Fraunhofer Austria Research GmbH

3D Gaussian Splatting (3DGS) has emerged as a cutting‐edge technique for real‐time radiance field rendering, offering state‐of‐the‐art performance in terms of both quality and speed. 3DGS models a scene as a collection of three‐dimensional Gaussians, with additional attributes optimized to conform to the scene's geometric and visual properties. Despite its advantages in rendering speed and image fidelity, 3DGS is limited by its significant storage and memory demands. These high demands make 3DGS impractical for mobile devices or headsets, reducing its applicability in important areas of computer graphics. To address these challenges and advance the practicality of 3DGS, this state‐of‐the‐art report (STAR) provides a comprehensive and detailed examination of two complementary yet fundamentally distinct strategies: compression and compaction. Compression techniques focus on reducing the file size by encoding Gaussian attributes more efficiently. In contrast, compaction methods directly optimize the scene's structure by optimizing the number of Gaussian primitives. Notably, while methods in both categories aim to maintain or improve quality, each while minimizing its respective attributes—file size for compression and the number of Gaussians for compaction—compaction does not necessarily lead to smaller file sizes; it specifically targets improved efficiency during rendering, making it distinct from compression. We introduce the basic mathematical concepts underlying the analyzed methods, as well as key implementation details and design choices. Our report thoroughly discusses similarities and differences among the methods, as well as their respective advantages and disadvantages. We establish a consistent framework for comparing the surveyed methods based on key performance metrics and datasets. Specifically, since these methods have been developed in parallel and over a short period of time, currently, no comprehensive comparison exists. This survey, for the first time, presents a unified framework to evaluate 3DGS compression techniques. To facilitate the continuous monitoring of emerging methodologies, we maintain a dedicated website that will be regularly updated with new techniques and revisions of existing findings. Overall, this STAR provides an intuitive starting point for researchers interested in exploring the rapidly growing field of 3DGS compression. By comprehensively categorizing and evaluating existing compression and compaction strategies, our work advances the understanding and practical application of 3DGS in computationally constrained environments.

This paper addresses the unique challenges in processing aluminum materials within metal forming technology, specifically focusing on complex wear conditions involving abrasion and adhesion. A promising research approach to avoid abrasive and adhesive tool wear and reduce the friction coefficient towards aluminum alloys is the use of sufficiently smooth CVD diamond coatings. To achieve this, two approaches are considered. First, wear resistance is enhanced by using tool inserts made of carbide or a tungsten alloy, directly coated with CVD diamond. Second, the friction coefficient is selectively influenced or reduced by refining the polished CVD diamond coating through laser ablation. The study investigates the impact of these surface treatments on friction coefficients during both dry and lubricated forming processes involving the aluminum alloy EN AW-5182. Comparative analyses of various surface treatments are conducted against reference tests using diamond-like carbon (DLC)-coated tools. Through application-oriented strip drawing tests, the paper systematically examines how different surface smoothing techniques affect the coefficient of friction. This research provides valuable insights into optimizing metal forming processes for aluminum alloys through tailored surface treatments, advancing our understanding of friction dynamics in these specific manufacturing conditions.

Over the past few decades, carbon materials, including fullerenes, carbon nanotubes, graphene, and porous carbons, have achieved tremendous success in the fields of energy, environment, medicine, and beyond, through their development and application. Due to their unique physical and chemical characteristics for enabling simultaneous interaction with ions and transport of electrons, carbon materials have been attracting increasing attention in the emerging field of iontronics in recent years. In this review, we first summarize the recent progress and achievements of carbon-based iontronics (ionic sensors, ionic transistors, ionic diodes, ionic pumps, and ionic actuators) for multiple bioinspired applications ranging from information sensing, processing, and actuation, to simple and basic artificial intelligent reflex arc units for the construction of smart and autonomous iontronics. Additionally, the promising potential of carbon materials for smart iontronics is highlighted and prospects are provided in this review, which provide new insights for the further development of nanostructured carbon materials and carbon-based smart iontronics.

