Evaporation is one of the major components of the hydrological cycle in the nature, thus its accurate estimation is so important in the planning and management of the irrigation practices and to assess water availability and requirements. The aim of this study is to investigate the ability of fuzzy inference system for estimating monthly pan evaporation form meteorological data. The study has been carried out depending on 261 monthly measurements of each of temperature (T), relative humidity (RH), and wind speed (W) which have been available in Emara meteorological station, southern Iraq. Three different fuzzy models comprising various combinations of monthly climatic variables (temperature, wind speed, and relative humidity) were developed to evaluate effect of each of these variables on estimation process. Two error statistics namely root mean squared error and coefficient of determination were used to measure the performance of the developed models. The results indicated that the model, whose input variables are T, W, and RH, perform the best for estimating evaporation values. In addition, the model which is dominated by (T) is significantly and distinctly helps to prove the predictive ability of fuzzy inference system. Furthermore, agreements of the results with the observed measurements indicate that fuzzy logic is adequate intelligent approach for modeling the dynamic of evaporation process.
The study highlights the critical need to focus on the physical, motor, and technical abilities of female fencing players by developing and testing modern, scientifically-based training methodologies tailored to the specific demands of the sport. The study's objective was to assess the effectiveness of targeted three-dimensional training in enhancing the participants' motor skills and technical performance. Utilizing an experimental design, the study involved forming both experimental and control groups. The sample comprised 16 female fencers from the College of Physical Education and Sports Sciences for Girls. After excluding two players during the exploratory phase, the remaining 14 were evenly divided into experimental and contro
... Show MoreAzo dyes like methyl orange (MO) are very toxic components due to their recalcitrant properties which makes their removal from wastewater of textile industries a significant issue. The present study aimed to study their removal by utilizing aluminum and Ni foam (NiF) as anodes besides Fe foam electrodes as cathodes in an electrocoagulation (EC) system. Primary experiments were conducted using two Al anodes, two NiF anodes, or Al-NiF anodes to predict their advantages and drawbacks. It was concluded that the Al-NiF anodes were very effective in removing MO dye without long time of treatment or Ni leaching at in the case of adopting the Al-Al or NiF-NiF anodes, respectively. The structure and surface morphology of the NiF electrode were inves
... Show MoreIn this study, several ionanofluids (INFs) were prepared in order to study their efficiency as a cooling medium at 25 °C. The two-step technique is used to prepare ionanofluid (INF) by dispersing multi-walled carbon nanotubes (MWCNTs) in two concentrations 0.5 and 1 wt% in ionic liquid (IL). Two types of ionic liquids (ILs) were used: hydrophilic represented by 1-ethyl-3-methylimidazolium tetrafluoroborate [EMIM][BF4] and hydrophobic represented by 1-hexyl-3-methylimidazolium hexafluorophosphate [HMIM][PF6]. The thermophysical properties of the prepared INFs including thermal conductivity (TC), density and viscosity were measured experimental
Software-defined networks (SDN) have a centralized control architecture that makes them a tempting target for cyber attackers. One of the major threats is distributed denial of service (DDoS) attacks. It aims to exhaust network resources to make its services unavailable to legitimate users. DDoS attack detection based on machine learning algorithms is considered one of the most used techniques in SDN security. In this paper, four machine learning techniques (Random Forest, K-nearest neighbors, Naive Bayes, and Logistic Regression) have been tested to detect DDoS attacks. Also, a mitigation technique has been used to eliminate the attack effect on SDN. RF and KNN were selected because of their high accuracy results. Three types of ne
... Show MoreBackground The appropriate disposal of medication is a well-recognized issue that has convened growing recognition in several contexts. Insufficient awareness relating to appropriate methods for the disposal of unneeded medicine may result in notable consequences. The current research was conducted among the public in Iraq with the aim of examining their knowledge, attitude, and practices regarding the proper disposal of unused and expired medicines. Methods The present study used an observational cross-sectional design that was community-based. The data were obtained from using an online questionnaire. The study sample included people of diverse genders, regardless of their race or occupational status. The study mandated that all pa
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