Self-archiving Research Articles

Batch-to-Batch Variation in Laser-Inscribed Graphene (LIG) Electrodes for Electrochemical Sensing.

Laser-inscribed graphene (LIG) is an emerging material for micro-electronic applications and is being used to develop supercapacitors, soft actuators, triboelectric generators, and sensors. The fabrication technique is simple, yet the batch-to-batch variation of LIG quality is not well documented in the literature. In this study, we conduct experiments to characterize batch-to-batch variation in the manufacturing of LIG electrodes for applications in electrochemical sensing. Numerous batches of 36 LIG electrodes were synthesized using a CO2 laser system on polyimide film. The LIG material was characterized using goniometry, stereomicroscopy, open circuit potentiometry, and cyclic voltammetry. Hydrophobicity and electrochemical screening (cyclic voltammetry) indicate that LIG electrode batch-to-batch variation is less than 5% when using a commercial reference and counter electrode. Metallization of LIG led to a significant increase in peak current and specific capacitance (area between anodic/cathodic curve). However, batch-to-batch variation increased to approximately 30%. Two different platinum electrodeposition techniques were studied, including galvanostatic and frequency-modulated electrodeposition. The study shows that formation of metallized LIG electrodes with high specific capacitance and peak current may come at the expense of high batch variability. This design tradeoff has not been discussed in the literature and is an important consideration if scaling sensor designs for mass use is desired. This study provides important insight into the variation of LIG material properties for scalable development of LIG sensors. Additional studies are needed to understand the underlying mechanism(s) of this variability so that strategies to improve the repeatability may be developed for improving quality control. The dataset from this study is available via an open access repository.

Adolescent health and well-being check-up programme in three African cities (Y-Check): protocol for a multimethod, prospective, hybrid implementation-effectiveness study

BackgroundDuring adolescence, behaviours are initiated that will have substantial impacts on the individual’s short-term and long-term health and well-being. However, adolescents rarely have regular contact with health services, and available...

Genomic incongruence accompanies the evolution of flower symmetry in Eudicots: a case study in the poppy family (Papaveraceae, Ranunculales).

Reconstructing evolutionary trajectories and transitions that have shaped floral diversity relies heavily on the phylogenetic framework on which traits are modelled. In this study, we focus on the angiosperm order Ranunculales, sister to all other eudicots, to unravel higher-level relationships, especially those tied to evolutionary transitions in flower symmetry within the family Papaveraceae. This family presents an astonishing array of floral diversity, with actinomorphic, disymmetric (two perpendicular symmetry axes), and zygomorphic flowers. We generated nuclear and plastid datasets using the Angiosperms353 universal probe set for target capture sequencing (of 353 single-copy nuclear ortholog genes), together with publicly available transcriptome and plastome data mined from open-access online repositories. We relied on the fossil record of the order Ranunculales to date our phylogenies and to establish a timeline of events. Our phylogenomic workflow shows that nuclear-plastid incongruence accompanies topological uncertainties in Ranunculales. A cocktail of incomplete lineage sorting, post-hybridization introgression, and extinction following rapid speciation most likely explain the observed knots in the topology. These knots coincide with major floral symmetry transitions and thus obscure the order of evolutionary events.

An AS-OCT image dataset for deep learning-enabled segmentation and 3D reconstruction for keratitis

Infectious keratitis is among the major causes of global blindness. Anterior segment optical coherence tomography (AS-OCT) images allow the characterizing of cross-sectional structures in the cornea with keratitis thus revealing the severity of inflammation, and can also provide 360-degree information on anterior chambers. The development of image analysis methods for such cases, particularly deep learning methods, requires a large number of annotated images, but to date, there is no such open-access AS-OCT image repository. For this reason, this work provides a dataset containing a total of 1168 AS-OCT images of patients with keratitis, including 768 full-frame images (6 patients). Each image has associated segmentation labels for lesions and cornea, and also labels of iris for full-frame images. This study provides a great opportunity to advance the field of image analysis on AS-OCT images in both two-dimensional (2D) and three-dimensional (3D) and would aid in the development of artificial intelligence-based keratitis management.

