A Network-Graph Based IT Artifact Aiding the Theory Building Process

To support theory building, we introduce a network-graph based IT artifact to provide high recall during exploratory searches and high precision using knowledge gained through the literature discovery process. The use of network graphs, where all data is represented as a node, relationship, or property of either, offers a flexible and tailorable methodology able to accommodate the highly iterative process of theory building. This IT artifact was developed to enable aggregation and normalization of data from varied sources and formats to support the acquisition and assessment of literature needed throughout this process. Our goal in presenting this IT artifact is to promote an accessible and pragmatic approach addressing the varied challenges of Information Systems researchers during the information seeking process

A Network-Graph Based IT Artifact Aiding the Theory Building Process

Proceedings of the 55th Hawaii International Conference on System Sciences | 2022The article of record at published may be found at https://hdl.handle.net/10125/80136To support theory building, we introduce a network-graph based IT artifact to provide high recall during exploratory searches and high precision using knowledge gained through the literature discovery process. The use of network graphs, where all data is represented as a node, relationship, or property of either, offers a flexible and tailorable methodology able to accommodate the highly iterative process of theory building. This IT artifact was developed to enable aggregation and normalization of data from varied sources and formats to support the acquisition and assessment of literature needed throughout this process. Our goal in presenting this IT artifact is to promote an accessible and pragmatic approach addressing the varied challenges of Information Systems researchers during the information seeking process

Digital Curation and Preservation: An Integrated Approach

The Binghamton University Libraries has created a suite of activities focused on customizable and personalized digital asset management for research data projects. Our activities encompass many aspects of the data life cycle including discovery, distribution and preservation. Support services including providing and discussing educational materials and policy documents for projects help ensure the success and sustainability of the program

Combining Green Metrics and Digital Twins for Sustainability Planning and Governance of Smart Buildings and Cities

Creating a more sustainable world will require a coordinated effort to address the rise of social, economic, and environmental concerns resulting from the continuous growth of cities. Supporting planners with tools to address them is pivotal, and sustainability is one of the main objectives. Modeling and simulation augmenting digital twins can play an important role to implement these tools. Although various green best practices have been utilized over time and there are related attempts at measuring green success, works in the published literature tend to focus on addressing a single problem (e.g., energy efficiency), and a comprehensive approach that takes the multiple facets of sustainable urban planning into consideration has not yet been identified. This paper begins with a review of recent research efforts in green metrics and digital twins. This leads to developing an approach that evaluates organizational green best practices to derive metrics, which are used for computational decision support by digital twins. Furthermore, it leverages these research results and proposes a metric-driven framework for sustainability planning that understands a city as a sociotechnical complex system. Such a framework allows the practitioner to take advantage of recent developments and provides computational decision support for the complex challenge of sustainability planning at the various levels of urban planning and governance

Field Evaluation of DNA Detection of Human Filarial and Malaria Parasites Using Mosquito Excreta/Feces

We recently developed a superhydrophobic cone-based method for the collection of mosquito excreta/feces (E/F) for the molecular xenomonitoring of vector-borne parasites show-ing higher throughput compared to the traditional approach. To test its field applicability, we used this platform to detect the presence of filarial and malaria parasites in two villages of Ghana and compared results to those for detection in mosquito carcasses and human blood. We compared the molecular detection of three parasites (Wuchereria bancrofti, Plas-modium falciparum and Mansonella perstans) in mosquito E/F, mosquito carcasses and human blood collected from the same households in two villages in the Savannah Region of the country. We successfully detected the parasite DNA in mosquito E/F from indoor resting mosquitoes, including W. bancrofti which had a very low community prevalence (2.5–3.8%). Detection in the E/F samples was concordant with detection in insect whole carcasses and human blood, and a parasite not vectored by mosquitoes was detected as well.Our approach to collect and test mosquito E/F successfully detected a variety of parasites at varying prevalence in the human population under field conditions, including a pathogen (M. perstans) which is not transmitted by mosquitoes. The method shows promise for further development and applicability for the early detection and surveillance of a variety of pathogens carried in human blood

Field evaluation of DNA detection of human filarial and malaria parasites using mosquito excreta/feces.

We recently developed a superhydrophobic cone-based method for the collection of mosquito excreta/feces (E/F) for the molecular xenomonitoring of vector-borne parasites showing higher throughput compared to the traditional approach. To test its field applicability, we used this platform to detect the presence of filarial and malaria parasites in two villages of Ghana and compared results to those for detection in mosquito carcasses and human blood. We compared the molecular detection of three parasites (Wuchereria bancrofti, Plasmodium falciparum and Mansonella perstans) in mosquito E/F, mosquito carcasses and human blood collected from the same households in two villages in the Savannah Region of the country. We successfully detected the parasite DNA in mosquito E/F from indoor resting mosquitoes, including W. bancrofti which had a very low community prevalence (2.5-3.8%). Detection in the E/F samples was concordant with detection in insect whole carcasses and human blood, and a parasite not vectored by mosquitoes was detected as well.Our approach to collect and test mosquito E/F successfully detected a variety of parasites at varying prevalence in the human population under field conditions, including a pathogen (M. perstans) which is not transmitted by mosquitoes. The method shows promise for further development and applicability for the early detection and surveillance of a variety of pathogens carried in human blood

Deep Learning for Distinguishing Normal versus Abnormal Chest Radiographs and Generalization to Unseen Diseases

Chest radiography (CXR) is the most widely-used thoracic clinical imaging modality and is crucial for guiding the management of cardiothoracic conditions. The detection of specific CXR findings has been the main focus of several artificial intelligence (AI) systems. However, the wide range of possible CXR abnormalities makes it impractical to build specific systems to detect every possible condition. In this work, we developed and evaluated an AI system to classify CXRs as normal or abnormal. For development, we used a de-identified dataset of 248,445 patients from a multi-city hospital network in India. To assess generalizability, we evaluated our system using 6 international datasets from India, China, and the United States. Of these datasets, 4 focused on diseases that the AI was not trained to detect: 2 datasets with tuberculosis and 2 datasets with coronavirus disease 2019. Our results suggest that the AI system generalizes to new patient populations and abnormalities. In a simulated workflow where the AI system prioritized abnormal cases, the turnaround time for abnormal cases reduced by 7-28%. These results represent an important step towards evaluating whether AI can be safely used to flag cases in a general setting where previously unseen abnormalities exist

Consumption and Consumer Footprint: methodology and results

This report presents the results of the Life Cycle Indicators (LCIND2) project, aimed at developing two sets of Life cycle Assessment-based indicators for assessing the environmental impact of EU consumption: the Consumption Footprint and the Consumer Footprint. The indicators have been designed aiming at: • monitoring the evolution of impacts over time in the EU and the Member States as well as the progress towards decoupling economic growth from environmental impacts • building an LCA-based framework for assessing relevant consumption and eco-innovation policies. The methodology shall measure the environmental impacts from three different perspectives at end consumer product group level; consumption areas (food, housing, mobility, consumer goods, and appliances); the average EU consumer. • developing a single headline indicator to monitor the evolution of the overall environmental impacts of EU consumption and production at macro level. This includes the elaboration of a specific framework on which to build such indicator and complete time-series for each Member State and European Union as a whole. • testing ecoinnovation scenarios along the supply chains, from extraction of raw materials, to consumer behaviour, up to end of life options. This science for policy report is complemented by a technical report, where methodological details and assumptions as well as comparison with other available studies are reported.JRC.D.1-Bio-econom