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Among the most critical structural deficiencies observed in existing reinforced concrete (RC) buildings worldwide are inadequately detailed beam–column joint regions, often constructed without reinforcement. Despite extensive research, the numerical modeling of these critical components still remains a major challenge, as a robust and universally accepted modeling framework has yet to be established, especially when extensive nonlinear analyses have to be performed. This study specifically addresses how joint reinforcement detailing governs the transition between flexure-dominated and shear-dominated joint behavior in non-ductile exterior sub-assemblages, and evaluates whether and how a simplified macro-model can reliably reproduce these mechanisms at full scale. The seismic behavior of exterior RC beam–column joints without adequate transverse reinforcement was first investigated herein through a full-scale experimental program. Five sub-assemblages were tested under quasi-static cyclic loading with increasing displacement history. They mainly differ for beam and column longitudinal reinforcement amount and joint panel (light or null) reinforcement layout, with equal geometric and material properties. The experimental results are first investigated in terms of global response, damage evolution, and energy dissipation capacity, comparing their seismic performance with varying beam or joint reinforcement. Then, nonlinear analyses were carried out by using a computationally efficient macro-modeling strategy in the OpenSees platform to numerically reproduce the observed response. The joint panel behavior was idealized through an empirical quadrilinear rotational spring, whereas flexural and fixed-end-rotation contributions are mechanically defined. The simulations reproduced the global load–drift envelopes, stiffness deterioration, and post-peak softening branch with satisfactory accuracy, although some discrepancies can be observed in the pinching effect. Nevertheless, the comparison between experimental and full-scale numerical results confirms that the adopted model provides reliable predictions of the cyclic response of non-ductile RC joints, also resulting in suitable solutions for extensive analyses as required, for example, for large-scale studies. Full article

This study re-examines two geologically and geotechnically similar geosynthetic-reinforced soil walls with modular block facings (GRS-MBs) that exhibited markedly different seismic performances during the 1999 Chi-Chi earthquake (M_L = 7.3). Integrating a multi-wedge failure mechanism that captures soil–facing–reinforcement interactions with a nonlinear hyperbolic soil model representing shear stress–displacement behavior along the slip surface, the Force–equilibrium-based Finite Displacement Method (FFDM) provides consistent and robust displacement evaluations over a wide range of input seismic inertial forces. A systematic sensitivity investigation confirms that the FFDM framework responds to parameter variations in a physically meaningful manner, and that displacement predictions remain stable with respect to reasonable uncertainties in soil, reinforcement, and facing properties. The analysis clarifies why two similar GRS-MBs responded so differently during strong shaking and demonstrates the broader applicability of FFDM for displacement-based seismic assessment, including under shaking levels (e.g., k_h ≈ 0.3) that would drive conventional limit–equilibrium calculations to F_s < 1.0, a physically impossible state requiring shear resistance greater than the soil’s ultimate strength. A comparative evaluation of seismic displacement predictions using the Newmark method and FFDM shows that FFDM successfully generates displacement-based seismic resisting curves and reproduces field-observed displacements. In contrast, the Newmark method yields order-of-magnitude variability in predicted movements and may be unsuitable for displacement-sensitive engineered slopes where deformations on the order of several 10⁻³–10⁻² m are practically significant. For interaction-rich GRS-MBs with high values of k_hc, beyond the predictive capability of Newmark’s equation, FFDM offers a practical and physically grounded tool for seismic displacement assessment of reinforced soil structures. Full article