Crosado-embalming has been successfully used as embalming technique in research and teaching for over 20 years. It is applied in biomechanical testing experiments if the fresh tissues are unavailable, e.g., for cultural, ethical, logistical or health and safety reasons. However, features of human Crosado-embalmed tissues biomechanical characteristics including its load-deformation properties in comparison to fresh tissues and its controllability through hydration fluids may be insightful and therefore need to be studied further. This study compared the uniaxial load-deformation properties and the cross-sectional area (CSA) measurements of fresh-frozen and Crosado-embalmed collagen-rich tissues, namely the iliotibial band (ITB, 16 unembalmed and 35 embalmed specimens) and cranial dura mater (DM, 60 unembalmed cadavers, and 25 embalmed specimens). The water content of 120 Crosado-embalmed ITB samples (30 cadavers) were analysed considering established rehydration treatments, including polyethylene glycol (PEG). Crosado-embalmed tissues presented an increased elastic modulus (EM) (all p < 0.050; e.g., Crosado ITB PEG only 306 ± 91 MPa vs. fresh-frozen ITB PEG only 108 ± 31 MPa; mean ± standard deviation; p < 0.001) and ultimate tensile strength (UTS) (e.g., Crosado ITB PEG only 46 ± 15 MPa vs. fresh-frozen ITB PEG only 21 ± 8 MPa; p < 0.001) when rehydrated similar to the fresh tissues. The maximum force was different for the dura mater (Crosado 25 ± 13 N vs. fresh 21 ± 20 N; mean ± standard deviation; p = 0.050) but not for the ITB. The CSA following rehydration in PEG only was decreased for Crosado-embalmed samples (3.4 ± 1.2mm², ITB; 1.1 ± 0.5 mm², DM) compared to fresh-frozen (5.8 ± 2.1mm², ITB; 3.1 ± 1.2mm², DM) (all p ≤ 0.003). Furthermore, rehydration effects were observed following 24 h of PEG treatment (untreated tissues, 49 ± 9% vs. PEG only, 77 ± 4%; p < 0.001), in comparison to fresh samples (69%) tissues were hyperhydrated. In conclusion, Crosado-embalming appears to alter collagen-rich tissues’ morphological and mechanical properties. While an increase in material properties of Crosado-embalmed tissues was observed (Emod and UTS), the overall load-bearing capacity and peak structural strength remained unaltered for ITB tissues. This may result from CSA-related, geometric or molecular alterations after the fixative and osmotic water protocols related to changes in the collagen backbone and water-binding capacity.

The MaterialDigital initiative represents a major driver toward the digitalization of material science. Next to providing a prototypical infrastructure required for building a shared data space and working on semantic interoperability of data, a core focus area of the Platform MaterialDigital (PMD) is the utilization of workflows to encapsulate data processing and simulation steps in accordance with findable, accessible, interoperable, and reusable principles. In collaboration with the funded projects of the initiative, the workflow working group strives to establish shared standards, enhancing the interoperability and reusability of scientific data processing steps. Central to this effort is the Workflow Store, a pivotal tool for disseminating workflows with the community, facilitating the exchange and replication of scientific methodologies. This article discusses the inherent challenges of adapting workflow concepts, providing the perspective on developing and using workflows in the respective domain of the various funded projects. Additionally, it introduces the Workflow Store's role within the initiative and outlines a future roadmap for the PMD workflow group, aiming to further refine and expand the role of scientific workflows as a means to advance digital transformation and foster collaborative research within material science.

Given the continuous global degradation of the Earth’s ecosystem due to unsustainable human activity, it is increasingly important for enterprises to evaluate the effects they have on the environment. Consequently, assessing the impact of business processes on sustainability is becoming an important consideration in the discipline of Business Process Management (BPM). However, existing practical approaches that aim at a sustainability-oriented analysis of business processes provide only a limited perspective on the environmental impact caused. Further, they provide no clear and practically applicable mechanism for sustainability-driven process analysis and re-design. Following a design science methodology, we here propose and study SOPA, a framework for sustainability-oriented process analysis and re-design. SOPA extends the BPM life cycle by use of Life Cycle Assessment (LCA) for sustainability analysis in combination with Activity-based Costing (ABC). We evaluate SOPA and its usefulness with a case study, by means of an implementation to support the approach, thereby also illustrating the practical applicability of this work.

