This paper investigates the practical implementation of models in the energy management of buildings for complex user behavior and the use of multiple heating technologies, focusing on the development of an accurate yet efficient model. The study is exemplified by the new Institute for Hydrogen and Energy Technology building at Hof University of Applied Sciences, designed as a research platform for innovative energy solutions. We address the integration of shading strategies and the subsequent model order reduction necessary for effective Model Predictive Control application. The research involves creating a simplified resistance-capacitance model of the building's thermal zones, including its heating systems and a dual façade with solar thermal collectors. This simplified model, generated using the BRCM Toolbox and validated against a detailed EnergyPlus model, accounts for dynamic discrepancies, particularly during periods of high solar radiation. Optimization techniques are applied to the simplified model across different seasons, revealing that season-specific optimizations are more effective for long-term simulations, while a combined optimization approach is suitable for short-term and year-round MPC applications. The results underscore the potential of advanced MPC strategies to enhance energy efficiency and sustainability in complex building systems with multiple renewable energy sources.

Es ist nahezu unmöglich, sich dem Hype um das Thema Künstliche Intelligenz (KI) zu entziehen. Insbesondere das Aufkommen der generativen KI, von Instrumenten wie ChatGPT oder Midjourney und der zugrundeliegenden großen Sprachmodelle, hat die Spielregeln für das Marketing neu definiert. Auch in der Mediaplanung, bei der Entwicklung und Umsetzung von Werbestrategien, entstehen neue Handlungsfelder, Chancen und Herausforderungen.

The density of memory cells in modern DRAM is so high that frequently accessing a memory row can flip bits in nearby rows. That effect is called Rowhammer, and an attacker can exploit this phenomenon to flip bits by rapidly accessing the contents of nearby memory rows. In recent years, researchers have developed sophisticated exploits based on this vulnerability, which enable privilege escalation on desktop computers, mobile devices, and even cloud systems without requiring any software vulnerability. However, rows are not equally vulnerable to Rowhammer. Therefore, an attacker has to massage the memory, for instance, with Page Table Entry (PTE) spraying, to increase the chance of successful exploitation. More bit flips mean the attacks become easier and faster to conduct. 

In this paper, we present Flipper, a Rowhammer amplification attack against DDR3, consisting of two components: cmpIST exploits the cmpsb and repe x86 instructions to get DRAM access with higher frequency. cmpP AR exploits the effect of hammering in multiple threads, which increases the number of bit flips found in a given time, as shown in previous work. As a result, we can increase the number of bit flips by a factor of 830 on the measured devices, even on systems featuring mitigation techniques, without using administrative privileges. We evaluate our technique on six DDR3 DIMMs. Although DDR3 memory has been superseded by DDR4 and DDR5 memory technologies, it is still widely used in devices that do not require frequent replacement, such as projectors, smart displays, servers, embedded devices, routers, and printers.

Map matching is a widely used technology for assigning tracks recorded by Global Navigation Satellite Systems (GNSS) to existing road networks. Due to the measurement uncertainty of GNSS positions, the biggest challenge is to map them accurately. To develop and verify suitable algorithms for map matching, ground truth, i.e., the traveled routes in the road network, is required as a reference. However, GNSS recorded tracks naturally lack the ground truth routes. Providing this data is time-consuming and costly in these cases, as it requires manual correction of the routes based on human memorization. This is not practical on a large scale, e.g., with floating car data (FCD). This is why there exist only a few isolated ground truth data sets that were created in this way for map matching. To close this gap, we introduce and evaluate in this work a new open source tool-chain for artificially generating large amounts of simulated ground truth routes for map matching. Based on these routes, we generate simulated FCD and we apply comparably authentic and parameterizable artificial GNSS noise with varying noise characteristics. The generated data allows to thoroughly evaluate and improve the performance of existing map matching algorithms and facilitates in future research the development of novel algorithms based on sufficiently large and diverse labeled data. In this work, we evaluate different scenarios of varying noise characteristics of our artificially generated ground truth data to compare the robustness, individual strengths, and weaknesses of existing open source map matching implementations. Our new approach of artificially generating ground truth data for map matching addresses the existing lack of sufficient available reference data for ongoing map matching research.

There is a huge demand for trying on clothing at home. Recent methods to capture your figure mostly work in a 2d image plane and despite recent improvements of available technology the simulation of clothing is still not satisfactory. Especially the rendering of different clothing sizes is still a major challenge, which is only addressed by COTTON [1]. We propose an improvement to this approach by adding more control over the image generation process. For this we employ a special type of conditional diffusion model, namely ControlNet, and take keypoints of the fashion as conditional input.

