Proactive Risk-Based Integrity Assessment of Water Distribution Networks

Sustainable management of urban water distribution networks should include not only new methods for monitoring, repairing or replacing aging infrastructure, but also (and more importantly) expanded methods for modelling deteriorating infrastructure, for pro-actively assessing the risk of failure and for devising replace or repair strategies. The study presented herein describes a framework for proactive risk-based integrity monitoring of urban water distribution networks and the results obtained from a case-study based on a 5-year data sample. A combination of artificial neural network and statistical modelling techniques stemming from parametric and nonparametric survival analysis (Kaplan–Meier survival curves with Epanechnikov’s kernel) are utilized in the investigation of identified risk factors and for estimation of the forecasted time to failure metric. The data is stratified for different pipe groups for a more targeted analysis.     

移动互联网业务对未来无线通信系统的影响分析

Based on the analysis of application status in real network, the trace model of some typical mobile Internet applications data is given and their impact on 2G/ 3G network is discussed in this paper. Furthermore, in order to support the mobile Internet application efficiently in future, the issues including the impact on the Long Term Evolution (LTE-A) system and some potential solutions for performance optimization are studied. Based on the trace data model of IM traffic, the performacne evaluaiton of LTE-A system shows that some specific configuration machanisms can play an important role in improving network system efficiency in the case of IM traffic.     

COMPARISON OF KINETIC MODELS FOR THE AQUEOUS SOLID-LIQUID EXTRACTION OF TILIA SAPWOOD IN A CONTINUOUS STIRRED TANK REACTOR

The kinetic study of the aqueous extraction of Tilia sapwood was based on temperature, while all the other factors were kept constant. Several models were used and compared to explain the kinetics of the aqueous solid-liquid extraction of Tilia sapwood. For each model, the rate constant, the equilibrium extraction capacity, and the initial extraction rate were evaluated to analyze the suitability of these kinetic models to describe the leaching process for temperatures ranging from 313 to 363 K. The pseudo first-order model could not be applied during the whole of the process. The Elovich model provided a good degree of correlation (from 0.929 to 0.988) and could be applied all along the extraction process. The pseudo second-order model was satisfactorily applied, with coefficients of correlation over 0.998, showing that it perfectly described the process. A physical explanation of these models was finally proposed.     

Bayesian design of experiments for generalized linear models and dimensional analysis with industrial and scientific application

ABSTRACT

The design of an experiment can always be considered at least implicitly Bayesian, with prior knowledge used informally to aid decisions such as the variables to be studied and the choice of a plausible relationship between the explanatory variables and measured responses. Bayesian methods allow uncertainty in these decisions to be incorporated into design selection through prior distributions that encapsulate information available from scientific knowledge or previous experimentation. Further, a design may be explicitly tailored to the aim of the experiment through a decision-theoretic approach using an appropriate loss function. We review the area of decision-theoretic Bayesian design, with particular emphasis on recent advances in computational methods. For many problems arising in industry and science, experiments result in a discrete response that is well described by a member of the class of generalized linear models. Bayesian design for such nonlinear models is often seen as impractical as the expected loss is analytically intractable and numerical approximations are usually computationally expensive. We describe how Gaussian process emulation, commonly used in computer experiments, can play an important role in facilitating Bayesian design for realistic problems. A main focus is the combination of Gaussian process regression to approximate the expected loss with cyclic descent (coordinate exchange) optimization algorithms to allow optimal designs to be found for previously infeasible problems. We also present the first optimal design results for statistical models formed from dimensional analysis, a methodology widely employed in the engineering and physical sciences to produce parsimonious and interpretable models. Using the famous paper helicopter experiment, we show the potential for the combination of Bayesian design, generalized linear models, and dimensional analysis to produce small but informative experiments.     

Hybrid Wireless Sensors Deployment Scheme with Connectivity and Coverage Maintaining in Wireless Sensor Networks

With the rapid growth of the internet of things (IoT), an impressive number of IoT’s application based on wireless sensor networks (WSNs) has been deployed in various domain. Due to its wide ranged applications, WSNs that have the capability to monitor a given sensing field, became the most used platform of IoT. Therefore, coverage becomes one of the most important challenge of WSNs. The search for better positions to assign to the sensors in order to control each point of an area of interest and the collection of data from sensors are major concerns in WSNs. This work addresses these problems by providing a hybrid approach that ensures sensors deployment on a grid for targets coverage while taking into account connectivity. The proposed sequential hybrid approach is based on three algorithms. The first places the sensors so as to all targets are covered. The second removes redundancies from the placement algorithm to reduce the number of sensors deployed. The third one, based on the genetic algorithm, aims to generate a connected graph which provide a minimal path that links deployed sensors and sink. Simulations and a comparative study were carried out to prove the relevance of the proposed method.

