From Grain to Main and Following Fish

Two story maps. One is about the rice-to-shrimp agricultural transition in Vietnam from the 1970s-1990s. The other is about Senegalese fishermen climate migrants and their impact on Spain

Handling disruptions in a network with cross-docking

Cross-docking (CD) is a commonly used technique to consolidate freight for more efficient delivery to customers; CD is continuing to see increased use by companies. Synchronization of inbound and outbound freight is clearly critical to operations and so is having the cross-dock able to support the freight flow with available doors and material handling equipment. The latter is particularly important when there is a disruption in the inbound freight. One delayed truck can impact several outbound trucks. A methodology is proposed to address explicitly both the scheduling of trucks and material handling within the CD. Two models are proposed – one for routing inbound and outbound trucks and the other to schedule the cross-dock. Results from each model when run separately are presented as well as results from when the two models are run iteratively

Assessment of the Nurse Medication Administration Workflow Process

This paper presents findings of an observational study of the Registered Nurse (RN) Medication Administration Process (MAP) conducted on two comparable medical units in a large urban tertiary care medical center in Columbia, South Carolina. A total of 305 individual MAP observations were recorded over a 6-week period with an average of 5 MAP observations per RN participant for both clinical units. A key MAP variation was identified in terms of unbundled versus bundled MAP performance. In the unbundled workflow, an RN engages in the MAP by performing only MAP tasks during a care episode. In the bundled workflow, an RN completes medication administration along with other patient care responsibilities during the care episode. Using a discrete-event simulation model, this paper addresses the difference between unbundled and bundled workflow and their effects on simulated redesign interventions

Towards personalised and adaptive QoS assessments via context awareness

Quality of Service (QoS ) properties play an important role in distinguishing between functionally-equivalent services and accommodating the different expectations of users. However, the subjective nature of some properties and the dynamic and unreliable nature of service environments may result in cases where the quality values advertised by the service provider are either missing or untrustworthy. To tackle this, a number of QoS estimation approaches have been proposed, utilising the observation history available on a service to predict its performance. Although the context underlying such previous observations (and corresponding to both user and service related factors) could provide an important source of information for the QoS estimation process, it has only been utilised to a limited extent by existing approaches. In response, we propose a context-aware quality learning model, realised via a learning-enabled service agent, exploiting the contextual characteristics of the domain in order to provide more personalised, accurate and relevant quality estimations for the situation at hand. The experiments conducted demonstrate the effectiveness of the proposed approach, showing promising results (in terms of prediction accuracy) in different types of changing service environments

Evaluating Atlantic bluefin tuna harvest strategies that use conventional genetic tagging data

An individual tagging model was implemented within the spatial, seasonal, multi-stock, multi-fleet operating models of the peer-reviewed Management Strategy Evaluation (MSE) framework for Atlantic bluefin tuna to evaluate the benefits of a harvest strategy that utilizes conventional gene tagging. A multi-year Brownie estimator was developed to test the accuracy and precision of exploitation rate estimates arising from gene tagging programs with various scenarios for spatial release distribution, release numbers and fishery exploitation rates. Harvest strategies that used the Brownie estimator were tested to evaluate yield and resource conservation performance relative to idealized management using perfect information. For the eastern stock, releasing 1,000 fish throughout the Atlantic and genotyping 27% of all landed fish at an estimated cost of US$2M was sufficient to obtain estimates of exploitation rate with a coefficient of variation of 20$2.5M. Harvest strategies using the gene tagging data provided expected yield and resource conservation performance that was not substantially lower than a harvest strategy assuming using perfect information regarding vulnerable biomass. Reducing the number of releases most strongly affected the worst-case ‘lower-tail’ outcomes for West area yield and eastern stock biomass. Conventional gene tagging harvest strategies offer a promising basis for calculating management advice for Atlantic bluefin tuna that may be cheaper, simpler, and more robust than the current conventional stock assessment paradigm

Automatic Extraction of Vehicle, Bicycle, and Pedestrian Traffic From Video Data

SPR No. 742This project investigated the use of traffic cameras to count and classify vehicles. The intent is to provide an alternative approach to pneumatic tubes for collecting traffic data at high volume locations and to eliminate safety risks to SCDOT personnel and contractors. The objective is to develop algorithms to post-process the 48-hour videos to determine the number of vehicles in each one of four categories: motorcycles, passenger cars and light trucks, buses/campers/tow trucks, and small to large trucks. To this end, background subtraction and foreground detection algorithms were implemented to detect moving vehicles, and a Convolutional Neural Network (CNN) model was developed to classify vehicles using thermal images obtained from a custom-built thermal camera and solar-powered trailer. Additionally, to overcome false detection of vehicles due to either camera motion or erratic light reflection from the pavement surface, an algorithm was developed to keep track of each vehicle\u2019s trajectory and the vehicle trajectories were used to determine the presence of an actual vehicle. The developed algorithms and CNN model were incorporated into a Windows-based application, named DECAF (detection and classification by functional class) to enable users to easily specify the folder that contains the video files to be processed, specify the region for which traffic should be analyzed, specify the time interval for which the data should be aggregated, and view the detection and classification results in two report formats: 1) a spreadsheet with vehicle-by-vehicle information, and 2) a PDF summary report with totals aggregated for the user-specified interval. DECAF was tested using videos collected from five different sites in Columbia, SC, and the overall detection and classification accuracy for the hours evaluated was found to be 95% or higher

Characteristics of non-AIDS-defining malignancies in the HAART era: a clinico-epidemiological study

ABSTRACT:Journal ArticleSCOPUS: ar.jinfo:eu-repo/semantics/publishe