Discrete Morse theory segmentation on high-resolution 3D lithic artifacts

Abstract Motivated by the question of understanding the roots of tool making by anatomically modern humans and coexisting Neanderthals in the Paleolithic, a number of shape classification methods have been tested on photographs and drawings of stone tools. Since drawings contain interpretation and photographs fool both human and computational methods by color and shadows on the surface, we propose an approach using 3D datasets as best means for analyzing shape, and rely on first open access repositories on lithic tools. The goal is to not only analyze shape on an artifact level, but allow a more detailed analysis of stone tools on a scar and ridge level. A Morse-Smale complex (MS complex) extracted from the triangular mesh of a 3D model is a reduced skeleton consisting of linked lines on the mesh. Discrete Morse theory makes it possible to obtain such a MS complex from a scalar function. Thus, we begin with Multi-Scale Integral Invariant filtering on the meshes of lithic artifacts, which provides curvature measures for ridges, which are convex, and scars, which are concave. The resulting values on the vertices can be used as our discrete Morse function and the skeleton we get is build up from lines that will coincide with the ridges and, implicitly, contains the scars as enclosed regions of those lines on the mesh. As this requires a few parameters, we provide a graphical user interface (GUI) to allow altering the predefined parameters to quickly find a good result. In addition, a stone tool may have areas that do not belong to the scar/ridge class. These can be masked and we use conforming MS complexes to ensure that the skeleton keeps these areas whole. Finally, results are shown on real and open access datasets. The source code and manually annotated ground truth for the evaluation are provided as Open Access with a Creative Commons license.

Multiyear surface wave dataset from the subsurface “DeepLev” eastern Levantine moored station

Abstract. Processed and analyzed sea surface wave characteristics derived from an up-looking acoustic Doppler current profiler (ADCP) for the period 2016–2022 are presented as a dataset available from the public open-access repository of SEA scieNtific Open data Edition (SEANOE) at https://doi.org/10.17882/96904 (Haim et al., 2022). The collected data include full two-dimensional wave fields, along with computed bulk parameters, such as wave heights, periods, and directions of propagation. The ADCP was mounted on the submerged Deep Levantine (DeepLev) mooring station located 50 km off the Israeli coast to the west of Haifa (bottom depth ∼1470 m). It meets the need for accurate and reliable in situ measurements in the eastern Mediterranean Sea as the area significantly lacks wave data compared to other Mediterranean sub-basins. The developed long-term time series of wave parameters contribute to the monitoring and analysis of the region's wave climate and the quality of wind–wave forecasting models.

Moving beyond the Content: 3D Scanning and Post-Processing Analysis of the Cuneiform Tablets of the Turin Collection

This work and manuscript focus on how 3D scanning methodologies and post-processing analyses may help us to gain a deeper investigation of cuneiform tablets beyond the written content. The dataset proposed herein is a key part of the archaeological collection preserved in the Musei Reali of Turin in Italy; these archaeological artefacts enclose further important semantic information extractable through detailed 3D documentation and 3D model filtering. In fact, this scanning process is a fundamental tool for better reading of sealing impressions beneath the cuneiform text, as well as for understanding micrometric evidence of the fingerprints of scribes. Most of the seal impressions were made before the writing (like a watermark), and thus, they are not detectable to the naked eye due to cuneiform signs above them as well as the state of preservation. In this regard, 3D scanning and post-processing analysis could help in the analysis of these nearly invisible features impressed on tablets. For this reason, this work is also based on how 3D analyses may support the identification of the unperceived and almost invisible features concealed in clay tablets. Analysis of fingerprints and the depths of the signs can tell us about the worker’s strategies and the people beyond the artefacts. Three-dimensional models generated inside the Artec 3D ecosystem via Space Spider scanner and Artec Studio software were further investigated by applying specific filters and shaders. Digital light manipulation can reveal, through the dynamic displacement of light and shadows, particular details that can be deeply analysed with specific post-processing operations: for example, the MSII (multi-scale integral invariant) filter is a powerful tool exploited for revealing hidden and unperceived features such as fingerprints and sealing impressions (stratigraphically below cuneiform signs). Finally, the collected data will be handled twofold: in an open-access repository and through a common data environment (CDE) to aid in the data exchange process for project collaborators and common users.

Repository of MRI-derived models of the breast with single and multiple benign and malignant tumors for microwave imaging research.

