Search Results (6,242)

Federated learning enables privacy-preserving distributed training but suffers from client drift and slow convergence under statistical data heterogeneity. Most existing federated optimization methods address client drift via parameter-space constraints or aggregation-level corrections, while fewer works directly shape the gradient covariance spectral structure of the optimization landscape. This paper analyzes the convergence problem from a spectral perspective, revealing that non-IID data causes spectral diffusion in the gradient covariance matrix and degrades convergence. Guided by random matrix theory, we propose federated spectral regularization (Fed-SR), a computationally efficient method that indirectly constrains spectral spread via gradient norm regularization. Although computing the regularizer gradient requires Hessian vector products, our optimized auto-differentiation implementation avoids storing full Hessian matrices and restricts extra computational overhead to a negligible level. Experiments on CIFAR-10, CIFAR-100, and other benchmarks show that Fed-SR outperforms baselines including FedAvg, FedProx, and SCAFFOLD in non-IID scenarios, reducing communication rounds and improving accuracy and stability. Ablation studies, spectral analysis, and controlled spectral feature manipulation experiments provide consistent empirical evidence showing a strong empirical association between the “spectral concentration” effect and performance gains, offering mechanistic interpretability consistent with our proposed theoretical framework within the tested experimental settings. Full article

H-bases provide an attractive alternative to Gröbner bases for the study of polynomial ideals because they are independent of monomial orderings and therefore preserve structural properties that may be obscured in Gröbner basis computations. However, the practical use of H-bases has been limited by the lack of efficient algorithms for their construction, particularly due to the difficulty of computing syzygy modules and eliminating redundant generators. In this paper, we present a new algorithm for computing syzygies and H-bases based on numerical linear algebraic techniques. The proposed approach utilizes QR decomposition to construct syzygies directly from coefficient matrices, avoiding the need for monomial orderings and Gröbner basis computations. A recursive framework derived from QR factorization is developed to compute both syzygy modules and H-bases while simultaneously defining an associated reduction process. In contrast to existing methods based on Gröbner bases, Schreyer’s theorem, or singular value decomposition, the proposed algorithm requires fewer matrix reductions and relies on computationally less demanding operations. Furthermore, its termination criterion is determined by an upper bound independent of Gröbner basis theory. Consequently, the method provides a more streamlined and conceptually simpler framework for the computation of syzygies and H-bases of polynomial ideals. Full article

The transition to a circular economy (CE) is central to achieving SDG 12, yet institutional transformation varies significantly across countries. This study conducts a comparative institutional analysis of CE transitions in China and Russia to quantify Russia’s institutional lag and assess the maturity of both systems in the context of SDG indicator 12.5.1 (Circular Material Use Rate—CMUR). Drawing on neo-institutional theory, multi-level governance, and institutional trap theory, we develop an original methodology that computes institutional lag through three key milestones, an integrated maturity index across eight institutional components, and CMUR estimates based on official statistics, legislative acts (1998–2025), and industry reports. Our results show that Russia’s average institutional lag relative to China is 15.3 years: Russia’s 2024 municipal solid waste recycling rate (13.9%) matched China’s 2009–2010 level, while China achieved Russia’s 25% target for 2030 in 2018. The integrated maturity index is 8.25 for China versus 5.25 for Russia, with the largest gaps in industrial symbiosis, R&D and human capital, and international integration. Russia’s CMUR stands at 5–7%, compared to 25–30% in China. We also demonstrate that Russia’s high recycling growth is driven by a low-base effect and is not evidence of institutional efficiency; the compound annual growth rate (CAGR) of Russian recycling volume (33.0%) significantly exceeds China’s (13.1%), but this reflects the much lower starting point rather than systemic maturity. We conclude that without systemic institutional reforms—including eco-industrial parks, green finance, and competence centres—Russia will remain at an early CE stage, failing to substantially contribute to SDG 12 by 2030. Full article

