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Magnetic fields remain one of the least understood aspects of exoplanetary systems. A deeper understanding of planetary dynamos and the evolution of surface magnetic properties throughout a planet's lifetime is a key scientific purpose, with implications for planetary evolution, habitability, and atmospheric dynamics. This study models the evolution of magnetic fields generated by dynamo action in cold giant gaseous planets. We solve the resistive magnetohydrodynamic (MHD) equations under anelastic approximation with a 3D pseudo-spectral spherical shell MHD code. We employ 1D thermodynamical hydrostatic profiles taken from gas giant evolutionary models as the background states of our MHD models. Numerical integration leads to saturated dynamo solutions. Such calculations are performed with radial profiles corresponding to different planetary ages so that we can interpret them as different snapshots of the magnetoconvection evolution during the long-term planetary evolution. We characterize magnetic fields across different evolutionary stages of a cold gaseous planet in terms of topology and strength. We find the occurrence of a transition from multipolar to dipolar-dominated dynamo regime throughout the life of a Jovian planet. During the planetary evolution and the cooling down phase, we observe a decrease in the average magnetic field strength near the dynamo surface as $\sim t^{-0.2}-t^{-0.3}$, a trend compatible with previously proposed scaling laws. We also find that some dimensionless parameters evolve differently for the multipolar to dipolar branch, possibly reflecting a force balance change. This approach can be extended to study hot gaseous planets, offering a versatile tool for interpreting the magnetic properties of giant planets., Comment: 19 pages, submitted for publication, comments are welcome

The metric tensor field equations for the general quadratic curvature gravity in four spacetime dimensions are derived by making use of the algebra of the exterior forms defined on pseudo-Riemannian manifolds and the identities satisfied by the Riemann curvature tensor., Comment: 17 pages

This study addresses the urban transit pattern design problem, optimizing stop sequences, headways, and fleet sizes across multiple routes simultaneously to minimize user costs (composed of riding, waiting, and transfer times) under operational constraints (e.g., vehicle capacity and fleet size). A destination-labeled multi-commodity network flow (MCNF) formulation is developed to solve the problem at a large scale more efficiently compared to the previous literature. The model allows for flexible pattern options without relying on pre-defined candidate sets and simultaneously considers multiple operational strategies such as express/local services, short-turning, and deadheading. It evaluates perceived headways of joint patterns for passengers, assigns passenger flows to each pattern accordingly, and allows transfers across patterns in different directions. The mixed-integer linear programming (MILP) model is demonstrated with a city-sized network of metro lines in Chicago, USA, achieving near-optimal solutions in hours. The total weighted journey times are reduced by 0.61% and 4.13% under single-route and multi-route scenarios respectively. The model provides transit agencies with an efficient tool for comprehensive service design and resource allocation, improving service quality and resource utilization without additional operational costs., Comment: 23 pages, 3 figures, a previous version accepted for presentation in the 104th Transportation Research Board Annual Meeting in Washington, D.C. in January 2025

Transit is essential for urban transportation and achieving net-zero targets. In urban areas like the Chicago Metropolitan Region, transit enhances mobility and connects people, fostering a dynamic economy. To quantify the mobility and selected economic impacts of transit, we use a novel agent-based simulation model POLARIS to compare baseline service against a scenario in which transit is completely removed. The transit-removal scenario assumes higher car ownership and results in higher traffic congestion, numerous activity cancellations, and economic decline. In this scenario, average travel times increase by 14.2% regionally and 34.7% within the City of Chicago. The resulting congestion causes significant activity cancellations despite increased car ownership: 11.8% of non-work and 2.8% of work/school activities regionally, totaling an 8.6% overall cancellation rate. In the city, non-work cancellations would reach 26.9%, and work/school cancellations 7.3%, leading to a 19.9% overall cancellation rate. The impact varies between groups. Women and lower-income individuals are more likely to cancel activities than men and higher-income groups. Women account for 53.7% of non-work and 53.0% of total cancellations. The lowest 40% income group experiences 50.2% of non-work and 48.0% of overall cancellations. Combined, activity cancellations, travel time losses, and increased car ownership cost the region $35.4 billion. With annual public transit funding at $2.7 billion, the ratio is 13 to 1, underscoring transit's critical role in mobility, equity, and economic health.

