Search Results (382)

During circumferential tensioning of prestressing strands in U-shaped aqueducts, longitudinal tensile stresses may develop and impair crack resistance. Most existing studies rely on three-dimensional finite element (FE) simulations. Although accurate, FE modeling is time-consuming and unsuitable for rapid scheme evaluation during construction. To overcome this limitation, the U-shaped aqueduct was first simplified as a cylindrical shell and the feasibility of this idealization was verified. An approximate analytical solution was then derived from cylindrical shell theory to predict the longitudinal stress induced by circumferential prestressing. Practical factors, including non-uniform wall thickness, non-equidistant strand spacing, and strand positional deviations, were incorporated to improve engineering applicability. FE results confirm good agreement, with RMSE of 0.055–0.169 MPa and NRMSE of 2.3–19.6%, where the upper bound occurs only in localized regions. The method was further applied to an engineering project to optimize the tensioning scheme. With a rational interval-tensioning procedure, the peak longitudinal tensile stress was reduced by 31.6%. Overall, the proposed approach enables rapid stress estimation and supports preliminary screening and optimization of circumferential tensioning schemes. Full article

Virtual power plants (VPPs) aggregate flexible resources, such as distributed photovoltaics (PV), energy storage, and flexible loads, to provide substantial reserve capacity for grid operation. However, the combined effects of renewable energy output uncertainty, load forecast errors, and user-bounded rationality responses lead to significant errors in traditional deterministic VPP reserve assessment methods, severely affecting the balance between system supply and demand. To address this challenge, this paper proposes a credible reserve assessment method that accounts for user-bounded rationality. First, thermodynamic models with on–off constraints for air conditioning loads, energy feasible region, and power constraint models for electric vehicles (EVs) and energy storage systems (ESSs), as well as PV forecast error models are established to characterize physical reserve boundaries. Second, prospect theory is introduced to describe user-bounded rationality and a logit-based response probability model is developed. Monte Carlo sampling and kernel density estimation are employed to derive credible reserve sets under different confidence levels, achieving a probabilistic quantification of VPP reserve capacity distribution. Case studies demonstrate that the proposed method accurately characterizes the probabilistic distribution characteristics of VPP reserve provision under multiple uncertainties, providing comprehensive and reliable assessment information for power dispatching agencies. Full article

Catalysis has entered everyday life through a range of technological processes that rely on different catalytic systems. The increasing demand for such systems requires rationalization of the use of their expensive components, such as noble-metal catalysts. As such, a catalyst with low noble-metal concentration, in which each one of the noble atoms is active, would reach the lowest price possible. Nevertheless, no clear reactivity descriptors have been outlined for this type of low-coordinated supported atom. Using DFT calculations, we consider three diverse systems as models of single-atom catalysts. We investigate monomers and bimetallic dimers of Ru, Rh, Pd, Ir, and Pt on MgO(001), Cu adatom on thin Mo(001)-supported films (NaF, MgO, and ScN), and single Pt adatoms on oxidized graphene surfaces. The reactivity of these metal atoms was probed by CO. In each case, we see the interaction through the donation–backdonation mechanism. In some cases, CO adsorption energies can be linked to the position of the d-band center and the adatom’s charge. A higher-lying d-band center and less-charged, supported single atoms bind CO more weakly. Also, in some cases, metal atoms that are less strongly bound to the substrate bind CO more strongly. The results suggest that the identification of common activity descriptor(s) for single metal atoms on foreign supports is a difficult task with no unique solution. However, it is also suggested that the stability of adatoms and strong anchoring to the support are prerequisites for the application of descriptor-based search to novel single-atom catalysts. Full article