The diagnosis of breast cancer through MicroWave Imaging (MWI) technology has been extensively researched over the past few decades. However, continuous improvements to systems are needed to achieve clinical viability. To this end, the numerical models employed in simulation studies need to be diversified, anatomically accurate, and also representative of the cases in clinical settings. Hence, we have created the first open-access repository of 3D anatomically accurate numerical models of the breast, derived from 3.0T Magnetic Resonance Images (MRI) of benign breast disease and breast cancer patients. The models include normal breast tissues (fat, fibroglandular, skin, and muscle tissues), and benign and cancerous breast tumors. The repository contains easily reconfigurable models which can be tumor-free or contain single or multiple tumors, allowing complex and realistic test scenarios needed for feasibility and performance assessment of MWI devices prior to experimental and clinical testing. It also includes an executable file which enables researchers to generate models incorporating the dielectric properties of breast tissues at a chosen frequency ranging from 3 to 10 GHz, thereby ensuring compatibility with a wide spectrum of research requirements and stages of development for any breast MWI prototype system. Currently, our dataset comprises MRI scans of 55 patients, but new exams will be continuously added.

Development of a Machine Learning (ML)-Based Computational Model to Estimate the Engineering Properties of Portland Cement Concrete (PCC)

Portland cement concrete (PCC) is the construction material most used worldwide. Hence, its proper characterization is fundamental for the daily-basis engineering practice. Nonetheless, the experimental measurements of the PCC’s engineering properties (i.e., Poisson’s Ratio -v-, Elastic Modulus -E-, Compressive Strength -ComS-, and Tensile Strength -TenS-) consume considerable amounts of time and financial resources. Therefore, the development of high-precision indirect methods is fundamental. Accordingly, this research proposes a computational model based on deep neural networks (DNNs) to simultaneously predict the v, E, ComS, and TenS. For this purpose, the Long-Term Pavement Performance database was employed as the data source. In this regard, the mix design parameters of the PCC are adopted as input variables. The performance of the DNN model was evaluated with 1:1 lines, goodness-of-fit parameters, Shapley additive explanations assessments, and running time analysis. The results demonstrated that the proposed DNN model exhibited an exactitude higher than 99.8%, with forecasting errors close to zero (0). Consequently, the machine learning-based computational model designed in this investigation is a helpful tool for estimating the PCC’s engineering properties when laboratory tests are not attainable. Thus, the main novelty of this study is creating a robust model to determine the v, E, ComS, and TenS by solely considering the mix design parameters. Likewise, the central contribution to the state-of-the-art achieved by the present research effort is the public launch of the developed computational tool through an open-access GitHub repository, which can be utilized by engineers, designers, agencies, and other stakeholders.

Digital stewardship for river stewardship: creating the Richmond River open access repository

ABSTRACT The catchment of the Richmond River in Lismore, Australia, presents a challenge for record collection and preservation. Firstly, material records belonging to the community are periodically destroyed in flooding. The places where these are physically located as historical archives are often situated in the floodplain. Secondly, relevant records are dispersed across multiple government levels and organizations, posing further threats to their preservation and access. This paper seeks to explore those challenges and describe the development of a river repository at Southern Cross University, funded in part through a grant for flood recovery projects. The repository and our research are motivated by the challenges and considerations surrounding the preservation and management of cultural and scientific materials in the current era, characterized by human impact on the environment and climate change and the integration the Anthropocene heralds between the human and non-human world. Digital stewardship encompasses caring for and managing records and data, goals we extend to the Richmond River and its health.

Artificial intelligence in higher education database (AIHE V1): Introducing an open-access repository

Artificial intelligence in higher education database (AIHE V1): Introducing an open-access repository

MelastomaTRAITs 1.0: A database of functional traits in Melastomataceae, a large pantropical angiosperm family.