Exoskeletons can boost human strength and provide assistance to individuals with physical disabilities. However, ensuring safety and optimal performance in their design poses substantial challenges. This study presents the design process for an underactuated hand exoskeleton (U-HEx), first including a single objective (maximizing force transmission), then expanding into multi objective (also minimizing torque variance and actuator displacement). The optimization relies on a Genetic Algorithm, the Big Bang-Big Crunch Algorithm, and their versions for multi-objective optimization. Analyses revealed that using Big Bang-Big Crunch provides high and more consistent results in terms of optimality with lower convergence time. In addition, adding more objectives offers a variety of trade-off solutions to the designers, who might later set priorities for the objectives without repeating the process - at the cost of complicating the optimization algorithm and computational burden. These findings underline the importance of performing proper optimization while designing exoskeletons, as well as providing a significant improvement to this specific robotic design., Comment: 12 pages, 7 figures, 8 talbes, submitted to IEEE Transactions on Robotics

Bus electrification plays a crucial role in advancing urban transportation sustainability. Battery Electric Buses (BEBs), however, often need recharging, making the Problem of Locating and Allocating Charging Equipment for BEBs (PLACE-BEB) essential for efficient operations. This study proposes an optimization framework to solve the PLACE-BEB by determining the optimal placement of charger types at potential locations under the stochastic charging demand. Leveraging the existing stochastic location literature, we develop a Mixed-Integer Non-Linear Program (MINLP) to model the problem. To solve this problem, we develop an exact solution method that minimizes the costs related to building charging stations, charger allocation, travel to stations, and average queueing and charging times. Queueing dynamics are modeled using an M/M/s queue, with the number of servers at each location treated as a decision variable. To improve scalability, we implement a Simulated Annealing (SA) and a Genetic Algorithm (GA) allowing for efficient solutions to large-scale problems. The computational performance of the methods was thoroughly evaluated, revealing that SA was effective for small-scale problems, while GA outperformed others for large-scale instances. A case study comparing garage-only, other-only, and mixed scenarios, along with joint deployment, highlighted the cost benefits of a collaborative and a comprehensive approach. Sensitivity analyses showed that the waiting time is a key factor to consider in the decision-making.

This study presents a transit routing, assignment, and simulation framework which is fully embedded in a multimodal, multi-agent transportation demand and supply modeling platform. POLARIS, a high-performance agent-based simulation platform, efficiently integrates advanced travel and freight demand modeling, dynamic traffic and transit assignment, and multimodal transportation simulation within a unified framework. We focus on POLARIS's transit routing, assignment, and simulation components, detailing its structural design and essential terminologies. We demonstrate how the model integrates upstream decision-making processes - activity generation, location and timing choices, and mode selection, particularly for transit-inclusive trips - followed by routing, assignment decisions, and the movement of travelers and vehicles within a multimodal network. This integration enables modeling of interactions among all agents, including travelers, vehicles, and transportation service providers. The study reviews literature on transportation system modeling tools, describes the transit modeling framework within POLARIS, and presents findings from large-scale analyses of various policy interventions. Results from numerical experiments reveal that measures such as congestion pricing, transit service improvements, first-mile-last-mile subsidies, increased e-commerce deliveries, and vehicle electrification significantly impact transit ridership, with some interactions between these levers exhibiting synergistic or canceling effects. The case study underscores the necessity of integrating transit modeling within a broader multimodal network simulation and decision-making context.

Employing a sixth order expression for the differential time evolution operator based on the Magnus expansion (ME), we conducted quantum dynamics calculations of a $\Lambda$-system driven by two sinusoidal time dependent fields. For a closed system dynamics, we confirmed the equivalence of the dynamics in the Hilbert space and the Liouville space numerically. We also conducted open system quantum dynamics calculation by generalizing the ME to the non-Hermitian dynamics in the Liouville space for the case where the effects of photonic bath are represented by Lindblad operators. In both cases, the accuracy of the rotating wave approximation (RWA) was assessed. We found significant errors of RWA during initial stages of the dynamics for representative cases where electromagnetically induced transparency or coherent population trapping can be observed. The presence of bath for open system quantum dynamics reduces the errors of RWA, but significant errors for off-diagonal elements of the density operator can still be seen. We also found that approaches to steady state limits of exact dynamics are slower than those for RWA. These results demonstrate the utility of the ME as a general and reliable tool for closed and open system quantum dynamics for time dependent Hamiltonians, and expose potential issues of drawing conclusions based solely on RWA., Comment: 20 pages, 20 figures

