BackgroundSafe water is essential for life but unsafe for human consumption if it is contaminated with pathogenic microorganisms. An acceptable quality of water supply (adequate, safe and accessible) must be ensured to all human beings for a healthy life.MethodsWe collected and analyzed a total of 12,650 drinking water samples, for the presence of Escherichia coli and faecal coliforms, from a large habitation of the displaced Rohingya population comprising of about 1.16 million people living within 4 km2.ResultsWe found that 28% (n = 893) water samples derived from tubewells were contaminated with faecal coliforms and 10.5% (n = 333) were contaminated with E. coli; also, 73.96% (n = 4644) samples from stored household sources (at point of use—POU) were found contaminated with faecal coliforms while 34.7% (n = 2179) were contaminated with E. coli. It was observed that a higher percentage of POU samples fall in the highest risk category than that of their corresponding sources.ConclusionsFrom our findings, it appears that secondary contamination could be a function of very high population density and could possibly occur during collection, transportation, and storage of water due to lack of knowledge of personal and domestic hygiene. Hence, awareness campaign is necessary, and the contaminated sources should be replaced. Further, the POU water should be treated by a suitable method.

Millimeter wave technology will be dominating the fifth-generation networks due to the clear advantage of higher frequency bands and hence wider spectrum. In this paper, the indoor radio wave propagation at 28 GHz is studied by developing an efficient three-dimensional ray tracing (ETRT) method. The simulation software based on the ETRT model has been verified by measurement data. The received signal strength indication and path loss have shown significant agreement between simulation and measurement. Compared with the conventional shooting bouncing ray tracing method, the proposed ETRT method has better agreement with measurement data.

Temporal fault trees (TFTs), an extension of classical Boolean fault trees, can model timedependent failure behaviour of dynamic systems. The methodologies used for quantitative analysis of TFTs include algebraic solutions, Petri nets (PN), and Bayesian networks (BN). In these approaches, precise failure data of components are usually used to calculate the probability of the top event of a TFT. However, it can be problematic to obtain these precise data due to the imprecise and incomplete information about the components of a system. In this paper, we propose a framework that combines intuitionistic fuzzy set theory and expert elicitation to enable quantitative analysis of TFTs of dynamic systems with uncertain data. Experts' opinions are taken into account to compute the failure probability of the basic events of the TFT as intuitionistic fuzzy numbers. Subsequently, for the algebraic approach, the intuitionistic fuzzy operators for the logic gates of TFT are defined to quantify the TFT. On the other hand, for the quantification of TFTs via PN and BN-based approaches, the intuitionistic fuzzy numbers are defuzzified to be used in these approaches. As a result, the framework can be used with all the currently available TFT analysis approaches. The effectiveness of the proposed framework is illustrated via application to a practical system and through a comparison of the results of each approach. INDEX TERMS Fault tree analysis, reliability analysis, fuzzy set, intuitionistic fuzzy set theory, expert judgement, temporal fault trees.

The indoor positioning system (IPS) is becoming increasing important in accurately determining the locations of objects by the utilization of micro-electro-mechanical-systems (MEMS) involving smartphone sensors, embedded sources, mapping localizations, and wireless communication networks. Generally, a global positioning system (GPS) may not be effective in servicing the reality of a complex indoor environment, due to the limitations of the line-of-sight (LoS) path from the satellite. Different techniques have been used in indoor localization services (ILSs) in order to solve particular issues, such as multipath environments, the energy inefficiency of long-term battery usage, intensive labour and the resources of offline information collection and the estimation of accumulated positioning errors. Moreover, advanced algorithms, machine learning, and valuable algorithms have given rise to effective ways in determining indoor locations. This paper presents a comprehensive review on the positioning algorithms for indoors, based on advances reported in radio wave, infrared, visible light, sound, and magnetic field technologies. The traditional ranging parameters in addition to advanced parameters such as channel state information (CSI), reference signal received power (RSRP), and reference signal received quality (RSRQ) are also presented for distance estimation in localization systems. In summary, the recent advanced algorithms can offer precise positioning behaviour for an unknown environment in indoor locations.

