Estimation of trip attraction and analyzing its main influencing factors are powerful for offering different classifications for business districts and presenting recommendations for improving attractiveness in long term. This is beneficial for designing transportation facilities and infrastructures. The paper presents the prediction of trip attraction using an artificial intelligence technology due to the profits that the technology can possess in shortening time, lowering expenses and saving effort. The new model has utilized six input parameters that have not been considered previously within the area of Nasiriyah city including; age and educational level of the passengers, mode of transport that the passengers use, purpose of the trip, frequency of the weekly visit, and the distance towards the central business district. In this study, the independences - trip attraction data of 224 sets are collected through field observations and home interviews within the area. Neural Network Toolbox in MATLAB is utilized, which is dealt with the six key independences as input whereas with the trip attraction as the output desired to be expected. The model has been generated by adoption of twenty-five artificial neurons in only one single hidden layer. The outcomes have showed a good performance in predicting the trip attraction by utilizing artificial neural network. The coefficient of correlation for training is 0.81445 and for all, including training, testing, and validation, it is 0.73825. The study produces a reliable model as an alternative to complex, high-priced and/or time-consuming models.
Type 2 diabetes mellitus (T2DM) is a prevalent metabolic disorder caused by impaired insulin secretion from pancreatic β-cells and insulin resistance in target tissues. Genome-wide association studies have identified over 50 genetic variants linked to T2DM, including polymorphisms associated with the disease. This study investigates the impact of the FADS1 (rs174547) polymorphism in T2DM patients compared to healthy controls and examines serum levels of omega-3 and omega-6 fatty acids, as well as D5D and D6D enzyme levels and activity. This case–control study included 120 participants: 60 newly diagnosed T2DM patients and 60 apparently healthy controls matched for age, sex, and other sociodemographic factors. Polyunsaturated fatt
... Show MoreThis paper presents a study of the application of gas lift (GL) to improve oil production in a Middle East field. The field has been experiencing a rapid decline in production due to a drop in reservoir pressure. GL is a widely used artificial lift technique that can be used to increase oil production by reducing the hydrostatic pressure in the wellbore. The study used a full field model to simulate the effects of GL on production. The model was run under different production scenarios, including different water cut and reservoir pressure values. The results showed that GL can significantly increase oil production under all scenarios. The study also found that most wells in the field will soon be closed due to high water cuts. Howev
... Show MoreMobile-based human emotion recognition is very challenging subject, most of the approaches suggested and built in this field utilized various contexts that can be derived from the external sensors and the smartphone, but these approaches suffer from different obstacles and challenges. The proposed system integrated human speech signal and heart rate, in one system, to leverage the accuracy of the human emotion recognition. The proposed system is designed to recognize four human emotions; angry, happy, sad and normal. In this system, the smartphone is used to record user speech and send it to a server. The smartwatch, fixed on user wrist, is used to measure user heart rate while the user is speaking and send it, via Bluetooth,
... Show MoreNew Schiff base ligand (E)-6-(2-(4-(dimethylamino)benzylideneamino)-2-(4-hydroxyphenyl)acetamido)-3,3- dimethyl-7-oxo-4-thia-1- azabicyclo[3.2.0]heptane-2-carboxylic acid = (HL) was synthesized via condensation of Amoxicillin and 4(dimethylamino)benzaldehyde in methanol. Figure -1 Polydentate mixed ligand complexes were obtained from 1:1:2 molar ratio reactions with metal ions and HL, 2NA on reaction with MCl2 .nH2O salt yields complexes corresponding to the formulas [M(L)(NA)2Cl],where M=Fe(II),Co(II),Ni(II),Cu(II),and Zn(II), A=nicotinamide .
The (E)-4-chloro-N-(2-(dimethylamino)ethyl)-5-((8-hydroxy quinolin-5-yl)diazenyl)-2-methoxybenzamide azo ligand (L) has been synthesized through the reaction of diazonium salt for 5-amino-4-chloro-N-(2-(dimethylamino) ethyl)-2-methoxybenzamide with 8-hydroxyquinoline and identified azo ligand (L) using spectroscopic studies (FTIR, UV-Vis, 1H and 13CNMR, mass), and micro-elemental analysis (C.H.N). Metal chelates of Co(II), Ni(II), Cu(II), as well as Zn(II) have been completed as well as characterized using mass spectra, flame atomic absorption, elemental analysis (C.H.N), infrared, UV-Vis spectroscopy, as well as conductivity, magnetic measurements. The metal-to-ligand ratio in all complexes, as determined by analytical data, was 1:2 and ex
... Show MoreAwsaj (Lycium barbarum) is a plant belong to family Solanaceae serves as a good source of bioactive compounds like phytosterols which have many important biological activity. Literature survey available so far revealed that there was no studies about Iraqi wild Awsaj phytosterols especially B-sitosterol, there for the objective of this study was to examine the efficiency of ultrasound assisted extraction (probe and bath) as compared to the conventional (Soxhlet) extraction method for extraction of phytosterols especially B-sitosterol from fruits, leaves, stems and roots of Iraqi wild Awsaj plant. This goal was achieved by comparing the extraction mass yield, also by a quick and easy approach for identification and quantification of bioac
... Show MoreCommunity detection is one of the most fundamental applications in understanding the structure of complicated networks. Furthermore, it is an important approach to identifying closely linked clusters of nodes that may represent underlying patterns and relationships. Networking structures are highly sensitive in social networks, requiring advanced techniques to accurately identify the structure of these communities.Most conventional algorithms for detecting communities perform inadequately with complicated networks. In addition, they miss out on accurately identifying clusters. Since single-objective optimization cannot always generate accurate and comprehensive results, as multi-objective optimization can.Therefore,we utilized two objective
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