Notes

Superior thermophilic denitrification performance and also possible microbial procedure throughout denitrifying granular gunge method.
adults with diabetes and incontinence should not only consider disease management of the individual conditions but pay attention to the broader social determinants of health in the context of multiple chronic conditions. Efforts to enhance health-care system integration would facilitate the provision of person-centred home care.This paper introduces a transition flow model to study fall-related emergency department (ED) revisits for elderly patients with diabetes. Five diabetes classes are used to classify patients at discharge, within 7-day revisits, and between 8 and 30-day revisits. Analytical formulas to evaluate patient revisiting risks are derived. To reduce revisits, sensitivity analysis is introduced to identify the most critical, i.e., dominant, factors whose changes can lead to the largest reduction in revisits. In addition, a case study at University of Wisconsin (UW) Hospital ED is described to illustrate the applicability of the model.Motion analysis is important in video surveillance systems and background subtraction is useful for moving object detection in such systems. However, most of the existing background subtraction methods do not work well for surveillance systems in the evening because objects are usually dark and reflected light is usually strong. To resolve these issues, we propose a framework that utilizes a Weber contrast descriptor, a texture feature extractor, and a light detection unit, to extract the features of foreground objects. We propose a local pattern enhancement method. For the light detection unit, our method utilizes the finding that lighted areas in the evening usually have a low saturation in hue-saturation-value and hue-saturation-lightness color spaces. Finally, we update the background model and the foreground objects in the framework. This approach is able to improve foreground object detection in night videos, which do not need a large data set for pre-training.A challenge of achieving intelligent marine ranching is the prediction of dissolved oxygen (DO). DO directly reflects marine ranching environmental conditions. Through accurate DO predictions, timely human intervention can be made in marine pasture water environments to avoid problems such as reduced yields or marine crop death due to low oxygen concentrations in the water. We use an enhanced semi-naive Bayes model for prediction based on an analysis of DO data from marine pastures in northeastern China from the past three years. Based on the semi-naive Bayes model, this paper takes the possible values of a DO difference series as categories, counts the possible values of the first-order difference series and the difference series of the interval before each possible value, and selects the most probable difference series value at the next moment. The prediction accuracy is optimized by adjusting the attribute length and frequency threshold of the difference sequence. The enhanced semi-naive Bayes model is compared with LSTM, RBF, SVR and other models, and the error function and Willmott's index of agreement are used to evaluate the prediction accuracy. The experimental results show that the proposed model has high prediction accuracy for DO attributes in marine pastures.Software is a complex entity, and its development needs careful planning and a high amount of time and cost. To assess quality of program, software measures are very helpful. Amongst the existing measures, coupling is an important design measure, which computes the degree of interdependence among the entities of a software system. Higher coupling leads to cognitive complexity and thus a higher probability occurrence of faults. Well in time prediction of fault-prone modules assists in saving time and cost of testing. This paper aims to capture important aspects of coupling and then assess the effectiveness of these aspects in determining fault-prone entities in the software system. We propose two coupling metrics, i.e., Vovel-in and Vovel-out, that capture the level of coupling and the volume of information flow. We empirically evaluate the effectiveness of the Vovel metrics in determining the fault-prone classes using five projects, i.e., Eclipse JDT, Equinox framework, Apache Lucene, Mylyn, and Eclipse PDE UI. Model building is done using univariate logistic regression and later Spearman correlation coefficient is computed with the existing coupling metrics to assess the coverage of unique information. Finally, the least correlated metrics are used for building multivariate logistic regression with and without the use of Vovel metrics, to assess the effectiveness of Vovel metrics. Baf-A1 The results show the proposed metrics significantly improve the predicting of fault prone classes. Moreover, the proposed metrics cover a significant amount of unique information which is not covered by the existing well-known coupling metrics, i.e., CBO, RFC, Fan-in, and Fan-out. This paper, empirically evaluates the impact of coupling metrics, and more specifically the importance of level and volume of coupling in software fault prediction. The results advocate the prudent addition of proposed metrics due to their unique information coverage and significant predictive ability.One of the essential concerns of Internet of Things (IoT) is in industrial systems or data architecture to support the evolutions in transportation and logistics. Considering the Industrial IoT (IIoT) openness, the need for accessibility, availability, and searching of data has rapidly increased. The primary purpose of this research is to propose an Efficient Two-Dimensional Filter (ETDF) to store multimedia data of IIoT applications in a specific