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.
Natural dolomite offers an inexpensive mineral matrix; however, its relatively low density limits its effectiveness in gamma-ray shielding applications. In the present work, Bi₀.₅Sr₀.₅FeO₃ − δ perovskite nanoparticles (nominally δ = 0.25, corresponding to Bi₀.₅Sr₀.₅FeO₂.₇₅) were synthesized via a citrate-assisted sol–gel route and incorporated into a dolomite matrix at loadings of 0–45 wt% to investigate their influence on gamma-ray attenuation performance. The prepared materials were characterized using XRD, FTIR, SEM, EDX, density, and porosity measurements, followed by experimental and theoretical evaluation of their gamma-ray attenuation behavior. XRD analysis of the structurally examined compositions confirmed the coexistence of the crystalline Bi₀.₅Sr₀.₅FeO₂.₇₅ perovskite and dolomite phases. SEM observations revealed nearly spherical Bi₀.₅Sr₀.₅FeO₂.₇₅ nanoparticles with an average particle size of approximately 26.5 nm, while EDX confirmed the presence of the constituent elements within the investigated material. The incorporation of Bi₀.₅Sr₀.₅FeO₂.₇₅ progressively increased the bulk density from 2.93 ± 0.44 to 4.02 ± 0.91 g cm−3, while the apparent porosity decreased from 0.86% to 0.41%. These changes reflect the increasing contribution of the higher-density Bi-containing perovskite phase and the reduced measured interconnected open-pore volume. The sample containing 45 wt% Bi₀.₅Sr₀.₅FeO₂.₇₅ exhibited the highest gamma-ray attenuation among the investigated compositions, achieving an experimental linear attenuation coefficient of 4.153 cm−1 and a Phy-X/PSD-calculated mass attenuation coefficient of 1.033 cm2 g−1 at 0.122 MeV, together with the lowest half-value layer (0.167 cm), tenth-value layer, and mean free path. The calculated effective atomic number increased to approximately 56, consistent with the increased contribution of the high-Z Bi-containing phase to photon attenuation. The experimental attenuation results showed good agreement with the corresponding Phy-X/PSD calculations. Overall, the results demonstrate that incorporating Bi₀.₅Sr₀.₅FeO₂.₇₅ into dolomite improves gamma-ray attenuation under the investigated conditions, supporting further evaluation of these materials for potential radiation-shielding applications.
SARS-CoV-2 main protease (Mpro) remains a clinically validated antiviral target; however, computational hit nomination must distinguish binding-site stability from experimentally confirmed inhibition. This study evaluates a focused positional series of four furan-substituted chromone derivatives (M1-M4) as computationally prioritized Mpro-binding candidates using the high-resolution Mpro co-structure PDB 9C8Q and its co-crystallized ligand A1AU4 as the primary structural reference. The workflow integrates SwissADME and ProTox-III profiling, validated AutoDock Vina docking, 100 ns molecular dynamics simulations of M1-M4 and A1AU4 complexes, endpoint MM/GBSA calculations over 100 equilibrated snapshots from 60-100 ns, per-residue energy decomposition and PCA-based free-energy landscape analysis. All chromone derivatives satisfied Lipinski and Veber criteria and showed low predicted acute toxicity classes. Docking indicated comparable scores for M2 and M3 (-7.0 kcal/mol), but M3 displayed a more specific active-site interaction pattern through conventional hydrogen bonds with His163 and Glu166, pi-sulfur contact with Cys145 and pi-alkyl stabilization near Met165. Comparative dynamics showed stable Mpro backbones in all systems, whereas the co-crystallized ligand remained the most stable ligand reference and M2 displayed the largest ligand RMSD drift. Among the chromone derivatives, M1, M3 and M4 maintained bound-state ensembles suitable for comparative MM/GBSA analysis, with favorable binding supported mainly by van der Waals interactions and moderated by polar desolvation. PCA/FEL analysis further suggested compact bound ensembles without implying experimental antiviral activity. Overall, M3 is proposed as a computationally prioritized chromone scaffold because it combines acceptable drug-likeness, low predicted acute toxicity and specific anchoring within the Mpro catalytic pocket. Biochemical protease assays, antiviral cell assays and toxicity validation are required before any inhibitory or therapeutic claim can be made.
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.
