Similarity Metrics-Based Uncertainty Analysis of River Water Quality Models

Uncertainty analysis (UA) is essential to reinforce the decisions made by water resource engineers and managers. In this study, the stepwise multiple linear regression procedure assessed the relationship between water quality parameters and physical characteristics of 48 catchments in the southwestern basin of the Caspian Sea, Iran. The results of the modeling showed that the coefficient of determination ranged between 0.47 and 0.68 and indicated a positive relationship between the area (%) of agricultural lands and the sodium adsorption ratio (SAR), potassium (K) and total dissolved solids (TDS). A negative relationship was also found between bicarbonate (HCO₃^?) and the area (%) of the intermediate-density forest. In contrast to previous studies focusing on analyzing the uncertainty of the model parameters, we addressed the uncertainty of the model variables. The results of the GLUE-based uncertainty analysis (UA) performed on the model’s variables indicated that the measures of the R-factor for all models were between 0.13 and 0.98. The lowest R-factor was obtained for the HCO₃^? model (0.13) suggesting it performed well when predicting HCO₃^?. To increase the degree of objectivity in the GLUE-UA method, a set of similarity metrics, including Czekanowski, Motyka, Ruzicka, Cosine, Kumar-Hassebrook, Jaccard and Dice was applied to determine the degree of proximity and or similarity between the probability density functions of the measured and simulated water quality parameters. The measures of the similarity metrics for the HCO₃^? model were generally close to 1, indicating good performance and low uncertainty, while it showed higher uncertainty (between 0.2487 and 0.897) for the other three models (SAR, K, and TDS).

     

Concentration-dependent diffusion of isobutane in silicalite-1 studied with the ZLC technique

The desorption of isobutane in silicalite-1 over a wide range of initial loadings was investigated with the ZLC technique. Model equations, in which the thermodynamic (correction) factor for intracrystalline diffusivities has been taken into account, were successfully used to describe the experimental ZLC curves. This verified the relation between diffusivity and loading for isobutane in silicalite-1 described by the Darken equation, in combination with the dual-site Langmuir isotherm. The determined diffusivities at zero coverage and their temperature dependence correspond well with those obtained by other macroscopic techniques. This agreement demonstrates that the ZLC technique can be successfully used to investigate concentration-dependent diffusivities if separately determined isotherms are available.     

Mutations in bdcA and valS Correlate with Quinolone Resistance in Wastewater Escherichia coli

Single mutations can confer resistance to antibiotics. Identifying such mutations can help to develop and improve drugs. Here, we systematically screen for candidate quinolone resistance-conferring mutations. We sequenced highly diverse wastewater E. coli and performed a genome-wide association study (GWAS) to determine associations between over 200,000 mutations and quinolone resistance phenotypes. We uncovered 13 statistically significant mutations including 1 located at the active site of the biofilm dispersal gene bdcA and 6 silent mutations in the aminoacyl-tRNA synthetase valS. The study also recovered the known mutations in the topoisomerases gyrase (gyrA) and topoisomerase IV (parC). In summary, we demonstrate that GWAS effectively and comprehensively identifies resistance mutations without a priori knowledge of targets and mode of action. The results suggest that mutations in the bdcA and valS genes, which are involved in biofilm dispersal and translation, may lead to novel resistance mechanisms.     

Design of a RFID System for Real-Time Tracking of Laboratory Animals

In this paper, a real-time RFID system capable of tracking laboratory animals is designed and implemented. Four passive RFID tags based on low frequency are designed and implemented. The tags can be read by any RFID reader that operates on the low frequency range 125–134 kHz. The tags are designed through the investigation of various antenna, encoding, modulation, and energy harvesting techniques. The tag receives the electromagnetic signal via the antenna, and converts it to a DC signal that the microcontroller can use to manipulate the electromagnetic signal with the data such that the reader can decode the unique tag identifier. RFID sensors are designed and implemented to collect data from various monitored areas of a semi natural environment. The data is sent to a central data coordinator for pre-processing and middleware for data error checking, display and storage. The RFID system can successfully detect and store movement data in real time. A read range of 14.5 cm is achieved.     

