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This Book of Abstracts of the ModVal 2025 symposium offers detailed insights into current research trends in the field of modeling and experimental validation of electrochemical energy technologies. The symposium program featured two plenary lectures, four keynote addresses, 36 contributed oral presentations in two parallel sessions, and more than 100 posters.
Background/Objectives: The subject of this work is the reconstruction of the inner mechanics of Götz von Berlichingen’s second iron hand. The complex inner mechanics were unknown until Christian von Mechel published a detailed description in 1815. In this artificial hand, each finger can be engaged individually in its three joints and the thumb in one joint. Methods: Based on this description, the individual components were reconstructed at an enlarged scale of 2:1 using computer-aided design (CAD) software and a three-dimensional (3D) printer for the mechanisms. In addition, a finite element method (FEM) analysis was carried out for the components exposed to the greatest stress in order to identify critical areas. Results: By making some adjustments to the mechanics, it was possible to reproduce the mechanisms on a scale of 2:1 on the basis of the index finger. However, when the model was rescaled to 1:1, the internal plastic components were too fragile. This problem was caused by the properties of the 3D printing materials and could be solved by manufacturing the springs from steel. Conclusions: This work aims to make a valuable contribution to the preservation and understanding of the historical artificial second iron hand of Götz von Berlichingen. It once again demonstrates the very precise and detailed craftsmanship of goldsmiths of that time.
Although there do exist a few aeroacoustic studies on harmful artificial phenomena related to the usage of non-uniform Cartesian grids in lattice Boltzmann methods (LBM), a thorough quantitative comparison between different categories of grid arrangement is still missing in the literature. In this paper, several established schemes for hierarchical grid refinement in lattice Boltzmann simulations are analyzed with respect to spurious aeroacoustic emissions using a weakly compressible model based on a D3Q19 athermal velocity set. In order to distinguish between various sources of spurious phenomena, we deploy both the classical Bhatnagar–Gross–Krook and other more recent collision models like the hybrid recursive-regularization operator, the latter of which is able to filter out detrimental non-hydrodynamic mode contributions, inherently present in the LBM dynamics. We show by means of various benchmark simulations that a cell-centered approach, either with a linear or uniform explosion procedure, as well as a vertex-centered direct-coupling method, proves to be the most suitable with regards to aeroacoustics, as they produce the least amount of spurious noise. Furthermore, it is demonstrated how simple modifications in the selection of distribution functions to be reconstructed during the communication step between fine and coarse grids affect spurious aeroacoustic artifacts in vertex-centered schemes and can thus be leveraged to positively influence stability and accuracy.
This contribution introduces the use of convolutional neural networks to detect humans and collaborative robots (cobots) in human–robot collaboration (HRC) workspaces based on their thermal radiation fingerprint. The unique data acquisition includes an infrared camera, two cobots, and up to two persons walking and interacting with the cobots in real industrial settings. The dataset also includes different thermal distortions from other heat sources. In contrast to data from the public environment, this data collection addresses the challenges of indoor manufacturing, such as heat distortions from the environment, and allows for it to be applicable in indoor manufacturing. The Work-Life Robotics Institute HRC (WLRI-HRC) dataset contains 6485 images with over 20 000 instances to detect. In this research, the dataset is evaluated for implementation by different convolutional neural networks: first, one-stage methods, i.e., You Only Look Once (YOLO v5, v8, v9 and v10) in different model sizes and, secondly, two-stage methods with Faster R-CNN with three variants of backbone structures (ResNet18, ResNet50 and VGG16). The results indicate promising results with the best mean average precision at an intersection over union (IoU) of 50 (mAP50) value achieved by YOLOv9s (99.4 %), the best mAP50-95 value achieved by YOLOv9s and YOLOv8m (90.2 %), and the fastest prediction time of 2.2 ms achieved by the YOLOv10n model. Further differences in detection precision and time between the one-stage and multi-stage methods are discussed. Finally, this paper examines the possibility of the Clever Hans phenomenon to verify the validity of the training data and the models’ prediction capabilities.
Aircraft ad hoc networks simplify airplane-to-airplane or airplane-to-service station communication. It evolved from MANET and VANET ad-hoc networks. MANET connects mobile networks and VANET for cars. Adhoc networks are popular because they can be built without routers or access points when no network exists. Flights are self-organizing nodes in AANET. This dynamic network requires only two nodes and no specific infrastructure. This method is important for GPS navigation, aircraft–ship communications, and navy signaling. These Ad Hoc networks let aircraft interact with the grounds and service stations to decrease traffic between aircraft. It can also connect with a network radar aircraft to avoid collisions. Planes ad-hoc networks connect planes spontaneously. It is versatile and simple. The aircraft adapts to the situation to make connections; thus, routers or networks are unnecessary. Planes’ fast mobility accelerates network evolution. No infrastructure is needed for aircraft to transmit and relay data. AANET optimizes performance despite restricted bandwidth, power, and processing. This paper describes the AANET network and the recommended solutions to improve airplane performance. It also covers existing methodologies, benefits and cons, and various scholars’ work on the AANET.
