Search Results (39,959)

Non-destructive in situ tests, such as the plate bearing (plate load) test, are widely used to estimate the equivalent deformation modulus (e.g., $E_{v2}$) of existing road embankments. However, the depth of influence sampled by such a test is governed by the loading plate diameter, so a single test yields only an average, diameter-dependent modulus rather than the actual variation in stiffness with depth. This study investigates whether systematically varying the plate diameter and inverting the resulting settlement–diameter (dispersion) curves can recover the full depth-dependent stiffness profile, $E\left(z\right)$. Synthetic settlement–diameter curves were generated using a Boussinesq-based forward model for four families of reference stiffness profiles, representing normal (stiffness increasing with depth) and reverse (stiffness decreasing with depth) linear and exponential trends, combined with six Poisson’s ratios and five profile slopes/exponents (30 cases per profile family, 120 cases in total). Two inversion strategies were applied to back-calculate $E\left(z\right)$ from each dispersion curve: a classical Occam-type, smoothness-constrained (Tikhonov-regularized) nonlinear inversion, and a direct, closed-form simplified inversion method (SIM) based on differencing the apparent-modulus-versus-diameter curve. The results were benchmarked against the known reference profiles. Once calibrated so that its governing parameters depend only on Poisson’s ratio and the shape of the measured dispersion curve, SIM could be applied blindly—without knowledge of the reference profile or a starting model, requiring only an assumed Poisson’s ratio and the established calibration—and recovered $E\left(z\right)$ with markedly lower error than Occam’s inversion (WAD = 2.2–4.7% and RMSPE = 2.6–5.8%, versus 7.4–19.1% and 9.4–28.3%, respectively, across the four profile families). For the Poisson’s ratio most typical of earth materials, $\nu =0.3$, the calibration further collapses to a single parameter set common to all four families investigated ($I=0.66$; $c=1.3$ for stiffness increasing with depth, $c=2.5$ for stiffness decreasing with depth), which attains WAD ≤ 4.2% across all four families with no calibration equation at all. Notably, Occam’s inversion reproduced the settlement–diameter curve itself with good accuracy in most cases, yet this close data fit did not guarantee an accurate stiffness profile—a direct manifestation of the intrinsic non-uniqueness of the settlement-based inverse problem. These findings are bounded by their evidence base: noise-free data from the same forward operator used in the inversion, smooth profiles, a calibration evaluated on the cases that produced it, and an Occam comparison specific to L-curve-selected regularization. Within these limits, SIM is a promising alternative to regularized inversion; measurement noise, layered profiles and field validation are the next steps. Full article

Renewable-driven operation exposes proton-exchange membrane (PEM) electrolyzers to ramps, starts, and partial-load conditions that accelerate degradation and weaken scheduling reliability. This paper develops a health-aware scheduling framework for an integrated electro-hydrogen system. It represents operating stress, delayed response, irreversible degradation, recoverable performance loss, and efficiency feedback. Scheduled rest partially relaxes the recoverable state, while per-unit health budgets yield degradation shadow prices that redirect load from health-scarce stacks. A Wasserstein distributionally robust model implements a health-conditioned risk-aversion policy by adjusting the protection radius with pre-horizon fleet health and filtered stress. The tractable finite-support formulation is evaluated through progressive ablations and five uncertainty treatments using chronological Liaoning wind, solar, and load data. Rotational recovery provides the main degradation mitigation, whereas shadow prices primarily improve allocation among heterogeneous stacks. Compared with fixed-radius DRO, the health-conditioned policy reduced the point estimates of ${\mathrm{CVaR}}_{95}$, energy-violation rate, and severe health-budget exceedance by 3.91%, 1.68 percentage points, and 3.36 percentage points, respectively. These results indicate the value of coordinating equipment health and uncertainty protection in short-term electro-hydrogen scheduling. Full article

Urban resilience is widely mobilised across built-environment research and practice, yet it remains conceptually contested, with definitions varying across disciplines and limiting translation into coherent planning and governance. This study examines how a multidisciplinary group of built-environment researchers conceptualise resilience and resilient futures, and how these understandings evolve through structured transdisciplinary engagement. Using an exploratory action research design, twelve academic staff and higher degree research candidates from a single built-environment school at the University of Technology Sydney in Australia participated in a two-day workshop combining individual reflection, facilitated group discussion, and engagement with the key resilience literature, followed by consolidation through collaborative writing. Thematic coding of participants’ definitions and discussions identified three primary framings: shocks and stresses, processes, and future outcomes. Participants shifted away from “bouncing back” narratives towards anticipatory preparedness, learning, adaptation, and, when needed, transformation. Cross-cutting themes highlighted the dual nature of disturbances, governance spanning formal and informal systems, multi-scalar interdependence, cultural identity and continuity through change. The study proposes a consolidated definition of urban resilience that makes these dimensions explicit and supports operationalising resilient futures. Given the single-institution sample, the definition is offered as a transferable starting point rather than a definitive interpretation. Full article

