In-Plant Testing of High-Efficiency Hydraulic Separators

Hydraulic separators are commonly used for particle size classification and gravity concentration of minerals and coal. Unfortunately, the efficiency of these processes can be quite low due to poor equipment design and variations in feed consistency. To help alleviate these problems, an industry-driven R&D program has been undertaken to develop a new generation of hydraulic separators that are more efficient and less costly to operate and maintain. These units, which are commercially called the CrossFlow separator and HydroFloat separator, have the potential to improve performance (separation efficiency and throughput) and reduce operating costs (power consumption, water and reagent usage). In Phase I of this project, laboratory and pilot-scale test units were evaluated at various industrial sites in both the coal and mineral industries. Based on promising results obtained from Phase I, full-scale prototypes were purchased and installed by a major U.S. phosphate producer and a large eastern U.S. coal company. The test data obtained from these sites demonstrate that significant performance improvements can be realized through the application of these high-efficiency separators

In-Plant Testing of High-Efficiency Hydraulic Separators

The mineral processing industry has commonly utilized hydraulic separators throughout history for classification and gravity concentration of various minerals. More commonly referred to as hindered-bed or fluidized-bed separators, these units make use of differential particle settling rates to segregate particles according to shape, size, and/or density. As with any equipment, there are inefficiencies associated with its operation, which prompted an industry driven research program to further evaluate two novel high-efficiency hindered bed separators. These units, which are commercially called the CrossFlow separator and HydroFloat separator, have the potential to improve performance (separation efficiency and throughput) and reduce operating costs (power consumption, water and reagent usage). This report describes the results of Phase I activities (laboratory and pilot-scale tests) conducted with the CrossFlow and HydroFloat separators at several locations in the minerals and coal industries. Details of the testing programs (equipment setup, shakedown testing and detailed testing) associated with four coal plants and two phosphate plants are summarized in this work. In most of these applications, the high-efficiency units proved to provide a higher quality product at reduced costs when compared against the performance of conventional separators. Based on promising results obtained from Phase I, full-scale prototypes will be purchased by several mining companies for use in Phase II of this project. Two of the prototype units, which will be constructed by Eriez Manufacturing, are expected to be installed by a major U.S. phosphate producer and a large eastern U.S. coal company. Negotiations are also underway to purchase and install additional prototype units by a mineral sands producer and a second phosphate producer. The data obtained from the full-scale evaluations will be used to further promote commercialization and industrial applications of these innovative technologies

A Spatio-Temporal Data Imputation Model for Supporting Analytics at the Edge

Current applications developed for the Internet of Things (IoT) usually involve the processing of collected data for delivering analytics and support efficient decision making. The basis for any processing mechanism is data analysis, usually having as an outcome responses in various analytics queries defined by end users or applications. However, as already noted in the respective literature, data analysis cannot be efficient when missing values are present. The research community has already proposed various missing data imputation methods paying more attention of the statistical aspect of the problem. In this paper, we study the problem and propose a method that combines machine learning and a consensus scheme. We focus on the clustering of the IoT devices assuming they observe the same phenomenon and report the collected data to the edge infrastructure. Through a sliding window approach, we try to detect IoT nodes that report similar contextual values to edge nodes and base on them to deliver the replacement value for missing data. We provide the description of our model together with results retrieved by an extensive set of simulations on top of real data. Our aim is to reveal the potentials of the proposed scheme and place it in the respective literature

Early adversity predicts adoptees’ enduring emotional and behavioral problems in childhood

Children adopted from the public care system are likely to experience a cluster of inter-related risk factors that place them on a trajectory of mental health problems that persist across the life course. However, the specific effects of putative risk factors on children’s mental health post-placement are not well understood. We conducted a prospective, longitudinal study of children placed for adoption between 2014 and 2015 (N = 96). Adoptive parents completed questionnaires at approximately 5-, 21-, 36-, and 48 months post-placement. We used time series analysis to examine the impact of pre-adoptive risk factors (adverse childhood experiences [ACEs], number of moves, days with birth parents and in care) on children’s internalizing and externalizing problems, and prosocial behaviour over four years post-placement. Adoptees’ internalizing and externalizing problems remained consistently high over the four-year study period but more ACEs predicted increases in internalizing and externalizing problems. Contrary to expectations, more pre-placement moves and time in care predicted fewer problems over time, but exploratory analyses of interactive effects revealed this was only the case in rare circumstances. We identify pre- and post-removal factors that may incur benefits or have a deleterious impact on adoptees’ outcomes in post-adoptive family life. Our findings provide knowledge for front-line professionals in the support of adoptive families and underscore the vital need for effective early intervention

