Hastyar Hama Rashid Najmuldeen
Abstract
This study was designed to determine the extent of contamination of water storage tanks by non-lactose fermenter Enterobacter spp, and to characterize the chlorine and antibiotic resistance status. Moreover, to find the correlation between biofilm formation and resistance to chlorine. For this ...
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This study was designed to determine the extent of contamination of water storage tanks by non-lactose fermenter Enterobacter spp, and to characterize the chlorine and antibiotic resistance status. Moreover, to find the correlation between biofilm formation and resistance to chlorine. For this purpose, a total of 60 water samples were collected from residential and restaurant water storage tanks. Bacterial analysis and antibiotic susceptibility profiles of the samples were assessed by the Most Probable Number (MPN) and Vitek 2 compact tests, respectively. The biofilm formation was quantified by crystal violet staining method and chlorine resistance test by microdilution technique. Obtained results indicated that water samples were contaminated by Escherichia coli (35%). Additionally, water samples that were assessed to be potable by the MPN test showed that (44%) positive for Enterobacter cloacae. Results of chlorine resistance test revealed variation in resistance of E. cloacae to different concentrations of chlorine, and relatively similar antibiotic susceptibility profiles. Moreover, biofilm analysis showed the isolates that were resistant to concentration of chlorine 400 mg L-1, have formed significantly more biofilm than those that were resistant to other concentrations. A positive non-linear correlation (r = 0.72) was found between the degree of biofilm formation and the ability of isolates to resist different chlorine concentrations, and no correlation has been detected between antibiotic and chlorine resistance. It can be concluded that the presence of chlorine resistant E. cloacae in drinking water can pose a real public health threat. The routine microbial water analysis should be modified to include detection of non-lactosefermenter Enterobacter.
Yadgar Sirwan Abdulrahman
Abstract
Clustering is one of the essential strategies in data analysis. In classical solutions, all features are assumed to contribute equally to the data clustering. Of course, some features are more important than others in real data sets. As a result, essential features will have a more significant impact ...
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Clustering is one of the essential strategies in data analysis. In classical solutions, all features are assumed to contribute equally to the data clustering. Of course, some features are more important than others in real data sets. As a result, essential features will have a more significant impact on identifying optimal clusters than other features. In this article, a fuzzy clustering algorithm with local automatic weighting is presented. The proposed algorithm has many advantages such as: 1) the weights perform features locally, meaning that each cluster's weight is different from the rest. 2) calculating the distance between the samples using a non-euclidian similarity criterion to reduce the noise effect. 3) the weight of the features is obtained comparatively during the learning process. In this study, mathematical analyzes were done to obtain the clustering centers well-being and the features' weights. Experiments were done on the data set range to represent the progressive algorithm's efficiency compared to other proposed algorithms with global and local features.
Rzgar Sirwan; muzhir al-ani
Abstract
Facilitating large-scale load-efficient Internet of things (IoT) connectivity is a vital step toward realizing the networked society. Although legacy wide-area wireless systems are heavily based on network-side coordination, such centralized methods will become infeasible in the future, by the unbalanced ...
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Facilitating large-scale load-efficient Internet of things (IoT) connectivity is a vital step toward realizing the networked society. Although legacy wide-area wireless systems are heavily based on network-side coordination, such centralized methods will become infeasible in the future, by the unbalanced signaling level and the expected increment in the number of IoT devices. In the present work, this problem is represented through self-coordinating for IoT networks and learning from past communications. In this regard, first, we assessed low-complexity distributed learning methods that can be applied to IoT communications. We presented a learning solution then, for adapting devices’ communication parameters to the environment to maximize the reliability and load balancing efficiency in data transmissions. Moreover, we used leveraging instruments from stochastic geometry to assess the behavior of the presented distributed learning solution against centralized coordinations. Ultimately, we analyzed the interplay amongst traffic efficiency, communications’ reliability against interference and noise over data channel, as well as reliability versus adversarial interference over feedback and data channels. The presented learning approach enhanced both reliability and traffic efficiency within IoT communications considerably. By such promising findings obtained via lightweight learning, our solution becomes promising in numerous low-power low-cost IoT uses.
Yadgar Mahmood; Halgurd Nasraden Hassan; Masood Saber Mohammed
Abstract
This study was carried out at the experiment field, Kalar Technical Institute, Garmian Region in two growing seasons of 2016-2017 and 2017-2018 in order to evaluate the growth and yield potentials of barley under water stressed using hybrids as a source of wide range of genotypic variations. Therefore, ...
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This study was carried out at the experiment field, Kalar Technical Institute, Garmian Region in two growing seasons of 2016-2017 and 2017-2018 in order to evaluate the growth and yield potentials of barley under water stressed using hybrids as a source of wide range of genotypic variations. Therefore, five F2 barley hybrids (Hordeum vulgare L.) were screened for grain yield, biomass dry matter, plant height and harvest index under irrigated and drought conditions. Results showed that there was no effect of drought on grain yield (P>0.05) in 2017, while significantly reduced yield in 2018 and across-year mean (P-2 (3//14) under irrigated condition, and 267.8 (3//5) to 302.3 g m-2 (3//4) under unirrigated condition (P=0.001), biomass dry matter was ranged from 1099.1 (3//1) to 1370.5 g m-2 (3//14) under irrigated condition, and 892.6 (3//1) to 1153.9 g m-2 (3//14) under unirrigated condition (P=0.05), and harvest index were from 25.1 (3//14) to 28.0 (3//1) under irrigated conditions, and 25.9 (3//14) to 31.2 (3//1) under unirrigated conditions (P=0.04). Regression analysis, averaging over years, showed a positive relationship between grain yield and biomass under irrigated (R2=0.76; P=0.05), despite that, any positive relation was not found under unirrigated conditions (R2=0.43; P=0.23) due to post-anthesis drought stress. A strong relationship was also found between plant height and biomass dry matter under both irrigated (R2=0.89; P=0.02) and unirrigated (R2=0.97; P=0.003) conditions due to the high contribution of plant height in increasing plant biomass. It is concluded that genotypes had different response to drought due to their genetic diversity, and relatively low impact of water stress was appeared on growth and grain yield of barley in this semi-arid region compared to worldwide expected range of yield reduction.
