This study investigates the impact of physical activity on the hemodynamic environment of intracranial aneurysms by comparing two patient-specific aneurysms located in the internal carotid artery (ICA) and middle cerebral artery (MCA), with closely matched geometrical and clinical characteristics. Using computational fluid dynamics (CFD) simulations based on the Casson non-Newtonian blood model and laminar flow assumptions, hemodynamic parameters were evaluated under two physiological states: normal body condition at rest and during exercise. The simulation domain was reconstructed from Aneurisk dataset cases, and boundary conditions were defined using time-dependent inlet mass flow rate and outlet pressure profiles for each condition. Results indicate that physical activity leads to significant alterations in local flow dynamics, including increased mass flow rate, intra-aneurysmal velocity, pressure, wall shear stress (WSS), helicity and oscillatory shear index (OSI). Notably, the maximum average wall shear stress (AWSS) in the ICA aneurysm increased by over 66% during exercise (from 30,147Pa at rest to 50,085Pa), while the average OSI in the same case more than doubled (from 0.0127 to 0.0277), indicating elevated mechanical stress and oscillatory flow behavior at the aneurysm wall. The MCA case exhibited higher OSI and vorticity near the aneurysmal neck, while the ICA case showed more stable flow structures. Similar trends were observed in the MCA aneurysm, although with slightly lower magnitudes, emphasizing the influence of aneurysm geometry and location on hemodynamic response. The study supports the potential value of personalized flow-based analysis in clinical decision-making related to activity guidelines for patients with unruptured cerebral aneurysms.
The depletion of natural resources has created an urgent need to identify alternative, sustainable materials for construction. Simultaneously, the rapid global accumulation and improper disposal of electronic waste (E-waste), particularly in developing countries, have raised significant environmental and public health concerns. This study investigates the use of electronic plastic waste (E-PW) as a partial replacement for fine aggregate in concrete, with replacement levels of 5
Youth migration has intensified in conflict-affected societies, where structural constraints undermine young people’s ability to achieve social and economic stability. This study examines the obstacles to youth empowerment under the conditions imposed by the Syrian crisis and analyzes their effect on external migration among youth in Al-Hasakah Governorate. Using a descriptive–analytical approach, data were collected in 2023 through a questionnaire administered to a convenience sample of 403 male and female youths. The instrument measured three dimensions of empowerment: national belonging, socio-economic empowerment, and self-empowerment. The findings indicate that the overall effect of obstacles to youth empowerment on migration was high (M = 3.56; relative weight = 71.27
Reservoir porosity determines how much fluid formation can store, making its accurate estimation a key element of petrophysical evaluation, reservoir characterization and geological studies. Core analysis provides reliable readings, but covers limited intervals and time-consuming, expensive. Log-based equations derived from well-logging interpretation introduce uncertainties that affect prediction reliability. Four ensemble machine learning models were used here to enhance porosity prediction, Decision Trees (DT), Random Forests (RF), Gradient Boosting (GB) and Extreme Gradient Boosting (XGB). Input dataset used 734 data point from seven wells in a Libyan field while a separate well with 202 data point kept as a blind well to evaluate model generalization. Bulk density (RHOB), gamma ray (GR), compressional travel time (DT), and neutron porosity (CNL) chosen as input features because each shows established relationship with porosity. Statistical metrics including correlation coefficients (R2) and root-mean squared error (RMSE) were used to assess the model. The results indicate that ensemble models provide a robust and efficient alternative to conventional porosity estimation methods, offering improved predictive accuracy and reliability. This study highlights the potential of machine learning in reservoir characterization, contributing to more data-driven decision-making in petroleum engineering applications. This scientific manuscript was presented at the sessions of the International Renewable Energy, Gas, Oil and Climate Change Conference "iREGO" in the period of April 25-27, 2026. Tripoli - Libya Keywords: Porosity Prediction, Ensemble models, Machine Learning, Well Logging Data.
This study reviews and lists the scorpion fauna of Syria according to current scorpion systematics. Critical evaluation of published records confirms 19 valid species and 2 subspecies across 11 genera and 3 families (Buthidae, Diplocentridae, and Scorpionidae); erroneous records (21 species) have been excluded. New locality records are reported for Aegaeobuthus nigrocinctus (Ehrenberg, 1828), Buthacus tadmorensis (Simon, 1892), Compsobuthus matthiesseni (Birula, 1905), and Scorpio kruglovi Birula, 1910. Additionally, Aegaeobuthus bishri (Lourenço, 2020) is herein treated as a subspecies of A. nigrocinctus.
