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This paper studies how to integrate rider mode preferences into the design of on-demand multimodal transit systems (ODMTSs). It is motivated by a common worry in transit agencies that an ODMTS may be poorly designed if the latent demand, that is, new riders adopting the system, is not captured. This paper proposes a bilevel optimization model to address this challenge, in which the leader problem determines the ODMTS design, and the follower problems identify the most cost efficient and convenient route for riders under the chosen design. The leader model contains a choice model for every potential rider that determines whether the rider adopts the ODMTS given her proposed route. To solve the bilevel optimization model, the paper proposes an exact decomposition method that includes Benders optimal cuts and no-good cuts to ensure the consistency of the rider choices in the leader and follower problems. Moreover, to improve computational efficiency, the paper proposes upper and lower bounds on trip durations for the follower problems, valid inequalities that strengthen the no-good cuts, and approaches to reduce the problem size with problem-specific preprocessing techniques. The proposed method is validated using an extensive computational study on a real data set from the Ann Arbor Area Transportation Authority, the transit agency for the broader Ann Arbor and Ypsilanti region in Michigan. The study considers the impact of a number of factors, including the price of on-demand shuttles, the number of hubs, and access to transit systems criteria. The designed ODMTSs feature high adoption rates and significantly shorter trip durations compared with the existing transit system and highlight the benefits of ensuring access for low-income riders. Finally, the computational study demonstrates the efficiency of the decomposition method for the case study and the benefits of computational enhancements that improve the baseline method by several orders of magnitude. Funding: This research was partly supported by National Science Foundation [Leap HI Proposal NSF-1854684] and the Department of Energy [Research Award 7F-30154]. [ABSTRACT FROM AUTHOR]

In this note we will describe a simple and practical approach to get rigorous bounds on the Hausdorff dimension of limits sets for some one dimensional Markov iterated function schemes. The general problem has attracted considerable attention, but we are particularly concerned with the role of the value of the Hausdorff dimension in solving conjectures and problems in other areas of mathematics. As our first application we confirm, and often strengthen, conjectures on the difference of the Lagrange and Markov spectra in Diophantine analysis, which appear in the work of Matheus and Moreira [Comment. Math. Helv. 95 (2020), pp. 593–633]. As a second application we (re-)validate and improve estimates connected with the Zaremba conjecture in number theory, used in the work of Bourgain–Kontorovich [Ann. of Math. (2) 180 (2014), pp. 137–196], Huang [ An improvement to Zaremba's conjecture , ProQuest LLC, Ann Arbor, MI, 2015] and Kan [Mat. Sb. 210 (2019), pp. 75–130]. As a third more geometric application, we rigorously bound the bottom of the spectrum of the Laplacian for infinite area surfaces, as illustrated by an example studied by McMullen [Amer. J. Math. 120 (1998), pp. 691-721]. In all approaches to estimating the dimension of limit sets there are questions about the efficiency of the algorithm, the computational effort required and the rigour of the bounds. The approach we use has the virtues of being simple and efficient and we present it in this paper in a way that is straightforward to implement. These estimates apparently cannot be obtained by other known methods. [ABSTRACT FROM AUTHOR]

The majority of adults suffering from alcohol use disorders (AUD) do not receive treatment. To address this gap in care, we must develop new models to increase identification, engagement and delivery of accessible and effective treatment. This paper describes the protocol for a randomized controlled trial (RCT) testing a novel telehealth treatment model for primary care patients with untreated AUD. We aim to recruit 300 adults across 2 healthcare systems for this two-arm RCT. Participants, initially identified for recruitment based on AUD-related indicators in their electronic health record (EHR), are RCT-eligible if they meet AUD criteria (mild, moderate, severe), report ≥ 3 drinking days/week in past 30 days, and have not received AUD psychotherapy in the past 90 days. Participants are randomized to an intervention or enhanced usual care control (EUC) condition, both individually-delivered. The intervention includes a telephone-delivered motivational interviewing (MI) engagement session and 8 sessions of MI-Cognitive Behavioral Therapy (MI-CBT). EUC involves AUD psychoeducation, advice to reduce drinking and seek treatment, and provision of community resources. Outcomes will be measured at 3-, 6-, and 12-months; primary outcomes include: AUD psychotherapy initiation and engagement (within the study and external community) and alcohol consumption (percent drinking days and heavy drinking days). This study addresses whether proactive patient identification and engagement and delivery of patient-centered telehealth psychotherapy to patients with untreated AUD is effective in increasing treatment use and improving alcohol outcomes. If effective, this could be a highly scalable model for reducing the public health impact of AUD. ClinicalTrials.gov # NCT05410561. University of Michigan HUM00204315. Ann Arbor VA IRB #1655886. [ABSTRACT FROM AUTHOR]

