IDA LAB
INTELLIGENT DATA ANALYTICS SALZBURG
advantage through applied research in
Data science, artificial intelligence,
Statistics
News Overview
Events
Upcoming
Practical course for small and medium-sized companies: Learn how to use AI and autonomous systems in a targeted manner to make data-based decisions and organise processes more efficiently.
💻 Please bring your own laptop!
Event centre Techno-Z, Jakob Haringer Straße 5, 5020 Salzburg
Jakob-Haringer-Strasse 5
5020 Salzburg
How can companies create real added value from data? At the upcoming Meet the Expert show you leading research partners from FH Salzburg, University of Salzburg and FH Vorarlberghow Artificial intelligence, predictive analytics and Digitisation Make processes more efficient and transparent.
You can look forward to three compact Keynote speeches on current research topics - from Machine Learning about Forecasting until Cyber Security - and then take the opportunity to Individual counselling sessions with the experts.
🎯 Small and medium-sized enterprises - no prior knowledge required
💻 Online (the link will be sent out a few days before the event)
👉 Further information can be found at: dih-west.at/events
Data products offer enormous opportunities for SMEs to open up new business areas or further develop existing fields. But how does an idea become a successful product - and how can it be turned into a sustainable business?
Format: Workshop with presentations, practical examples and interactive exercises
Target group: Small and medium-sized enterprises (SMEs) that want to utilise the potential of data products in the B2B sector to establish a new business model or advance their existing one. The course is aimed at beginners and interested parties without in-depth prior knowledge of data science
Techno-Z, seminar room C, Jakob Haringer Straße 5, 5020 Salzburg
Jakob-Haringer-Strasse 5
5020 Salzburg
The agenda includes theoretical lectures, practical workshops (such as "RL for business applications"), guest lectures from renowned institutions and networking opportunities.
Organiser: IDA Lab, Smart Analytics & Reinforcement Learning team in co-operation with AI Austria, Fraunhofer Austria, JOANNEUM RESEARCH ROBOTICS, ExtensityAI and DIH West
Registration is already possible!
RoomE.002 U1.002 (HS Agnes Muthspiel)
Erzabt-Klotz-Straße 1
5020 Salzburg
Find out how small and medium-sized companies can generate real added value from their data using specific use cases. Ask your individual questions to our experts and discover practical solutions.
No previous knowledge necessary!
Target group: Small and medium-sized enterprises
Location: Online
The link will be sent out a few days before the event!
Are you fascinated by the rapid advances in the fields of artificial intelligence and data science? Entrepreneurs who would like to utilise these developments in their own company in a targeted and successful manner and want to delve deep into the subject matter together with the experts at IDA Lab Salzburg are very welcome to join us.
Target group: Small and medium-sized enterprises (SMEs)
Meeting room IDA Lab: Techno-Z, Jakob Haringer Straße 6, 2nd floor right, 209, 5020 Salzburg
The time and format of the meeting (online or on site) are agreed individually.
Jakob-Haringer-Strasse 6
5020 Salzburg
About
IDA LAb SAlzburg
Information on
The IDA Lab Salzburg (Lab for Intelligent Data Analytics), funded by the state of Salzburg as part of WISS 2025, is a competence centre for basic and applied research, as well as for knowledge and technology transfer in the fields of data science, machine learning, AI and statistics.