Reliable integration of wind energy into modern power grids requires control strategies capable of maintaining stable operation under stochastic wind conditions and grid-side disturbances. This paper presents an adaptive metaheuristic optimization and numerical modeling framework for robust multi-scenario tuning of proportional–integral controller parameters in a doubly fed induction generator (DFIG)-based wind-energy conversion system. The optimized control loops include the rotor-side converter, grid-side converter, rotor-speed loop, and DC-link voltage loop. Unlike conventional tuning approaches that rely on nominal operating points or limited deterministic cases, the proposed formulation evaluates each candidate controller over multiple operating scenarios, including start-up dynamics, step wind-speed variation, random wind fluctuation, and grid-voltage dip conditions. An Adaptive Whale Optimization Algorithm (AWOA) is developed by incorporating diversity-aware adaptation and stagnation-handling mechanisms into the standard WOA structure to improve the exploration–exploitation balance during the search process. The tuning objective combines aggregate transient-performance minimization with robustness-oriented scenario evaluation, thereby promoting controller gains that remain effective across uncertain operating conditions. Comparative numerical simulations against Grey Wolf Optimizer, Generalized Grey Wolf Optimizer, Moth-Flame Optimizer, and standard WOA show that the proposed AWOA achieves lower aggregate Integral Time Squared Error values across the considered cases. Convergence assessment, ablation analysis, and hold-out robustness testing further confirm the contribution of the adaptive mechanisms. Time-domain responses also demonstrate improved DC-link voltage regulation and reactive-power recovery under severe grid disturbances. These results indicate that the proposed framework can enhance the reliability and resilience of grid-connected DFIG wind-energy systems, supporting more robust and sustainable renewable-energy integration. Full article

A fundamental concern for coaches and sport practitioners is developing athlete performance while preserving health and well-being. An emerging topic is that performing in nature-based environments may have more benefits than performing in typical built/indoor environments. However, to understand athlete performance and well-being, it is essential to recognize how performers accommodate (adapt) skills and resources under environmental constraints in pursuit of sport and exercise task goals. This article presents a narrative review to support a hypothesis on the sustained benefits of performing in nature-based environments, with health and well-being as necessary aspects for performance, grounded in the ecological dynamics theoretical framework. We start by discussing: (i) performance in nature-based and indoor environments underpinned by affordance perception; (ii) evidence for the role of nature-based environmental constraints on movement and psychological aspects; (iii) the hypothesis that prolonged engagement with nature-based environments during sport and exercise may promote adaptive skill development, health, and well-being, and may facilitate the transfer of these adaptations to performance in built indoor competitive settings; (iv) the distinct lived experiences (e.g., emotion, well-being) that emerge from exercising in nature-based and built indoor environments; and (v) suggestions on how to include nature-based constraints on sport and exercise training, and future research considerations. Overall, the reviewed theoretical and empirical evidence provides a rationale for the hypothesis that prolonged engagement with nature-based environments during sport and exercise may promote adaptive skill development, health, and well-being, with potential transfer of these adaptations to performance in built indoor settings. Future longitudinal and experimental studies are required to test this hypothesis and determine whether, and under what conditions, these proposed adaptations transfer to performance in built indoor sport settings. Full article

The current study examined judgments of learning (JOL) reactivity in social contexts. Participants studied related and unrelated cue–target word pairs. Half of the participants were asked to predict the likelihood that other people would remember the cue–target pairs on a later memory test, whereas the other half were not. All participants then completed a final cued recall test. The results show that making JOLs for others, like making JOLs for oneself, produces positive reactivity. Specifically, making JOLs for others enhanced memory for related word pairs but not unrelated pairs, supporting the cue-strengthening hypothesis. This study serves as a starting point to examine the influence of making JOLs in social contexts and to understand the impact of cue utilization on JOL reactivity. Full article

