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More than 1.3 billion tons of foodstuffs are wasted each year because of their storage, handling, transportation or sale, resulting in the world suffering from famine and food shortages. This means that huge amounts of the resources utilized in food production are wasted. Greenhouse gas emissions caused by food degradation that gets wasted results in serious environmental and human health impacts. Food losses could be caused by microbes, temperature, humidity, UV-lights, or other factors related to the current pandemics, that have serious jeopardy impacts on the food security and the environment. Thus, smart packaging-based 2D-nanomaterials (2DMs) including sensors and pH-responsive tags have developed for achieving the quality and prolong the shelf-life of foodstuffs. Because they are capable to detect, sense, record internal or external changes in the product's area. They also can give a prompt message or color changing to the producer or consumer to decide about the shelf lives and expiration dates for consuming the foodstuffs without deteriorating food and packaging. This study reviews the recent advancements in 2DMs regarding preparation, characterization, and applications including active/intelligent food packaging industries. Facile and green functionalization of 2DMs-based graphene oxide (GO) by folic acid or natural dyes enabling them be used in several sensor/artificial intelligence disciplines are discussed. Future insights on the challenges towards the potential outbreak prevention in foodstuffs and their control in long-term use and risk management of these materials are also discussed. [ABSTRACT FROM AUTHOR] more...

The most frequent type of cancer among women is breast cancer. Artificial intelligence (AI) researchers are developing automated systems to assist in the detection and classification of breast cancer. This study explores machine learning (ML) and deep learning (DL) as two AI methods for identifying benign and malignant breast tumors in ultrasound images. The investigation assesses the performance of various computer-aided detection and diagnosis (CAD) systems, which utilize either handcrafted features or deep features extracted from DL models. Furthermore, three models for CAD deep learning-based systems were implemented using convolutional neural networks (CNN), convolutional autoencoders (CAE), and deep features with CNN models, and compared with three traditional ML models based on handcrafted (texture) features. The results indicate that the deep features of the CNN model are promising, achieving a mean accuracy of 95% with a standard deviation of 1.1%. [ABSTRACT FROM AUTHOR] more...

In the current project, the National Institute of Standards (NIS), Egypt, initiated a preliminary investigation to create a certified reference material for certain elements, including Fe, Mg, Mn, Al, Cu, and Zn, in human hair. This reference material would be a quality control sample for trace element determination. This paper studied homogeneity and short-term stability as critical parameters for producing certified reference materials. ICP-OES and AAS were used to measure human hair powder samples, and the data was statistically evaluated for normality and outliers, which resulted in the measurement results being normal after removing the outliers. Also, sample homogeneity was evaluated and assessed using analysis of variance (ANOVA), which revealed that the human hair samples produced were homogenous and stable during transportation. This CRM mainly intends to develop methods and check instrumental performance, analytical trueness, and accuracy related to trace element analysis in human hair and similar matrices. [ABSTRACT FROM AUTHOR] more...

Copyright of Iraqi Journal of Desert Studies is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) more...

Brain tumors have recently become a widespread phenomenon that affects almost all age ranges. Magnetic resonance imaging (MRI) is a widely used technique nowadays to diagnose this life‐threatening disease. Therefore, using a computer‐aided diagnosis system is essential for the early identification and classification of brain tumors. This work aims to develop a fully automated classification framework for MRI brain tumor images using machine learning (ML) algorithms. In this article, four ML algorithms such as support vector machines (Cubic SVM), k‐nearest neighbors (fine KNN), decision tree (fine tree), and naive Bayes (Kernel) were implemented with two feature extraction approaches: histogram of oriented gradients (HOG) and a bag of features (BOF) approach. The novelty of this work is based on the original usage of BOF and HOG approaches over different types of ML algorithms for MRI brain tumor image classification. In addition, combining two public datasets that contain three brain tumor types to generalize and improve the proposed work. The results showed that the fine KNN algorithm achieved the greatest success among all algorithms using the HOG method with an accuracy of 98.62% on 6328 MRI images. [ABSTRACT FROM AUTHOR] more...

Copyright of Iraqi Journal of Desert Studies is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) more...

Copyright of Iraqi Journal of Desert Studies is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) more...