This research introduces and optimizes a novel multi-generation power system integrating a steam Rankine cycle (SRC), a gas turbine (GT), an absorption refrigeration cycle (ARC), a proton exchange membrane (PEM) electrolyzer, and a CO2 separation unit. This system is designed to improve energy efficiency while simultaneously capturing CO2 and producing hydrogen through electrolysis. Two configurations-with and without ARC-are evaluated using a genetic algorithm-based multi-objective optimization framework, which considers exergetic efficiency, CO2 emission reduction, and total cost rate. The findings demonstrate that the proposed system improves exergetic efficiency by up to 71% and reduces CO2 emissions by up to 3.9% compared to a standalone GT system. Furthermore, the system without ARC achieves higher hydrogen production, while the system with ARC provides valuable cooling. These findings demonstrate the feasibility and environmental advantages of integrated power, CO2 capture, and H 2 blending systems for sustainable energy generation.
Accurate prediction of the Klinkenberg slippage factor (KSF) is crucial for characterizing low permeability gas reservoirs, but conventional laboratory determination methods are prohibitively costly and time-consuming, constituting a major research gap. This study addresses this limitation by presenting a novel, experimentally validated workflow that seamlessly integrates comprehensive laboratory measurements with advanced machine learning (ML) models to achieve rapid and accurate KSF prediction. A robust dataset of 253 limestone core samples was utilized, employing air permeability, cation exchange capacity (Qv), porosity, tortuosity and grain density as input features. After a systematic comparative evaluation of ten algorithms on the capacity of ML to model the complex, non-linear relationships in KSF estimation are demonstrated. The key distinction and novelty reside in the superior performance of the AdaBoost algorithm, which achieved the highest predictive accuracy (R2=0.998, AARE%=1.276). This research offers a robust, efficient, and cost-effective alternative to traditional permeability correction techniques, establishing an innovative, data-driven framework that significantly enhances predictive modeling for reservoir evaluation in tight gas formations.
Procrastination—the habitual postponement of intended tasks despite anticipating negative consequences—is increasingly understood as a neurocognitive syndrome rooted in affective, motivational, and executive dysregulation. This systematic literature review synthesizes findings from 23 neuroimaging studies involving a total of 6,087 participants, predominantly university students aged 16–26 years, to examine the structural, functional, and psychological underpinnings of academic procrastination. Using voxel-based morphometry (VBM), resting-state functional connectivity (RSFC), and task-based fMRI, these studies consistently implicate reduced gray matter volume in the dorsolateral prefrontal cortex, hippocampus, insula, and precuneus, alongside disrupted connectivity within the Cognitive Control Network and Default Mode Network. Psychological mediators such as low self-control, high reward sensitivity, rumination, and boredom proneness were found to bridge neural alterations and procrastinatory behavior. Temporal activation patterns further indicate that procrastination is not a static trait but a dynamic process influenced by real-time fluctuations in attention, motivation, and emotional engagement. Key limitations include the cross-sectional design of all included studies, reliance on self-report measures, and limited demographic diversity, primarily East Asian student samples. These findings support reconceptualizing procrastination as a multidimensional neurocognitive phenomenon and highlight the potential for targeted interventions—such as cognitive training, mindfulness, and non-invasive brain stimulation—that address its core neural mechanisms to improve academic performance, mental health, and productivity.
Achillea arabica Kotschy, known locally as “Thafera’a” in Saudi Arabia, has been widely used in traditional medicine for treating various human ailments, including diabetes and skin inflammation. In the current investigation, we sought to unravel the phytochemical profile, antioxidant, antidiabetic, and anti-inflammatory activities of A. arabica ethanolic extract (AAEE) using in vitro and in silico approaches. The extract contained substantial total phenolic and flavonoid content (TPC = 87.15 ± 1.15 mg GAE/g DE and TFC = 26.2 ± 0.15 mg QE/g DE). Furthermore, UHPLC-QTOF-MS2 analysis exhibited a broad spectrum of metabolites, chiefly phenolic acids and flavonoids. Key compounds included chlorogenic acid, isorhamnetin, kaempferide, Kaempferol-3-O-glucoside, cyanidin-3-O-glucoside, delphinidin-3-O-β-glucopyranoside, naringenin and apigenin. This rich phytochemical profile underpinned the extract’s potent bioactivities, as demonstrated by its ability to scavenge DPPH• radicals (IC50 = 135.99 ± 0.87 µg/mL) and ABTS+• radicals (IC50 = 422.02 ± 11.02 µg/mL), reduce metals (FRAP EC50 = 548.70 ± 0.06 µmol Trolox/g dry extract), inhibit α-amylase enzyme (IC50 = 233.65 ± 5.03 µg/mL), and suppression of protein denaturation (IC50 = 138.33 ± 2.23 µg/mL). Docking analysis showed strong binding of flavonoids to the target proteins with energies of −8.3 to −9.8 kcal/mol, while 200 ns molecular dynamics confirmed stable binding of the 1OSE–cosmosiin complex. ADMET predictions indicated favorable pharmacokinetic and safety profiles for naringenin and apigenin, and DFT calculations supported these findings by revealing suitable electronic properties. These results demonstrate that A. arabica is recognized as a significant source of biologically active metabolites with therapeutic potency, validating its traditional medicinal use and warranting further in vivo and clinical investigations to confirm its effectiveness.
This work reports the fabrication and comprehensive characterization of polyvinyl alcohol-chitosan/graphene oxide (PVA-CS/GO) nanocomposite films with different graphene oxide (GO) nanoparticle concentrations (0-6 wt%). The nanocomposites were prepared using a simple solution casting technique and systematically investigated to understand the influence of GO incorporation on the structural, optical, dielectric, and sensing properties of the polymer matrix. FTIR analysis confirmed strong interfacial interactions between PVA-CS chains and GO nanoparticles through hydrogen bonding, while optical microscopy revealed a homogeneous dispersion of GO within the polymer network. The optical analysis demonstrated a significant enhancement in light-matter interaction after GO incorporation. The optical band gap decreased from 5.49 to 4.60 eV for allowed indirect transitions and from 5.12 to 4.19 eV for forbidden transitions with increasing GO content, indicating the formation of localized energy states within the polymer matrix. Additionally, key optical parameters such as the refractive index, dielectric constants (epsilon ' and epsilon ''), and optical conductivity increased with GO loading, whereas transmittance decreased due to enhanced photon absorption. Nonlinear optical parameters including linear susceptibility chi (1), third-order susceptibility chi (3), and nonlinear refractive index (n2) exhibited noticeable improvement, suggesting enhanced optical polarizability of the nanocomposite films. Furthermore, dielectric analysis showed that the dielectric constant, dielectric loss, and AC electrical conductivity increase with nanoparticle concentration due to enhanced charge carrier mobility and interfacial polarization. The Urbach energy also increased, confirming the creation of additional defect states in the electronic structure. Pressure sensing measurements revealed improved mechanical flexibility, environmental stability, and high-pressure sensitivity compared with conventional polymer sensors. These results demonstrate that the incorporation of graphene oxide significantly tailors the optical and electrical properties of PVA-CS matrices, highlighting the novelty of this nanocomposite system as a promising material for flexible pressure sensors, optoelectronic devices, and nonlinear optical nanodevices.
