哈拉齐大学

The effect of different entrance walls (including graphene, graphene oxide (GO), boron nitride (BN), silicone carbide (SiC), and Ti2C MXene layer) has been examined on water and ion fluxes in desalination process using carbon nanotube by molecular dynamics simulations. Our results indicated that the kind of entrance wall has significant effect on the desalination process. The Ti2C MXene layer showed higher water and ion fluxes than the other systems at 300 and 320 K at high pressure of 250 MPa. The GO surface exhibits the least water and ions fluxes at all pressures and temperatures which is due to the strong water-wall interactions. Therefore, the GO surface is the best choice of the entrance wall if the goal is only the greatest rejection rate. It is also shown that there are not significant differences between graphene, BN, and SiC surfaces in desalination process.

Pesticide pollution in aquatic environments poses important risks to human health and ecosystems. This review presents the current state of knowledge regarding pesticide residues in surface water, groundwater, seawater, and wastewater in the Mediterranean region. It examines current analytical methods for pesticide identification, emphasizing spectroscopic and chromatographic techniques, including high-performance liquid chromatography-mass spectrometry, gas chromatography-mass spectrometry, electrochemical sensors, and Raman spectroscopy. The review also explores membrane-based technologies for pesticide removal, such as nanofiltration, reverse osmosis, and functionalized membranes. Comparisons of sample preparation techniques, detection limits, and regulatory standards are provided, highlighting the need for enhanced detection and removal strategies. Findings indicate that while gas chromatography remains the most effective detection technique, advances in membrane filtration offer promising solutions for water treatment. The study highlights the necessity of stricter regulations and improved water treatment methods to reduce pesticide contamination and protect aquatic ecosystems and public health.

Obesity exacerbates rheumatoid arthritis (RA). However, the underlying mechanisms remain incompletely defined. Elucidating these mechanisms can help the identification of novel therapeutic targets. Herein, we used high-fat diet (HFD)-induced obese collagen-induced arthritis (CIA) mice to investigate these mechanisms. Immunohistochemistry revealed that obesity exacerbated joint inflammation and cartilage degradation. Next, integrated label-free quantitative proteomics and cytometry by time-of-flight (CyTOF) were used to characterize lymphocyte subsets. Proteomic profiling identified 26 differentially expressed proteins in obese versus lean CIA mice, including the transcription factors EOMES and KLF2, the TGFβ receptor (TGFβR) signaling component TGFBR2, and the tissue-resident memory (TRM) T cell marker CD103. CyTOF analysis revealed a robust 3.0-fold increase (P = 0.0043) in the proportion of CD103⁺ TRM cells among CD3⁺ T cells in obese CIA mice, characterized by a large effect size. Immunofluorescence results confirmed this increase in synovial tissues. Treatment with asiaticoside (a TGF-β/Smad-suppressing triterpenoid) significantly reduced TRM cell proportions (P < 0.05) and ameliorated symptoms in obese CIA mice. Collectively, these findings establish a novel mechanistic axis in which obesity-induced TGFβR-hyperactivation promotes TRM cell accumulation, which exacerbates arthritis severity in this RA model. Our findings provide a preclinical rationale for targeting TGFβR/TRM in human RA with obesity as a comorbidity.

Glass fiber polymer-reinforced (GFRP) composite profiles offer advantages such as corrosion resistance and a favorable strength-to-weight ratio, but their limited ductility and poor fire resistance hinder broader structural use. This study examines the thermal and compressive behavior of carbon fiber-reinforced polymer (CFRP)-confined, geopolymer concrete-filled pultruded GFRP square tubes under elevated temperatures. A total of 90 specimens were prepared using geopolymer concrete with three different compressive strengths (average strengths of 59.8, 68.3, and 89.6 MPa), controlled by sodium hydroxide molarity: 4 M (geopolymer concrete with 4 M sodium hydroxide, denoted as GC4), 8 M (GC8), and 12 M (GC12). Specimens were externally wrapped with CFRP wraps and exposed to temperatures ranging from 25 degrees C to 350 degrees C. Results show that CFRP confinement significantly enhanced compressive capacity, particularly in lower-strength cores, with average strength gains of 87%, 63%, and 27% for GC4, GC8, and GC12 specimens, respectively. Interestingly, elevated temperatures improved strength further, with peak load increases of up to 25% at 350 degrees C. Average ductility indices decreased with increasing concrete strength, ranging from 1.42 (GC4) to 1.30 (GC12). One-way ANOVA revealed that temperature accounted for 82%, 79%, and 63% of variance in compressive capacity for GC4, GC8, and GC12 groups, respectively. These findings highlight the effectiveness of CFRP confinement and the potential of sustainable geopolymer-filled GFRP systems in fire-prone structural applications.

Accurately predicting hydro/aero dynamic coefficients of vortex-induced vibration is crucial for designing and optimizing marine and oceanic structures exposed to fluid flows. In this study, we employ a long short-term memory network to predict lift and drag forces applied to an elastically-mounted circular cylinder vibrating freely in the x and y directions. The predictions are based on the response of the transverse and streamwise displacements at selected reduced velocities within the frequency synchronization region. We conduct fluid-structure interaction simulations for low mass damping (m*ζ=0.013 ) using 2D unsteady incompressible Reynolds-averaged Navier-Stokes equations with the finite volume method. The simulations cover a Reynolds number range of 1700–13000 (reduced velocities 2–14.9). The LSTM network architecture comprises 1 input layer, 3 hidden layers, and 1 output layer, with a previous data window of 20 and 32 neurons in each layer. We use the first 80 V_r=5.75 for training the LSTM network, while the remaining 20 V_r=5, and 6.5 , which were not part of the training process. The results demonstrate the LSTM model’s success in predicting lift and drag coefficients based on the time evolution of transverse and streamwise displacement amplitudes. At V_r=5.75 , the correlation factors for the lift coefficient are calculated as R=0.996 (training) and R=0.993 (test), while for the drag coefficient, they are R=0.973 (training) and R=0.940 (test). Overall, this study highlights the potential of the LSTM network in accurately predicting hydro/aero dynamic coefficients for flow-induced vibration, offering insights into the behavior of marine and oceanic structures under fluid flow conditions.