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Emergence of Scale-Free Traffic Jams in Highway Networks:A Probabilistic Approach
Get PDFTraffic congestion continues to escalate with urbanization and socioeconomic development, necessitating advanced modeling to understand and mitigate its impacts. In large-scale networks, traffic congestion can be studied using cascade models, where congestion not only impacts isolated segments, but also propagates through the network in a domino-like fashion. One metric for understanding these impacts is congestion cost, which is typically defined as the additional travel time caused by traffic jams. Recent data suggests that congestion cost exhibits a universal scale-free-tailed behavior. However, the mechanism driving this phenomenon is not yet well understood. To address this gap, we propose a stochastic cascade model of traffic congestion. We show that traffic congestion cost is driven by the scale-free distribution of traffic intensities. This arises from the catastrophe principle, implying that severe congestion is likely caused by disproportionately large traffic originating from a single location. We also show that the scale-free nature of congestion cost is robust to various congestion propagation rules, explaining the universal scaling observed in empirical data. These findings provide a new perspective in understanding the fundamental drivers of traffic congestion and offer a unifying framework for studying congestion phenomena across diverse traffic networks
Development of a cerebral CT perfusion phantom:A structured approach
Get PDFIntroduction: Computed tomography perfusion (CTP) imaging is crucial in diagnosing and managing vascular diseases, e.g, stroke. Differences in scanners and protocols may lead to different results, affecting clinical decision-making. Objective validation and evaluation of CTP imaging are therefore important. Perfusion phantoms are essential test objects to facilitate the validation and evaluation of perfusion imaging. Therefore, this study aimed to develop, validate and evaluate a brain perfusion phantom for the evaluation of cerebral CTP. Methods: A cerebral perfusion phantom was developed to evaluate CTP imaging of the brain using a workflow based on the Design Science Research Methodology. The reliability and repeatability of the phantom's perfusion parameters derived from the time-density curves (TDCs) in CTP were evaluated. Results: A 3D-printed modular perfusion phantom was developed, filled with sodium alginate beads, and connected to a pumping system to mimic microvasculature and flow dynamics. The phantom consisted of three compartments that simulated different states of perfusion. The phantom showed reliable TDCs, with a relative standard deviation of <6.6 % for peak intensity and time-to-peak (TTP) over two sets of five repeated experiments for all compartments, and repeatable TTP and mean transit time values with a repeatability coefficient of <2.3 s compared to the mean. Conclusions: The developed perfusion phantom demonstrated high reliability and could be employed for investigating CTP imaging under various flow speeds. The presented workflow promotes transparency in the development, validation, and application of CTP phantoms, and facilitates cross-study comparisons through structured iterative development and unified evaluation metrics.</p
A framework to analyze inclusion in smart energy city development:The case of Smart City Amsterdam
Get PDFIn response to unprecedented global urbanization, the smart city concept has emerged, leveraging ICT to enhance municipal efficiency and improve the quality of urban life. The concept of smart energy city (SEC) is closely related to smart cities, however, energy system development in a smart city context is often found eluding certain segments of society, which calls for more attention to inclusion in SEC development. In this paper, the research question is: How can inclusion be effectively integrated into a framework of SEC design? A framework is developed comprising three key principles - energy conservation, energy efficiency, and renewable energy. These principles are aligned with collaboration among stakeholders, smart energy solutions applications, and integration of these solutions. The framework is illustrated using two real-world cases of demonstration projects in the City of Amsterdam, the Netherlands. The paper concludes by presenting several strategies for fostering inclusion in SEC development. They pertain to including utilization of the framework as a guideline to promote inclusion, establishing a clear understanding of inclusion, and involving all relevant stakeholders, including citizens' rights from the project's inception, and fostering transparency regarding the objectives, interests, and individual stakeholders' value.</p
Orthonormalization of phase-only basis functions
Get PDFGeneration of orthonormal optical fields using phase-only spatial light modulators (SLM) or amplitude-only digital micromirror devices (DMD) is an active and diverse research field, with a wide variety of applications. However, these approaches typically come with limited accuracy, and a significant loss in resolution and intensity. We present a different approach: we construct orthonormal fields that can be generated exactly on phase-only hardware without loss of resolution or intensity. Our method can use any set of fields as a starting point and orthonormalize them. Our approach allows control over application-specific requirements such as smoothness, symmetry and overall shape. In many use cases, sets of orthonormal fields can be used as a ‘drop-in replacement’ for other sets of fields. We demonstrate the practical benefit of our approach in a wavefront shaping experiment, achieving a factor 1.5 increase in performance over a non-orthonormal phase-only basis.</p
Dual Electronic and Optical Monitoring of Biointerfaces by a Grating-Structured Coplanar-Gated Field-Effect Transistor
Get PDFWe present a novel, portable sensor platform that enables concurrent monitoring of surface mass and charge density variations at thin biointerfaces. This platform combines a coplanar-gated field-effect transistor (FET) architecture with grating-coupled surface plasmon resonance (SPR), yielding an integrated disposable sensor chip prepared by nanoimprint and maskless photolithography techniques. The sensor chip design is suitable for scalable production and relies on reduced graphene oxide (rGO), serving as the FET's semiconductor material for the electronic readout, and a metallic gate electrode surface that is corrugated with a multi-diffractive structure for optical probing with resonantly excited surface plasmons. Together with its integration in a compact instrumentation this results in a form factor optimized solution for dual-mode investigations without compromising the optical or electronic sensor performance. A poly-L-lysine (PLL) - based thin linker layer was deployed at the sensor surface to covalently attach azide-conjugated biomolecules by using incorporated "clickable"dibenzocyclooctyne (DBCO) moieties. Interestingly, the dual-mode measurements allow elucidating the role of the globular nature of the PLL chains when increasing the density of DBCO attached to their backbone, leading to PLL folding and internalization of DBCO moieties, and thus reducing the coupling yield for the used DNA oligomers. We envision that this platform can be employed to studying a range of other biointerface architectures and biomolecular interaction phenomena, which are inherently tied to mass and charge density variations.</p
