Search Results (6)

The atmosphere plays a pivotal role in modulating the interactions between microorganisms and their surrounding environments, influencing ecological cycles, heritage conservation, and providing opportunities for novel applications. Recent studies have highlighted the role of microbial responses to atmospheric conditions as indicators of environmental change. This study highlights the role of climate change, particularly rising temperatures, on the growth of cyanobacteria and, consequently, the impact of this on the conservation of cultural heritage, as in the case study of the rock paintings of the Majella Massif (Lama dei Peligni—Abruzzo Region, central Italy). The region’s rock art, characterized by red and black schematic motifs, is increasingly impacted by microbial colonization, driven by climate-induced temperature variations. These impacts are consistent with broader research demonstrating the link between microbial growth patterns and climatic factors. Laboratory analyses were carried out on cyanobacteria samples collected near the rock paintings at the study site in the Majella National Park. Results revealed a significant increase in growth rates at the higher temperature, demonstrating their sensitivity to climatic shifts. These findings underscore the dynamic role of atmospheric factors in shaping microbial survival and propagation. Consequently, certain atmospheric parameters appear to play a crucial role in the deterioration of fragile cultural assets. Indeed, the enhanced growth of cyanobacteria due to rising temperatures also poses a challenge: their proliferation can degrade cultural heritage sites, threatening their preservation. This research advocates for interdisciplinary approaches that integrate atmospheric sciences, microbial ecology, and heritage studies to explore the role of temperature in affecting cyanobacteria growth and the conservation of a peculiar cultural heritage in the Majella Massif. By leveraging their biological traits, cyanobacteria can provide valuable insights into climate dynamics while emphasizing the urgency for proactive strategies to mitigate environmental impacts on vulnerable ecosystems and heritage sites. Full article

Repeated expeditions across various regions of Georgia in the early 2000s led to the identification of 434 wild grapevine individuals (Vitis vinifera L. subsp. sylvestris (C.C. Gmel.) Hegi) across 127 different sites, with 45% of these sites containing only a single vine and only 7% more than 9 vines. A total of 70 accessions were propagated in a germplasm collection, 41 of them were descripted from the ampelographic point of view and 32 from the phenological one. The geographical and ecological analysis confirmed that wild grapevines primarily grow in humid environments with warm and fully humid climates, often near rivers. They favor deep, fertile, and evolved soils, mainly alluvial and cinnamonic types (80%), with a marginal presence on strongly eroded soils. Their main natural vegetations are forests and open woodlands, with some individuals in the Southeast found in steppes. The altitudinal range spans from 0 to 1200 m, with 80% of vines distributed between 400 and 900 m. The phenological analysis revealed significant differences among the accessions but no difference among populations, with only a slight variation in bud-break timing, indicating a high level of synchronicity overall. Flowering timing proved to be the most uniform stage, suggesting minimal environmental pressure on genetic adaptation. The mature leaf morphology exhibited significant polymorphism, though leaves were generally three- or five-lobed, weak-wrinkling, and -blistering, with a low density of hairs. Bunch and berry morphology were more uniform. Bunches were consistently very small, cylindrical, and never dense or winged. Berries were also very small, mostly globular, always blue-black in color, and non-aromatic. A striking feature was the frequency of red flesh coloration, which ranged from weak to strong, with uncolored flesh being rare. The Georgian population of wild grapevines was found to be fragmented, often consisting of scattered single individuals or small groups. Therefore, we believe it is urgent for Georgia to implement specific protection measures to preserve this vital genetic resource. Full article

The non-destructive testing of wood fibre properties is crucial for informing forest management decisions and achieving optimal resource utilization. Moisture content (MC) is an important indicator of wood freshness and may reveal the presence of wood degradation. However, efficient methods are still needed to better monitor this property along the forest–wood value chain. The objective of the study was to develop prediction models to evaluate log MC based on the propagation of ground penetrating radar (GPR) signals. A total of 165 trees representing four species (black spruce (Picea mariana (Mill.) B.S.P.), white spruce (Picea glauca (Moench) Voss), red spruce (Picea rubens Sarg.), and balsam fir (Abies balsamea (L.) Mill.)) were harvested in two regions of the province of Quebec. GPR signals were acquired in the green (fresh) state and at three subsequent drying stages. Partial least squares regression (PLSR) and locally weighted PLSR (LWPLSR) were employed to establish relationships between GPR signals (antenna frequency: 1.6 GHz) and log properties. The models were fitted on three calibration sets containing four drying stages and different species mixes. The LWPLSR models performed better than the PLSR models for predicting log MC, with a lower root mean square error (RMSEp range: 10.8%–20.2% vs. 13.0%–20.5%) and a higher R²p (0.63–0.87 vs. 0.62–0.82). Spruce-only models performed considerably better than fir-only models while multi-species models were in-between. Despite the complex anisotropy of wood and the physics of wave propagation, the GPR technology can be successfully used to estimate log moisture content, but the GPR-based MC models should be calibrated for each specific type of wood material. Full article

