Search Results (169)

Systematic spatiotemporal records of the International Roughness Index (IRI) for South American Andean rural corridors remain scarce, and available deterioration models, calibrated mostly under temperate or arid conditions, transfer to Andean tropical contexts with considerable uncertainty. This exploratory baseline study addresses that gap on the 36.50 km Loja–Catamayo corridor in southern Ecuador under three a priori constraints: eleven IRI campaigns, one meteorological station whose record starts ten months after the first campaign, and a traffic series anchored on a base-year count conducted ten years before the monitoring window. The campaigns, conducted with a Roughometer III between 2023 and 2025, were integrated with daily climate records from the INAMHI Villonaco station, a yearly AADT series cross-validated against a contemporary classified count, and the as-designed pavement structural section. The non-parametric framework combined the Mann–Kendall trend test with a 25-cell Antecedent Moisture Index sensitivity grid, AASHTO 1993 Structural Number computation, Sayers-derived Present Serviceability Index, and linear, exponential, and Gompertz modelling. The results revealed a statistically significant positive monotonic trend robust to post-peak truncation (H₁ supported) and no detectable short-term climate–IRI association under any of the twenty-five AMI specifications tested (H₂ not supported at the available resolution). The corridor exhibits a structural reserve exceeding projected cumulative ESAL demand by an order of magnitude yet reached the functional intervention threshold at one-third of its design service life. This decoupling between structural adequacy and functional decay locates the dominant deterioration mechanism in the bituminous surface and the drainage regime, supporting surface preservation interventions as the operationally appropriate response. Full article

Public health suffers from noxious gas emitted by massive beached Sargassum algae. Net-barrages deployed in near-shore seas can contain Sargassum, provided they efficiently resist the additional hydrodynamic pressure induced by the catch. Nowadays, the design and installation of net-barrages are empiric. Structural breaks and anchor and mooring chain drifts can arise. We provide a mechanical model to evaluate stresses and loads on a structure made of fishing nets and buoy moorings. Hydrodynamic uncertainties occur through catches, fouling and sea current amplitudes appearing in lagoons or sheltered bays. This study presents a non-linear four-node finite-element model for continuous elastic membranes undergoing large displacements and small strains. The model relies on the Lagrangian linearly elastic membrane theory, employing the non-linear Green strain tensor and a non-updated hydrodynamic loading. We study forcings fixed a priori on a netting section of barrage that is 50 m long and 1 m high with double layer, e.g., two net-faces. We consider low and moderate current velocities, 0.05 and 0.35 m∙s⁻¹, while assuming specific vertical and horizontal catch pressures. A barrage installed in the reef lagoon at Le François on Martinique Island that is observable by satellite imagery could benefit of the computed net and mooring tensions. Full article

Preoperative risk stratification for laparoscopic cholecystectomy (LC) remains imperfect, particularly in patients with chronic inflammatory remodeling and biliary anatomic variants. Existing tools often focus on acute presentations or intraoperative variables, resulting in uncertainty on how congenital anatomy, recurrent biliary colic, and cystic pediculitis interact. We synthesize a hypothesis-generating conceptual framework and propose an illustrative candidate preoperative rubric for future validation. We performed a structured narrative review of PubMed, Scopus, and Web of Science (January 1990–December 2024; last search: 15 December 2024). Eligible primary studies evaluated clinical history, imaging-defined anatomy, inflammatory biomarkers, and/or operative outcomes (conversion, intraoperative complications, or operative difficulty) in the setting of LC. Acute cholecystitis and chronic/elective cohorts were interpreted separately during the narrative synthesis. Two reviewers screened titles/abstracts and assessed full texts using predefined inclusion/exclusion criteria; due to heterogeneity, no meta-analysis and no formal risk-of-bias tool were applied. The literature supports a plausible vicious cycle in which biliary anatomic variants may impair drainage and promote stasis, recurrent biliary colic, and chronic inflammation, ultimately leading to fibrosis/pediculitis and a “frozen” Calot’s triangle. We translate these signals into an illustrative candidate rubric (0–16 points) spanning three domains: clinical history (0–6), imaging (0–6), and inflammatory biomarkers (0–4). Weights and cut-offs (low: 0–4; moderate: 5–9; high: 10–16) were chosen a priori for conceptual clarity and are not data-derived. This review provides a conceptual map and a candidate variable set to support hypothesis generation, standardized data collection, and staged validation. The rubric is not validated and must not be used for clinical decision-making. Planned next steps include feasibility-oriented derivation, followed by prospective multicenter external validation and impact assessment. Full article

