3D Hydrodynamic Model Development and Verification

A three-dimensional numerical model was developed to simulate hydrodynamic, temperature, and water quality distributions in rivers and lakes. In an attempt to get rid of the extra approximation and complexity, no coordinate transformation has been done and z-coordinate system has been employed. The governing equations are the continuity equation, free surface equation, momentum equations, and conservation equations of temperature and water quality. The model employs the time splitting technique which allows splitting the directions in which we end with two-dimensional governing equations and eventually the solution ends with a tri-diagonal matrix, which is easily solved by Thomas algorithm. The first step after developing a numerical model and before adding more features or applying the model to a real case, the model should be verified. The verification of the model was done by implementing the model to known solutions test cases in additional to evaluating whether the code preserves fluid mass. A series of test cases is performed by comparing the model results with the analytical solutions as proposed by many modelers. The model showed good agreement between the analytical and the numerical solution

Air Pollution Levels by Re-suspended and Airborne Dust Due to Traffic Movement at the Main High Traffic Crossroads of Hilla City, Iraq

يتضمن هذا البحث عمليه مراقبه وتقييم لمستويات تلوث الهواء الناتج عن الغبار المتصاعد والمحمول بالهواء بسبب حركه المرور في تقاطعات الطرق الأكثر ازدحاما في مدينه ألحله وهي تقاطع نادر وتقاطع ألثوره. إن الغبار المتصاعد هو واحد من أكثر مصادر تلوث الهواء المساهم في التلوث الجوي الكلي وخصوصا عندما تكون الطرق غير مبلطه أو تحت الصيانة وبحمل مروري عالي مثل مايحصل ألان في تقاطع نادر الذي هو تقاطع رئيسي غير مبلط حاليا كونه تحت عمليه صيانة لأقامه مجسر عليه. تم إيجاد تراكير الدقائق العالقة الكلية للموقعين بواسطة جهاز اخذ العينات المحمول خلال ساعة الازدحام المروري وفي جو مشمس ومعتدل ولأربعه شهور( كانون الأول 2011, شباط 2012, نيسان 2012, أيار 2012 ). قد أكدت النتائج مساهمه الطرق غير ألمبلطه في تلوث الهواء. بينت النتائج أن معدل مستويات الدقائق العالقة في تقاطع نادر أعلى من معدلاتها في تقاطع ألثوره طوال فتره الدراسة حيث كان اقل مستوى للدقائق العالقة في تقاطع نادر(5676,67 مايكرو غرام\م3 ) هو أعلى من أعلى مستوى للدقائق العالقة في تقاطع ألثوره (4096,41 مايكرو غرام\م3 ). كما بينت النتائج أن تركيز الغبار المتصاعد ألمقاسه في هذه الدراسة (426.06-9348.95) مايكرو غرام\م3 هي أعلى بكثير من الحدود المسموحه في المواصفات ألقياسيه لوكالة حماية البيئة الامريكيه.This research includes a monitoring and an evaluation of the air pollution levels generated by the re-suspended and airborne dust due to traffic movement at the main busy crossroads of Hilla City, Nader Crossroad and Al-Thowra Crossroad, Iraq. The re-suspended dust is one of the most important contributors towards overall atmospheric pollution, especially when the roads are unpaved or under maintenance with high traffic load such as Nader Crossroad, which was under maintenance to construct a bridge on it. The concentrations of the total suspended particulate matters were determined at the two locations using portable air sampler during traffic rush hour on sunny moderated weekdays for four months, December 2011, February 2012, April 2012, and May 2012. The results have confirmed the contribution of the unpaved roads in air pollution. The results showed that the average TSP levels at Nader Crossroad was higher than the average TSP levels at Al-Thowra Crossroad during the total period of the study in which the minimum TSP level at Nader Crossroad was 5676.67 μg/m3, which was higher than the maximum TSP level at Al-Thowra Crossroad, 4096.41 μg/m3. In addition, the re-suspended dust concentrations that were measured in this study and ranged from 426.06 to 9348.95 μg/m3 are much higher than the American Environmental Protection Agency acceptable limits of national ambient air quality standards for the particulate matter. &nbsp

