Piezoelectric transducers have attracted considerable attention in structural repair and health monitoring because of their dual actuation and sensing capabilities. This review critically examines their application in repairing and monitoring damaged engineering structures, particularly those containing cracks, notches, delamination, and debonding. First, analytical, numerical, and experimental approaches for piezoelectric-based structural repair are evaluated, with emphasis on crack suppression, stress intensity factor reduction, stress concentration control, and structural response improvement. The effects of actuator location, applied voltage, patch geometry, adhesive prop-erties, loading conditions, and optimization techniques are also discussed. The review then examines piezoelec-tric-based structural health monitoring techniques, including guided Lamb waves, electromechanical impedance, vibration-based sensing, sensor placement, and machine-learning-assisted damage assessment. The critical analysis shows that numerical investigations dominate the existing literature, whereas experimental validation, mixed-mode fracture studies, and long-term performance assessments remain limited. Finally, current research gaps, challenges, and future opportunities for intelligent and adaptive engineering structures are identified.
Multi-scale registration and adaptive feature fusion provide established mechanisms for combining spatial information, but their effectiveness does not establish the benefit of combining drift estimates from independently generated reference patterns. This study proposes a multi-scale latent reference pattern model and its mathematical formulation for geometric drift estimation in printing. Coarse, medium, and fine Poisson-disk patterns provide separate observations for estimating translation, small-angle rotation, and fixed-frequency sinusoidal deformation. Three scale-specific estimators are compared with equal-weight and adaptive multi-scale combinations. The main experiment comprises 250 independent synthetic realizations, each evaluated under 16 combinations of Gaussian noise and blur. All three scales produced valid estimates in all 4000 scenario realizations. The fine scale had the lowest individual estimation errors, while equal weighting achieved the lowest mean errors among the five estimators in the main experiment. Mean deformation RMSE was 0.056848 px for equal weighting and 0.058735 px for adaptive weighting. Additional synthetic experiments examine threshold sensitivity, rigid and thin-plate-spline baselines, pooled estimation, and the relationship between adaptive weights and estimation error. Rigid registration was most precise in the rigid-only control, while pooled estimation achieved the lowest mean exact-field error under sinusoidal deformation. The contribution is a mathematically specified reference-pattern model and a controlled evaluation that identifies scale-dependent precision and limitations of the tested reliability indicator. The results support computational feasibility under the specified synthetic conditions. They do not establish an accuracy advantage for adaptive weighting or measurement performance on physically printed and captured patterns.
Rapid urbanization and climate change increase heat exposure in large cities, and reproducible baselines are needed to compare their long-term thermal behavior. We derive 1994–2024 near-surface air temperature (T2m) climatologies and trends for eight American megacities from hourly ERA5 reanalysis. The multiannual mean, hourly 90th percentile, interannual standard deviation, Mann–Kendall test, and Sen’s slope were computed on the native 0.25° grid and weighted by area, with 2km bilinear regridding used only for boundary-conformal cartographic visualization. Compared with one NOAA ISD airport station per city, ERA5 achieved CC=0.92–0.99 and RMSE=1.24–2.04 °C in seven cities, with values of 0.84 and 2.66 °C in Bogotá. Multiannual means ranged from 12.2 (Bogotá) to 23.6 °C (Rio de Janeiro). Warming was robust to serial dependence and the analysis period in New York, Buenos Aires, Mexico City, and Bogotá (+0.032 to +0.045 °C/year), conditional in Los Angeles (+0.048 °C/year; not significant after the Hamed–Rao correction), and not detected in São Paulo, Rio de Janeiro (+0.017 and +0.014 °C/year, p≈0.10), and Lima (+0.001 °C/year, p=0.92), where the 31-year record cannot distinguish these slopes from zero. Robust within-city slope contrasts reached 0.4–0.8 °C per 30 years in Mexico City and Bogotá. Because ERA5 resolves at about 28 km, the results describe city-scale baselines, not intra-urban variability or urban effects.
Resource-efficient conversion of biomass and organic residues into humic substances represents an important pathway for waste valorization within the circular bioeconomy. This review systematizes feedstock sources for humic substance extraction and compares their characteristics, sustainability, processing requirements, and assessment approaches. The scientific literature was analyzed together with regulatory and statistical sources relevant to feedstock classification and resource availability. The review synthesized evidence from 156 sources and shows that humic substance potential cannot be evaluated from resource volume or origin alone; it also depends on humification, organic-matter stability, physicochemical composition, contamination risks, and extraction technology. Primary resources such as peat and sapropel provide naturally humified fractions but are constrained by slow regeneration and environmental considerations. Secondary resources, including agricultural and forestry residues, fresh manure, and food waste, require flow- and coefficient-based assessment and may undergo further processing or stabilization. Tertiary resources, such as compost, digestate, treated sewage sludge, and processed or stabilized manure, are evaluated by linking material flows with quality, humification, safety, and recovery-yield indicators. For selected feedstocks, reported humic-substance content varied substantially, ranging from 2–20% dry matter in sapropel and 15–40% dry matter in peat, with different source-specific ranges reported for other organic materials. Because these values also depend on the analytical and extraction procedures applied, they should not be interpreted as directly comparable without harmonized analytical and reporting criteria. The quantitative assessment framework distinguishes theoretical, technical, and sustainably available resource potential and incorporates residue-to-product ratios, availability coefficients, material composition, and humic-substance content or recovery factors. The proposed differentiated framework supports more consistent comparison of renewable and waste-derived feedstocks and provides a methodological basis for assessing resource suitability, while highlighting the need for standardized analytical procedures, uncertainty assessment, contaminant-fate evaluation, and validation using regional or national case studies.
High-quality images are essential for several computer-vision applications. Hence, low-contrast images often pose significant challenges because they can cause failures in automated detection and analysis processes. This study presents an innovative strategy to improve image quality by segmenting an image into interleaved cell groups and processing each group independently using equalization. For each group, we construct a mesh of images with local equalization, then integrate it using a set of fusion functions. The method enables equalization not only based on the intensity histogram but also on the spatial properties of the elements, ensuring overall contrast enhancement across the entire image. Likewise, because the algorithm is deterministic, it is possible to estimate its latency and accelerate it reliably through hardware implementation. The performance of the proposed method was validated qualitatively and quantitatively against other state-of-the-art algorithms, demonstrating outstanding results.