Our teams
Employees Board
Cooperations & Projects
Ongoing projects
INSPIRE: Intelligent Novel Support for Personalised Instruction and Robust Evaluation in STEM Lessons at Primary School | PI: S. Hirländer
Research co-operation with Salzburg University of Teacher Education (PH Salzburg) and the Paris Lodron University Salzburg (PLUS) / Dept. of Artificial Intelligence & Human Interfaces | funded by the Province of Salzburg (WISS2030) | Application-oriented basic research | PLUS Research
Term: 01.01.2026 - 01.01.2029
Christina Egger (project management, PH Salzburg), Simon Hirländer (PLUS)
FOCUS: Forecasting and optimisation under constraints and uncertainty for sustainable industrial energy systems | PI: S. Hirländer
Research co-operation with Ing. Punzenberger COPA-DATA GmbH and the Paris Lodron University Salzburg (PLUS) / Dept. of Artificial Intelligence & Human Interfaces | funded by COPA-DATA GmbH | Contract research PLUS Research
Term: 01.10.2025 - 30.09.2028
Simon Hirländer (Project management, PLUS), Sarah Trausner (Predoc, PLUS)
Data Science PhD student II | PI: W. Trutschnig
Research co-operation with Red Bull GmbH and the Paris Lodron University Salzburg (PLUS) / FB Artificial Intelligence & Human Interfaces | funded by Red Bull GmbH I Application-orientated basic research | PLUS Research
Term: 01.10.2025 - 30.09.2028
Wolfgang Trutschnig (Project management, PLUS)
KRONUS: Continuous ROL process optimisation for sustainable US scrap reduction | PI: W. Trutschnig
Research co-operation with Austria Metall AG (AMAG) and the Paris Lodron University Salzburg (PLUS) / FB Artificial Intelligence & Human Interfaces | funded by the AMAG | Application-orientated basic research PLUS Research
Term: 01.09.2025 - 31.08.2028
Wolfgang Trutschnig (Project management, PLUS), Patrick Langthaler (Predoc, PLUS)
DIH-West: Digital Innovation Hub West | PI: A. Bathke
Partner consortium of universities and interest groups in Vorarlberg, Tyrol, Salzburg and the Paris Lodron University Salzburg (PLUS) / FB Artificial Intelligence & Human Interfaces | funded by the FFG and the State of Salzburg | Knowledge and Technology Transfer | PLUS Research
Term: 01.03.2024 - 29.02.2028
Arne Bathke (Project management, PLUS), Wolfgang Trutschnig (PLUS)
AI4GREEN: Data Science for Sustainability | PI: C. Borgelt
Research co-operation with FH Kufstein, FH Salzburg, FH Vorarlberg, University of Applied Sciences Kempten, TH Rosenheim, Software Competence Centre Hagenberg GmbH, TH Deggendorf and the Paris Lodron University Salzburg (PLUS) / FB Artificial Intelligence & Human Interfaces | funded by EFRE (EU) & Land Salzburg | application-orientated basic research | PLUS Research
Term: 01.05.2024 - 30.04.2027
Christian Borgelt (Project management, PLUS), Raoul Kutil (Predoc, PLUS)
OMI-IA: Open Medical Institute Impact Analysis
Research co-operation with Salzburg Foundation of the American Austrian Foundation and the Paris Lodron University Salzburg (PLUS) / Dept. of Artificial Intelligence & Human Interfaces | funded by Salzburg Foundation of the American Austrian Foundation | Applied Research | PLUS Research
Duration: 1 January 2026 – 31 December 2026
Arne Bathke (Project management)
Completed projects
Danieli X - PLUS | PI: S. Hirländer
Research co-operation with Danieli Automation and Paris Lodron University Salzburg (PLUS) / Dept. of Artificial Intelligence & Human Interfaces | funded by Danieli Automation | Contract research PLUS Research
Duration: 01.03.2025 - 31.10.2025
