Microplastics (MPs), including polymers such as polyethylene, polyvinyl chloride (PVC), and polystyrene (PS), are widespread environmental contaminants detected in air, water, soil, and food. These particles originate from the breakdown of larger plastics and from direct industrial and consumer sources, including packaging, textiles, and personal care products. MPs enter the human body primarily through ingestion, inhalation, and dermal contact, with food, water, and air serving as major exposure pathways. Once internalized, MPs have been found in various human tissues and biological fluids, indicating their capacity for bioaccumulation. Toxicological studies in experimental models and occupational settings link MP exposure to oxidative stress, inflammation, cellular dysfunction, and potential organ toxicity, including effects on the gastrointestinal, respiratory, immune, reproductive, and nervous systems. PVC microplastics, in particular, are associated with liver toxicity and increased cancer risk in occupationally exposed populations. MPs can also act as vectors for environmental pollutants and plastic-associated chemicals, further amplifying health risks. This review summarizes the classification, major sources, exposure routes, and toxicological activity of MPs. A comprehensive understanding of MP properties is essential for developing effective strategies to mitigate their persistent harmful effects on public health and the environment. Copyright © 2025. Published by Elsevier Inc.
Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class
... Show MoreThe study aimed to reach the best rating for the views and variables in the totals characterized by qualities and characteristics common within each group and distinguish them from aggregates other for the purpose of distinguishing between Iraqi provinces which suffer from deprivation, for the purpose of identifying the status of those provinces in the early allowing interested parties and regulators to intervene to take appropriate corrective action in a timely manner. Style has been used cluster analysis Cluster analysis to reach the best rating to those totals from the provinces that suffer from problems, where the provinces were classified, based on the variables (Edu
... Show MoreSensing insole systems are a promising technology for various applications in healthcare and sports. They can provide valuable information about the foot pressure distribution and gait patterns of different individuals. However, designing and implementing such systems poses several challenges, such as sensor selection, calibration, data processing, and interpretation. This paper proposes a sensing insole system that uses force-sensitive resistors (FSRs) to measure the pressure exerted by the foot on different regions of the insole. This system classifies four types of foot deformities: normal, flat, over-pronation, and excessive supination. The classification stage uses the differential values of pressure points as input for a feedforwar
... Show MoreA new and hybrid deep learning-based approach for diagnosing faults in electric vehicle (EV) drive motors is proposed in this article. This article presents a new and hybrid deep learning-based method of diagnosing faults in the drive motors of electric vehicles (EV). In contrast to standard CNNLSTM approaches that depend on SoftMax classification, the introduced framework combines a Random Forest (RF) classifier to enhance the generalization, interpretability, and robustness of fault prediction. Furthermore meant for use on edge computing equipment with IoT integration, the design allows for real-time monitoring in resource-limited settings. The introduced algorithm utilizes a Random Forest (RF) classifier for accurate fault classification
... Show MoreThe research involves using phenol – formaldehyde (Novolak) resin as matrix for making composite material, while glass fiber type (E) was used as reinforcing materials. The specimen of the composite material is reinforced with (60%) ratio of glass fiber.
The impregnation method is used in test sample preparation, using molding by pressure presses.
All samples were exposure to (Co60) gamma rays of an average energy (2.5)Mev. The total doses were (208, 312 and 728) KGy.
The mechanical tests (bending, bending strength, shear force, impact strength and surface indentation) were performed on un irradiated and irrad
... Show MoreMotives for public exposure to specialized sports satellite channels and the gratifications achieved about it - Research presented by (Dr. Dr. Laila Ali Jumaa), Imam Al-Kadhim College (peace be upon him) - Department of Information-2021.
The research aims to know the extent of public exposure to specialized sports satellite channels, and what gratifications are achieved from them, and to reach scientific results that give an accurate description of exposure, motives and gratifications verified by that exposure, and the research objectives are summarized in the following:
- Revealing the habits and patterns of public exposure to specialized sports satelli
A field study aimed at identifying the sources of mutual complaints among the Directorates of Education staff of their departments and management and run it in their daily dealings with principals in the province of Baghdad, and adopt approach. It was determined the research consists of (2357) male and female employees and 305 randomly stratified simple by the rate of (7%) from the research community as the number of sample reached (167) male and female employees, and selected sample was randomly stratified simple by the rate of (39.67%) of the research community, as the number of sample was (121) principals. It was constructed two questionnaires, the first included (28) items and the second contained (28) items. And the two researchers
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