Search Results (263)

Background/Objectives: Cardiorespiratory fitness (CRF) is of great interest in children and adolescents. Due to the limited availability of cardiopulmonary exercise testing, simple and reliable alternatives are needed. A stair climbing test (SCT) for the assessment of CRF developed at the Department of Pediatric Cardiology of the LMU University Hospital in Munich showed a strong correlation with VO₂max. The aim of this study is to prove its feasibility in a non-clinical setting and to analyse its results in a larger study population. Methods: During the “Hand aufs Herz” study, a comprehensive cardiovascular examination was carried out on 922 pupils and siblings (13.2 ± 7.8 years) at a high school in Bavaria. The SCT was performed to evaluate CRF: participants had to run up and down a total of four floors (14.8 m) as quickly as possible without skipping steps or holding on to the banister. Absolute time has been normalized over the standard height of 12 m to allow comparisons with different settings. An SCT Index was calculated to adjust results to the different weights of participants and the exact height of the staircase. Results: The SCT proved to be easily feasible and safe in non-clinical contexts. Out of 922 participants, 13 (1.4%) were not able to perform the test, and 3 (0.3%) had to interrupt it following fatigue or stumbling. A total of 827 participants aged from 9 to 17 years (13.1 ± 2.1 years, 45.8% girls) had a mean absolute SCT time of 53.4 ± 6.2 s and 43.3 ± 5.1 s when normalized over 12 m. Conclusions: The SCT represents a simple, cost- and time-saving test that allows a rapid and solid assessment of cardiorespiratory fitness in children and adolescents. We could demonstrate that it is safe and feasible in non-clinical contexts. Its short duration and universal applicability are valuable advantages that could facilitate the establishment of a repetitive cardiovascular screening in the pediatric population, particularly in outpatient departments or settings with low-resource systems. Full article

The capability of U-Net methods and aerial orthoimagery to identify tree crown mortality in study areas in Bavaria, Germany was evaluated and aspects such as model transferability were investigated. We trained the models with imagery from May to September for the years 2019–2023. One goal was to differentiate between damaged crowns of deciduous, coniferous, and pine trees. The results from a validation area containing an independent dataset showed the best average F1-scores of 68%, 52%, and 66% for deciduous, coniferous, and pine trees, respectively. This study highlights the potential of U-Net methods for detecting tree mortality in large areas. Full article

Anthropogenic and climatic pressures can transform contiguous forests into smaller, less connected fragments. Forest biodiversity and ecosystem functioning can furthermore be compromised or enhanced. We present a descriptive analysis of forest fragmentation in Bavaria, the largest federal state in Germany. We calculated 22 metrics of fragmentation using forest polygons, aggregated within administrative units and with respect to both elevation and aspect orientation. Using a forest mask from September 2024, we found 2.384 million hectares of forest across Bavaria, distributed amongst 83,253 forest polygons 0.1 hectare and larger. The smallest patch category (XS, <25 ha) outnumbered all other size classes by nearly 13 to 1. Edge zones accounted for more than 1.68 million hectares, leaving less than 703,000 hectares as core forest. Although south-facing slopes dominated the state, the highest forest cover (~36%) was found on the least abundant east-oriented slopes. Most of the area is located at 400–600 m.a.s.l., with around 30% of this area covered by forests; however, XL forest patches (>3594 ha) dominated higher elevations, covering 30–60% of land surface area between 600 and 1400 m.a.s.l. The distribution of the largest patches follows the higher terrain and corresponds well to protected areas. K-Means clustering delineated 3 clusters, which corresponded well with the predominance of patchiness, aggregation, and edginess within districts. Full article

Objectives: The relationship between oral health and general health of geriatric in-hospital patients (GIH) who are poly-morbid and edentulous is currently unclear. This study determined the relationship between oral health and general health, and further implications and recommendations were derived. Methods: This retrospective cohort study included 81 GIH patients with impairment of oral state and masticatory function and need for immediate prosthetic treatment. The number of medical diagnoses, particularly main diagnoses of being hospitalized, comorbid diagnoses and the dental/oral state, were evaluated. Laboratory data of vitamin D3 and albumin concentrations were measured. Intraoral risk factors (IRF) affecting the masticatory function were intraoral inflammation, mucogingival impairment (MGI) and severe bone crest atrophy (SBCA). Masticatory function was evaluated by DMF*-T Index (number of destroyed/diseased, missing teeth and artificial fabrication), Eichner Index and Scores. The clinical relevance was surveyed by significance and effect size calculations. Results: In GIH, the number of medical diagnoses correlated significantly with the occurrence of IRFs. SBCA was the most affecting IRF, as measured by Eichner Index at baseline (p = 0.001). Single main diagnoses CNS and gastro-intestinal disease (GID) correlated with both deficiency of vitamin D3 levels (p = 0.011; p = 0.028) and hypoalbuminemia (p = 0.013; p = 0.023). Single comorbid diagnoses significantly correlated with both vitamin D3 deficiency and hypoalbuminemia (CVD (p = 0.031); DM (p = 0.042). Hypoalbuminemia was further found to be correlated with the sum of comorbid diagnoses (p = 0.033). Conclusions: GIH patients suffered from general and dental poly-morbidity. The prevalence of diseases was higher due to SBCA and impaired masticatory function. Deficiency of vitamin D3 and hypoalbuminemia were possible malnutrition markers. Full article