Simon Hirländer (Project management, PLUS), Olga Mironova (Master's student, PLUS)
ProSA 2.1: Process analyses effects of hot rolling process on discontinuities in aerospace panels | PI: W. Trutschnig
Research co-operation with Austria Metall AG (AMAG) and the Paris Lodron University Salzburg (PLUS) / FB Artificial Intelligence & Human Interfaces | funded by the AMAG | Application-orientated basic research PLUS Research
Duration: 01.04.2025 - 31.08.2025
Wolfgang Trutschnig (Project management, PLUS), Patrick Langthaler (Predoc, PLUS)
DEOP 2.5: Dynamic energy optimisation | PI: S. Hirländer
Research co-operation with Ing. Punzenberger COPA-DATA GmbH and Paris Lodron University Salzburg (PLUS) / Dept. of Artificial Intelligence & Human Interfaces | funded by COPA-DATA GmbH | Contract research PLUS Research
Duration: 15.02.2025 - 31.08.2025
Simon Hirländer (Project management, PLUS), Sarah Trausner (Master's student, PLUS)
publiCations
Research
2025
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[175] , , F. Schürrer: On exact regions between measures of concordance and Chatterjee's rank correlation for lower semilinear copulas. (2025) https://doi.org/10.1016/j.ijar.2025.109588
[174] : An asymptotic expansion for the Mellin transform of a beta function and applications. (2025) https://doi.org/10.1080/10652469.2025.2565257
[173] G. B. Bottini, , W. Hitzl, B. Walch, et al.: Is there an „ideal“ sequence for open reduction and internal fixation of multiple mandibular fractures involving the condylar neck? A retrospective cohort study. (2025) https://doi.org/10.3390/jcm14207142
[172] S. Clemens, C. Simon, , O. Rose, , et al: Development and validation of a risk prediction tool for drug-related problems in pre-operative elective surgical patients (mediPORT): A case-control study. (2025) https://doi.org/10.1371/journal.pone.0326088
[171] B. Taxer, , H. von Piekartz, E. Trinka, S. Leis: Investigation of Sensory and Neuropsychological Parameters in Migraine Sufferers: A Cross-Sectional Study with Negative Findings. (2025) https://doi.org/10.1007/s40120-025-00824-9
[170] M. Delporte, J. Verbeck, I. Brambilla, , G. Molenberghs, et al.: Dravet syndrome: Insights into seizure and speech progression from registry data. (2025) https://doi.org/10.1016/j.yebeh.2025.110459
[169] , G. Gruber, D. Lahnsteiner, : Spatiotemporal variability of passenger distribution to destination regions using the example of Salzburg Airport. (2025) https://doi.org/10.25598/agit/2025-29
[168] H. Sterzik, J. Arand, C. E. Schwarz, M. Kumpf, M. Wald, A. Kribs, , et al: Imposed work of breathing of 16 neonatal CPAP devices using different mechanisms of CPAP generation. (2025) https://doi.org/10.1038/s41390-025-04265-w
[167] S. Laner-Plamberger, A. Siller, , J.M. Kern, et al.: Stable SARS-CoV-2 antibody levels and functionality in serum and COVID-19 convalescent plasma after long-term storage. (2025) https://doi.org/10.1111/vox.70059
[166] J. Verbeeck, , J. Nyberg, K. E. Thiel, , , et al: Reflection on clinical and methodological issues in rare disease clinical trials. (2025) https://doi.org/10.1186/s13023-025-03805-1
[165] B. Walch, A. Gaggl, G. B. Bottini, , et al: Comparison of Anatomical Maxillary Sinus Implant and Polydioxanone Sheets in Treatment of Orbital Floor Blowout Fractures: A Retrospective Cohort Study. (2025) https://doi.org/10.3390/jfb16060204
[164] S. Schaible, E. Hofstätter, , M. Wald: The Effect of the COVID-19 Pandemic and the Establishment of a Ronald McDonald House on Skin-to-Skin Times in the Neonatal Intensive Care Unit: A Retrospective Study. (2025) https://doi.org/10.3390/children12060803
