An event service supporting autonomic management of ubiquitous systems for e-health

An event system suitable for very simple devices corresponding to a body area network for monitoring patients is presented. Event systems can be used both for self-management of the components as well as indicating alarms relating to patient health state. Traditional event systems emphasise scalability and complex event dissemination for internet based systems, whereas we are considering ubiquitous systems with wireless communication and mobile nodes which may join or leave the system over time intervals of minutes. Issues such as persistent delivery are also important. We describe the design, prototype implementation, and performance characteristics of an event system architecture targeted at this application domain

Economic Analysis of Stand-Alone Hybrid Wind/PV/Diesel Water Pumping System: A Case Study in Egypt

The design and evaluation of a stand-alone hybrid renewable energy system for pumping underground water for small farm irrigation is presented. Given environmental conditions, system specifications and daily load demand data, the optimal size of main system components is obtained using a sizing algorithm. Different renewable energy systems are compared using yearly simulations, on hourly base via specialized commercial software simulation packages PVSYST and HOMER, to simulate the system performance and to reach the optimum configurations based on the objective criteria. The criteria used in economic optimization are the net present cost and the cost of energy, with the percent of the capacity shortage. The following systems can be compared: PV only, PV with horizontal axis wind turbine, PV with vertical axis wind turbine, and PV with horizontal axis wind turbine and diesel generator and diesel generator only. The simulation also was carried out for different load patterns for optimum operation. The study was illustrated for climatic conditions of an isolated area in El-Tour City, Sinai, Egypt. The installed 3.42 kW PV water pumping system for irrigation purposes in the same site was also described

Fetuin-A and Ghrelin Levels in Children with End Stage Renal Disease and the Effect of a Single Hemodialysis Session on Them

BACKGROUND: Fetuin-A and ghrelin have been implicated in cardiovascular diseases and mortality among end stage renal disease patients. The exact mechanisms have not been fully elucidated. There is robust data supporting an association between ghrelin and various cardiovascular conditions, and some common processes such as inflammation, oxidative stress, and endoplasmic reticulum stress have been implicated.AIM: This study was conducted to assay serum fetuin-A and ghrelin in chronic renal failure pediatric patients and to study changes in their level that may occur after a single hemodialysis.MATERIAL AND METHODS: Forty nine pediatric patients suffering from ESRD on maintenance hemodialysis (HD), 20 patients with chronic renal failure (CRF) not on dialysis and 35 healthy subjects as control group were included. The mean age of the study population was 10.58 ± 3.94, 10.62 ± 3.24 and 10.61 ± 3.97 years respectively. Serum fetuin-A and plasma acyl ghrelin levels were measured by using ELISA method.RESULTS: The present study revealed that predialysis serum fetuin-A level was significantly increased in pediatric HD patients compared with the normal population, while ghrelin levels were significantly reduced. Furthermore, serum levels of fetuin-A decreased significantly after a single HD session.CONCLUSION: Our study concluded that fetuin-A and acyl ghrelin may play a role in inflammatory process among HD pediatric patients which may account for cardiovascular insults and mortality but their use as biochemical markers among ESRD pediatric patients have limitations due to wide fluctuations

The Spatiotemporal Data Fusion (STDF) Approach: IoT-Based Data Fusion Using Big Data Analytics

Enormous heterogeneous sensory data are generated in the Internet of Things (IoT) for various applications. These big data are characterized by additional features related to IoT, including trustworthiness, timing and spatial features. This reveals more perspectives to consider while processing, posing vast challenges to traditional data fusion methods at different fusion levels for collection and analysis. In this paper, an IoT-based spatiotemporal data fusion (STDF) approach for low-level data in–data out fusion is proposed for real-time spatial IoT source aggregation. It grants optimum performance through leveraging traditional data fusion methods based on big data analytics while exclusively maintaining the data expiry, trustworthiness and spatial and temporal IoT data perspectives, in addition to the volume and velocity. It applies cluster sampling for data reduction upon data acquisition from all IoT sources. For each source, it utilizes a combination of k-means clustering for spatial analysis and Tiny AGgregation (TAG) for temporal aggregation to maintain spatiotemporal data fusion at the processing server. STDF is validated via a public IoT data stream simulator. The experiments examine diverse IoT processing challenges in different datasets, reducing the data size by 95% and decreasing the processing time by 80%, with an accuracy level up to 90% for the largest used dataset

An Enhanced Testing Approach for Mobile Applications

Nowadays, there is an enormous number of mobile applications that are continuously being launched to the market. As a result of this rapid process, there is a need to increase the speed of testing process using enhanced approaches. This research aims to increase the effectiveness of the graphical user interface testing process of mobile applications. This is achieved by proposing an enhanced combinatorial-based metaheuristic approach. The proposed approach aims to maximize statement and branch coverage by applying Cuckoo search, for event selection. The approach was compared to monkey, frequency, random and greedy approaches. Experiments were conducted on different mobile applications. During the same testing time duration, the proposed approach achieved higher coverage than the other approaches. The proposed approach proved its effectiveness in mobile application testing compared to the other approaches.

