Hybrid PSO-MPC-based dynamic tuning of battery management parameters for enhanced lithium-ion battery performance in electric vehicles

UAV-thermal driven machine learning framework for predicting methane emissions in tropical landfills

Unraveling the thermal release dynamics and pyrolysis signatures of agarwood by gas-liquid microextraction and gas chromatography-mass spectrometry.

Physics vs structure: A systematic benchmark of learning strategies for multi-zone building thermal dynamics

PINN with dynamic constraint optimization for complex air-based TABS thermal dynamics prediction

Lichtenberg drinking water reservoir, Germany. Coupled hydrological-hydrodynamic modeling is used to evaluate an adaptation strategy for the dimictic Lichtenberg reservoir under climate warming in a realistic operational setup. An ensemble of three one-dimensional lake models, coupled with a rainfall-runoff model, simulated reservoir thermal dynamics through the end of the century under RCP2.6, RCP4.5, and RCP8.5, comparing current and adapted management. The current management strategy releases cold water from near the reservoir bottom to the downstream river, facilitating downward heat transfer within the reservoir. Under this strategy, the ensemble predicted consistent increases in surface and deep water temperatures, highest under RCP8.5 at 0.4 and 0.1 K/decade, respectively. To mitigate this impact, the water release depth to the downstream river is shifted closer to the surface. Surface water temperature, which is primarily driven by meteorology, was insensitive to this strategy. Conversely, the adapted strategy kept deep water isolated through thermal stratification for a longer period and reduced its temperature by about 1.5 K over time and across climate scenarios. This prevented early-summer hypolimnetic depletion and increased the availability of cold deep water for drinking water production. Epilimnetic withdrawal thus emerges as an effective, operationally feasible measure to help preserve water quality and supply in dimictic reservoirs under climate change. • Epilimnetic withdrawal counteracts hypolimnetic warming in a dimictic reservoir. • Coupled hydrological-hydrodynamic ensemble simulation reduces uncertainty. • Reproducing site-specific operation improves simulation realism and applicability.

Femtosecond laser-pumped spintronic terahertz (THz) emissions have attracted intense academic interest, thanks to their intrinsic capability to generate ultrafast THz pulses that cover a wide spectral range. To gain deep insight into the interplay between the ultrafast thermal dynamics in ferromagnets and the spin-to-charge conversion is critical for advancing this field, yet up to now it has not been fully explored, partially due to a lack of efficient tools to measure these two processes simultaneously. Here, we experimentally employ optical-pump THz-probe (OPTP) spectroscopy to promote this study in a typical ferromagnet (Ni80Fe20) nano-film. It not only elucidates the electron and lattice temperature dynamics by quantifying the optical-pump-induced THz transmission changes, but also provides time-resolved THz spectral maps to analyze the sub-picosecond changes, which determines a time lag of 63 ± 8 fs between THz emission and laser excitation. This is an intrinsic parameter constraining the upper frequency limit of spintronic THz emission. Our work deepens the fundamental understanding of ultrafast laser excitation mechanisms for spintronic THz emissions and may offer a new perspective to achieve high-performance THz emitters.

Tissue surface temperature may be influenced by physiological processes related to microcirculation and vascular regulation. Due to its rich vascularisation and thin epithelial layer, the tongue may serve as a sensitive site for assessing dynamic thermal responses. This study aimed to quantitatively evaluate the tongue's response to a cold stimulus using infrared thermography and to analyse its relationship with age in healthy adults. An observational study was conducted in 117 volunteers aged 18-64 years. Tongue temperature was measured at baseline, immediately after cold exposure (20ml of water at 8°C for 60s), and after 2, 5, and 10min. Baseline temperature, as well as absolute and relative indices of temperature recovery, were analysed across three predefined regions of interest. A statistically significant association was observed between age and both baseline tongue temperature (r = - 0.21, p = 0.024) and post-stimulus thermal dynamics, including temperature changes 2min after stimulation (r = 0.33, p = 0.0002) and relative recovery indices (W2-W10: r = 0.29-0.30, p ≤ 0.002). Younger individuals exhibited a greater immediate decrease in temperature, whereas older participants showed higher relative temperature recovery indices; however, this may be partly influenced by differences in the magnitude of the initial temperature decrease. No significant correlations were found between thermal parameters and basic physiological or anthropometric variables. These findings suggest a potential association between age and selected thermal parameters; however, this relationship was not confirmed after accounting for the repeated-measures structure of the data in the mixed-effects model.