This study develops a Constraint-Driven Model of Intelligence to explain the emergence of structured meaning in complex systems, reconciling probability and cybernetics. It applies a conceptual–analytic procedure, conducted entirely through logical reasoning and theoretical analysis, without empirical measurement, data acquisition, experimental manipulation, or statistical testing, and is therefore methodologically separate from empirical artificial intelligence research. Phenomena such as model collapse are cited as theoretical instances for epistemic argumentation, without asserting empirical verification. Building on Émile Borel’s Infinite Monkey Theorem, which demonstrates the theoretical inevitability of order in unbounded stochastic processes, and Gregory Bateson’s principle of negative explanation, which defines structure as the result of systematically eliminated alternatives, the analysis formalizes how constraints break ergodicity and generate asymmetry. Shannon’s entropy quantifies the informational effects of constraints, while Simon’s bounded rationality and Turing’s algorithmic limits show how cognitive and computational boundaries produce tractable outcomes. Applied to modern AI, the model accounts for model collapse in recursive training, showing that the loss of asymmetric constraints produces low-entropy, repetitive outputs, demonstrating the epistemic necessity of constraint regulation. Comparing probabilistic and cybernetic accounts of emergence, the study shows that structured intelligence arises not from stochastic exploration alone, but from bounded, recursive, selective processes. This model is transdisciplinary, formalizing how constraints from socioeconomic pressures to subcultural circulation shape diversity, innovation, and functional asymmetry, establishing a generalizable cybernetic epistemology for the generation of structured intelligence and meaning across domains. By formalizing these concepts through set-theoretic derivations and integrative synthesis, this non-empirical model advances a cybernetic epistemology, separate from quantitative AI evaluations or experimental designs. Full article

This study investigates two graphene oxide (GO)-based coating architectures on urinary catheter substrates—layered (PEI+GO) and embedded (PEI/GO)—loaded with antimicrobial peptides (E14LKK and fLFB), with the aim of elucidating how coating structure governs peptide retention and release. Physicochemical and morphological characterization confirmed distinct coating architectures and thicknesses. Molecular dynamics simulations were employed to probe GO–peptide and PEI–peptide interactions, revealing weaker binding of fLFB to GO relative to PEI, consistent with enhanced peptide mobility. Antibacterial performance against Escherichia coli and Enterococcus faecalis was evaluated using agar diffusion assays as a comparative indicator of peptide release from surface-bound coatings. The layered PEI+GO–fLFB system exhibited the highest antibacterial activity, in agreement with simulation-predicted interaction energetics and structural fluctuations. Rather than targeting immediate clinical translation, this work provides mechanistic insight into how GO–polymer architecture modulates antimicrobial peptide availability, offering a molecular dynamics simulation-guided framework for the rational design of peptide-releasing antimicrobial coatings. Full article

Reservation prices determine which goods consumers are willing to buy and, therefore, shape demand curves in markets. Neoclassical economics postulates that reservation prices optimally reflect the marginal utility provided by a good given all other possible uses of the consumer’s budget, as well as a rational response to the information environment. In contrast, behavioral economics suggests that reservation prices are influenced by extraneous factors and are, thus, less stable and more difficult to predict. In this article, I propose a behavioral model of how the reservation price changes during sequential price searches. The model assumes bounded rationality, is rooted in the psychological theory of aspiration levels, and posits that the reservation price adjusts towards the lowest price known. A corollary is that when higher prices are charged in a market, consumers become willing to pay more in the short term. Results from an online laboratory experiment with more than 400 participants from the general population suggest that the model performs well in explaining the dynamics of the reservation price during a search spell. While the results imply that reservation prices are malleable, competition can protect consumers from sellers exploiting their adaptiveness. Full article