     

A framework for optimal battery management for wireless nodes

The focus of this paper is to extend the lifetime of a battery powered node in wireless context. The lifetime of a battery depends on both the manner of discharge and the transmission power requirements. We present a framework for computing the optimal discharge strategy which maximizes the lifetime of a node by exploiting the battery characteristics and adapting to the varying power requirements for wireless operations. The complexity of the optimal computation is linear in the number of system states. However, since the number of states can be large, the optimal strategy can only be computed offline and executed via a table lookup. We present a simple discharge strategy which can be executed online without any table lookup and attains near maximum lifetime.     

Road network layout based multi-hop broadcast protocols for Urban Vehicular Ad-hoc Networks

Wireless Networks - In recent years, the decentralized wireless Vehicular Ad hoc Networks (VANETs) have emerged as a key technology for Intelligent Transportation Systems (ITS). The need...     

Fc Gamma Receptor IIIB NA1/NA2/SH Polymorphisms Are Associated with Malaria Susceptibility and Antibody Levels to P. falciparum Merozoite Antigens in Beninese Children

This paper aimed to investigate the influence of polymorphisms in the FCGR2A gene encoding R131H FcgRIIA variants and in the FCGR3B gene (108G > C, 114C > T, 194 A > G, 233C > A, 244 G > A and 316G > A) encoding FcgRIIIB-NA1, -NA2 and -SH variants on malaria susceptibility and antibody responses against P. falciparum merozoite antigens in Beninese children. An active malaria follow-up was conducted in infants from birth to 24 months of age in Allada, Benin. FCGR3B exon 3 was sequenced and FCGR2A exon 4 was genotyped. Antibodies directed to GLURP and MSP3 were quantified by ELISA. Association studies were performed using mixed-effect models. Individual carriage of FCGR3B 194 AA genotype was associated with a high number of malaria infections and a low level of IgG1 against MSP3 and GLURP-R0. High parasitemia and increased malaria infections were observed in infants carrying the FCGR3B*05 108C-114T-194A-233C-244A-316A haplotype. A reduced risk of malaria infections and low parasitemia were related to the carriages of the FCGR3B 108C-114T-194G-233C-244G-316A (FCGR3B*06), FCGR3B 108C–114T–194G–233A–244A–316A (FCGR3B*03 encoding for FcgRIIIB-SH) haplotypes and FCGR3B 297 TT genotype. Our results highlight the impact of FCGR3B polymorphisms on the individual susceptibility to malaria and antibody responses against MSP3 and GLURP in Beninese children.     

Distribution des combretums en rapport avec l’aridité au Cameroun

Dans la perspective de recherche des indicateurs biologiques susceptibles d’interpréter les changements des conditions d’un milieu, une étude, sur la distribution des combretums, a été effectuée dans les provinces administratives de l’Est, Adamaoua et extrême nord du Cameroun; suivant le gradient d’aridité croissant. A l’issus des prospections de terrain, il ressort que la diversité des combretums le long du gradient climatique Est–extrême nord est très perceptible. D’une manière générale, la densité de ces espèces croit au fur et à mesure que l’on va des régions humides aux régions plus sèches. Toutefois, une étude plus fine se doit d’être menée pour confirmer les résultats de ce travail.

Mots clés: Combretum; Distribution; Aridité; Cameroun

In order to determine the biological indicator which can permit understanding of environmental changes, a study was carried out on combretum distribution in the administrative provinces of East, Adamawa and Extreme‐North, in Cameroon, along a gradient of increasing aridity. After field exploration, it appears that there is combretum diversity along the East–Extreme north climatic gradient. In general, the species density increases also from the humid to the driest zone. There is a need for further research to confirm this trend.     

Improved detection of breast cancer nuclei using modular neuralnetworks

Discusses the analysis of nuclei in histopathological sections with a system that closely simulates human experts. The evaluation of immunocytochemically stained histopathological sections presents a complex problem due to many variations that are inherent in the methodology. In this respect, many aspects of immunocytochemistry remain unresolved, despite the fact that results may carry important diagnostic, prognostic, and therapeutic information. In this article, a modular neural network-based approach to the detection and classification of breast cancer nuclei stained for steroid receptors in histopathological sections is described and evaluated