Soil temperature forecasting plays a key role in agriculture, hydrology, and climate modeling; however, existing deep learning models often show degraded performance in long-term prediction due to error accumulation, insufficient physical interpretability, and limited spatial generalization. To overcome these limitations, this study proposes a Thermodynamic-Enhanced Physics-Informed Neural Network (TE-PINN), a forecasting framework based on an LSTM backbone that integrates domain-specific physical knowledge through the Latent Thermodynamic Potential Inference (LTPI) and Multi-Pathway Physics-Guided Loss Integration (MPPGLI) modules. LTPI applies free-energy principles and dissipation constraints to characterize internal thermal dynamics, addressing the difficulty of LSTM in capturing long-range temporal dependencies. MPPGLI provides a multi-path physics-guided loss formulation that effectively narrows the discrepancy between predictions and observations, improving robustness. TE-PINN exhibits slower performance degradation across multi-day forecast horizons and maintains stable predictive behavior across datasets from different latitudes. In comparison with both shallow and deep baseline models, the results indicate that introducing thermodynamic priors substantially improves the accuracy and physical consistency of soil temperature forecasting.

This study employs Physics-Informed Neural Networks (PINNs) to simulate the thermal dynamics of biological tissue under laser irradiation by embedding the heat transfer and radiative transport equations into the training process. The PINN architecture, comprising three hidden layers with 50 neurons each and Tanh activation, accurately predicts temperature distributions, achieving close agreement with the analytical solutions (final MSE [Formula: see text]). The thermal penetration depths, calculated under uniform conditions of P = 5 W and beam diameter = 1 mm, were CO[Formula: see text] (0.11 mm), Nd:YAG (0.077 mm), Er:YAG (0.063 mm) and Diode (0.055 mm), lasers. These values are strongly dependent on laser power density and absorption coefficients, and thus represent reference conditions rather than generalizable results. CO[Formula: see text] lasers concentrated energy at the surface, enabling precise incisions; Nd:YAG achieved deeper subsurface heating suited for coagulation; Er:YAG provided highly efficient superficial ablation; and Diode lasers offered balanced heating for minimally invasive procedures. The analysis further showed that laser-tissue interactions are strongly influenced by fluence, power, and pulse duration. Higher power leads to more superficial heating, while lower power favors deeper diffusion. Simulations in age groups revealed that elderly tissue, with lower absorption coefficients, exhibits greater penetration, whereas adolescent tissue shows more superficial confinement, underscoring the importance of patient-specific parameter adjustment. Critical thermal thresholds for coagulation, vaporization, and irreversible cellular damage were identified, providing clinically relevant safety margins. These findings demonstrate that PINNs provide a robust, physics-based framework for predicting laser-tissue interactions, showing close agreement with analytical benchmarks and offering a computationally efficient alternative to traditional solvers, although the current model is one-dimensional and still not experimentally validated, which will be the focus of future work. Beyond clinical optimization, this work establishes a computational foundation for future extensions, such as incorporating Arrhenius damage integrals, heterogeneous tissue layers, nonlinear optical effects, and real-time feedback modalities.

This study develops an advanced optimization framework for heating, ventilation, and air conditioning (HVAC) systems in multi-zone buildings with highly dynamic and uncertain internal heat loads. Unlike conventional models that assume static occupancy, the proposed approach captures time-varying, spatially heterogeneous thermal disturbances driven by occupant activity. The building is modeled as a coupled cyber-physical system integrating multi-zone thermal dynamics, nonlinear HVAC energy consumption, and behavior-driven heat generation. To address uncertainty, a distributionally robust optimization framework based on Wasserstein ambiguity sets is employed, enabling reliable performance without requiring precise probability distributions. In addition, a physics-informed reinforcement learning mechanism is incorporated to derive adaptive control policies while ensuring thermodynamic feasibility. A multi-zone coordination strategy is further introduced to manage spatial thermal interactions and maintain stable comfort across different areas. Case study results demonstrate that the proposed method reduces peak energy consumption by 28–32%, decreases comfort violation rates by 65–75%, and improves thermal stability, with temperature variance reduced by over 60% compared to baseline strategies.

Abstract Direct laser metal deposition is a versatile additive manufacturing technique known for its ability to fabricate complex geometries with high precision. However, the thermal history during deposition can significantly influence the microstructure, distortions, and mechanical properties of the final product. This study investigates the influence of scanning path strategies, namely linear bidirectional (conventional), flattened trochoidal (fully circular), and adaptive trochoidal (semi-circular), on the thermal dynamics and resulting material characteristics in direct laser metal deposition (DLMD) of Ti-6Al-4V alloy. Using infrared thermal imaging, detailed comparisons were made of the thermal history, cooling rates, and spatial heat distribution at different scan speeds (100, 250, and 400 mm/min). Results revealed that the adaptive trochoidal path produced the most thermally balanced behaviour, with up to 18% lower peak temperatures and 25–30% lower temperature fluctuation range compared to the linear bidirectional strategy. Cooling rate analysis showed that adaptive trochoidal scanning maintained a more gradual cooling slope, especially at the middle point. Microstructural examination confirmed a finer grain structure in trochoidal paths, particularly adaptive ones, correlating with a 10–15% increase in microhardness compared to linear scanning. The study highlights how path shape optimization can significantly improve thermal management and enhance mechanical performance in DLMD applications, providing valuable insights for the design of complex geometries in additive manufacturing.