We propose Adaptive Inference, a portfolio management framework extending Active Inference to non-stationary financial environments. The framework integrates inference, control, and execution under endogenous uncertainty, modeling investment decisions as coupled dynamics of belief updating, preference encoding, and action selection rather than optimization over fixed objectives. In this approach, portfolio behavior is governed by the expected free energy (EFE) minimization, showing that classical valuation models emerge as limiting cases when epistemic components vanish. Using train–test evaluation on the ARKK Innovation ETF (2015–2025), we identify a Passivity Paradox: frozen belief transfer outperforms naive adaptive learning. A Professional Agent achieves a Sharpe ratio of 0.39 while its adaptive counterpart degrades to $-0.28$, reflecting belief contamination when learning from policy-dependent signals. Crucially, the architecture is not designed to generate alpha but to perform endogenous risk management that mitigates overtrading under regime ambiguity and distributional shift. Adaptive Inference Agents maintain long exposure most of the time while tactically reducing positions during high-entropy periods, implementing uncertainty-aware passive investing. All agents reduce realized volatility relative to ARKK Buy-and-Hold (43.0% annualized). Cross-asset validation on the S&P 500 ETF (SPY) shows that inference-guided risk shaping achieves a positive Entropic Sharpe Ratio (ESR), defined as excess return per unit of informational work, thereby quantifying the economic value of information under thermodynamic constraints on inference. Full article

Mineral scale deposition remains a major flow-assurance constraint in oil and gas operations, especially in water-flooding and produced-water reinjection, where mixing between incompatible brines promotes super-saturation and precipitation of poorly soluble salts. This work introduces a novel extension of traditional methods used for modeling chemical inhibition and the predictive evaluation of oilfield scale-inhibitor molecules. A systematically optimized Two-Dimensional Quantitative Structure–Activity Relationship Model based on the k-Nearest Neighbors algorithm 2D-QSAR-KNN model was developed to quantitatively link molecular constitution of phosphonate inhibitors, brine chemistry, and operating factors with inhibition efficiency IE %. The optimized model achieved strong accuracy and generalization R²_train = 0.9182, R²_test = 0.9306, and R²_global = 0.9208 with low prediction errors RMSE_train = 4.7888%, RMSE_test = 4.5485%, and RMSE_global = 4.7421%. Median absolute errors remained minimal for the train set = 0.80%, and test set = 1.63%, and model stability was confirmed by high correlation with experimental IE % r = 0.94 and R²_train/R²_test ≈ 0.99, showing no sign of overfitting. Additionally, an inverse-2D-QSAR framework was applied to identify the optimal molecular descriptor profile expected to maximize inhibitory performance within normalized bounds, providing rational rules for next-generation inhibitor design. The findings highlight the practical value of QSAR-inspired AI modeling to accelerate molecule screening and dosage exploration prior to laboratory validation, supporting more cost-effective, interpretable, and environmentally aware sulfate-scale inhibition strategies under high-salinity reservoir conditions. Full article

Motivated by the fact that the Fibonacci sequence is the simplest nontrivial second-order recurrence with a rational generating function, we develop a Fibonacci-weighted Hardy theory for bicomplex holomorphic functions. Starting from the coefficient norm ${\sum }_{n\ge 0}{\left|a_n\right|}^2/F_{n+1}$, we obtain a bicomplex Hilbert module whose reproducing kernel is governed by ${(1-t-t^2)}^{-1}$ and whose maximal disk of holomorphy is determined sharply by the nearest kernel singularity, giving the radius ${\rho }_F={\phi }^{-1/2}$ (the square-root inverse of the golden ratio $\phi$). The arithmetic recurrence makes several objects fully explicit: we derive closed formulas for the kernels through the idempotent decomposition of $\mathbb{BC}$, compute exact norms of the shift powers and a golden-ratio spectral radius, and package the local theory into a sheaf of Fibonacci-holomorphic germs that are compatible with the bicomplex idempotent splitting. We also treat $(p,q)$-Fibonacci weights, obtaining a one-parameter family of rational kernels ${(1-pt-q{t}^2)}^{-1}$ and corresponding operator bounds. In addition to providing a concrete bicomplex model within weighted Hardy theory, the resulting explicit kernels furnish benchmark examples for kernel-based interpolation and for the operator theory of unilateral weighted shifts. Full article

