In recent years, the frequency and severity of large-scale forest fires have increased globally, threatening forest ecosystems, human lives, and property while potentially triggering cascading ecological and social crises. Despite significant advancements in remote sensing-based forest fire monitoring, early warning systems, and fire risk zoning, post-fire thermal damage assessment remains insufficiently addressed. This study introduces an innovative approach combining multispectral imaging with a dual annealing constrained optimization algorithm to enable dynamic monitoring of fire temperature distribution. Based on this method, we develop a dynamic thermal damage assessment model to quantify thermal impacts during forest fires. The proposed model provides valuable insights for defining thermal damage zones, optimizing evacuation strategies, and supporting firefighting operations, ultimately enhancing emergency response and forest fire management efficiency.

Sweep-field operation in a single-beam spin-exchange relaxation-free (SERF) magnetometer requires stable extraction of the dispersion zero-crossing. A frequency mismatch between the modulation signal and the demodulation references, or an unsuitable low-pass filter, can shift this zero-crossing and affect working-point determination. This paper presents a zero-crossing-stability-oriented FPGA quadrature lock-in module for SERF sweep-field demodulation. The module is designed around two requirements of sweep-field operation: maintaining a common frequency basis between the modulation output and the demodulation references, and preserving the dispersion zero-crossing when the low-pass-filter cutoff frequency is adjusted. A shared direct digital synthesizer generates both the sinusoidal modulation output and the I/Q references, keeping the excitation and demodulation signals on the same frequency basis. After quadrature multiplication, CIC decimation and a reloadable Kaiser-window FIR filter are used for low-pass processing. Board-level tests show a 1000.054 Hz spectral peak for a 1000 Hz setting and a loopback amplitude of 0.496 V, close to the ideal 0.500 V baseband amplitude. On the SERF platform, I/Q rotation reduces the quadrature residual ratio from 32.1% to 0.10%. When the FIR cutoff frequency is changed from 3 to 15 Hz, the maximum zero-crossing difference is about 0.58 ms, corresponding to 0.12% of the 2 Hz sweep period. These results show that the module supports stable zero-crossing extraction and working-point determination during sweep-field operation in a single-beam SERF magnetometer.

To address the challenges of data processing caused by uncertain emissivity in multispectral radiation thermometry, this paper proposes a temperature retrieval method based on the Chaotic Artificial Hummingbird Algorithm (CAHA). Without relying on an assumed emissivity model, the method can automatically identify the emissivity distribution and selects the optimal output through multiple iterations to enhance accuracy. Simulations and offline tests conducted on rocket nozzles demonstrate that CAHA maintains high accuracy both in noise-free conditions and under 5% noise, with a single execution time of approximately 0.15 s. Furthermore, the method is validated through experiments on blackbody sources and candle flames: the relative error in retrieved temperature for blackbody sources remains below 0.93%, while the retrieved outer flame temperature of candle flames shows a relative error of 0.66% compared with thermocouple measurements. Combining high precision with rapid computation, this method is suitable for practical applications in radiation thermometry.

This study assesses physics-informed neural networks (PINNs) for the unsteady two-dimensional flow past a rotating circular cylinder confined in a channel (blockage ratio 0.2) at Reynolds numbers of 50, 200 and 500. The networks are trained on the non-dimensional Navier–Stokes residuals, the boundary conditions including the rotating wall, and an initial velocity field, using adaptive loss weighting and time marching. A finite-volume reference solver, verified against the Schäfer–Turek benchmark to within 1.2%, is used to quantify the PINN accuracy and to characterise the flow for rotation ratios between $-1$ and 1.5. Rotation produces a Magnus lift that increases almost linearly with the rotation ratio ($\overline{C_l}\approx -3.7\alpha$), reduces the mean drag and leaves the Strouhal number nearly unchanged, while all cases, including the stationary cylinder at $Re=50$, exhibit periodic vortex shedding. The data-free PINN reproduces the wake topology and the sign of the Magnus lift, with relative $L_2$ velocity errors of 14–24%, but underestimates the pressure and the hydrodynamic forces. A systematic ablation shows that sparse velocity data (0.4% of the domain) halve the velocity error, and that a hard rotating-wall constraint with boundary-layer sampling further reduces it to 6% and lowers the lift error to 30% at $Re=50$. The persistent force errors at higher Reynolds numbers identify the representation of the thin rotating boundary layer as the principal limitation of PINNs for this class of flows.