Inverse scattering problems seek anomalies in a medium given data measured after the interaction with emitted waves. Due to noise, predictions about the nature of these inclusions should be complemented with uncertainty estimates. To this end, we propose a progressive framework for inverse scattering from low- to high-dimensional Bayesian formulations depending on the prior information and the problem complexity. We aim to reduce computational costs by exploiting educated prior information. When we look for a few well-separated inclusions in a known medium with information about their number, we resort to low-dimensional parameterizations in terms of a few random variables representing their shape and material constants. We test this approach detecting anomalies in tissues and deposits in stratified subsoils. In more complex situations where the anomalies may overlap, we propose high-dimensional parameterizations obtained from Karhunen–Loève (KL) or Fourier expansions of the density and velocity fields. We employ these methods to characterize oil and gas reservoirs in a salt dome configuration, where the screening effect of the dome cap prevents the obtention of adequate prior information. We characterize the posterior probability by means of affine invariant ensemble and functional ensemble MCMC samplers depending on dimensionality. This provides information on configurations with the highest a posteriori probability and the uncertainty around them, identifying factors that could reduce the uncertainty. In high-dimensional setups, techniques based on KL developments are more effective and stable. A recurring issue is the choice of the a priori covariance (which strongly affects the results) and the choice of its hyperparameters. Here, we use educated choices. Formulations that include them as additional parameters could be a next step at a higher cost. Full article

This paper studies prescribed-time (PT) stabilization for a heat equation with an unstable term at the uncontrolled boundary, subject to external disturbances and internal unknown mode uncertainties at the controlled boundary. A boundary time-varying output feedback control scheme based on disturbance estimation is developed, where the convergence time is independent of the initial condition and can be specified a priori. A disturbance estimator using boundary measurements and a time-varying tuning function enables prescribed-time estimation of both the disturbance and the system state. By integrating active disturbance rejection control with the backstepping technique, a boundary output feedback controller is derived. A simulation example from the burning process of a solid propellant rocket demonstrates the effectiveness of the proposed approach. Full article

Here we perform a detailed information-theoretic (IT) analysis of atomic electron densities in the periodic table, from hydrogen (Z = 1) to lawrencium (Z = 103). By use of the Shannon entropy, the Fisher information and the disequilibrium functionals in both position and momentum spaces as fundamental descriptors of the atomic densities, the periodic table can be represented in a three-dimensional information space as a continuous, highly ordered manifold. The analysis shows that chemical periodicity naturally emerges as a helicoidal manifold (reminiscent of a helix) at the coordinates of a 3D theoretic-information space (Shannon, Fisher, Disequilibrium), with each period forming one segment within the continuous global trajectory. We find information-theoretic signatures of shell structure, sub-shell filling, and electron-configuration anomalies, such as the familiar irregularities seen in chromium and copper. Therefore, the helicoidal character emerges naturally and is not imposed a priori. Further, through the uncertainty principle of the complementary analysis in momentum space, more insights are gained by exposing maximal information-theoretic differentiation for lighter atoms and compression among heavy elements. Notably, momentum-space analysis reveals that hydrogen occupies a natural intermediate position between helium and lithium based on kinetic energy distribution—contrasting with IT position-space results that emphasize hydrogen’s unique delocalized electron density. Indeed, the 3D IT representation of the elements in position space aligns with the view that H does not belong to either the alkali metals or the halogens, but rather stands as a unique, standalone element. This complementary perspective provides new quantitative support for understanding hydrogen’s dual chemical nature, providing new quantitative insight into ongoing debates about hydrogen’s optimal periodic table position. Furthermore, by considering triadic relationships and complexity properties in relation to the López–Mancini–Ruiz (LMC) and Fisher–Shannon (FS) functionals, we show that atomic complexity increases monotonically along with nuclear charge, and we provide a quantitative measure of how organized atomic electron densities are distributed throughout the periodic system. Based on our IT analyses, the fundamental character of periodicity could be addressed by employing helicoidal representations that highlight the characteristics of hydrogen, while simultaneously preserving the autonomy of the blocks of elements. Full article