The recent availability of open-access repositories of functional traits has revolutionized trait-based approaches in ecology and evolution. Nevertheless, the underrepresentation of tropical regions and lineages remains a pervasive bias in plant functional trait databases, which constrains large-scale assessments of plant ecology, evolution, and biogeography. Here, we present MelastomaTRAITs 1.0, a comprehensive and updatable database of functional traits for the pantropical Melastomataceae, the ninth-largest angiosperm family with 177 genera and more than 5800 species. Melastomataceae encompass species with a wide diversity of growth forms (herbs, shrubs, trees, epiphytes, and woody climbers), habitats (including tropical forests, savannas, grasslands, and wetlands from sea level to montane areas above the treeline), ecological strategies (from pioneer, edge-adapted and invasive species to shade-tolerant understory species), geographic distribution (from microendemic to continental-wide distribution), reproductive, pollination, and seed dispersal systems. MelastomaTRAITs builds on 581 references, such as taxonomic monographs, ecological research, and unpublished data, and includes four whole-plant traits, six leaf traits, 11 flower traits, 18 fruit traits, and 27 seed traits for 2520 species distributed in 144 genera across all 21 tribes. Most data come from the Neotropics where the family is most species-rich. Miconieae (the largest tribe) contains the highest number of trait records (49.6%) and species (41.1%) records. The trait types with the most information in the database were whole-plant traits, flowers, and leaf traits. With the breadth of functional traits recorded, our database helps to fill a gap in information for tropical plants and will significantly improve our capacity for large-scale trait-based syntheses across levels of organization, plant-animal interactions, regeneration ecology, and thereby support conservation and restoration programs. There are no copyright restrictions on the dataset; please cite this data paper when reusing the data.

Global status of dataset repositories at a glance: study based on OpenDOAR

PurposeThe study aims to shed light on the current state of “Dataset repositories” indexed in Directory of Open Access Repositories (OpenDOAR).Design/methodology/approachFrom each repository/record information, the Open-Access Policies, Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH), year of creation and the number of data sets archived in the repositories were manually searched, documented and analyzed.FindingsDeveloped countries like the United Kingdom and the USA are primarily involved in the development of institutional open-access repositories comprising significant components of OpenDOAR. The most extensively used software is DSpace. Most data set archives are OAI-PMH compliant but do not follow open-access rules. The study also highlights the sites’ embrace of Web 2.0 capabilities and discovers really simple syndication feeds and Atom integration. The use of social media has made its presence known. Furthermore, the study concludes that the number of data sets kept in repositories is insufficient, although the expansion of such repositories has been consistent over the years.Practical implicationsThe work has the potential to benefit both researchers in general and policymakers in particular. Scholars interested in research data, data sharing and data reuse can learn about the present state of repositories that preserve data sets in OpenDOAR. At the same time, policymakers can develop recommendations and policies to assist in the construction and maintenance of repositories for data sets.Originality/valueAccording to the literature, there have been numerous studies on open-access repositories and OpenDOAR internationally, but no research has focused on repositories preserving content-type data sets. As a result, the study attempts to uncover various characteristics of OpenDOAR Data set repositories.

Open Access, Scholarly Communication, and Open Science in Psychology: An Overview for Researchers

Scholarly communication, Open Access (OA), and open science practices in Psychology are rapidly evolving. However, most published works that focus on scholarly communication issues do not target the specific discipline, and instead take a more “one size fits all” approach. When it comes to scholarly communication, research practices and traditions vary greatly across and within disciplines. This monograph presents a current overview that aims to cover Open Access (OA) and some of the newer open science-related issues that are affecting Psychology. Issues covered include topics around OA of all types, as well as other important scholarly communication-related issues such as the emergence of preprint options, the evolution of new peer review models, citation metrics, persistent identifiers, coauthorship conventions, field-specific OA megajournals, and other “gold” OA psychology journal options, the challenges of interdisciplinarity, and how authors are availing themselves of green and gold OA strategies or using scholarly networking sites such as ResearchGate. Included are discussions of open science strategies in Psychology such as reproducibility, replication, and research data management. This overview will allow psychology researchers to get up to speed on these expansive topics. Further study into researcher behavior in terms of scholarly communication in Psychology would create more understanding of existing culture as well as provide researchers with a more effective roadmap to the current landscape. As no other single work is known to provide a current look at scholarly communication topics that is specifically focused on Psychology, this targeted overview aims to partially fill that niche.

