Shale gas is a form of natural gas found trapped within shale formations. Shale gas is vastly distributed around the world. Recently, there has been increasing interest in exploiting shale gas for energy production. As a source of energy, many countries started to investigate their shale gas potential. To understand the public’s views on the extraction of shale gas and its long and short-term health and environmental consequences, this study was undertaken to recognize Ghadames region residents’ awareness of the environmental and health effects resulting from the extraction of shale gas in their areas. Ghadames region is located in northwest Libya, where a potential deposit of shale gas is spotted; therefore, it was perfect as a study area. A population survey from a random sample (N=251) drawn from local residents; it was designed to detect the public opinion of the most likely adverse direct and indirect health and environmental outcomes as a result of fracking; the possible benefits were also included as well. The results show that the majority of respondents support shale gas extraction in their hometown, no matter how the consequences will affect their health and environment, about 70% of the respondents show support for shale gas exploitation

Shale gas and shale oil have rapidly revolutionized the energy supply and security landscape in various parts of the world. As Libya has a notable deposit of shale gas, this study sought to gain insight into the public perception of potential health and environmental impacts of a hydraulic fracturing and its associated controversies. It also sought to determine whether there is a need for public awareness of the science and economics associated with hydraulic fracturing. In this study, 304 local residents in Zawia city in the north west of Libya were surveyed on their views on the exploration of shale gas. The purpose of the survey is to understand the factors that influence local residents‖ support or opposition to the use of shale gas, such as energy independence, environmental consciousness, and risk/benefit perceptions. The results show that the respondents are generally supportive of shale gas extraction, the study show that 52% of the respondents express support or strong support for shale gas exploitation. Both this and future studies have the potential to improve public discourse and decision-making around the development of unconventional gas and oil industries.

A new robust blind image watermarking method based on lifting wavelet transform (LWT), and Schur decomposition in combination with discrete cosine transform (DCT), and entropy analysis for copyright protection of multimedia information is introduced in this paper. At first, the 2-level LWT is applied to the original image to improve the invisibility of the watermarking method and then the high-frequency sub-band of 2-level LWT is decomposed by DCT. Then, the DCT coefficients are divided into 4×4 non-overlapping blocks. After that, Schur decomposition applies to each selected block, while the first row, the first column element of the upper triangular matrix is used to embed the watermark. For evaluation, the invisibility and robustness of the proposed watermarking method, the peak signal to noise ratio (PSNR), and normalized cross-correlation (NC) are used to measure the quality and the ability of the proposed watermarking method to be robust against signal processing operations and geometric attacks. Experimental results have demonstrated that the proposed method is achieved a very good tradeoff between invisibility and robustness. The comparisons with other scheme have shown that the proposed digital watermarking scheme has a superior performance in terms of invisibility than other. 

Abstract:

In this research study, we investigate the effectiveness of a proposed automated framework that leverages AI algorithms to improve the accuracy, scalability, and efficiency of data analysis. We compare the performance of the proposed framework to traditional data analysis methods in terms of accuracy, time and effort required for data analysis, and scalability. We conducted a questionnaire-based survey to collect data on the application of the proposed framework and traditional data analysis methods in real-world scenarios. We used statistical analyses, including t-tests and regression analyses, to analyze the data and draw conclusions. Our findings indicate that the proposed automated framework outperforms traditional data analysis methods in terms of accuracy, reduces the time and effort required for data analysis, and has better scalability for processing large datasets in real-time. Our results suggest that the proposed framework offers significant benefits for organizations seeking to improve their data analysis processes. However, we also note the importance of considering the costs and benefits of implementing the framework, as well as any ethical or legal implications. Further research is recommended to confirm the findings and to investigate the effectiveness of the proposed framework in different contexts and with different datasets.