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Knotenpunkt Führungskraft
No full textFührungskräfte nehmen in Netzwerken des Sozial- und Gesundheitswesens eine Schlüsselrolle ein: Sie moderieren zwischen internen Strukturen und externen Einflüssen, prägen durch ihr Handeln die Kultur und den Zusammenhalt der Einrichtung, sind Bezugsperson und Vorbild für die Mitarbeitenden. Zugleich müssen sie administrative und pflegerische Leitungsaufgaben vereinen. Ihre Bereitschaft zur Vernetzung entscheidet dabei über den Zusammenhalt und die Resilienz einer Organisation. Am Beispiel von Organisationsdiagnosen in Einrichtungen des Sozial- und Gesundheitswesens lässt sich verdeutlichen, dass Führungskräfte nicht nur innerhalb von Netzwerken agieren und delegieren, sondern diese auch aktiv gestalten, um Veränderungsprozesse zu fördern und Herausforderungen zwischen Versorgungssicherheit und organisatorischer Effizienz zu bewältigen
Global, regional, and national prevalence of adult overweight and obesity, 1990–2021, with forecasts to 2050: a forecasting study for the Global Burden of Disease Study 2021
No full textBackground
Overweight and obesity is a global epidemic. Forecasting future trajectories of the epidemic is crucial for providing an evidence base for policy change. In this study, we examine the historical trends of the global, regional, and national prevalence of adult overweight and obesity from 1990 to 2021 and forecast the future trajectories to 2050.
Methods
Leveraging established methodology from the Global Burden of Diseases, Injuries, and Risk Factors Study, we estimated the prevalence of overweight and obesity among individuals aged 25 years and older by age and sex for 204 countries and territories from 1990 to 2050. Retrospective and current prevalence trends were derived based on both self-reported and measured anthropometric data extracted from 1350 unique sources, which include survey microdata and reports, as well as published literature. Specific adjustment was applied to correct for self-report bias. Spatiotemporal Gaussian process regression models were used to synthesise data, leveraging both spatial and temporal correlation in epidemiological trends, to optimise the comparability of results across time and geographies. To generate forecast estimates, we used forecasts of the Socio-demographic Index and temporal correlation patterns presented as annualised rate of change to inform future trajectories. We considered a reference scenario assuming the continuation of historical trends.
Findings
Rates of overweight and obesity increased at the global and regional levels, and in all nations, between 1990 and 2021. In 2021, an estimated 1·00 billion (95% uncertainty interval [UI] 0·989–1·01) adult males and 1·11 billion (1·10–1·12) adult females had overweight and obesity. China had the largest population of adults with overweight and obesity (402 million [397–407] individuals), followed by India (180 million [167–194]) and the USA (172 million [169–174]). The highest age-standardised prevalence of overweight and obesity was observed in countries in Oceania and north Africa and the Middle East, with many of these countries reporting prevalence of more than 80% in adults. Compared with 1990, the global prevalence of obesity had increased by 155·1% (149·8–160·3) in males and 104·9% (95% UI 100·9–108·8) in females. The most rapid rise in obesity prevalence was observed in the north Africa and the Middle East super-region, where age-standardised prevalence rates in males more than tripled and in females more than doubled. Assuming the continuation of historical trends, by 2050, we forecast that the total number of adults living with overweight and obesity will reach 3·80 billion (95% UI 3·39–4·04), over half of the likely global adult population at that time. While China, India, and the USA will continue to constitute a large proportion of the global population with overweight and obesity, the number in the sub-Saharan Africa super-region is forecasted to increase by 254·8% (234·4–269·5). In Nigeria specifically, the number of adults with overweight and obesity is forecasted to rise to 141 million (121–162) by 2050, making it the country with the fourth-largest population with overweight and obesity.
Interpretation
No country to date has successfully curbed the rising rates of adult overweight and obesity. Without immediate and effective intervention, overweight and obesity will continue to increase globally. Particularly in Asia and Africa, driven by growing populations, the number of individuals with overweight and obesity is forecast to rise substantially. These regions will face a considerable increase in obesity-related disease burden. Merely acknowledging obesity as a global health issue would be negligent on the part of global health and public health practitioners; more aggressive and targeted measures are required to address this crisis, as obesity is one of the foremost avertible risks to health now and in the future and poses an unparalleled threat of premature disease and death at local, national, and global levels
Künstliche Intelligenz in der Sozialwirtschaft: Der Wandel beginnt nicht im Serverraum
No full textKünstliche Intelligenz (KI) hält Einzug in die Sozialwirtschaft – langsam, aber spürbar. In Einrichtungen der Pflege, Jugendhilfe oder Behindertenarbeit eröffnen algorithmische Systeme neue Möglichkeiten: von der Optimierung von Dienstplänen über automatisierte Verwaltungsprozesse bis hin zu KI-gestützter Spracherkennung oder Übersetzungshilfen für die Klient:innenkommunikation (Fraunhofer IAO o. J.). Viele Organisationen experimentieren bereits mit Anwendungen generativer KI wie ChatGPT, meist für unterstützende Aufgaben wie Textgenerierung oder visuelle Gestaltung. Tiefgreifende, systemische KI-Implementierungen sind hingegen noch die Ausnahme
Methodological Approach of Sensor System Testing for Autonomous Agricultural Machinery
No full textThe validation of autonomous functions in agricultural machinery faces unique challenges due to highly variable environmental conditions, task diversity, and the open-world nature of agricultural f ields. While safety-critical system development in other domains – especially automotive – has led to mature and widely adopted standards, the agricultural domain still lacks a standardized framework for evaluating the performance of sensor systems, particularly for object detection. The aim of this paper is to examine how testing methods and frameworks from adjacent domains, particularly the automotive and industrial sectors, can be adapted and transferred to the agricultural context. The study draws on existing literature and early findings, using a machine specific agricultural operational design domain to describe a procedural concept for deriving test cases for each function and test level
