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Artificial intelligence‐based modeling of novel non‐thermal milk pasteurization to achieve desirable color and predict quality parameters during storage
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Abstract<sec><label></label><p>This study proposed using color components as artificial intelligence (AI) input to predict milk moisture and fat contents. In this sense, an adaptive neuro‐fuzzy inference system (ANFIS) was applied to milk processed by moderate electrical field‐based non‐thermal (NP) and conventional pasteurization (CP). The differences between predicted and experimental data were not significant (<italic>p</italic> > 0.05) for lightness (<italic>L</italic>*), redness‐greenness (<italic>a</italic>*), yellowness‐blueness (<italic>b</italic>*), total color differences (∆<italic>E</italic>), hue angle (<italic>h</italic>), chroma (<italic>C</italic>), whiteness (WI), yellowness (YI), and browning index (BI). ANFIS well‐predicted milk fat and moisture content using quadratic and two‐factor interaction models with mean errors of .00858–.01260 and correlation coefficient of .8051–.8205. Stability tests showed <italic>L</italic>* and WI reduced while <italic>a</italic>*, <italic>b</italic>*, Δ<italic>E</italic>, <italic>h</italic>, <italic>C</italic>, YI, and BI increased during the storage. NP milk had 77.21% higher half‐life than CP, as predicted by ANFIS modeling. Findings indicated milk quality characteristics could be estimated based on physical parameters (e.g., color components), contributing to sustainable food production.</p></sec><sec><title>Practical applications

The findings offer practical applications of artificial intelligence (AI) as an innovative monitoring and prediction technique to enhance food quality and sustainability. The proposed methodology makes the real‐time prediction of milk quality feasible by leveraging AI and physical parameters. An adaptive neuro‐fuzzy inference system (ANFIS) accurately predicts moisture and fat contents according to color values, facilitating quality assessment. Stability tests during cold storage provide insights into milk quality changes over time, aiding in determining key parameters in predictive modeling. The proposed approach was found to be applicable to both conventional and non‐thermal pasteurized milk. This study also provides a step‐by‐step protocol, facilitating the implementation of emerging technologies in the food industry.

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Publication Date
Thu Dec 15 2022
Journal Name
Al-academy
color methodology to Re-reading the musical notes
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many painters tried to mix colors with Music by direct employment through colorful musical pieces or the use of multiple instruments and techniques , or vice versa, including the French artist )Robert Stroben(, he transferred the piece of music to be depicted on the painting and worked on the tones of music (Johann Sebastian Bach) by dropping the color on the lines of the musical scale, for example (the C tone) ranging from brown to red ( Tone La A) from gray to orange, and so on, the presence of links and similarity factors between the world of music and the world of colors facilitated the process of linking musical notes with colors, the most famous of which was presented by the world (Newton) in the circle of basic colors and linking

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Publication Date
Thu Mar 31 2022
Journal Name
Iraqi Geological Journal
Development of Artificial Intelligence Models for Estimating Rate of Penetration in East Baghdad Field, Middle Iraq
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It is well known that the rate of penetration is a key function for drilling engineers since it is directly related to the final well cost, thus reducing the non-productive time is a target of interest for all oil companies by optimizing the drilling processes or drilling parameters. These drilling parameters include mechanical (RPM, WOB, flow rate, SPP, torque and hook load) and travel transit time. The big challenge prediction is the complex interconnection between the drilling parameters so artificial intelligence techniques have been conducted in this study to predict ROP using operational drilling parameters and formation characteristics. In the current study, three AI techniques have been used which are neural network, fuzzy i

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Publication Date
Wed Jan 01 2020
Journal Name
Energy Conversion And Management
Improved PCM melting in a thermal energy storage system of double-pipe helical-coil tube
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Publication Date
Sat Dec 02 2017
Journal Name
Al-khwarizmi Engineering Journal
Mathematical Modeling for the Clarifier Units and Turbidity Parameters in AL-KARAMA Treatment Plant
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The high cost of chemical analysis of water has necessitated various researches into finding alternative method of determining portable water quality. This paper is aimed at modelling the turbidity value as a water quality parameter. Mathematical models for turbidity removal were developed based on the relationships between water turbidity and other water criteria. Results showed that the turbidity of water is the cumulative effect of the individual parameters/factors affecting the system. A model equation for the evaluation and prediction of a clarifier’s performance was developed:

