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Parametric texture models have been applied successfully to synthesize artificial images. Psychophysical studies show that under defined conditions observers are unable to differentiate between model-generated and original natural textures. In industrial applications the reverse case is of interest: a texture analysis system should decide if human observers are able to discriminate between a reference and a novel texture. Here, we implemented a human-vision-inspired novelty detection approach. Assuming that the features used for texture synthesis are important for human texture perception, we compare psychophysical as well as learnt texture representations based on activations of a pretrained CNN in a novelty detection scenario. Based on a digital print inspection scenario we show that psychophysical texture representations are able to outperform CNN-encoded features.
Model predictive path integral (MPPI) control has recently received a lot of attention, especially in the robotics and reinforcement learning communities. This letter aims to make the MPPI control framework more accessible to the optimal control community. We present three classes of optimal control problems and their solutions by MPPI. Further, we investigate the suboptimality of MPPI to general deterministic nonlinear discrete-time systems. Here, suboptimality is defined as the deviation between the control provided by MPPI and the optimal solution to the deterministic optimal control problem. Our findings are that in a smooth and unconstrained setting, the growth of suboptimality in the control input trajectory is second-order with the scaling of the MPPI exploration uncertainty. The results indicate that the suboptimality of the MPPI solution can be modulated by tuning the hyperparameters. We illustrate our findings using numerical examples.
Die Pandemiesituation 2020-2021 hat gezeigt, dass schützende Maßnahmen zwar unumgänglich sind, aber unser alltägliches Leben und Wirtschaften extrem einschränken. Meist fehlte es an Kenntnissen, um ein optimales Maß der Einschränkungen festzulegen. Es wird ein Softwaretool präsentiert, mit dessen
Hilfe Personenströme in Räumen und gleichzeitig die damit verbundene Ansteckungswahrscheinlichkeit verschiedener Infektionskrankheiten simuliert werden können. Dabei geht es nicht nur um freie und einfache Bedienbarkeit der Software, sondern auch um mathematische Fragestellungen, die eine robuste Simulation mit korrekten Ergebnissen garantiert.
Traditional industrial training methods often require extensive time, cost, and resources for effective preparation and execution. This paper presents a virtual reality (VR)-based solution for industrial training, enhanced by a cutaneous feedback haptic glove that improves the immersive learning experience. The developed haptic glove integrates a high-resolution tactile pin array that provides realistic feedback through the fingertips, allowing users to sense interactions with virtual objects. The VR training environment, created using Unity, simulates a crane sorting system that replicates realworld industrial operations. This setup enables users to manipulate a virtual crane through hand motions, with the haptic glove delivering tactile feedback that enhances the sense of realism. This approach not only advances skill acquisition but also opens opportunities for safe and cost-effective training in complex industrial tasks. By leveraging VR and cutaneous feedback, this study contributes to the modernization of training methods, reducing risks associated with real-world training scenarios and offering a pathway toward more immersive, effective industrial training.
Challenges in calculating the AHI to diagnose sleep apnoea using deep learning and portable monitors
(2025)
Automatic detection of the apnoea–hypopnoea index (AHI) using a portable monitor (PM) with artificial intelligence (AI) represents a significant challenge. The objective of this study was to examine factors that affect the performance of an AI algorithm that had been previously trained in calculating the AHI with polysomnography (PSG) data using signals collected by a PM.