When looking towards the goal of reducing greenhouse gas (GHG) emissions, automotive manufacturers face several challenges when planning future vehicle offerings in different markets. The planned vehicle offerings must cope with uncertainties in the supply chains of critical materials and adhere to regulatory requirements in different regions, all while appealing to customer preferences and maintaining low cost. Regulatory requirements, which are often based on tailpipe GHG emissions, do not necessarily align with Lifecycle Analysis (LCA) of GHG emissions, which becomes yet another challenge towards attaining sustainability goals. Planning the future mix of vehicles to be manufactured under all such considerations can be a complex task, often relying on methods with poor transparency, unguaranteed optimality, or requiring difficult-to-predict a priori knowledge. This paper considers the special case of a short time window (one future model–year), which allows for modelling the future planning decisions as a linear programming (LP) problem, which in turn, can be solved to global optimality via well-established algorithms, such as Dual-Simplex. The proposed formulation is demonstrated via one simple example, as well as a scaled-up study with two regions, two vehicle size categories, and four powertrain configurations. A key insight that the proposed formulation is able to demonstrate in the scaled-up study is how the optimum (lowest) LCA GHG solution depends on the availability of battery materials, ranging from an increased share of hybrids under low battery supply to an increased share of electric vehicles for abundant battery supply. Full article

Objectives: This paper presents an innovative control framework for the classical Cart–Pole problem. Methods: The proposed framework combines Interval Type-2 Fuzzy Logic, the Dueling Double DQN deep reinforcement learning algorithm, and adaptive reward shaping techniques. Specifically, fuzzy logic acts as an a priori knowledge layer that incorporates measurement uncertainty in both angle and angular velocity, allowing the controller to generate adaptive actions dynamically. Simultaneously, the deep Q-network is responsible for learning the optimal policy. To ensure stability, the Double DQN mechanism successfully alleviates the overestimation bias commonly observed in value-based reinforcement learning. An accelerated convergence mechanism is achieved through a multi-component reward shaping function that prioritizes angle stability and survival. Results: Given the training results, the method stabilizes rapidly; it achieves a 100% success rate by episode 20 and maintains consistent high rewards (650–700) throughout training. While Standard DQN and other baselines take 100+ episodes to become reliable, our method converges in about 20 episodes (4–5 times faster). It is observed that in comparison with advanced baselines like C51 or PER, the proposed method is about 15–20% better in final performance. We also found that PPO and QR-DQN surprisingly struggle on this task, highlighting the need for stability mechanisms. Conclusions: The proposed approach provides a practical solution that balances exploration with safety through the integration of fuzzy logic and deep reinforcement learning. This rapid convergence is particularly important for real-world applications where data collection is expensive, achieving stable performance much faster than existing methods without requiring complex theoretical guarantees. Full article

