Perceived control in one context can affect behaviour in novel contexts. One potentially important variable determining generalisation is how perceived control in one context shapes beliefs about the self. Typically, learned helplessness studies do not control or manipulate beliefs about the self. Here, we test whether observing others' ability to exert control helps to inform inferences about whether the controllability is primarily due to one's own ability or a feature of the current environment, and thereby determines the degree of generalisation. In an initial study (N = 200) and pre-registered replication study (N = 436) we used comparative social feedback about performance in a novel task (the Wheel Stopping task) to assess how self- or environment-specific inferences shape control beliefs. Linear mixed effects models in both studies revealed that both task controllability and social feedback uniquely predicted participants' local control beliefs (trial-by-trial), after accounting for perceived task difficulty. Additionally, in Study 2, there was a significant decrease of internal locus of control in the group given low relative feedback in the low control condition, suggesting that in low control, social feedback impacts global control beliefs. Study 2 further revealed that task controllability and feedback were not related to changes in reported self- versus task-specific attribution of control. Social feedback generalised to influence behaviour in a second task in terms of the number of actions performed but not the time taken to escape or the proportion of those who learned to escaped. These results suggest that comparative social feedback shapes local control beliefs, global control beliefs in low control scenarios, and generalises to some aspects of behaviour in a second task, but does not change reported self- or task-attribution of control.
The global energy crisis and environmental challenges can be addressed by replacing fossil fuels with hydrogen energy, which is recognized as a promising future energy source. The hydrogen evolution reaction (HER) aims to achieve low overpotentials at potentially high current densities, but its industrial process is hindered by the substantial economic and time-intensive costs. In this study, we synthesized the acicular NiMoO4 nanowire arrays on Ni foam (NiMoO4 aNWs/NF) through the hydrothermal method, and constructing the Ni heterolayer with a unique nano-island structure on NiMoO4 nano units via electrodeposition. A two-step strategy was adopted to ultimately yield the Ni-NiMoO4 aNWs/NF catalyst. There are abundant oxygen vacancies on the nano units of NiNiMoO4 aNWs/NF to expose active sites. It also capitalizes on the structural advantages of the nanoarray to establish rapid water-gas exchange channels on the catalyst surface, thereby enhancing mass and charge transport efficiency. Consequently, Ni-NiMoO4 aNWs/NF exhibits enhanced HER activity, demonstrating a low overpotential of 188 mV at 1 A center dot cm(-2) and maintaining long-term stability for 550h. A two-electrode electrolytic cell assembled by employing Ni-NiMoO4 aNWs/NF and NiMoO4 aNWs/NF as the cathode and anode, achieved an overall water splitting performance of 2.02 V at 1 A center dot cm(-2) in 1 M KOH.
Time-dependent partial differential equations (PDEs) underpin modeling of dynamical phenomena across science and engineering, but repeatedly solving them at high fidelity remains expensive. Neural operators offer reusable surrogates for such systems, yet current approaches often lose robustness under sparse or irregular temporal supervision and can be costly to train for high-resolution or long-trajectory problems. Here we introduce the Time-Attentional Neural Operator (TANO), a composite neural operator whose stacked layers each extract multiscale spatial features, model their temporal interactions with attention, and reconstruct the spatial representation. We further develop a stochastic temporal subsampling strategy in which each training step uses only a random subset of time points, improving training efficiency while retaining one-pass full-trajectory prediction over the supervised horizon. Across five canonical PDE benchmarks, TANO reduces prediction errors by 1.87–3.94× relative to existing baselines and remains robust under sparse temporal supervision or limited data. It accurately simulates wave propagation over long trajectories in heterogeneous media, trains about 4× faster than the competing approach on high-resolution problems, and can be extended to long-horizon extrapolation through a windowed rollout scheme. Our findings establish TANO as an efficient and robust neural-operator framework that makes training on complex spatiotemporal problems more computationally tractable and supports practical surrogate modeling.
In this article we report on numerical results for soot formation in n-dodecane and biodiesel spray flames, ob tained via Large Eddy Simulations (LES). Emphasis is placed on the influence of the flame structure on the spatial distribution and temporal evolution of soot. The biodiesel fuel is a surrogate of Karanja Methyl Ester (KME), composed of n-dodecane and methyl butanoate and in our study we consider the well-known reactive Spray A configuration. In terms of combustion modeling, we employ a specific formulation of the Flamelet Generated Manifold (FGM) approach that involves 4 control variables (progress variable, mixture fraction and their vari ances). Also, in our formulation, the temperature is computed directly from the energy equation rather than from the FGM database. Soot formation is described by a multi-step phenomenological model accounting for inception from acetylene and key Polycyclic Aromatic Hydrocarbons (PAH), surface growth, coagulation, and oxidation by O2 and OH. The results show that soot formation in spray flames is controlled by the coupled interaction of fuel thermophysical properties, evaporation processes, and combustion chemistry. Despite similar ignition behavior, biodiesel develops a less rapidly spreading flame and produces markedly lower soot levels than n-dodecane. This reduction is linked to modified local mixture conditions and decreased availability of soot-forming precursors. Overall, the study highlights the strong sensitivity of soot evolution to fuel-dependent spray and flame charac teristics and demonstrates the capability of the proposed LES-FGM framework for predictive simulations of soot in alternative-fuel spray combustion.
Biochar-poly(lactic acid) (PLA) composites are emerging as waste-derived biocomposites that integrate biomass valorization, biodegradable polymer development, and circular bioeconomy strategies. This review critically synthesizes how biochar feedstock, pyrolysis temperature, ash content, inorganic composition, surface chemistry, particle size, filler loading, and processing route influence the thermal, mechanical, degradability, and functional performance of PLA-based composites. Current evidence shows that optimized biochar incorporation can improve stiffness, tensile or flexural modulus, crystallization behaviour, impact resistance, dimensional stability, and composting-driven degradation. These benefits are mainly linked to biochar's carbon-rich structure, porous morphology, nucleating ability, surface functionality, and interfacial interactions with PLA. However, performance gains are not universal. Excessive loading or poor dispersion can reduce tensile strength, elongation at break, thermal stability, melt flow, and processability because of particle agglomeration, weak filler-matrix adhesion, moisture sensitivity, pore blockage, and processing-induced PLA chain scission. Particular attention is given to ash and inorganic residues, including alkali and alkaline-earth metals, carbonates, phosphates, silicates, and metal oxides, which may either promote crystallization and char formation or catalyze PLA degradation depending on their speciation, concentration, and dispersion. The review compares solvent casting, melt mixing, extrusion, compression and injection molding, filament production, and additive manufacturing, highlighting their advantages and processing constraints. Application opportunities in packaging, agriculture, water treatment, construction-related materials, biomedical systems, and 3D printing are discussed alongside food-contact safety, migration, durability, biocompatibility, regulatory, and end-of-life considerations. Wider adoption requires feedstock standardization, ash chemistry control, improved interfacial design, application-specific validation, and life-cycle assessment.
