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Perceptually Important Points-Based Data Aggregation Method for Wireless Sensor Networks

The transmitting and receiving of data consume the most resources in Wireless Sensor Networks (WSNs). The energy supplied by the battery is the most important resource impacting WSN's lifespan in the sensor node. Therefore, because sensor nodes run from their limited battery, energy-saving is necessary. Data aggregation can be defined as a procedure applied for the elimination of redundant transmissions, and it provides fused information to the base stations, which in turn improves the energy effectiveness and increases the lifespan of energy-constrained WSNs. In this paper, a Perceptually Important Points Based Data Aggregation (PIP-DA) method for Wireless Sensor Networks is suggested to reduce redundant data before sending them to the sink. By utilizing Intel Berkeley Research Lab (IBRL) dataset, the efficiency of the proposed method was measured. The experimental findings illustrate the benefits of the proposed method as it reduces the overhead on the sensor node level up to 1.25% in remaining data and reduces the energy consumption up to 93% compared to prefix frequency filtering (PFF) and ATP protocols.

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Publication Date
Mon Apr 24 2017
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Thermal Degradation Kinetics of Polyamide 6,6 Cable Ties by Thermogravimetric Analysis

  The thermal degradation of cable ties of polyamide (PA6,6) neat and UV stabilized was investigated by thermogravimetry (TG) and its derivative (DTG) at several heating rates between 5 and 80 oC min-1  in helium atmosphere. High heating rates signal novel peaks in the DTG curves that indicate melting temperature of PA6,6. The kinetic parameters calculated via isoconversion and nonisothermal data using the Flynn-Wall-Ozawa, Kissinger and CoatsRedfern methods showed comparable activation energy values. Exposure of the ties to outdoor environment causes pre-mature stress cracking and brittle failure due to prevalence of crosslinking reaction occurring in the polymer chains

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Publication Date
Fri Oct 21 2022
Journal Name
Cuadernos De Economia
The Role of Green Spaces, Technological Innovation, and Environmental Taxes on Environmental Sustainability in Iraq

Abstract: In recent times, global attention has increasingly focused on the critical issue of environmental sustainability, owing to escalating environmental degradation exacerbated by the utilization of green spaces and technological innovation. This phenomenon necessitates thorough examination, prompting the present study to scrutinize the impact of various factors, namely green spaces, technological innovation, environmental taxes, renewable energy consumption (REC), inflation, and economic growth (EG), on environmental sustainability within the context of Iraq. Secondary data extracted from the World Development Indicators (WDI) spanning the period from 1991 to 2022 served as the foundation for this investigation. Methodologically, the

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Publication Date
Sun Dec 29 2019
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Kinetics and Thermodynamics of Peppermint Oil Extraction from Peppermint Leaves

This study aimed to extraction of essential oil from peppermint leaves by using hydro distillation methods. In the peppermint oil extraction with hydro distillation method is studied the effect of the extraction temperature to the yield of peppermint oil. Besides it also studied the kinetics during the extraction process. Then, 2nd -order mechanism was adopted in the model of hydro distillation for estimation many parameters such as the initial extraction rate, capacity of extraction and the constant rat of extraction with various temperature. The same model was also used to estimate the activation energy. The results showed a spontaneous process, since the  Gibbs free energy had a value negative sign.

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Publication Date
Tue Sep 01 2015
Journal Name
Journal Of Engineering
Indoor Positioning and Monitoring System Using Smartphone and WLAN (IPMS)

 

Buildings such as malls, offices, airports and hospitals nowadays have become very complicated which increases the need for a solution that helps people to find their locations in these buildings. GPS or cell signals are commonly used for positioning in an outdoor environment and are not accurate in indoor environment. Smartphones are becoming a common presence in our daily life, also the existing infrastructure, the Wi-Fi access points, which is commonly available in most buildings, has motivated this work to build hybrid mechanism that combines the APs fingerprint together with smartphone barometer sensor readings, to accurately determine the user position inside building floor relative to well-known lan

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Publication Date
Wed May 17 2017
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Evaluation of Thermal Reactor Fission Products Cross Sections

      The production of fission products during reactor operation has a very important effect on  reactor reactivity .Results of neutron cross section evaluations are presented for the main product nuclides considered as being the most important  for reactor calculation and burn-up consideration . Data from the main international libraries considered as containing the most up-to-date nuclear data   and the latest experimental measurements are considered in the evaluation processes, we describe the evaluated cross sections of the fission product nuclides by making inter comparison of the data and point out the discrepancies among libraries.

