An intelligent energy management system for educational buildings

There is a wide variation in the energy consumption between different educational institutions due to the adoption of different management strategies and different levels of occupants’ environmental understanding. The presence of large amounts of information and communication technology (ICT) equipment and heating, ventilation, and air conditioning (HVAC) system causes a major consumption of energy in higher education institution (HEI) buildings. The main objective of this research is to investigate the use of ICT to optimize the energy consumption in HEI buildings and reduce carbon dioxide (CO 2 ) emission. The first phase of the system has been implemented at King Saud University to measure energy consumption through sensors that read energy consumption of electrical appliances and devices every 10 seconds. The analysis of collected data allows us to develop and employ energy saving strategies that lead to a reduction in total energy consumption. Our preliminary results show that up to 17% of energy consumption can be reduced by simply dealing with standby energy loss of labs’ computers. The novelty of this research comes from the use of a functional database approach to deal with high volume of data and query performance and the incorporation of a timetabling system in energy management system

Proof Explanation in the DR-DEVICE System

Trust is a vital feature for Semantic Web: If users (humans and agents) are to use and integrate system answers, they must trust them. Thus, systems should be able to explain their actions, sources, and beliefs, and this issue is the topic of the proof layer in the design of the Semantic Web. This paper presents the design and implementation of a system for proof explanation on the Semantic Web, based on defeasible reasoning. The basis of this work is the DR-DEVICE system that is extended to handle proofs. A critical aspect is the representation of proofs in an XML language, which is achieved by a RuleML language extension

Evaluating the accuracy of a functional SNP annotation system

Many common and chronic diseases are influenced at some level by genetic variation. Research done in population genetics, specifically in the area of single nucleotide polymorphisms (SNPs) is critical to understanding human genetic variation. A key element in assessing role of a given SNP is determining if the variation is likely to result in change in function. The SNP Integration Tool (SNPit) is a comprehensive tool that integrates diverse, existing predictors of SNP functionality, providing the user with information for improved association study analysis. To evaluate the SNPit system, we developed an alternative gold standard to measure accuracy using sensitivity and specificity. The results of our evaluation demonstrated that our alternative gold standard produced encouraging results

Probabilistic reasoning with a bayesian DNA device based on strand displacement

We present a computing model based on the DNA strand displacement technique which performs Bayesian inference. The model will take single stranded DNA as input data, representing the presence or absence of a specific molecular signal (evidence). The program logic encodes the prior probability of a disease and the conditional probability of a signal given the disease playing with a set of different DNA complexes and their ratios. When the input and program molecules interact, they release a different pair of single stranded DNA species whose relative proportion represents the application of Bayes? Law: the conditional probability of the disease given the signal. The models presented in this paper can empower the application of probabilistic reasoning in genetic diagnosis in vitro

A Scalable Architecture for Incremental Specification and Maintenance of Procedural and Declarative Clinical Decision-Support Knowledge

Clinical guidelines have been shown to improve the quality of medical care and to reduce its costs. However, most guidelines exist in a free-text representation and, without automation, are not sufficiently accessible to clinicians at the point of care. A prerequisite for automated guideline application is a machine-comprehensible representation of the guidelines. In this study, we designed and implemented a scalable architecture to support medical experts and knowledge engineers in specifying and maintaining the procedural and declarative aspects of clinical guideline knowledge, resulting in a machine comprehensible representation. The new framework significantly extends our previous work on the Digital electronic Guidelines Library (DeGeL) The current study designed and implemented a graphical framework for specification of declarative and procedural clinical knowledge, Gesher. We performed three different experiments to evaluate the functionality and usability of the major aspects of the new framework: Specification of procedural clinical knowledge, specification of declarative clinical knowledge, and exploration of a given clinical guideline. The subjects included clinicians and knowledge engineers (overall, 27 participants). The evaluations indicated high levels of completeness and correctness of the guideline specification process by both the clinicians and the knowledge engineers, although the best results, in the case of declarative-knowledge specification, were achieved by teams including a clinician and a knowledge engineer. The usability scores were high as well, although the clinicians’ assessment was significantly lower than the assessment of the knowledge engineers

Development and implementation of clinical guidelines : an artificial intelligence perspective

Clinical practice guidelines in paper format are still the preferred form of delivery of medical knowledge and recommendations to healthcare professionals. Their current support and development process have well identified limitations to which the healthcare community has been continuously searching solutions. Artificial intelligence may create the conditions and provide the tools to address many, if not all, of these limitations.. This paper presents a comprehensive and up to date review of computer-interpretable guideline approaches, namely Arden Syntax, GLIF, PROforma, Asbru, GLARE and SAGE. It also provides an assessment of how well these approaches respond to the challenges posed by paper-based guidelines and addresses topics of Artificial intelligence that could provide a solution to the shortcomings of clinical guidelines. Among the topics addressed by this paper are expert systems, case-based reasoning, medical ontologies and reasoning under uncertainty, with a special focus on methodologies for assessing quality of information when managing incomplete information. Finally, an analysis is made of the fundamental requirements of a guideline model and the importance that standard terminologies and models for clinical data have in the semantic and syntactic interoperability between a guideline execution engine and the software tools used in clinical settings. It is also proposed a line of research that includes the development of an ontology for clinical practice guidelines and a decision model for a guideline-based expert system that manages non-compliance with clinical guidelines and uncertainty.This work is funded by national funds through the FCT – Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within project PEst-OE/EEI/UI0752/2011"