Background. Bone marrow infiltration of lymphoma cells is a candidate risk factor for infusion-related reactions (IRRs) in patients with CD20-positive B-cell non-Hodgkin lymphoma (B-NHL). However, despite with the lack of sufficient data, the effect of bone marrow infiltration of B-NHL cells on the incidence rate of grade 2 or higher IRRs with the administration of rituximab has been retrospectively studied in this paper. Methods. Patients with CD20-positive B-NHL who received the rituximab induction therapy for the first time were enrolled in this study. To evaluate the bone marrow infiltration of B-NHL cells, May–Giemsa stain of bone marrow films and flow cytometry examination of bone marrow aspiration samples were performed. IRR grade was determined using the IRR criteria in the Common Terminology Criteria for Adverse Events version 4.0. Results. A total of 127 patients were eligible for this study. Grade 2 or higher IRRs were observed in 43 (34%) patients. In univariate analysis, use of glucocorticoid before rituximab infusion was a strong risk-avoiding factor for grade 2 or higher IRRs. Advanced stage of disease (Ann Arbor: stages III and IV) or bone marrow infiltration of B-NHL cells revealed the risk factors, regardless of glucocorticoid premedication. Using multivariate analysis, bone marrow infiltration was found to be an independent risk factor for patients without prior glucocorticoid use. Conclusion. Bone marrow infiltration of B-NHL cells is a risk factor for grade 2 or higher IRRs at the first rituximab induction therapy without glucocorticoid premedication. [ABSTRACT FROM AUTHOR]

A synchronization problem in cellular automata has been known as the Firing Squad Synchronization Problem (FSSP), where the FSSP gives a finite-state protocol for synchronizing a large scale of cellular automata. A quest for smaller state FSSP solutions has been an interesting problem for a long time. It has been shown by Balzer (Inf Control 10:22–42, 1967), Sanders (in: Jesshope, Jossifov, Wilhelmi (eds) Proceedings of the VI international workshop on parallel processing by cellular automata and arrays, Akademie, Berlin, 1994) and Berthiaume et al. (Theoret Comput Sci 320:213–228, 2004) that there exists no 4-state FSSP solution in arrays and rings. The number four is the state lower bound in the class of FSSP protocols. Umeo et al. (Parallel Process Lett 19(2):299–313, 2009), by introducing a concept of full versus partial FSSP solutions, provided a list of the smallest 4-state symmetric powers-of-2 FSSP protocols that can synchronize any one-dimensional (1D) ring cellular automata of length n = 2 k for any positive integer k ≥ 1 . Afterwards, Ng (in: Partial solutions for the firing squad synchronization problem on rings, ProQuest Publications, Ann Arbor, MI, 2011) also added a list of asymmetric FSSP partial solutions, thus completing the 4-state powers-of-2 FSSP partial solutions. A question whether there are any 4-state partial solutions for ring lengths other than powers-of-2 has remained open. In this paper, we answer the question by providing a new class of the smallest symmetric and asymmetric 4-state FSSP protocols that can synchronize any 1D ring of length n = 2 k - 1 for any positive integer k ≥ 2 . We show that the class includes a rich variety of FSSP protocols that consists of 39 symmetric and 132 asymmetric solutions, ranging from minimum to linear synchronization time. In addition, we make an investigation into several interesting properties of those partial solutions, such as swapping general states, transposed protocols, a duality property between them, and an inclusive property of powers-of-2 solutions. [ABSTRACT FROM AUTHOR]

