Fossil Leaves of Meliosma (Sabiaceae) With Associated Pollen and a Eupodid Mite From the Eocene of Maoming Basin, South China

A first occurrence of the genus Meliosma (Sabiaceae) is reported from the upper Eocene of the Maoming Basin of South China. This fossil is one of the oldest reliable records of the genus within its modern center of diversity. Fossil leaves are assigned to a new species, Meliosma eosinica sp. nov. based on leaf morphology and epidermal characters. The leaf epidermal anatomy of fossil Meliosma is illustrated for the first time. We also provide the first SEM observation of pollen grains associated with Meliosma. This study also documents an occurrence of mites within the leaf domatia previously unknown from the fossil record. We presume that the studied mite belongs to the superfamily Eupodoidea (Arthropoda), and probably the family Eupodidae, which comprises very small soft-bodied cosmopolitan mites occupying a wide range of terrestrial habitats. Additionally, we analyze the damage types on the fossil leaves of Meliosma. They exhibit exclusively external foliage feeding damage caused by arthropods and traces of probable fungal infection. A review of currently known fossil occurrences of leaves, fruits, and wood of Meliosma provides evidence for the geological and geographical distribution of the genus.

Study on the Applicability of the Exterior Condition Assessment to Evaluate The Physical Vulnerability of Check Dams

Check dams are a typical structural approach used in watershed disaster management systems. Currently, approximately 12,000 check dams have been estimated to be constructed on mountain streams in Korea. More than 90% of these have been constructed in the last 20 years. This rapid increase over a short period of time has attracted attention to the necessity of maintenance strategies for check dams. The purpose of this study is to examine the applicability of the exterior condition assessment to evaluate the degree of deterioration in check dams. We classify the typical damage types of check dams and describe its key characteristics. Moreover, we apply a modified version of the condition assessment for large dams to meet the characteristics of check dams.

Health Condition of European Beech (Fagus sylvatica L.) According to Provenances in International Provenance Trial

The performance of European beech ( Fagus sylvatica L.) is severely threatened by abiotic and biotic stresses, but the resilience of its provenances from distinct geographic areas has not been sufficiently studied. Therefore, the performance of European beech was investigated in an international provenance trial consisting of twenty-one natural populations originating from Central Europe and Southeast Europe located on Medvednica Mountain (Croatia). The performance of European beech was investigated by characterizing I) damage types, II) crown damage intensity, III) damage frequency, and IV) clustering of provenances based on damage types. Anthracnose, galls, chewing damages, and aphids were recorded on leaves, and canker on the trunk and branches. The crown damage intensity was minute (less than 10%) for all types of disease. Anthracnose was the most common damage, followed in descending order by galls, canker, chewing damages and aphids. When the types of damage were analysed using descriptive statistics (frequency analysis and clustering), significant differences were recorded between provenances (χ2=322.19, p<0.0001). European beech provenances were classified into four clusters. Aphids and galls caused the least and the highest damage, respectively, in each cluster, except for Cluster 4, where anthracnose caused the highest damage. The results of this study showed generally good health condition of European beech provenances originating from Central and Southeast Europe in the period of the investigation.

Linking host plants to damage types in the fossil record of insect herbivory

Studies of insect herbivory on fossilized leaves tend to focus on a few, relatively simple metrics that are agnostic to the distribution of insect damage types among host plants. More complex metrics that link particular damage types to particular host plants have the potential to address additional ecological questions, but such metrics can be biased by sampling incompleteness due to the difficulty of distinguishing the true absence of a particular interaction from the failure to detect it---a challenge that has been raised in the ecological literature. We evaluate a range of methods for characterizing the relationships between damage types and host plants by performing resampling and subsampling exercises on a variety of datasets. We found that the components of beta diversity provide a more valid, reliable, and interpretable method for comparing component communities than do bipartite network metrics. We found the rarefaction of interactions to be a valid, reliable, and interpretable method for comparing compound communities. Both of these methods avoid the potential pitfalls of multiple comparisons. Lastly, we found that the host specificity of individual damage types is challenging to assess. Whereas some previously used methods are sufficiently biased by sampling incompleteness to be inappropriate for fossil herbivory data, alternatives exist that are perfectly suitable for fossil datasets with sufficient sample coverage.

