Heavy Metals Removal Using Natural Jordanian Volcanic Tuff

The removal performance and the selectivity sequence of separate metal ions (Fe2+, Cr3+, Cu2+, Zn2+ and Ni2+) in aqueous solution were studied by adsorption process on untreated and natural volcanic tuff. A series of experiments were conducted in batch-wise and fixed-bed columns to investigate the removal efficiency of natural Jordanian volcanic tuff as low cost and an effective adsorbent for heavy metal ions and to examine its economical application in water purification and treatment practices.Water and wastewater samples containing metal ions with concentrations ranging from 1 to 15 mg/L were used. The plexi glas columns were filled with natural occurring volcanic tuff particles ranging between (0.350 – 3.000) mm. Photometric methods were used for laboratory analysis of samples.The experiments were carried out under changing conditions as a function of different pH-values (2,4,6 and 7), initial solute concentrations (1, 5, 10, 15) mg/L, and different room temperatures (20, 25 and 30 Cº ), and varying tuff particle sizes (0.35 -3.0) mm. The breakthrough curves were derived by plotting the normalized effluent metal concentrations (C/C0) versus bed volume.Obtained results showed that natural Jordanian volcanic tuff has an adsorption capacity of 0.417 mg/g for Fe 2+ and 0.151mg/g for C 2+. Factors in the reaction medium such as pH and ionic strength influenced the adsorption process. The quantity of particular ionic species (Cu2+, Pb2+, Cr2+ ,Fe2+, Zn2+) bound in dependence on the initial concentrations, indicates that the removal efficiency from the liquid phase follows the sequence Fe2+>Cu2+>Pb2+> Cr2+>Zn2+ when keeping the pH at 4 and follows the sequence Cu2+>Zi2+>Fe2+>Cr2+>Pb2+ when keeping the pH at 6. Equilibrium modeling of the removal showed that the adsorption of the metal cations Cr2+, Pb2+, Zn2+ , Cu2+ and Fe2+ were fitted to one of the adsorption isotherms

The Effects of Platforms and Languages on the Memory Footprint of the Executable Program: A Memory Forensic Approach

Identifying the software used in a cybercrime can play a key role in establishing the evidence against the perpetrator in the court of law. This can be achieved by various means, one of which is to utilize the RAM contents. RAM comprises vital information about the current state of a system, including its running processes. Accordingly, the memory footprint of a process can be used as evidence about its United States of Americage. However, this evidence can be influenced by several factors. This paper evaluates three of these factors. First, it evaluates how the used programming language affects the evidence. Second, it evaluates how the used platform affects the evidence. Finally, it evaluates how the search for this evidence is influenced by the implicitly used encoding scheme. Our results should assist the investigator in its quest to identify the best amount of evidences about the used software based on its execution logic, host platform, language used, and the encoding of its string values. Results show that the amount of digital evidence is highly affected by these factors. For instance, the memory footprint of a Java based software is often more traceable than the footprints of languages such as C++ and C#. Moreover, the memory footprint of a C# program is more visible on Linux than it is on Windows or Mac OS. Hence, often software related values are successfully identified in RAM memory dumps even after the program is stopped

Climate Resilience and Environmental Sustainability: How to Integrate Dynamic Dimensions of Water Security Modeling

Considering hydro-climatic diversity, integrating dynamic dimensions of water security modeling is vital for ensuring environmental sustainability and its associated full range of climate resilience. Improving climate resiliency depends on the attributing uncertainty mechanism. In this study, a conceptual resilience model is presented with the consideration of input uncertainty. The impact of input uncertainty is analyzed through a multi-model hydrological framework. A multi-model hydrological framework is attributed to a possible scenario to help apply it in a decision-making process. This study attributes water security modeling with the considerations of sustainability and climate resilience using a high-speed computer and Internet system. Then, a subsequent key point of this investigation is accounting for water security modeling to ensure food security and model development scenarios. In this context, a four-dimensional dynamic space that maps sources, resource availability, infrastructure, and vibrant economic options is essential in ensuring a climate-resilient sustainable domain. This information can be disseminated to farmers using a central decision support system to ensure sustainable food production with the application of a digital system

Multi-Techniques for Analyzing X-ray Images for Early Detection and Differentiation of Pneumonia and Tuberculosis Based on Hybrid Features

An infectious disease called tuberculosis (TB) exhibits pneumonia-like symptoms and traits. One of the most important methods for identifying and diagnosing pneumonia and tuberculosis is X-ray imaging. However, early discrimination is difficult for radiologists and doctors because of the similarities between pneumonia and tuberculosis. As a result, patients do not receive the proper care, which in turn does not prevent the disease from spreading. The goal of this study is to extract hybrid features using a variety of techniques in order to achieve promising results in differentiating between pneumonia and tuberculosis. In this study, several approaches for early identification and distinguishing tuberculosis from pneumonia were suggested. The first proposed system for differentiating between pneumonia and tuberculosis uses hybrid techniques, VGG16 + support vector machine (SVM) and ResNet18 + SVM. The second proposed system for distinguishing between pneumonia and tuberculosis uses an artificial neural network (ANN) based on integrating features of VGG16 and ResNet18, before and after reducing the high dimensions using the principal component analysis (PCA) method. The third proposed system for distinguishing between pneumonia and tuberculosis uses ANN based on integrating features of VGG16 and ResNet18 separately with handcrafted features extracted by local binary pattern (LBP), discrete wavelet transform (DWT) and gray level co-occurrence matrix (GLCM) algorithms. All the proposed systems have achieved superior results in the early differentiation between pneumonia and tuberculosis. An ANN based on the features of VGG16 with LBP, DWT and GLCM (LDG) reached an accuracy of 99.6%, sensitivity of 99.17%, specificity of 99.42%, precision of 99.63%, and an AUC of 99.58%

Analysis of Climate Change Impacts on the Food System Security of Saudi Arabia

Climate change poses a challenge to the security and long-term viability of the global food supply chain. Climate unpredictability and extreme weather events have significant impacts on Saudi Arabia’s vulnerable food system, which is already under stress. The Kingdom of Saudi Arabia faces distinct challenges in comparison to other dry locations across the world. Here, the per capita water demand is high, the population is growing, the water resources are extremely limited, and there is little information on the existing groundwater supplies. Consequently, it is anticipated that there will be formidable obstacles in the future. In order to make data-driven decisions, policymakers should be aware of causal links. The complex concerns pertaining to the Saudi Arabian food system were analyzed and rationally explained in the current study. A causality analysis examined different driving factors, including temperature, greenhouse gas (GHG) emission, population, and gross domestic product (GDP) that cause vulnerabilities in the country’s food system. The results of the long-run causality test show that GDP has a positive causal relationship with the demand for food, which implies that the demand for food will increase in the long run with an increase in GDP. The result also shows that Saudi Arabia’s GDP and population growth are contributing to the increase in their total GHG emissions. Although the Kingdom has made some efforts to combat climate change, there are still plenty of opportunities for it to implement some of the greatest strategies to guarantee the nation’s food security. This study also highlights the development of appropriate policy approaches to diversify its import sources to ensure future food security