A deep mutational scanning platform to characterize the fitness landscape of anti-CRISPR proteins
(2024)
Deep mutational scanning is a powerful method for exploring the mutational fitness landscape of proteins. Its adaptation to anti-CRISPR proteins, which are natural CRISPR-Cas inhibitors and key players in the co-evolution of microbes and phages, facilitates their characterization and optimization. Here, we developed a robust anti-CRISPR deep mutational scanning pipeline in Escherichia coli that combines synthetic gene circuits based on CRISPR interference with flow cytometry coupled sequencing and mathematical modeling. Using this pipeline, we characterized comprehensive single point mutation libraries for AcrIIA4 and AcrIIA5, two potent inhibitors of CRISPR-Cas9. The resulting mutational fitness landscapes revealed considerable mutational tolerance for both Acrs, suggesting an intrinsic redundancy with respect to Cas9 inhibitory features, and – for AcrIIA5 – indicated mutations that boost Cas9 inhibition. Subsequent in vitro characterization suggested that the observed differences in inhibitory potency between mutant inhibitors were mostly due to changes in binding affinity rather than protein expression levels. Finally, to demonstrate that our pipeline can inform Acrs-based genome editing applications, we employed a selected subset of mutant inhibitors to increase CRISPR-Cas9 target specificity by modulating Cas9 activity. Taken together, our work establishes deep mutational scanning as a powerful method for anti-CRISPR protein characterization and optimization.
In the quest for effective lung cancer treatments, the potential of 3,6-diaminoacridine-9-carbonitrile (DAC) has emerged as a game changer. While DAC's efficacy against glioblastoma is well documented, its role in combating lung cancer has remained largely untapped. This study focuses on CTX-1, exploring its interaction with the pivotal EGFR-TKD protein, a crucial target in lung cancer therapeutics. A meticulous molecular docking analysis revealed that CTX-1 exhibits a noteworthy binding affinity of −7.9 kcal/mol, challenging Erlotinib, a conventional lung cancer medication, which displayed a binding affinity of −7.3 kcal/mol. For a deeper understanding of CTX-1's molecular mechanics, this study employed rigorous 100-ns molecular dynamics simulations, demonstrating CTX-1's remarkable stability in comparison with erlotinib. The Molecular Mechanics Poisson–Boltzmann Surface Area (MM-PBSA) method further corroborated these results, with CTX-1 showing a free binding energy of −105.976 ± 1.916 kJ/mol. The true prowess of CTX-1 was tested against diverse lung cancer cell lines, including A549, Hop-62 and H-1299. CTX-1 not only significantly outperformed erlotinib in anticancer activity but also exhibited a spectrum of therapeutic effects. It effectively diminished cancer cell viability, induced DNA damage, halted cell cycle progression, generated reactive oxygen species (ROS), impaired mitochondrial transmembrane potential, instigated apoptosis and successfully inhibited EGFR-TKD. This study not only underscores the potential of CTX-1 a formidable contender in lung cancer treatment but also marks a paradigm shift in oncological therapeutics, offering new horizons in the fight against this formidable disease.
Wood species identification plays a crucial role in various industries, from ensuring the legality of timber products to advancing ecological conservation efforts. This paper introduces WoodYOLO, a novel object detection algorithm specifically designed for microscopic wood fiber analysis. Our approach adapts the YOLO architecture to address the challenges posed by large, high-resolution microscopy images and the need for high recall in localization of the cell type of interest (vessel elements). Our results show that WoodYOLO significantly outperforms state-of-the-art models, achieving performance gains of 12.9% and 6.5% in F2 score over YOLOv10 and YOLOv7, respectively. This improvement in automated wood cell type localization capabilities contributes to enhancing regulatory compliance, supporting sustainable forestry practices, and promoting biodiversity conservation efforts globally.
Carbon plate running shoes (CPRSs) have gained widespread popularity among elite and amateur runners, representing one of the most substantial changes in running gear over the past decade. Compared to elite runners, however, amateurs run at lower speeds and show more diverse running styles. This is a meaningful difference as many previous studies on CPRSs focus either on highly trained male runners and higher speeds or only on a single CPRSs manufacturer. The present study aims at bridging this gap by investigating how CPRSs from four different manufacturers affect running economy in amateurs of both sexes at their individual running speeds. For this purpose, 21 trained amateur triathletes (12 men; 9 women) completed an incremental treadmill test until volitional exhaustion, yielding running speeds at ventilatory thresholds 1 (vVT1) and 2 (vVT2). In a second session, subjects ran five trials of 3 × 3 min (speeds of 90% vVT1, ½ (vVT1 + vVT2), and 100% vVT2), wearing one out of four different pairs of CPRSs or their own preferred non-CPRS shoes in each trial. Our results show that tested CPRS models resulted in a significant reduction in the mean energy cost of transport, compared to the non-CPRS control condition, with Cohen’s d amounting to −1.52 (p = 0.016), 2.31 (p < 0.001), 2.57 (p < 0.001), and 2.80 (p < 0.001), respectively, although effect sizes varied substantially between subjects and running speeds. In conclusion, this study provides evidence that amateur athletes may benefit from various manufacturers’ CPRS models at their typical running speeds to a similar degree as highly trained runners. It is recommended that amateur athletes evaluate a range of CPRSs and select the shoe that elicits the least subjective sensation of fatigue over a testing distance of at least 400–1000 m.
AbstractAccurate and automatic assessments of body segment kinematics via wearable sensors are essential to provide new insights into the complex interactions between active lifestyle and fall risk in various populations. To remotely assess near-falls due to balance disturbances in daily life, current approaches primarily rely on biased questionnaires, while contemporary data-driven research focuses on preliminary fall-related scenarios. Here, we worked on an automated framework based on accurate trunk kinematics, enabling the detection of near-fall scenarios during locomotion. Using a wearable inertial measurement cluster in conjunction with evaluation algorithms focusing on trunk angular acceleration, the proposed sensor-framework approach revealed accurate distinguishment of balance disturbances related to trips and slips, thereby minimising false detections during activities of daily living. An important factor contributing to the framework’s high sensitivity and specificity for automatic detection of near-falls was the consideration of the individual’s gait characteristics. Therefore, the sensor-framework presents an opportunity to substantially impact remote fall risk assessment in healthy and pathological conditions outside the laboratory.