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In this paper, we present an extended evaluation of a course recommender system designed to support students who struggle in the first semesters of their studies and are at risk of dropping out. The system, which was developed in earlier work using a student-centered design, is based on the explainable k-nearest neighbor algorithm and recommends a set of courses that have been passed by the majority of successful neighbors, that is, students who graduated from the study program. In terms of the number of recommended courses, we found a discrepancy between the number of courses that struggling students are recommended to take and the actual number of courses they take. This indicates that there may be an alternative path that these students could consider. However, the recommended courses align well with the courses taken by students who successfully graduated. This suggests that even students who are performing well could still benefit from the course recommender system designed for at-risk students. In the present work, we investigate a second type of success--a specific minimum number of courses passed--and compare the results with our first approach from previous work. With the second type, the information about success might be already available after one semester instead of after graduation which allows faster growth of the database and faster response to curricular changes. The evaluation of three different study programs in terms of dropout risk reduction and recommendation quality suggests that course recommendations based on students passing at least three courses in the following semester can be an alternative to guide students on a successful path.

Formative assessment is about providing and using feedback and diagnostic information. On this basis, further learning or further teaching should be adaptive and, in the best case, optimized. However, this aspect is difficult to implement in reality, as teachers work with a large number of students and the whole process of formative assessment, especially the evaluation of student performance takes a lot of time. To address this problem, this paper presents an approach in which student performance is collected through a concept map and quickly evaluated using Machine Learning techniques. For this purpose, a concept map on the topic of mechanics was developed and used in 14 physics classes in Germany. After the student maps were analysed by two human raters on the basis of a four-level feedback scheme, a supervised Machine Learning algorithm was trained on the data. The results show a very good agreement between the human and Machine Learning evaluation. Based on these results, an embedding in everyday school life is conceivable, especially as support for teachers. In this way, the teacher can use and interpret the automatic evaluation and use it in the classroom.

Although there are several indirect methods that can be used to verify reference limits, they have a common weakness in that they assume a low proportion of pathological values. This paper investigates whether a Gaussian decomposition algorithm can identify the non-pathological fraction even if it is not the main subset of mixed data. All investigations are carried out in the R programming environment. The mclust package is used for Gaussian mixture modelling via the expectation maximization (EM) algorithm. For right-skewed distributions, logarithms of the original values are taken to approximate the Gaussian model. We use the Bayesian information criterion (BIC) for evaluation of the results. The reflimR and refineR packages serve as comparison procedures. We generate synthetic data mixtures with known normal distributions to demonstrate the feasibility and reliability of our approach. Application of the algorithm to real data from a Nigerian and a German population produces results, which help to interpret reference intervals of reflimR and refineR that are obviously too wide. In the first example, the mclust analysis of hemoglobin in Nigerian women supports the medical hypothesis that an anemia rate of more than 50 % leads to falsely low reference limits. Our algorithm proposes various scenarios based on the BIC values, one of which suggests reference limits that are close to published data for Nigeria but significantly lower than those established for the Caucasian population. In the second example, the standard statistical analysis of creatine kinase in German patients with predominantly cardiac diseases yields a reference interval that is clearly too wide. With mclust we identify overlapping fractions that explain this false result. Gaussian mixture modelling does not replace standard methods for reference interval estimation but is a valuable adjunct when these methods produce discrepant or implausible results. [ABSTRACT FROM AUTHOR]

The article presents a study which investigates the development of an algorithm to retrieve atmospheric water vapour column from observations in Multi-AXis Differential Optical Absorption Spectroscopy (MAX-DOAS). The study has applied the algorithm to observations at the Mainz, Germany-based Max Planck Institute for Chemistry from March to August 2011. Study reveals that the retrieval of vertical column density (VCDs) of water in cloudy conditions serves as the algorithm's important advantage.

As one of the automated guided vehicle (AGV) positioning methods, the LiDAR positioning method, based on artificial landmarks, has been widely used in warehousing logistics industries in recent years. However, the traditional LiDAR positioning method based on artificial landmarks mainly depends on the three-point positioning method, the performance of which is limited due to landmarks' layout and detection requirements. This paper proposes a LiDAR positioning algorithm based on iterative closest point (ICP) and artificial landmarks assistance. It provides improvements based on the traditional ICP algorithm. The result of positioning provided by the landmarks is used as the initial iteration ICP value. The combination of the ICP algorithm and landmarks enables the positioning algorithm to maintain a certain positioning precision when landmark detection is disturbed. By comparing the proposed algorithm with the positioning scheme developed by SICK in Germany, we prove that the combination of the ICP algorithm and landmarks can effectively improve the robustness under the premise of ensuring precision.

Classification of outdoor point clouds is an intensely studied topic, particularly with respect to the separation of vegetation from the terrain and manmade structures. In the presence of many overhanging and vertical structures, the (relative) height is no longer a reliable criterion for such a separation. An alternative would be to apply supervised classification; however, thousands of examples are typically required for appropriate training. In this paper, an unsupervised and rotation-invariant method is presented and evaluated for three datasets with very different characteristics. The method allows us to detect planar patches by filtering and clustering so-called superpoints, whereby the well-known but suitably modified random sampling and consensus (RANSAC) approach plays a key role for plane estimation in outlier-rich data. The performance of our method is compared to that produced by supervised classifiers common for remote sensing settings: random forest as learner and feature sets for point cloud processing, like covariance-based features or point descriptors. It is shown that for point clouds resulting from airborne laser scans, the detection accuracy of the proposed method is over 96% and, as such, higher than that of standard supervised classification approaches. Because of artifacts caused by interpolation during 3D stereo matching, the overall accuracy was lower for photogrammetric point clouds (74-77%). However, using additional salient features, such as the normalized green-red difference index, the results became more accurate and less dependent on the data source.

