In the context of the Internet of Things (IoT), lightweight block ciphers are of vital importance. Due to the nature of the devices involved, traditional security solutions can add overhead and perhaps inhibit the application's objective due to resource limits. Lightweight cryptography is a novel suite of ciphers that aims to provide hardware-constrained devices with a high level of security while maintaining a low physical cost and high performance. In this paper, we are going to evaluate the performance of some of the recently proposed lightweight block ciphers (GIFT-COFB, Romulus, and

Digital images have emerged as the most popular means for sharing information in articles, newspapers, and even courtrooms. However, the widespread use of advanced digital imaging tools has made it easier to forge images. One such technique is image splicing, where multiple source images are merged into a single destination image to conceal or alter its content. Image splicing is an effective forgery technique, as it is difficult to detect by the naked eye. Detection of image splicing is a pattern recognition problem, based on finding image features that are sensitive to splicing. In this

As a case study for our research, COVID-19, that was caused by a unique coronavirus, has substantially affected the globe, not only in terms of healthcare, but also in terms of economics, education, transportation, and politics. Predicting the pandemic's course is critical to combating and tracking its spread. The objective of our study is to evaluate, optimize and fine-Tune state of the art prediction models in order to enhance its performance and to automate its function as possible. Therefore, a comparison between statistical versus compartmental methods for time series-based modeling and

Plain concrete is used for water canal lining due to its low permeability to reduce water losses due to seepage. However, cement manufacture has a negative environmental impact as it produces large amount of CO2 emissions in addition to high energy consumption. In this study, bio-sludge of sewage plants was used an alternative for cement, mixed with sand and crushed stone, and used as an alternative to plan concrete for canal lining. An experimental testing program was designed based on percentages of sludge and soil equal to 2.5%, 5%, and 10% by weight. For each sludge mix, properties were

Concrete industry is challenged by sustainability and technical concerns. Sustainability includes minimization of raw material usage, energy consumption, and emission of greenhouse gases, while technical concerns comprise the enhancement of mechanical properties and durability such as compressive strength, resistance to chloride, acids, and elevated temperatures. Therefore, recycling of industrial waste in manufacturing of green concrete has become a robust viable alternative to disposal, due to the limited natural resources and raw materials which contribute to sustainable construction

As noted by several researchers, the Grand Ethiopian Renaissance Dam (GERD) on the Blue Nile River is expected to have unfavorable consequences for downstream countries like Egypt and Sudan. To limit GERD's negative effects on downstream countries, its operation should be secure, and its upstream water level should be ideal. However, none of the studies carried out the ideal operating scenarios from the perspective of controlling the number of gate openings. Accordingly, this study evaluates the optimal operating scenarios of the GERD and its impact on downstream countries by adopting a

Integrating artificial intelligence and green concrete in the construction industry is a challenge that can help to move towards sustainable construction. Therefore, this research aims to predict the compressive strength of green concrete that includes a ratio of cement kiln dust (CKD) and fly ash (FA), then recommend the optimum sustainable mixture design. The artificial neural network (ANN) and multiple linear regression techniques are used to build prediction models and statistics using MATLAB and IBM SPSS software. The input parameters are based on 156 data points of concrete components

The slaughterhouse industry generates substantial wastewater rich in proteins, lipids, fibers, and carbohydrates. This study integrates experimental investigations into artificial neural network (ANN) optimization and commerce design studies for treating slaughterhouse and meat processing wastewater (SMW). Batch coagulation/flocculation experiments identified optimal conditions for three coagulants: Ferric Chloride (FeCl3·6H2O), Poly Aluminum Chloride (PAC), and Aluminum Sulfate Al2(SO4)3, aiming for optimum removal of chemical oxygen demand (COD), total suspended solids (TSS), and total

The existence of the Grand Ethiopian Renaissance Dam (GERD) has a significant impact on Egypt's water share and future water usage. The filling time of the reservoir will have a great impact on the Nile River of the downstream users, especially Egypt. Many of the GERD impacts on Egypt have been previously studied, but no study has addressed the effect of the GERD existence on the hydropower water footprint of the Aswan High dam. This research is studying and simulating the effect of the different GERD reservoir filling scenarios on the water footprint of the hydropower generated from the High

Providing an accurate spatiotemporal distribution of rainfall and filling data gaps are pivotal for effective water resource management. This study focuses on the Asir region in the southwest of Saudi Arabia. Given the limited accuracy of satellite data in this arid/mountain-dominated study area, geospatial interpolation has emerged as a viable alternative approach for filling terrestrial records data gaps. Furthermore, the irregularity in rain gauge data and the yearly spatial variation in data gaps hinder the creation of a coherent distribution pattern. To address this, the Centered Root