Material flow analysis and integration of watersheds and drain systems: II. Integration and solution strategies with application to ammonium management in Bahr El-Baqar drain system

The objective of this paper is to develop a systematic methodology for mass integration in drain systems and watersheds. Mass integration is a holistic approach to the tracking, transformation, and allocation of species and streams. The watershed and drain system is first discretized into reaches. The MFA model developed in part I of this work (Simulation and Application to Ammonium Management in Bahr El-Baqar Drain System) is used to describe the environmental phenomena that affect the fate and transport of targeted species and the operators that characterize the system inputs and outputs as they relate to the surroundings. Next, we develop an integration framework which encompasses sources, sinks, and interception technologies to aid in the development for nitrogen-management strategies. The simulation model was transformed into a synthesis model by introducing optimization variables and including models for the potential management strategies. The problem of minimizing negative environmental impact subject to technical, social, economic, and regulatory constraints was posed as a nonlinear optimization program whose solution identified and synthesized the most effective solution strategies. These mathematical models and management strategies were coded into a computer-aided tool using LINGO programming platform. The program can be readily modified to address a variety of cases. Tradeoffs and sensitivity analysis were established using the devised model. The devised framework was applied to an Egyptian drain system (Bahr El-Baqar) along with the outfall to Lake Manzala. The results of the case study provide solution strategies for nitrogen management along with their technical, economic, and environmental implications.     

Material flow analysis and integration of watersheds and drainage systems: I. Simulation and application to ammonium management in Bahr El-Baqar drainage system

This paper is aimed at developing a systematic and generally applicable methodology for material flow analysis in drainage systems and watersheds. In particular, this research has focused on developing a mathematical framework and application for the management of nitrogenous species (primarily ammonium ions). Nitrogen compounds are among the most important species contributing to ecological cycles. Indeed, the environmental and biological aspects of water systems and their surrounding systems are highly impacted by nitrogen compounds as they contribute to the quality, nutrition, and toxicity of these systems. A material flow model was developed to deal primarily with the water phase while including pertinent information on the solid and air phases as they interface with the water medium. Both spatial and discrete temporal dimensions were included to account for nitrogen flow and transformation. The model includes the various environmental phenomena that influence the fate and transport of targeted species (e.g., volatilization, precipitation, sedimentation, uptake by biota, adsorption, chemical and biochemical reactions, etc.). Furthermore, the model includes material flow analysis operators (or transfer functions) that characterize the system inputs and outputs as they relate to the surroundings. The aforementioned material flow analysis tools were combined in a computer-aided modeling platform to provide a complete material flow analysis and yield useful insights on the transport and fate of targeted species. The simulation results shed light on the system performance. Actual data for an Egyptian drainage system (Bahr El-Baqar) along with the outfall to Lake Manzala were used to illustrate the usefulness and applicability of the developed model. Comparison with the measured data confirmed the validity and fidelity of the model.     

Bayesian belief-based model for reliability improvement of the digital reactor protection system

Nuclear Science and Techniques - This paper introduces some latest developments regarding the X-ray imaging methodology and applications of the X-ray imaging and biomedical application beamline...     

Lightweight Transfer Learning Models for Ultrasound-Guided Classification of COVID-19 Patients

Lightweight deep convolutional neural networks (CNNs) present a good solution to achieve fast and accurate image-guided diagnostic procedures of COVID-19 patients. Recently, advantages of portable Ultrasound (US) imaging such as simplicity and safe procedures have attracted many radiologists for scanning suspected COVID-19 cases. In this paper, a new framework of lightweight deep learning classifiers, namely COVID-LWNet is proposed to identify COVID-19 and pneumonia abnormalities in US images. Compared to traditional deep learning models, lightweight CNNs showed significant performance of real-time vision applications by using mobile devices with limited hardware resources. Four main lightweight deep learning models, namely MobileNets, ShuffleNets, MENet and MnasNet have been proposed to identify the health status of lungs using US images. Public image dataset (POCUS) was used to validate our proposed COVID-LWNet framework successfully. Three classes of infectious COVID-19, bacterial pneumonia, and the healthy lung were investigated in this study. The results showed that the performance of our proposed MnasNet classifier achieved the best accuracy score and shortest training time of 99.0% and 647.0 s, respectively. This paper demonstrates the feasibility of using our proposed COVID-LWNet framework as a new mobile-based radiological tool for clinical diagnosis of COVID-19 and other lung diseases.     

A review of one aspect of the thermal-explosion theory

The historical development of the thermal-explosion theory is examined and reviewed, It is shown that the original formulation of the problem by Semenov accurately defines the phenomenon and explains the reason for the explosion. He showed that, when the heat generation within the solid exceeds the heat dissipation to the surroundings, explosion occurs. Frank-Kamenetskii’s disapproval of Semenov’s logic theorized that the difference between the temperatures at the center of the solid and its surface is the cause of the explosion. His famous and ingenious small-temperature model and the solution to the differential equation that resulted from that distorted the problem and delayed the progress to a full understanding of the problem. He concluded that explosion occurs when no solution to the problem exists. The exact solution to the problem by Shouman, Donaldson and Tsao reaffirmed the validity of the Semenov formulation. Further examination of the effect of reactant consumption on the problem produced full understanding of the physics of the problem.     

The Frank-Kamenetskii problem revisited,part III: The moving flame front

The basic flame propagation theory proposed in part by Frank-Kamenetskii was modified to show that flame behavior can be predicted in a two-zone flame structure without eliminating the ignition temperature. The ignition temperature can be retained when the inert zone maintains the reaction zone in a critical condition. By retaining the ignition temperature and by maintaining the integrity of the zone matching equations, the two step flame model we have developed predicts flame behavior in either zone.     

Handling missing and outliers values by enhanced algorithms for an accurate diabetic classification system

Recently, healthcare data analysis has become an attractive research topic. Data gathering is the first step in data analysis and processing. During the collection of the data, some errors may occur due to human mistakes, devices’ errors, or the transmission process noise. The correct treatment of the missed data and outliers conserve the data size and improve the model’s performance. This paper provides two enhanced algorithms to handle missing values and outliers in big datasets. The main idea is dividing the dataset into its different classes, or clustering it by using k-means++, then calculate the average value of each part, finally replace the missed data and outliers with its corresponding part mean value. The projected imputation and outliers’ data handling algorithms are tested on a dataset called Pima Indian diabetic, which contains 2768 patients dividing into 952 diabetic and 1816 controls. Four classifiers (Random Forest, Decision Tree, Support Vector Machine, and Naïve Bayes) are used to evaluate the effect of the proposed algorithms. The results show that the proposed algorithms improve classification accuracy by 8% and decrease the RMSE by 17% over Deep Learning (DL). DL is the most powerful algorithms used in repairing the missed data.

     

Correction to: Handling missing and outliers values by enhanced algorithms for an accurate diabetic classification system

Multimedia Tools and Applications - A Correction to this paper has been published: https://doi.org/10.1007/s11042-021-10843-x