The study develops a game-theoretic evaluation framework for cyber deception that quantifies deception benefit relative to an otherwise matched non-deceptive baseline and links strategic outcomes to information disclosure. A defender–attacker interaction is modeled through a paired design consisting of a baseline game without deception and a corresponding decoy-enabled deception game, enabling direct measurement of deception impact through two operational metrics: the value of deception, defined as the baseline-referenced change in defender equilibrium utility attributable to deception, and the price of transparency, defined as the marginal loss induced by increased observability of the true system state. The analysis characterizes defender-optimal deception strategies, derives interpretable bounds and break-even conditions under which deception becomes ineffective due to cost or detectability, and establishes approximation properties that support scalable allocation rules. To complement equilibrium-based evaluation, the study introduces an information-theoretic uncertainty construct that captures the extent to which deception preserves attacker uncertainty after observation, providing a mechanism-level interpretation of when and why value of deception degrades as transparency increases. Computational experiments across heterogeneous scenarios demonstrate consistent cross-setting comparability, reveal tradeoffs among decoy realism, budget, and attacker rationality, and identify regimes in which simplified allocation heuristics approach optimal performance. Full article

The pre-prepared meal industry is a vital engine for food sector upgrading in China. This study investigates the key drivers of consumer purchasing decisions and identifies strategic pathways to support high-quality industry development. Grounded in behavioral decision theory and the stimulus–organism–response framework, we propose two central research questions: (1) What are the dominant determinants of consumer purchase intention for pre-prepared meals? and (2) How do these determinants interact in nonlinear and asymmetric ways to shape final decisions? To address these questions, we analyzed 805 valid questionnaires collected in Jilin Province using an integrated machine learning framework. Data quality and validity were ensured through baseline balance tests, and sample imbalance was corrected using the SMOTE–Tomek algorithm. Six models, including Random Forest (RF) and XGBoost, were optimized via Gaussian process-based Bayesian optimization. The RF model achieved optimal performance on the test set, with an F1 score of 0.907, an AUC of 0.928, and a prediction accuracy of 0.876. To enhance model interpretability, Mean Decrease Impurity (MDI) was integrated with the SHAP framework. Our findings reveal that: (1) purchase decisions are predominantly willingness-driven, with behavioral tendency—especially recommendation willingness—accounting for over 72% of predictive importance; (2) rational considerations, such as convenience and channel accessibility, serve as foundational enablers; and (3) recommendation willingness exhibits a significant S-shaped nonlinear threshold, where a shift to “relatively willing” marks a critical marketing intervention window. SHAP force plot analysis further uncovers an asymmetric decision logic: high willingness can compensate for perceived product shortcomings, whereas the absence of core intention functions as a non-compensatory barrier. Theoretically, these findings synthesize machine learning outputs with classical behavioral models (e.g., the Theory of Planned Behavior and Prospect Theory) by empirically quantifying bounded rationality and nonlinear activation mechanisms. These findings suggest that enterprises should transition from traffic-centric to retention-oriented strategies by leveraging word-of-mouth and proximity-based channels. Moreover, establishing a collaborative governance system is essential to mitigate risk perception and ensure long-term industry prosperity. Full article

The rapid rise in cryptocurrencies has created an investment environment marked by unprecedented levels of information volume, fragmentation, and volatility. While prior research has examined drivers of trust and adoption in crypto markets, far less is known about the psychological consequences of information overload on investor decision-making. This study addresses this gap through nineteen semi-structured interviews with individual cryptocurrency investors, analyzed using an inductive, manually conducted thematic approach. Findings reveal four interconnected dynamics: decision fatigue and paralysis, heuristic reliance on influencers and peers, emotional strain characterized by anxiety and fear of missing out (FOMO), and diverse coping strategies ranging from selective filtering to withdrawal. These results demonstrate that crypto investing is not only a financial process but also a cognitively and emotionally taxing experience. By linking investor narratives to broader theories of decision fatigue, bounded rationality, and consumer vulnerability, the study contributes to interdisciplinary debates in marketing, behavioral finance, and consumer psychology. Practically, the findings highlight the need for clearer communication strategies, supportive platform design, and financial education initiatives that help investors manage cognitive strain and decision fatigue. In a market where credibility is fluid and decisions are often made under conditions of overload, understanding the psychological dimensions of investment behavior is essential. Full article