A comparative study on characteristics of retracted publications across different open access levels

Abstract Purpose Recently, global science has shown an increasing open trend, however, the characteristics of research integrity of open access (OA) publications have rarely been studied. The aim of this study is to compare the characteristics of retracted articles across different OA levels and discover whether OA level influences the characteristics of retracted articles. Design/methodology/approach The research conducted an analysis of 6,005 retracted publications between 2001 and 2020 from the Web of Science and Retraction Watch databases. These publications were categorized based on their OA levels, including Gold OA, Green OA, and non-OA. The study explored retraction rates, time lags and reasons within these categories. Findings The findings of this research revealed distinct patterns in retraction rates among different OA levels. Publications with Gold OA demonstrated the highest retraction rate, followed by Green OA and non-OA. A comparison of retraction reasons between Gold OA and non-OA categories indicated similar proportions, while Green OA exhibited a higher proportion due to falsification and manipulation issues, along with a lower occurrence of plagiarism and authorship issues. The retraction time lag was shortest for Gold OA, followed by non-OA, and longest for Green OA. The prolonged retraction time for Green OA could be attributed to an atypical distribution of retraction reasons. Research limitations There is no exploration of a wider range of OA levels, such as Hybrid OA and Bronze OA. Practical implications The outcomes of this study suggest the need for increased attention to research integrity within the OA publications. The occurrences of falsification, manipulation, and ethical concerns within Green OA publications warrant attention from the scientific community. Originality/value This study contributes to the understanding of research integrity in the realm of OA publications, shedding light on retraction patterns and reasons across different OA levels.

Assessing the incorporation of the soil health concept in postsecondary education in Canada

Incorporation of soil health concept into professional practice, supported by integration into postsecondary programs, may lead to the successful maintenance and improvement of Canadian soils. The Canadian Society of Soil Science (CSSS) has identified the need to promote the teaching of the soil health concept to instructors of soil science and related courses across Canada. In summer 2023, a 37-question survey was emailed to CSSS members with the objectives of (1) gauging the understanding and interest among instructors of soil science (and related) courses across Canada in soil health, (2) describing the current status of the soil health concept in Canadian postsecondary soil science (and related) courses, and (3) assessing the need for an open access repository of educational resources focused on soil health. Survey results of 46 respondents suggest that inclusion of the concept in courses has been increasing steadily for the last 20 years, with estimated 1400 students being exposed to the concept annually in 30 courses in 6 majors, where 60% had soil in the course title. The main reasons for not including soil health in courses included lack of time and experience. Creation of a Canadian soil health repository of teaching materials would provide resources to 28% respondents (13/46) who do not include the soil health concept into their courses. Respondents indicated that the soil health concept is useful in teaching because it requires integration of a range of basic soil disciplinary concepts, a process that might be best implemented through active learning strategies.

Compositional Simulation for Carbon Storage in Porous Media Using an Electrolyte Association Equation of State

Summary We present a new algorithm based on automatic differentiation that enables precise computation of the derivatives of the Z-factor, facilitating the utilization of Newton’s method or coupling with a robust flow solver. Leveraging a free open-source code [MATLAB Reservoir Simulation Toolbox (MRST)], we develop an electrolyte cubic plus association (e-CPA) equation of state (EoS) model to accurately represent the injection of carbon dioxide (CO2) in brine. By integrating flow and thermodynamics, we construct an advanced compositional simulator using MRST’s object-oriented, automatic differentiation framework and the newly developed e-CPA EoS model. This simulator offers flexibility through both overall-composition and natural-variable formulations, achieved by selecting different primary variables. The Péneloux volume translation technique is employed to modify the EoS model’s volume, ensuring accurate density calculation for the mixture. Additionally, we introduce a viscosity model, e-CPA-FV, which accurately predicts the viscosity of carbon capture and storage (CCS) fluids, surpassing the accuracy of the traditional Lohrenz-Bray-Clark (LBC) model. Our simulator demonstrates superior performance in predicting CO2-brine systems compared with the standard formulation based on the Peng-Robinson (PR) EoS and can handle brine with various salts. The self-contained source code necessary to reproduce all examples is available on the open-access Zenodo digital repository (doi: 10.5281/zenodo.10691505).

Diamond is a scientist’s best friend

The field of applied linguistics is increasingly adopting open science practices. As open access publication gains traction, ethical issues emerge that need to be addressed by the field. This viewpoint paper addresses the concern that open science is not equally open for everyone. This paper describes how open access publication is increasingly being commercialized and explains how open access publication coincides with systemic inequality. We offer the following viewpoints for the field to consider: We are morally obligated to make our research output accessible. Hybrid, Gold, and Green open access publishing lead to systemic inequality in open access publishing, benefiting commercial publishers and those working in research-intensive universities and rich countries. Diamond open access publication removes the systemic inequalities; hence, Diamond open access should be prioritized over Hybrid, Gold, and Green open access publication models. We should move away from publish-and-read agreements and Green open access publishing, because they prevent system change. Through our choices in our work as researchers, editors, reviewers, authors and teachers, we can contribute to the transition towards truly equitable open access publishing practices. Senior researchers are in the position and have the moral obligation to be drivers of these changes.

