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Aqueous redox flow batteries (ARFBs) have attracted significant attention in the field of electrochemical energy storage due to their high intrinsic safety, low cost, and flexible system configuration. However, the advancement of this technology is still hindered by several critical challenges, including capacity decay, structural optimization, and the design and application of key materials as well as their performance within battery systems. Addressing these issues requires systematic theoretical foundations and scientific guidance. Numerical modeling has emerged as a powerful tool for investigating the complex physical and electrochemical processes within flow batteries across multiple spatial and temporal scales. It also enables predictive performance analysis and cost-effective optimization at both the component and system levels, thus accelerating research and development. This review provides a comprehensive overview of recent progress in the modeling of ARFBs. Taking the all-vanadium redox flow battery as a representative example, we summarize the key multiphysics phenomena involved and introduce corresponding multi-scale modeling strategies. Furthermore, specific modeling considerations are discussed for phase-change ARFBs, such as zinc-based ones involving solid–liquid phase transition, and hydrogen–bromine systems characterized by gas–liquid two-phase flow, highlighting their distinctive features compared to vanadium systems. Finally, this paper explores the major challenges and potential opportunities in the modeling of representative ARFB systems, aiming to provide theoretical guidance and technical support for the continued development and practical application of ARFB technology. Full article

Accurately predicting the compressive strength of high-performance concrete (HPC) is critical for ensuring structural integrity and promoting sustainable construction practices. However, HPC exhibits highly complex, nonlinear, and multi-factorial interactions among its constituents (such as cement, aggregates, admixtures, and curing conditions), which pose significant challenges to conventional predictive models. Traditional approaches often fail to adequately capture these intricate relationships, resulting in limited prediction accuracy and poor generalization. Moreover, the high dimensionality and noisy nature of HPC mix data increase the risk of model overfitting and convergence to local optima during optimization. To address these challenges, this study proposes a novel bio-inspired hybrid optimization model, AP-IVYPSO-BP, which is specifically designed to handle the nonlinear and complex nature of HPC strength prediction. The model integrates the ivy algorithm (IVYA) with particle swarm optimization (PSO) and incorporates an adaptive probability strategy based on fitness improvement to dynamically balance global exploration and local exploitation. This design effectively mitigates common issues such as premature convergence, slow convergence speed, and weak robustness in traditional metaheuristic algorithms when applied to complex engineering data. The AP-IVYPSO is employed to optimize the weights and biases of a backpropagation neural network (BPNN), thereby enhancing its predictive accuracy and robustness. The model was trained and validated on a dataset comprising 1,030 HPC mix samples. Experimental results show that AP-IVYPSO-BP significantly outperforms traditional BPNN, PSO-BP, GA-BP, and IVY-BP models across multiple evaluation metrics. Specifically, it achieved an R² of 0.9542, MAE of 3.0404, and RMSE of 3.7991 on the test set, demonstrating its high accuracy and reliability. These results confirm the potential of the proposed bio-inspired model in the prediction and optimization of concrete strength, offering practical value in civil engineering and materials design. Full article

 Synthetic aperture radar (SAR) systems are of paramount importance to remote sensing applications, including Earth observation and environmental monitoring. Accurate calibration of these systems is imperative to ensuring the accuracy and reliability of the acquired data. A central component of the calibration process is the antenna model, which serves as a fundamental reference for characterizing the radiation pattern, gain, and overall performance of SAR systems. The present paper sets out to describe the implementation and validation of a phased-array antenna model for Synthetic Aperture Radar Systems (SARAS) in MATLAB R2024a. The antenna model was developed for utilization in the Spanish Earth observation missions PAZ and PRECURSOR-ECO. The antenna model incorporates a number of functions, which are divided into two primary modules: the first of these is the antenna pattern generation (APG) module, and the second is the antenna excitation generation (AEG) module. The present document focuses on the AEG, the function of which is to generate patterns for all required beams. These patterns are optimized and matched to specific calculated masks using an ad hoc genetic algorithm (GA). In consideration of the aforementioned factors, the AEG module generates a set of complex excitations corresponding to the required beam from different satellite operational beams, based on several radiometrically defined parameters.  Full article

