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A three-dimensional risk matrix to support investment strategies for network planning by analyzing the impact from congestion on the remaining lifetime of assets
No full textReinforcement to increase the capacity of distribution networks is necessary to prevent congestion if the load exceeds the capacity limit of assets. However, distribution system operators (DSOs) face difficulties in keeping up with the pace of all required reinforcements due to the energy transition. Therefore, this paper proposes a new framework for DSOs to support their investment strategy for network planning that can indicate for which assets reinforcement is most urgent and for which congestion management (CM) is sufficient. This new framework is based on a three-dimensional risk matrix which distinguishes three levels to categorize the impact of congestion on the fault probability. Three properties are used to reflect the dimensions of the risk matrix which are the maximum power, frequency, and maximum duration of the congestion. Two use cases to which the proposed framework is applied are presented in this paper. First, both use cases are studied without CM. Then, the use cases were studied with the application of CM. For both use cases, the results indicated that the application of CM can not avoid the need for reinforcement.</p
Adaptive Dual-Headway Unicycle Pose Control and Motion Prediction for Optimal Sampling-Based Feedback Motion Planning
No full textSafe, smooth, and optimal motion planning for nonholonomically constrained mobile robots and autonomous vehicles is essential for achieving reliable, seamless, and efficient autonomy in logistics, mobility, and service industries. In many such application settings, nonholonomic robots, like unicycles with restricted motion, require precise planning and control of both translational and orientational motion to approach specific locations in a designated orientation, such as for approaching charging, parking, and loading areas. In this paper, we introduce a new dual-headway unicycle pose control method by leveraging an adaptively placed headway point in front of the unicycle pose and a tailway point behind the goal pose. In summary, the unicycle robot continuously follows its headway point, which chases the tailway point of the goal pose and the asymptotic motion of the tailway point towards the goal position guides the unicycle robot to approach the goal location with the correct orientation. The simple and intuitive geometric construction of dual-headway unicycle pose control enables an explicit convex feedback motion prediction bound on the closed-loop unicycle motion trajectory for safety verification. We present an application of dual-headway unicycle control and motion prediction for optimal sampling-based motion planning around obstacles. In numerical simulations, we show that optimal unicycle motion planning using dual-headway translation and orientation distances significantly outperforms Euclidean translation and cosine orientation distances in generating smooth motion with minimal travel and turning effort
Enhanced Extinction Ratio in QKD Transmitters Using On-Chip Saturable Absorbers
No full textWe demonstrate an integrated quantum key distribution (QKD) transmitter with enhanced extinction ratio (ER) using a monolithically integrated saturable absorber (SA). Since the light is later attenuated to the weak-coherent-state level, the additional signal loss in the SA does not impair performance. The SA improves the ER by 3.6 dB at 1 GHz, corresponding to an estimated ∼50% relative reduction in quantum bit error rate and a ∼17% increase in secret key rate, offering a compact, low-complexity route to improved secure key rates in practical QKD systems.</p
The Shannon Capacity of Graph Powers
No full textFor a graph G, its k-th graph power Gk is constructed by placing an edge between two vertices if they are within distance k. We consider the problem of deriving upper bounds on the Shannon capacity of graph powers by using spectral graph theory and linear optimization methods. First, we use the so-called ratio-type bound to provide an alternative and spectral proof of a result by Lovász [IEEE Trans. Inform. Theory 1979], which states that, for a regular graph, the Hoffman ratio bound on the independence number is also an upper bound on the Lovász theta number and, hence, also on the Shannon capacity. In fact, we show that Lovász’ result holds in the more general context of graph powers. Secondly, we derive another bound on the Shannon capacity of graph powers, the so-called rank-type bound, which depends on a new family of polynomials that can be computed by running a simple algorithm. Lastly, we provide several computational experiments that demonstrate the sharpness of the two proposed algebraic bounds. As a by-product, when these two new algebraic bounds are tight, they can be used to easily derive the exact values of the Lovász theta number (which relies on solving an SDP) and the Shannon capacity (which is not known to be computable) of the corresponding graph power.</p
A frequency-domain approach for estimating continuous-time diffusively coupled linear networks
No full textThis paper addresses the problem of consistently estimating a continuous-time (CT) diffusively coupled network (DCN) to identify physical components in a physical network. We develop a three-step frequency-domain identification method for linear CT DCNs that allows to accurately recover all the physical component values of the network while exploiting the particular symmetric structure in a DCN model. This method uses the estimated noise covariance as a non-parametric noise model to minimize variance of the parameter estimates, obviating the need to select a parametric noise model. The method is illustrated with an application from In-Circuit Testing of printed circuit boards. Experimental results highlight the method's ability to consistently estimate component values in a complex network with only a single excitation.</p
Predicting DNA Origami Stability in Physiological Media by Machine Learning
