RESTORING THE WATERSHED: THE AFTERMATH OF THE MARSH CREEK RESTORATION AT CREEKSIDE PARK IN OAKLEY, CALIFORNIA

In 2012, the City of Oakley and American Rivers completed the first Marsh Creek Restoration Project at Creekside Park. Marsh Creek, prior to restoration, was a trapezoidal engineered channel to mitigate flooding events affecting agricultural spaces and residential areas. This research was conducted to test the effectiveness of restoration projects and identify drawbacks that will be beneficial in the planning and design phases of future project sites. Several parameters were analyzed to understand the aftermath of the Marsh Creek at Creekside Park Restoration Project. This includes collecting water quality data, interpreting Landsat imagery analysis, performing physical characteristic assessments, and reviewing biological indicators. In addition, a compilation of fieldwork performed by American Rivers and Brianne Visaya prior-, during-, and post-restoration project completion was used to determine the success of enhancing the creek habitat and recreating the floodplain. Marsh Creek at Creekside Park Restoration Project’s monitoring program began in 2021. Within the two years, the results have remained the same. The three sites nearest to and within the proposed restoration area are classified as ‘in poor condition. The continuation of monitoring and data collection can better inform the condition of Marsh Creek

Analysis of binary multivariate longitudinal data via 2-dimensional orbits: An application to the Agincourt Health and Socio-Demographic Surveillance System in South Africa.

© 2015 Visaya et al. This is an open access article distributed under the terms of the Creative Commons Attribution License.We analyse demographic longitudinal survey data of South African (SA) and Mozambican (MOZ) rural households from the Agincourt Health and Socio-Demographic Surveillance System in South Africa. In particular, we determine whether absolute poverty status (APS) is associated with selected household variables pertaining to socio-economic determination, namely household head age, household size, cumulative death, adults to minor ratio, and influx. For comparative purposes, households are classified according to household head nationality (SA or MOZ) and APS (rich or poor). The longitudinal data of each of the four subpopulations (SA rich, SA poor, MOZ rich, and MOZ poor) is a five-dimensional space defined by binary variables (questions), subjects, and time. We use the orbit method to represent binary multivariate longitudinal data (BMLD) of each household as a two-dimensional orbit and to visualise dynamics and behaviour of the population. At each time step, a point (x, y) from the orbit of a household corresponds to the observation of the household, where x is a binary sequence of responses and y is an ordering of variables. The ordering of variables is dynamically rearranged such that clusters and holes associated to least and frequently changing variables in the state space respectively, are exposed. Analysis of orbits reveals information of change at both individual- and population-level, change patterns in the data, capacity of states in the state space, and density of state transitions in the orbits. Analysis of household orbits of the four subpopulations show association between (i) households headed by older adults and rich households, (ii) large household size and poor households, and (iii) households with more minors than adults and poor households. Our results are compared to other methods of BMLD analysis

AI-enabled case detection model for infectious disease outbreaks in resource-limited settings

IntroductionThe utility of non-contact technologies for screening infectious diseases such as COVID-19 can be enhanced by improving the underlying Artificial Intelligence (AI) models and integrating them into data visualization frameworks. AI models that are a fusion of different Machine Learning (ML) models where one has leveraged the different positive attributes of these models have the potential to perform better in detecting infectious diseases such as COVID-19. Furthermore, integrating other patient data such as clinical, socio-demographic, economic and environmental variables with the image data (e.g., chest X-rays) can enhance the detection capacity of these models.MethodsIn this study, we explore the use of chest X-ray data in training an optimized hybrid AI model based on a real-world dataset with limited sample size to screen patients with COVID-19. We develop a hybrid Convolutional Neural Network (CNN) and Random Forest (RF) model based on image features extracted through a CNN and EfficientNet B0 Transfer Learning Model and applied to an RF classifier. Our approach includes an intermediate step of using the RF's wrapper function, the Boruta Algorithm, to select important variable features and further reduce the number of features prior to using the RF model.Results and discussionThe new model obtained an accuracy and recall of 96% for both and outperformed the base CNN model and four other experimental models that combined transfer learning and alternative options for dimensionality reduction. The performance of the model fares closely to relatively similar models previously developed, which were trained on large datasets drawn from different country contexts. The performance of the model is very close to that of the “gold standard” PCR tests, which demonstrates the potential for use of this approach to efficiently scale-up surveillance and screening capacities in resource limited settings

Childhood Development after Cochlear Implantation (CDaCI) study: Design and baseline characteristics

