Transgender people experience harassment, denial of services, and physical assault during healthcare visits. Electronic health record (EHR) structure and language can exacerbate the harm they experience by using transphobic terminology, emphasizing binary genders, and pathologizing transness. Here, we investigate the ways in which SNOMED CT and ICD-10-CM record gender-related terminology and explore their shortcomings as they contribute to this EHR-mediated violence. We discuss how this "standardized" gender-related medical terminology pathologizes transness, fails to accommodate nonbinary patients, and uses derogatory and outmoded language. We conclude that there is no easy fix to the transphobia beleaguering healthcare, provide options to reduce harm to patients, and ultimately call for a critical examination of medicine's role in transphobia. We aim to demonstrate the ways in which the [mis]use and [mis]understanding of gender-specific terminology in healthcare settings has harmed and continues to harm trans people by grounding our discussion in our personal experiences.

Guaranteeing continuous streaming of multimedia data from service providers to the users is a challenging task in wireless ad hoc networks, particularly when node mobility is considered. The topological dynamics introduced by node mobility are further exacerbated by the natural grouping behavior of mobile users, which leads to frequent network partitioning. Network partitioning poses significant challenges to the provisioning of continuous multimedia streaming services in wireless ad hoc networks, since the partitioning disconnects many mobile users from the centralized streaming service. In this paper, we propose NonStop, a collection of novel middleware-based run-time algorithms that ensures the continuous availability of such multimedia streaming services, while minimizing the overhead involved. The network-wide continuous streaming coverage is achieved by partition prediction and service replication on the streaming sources and assisted by distributed selection of streaming sources on regular mobile nodes and users. The proposed algorithms are validated by extensive results from performance evaluations.

In wireless ad-hoc networks, network partitioning occurs when the mobile nodes move with diverse patterns and cause the network to separate into completely disconnected portions. Network partitioning is a wide-scale topology change that can cause sudden and severe disruptions to ongoing network routing and upper layer applications. Its occurrence can be attributed to the aggregate group motion exhibited in the movements of the mobile nodes. By exploiting the group mobility pattern, we can predict the future network partitioning, and thus minimize the amount of disruption. We propose a new characterization of group mobility, based on existing group mobility models, which provides parameters that are sufficient for network partition prediction. We then demonstrate how partition prediction can be made using the mobility model parameters and illustrate the applicability of the prediction information. Furthermore, we use a simple but effective data clustering algorithm that, given the velocities of the mobile nodes in an ad-hoc network, can accurately determine the mobility groups and estimate the characteristic parameters of each group.

In wireless ad-hoc networks, the network topology changes dynamically and unpredictably due to node mobility. Such topological dynamics are further exacerbated by the natural grouping behavior in the mobile user's movement, which leads to frequent network partitioning. Network partitioning poses significant challenges to the provisioning of centralized services in ad-hoc networks, since partitioning disconnects many mobile users from the central server. We propose a collection of novel run-time algorithms that adaptively ensure the centralized service is available to all mobile nodes during network partitioning, while minimizing the number of servers required. The network-wide service coverage is achieved by partition prediction and service replication on the servers, and assisted by distributed service selection on regular mobile nodes. Simulation results show that our algorithm efficiently achieves guaranteed service coverage to all nodes. To the best of our knowledge, there have been no similar approaches that use partition prediction to provision centralized services adaptively in partitionable mobile ad-hoc networks.