Crime In 2016: A Preliminary Analysis

Earlier this year, the Brennan Center analyzed crime data from the 30 largest cities in 2015, finding that crime overall remained the same as in 2014. It also found that murder increased by 14 percent, with just three cities — Baltimore, Chicago, and Washington, D.C. — responsible for half that increase. All told, 2015's murder rate was still near historic lows. The authors concluded that reports of a national crime wave were premature and unfounded, and that "the average person in a large urban area is safer walking on the street today than he or she would have been at almost any time in the past 30 years."This report updates those findings. It collects midyear data from police departments to project overall crime, violent crime, and murder for all of 2016

Crime Trends: 1990-2016

This report examines crime trends at the national and city level during the last quarter century. It covers the years 1990 through 2016, as crime rates peaked in 1991. It analyzes data from the Federal Bureau of Investigation and from police departments from the nation's 30 largest cities. Data for 2016 are estimated, as full year data was not available at the time of publication.This report concludes that although there are some troubling increases in crimes in specific cities, there is no evidence of a national crime wave

Coming Out of YouTube: Self-Disclosure in Online Spaces

Coming out, the act of disclosing a queer identity by an individual, is a fairly new phenomenon that both brings awareness to a marginalized community and allows individuals to self-accept. With the rise of media (television, movies, and social media), coming out has become a more public act where individuals are self-disclosing their identities to mass audiences. With these acts of self-disclosure now happening more frequently in online spaces (especially on social media), the research focused on computer-mediated communication sites with an interest on how identities were disclosed and how other users responded to the disclosure. YouTube has become a popular site for users to post coming out videos as well as view queer content that provides authentic representation. Within the queer community, a disparity between gay and transgender individuals has persisted, and the treatment of these individuals once out has differed as well. While YouTube has labelled itself as a welcoming, all-inclusive platform, the presence of anti-queer (both homophobic and transphobic) language remains on this site, especially in the comment sections. Therefore, with the threat of anti-queer language, a textual analysis was conducted in these online spaces to determine overall reaction and the difference in reactions in response to gay and transgender individuals

Values of Ecosystem Services Associated with Intense Dairy Farming in New Zealand

The increase in greenhouse gas emissions and degradation of water quality and quantity in waterways due to dairy farming in New Zealand have become of growing concern. Compared to traditional sheep and beef cattle farming, dairy farming is more input intensive and more likely to cause such environmental damage. Our study uses choice modeling to explore New Zealanders' willingness to pay for sustainable dairy and sheep/beef cattle farming. We investigate respondents' level of awareness of the environmental degradation caused by dairy farming and their willingness to make trade-offs between economic growth and improvements in the level of ecosystem services associated with pastoral farming.ecosystem services, greenhouse gas emissions, dairy farming, choice modeling, Environmental Economics and Policy, Livestock Production/Industries,

Ecosystem Services on New Zealand Arable Farms

Researchers have estimated the total economic value of global ecosystem goods and services showing that a significant portion of humanity's economic well being is unaccounted for in conventional GNP accounting (Constanza et al., 1997). To demonstrate this point, authors have conventionally used highly aggregated landscape units for analysis (e.g., biomes), and average, not marginal values, of each ecosystem good or service are estimated for each unit using value transfer methodologies (Wilson et al., 2004). For example, Patterson and Cole (1999a, b) replicated the Constanza et al., (1997) approach by estimating economic values for Waikato and New Zealand ecosystem goods and services associated with standard land cover classes including horticulture, agriculture and cropping. As a result, Patterson and Cole (1999b) argue that only five ecosystem services associated with cropping have non-zero value. One of the reasons for this low number of non-zero values assorted with arable lands is that the original economic studies used by Patterson and Cole, are heavily weighted towards natural and undisturbed ecosystems rather than disturbed systems like agricultural or urban landscapes. To address this issue, more recently researchers have noted that many landscapes are actively modified by humans who seek to realise economic gain and this topic is thus an important one because in the 21st century, many of our homes, workplaces and recreational spaces are embedded within, or adjacent to, landscape mosaics that are to a greater or lesser degree affected by the conscious efforts of people to harness goods and services provided by ecological systems (Palmer et al., 2004). An engineered or designed ecosystem is one that has been extensively modified by humans to explicitly provide a set of ecosystem goods and services including more fresh water, trees, and food products and fewer floods and pollutants. These modified landscapes provide a range of ecosystem goods and services, particularly food production as farmers seek to maximize commercial gain from land use. The current paper examines issues in valuation of ecosystem goods and services derived from land used for arable farming in New Zealand and proposes ways to provide more detailed estimates of the flow and value of the flow of ecosystem services provided.Ecosystem management, Arable farming, Engineered ecosystem, Agricultural and Food Policy, Community/Rural/Urban Development, Crop Production/Industries, Environmental Economics and Policy, Farm Management, Land Economics/Use,

