Search Results (777)

As climate change intensifies, transitioning the construction sector away from fossil fuels is vital to reducing global greenhouse gas emissions and localized urban pollution. This paper assesses the economic feasibility of electrifying construction machinery by developing an Annualized Cost of Ownership framework that incorporates mobile charging solutions, internalizes environmental and public health operational externalities (CO₂, PM_2.5, NO_x, and SO₂), and relies on Monte Carlo simulation with Cholesky decomposition to capture the interdependencies among cost drivers. We analyze twenty distinct models of excavators and wheel loaders—the two largest contributors to construction-machinery emissions—comprising functionally equivalent diesel and battery-electric variants. Our results show that several compact electric models are already cost-competitive even without internalizing environmental and public health operational externalities. When these are accounted for, the economic advantage of electric machinery increases, particularly in denser urban areas where local air pollution damages are severe. While projected battery cost reductions further lower electric ownership costs, the magnitude of this effect is modest. However, the weak penetration of electric construction equipment in the US underscores that targeted policy interventions—such as point-of-sale rebates, green procurement mandates, tax credits, charging infrastructure subsidies, or the creation of low-emission zones and noise ordinances that advantage electric construction machinery—are needed to accelerate market adoption. These measures are particularly critical in densely populated urban areas, where internalizing local air pollution and public health externalities significantly amplifies the economic value of zero-emission machinery. Full article

Let N be a finite set of cardinality n, and let $a\in N$. A submodular function f on N with $f\left(a\right)=1$ is defined to be a-reduced if, for any decomposition $f=g+h$ into submodular functions, where h does not depend on a, it follows that h is identically zero. The maximal possible value of f on the remaining singletons defines a quantity $\lambda$ that characterizes the degree to which one variable can constrain the value of another; geometrically, it also limits the possible elongation of the associated submodular base polytope. The parameter has concrete relevance: it caps the share-size lower bounds provable for secret-sharing schemes via the basic Shannon inequalities, and it controls the geometry of the base polytopes on which greedy submodular-optimization algorithms operate. We construct an example demonstrating that $\lambda$ can be as large as $\Omega (n/logn)$. Furthermore, we establish a doubly exponential upper bound on $\lambda$. The problem of narrowing the gap between these bounds remains open. Full article

To solve the low-precision prediction problem of noisy non-stationary goaf subsidence sequences, this study aims to establish a high-accuracy hybrid prediction model for mining surface deformation monitoring. The Global Navigation Satellite System (GNSS) monitoring data of surface subsidence in goaf areas exhibits non-stationary and noisy characteristics, which limits the accuracy of traditional prediction models. In this paper, a hybrid prediction model, namely the Improved Radial Movement Optimization–Variational Mode Decomposition–Gated Recurrent Unit (IRMO-VMD-GRU) model, is proposed. The IRMO algorithm is employed to globally optimize the key parameters of VMD, achieving adaptive and stable decomposition of the settlement sequences. The obtained Intrinsic Mode Function (IMF) sub-sequences are input into the GRU network for independent training and prediction, followed by superposition and reconstruction. The model is validated using the GNSS monitoring data from three monitoring points at a coal mine in Shaanxi Province, China. The results show that the proposed model outperforms the comparison models in all four evaluation indicators, namely Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R²), with all R² values exceeding 0.8. The model demonstrates superior fitting performance, correlation, and generalization ability, which provides important practical technical support for goaf subsidence early warning, geological disaster prevention and engineering safety management in mining areas. Full article

CT perfusion (CTP) is widely used in acute ischemic stroke imaging, particularly for treatment selection beyond conventional time windows. However, automated perfusion maps are not direct measurements of irreversible tissue injury, but estimates shaped by deconvolution strategy, temporal correction, dispersion handling, and software-specific thresholds. This review provides a clinically oriented explanation of how CTP algorithms influence the estimation of ischemic core and hypoperfused tissue. Particular attention is given to singular value decomposition (SVD) methods, Bayesian approaches, and timing parameters, including time to maximum (Tmax), Delay, time to peak (TTP), and mean transit time (MTT). Differences in residue function estimation and threshold definition may generate variable outputs across software platforms, even from the same source dataset. Perfusion thresholds should therefore not be treated as universally interchangeable. CTP findings should be integrated with clinical status, non-contrast CT, CT angiography (CTA), collateral status, occlusion site, and imaging-to-treatment context, serving as decision-support tools rather than isolated measures of tissue viability. Full article