The development of More Electric Aircraft (MEA) necessitates that Electro-Hydrostatic Actuator (EHA) controllers achieve exceptional power density within rigorously constrained volumes. However, the compact layout design of these controllers constitutes a challenging NP-hard problem, characterized by strong multi-physics coupling—such as electromagnetic, thermal, and structural fields—and complex nonlinear constraints. Traditional meta-heuristic algorithms frequently suffer from premature convergence and struggle to balance global exploration with local exploitation. To address these challenges, the core contribution of this paper is the proposal of a novel Fractional-Order Anteater Foraging Optimization Algorithm (AFO), which is successfully applied to an established EHA controller layout optimization model. At the algorithmic level, by incorporating the Grünwald–Letnikov fractional derivative, the algorithm exploits the inherent memory property of fractional calculus to dynamically adjust the search step size and direction based on historical evolutionary information, thereby preventing stagnation in local optima. At the engineering application level, a high-fidelity mathematical model of the EHA controller is established, comprising 11 design variables and 10 critical physical constraints, including parasitic inductance minimization, thermal radiation efficiency, and electromagnetic interference (EMI) isolation. Extensive validation against the CEC2005 and CEC2022 benchmark functions demonstrates the superior convergence accuracy and stability of the AFO algorithm. In a specific EHA case study, the proposed method reduced the controller volume by 33.9% while strictly satisfying all multi-physics constraints, compared to traditional methods. Furthermore, a physical prototype was fabricated based on the optimized layout, and experimental tests confirmed its stable operation and excellent thermal performance. The results validate the efficacy of incorporating fractional calculus into bio-inspired algorithms to solve complex, high-dimensional engineering optimization problems. Full article

3D bioprinting of hydrogel–cell composites requires simultaneous consideration of the biomechanical properties of the printed structures, the construct’s geometric stability, and conditions conducive to cell survival and function. Hydrogel cross-linking techniques and their kinetics play a key role in this process, determining the time of shape fixation, the mechanical strength of the structures, and the mechanical environment in which the cells are located immediately after printing. The relationships between bioprinting parameters, material properties, cross-linking strategies, and the presence of cells are highly nonlinear and often investigated through trial and error, leading to significant time and material costs. This paper proposes an approach based on artificial intelligence-assisted simulation, focusing on computer modeling of the biomechanical and functional parameters of hydrogel–cell composites produced by 3D bioprinting. The methodology is based on data generated from computer simulations and allows for analysis of the impact of printing parameters and different cross-linking strategies on mechanical strength, time-dependent geometric stability, and limitations related to cellular function, including exposure time to non-cross-linked matrices. The use of artificial intelligence methods allows for the integration of simulation results and predictive assessment of material behavior, providing a basis for future optimization of bioprinting parameters and process costs prior to experimental validation. Full article

Particulate organic carbon (POC) and mineral-associated organic carbon (MAOC) are two operationally defined fractions frequently used in studies related to soil organic carbon (SOC) dynamics. However, the changes and governing mechanisms of these fractions, particularly along a restoration chronosequence, remain poorly understood. Here, we investigated changes in SOC fractions, soil properties, and microbial communities across a restoration chronosequence (1, 5, 7, 13, and 20 years) of alpine meadows using a space-for-time substitution approach on the Qinghai–Tibet Plateau. We quantified the contributions of biotic and abiotic drivers using Spearman correlation analysis, linear regression and random forest analysis. The results revealed a unimodal pattern in SOC, POC, and MAOC contents, peaking at 7, 5, and 7 years, respectively, with no further increase thereafter. Restoration duration strongly shaped microbial community structure and observed species richness, but had no significant effect on Shannon index and Pielou index. Random forest analysis identified soil water content (SWC) and total nitrogen (TN) as the primary predictors of SOC. The microbial community composition dominated the variation in POC while enzyme activity was the key driver of MAOC. Our findings highlight that soil carbon accumulation during alpine meadow restoration is a nonlinear process with a temporal threshold, and POC and MAOC are regulated by distinct biotic and abiotic mechanisms. This study provides a theoretical basis for understanding carbon sequestration mechanisms during alpine meadow restoration and developing sustainable grassland management strategies. Full article

A method for analyzing long-term (1997–2025) continuous records of low-frequency global seismic noise measured at a network of 229 broadband seismic stations distributed across the Earth’s surface is proposed in this study. The method is based on the use of nonlinear multifractal and entropy statistics, evaluated daily in successive time intervals, of first-principal component analysis, correlation analysis, and parametric models of point process intensity. The relationships between changes in seismic noise properties and the response of noise properties to the irregularity of the Earth’s rotation with the sequence of strong earthquakes, including those of a predictive nature, are investigated. Full article

