An Ontology for Submarine Feature Representation on Charts

A landform is a subjective individuation of a part of a terrain. Landform recognition is a difficult task because its definition usually relies on a qualitative and fuzzy description. Achieving automatic recognition of landforms requires a formal definition of the landforms properties and their modelling. In the maritime domain, the International Hydrographic Organisation published a standard terminology of undersea feature names which formalises a set of definition mainly for naming and communication purpose. This terminology is here used as a starting point for the definition of an ontology of undersea features and their automatic classification from a terrain model. First, an ontology of undersea features is built. The ontology is composed of an application domain ontology describing the main properties and relationships between features and a representation ontology deals with representation on a chart where features are portrayed by soundings and isobaths. A database model was generated from the ontology. Geometrical properties describing the feature shape are computed from soundings and isobaths and are used for feature classification. An example of automatic classification on a nautical chart is presented and results and on-going research are discussed

Generation and Validation of Workflows for On-demand Mapping

The paper presents a method to automatically select and sequence the tasks required to build maps according to user requirements. Workflows generated are analysed using Petri nets to assess their validity before execution. Although further work is required to select the optimal method for generating the workflow and to execute the workflow, the proposed method can be used on any workflow to assess its validity

An Ontological approach to On-demand Mapping

Automatic generalisation for map production has been in use for decades. The process is still, however, only semi-automatic in that the expert selects and sequences the required generalisation operators and the algorithms that implement them and provides parameter values. Different techniques can be applied to rural and urban areas at the discretion of the expert, working to a fixed target scale and with familiar feature types. But in the case of on-demand mapping the expert will be replaced by a system that will be able to automatically select, sequence and execute map generalisation operations according to user requirements

ChaseMe: a heuristic scheme for electric vehicles mobility management on charging stations in a smart city scenario

Towards achieving the goal of green transportation, the usage of battery powered electric vehicles (BEVs) has been continuously growing across the globe. However, considering the limited number of Charging Stations (CSs) in the cities, electric vehicle charging problem has become a challenging task, especially, due to the constraints of longer waiting time and dynamic pricing at the CHs. This issue has led to the degradation in Quality of Experience (QoE) for BEV drivers. Moreover, Charging Point (CP) service providers in the cities also suffer from lack of space which causes higher congestion at the CSs. In this context, we propose ChaseMe, a heuristic scheme for optimizing CS management by scheduling BEVs based on availability and type (fast/ultra-fast) of CPs by considering delay and charging time for CPs reservation. The proposed heuristic scheme consists of two soft computing techniques i) Harris Hawk Optimization (HHO) and ii) Fuzzy Inference System (FIS). Former technique is used to map the CP reservation requests to the best-suited CS by considering Quality of Service (QoS) parameters and acting as a global optimizer. FIS locally manages CPs at a particular CS in coordination with proposed meta-heuristic technique. The experimental results prove the benefits of the proposed ChaseMe framework as compared to the state-of-the-art techniques considering various charging metrics for BEVs

ACE inhibitor and angiotensin receptor-II antagonist prescribing and hospital admissions with acute kidney injury:a longitudinal ecological study

BACKGROUND: ACE Inhibitors (ACE-I) and Angiotensin-Receptor Antagonists (ARAs) are commonly prescribed but can cause acute kidney injury (AKI) during intercurrent illness. Rates of hospitalization with AKI are increasing. We aimed to determine whether hospital AKI admission rates are associated with increased ACE-I/ARA prescribing. METHODS AND FINDINGS: English NHS prescribing data for ACE-I/ARA prescriptions were matched at the level of the general practice to numbers of hospital admissions with a primary diagnosis of AKI. Numbers of prescriptions were weighted for the demographic characteristics of general practices by expressing prescribing as rates where the denominator is Age, Sex, and Temporary Resident Originated Prescribing Units (ASTRO-PUs). We performed a mixed-effect Poisson regression to model the number of admissions for AKI occurring in each practice for each of 4 years from 1/4/2007. From 2007/8-2010/11, crude AKI admission rates increased from 0.38 to 0.57 per 1000 patients (51.6% increase), and national annual ACE-I/ARA prescribing rates increased by 0.032 from 0.202 to 0.234 (15.8% increase). There was strong evidence (p<0.001) that increases in practice-level prescribing of ACE-I/ARA over the study period were associated with an increase in AKI admission rates. The increase in prescribing seen in a typical practice corresponded to an increase in admissions of approximately 5.1% (rate ratio = 1.051 for a 0.03 per ASTRO-PU increase in annual prescribing rate, 95%CI 1.047-1.055). Using the regression model we predict that 1,636 (95%CI 1,540-1,780) AKI admissions would have been avoided if prescribing rates were at the 2007/8 level, equivalent to 14.8% of the total increase in AKI admissions. CONCLUSION: In this ecological analysis, up to 15% of the increase in AKI admissions in England over a 4-year time period is potentially attributable to increased prescribing of ACE-I and ARAs. However, these findings are limited by the lack of patient level data such as indication for prescribing and patient characteristics

Telemonitoring for Patients With Chronic Heart Failure: A Systematic Review

Background Telemonitoring, the use of communication technology to remotely monitor health status, is an appealing strategy for improving disease management. Methods and Results We searched Medline databases, bibliographies, and spoke with experts to review the evidence on telemonitoring in heart failure patients. Interventions included: telephone-based symptom monitoring (n = 5), automated monitoring of signs and symptoms (n = 1), and automated physiologic monitoring (n = 1). Two studies directly compared effectiveness of 2 or more forms of telemonitoring. Study quality and intervention type varied considerably. Six studies suggested reduction in all-cause and heart failure hospitalizations (14% to 55% and 29% to 43%, respectively) or mortality (40% to 56%) with telemonitoring. Of the 3 negative studies, 2 enrolled low-risk patients and patients with access to high quality care, whereas 1 enrolled a very high-risk Hispanic population. Studies comparing forms of telemonitoring demonstrated similar effectiveness. However, intervention costs were higher with more complex programs ($8383 per patient per year) versus less complex programs ($1695 per patient per year). Conclusion The evidence base for telemonitoring in heart failure is currently quite limited. Based on the available data, telemonitoring may be an effective strategy for disease management in high-risk heart failure patients