Children with severe to profound sensorineural hearing loss face communication challenges that influence language, psychosocial and scholastic performance. Clinical studies over the past 20 years have supported wider application of cochlear implants in children. The Childhood Development after Cochlear Implantation (CDaCI) study is the first longitudinal multicentre, national cohort study to evaluate systematically early cochlear implant (CI) outcomes in children. The objective of the study was to compare children who have undergone cochlear implantation, with similarly aged hearing peers across multiple domains, including oral language development, auditory performance, psychosocial and behavioural functioning, and quality of life. The CDaCI study is a multicentre national cohort study of CI children and normal hearing (NH) peers. Eligibility criteria include informed consent, age less than 5 years, pre- or post-lingually deaf, developmental criteria met, commitment to educate the child in English and bilateral cochlear implants. All children had a standardised baseline assessment that included demographics, hearing and medical history, communication history, language measures, cognitive tests, speech recognition, an audiological exam, psychosocial assessment including parent-child videotapes and parent reported quality of life. Follow-up visits are scheduled at six-month intervals and include a standardised assessment of the full battery of measures. Quality assurance activities were incorporated into the design of the study. A total of 188 CI children and 97 NH peers were enrolled between November 2002 and December 2004. The mean age, gender and race of the CI and NH children are comparable. With regard to parental demographics, the CI and NH children's families are statistically different. The parents of CI children are younger, and not as well educated, with 49% of CI parents reporting college graduation vs. 84% of the NH parents. The income of the CI parents is also lower than the NH parents. Assessments of cognition suggest that there may be baseline differences between the CI and NH children; however the scores were high enough to suggest language learning potential. The observed group differences identified these baseline characteristics as potential confounders which may require adjustment in analyses of outcomes. This longitudinal cohort study addresses questions related to high variability in language outcomes. Identifying sources of that variance requires research designs that: characterise potential predictors with accuracy, use samples that adequately power a study, and employ controls and approaches to analysis that limit bias and error. The CDaCI study was designed to generate a more complete picture of the interactive processes of language learning after implantation. Copyright © 2007 John Wiley & Sons, Ltd.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/56091/1/333_ftp.pd

Surgical Factors in Pediatric Cochlear Implantation and Their Early Effects on Electrode Activation and Functional Outcomes

To assess the impact of surgical factors on electrode status and early communication outcomes in young children in the first 2 years of cochlear implantation

The identification of socio-economic classes of Metro Manila household population

Segmenting the household population into socio-economic classes (AB - upper class, C - middle class, D - lower class, and E - lower-lower class) is a tool which marketing men use to identify viable markets to target for their product offerings. To support the marketing men\u27s need for this data, marketing researchers routinely classify survey households according to their socio-economic classes. Past studies, however, show that the way that the households are classified, and the proportions of these classes to total population, vary from one researcher to another. It is the intent of this dissertation study to look into the basic issue of whether there exist segments of households that can be considered as socio-economic classes. And if so, the study further attempts to establish the proportions of these segments to total household population, and to determine which variables will consistently and distinctively classify socio-economic status. To achieve the study\u27s objectives, an ideal sampling approach was adopted. This approach is different from current industry practice which commonly generates samples that are random only within each segment. For this study, a representative sample of 1,067 Metro Manila households was generated using the sample size formula for population data.Using the multi stage sampling procedure, a household was randomly selected and the housewife interviewed. They were asked to provide information on the variables commonly used for socio-economic classification. These include occupation of the household head, educational attainment, possession of household facilities, income and expenses. The dwelling unit was observed by the interviewer and classified according to type, make and painting. All these information were subjected to the multivariate statistical tool of cluster analysis. Five computer runs were done. The results revealed the following: 1. Cluster analysis confirms the existence of socio-economic segments. 2. The distribution of the segments is highly skewed to the E class. 3. Comparing the proportions derived from this study with the proportions currently used by marketing researchers: the proportion of AB\u27s are just about the same, the C\u27s and the D\u27s are currently over-represented, and the E\u27s are grossly underestimated

The Data Age Is Here, Are You Ready?

In this video, provided by the National Convergence Technology Center (CTC) to attendees of the July 2022 Summer Working Connections professional development event, Chris Visaya, Senior Manager of Education Delivery for Splunk, discusses both broad data science and data analytics workforce needs and specific benefits for colleges joining Splunk's Academic Alliance. The increasing importance of data in society, new technologies, Splunk's data-to-everything platform, Splunk's services and software, and Splunk's customers and users are also highlighted. The video recording runs 17:18 minutes in length.Presentation slides are also provided