Using Character-Level Sequence-to-Sequence Model for Word Level Text Generation to Enhance Arabic Speech Recognition

Owing to the linguistic richness of the Arabic language, which contains more than 6000 roots, building a reliable Arabic language model for Arabic speech recognition systems faces many challenges. This paper introduces a language model free Arabic automatic speech recognition system for Modern Standard Arabic based on an end-to-end-based Deep Speech architecture developed by Mozilla. The proposed model uses a character-level sequence-to-sequence model to map the character alignment produced by the recognizer model onto the corresponding words. The developed system outperformed recent studies on single-speaker and multi-speaker Arabic speech recognition using two different state-of-the-art datasets. The first was the Arabic Multi-Genre Broadcast (MGB2) corpus with 1200 h of audio data from multiple speakers. The system achieved a new milestone in the MGB2 challenge with a word error rate (WER) of 3.2, outperforming related work using the same corpus with a word error reduction of 17%. An additional experiment with a 7-hour Saudi Accent Single Speaker Corpus (SASSC) was used to build an additional model for single male speaker-based Arabic speech recognition using the same proposed network architecture. The single-speaker model outperformed related experiments with a WER of 4.25 with a relative improvement of 33.8%

Enriching Ontology Concepts Based on Texts from WWW and Corpus

In spite of the growing of ontological engineering tools, ontology knowledge acquisition remains a highly manual, time-consuming and complex task. Automatic ontology learning is a well-established research field whose goal is to support the semi-automatic construction of ontologies starting from available digital resources (e.g., A corpus, web pages, dictionaries, semi-structured and structured sources) in order to reduce the time and effort in the ontology development process. This paper proposes an enhanced methodology for enriching Lexical Ontologies such as the popular open-domain vocabulary –WordNet. Ontologies like WordNet can be semantically enriched to obtain extensions and enhancements to its lexical database. The proliferation of senses in WordNet is considered as one of its main shortcomings for practical applications. Therefore, the presented methodology depends on the Coarse-Grained word senses. These senses are generated from applying WordNet Fine-Grained word senses to a Merging Sense algorithm. This algorithm merges only semantically similar word senses instead of applying traditional clustering techniques. A performance comparison is illustrated between two different data sources (Web, Corpus) used in the Enrichment process. The results obtained from using Coarse-Grained word senses in both cases yields better precision than Fine-Grained word senses in the Word Sense Disambiguation task

An Ontology Development Methodology Based on Ontology-Driven Conceptual Modeling and Natural Language Processing: Tourism Case Study

Ontologies provide a powerful method for representing, reusing, and sharing domain knowledge. They are extensively used in a wide range of disciplines, including artificial intelligence, knowledge engineering, biomedical informatics, and many more. For several reasons, developing domain ontologies is a challenging task. One of these reasons is that it is a complicated and time-consuming process. Multiple ontology development methodologies have already been proposed. However, there is room for improvement in terms of covering more activities during development (such as enrichment) and enhancing others (such as conceptualization). In this research, an enhanced ontology development methodology (ON-ODM) is proposed. Ontology-driven conceptual modeling (ODCM) and natural language processing (NLP) serve as the foundation of the proposed methodology. ODCM is defined as the utilization of ontological ideas from various areas to build engineering artifacts that improve conceptual modeling. NLP refers to the scientific discipline that employs computer techniques to analyze human language. The proposed ON-ODM is applied to build a tourism ontology that will be beneficial for a variety of applications, including e-tourism. The produced ontology is evaluated based on competency questions (CQs) and quality metrics. It is verified that the ontology answers SPARQL queries covering all CQ groups specified by domain experts. Quality metrics are used to compare the produced ontology with four existing tourism ontologies. For instance, according to the metrics related to conciseness, the produced ontology received a first place ranking when compared to the others, whereas it received a second place ranking regarding understandability. These results show that utilizing ODCM and NLP could facilitate and improve the development process, respectively