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Predicting Bitter Orange (Citrus aurantium L.) Maturity by Machine Learning Based on Picking Force in Smart Picker
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Manual fruit picking is labor-intensive and can damage fruit. Fully mechanized picking is efficient, but it also risks fruit damage. Therefore, semi-automated tools are needed to improve bitter orange picking. This paper presents a smart manual picker designed to facilitate picking while predicting fruit maturity based on picking force as well as various chemical and physical parameters using machine learning (ML). The study methodology consists of five stages: (1) manufacturing the smart picker, (2) picking 50 bitter orange samples, (3) measuring the characteristics of the bitter oranges in the laboratory, (4) training different ML models, and (5) identifying the most accurate model for predicting fruit maturity. The results indicate that as fruits mature, their weight, CIE-L*a*b* values, and pH levels increase, while picking force and hardness decrease. Notably, picking force exhibited a strong correlation (93.5%) with maturity compared to other physical parameters. The Kruskal–Wallis test also showed that the relationship between picking force and bitter orange physical parameters, including weight, CIE-L*a*b*, pH, and hardness, was statistically significant. The extreme gradient boosting (XGBoost) model achieved the highest training accuracy (100%), outperforming stacking (99.91%), random forest (91.17%), and gradient boosting machine (89.08%) on all evaluation metrics. However, the stacking model is considered better, even though XGBoost achieved 100% training accuracy, as the former showed a better balance between training, testing, and validation. This study contributes to improving bitter orange quality by accurately predicting maturity through data collected from the smart picker.

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Publication Date
Sat Apr 01 2017
Journal Name
Iraqi Journal Of Pharmaceutical Sciences ( P-issn 1683 - 3597 E-issn 2521 - 3512)
In Vitro Release Study on Capsules and Tablets Containing Enteric - Coated Granules Prepared by Wet Granulation
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Wet granulation method was used instead of conventional pan coating  or fluidized –bed coating technique to prepare enteric-coated diclofenac sodium granules, using ethanolic solution of EudragitTM L100 as coating, binding and granulating agent .Addition of PEG400 or di-n-butyl phthalate as a plasticizer was found to improve the enteric property of the coat.

Part of the resulted granules was filled in hard gelatin capsules (size 0), while the other part was compressed into tablets with and without disintegrant.

The release profile of these two dosage forms in 0.1N HCl (pH 1.2)for 2 hours, and in phosphate buffer (pH 6.8) for 45 minutes as well as the release kinetic were compared with that of the en

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Publication Date
Mon Mar 01 2021
Journal Name
Iraqi Journal Of Physics
The Toxic Effect of Pb Nanoparticles Prepared by Laser ablation on Some Biochemical Aspects in Rats
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This work aimed to prepare and study the characteristic feature of lead nanoparticles (PbNPS) and follow its effects on some physiological aspects in rats.PbNPS was prepared by laser ablation of pure lead mass with a pulse of 500 and 100 mJ of energy. The results indicated that the wavelength was approximately 196 and the concentration was reported at 53,8967 mg / L. AFM, as the average diameter has been estimated at 69.93 nm. EFSEM shows the spherical shape of the particle.The experimental animals (rats) were divided into two groups, with seven rats for each one. The first group was a control and the second group was injected with 1 milliliter of PbNPS (53.8673 mg/l) per day for 45 days. Bioaccumulated lead ( in liver, spleen kidney and

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Publication Date
Sun Jan 01 2023
Journal Name
Journal Of Molecular Liquids
Theoretical modeling study on preparation of nanosized drugs using supercritical-based processing: Determination of solubility of Chlorothiazide in supercritical carbon dioxide
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Publication Date
Fri Oct 02 2020
Journal Name
International Journal Of Pharmaceutical Research
A turbidimetric method for the quantitative determination of cyproheptadine hydrochloride in tablets using an optoelectronic detector based on the LEDs array
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Publication Date
Fri Oct 02 2020
Journal Name
International Journal Of Pharmaceutical Research
A turbidimetric method for the quantitative determination of cyproheptadine hydrochloride in tablets using an optoelectronic detector based on the LEDs array
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Publication Date
Sun Aug 30 2020
Journal Name
Periodicals Of Engineering And Natural Sciences (pen)
Kernel estimation of returns of retirement funds of employers based on monetary earnings (subscriptions and compensation) via regression discontinuity in Iraq
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Regression Discontinuity (RD) means a study that exposes a definite group to the effect of a treatment. The uniqueness of this design lies in classifying the study population into two groups based on a specific threshold limit or regression point, and this point is determined in advance according to the terms of the study and its requirements. Thus , thinking was focused on finding a solution to the issue of workers retirement and trying to propose a scenario to attract the idea of granting an end-of-service reward to fill the gap ( discontinuity point) if it had not been granted. The regression discontinuity method has been used to study and to estimate the effect of the end -service reward on the cutoff of insured workers as well as t

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Publication Date
Fri Oct 17 2025
Journal Name
Journal Of Pharmaceutical Health Services Research
Cost-effectiveness of on-demand rFVIIa vs prophylactic emicizumab in hemophilia a with inhibitors: clinical, QoL, and ICER-based field insights
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Abstract<sec> <title>Objective

To evaluate the cost-effectiveness of emicizumab compared to recombinant activated factor VII (rFVIIa) in Iraqi patients with hemophilia A and inhibitors.

Method

A retrospective cost-effectiveness analysis was conducted on 46 male patients with hemophilia A and inhibitors treated at a public children’s hospital in Baghdad. Data collection was conducted between November 2024 and March 2025. Clinical and economic data were retrospectively collected for a 12-m

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Publication Date
Mon Mar 07 2022
Journal Name
Journal Of Educational And Psychological Researches
The Impact of Cognitive Behavior Program Based on Meichenbaum Theory in Reducing the Negative Emotional Sensitivity among the Intermediate Stage Students
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The aim of this research is to construct a cognitive behavior program based on the theory of Meichenbaum in reducing the emotional sensitivity among Intermediate school students. To achieve the aims of the research, two hypotheses were formulated and the experimental design with equal groups was chosen. The population of research and its sample are determined. The test of negative emotional sensitivity, which is constructed by the researcher, was adopted. The test contains (20) items proved validity and reliability as a reliable test by presenting it to a group of arbitrators and experts in education and psychology. An educational program is constructed based on the theory of Meichenbaum. The test was applied to a sample of (60) second i

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Publication Date
Thu Feb 01 2024
Journal Name
International Journal Of Biological Macromolecules
A novel designed nanofibrous mat based on hydroxypropyl methyl cellulose incorporating mango peel extract for potential use in wound care system
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Publication Date
Mon Jan 01 2024
Journal Name
Aip Conference Proceedings
Comparative analysis of deep learning techniques for lung cancer identification
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One of the diseases on a global scale that causes the main reasons of death is lung cancer. It is considered one of the most lethal diseases in life. Early detection and diagnosis are essential for lung cancer and will provide effective therapy and achieve better outcomes for patients; in recent years, algorithms of Deep Learning have demonstrated crucial promise for their use in medical imaging analysis, especially in lung cancer identification. This paper includes a comparison between a number of different Deep Learning techniques-based models using Computed Tomograph image datasets with traditional Convolution Neural Networks and SequeezeNet models using X-ray data for the automated diagnosis of lung cancer. Although the simple details p

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