Performance assessment of real-time data management on wireless sensor networks

Technological advances in recent years have allowed the maturity of Wireless Sensor Networks (WSNs), which aim at performing environmental monitoring and data collection. This sort of network is composed of hundreds, thousands or probably even millions of tiny smart computers known as wireless sensor nodes, which may be battery powered, equipped with sensors, a radio transceiver, a Central Processing Unit (CPU) and some memory. However due to the small size and the requirements of low-cost nodes, these sensor node resources such as processing power, storage and especially energy are very limited. Once the sensors perform their measurements from the environment, the problem of data storing and querying arises. In fact, the sensors have restricted storage capacity and the on-going interaction between sensors and environment results huge amounts of data. Techniques for data storage and query in WSN can be based on either external storage or local storage. The external storage, called warehousing approach, is a centralized system on which the data gathered by the sensors are periodically sent to a central database server where user queries are processed. The local storage, in the other hand called distributed approach, exploits the capabilities of sensors calculation and the sensors act as local databases. The data is stored in a central database server and in the devices themselves, enabling one to query both. The WSNs are used in a wide variety of applications, which may perform certain operations on collected sensor data. However, for certain applications, such as real-time applications, the sensor data must closely reflect the current state of the targeted environment. However, the environment changes constantly and the data is collected in discreet moments of time. As such, the collected data has a temporal validity, and as time advances, it becomes less accurate, until it does not reflect the state of the environment any longer. Thus, these applications must query and analyze the data in a bounded time in order to make decisions and to react efficiently, such as industrial automation, aviation, sensors network, and so on. In this context, the design of efficient real-time data management solutions is necessary to deal with both time constraints and energy consumption. This thesis studies the real-time data management techniques for WSNs. It particularly it focuses on the study of the challenges in handling real-time data storage and query for WSNs and on the efficient real-time data management solutions for WSNs. First, the main specifications of real-time data management are identified and the available real-time data management solutions for WSNs in the literature are presented. Secondly, in order to provide an energy-efficient real-time data management solution, the techniques used to manage data and queries in WSNs based on the distributed paradigm are deeply studied. In fact, many research works argue that the distributed approach is the most energy-efficient way of managing data and queries in WSNs, instead of performing the warehousing. In addition, this approach can provide quasi real-time query processing because the most current data will be retrieved from the network. Thirdly, based on these two studies and considering the complexity of developing, testing, and debugging this kind of complex system, a model for a simulation framework of the real-time databases management on WSN that uses a distributed approach and its implementation are proposed. This will help to explore various solutions of real-time database techniques on WSNs before deployment for economizing money and time. Moreover, one may improve the proposed model by adding the simulation of protocols or place part of this simulator on another available simulator. For validating the model, a case study considering real-time constraints as well as energy constraints is discussed. Fourth, a new architecture that combines statistical modeling techniques with the distributed approach and a query processing algorithm to optimize the real-time user query processing are proposed. This combination allows performing a query processing algorithm based on admission control that uses the error tolerance and the probabilistic confidence interval as admission parameters. The experiments based on real world data sets as well as synthetic data sets demonstrate that the proposed solution optimizes the real-time query processing to save more energy while meeting low latency.Fundação para a Ciência e Tecnologi