Mechanical and plant engineering, a key industry in the German economy, is facing challenges such as a shortage of skilled workers, international competitive pressure as well as increasing customer requirements. Small and medium-sized enterprises (SMEs) in this sector are therefore showing a growing interest in AI-supported technologies in service intending to improve customer engagement (CE) and ensure the long-term success of the company. Based on interviews with professionals and leaders from SMEs in the mechanical and plant engineering sector, this study examines their approaches to exploiting AI-based digitalization potential in after-sales service. The aim is to analyse and evaluate how these technologies promote CE and to identify the possible impact on value creation and corporate success. The focus is on questions relating to the transfer of performance factors to the customer, increasing demands on service quality, and the role of AI-based applications in after-sales service to ensure the success of the company. The findings highlight the positive impact of using AI software solutions on service quality, for example, and therefore on customer satisfaction. In addition, there is a shift of tasks from the company to the customer, relieving internal processes and changing interaction. The customer is actively involved in the service process, e.g. through self-service platforms and real-time data access. This co-creation approach creates a positive synergy, where the customer’s continuous use of platforms and access to data increases value on both sides and deepens CE. Despite the limited spread of AI solutions to date, companies are increasingly recognizing the strategic benefits of these technologies but face technological and organizational challenges that affect implementation.
Chancen und Hemmnisse einer Installation von Viti-PV am deutschen und schweizerischen Bodensee
(2025)
Der Klimawandel stellt den Weinbau vor große Herausforderungen, eröffnet aber mit Technologien wie der Viti-Photovoltaik auch neue Chancen, indem diese sowohl die Resilienz der Reben stärken als auch erneuerbaren Strom erzeugen kann. Eine Umfrage unter Winzern am deutschen und schweizerischen Bodensee zeigt ein grundsätzliches Interesse an Viti-PV, jedoch auch verbreitete Bedenken hinsichtlich Investitionskosten, rechtlicher Hürden und möglicher mikroklimatischer Auswirkungen.
Small Island Developing States (SIDS) like Seychelles face challenges in food and energy security, relying heavily on imports for both. Global crises exacerbate these vulnerabili-ties, causing price and supply chain issues. To increase self-sufficiency, Seychelles aims to promote backyard farming, strengthen sustainability, and strive for 100% renewable energy. However, limited land poses challenges. Agrivoltaics, combining solar energy and agriculture, may offer a solution. This examination explores its potential through a survey of backyard farmers on Mahé, focusing on farming practices, energy use, and per-ceptions of Agrivoltaics.
A survey of 29 backyard farmers on Mahé revealed that 58.5% of their land is suitable for Agrivoltaics, highlighting its potential to enhance electricity self-sufficiency, currently at 66%. Bifacial PV modules and interspace or overhead mounting systems are recom-mended, as many local crops could thrive under shading. Farmers value self-sufficiency highly, with 45% open to community projects. Opportunities for Agrivoltaics were rated positively, while barriers scored lower. Establishing a trial site and conducting further research are key steps to advancing this approach.
Seagrass ecosystems provide essential ecological services and are increasingly recog-nized for their potential as sustainable building insulation. While prior studies have examined seagrass insulation in temperate climates, its suitability for tropical con-struction remains largely unexplored. This study assesses the insulation performance, practical challenges, and adoption barriers of seagrass insulation in tropical climates, using building physics simulations and structured expert interviews, with case studies in Seychelles and Auroville, India. Simulation results indicate that seagrass insulation with its high specific heat capacity effectively reduces overheating risks and demon-strates consistently low mould-growth potential under persistently humid tropical conditions. Despite these technical advantages, expert interviews reveal significant non-technical barriers, including negative public perception, regulatory uncertainties, and logistical complexities. Seychelles faces particular hurdles such as limited coastal storage capacity and stringent environmental regulations. In contrast, Auroville emerges as an ideal demonstration site due to its strong sustainability culture and openness to innovative building materials. The study further identifies that integrating seagrass insulation into a structured, regulated supply chain—from sustainable har-vesting and processing to quality assurance—could simultaneously enhance ecosystem conservation and material availability. Implementing a harvesting framework analo-gous to sustainable forestry could ensure environmental protection alongside supply stability. The findings emphasize the urgent need for targeted awareness initiatives, regulatory alignment, and economic feasibility assessments to overcome barriers and enable wider adoption. Overall, this research highlights seagrass insulation as a prom-ising, climate-positive construction material with strong potential under tropical con-ditions, provided that identified logistical, societal, and regulatory challenges are ad-dressed through dedicated research, stakeholder collaboration, and practical pilot projects.