This study examines the effects of graph aggregation and candidate-level scoring on PPO-based coverage planning for simulated curved point clouds. In a static uniform-coverage benchmark, a parameter-matched global MLP reaches 22.13% overall coverage, GCN-PPO reaches 76.97%, and Graph-Enhanced PPO reaches 88.37%; DFS remains the strongest at 95.80%. A separate 50-decision task introduces nonuniform priorities and motion costs. In that setting, GCN-PPO and Graph-Enhanced PPO obtain nearly identical weighted coverage (0.812 and 0.811, respectively), and no statistically reliable difference is detected. On a separate 1000-node scan-derived Stanford Bunny surface with retained holes and mesh-geodesic connectivity, GCN-PPO and Graph-Enhanced PPO again show comparable weighted coverage (0.450 and 0.445, respectively) and task utility (0.321 and 0.315, respectively), with no statistically reliable difference. Across the physically normalized zero-shot density and area tests, the direction of the small mean differences varies by condition, and none remain significant after multiplicity correction; both policies lose performance as the sampling density or workspace size changes. Direct adaptation on the known fivefold-area target graphs raises Graph-Enhanced PPO coverage from 0.631 to 0.753 for held-out starts. Because adaptation and evaluation use the same target graph instances, unseen large-graph generalization is not tested. Across the experiments, graph aggregation provides the clearest learned improvement. The auxiliary scorer improves the static benchmark result, but the added parameters prevent assigning that gain solely to a separate scoring mechanism; no consistent benefit appears in the other tests. Systematic traversal remains preferable when exhaustive uniform coverage is feasible. The study remains simulation-based: scan-derived geometry is evaluated, but validation on physical hardware is still required. Full article

Accurate online state of health (SoH) estimation, especially for highly degraded second-life batteries (SLBs), is critical for the safe and efficient operation of any Battery Management System (BMS). However, existing data-driven estimation methods typically rely on complete charge/discharge cycles or assume a fixed starting state of charge (SoC) and fixed voltage windows. These assumptions are highly restrictive and rarely align with the random, partial charging behaviors of real-world electric vehicle (EV) operations and Battery Energy Storage System (BESS) applications. To overcome this limitation, this paper presents the following framework: an online SoH estimation approach utilizing long short-term-memory (LSTM)-ensembled learning using consecutive constant current (CC) charging segments. Instead of relying on a rigid voltage window, the presented method utilizes nested, expanding slices of highly flexible CC charging intervals to capture sequential degradation dynamics. Furthermore, this study maps suggestive ranges of optimal voltage intervals that dynamically adapt to different battery health conditions, ensuring high estimation accuracy despite the type of degradation. The framework is validated using four commercially available lithium-ion batteries (LIBs), with a moderate to low SoH down to ~40%. To ensure practical viability, the hybrid deep neural network (DNN) and LSTM architecture has been highly optimized, requiring only 474 trainable parameters. The framework has been evaluated utilizing multiple performance matrices, showing minimal discrepancy with excellent correlation to the true SoH throughout the lifespan of the testing cell. Full article

Background: Iatrogenic fungal infections associated with contaminated parenteral products are uncommon but potentially severe, particularly when the affected patients are critically ill and the microorganism is not included in routine diagnostic panels. Here, we describe C. viswanathii candidemia associated with contaminated milrinone in an extremely preterm infant and place the case within the context of the published clinical evidence. Methods: The infant’s clinical course, microbiological findings, milrinone exposure and pharmaceutical-source investigation were retrospectively reconstructed. Published human C. viswanathii infections were reviewed systematically, together with Italian pharmacovigilance communications concerning milrinone. Results: A 600 g preterm infant developed candidemia on Day 7 of life. A central-venous-catheter (CVC) blood culture became positive for yeasts after 7 h, while the BIOFIRE^® Blood Culture Identification 2 (BCID2) Panel remained negative. C. viswanathii was recovered from blood, urine and bronchoalveolar lavage fluid (BAL). Voriconazole was started, and anidulafungin was subsequently added. The infant died on Day 13 following accidental extubation and unsuccessful resuscitation. Exposure reconstruction identified a contaminated milrinone lot, and the source investigation linked the case to the wider Italian multicenter cluster. The literature review showed that C. viswanathii can cause bloodstream, central nervous system, deep-seated and osteoarticular infection, with variable susceptibility, particularly for azoles, but no previous iatrogenic sources were found, and its ecological niche remains undefined. Conclusion: This case provides additional evidence that the Italian C. viswanathii outbreak was an iatrogenic event associated with contaminated milrinone. Recognition depended on combining extended yeast identification with medication-lot traceability and interinstitutional pharmacovigilance. Full article

