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Bayesian Regularized Neural Network Model Development for Predicting Daily Rainfall from Sea Level Pressure Data: Investigation on Solving Complex Hydrology Problem
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Prediction of daily rainfall is important for flood forecasting, reservoir operation, and many other hydrological applications. The artificial intelligence (AI) algorithm is generally used for stochastic forecasting rainfall which is not capable to simulate unseen extreme rainfall events which become common due to climate change. A new model is developed in this study for prediction of daily rainfall for different lead times based on sea level pressure (SLP) which is physically related to rainfall on land and thus able to predict unseen rainfall events. Daily rainfall of east coast of Peninsular Malaysia (PM) was predicted using SLP data over the climate domain. Five advanced AI algorithms such as extreme learning machine (ELM), Bayesian regularized neural networks (BRNNs), Bayesian additive regression trees (BART), extreme gradient boosting (xgBoost), and hybrid neural fuzzy inference system (HNFIS) were used considering the complex relationship of rainfall with sea level pressure. Principle components of SLP domain correlated with daily rainfall were used as predictors. The results revealed that the efficacy of AI models is predicting daily rainfall one day before. The relative performance of the models revealed the higher performance of BRNN with normalized root mean square error (NRMSE) of 0.678 compared with HNFIS (NRMSE = 0.708), BART (NRMSE = 0.784), xgBoost (NRMSE = 0.803), and ELM (NRMSE = 0.915). Visual inspection of predicted rainfall during model validation using density-scatter plot and other novel ways of visual comparison revealed the ability of BRNN to predict daily rainfall one day before reliably.

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Publication Date
Wed Mar 10 2021
Journal Name
Baghdad Science Journal
Compression-based Data Reduction Technique for IoT Sensor Networks
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Energy savings are very common in IoT sensor networks because IoT sensor nodes operate with their own limited battery. The data transmission in the IoT sensor nodes is very costly and consume much of the energy while the energy usage for data processing is considerably lower. There are several energy-saving strategies and principles, mainly dedicated to reducing the transmission of data. Therefore, with minimizing data transfers in IoT sensor networks, can conserve a considerable amount of energy. In this research, a Compression-Based Data Reduction (CBDR) technique was suggested which works in the level of IoT sensor nodes. The CBDR includes two stages of compression, a lossy SAX Quantization stage which reduces the dynamic range of the

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Publication Date
Sat Apr 12 2025
Journal Name
Mustansiriyah Journal Of Sports Science
A Review of the Use of Artificial Intelligence Algorithms for Predicting Injuries and Performance in Football Players
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The purpose of this study is to investigate the research on artificial intelligence algorithms in football, specifically in relation to player performance prediction and injury prevention. To accomplish this goal, scholarly resources including Google Scholar, ResearchGate, Springer, and Scopus were used to provide a systematic examination of research done during the last ten years (2015–2025). Through a systematic procedure that included data collection, study selection based on predetermined criteria, categorisation based on AI applications in football, and assessment of major research problems, trends, and prospects, almost fifty papers were found and analysed. Summarising AI applications in football for performance and injury p

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Publication Date
Fri Dec 27 2024
Journal Name
Epitheorese Klinikes Farmakologias Kai Farmakokinetikes – Greek Edition
Examine the influence of treatment and age on patients affected by high blood pressure
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Background: Hypertension is a major global health concern that increases the risk of cardiovascular disease. Understanding the impact of age and treatment types on blood pressure control is essential for optimizing therapeutic strategies. Aim: This study aims to assess how different treatment types and patient age influence blood pressure control in hypertensive patients. Methodology: A binary logistic regression model was employed to analyze data from 48 patients diagnosed with hypertension. The study investigated the impact of two treatment regimens and patient age on the likelihood of achieving optimal blood pressure levels. The statistical significance of the findings was evaluated using chi-square tests and p-values. Results: T

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Publication Date
Sat Jan 01 2022
Journal Name
Journal Of The Asabe
Effects of Pressure and Moisture Content on Bulk Density of Triticale Grain under Compaction
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Highlights

Compaction of triticale grain with three moisture contents (8%, 12%, and 16% wet basis) was measured at five applied pressures (0, 7, 14, 34, and 55 kPa).