How moss affects urban temperatures: The effects of moss on the thermal dynamics of an urban cementitious surface.

Graph neural network-based surrogate modeling for fast and scalable simulations of meshed district heating networks

An iterative MILP-based model for optimal V2G scheduling considering battery degradation and thermal dynamics

Digital twin enhancement of real-time predictive EMS for FCEV truck operating under varying conditions

The conventional approach to converting kraft lignin (KL) into hard carbons is to start with highly purified, low-ash KL feedstocks and then rely on slow, energy-intensive oxidative stabilization and added crosslinkers to keep melting and foaming associated thermal challenges under control. Herein, we deliberately invert this paradigm. Instead of starting with highly purified KL, we retain pulping inorganics and use them as catalytic centers for oxidative crosslinking and melt suppression of KL. Spherical KL microparticles (KL-MP) were recovered from softwood black liquor by membrane filtration and spray-drying steps, intentionally retaining inorganic sodium (Na) salts as well as organically bound Na in KL-MP, and were compared to acid-precipitated, low-ash reference KL (KL-REF). During thermo-oxidative pretreatment (250°C, 5°C/min) in air, KL-MP undergoes inorganic-catalyzed rapid oxidative crosslinking that converts thermoplastic lignin into a rigid network, whereas KL-REF softens, foams, and fuses. Experimental analysis identifies organically bound Na-phenoxide type species as key catalytic sites. Proton magnetic resonance thermal analysis and molecular dynamics simulations reveal strongly reduced segmental mobility and Na-driven ionic clusters acting as physical crosslinking points. After pretreatment, inorganics are removed by a washing step, and the crosslinked KL-MP is carbonized, yielding low-surface-area hard carbons that retain their initial micron size and spherical morphology. As Li-ion battery anodes, the derived hard carbon shows better electrochemical performance than carbons derived from KL-REF. Overall, the work shows how otherwise undesirable inorganic impurities can simplify thermal conversion of KL, with potential for diverse applications where particle size and shape are critical.

Climate-driven increases in mean water temperature and the frequency of heatwaves affect the thermal tolerance of ectotherms, including fishes. Fishes can increase their thermal tolerance through acclimation, optimizing survival during extreme weather events. Few studies have investigated the exposure duration necessary for individuals to acclimate to warmer conditions or differences in acclimation dynamics among populations, limiting our understanding of how species deal with acute heat stress. Warmer waters can also increase parasite transmission. Although infections can reduce host thermal tolerance, the impact of parasite prevalence and abundance on fish acclimation capacity has not been explored, and may help explain population-level differences in thermal tolerance. We assessed thermal tolerance and acclimation dynamics across three populations of pumpkinseed sunfish (Lepomis gibbosus) from lakes in Quebec, Canada, differing in trematode and cestode infection prevalence. Pumpkinseed were acclimated to 22 °C or 27 °C from 3 h to 60 days before measuring critical thermal maximum (CTmax). CTmax increased with acclimation duration, with detectable increases after only 3 h, indicating rapid induction of acclimatory mechanisms. Fish from all populations appeared to reach full acclimation after 10 days at 27 °C. However, thermal tolerance was consistently highest in the control lake (Lake Triton, no cestodes or trematodes) compared to populations with intermediate (Lake Croche) and high (Lake Cromwell) infection prevalence, despite no relationship with parasite abundance. Although our design does not permit causal inference, these results suggest pumpkinseed rapidly acclimate to higher temperatures, but natural exposure to parasites could contribute to population-level differences in thermal tolerance.

Enhanced thermal transmission and flow dynamics of nanofluids based on soft computing optimization approach subject to heat source and slip effects

Abstract Ground-level ozone pollution in the Yangtze River Delta (YRD) poses a persistent air quality challenge, yet the role of the vertical atmospheric structure remains poorly quantified. In this study, we employ an explainable machine learning framework to quantify the contribution of lower-tropospheric thermal dynamics on seasonal surface ozone variability from 2015 to 2022. We focus on the temperature difference between 850 hPa and 1000 hPa, denoted as T diff , which captures the vertical thermal gradient between the free troposphere and the near-surface layer. This gradient serves as a robust indicator of atmospheric stability: a strongly negative T diff reflects a steep lapse rate that enhances vertical mixing and pollutant dispersion, whereas a weakly negative or positive T diff indicates the presence of a thermal inversion that suppresses vertical exchange and promotes ozone accumulation near the surface. Our results reveal that T diff is the most statistically important predictor of surface ozone, outperforming both chemical precursors and surface meteorological variables. Although the influence of thermal stratification on air quality is well recognized, this study provides, the first systematic quantification of its dominant role relative to other drivers in the YRD using a data-driven, interpretable modeling approach. These findings identify the lower-tropospheric thermal structure as a key physical driver of surface ozone pollution and offer a practical metric for improving operational air quality forecasting.