Enhanced rock weathering (ERW) is a promising carbon dioxide removal strategy that accelerates silicate mineral dissolution to generate alkalinity and sequester carbon in soils and aquatic systems. The frequency and severity of fires are increasing globally, and fire-prone regions such as agricultural lands, forests, and grasslands overlap substantially with potential ERW deployment areas. However, fire–ERW interactions remain unexamined. This perspective synthesizes the literature on fire effects on soil properties to develop a conceptual framework for predicting fire impacts on ERW performance. An assessment of the available literature reveals that the effects of fire on soil pH and inorganic carbon are nonlinear with respect to severity, complicating both dissolution kinetics and carbon verification. Base cation pulses from ash are temporary and subject to rapid export. Fire-induced soil water repellency and erosion may dominate chemical effects in controlling ERW material fate, particularly during the first year post-fire. Pyrogenic carbon and thermally altered minerals create novel soil‒rock interactions with unknown consequences for weathering rates. The authors concluded that fire history must be incorporated as a covariate in ERW deployment planning and monitoring, reporting, and verification design. Full article

The urgent global need for sustainable infrastructure drives the demand for high-value buildings and waste removal. This paper studies the feasibility of using recycled foam concrete aggregate (FCA) as a substitute for natural coarse aggregate (NCA) in concrete and studies its impact on rheology, mechanical degradation, shear resistance, and the full-range replacement ratio (0–100). The experimental results show that the monotonic change in the workability of fresh concrete determines the lubrication threshold at 60% replacement, which is driven by the volume proportion effect. Beyond this value, capillary suction dominates, and the viscosity rises rapidly. From a mechanical perspective, the porous structure of FCA is conducive to “internal curing” so that moisture is released from the drying interface, but it also becomes a source of defects that change the fault topology. Specifically, the critical transition of the shear failure mode shifts from the debonding of the interface to the crushing of the cross-particle aggregate. At this time, the shear capacity decreases substantially, experiencing a reduction of 71.8% when completely replaced. There is a strong correlation between ultrasonic pulse velocity (UPV), rebound number, and compressive strength, and a multivariate nonlinear regression model (R² > 0.85) with non-destructive strength prediction is ultimately obtained. Based on the balance between mechanical capacity and resource cyclability, an optimal alternative zone of 20% to 40% is proposed. This work not only provides a mechanism for multi-scale coupling between pore structure and structural properties but also provides a data-driven method for the safety assessment of lightweight recycled aggregate concrete (RAC). Full article

A difficult NP-hard optimization problem, designing cryptographically robust substitution-boxes (S-boxes) necessitates a careful balancing act between several conflicting properties, such as differential uniformity and nonlinearity. Genetic Algorithms (GAs) have been widely used for this task; however, their performance is often limited by premature convergence and insufficient diversity during crossover operations. This primarily occurs because genetic algorithms commence with limited a priori knowledge. This sort of “blindness” and failure to utilize local knowledge results in diminished performance. In GA, the crossover operations facilitate the dissemination of robust candidates within the population. Conventionally, GA implements crossover for each pair of parents for diversity and a robust solution. However, this is not invariably the situation. To enhance children’s candidacy, parental diversity is quite crucial. This paper proposes a similarity-aware crossover strategy, integrated with a Siamese learning framework, to guide the genetic algorithm for improved S-box optimization with better diversity and faster convergence by utilizing parental local information. The proposed model is similarity-aware to guarantee that the GA improves parental diversity. When the parents exhibit excessive similarity, a “regressive” crossover is opted, which ensures the propagation of a parental couple with sufficient diversity to produce superior offspring. The proposed similarity-aware GA model is applied and evaluated to generate cryptographically robust and optimized S-boxes. To verify the robustness in terms of diversity, the model has been tested using three different loss functions: contrastive loss, KL divergence loss, and the suggested method of combining both loss functions to form a hybrid loss function. The effectiveness of the proposed approach is demonstrated through the generation of high-quality S-boxes with strong cryptographic properties. Full article

