The instruction of practical competencies within the domain of healthcare education, particularly in nursing and midwifery, presents unique challenges. Virtual Reality (VR) simulation has emerged as a promising high-fidelity method to support learning in clinical contexts. This study investigates the perceived usefulness and didactic potential of a VR-based “Room of Errors” (ROE) scenario developed at Hochschule Niederrhein, which replicates a delivery room containing intentional safety risks. A mixed-methods approach was used, combining quantitative survey data with qualitative content analysis. A total of 29 people participated in the survey. Among them 6 were nurses and 5 midwives. In addition, 18 students participated, including 7 nursing students and 11 midwifery students. Of the participants, 15 had never used VR before, and 14 stated they had used VR before. The results suggest that the VR learning environment is generally perceived as useful and engaging, with the potential to complement traditional instruction. However, practical usability issues and the necessity for simulation supervision were also emphasized. The findings contribute to the understanding of VR in health education and offer recommendations for improving VR-based learning scenarios.

Current IT tools in the medical system often lack gender-sensitive design, which can compromise both diagnosis and treatment. Since clinical evidence shows that men and women may respond differently to medications, this study developed an AI-based system to incorporate gender-specific data into clinical decision-making. A Retrieval-Augmented Generation (RAG) pipeline was implemented to analyze a curated dataset of 20 scientific articles using three distinct Large Language Models (LLMs): kronos483/Llama-3.2-3B-PubMed, deepseek-v2, and mistral-small-3.2. The evaluation revealed distinct trade-offs between the models. Mistral-small-3.2 achieved the highest average F 1 score, while deepseek-v2 delivered the highest average Precision but failed to assign gender-sensitivity scores. The performance of Llama-3.2 was comparable to Mistral’s, but its responses occasionally included faulty information. The findings confirm that this RAG-based approach could be a feasible method for generating promising, gender-sensitive clinical insights, demonstrating a path toward more equitable and personalized AI-supported healthcare.

Teaching students about Artificial Intelligence (AI) in healthcare presents significant challenges for both educators and students. This study examines the lecture AI in Healthcare that was conducted at the Niederrhein University of Applied Sciences as part of the master's programme Health Care. The lecture is intended for students from both the Faculty of Health Care and the Faculty of Computer Sciences. It aims to provide them with an understanding of the fundamental concepts of AI and the practical usage in healthcare through exercises and interdisciplinary group work. Despite the positive feedback on the relevance of AI and ML, students identified several challenges. These challenges include the lack of fundamental knowledge about healthcare specific datasets, fundamental AI concepts, and also difficulties in the context of the interdisciplinary collaboration. These issues were provided by the implementation of a Teaching Analysis Poll (TAP) halfway through the lecture. This study shows the necessity for preparatory courses and the provision of resources. This could bridge knowledge gaps and enhance the practical relevance of the lecture within the curriculum. Additionally, the use of visual programming tools like Orange Data Mining proved beneficial, especially for nontechnical students. The findings suggest, that by addressing these gaps and refining instructional methods, the learning experience can be improved and the students can be better prepared for the complexities of the healthcare industry.

In ACM TOMS Algorithm 1012, the DELAUNAYSPARSE software is given for performing Delaunay interpolation in medium to high dimensions. When extrapolating outside the convex hull of the training set, DELAUNAYSPARSE calls the nonnegative least squares solver DWNNLS to compute projections onto the convex hull. However, DWNNLS and many other available sum-of-squares optimization solvers were not intended for usage with many variable problems, which result from the large training sets that are typical in machine learning applications. Thus, a new PROJECT subroutine is given, based on the highly customizable quadratic program solver BQPD . This solution is shown to be as robust as DELAUNAYSPARSE for projection onto both synthetic and real-world datasets, where other available solvers frequently fail. Although it is intended as an update for DELAUNAYSPARSE , due to the difficulty and prevalence of the problem, this solution is likely to be of external interest as well.

This paper presents an evaluation of the introduction of artificial intelligence (AI) in healthcare lectures. Given the increasing importance of AI in transforming healthcare, the study assesses students' perceptions of AI and their experience with the BERT Natural Language Processing (NLP) network during the lecture. A mixed-methods approach was used, including pre- and post-lecture surveys and hands-on interaction with the BERT NLP network. The integration of AI in healthcare has become crucial in addressing challenges in hospital management and patient treatment. The results indicate diverse preconceptions about AI in healthcare among students, with most participants reporting enhanced understanding and positive attitudes towards AI integration after the lecture. The hands-on experience with the BERT NLP network received positive feedback, suggesting its potential in analysing healthcare data. This study emphasizes the significance of incorporating AI topics in healthcare lectures and provides valuable insights for improving AI education in healthcare curricula.