A data analytics approach to gas turbine prognostics and health management

As a consequence of the recent deregulation in the electrical power production industry, there has been a shift in the traditional ownership of power plants and the way they are operated. To hedge their business risks, the many new private entrepreneurs enter into long-term service agreement (LTSA) with third parties for their operation and maintenance activities. As the major LTSA providers, original equipment manufacturers have invested huge amounts of money to develop preventive maintenance strategies to minimize the occurrence of costly unplanned outages resulting from failures of the equipments covered under LTSA contracts. As a matter of fact, a recent study by the Electric Power Research Institute estimates the cost benefit of preventing a failure of a General Electric 7FA or 9FA technology compressor at $10 to$20 million. Therefore, in this dissertation, a two-phase data analytics approach is proposed to use the existing monitoring gas path and vibration sensors data to first develop a proactive strategy that systematically detects and validates catastrophic failure precursors so as to avoid the failure; and secondly to estimate the residual time to failure of the unhealthy items. For the first part of this work, the time-frequency technique of the wavelet packet transforms is used to de-noise the noisy sensor data. Next, the time-series signal of each sensor is decomposed to perform a multi-resolution analysis to extract its features. After that, the probabilistic principal component analysis is applied as a data fusion technique to reduce the number of the potentially correlated multi-sensors measurement into a few uncorrelated principal components. The last step of the failure precursor detection methodology, the anomaly detection decision, is in itself a multi-stage process. The obtained principal components from the data fusion step are first combined into a one-dimensional reconstructed signal representing the overall health assessment of the monitored systems. Then, two damage indicators of the reconstructed signal are defined and monitored for defect using a statistical process control approach. Finally, the Bayesian evaluation method for hypothesis testing is applied to a computed threshold to test for deviations from the healthy band. To model the residual time to failure, the anomaly severity index and the anomaly duration index are defined as defects characteristics. Two modeling techniques are investigated for the prognostication of the survival time after an anomaly is detected: the deterministic regression approach, and parametric approximation of the non-parametric Kaplan-Meier plot estimator. It is established that the deterministic regression provides poor prediction estimation. The non parametric survival data analysis technique of the Kaplan-Meier estimator provides the empirical survivor function of the data set comprised of both non-censored and right censored data. Though powerful because no a-priori predefined lifetime distribution is made, the Kaplan-Meier result lacks the flexibility to be transplanted to other units of a given fleet. The parametric analysis of survival data is performed with two popular failure analysis distributions: the exponential distribution and the Weibull distribution. The conclusion from the parametric analysis of the Kaplan-Meier plot is that the larger the data set, the more accurate is the prognostication ability of the residual time to failure model.PhDCommittee Chair: Mavris, Dimitri; Committee Member: Jiang, Xiaomo; Committee Member: Kumar, Virendra; Committee Member: Saleh, Joseph; Committee Member: Vittal, Sameer; Committee Member: Volovoi, Vital

Performance Contracting Effects on Employee’s Motivation in the Senegalese Public Media Sector

Public sector reforms have become a common phenomenon around the world for all countries; it is a great challenge for developing countries especially African countries. This paper was designed to analyze the effects of performance contracting on employee’s motivation in the 3 selected public media organizations in Senegal (Senegalese Radio Television (SRT), Senegalese Press Agency (SPA) and the “SUN”). The literature review in the media sector is death. In addition, many researchers who have investigated the variables focused their studies essentially on performance contract implementation in some state corporations. The paper tries to explore the link between performance contract and employee’s motivation. From empirical data and using spss 21.0 with chi-square, and descriptive statistics, data were collected from employees through questionnaire. The results show an existence relationship between the variables. Analysis of variance (ANOVA) established that Performance Contracting is significant on employee’s motivation.  Chi-square Pearson’s shows (r=1)(c=2) with a degree of freedom (df=2), The p-value is pv= 0.000 therefore, less than 0.5% this means the correlation is positive. The regression model revealed that the increase in performance contracting resulted to an increase of employee’s motivation. The Cramer’s coefficient is 59% confirms that the relationship is significant. It is concluded that performance contract affects employee’s motivation in the public media sector.

Analysis of The Influence of Liquidity, Credit and Operational Risk, in Indonesian Islamic Bank’s Financing for The Period 2007-2013

The purpose of this paper is to analyze the influence of credit, liquidity and operational risks in six Indonesian’s islamic banking financing products namely mudharabah, musyarakah, murabahah, istishna, ijarah and qardh, in order to try to discover whether or not Indonesian islamic banking is based on the “risk-sharing” system. This paper relies on a fixed effect model test based on the panel data analysis method, focusing on the period from 2007 to 2013. The research is an exploratory and descriptive study of all the Indonesian islamic banks that were operating in 2013. The results of this study show that the Islamic banking system in Indonesia truly has banking products based on “risk-sharing.” We found out that credit, operational and liquidity risks as a whole, have significant influence on mudarabah, musyarakah, murabahah, istishna, ijarah and qardh based financing. There is a correlation between the credit risk and mudarabah based financing, and no causal relationship between the credit risk and musharaka, murabahah, ijarah, istishna and qardh based financing. There is also correlation between the operational risk and mudarabah and murabahah based financing, and no causal relationship between the operational risk and musharaka, istishna, ijarah and qardh based financing. There is correlation between the liquidity risk and istishna based financing, and no causal relationship between the liquidity risk and musharaka, mudarabah, murabahah, ijarah and qardh based financing. A major implication of this study is the fact that there is no causal relationship between the credit risk and musharakah based financing, which is the mode of financing where the islamic bank shares the risk with its clients, but there is an influence of credit risk toward mudarabah mode financing, a financing mode where the Islamic bank bears all the risk. These findings can lead us to conclude that the Indonesian Islamic banking sector is based on the “risk sharing” system