 

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Publication Date
Thu May 18 2023
Journal Name
Journal Of Engineering
Spatial Prediction of Monthly Precipitation in Sulaimani Governorate using Artificial Neural Network Models

ANN modeling is used here to predict missing monthly precipitation data in one station of the eight weather stations network in Sulaimani Governorate. Eight models were developed, one for each station as for prediction. The accuracy of prediction obtain is excellent with correlation coefficients between the predicted and the measured values of monthly precipitation ranged from (90% to 97.2%). The eight ANN models are found after many trials for each station and those with the highest correlation coefficient were selected. All the ANN models are found to have a hyperbolic tangent and identity activation functions for the hidden and output layers respectively, with learning rate of (0.4) and momentum term of (0.9), but with different data

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Publication Date
Fri Apr 01 2022
Journal Name
Baghdad Science Journal
Tourism Companies Assessment via Social Media Using Sentiment Analysis

In recent years, social media has been increasing widely and obviously as a media for users expressing their emotions and feelings through thousands of posts and comments related to tourism companies. As a consequence, it became difficult for tourists to read all the comments to determine whether these opinions are positive or negative to assess the success of a tourism company. In this paper, a modest model is proposed to assess e-tourism companies using Iraqi dialect reviews collected from Facebook. The reviews are analyzed using text mining techniques for sentiment classification. The generated sentiment words are classified into positive, negative and neutral comments by utilizing Rough Set Theory, Naïve Bayes and K-Nearest Neighbor

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Publication Date
Thu Oct 01 2020
Journal Name
Journal Of Engineering Science And Technology (jestec)
Water Quality Assessment and Sodium Adsorption Ratio Prediction of Tigris River Using Artificial Neural Network

Sodium adsorption ratio (SAR) is considered as a measure of the water suitability for irrigation usage. This study examines the effect of the physicochemical parameters on water quality and SAR, which included Calcium(Ca+2), Magnesium(Mg+2), Sodium (Na+), Potassium (K), Chloride (Cl-), Sulfate(SO4-2), Carbonate (CO3-2), Bicarbonate (HCO3-), Nitrate (NO3-), Total Hardness (TH), Total Dissolved Salts (TDS), Electrical Conductivity (EC), degree of reaction (DR), Boron (B) and the monthly and annually flow discharge (Q). The water samples were collected from three stations across the Tigris River in Iraq, which flows through Samarra city (upstream), Baghdad city (central) and the end of Kut city (downstream) for the periods of 2016-201

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Publication Date
Tue Jun 30 2020
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Using Artificial Neural Network to Predict Rate of Penetration from Dynamic Elastic Properties in Nasiriya Oil Field

   The time spent in drilling ahead is usually a significant portion of total well cost. Drilling is an expensive operation including the cost of equipment and material used during the penetration of rock plus crew efforts in order to finish the well without serious problems. Knowing the rate of penetration should help in speculation of the cost and lead to optimize drilling outgoings. Ten wells in the Nasiriya oil field have been selected based on the availability of the data. Dynamic elastic properties of Mishrif formation in the selected wells were determined by using Interactive Petrophysics (IP V3.5) software based on the las files and log record provided. The average rate of penetration and average dynamic elastic propert

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Publication Date
Thu Dec 01 2022
Journal Name
Baghdad Science Journal
Diagnosing COVID-19 Infection in Chest X-Ray Images Using Neural Network

With its rapid spread, the coronavirus infection shocked the world and had a huge effect on billions of peoples' lives. The problem is to find a safe method to diagnose the infections with fewer casualties. It has been shown that X-Ray images are an important method for the identification, quantification, and monitoring of diseases. Deep learning algorithms can be utilized to help analyze potentially huge numbers of X-Ray examinations. This research conducted a retrospective multi-test analysis system to detect suspicious COVID-19 performance, and use of chest X-Ray features to assess the progress of the illness in each patient, resulting in a "corona score." where the results were satisfactory compared to the benchmarked techniques.  T

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