In Part I of this two-part paper a new method of predicting the total lightning flash rate in thunderstorms was introduced. In this paper, the implementation of this method into the convection-permitting Consortium for Small Scale Modeling (COSMO) model is presented. The new approach is based on a simple theoretical model that consists of a dipole charge structure, which is maintained by a generator current and discharged by lightning and, to a small extent, by a leakage current. This approach yields a set of four predictor variables, which are not amenable to direct observations and consequently need to be parameterized (Part I). Using an algorithm that identifies thunderstorm cells and their properties, this approach is applied to determine the flash frequency of every thunderstorm cell in the model domain. With this information, the number of flashes that are accumulated by each cell and during the interval between the activation of the lightning scheme can be calculated. These flashes are then randomly distributed in time and beneath each cell. The output contains the longitude, the latitude, and the time of occurrence of each simulated discharge. Simulations of real-world scenarios are presented, which are compared to measurements with the lightning detection network, LINET. These comparisons are done on the cloud scale as well as in a mesoscale region composing southern Germany (two cases each). The flash rates of individual cumulonimbus clouds at the extreme ends of the intensity spectrum are realistically simulated. The simulated overall lightning activity over southern Germany is dominated by spatiotemporal displacements of the modeled convective clouds, although the scheme generally reproduces realistic patterns such as coherent lightning swaths. [ABSTRACT FROM AUTHOR]

Background: In cardiology, cardiac output (CO) is an important parameter for assessing cardiac function. While invasive thermodilution procedures are the gold standard for CO assessment, transthoracic Doppler echocardiography (TTE) has become the established method for routine CO assessment in daily clinical practice. However, a demand persists for non-invasive approaches, including oscillometric pulse wave analysis (PWA), to enhance the accuracy of CO estimation, reduce complications associated with invasive procedures, and facilitate its application in non-intensive care settings. Here, we aimed to compare the TTE and oscillometric PWA algorithm Antares for a non-invasive estimation of CO. Methods: Non-invasive CO data obtained by two-dimensional TTE were compared with those from an oscillometric blood pressure device (custo med GmbH, Ottobrunn, Germany) using the integrated algorithm Antares (Redwave Medical GmbH, Jena, Germany). In total, 59 patients undergoing elective cardiac catheterization for clinical reasons (71±10 years old, 76% males) were included. Agreement between both CO measures were assessed by Bland-Altman analysis, Student's t-test, and Pearson correlations. Results: The mean difference in CO was 0.04 ± 1.03 l/min (95% confidence interval for the mean difference: -0.23 to 0.30 l/min) for the overall group, with lower and upper limits of agreement at -1.98 and 2.05 l/min, respectively. There was no statistically significant difference in means between both CO measures (P = 0.785). Statistically significant correlations between TTE and Antares CO were observed in the entire cohort (r = 0.705, P<0.001) as well as in female (r = 0.802, P<0.001) and male patients (r = 0.669, P<0.001). Conclusions: The oscillometric PWA algorithm Antares and established TTE for a non-invasive estimation of CO are highly correlated in male and female patients, with no statistically significant difference between both approaches. Future validation studies of the Antares CO are necessary before a clinical application can be considered. [ABSTRACT FROM AUTHOR]

Background: Staffing ratios in nursing homes vary among the federal states of Germany, but there are no rational grounds for these variations. In a previous study, a new instrument for the standardized calculation of staffing requirements in nursing homes was developed (Algorithm1.0). The development was based on a new empirical data collection method that derives actual and target values for the time and number of care interventions provided. Algorithm1.0 found an increased requirement of 36% of staff in German nursing homes. Based on these results, the German legislature has commissioned a model program to trial and evaluate a complex intervention comprising increased staffing combined with strategies for organizational development. Methods: The mixed-methods study consists of (i) developing a concept for restructuring the work organization, (ii) the application of this concept combined with increased staffing in 10 nursing homes (complex intervention), and the further development of the concept using a participatory and iterative formal evaluation process. The intervention consists of (a) quantitative measures of increased staffing based on a calculation using Algorithm1.0 and (b) qualitative measures regarding organizational development. The intervention will be conducted over one year. The effects of the intervention on job satisfaction and quality of care will be evaluated in (iii) a comprehensive prospective, controlled summative evaluation. The results will be compared with ten matched nursing homes as a control group. Finally, (iv) prototypical concepts for qualification-oriented work organization, a strategy for the national rollout, and the further development of Algorithm1.0 into Algorithm 2.0 will be derived. Discussion: In Germany, there is an ongoing dynamic legislation process regarding further developing the long-term care sector. The study, which is the subject of the study protocol presented here, generates an evidence-based strategy for the staffing requirements for nursing homes. Ethics and dissemination. This study was approved by the Ethics Committee of the German Association of Nursing Science (Deutsche Gesellschaft für Pflegewissenschaft) on 02.08.2023 (amended on 20.09.2023). Research findings are disseminated through presentations at national and international conferences and publications in peer-reviewed scientific journals. Trial registration number: German Clinical Trails Register DRKS00031773 (Date of registration 09.11.2023). [ABSTRACT FROM AUTHOR]