OpenWFS—a library for conducting and simulating wavefront shaping experiments
Get PDFWavefront shaping (WFS) is a technique for controlling the propagation of light. With applications ranging from microscopy to free-space telecommunication, this research field is expanding rapidly. As the field advances, it stands out that many breakthroughs are driven by the development of better software that incorporates increasingly advanced physical models and algorithms. Typical WFS software involves a complex combination of low-level hardware control, signal processing, calibration, troubleshooting, simulation, and the WFS algorithm itself. This complexity makes it hard to compare different algorithms and to extend existing software with new hardware or algorithms. Moreover, the complexity of the software can be a significant barrier for end users of microscopes to adopt WFS. OpenWFS addresses these challenges by providing a modular Python library that separates hardware control from the WFS algorithm itself. Using these elements, a WFS algorithm can be written in a minimal amount of code, with OpenWFS taking care of low-level hardware control, synchronization, and troubleshooting. Algorithms can be used on different hardware or in a completely simulated environment without changing the code. Moreover, we provide full integration with the Micro-Manager microscope control software, enabling WFS experiments to be executed from a user-friendly graphical user interface.</p
Modeling commodity price co-movement:building on traditional time series models and exploring applications of machine learning algorithms
Get PDFIn this study, we explore the efficacy of various methodologies and co-movement measures for modeling the co-movement of cross-commodity prices, using macroeconomic variables. Applying Vector Autoregression (VAR), VAR with exogenous variables (VARX), multiple regressions, and Random Forest regressions, alongside Pearson correlations and Gerber statistics, we analyze the price co-movement of 20 key commodities over the period from mid-2003 to early 2023. Our results reveal that VAR and VARX models notably outperform Random Forests and multiple regressions, achieving R2 values of up to 89%. Although Random Forests marginally outperform multiple regressions, they are still inferior to VAR-based approaches. Furthermore, we observe that the inclusion of Gerber statistics (differently from the Pearson correlation metric) enhances the effectiveness of VAR and VARX models, while their influence on Random Forests and multiple regression models remains indeterminate. This research contextualizes and contributes to the existing literature on commodity price dynamics, providing insights into the relative strengths of various modeling approaches and setting the stage for future methodological advancements, especially in the applications of Machine Learning (ML) in the field.</p
Data Driven Approach towards More Efficient Newton-Raphson Power Flow Calculation for Distribution Grids
No full textPower flow (PF) calculations are fundamental to power system analysis to ensure stable and reliable grid operation. The Newton-Raphson (NR) method is commonly used for PF analysis due to its rapid convergence when initialized properly. However, as power grids operate closer to their capacity limits, ill-conditioned cases and convergence issues pose significant challenges. This work, therefore, addresses these challenges by proposing strategies to improve NR initialization, hence minimizing iterations and avoiding divergence. We explore three approaches: (i) an analytical method that estimates the basin of attraction using mathematical bounds on voltages, (ii) a data-driven model leveraging supervised learning and physics-informed neural networks (PINNs) to predict optimal initial guesses, and (iii) a reinforcement learning (RL) approach that incrementally adjusts voltages to accelerate convergence. These methods are tested on benchmark systems. This research is particularly relevant for modern power systems, where high penetration of renewables and decentralized generation require robust and scalable PF solutions. Our findings provide a pathway for more efficient real-time grid operations, which, in turn, support the transition toward smarter and more resilient electricity networks
Soft back exosuit controlled by neuro-mechanical modeling provides adaptive assistance while lifting unknown loads and reduces lumbosacral compression forces
Get PDFState-of-the-art controllers for active back exosuits rely on body kinematics and state machines. These controllers do not continuously target the lumbosacral compression forces or adapt to unknown external loads. The use of additional contact or load detection could make such controllers more adaptive; however, it can be impractical for daily use. Here, we developed a novel neuro-mechanical model-based controller (NMBC) that uses a personalized electromyography (EMG)-driven musculoskeletal (MSK) model to estimate lumbosacral joint loading. NMBC provided adaptive, subject- and load-specific assistive forces proportional to estimates of the active part of biological joint moments through a soft back support exosuit. Without a priori information, the maximum assistive forces of the cable were modulated across weights. Simultaneously, we applied a non-adaptive, kinematic-dependent, trunk inclination-based controller (TIBC). Both NMBC and TIBC reduced the mean and peak biomechanical metrics, although not all reductions were significant. TIBC did not modulate assistance across weights. NMBC showed larger reductions of mean than peak values, significant reductions during the erect stance and the cumulative compressive loads by 21% over multiple cycles in a cohort of 10 participants. Overall, NMBC targeted mean lumbosacral compressive forces during lifting without a priori information of the load being carried. This may facilitate the adoption of non-hindering wearable robotics in real-life scenarios. As NMBC is informed by an EMG-driven MSK model, it is possible to tune the timing of NMBC-generated torque commands to the exosuit (delaying or anticipating commands with respect to biological torques) to target further reduction of peak or mean compressive forces and muscle fatigue.</p