Bulk density increased with increasing pressure for all moisture contents and was significantly (p < 0.0001) dependent on both moisture content and applied pressure.

A Verhulst logistic equation was found to model the changes in bulk density of triticale grain with R2 of 0.986.

The model showed similar beha

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Publication Date
Sun Sep 22 2019
Journal Name
Baghdad Science Journal
Thermal Conductivity Performance of Silica Aerogel after Exposition on Different Heating under Ambient Pressure
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         The varied thermal conductivity (insulation) of silica aerogel with heating for different pH has been investigated, it has been depended on ambient pressure drying method in the preparing silica aerogel samples, also six different pH of samples (1, 2, 3, 7, 8 and 9) were treated under five degree of heating with (50,100,150,200 and 250) ᴼC. This technique is important to test the carry-outs hydrophobic silica to temperature without high-quality material changes in the basic characteristics. The hot-wire technique is used in this work to examine the thermal conductivity, Fourier Transform Infrared Spectroscopy (FTIR) depended to characterize the bonds and their artificial by heating. Resu

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Publication Date
Sat Dec 01 2018
Journal Name
Journal Of Colloid And Interface Science
Impact of nanoparticles on the CO2-brine interfacial tension at high pressure and temperature
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Hypothesis Nanofluid flooding has been identified as a promising method for enhanced oil recovery (EOR) and improved Carbon geo-sequestration (CGS). However, it is unclear how nanoparticles (NPs) influence the CO2-brine interfacial tension (γ), which is a key parameter in pore-to reservoirs-scale fluid dynamics, and consequently project success. The effects of pressure, temperature, salinity, and NPs concentration on CO2-silica (hydrophilic or hydrophobic) nanofluid γ was thus systematically investigated to understand the influence of nanofluid flooding on CO2 geo-storage. Experiments Pendant drop method was used to measure CO2/nanofluid γ at carbon storage conditions using high pressure-high temperature optical cell. Findings CO2/nano

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Publication Date
Sun Jun 30 2013
Journal Name
Al-khwarizmi Engineering Journal
Effect of Solid Particle Properties on Heat Transfer and Pressure Drop in Packed Duct
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This work examines numerically the effects of particle size, particle thermal conductivity and inlet velocity of forced convection heat transfer in uniformly heated packed duct. Four packing material (Aluminum, Alumina, Glass and Nylon) with range of thermal conductivity (from200 W/m.K for Aluminum to 0.23 W/m.K for Nylon), four particle diameters (1, 3, 5 and 7 cm), inlet velocity ( 0.07, 0.19 and 0.32 m/s) and constant heat flux ( 1000, 2000 and 3000 W/ m 2) were investigated. Results showed that heat transfer (average Nusselt number Nuav) increased with increasing packing conductivity; inlet velocity and heat flux, but decreased with increasing particle size.Also, Aluminum average Nusselt number is about (0.85,2.

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Publication Date
Tue Dec 01 2015
Journal Name
Journal Of Engineering
Calibration and Verification of the Hydraulic Model for Blue Nile River from Roseires Dam to Khartoum City
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This research represents a practical attempt applied to calibrate and verify a hydraulic model for the Blue Nile River. The calibration procedures are performed using the observed data for a previous period and comparing them with the calibration results while verification requirements are achieved with the application of the observed data for another future period and comparing them with the verification results. The study objective covered a relationship of the river terrain with the distance between the assumed points of the dam failures along the river length. The computed model values and the observed data should conform to the theoretical analysis and the overall verification performance of the model by comparing i

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Publication Date
Tue Dec 01 2015
Journal Name
Journal Of Engineering
Calibration and verification of the hydraulic model for Blue Nile river from Roseires dam to Khartoum city
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This research represents a practical attempt applied to calibrate and verify a hydraulic model for the Blue Nile River. The calibration procedures are performed using the observed data for a previous period and comparing them with the calibration results while verification requirements are achieved with the application of the observed data for another future period and comparing them with the verification results. The study objective covered a relationship of the river terrain with the distance between the assumed points of the dam failures along the river length. The computed model values and the observed data should conform to the theoretical analysis and the overall verification performance of the model by comparing it with anothe

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Publication Date
Thu Nov 17 2022
Journal Name
Journal Of Information And Optimization Sciences
Hybrid deep learning model for Arabic text classification based on mutual information
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