Wichtig ist, dass wir KI als Instrument zur Unterstützung begreifen. Der Einsatz von KI aus Marketingsicht und Möglichkeiten für den Bereich Pflege.

Background

It is controversial to what extent afterimages, as distinct visual phenomena, are altered in patients with migraine and whether they have a specific role in migraine pathophysiology.

Objective

The aim of this cross-sectional study was to investigate the duration of afterimages in patients with migraine, migraine with aura (MwA), and migraine without aura (MwoA), compared to healthy controls (HCs).

Methods

Adults with migraine, MwA, and MwoA, diagnosed according to The International Classification of Headache Disorders, third edition criteria and HCs without relevant headache history were included. Initially, factors affecting the experimental setting of testing afterimage latency were determined. Then, afterimage duration was measured in the two study groups (MwA and MwoA) and the HC group. Patient characteristics, intraocular pressure, and relevant comorbid conditions, as well as scales on depressive symptoms (nine-item Patient Health Questionnaire) and headache-specific psychosocial impairment (six-item Headache Impact Test) were recorded. Lastly, the role of different stimulus colors, as well as habituation effects after repeated stimulation, were investigated.

Results

The main study included 174 participants (40 with MwA, 53 with MwoA, and 81 HCs). The duration of the afterimage in patients with MwA was significantly longer than in HCs, at a mean (standard error of the mean [SEM]) of 12.6 (2.6) versus 5.5 ( 0.3) s (p = 0.035), while there was no significant difference between patients with MwoA (mean [SEM] 7.7 [1.6] s; p = 0.510) and HCs. There was also no significant effect of stimulus color on afterimage latency (mean [SEM] red: 8.9 [1.2] s and black: 8.4 [1.2] s).

Conclusion

We found significantly longer afterimage duration in patients with MwA compared to both HCs and patients with MwoA. Furthermore, partially selective stimulation of retinal rods and cones by different stimulus colors had no effect on afterimage duration suggesting a relevant subcortical and/or cortical modulation in migraine aura with increased excitability.

Weinbau dürfte zu den ältesten und traditionsreichsten Branchen der Welt gehören. Politische Umbrüche und – natürlich vor allem in jüngerer Zeit – klimatische und ökologische Veränderungen sorgen, genauso wie technische Innovationen, für stetigen Anpassungsdruck bei der Erzeugung und Vermarktung von Wein. Auch die Digitalisierung der Weinwirtschaft schreitet immer weiter voran. Künstliche Intelligenz (KI) spielt im Weinbau eine zunehmend wichtige Rolle, über alle Glieder der Wertschöpfungskette hinweg.

Große Sprachmodelle wie ChatGPT benötigen große und teure Server und viel Energie. Man kann sie aber quantisieren, sodass sie mit viel weniger Speicher und Strom auskommen und sogar lokal auf einem Smartphone laufen. Wir erklären, warum quantisierte Modelle viel schneller antworten und trotzdem fast so schlau sind wie die großen Originale.

During the corona pandemic, the role of science was seen as something very important in broad sections of society. Unfortunately, this appears to be less the case with regard to climate change. Increasingly, subjective and emotionally based statements are dominating the discussion, which must be viewed extremely critically in view of the complex interactions and extreme effects. On the other hand, great hope is being placed in science to master the complex challenges with the help of artificial intelligence (AI). With the public appearance of generative AI (GenAI) in the form of ChatGPT, a new, partly critical discussion is being held, although AI-based technologies have already made impressive progress as a result of scientific research for many years and numerous developments are already in real use. On the one hand, science is expected to provide insights and recommendations for the responsible use of AI. On the other hand, the use of AI in research work is also discussed critically because it is used, for example, in the application phase of research projects or to evaluate the data obtained and to write down and communicate the results. This gives rise to questions such as “How valid are scientific achievements that are produced with (the help of) AI?” Especially when using generative AI, there is a risk that the own creation process will degenerate due to uncontrolled use, excessive dependency and distortion of results [1], because activities that go beyond routine processes are conveniently left to generative AI, which can be a problem especially when processing complex tasks. An analysis of the authors of around 30 master’s theses from 2023 and 2024 in an international engineering master’s program showed that around 1/3 of the theses are strongly influenced by GenAI results, recognizable by the usual listing form, the text style and the lack of reference to the task. These papers were also among the 1/3 with the lowest grading. Without guidance on the effective use of GenAI, its use does not appear to lead to improved performance, but instead encourages the unthinking copying of text modules.

