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The massive growth of ride-hailing and mobility-on-demand (MoD) platforms like Uber, Lyft, and DiDi, as well as advances in connected and automated vehicle (CAV) technology over the past decade have brought promising alternatives to car ownership within reach for many city residents. However, these services come with potentially severe negative effects on cities, such as increasing congestion and emissions due to empty miles. A useful tool to reduce these drawbacks is the introduction and promotion of ride-pooling, which accommodates multiple passenger requests in a single trip. Adopting ride-pooling on a city-wide scale though has significant challenges including customer preferences, computational complexity, and demand uncertainty, all of which affect the benefits of the service. This dissertation aims to examine and address these issues in the context of ride-pooling operations. The success of ride-pooling platforms hinges on whether customer demand will accept it over other alternatives; without enough demand, the system loses the efficiency of multiple customers per vehicle. To this end, we propose a community-based ride-sharing scheme where a system operator recommends travelers with similar travel patterns in order to address concerns about delay and safety while promoting a shared-vehicle environment. By leveraging trajectory data from routing apps, smartphones and CAVs, we can gather information about consumer preferences and construct a mobility profile for them. We modify a traditional Dynamic Time Warping (DTW) algorithm to compare trajectories in users’ profiles and use the resulting measures as a basis for offline recommendation matching. We demonstrate this framework on data from the Microsoft Geolife Dataset. Most ride-pooling platforms operate in a real-time environment, rather than offline, so it is important to consider operational challenges as well. Most notably, solving a matching problem to not only pair riders but also assign groups of requests to vehicles is incredibly complex and time-consuming at scale. First, we develop and examine a family of scalable heuristic methods for ride-to-ride and ride-to-vehicle assignment that improves the customers’ ride-pooling experience. In order to evaluate how changes in these methods and platform decisions affect all aspects of the system including customer waiting time and delay, we propose a family of metrics for evaluating ride-pooling performance. We show that the proposed heuristics for the ride-pooling assignment are scalable and easily implementable methods and can be substitutes for centralized optimization in many scenarios, with only minor sacrifices in platform performance. Second, while heuristics can achieve good performance in many scenarios, they provide no guarantees on performance in the worst cases. To address this, we formulate a joint vehicle repositioning and ride-pooling assignment problem as a two-stage stochastic integer program and expand it to the dual- or multi-source scenario in which the service provider can use different fleets of vehicles. Two approximation algorithms are proposed that provide competitive bounds on worst case performance. We then evaluate these approximation algorithms on real-world data using a simulator, demonstrating that these algorithms can parallelize computations and achieve solutions with small optimality gaps (typically within 1%) The algorithms, frameworks, and takeaways presented in this dissertation were derived and evaluated for ride-pooling specifically, but many are generalizable to other shared mobility and multi-modal use cases.

In recent years polynuclear transition metal molecules have been synthesized and proposed for example as magnetic storage units or qubits in quantum computers. They are known as molecular nanomagnets and belong in the class of mesoscopic systems, which are large enough to display many-body effects but small enough to be away from the finite-size scaling regime. It is a challenge for physicists to understand their magnetic properties, and for synthetic chemists to efficiently tailor them by assembling fundamental units. They are complementary to artificially engineered spin systems for surface deposition, as they support a wider variety of complex states in their low energy spectrum. Here a few characteristic examples of molecular nanomagnets showcasing unusual many-body effects are presented. Antiferromagnetic wheels and chains can be described in classical terms for small sizes and large spins to a great extent, even though their wavefunctions do not significantly overlap with semiclassical configurations. Hence, surprisingly, for them the transition from the classical to the quantum regime is blurred. A specific example is the Fe18 wheel, which displays quantum phase interference by allowing Néel vector tunneling in a magnetic field. Finally, the Co5Cl single-molecule magnet is shown to have an unusual anisotropic response to a magnetic field.

Ride-pooling has become an important service option offered by ride-hailing platforms as it serves multiple trip requests in a single ride. By leveraging customer data, connected vehicles, and efficient assignment algorithms, ride-pooling can be a critical instrument to address driver shortages and mitigate the negative externalities of ride-hailing operations. Recent literature has focused on computationally intensive optimization-based methods that maximize system throughput or minimize vehicle miles. However, individual customers may experience substantial service quality degradation due to the consequent waiting and detour time. In contrast, this paper examines heuristic methods for real-time ride-pooling assignments that are highly scalable and easily computable. We propose a restricted subgraph method and compare it with other existing heuristic and optimization-based matching algorithms using a variety of metrics. By fusing multiple sources of trip and network data in New York City, we develop a flexible, agent-based simulation platform to test these strategies on different demand levels and examine how they affect both the customer experience and the ride-hailing platform. Our results find a trade-off among heuristics between throughput and customer matching time. We show that our proposed ride-pooling strategy maintains system performance while limiting trip delays and improving customer experience. This work provides insight for policymakers and ride-hailing operators about the performance of simpler heuristics and raises concerns about prioritizing only specific platform metrics without considering service quality.

Ride-pooling, which accommodates multiple passenger requests in a single trip, has the potential to significantly increase fleet utilization in shared mobility platforms. The ride-pooling assignment problem finds optimal co-riders to maximize the total utility or profit on a shareability graph, a hypergraph representing the matching compatibility between available vehicles and pending requests. With mixed fleets due to the introduction of automated or premium vehicles, fleet sizing and relocation decisions should be made before the requests are revealed. Due to the immense size of the underlying shareability graph and demand uncertainty, it is impractical to use exact methods to calculate the optimal trip assignments. Two approximation algorithms for mid-capacity and high-capacity vehicles are proposed in this paper; The respective approximation ratios are \\(\\frac1{p^2}\\) and \\(\\frac{e-1}{(2e+o(1)) p \\ln p}\\), where \\(p\\) is the maximum vehicle capacity plus one. The performance of these algorithms is validated using a mixed autonomy on-demand mobility simulator. These efficient algorithms serve as a stepping stone for a variety of multimodal and multiclass on-demand mobility applications.

This study aims to compare treatments and outcomes of mechanical complications of acute myocardial infarction (MI) during the Covid-19 and in the pre-Covid-19 era. Electronic databases have been searched for MI mechanical complications during the Covid-19 era and in the previous period from January 1998 to January 2020 (pre-Covid-19 era), until October 2021. To perform a quantitative analysis of non-comparative series, a meta-analysis of proportion has been conducted. Early mortality after surgical treatment was 15.0% while it was significantly higher after conservative treatment (62.4%) ( = 0.026). Early mortality after surgical treatment was seemingly higher in the pre-Covid-19 era but the difference did not reach statistical significance (15.0% vs 38.9%; = 0.13). Mortality in patients treated conservatively, or turned down for surgery, was lower during the Covid-19 pandemic (62.4% vs 97.7%; = 0.001). The crude mean prevalence of the use rate of conservative or surgical treatment across the studies during Covid-19 and in the pre-Covid-19 era was comparable. The current increased incidence of MI mechanical complications might be a consequence of delayed presentation or restricted access to hospital facilities. Despite the general negative impact of Covid-19 on cardiac surgery volumes and outcomes and the apparent increase of the incidence of MI complications, the outcomes of their surgical and clinical treatment seem not to have been affected during the pandemic.