Distribution systems’ vulnerability to power losses remains high, among other parts of the power system, due to the high currents and lower voltage ratio. Connecting distributed generation (DG) units can reduce power loss and improve the overall performance of the distribution networks if sized and located correctly. However, existing studies have usually assumed that DGs operate only at the unity power factor (i.e., type-I DGs) and ignored their dynamic capability to control reactive power, which is unrealistic when optimizing DG allocation in power distribution networks. In contrast, optimizing the allocation of DG units injecting reactive power (type-II), injecting both active and reactive powers (type-III), and injecting active power and dynamically adjusting (absorbing or injecting) reactive power (type-IV) is a more likely approach, which remains unexplored in the current literature. Additionally, various metaheuristic optimization techniques are employed in the literature to optimally allocate DGs in distribution networks. However, the no-free-lunch theorem emphasizes employing novel optimization approaches, as no method is best for all optimization problems. This study demonstrates the potential of optimally allocating different DG types simultaneously to improve power distribution network performance using a parameter-free Jaya optimization technique. The primary objective of optimally allocating DG units is minimizing the distribution network’s power losses. The simulation validation of this study is conducted using the IEEE 33-bus test system. The results revealed that optimally allocating a multiunit DG mix instead of a single DG type significantly reduces power losses. The highest reduction of 96.14% in active power loss was obtained by placing three type-II, two type-III, and three type-IV units simultaneously. In contrast, the minimum loss reduction of 87.26% was observed by jointly allocating one unit of the aforementioned three DG types. Full article

Retrieval-Augmented Generation (RAG) systems have shown significant potential for domain-specific Question Answering (QA) tasks, although persistent challenges in retrieval precision and context selection continue to hinder their effectiveness. This study introduces Document Graph RAG (GraphRAG), a novel framework that bolsters retrieval robustness and enhances answer generation by incorporating Knowledge Graphs (KGs) built upon a document’s intrinsic structure into the RAG pipeline. Through the application of the Design Science Research methodology, we systematically design, implement, and evaluate GraphRAG, leveraging graph-based document structuring and a keyword-based semantic linking mechanism to improve retrieval quality. The evaluation, conducted on well-established datasets including SQuAD, HotpotQA, and a newly developed manufacturing dataset, demonstrates consistent performance gains over a naive RAG baseline across both retrieval and generation metrics. The results indicate that GraphRAG improves Context Relevance metrics, with task-dependent optimizations for chunk size, keyword density, and top-k retrieval further enhancing performance. Notably, multi-hop questions benefit most from GraphRAG’s structured retrieval strategy, highlighting its advantages in complex reasoning tasks. Full article

Background: Activities of Daily Living (ADLs) are essential tasks performed at home and used in healthcare to monitor sedentary behavior, track rehabilitation therapy, and monitor chronic obstructive pulmonary disease. The Barthel Index, used by healthcare professionals, has limitations due to its subjectivity. Human activity recognition (HAR) is a more accurate method using Information and Communication Technologies (ICTs) to assess ADLs more accurately. This work aims to create a singular, adaptable, and heterogeneous ADL dataset that integrates information from various sources, ensuring a rich representation of different individuals and environments. Methods: A literature review was conducted in Scopus, the University of California Irvine (UCI) Machine Learning Repository, Google Dataset Search, and the University of Cauca Repository to obtain datasets related to ADLs. Inclusion criteria were defined, and a list of dataset characteristics was made to integrate multiple datasets. Twenty-nine datasets were identified, including data from various accelerometers, gyroscopes, inclinometers, and heart rate monitors. These datasets were classified and analyzed from the review. Tasks such as dataset selection, categorization, analysis, cleaning, normalization, and data integration were performed. Results: The resulting unified dataset contained 238,990 samples, 56 activities, and 52 columns. The integrated dataset features a wealth of information from diverse individuals and environments, improving its adaptability for various applications. Conclusions: In particular, it can be used in various data science projects related to ADL and HAR, and due to the integration of diverse data sources, it is potentially useful in addressing bias in and improving the generalizability of machine learning models. Full article

