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H<sup>∞</sup>-control for a class of boundary controlled hyperbolic PDEs
No full textA solution to the suboptimal H∞-control problem is given for a class of hyperbolic partial differential equations (PDEs). The first result of this manuscript shows that the considered class of PDEs admits an equivalent representation as an infinite-dimensional discrete-time system. Taking advantage of this, this manuscript shows that it is equivalent to solving the suboptimal H∞-control problem for a finite-dimensional discrete-time system whose matrices are derived from the PDEs. After computing the solution to this much simpler problem, the solution to the original problem can be deduced easily. In particular, the optimal compensator solution to the suboptimal H∞-control problem is governed by a set of hyperbolic PDEs, actuated and observed at the boundary. We illustrate our results with a boundary-controlled and boundary-observed vibrating string.</p
Combinatorial designs and cellular automata:A survey
Get PDFCellular Automata (CA) are commonly investigated as a particular type of dynamical systems, defined by shift-invariant local rules. In this paper, we consider instead CA as algebraic systems, focusing on the combinatorial designs induced by their short-term behavior. Specifically, we review the main results published in the literature concerning the construction of mutually orthogonal Latin squares via bipermutive CA, considering both the linear and nonlinear cases. We then survey some significant applications of these results to cryptography, and conclude with a discussion of open problems to be addressed in future research on CA-based combinatorial designs.</p
Impact of pandemic measures on air quality and meteorological parameters during the COVID-19 spread in the Euphrates Basin, Türkiye
No full textThis study investigates the impact of COVID-19 pandemic measures on air quality and their relationship with meteorological parameters in the Euphrates Basin, Türkiye. It provides a basin-specific analysis of air quality trends during the pandemic, exploring the interplay between meteorological variables and air quality indicators. The analysis examines the COVID-19 rates across 15 provinces about air quality indicators PM10 and SO2 and includes weekly average temperature (Tw) and weekly total precipitation (Pw). Three periods were defined: before the pandemic (Period 1), during the pandemic (Period 2), and after the pandemic (Period 3), each spanning 77 weeks. The spatial–temporal changes in PM10 and SO2 concerning Pw and Tw were analyzed during Periods 1 and 3, while in Period 2, they were related to the COVID-19 rates. The results of this study show that the COVID-19 outbreak was more intense in large cities, while the opposite was true in small cities. Using the Multivariate Auto-Regressive State-Space (MARSS) model, we found that PM10 and SO2 significantly influenced the COVID-19 rate during the second and third waves of the pandemic, most likely due to the decreased social and urban activities during the quarantine period. Moreover, the study identified noteworthy, though statistically non-significant, associations between population density and COVID-19 transmission patterns. These preliminary findings warrant further validation through future, more granular investigations.</p
Rapid-cycling ReBCO dipole magnet concept for muon acceleration
No full textWe present a concept of the superconducting ReBCO dipole magnet for ramping range up to 10 kT/s as possibly required for the muon acceleration (Chance 2023) in a future Muon Collider presently under study (Palmer 2013), (Accettura et al., 2024), (Jindariani et al., 2025). This approach is based on the 6 kA CORC-like cable constructed with 12 ReBCO tapes of 2 mm width. Based on theoretical prediction (Solovyev et al., 2023) there is a linear scaling of the ReBCO cable hysteresis loss with the crossing magnetic field possibly generating power loss independent of the magnetic field ramping rate. This feature makes this ReBCO cable suitable for the construction of rapid-cycling magnets. In this work we outline the design of a dipole magnet for 2T gap magnetic field and discuss the helium coolant parameters for the minimal electric power.</p
Learning deep generative models based on binomial log-likelihood
No full textLikelihood-based learning algorithms for deep generative models mostly use the Gaussian log-likelihood. One notable exception is the binomial log-likelihood used in the Wasserstein autoencoder; however, it is not commonly used in practice because it does not generalize well. In this paper, we reconsider the binomial log-likelihood for learning deep generative models and study its theoretical properties. We propose two modifications to the original binomial log-likelihood and derive the convergence rates of the corresponding maximum likelihood estimators. These theoretical results explain why the original binomial log-likelihood performs poorly. In addition, motivated by the modified binomial log-likelihood, we propose a parametric heterogeneous Gaussian log-likelihood, which is novel in learning deep generative models. By analyzing various benchmark image datasets, we show that the proposed parametric heterogeneous Gaussian log-likelihood outperforms the standard homogeneous Gaussian log-likelihood. Additionally, we provide several pieces of evidence to explain why the proposed heterogeneous Gaussian log-likelihood works better than others.</p
Optimal network geometry detection for weak geometry