Ideathons and pitch competitions have expanded rapidly as experiential learning devices, but the textual artefacts they produce—presentation slides—remain under-examined. This study applies interpretable computational text analysis to 104 ideathon decks (1289 content slides) from four cohorts: Cambodia 2023, 2024, 2025 and Thailand 2025. Measures include lexical frequency, TF-IDF, lexicon-based sentiment, a ten-component pitch-completeness proxy, numerical density, and Jaccard similarity. Because sector designation was absent in Cambodia 2023 and present from 2024 onward, the longitudinal Cambodian data support an observational cohort comparison with an institutional change between cohorts; year effects, programme evolution, and sector designation cannot be separated. The Cambodia 2025 vs. Thailand 2025 contrast is a single-year cross-country comparison, not a longitudinal one. Between Cambodia 2023 and 2024, presentations show large Cohen’s d differences with 95% bootstrap confidence intervals (CIs) in total words, unique words, slide count, pitch completeness, and market-related vocabulary, alongside a small decline in type–token ratio. At fixed sector composition, Cambodia 2025 and Thailand 2025 differ sharply in surface vocabulary (top-50 Jaccard = 0.176): Cambodia leans toward agriculture, rural markets, and community development, while Thailand leans toward AI, learning, and cassava-centric agronomy. AI use was not directly measured, so all claims about generative AI are hypothesis-generating; the drop in within-cohort pairwise Jaccard from 2024 to 2025 (0.061 → 0.041) is consistent with—but does not establish—an augmentative rather than homogenising effect of AI assistance. Findings are reported as descriptive associations and interpreted through the lens of constraint-based creativity and institutional theory. We discuss implications for curriculum designers who wish to balance structural templates with exercises that promote diverse problem framings. Full article

The mathematical representation of discrete signals is classically governed by linear basis transforms, such as the Discrete Fourier Transform (DFT), which treat spectral projection as a rigid, deterministic geometric operation. Under this paradigm, signal compression and thresholding rely on heuristic error metrics like the Minimum Mean Squared Error (MMSE). To mathematically axiomatize these fundamental operations, this paper reformulates computational basis transforms through the lens of Cooperative Game Theory. By defining discrete signal reconstruction as a Grand Coalition of orthogonal frequency players, we establish a strict mathematical isomorphism between functional analysis and cooperative game theory. We prove that the spectral energy assigned to each frequency coefficient is exactly its Shapley Value and that classical MMSE minimization is mathematically equivalent to maximizing the retained Shapley payout. Furthermore, we extend this framework to physical hardware and linear shift-invariant (LSI) systems, modeling 8-bit quantization and the Modulation Transfer Function (MTF) as sub-additive “economic taxes” on the coalition. Finally, by intentionally violating the Shapley Symmetry Axiom to mimic the Contrast Sensitivity Function (CSF) of the human visual system, we propose a fundamentally new mathematical basis: the Perceptual Game-Theoretic Transform (PGTT). Unlike classical methods that rely on post hoc quantization for signal compression, the PGTT acts as an inherently efficient transform that structurally guarantees sub-Nyquist computational complexity and dynamic range reallocation prior to physical hardware saturation. Full article

The assembly line balancing problem (ALBP) is an optimization problem which involves assigning tasks to stations to maximize line efficiency. In the context of Industry 4.0, assembly lines must be flexible and adaptable to stochastic and real-world conditions. However, in the literature, line balancing is mostly studied in deterministic environments. As a result, this paper presents a robust optimization approach to bridge the gap between theory and practice by addressing a real-world ALBP in the furniture industry. A mixed integer programming (MIP) model is proposed, considering uncertainty in task execution times, which are expressed as intervals of possible values, and the sequence of module assembly in addition to the standard ALBP constraints. Then, it is linearized by duality to be solved by Gurobi. Two objectives, (1) cycle time minimization and (2) tool efficiency, are optimized lexicographically using the proposed MILP models. Computational experiments and Monte Carlo simulations demonstrate that robust solutions outperform deterministic ones, ensuring stable throughput under variability. The study highlights the trade-off between efficiency and robustness, offering practical insights for furniture manufacturers. Full article