Context: YOLO (You Look Only Once) is an algorithm based on deep neural networks with real-time object detection capabilities. This state-of-the-art technology is widely available, mainly due to its speed and precision. Since its conception, YOLO has been applied to detect and recognize traffic signs, pedestrians, traffic lights, vehicles, and so on. Objective: The goal of this research is to systematically analyze the YOLO object detection algorithm, applied to traffic sign detection and recognition systems, from five relevant aspects of this technology: applications, datasets, metrics, hardware, and challenges. Method: This study performs a systematic literature review (SLR) of studies on traffic sign detection and recognition using YOLO published in the years 2016–2022. Results: The search found 115 primary studies relevant to the goal of this research. After analyzing these investigations, the following relevant results were obtained. The most common applications of YOLO in this field are vehicular security and intelligent and autonomous vehicles. The majority of the sign datasets used to train, test, and validate YOLO-based systems are publicly available, with an emphasis on datasets from Germany and China. It has also been discovered that most works present sophisticated detection, classification, and processing speed metrics for traffic sign detection and recognition systems by using the different versions of YOLO. In addition, the most popular desktop data processing hardwares are Nvidia RTX 2080 and Titan Tesla V100 and, in the case of embedded or mobile GPU platforms, Jetson Xavier NX. Finally, seven relevant challenges that these systems face when operating in real road conditions have been identified. With this in mind, research has been reclassified to address these challenges in each case. Conclusions: This SLR is the most relevant and current work in the field of technology development applied to the detection and recognition of traffic signs using YOLO. In addition, insights are provided about future work that could be conducted to improve the field. [ABSTRACT FROM AUTHOR]

Detecting changes in soil micro-relief in farmland helps to understand degradation processes like sheet erosion. Using the high-resolution technique of terrestrial laser scanning (TLS), we generated point clouds of three 2 × 3 m plots on a weekly basis from May to mid-June in 2022 on cultivated farmland in Germany. Three well-known applications for eliminating vegetation points in the generated point cloud were tested: Cloth Simulation Filter (CSF) as a filtering method, three variants of CANUPO as a machine learning method, and ArcGIS PointCNN as a deep learning method, a sub-category of machine learning using deep neural networks. We assessed the methods with hard criteria such as F1 score, balanced accuracy, height differences, and their standard deviations to the reference surface, resulting in data gaps and robustness, and with soft criteria such as time-saving capacity, accessibility, and user knowledge. All algorithms showed a low performance at the initial measurement epoch, increasing with later epochs. While most of the results demonstrate a better performance of ArcGIS PointCNN, this algorithm revealed an exceptionally low performance in plot 1, which is describable by the generalization gap. Although CANUPO variants created the highest amount of data gaps, we recommend that CANUPO include colour values in combination with CSF. [ABSTRACT FROM AUTHOR]

Online-monitoring systems in intensive care are affected by a high rate of false threshold alarms. These are caused by irrelevant noise and outliers in the measured time series data. The high false alarm rates can be lowered by separating relevant signals from noise and outliers online, in such a way that signal estimations, instead of raw measurements, are compared to the alarm limits. This paper presents a clinical validation study for two recently developed online signal filters. The filters are based on robust repeated median regression in moving windows of varying width. Validation is done offline using a large annotated reference database. The performance criteria are sensitivity and the proportion of false alarms suppressed by the signal filters.

In late 2001, the Object Management Group issued a Request for Proposal to develop a testing profile for UML 2.0. In June 2003, the work on the UML 2.0 Testing Profile was finally adopted by the OMG. Since March 2004, it has become an official standard of the OMG. The UML 2.0 Testing Profile provides support for UML based model-driven testing. This paper introduces a methodology on how to use the testing profile in order to modify and extend an existing UML design model for test issues. The application of the methodology will be explained by applying it to an existing UML Model for a Bluetooth device.

In the past, many algorithms have been applied for three-dimensional (3-D) single tree extraction using Airborne Laser Scanner (ALS) data. Clustering based algorithms are widely used in different applications but rarely being they used in the field of forestry using ALS data as an input. In this paper, a comparative qualitative study was conducted using the iterative partitioning and hierarchical clustering based mechanisms and full waveform ALS data as an input to extract the individual trees/tree crowns in their most appropriate shape. The full waveform LIght Detection And Ranging (LIDAR) data was collected from the Waldkirch black forest area in the south-western part of Germany in August 2005 with density of 4-5 points/m². Both the clustering algorithms were used in their original and modified form for a comparative qualitative analysis of the results obtained in the form of individual clusters containing 3-D points for each tree/tree crown. A total of 378 trees were found in all the 1.2 ha area with height ranging from 15 m to 50.9 m. The forest contains mainly older trees with deciduous, coniferous and mixed stands. The findings showed that among the three kind of clustering methods applied (normal k-means, modified k-means and hierarchical clustering), the modified k-means algorithm using external seed points and scaling down the height for initialization of the clustering process was the most promising method for the extraction of clusters of individual trees/tree crowns. A 3-D reconstruction of extracted individual clusters was carried out using QHull algorithm. In this study, the result was not possible to validate quantitatively due to lack of the field inventory data. [ABSTRACT FROM AUTHOR]