Automated Fault Detection in the Arabian Basin

In recent years, there has been a rapid development of the computer-aided interpretation of seismic data to reduce the otherwise intensive manual labor. A variety of seed detection algorithms for horizon and fault identification are integrated into popular seismic software packages. Recently, there has been an increasing focus on using neural networks for fully automatic faults detection without manually seeding each fault. These networks are usually trained with synthetic fault data sets. These data sets can be used across multiple seismic data sets; however, they are not as accurate as real seismic data, particularly in structurally complex regions associated with several generations of faults. The approach taken here is to combine the accuracy of manual fault identification in certain parts of the data set with a convolutional neural network that can then sweep through the entire data set to identify faults. We have implemented our method using 3D seismic data acquired from the Arabian Basin in Saudi Arabia covering an area of 1051 km2. The network is trained, validated, and tested with samples that included a seismic cube and fault images that are labeled manually corresponding to the seismic cube. The model successfully identified faults with an accuracy of 96% and an error rate of 0.12 on the training data set. To achieve a robust model, we further enhanced the prediction results using postprocessing by linking discontinued segments of the same fault line, thus reducing the number of detected faults. The postprocessing improved the prediction results from the test data set by 77.5%. In addition, we introduced an efficient framework to correlate the predictions and the ground truth by measuring their average distance value. Furthermore, tests using this approach also were conducted on the F3 Netherlands survey with complex fault geometries and find promising results. As a result, fault detection and diagnosis were achieved efficiently with structures similar to the trained data set

Methods of Diagnosing Immunodeficiency in Adolescents

Immunodeficiency disorders in adolescents represent a complex and multifaceted challenge in clinical practice. A timely and accurate diagnosis is crucial for appropriate management and improved patient outcomes. This review paper comprehensively examines the methods employed in diagnosing immunodeficiency disorders in adolescents. We explore clinical assessments, laboratory tests, genetic analyses, imaging techniques, and functional assays, highlighting their respective advantages and disadvantages. A critical understanding of these diagnostic approaches equips healthcare professionals with valuable tools to enhance the healthcare of adolescents with immunodeficiency disorders. Furthermore, some systems for diagnosing diseases that affect immunity are explained

Bigger tides, less flooding: Effects of dredging on barotropic dynamics in a highly modified estuary.

Author Posting. © American Geophysical Union, 2019. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research-Oceans 124 (2019): 196-211, doi:10.1029/2018JC014313.Since the late nineteenth century, channel depths have more than doubled in parts of New York Harbor and the tidal Hudson River, wetlands have been reclaimed and navigational channels widened, and river flow has been regulated. To quantify the effects of these modifications, observations and numerical simulations using historical and modern bathymetry are used to analyze changes in the barotropic dynamics. Model results and water level records for Albany (1868 to present) and New York Harbor (1844 to present) recovered from archives show that the tidal amplitude has more than doubled near the head of tides, whereas increases in the lower estuary have been slight (<10%). Channel deepening has reduced the effective drag in the upper tidal river, shifting the system from hyposynchronous (tide decaying landward) to hypersynchronous (tide amplifying). Similarly, modeling shows that coastal storm effects propagate farther landward, with a 20% increase in amplitude for a major event. In contrast, the decrease in friction with channel deepening has lowered the tidally averaged water level during discharge events, more than compensating for increased surge amplitude. Combined with river regulation that reduced peak discharges, the overall risk of extreme water levels in the upper tidal river decreased after channel construction, reducing the water level for the 10‐year recurrence interval event by almost 3 m. Mean water level decreased sharply with channel modifications around 1930, and subsequent decadal variability has depended both on river discharge and sea level rise. Channel construction has only slightly altered tidal and storm surge amplitudes in the lower estuary.Funding for D. K. R., W. R. G., and C. K. S. was provided by NSF Coastal SEES awards OCE-1325136 and OCE-1325102. Funding for S.T. and H. Z. was provided by the U.S. Army Corps of Engineers (award W1927 N-14-2-0015), and NSF (Career Award 1455350). Data supporting this study are posted to Zenodo (https://doi.org/10.5281/zenodo.1298636).2019-06-1

Optically powered radio-over-fiber systems in support of 5G cellular networks and IoT