[163] V. Wally, T. Welponer, H. P. Wiesinger, A. Diem, K. Thiel, , , et al: Keratin-associated epidermolysis bullosa simplex: phenotypes and challenges in clinical trials – a narrative review and systematic update. (2025) https://doi.org/10.1186/s13023-025-03822-0
[162] J. F. Sánchez, : On bivariate Archimedean copulas with fractal support. (2025) https://doi.org/10.1515/demo-2025-0013
[161] M. Laimer, A. P. South, E. Mayer, S. Kitzmueller, L. Banner, M. A. Hosler, , et al: Efficacy and safety of rigosertib in patients with recessive dystrophic epidermolysis bullosa-associated advanced/metastatic cutaneous squamous cell carcinoma. (2025) https://doi.org/10.1093/bjd/ljaf205
[160] M. C. Hribljan, , S. Beniczky: Lateralising value of ictal head turning: A systematic review and meta-analysis. (2025) https://doi.org/10.1002/epd2.70046
[159] , : On bivariate lower semilinear copulas and the star product. (2025) https://doi.org/10.1016/j.ijar.2025.109366
[158] F. Durante, R. Pappadà: Clustering of compound events based on multivariate comonotonicity. (2025) https://doi.org/10.1016/j.spasta.2025.100881
[157] M. Oeller, O. Kartal, I. Trifonova, N. Held, , et al: Long-term retrospective analysis of parvovirus B19 infections in blood donors (2012–2014): significant increase in prevalence following the SARS-CoV-2 pandemic. (2025) https://doi.org/10.3390/diagnostics15182313
[156] M. Schreyer, : Revisiting the region determined by Spearman's ρ and Spearman's footrule ϕ. (2025) https://doi.org/10.1016/j.cam.2024.116259
[155] J. Beck, , : Combining stochastic tendency and distribution overlap towards improved nonparametric effect measures and inference. (2025) https://doi.org/10.1111/sjos.12783
[154] A. E. Carrozzo, , , D. Neunhaeuserer, et al.: Two-arm crossover randomised controlled trial versus meta-analysis of N-of-1 studies: comparison of statistical efficiency in determining an intervention effect. (2025) https://doi.org/10.1002/bimj.70045
[153] D. Lahnsteiner, J. Schmitt, : Spatiotemporal clustering based on internationaltourists' overnight stay data in Salzburg, Austria: aseasonal analysis using space-time data cubes toenhance airport connectivity. (2025) https://doi.org/10.1080/02508281.2024.2443728
[152] , : On differentiability and mass distributions of typical bivariate copulas. (2025) https://doi.org/10.1016/j.fss.2024.109150
[151] C. Böck, , M. Wald: Evaluation of Cuff Pressure Behaviour in Neonates During Mechanical Ventilation Using Endotracheal Microcuff Tubes: An In Vitro Study on a Neonatal Lung Model. (2025) https://doi.org/10.26717/BJSTR.2025.60.009434
2024
[150] J. Freidl: Nonparametric Analysis of Multivariate Data in Factorial Designs with Nondetects: A Case Study with Microbiome Data. (2024) https://doi.org/10.1007/s13253-024-00671-5
[149] A. Reiss, , M. Wald: Premature infants show consistently good lung compliance during conventional mechanical ventilation. (2024) https://doi.org/10.1002/ppul.27419
[148] F. Petersen, H. Kuehne, J. Welzel, S. Ermon: Convolutional Differentiable Logic Gate Networks. (2024) https://openreview.net/pdf?id=4bKEFyUHT4
[147] J. Nyberg, , K. E. Thiel, et al: Optimising designs in clinical trials with an application in treatment of Epidermolysis bullosa simplex, a rare genetic skin disease. (2024) https://doi.org/10.1016/j.csda.2024.108015
[146] V. N. Frey, , , et al: Stress and the City: Mental Health in Urbanised vs. Rural Areas in Salzburg, Austria. (2024) https://doi.org/10.3390/ijerph21111459
