The main challenge is to protect the environment from future deterioration due to pollution and the lack of natural resources. Therefore, one of the most important things to pay attention to and get rid of its negative impact is solid waste. Solid waste is a double-edged sword according to the way it is dealt with, as neglecting it causes a serious environmental risk from water, air and soil pollution, while dealing with it in the right way makes it an important resource in preserving the environment. Accordingly, the proper management of solid waste and its reuse or recycling is the most important factor. Therefore, attention has been drawn to the use of solid waste in different ways, and the most common way is to use it as an alternative material for raw materials in engineering construction. Countless types of solid waste can be used in different ways and quantities according to the purpose for which they are intended, for example the use of rubber, construction waste, ash and many others. This study discusses the problem of solid waste and methods of its management, in addition to its use with building materials in order to improve their properties and reduce costs.
Signature verification involves vague situations in which a signature could resemble many reference samples or might differ because of handwriting variances. By presenting the features and similarity score of signatures from the matching algorithm as fuzzy sets and capturing the degrees of membership, non-membership, and indeterminacy, a neutrosophic engine can significantly contribute to signature verification by addressing the inherent uncertainties and ambiguities present in signatures. But type-1 neutrosophic logic gives these membership functions fixed values, which could not adequately capture the various degrees of uncertainty in the characteristics of signatures. Type-1 neutrosophic representation is also unable to adjust to various
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Ecological risk assessment of mercury contaminant has a means to analyze the ecological risk aspect of ecosystem using the potential impact of mercury pollution in soil, water and organism. The ecological risk assessment in a coastal area can be shown by mangrove zonation, clustering and interpolation of mercury accumulation. This research aims to analyze ecological risk assessment of potential mercury (including bioaccumulation and translocation) using indicators of species distribution, clustering, zonation and interpolation of mercury accumulation. The results showed that the Segara Anakan had a high risk of mercury pollution, using indicators like as the potential of mercury contaminant in water body was 0137±0.0137 ppm, substrate a
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This paper aims at the analytical level to know the security topics that were used with data journalism, and the expression methods used in the statements of the Security Media Cell, as well as to identify the means of clarification used in data journalism. About the Security Media Cell, and the methods preferred by the public in presenting press releases, especially determining the strength of the respondents' attitude towards the data issued by the Security Media Cell. On the Security Media Cell, while the field study included the distribution of a questionnaire to the public of Baghdad Governorate. The study reached several results, the most important of which is the interest of the security media cell in presenting its data in differ
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Vaccine hesitancy poses a significant risk to global recovery from COVID-19. To date however, there is little research exploring the psychological factors associated with vaccine acceptability and hesitancy in Iraq.
To explore attitudes towards COVID-19 vaccination in Iraq. To establish the predictors of vaccine uptake and vaccine hesitancy in an Iraqi population.
Using a cross-sectional design, 7,778 participants completed an online questionnaire exploring their vaccination status, likelihood of infection, perc
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A hand gesture recognition system provides a robust and innovative solution to nonverbal communication through human–computer interaction. Deep learning models have excellent potential for usage in recognition applications. To overcome related issues, most previous studies have proposed new model architectures or have fine-tuned pre-trained models. Furthermore, these studies relied on one standard dataset for both training and testing. Thus, the accuracy of these studies is reasonable. Unlike these works, the current study investigates two deep learning models with intermediate layers to recognize static hand gesture images. Both models were tested on different datasets, adjusted to suit the dataset, and then trained under different m
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