MQSI is a Fortran 2003 subroutine for constructing monotone quintic spline interpolants to univariate monotone data. Using sharp theoretical monotonicity constraints, first and second derivative estimates at data provided by a quadratic facet model are refined to produce a univariate C 2 monotone interpolant. Algorithm and implementation details, complexity and sensitivity analyses, usage information, a brief performance study, and comparisons with other spline approaches are included.

The hospital is a place of high complexity, high risk and intensive use of resources. In addition, processes are highly individual, non-standardized and vary from hospital to hospital. Thus, process modification is challenging and difficult to implement. Pre-medication is one of these complex and individual hospital processes. 2D tools such as the event-driven process chain, which can be represented with ARIS, and Ema WorkDesigner support digital production planning and prospective ergonomics as well as productivity assessment by providing an efficient and accurate approach to 3D human simulation of manual and semi-automatic activities. For this purpose, the pre-medication process in a hospital presented vulnerabilities identified. The weak points: an incomplete patient record as well as a crowded waiting area can potentially be minimized by process optimization. The simple representation in the 2D tool and the more complex modification using emaWD allow an analysis of these processes and identification of solution approaches, such as the creation of a waiting room and a modified process flow for the patient file. 2D and 3D tools should not be understood as competing, but as complementary.

VTMOP is a Fortran 2008 software package containing two Fortran modules for solving computationally expensive bound-constrained blackbox multiobjective optimization problems. VTMOP implements the algorithm of [ 32 ], which handles two or more objectives, does not require any derivatives, and produces well-distributed points over the Pareto front. The first module contains a general framework for solving multiobjective optimization problems by combining response surface methodology, trust region methodology, and an adaptive weighting scheme. The second module features a driver subroutine that implements this framework when the objective functions can be wrapped as a Fortran subroutine. Support is provided for both serial and parallel execution paradigms, and VTMOP is demonstrated on several test problems as well as one real-world problem in the area of particle accelerator optimization.

The operating room is a site of high complexity, high risk and an intensive use of resources. Analysis and modification of processes are challenging and difficult to implement in practice. The planning software EMA WorkDesigner supports digital production planning and prospective ergonomics as well as productivity assessment by providing an efficient and accurate approach to 3D-human-simulation of manual and semi-automatic and human robot tasks. Main objective of the paper is the transfer of the application of EMA WorkDesigner in healthcare settings. For this purpose, we simulated a preoperative process in a 3D operating room and derived characteristic lines for ergonomics and cycle times for different human models. The results of the process simulation show that most of the workers have a higher risk of musculoskeletal overload according to the Ergonomic Assessment Worksheet evaluation method; on average, women have worse individual evaluation scores than men. There is no difference for the cycle times depending on the various human models. The assessed operating-room nurses have to cover a distance of 1222.5 m in one shift, for this process sequence alone. The EMA WorkDesigner has an enormous potential for use in healthcare simulation. A strength lies in the simple, quick and intuitive creation of working processes with object interaction by using the EMA WorkDesigner tasks library. On the other hand, there is a need for improvement in the creation of processes that require direct interaction with people, e.g. put patients from bed to divan bed. These require more effort. In addition a combination of EMA WorkDesigner with virtual reality methods would increase the spatial perception and interaction in groups.

Mobile health, or mHealth for short, is considered a trend and growth market in healthcare. Start-ups are increasingly entering the market with innovative applications in a narrow medical context. However, the German healthcare market is very complex in terms of its structure and regulatory requirements, which makes the transfer of innovations difficult. As a result, only a small amount of applications actually manages to successfully enter and establish themselves in the healthcare market in the long term.The aim of this study is to identify factors for successful market entry. Success factors for start-ups and innovations in other industries also apply to the mHealth sector, however, success factors for the healthcare market need to be specified in terms of content. The result is the identification of relevant success factors.

DELAUNAYSPARSE contains both serial and parallel codes written in Fortran 2003 (with OpenMP) for performing medium- to high-dimensional interpolation via the Delaunay triangulation. To accommodate the exponential growth in the size of the Delaunay triangulation in high dimensions, DELAUNAYSPARSE computes only a sparse subset of the complete Delaunay triangulation, as necessary for performing interpolation at the user specified points. This article includes algorithm and implementation details, complexity and sensitivity analyses, usage information, and a brief performance study.