[145] M. Oeller, T. Schally, , , K. Schallmoser, E. Rohde, S. Laner-Plamberger: Heparin differentially regulates the expression of specific miRNAs in mesenchymal stromal cells. (2024) https://doi.org/10.3390/ijms252312589
[144] A. M. Wiesinger, B. Bigger, R. Giugliani, C. Lampe, , et al: Development of a novel tool for individual treatment trials in mucopolysaccharidosis. (2024) https://doi.org/10.1002/jimd.12816
[143] K. P. Gladow, O. Krüger, J. Beck: A Novel Method for Nonparametric Statistical Inference for Niche Overlap in Multiple Species. (2024) https://doi.org/10.1002/bimj.202400013
[142] O. Kartal, S. Laner-Plamberger, E. Rohde, C. Mrazek, , C. Grabmer: Evaluating a New Photopheresis System: A Comparison with Two Established Systems on Cell Yield and Collection Efficiency. (2024) https://doi.org/10.3390/diagnostics14202290
[141] K. Zeman-Kuhnert, A. J. Gaggl, G. B. Bottini, J. Wittig, C. Steiner, , C. Brandtner: Quality of Life after Microvascular Alveolar Ridge Reconstruction with Subsequent Dental Rehabilitation. (2024) https://doi.org/10.3390/jcm13206229
[140] J. Lischka, T. Pixner, K. Mörwald, , D. Furthner, et al.: Validation of fat mass metrics in paediatric obesity. (2024) https://doi.org/10.1159/000542029
[139] B. Taxer, , M. Christova, et al: Exploring Facial Somatosensory Distortion in Chronic Migraine: The Role of Laterality and Emotion Recognition-A Cross-Sectional Study. (2024) https://doi.org/10.3390/app14188102
[138] , , , : Constructing measures of dependence via sensitivity of conditional distributions. (2024) https://doi.org/10.1007/978-3-031-65993-5_28
[137] , : Quantifying Directed Dependence with Kendall's Tau. (2024) https://doi.org/10.1007/978-3-031-65993-5_30
[136] , : Hierarchical Variable Clustering Based on Measures of Predictability. (2024) https://doi.org/10.1007/978-3-031-65993-5_67
[135] , , , et al: Combining, Modelling and Analyzing Imprecision, Randomness and Dependence. (2024) https://doi.org/10.1007/978-3-031-65993-5
[134] , , C. Xu, et al.: Deep Meta Reinforcement Learning for Rapid Adaptation In Linear Markov Decision Processes: Applications to CERN's AWAKE Project. (2024) https://doi.org/10.1007/978-3-031-65993-5_21
[133] , , , et al: Multi-agent Reinforcement Learning and Its Application to Wireless Network Communication. (2024) https://doi.org/10.1007/978-3-031-65993-5_45
[132] G. Schäfer, S. Huber, , et al: Python-Based Reinforcement Learning on Simulink Models. (2024) https://doi.org/10.1007/978-3-031-65993-5_55
[131] : Common Models of Errors in Variables. (2024) https://doi.org/10.1007/978-3-031-65993-5_25
[130] E. Lütkebohmert, M. Rockel: An Empirical Study on New Model-Free Multi-output Variable Selection Methods. (2024) https://doi.org/10.1007/978-3-031-65993-5_2
[129] A. Romagna, , et al: Wound healing after intracutaneous vs. staple-assisted skin closure in lumbar, non-instrumented spine surgery: a multicentre prospective randomized trial. (2024) https://doi.org/10.1007/s00701-024-06227-3
[128] A. Astner-Rohracher, B. Frauscher, et al: Prognostic value of the 5-SENSE Score to predict focality of the seizure-onset zone as assessed by stereoelectroencephalography: a prospective international multicentre validation study. (2024) https://doi.org/10.1136/bmjno-2024-000765
[127] A. E. Carrozzo, , , et al: Applying Exercise Capacity and Physical Activity as Single vs Composite Endpoints for Trials of Cardiac Rehabilitation Interventions: Rationale, Use-case, and a Blueprint Method for Sample Size Calculation. (2024) https://doi.org/10.1016/j.apmr.2024.04.004
