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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
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
Tue Dec 31 2019
Journal Name
Journal Of Economics And Administrative Sciences
A Try to combine more than approaches and competitive strategies to achieve a sustainable competitive advantage.
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The objective of this research paper is two-fold. The first is a precise reading of the theoretical underpinnings of each of the strategic approaches: "Market approach" for (M. Porter), and the alternative resource-based approach (R B V), advocates for the idea that the two approaches are complementary. Secondly, we will discuss the possibility of combining the two competitive strategies: cost leadership and differentiation. Finally, we propose a consensual approach that we call "dual domination". 

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Publication Date
Sun Jan 01 2017
Journal Name
Proceeding Of Second Thermal And Fluids Engineering Conference
Solidification Enhancement in Triplex-Tube Latent Thermal Energy Storage System Using a Combination of Nanoparticles and Fins
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Publication Date
Sun Jul 09 2023
Journal Name
Journal Of Engineering
Parametric Study of Active Solar Heating Using a Pebble Bed as a Thermal Collector and Storage Unit
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In this study, pebble bed as an absorber and storage material was placed in a south facing, flat plate air-type solar collector at fixed tilt angle of (45°). The effect of this material and differ- ent parameters on collector efficiency has been investigated experimentally and
theoretically. Two operation modes were employed to study the performance of the solar air heater. An inte- grated mode of continuous operation of the system during the period of (11:00 am – 3:00 pm) and non-integrated mode in which the system stored the solar energy through the day then used the stored energy during the period of (3:00 pm – 8:00 pm). The results of parametric study in case of continuous operating showed that the maximum average temperatur

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Publication Date
Mon Jul 01 2019
Journal Name
International Journal Of Heat And Mass Transfer
Hybrid heat transfer enhancement for latent-heat thermal energy storage systems: A review
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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
Sun Mar 04 2012
Journal Name
Baghdad Science Journal
Levels of Elements in Milk and Yoghurt
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This study aimed to know the concentration of elements content in dairy products. Nine samples collected from the local market in Baghdad, two samples of milk and seven of yoghurt.The results were followed: The ratio of Aluminum In Aluminum cans That is used for storage milk and industrialize yoghurt is about 95.5% ,this ratio is against [1] which said that pureness of Aluminum used in Aluminum cans should not be less than 98-99% . and the ratio of Cadmium reached to 0.001% which is also against [1] which assure that the Aluminum cans should be clean from that element, mean while the concentration of Iron, Copper and Zinc were within the standard level. The highest concentration of Iron was in treatment B2(canon yoghurt)it was 9.95 m

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Publication Date
Sat Dec 28 2024
Journal Name
Dental Hypotheses
Assessment of Color Change of Artificial White Spot Lesions after Sandblasting with Bioactive Glass, Resin Infiltration, or Microabrasion Followed by Color Stability Test by pH Cycling: An in Vitro Study
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Introduction: This study aimed to assess the color change of human teeth with artificial enamel white spot lesions (WSLs) after sandblasting with bioactive glass, resin infiltration, and microabrasion and to test color stability after pH cycling. Methods: Fifty extracted human mandibular first molars were randomly assigned into five groups: Sound, WSLs (untreated), and WSLs sandblasted with bioactive glass (Sylc), WSLs treated by resin infiltration (ICON), and WSLs treated by microabrasion (Opalustre), respectively. All specimens underwent a pH cycling procedure. The color parameters for each specimen were assessed using an Easyshade dental spectrophotometer at different time stages then the color changes (ΔE) were calculated. Results: The

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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
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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