drilling simulation. • Comparison and sensitivity analysis of empirical and data-driven models. • Implementation of framework in existing software simulator. [ABSTRACT FROM AUTHOR]

• A method for real-time road safety evaluation based on traffic conflict was proposed. • Effects of short-term traffic states on conflict risk were investigated and quantified. • A high-precision evaluation model of traffic safety was developed by machine learning algorithms. • Findings indicated the practicability of the proposed method for real-time safety evaluation. Real-time safety evaluation is essential for developing proactive safety management strategy and improving the overall traffic safety. This paper proposes a method for real-time evaluation of road safety, in which traffic states and conflicts are combined to explore the internal relationship based on high-resolution trajectory data. In order to assess the real-time traffic safety at a lane level, the trajectory data of the HighD dataset from Germany are utilized to collect lane-based dataset. A surrogate safety measure, time-to-collision (TTC) index, is used for the conflict identification. A binary logistic regression model is employed to quantify the relationship between traffic states and conflicts. Moreover, machine learning methods, including support vector machine, decision tree, random forest, and gradient boosting decision tree, are applied for real-time evaluation. A total of 24 models are trained using the selected four classifier algorithms, and random forest achieves the best performance with 0.85 of the overall accuracy. The results show that the conflict risk can be well estimated by the proposed method. The findings of this study contribute to the high-precision evaluation of real-time traffic safety and the development of proactive safety management. [ABSTRACT FROM AUTHOR]

NeuLAND, the New Large Area Neutron Detector, is a key component to investigate the origin of matter in the universe with experimental nuclear physics. It is a core component of the Reactions with Relativistic Radioactive Beams setup at the Facility for Antiproton and Ion Research, Germany. Neutrons emitted from these reactions create a wide range of patterns in NeuLAND. From these patterns, the number of neutrons (multiplicity) and their first interaction points must be reconstructed to determine the neutrons' four-momenta. In this paper, we detail the challenges involved in this reconstruction and present a range of possible solutions. Scikit-Learn classification models and simple Keras-based neural networks were trained on a wide range of input-scaler combinations and compared to classical models. While the improvement in multiplicity reconstruction is limited due to the overlap between features, the machine learning methods achieve a significantly better first interaction point selection, which directly improves the resolution of physical quantities. [ABSTRACT FROM AUTHOR]

Since climate change impacts threaten the coastal regions of the North Sea, consistent sea state time series are essential for building coastal protection or offshore structures. Vast gaps in buoy data, caused by false measurements or maintenance periods, require reconstruction and forecasting of ocean waves. Morphodynamic changes of the Ebb-Tidal Delta (ETD) sandbanks exert a huge impact on the local wave climate off the island of Norderney, Germany. Therefore, the objective of this paper is to develop a machine learning model based on Long Short-Term Memory (LSTM) neural networks for reconstruction and short- and long-term prediction of nearshore significant wave height (SWH), integrating bathymetric data for the first time. Time series of sea state and weather data of adjacent buoys as well as bathymetric data of the ETD sandbanks from 2004 to 2017 were used and the networks were tuned with Bayesian hyperparameter optimization. Including the bathymetries improved the performance of the LSTM for SWH reconstruction, short-, and long-term predictions by 16.7%, 7.4–11.7%, and 8.8–9.1% in terms of RMSE, respectively. The LSTM outperformed deep feed-forward neural networks and other state-of-the-art machine learning algorithms. With an RMSE of 0.069 m, a parallel LSTM structure (P-LSTM) is the proposed method for SWH reconstruction. • Including bathymetry features improved wave height reconstruction and prediction. • LSTM reconstruction RMSE improved by 16.7% with included bathymetries. • Significant wave height reconstruction RMSE of 0.069 m for parallel LSTM structure. • LSTM neural networks outperformed other algorithms. [ABSTRACT FROM AUTHOR]