CT and MRI radiomics of bone and soft-tissue sarcomas: an updated systematic review of reproducibility and validation strategies.

To systematically review radiomic feature reproducibility and model validation strategies in recent studies dealing with CT and MRI radiomics of bone and soft-tissue sarcomas, thus updating a previous version of this review which included studies published up to 2020. A literature search was conducted on EMBASE and PubMed databases for papers published between January 2021 and March 2023. Data regarding radiomic feature reproducibility and model validation strategies were extracted and analyzed. Out of 201 identified papers, 55 were included. They dealt with radiomics of bone (n= 23) or soft-tissue (n = 32) tumors. Thirty-two (out of 54 employing manual or semiautomatic segmentation, 59%) studies included a feature reproducibility analysis. Reproducibility was assessed based on intra/interobserver segmentation variability in 30 (55%) and geometrical transformations of the region of interest in 2 (4%) studies. At least one machine learning validation technique was used for model development in 34 (62%) papers, and K-fold cross-validation was employed most frequently. A clinical validation of the model was reported in 38 (69%) papers. It was performed using a separate dataset from the primary institution (internal test) in 22 (40%), an independent dataset from another institution (external test) in 14 (25%) and both in 2 (4%) studies. Compared to papers published up to 2020, a clear improvement was noted with almost double publications reporting methodological aspects related to reproducibility and validation. Larger multicenter investigations including external clinical validation and the publication of databases in open-access repositories could further improve methodology and bring radiomics from a research area to the clinical stage. An improvement in feature reproducibility and model validation strategies has been shown in this updated systematic review on radiomics of bone and soft-tissue sarcomas, highlighting efforts to enhance methodology and bring radiomics from a research area to the clinical stage. • 2021-2023 radiomic studies on CT and MRI of musculoskeletal sarcomas were reviewed. • Feature reproducibility was assessed in more than half (59%) of the studies. • Model clinical validation was performed in 69% of the studies. • Internal (44%) and/or external (29%) test datasets were employed for clinical validation.

Cognitive, behavioral, and affective mechanisms underlying the efficacy of Applied Relaxation in reducing psychopathological symptoms: A randomized controlled trial

BackgroundThis study examined the cognitive, behavioral, and affective mechanisms underlying the efficacy of Applied Relaxation (AR) in reducing psychopathological symptoms. AR is a cognitive-behavioral technique that allows for rapid relaxation at the first sign of stress or tension in daily life. MethodsA randomized controlled trial was conducted in 277 adults (18–55 years) with elevated symptoms of depression, anxiety, or stress but without a 12-month DSM-5 mental disorder at study entry. Participants were randomized to an intervention group receiving AR training (10 weeks, N = 139) and an assessment-only control group (N = 138). Mental health outcomes (depressive, anxiety, and stress symptoms) and potential cognitive (self-efficacy, perceived control), behavioral (coping behaviors) and affective (positive and negative affect) mediators of the intervention efficacy were assessed at baseline and post-assessment in both groups. ResultsStructural equation models indicated that baseline to post reductions in psychopathological symptoms due to AR partially passed through less avoidance-oriented and less other dysfunctional coping (proportion of total effect mediated; ratio of indirect to total effect: resignation: 55.0%, rumination: 27.9%, escape: 27.4%, aggression: 21.3%), less negative affect (46.3%), more positive affect (41.8%), lower external control beliefs (14.5%), and higher self-efficacy (13.3%). ConclusionsOur results suggest that improvements in cognitive, behavioral, and affective mediator variables partially explain the intervention efficacy of AR in improving mental health. Trial registration and data statementThe study protocol has been pre-registered on ClinicalTrials.gov (NCT03311529). The study protocol, minimum dataset, and analysis codes are available at OpARA - Open Access Repository and Archive. Supplementary materials (e.g., the course manual and additional training materials) are available on request from the last author (katja.beesdo-baum@tu-dresden.de).