Mining (1) operations in remote areas (2) face significant challenges related to energy supply, high fuel costs, and limited infrastructure. This study investigates the potential for achieving energy independence (3) and resilience (4) in such environments through the integration of renewable energy sources (5) and battery–electric mining equipment. Using the “Studena Vrila” underground bauxite mine as a case study, a comprehensive techno-economic and environmental analysis was conducted across three development models. These models explore incremental scenarios of solar and wind energy adoption combined with electrification of mobile machinery. The methodology includes calculating levelized cost of energy (LCOE), return on investment (ROI), and greenhouse gas (GHG) reductions under each scenario. Results demonstrate that a full transition to RES and electric machinery can reduce diesel consumption by 100%, achieve annual savings of EUR 149,814, and cut GHG emissions by over 1.7 million kg CO₂-eq. While initial capital costs are high, all models yield a positive Net Present Value (NPV), confirming long-term economic viability. This research provides a replicable framework for decarbonizing mining operations in off-grid and infrastructure-limited regions. Full article

Ready-to-eat meat products face quality challenges during storage and reheating. This study aimed to (i) characterize the physicochemical/microbiological changes in stewed mutton during storage (4 °C/−18 °C, 0–28 days) and (ii) evaluate reheating methods (boiling vs. microwaving) on day-7 samples. The nutritional analysis confirmed moisture reduction (57.32 vs. 72.12 g/100 g)-concentrated protein/fat levels. Storage at −18 °C suppressed microbial growth (the total plate count (TPC), 3.73 vs. 4.80 log CFU/g at 28 days; p < 0.05) and lipid oxidation (thiobarbituric acid reactive substances (TBARS): 0.14 vs. 0.19 mg/kg) more effectively than storage at 4 °C. The total volatile basic nitrogen (TVB-N) kinetics projected a shelf life ≥90 days (4 °C) and ≥120 days (−18 °C). Microwave reheating after frozen storage (−18 °C) maximized the yield (86.21% vs. 75.90% boiling; p < 0.05) and preserved volatile profiles closest to those in the fresh samples (gas chromatography–mass spectrometry (GC-MS)/electronic nose). The combination of freezing storage and subsequent microwave reheating has been demonstrated to be an effective method for preserving the quality of a precooked lamb dish, thereby ensuring its nutritional value. Full article

The induction of type I interferon (IFN) via intracellular nucleic acid sensors may be useful in preventing viral infections. However, little is known about the effect of natural plant materials on sensor responses. We previously found that cardamom (Elettaria cardamomum (L.) Maton) seed extract (CSWE) enhanced type I IFN expression and prevented influenza virus infection. In this study, we investigated the effect of CSWE on type I IFN responses using intracellular nucleic acid sensor molecules. Human lung epithelial A549 cells were treated with CSWE and transfected with poly(dA:dT) or poly(I:C) using lipofection. CSWE and 1,8-cineole, the major CSWE components, dose-dependently induced type I IFNs and IFN-stimulated genes in both poly(dA:dT)- and poly(I:C)-transfected A549 cells. The type I IFN-enhancing effect of CSWE was dependent on the stimulator of interferon genes (STING), whereas the effect of 1,8-cineole was independent of STING and mediated by the down-regulation of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD)-inducible poly-ADP-ribose polymerase expression. Our study suggests that CSWE has the potential to act as a beneficial antiviral agent by enhancing homeostatic type I IFN production. Full article