Polymer flooding is a well-established chemical enhanced oil recovery (EOR) method, primarily aimed at improving sweep efficiency. However, the interplay between polymer properties and porous media, particularly the influence on permeability reduction, remains poorly understood. In this study, we investigate how polymer molecular weight, chemistry, and mechanical pre-shearing influence residual resistance factor (RRF) and in situ rheology in Berea sandstone core floods. Post-polymer brine flow exhibits clear non-Darcy behavior, indicating that permeability becomes rate-dependent after polymer adsorption. Application of a Forchheimer-based approach demonstrates that inertial contributions become significant at reservoir-relevant velocities, suggesting enhanced microscopic inertia dissipation associated with interaction between flowing brine and the stationary adsorbed polymer layer. Applying conventional Darcy-based interpretation systematically overestimates RRF under these conditions. RRF increases with polymer molecular weight for polymers with similar bulk viscosities, suggesting that permeability reduction is primarily controlled by effective hydrodynamic size and pore-scale interactions rather than polymer concentration. Mechanical pre-shearing substantially reduces RRF and the non-linear flow contribution, suggesting that laboratory measurements performed on unsheared solutions may overestimate field-scale injectivity impairment. In contrast, an ATBS-containing polymer exhibits relatively low RRF but high apparent viscosity, indicating that alterations in polymer chemistry may override molecular weight as the main factor. The results demonstrate that polymer–surface interactions can induce rate-dependent permeability at reservoir-relevant velocities, and highlight the need for non-Darcy analysis when interpreting polymer core flood experiments for field application. Full article

The accumulation of highly alkaline red mud poses serious environmental risks due to land occupation and potential soil/groundwater contamination. Recycling red mud as a secondary resource offers an eco-friendly solution, yet its influence on the performance of high-performance mortar (HPM) remains incompletely understood, particularly in aggressive environments. This study aims to systematically evaluate the effects of red mud on the fresh and hardened properties of HPM, including rheological parameters, setting time, mechanical strength, drying shrinkage, and sulfate dry–wet erosion resistance. The novelty lies in (1) quantifying the nonlinear relationships between red mud content and rheological/setting behaviors, (2) revealing the dual effect of red mud with curing age, and (3) using XRD/SEM-EDS to elucidate the micro-mechanisms related to hydration products and elemental changes (Al and Fe). The results show that increasing red mud content reduces slump flow (max 76.03%), plastic viscosity (46.7%), and yield stress (42.3%) while also shortening initial/final setting times (67.91% and 76.18% max reductions). At curing ages below 7 days, flexural and compressive strength increase (up to 64.53% and 33.35%, respectively), following cubic functions; however, at 7 and 28 days, both strength values decrease (max reductions of 13.43% and 12.98%). Red mud increases drying shrinkage and delays sulfate-induced degradation. Microstructural analysis reveals improved compactness of hydration products at early ages but reduced compactness at later ages, accompanied by increased Al/Fe content and enhanced SiO₂/calcium silicate hydrate crystals. These findings provide valuable insights for applying red mud HPM in marine environments. Full article

Business development unfolds within complex adaptive environments marked by nonlinear interaction, structural asymmetry, and recurrent instability. Sustained performance under such conditions requires regulatory structures that preserve coherence while enabling structural transformation. This study advances symmetry evolution as a systems principle that explains the emergence of balance through interaction among decision bias, structural symmetry, and regulatory intensity. An evolutionary regulation framework represents this interaction as a closed-loop dynamic that drives coevolution of regulation and symmetry through recursive feedback. Stability emerges as a property of proportional coupling rather than correction of deviations. Multi-modal simulations representing turbulent decision landscapes demonstrate formation of bounded oscillatory equilibrium under perturbation while preserving exploratory capacity, with a mean recovery interval of 1.01 iterations, compared with 9.56 under fixed regulatory intensity and 47.29 under exogenous adjustment, indicating a substantial reduction in recovery time. Coordinated evolution of regulatory gain and structural symmetry sustains adaptive stability without suppressing innovation dynamics. The study establishes a systemic foundation for resilience and endogenous governance in complex business systems and reframes decision optimization as structural adaptation within evolving regulatory architectures. Full article