The structural, electronic, optical, transport and photocatalytic properties of the BTe/PtS₂ vdW heterojunction are investigated by the density functional theory approach. The results reveal that applying the tensile effect can significantly impact the material’s properties. The bandgap values of BTe, PtS₂ and BTe/PtS₂ vdW heterojunction are 1.599 eV, 1.756 eV and 1.19 eV, respectively. The bandgap decreases as the percentage of applied tensile effect increases. Moreover, the photocatalytic water decomposition of BTe/PtS₂ vdW heterojunction is significantly broadened in the pH range compared with that of the two monolayers. In conclusion, the BTe/PtS₂ vdW heterojunction can be used as an efficient photocatalytic material for optoelectronic devices and photocatalysis. Full article

Compressed sensing (CS) and matrix completion algorithms (MCA) have each introduced sparse and low-rank priors into synthetic aperture radar (SAR) imaging. However, their combined use reveals a fundamental zero-sum trade-off: enhancing spatial continuity tends to obscure weak targets, while strengthening sparse recovery amplifies off-grid artifacts. This inherent conflict is further exacerbated by static regularization, which imposes a rigid global compromise and prevents genuine synergy between the two priors. To overcome this limitation, this paper proposes a Spatio-Temporal Adaptive Synergistic Constraint Imaging (STASCI) algorithm, which dynamically balances the two priors in a scene-aware manner. The core of STASCI is a unified regularization framework. The low-rank constraint models’ spatial continuity in the background to suppress off-grid artifacts. The sparse constraint, enhanced by a non-convex Geman-McClure function, is employed to detect weak targets and compensate for detail loss. A key innovation is a spatio-temporal dual-dimensional regularization mechanism that employs Sobel operators to probe local spatial gradients and dynamically adjusts the strength of each prior according to regional scene characteristics. This enables adaptive synergy rather than a fixed trade-off. The optimization is solved via the alternating direction method of multipliers (ADMM), with the low-rank subproblem accelerated by randomized singular value decomposition (RSVD). Final imaging is performed using the Range Migration Algorithm (RMA). Experiments on real measurements and public datasets demonstrate that STASCI breaks the conventional detail-background trade-off. It effectively suppresses off-grid artifacts while retaining weak targets, leading to significant improvements in imaging accuracy and robustness across complex scenarios. Full article

This paper investigates the problem of exponential synchronization for a class of quaternion-valued inertial neural networks with mixed time delays under aperiodic intermittent control. First, a neural network model incorporating both discrete and distributed delays is established. To overcome the limitations of conventional approaches, a novel quaternion-based controller is proposed, which operates without relying on model order reduction or quaternion decomposition techniques, thereby achieving global exponential synchronization of the system. Furthermore, by constructing an appropriate Lyapunov function and combining the algebraic properties of quaternions with inequality techniques, sufficient conditions for synchronization are rigorously derived within the Lyapunov stability framework. Numerical simulations are conducted to demonstrate the effectiveness of the proposed control strategy and validate the theoretical results. Finally, an image encryption application is developed to further corroborate the practical viability of the proposed scheme, wherein the original image is encrypted into a noise-like pattern without information leakage and perfectly recovered upon synchronization, with quantitative error analysis confirming high-precision exponential synchronization. Full article

Corrosion-driven section loss in steel tower structures erodes load-carrying capacity, yet field assessment still relies on subjective visual grading. This paper presents a closed-loop framework coupling quantitative image-based corrosion measurement with stochastic degradation modeling, Monte Carlo reliability simulation, and Sobol’ variance-based global sensitivity decomposition. Two complementary segmentation paths—hue–saturation–value (HSV) color-space thresholding for fleet-scale screening and DeepLabV3+ deep learning for detailed evaluation—convert imagery into calibrated section-loss estimates via nonlinear regression. Three analysis modes (single-image, multi-angle weighted-median fusion, and Oriented FAST and Rotated BRIEF (ORB) feature-matched temporal differencing) feed a Bayesian-updated power-law corrosion growth model whose outputs propagate through a time-dependent limit-state function via 10⁶-sample Monte Carlo simulation. Sobol’ indices rank each uncertain input’s contribution to the reliability-index variance. A field demonstration on a 40-year-old galvanized lattice tower in an ISO 9223 C4 coastal environment shows that the corrosion rate constant and zinc coating thickness together govern 65% of the total reliability variance and that a risk-ranked selective maintenance strategy reduces expected life-cycle cost by 71% relative to blanket intervention. Full article