Urban forests are vital to cities because they provide a range of ecosystem services, including carbon (C) sequestration, air purification, and urban cooling. However, urban forestry also generates significant amounts of organic waste, such as grass clippings, pruned tree branches, and fallen tree leaves and woody debris that can contribute to greenhouse gas (GHG) emissions if not properly managed. In this study, we investigated the effect of wheat straw biochar (produced at 500 °C) on GHG emissions from two types of urban forestry waste: green waste (GW) and yard waste (YW), using a 100-day laboratory incubation experiment. Overall, GW released more CO₂ than YW, but biochar addition reduced cumulative CO₂ emissions by 9.8% in GW and by 17.6% in YW. However, biochar increased CH₄ emissions from GW and reduced the CH₄ sink strength of YW. Biochar also had contrasting effects on N₂O emissions, increasing them by 94.3% in GW but decreasing them by 61.4% in YW. Consequently, the highest global warming potential was observed in biochar-amended GW (125.3 g CO₂-eq kg⁻¹). Our findings emphasize that the effect of biochar on GHG emissions varies with waste type and suggest that selecting appropriate biochar types is critical for mitigating GHG emissions from urban forestry waste. Full article

Chromium–aluminum nitride (CrAlN) coatings were deposited on polished H13 tool steel substrates using direct current (DC) magnetron sputtering. The Cr/Al composition in the target was varied by inserting either four or eight chromium (Cr) plugs into cavities machined into an aluminum (Al) plate target. Nitrogen was introduced as a reactive gas to facilitate the formation of the nitride phase. Coatings were deposited at substrate bias voltages of −30 V, −50 V, and −60 V to study the combined effects of composition and ion energy on coating properties. Compositional analysis of coatings deposited at a −50 V bias revealed Cr/Al ratios of approximately 0.8 and 1.7 for the 4- and 8-plug configurations, respectively. This increase in the Cr/Al ratio led to a 2.6-fold improvement in coating hardness. Coatings produced using the eight-Cr-plug target exhibited a nearly linear increase in hardness with increasing substrate bias voltage. Cross-sectional scanning electron microscopy revealed a uniform bilayer structure consisting of an approximately 0.5 µm metal interlayer beneath a 2–3 µm CrAlN coating. Surface morphology analysis indicated the presence of coarse microdroplets in coatings with the lower Cr/Al ratio. These microdroplets were significantly suppressed in coatings with higher Cr/Al content, especially at increased bias voltages. This suppression is likely due to enhanced ion bombardment associated with the increased Cr content, attributed to Cr’s relatively higher atomic mass compared to Al. Coatings with lower hardness exhibited greater scratch resistance, likely due to the influence of residual compressive stresses. The findings highlight the critical role of both Cr/Al content and substrate bias in tailoring the tribo-mechanical performance of PVD CrAlN coatings for wear-resistant applications. Full article

Petrological knowledge on weathering processes controlling the mobility of chemical elements is still limited in the dry tropical zone of Cameroon. This study aims to investigate the mobility of major and trace elements during rhyolite weathering and soil formation in Mobono by understanding the mineralogical and elemental vertical variation. The studied soil was classified as Cambisols containing mainly quartz, K-feldspar, plagioclase, smectite, kaolinite, illite, calcite, lepidocrocite, goethite, sepiolite, and interstratified clay minerals. pH values ranging between 6.11 and 8.77 indicated that hydrolysis, superimposed on oxidation and carbonation, is the main process responsible for the formation of secondary minerals, leading to the formation of iron oxides and calcite. The bedrock was mainly constituted of SiO₂, Al₂O₃, Na₂O, Fe₂O₃, Ba, Zr, Sr, Y, Ga, and Rb. Ce and Eu anomalies, and chondrite-normalized La/Yb ratios were 0.98, 0.67, and 2.86, respectively. SiO₂, Al₂O₃, Fe₂O₃, Na₂O, and K₂O were major elements in soil horizons. Trace elements revealed high levels of Ba (385 to 1320 mg kg⁻¹), Zr (158 to 429 mg kg⁻¹), Zn (61 to 151 mg kg⁻¹), Sr (62 to 243 mg kg⁻¹), Y (55 to 81 mg kg⁻¹), Rb (1102 to 58 mg kg⁻¹), and Ga (17.70 to 35 mg kg⁻¹). LREEs were more abundant than HREEs, with LREE/HREE ratio ranging between 2.60 and 6.24. Ce and Eu anomalies ranged from 1.08 to 1.21 and 0.58 to 1.24 respectively. The rhyolite-normalized La/Yb ratios varied between 0.56 and 0.96. Mass balance revealed the depletion of Si, Ca, Na, Mn, Sr, Ta, W, U, La, Ce, Pr, Nd, Sm, Gd and Lu, and the accumulation of Al, Fe, K, Mg, P, Sc, V, Co, Ni, Cu, Zn, Ga, Ge, Rb, Y, Zr, Nb, Cs, Ba, Hf, Pb, Th, Eu, Tb, Dy, Ho, Er, Tm and Yb during weathering along the soil profile. Full article