This paper presents a systematic approach for rapid assessment of the performance and robustness of linear control systems through geometric analysis in the complex plane. By combining indirect performance indices within a defined zone of desired performance in the complex s-plane, a connection is established with direct performance indices, forming a foundation for the synthesis of control algorithms that ensure root placement within this zone. Analytical relationships between the complex variables s and z are derived, thereby defining an equivalent zone of desired performance for discrete-time systems in the complex z-plane. Methods for verifying digital algorithms with respect to the desired performance zone in the z-plane are presented, along with a visual assessment of robustness through radii describing robust stability and robust performance, representing performance margins under parameter variations. Through parametric modeling of controlled processes and their projections in the complex s- and z-domains, the influence of the discretization method and sampling period, as forms of a priori uncertainty, is analyzed. This paper offers original derivations for MISO systems, facilitating the analysis, explanation, and understanding of the dynamic behavior of real-world controlled processes in both the continuous and discrete-time domains, and is aimed at integration into expert systems supporting control strategy selection. The practical applicability of the proposed methodology is related to discrete control systems in energy, electric drives, and industrial automation, where parametric uncertainty and choice of method and period of discretization significantly affect both robustness and control performance. Full article

Accurate and reliable multi-scene positioning remains a critical challenge in autonomous driving systems, as conventional fixed-noise fusion strategies struggle to handle the dynamic error characteristics of heterogeneous sensors in complex operational environments. This paper proposes a novel noise-adaptive fusion framework integrating Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS) measurements. Our key innovation lies in developing a dual noise estimation model that synergizes priori weighting with posterior variance compensation. Specifically, we establish an a priori weighting model for satellite pseudorange errors based on elevation angles and signal-to-noise ratios (SNRs), complemented by a Helmert variance component estimation for posterior refinement. For INS error modeling, we derive a bias instability noise accumulation model through Allan variance analysis. These adaptive noise estimates dynamically update both process and observation noise covariance matrices in our Error-State Kalman Filter (ESKF) implementation, enabling real-time calibration of GNSS and INS contributions. Comprehensive field experiments demonstrate two key advantages: (1) The proposed noise estimation model achieves 37.7% higher accuracy in quantifying GNSS single-point positioning uncertainties compared to conventional elevation-based weighting; (2) in unstructured environments with intermittent signal outages, the fusion system maintains an average absolute trajectory error (ATE) of less than 0.6 m, outperforming state-of-the-art fixed-weight fusion methods by 36.71% in positioning consistency. These results validate the framework’s capability to autonomously balance sensor reliability under dynamic environmental conditions, significantly enhancing positioning robustness for autonomous vehicles. Full article

In mobile edge computing networks, achieving effective load balancing across edge server nodes is essential for minimizing task processing latency. However, the lack of a priori knowledge regarding the current load state of edge nodes for user devices presents a significant challenge in multi-user, multi-edge node scenarios. This challenge is exacerbated by the inherent dynamics and uncertainty of edge node load variations. To tackle these issues, we propose a deep reinforcement learning-based approach for task offloading and resource allocation, aiming to balance the load on edge nodes while reducing the long-term average cost. Specifically, we decompose the optimization problem into two subproblems, task offloading and resource allocation. The Karush–Kuhn–Tucker (KKT) conditions are employed to derive the optimal strategy for communication bandwidth and computational resource allocation for edge nodes. We utilize Long Short-Term Memory (LSTM) networks to forecast the real-time activity of edge nodes. Additionally, we integrate deep compression techniques to expedite model convergence, facilitating faster execution on user devices. Our simulation results demonstrate that our proposed scheme achieves a 47% reduction in terms of the task drop rate, a 14% decrease in the total system cost, and a 7.6% improvement in the runtime compared to the baseline schemes. Full article

The critical design challenge for a class of servomechanisms is to reject unknown dynamics (including internal uncertainties and external disturbances) and achieve the prescribed performance of the tracking error. To get rid of the influence of unknown dynamics, an extended state observer (ESO) is employed to estimate system states and total unknown dynamics and does not require a priori information of the known dynamic. Meanwhile, an improved prescribed performance function is presented to guarantee the transient performance of the tracking error (e.g., the overshoot, convergence rate, and the steady state error). Consequently, a modified dynamic surface control strategy is designed based on the estimations of the ESO and error constraints. The stability of the proposed control strategy is demonstrated using Lyapunov theory. Finally, some simulation results based on a turntable servomechanism show that the proposed method is effective, and it has a better control effect and stronger anti-disturbance ability compared with the traditional control method. Full article