Digital infrastructure is increasingly regarded as a key enabler of economic modernization and urban sustainability, but its sustainability implications depend on whether digitalization guides innovation activities toward greener technological directions. Against the backdrop of China’s “dual carbon” goals and the deepening of low-carbon transformation, this study examines the relationship between digital infrastructure development and the green orientation of urban innovation from the perspective of innovation structure. Using panel data for 284 prefecture-level cities in China from 2011 to 2023, we measure the share of green innovation by the proportion of green invention patents in total granted patents, and use broadband Internet access users per 100 residents, denoted as InternetRate, as a proxy for digital infrastructure development. A two-way fixed effects model is employed to investigate the empirical relationship between the two. The results show that digital infrastructure development is significantly negatively associated with the relative share of green innovation within total innovation. This finding remains robust to alternative functional-form specifications, extreme-value treatment, alternative measures of digital infrastructure, and alternative measures of green innovation structure, and remains directionally consistent in a supplementary instrumental-variable test. Decomposition of scale effects indicates that this negative association reflects the relatively faster expansion of non-green innovation rather than an absolute contraction in green innovation, suggesting a structural reallocation pattern within urban innovation activities. Heterogeneity analysis shows that the negative association is mainly concentrated in cities with lower levels of economic development and higher text-based environmental governance attention, and is more pronounced in cities with a lower degree of industrial servitization. Moderation analysis further shows that this negative association becomes weaker in cities with stronger local green fiscal support. Spatial analysis indicates that the share of green innovation exhibits significant spatial dependence; however, the association between digital infrastructure development and innovation structure is mainly localized, with no significant spatial spillover detected. These findings contribute to sustainability research by showing that digital infrastructure does not automatically improve the green composition of innovation and that sustainable digital transformation requires complementary green fiscal support, environmental governance, and industrial upgrading policies. Full article

Cryptocurrency markets exhibit persistent temporal dependence and multifractal scaling behavior, yet these properties remain only partially incorporated into existing deep learning architectures. This study proposes the Fractional Attention-Driven LSTM–N-BEATS (FA-LSTM-NBEATS) framework, integrating Grünwald–Letnikov fractional memory operators, adaptive attention, interpretable N-BEATS decomposition, and an asymmetric loss function within a unified forecasting and risk estimation model. The framework is evaluated using daily BTC, ETH, and BNB data from 2018 to 2025 through hierarchical ablation analysis, walk-forward validation, Diebold–Mariano testing, residual diagnostics, and Fractional Value-at-Risk (VaR) evaluation. Results indicate persistent scaling behavior with Hurst exponents above 0.76 and multifractal spectrum widths of $\mathrm{\Delta }\alpha \approx 0.51–0.54$. FA-LSTM-NBEATS achieves the strongest relative forecasting performance for BNB, with the lowest RMSE (0.02789) and MAE (0.01950) among all evaluated models. The learned fractional parameter $\left(\alpha ≈0.6106\right)$ remains stable across assets, suggesting convergence toward a persistent memory regime. In addition, Fractional VaR produces coverage ratios closer to unity than Historical and LSTM-based benchmarks under high-volatility conditions. Full article

This paper develops a robust numerical scheme based on a frame collocation method for solving multi-term fractional ordinary differential equations (FODEs) whose solutions exhibit multiple singularities at the origin. To adaptively capture the singular behavior, we construct a hybrid basis-function frame by combining shifted fractional Legendre polynomials. An efficient computational formula for the Caputo fractional derivative is derived, which transforms the original problem into a nonlinear algebraic system at the collocation points. Due to the over-completeness of the fractional polynomial frame, the resulting linear system becomes rank-deficient, with only a small subset of singular components carrying meaningful solution information. To eliminate the adverse effects of numerical null-space components, we employ truncated singular value decomposition (TSVD) regularization, thereby enabling stable and high-precision solutions. Extensive numerical experiments on several benchmark problems, including the fractional Bagley–Torvik equation, linear multi-term FODEs, and nonlinear cases, demonstrate that the proposed method achieves exponential convergence rates. Notably, when the singular exponent of the solution matches a tunable parameter ($\delta$) in the basis functions, superconvergence is observed, significantly outperforming standard spectral methods. Compared with traditional spectral approaches, the proposed frame collocation framework retains spectral accuracy while exhibiting superior capability in handling complex singular structures, providing a powerful and reliable tool for high-precision simulations of multi-term fractional differential equations. Full article

Efficient coordination under limited communication is a central challenge in multi-agent reinforcement learning (MARL). Existing approaches often focus on message exchange without explicitly modeling how communication affects policy learning, leading to redundant interactions and limited coordination gains. In this paper, we propose a threshold-gated sparse communication framework built upon QMIX, a monotonic value-decomposition method that mixes individual agent action values into a global team action value. In the proposed framework, communication is integrated into the agent utility function to directly influence policy learning. Each agent encodes local observations into structured representations and activates communication through a learned trigger mechanism. Messages are aggregated via neighbor-constrained attention and incorporated into utility estimation for decentralized decision-making. Experimental results on the StarCraft Multi-Agent Challenge (SMAC) benchmark show that the proposed method improves coordination quality and training stability while significantly reducing communication frequency. On MMM, the Marine–Marauder–Medivac heterogeneous scenario, the communication rate is reduced to approximately 30–38% while achieving up to 96.6% win rate, compared to 92.1% for QMIX. On 10m_vs_11m, a homogeneous scenario where ten allied Marines fight against eleven enemy Marines, communication remains within 28–37% while reaching 88.4% win rate, compared to 85.6% for QMIX. Moreover, on the same task, varying communication thresholds induce clearly differentiated policy behaviors, indicating that sparse communication not only reduces overhead but also plays a critical role in shaping coordination policies. These results demonstrate that selective communication enables efficient coordination while explicitly regulating policy formation. Full article