On the other hand, observations of the use of generative AI in everyday teaching and research show that GenAI can promote complexity competence in particular when used in a targeted manner on the basis of appropriate training. The authors use GenAI in various contexts of their research, increasingly to support the solution of complex problems and tasks in complex environments. The main area of application is water management and increasingly the analysis of urban areas to adapt to water-related challenges caused by climate change. The main focus here is on the massive impact of extreme weather events such as heavy rainfall and prolonged periods of drought on urban and regional infrastructure as well as forest and agricultural areas. These main areas of application are not only highly complex in thematic terms. Solutions must be implemented at urban and municipal level, and here too, the spatial, infrastructural and organizational environment is highly complex. It is against this background that the observations described in the use of generative AI were made, the theoretical framework described in this article was created and the hypothetical analysis was carried out, for which empirical evidence is still pending, but in preparation.

Despite being a specific sustainable development goal (SDG), the role of consumers for sustainable consumption is still ambiguous. This is exemplified by a vast amount of research on the attitude-intention-behavior gap, which generally describes consumers’ failures to behave as sustainable as theoretically predicted. Recent reviews have prompted further investigations beyond the existing literature on factors influencing this gap. We contribute to this call by quantitatively investigating five antagonistic dimensions – both intrapsychic and situation-related – of smartphone usage and sustainable consumer behavior in Germany (n=800). Our results indicate two novel concepts. Emotional connection – i.e., consumers’ connections with the consumption experience – can either promote or prevent sustainable behavior, while exploration-driven consumerism – i.e., new purchases due to exploration tendencies – typically attenuates sustainable behavior. This illustrates how and when sustainability is outweighed by other consumer attitudes. We contextualize these results and conclude our study by highlighting limitations and further research opportunities.

Seien Sie dabei, wenn wir am Dienstag, den 17. Dezember, einen aufschlussreichen Webcast zum Thema „CX und KI - eine deutsche Perspektive“ anbieten! 🌟 Entdecken Sie die einzigartige Sichtweise auf Customer Experience (CX) und Künstliche Intelligenz (KI) aus Deutschland. Unsere Experten werden sich mit folgenden Themen befassen: Die deutsche CX-Perspektive und ihre Unterschiede zu Europa und den USA. Der deutsche KI-Ansatz mit Fokus auf Branchen, Datenoptimierung und Qualitätsmanagement. KI-Anwendungen aus der Praxis in Vertrieb, Marketing und dem öffentlichen Sektor. Die Landschaft der KI-Startups in Deutschland und der DACH-Region. Herausforderungen und Chancen der KI-Finanzierung, Regulierung und Nachhaltigkeit. Verpassen Sie nicht die Gelegenheit, wertvolle Einblicke zu gewinnen und sich mit Referenten und Experten der Branche auszutauschen! Join us for an insightful webcast on "CX and AI - A German Perspective" on Tuesday, December 17th! 🌟 Explore the unique viewpoints on Customer Experience (CX) and Artificial Intelligence (AI) from Germany. Our experts will delve into: The German CX perspective and its differences from Europe and the USA. The German AI approach, focusing on sectors, data optimization, and quality management. Real-world AI applications in sales, marketing, and the public sector. The landscape of AI startups in Germany and the DACH region. Challenges and opportunities in AI financing, regulation, and sustainability. Don't miss this opportunity to gain valuable insights and engage with industry speakers and experts!

Literature reviews play a crucial role in Information Systems (IS) research. However, scholars have expressed concerns regarding the reproducibility of their results and the quality of documentation. The involvement of human reproducers in these reviews is often hindered by the time-consuming nature of the procedures. The emergence of Large Language Models (LLMs) seems promising to support researchers and to enhance reproducibility. To explore this potential, we conducted experiments using various LLMs, focusing on abstract scanning, and have presented initial evidence suggesting that the application of LLMs in structured literature reviews could assist researchers in refining and formulating rules for abstract scanning. Based on our preliminary findings, we identify potential future research directions in this research in progress paper.

Angesichts des enormen Veränderungspotenzials, das ChatGPT, Perplexity & Co für die Arbeitswelt bergen, stellt sich die Frage, wie man diese Instrumente der generativen KI sinnvoll in den Arbeitsalltag integriert. Einen entsprechenden Analyseansatz bietet das Kategorisierungsmodell der „Cyborgs und Zentauren“.