Hydrogen (H₂) is expected to play a critical role in modern industry, particularly in ammonia synthesis, petroleum refining, and low-carbon transportation. The safe storage of H₂ remains a major challenge due to its low volumetric density under ambient conditions. Metal–Organic Frameworks (MOFs), highly porous crystalline materials, have emerged as promising H₂ storage candidates owing to their high surface areas and tuneable pore structures. Molecular simulations such as grand canonical Monte Carlo or density functional theory are costly and limited in exploring large material spaces, motivating efficient predictive tools to accelerate discovery. Here, Machine Learning (ML) techniques are compared to an explainable artificial intelligence (XAI) approach using symbolic regression (SR), trained on 10,123 experimentally measured H₂ adsorption datapoints from real-world MOFs. The best performing model achieved a goodness of fit of 0.9986 with lower computational demand, but reduced interpretability, addressed using XAI analysis and clustering. SR achieves a lower goodness of fit of 0.914 but produces a physically meaningful equation highlighting structural features driving high gravimetric efficiencies. These results demonstrate strong ML capability for predicting how MOF properties and environmental conditions affect H₂ uptake. This offers engineers and researchers a practical means of screening potential MOFs for H₂ storage applications, with the XAI analyses providing additional confidence in the predictions. They allow researchers to understand the physical reasoning behind each output, assess the reliability of individual predictions, and make fully informed decisions, enabling predictive models to be acted upon with confidence in real-world contexts. Full article

This study addresses an integrated security control and communication co-design problem for nonlinear CPSs subject to coexisting FDI attacks and actuator faults. A novel adaptive discrete event-triggered communication scheme (ADETCS) is proposed. Its triggering threshold adapts to the system state. State estimation, fault estimation, and attack detection are all migrated to the control unit. Based on this framework, a closed-loop T–S fuzzy model is established for active defense against actuator faults and dual-end FDI attacks. A robust augmented observer is then developed via Lyapunov stability theory to jointly estimate system states, actuator faults, and FDI attacks. Sufficient conditions are further derived for an integrated security controller that unifies attack tolerance and fault tolerance. Simulation results on a quadruple-tank system show that the proposed method effectively counteracts coexisting attacks and faults while significantly reducing resource consumption. Over an 800-s horizon, data transmissions drop to 712 (8.9% transmission rate). The sensor-node computational load is also reduced from 8000 time-triggered executions to 712 event-triggered ones. Full article

To address the challenges in risk assessment of complex emergency systems, such as difficulties in closed-loop structure modeling, insufficient quantification of dynamic evolution, and poor adaptability to multiple scenarios, this study proposes a dynamic risk assessment method that integrates the System-Theoretic Accident Model and Processes (STAMP) and the Cascading Failure Propagation Model (CFPM). The novelty of this coupling lies in a bidirectional “qualitative diagnosis → quantitative prediction” logic: STAMP’s identification of Unsafe Control Actions (UCAs) provides a theory-grounded blueprint for configuring the CFPM network topology and propagation parameters, while CFPM’s dynamic simulation translates these qualitative control flaws into computable risk evolution trajectories. The proposed framework adopts a two-layer structure of “qualitative modeling–quantitative analysis”. STAMP is used to construct a hierarchical control structure, identify Unsafe Control Actions (UCAs), and analyze the nonlinear interaction mechanisms among “human–organization–technology” factors. For typical scenarios of “fault not processed” and “online fault processing”, CFPM is employed to abstract the system into a node network, quantify the time-step propagation process of node failure probability, calculate the system residual performance index, and generate real-time risk evolution curves. A case study of the Tianjin Port ‘8·12’ explosion accident demonstrates that this method effectively captures the closed-loop interaction characteristics and dynamic risk evolution patterns of emergency systems. Quantitative results reveal a distinct contrast between the two handling scenarios: in the absence of maintenance intervention, system residual performance deteriorates exponentially and rapidly approaches a critical threshold; in contrast, effective online maintenance significantly retards risk accumulation and facilitates gradual system recovery, thereby preventing further escalation of consequences. Compared to traditional methods like Bayesian Networks, it shows stronger applicability by explicitly modeling closed-loop feedback structures and enabling discrete time-step quantification of risk accumulation, and can accurately identify control flaws and quantify risk accumulation effects, thereby providing support for optimizing emergency strategies. Future research should focus on enhancing the method’s adaptability to data uncertainty and cybersecurity threats. Full article