This study presents an innovative operando analysis of lithium‐sulfur (Li/S) multilayer pouch cells, employing a combination of lab‐source and synchrotron x‐ray imaging to investigate sulfur crystallite dissolution and lithium dendrite formation. By integrating advanced X‐ray imaging, impedance spectroscopy, and simultaneous monitoring of temperature and pressure, the research uncovers critical insights into the behavior of active and inactive cell components. The analysis reveals significant degradation increments, primarily driven by side product accumulation and the deterioration of lithium microstructures, which contribute to performance loss over cycling. Additionally, temperature distribution analysis shows a strong correlation between joule heating, polarization resistance, and the observed endothermic processes during crystallization. These findings provide a comprehensive understanding of the mechanistic processes within industrially relevant pouch cells, highlighting opportunities for optimizing Li/S cell designs and advancing high‐energy‐density battery systems for commercial applications.

Objective. Magnetic particle imaging (MPI) was introduced in 2005 as a promising, tracer-based medical imaging modality with the potential for high sensitivity and spatial resolution. Since then, numerous preclinical devices have been built but only a few human-scale devices, none of which targeted functional neuroimaging. In this work, we probe the challenges of scaling the technology to meet the needs of human functional neuroimaging with sufficient sensitivity for detecting the hemodynamic changes following brain activation with a spatio-temporal resolution comparable to current functional magnetic resonance imaging approaches. Approach. We built a human brain-scale MPI system using a mechanically-rotated, permanent-magnet-based field-free line (FFL) ( 1.1Tm−1) with a water-cooled, 26 kHz drive coil producing a field of up to 7 mT peak , and receive coil that can fit over a human head. Images are acquired continuously at a temporal resolution of 5 s/image, controlled by in-house LabView-based acquisition software with online reconstruction. We used a dilution series to quantify the detection limit, a series of parallel-line phantoms to assess the spatial resolution, and a large ‘G’ shaped phantom to demonstrate the human-scale field of view (FOV). Main results. The imager has a sensitivity of about 1 µg Fe over a 2D imaging FOV of 181 mm diameter(132 pixels) in a 5 s image. Depending on the image reconstruction used, the spatial resolution defined by 50% contrast between adjacent lines was 5–7 mm. Significance. This proof-of-concept system demonstrates a pathway for human MPI functional neuroimaging with the potential for an order of magnitude increase of sensitivity compared to the other human hemodynamic imaging methods. It demonstrates the successful transition of the FFL based MPI architecture from the rodent to human scale and identifies areas which could benefit from further work.

Background Exergames are interactive technology-based exercise programs. By combining physical and cognitive training components, they aim to preserve independence in older adults and reduce their risk of falling. This study explored whether primary end users (PEU, healthy older adults and patients with neurological and geriatric diagnoses) and secondary end users (SEU, health professionals) evaluated the ExerG functional model to be usable, providing a positive experience and therefore acceptable. Methods We conducted a multi-methods study using several assessments to quantify usability and enjoyment outcomes, along with semi-structured interviews to gain an in-depth understanding of the users’ experiences. Descriptive statistics were used for quantitative outcome measures. For qualitative data, a thematic analysis (TA) using an inductive, data-driven approach was carried out to develop themes for each user group. Results We interviewed 20 PEUs (13 healthy older adults, 7 patients) and 22 SEUs at two rehabilitation centres in Austria and Switzerland. Users' scores of over 70 on the System Usability Scale denoted good usability. On the Physical Activity Enjoyment Scale-16, both PEU groups rated the ExerG highly. Our TA approach identified four themes per user group. Themes from both PEU groups confirmed their enjoyment of training with the ExerG, however more variety and greater challenges were requested. Whilst the patient group appreciated the security given by the harness system, the healthy older adults reported feeling restricted. SEU themes reflected their approval of this novel training device, although a desire for increased difficulty and more individualisation was expressed. Clear instructions and an easy-to-use harness system were acknowledged and useful feedback for the developers emerged. Conclusions The ExerG is usable, offers a positive experience, and can therefore be regarded as an acceptable solution for the combined physical and mental training of older adults. Our findings contribute to the ongoing development of the ExerG, which will be a welcome addition to current training options for this target group. Further research is needed to confirm its effectiveness in preserving or improving functional independence in daily life and reducing the risk of falling.