Triumvirat - Digitales Lernen, KI und Kompetenzmanagement
No full textDie Digitalisierung und die Integration von künstlicher Intelligenz (KI) verändern die Arbeitswelt und die Bildungsprozesse grundlegend. KI ermöglicht individuelles, flexibles und effektives Lernen, indem Lerninhalte gezielt auf die Bedürfnisse der Lernenden zugeschnitten werden und strategische Unternehmensziele unterstützen (North et al., 2018). KI-gestützte Lernplattformen analysieren das Lernverhalten in Echtzeit und bieten personalisierte Empfehlungen zur gezielten Kompetenzentwicklung (Wegenberger & Wegenberger, 2021). Dies stärkt die Anpassungsfähigkeit von Unternehmen und Mitarbeitenden in einer sich wandelnden Arbeitswelt. Die Förderung von Future Skills wie kritischem Denken, digitalem Problemlösen und Kommunikationsfähigkeit wird zunehmend wichtiger, um den Anforderungen der digitalen Zukunft gerecht zu werden. Strategische Modelle wie die Future Skills Triple Helix bieten dabei eine wichtige Orientierung
BattProDeep: A Deep Learning-Based Tool for Probabilistic Battery Aging Prediction
No full textProfitability, reliability, and efficiency of battery systems across a broad spectrum of applications, including both stationary energy storage and automobile sectors, are critically dependent on accurate battery lifespan predictions. Traditional deterministic models for estimating battery longevity are inadequate, as they do not fully capture the complex and stochastic nature of battery degradation. In this contribution BattProDeep is introduced as a groundbreaking tool that employs a deep learning-based framework to offer probabilistic predictions of battery aging, thereby addressing the uncertainties according to the experimental dataset. BattProDeep sets itself apart with its innovative features. It adopts an open-source approach, enhancing transparency and fostering collaboration across the global research community. This not only enriches the tool with a diverse range of insights but also accelerates advancements in the field. Utilizing cutting-edge TensorFlow and TensorFlow probability libraries, BattProDeep offers a data-driven method for battery aging prediction, improving accuracy and applicability across different battery types and conditions. Furthermore, its probabilistic predictions include confidence intervals, providing crucial information about prediction uncertainty, which is invaluable for risk management and decision-making in critical sectors. The validation results show that the mean prediction error for our
approach stays within ±0.2 % for high-cyclic applications, with all true measured capacity loss values falling within the 95 % confidence interval, affirming its reliability for risk management. These qualities, coupled with the benchmarking of BattProDeep according to the literature, make BattProDeep a key instrument for advancing battery health management, leading to more dependable and sustainable battery-powered solutions
Simulating Morphology and Degradation of PEMFC Cathode Catalyst Layers with Porous Carbon Supports: Part I. Effect of Relative Humidity on Catalyst Utilization
No full textWe present a physical model of the cathode catalyst layer morphology which simulates the electrochemically active catalyst surface area at varying degrees of humidification. The model considers pore, particle and ionomer distributions and resulting interfaces on the nanoscale to create a unique mathematical representation of each material. Specifically, the model discriminates between catalyst particles on the support surface and inside primary pores of the support and their respective connection to the proton-conducting phase.
Catalyst particles may be protonically activated by coverage with ionomer or water or be protonically inaccessible. The exact configuration depends on the particle size and location as well as the cell operating conditions, sensitizing the simulated catalyst utilization to these properties.
Model results are compared against data for five samples with different support materials and ionomer loadings manufactured in-house. Further, trends in catalyst utilization which were observed in recent literature are reproduced with the model. This work provides a comprehensive analysis of material configurations and simulations at begin of test. In Part II, the presented model is integrated into a degradation model to predict the evolution of the electrode morphology and catalyst utilization during ageing
Machine Learning Based Parameter Estimation of Energy Models in Digital Production Environments
No full textThe growing relevance of data science in engineering highlights the challenges of modelling complex systems using conventional methods, which are often time-consuming. This paper explores the application of various machine learning techniques to create energy models, aiming to minimize model deviation errors against real measurements. By comparing different approaches, this study seeks to optimize energy models, enhancing both accuracy and efficiency
Marke und Markenführung im Tourismus: Im Gespräch mit Top-Führungskräften aus der Tourismusindustrie
No full textEine starke Marke gilt in vielen Bereichen des Tourismus als ein zentraler Werttreiber und als essenzielle Grundbedingung für den Unternehmenserfolg. Die Markenführung ist deshalb ein zentrales Aufgabengebiet von Unternehmen und so ist die Bedeutung der Tourismusmarke als strategischer Erfolgsfaktor seit Jahren ein Dauerthema auf den Managementagenden in der Tourismusindustrie. In den Interviews mit Top-Führungskräften aus verschiedenen Bereichen der Tourismusindustrie werden die besondere Rolle der Marke im Tourismus und die spezifischen Herausforderungen und Aufgaben des Markenmanagements aus den jeweiligen Blickwinkeln der touristischen Teilbranchen analysiert und diskutiert
Analysis of Tourism Data
No full textIn an era where technology drives innovation and shapes the world around us, the ability to think critically and solve problems has never been more essential. Programming serves as a powerful tool to develop these skills, and Python, with its simplicity and versatility, is the perfect language to get started.
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