Item-Focused Trees for the Detection of Differential Item Functioning in Partial Credit Models

Various methods to detect differential item functioning (DIF) in item response models are available. However, most of these methods assume that the responses are binary, and so for ordered response categories available methods are scarce. In the present article, DIF in the widely used partial credit model is investigated. An item-focused tree is proposed that allows the detection of DIF items, which might affect the performance of the partial credit model. The method uses tree methodology, yielding a tree for each item that is detected as DIF item. The visualization as trees makes the results easily accessible, as the obtained trees show which variables induce DIF and in which way. In the present paper, the new method is compared with alternative approaches and simulations demonstrate the performance of the method

What can the Real World do for simulation studies? A comparison of exploratory methods

For simulation studies on the exploratory factor analysis (EFA), usually rather simple population models are used without model errors. In the present study, real data characteristics are used for Monte Carlo simulation studies. Real large data sets are examined and the results of EFA on them are taken as the population models. First we apply a resampling technique on these data sets with sub samples of different sizes. Then, a Monte Carlo study is conducted based on the parameters of the population model and with some variations of them. Two data sets are analyzed as an illustration. Results suggest that outcomes of simulation studies are always highly influenced by particular specification of the model and its violations. Once small residual correlations appeared in the data for example, the ranking of our methods changed completely. The analysis of real data set characteristics is therefore important to understand the performance of different methods

Evaluation of a new k-means approach for exploratory clustering of items

Evaluation of a new k-means approach for exploratory clustering of item

Challenges of Industrial-Scale Testing Infrastructure for Green Hydrogen Technologies

Green hydrogen is set to become the energy carrier of the future, provided that production technologies such as electrolysis and solar water splitting can be scaled to global dimensions. Testing these hydrogen technologies on the MW scale requires the development of dedicated new test facilities for which there is no precedent. This perspective highlights the challenges to be met on the path to implementing a test facility for large-scale water electrolysis, photoelectrochemical and photocatalytic water splitting and aims to serve as a much-needed blueprint for future test facilities based on the authors’ own experience in establishing the Hydrogen Lab Leuna. Key aspects to be considered are the electricity and utility requirements of the devices under testing, the analysis of the produced H2 and O2 and the safety regulations for handling large quantities of H2. Choosing the right location is crucial not only for meeting these device requirements, but also for improving financial viability through supplying affordable electricity and providing a remunerated H2 sink to offset the testing costs. Due to their lower TRL and requirement for a light source, large-scale photocatalysis and photoelectrochemistry testing are less developed and the requirements are currently less predictable

Boundary learning by optimization with topological constraints

Recent studies have shown that machine learning can improve the accuracy of detecting object boundaries in images. In the standard approach, a boundary detector is trained by minimizing its pixel-level disagreement with human boundary tracings. This naive metric is problematic because it is overly sensitive to boundary locations. This problem is solved by metrics provided with the Berkeley Segmentation Dataset, but these can be insensitive to topological differences, such as gaps in boundaries. Furthermore, the Berkeley metrics have not been useful as cost functions for supervised learning. Using concepts from digital topology, we propose a new metric called the warping error that tolerates disagreements over boundary location, penalizes topological disagreements, and can be used directly as a cost function for learning boundary detection, in a method that we call Boundary Learning by Optimization with Topological Constraints (BLOTC). We trained boundary detectors on electron microscopic images of neurons, using both BLOTC and standard training. BLOTC produced substantially better performance on a 1.2 million pixel test set, as measured by both the warping error and the Rand index evaluated on segmentations generated from the boundary labelings. We also find our approach yields significantly better segmentation performance than either gPb-OWT-UCM or multiscale normalized cut, as well as Boosted Edge Learning trained directly on our data

Alpine glacial relict species losing out to climate change: The case of the fragmented mountain hare population (Lepus timidus) in the Alps

Alpine and Arctic species are considered to be particularly vulnerable to climate change, which is expected to cause habitat loss, fragmentation and—ultimately—ex- tinction of cold-adapted species. However, the impact of climate change on glacial relict populations is not well understood, and specific recommendations for adaptive conservation management are lacking. We focused on the mountain hare (Lepus timidus) as a model species and modelled species distribution in combination with patch and landscape-based connectivity metrics. They were derived from graph-the- ory models to quantify changes in species distribution and to estimate the current and future importance of habitat patches for overall population connectivity. Models were calibrated based on 1,046 locations of species presence distributed across three biogeographic regions in the Swiss Alps and extrapolated according to two IPCC scenarios of climate change (RCP 4.5 & 8.5), each represented by three down- scaled global climate models. The models predicted an average habitat loss of 35% (22%–55%) by 2100, mainly due to an increase in temperature during the reproduc- tive season. An increase in habitat fragmentation was reflected in a 43% decrease in patch size, a 17% increase in the number of habitat patches and a 34% increase in inter-patch distance. However, the predicted changes in habitat availability and connectivity varied considerably between biogeographic regions: Whereas the great- est habitat losses with an increase in inter-patch distance were predicted at the southern and northern edges of the species’ Alpine distribution, the greatest increase in patch number and decrease in patch size is expected in the central Swiss Alps. Finally, both the number of isolated habitat patches and the number of patches crucial for maintaining the habitat network increased under the different variants of climate change. Focusing conservation action on the central Swiss Alps may help mitigate the predicted effects of climate change on population connectivity