We develop a stochastic discrete-time framework for Wolbachia invasion in mosquito populations under environmental variability. Starting from a deterministic frequency-dependent difference equation with an Allee-type threshold, we consider the random iteration $X_{n+1}=F_{{\mathsf{\Theta }}_n}\left(X_n\right)$, where ${\mathsf{\Theta }}_n$ represents fluctuations in fitness cost, cytoplasmic incompatibility, and maternal transmission. We establish existence, uniqueness, positivity, forward invariance of $[0,1]$, continuity, and pathwise order preservation, and formulate the model as a random dynamical system. Under independent environmental forcing, the process is Markovian; the associated Markov operator is Feller, and compactness of the state space implies the existence of invariant probability measures. Local behavior near extinction is characterized by the stochastic Lyapunov exponent $\lambda =\mathbb{E}\left[\log \left((1-M)(1-S_f)\right)\right],$ with $\lambda <0$ yielding local exponential stability of the Wolbachia-free state. We further introduce finite-horizon establishment probabilities and probability-based release thresholds, whose monotonicity follows from the pathwise comparison principle. Numerical simulations show how the magnitude, source, and dependence structure of environmental variability affect invasion probabilities and release requirements. The framework combines nonlinear difference equations, random dynamical systems, Markov operators, invariant measure theory, and stochastic stability in a unified analysis of threshold-dependent Wolbachia invasion. Full article

We present a computational framework for constructing effective, intrinsically screened heavy-particle potentials in dense plasmas from first-principles pseudopotentials. Starting from available projector-augmented-wave (PAW) data in the Unified Pseudopotential Format (UPF), the angular-momentum-dependent channel potentials (s, p, d, f) are reconstructed and mixed according to Boltzmann occupation probabilities at the plasma temperature. The intrinsic screening by thermally populated valence electrons is included through a self-consistent Hartree potential computed for each charge state, yielding element-specific temperature-dependent effective scattering potentials for neutral atoms and ions. The Saha ionisation equilibrium, corrected for ionisation-potential depression via the Stewart–Pyatt model, determines the species composition. A number-density-weighted mixture average then produces a single effective heavy-particle potential for multi-component plasmas. The procedure is implemented in D_plas_V, a C++17 code with zero external dependencies, and all methods are validated against analytical test cases. Full article

Background: The gluten-free diet (GFD) remains the cornerstone of treatment for celiac disease (CeD), yet it is associated with a substantial psychosocial and practical burden. As novel therapies are being developed, understanding patient priorities, and their alignment with physician perceptions, is essential to guide future therapeutic strategies. Methods: We conducted a multicenter, cross-sectional online survey of adults with confirmed CeD followed at tertiary referral centers in Italy (Salerno), the UK (Sheffield) and Malta (La Valletta). The questionnaire collected demographic data, a 0–10 visual analogue scale (VAS) assessing self-reported adherence to the GFD, and responses to a single-choice question exploring what participants missed most since starting the GFD. A parallel survey was administered to healthcare providers to assess their perception of patients’ unmet needs. Descriptive statistics and between-groups comparisons were performed. Results: A total of 486 patients (74.7% female; mean age 43.2 years; 215 from Italy, 196 from the UK and 75 from Malta) and 134 physicians were included. GFD adherence was high, with a median score of 10. The most frequently reported unmet need among patients was the desire for an unrestricted diet (25.9%), followed by a preference for a medication allowing occasional gluten exposure (16.9%), greater availability of gluten-free options during travel (15.0%), and improved safety when eating out (13.6%). Interest in a lifelong pharmacological alternative to the GFD was limited (9.5%). Despite minor differences in the distribution of responses across centers, the overall pattern of patient priorities was remarkably consistent. Among 134 physicians (76 (56.7% from the United Kingdom, 20 (14.9%) from Malta, and 38 (28.4%) from Italy)), the most frequently perceived patient need was also an unrestricted diet (17.9%), but a higher proportion attributed importance to lifelong pharmacological therapy (16.4%). The distribution of responses differed significantly between patients and physicians (χ² test, p < 0.001). Conclusions: Patients with CeD across different European healthcare systems report consistent unmet needs driven primarily by lifestyle constraints and the burden of maintaining a strict GFD. Importantly, most patients do not seek complete replacement of the GFD, but rather strategies that provide greater flexibility and protection against occasional gluten exposure. A significant mismatch exists between patient priorities and physician perceptions, with clinicians overestimating the demand for a definitive cure and underestimating the importance of day-to-day disease burden. Future efforts should improve GFD practicality and prevent accidental gluten exposure, while an unrestricted diet remains the ultimate therapeutic goal. Full article