Injecting industrial high-temperature flue gas into hydrocarbon reservoirs has emerged as a novel approach for carbon sequestration. However, the complex high-temperature phase behavior between flue gas (CO₂, N₂) and reservoir fluids challenges this technology’s development, as traditional experimental methods and theoretical models often fall short in capturing it accurately. To address this, molecular dynamics simulations were employed in this study to investigate the phase behavior of single-component alkanes, multicomponent alkane mixtures, and multicomponent alkane–flue gas systems under high-temperature conditions. The results reveal that CO₂ can become miscible with alkanes, while N₂ diffuses into the system, causing volumetric expansion and a reduction in density. The initially distinct phase interface between the multicomponent alkanes and the flue gas becomes progressively blurred and eventually disappears, indicating the formation of a fully miscible phase. Comparative simulations revealed that the diffusion coefficients of N₂ and CO₂ increased by up to 20% with rising temperature and pressure, while variations in flue gas composition had negligible effects, indicating that high-temperature and high-pressure conditions significantly enhance flue gas–alkane miscibility. Full article

Essential oils (EOs) constitute intricate mixtures of volatile phytochemicals that have garnered significant attention due to their multifaceted biological effects. Notably, the presence of bioactive constituents capable of inhibiting tyrosinase enzyme activity and scavenging reactive oxygen species (ROS) underpins their potential utility in skin-related applications, particularly through the modulation of melanin biosynthesis and protection of skin-relevant cells from oxidative damage—a primary contributor to hyperpigmentation disorders. Zingiber officinale Rosc. (ginger) and Humulus lupulus L. (hop) are medicinal plants widely recognized for their diverse pharmacological properties. To the best of our knowledge, this study provides the first report on the synergistic interactions between essential oils derived from these species (referred to as EOZ and EOH) offering novel insights into their combined bioactivity. The purpose of this study was to evaluate essential oils extracted from ginger rhizomes and hop strobiles with respect to the following: (1) chemical composition, determined by gas chromatography–mass spectrometry (GC-MS); (2) tyrosinase inhibitory activity; (3) capacity to inhibit linoleic acid peroxidation; (4) ABTS^•+ radical scavenging potential. Furthermore, the study utilizes both the combination index (CI) and dose reduction index (DRI) as quantitative parameters to evaluate the nature of interactions and the dose-sparing efficacy of essential oil (EO) combinations. GC–MS analysis identified EOZ as a zingiberene-rich chemotype, containing abundant sesquiterpene hydrocarbons such as α-zingiberene, β-bisabolene, and α-curcumene, while EOH exhibited a caryophyllene diol/cubenol-type profile, dominated by oxygenated sesquiterpenes including β-caryophyllene-9,10-diol and 1-epi-cubenol. In vitro tests demonstrated that both oils, individually and in combination, showed notable anti-tyrosinase, radical scavenging, and lipid peroxidation inhibitory effects. These results support their multifunctional bioactivity profiles with possible relevance to skin care formulations, warranting further investigation. Full article

To remain competitive in the decarbonization process of the economy worldwide, energy companies must preserve their market value to attract new investors and remain resilient throughout the transition to net zero. This article examines the market value of US energy companies during the period 2012–2024 in relation to their power generation mix. Panel regression analyses reveal that Tobin’s q and price-to-book ratios increase significantly for solar and wind power, while they experience moderate increases for natural gas power. In contrast, Tobin’s q and price-to-book ratios decline for nuclear and coal power. Furthermore, accounting-based profitability, measured by the return on assets (ROA), does not show significant variation with any type of power generation. The findings suggest that market investors prefer solar, wind, and natural gas power generation, thereby attributing greater value (that is, demanding lower risk compensation) to green companies compared to traditional ones. These insights provide guidance to executives, investors, and policy makers on how the power generation mix can influence strategic decisions in the energy sector. Full article