We address a gap in Machine Learning–human alignment research by proposing that methods from Explainable AI (XAI) can be repurposed to quantitatively model human learning. To achieve alignment between human experts and Machine Learning (ML) models, we must first be able to explain the problem-solving strategies of human experts with the same rigor we apply to ML models. To demonstrate this approach, we model expertise in the complex domain of particle accelerator operations. Analyzing 14 years of operational text logs, we construct weighted graphs where nodes represent operational subtasks and edges capture their strategic relationships. We then examine these strategic models across four granularity levels. Our analysis reveals statistically significant changes with expertise at three of four graph levels. Remarkably, despite numerous possible ways to partition subtasks, operators across all expertise levels demonstrate a striking consistency in high-level strategy, partitioning the task into the same three functional communities. This suggests a shared “divide and conquer” cognitive framework. Expertise develops within this stable framework, as experts exhibit greater cognitive flexibility (forming more cross-community connections) and build more refined internal models. The primary contribution of this work is a methodology for creating a quantitative, interpretable baseline of expert human performance. This provides a “ground truth” for future research in alignment between humans and ML models, enabling a new approach to verification: the ML model’s representation of the task can be quantitatively compared against the human expert benchmark to measure their alignment. This paves the way for building safer, more interpretable partnerships between humans and ML models. Full article

This study investigated the α-glucosidase inhibitory potential of enzymatic/alkaline treatments from Palmaria palmata using different proteases and pairwise combinations thereof. Treatments prepared with Alcalase^®, Flavourzyme^®, and Formea^® Prime, alone or in combination, were evaluated for dose-dependent inhibitory activity. Alcalase^®-derived treatments exhibited the highest α-glucosidase inhibition, achieving an IC₅₀ of 2.48 mg·mL⁻¹, outperforming other treatments and combinations. Membrane fractionation of the Alcalase^®-derived treatment into >5 kDa, 3–5 kDa, 1–3 kDa, and <1 kDa fractions revealed a size-dependent trend, with the <1 kDa fraction showing the strongest inhibition (IC₅₀ of 1.94 mg·mL⁻¹). Three peptides, RADIPFRRA, DGIAEAWLG, and FWSQIFGVAF, from the <1 kDa fraction were identified as potential α-glucosidase inhibitors using the BIOPEP-UWM database and were further selected based on a Peptide Ranker score above 0.6 for in silico docking analyses. Docking revealed distinct binding modes: RADIPFRRA and DGIAEAWLG occupied the catalytic cleft, interacting with key residues (Asp518, Asp616, Trp481, Trp613) consistent with competitive inhibition, whereas FWSQIFGVAF bound to a peripheral site, suggesting potential allosteric modulation. Physicochemical analysis further highlighted differences in charge and isoelectric point correlating with their binding behavior. Together, these findings demonstrate that low-molecular-weight peptides derived from P. palmata proteins, particularly those generated by Alcalase^®, possess significant α-glucosidase inhibitory activity, and provide structural insights for the rational design of peptide-based modulators of carbohydrate metabolism. Full article

This paper addresses the position control problem for a Lagrangian pendulum. Using a strict Lyapunov function, a rigorous analysis is presented to prove that the closed-loop system equilibrium point composed of the pendulum dynamics and a classical linear PID control is globally asymptotically stable. Motivated by these results, the theoretical proposal is extended to analyze a novel hyperbolic PID-type control scheme; reformulating the Lyapunov function, global asymptotic stability of the equilibrium point for the corresponding closed-loop equation is demonstrated. The proposed hyperbolic scheme is a rational function with bounded control action composed of a suitable combination of hyperbolic sine and cosine functions. The hyperbolic structure is used in the proportional, integral, and derivative terms of the control algorithm to drive the position error and joint velocity to zero. Experimental results of both a linear PID and a novel hyperbolic PID-type controller on a direct-drive pendulum are presented to illustrate the effectiveness and performance of the proposed control algorithm. Full article