Cardiovascular disease is a global health burden. To develop novel treatment options complex in vitro model systems are needed that resemble the pathophysiological situation ex vivo. Nevertheless, current pre-clinical in vitro models for pharmacological research are limited in complexity. Basic cell culture models lack cardiac tissue architecture and intercellular communication, limiting their translational capability. Force measurement methods on ex vivo cultured living myocardial slices (LMS) have been described for contraction analysis studies after compound treatment. Here, we combined LMS with a microphysiological system (MPS) to develop MPSlms as heart-on-chip approach that enables advanced nutrition circulation and integrates electrical pacing (MPSpacer) of the ex vivo cardiac tissue. To optimize the LMS technique, we designed a novel isometric tissue holder (ITH) and miniaturized the LMS format, allowing for extended condition testing and thus refinement of animal experiments. The contractile performance of cardiomyocytes was quantified by applying optical mapping of movement detection, which revealed precise and local variations in contraction within one LMS.

This paper presents the development of an optimized planning and control method for flexible manufacturing and assembly systems. While the significant potential of flexible manufacturing concepts to help producers adapt to market developments is recognized, the complexity of the flexible systems and the need to optimally plan and control them is a major obstacle in their practical implementation. Thus, this paper aims to develop a comprehensive digital planning method, based on a digital twin and to demonstrate the feasibility of the approach for practical application scenarios. The approach consists of four modules: (1) a simulation-based optimization module that applies reinforcement learning and genetic algorithms to optimize the module configuration and job routing in cellular reconfigurable manufacturing systems; (2) a synchronization module that links the physical and virtual systems via sensors and event handling; (3) a sensor module that enables a continuous status update for the digital twin; and (4) a visualization module that communicates the optimized plans and control measures to the shop floor staff. The demonstrator implementation and evaluation are implemented in a learning factory. The results include solutions for the method components and demonstrate their successful interaction in a digital twin, while also pointing towards the current technology readiness and future work required to transfer this demonstrator implementation to a full-scale industrial implementation.

In order to better facilitate the need for continuous business process improvement, the application of DevOps principles has been proposed. In particular, the AB-BPM methodology applies AB testing—a DevOps practice—and reinforcement learning to increase the speed and quality of business process improvement efforts. In this paper, we provide an industry perspective on this approach, assessing prerequisites, suitability, requirements, risks, and additional aspects of the AB-BPM methodology and supporting tools. Our qualitative study follows the grounded theory research methodology, including 16 semi-structured interviews with BPM practitioners. The main findings indicate: (1) a need for expert control during reinforcement learning-driven experiments in production, (2) the importance of involving the participants and aligning the method culturally with the respective setting, (3) the necessity of an integrated process execution environment, and (4) the long-term potential of the methodology for effective and efficient validation of algorithmically (co-)created business process variants, and their continuous management.

Agrivoltaic systems, combining solar energy generation with agricultural activities, offer a sustainable approach to maximising land efficiency. However, these systems can present challenges, such as potential shading effects that may impact fruit quality or crop yields. This study evaluated the impact of overhead agrivoltaic systems on apple ( Malus domestica L. cv. Gala) ripening and maturation patterns in a temperate orchard near Lake Constance, Germany. Experiments compared apples grown under conventional conditions (control) with those under agrivoltaic setups equipped with semi-transparent photovoltaic panels utilizing spatially distributed cells for 40% light transparency installed with a 70% ground-coverage ratio. Key metrics, including fruit diameter, length, volume, and BBCH phenology stages, were monitored throughout the 2024 growing season. An IoT-capable fixed RGB camera system captured daily images, and a machine learning algorithm assessed ripeness based on colour changes. Results indicated that apples under agrivoltaic conditions experienced a significant delay in ripening, reaching full maturity approximately 12 days later than the control group. On September 13 (harvest), no significant differences were found in mean length (67.54 mm for agrivoltaic apples and 70.05 mm for control apples), while the diameter of agrivoltaic apples was significantly smaller (65.59 mm versus 70.98 mm), indicating slightly smaller dimensions under shaded conditions. Fruit volume and weight were approximately 16% lower under agrivoltaic conditions, averaging 161.16 cm³ (138.6 g) versus 191.58 cm³ (164.8 g) in the control. The delayed maturation is attributed to reduced sunlight due to shading from the solar panels, affecting physiological processes essential for ripening. These findings indicate that overhead agrivoltaic systems can significantly delay apple phenology and fruit maturation. Depending on the agricultural goals, the desired harvest timing and the cultivar, this may be challenging or beneficial, e.g., if it adapts the crop against climate change impacts or other factors such as local climate conditions, latitude and geographic region, and market demand. Integrating IoT-based monitoring with machine learning enhances the precision of agricultural assessments, providing valuable data for managing the effects of agrivoltaic systems on crop development.