The growing use of generative artificial intelligence (GenAI) tools like ChatGPT presents both opportunities and challenges for programming education, especially for novices. While these tools can assist with code generation, debugging and conceptual understanding, there is a risk that they will promote superficial learning and the uncritical adoption of generated code. This paper presents the development and empirical evaluation of a best-practice guideline designed to support students in the reflective and effective use of generative AI in programming contexts. We conducted an experimental study with 36 first-semester computer science students, comparing a control group using AI without guidance against an experimental group equipped with our structured guide. The results show that while the quantitative performance metrics remained similar between the groups, the students using the guideline demonstrated a significantly higher self-reported understanding of their solutions and exhibited more reflective AI interaction patterns. Observational data revealed that guided users critically evaluated (66.7% vs 55.6%), modified (61.1% vs 22.2%), and rejected (38.9% vs 16.7%) AI-generated suggestions more frequently. A supplementary AI guidance workshop conducted provided convergent validation: students independently identified debugging and conceptual understanding as their primary skill gaps requiring AI support.

Businesses looking to expand into international markets need high-quality market research to make strategic decisions and develop effective market entry strategies. However, conducting extensive secondary market research that contains all the necessary data is very time-consuming and resource-intensive. In recent years, Artificial Intelligence (AI) has been widely used for marketing applications. Conducting market research and generating market entry strategies are functions that AI, especially Large Language Models (LLMs), could support businesses with. In this paper we propose a theoretical model of how an LLM could be trained to ensure the quality of the output by conducting secondary market research and, based on it, to generate realistic market entry strategy for decision making about international expansion. One of the key elements of the proposed pipeline is an adjustable scoring system which ensures input data reliability and transparency and, as a result, helps to provide a reliable output which could be used for strategic decision-making. Compared to traditional market research approaches, the proposed methodology offers significant improvements in speed, resource efficiency, and accessibility and is aimed to support the businesses in their expansion into new international markets.

Driving safety is an important matter that, if violated, can have serious consequences such as injury or even death. Several studies have shown that fatigue, and the drowsiness that often results from it, has a significant impact on road safety. It is therefore essential to detect the symptoms of fatigue at an early stage so that appropriate countermeasures can be taken before a dangerous situation arises. Various systems can be used to detect fatigue, including those that monitor physiological signals and look for specific patterns to detect changes and trigger appropriate action if necessary. Mobile systems, which offer greater flexibility, are the focus of some of these efforts. In order to facilitate a methodological approach in the development of new systems, it is imperative to obtain a systematic overview within the initial phase. This enables the identification of both established and promising approaches, as well as the identification of actual gaps that necessitate further research. The aim of this article is to set out a protocol for the conduct of a systematic review in order to be able to subsequently carry it out. This includes researching and defining features such as eligibility criteria, selecting appropriate databases, search strategies and, most importantly, defining the specific research question. The result of this work is a clear and methodologically prepared summary of the most important points to be considered when conducting the systematic review on the subject of ”Mobile Physiological Monitoring Systems for Driver Fatigue Detection”.

The mental health of children and adolescents is susceptible to being adversely influenced by external stressors and events. A particularly notable example is the COVID-19 pandemic, which has profoundly disrupted the lives of billions of children and families and has been associated with an escalation in mental health issues, including depression, anxiety, and stress-related difficulties. As cited by the World Health Organization, there has been a documented 25% increase in the incidence of anxiety and depression. Before the pandemic, German data indicated a considerable prevalence of anxiety (15%) and depressive symptoms (10%), with similar trends observed worldwide. Impulsivity, in addition, is linked to various behavioral problems in childhood and adolescence, potentially resulting in challenges regarding emotional regulation as individuals advance in age. This protocol seeks to examine the interrelations between stress, sleep, and impulsivity in children and adolescents. Hence, herein, we present a methodological protocol designed to systematically collect data across these domains. The presented method uses mixed longitudinal methods to investigate the relationship between sleep, stress, and impulsivity in children, combining questionnaires, specialist evaluations, and data obtained from mobile sensors and speech analysis aimed at detecting impulsivity-related discourse patterns. The data obtained will be used to develop a cost-effective approach involving automatic and structured speech analysis, sleep pattern assessment and stress indicators captured by mobile devices. This will aid in the diagnosis of Impulse Control Disorders (ICD) in children and adolescents and provide valuable information for the diagnosis and implementation of the intervention program. Combining psychological expertise with technological innovation, the project will contribute to fundamental research and the development of digital tools to support young people’s mental health.