This paper considers the design of On-Demand Multimodal Transit Systems (ODMTS) that combine fixed bus/rail routes between transit hubs with on-demand shuttles that serve the first/last miles to/from the hubs. The design problem aims at finding a network design for the fixed routes to allow a set of riders to travel from their origins to their destinations, while minimizing the sum of the travel costs, the bus operating costs, and rider travel times. The paper addresses two gaps in existing tools for designing ODMTS. First, it generalizes prior work by including ridesharing in the shuttle rides. Second, it proposes novel fleet-sizing algorithms for determining the number of shuttles needed to meet the performance metrics of the ODMTS design. Both contributions are based on Mixed-Integer Programs (MIP). For the ODMTS design, the MIP reasons about pickup and dropoff routes in order to capture ridesharing, grouping riders who travel to/from the same hub. The fleet-sizing optimization is modeled as a minimum flow problem with covering constraints. The natural formulation leads to a dense graph and computational issues, which is addressed by a reformulation that works on a sparse graph. The methodological contributions are evaluated on a real case study: the public transit system of the broader Ann Arbor and Ypsilanti region in Michigan. The results demonstrate the substantial potential of ridesharing for ODMTS, as costs are reduced by about 26% with respect to allowing only individual shuttle rides, at the expense of a minimal increase in transit times. Compared to the existing system, the designed ODMTS also cuts down costs by 35% and reduces transit times by 38%. • The paper studies a two-modal system combining bus and shuttles modes. • It solves the bus network design and the shuttle fleet sizing of the system. • It models ridesharing in the network design as a HALP via shuttle route enumeration. • It proposes a totally unimodular flow formulation for the shuttle fleet sizing. • It illustrates potential benefits of ridesharing using real world data. [ABSTRACT FROM AUTHOR]

• Life-cycle impacts of SAV and SAEV fleets are examined. • SAEV fleets have higher VMT and require additional infrastructure compared to SAVs. • V2G services can reduce the operating cost of SAEV fleets by up to 19.6% over 30 years. • V2G can further leverage the environmental benefits of SAEVs. Vehicle-to-Grid (V2G) services utilize idle electric vehicle (EV) batteries as distributed energy storage units for the power grid to stabilize the fluctuating demand. The commercial implementation and deployment of V2G services are hindered by concerns about battery degradation, inconvenience, distrust, and further range anxiety, making private mainstream EV owners reluctant to participate in V2G program offered by utilities. The centralized operation and fleet-level ownership of shared autonomous electric vehicles (SAEV) can alleviate these barriers. This paper examines the economic and environmental co-benefits of V2G services in SAEV fleets, using results from operational simulations in a mid-size city (Ann Arbor, MI). We present a life-cycle assessment framework by considering SAEV fleets with 100-mile short-range (SAEV100) and 250-mile long-range (SAEV250) powertrain vehicles and compare the trade-offs of higher vehicle-mile-traveled (VMT) and additional infrastructure requiem nets in the electrified fleet with SAV counterpart. Incorporating a 30-year timeframe, we show that from an economic point of view, the operating cost of the SAEV fleet is 3.4%-8.4% higher than SAV fleet. However, providing V2G service can reduce the cost of SAEV250 fleet by 19.6% compared to SAV fleet by generating revenue, thus making it economically feasible to replace the SAV fleet. We find that on average, V2G service yields a revenue of $2,272 per vehicle annually while saving 66.5 tons of GHG emissions per vehicle per year. We also conduct a thorough sensitivity analysis to quantify the uncertainty of results from key parameters. Finally, we highlight the major opportunities for maximizing the environmental and economic performance of SAEV fleets under the provision of V2G services over the long term. [ABSTRACT FROM AUTHOR]