ForgetMeNot: Active Reminder Entry Support for Adults with Acquired Brain Injury

Smartphone reminding apps can compensate for memory impairment after acquired brain injury (ABI). In the absence of a caregiver, users must enter reminders themselves if the apps are going to help them. Poor memory and apathy associated with ABI can result in failure to initiate such configuration behaviour and the benefits of reminder apps are lost. ForgetMeNot takes a novel approach to address this problem by periodically encouraging the user to enter reminders with unsolicited prompts (UPs). An in situ case study investigated the experience of using a reminding app for people with ABI and tested UPs as a potential solution to initiating reminder entry. Three people with severe ABI living in a post-acute rehabilitation hospital used the app in their everyday lives for four weeks to collect real usage data. Field observations illustrated how difficulties with motivation, insight into memory difficulties and anxiety impact reminder app use in a rehabilitation setting. Results showed that when 6 UPs were presented throughout the day, reminder-setting increased, showing UPs are an important addition to reminder applications for people with ABI. This study demonstrates that barriers to technology use can be resolved in practice when software is developed with an understanding of the issues experienced by the user group

Is the Schoharie Really that Scary?

Use analytical methods and a variety of instruments to compare water samples from different rivers and creeks within the Capital Region and determine what is in Union College’s tap water.https://digitalworks.union.edu/waterprojectposters/1003/thumbnail.jp

The use of flow cytometry in the diagnosis of the Myelodysplastic Syndromes

The Myelodysplastic Syndromes (MDS) are a biologically and clinically heterogeneous group of bone marrow haematopoietic cell disorders that result in ineffective haematopoiesis. Unlike most forms of haematological malignancy, the diagnosis of MDS remains heavily reliant on subjective morphological interpretation which can result in inaccurate and missed diagnoses. The use of flow cytometric immunophenotyping offers a potential solution to aid in the diagnosis of MDS, and numerous flow cytometric scoring schemes have been already been proposed and tested. However, most flow cytometric scoring schemes are user-defined, with simple schemes lacking diagnostic sensitivity, whilst the more comprehensive schemes may be unfeasible to implement in a large-scale diagnostic setting. The use of machine learning classifiers offered a more subjective approach to the use of flow cytometric data. Therefore, we have tested a series of classifiers both by combining simple immunophenotypic and demographic features, and by utilising a 2 tube-immunophenotyping panel which contained a large array of numerical and immunophenotypic attributes which had been identified as being abnormal in MDS patients. We have shown that machine learning classifier-based approaches could reproducibly identify patients with definite abnormalities in MDS, and those with normal haematopoietic populations in non-diagnostic, reactive conditions. The classifiers further offered the ability to aid in the triage of patients unlikely to be MDS by providing the basis to a diagnostic confidence score. The application of multiple classifiers also identified a grey-area of MDS patients who were consistently misclassified and who may prove to be challenging to diagnose by flow cytometry, due to an absence of aberrant immunophenotypic features. Finally, we have also shown that a combination of immunophenotyping and targeted gene mutation analysis provides the potential to identify non-diagnostic cases which may progress to MDS. It is in a combination of these two techniques where the future of MDS diagnosis may lie

The Houston Lightning Mapping Array: Network Installation and Preliminary Analysis

The Houston Lightning Mapping Array (LMA) is a lightning detection network providing total lightning mapping for the Houston metropolitan area and southeast Texas. The network is comprised of twelve Very High Frequency (VHF) time-of-arrival total lightning mapping sensors built by New Mexico Institute of Mining and Technology and purchased by Texas A&M University. The sensors, installed in April 2012, are of the latest, modular design and built to be independent stations that utilize a solar panel for electricity and cellular data modems for communication. Each sensor detects the time of arrival of a VHF impulse emitted as part of the electrical breakdown and lightning propagation process. Data from each sensor are processed on a central LMA server to provide three-dimensional mapping of these impulses, also called LMA sources. This processing facilitates the analysis of variations in thunderstorm structure and the associated changes in both space and time. The primary objectives for the installation of the Houston LMA network are twofold: first, to provide a dataset enabling research into thunderstorm electrification in the context of a coastal, urban, polluted environment; and second, to enable improvements in operational forecasting and public safety by providing total lightning data to partners including the National Weather Service (NWS). A workflow was established to create and share real-time data to these partners, while simultaneously maintaining a full, research-quality dataset. Data are retrieved from the field sensors and backed up to a central LMA server for processing and storage. Archived network data are available from July 2012 through the present. The network measures 150 km from north to south, with stations in College Station and Galveston complementing the ten sites surrounding downtown Houston. This extends the region constrained by the network beyond the immediate metropolitan Houston area, resulting in increased accuracy in locating sources further from the network center. Based on initial analyses, the effective range of the Houston LMA is 75 km for three-dimensional mapping and approximately 250 km for two-dimension mapping