Channel charting is an emerging self-supervised method that maps channel-state information (CSI) to a low-dimensional latent space (the channel chart) that represents pseudo-positions of user equipments (UEs). While channel charts preserve local geometry, i.e., nearby UEs are nearby in the channel chart (and vice versa), the pseudo-positions are in arbitrary coordinates and global geometry is typically not preserved. In order to embed channel charts in real-world coordinates, we first propose a bilateration loss for distributed multiple-input multiple-output (D-MIMO) wireless systems in which only the access point (AP) positions are known. The idea behind this loss is to compare the received power at pairs of APs to determine whether a UE should be placed closer to one AP or the other in the channel chart. Second, we propose a line-of-sight (LoS) bounding-box loss that places the UE in a predefined LoS area of each AP that is estimated to have a LoS path to the UE. We demonstrate the efficacy of combining both of these loss functions with neural-network-based channel charting using ray-tracing-based and measurement-based channel vectors. Our approach outperforms several baselines and maintains the self-supervised nature of channel charting as it does not rely on geometrical propagation models or require ground-truth UE position information., Comment: Submitted to a journal. arXiv admin note: substantial text overlap with arXiv:2308.14498

High-pressure torsion (HPT) can facilitate phase transformations in titanium dioxide (TiO2) and stabilize its high-pressure columbite phase, as an active photocatalyst, by shear straining under high pressure. This study aims to understand the mechanism underlying the acceleration of the anatase-to-columbite phase transformation by shear strain. A mechanism by considering sheared crystal structures as intermediate phases was proposed and examined using quantum mechanics in the framework of density functional theory (DFT) and HPT experiments. DFT energy and phonon calculations demonstrated the viability of the sheared structures as intermediate phases. Furthermore, the sheared structures were observed experimentally as new metastable phases using high-resolution transmission electron microscopy. These findings can explain the significant effect of shear strain on pressure-induced phase transitions, reported during severe plastic deformation of various metals and ceramics.

High-entropy oxides (HEOs), containing at least five principal cations, have recently emerged as promising photocatalysts for hydrogen production via water splitting. Despite their high potential, the impact of the cation mixtures on photocatalytic activity remains poorly understood. This study investigates the high-entropy photocatalyst TiZrHfNbTaO11 using first-principles calculations combined with experimental methods to elucidate the effects of various elements on electronic structure and water splitting performance. The results indicate that the HEO exhibits a bandgap comparable to TiO2 polymorphs rutile, brookite and anatase. Cations with lower electronegativity, such as hafnium and zirconium, provide the strongest water adsorption energy, serving as active sites for water adsorption. Additionally, the co-presence of highly electronegative cations like niobium and tantalum adjacent to hafnium and zirconium enhances charge transfer to water molecules, improving splitting efficiency. These findings suggest novel strategies for designing high-entropy photocatalysts by synergistic incorporating cations with different electronegativities.

Grammatical Error Correction has seen significant progress with the recent advancements in deep learning. As those methods require huge amounts of data, synthetic datasets are being built to fill this gap. Unfortunately, synthetic datasets are not organic enough in some cases and even require clean data to start with. Furthermore, most of the work that has been done is focused mostly on English. In this work, we introduce a new organic data-driven approach, clean insertions, to build parallel Turkish Grammatical Error Correction datasets from any organic data, and to clean the data used for training Large Language Models. We achieve state-of-the-art results on two Turkish Grammatical Error Correction test sets out of the three publicly available ones. We also show the effectiveness of our method on the training losses of training language models.

Robotic exoskeletons can enhance human strength and aid people with physical disabilities. However, designing them to ensure safety and optimal performance presents significant challenges. Developing exoskeletons should incorporate specific optimization algorithms to find the best design. This study investigates the potential of Evolutionary Computation (EC) methods in robotic design optimization, with an underactuated hand exoskeleton (U-HEx) used as a case study. We propose improving the performance and usability of the U-HEx design, which was initially optimized using a naive brute-force approach, by integrating EC techniques such as Genetic Algorithm and Big Bang-Big Crunch Algorithm. Comparative analysis revealed that EC methods consistently yield more precise and optimal solutions than brute force in a significantly shorter time. This allowed us to improve the optimization by increasing the number of variables in the design, which was impossible with naive methods. The results show significant improvements in terms of the torque magnitude the device transfers to the user, enhancing its efficiency. These findings underline the importance of performing proper optimization while designing exoskeletons, as well as providing a significant improvement to this specific robotic design., Comment: 6 pages (+ref), 4 figures, IEEE International Conference on Robotics and Automation (ICRA) 2024