In high energy density physics, the demand for precise detection of nanosecond-level fast physical processes is high. Ga:ZnO (GZO), GaN, and other fast scintillators are widely used in pulsed signal detection. However, many of them, especially wide-bandgap materials, still face issues of low luminous intensity and significant self-absorption. Therefore, an enhanced method was proposed to tune the wavelength of materials via coating perovskite quantum dot (QD) films. Three-layer samples based on GZO were primarily investigated and characterized. Radioluminescence (RL) spectra from each face of the samples, as well as their decay times, were obtained. Lower temperatures further enhanced the luminous intensity of the samples. Its overall luminous intensity increased by 2.7 times at 60 K compared to room temperature. The changes in the RL processes caused by perovskite QD and low temperatures were discussed using the light tuning and transporting model. In addition, an experiment under a pico-second electron beam was conducted to verify their pulse response and decay time. Accordingly, the samples were successfully applied in beam state monitoring of nanosecond pulsed proton beams, which indicates that GZO wafer coating with perovskite QD films has broad application prospects in pulsed radiation detection. Full article

Understanding how energy use and economic activity shape carbon emissions is pivotal for achieving global climate targets. This study quantifies the dynamic nexus between disaggregated energy consumption, economic growth, and CO₂ emissions in India and China—two economies that together account for more than one-third of global emissions. Using annual data from 1990 to 2021, we implement Long Short-Term Memory (LSTM) neural networks, which outperform traditional linear models in capturing nonlinearities and lagged effects. The dataset is split into training (1990–2013) and testing (2014–2021) intervals to ensure rigorous out-of-sample validation. Results reveal stark national differences. For India, coal, natural gas consumption, and economic growth are the strongest positive drivers of emissions, whereas renewable energy exerts a significant mitigating effect, and nuclear energy is negligible. In China, emissions are dominated by coal and petroleum use and by economic growth, while renewable and nuclear sources show weak, inconsistent impacts. We recommend retrofitting India’s coal- and gas-plants with carbon capture and storage, doubling clean-tech subsidies, and tripling annual solar-plus-storage auctions to displace fossil baseload. For China, priorities include ultra-supercritical upgrades with carbon capture, utilisation, and storage, green-bond-financed solar–wind buildouts, grid-scale storage deployments, and hydrogen-electric freight corridors. These data-driven pathways simultaneously cut flagship emitters, decouple GDP from carbon, provide replicable models for global net-zero research, and advance climate-resilient economic growth worldwide. Full article

Underground gas storage (UGS) in heterogeneous carbonate reservoirs is crucial for energy security but frequently faces wellbore instability challenges, which traditional static methods struggle to address due to dynamic full life cycle changes. This study systematically analyzes the dynamic evolution of wellbore stress in the Bs8 well (Qianmiqiao carbonate UGS) during drilling, acidizing, and injection-production operations, establishing a quantitative risk assessment model based on the Mohr–Coulomb criterion. Results indicate a significantly higher wellbore instability risk during drilling and initial gas injection stages, primarily manifested as shear failure, with greater severity observed in deeper well sections (e.g., 4277 m) due to higher in situ stresses. During acidizing, while the wellbore acid column pressure can reduce principal stress differences, the process also significantly weakens rock strength (e.g., by approximately 30%), inherently increasing the risk of wellbore instability, though the primary collapse mode remains shallow shear breakout. In the injection-production phase, increasing formation pressure is identified as the dominant factor, shifting the collapse mode from initial shallow shear failure to predominant wide shear collapse, notably at 90°/270° from the maximum horizontal stress direction, thereby significantly expanding the unstable zone. This dynamic assessment method provides crucial theoretical support for full life cycle integrity management and optimizing safe operation strategies for carbonate gas storage wells. Full article