In risk analysis there is a spatial mismatch of hazard data that are commonly modelled on an explicit raster level and exposure data that are often only available for aggregated units, e.g. communities. Dasymetric mapping techniques that use ancillary information to disaggregate data within a spatial unit help to bridge this gap. This paper presents dasymetric maps showing the population density and a unit value of residential assets for whole Germany. A dasymetric mapping approach, which uses land cover data (CORINE Land Cover) as ancillary variable, was adapted and applied to regionalize aggregated census data that are provided for all communities in Germany. The results were validated by two approaches. First, it was ascertained whether population data disaggregated at the community level can be used to estimate population in postcodes. Secondly, disaggregated population and asset data were used for a loss evaluation of two flood events that occurred in 1999 and 2002, respectively. It must be concluded that the algorithm tends to underestimate the population in urban areas and to overestimate population in other land cover classes. Nevertheless, flood loss evaluations demonstrate that the approach is capable of providing realistic estimates of the number of exposed people and assets. Thus, the maps are sufficient for applications in large-scale risk assessments such as the estimation of population and assets exposed to natural and man-made hazards. [ABSTRACT FROM AUTHOR]

This paper illustrates a method based on local likelihood (LL) for detecting disease clusters. The approach is based on estimating a lasso distance for each region: within which regions are considered to be clustered. An important advantage in implementing this approach is that it does not require any special Monte Carlo Markov Chain (MCMC) algorithm, e.g., reversible jump MCMC, which is essential in hidden Markov model approach. Another advantage is that extending the model to incorporate covariates is straightforward. We illustrate three ways of doing this by using Eastern Germany lip cancer data. By using simulated data, we have made a comparison with the BYM model [Besag et al. (1991) Annals of the Institute of Statistical Mathematics, 43, 1–59] and the mixture model [Lawson and Clark (2002) Disease Mapping and Risk Assessment for Public Health, Chapman and Hall]. We also did a limited examination of the ability of the LL model to recover true relative risk under different priors for lasso parameter. In order to check the edge effects, which has been overlooked in many spatial clustering models for disease mapping but deserves special attention as it lacks observable neighbors, we have adapted here a simple approach to observe the changes in relative risks when the edge regions are omitted. [ABSTRACT FROM AUTHOR]

The management of moving objects has been intensively studied in recent years. A wide and increasing range of database applications has to deal with spatial objects whose position changes continuously over time, called moving objects. The main interest of these applications is to efficiently store and query the positions of these continuously moving objects. To achieve this goal, index structures are required. The main proposals of index structures for moving objects deal with unconstrained 2-dimensional movement. Constrained movement is a special and a very important case of object movement. For example, cars move in roads and trains in railroads. In this paper we propose a new index structure for moving objects on networks, the MON-Tree. We describe two network models that can be indexed by the MON-Tree. The first model is edge oriented, i.e., the network consists of nodes and edges and there is a polyline associated to each edge. The second one is more suitable for transportation networks and is route oriented, i.e., the network consists of routes and junctions. In this model, a polyline also serves as a representation of the routes. We propose the index in terms of the basic algorithms for insertion and querying. We test our proposal in an extensive experimental evaluation with generated data sets using as underlying networks the roads of Germany. In our tests, the MON-Tree shows good scalabiliy and outperforms the competing index structures in updating (index creation) as well as in querying. [ABSTRACT FROM AUTHOR]

In March 1964, the cover page of a popular German weekly magazine entitled Der Spiegel painted a frightening picture: An anthropomorphic robot with six mechanical arms commands an assembly line while a displaced human worker floats aimlessly in the foreground. Ejected from his station, the worker throws up his hands in despair next to a headline that reads, "Automation in Germany, the arrival of robots." Over fifty years later, a cover page from the same magazine evoked similar themes: A giant robot arm yanks an office worker away from his computer under the headline, "You're fired! How computers and robots steal our jobs - and which jobs will be safe." The more things change, the more they stay the same. [ABSTRACT FROM AUTHOR]

Introduction: This pilot study aimed to investigate the effects of using an app-based certified medical product named PINK! on breast cancer patients and survivors. The objectives were to measure psychological distress, physical activity, and therapy-related fatigue of patients using PINK! to identify trends and develop a study design for a subsequent multicentric proof of efficacy RCT. Materials and Methods: PINK! offers individualized, evidence-based therapy and side-effect management, mindfulness-based stress reduction, nutritional and psychological education, physical activity tracking, and motivational exercises to implement lifestyle changes sustainably in daily routine. A prospective, intraindividual RCT was performed with n = 60 patients in 2021 at Comprehensive Cancer Center Munich. Patients with BC were included independent of the stage of diseases. The intervention group got access to PINK! over 12 weeks. Control group served as a waiting-list comparison to "standard of care." Results: Primary efficacy variable analysis revealed a relative average decrease of 32.9% in psychological distress, which corresponds to a statistically significant reduction (p < 0.001) within 12 weeks compared to the control group. Linear regressions within usage groups showed a correlation of high app usage and a reduction of psychological distress. Fatigue data presented a statistically significant antifatigue efficacy (p < 0.001) and physical activity increased by 63.9%. Conclusion: App-based supportive care offers a promising, low-threshold, and cost-efficient opportunity to improve psychological well-being, quality of life, fatigue, and physical activity. More research is needed to implement eHealth solutions in clinical cancer care. [ABSTRACT FROM AUTHOR]