Fast reconstruction of power lines and corridors is a critical task in UAV (unmanned aerial vehicle)-based inspection of high-voltage transmission corridors. However, recent dense matching algorithms suffer the problem of low efficiency when processing large-scale high-resolution UAV images. This study proposes an efficient dense matching method for the 3D reconstruction of high-voltage transmission corridors with fine-scale power lines. First, an efficient random red-black checkerboard propagation is proposed, which utilizes the neighbor pixels with the most similar color to propagate plane parameters. To combine the pixel-wise view selection strategy adopted in Colmap with the efficient random red-black checkerboard propagation, the updating schedule for inferring visible probability is improved; second, strategies for decreasing the number of matching cost computations are proposed, which can reduce the unnecessary hypotheses for verification. The number of neighbor pixels necessary to propagate plane parameters is reduced with the increase of iterations, and the number of the combinations of depth and normal is reduced for the pixel with better matching cost in the plane refinement step; third, an efficient GPU (graphics processing unit)-based depth map fusion method is proposed, which employs a weight function based on the reprojection errors to fuse the depth map. Finally, experiments are conducted by using three UAV datasets, and the results indicate that the proposed method can maintain the completeness of power line reconstruction with high efficiency when compared to other PatchMatch-based methods. In addition, two benchmark datasets are used to verify that the proposed method can achieve a better $F_1$ score, 4–7 times faster than Colmap. Full article

A banker plant system is a rearing and release method for biological control agents, and in recent years has gained serious attention among plant propagators for its use in regulating common greenhouse and nursery pests. In the current study, the suitability of four ornamental pepper (Capsicum annuum L.; Solanales: Solanaceae) banker plant candidates, Black Pearl (BP), Explosive Ember (EE), Masquerade (MA), Red Missile (RM), and a commercial pepper cultivar, Blitz (BL), were evaluated with three main objectives: (1) to assess host preference of three major arthropod pests of agricultural importance, Bemisia tabaci Gennadius, Polyphagotarsonemus latus Banks, and Frankliniella occidentalis Pergande among selected pepper cultivars; (2) to determine the susceptibility of plant cultivars to three different pests; and (3) to assess the effect of tuft domatia on the abundance of the predatory mite Amblyseius swirskii Athias–Henriot. In choice and no-choice assays, BL and BP were highly susceptible to P. latus with a moderately high damage rating index of >3.5/5; B. tabaci and F. occidentalis were abundant on BL and EE. A positive correlation was observed between the number of tuft domatia and the A. swirskii count. Although all ornamental pepper cultivars exhibited varying degrees of susceptibility to different arthropod pests, if used strategically, cultivars MA and RM can be used to develop a banker plant system and help reduce multiple pests in greenhouses or interiorscapes. Full article

Compared with the chemical analytical technique, the soil nitrogen acquisition method based on near infrared (NIR) sensors shows significant advantages, being rapid, nondestructive, and convenient. Providing an accurate grasp of different soil types, sensitive wavebands could enhance the nitrogen estimation efficiency to a large extent. In this paper, loess, calcium soil, black soil, and red soil were used as experimental samples. The prediction models between soil nitrogen and NIR spectral reflectance were established based on three chemometric methods, that is, partial least squares (PLS), backward interval partial least squares (BIPLS), and back propagation neural network (BPNN). In addition, the sensitive wavebands of four kinds of soils were selected by competitive adaptive reweighted sampling (CARS) and BIPLS. The predictive ability was assessed by the coefficient of determination R² and the root mean square error (RMSE). As a result, loess ( $0.93<R_p^2<0.95,0.066\mathrm{g}/\mathrm{kg}<{\mathrm{RMSE}}_p<0.075\mathrm{g}/\mathrm{kg}$ ) and calcium soil ( $0.95<R_p^2<0.96,0.080\mathrm{g}/\mathrm{kg}<{\mathrm{RMSE}}_p<0.102\mathrm{g}/\mathrm{kg}$ ) achieved a high prediction accuracy regardless of which algorithm was used, while black soil ( $0.79<R_p^2<0.86,0.232\mathrm{g}/\mathrm{kg}<{\mathrm{RMSE}}_p<0.325\mathrm{g}/\mathrm{kg}$ ) obtained a relatively lower prediction accuracy caused by the interference of high humus content and strong absorption. The prediction accuracy of red soil ( $0.86<R_p^2<0.87,0.231\mathrm{g}/\mathrm{kg}<{\mathrm{RMSE}}_p<0.236\mathrm{g}/\mathrm{kg}$ ) was similar to black soil, partly due to the high content of iron–aluminum oxide. Compared with PLS and BPNN, BIPLS performed well in removing noise and enhancing the prediction effect. In addition, the determined sensitive wavebands were 1152 nm–1162 nm and 1296 nm–1309 nm (loess), 1036 nm–1055 nm and 1129 nm–1156 nm (calcium soil), 1055 nm, 1281 nm, 1414 nm–1428 nm and 1472 nm–1493 nm (black soil), 1250 nm, 1480 nm and 1680 nm (red soil). It is of great value to investigate the differences among the NIR spectral characteristics of different soil types and determine sensitive wavebands for the more efficient and portable NIR sensors in practical application. Full article