The inflated radii observed in hundreds of Hot Jupiters represent a long-standing open issue. The observed correlation between radii and irradiation strength, and the occasional extreme cases, nearly double the size of Jupiter, remain without a comprehensive quantitative explanation. In this investigation, we delve into this issue within the framework of Ohmic dissipation, one of the most promising mechanisms for explaining the radius anomaly. Using the evolutionary code MESA, we simulate the evolution of irradiated giant planets, spanning the range 1 to 8 Jupiter masses, incorporating an internal source of Ohmic dissipation located beneath the radiative-convective boundary. Our modeling is based on physical parameters, and accounts for the approximated conductivity and the evolution of the magnetic fields, utilizing widely-used scaling laws. We compute the radius evolution across a spectrum of masses and equilibrium temperatures, considering varying amounts of Ohmic dissipation, calculated with the internal conductivity profile and an effective parametrization of the currents, based on the typical radius of curvature of the field lines. Our analysis reveals that this internal Ohmic dissipation can broadly reproduce the range of observed radii using values of radius of curvature up to about one order of magnitude lower than what we estimate from the Juno measurements of the Jovian magnetosphere and from MHD dynamo simulations presented herein. The observed trend with equilibrium temperature can be explained if the highly-irradiated planets have more intense and more small-scale magnetic fields. This suggests the possibility of an interplay between atmospherically induced currents and the interior, via turbulence, in agreement with recent box simulations of turbulent MHD in atmospheric columns., Comment: 13 pages, 14 figures

Photocatalytic CO2 conversion is a clean technology to deal with CO2 emissions, and titanium oxide (TiO2) polymorphs are the most investigated photocatalysts for such an application. In this study, black TiO2 brookite is produced by a high-pressure torsion (HPT) method and employed as an active photocatalyst for CO2 conversion. Black brookite with a large concentration of lattice defects (vacancies, dislocations and grain boundaries) showed enhanced light absorbance, narrowed optical bandgap and diminished recombination rate of electrons and holes. The photocatalytic activity of the black oxide for CO2 conversion was higher compared to commercial brookite and benchmark P25 catalyst powders. First-principles calculations suggested that the presence of oxygen vacancies in black brookite is effective not only for reducing optical bandgap but also for providing active sites for the adsorption of CO2 on the surface of TiO2.

Channel charting (CC) applies dimensionality reduction to channel state information (CSI) data at the infrastructure basestation side with the goal of extracting pseudo-position information for each user. The self-supervised nature of CC enables predictive tasks that depend on user position without requiring any ground-truth position information. In this work, we focus on the practically relevant streaming CSI data scenario, in which CSI is constantly estimated. To deal with storage limitations, we develop a novel streaming CC architecture that maintains a small core CSI dataset from which the channel charts are learned. Curation of the core CSI dataset is achieved using a min-max-similarity criterion. Numerical validation with measured CSI data demonstrates that our method approaches the accuracy obtained from the complete CSI dataset while using only a fraction of CSI storage and avoiding catastrophic forgetting of old CSI data., Comment: Presented at the 2023 Asilomar Conference on Signals, Systems, and Computers

Magnus expansion provides a general way to expand the real time propagator of a time dependent Hamiltonian within the exponential such that the unitarity is satisfied at any order. We use this property and explicit integration of Lagrange interpolation formulas for the time dependent Hamiltonian within each time interval and derive approximations that preserve unitarity for the differential time evolution operators of general time dependent Hamiltonians. The resulting second order approximation is the same as using the average of Hamiltonians for two end points of time. We identify three fourth order approximations involving commutators of Hamiltonians at different times, and also derive a sixth order expression. Test of these approximations along with other available expressions for a two state time dependent Hamiltonian with sinusoidal time dependences provide information on relative performance of these approximations, and suggest that the derived expressions can serve as useful numerical tools for time evolution for time resolved spectroscopy, quantum control, quantum sensing, and open system quantum dynamics., Comment: 11 pages, 2 figures