Glucose testing and insufficient follow-up of abnormal results: a cohort study

BACKGROUND: More than 6 million Americans have undiagnosed diabetes. Several national organizations endorse screening for diabetes by physicians, but actual practice is poorly understood. Our objectives were to measure the rate, the predictors and the results of glucose testing in primary care, including rates of follow-up for abnormal values. METHODS: We conducted a retrospective cohort study of 301 randomly selected patients with no known diabetes who received care at a large academic general internal medicine practice in New York City. Using medical records, we collected patients' baseline characteristics in 1999 and followed patients through the end of 2002 for all glucose tests ordered. We used multivariate logistic regression to measure associations between diabetes risk factors and the odds of glucose testing. RESULTS: Three-fourths of patients (78%) had at least 1 glucose test ordered. Patient age (≥45 vs. <45 years), non-white ethnicity, family history of diabetes and having more primary care visits were each independently associated with having at least 1 glucose test ordered (p < 0.05), whereas hypertension and hyperlipidemia were not. Fewer than half of abnormal glucose values were followed up by the patients' physicians. CONCLUSION: Although screening for diabetes appears to be common and informed by diabetes risk factors, abnormal values are frequently not followed up. Interventions are needed to trigger identification and further evaluation of abnormal glucose tests

Equity in development and access to health services in the Wild Coast of South Africa: the community view through four linked cross-sectional studies between 1997 and 2007

<p>Abstract</p> <p>Background</p> <p>After election in 1994, the South African government implemented national and regional programmes, such as the Wild Coast Spatial Development Initiative (SDI), to provoke economic growth and to decrease inequities. CIET measured development in the Wild Coast region across four linked cross-sectional surveys (1997-2007). The 2007 survey was an opportunity to look at inequities since the original 1997 baseline, and how such inequities affect access to health care.</p> <p>Methods</p> <p>The 2000, 2004 and 2007 follow-up surveys revisited the communities of the 1997 baseline. Household-level multivariate analysis looked at development indicators and access to health in the context of inequities such as household crowding, access to protected sources of water, house roof construction, main food item purchased, and perception of community empowerment. Individual multivariate models accounted for age, sex, education and income earning opportunities.</p> <p>Results</p> <p>Overall access to protected sources of water increased since the baseline (from 20% in 1997 to 50% in 2007), yet households made of mud and grass, and households who bought basics as their main food item were still less likely to have protected sources of water. The most vulnerable, such as those with less education and less water and food security, were also less likely to have worked for wages leaving them with little chance of improving their standard of living (less education OR 0.59, 95%CI 0.37-0.94; less water security OR 0.67, 95%CI 0.48-0.93; less food security OR 0.43, 95%CI 0.29-0.64). People with less income were more likely to visit government services (among men OR 0.28, 95%CI 0.13-0.59; among women OR 0.33, 95%CI 0.20-0.54), reporting decision factors of cost and distance; users of private clinics sought out better service and medication. Lower food security and poorer house construction was also associated with women visiting government rather than private health services. Women with some formal education were nearly eight times more likely than women with no education to access health services for prevention rather than curative reasons (OR 7.65, 95%CI 4.10-14.25).</p> <p>Conclusion</p> <p>While there have been some improvements, the Wild Coast region still falls well below provincial and national standards in key areas such as access to clean water and employment despite years of government-led investment. Inequities remain prominent, particularly around access to health services.</p

Identifying Biological Network Structure, Predicting Network Behavior, and Classifying Network State With High Dimensional Model Representation (HDMR)

This work presents an adapted Random Sampling - High Dimensional Model Representation (RS-HDMR) algorithm for synergistically addressing three key problems in network biology: (1) identifying the structure of biological networks from multivariate data, (2) predicting network response under previously unsampled conditions, and (3) inferring experimental perturbations based on the observed network state. RS-HDMR is a multivariate regression method that decomposes network interactions into a hierarchy of non-linear component functions. Sensitivity analysis based on these functions provides a clear physical and statistical interpretation of the underlying network structure. The advantages of RS-HDMR include efficient extraction of nonlinear and cooperative network relationships without resorting to discretization, prediction of network behavior without mechanistic modeling, robustness to data noise, and favorable scalability of the sampling requirement with respect to network size. As a proof-of-principle study, RS-HDMR was applied to experimental data measuring the single-cell response of a protein-protein signaling network to various experimental perturbations. A comparison to network structure identified in the literature and through other inference methods, including Bayesian and mutual-information based algorithms, suggests that RS-HDMR can successfully reveal a network structure with a low false positive rate while still capturing non-linear and cooperative interactions. RS-HDMR identified several higher-order network interactions that correspond to known feedback regulations among multiple network species and that were unidentified by other network inference methods. Furthermore, RS-HDMR has a better ability to predict network response under unsampled conditions in this application than the best statistical inference algorithm presented in the recent DREAM3 signaling-prediction competition. RS-HDMR can discern and predict differences in network state that arise from sources ranging from intrinsic cell-cell variability to altered experimental conditions, such as when drug perturbations are introduced. This ability ultimately allows RS-HDMR to accurately classify the experimental conditions of a given sample based on its observed network state