Annual Highlights of Results from the International Space Station October 1, 2017 - October 1, 2018

The International Space Station (ISS) is a unique place a convergence of science, technology and human innovation that demonstrates new technologies and makes research breakthroughs that cannot be accomplished on Earth. As an international laboratory for scientific research in microgravity, the space stations international crew lives and works while traveling at a speed of about five miles per second as they make new discoveries in the disciplines of biology and biotechnology, Earth and space science, human research, physical science, educational activities, and technology development and demonstrations

Quantitative real-time PCR detection of Zika virus and evaluation with field-caught mosquitoes

BACKGROUND Zika virus (ZIKV), a mosquito borne flavivirus is a pathogen affecting humans in Asia and Africa. ZIKV infection diagnosis relies on serology-which is challenging due to cross-reactions with other flaviviruses and/or absence or low titer of IgM and IgG antibodies at early phase of infection- virus isolation, which is labor intensive, time consuming and requires appropriate containment. Therefore, real-time RT-PCR (rRT-PCR) is an appealing option as a rapid, sensitive and specific method for detection of ZIKV in the early stage of infection. So far, only one rRT-PCR assay has been described in the context of the outbreak in Micronesia in 2007. In this study, we described a one step rRT-PCR for ZIKV which can detect a wider genetic diversity of ZIKV isolates from Asia and Africa. RESULTS The NS5 protein coding regions of African ZIKV isolates were sequenced and aligned with representative flaviviruses sequences from GenBank to design primers and probe from conserved regions. The analytical sensitivity of the assay was evaluated to be 32 genome-equivalents and 0.05 plaque forming unit (pfu). The assay was shown to detect 37 ZIKV isolates covering a wide geographic in Africa and Asia over 36 years but none of the 31 other flaviviruses tested showing high analytical specificity. The rRT-PCR could be performed in less than 3 hours. This method was used successfully to detect ZIKV strains from field-caught mosquitoes. CONCLUSION We have developed a rapid, sensitive and specific rRT-PCR for detection of ZIKV. This assay is a useful tool for detection of ZIKV infection in regions where a number of other clinically indistinguishable arboviruses like dengue or chikungunya co-circulate. Further studies are needed to validate this assay in clinical positive samples collected during acute ZIKV infection

Prophylactic and emergency cesareans: a comparative study on 718 observations at the maternity ward of Ignace Deen National hospital

Background: The objective of the study is to compare the frequency, the socio-demographic characteristics, the indications, the fetal maternal prognosis and the Robson classification of prophylactic and emergency caesarean sections.Methods: This was a comparative study of prophylactic and emergency caesarean sections at the maternity of Ignace Deen national hospital. It was a 12 month (July 1, 2016 to June 30, 2017) descriptive and analytical study.Results: Prophylactic caesarean sections accounted for 12, 51% of caesarean sections and 3.96% of deliveries at the ward. Prophylactic caesarean sections involved pregnant women aged from 20 to 29, holder of higher education degrees (51.54%), married (92.76%) employed (56.83%) and whose prenatal visit was provided by the obstetrician (73.54%). While the emergency caesarean section concerned parturient aged between 20 and 34, mostly non-schooled (36.49%), transferred patients (80.22%) and nulliparous (58.5%). Surgical indications were mainly scarred uterus (32.32%) and maternal pathologies (18.11%) prophylaxis; bleeding in the last quarter (25.90%) acute fetal distress (20.33%) in emergency. Groups 6 and 5 of the Robson classification were the most represented with a 2.23% morbidity and a zero maternal lethality in prophylaxis versus groups 5 and 6 with a 10.03% morbidity and a 1.67% maternal lethality in emergency.Conclusions: Improving this prognosis would be achieved through an increase in the frequency of prophylactic caesarean sections

Caractérisation des peuplements ligneux sur le tracé de la Grande Muraille Verte au Tchad

