Consumers’ Mental Health Recovery in a Psychiatric Outreach Program: A Process and Outcome Evaluation

This study was a process and outcome evaluation of a relatively innovative Canadian psychiatric outreach program that was multi-disciplinary, shared-care, community-based, consumer-directed, and self-determination oriented. Using structured interviews with participants (n=32), both quantitative and qualitative approaches were employed. The quantiative process measures included a demographic survey, a frequency of service utilization and satisfaction survey, and a shortened version of the Recovery Oriented System Indicator (ROSI) instrument. These measures revealed that the program was serving the target population; the conumsers utilized the services frequently and they were satisfied with the program; and, in the eyes of consumers, the program conformed to the concept of mental health recovery. The qualitative evaluation of process revealed seven themes: the program process is consumer-friendly; the program is supportive; the program is seamless; the program is wellness-oriented; the program is strengths-based; the program is collaborative; and the staff goes above and beyond their regular duties in the process. The quantitative outcome survey completed by consumers and staff revealed predominantly positive ratings. Qualitative outcome themes included: realizing potentiality, healing and wellness, self-determination, managing symptoms, incorporating illness, thriving, becoming more responsible and committed citizens, social connectedness and relationships, more positive day-to-day functioning, and renewing hope and commitment. Correlations between the process and outcome measures indicated that the consumer-rated outcome total score was associated with the frequency of utilization, and that the ROSI total score was associated with consumer satisfaction. The overall results suggested that the psychiatric outreach program operationalized the principles of recovery and became an indispensable part of consumers’ mental health recovery. However, the overall outlook for homeless Canadians who are affected by mental health issues remains bleak given existing housing policy

Dynamical Phase Transitions in a 2D Classical Nonequilibrium Model via 2D Tensor Networks

We demonstrate the power of 2D tensor networks for obtaining large deviation functions of dynamical observables in a classical nonequilibrium setting. Using these methods, we analyze the previously unstudied dynamical phase behavior of the fully 2D asymmetric simple exclusion process with biases in both the x and y directions. We identify a dynamical phase transition, from a jammed to a flowing phase, and characterize the phases and the transition, with an estimate of the critical point and exponents

Dynamical phase behavior of the single- and multi-lane asymmetric simple exclusion process via matrix product states

The open asymmetric simple exclusion process (ASEP) has emerged as a paradigmatic model of nonequilibrium behavior, in part due to its complex dynamical behavior and wide physical applicability as a model of driven diffusion. We compare the dynamical phase behavior of the one-dimensional (1D) ASEP and the multi-lane ASEP, a previously unstudied extension of the 1D model that may be thought of as a finite-width strip of the fully two-dimensional (2D) system. Our characterization employs large deviation theory (LDT), matrix product states (MPS), and the density matrix renormalization group (DMRG) algorithm, to compute the current cumulant generating function and its derivatives, which serve as dynamical order parameters. We use this measure to show that when particles cannot exit or enter the lattice vertically, the phase behavior of the multi-lane ASEP mimics that of its 1D counterpart, exhibiting the macroscopic and microscopic signatures of the maximal current, shock, and high-density–low-density coexistence phases. Conversely, when particles are allowed to freely enter and exit the lattice, no such transition is observed. This contrast emphasizes the complex interplay between latitudinal and longitudinal hopping rates and the effect of current biasing. Our results support the potential of tensor networks as a framework to understand classical nonequilibrium statistical mechanics

Sustainable Approaches to Food Production

Permaculture is a system of ecological design that aims to create more sustainable communities: its principles reinforce to participants understanding patterns of nature, learning food production, managing water catchment and storage, utilizing renewable energy, and building communities. A permaculture system is the exemplary sustainable approach to food production systems that the Campus Garden aims to bring to the University at Buffalo. The belief is that through the building of this garden, we have created a community at UB that has a heightened understanding of where its food comes from (fair share), how that food affects individuals’ bodies (people care), and how food production affects the environment (earth care). With the framework of “Grow better, not bigger” in mind, the ultimate goal of this research is to double the amount of food production to forty-pounds, in the same 20’x20’ plot of the UB Campus Garden. To advance the Garden’s vision and further emphasize the importance of sustainability, it is our goal to explore different gardening techniques for implementation during the growing season. The four components to this alternative growing research include: 1) Community engagement and education; 2) Permaculture and companion planning; 3) Container gardening; and, 4) Vertical gardening. This research allows us to utilize the Garden’s space as best possible and be a representation to the campus community of how food production can occur despite space constraints. Wholly, we aim to educate individuals on alternative gardening techniques, prove that implementation of these techniques is plausible at other sites, and expand the campus community’s understanding of the importance of food production processes

EHR-Based Care Coordination Performance Measures in Ambulatory Care

Describes electronic health record-based measures for assessing coordination in referrals, including information communicated with referral, communication to patient, and specialist report to primary care physician. Offers preliminary evaluation findings

Automatic transformation of irreducible representations for efficient contraction of tensors with cyclic group symmetry

Tensor contractions are ubiquitous in computational chemistry and physics, where tensors generally represent states or operators and contractions are transformations. In this context, the states and operators often preserve physical conservation laws, which are manifested as group symmetries in the tensors. These group symmetries imply that each tensor has block sparsity and can be stored in a reduced form. For nontrivial contractions, the memory footprint and cost are lowered, respectively, by a linear and a quadratic factor in the number of symmetry sectors. State-of-the-art tensor contraction software libraries exploit this opportunity by iterating over blocks or using general block-sparse tensor representations. Both approaches entail overhead in performance and code complexity. With intuition aided by tensor diagrams, we present a technique, irreducible representation alignment, which enables efficient handling of Abelian group symmetries via only dense tensors, by using contraction-specific reduced forms. This technique yields a general algorithm for arbitrary group symmetric contractions, which we implement in Python and apply to a variety of representative contractions from quantum chemistry and tensor network methods. As a consequence of relying on only dense tensor contractions, we can easily make use of efficient batched matrix multiplication via Intel's MKL and distributed tensor contraction via the Cyclops library, achieving good efficiency and parallel scalability on up to 4096 Knights Landing cores of a supercomputer