This work investigates the adaptive event-triggered distributed estimation problem for discrete time-varying nonlinear stochastic systems over sensor networks exposed to replay attacks within a finite-horizon setting. The sensor network comprises multiple nodes whose interaction structure is described by two randomly switching directed graphs. The plant under consideration is formulated as a discrete time-varying nonlinear stochastic system obeying a sector-bounded condition. To mitigate communication overhead, an adaptive event-triggered scheme is employed, where the triggering threshold is dynamically updated based on the triggering error. In addition, replay attacks are considered, wherein an adversary randomly replaces current data packets with previously recorded ones. A compensation mechanism is devised to neutralize the impact of such attacks. By building a distributed estimator and formulating an augmented estimation error system, sufficient criteria are established via Lyapunov theory and stochastic analysis to ensure the prescribed average $H_{\infty }$ performance level is attained. The estimator gains are computed recursively by solving a sequence of recursive linear matrix inequalities (RLMIs). A design algorithm for the distributed estimator is also provided to support online implementation. Finally, a numerical simulation example is given to demonstrate the effectiveness of the proposed estimation approach. Full article

Traditional modular pipelines in autonomous driving (AD) frequently suffer from error accumulation and delayed responsiveness during safety-critical events. Although Embodied Intelligence (EI) introduces a paradigm shift through internal “World Models” for proactive risk mitigation, a substantial gap remains between high-level cognitive theories and real-time, safety-certified deployment. This paper bridges that gap by proposing an Embodied System Integration Methodology (ESIM), which translates cognitive models into fielded robotic systems. Grounded in a “Perception-Imagination-Execution” (PIE) cognitive architecture, ESIM treats risk prediction as an uncertainty-driven, counterfactual closed-loop sensorimotor process. Unlike passive prediction models, the framework employs a Bayesian uncertainty-gated mechanism that selectively triggers a World Model to simulate future risk scenarios only when perceptual degradation occurs. We validate this methodology through a multi-paradigm study spanning three distinct levels: an academic prototype on edge computing platforms, an industrial implementation adhering to ASIL-D (Automotive Safety Integrity Level D) constraints, and an open-source simulation platform. The results demonstrate that by applying hardware acceleration and asynchronous pipelines, the ESIM framework consistently maintains end-to-end latencies within 10–20 ms across heterogeneous hardware. We explicitly address the engineering trade-offs in latency, hardware heterogeneity, and optimization, and establish mathematically grounded probabilistic safety boundaries for black-box neural architectures. Finally, we discuss the framework’s scalability in extreme scenarios, coupling with SLAM pipelines, privacy-preserving federated learning, and generalization potential in the low-altitude economy. Full article

The concurrent disposal of industrial lithium slag (LS) and the remediation of heavy-metal-contaminated water remain critical environmental imperatives. Herein, industrial lithium slag was successfully upcycled into a high-capacity geopolymer via alkali activation to systematically evaluate its Pb(II) removal mechanisms. Synthesized under optimal conditions (11 mol/L alkali concentration, 0.616 solid-to-liquid ratio), the geopolymer showed exceptional Pb(II) capture, achieving ~99% removal efficiency within 120 min for a 100 mg/L Pb(II) solution at pH 6.0. The adsorption kinetics obeyed the pseudo-first-order model, yielding a remarkable theoretical equilibrium capacity of 284 mg/g. Thermodynamic results reveal a spontaneous (ΔG < 0), endothermic (ΔH = 17.66 kJ/mol) process with increased interfacial randomness (ΔS > 0). Integrating macroscopic performance with characterizations and density functional theory (DFT) computations elucidated a site-specific chemisorption mechanism and the precipitation of PbSO₄ caused by Pb(II) and SO₄²⁻ in LS. Ultimately, this work provides a sustainable paradigm for the value-added upcycling of industrial solid waste. Full article