With the digitalization of the health care system, health apps are taking on a new role as potential helpers for doctors and patients. For the acceptance of these digital solutions, however, it is necessary to know the requirements of the users in order to satisfy their needs. Aims of the conducted survey study were to determine, prioritise and analyze requirements for applications in the field of prevention and health promotion on the basis of the Kano model of customer satisfaction. In a first step, user requirements were identified by expert interviews. In the following, 160 participants took part in a questionnaire survey based on Kano model and identified user requirements. The results show 10 different product requirements evaluated by the participants using the Kano method resulting in important insights into the requirements from user perspective. The wishes for individualization can very clearly be classified as a performance criterion and data protection as a basic requirement. Disproportionate satisfaction can be achieved through communication with the doctor or with usage of reward systems within an app, while reminder functions were seen indifferent.

Large volumes of data allow for modern application of statistical and mathematical models to practical social issues. Many applications of predictive models like criminal activity heat mapping, recidivism estimation, and child safety scoring rely on data that may be incomplete, incorrect, or biased. Many sensitive social and historical issues can unintentionally be incorporated into predictions causing ethical mistreatment. This work proposes a mechanism for continuously mitigating model bias by using algorithms that produce predictions from reasonably small subsets of data, allowing a human-in-the-loop approach to model application. The benefits offered by this framework are twofold: (1) bias can be identified either statistically or by human users on a per-prediction basis; (2) data can be cleaned for bias on a per-prediction basis. A modeling and data management methodology similar to that presented here could strengthen the ethical application of data science and make the process of cleaning and validating data manageable in the long term.

Exponential increases in complexity and scale make variability a growing threat to sustaining HPC performance at exascale. Performance variability in HPC I/O is common, acute, and formidable. We take the first step towards comprehensively studying linear and nonlinear approaches to modeling HPC I/O system variability in an effort to demonstrate that variability is often a predictable artifact of system design. Using over 8 months of data collection on 6 identical systems, we propose and validate a modeling and analysis approach (MOANA) that predicts HPC I/O variability for thousands of software and hardware configurations on highly parallel shared-memory systems. Our findings indicate nonlinear approaches to I/O variability prediction are an order of magnitude more accurate than linear regression techniques. We demonstrate the use of MOANA to accurately predict the confidence intervals of unmeasured I/O system configurations for a given number of repeat runs - enabling users to quantitatively balance experiment duration with statistical confidence.

The use of Electronic Medical Information and Health Systems, by both health and IT professionals, support staff and patients, is continually growing. However, to use or develop such systems properly, these users require well-founded training, which has increasingly become part of their formal university education. In this education, a necessary set of health informatics topics need to be covered by lectures for both computer science students, as developers of such systems, and health students, as future users of these systems, to be able to master the needed skills. Within the ERASMUS PLUS HiCure project, which aims to develop an integrated health informatics curricula, this work was conducted to identify the necessary topics and needed skills. To achieve, a literature analysis was conducted to gain a comprehensive insight of existing health informatics curricula and recommendations of both scientific community and professional bodies. This analysis resulted in identifying some key competencies, that students should learn within a health informatics education, to gain the critical skills needed for specifying, evaluating and developing health information systems as well as performing health information management.

Computer systems form the backbone of computing from very small, mobile devices to the huge datacenters that power the digital economy. These systems often exhibit large degrees of variability in their performance that is little understood, but such variability threatens to severely diminish the effectiveness of critical systems upon which society relies. Funded by a large NSF grant, the VarSys project at Virginia Tech researches the sources of variability in computer systems and develops methods to overcome it. We believe it is crucial to raise awareness of the phenomena surrounding variability in computer systems at the undergraduate level. Towards this end, we are connecting the research techniques developed as part of this NSF award to ongoing classroom projects in a core systems course. Our key insight is to expose students to the phenomenon as it occurs in the systems software modules (e.g. a memory allocator, a fork-join thread pool) they are themselves developing in the course. We have implemented a web-based system that allows students to submit their own systems-level code to a specialized cluster which then benchmarks it while systematically varying a number of ordinal and categorical variables. These variables reflect environmental factors that can influence the performance of complex systems. Students are then presented with a visual statistical analysis of the results and asked to interpret those results. We have successfully deployed this system in 2 semesters to over 250 students and collected student data about their experience with this system and are documenting our progress towards these important learning objectives.

This paper revisits the Kareken-Wallace model of exchange rate formation. Following the seminal paper by Arifovic (1996) we investigate a dynamic version of the model in which agents' decision rules are updated using genetic algorithms. Time series analysis of simulated data indicates that for particular parameterizations, the characteristics of the exchange rate dynamics are very similar to those of empirical data. The similarity appears to be quite insensitive with respect to the ingredients of the GA algorithm. However, appearance or not of realistic time series characteristics depends crucially on the mutation probability (which should be low) and the number of agents (not more than about 1000).