A novel and secure IoT based cloud centric architecture to perform predictive analysis of users activities in sustainable health centres

Diabetes, blood pressure, heart, and kidney, some of the diseases common across the world, are termed ’silent killers’. More than 50 % of the world’s population are affected by these diseases. If suitable steps are not taken during the early stages then severe complications occur from these diseases. In the work proposed, we have discussed the manner in which the Internet-of-Things based Cloud centric architecture is used for predictive analysis of physical activities of the users in sustainable health centers. The architecture proposed is based on the embedded sensors of the equipment rather than using wearable sensors or Smartphone sensors to store the value of the basic health-related parameters. Cloud centric architecture is composed of a Cloud data center, Public cloud, Private cloud, and uses the XML Web services for secure and fast communication of information. The architecture proposed here is evaluated for its adoption, prediction analysis of physical activities, efficiency, and security. From the results obtained it can be seen that the overall response between the local database server and Cloud data center remains almost constant with the rise in the number of users. For prediction analysis, If the results collected in real time for the analysis of physical activities exceed any of the parameter limits of the defined threshold value then an alert is sent to the health care personnel. Security analysis also shows the effective encryption and decryption of information. The architecture presented is effective and reduces the proliferation of information. It is also suggested, that a person suffering from any of the diseases mentioned above can defer the onset of complications by doing regular physical activities.     

Development of a New Integrated Framework for Improved Rainfall-Runoff Modeling under Climate Variability and Human Activities

The relationship between rainfall and runoff is a complex phenomenon and understanding the physical processes, hydrological components and their impacts on response of watershed to precipitation is one of the challenging issues in watershed hydrology and planning. There is still a need to improve conceptual hydrological models in water scarce regions, such as Iran mainly because in many cases there is not enough data to fully describe this phenomenon. In this research, we aimed to present an improved and parsimonious framework that increases the performance of a conceptual model in water balance and discharge modeling for Delichay watershed located in Hablehroud basin, Iran as one of the main source of water supply for downstream fertile agricultural areas that produce a considerable amount of cereals and play a major role for food and water security of the region. In areas where data for water cycle components are not available or limited, it is recommended to use parsimonious approach in order to have an acceptable level of understanding of the system with minimum possible predictor variables. The Salas model used in current research to model water balance over the period 1983–2012 and evaluation of the results indicated an unsatisfactory performance when the entire period was modeled altogether (NSE?=?0.35, d?=?0.70, R²?=?0.63, RSR?=?0.80, PBIAS?=?4.96 and RMSE?=?41.87). A key reason is that this watershed is intensively impacted by human activities and homogeneity analysis confirmed a sudden shift in runoff data during 1998–1999. Such a sudden shift reveals the role of human activities impacts on the watershed with a total reduction of 58 mm of runoff per year while the climate variability has not occurred in the region. Thus, the entire period (i.e. 1983–2012) was divided into two homogenous sub-periods of before and after the change point (i.e., pre-change and post-change periods). The results indicated that modeling performance in the sub-periods improved (e.g. the NSE was 0.77 and 0.66 for pre-change and post-change, respectively, vs. 0.35 for entire period). Meanwhile, it is revealed that water balance affected by human activities over the time and application of historical data for water balance modeling cannot be reliable without considering the homogeneous data. Since, many watersheds in the world have been affected by human activities or climate variability, it is recommended to consider the homogeneity of observed data before any application.

     

Home automation - an IoT based system to open security gates using number plate recognition and artificial neural networks

This paper proposed a system that automatically opens security gates. A system is designed and implemented to automatically open security gates for vehicles using Licence Plate Recognition. Image processing is used to extract the licence plate and characters, and an Artificial Neural Network is used to perform Optical Character Recognition on licence plate characters. Internet of Things principles are introduced to the system to allow for web and mobile application integration. A proximity sensor is designed to detect vehicles and to start the recognition process. An ambient light sensor and control circuit is developed to control ambient lighting conditions using an ambient light source. The neural network achieved an accuracy of 88% on training data and 93% on licence plate characters. A unique strong point of the system is the ability to monitor and control the system using a web interface or mobile application. The system is also able to produce mobile notifications regarding security gate access attempts.     

Improved Approximation Algorithms for Data Migration

Our work is motivated by the need to manage data items on a collection of storage devices to handle dynamically changing demand. As demand for data items changes, for performance reasons, the system needs to automatically respond to changes in demand for different data items. The problem of computing a migration plan among the storage devices is called the data migration problem. This problem was shown to be NP-hard, and an approximation algorithm achieving an approximation factor of 9.5 was presented for the half-duplex communication model in Khuller, Kim and Wan (Algorithms for data migration with cloning. SIAM J. Comput. 33(2):448–461, 2004). In this paper we develop an improved approximation algorithm that gives a bound of 6.5+o(1) using new ideas. In addition, we develop better algorithms using external disks and get an approximation factor of 4.5 using external disks. We also consider the full duplex communication model and develop an improved bound of 4+o(1) for this model, with no external disks.