[126] G. Kalss, , et al: The Fingerprint of Scalp-EEG in Drug-Resistant Frontal Lobe Epilepsies. (2024) https://doi.org/10.1097/WNP.0000000000001106
[125] M. Rockel: Dependence properties of bivariate copula families. (2024) https://doi.org/10.1515/demo-2024-0002
[124] E. Lütkebohmert, A. Neufeld, J. Sester: Improved robust price bounds for multi-asset derivatives under market-implied dependence information. (2024) https://doi.org/10.1007/s00780-024-00539-z
[123] T. Pixner, , , et al: Rise in fasting and dynamic glucagon levels in children and adolescents with obesity is moderate in subjects with impaired fasting glucose but accentuated in subjects with impaired glucose tolerance or type 2 diabetes. (2024) https://doi.org/10.3389/fendo.2024.1368570
[122] , , J. Kaiser, C. Xu, A. Santamaría, L. Scomparin, V. Kain: Towards few-shot reinforcement learning in particle accelerator control. (2024) https://doi.org/10.18429/JACoW-IPAC2024-TUPS59
[121] S. Appel, N. Madysa: Data-Driven model predictive control for automated optimitisation of injection into the SIS18 synchrotron. (2024) https://doi.org/10.18429/JACoW-IPAC2024-TUPS59
[120] A. Santamaría, C. Xu, L. Scomparin, , A. Eichler, J. Kaiser, M. Schenk: The Reinforcerment Learning for Autonomous Accelerators Collaboration. (2024) https://doi.org/10.18429/JACoW-IPAC2024-TUPS62
[119] A. Domnica Hoeggerl, , , et al: Dissecting the dynamics of SARS-CoV-2 reinfections in blood donors with pauci- or asymptomatic COVID-19 disease course at initial infection. (2024) https://doi.org/10.1080/23744235.2024.2367112
[118] : Quantifying directed dependence via dimension reduction. (2024) https://doi.org/10.1016/j.jmva.2023.105266
[117] K. Zeman-Kuhnert, , , et al: Long-Term Outcomes of Dental Rehabilitation and Quality of Life after Microvascular Alveolar Ridge Reconstruction in Patients with Head and Neck Cancer. (2024) https://doi.org/10.3390/jcm13113110
[116] V. N. Frey, P. Langthaler, , et al: Influence of sports on cortical excitability in patients with spinal cord injury: a TMS study. (2024) https://doi.org/10.3389/fmedt.2024.1297552
[115] P. Sattler, : Choice of the hypothesis matrix for using the Wald-type-statistic. (2024) https://doi.org/10.1016/j.spl.2024.110038
[114] , : Hierarchical variable clustering based on the predictive strength between random vectors. (2024) https://doi.org/10.1016/j.ijar.2024.109185
[113] T. Pollhammer, B. Salcher, : MAMU: an R package for GIS-based river terrace mapping, morphostratigraphic evaluation of terrace maps and outcrop data and river long profile modelling. (2024) https://doi.org/10.5194/egusphere-egu24-16766
[112] S. Deininger, , et al: Functional Outcome and Safety of Endoscopic Treatment Options for Benign Prostatic Obstruction (BPO) in Patients ≥ 75 Years of Age. (2024) https://doi.org/10.3390/jcm13061561
[111] L. Rüschendorf: Supermodular and directionally convex comparison results for general factor models. (2024) https://doi.org/10.1016/j.jmva.2023.105264
[110] S. Schoenen, , et al: Istore: a project on innovative statistical methodologies to improve rare diseases clinical trials in limited populations. (2024) https://doi.org/10.1186/s13023-024-03103-2
[109] , : A novel positive dependence property and its impact on a popular class of concordance measures. (2024) https://doi.org/10.1016/j.jmva.2023.105259