The atypical proliferation of Sargassum (Sargassum spp.) in the tropical Atlantic and the Caribbean Sea over the past decade has triggered an unprecedented environmental and socioeconomic crisis along the Mexican coastline. Continuous beaching events of this macroalga on the Riviera Maya have caused coastal ecosystem degradation, severe impacts on the tourism sector, toxic gas emissions during decomposition, and high cleanup costs. To address this challenge, the valorization of Sargassum as a raw material for synthesizing functional materials represents a sustainable management strategy. In this study, a superabsorbent hydrogel was developed from Sargassum biomass (collected in Cancún, Quintana Roo, in 2025) using an innovative process that bypasses the conventional cellulose isolation step. The biomass was subjected to high-energy milling (15 and 30 min) to prepare Sargassum powder, which was subsequently carboxymethylated using monochloroacetic acid. This modified biomass was then crosslinked with citric acid, a process evaluated at three different citric acid/carboxymethylated Sargassum mass ratios. The hydrogel synthesized with the lowest crosslinking agent ratio achieved a maximum water absorption capacity of 1160 wt%, a value that exceeds the typical absorption capacities of 700–900% for biopolymer hydrogels. Successful material formation was confirmed by Fourier transform infrared spectroscopy (FTIR), which revealed the characteristic functional groups of CMC and the ester bonds formed during crosslinking. Additionally, scanning electron microscopy (SEM) analysis showed a well-defined porous structure with pore sizes ranging from 8.5 to 19.5 µm, which is essential for its high absorption performance. This study demonstrates the feasibility of producing high performance hydrogels from Sargassum through a simplified, cost-effective, and environmentally friendly process. These findings open a promising avenue for the integrated management of this problematic biomass, transforming it into value-added materials with potential applications in agriculture, hygiene, and environmental remediation. Full article

Architectural and urban quality, or spatial quality, is inherently difficult to objectify due to its subjective nature. Traditional surveys can identify general trends but lack numerical precision. This study proposes a “qualitative quantification” model that transforms subjective judgments into measurable values and tracks their evolution throughout the production process. Based on evaluation criteria decomposition and Hanrot’s MATEA framework, the model measures a large set of indicators related to the spatial quality of the built environment, including accessibility, density, visual comfort, functionality, maintenance and several other aspects. It generates a conceptual and graphical radar evaluation scheme, integrating production stages with quantified indicators. Applied to the outdoor spaces of 40 collective housing estates in Biskra, southeastern Algeria, the model objectively confirms the poor spatial quality perceived subjectively by residents and other stakeholders. Results reveal negative evaluations in design, construction, use and maintenance reflecting systemic deficiencies and governance challenges. This approach contributes to post-occupancy evaluation and sustainable urban development assessment, providing a reproducible framework for quantifying and visualizing spatial quality throughout architectural and urban production, enabling planners and designers to identify weaknesses and monitor improvements over time. Full article

Lithium-ion batteries are widely used in applications ranging from portable electronics to electric vehicles and grid energy storage, owing to their high energy density, efficiency, and long lifetime. However, their performance degrades over time due to aging mechanisms such as solid electrolyte interface growth, lithium plating, and electrolyte decomposition, leading to capacity fade and reduced power capability. Accurate state of charge (SOC) estimation is therefore essential for ensuring safe and efficient battery operation, particularly within battery management systems. While many existing methods rely on the direct relationship between open circuit voltage (OCV) and SOC, practical applications require the inverse mapping, i.e., the estimation of SOC from measured OCV values. This inversion is not always straightforward: analytical solutions are only available for simple models, whereas more accurate formulations often require computationally intensive numerical methods. Direct analytical SOC–OCV relationships (inverse OCV–SOC models) provide an effective alternative, enabling simplified SOC estimation without numerical inversion. Previous work proposed a direct generalized Gaussian analytical relationship expressing the absolute state of charge as a function of OCV, thereby simplifying SOC estimation and avoiding numerical inversion, developed and validated on a lithium cobalt oxide battery cycled in the linear region of the OCV curve at constant battery temperature. Building upon this study, the proposed approach was extended to investigate the effects of cycle aging across a wider operating range, considering low, medium, and high voltage/SOC conditions. The model was experimentally validated, at constant battery temperature, on the same type of lithium cobalt oxide batteries through an extensive testing campaign, demonstrating its effectiveness in capturing battery behavior under different operating conditions. Full article