On dynamical bit sequences

Let X^{(k)}(t) = (X_1(t), ..., X_k(t)) denote a k-vector of i.i.d. random variables, each taking the values 1 or 0 with respective probabilities p and 1-p. As a process indexed by non-negative t, $X^{(k)}(t)$ is constructed--following Benjamini, Haggstrom, Peres, and Steif (2003)--so that it is strong Markov with invariant measure ((1-p)\delta_0+p\delta_1)^k. We derive sharp estimates for the probability that ``X_1(t)+...+X_k(t)=k-\ell for some t in F,'' where F \subset [0,1] is nonrandom and compact. We do this in two very different settings: (i) Where \ell is a constant; and (ii) Where \ell=k/2, k is even, and p=q=1/2. We prove that the probability is described by the Kolmogorov capacitance of F for case (i) and Howroyd's 1/2-dimensional box-dimension profiles for case (ii). We also present sample-path consequences, and a connection to capacities that answers a question of Benjamini et. al. (2003)Comment: 25 pages. This a substantial revision of an earlier paper. The material has been reorganized, and Theorem 1.3 is ne

Interim report, Bocono Dam river outlet tests

CER58ARC17.May 1958.Includes bibliographical references.Prepared for Tipton and Kalmbach, Inc

A Comparative Review Study on the Manufacturing Processes of Composite Grid Structures

Filament winding and fiber placement are low-cost, fast, and suitable processes for manufacturing composite grid structures. Resulted structures are high quality products. They have the advantage of carrying heavy structural loads as well as light structural weight. Composite Grid Structures (CGS) are manufactured with varying geometries such as circular (cylindrical and conic) and flat. They are applied in hightech industries including aerospace industry. In this paper, the manufacturing processes of these structures and their various aspects (including winding method, mandrel material and curing method) are reviewed and compared in detail

The construction of mental health as a technological problem in India

This paper points to an underexplored relationship of reinforcement between processes of quantification and digitization in the construction of mental health as amenable to technological intervention, in India. Increasingly, technology is used to collect mental health data, to diagnose mental health problems, and as a route of mental health intervention and clinical management. At the same time, mental health has become recognized as a new public health priority in India, and within national and global public health agendas. We explore two sites of the technological problematisation of mental health in India: a large-scale survey calculating prevalence, and a smartphone app to manage stress. We show how digital technology is deployed both to frame a ‘need’ for, and to implement, mental health interventions. We then trace the epistemologies and colonial histories of ‘psy’ technologies, which question assumptions of digital empowerment and of top-down ‘western’ imposition. Our findings show that in India such technologies work both to discipline and liberate users. The paper aims to encourage global debate inclusive of those positioned inside and outside of the ‘black box’ of mental health technology and data production, and to contribute to shaping a future research agenda that analyzes quantification and digitization as key drivers in global advocacy to make mental health count

Ethnographic perspectives on global mental health

The field of Global Mental Health (GMH) aims to influence mental health policy and practice worldwide, with a focus on human rights and access to care. There have been important achievements, but GMH has also been the focus of scholarly controversies arising from political, cultural and pragmatic critiques. These debates have become increasingly polarized, giving rise to a need for more dialogue and experience-near research to inform theorizing. Ethnography has much to offer in this respect. This paper frames and introduces five articles in the issue of Transcultural Psychiatry that illustrate the role of ethnographic methods in understanding the effects and implications of the field of global mental health on mental health policy and practice. The papers include ethnographies from South Africa, India and Tonga, that show the potential for ethnographic evidence to inform GMH projects. These studies provide nuanced conceptualizations of GMH’s varied manifestations across different settings, the diverse ways that GMH’s achievements can be evaluated, and the connections that can be drawn between locally observed experiences and wider historical, political and social phenomena. Ethnography can provide a basis for constructive dialogue between those engaged in developing and implementing GMH interventions and those critical of some of its approaches

A workflow for patient-specific fluid-structure interaction analysis of the mitral valve: A proof of concept on a mitral regurgitation case

The mechanics of the mitral valve (MV) are the result of the interaction of different anatomical structures complexly arranged within the left heart (LH), with the blood flow. MV structure abnormalities might cause valve regurgitation which in turn can lead to heart failure. Patient-specific computational models of the MV could provide a personalised understanding of MV mechanics, dysfunctions and possible interventions. In this study, we propose a semi-automatic pipeline for MV modelling based on the integration of state-of-the-art medical imaging, i.e. cardiac magnetic resonance (CMR) and 3D transoesophageal-echocardiogram (TOE) with fluid-structure interaction (FSI) simulations. An FSI model of a patient with MV regurgitation was implemented using the finite element (FE) method and smoothed particle hydrodynamics (SPH). Our study showed the feasibility of combining image information and computer simulations to reproduce patient-specific MV mechanics as seen on medical images, and the potential for efficient in-silico studies of MV disease, personalised treatments and device design

Predicting Cancer Immunotherapy Response From Gut Microbiomes Using Machine Learning Models

Cancer immunotherapy has significantly improved patient survival. Yet, half of patients do not respond to immunotherapy. Gut microbiomes have been linked to clinical responsiveness of melanoma patients on immunotherapies; however, different taxa have been associated with response status with implicated taxa inconsistent between studies. We used a tumor-agnostic approach to find common gut microbiome features of response among immunotherapy patients with different advanced stage cancers. A combined meta-analysis of 16S rRNA gene sequencing data from our mixed tumor cohort and three published immunotherapy gut microbiome datasets from different melanoma patient cohorts found certain gut bacterial taxa correlated with immunotherapy response status regardless of tumor type. Using multivariate selbal analysis, we identified two separate groups of bacterial genera associated with responders versus non-responders. Statistical models of gut microbiome community features showed robust prediction accuracy of immunotherapy response in amplicon sequencing datasets and in cross-sequencing platform validation with shotgun metagenomic datasets. Results suggest baseline gut microbiome features may be predictive of clinical outcomes in oncology patients on immunotherapies, and some of these features may be generalizable across different tumor types, patient cohorts, and sequencing platforms. Findings demonstrate how machine learning models can reveal microbiome-immunotherapy interactions that may ultimately improve cancer patient outcomes