[108] P. Bosque Varela, E. Trinka, et al: Magnetic resonance imaging fingerprints of status epilepticus: A case-control study. (2024) https://doi.org/10.1111/epi.17949
[107] , T. Shushi, S. Vanduffel: Up- and down-correlations in normal variance mixture models. (2024) https://doi.org/10.1016/j.spl.2023.109949
[106] R. Kozlica, G. Schäfer, S. Wegenkittl: A Modular Test Bed for Reinforcement Learning Incorporation into Industrial Applications. (2024) https://doi.org/10.1007/978-3-031-42171-6_15
[105] T. Kasper, , : On convergence and mass distributions of multivariate Archimedean copulas and their interplay with the Williamson transform. (2024) https://doi.org/10.1016/j.jmaa.2023.127555
2023
[104] J. Verbeeck, K. Thiel, [...], : How to analyse continuous and discrete repeated measures in small sample cross-over trials?. (2023) https://doi.org/10.1111/biom.13920
[103] J. Verbeeck, A.C. Hooker, K.E. Thiel, G. Molenberghs, J. Nyberg, J. Bauer, M. Laimer, V. Wally, , : Statistical recommendations for count, binary, and ordinal data in rare disease cross-over trials. (2023) https://doi.org/10.1186/s13023-023-02990-1
[102] F. Berns, , , et al: Trustworthy Medical Operational AI: Marrying AI and Regulatory Requirements. (2023) https://doi.org/10.1109/BigData59044.2023.10386683
[101] J. Fernández Sánchez, : A link between Kendall's τ , the length measure andthe surface of bivariate copulas, and a consequence to copulas with self-similar support. (2023) https://doi.org/10.1515/demo-2023-0105
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[98] , G. Zevi-Della-Pora, V. Kain: Ultra fast reinforcement learning in accelerator control demonstrated on CERN AWAKE. (2023) https://doi.org/10.18429/jacow-ipac2023-thpl038
[97] A. Oeftiger, S. Garcia, J. Lagrange, : Active Deep Learning for Nonlinear Optics Design of a Vertical FFA Accelerato. (2023) https://doi.org/10.18429/jacow-ipac2023-wepa026
[96] T. Moser, , et al: Long-term outcome of natalizumab-associated progressive multifocal leukoencephalopathy in Austria: a nationwide retrospective study. (2023) https://doi.org/10.1007/s00415-023-11924-7
[95] R. Kozlica, S. Wegenkittl, : Deep Q-Learning versus Proximal Policy Optimisation: Performance Comparison in a Material Sorting Task. (2023) https://doi.org/10.1109/isie51358.2023.10228056
[94] M. Hanusch, X. He, S. Janssen, J. Selke, R.R. Junker: Exploring the Frequency and Distribution of Ecological Non-monotonicity in Associations among Ecosystem Constituents. (2023) https://doi.org/10.1007/s10021-023-00867-9
[93] J. Fernández-Sánchez, J. López-Salazar Codes, J.B. Seoane Sepúlveda, : Generalised Notions of Continued Fractions: Ergodicity and Number Theoretic Applications (1st ed.). (2023) https://doi.org/10.1201/9781003404064
[92] O. Kartal, N. Lindlbauer, S. Laner-Plamberger, E. Rohde, F. Föttinger, L. Ombres, C. Mrazek, C. Grabmer: Collection efficiency of mononuclear cells in offline extracorporeal photopheresis: can processing time be shortened?. (2023) https://doi.org/10.2450/BloodTransfus.442
[91] Y. Deng, D. Lahnsteiner, : Promoting Active and Sustainable Commuting: A Tool for Analysing Location-specific Conditions and Potentials for Walking, Cycling and Public Transport. (2023) https://doi.org/10.1553/giscience2023_01_s101
[90] A.M. Wiesinger, B. Bigger, R. Giugliani, C. Lampe, M. Scarpa, T. Moser, C. Kampmann, F.B. Lagler: An Innovative Tool for Evidence-Based, Personalised Treatment Trials in Mucopolysaccharidosis. (2023) https://doi.org/10.3390/pharmaceutics15051565
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