Model: T = T0(-1.36729 + 0.037101∙10λpH + 0.048928t + 0.00741387∙alk)

The developed model will aid the predictiv

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Publication Date
Mon Oct 14 2019
Journal Name
Applied Sciences
Synthesis of Novel Heteroatom-Doped Porous-Organic Polymers as Environmentally Efficient Media for Carbon Dioxide Storage
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The high carbon dioxide emission levels due to the increased consumption of fossil fuels has led to various environmental problems. Efficient strategies for the capture and storage of greenhouse gases, such as carbon dioxide are crucial in reducing their concentrations in the environment. Considering this, herein, three novel heteroatom-doped porous-organic polymers (POPs) containing phosphate units were synthesized in high yields from the coupling reactions of phosphate esters and 1,4-diaminobenzene (three mole equivalents) in boiling ethanol using a simple, efficient, and general procedure. The structures and physicochemical properties of the synthesized POPs were established using various techniques. Field emission scanning elect

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Publication Date
Sat May 01 2021
Journal Name
Proceedings Of The Thermal And Fluids Engineering Summer Conference
HEAT TRANSFER ENHANCEMENT IN PCM THERMAL ENERGY STORAGE VIA THE TRIPLEX TUBE HEAT EXCHANGER
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Publication Date
Thu Jun 01 2017
Journal Name
International Journal Of Heat And Mass Transfer
Melting enhancement in triplex-tube latent thermal energy storage system using nanoparticles-fins combination
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Publication Date
Sun Jan 01 2012
Journal Name
The Iraqi Journal Of Agricultural Sciences
A FORMULA TO PREDICT INBREEDING DEPRESSION IN F2 POPULATION OF MAIZE
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A field experiment was conducted on the form of the Dept. of Field Crop Sci. / College of Agriculture / University of Baghdad in spring and fall seasons of 2009 and 2010 . Ten inbreds of maize were planted and crossed to each other to produce single crosses . In the second season, single crosses were planted along with thin parent to produce three – way and double crosses . In the third seasons panet and crosses were planted . Crosses were selfed to produce F2 seeds and increase seeds of inbreds . In the fourth season, all grin types were planted , and their agronomic traits were evaluated . Values of P of inbreds , F1 and F2 were calculated for agronomic traits . The new formula to predict inbreeding depression ( ID ) F2 plant without gr

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Publication Date
Tue Mar 02 2021
Journal Name
Sustainability
Effect of Twisted Fin Array in a Triple-Tube Latent Heat Storage System during the Charging Mode
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This study aims to assess the effect of adding twisted fins in a triple-tube heat exchanger used for latent heat storage compared with using straight fins and no fins. In the proposed heat exchanger, phase change material (PCM) is placed between the middle annulus while hot water is passed in the inner tube and outer annulus in a counter-current direction, as a superior method to melt the PCM and store the thermal energy. The behavior of the system was assessed regarding the liquid fraction and temperature distributions as well as charging time and energy storage rate. The results indicate the advantages of adding twisted fins compared with those of using straight fins. The effect of several twisted fins was also studied to discover

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Publication Date
Sun Mar 01 2009
Journal Name
Al-khwarizmi Engineering Journal
A Proposed Artificial Intelligence Algorithm for Assessing of Risk Priority for Medical Equipment in Iraqi Hospital
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This paper presents a robust algorithm for the assessment of risk priority for medical equipment based on the calculation of static and dynamic risk factors and Kohnen Self Organization Maps (SOM). Four risk parameters have been calculated for 345 medical devices in two general hospitals in Baghdad. Static risk factor components (equipment function and physical risk) and dynamics risk components (maintenance requirements and risk points) have been calculated. These risk components are used as an input to the unsupervised Kohonen self organization maps. The accuracy of the network was found to be equal to 98% for the proposed system. We conclude that the proposed model gives fast and accurate assessment for risk priority and it works as p

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