During the outbreak of a disease caused by a pathogen with unknown characteristics, the uncertainty of its progression parameters can be reduced by devising methods that, based on rational assumptions, exploit available information to provide actionable insights. In this study, performed a few (~6) weeks into the outbreak of COVID-19 (caused by SARS-CoV-2), one of the most important disease parameters, the average time-to-recovery, was calculated using data publicly available on the internet (daily reported cases of confirmed infections, deaths, and recoveries), and fed into an algorithm that matches confirmed cases with deaths and recoveries. Unmatched cases were adjusted based on the matched cases calculation. The mean time-to-recovery, calculated from all globally reported cases, was found to be 18.01 days (SD 3.31 days) for the matched cases and 18.29 days (SD 2.73 days) taking into consideration the adjusted unmatched cases as well. The proposed method used limited data and provided experimental results in the same region as clinical studies published several months later. This indicates that the proposed method, combined with expert knowledge and informed calculated assumptions, could provide a meaningful calculated average time-to-recovery figure, which can be used as an evidence-based estimation to support containment and mitigation policy decisions, even at the very early stages of an outbreak. [ABSTRACT FROM AUTHOR]

Grasslands are an important part of pre-Alpine and Alpine landscapes. Despite the economic value and the significant role of grasslands in carbon and nitrogen (N) cycling, spatially explicit information on grassland biomass and quality is rarely available. Remotely sensed data from unmanned aircraft systems (UASs) and satellites might be an option to overcome this gap. Our study aims to investigate the potential of low-cost UAS-based multispectral sensors for estimating above-ground biomass (dry matter, DM) and plant N concentration. In our analysis, we compared two different sensors (Parrot Sequoia, SEQ; MicaSense RedEdge-M, REM), three statistical models (linear model; random forests, RFs; gradient-boosting machines, GBMs), and six predictor sets (i.e. different combinations of raw reflectance, vegetation indices, and canopy height). Canopy height information can be derived from UAS sensors but was not available in our study. Therefore, we tested the added value of this structural information with in situ measured bulk canopy height data. A combined field sampling and flight campaign was conducted in April 2018 at different grassland sites in southern Germany to obtain in situ and the corresponding spectral data. The hyper-parameters of the two machine learning (ML) approaches (RF, GBM) were optimized, and all model setups were run with a 6-fold cross-validation. Linear models were characterized by very low statistical performance measures, thus were not suitable to estimate DM and plant N concentration using UAS data. The non-linear ML algorithms showed an acceptable regression performance for all sensor–predictor set combinations with average (avg; cross-validated, cv) Rcv2 of 0.48, RMSE cv,avg of 53.0 g m 2 , and rRMSE cv,avg (relative) of 15.9 % for DM and with Rcv,avg2 of 0.40, RMSE cv,avg of 0.48 wt %, and rRMSE cv, avg of 15.2 % for plant N concentration estimation. The optimal combination of sensors, ML algorithms, and predictor sets notably improved the model performance. The best model performance for the estimation of DM (Rcv2=0.67 , RMSE cv=41.9 g m 2 , rRMSE cv=12.6 %) was achieved with an RF model that utilizes all possible predictors and REM sensor data. The best model for plant N concentration was a combination of an RF model with all predictors and SEQ sensor data (Rcv2=0.47 , RMSE cv=0.45 wt %, rRMSE cv=14.2 %). DM models with the spectral input of REM performed significantly better than those with SEQ data, while for N concentration models, it was the other way round. The choice of predictors was most influential on model performance, while the effect of the chosen ML algorithm was generally lower. The addition of canopy height to the spectral data in the predictor set significantly improved the DM models. In our study, calibrating the ML algorithm improved the model performance substantially, which shows the importance of this step. [ABSTRACT FROM AUTHOR]

Abstract: The present paper addresses the issue of urban sprawl in Europe from a multidimensional point of view, identifying the most sprawled areas and characterizing them in terms of population size. The literature is reviewed to categorize and extract the most relevant six dimensions that define the concept and several indices are specified to implement them. These are then calculated for a sample of the main European cities that uses several sources to obtain the best possible dataset to measure urban sprawl. All this information is brought together using the self-organizing map (SOM) algorithm to be visualized and further studied, taking advantage of its properties as a data-reduction as well as a clustering technique. The analysis locates the hot-spots of urban sprawl in Europe in the centre of the continent, around Germany, and characterizes such urban areas as small, always half the size of the average city of the sample. [Copyright &y& Elsevier]

Abstract: In this paper, we are concerned with a special timetabling problem. It was posed to us by the administration of our university and stems from the adoption of the British-American system of university education in Germany. This change led to the concrete task of constructing a timetable that enables the undergraduate education of secondary school teachers within three years in the “normal case” and within four years in the case of exceptional combinations of subjects. We develop two relation-algebraic models of the timetabling problem and in each case algorithms for computing solutions. The latter easily can be implemented in the Kiel RelView tool showing that RelView can be used for timetabling. [ABSTRACT FROM AUTHOR]

[Display omitted] • We simulate renewables pathways under different support phase-out scenarios. • We extend an investment algorithm by preferences and calibrated returns. • Our improved algorithm incorporates more heterogeneity resulting in stronger effects. • Further auction support is necessary for most capacity targets in the case countries. • Countries should coordinate policy changes due to cross-border effects. Support schemes have been central to the expansion of renewable electricity globally and in the European Union. As technologies mature, individual member states may decide to phase out these policies. While previous research has shown that such policy changes affect investors' decisions, we investigate how they affect pathways and electricity prices by simulating investment decisions in an agent-based model in two case countries. This paper contributes and applies an adapted investment decision algorithm that incorporates empirically observed technology and return preferences and is calibrated by return observations. The new algorithm yields more refined and stronger effects compared to its predecessor. Results show that the phase-out of auctions in Germany and the Netherlands slows down their deployment of renewable capacity by up to ∼60% and ∼35%, respectively. With the exception of photovoltaics and onshore wind projects in the Netherlands, the targeted capacities can only be reached by continuing support in both countries. Furthermore, ending support in a large country like Germany leads to higher electricity prices and fosters a market-driven but insufficient capacity expansion in smaller neighbours like the Netherlands. As the electricity grids in many countries are strongly interconnected, such cross-border effects are of international relevance. Our findings suggest that continued auctions may be necessary and that countries should coordinate policy changes to stay on track for meeting their renewables targets. [ABSTRACT FROM AUTHOR]