We propose using power-over-fiber (PoF) in some part of future 5G cellular solutions based on radio access networks considering currently installed front-haul solutions with single mode fiber to optically power communication systems for 5G new radio (NR) data transmission. Simulations addressing design parameters are presented. Radio-over-fiber (RoF) transmission over single mode fiber (SMF) is experimentally implemented and tested for link lengths ranging from 100 m up to 10 km with injected PoF signals up to 2 W. 64QAM, 16QAM and QPSK data traffic of 100 MHz bandwidth are transmitted simultaneously with the PoF signal showing an EVM compliant with 5G NR standard, and up to 0.5 W for 256QAM. EVM of 4.3% is achieved with RF signal of 20 GHz and QPSK modulation format in coexistence with delivering 870 mW of optical power to a photovoltaic cell (PV) after 10 km-long SMF link. Using PoF technology to optically powering remote units and Internet-of-Things (IoT) solutions based on RoF links is also discussed.This work was supported in part by the Spanish Ministerio de Ciencia, Innovación y Universidades, Comunidad de Madrid and H2020 European Union programme under Grants RTI2018-094669-B-C32, Y2018/EMT-4892, and 5G PPP Bluespace project grant n°.762055, respectively

Hybridised Artificial Neural Network model with Slime Mould Algorithm: A novel methodology for prediction urban stochastic water demand

Urban water demand prediction based on climate change is always challenging for water utilities because of the uncertainty which results from a sudden rise in water demand due to stochastic patterns of climatic factors. For this purpose, a novel combined methodology including, firstly, data pre-processing techniques were employed to decompose the time series of water and climatic factors by using Empirical Mode Decomposition and identifying the best model input via tolerance to avoid multi-collinearity. Second, the Artificial Neural Network (ANN) model was optimised by an up-to-date Slime Mould Algorithm (SMA-ANN) to predict the medium term of the stochastic signal of monthly urban water demand. Ten climatic factors over 16 years were used to simulate the stochastic signal of water demand. The results reveal that SMA outperforms Multi-Verse Optimiser and Backtracking Search Algorithm based on error scale. The performance of the hybrid model SMA-ANN is better than ANN (stand-alone) based on the range of statistical criteria. Generally, this methodology yields accurate results with a coefficient of determination of 0.9 and a mean absolute relative error of 0.001. This study can assist local water managers to efficiently manage the present water system and plan extensions to accommodate the increasing water demand

Prediction and Forecasting of Maximum Weather Temperature Using a Linear Autoregressive Model

This paper investigates the autoregressive (AR) model performance in prediction and forecasting the monthly maximum temperature. The temperature recordings are collected over 12 years (i.e., 144 monthly readings). All the data are stationaries, which is converted to be stationary, via obtaining the normal logarithm values. The recordings are then divided into 70% training and 30% testing sample. The training sample is used for determining the structure of the AR model while the testing sample is used for validating the obtained model in forecasting performance. A wide range of model order is selected and the most suitable order is selected in terms of the highest modelling accuracy. The study shows that the monthly maximum temperature can accurately be predicted and forecasted using the AR model

Assessing the Potential of Hybrid-Based Metaheuristic Algorithms Integrated with ANNs for Accurate Reference Evapotranspiration Forecasting

Evapotranspiration (ETo) is one of the most important processes in the hydrologic cycle, with specific application to sustainable water resource management. As such, this study aims to evaluate the predictive ability of a novel method for monthly ETo estimation, using a hybrid model comprising data pre-processing and an artificial neural network (ANN), integrated with the hybrid particle swarm optimisation–grey wolf optimiser algorithm (PSOGWO). Monthly data from Al-Kut City, Iraq, over the period 1990 to 2020, were used for model training, testing, and validation. The predictive accuracy of the proposed model was compared with other cutting-edge algorithms, including the slime mould algorithm (SMA), the marine predators algorithm (MPA), and the constriction coefficient-based particle swarm optimisation and chaotic gravitational search algorithm (CPSOCGSA). A number of graphical methods and statistical criteria were used to evaluate the models, including root mean squared error (RMSE), Nash–Sutcliffe model efficiency (NSE), coefficient of determination (R2), maximum absolute error (MAE), and normalised mean standard error (NMSE). The results revealed that all the models are efficient, with high simulation levels. The PSOGWO–ANN model is slightly better than the other approaches, with an R2 = 0.977, MAE = 0.1445, and RMSE = 0.078. Due to its high predictive accuracy and low error, the proposed hybrid model can be considered a promising technique