Touch, as one of the five primary senses, provides critical somatosensory information, including pressure, vibration, temperature, pain, and skin stress. Somatosensory deficits, often resulting from stroke or aging, significantly impair fingertip sensitivity, affecting daily function and quality of life. Vibrotactile stimulation devices have emerged as a modern therapeutic approach to address these deficits. A well-established bidirectional relationship exists between somatosensory function and sleep, where somatosensory stimulation aids sleep regulation, and sleep enhances somatosensory recovery. However, the potential of somatosensory therapy in ambulatory settings remains largely unexplored. While MRI and EEG have been used to measure the effects of somatosensory stimulation, current therapeutic evaluations still rely primarily on patient feedback, highlighting the need for objective assessment methods. This research initiates an in-depth literature review on the interplay between somatosensory therapy and sleep, alongside the application of EEG for therapy evaluation in home environments. The study aims to guide data selection strategies by sourcing information from patients, therapy sessions, sleep monitoring, and EEG recordings. A system architecture will be designed to integrate somatosensory therapy with sleep monitoring and EEG, addressing hardware requirements, communication protocols, and information architecture. Furthermore, deep learning models will be developed to analyze the interaction between sleep and somatosensory therapy, enabling personalized therapy adaptations.

Introduction: This study aims to establish normal ranges for the metabolites N-acetyl aspartate (NAA), choline (Cho), creatine (Cr), myo-inositol (mI), and lipids/lactate (Lip/Lac) in healthy brain tissue using proton magnetic resonance spectroscopy ( 1 H-MRS) with 1.5T MRI equipment. Objective: Determine the normal ranges of NAA, Cho, Cr, mI, and Lip/Lac in healthy brain parenchyma. These normal ranges are necessary for the training of Artificial intelligence or the implementation of machine learning approaches. Without these ranges, automatic classification based on NAA, Cho, and Cr is not feasible. Methodology: The analysis included 60 patients (24 females and 36 males) with a total of 150 spectra (some patients contributed more than one spectrum). All spectra were acquired from structurally normal brain regions with no signal abnormalities in any MRI sequence. Normal ranges were established for each metabolite concentration, and linear regressions were performed to assess age-related changes. ROC curves were constructed to evaluate diagnostic accuracy. Results: The normal ranges established were: [Cho]n: 42.8 ± 1.66, [Cr]n: 42.3 ± 1.22, [NAA]n: 78.9 ± 1.04, [Lip/Lac]n: 28.7 ± 3.16, [mI]n: 10.8 ± 0.80. No significant differences were observed in the metabolites in terms of gender or echo time (TE). Normalized concentrations of Cho, Lip/Lac, and mI increased linearly with age, whereas NAA decreased significantly with age, indicating a loss of neurons related to ageing. Based on the results, we propose the following equation to calculate the normal range of metabolite concentrations according to age: [M]a = β coefficient× age + [M]x, where [M]a represents the age-adjusted normalized concentration of the metabolite, and [M]x is the average concentration of the metabolite. Conclusions: 1 H-MRS is a non-invasive diagnostic technique that allows the establishment of normal ranges for cerebral metabolites in healthy brain parenchyma. These reference points aid in the clinical interpretation of spectroscopy studies in patients with brain pathologies.

Driving behaviour is a critical factor in accidents today. Physiological factors have a significant impact on driving behaviour. A potential solution lies in vehicle services that benefit from sensing environmental conditions to improve road safety, such as collision avoidance routines in driver assistance systems. Stress, assessed subjectively or physiologically, influences decision making and behaviour, with implications for individuals and the economy. In this paper we present a novel approach to formulate a risk index by combining data from subjective self-reports and objective physiological measures (in particular heart rate). The model identifies stress tendencies in driving behaviour by monitoring behavioural and physiological markers. We present our evaluation results and explore potential ways to implement the model in vehicle systems and its implications for improving road safety. We discuss potential enhancements to improve driving safety and enable timely responses in situations with an increased risk of accidents due to stress or drowsiness.

The heart of the project is the early and cost-effective diagnosis of impulse control disorders in children and adolescents. The methodology is based on the automatic analysis of speech and sleep patterns, which is being carried out in cooperation with Colombian and German partners. The group has set itself three project goals. In the first step, the synchronization of ongoing project work will be carried out so that, on the one hand, available results can be incorporated into this project and, on the other hand, cooperation results can be taken into account in ongoing work. Parallel to this, the second project objective is to set up competence groups that, as specialist groups, are familiar with the regional characteristics and help to record the current situation. The first two objectives are supported in particular by workshops and the exchange of researchers. In this way, the partners’ methodology is made accessible to both groups, which significantly promotes the analysis of research topics and the approach. Finally, in the third project objective, practice-oriented and target group-oriented results based on validated case studies will be provided so that the jointly developed methodology can be created. This contribution provides an overview of the activities.

The principal objective of this study is to investigate the impact of perceived stress on traffic and road safety. Therefore, we designed a study that allows the generation and collection of stress-relevant data. Drivers often experience stress due to their perception of lack of control during the driving process. This can lead to an increased likelihood of traffic accidents, driver errors, and traffic violations. To explore this phenomenon, we used the Stress Perceived Questionnaire (PSQ) to evaluate perceived stress levels during driving simulations and the EPQR questionnaire to determine the personality of the driver. With the presented study, participants can categorised based on their emotional stability and personality traits. Wearable devices were utilised to monitor each participant's instantaneous heart rate (HR) due to their non-intrusive and portable nature. The findings of this study deliver an overview of the link between stress and traffic and road safety. These findings can be utilised for future research and implementing strategies to reduce road accidents and promote traffic safety.