Background/Objectives: Activities of Daily Living (ADLs) are crucial for assessing an individual’s autonomy, encompassing tasks such as eating, dressing, and moving around, among others. Predicting these activities is part of health monitoring, elderly care, and intelligent systems, improving quality of life, and facilitating early dependency detection, all of which are relevant components of personalized health and social care. However, the automatic classification of ADLs from sensor data remains challenging due to high variability in human behavior, sensor noise, and discrepancies in data acquisition protocols. These challenges limit the accuracy and applicability of existing solutions. This study details the modeling and evaluation of real-time ADL classification models based on batch learning (BL) and stream learning (SL) algorithms. Methods: The methodology followed is the Cross-Industry Standard Process for Data Mining (CRISP-DM). The models were trained with a comprehensive dataset integrating 23 ADL-centric datasets using accelerometers and gyroscopes data. The data were preprocessed by applying normalization and sampling rate unification techniques, and finally, relevant sensor locations on the body were selected. Results: After cleaning and debugging, a final dataset was generated, containing 238,990 samples, 56 activities, and 52 columns. The study compared models trained with BL and SL algorithms, evaluating their performance under various classification scenarios using accuracy, area under the curve (AUC), and F1-score metrics. Finally, a mobile application was developed to classify ADLs in real time (feeding data from a dataset). Conclusions: The outcome of this study can be used in various data science projects related to ADL and Human activity recognition (HAR), and due to the integration of diverse data sources, it is potentially useful to address bias and improve generalizability in Machine Learning models. The principal advantage of online learning algorithms is dynamically adapting to data changes, representing a significant advance in personal autonomy and health care monitoring. Full article

Historical archives hold untapped potential for understanding long-term biodiversity change. This study introduces computational approaches to historical ecology, combining archival research, text analysis, and spatial mapping to reconstruct past biodiversity patterns. Using the 1845 Bavarian Animal Observation Dataset (AOD1845), a comprehensive survey of vertebrate species across 119 districts, we transform 5400 prose records into structured ecological data. Our analyses reveal how species distributions, habitat associations, and human–wildlife interactions were shaped by land use and environmental pressures in pre-industrial Bavaria. Beyond documenting ecological baselines, the study captures early perceptions of habitat loss and species decline. We emphasise the critical role of historical expertise in interpreting archival sources and avoiding anachronisms when integrating historical data with modern biodiversity frameworks. By bridging the humanities and environmental sciences, this work shows how digitised archives and computational methods can open new frontiers for conservation science, restoration ecology, and Anthropocene studies. The findings advocate for the systematic mobilisation of historical datasets to better understand biodiversity change over time. Full article

Ground-based ground-penetrating radar (GPR) has been applied successfully for decades in archaeological geophysics. However, there are sometimes severe problems arising in cases of rough terrain, permission to enter a site, or due to vegetation. Other issues may also make it impossible to use conventional ground-based GPR. Therefore, mounting the GPR antenna below a drone could be a potential alternative. Successful applications of drone-based GPR have already been reported, e.g., in the fields of geological mapping, glaciology, and UXO-detection. However, it is not clear whether faint archaeological remains can also be mapped using this approach. In the survey discussed below, we tested such a drone-based GPR setup at an archaeological site in Bavaria, where well-preserved Roman foundations at a shallow depth are known from previous geophysical surveys with magnetics and ground-based GPR. The aim was to evaluate the possibilities and problems arising with this new approach through a comparison with the afore-mentioned data, obtained in previous ground-based surveys of this site. The results show that under certain circumstances, the archaeological remains can be resolved while using a drone. However, the remains are much harder to detect with a lower degree of resolution and survey setup and acquisition time play a crucial role for a successful survey. Especially relevant are two factors: First, the correct choice of profile orientation, as there are strong reflections caused by near-surface features (like field boundaries) due to decoupling the antenna from the ground. Second, a very dry soil is mandatory, as otherwise too much signal is lost at the air-ground-interface. Considering these factors, drone-based GPR represents a valuable tool for modern archaeological geophysics. Full article

Coal mining in Upper Bavaria ended in the 1960s and the mines were flooded. This study investigates the mining-influenced water and its environmental implications in the Hausham Mine, one of many unmonitored coal mines in the region and along the northern edge of the Molasse zone in Austria, Germany and Switzerland. Water and solid samples were collected in the vicinity of the discharge area within a waste rock pile and downstream of a nearby lake. The samples were subjected to chemical and isotopic analysis, with a focus on the potential for natural attenuation. The mine waste discharge has high initial concentrations of calcium, sulfate, and iron, and elevated concentrations of nickel, zinc, and strontium. These element concentrations are significantly reduced along the flow path so that the water is environmentally safe for discharge into the Loidlsee. The reduced contaminant levels are related to the formation of secondary iron precipitates and associated sorption processes, the formation of secondary calcium carbonates, and mixing with another groundwater source. The results indicate that the carbonate-dominated sediments of the Molasse zone contribute substantially to the natural remediation of a potential environmental problem. Full article