The estimation of hydrological model parameters is a challenging task. With increasing capacity of computational power several complex optimization algorithms have emerged, but none of the algorithms gives a unique and very best parameter vector. The parameters of fitted hydrological models depend upon the input data. The quality of input data cannot be assured as there may be measurement errors for both input and state variables. In this study a methodology has been developed to find a set of robust parameter vectors for a hydrological model. To see the effect of observational error on parameters, stochastically generated synthetic measurement errors were applied to observed discharge and temperature data. With this modified data, the model was calibrated and the effect of measurement errors on parameters was analysed. It was found that the measurement errors have a significant effect on the best performing parameter vector. The erroneous data led to very different optimal parameter vectors. To overcome this problem and to find a set of robust parameter vectors, a geometrical approach based on Tukey's half space depth was used. The depth of the set of N randomly generated parameters was calculated with respect to the set with the best model performance (Nash-Sutclife efficiency was used for this study) for each parameter vector. Based on the depth of parameter vectors, one can find a set of robust parameter vectors. The results show that the parameters chosen according to the above criteria have low sensitivity and perform well when transferred to a different time period. The method is demonstrated on the upper Neckar catchment in Germany. The conceptual HBV model was used for this study. [ABSTRACT FROM AUTHOR]

Two-thirds of the final energy consumption of the EU residential sector goes towards space heating of buildings, yet a huge portion of the population still suffers from energy poverty. Identifying optimum heating strategies of current buildings would be a solution to this crisis, which is the main aim of the developed algorithm in this research. The algorithm incorporates a modified version of the simple hourly method from the ISO 13790 standard to determine the hourly heating load and indoor temperatures of buildings based on any heating strategy. Flexibility in the input of building and weather data make this tool versatile with practicality towards building users and policymakers. With this algorithm, a case study to evaluate three commonly used domestic heating strategies has been established for nine different residential buildings in typical cold winter conditions in Germany. Most EU households heat their buildings either continuously throughout the day at fixed temperatures, sporadically at fixed times or at peak loads during the evening. The continuous heating strategy is rated the best consuming minimal energy with consistent temperatures and optimal thermal comfort ranges. The sporadic and peak load heating strategies provide fluctuating indoor temperatures with high standard deviations of up to 8.70°C while consuming a similar cumulative energy to the continuous heating strategy. Additionally, both these aforementioned strategies augment energy poverty and promote indoor mould formation on the building envelope caused by water vapor condensation. Consequently, this algorithm can be applicable to any building type of any region. [ABSTRACT FROM AUTHOR]

Electrical anisotropy of formations has been long recognized by field and laboratory evidence. However, most interpretations of long-offset transient electromagnetic (LOTEM) data are based on the assumption of an electrical isotropic earth. Neglecting electrical anisotropy of formations may cause severe misleading interpretations in regions with strong electrical anisotropy. During a large scale LOTEM survey in a former mining area in Eastern Germany, data was acquired over black shale formations. These black shales are expected to produce a pronounced bulk anisotropy. Here, we investigate the effects of electrical anisotropy on LOTEM responses through numerical simulation using a finite-volume time-domain (FVTD) algorithm. On the basis of isotropic models obtained from LOTEM field data, various anisotropic models are developed and analysed. Numerical results demonstrate that the presence of electrical anisotropy has a significant influence on LOTEM responses. Based on the numerical modelling results, an isolated deep conductive anomaly presented in the 2-D isotropic LOTEM electric field data inversion result is identified as a possible artifact introduced by using an isotropic inversion scheme. Trial-and-error forward modelling of the LOTEM electric field data using an anisotropic conductivity model can explain the data and results in a reasonable quantitative data fit. The derived anisotropic 2-D model is consistent with the prior geological information. [ABSTRACT FROM AUTHOR]

The current media environment is primarily characterised by a large amount of information and, in contrast, rather fragmented audience attention. This is especially true for social media, particularly Facebook, which have become important news sources for many people. Journalists cannot help but publish content on Facebook if they want to reach the part of their audience that mainly--or even only--consumes news there. On Facebook, journalists are at the mercy of the algorithm that determines the visibility of their content. Because user engagement is a crucial factor in the algorithm, concerns have been raised that journalists are abandoning their normative quality standards to make the news as attractive as possible to the audience--at the expense of media performance. A softened presentation of the news, particularly in Facebook posts, may help achieve this aim, but research on this subject is lacking. The present study analyses this practice of softening the news in four German media outlets' (BILD, FAZ, Der Spiegel, Tagesschau) political Facebook posts. The results show that the overall level of news softening is low to medium. Furthermore, comparing them to website teasers reveals that news softening is only slightly higher on Facebook (mainly BILD and Der Spiegel), and that there are no converging trends between quality or public service media and tabloid media. Exaggerated fears about news softening are therefore unnecessary. Continued analysis of news softening, as well as ongoing adaption of the concept according to dynamic developments, is nevertheless important. [ABSTRACT FROM AUTHOR]

