Search

Aiming to design, produce and test a model rocket capable of landing. Different systems to accomplish the objective are implemented (aerodynamics brakes, landing legs, thrust vector control). A flight computer capable of managing these systems is used. A PID feedbacked with the attitude of the rocket to control the direction of the engine through two servos mounted on a two-axes gimbal is designed. Furthermore, the algorithm logs data from sensors and from a Kalman filter to estimate velocity, altitude, accelerations and the attitude of the rocket for later review. The mission is tested only for landing, it is to say, there is no launch. The results conclude that the idea is feasible and works properly but can be improved. Moreover, trying to launch and land is discussed at the end as the results from landing tests are a success

This work presents methods to combine data from the Semantic Web into existing EHRs, leading to an augmented EHR. An existing EHR extract is augmented by combining it with additional information from external sources, typically linked data sources. The starting point is a standardized EHR extract described by an archetype. The method consists of combining specific data from the original EHR with contents from the external information source by building a semantic representation, which is used to query the external source. The results are converted into a standardized EHR extract according to an archetype. This work sets the foundations to transform Semantic Web contents into normalized EHR extracts. Finally, to exemplify the approach, the work includes a practical use case in which the summarized EHR is augmented with drug–drug interactions and disease-related treatment information.

Background SNOMED CT Expression Constraint Language (ECL) is a declarative language developed by SNOMED International for the definition of SNOMED CT Expression Constraints (ECs). ECs are executable expressions that define intensional subsets of clinical meanings by stating constraints over the logic definition of concepts. The execution of an EC on some SNOMED CT substrate yields the intended subset, and it requires an execution engine able to receive an EC as input, execute it, and return the matching concepts. An important issue regarding subsets of clinical concepts is their use in terminology binding between clinical information models and terminologies for defining the set of valid values of codified data. Objective To define and implement methods for the simplification, semantic validation and execution of ECs over a graph-oriented SNOMED CT database, and to provide a method for the visual representation of subsets in order to explore, understand and validate its content, as well as to develop an EC execution platform, called SNQuery, which makes use of these methods. Methods Since SNOMED CT is a directed and acyclic graph, we have used a graph-oriented database to represent the content of SNOMED CT, where the schema and instances are represented as graphs and the data manipulation is expressed by graph-oriented operations. For the execution of ECs over the graph database, it is performed a translation process in which ECs are translated into a set of Cypher Query Language queries. We have defined some EC simplification methods that leverage the logic structure underlying SNOMED CT. The purpose of these methods is to reduce the complexity of ECs and, in turn, its execution time, as well as to validate them from a SNOMED CT Concept Model and logical definition points of view. We also have developed a graphic representation based on the circle packing geometrical concept, which allows validating subsets, as well as pre-defined refsets and the terminology itself. Results We have developed SNQuery, a platform for the definition of intensional subsets of SNOMED CT concepts by means of the execution of ECs over a graph-oriented SNOMED CT database. Additionally, we have incorporated methods for the simplification and semantic validation of ECs, as well as for the visualization of subsets as a mechanism to understand and validate them. SNQuery has been evaluated in terms of EC execution times. Conclusion In this paper, we provide methods to simplify, semantically validate and execute ECs over a graph-oriented database. We also offer a method to visualize the intensional subsets obtained by executing ECs to explore, understand and validate them, as well as refsets and the terminology itself. The definition of intensional subsets is useful to bind content between clinical information models and clinical terminologies, which is a necessary step to achieve semantic interoperability between EHR systems.

OBJECTIVE: To assess the variability in data distributions among data sources and over time through a case study of a large multisite repository as a systematic approach to data quality (DQ). MATERIALS AND METHODS: Novel probabilistic DQ control methods based on information theory and geometry are applied to the Public Health Mortality Registry of the Region of Valencia, Spain, with 512 143 entries from 2000 to 2012, disaggregated into 24 health departments. The methods provide DQ metrics and exploratory visualizations for (1) assessing the variability among multiple sources and (2) monitoring and exploring changes with time. The methods are suited to big data and multitype, multivariate, and multimodal data. RESULTS: The repository was partitioned into 2 probabilistically separated temporal subgroups following a change in the Spanish National Death Certificate in 2009. Punctual temporal anomalies were noticed due to a punctual increment in the missing data, along with outlying and clustered health departments due to differences in populations or in practices. DISCUSSION: Changes in protocols, differences in populations, biased practices, or other systematic DQ problems affected data variability. Even if semantic and integration aspects are addressed in data sharing infrastructures, probabilistic variability may still be present. Solutions include fixing or excluding data and analyzing different sites or time periods separately. A systematic approach to assessing temporal and multisite variability is proposed. CONCLUSION: Multisite and temporal variability in data distributions affects DQ, hindering data reuse, and an assessment of such variability should be a part of systematic DQ procedures., This work was supported by the Spanish Ministry of Economy and Competitiveness grant numbers RTC-2014-1530-1 and TIN-2013-43457-R, and by the Universitat Politecnica de Valencia grant number SP20141432.

My passion for both software and hardware has brought me into the idea of this project which is intended to test if it’s possible to modify a common Radio Controlled car into a vehicle that has self-driving behaviours like line following, obstacle detection, etc. So the main objective is to create an unmanned ground vehicle robot (from now on, UGV) that will be able to act totally by itself in local mode (without any help from another computer nor human)

We present the results of a pilot project of the Spanish Ministry of Health, Social Services and Equality, envisaged to the development of a national integrated data repository of maternal-child care information. Based on health information standards and data quality assessment procedures, the developed repository is aimed to a reliable data reuse for (1) population research and (2) the monitoring of healthcare best practices. Data standardization was provided by means of two main ISO 13606 archetypes (composed of 43 sub-archetypes), the first dedicated to the delivery and birth information and the second about the infant feeding information from delivery up to two years. Data quality was assessed by means of a dedicated procedure on seven dimensions including completeness, consistency, uniqueness, multi-source variability, temporal variability, correctness and predictive value. A set of 127 best practice indicators was defined according to international recommendations and mapped to the archetypes, allowing their calculus using XQuery programs. As a result, a standardized and data quality assessed integrated data respository was generated, including 7857 records from two Spanish hospitals: Hospital Virgen del Castillo, Yecla, and Hospital 12 de Octubre, Madrid. This pilot project establishes the basis for a reliable maternal-child care data reuse and standardized monitoring of best practices based on the developed information and data quality standards.