Car safety can be enhanced by enabling the Airbag Control Unit (ACU) to adaptively deploy different charges based on the occupant’s position once the crash occurs. In this context, monitoring the occupant’s position using a sensorized seat integrated with an Inertial Measurement Unit (IMU) offers a practical and cost-effective solution. However, certain challenges still need to be addressed. The adoption of sensorized seats in research and vehicle set-up is still under consideration. This study investigates an interface device that can be reconfigured to suit almost any seat model. This reconfigurability makes it easily adaptable to new vehicles under development and applicable to any passenger seat in the vehicle. This paper details the device’s design, including its programming using calibration and monitoring features, which significantly improves its reliability compared to earlier prototypes. Extensive testing through real driving experiments with multiple participants demonstrated an accuracy range of 45–100%. The testing involved both drivers and passengers, showcasing the device’s ability to effectively monitor various in-car scenarios. Full article

Future decarbonization targets demand a higher penetration of renewable energy (RE) sources into the system. However, challenges such as an uneven spatial and temporal distribution of various RE sources’ potential for green electricity (GE) generation demand alternative ways to store and later utilize the generated energy. In addition to that, sustainable development goals (SDGs) highlight the need for the responsible use of resources with increased recycling and a reduction in corresponding waste generation while ensuring access to affordable, reliable, sustainable, and modern energy for all. In this paper, an attempt is made to address both the issues of biodegradable waste (BW) processing and sustainable hydrogen (SH) production through it. Thermochemical conversion technology (TCC) and, within that, especially ‘thermocatalytic reforming’ (TCR^®) technology have been explored as options to provide viable solutions. An added advantage of decentralized hydrogen production can be envisioned here that can also contribute to regional energy security to some degree. To analyze the concept, the Bavarian region in Germany, along with open-source data for bio-waste from two main sources, namely domestic household and sewage sludge (SS), were considered. Based on that, the corresponding regional hydrogen demand coverage potential was analyzed. Full article

In the 21st century, the adoption of solar energy has witnessed significant growth, driven by the increased use of ground-mounted photovoltaic (GPV) systems, recognized as solar farms, which have emerged as major players in this sector. Nevertheless, their extensive land utilization may impact local ecosystem services (ESSs), especially those related to water resources. In the context of the water–energy–food–ecosystem (WEFE) nexus, it becomes vital to investigate how solar park construction will impact water-related ESSs. This paper developed a framework that assesses the effect of constructing a solar park on water-related ecosystem services. We focused on solar farm construction and its interactions with various hydrological cycle components; then, we evaluated the implications for water-related ESSs. This approach encompasses a systematic literature review that identifies the hydrological factors most affected by the construction of solar farms. As a result, thirteen ESSs were selected to be included in an evaluation framework, and a definition of a scoring system of each ESS was defined based on the economic value, a predetermined indicator, or land use and land cover (LULC) properties. The allocation of weighting factors for these scores can be determined based on individual experience and stakeholders. This study presents a DSS-integrated framework to assess the impact of solar park constructions on water-related ecosystem services (ESSs) within the WEFE nexus. The framework was applied to a case study in Darstadt, Bavaria, revealing that, among the water-related ESSs in favor of ground-mounted PV systems (GPVs) compared to traditional agricultural practices, there could be soil erosion and nitrate leaching reduction. The DSS tool enables stakeholders to efficiently evaluate trade-offs between energy production and ecosystem impacts. The findings underscore the potential of integrating renewable energy projects with ecosystem management strategies to promote sustainable land-use practices. Full article

Background/Objectives: The Munich Oktoberfest is the largest Volksfest in the world, attracting more than 6 million visitors every year to the city of Munich and surroundings, posing challenges to healthcare providers. Since 2022, a CT has been installed on the festival site to decrease patient transport to emergency departments (EDs) and relieve EDs of a significant number of patients. The aim of our studies was to determine the economic impact of on-site CT on the healthcare system, both from a German and a U.S. perspective. Methods: A decision model was built using patient data from the Munich Oktoberfest. Two scenarios were investigated where patients with mild traumatic brain injuries were either scanned for intracranial hemorrhage (ICH) with CT on-site or brought directly to an ED for further investigation. Costs for patient transportation and CT scans were derived from German national reimbursement rates as well as U.S. Medicare data. Costs were calculated by diagnosis in the national currency. Results: In all scenarios, on-site CT scans were associated with reduced costs per patient (EUR 243 vs. EUR 908 in the German setting and EUR 438 vs. EUR 1635 in the international setting, as well as USD 160 vs. USD 403 in the U.S. setting). For the U.S. scenario, the proportion of ICH in the patient group, as well as the transport distance, had the strongest impact on average costs per patient. Conclusions: On-site CT scanning is a cost-reducing as well as clinically beneficial method for triaging patients at the Munich Oktoberfest. Full article