Accurate relationships between water diffusivity and chemical–morphological characteristics of membranes are mandatory for membrane material optimizations. To meet this challenge, careful post-processing of previous extensive Molecular Dynamics (MD) simulations was performed in this work to obtain the distribution of water molecules in nanometer-thick polyamide active layers, both at equilibrium and during water permeation. The polyamide active layer was modeled as a linear, uncross-linked TMC–MPD network, representing the low-crosslink-density limit of the FT-30 chemistry. This allowed us to gain insights into molecular-level structures and macroscopic transport patterns. The water volume fractions and Fick diffusivities, extracted from the aforementioned MD simulations, were also used to evaluate the thermodynamic factor accounting for the water activity gradient and the Maxwell–Stefan diffusivity, directly related to the chemical structure of the ultrathin polyamide active layer. Furthermore, three effective-medium-based approaches (Mackie–Meares, Bruggeman, and Maxwell models) were used to predict the water diffusion coefficients using, as for MS coefficients, input derived from MD simulations. Hence the results were compared for a rigorous assessment of the predictive capability of these models. Using the equilibrium membrane hydration, the analysis shows that Bruggeman and Maxwell models provide more accurate predictions compared to the Mackie–Meares model; indeed, the former provide diffusion coefficients in the range of experimental values available in the literature. On the contrary, the Mackie–Meares model yields less accurate values, even compared to diffusivities obtained using the previous molecular dynamics simulations. Ultimately, the results demonstrate that the effective medium-based approaches lose accuracy when transient hydration is used. Full article

To address the mismatch between solid conveying efficiency and melting efficiency in grooved barrel single-screw extruders (SSEs), the effects of barrel groove and screw channel structural parameters on the melting start point and melting length were systematically investigated based on the groove-channel coupled melting (GCCM) theory. Three extruder configurations—smooth barrel, spiral-grooved IKV (Institut für Kunststoffverarbeitung), and GCCM—were designed and tested on a hydraulically driven clamshell barrel SSE platform with a screw diameter of 45 mm and a length-to-diameter ratio of 30:1. Low-density polyethylene (LDPE) grade 607 was used as the model material. The results demonstrate that increasing the barrel groove depth shifts both the melting start point and melting length downstream, whereas the groove width has negligible effects. A minimum melting start point is achieved at a groove pitch of 4D. At a screw speed of 30 r/min, the GCCM extruder equipped with a BARR barrier screw achieves a melting start point 24.0% earlier than the IKV extruder and a melting length shortened to 62.7% of the IKV value, representing reductions of 27–35% and 21–31% compared to reported barrier screw and Maddox screw melting lengths, respectively. The actual throughput reaches 93.7–95.7% of the theoretical solid conveying throughput, with specific energy consumption only 8.5% higher than the IKV extruder and throughput fluctuation reduced to 23.7%. Complete melting is achieved at a melting zone temperature of only 110 °C, confirming the dominant role of internal frictional heat. This study provides systematic experimental data and theoretical guidance for the structural optimization of grooved barrel SSEs. Full article

The strong coupling among guidance laws, control loops, aerodynamics, and mission constraints poses growing challenges to the design of modern tactical missile guidance systems for autonomous flight. Conventional manual tuning and simulation-based trial-and-error result in long iteration cycles, limited reuse of design knowledge, and poor adaptability to changing scenarios. To address these limitations, we propose GSMultiAgent, a multi-agent collaborative cascade framework built atop Hermes Agent, which transforms natural-language mission requirements into optimized guidance system models through structured agent cooperation with feedback-driven iterative refinement. Three innovations are introduced: (1) a three-layer correction pipeline covering syntactic checking, deterministic mathematical verification, and semantic reasoning; (2) a bimodal experience repository supporting similarity-guided retrieval with access-count decay and best-quality retrieval for PPO warm-start initialization; and (3) a self-adjudicating optimizer that autonomously decides between PPO-based systematic parameter search and heuristic LLM-tuning guided by a reflection agent. Across four engagement scenarios, GSMultiAgent consistently attains high feasibility at a small fraction of the simulation budget required by conventional optimizers and single-agent baselines, and its design paths escalate autonomously from parameter tuning to structural law modification as task difficulty increases. Ablation studies confirm that the reflection agent, the optimization agent, and structured memory each contribute essential and complementary gains. These results establish multi-agent coordination with structured memory and self-adjudicating optimization as an effective paradigm for intelligent, reusable guidance system design. Full article