Background The article examines gender differences in happy life expectancy at age 50 (LE50) and computes the age-specific contributions of mortality and happiness effects to gender differences in happy LE50 in 16 European countries. Methods Abridged life tables and happy LE50 were calculated using conventional life tables and Sullivan's method. Age-specific death rates were calculated from deaths and population exposures in the Human Mortality Database. Happiness prevalence was estimated using the 2010–11 Survey of Health, Ageing and Retirement in Europe. Happiness was defined using a single question about life satisfaction on a scale of 0–10. A decomposition algorithm was applied to estimate the exact contributions of the differences in mortality and happiness to the overall gender gap in happy LE50. Results Gender differences in happy LE50 favour women in all countries except Portugal (0.43 years in Italy and 3.55 years in Slovenia). Generally, the contribution of the gender gap in happiness prevalence is smaller than the one in mortality. The male advantage in the prevalence of happiness partially offsets the effects of the female advantage in mortality on the total gender gap in happy LE50. Gender differences in unhappy life years make up the greatest share of the gender gap in total LE50 in all countries except Denmark, Germany, Netherlands, Slovenia and Sweden. Conclusion Countries with the largest gender gap in LE are not necessarily the countries with larger differences in happy LE50. The remaining years of life of women are expected to be spent not only in unhealthy but also in unhappy state. [ABSTRACT FROM AUTHOR]

Phenological metrics extracted from satellite data (phenometrics) have been increasingly used to access timely, spatially explicit information on crop phenology, but have rarely been calibrated and validated with field observations. In this study, we developed a calibration procedure to make phenometrics more comparable to ground-based phenological stages by optimising the settings of Best Index Slope Extraction (BISE) and smoothing algorithms together with thresholds. We used a six-year daily Moderate Resolution Imaging Spectrometer (MODIS) Normalized Difference Vegetation Index (NDVI) time series and 211 ground-observation records from four major crop species (winter wheat/barley, oilseed rape, and sugar beet) in central Germany. Results showed the superiority of the Savitzky–Golay algorithm in combination with BISE. The satellite-derived senescence dates matched ripeness stages of winter crops and the dates with maximum NDVI were closely related to the field-observed heading stage of winter cereals. We showed that the emergence of winter crops corresponded to the dates extracted with a threshold of 0.1, which translated into 8.89 days of root-mean-square error (RMSE) improvement compared to the standard threshold of 0.5. The method with optimised settings and thresholds can be easily transferred and applied to areas with similar growing conditions. Altogether, the results improve our understanding of how satellite-derived phenometrics can explain in situ phenological observations. [ABSTRACT FROM AUTHOR]

This prospective observational study evaluates the validity of an algorithm for assigning patients to a multidisciplinary modularized managed care headache treatment program. N = 545 chronic headache sufferers [migraine (53.8 %), migraine + tension type (30.1 %), tension type (8.3 %) or medication overuse headache (6.2 %), other primary headaches (1.5 %)] were assigned to one of four treatment modules differing with regard to the number and types of interventions entailed (e.g., medication, psychological intervention, physical therapy, etc.). A rather simple assignment algorithm based on headache frequency, medication use and psychiatric comorbidity was used. Patients in the different modules were compared with regard to the experienced burden of disease. 1-year follow-up outcome data are reported ( N = 160). Headache frequency and analgesic consumption differed significantly among patients in the modules. Headache-related disability was highest in patients with high headache frequency with/without medication overuse or psychiatric comorbidity (modules 2/3) compared to patients with low headache frequency and medication (module 0). Physical functioning was lowest in patients with chronic headache regardless of additional problems (modules 1/2/3). Psychological functioning was lowest in patients with severe chronicity with/without additional problems (module 2/3) compared to headache suffers with no/moderate chronicity (module 0/1). Anxiety or depression was highest in patients with severe chronicity. In 1-year follow-up, headache frequency (minus 45.3 %), consumption of attack-aborting drugs (minus 71.4 %) and headache-related disability decreased (minus 35.9 %). Our results demonstrate the clinical effectiveness and the criterion validity of the treatment assignment algorithm based on headache frequency, medication use and psychiatric comorbidity. [ABSTRACT FROM AUTHOR]

Existing simple but theoretically based clear-sky models for longwave down-welling radiation (LDR) and cloud impact algorithms transforming them to all-sky radiation (LDR) are checked against locally calibrated empirical algorithms. They are evaluated for daylight hours based on measurements in regionally differing climates of Germany. The Prata clear-sky scheme is additionally tested with adjusted coefficients so that LDR converges against a realistic emissivity for a completely dry atmosphere. This version is characterised by an improved modelled variance. Compared with locally calibrated schemes, bias and root mean square error (RMSE) of the more theoretical clear-sky schemes do not differ significantly and yield even better results at a mountain site. In contrast, the locally calibrated algorithms yield biases up to 9% and an increase in RMSE between 6% and 67%, if applied for other sites. For daylight hours, the cloud impact on LDR can be calculated via the ratio of observed to clear-sky global irradiation (CMF). With CMF, the Crawford and Duchon scheme reveals the lowest bias and a decrease in RMSE by 22% against the next best performing algorithms. Compared with synoptic cloud observations as input, the bias is reduced by 9 to 28 W m and the scattering of the residuals decreases by 20% to 30%. Based on published results for also non-European sites, it is inferred that the more theoretically based LDR schemes and cloud impact evaluated via CMF are universally applicable and perform at least in the order of magnitude of locally calibrated empirical algorithms. [ABSTRACT FROM AUTHOR]