La connaissance des caractéristiques du peuplement ligneux du tracé de la Grande Muraille Verte (GMV) du Tchad permet de comprendre leur structure et fonctionnement afin de proposer des stratégies de gestion durable. C’est dans ce cadre que ce travail vise à caractériser la flore et la végétation ligneuses sur le tracé de la GMV du Tchad. La méthodologie utilisée consiste à déterminer la composition floristique et la structure du peuplement ligneux dans les trois sites retenus (Lac, Kanem et Bahr El Ghazal). Les inventaires floristiques ont permis de recenser 18 espèces réparties en 15 genres et 9 familles. La flore ligneuse est dominée par la famille des Mimosaseae. La densité des ligneux (34,67 ind.ha-1) et le recouvrement (1604,30 m².ha-1) sont plus importants au Lac et faible au Bahr El Ghazal (densité de 21,39 ind.ha-1 et recouvrement de 465,66 m².ha-1), quant à la surface terrière, elle est plus élevée au Kanem (3,39 m².ha-1). La distribution par classes de hauteur et de circonférence des ligneux révèle une prédominance des individus de la strate arbustive dans les trois sites. L’étude de la régénération naturelle montre que Acacia raddiana et Balanites aegyptiaca présentent le potentiel de régénération le plus élevé dans les trois sites. Globalement, les résultats obtenus montrent que les espèces les plus adaptées aux conditions écologiques de la GMV au Tchad sont Acacia raddiana et Balanites aegyptiaca et par conséquent, doivent être choisies pour la restauration de ces écosystèmes.© 2015 International Formulae Group. All rights reserved.Mots clés: Tchad, GMV, végétation, peuplement, structure, régénérationEnglish Title:  Characterization of the ligneous populatings on the route of the Great Green Wall in ChadEnglish AbstractThe knowledge of ligneous populating characteristics of the route of the Great Green Wall (GGW) of Chad helps to understand their structure and function in order to propose strategies for sustainable management. This study was carried out to characterize the flora and the tree vegetation present on the Chad  Great Green Wall (GGW) line. The methodology consisted in determining the floristic composition and the structure of the tree population in three sites: Lac, Kanem, and Bahr El Ghazal. The floristic inventories have identified 18 species distributed into 15 genera and 9 families. Trees are dominated by the Mimosaseae family. Their density (34.67 individuals ha-1) and their recovery (1604.3 m2 ha-1) are more important at Lac and lower at Bahr El Ghazal with 21.39 individuals ha -1 and 465.66 m2 ha-1, respectively. However, the basal area of the vegetation was more important at Kanem with 3.39 m2 ha-1. The distribution by classes of height and circumference of the trees indicates a predominance of the shrub layer in all the three sites. The study of the natural regeneration reveals that Acacia raddiana and Balanites aegyptiaca present the highest regeneration potential in the three sites. Globally, the results obtained in this study show that the most adapted species to the ecological conditions of the Chad’s GGW are Acacia raddiana and Balanites aegyptiaca. Consequently, these species must be considered during the restoration of the given ecosystems.© 2015 International Formulae Group. All rights reserved.Keywords: Chad, GGW, vegetation, population, structure, regeneratio

Distributed Database Management Techniques for Wireless Sensor Networks

Authors and/or their employers shall have the right to post the accepted version of IEEE-copyrighted articles on their own personal servers or the servers of their institutions or employers without permission from IEEE, provided that the posted version includes a prominently displayed IEEE copyright notice and, when published, a full citation to the original IEEE publication, including a link to the article abstract in IEEE Xplore. Authors shall not post the final, published versions of their papers.In sensor networks, the large amount of data generated by sensors greatly influences the lifetime of the network. In order to manage this amount of sensed data in an energy-efficient way, new methods of storage and data query are needed. In this way, the distributed database approach for sensor networks is proved as one of the most energy-efficient data storage and query techniques. This paper surveys the state of the art of the techniques used to manage data and queries in wireless sensor networks based on the distributed paradigm. A classification of these techniques is also proposed. The goal of this work is not only to present how data and query management techniques have advanced nowadays, but also show their benefits and drawbacks, and to identify open issues providing guidelines for further contributions in this type of distributed architectures.This work was partially supported by the Instituto de Telcomunicacoes, Next Generation Networks and Applications Group (NetGNA), Portugal, by the Ministerio de Ciencia e Innovacion, through the Plan Nacional de I+D+i 2008-2011 in the Subprograma de Proyectos de Investigacion Fundamental, project TEC2011-27516, by the Polytechnic University of Valencia, though the PAID-05-12 multidisciplinary projects, by Government of Russian Federation, Grant 074-U01, and by National Funding from the FCT-Fundacao para a Ciencia e a Tecnologia through the Pest-OE/EEI/LA0008/2013 Project.Diallo, O.; Rodrigues, JJPC.; Sene, M.; Lloret, J. (2013). Distributed Database Management Techniques for Wireless Sensor Networks. IEEE Transactions on Parallel and Distributed Systems. PP(99):1-17. https://doi.org/10.1109/TPDS.2013.207S117PP9