Mapping of population growth influence on land use transformation from 1994 to 2015 in Madaba Governorate, Jordan.

The Madaba Governorate, as the second-largest wheat producer in Jordan, holds a crucial position in safeguarding regional food security. Its evolving landscape, marked by changes in land use, presents environmental and socio-economic challenges that necessitate sustainable urban planning and land management practices. This study delves into the intricate relationship between the conversion of agricultural lands into urban areas and the concurrent rise in population within the Madaba Governorate. Utilizing a Markov model, this research employs land use and land cover (LULC) data from 1994, 2004, and 2015 to project future changes in 2025 and 2035 with an impressive 80% accuracy (kappa coefficient). The findings reveal a projected 6% increase in urban areas over the next decade and a notable 11.81% decline in rural lands, signifying a substantial urbanization trend. In response to these population-driven LULC dynamics, there is an urgent need for the implementation of sustainable land use planning and management solutions. Given the constraints of limited water resources in the region, this study also places emphasis on water resource management. Recommendations include measures such as restricting urban sprawl, preserving agricultural lands, managing population growth, and implementing water conservation strategies. These insights provide invaluable information for stakeholders in the Madaba Governorate, including policymakers and land use planners, fostering a comprehensive understanding of the complex interplay between regional water resources, population expansion, and land use changes.

The relationship between fatigue severity and mild cognitive impairment in Saudi patients with type 2 diabetes mellitus.

Type 2 Diabetes Mellitus (T2DM) is a major health issue in Saudi Arabia, with a prevalence of 23.7% in 2015. Several factors contribute to the occurrence of Mild Cognitive Impairment (MCI) and its progression to Alzheimer's disease in patients with T2DM. This study assesses MCI and fatigue severity and their relationship in patients with T2DM. Out of the 160 Saudi adults interviewed at the King Khalid University Hospital in Riyadh from October 2019 till March 2020, 80 were known cases of T2DM while the rest were non-diabetic individuals. The Montreal Cognitive Assessment (MoCA) test, Mini Mental State Exam (MMSE) and Fatigue Severity Score (FSS) were used to evaluate MCI and fatigue severity, respectively. According to the MoCA scale, 68.7% diabetic individuals as against 42.5% from the non-diabetic group had MCI. While the FSS showed that 40% of the diabetic group vs 26.3% of the non-diabetic were fatigued. In conclusion, patients with T2DM are at a higher risk of developing MCI.

Development of Prediction Model to Predict the Compressive Strength of Eco-Friendly Concrete Using Multivariate Polynomial Regression Combined with Stepwise Method.

Concrete is the most widely used building material, but it is also a recognized pollutant, causing significant issues for sustainability in terms of resource depletion, energy use, and greenhouse gas emissions. As a result, efforts should be concentrated on reducing concrete's environmental consequences in order to increase its long-term viability. In order to design environmentally friendly concrete mixtures, this research intended to create a prediction model for the compressive strength of those mixtures. The concrete mixtures that were used in this study to build our proposed prediction model are concrete mixtures that contain both recycled aggregate concrete (RAC) and ground granulated blast-furnace slag (GGBFS). A white-box machine learning model known as multivariate polynomial regression (MPR) was developed to predict the compressive strength of eco-friendly concrete. The model was compared with the other two machine learning models, where one is also a white-box machine learning model, namely linear regression (LR), and the other is the black-box machine learning model, which is a support vector machine (SVM). The newly suggested model shows robust estimation capabilities and outperforms the other two models in terms of R2 (coefficient of determination) and RMSE (root mean absolute error) measurements.

Effects of pollution on hydrogeochemistry and water quality of the Damietta branch (Nile River, Egypt).

Currently, different sources of contamination threaten water quality in Damietta, Egypt. The aim of this study was to assess the pollution, hydrogeochemistry and suitability for use in agriculture of water collected atseven stations along the Damietta branch of the Nile River. In addition to pH, temperature, dissolved oxygen (DO), and electrical conductivity (EC), levels of Mg2+, Ca2+, K+, Na+, Cl-, HCO3-, SO42- and CO32- were measured. From the highest to the lowest, the concentrations of cations were Na+ > Ca2+ > Mg2+ > K+, whereas those of anions were HCO3- > SO42- > Cl- in water samples collected at seven stations along the Damietta branch. Moreover, water quality indexes, such as sodium percentage (Na%) ranged between 21.6 and 71.4, Kelly's ratio from 0.37 to 3.85, sodium adsorption ratio (SAR) varied from 1.73 to 61.8, residual sodium carbonate (RSC) found to be in range of 0.2 to 3.8. Also, the potential salinity (PS) and permeability index (PI), magnesium hazard (MH), were calculated. Most samples (71%) belonged to the Mg-Ca-Na-HCO3 watertype. Fertilization wastes were identified as the main contributors of alkaline earth metals, whereas ion exchange was the main source of alkali metals. This work also provides information on distinguishing geochemical behaviors and the pollution of water samples.