We discuss the clustering of 234 environmental samples resulting from an extensive monitoring program concerning soil lead content, plant lead content, traffic density, and distance from the road at different sampling locations in former East Germany. Considering the structure of data and the unsatisfactory results obtained applying classical clustering and principal component analysis, it appeared evident that fuzzy clustering could be one of the best solutions. In the following order we used different fuzzy clustering algorithms, namely, the fuzzy c-means (FCM) algorithm, the Gustafson–Kessel (GK) algorithm, which may detect clusters of ellipsoidal shapes in data by introducing an adaptive distance norm for each cluster, and the fuzzy c-varieties (FCV) algorithm, which was developed for recognition of r-dimensional linear varieties in high-dimensional data (lines, planes or hyperplanes). Fuzzy clustering with convex combination of point prototypes and different multidimensional linear prototypes is also discussed and applied for the first time in analytical chemistry (environmetrics). The results obtained in this study show the advantages of the FCV and GK algorithms over the FCM algorithm. The performance of each algorithm is illustrated by graphs and evaluated by the values of some conventional cluster validity indices. The values of the validity indices are in very good agreement with the quality of the clustering results. [Figure not available: see fulltext.] [ABSTRACT FROM AUTHOR]

Multi-objective optimization algorithms are widely used for the calibration of conceptual hydrological models. Such algorithms yield a set of Pareto-optimal solutions, reflecting the model structure uncertainty. In this study, a multi-objective optimization strategy is suggested, which aims at reducing the model structure uncertainty by considering parameter interaction within Pareto-optimal solutions. The approach has been used to develop a nested setup of a rainfall-runoff model, which is integrated in a probabilistic meso-/macroscale flood forecasting system. The optimization strategy aided in determining the best combination of a lumped (computationally efficient in operational real time forecasting) and a semi-distributed parameterization of the hydrological model. First results are shown for two sub-basins of the Mulde catchment in Germany. The different phenomena of parameter interaction were analysed in this case study to reduce the model structure uncertainties. [ABSTRACT FROM AUTHOR]

Even in physically based distributed hydrological models, various remaining parameters must be estimated for each sub-catchment. This can involve tremendous effort, especially when the number of sub-catchments is large and the applied hydrological model is computationally expensive. Automatic parameter estimation tools can significantly facilitate the calibration process. Hence, we combined the nonlinear parameter estimation tool PEST with the distributed hydrological model WaSiM. PEST is based on the Gauss-Marquardt-Levenberg method, a gradient-based nonlinear parameter estimation algorithm. WaSiM is a fully distributed hydrological model using physically based algorithms for most of the process descriptions. WaSiM was applied to the alpine/prealpine Ammer River catchment (southern Germany, 710 km²) in a 100x100m² horizontal resolution. The catchment is heterogeneous in terms of geology, pedology and land use and shows a complex orography (the difference of elevation is around 1600 m). Using the developed PEST-WaSiM interface, the hydrological model was calibrated by comparing simulated and observed runoff at eight gauges for the hydrologic year 1997 and validated for the hydrologic year 1993. For each sub-catchment four parameters had to be calibrated: the recession constants of direct runoff and interflow, the drainage density, and the hydraulic conductivity of the uppermost aquifer. Additionally, five snowmelt specific parameters were adjusted for the entire catchment. Altogether, 37 parameters had to be calibrated. Additional a priori information (e.g. from flood hydrograph analysis) narrowed the parameter space of the solutions and improved the non-uniqueness of the fitted values. A reasonable quality of fit was achieved. Discrepancies between modelled and observed runoff were also due to the small number of meteorological stations and corresponding interpolation artefacts in the orographically complex terrain. Application of a 2-dimensional numerical groundwater model partly yielded a slight decrease of overall model performance when compared to a simple conceptual groundwater approach. Increased model complexity therefore did not yield in general increased model performance. A detailed covariance analysis was performed allowing to derive confidence bounds for all estimated parameters. The correlation between the estimated parameters was in most cases negligible, showing that parameters were estimated independently from each other. [ABSTRACT FROM AUTHOR]

Image segmentation is a cost-effective way to obtain information about the sizes and structural composition of agricultural parcels in an area. To accurately obtain such information, the parameters of the segmentation algorithm ought to be optimized using supervised or unsupervised methods. The difficulty in obtaining reference data makes unsupervised methods indispensable. In this study, we evaluated an existing unsupervised evaluation metric that minimizes a global score (GS), which is computed by summing up the intra-segment uniformity and inter-segment dissimilarity within a segmentation output. We modified this metric and proposed a new metric that uses absolute difference to compute the GS. We compared this proposed metric with the existing metric in two optimization approaches based on the Multiresolution Segmentation (MRS) algorithm to optimally delineate agricultural parcels from Sentinel-2 images in Lower Saxony, Germany. The first approach searches for optimal scale while keeping shape and compactness constant, while the second approach uses Bayesian optimization to optimize the three main parameters of the MRS algorithm. Based on a reference data of agricultural parcels, the optimal segmentation result of each optimization approach was evaluated by calculating the quality rate, over-segmentation, and under-segmentation. For both approaches, our proposed metric outperformed the existing metric in different agricultural landscapes. The proposed metric identified optimal segmentations that were less under-segmented compared to the existing metric. A comparison of the optimal segmentation results obtained in this study to existing benchmark results generated via supervised optimization showed that the unsupervised Bayesian optimization approach based on our proposed metric can potentially be used as an alternative to supervised optimization, particularly in geographic regions where reference data is unavailable or an automated evaluation system is sought. [ABSTRACT FROM AUTHOR]