A game problem of structural design is defined as a problem of playing against external circumstances. There are two classes of players, namely the “ordinal” and “cardinal” players. The ordinal players, designated as the “operator” and “nature”, endeavor to, respectively, minimize or maximize the payoff function, operating within the constraints of limited resources. The fundamental premise of this study is that the action of player “nature” is a priori unknown. Statistical decision theory addresses decision-making scenarios where these probabilities, whether or not they are known, must be considered. The solution to the substratum game is expressed as a value of the game “against nature”. The structural optimization extension of the game considers the value of the game “against nature” as the function of certain parameters. Thus, the value of the game is contingent upon the design parameters. The cardinal players, “designers”, choose the design parameters. There are two formulations of optimization. For the single cardinal player, the pursuit of the maximum and minimum values of the game reduces the problem of optimal design. In the second formulation, there are multiple cardinal players with conflicting objectives. Accordingly, the superstratum game emerges, which addresses the interests of the superstratum players. Finally, the optimal design problems for games with closed forms are presented. The game formulations could be applied for optimal design with uncertain loading, considering “nature” as the source of uncertainty. Full article

Microelectromechanical systems (MEMSs) are attracting increasing interest from the scientific community for the large variety of possible applications and for the continuous request from the market to improve performances, while keeping small dimensions and reduced costs. To be able to simulate a priori and in real time the dynamic response of resonant devices is then crucial to guide the mechanical design and to support the MEMSs industry. In this work, we propose a simplified modeling procedure able to reproduce the nonlinear dynamics of MEMS resonant devices of arbitrary geometry. We validate it through the fabrication and testing of a cantilever beam resonator functioning in the nonlinear regime and we employ it to design a ring resonator working in the linear regime. Despite the uncertainties of a fabrication process available in the university facility, we demonstrate the predictability of the model and the effectiveness of the proposed design procedure. The satisfactory agreement between numerical predictions and experimental data proves indeed the proposed a priori design tool based on reduced-order numerical models and opens the way to its practical applications in the MEMS industry. Full article

To investigate deep Earth information, researchers often utilize geomagnetic observatories and satellite data to obtain the conversion function of geomagnetic sounding, C-response data, and employ traditional inversion techniques to reconstruct subsurface structures. However, the traditional gradient-based inversion produces geophysical models with artificial structure constraint enforced subjectively to guarantee a unique solution. This method typically requires the model parameterization knowledge a priori (e.g., based on personal preference) without uncertainty estimation. In this paper, we apply an efficient trans-dimensional (trans-D) Bayesian algorithm to invert C-response data from observatory and satellite geomagnetic data for the electrical conductivity structure of the Earth’s mantle, with the model parameterization treated as unknown and determined by the data. In trans-D Bayesian inversion, the posterior probability density (PPD) represents a complete inversion solution, based on which useful inversion inferences about the model can be made with the requirement of high-dimensional integration of PPD. This is realized by an efficient reversible-jump Markov-chain Monte Carlo (rjMcMC) sampling algorithm based on the birth/death scheme. Within the trans-D Bayesian algorithm, the model parameter is perturbated in the principal-component parameter space to minimize the effect of inter-parameter correlations and improve the sampling efficiency. A parallel tempering scheme is applied to guarantee the complete sampling of the multiple model space. Firstly, the trans-D Bayesian inversion is applied to invert C-response data from two synthetic models to examine the resolution of the model structure constrained by the data. Then, C-response data from geomagnetic satellites and observatories are inverted to recover the global averaged mantle conductivity structure and the local mantle structure with quantitative uncertainty estimation, which is consistent with the data. Full article