No full textNetwork geometry, characterized by nodes with associated latent variables, is a fundamental feature of real-world networks. Still, when only the network edges are given, it may be difficult to assess whether the network contains an underlying source of geometry. This paper investigates the limits of geometry detection in networks in a wide class of models that contain geometry and power-law degrees, which include the popular hyperbolic random graph model. We specifically focus on the regime in which the geometric signal is weak. We show that the dependencies between edges in these models can be tackled through mixed-integer linear problems, which lift the nonlinear nature of network analysis into an exponential space in which simple linear optimization techniques can be employed. This approach allows us to investigate which subgraph and degree-based statistic is most effective at detecting the presence of an underlying geometric space. Interestingly, we show that even when the geometric effect is extremely weak, our mixed-integer programming approach identifies a network statistic that efficiently distinguishes geometric and nongeometric random graph models.</p
Load-Balancing Versus Anycast: A First Look at Operational Challenges
No full textLoad Balancing (LB) is a routing strategy that increases performance by distributing traffic over multiple outgoing paths. In this work, we introduce a novel methodology to detect the influence of LB on anycast routing, which can be used by operators to detect networks that experience anycast site flipping, where traffic from a single client reaches multiple anycast sites. We use our methodology to measure the effects of LB-behavior on anycast routing at a global scale, covering both IPv4 and IPv6. Our results show that LB-induced anycast site flipping is widespread. The results also show our method can detect LB implementations on the global Internet, including detection and classification of Points-of-Presence (PoP) and egress selection techniques deployed by hypergiants, cloud providers, and network operators. We observe LB-induced site flipping directs distinct flows to different anycast sites with significant latency inflation. In cases with two paths between an anycast instance and a load-balanced destination, we observe an average RTT difference of 30 ms with 8% of load-balanced destinations seeing RTT differences of over 100 ms. Being able to detect these cases can help anycast operators significantly improve their service for affected clients.</p
DADO: A Depth-Attention Framework for Object Discovery
No full textUnsupervised object discovery, the task of identifying and localizing objects in images without human-annotated labels, remains a significant challenge and a growing focus in computer vision. In this work, we introduce a novel model, DADO (Depth-Attention self-supervised technique for Discovering unseen Objects), which combines an attention mechanism and a depth model to identify potential objects in images. To address challenges such as noisy attention maps or complex scenes with varying depth planes, DADO employs dynamic weighting to adaptively emphasize attention or depth features based on the global characteristics of each image. We evaluated DADO on standard benchmarks, where it outperforms state-of-the-art methods in object discovery accuracy and robustness without the need for fine-tuning
Transformation of H<sub>m0</sub> and T<sub>m−1,0</sub> over a model salt marsh
No full textThis research investigates how salt marshes contribute to both wave energy dissipation and spectral period transformation, advancing their role as a nature-based solution for coastal protection. Using laboratory simulations with a scaled barren foreshore, salt marsh and dike model, we examine the interactions between vegetation, water depth, and wave properties under varied conditions, including storm scenarios with irregular waves. Results indicate a case specific threshold at which the salt marsh model attenuates energy optimally, as for very shallow water depths wave energy is predominantly dissipated by the barren foreshore. The spectral wave period T m − 1 , 0 increases when waves propagate from deep to shallow water depths, as a result of wave breaking and generation of infragravity waves. The presence of salt marsh vegetation further enhances this effect by preferentially damping high frequency components. This highlights that an increase in T m − 1 , 0 in vegetated environments may not always correspond to an increased hydrodynamic load on the dike.</p
Low-emission Ammonia Production and Utilization
No full textBy virtue of not containing carbon, ammonia (NH3) is considered a zero-carbon fuel. This book provides an introduction and technological context for the current role of ammonia as a fertilizer and chemical, as well as ammonia as a zero-carbon fuel and hydrogen carrier.The book emphasises industrial aspects of low-emission ammonia with scientific explanations for low-carbon fossil ammonia production and renewable ammonia production via various electrolysis technologies, as well as storage, handling, and utilization of ammonia for energy applications, while also covering safety, regulations, environmental considerations, business methods, and policy. Thus, this book presents the state-of-the-art of relevant technologies in the energy transition.All in all, ammonia production is set to triple over the coming decades, while the ammonia infrastructure is expected to scale up by an order of magnitude.Low-emission Ammonia Production and Utilization is an invaluable reference source for academics and industry workers, as well as policy makers, investigating all relevant sustainable ammonia production and utilisation technologies