This study presents a comparative investigation of MgCu intermetallic compounds, CuCoMnSn Heusler alloys, and carbon steel for spur gear applications using a novel tooth contact analysis (TCA) method. The TCA employs a nonlinear two-variable equation, providing a fast and accurate computational tool for evaluating gear contact behavior. By integrating material-specific elastic properties from density functional theory (DFT) studies, the analysis predicts contact paths, stress distributions, and responses to angular misalignments. Material selection strongly influences gear performance: MgCu is promising for lightweight applications, while CuCoMnSn is better suited where mechanical performance is prioritized. The CuCoMnSn alloy also exhibits half-metallic ferromagnetic behavior, offering potential functional advantages beyond mechanical performance. These results highlight the promise of intermetallics and Heusler alloys for high-performance, misalignment-tolerant gears and demonstrate the effectiveness of combining DFT-informed material modeling with the novel TCA method for optimized spur gear design. Full article

The development of geopolymers as sustainable alternative binders has been accelerated by the environmental requirement to reduce the carbon footprint of cement. However, predicting their key properties, such as compressive strength, from their complex chemical composition remains a significant challenge. Although mixture ratios prepared on a macro-scale are widely used for quality control purposes, they do not account for the chemical structure, despite this having a direct impact on the materials’ structural properties. Predicting fundamental properties such as compressive strength from complex chemical compositions remains a significant challenge due to the nonlinear relationships between the elemental components. This research paper introduces a tailored hybrid machine learning framework that combines K-means clustering with classification algorithms. The method uses energy-dispersive X-ray spectroscopy (EDS) data to classify geopolymer samples into their specific mixture numbers, which allows scientists to predict material properties through compositional analysis. A new dataset featuring the elemental compositions of Si, Al, Na, Ca, O, and C, as well as the critical ratios of Si/Al and Ca/Si, was analyzed. The initial step involved clustering the data to discover natural compositional clusters, which served as the basis for training and testing five different classifiers, which included Random Forest (RF), Artificial Neural Networks (ANN), LightGBM, Naive Bayes (NB), and Linear Discriminant Analysis (LDA). The consequences proved that the hybrid method worked with outstanding efficiency. RF achieved the highest performance results through its 98% accuracy, 96% recall, 94% precision, and 95% F1-score results when it classified samples according to their clustered groups. SHAP (SHapley Additive exPlanations) and permutation feature importance analyses both showed that Si/Al proportion functioned as the most crucial predictive variable, while oxygen (O) content and silicon (Si) content followed in importance. The K-means cluster labels produced high accuracy results because they demonstrated that compositional data had strong natural groups, which matched the target property. The system delivers an efficient method which enables fast and dependable geopolymer property forecasts through direct analysis of chemical composition with chemical composition analysis, thus delivering essential information to enhance mix design processes and boost sustainable building material production. Full article

The mineral composition of cereals is one of the key indicators of the quality of agricultural raw materials, determining both nutritional value and technological and processing properties. Complex interactions between nutrients, especially sulfur and selenium, can significantly modify the accumulation of macroelements in plant tissues. The aim of the study was to assess the effect of different doses of sulfur (S₁—15 kg S ha⁻¹ and S₂—30 kg S ha⁻¹) and selenium (Se₁—10 g Se ha⁻¹ and Se₂—20 g Se ha⁻¹), as well as the timing of selenium application, on the phosphorus, potassium, calcium and magnesium contents in the grain and straw of spelt and common wheat. The results obtained indicate clear interspecies differences and a non-linear, often species-specific response to selenium doses. In common wheat grain, the application of selenium at two doses increased potassium and magnesium contents by 4–9% and 4–11%, respectively, and it reduced calcium content by 14–18% in spelt wheat grain. In spelt wheat straw, selenium application resulted in an 11% decrease in potassium content and an 8–10% decrease in calcium content. In common wheat, on the other hand, the straw responded with a 17% (Se₁) and 13% (Se₂) increase in magnesium content, accompanied by an 8–10% decrease in potassium content. Sulfur exhibited species-specific effects. In spelt wheat straw, it increased phosphorus content by 5–10%, calcium by 11% and magnesium by 15%. In common wheat straw, sulfur also reduced potassium accumulation by 5% and calcium by 23% (S₁) and 9% (S₂). The timing of selenium application modified the results of their content, but did not show a universal reaction pattern: earlier application increased the P content in spelt straw, while later application promoted an increase in Ca content in common wheat grain. Full article