Non-heme iron enzyme-catalyzed enantioselective alkene trifluoromethyl azidation represents a powerful strategy for constructing valuable chiral organofluorine compounds, yet the mechanistic origins of enantioselectivity controlled by the key residues in the enzyme’s chiral environment remain elusive. Herein, we integrate density functional theory (DFT) calculations, classical molecular dynamics (MD) simulations, and quantum mechanical/molecular mechanical (QM/MM) calculations to elucidate the mechanism and the origin of enantiocontrol in this transformation. Our computational study reveals that radical addition from the CF₃ radical to the 4-methoxystyrene substrate constitutes the enantioselectivity-determining step. The enantioselectivity arises from energetic differences driven by H···H repulsions and C-H···O and N-H···F interactions between the substrate and the binding pocket defined by key residues I335V, F188Q, and R324 near the active site, which stabilize the transition state leading to the major enantiomer. These findings provide mechanistic insights into non-heme iron enzyme-catalyzed asymmetric azido-trifluoromethylation of alkenes and establish a structural framework for rational biocatalyst design. Full article

Natural plant extracts rich in polyphenols are increasingly being studied as multifunctional food ingredients with antioxidant and stabilizing properties. In this study, Ocimum basilicum L. extracts were evaluated as biological cryoprotective agents for Streptococcus thermophilus. The extracts contained high levels of phenolic compounds (~1350–2200 mg GAE equivalents/L) and exhibited strong antioxidant activity (up to 5.6 mM Trolox equivalents). Density functional theory calculations showed low O–H bond dissociation energies (~72–74 kcal/mol in ethanol) for key components, including luteolin and rosmarinic acid. These calculations indicate a high hydrogen donation capacity comparable to or exceeding that of ascorbic acid. Molecular dynamics simulations demonstrated the preferential localization of major phenolic compounds at the membrane–water interface in a POPC bilayer membrane. The interaction of molecules with POPC increased membrane thickness and formed stable hydrogen-bond networks with lipid head groups. Experiments showed that systems based on basil extract significantly increased the survival of bacteria after storage at −25 °C, with the number of viable cells reaching (1.5–2.75) × 10⁸ CFU/mL. This effect was observed in comparison with control groups that used saline or sucrose. Fluorescent analysis of live/dead cells confirmed the improvement in cell membrane preservation. At the same time, no signs of metabolic inhibition were detected. Taken together, the experimental and computational results support the hypothesis that the cryoprotective effect may involve complementary antioxidant and membrane-associated interactions. However, direct biophysical validation of the proposed membrane mechanism is still required. These results emphasize that polyphenol extracts are promising natural functional ingredients for improving the stability and shelf life of probiotic and starter cultures in food systems. Full article

The misalignment between workforce competences and the requirements of digitally evolving occupations is a critical barrier to SME competitiveness. This study’s primary contribution is theoretical and methodological: it reconceptualizes the workforce skill gap as a firm-level human-capital–technology complementarity constraint rendered observable and commensurable through the ESCO taxonomy, and abstracts four transferable design principles—commensurability, macro–micro integration, a transferable metric, and modular extraction. Drawing on human capital theory, the knowledge-based view, and skill-biased technical change, the framework maps anonymized employee CVs to ESCO occupational requirements through a deterministic natural language processing procedure and computes a Skill Gap Indicator as the complement of evidenced competence coverage. A prototype Intelligent Learning Management System, developed within the LUCE project, instantiates the framework as a proof of concept, translating identified gaps into targeted training recommendations. Applied to a convenience sample of publicly available professional profiles, the indicator has a mean of 0.956, interpreted as a conservative upper-bound estimate rather than a literal deficit. The empirical results are an exploratory demonstration that motivates, rather than confirms, the posited link between skill gaps and firm performance; a cross-sectional test found no significant association, which the design cannot adjudicate. Confirmatory testing would require sample expansion, employer-provided workforce records, and a longitudinal design, identified as priorities for future research. The study thus contributes a standardised, interoperable, and transferable approach to measuring and comparing workforce skill gaps in SMEs. Full article