Nowadays, the importance of early active patient mobilization in the recovery and rehabilitation phase has increased significantly. One way to involve patients in the treatment is a gamification-like approach, which is one of the methods of motivation in various life processes. This article shows a system prototype for patients who require physical activity because of active early mobilization after medical interventions or during illness. Bedridden patients and people with a sedentary lifestyle (predominantly lying in bed) are also potential users. The main idea for the concept was non-contact system implementation for the patients making them feel effortless during its usage. The system consists of three related parts: hardware, software, and game application. To test the relevance and coherence of the system, it was used by 35 people. The participants were asked to play a video game requiring them to make body movements while lying down. Then they were asked to take part in a small survey to evaluate the system's usability. As a result, we offer a prototype consisting of hardware and software parts that can increase and diversify physical activity during active early mobilization of patients and prevent the occurrence of possible health problems due to predominantly low activity. The proposed design can be possibly implemented in hospitals, rehabilitation centers, and even at home.

Home health applications have evolved over the last few decades. Assistive systems such as a data platform in connection with health devices can allow for health-related data to be automatically transmitted to a database. However, there remain significant challenges concerning intermodular communication. Central among them is the challenge of achieving interoperability, the ability of devices to communicate and share data with each other. A major goal of this project was to extend an existing data platform (COMES®) and establish working interoperability by connecting assistive devices with differing approaches. We describe this process for a sleep monitoring and a physical exercise device. Furthermore, we aimed to test this setup and the implementation with a data platform in both a laboratory and an in-home setting with 11 elderly participants. The platform modification was realized, and the relevant changes were made so that the incoming data could be processed by the data platform, as well as visually displayed in real-time. Data was recorded by the respective device and transmitted into the data server with minor disruptions. Our observations affirmed that difficulties and data loss are far more likely to occur with increasing technical complexity, in the event of instable internet connection, or when the device setup requires (elderly) subjects to take specific steps for proper functioning. We emphasize the importance for tests and evaluations of home health technologies in real-life circumstances.

Cardiovascular diseases are directly or indirectly responsible for up to 38.5% of all deaths in Germany and thus represent the most frequent cause of death. At present, heart diseases are mainly discovered by chance during routine visits to the doctor or when acute symptoms occur. However, there is no practical method to proactively detect diseases or abnormalities of the heart in the daily environment and to take preventive measures for the person concerned. Long-term ECG devices, as currently used by physicians, are simply too expensive, impractical, and not widely available for everyday use. This work aims to develop an ECG device suitable for everyday use that can be worn directly on the body. For this purpose, an already existing hardware platform will be analyzed, and the corresponding potential for improvement will be identified. A precise picture of the existing data quality is obtained by metrological examination, and corresponding requirements are defined. Based on these identified optimization potentials, a new ECG device is developed. The revised ECG device is characterized by a high integration density and combines all components directly on one board except the battery and the ECG electrodes. The compact design allows the device to be attached directly to the chest. An integrated microcontroller allows digital signal processing without the need for an additional computer. Central features of the evaluation are a peak detection for detecting R-peaks and a calculation of the current heart rate based on the RR interval. To ensure the validity of the detected R-peaks, a model of the anatomical conditions is used. Thus, unrealistic RR-intervals can be excluded. The wireless interface allows continuous transmission of the calculated heart rate. Following the development of hardware and software, the results are verified, and appropriate conclusions about the data quality are drawn. As a result, a very compact and wearable ECG device with different wireless technologies, data storage, and evaluation of RR intervals was developed. Some tests yelled runtimes up to 24 hours with wireless Lan activated and streaming.

In previous studies, we used a method for detecting stress that was based exclusively on heart rate and ECG for differentiation between such situations as mental stress, physical activity, relaxation, and rest. As a response of the heart to these situations, we observed different behavior in the Root Mean Square of the Successive differences heartbeats (RMSSD). This study aims to analyze Virtual Reality via a virtual reality headset as an effective stressor for future works. The value of the Root Mean Square of the Successive Differences is an important marker for the parasympathetic effector on the heart and can provide information about stress. For these measurements, the RR interval was collected using a breast belt. In these studies, we can observe the Root Mean Square of the successive differences heartbeats. Additional sensors for the analysis were not used. We conducted experiments with ten subjects that had to drive a simulator for 25 minutes using monitors and 25 minutes using virtual reality headset. Before starting and after finishing each simulation, the subjects had to complete a survey in which they had to describe their mental state. The experiment results show that driving using virtual reality headset has some influence on the heart rate and RMSSD, but it does not significantly increase the stress of driving.