During recent decades, Bangladesh has experienced a rapid epidemiological transition from communicable to non-communicable diseases. Coronary heart disease (CHD), with myocardial infarction (MI) as its main manifestation, is a major cause of death in the country. However, there is limited reliable evidence about its determinants in this population. The Bangladesh Risk of Acute Vascular Events (BRAVE) study is an epidemiological bioresource established to examine environmental, genetic, lifestyle and biochemical determinants of CHD among the Bangladeshi population. By early 2015, the ongoing BRAVE study had recruited over 5000 confirmed first-ever MI cases, and over 5000 controls “frequency-matched” by age and sex. For each participant, information has been recorded on demographic factors, lifestyle, socioeconomic, clinical, and anthropometric characteristics. A 12-lead electrocardiogram has been recorded. Biological samples have been collected and stored, including extracted DNA, plasma, serum and whole blood. Additionally, for the 3000 cases and 3000 controls initially recruited, genotyping has been done using the CardioMetabochip+ and the Exome+ arrays. The mean age (standard deviation) of MI cases is 53 (10) years, with 88 % of cases being male and 46 % aged 50 years or younger. The median interval between reported onset of symptoms and hospital admission is 5 h. Initial analyses indicate that Bangladeshis are genetically distinct from major non-South Asian ethnicities, as well as distinct from other South Asian ethnicities. The BRAVE study is well-placed to serve as a powerful resource to investigate current and future hypotheses relating to environmental, biochemical and genetic causes of CHD in an important but under-studied South Asian population.Electronic supplementary materialThe online version of this article (doi:10.1007/s10654-015-0037-2) contains supplementary material, which is available to authorized users.

Inorganic arsenic (iAs) and lead (Pb) rank first and second on the U.S. Environmental Protection Agency’s priority list of hazardous substances. Both are known neurotoxic metals that cause detrimental effects on brain development and lead to deficits in cognitive function and behavioral performance in children. Studies have indicated a potential link between iAs and Pb exposure and a higher risk for autism spectrum disorder (ASD). To provide further insight into whether developmental exposure to iAs or Pb is associated with ASD, we conducted a systematic review and combined data into a meta-analysis to evaluate the available human evidence on the relationships. We systematically reviewed relevant studies published through December 30, 2018 and identified 14 studies on iAs and 37 studies on Pb exposure and their respective associations with ASD. Among them, 8 (53.3%) and 19 (51.3%) studies reported a positive association for iAs and Pb, respectively, and none reported a sole inverse association. In the following meta-analysis, we found statistically significant higher iAs concentrations, in hair and in blood, for children diagnosed with ASD compared with controls across studies. However, the findings on Pb exposure were inconsistent, with a significant association for hair Pb, no association for urinary Pb, and an inverse association for blood Pb. After considering strengths and limitations of the body of research, we concluded that there is consistent evidence supporting a positive association between early life iAs exposure and diagnosis of ASD and inconsistent evidence for Pb exposure and ASD risk. We believe it is in the best interest of policy makers and the public to reduce exposures to iAs and Pb among pregnant women and children. Further, our research supports the need for large perspective human studies with accurate measurement and determination of the long-term body burden of iAs and Pb exposures to assess the impact of iAs and Pb exposures on ASD risk.

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Radio propagation prediction simulation methods based on deterministic technique such as ray launching is extensively used to accomplish radio channel characterization. However, the superiority of the simulation depends on the number of rays launched and received. This paper presented the indoor three-dimensional (3D) Minimum Ray Launching Maximum Accuracy (MRLMA) technique, which is applicable for an efficient indoor radio wave propagation prediction. Utilizing the novel MRLMA technique in the simulation environment for ray lunching and tracing can drastically reduce the number of rays that need to be traced, and improve the efficiency of ray tracing. Implementation and justification of MRLMA presented in the paper. An indoor office 3D layouts are selected and simulations have been performed using the MRLMA and other reference techniques. Results showed that the indoor 3D MRLMA model is appropriate for wireless communications network systems design and optimization process with respect to efficiency, coverage, number of rays launching, number of rays received by the mobile station, and simulation time.