The damage profile of kerai payung (Filicium desipiens) in University of Sumatera Utara green space based on Forest Health Monitoring (FHM) method

The University of Sumatera Utara (USU) campus in Medan is a green space area with numerous social and environmental benefits. In the USU campus, trees were planted for providing inhabitant needs such as fresh air, pollutant absorber, medicine, wood, fuel, fiber, aesthetic, and food. One of the medicine, wood, and aesthetic species that grows on the USU campus is kerai payung (Filicium decipiens). Along with fallen trees and broken branches incidents, detection of kerai payung trees damage in the campus USU was needed to prevent undesirable incidents and provide recommendations for maintenance. This research aims to get information on the distribution and damage profile of Kerai payung. To determine and assess the damage profile, the Forest Health Monitoring (FHM) approach was used. Research showed that the Kere payung diameter structure ranges from 8 cm to 74 cm. Based on FHM, damage types of Kerai payung trees in USU campus are as follows; broken or dead branch, open wound, brum in branch, loss of dominant tips, leaves change color and konk. While the damage location is found in branches, buds and tips, lower and upper bole, leaves and upper bole. Our research pointed out that maintaining activities still be needed to avoid further damages.

Nondestructive Measurement of Emissivity of Damaged Parts of Coatings

Low Infrared emissivity coating (LIREC) is prone to generating some problems such as bulges, degumming, and abrasion. In order to study whether the performance of LIREC under different damages can meet the work needs, it is essential to timely measure and evaluate the performance state of LIREC in the application process. The existing methods for measuring the damage of LIREC have some disadvantages such as expensive equipment, complex operation, and inaccurate measurement results. In this paper, a measurement method of LIREC damage capability based on thermal imager is proposed. The radiation temperature is measured by thermal imager, the real temperature and ambient temperature of coating surface are measured by thermocouple, and the emittance of coating surface is calculated. Non-contact and continuous large-area emissivity measurements are carried out on the damaged parts of the coating and verified by experiments. The measurement results show that the different damage types and damage degrees directly affect the measurement results of LIREC. Wear damage increases the emissivity of the coating while debonding damage basically does not change the coating emissivity. Shedding damage of small diameter forms voids, which causes the increase of the damage parts of emittance. In addition, bulge damage impedes temperature transfer and reduces emissivity. This method can timely and accurately measure and evaluate the performance state of LIREC and can provide a new idea for the accurate measurement of damage emissivity of LIREC.

Spatial Frequency Domain Imaging System Calibration, Correction and Application for Pear Surface Damage Detection

Spatial frequency domain imaging (SFDI) is a non-contact wide-field optical imaging technique for optical property detection. This study aimed to establish an SFDI system and investigate the effects of system calibration, error analysis and correction on the measurement of optical properties. Optical parameter characteristic measurements of normal pears with three different damage types were performed using the calibrated system. The obtained absorption coefficient μa and the reduced scattering coefficient μ’s were used for discriminating pears with different surface damage using a linear discriminant analysis model. The results showed that at 527 nm and 675 nm, the pears’ quadruple classification (normal, bruised, scratched and abraded) accuracy using the SFDI technique was 92.5% and 83.8%, respectively, which has an advantage compared with the conventional planar light classification results of 82.5% and 77.5%. The three-way classification (normal, minor damage and serious damage) SFDI technique was as high as 100% and 98.8% at 527 nm and 675 nm, respectively, while the classification accuracy of conventional planar light was 93.8% and 93.8%, respectively. The results of this study indicated that SFDI has the potential to detect different damage types in fruit and that the SFDI technique has a promising future in agricultural product quality inspection in further research.

Bridge damage identification using deep learning-based Convolutional Neural Networks (CNNs)

In this paper, a novel method is proposed based on windowed-one dimensional convolutional neural network for multiclass damage detection using acceleration responses. The data is pre-processed and augmented by extracting samples of windows of the original acceleration time-series. 1D CNN is developed to classify the signals in multiple classes. The damage is detected if the predicted classification is one of the indicated damage levels. The damage is quantified using the predicted class probabilities. Various signals from the accelerometers are provided as input to 1D CNN model, and the resulting class probabilities are used to identify the location of the damage. The proposed method is validated using Z24 bridge benchmark data for multiclass classification for two damage scenarios. The results show that the proposed 1D CNN methods performs with superior accuracy for severe damage cases and works well with different type of damage types.