format to achieve faster response and dynamic updating. This filter consists of a two-dimensional array and a hash function integrated into a cuckoo filter for efficient use of memory. This study evaluates the scalability of the filter by increasing the number of requests from 10,000 to 100,000. To assess the performance of the proposed filter, we measure the parameters of access time and lookup message latency. The results show that the proposed filter improves the access time by 12%, compared to a Fast Two-Dimensional Filter (FTDF). Moreover, it improves memory usage by 20% compared to FTDF. Experiments indicate a better access time of the proposed filter compared to other filters (i.e., Bloom, quotient, cuckoo, and FTD filters). Insertion and deletion times are essential parameters in comparing filters, so they are also analyzed.Mass spectrometry imaging (MSI) enables the unbiased characterization of surfaces with respect to their chemical composition. In biological MSI, zones with differential mass profiles hint towards localized physiological processes, such as the tissue-specific accumulation of secondary metabolites, or diseases, such as cancer. Thus, the efficient discovery of 'regions of interest' (ROI) is of utmost importance in MSI. However, often the discovery of ROIs is hampered by high background noise and artifact signals. Especially in ambient ionization MSI, unmasking biologically relevant information from crude data sets is challenging. Therefore, we implemented a Threshold Intensity Quantization (TrIQ) algorithm for augmenting the contrast in MSI data visualizations. The simple algorithm reduces the impact of extreme values ('outliers') and rescales the dynamic range of mass signals. We provide an R script for post-processing MSI data in the imzML community format (https//bitbucket.org/lababi/msi.r) and implemented the TrIQ in our open-source imaging software RmsiGUI (https//bitbucket.org/lababi/rmsigui/). Applying these programs to different biological MSI data sets demonstrated the universal applicability of TrIQ for improving the contrast in the MSI data visualization. link2 We show that TrIQ improves a subsequent detection of ROIs by sectioning. In addition, the adjustment of the dynamic signal intensity range makes MSI data sets comparable.The use of low-field magnetic resonance imaging (LF-MRI) scanners has increased in recent years. The low economic cost in comparison to high-field (HF-MRI) scanners and the ease of maintenance make this type of scanner the best choice for nonmedical purposes. However, LF-MRI scanners produce low-quality images, which encourages the identification of optimization procedures to generate the best possible images. In this paper, optimization of the image acquisition procedure for an LF-MRI scanner is presented, and predictive models are developed. The MRI acquisition procedure was optimized to determine the physicochemical characteristics of pork loin in a nondestructive way using MRI, feature extraction algorithms and data processing methods. The most critical parameters (relaxation times, repetition time, and echo time) of the LF-MRI scanner were optimized, presenting a procedure that could be easily reproduced in other environments or for other purposes. In addition, two feature extraction algorithms (gray level co-occurrence matrix (GLCM) and one point fractal texture algorithm (OPFTA)) were evaluated. The optimization procedure was validated by using several evaluation metrics, achieving reliable and accurate results (r > 0.85; weighted absolute percentage error (WAPE) lower than 0.1%; root mean square error of prediction (RMSEP) lower than 0.1%; true standard deviation (TSTD) lower than 2; and mean absolute error (MAE) lower than 2). These results support the high degree of feasibility and accuracy of the optimized procedure of LF-MRI acquisition. No other papers present a procedure to optimize the image acquisition process in LF-MRI. Eventually, the optimization procedure could be applied to other LF-MRI systems.Demand for high-speed wireless broadband internet service is ever increasing. Multiple-input-multiple-output (MIMO) Wireless LAN (WLAN) is becoming a promising solution for such high-speed internet service requirements. This paper proposes a novel algorithm to efficiently model the address generation circuitry of the MIMO WLAN interleaver. The interleaver used in the MIMO WLAN transceiver has three permutation steps involving floor function whose hardware implementation is the most challenging task due to the absence of corresponding digital hardware. In this work, we propose an algorithm with a mathematical background for the address generator, eliminating the need for floor function. The algorithm is converted into digital hardware for implementation on the reconfigurable FPGA platform. Hardware structure for the complete interleaver, including the read address generator and memory module, is designed and modeled in VHDL using Xilinx Integrated Software Environment (ISE) utilizing embedded memory and DSP blocks of Spartan 6 FPGA. The functionality of the proposed algorithm is verified through exhaustive software simulation using ModelSim software. Hardware testing is carried out on Zynq 7000 FPGA using Virtual Input Output (VIO) and Integrated Logic Analyzer (ILA) core. Comparisons with few recent similar works, including the conventional Look-Up Table (LUT) based technique, show the superiority of our proposed design in terms of maximum improvement in operating frequency by 196.83%, maximum reduction in power consumption by 74.27%, and reduction of memory occupancy by 88.9%. link3 In the case of throughput, our design can deliver 8.35 times higher compared to IEEE 802.11n requirement.
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