No full textDNA origami nanostructures offer substantial potential as programable, biocompatible platforms for drug delivery and diagnostics. However, their structural instability under physiological conditions remains a major barrier to practical applications. Stability assessment of DNA origami nanostructures has traditionally relied on image-based and empirical approaches, which are time-consuming and difficult to generalize across conditions. Here, a proof-of-concept framework coupling dynamic light scattering (DLS) with machine learning (ML) to estimate diffusion coefficient-based stability responses is presented. We use DLS as a screening proxy, supported by gel electrophoresis and atomic force microscopy (AFM) for selected conditions. A dataset of over 1400 measurements across three DNA origami shapes is assembled under physiologically relevant variations in temperature, incubation time, MgCl2 concentration, pH, and DNase I concentration. The dataset is used to train a consensus ML model built from Gaussian Process Regressor (GPR) and Random Forest (RF) capable of estimating diffusion coefficients for new condition combinations within and near explored ranges. The dataset and ML model are provided as a resource for the community, enabling others to extend and refine stability prediction for diverse nanostructures and conditions. This work establishes a scalable, data-driven framework for guiding the rational design of robust DNA origami nanostructures for biomedical applications. While qualitative and shape-dependent, the framework and dataset provide a scalable basis for community benchmarking and extension.</p
Investigating Spectral Biomarker Candidates for Migratory Potential in Cancer Cells Using Micro-FTIR and O-PTIR Spectroscopy
No full textRoutine diagnostic practice for cancer and metastasis relies on a time-consuming staining process and the use of antibodies to detect selected molecular markers and is hence limited by a lack of real-time data and the availability of molecular information. Against this background, techniques based on rapid chemical analysis to identify migratory properties are highly desirable. Fourier-Transform Infrared (FTIR) microspectroscopy has a long history in the label-free identification of infrared marker bands for cancer detection. However, it requires extensive postprocessing of the acquired spectra, is of limited suitability for analysis in aqueous environments, and has poor spatial resolution. To overcome these challenges, we are using a new method termed Optical Photothermal Infrared (O-PTIR) spectroscopy to detect local absorption to establish potential IR tumor markers and classification models. We report on experimental outcomes using machine learning and FTIR microspectroscopy for the classification of cells and the analysis of spectral features reflecting cancer and migratory properties, comparing a commercial FTIR microspectrometer to a custom-built O-PTIR instrument dedicated to spectroscopic measurement and imaging in microfluidic channels.</p
An AI-based approach accelerates the discovery of protein–protein interaction modulators targeting NCS-1
No full textDrug repurposing is an efficient strategy to accelerate the identification of therapeutic compounds by finding new uses for existing drugs. Here, we leveraged artificial intelligence (AI) to optimize this process and discover protein–protein interaction (PPI) modulators targeting the Neuronal Calcium Sensor 1. AI models were trained on large datasets of known PPIs, protein structures, and small molecular graphs to predict binding score. Using virtual screening of an FDA-approved drug library, the models generated a prioritized list of candidates. The most promising compounds were selected for experimental validation. This integrated approach efficiently reduced experimental workload, time, and cost, leading to the identification of dipyridamole as a selective modulator of the interaction between NCS-1 and the dopamine D2 receptor. The crystal structure of NCS-1 in complex with dipyridamole elucidates its mechanism of modulation. Our findings highlight the potential of AI to streamline and accelerate the discovery of novel therapeutic PPI modulators
Hexagonal Boron Nitride for Nanoscale Heat Dissipation in Electronic and Photonic Chips
No full textEfficient heat dissipation is critical for chip components, in particular around (sub)microscopic regions with locally elevated power densities, where performance degradation and failure can originate. Here, we experimentally demonstrate enhanced heat dissipation using hexagonal boron nitride (hBN) flakes and heterostructures on gold nanostrips and hexagonal SiGe nanowires. These nanoscale building blocks for electronic and photonic chips simultaneously serve as local temperature sensors. We transferred flakes using dry transfer, ensuring pristine interfaces. Covering gold nanostrips with hBN flakes or hBN/graphene/hBN stacks decreases the temperature ramp rate by up to 40%, and increases the breakdown current density by up to 30%. This occurs through improved in-plane heat dissipation, according to our simulations. Covering hexagonal SiGe nanowires with hBN decreases the operating temperature by up to 500 K under optical excitation, due to improved thermal boundary conductance. These findings pave the way for targeted thermal management in miniaturized electronic and photonic devices.</p
Implications for improving evacuation safety in primary school corridors:a video-based analysis on evacuees’ speed and density
No full textPurpose: This study aims to enhance evacuation safety and efficiency measures in primary school corridors by considering the impact of adult guidance and evacuation graphical signs on evacuation speed and density by considering different visibility conditions and corridor design. Design/methodology/approach: The experiment setup involved ten carefully designed drills exploring the evacuation behavior of 6–7-year-old students in a primary school, varying factors such as adult guidance, smoke conditions and graphical evacuation signs. Kinovea software was employed for data extraction to transform video footage into frames, facilitating meticulous manual tallies of children’s movements in designated sub-areas during the drills. The research utilized statistical tests, a generalized linear model and curve-fitting techniques to analyze the extracted data. Findings: The findings highlight the vital role of adult guidance in expediting evacuations, emphasizing the importance of trained personnel during emergencies. Additionally, graphical evacuation signs were identified as powerful tools for enhancing evacuation speed during low visibility, as they provide clear visual cues to guide evacuees effectively. Signage and adult guidance are beneficial when the classrooms’ gates opening to the passage are far from each other. In contrast, in areas with close and multiple exits, guidance strategies, especially those involving adults, are more effective in reducing population density during evacuations. Originality/value: These findings have practical implications for improving emergency preparedness, guiding the design of primary school corridors and informing evacuation protocols. School administrators, architects and emergency planners can utilize these findings to inform the development of safety protocols, enhance evacuation guidance strategies and improve the design of primary school corridors. Further research can expand on these findings by exploring their applicability in diverse educational settings and evaluating the real-world implementation of evacuation measures.</p