Adaptation of Deeplab V3+ for Damage Detection on Port Infrastructure Imagery

Regular inspection and maintenance of infrastructure facilities are crucial to ensure their functionality and safety for users. However, current inspection methods are labor-intensive and can vary depending on the inspector. To improve this process, modern sensor systems and machine learning algorithms can be deployed to detect defects based on rapidly acquired data, resulting in lower downtime. A quality-controlled processing chain allows to provide hence informed uncertainty assessments to inspection operators. In this study, we present several Deeplab V3+ models optimized to predict corroded segments of the quay wall at JadeWeserPort, Germany, which is a dataset from the 3D HydroMapper research project. Our models achieve generally high accuracy in detecting this damage type. Therefore, we examine the use of a Region Growing-based weakly supervised approach to efficiently extend our model to other common types in the future. This approach achieves about 90 % of the results compared to corresponding fully supervised networks, of which a ResNet-50 variant peaks at 55.6 % Intersection-over-Union regarding the test set's corrosion class

Integrating remote sensing and a Markov-FLUS model to simulate future land use changes in Hokkaido, Japan

As the second largest island in Japan, Hokkaido provides precious land resources for the Japanese people. Meanwhile, as the food base of Japan, the gradual decrease of the agricultural population and more intensive agricultural practices on Hokkaido have led its arable land use to change year by year, which has also caused changes to the whole land use pattern of the entire island of Hokkaido. To realize the sustainable use of land resources in Hokkaido, past and future changes in land use patterns must be investigated, and target-based land use planning suggestions should be given on this basis. This study uses remote sensing and GIS technology to analyze the temporal and spatial changes of land use in Hokkaido during the past two decades. The types of land use include cultivated land, forest, waterbody, construction, grassland, and others, by using the satellite images of the Landsat images in 2000, 2010, and 2019 to achieve this goal to make classification. In addition, this study used the coupled Markov-FLUS model to simulate and analyze the land use changes in three different scenarios in Hokkaido in the next 20 years. Scenario-based situational analysis shows that the cultivated land in Hokkaido will drop by about 25% in 2040 under the natural development scenario (ND), while the cultivated land area in Hokkaido will remain basically unchanged in cultivated land protection scenario (CP). In forest protection scenario (FP), the area of forest in Hokkaido will increase by 1580.8 km2. It is believed that the findings reveal that the forest land in Hokkaido has been well protected in the past and will be protected well in the next 20 years. However, in land use planning for future, Hokkaido government and enterprises should pay more attention to the protection of cultivated land.</jats:p

A Debiasing Variational Autoencoder for Deforestation Mapping

Deep Learning (DL) algorithms provide numerous benefits in different applications, and they usually yield successful results in scenarios with enough labeled training data and similar class proportions. However, the labeling procedure is a cost and time-consuming task. Furthermore, numerous real-world classification problems present a high level of class imbalance, as the number of samples from the classes of interest differ significantly. In various cases, such conditions tend to promote the creation of biased systems, which negatively impact their performance. Designing unbiased systems has been an active research topic, and recently some DL-based techniques have demonstrated encouraging results in that regard. In this work, we introduce an extension of the Debiasing Variational Autoencoder (DB-VAE) for semantic segmentation. The approach is based on an end-to-end DL scheme and employs the learned latent variables to adjust the individual sampling probabilities of data points during the training process. For that purpose, we adapted the original DB-VAE architecture for dense labeling in the context of deforestation mapping. Experiments were carried out on a region of the Brazilian Amazon, using Sentinel-2 data and the deforestation map from the PRODES project. The reported results show that the proposed DB-VAE approach is able to learn and identify under-represented samples, and select them more frequently in the training batches, consequently delivering superior classification metrics

Broadband terahertz modulators using self-gated graphene capacitors

We demonstrate a terahertz intensity modulator using a graphene supercapacitor which consists of two large-area graphene electrodes and an electrolyte medium. The mutual electrolyte gating between the graphene electrodes provides very efficient electrostatic doping with Fermi energies of 1 eV and a charge density of 8 × 1013 cm-2. We show that the graphene supercapacitor yields more than 50% modulation between 0.1 and 1.4 THz with operation voltages less than 3 V. The low insertion losses, high modulation depth over a broad spectrum, and the simplicity of the device structure are the key attributes of graphene supercapacitors for THz applications. © 2015 Optical Society of America

Broadband THz modulators based on multilayer graphene on PVC

In this study we present the direct terahertz time-domain spectroscopic measurement of CVD-grown multilayer graphene (MLG) on PVC substrate with an electrically tunable Fermi level. In a configuration consisting MLG and injected organic dopant, the transmitted intensity loss of terahertz radiation was observed with an applied voltage between 0 and 3.5 V. We showed that MLG on PVC devices provided approximately 100 % modulation between 0.2 and 1.5 THz at preferentially low operation voltage of ca. 3V. The observed modulation bandwidth in terahertz frequencies appears to be instrument limited. © 2016 IEEE

Phylogeny and S1 Gene Variation of Infectious Bronchitis Virus Detected in Broilers and Layers in Turkey

Citation: Yilmaz, H., Altan, E., Cizmecigil, U. Y., Gurel, A., Ozturk, G. Y., Bamac, O. E., . . . Turan, N. (2016). Phylogeny and S1 Gene Variation of Infectious Bronchitis Virus Detected in Broilers and Layers in Turkey. Avian Diseases, 60(3), 596-602. doi:10.1637/11346-120915-Reg.1The avian coronavirus infectious bronchitis virus (AvCoV-IBV) is recognized as an important global pathogen because new variants are a continuous threat to the poultry industry worldwide. This study investigates the genetic origin and diversity of AvCoV-IBV by analysis of the S1 sequence derived from 49 broiler flocks and 14 layer flocks in different regions of Turkey. AvCoV-IBV RNA was detected in 41 (83.6%) broiler flocks and nine (64.2%) of the layer flocks by TaqMan real-time RT-PCR. In addition, AvCoV-IBV RNA was detected in the tracheas 27/30 (90%), lungs 31/49 (62.2%), caecal tonsils 7/22 (31.8%), and kidneys 4/49 (8.1%) of broiler flocks examined. Pathologic lesions, hemorrhages, and mononuclear infiltrations were predominantly observed in tracheas and to a lesser extent in the lungs and a few in kidneys. A phylogenetic tree based on partial S1 sequences of the detected AvCoV-IBVs (including isolates) revealed that 1) viruses detected in five broiler flocks were similar to the IBV vaccines Ma5, H120, M41; 2) viruses detected in 24 broiler flocks were similar to those previously reported from Turkey and to Israel variant-2 strains; 3) viruses detected in seven layer flocks were different from those found in any of the broiler flocks but similar to viruses previously reported from Iran, India, and China (similar to Israel variant-1 and 4/91 serotypes); and 4) that the AVCoV-IBV, Israeli variant-2 strain, found to be circulating in Turkey appears to be undergoing molecular evolution. In conclusion, genetically different AvCoV-IBV strains, including vaccine-like strains, based on their partial S1 sequence, are circulating in broiler and layer chicken flocks in Turkey and the Israeli variant-2 strain is undergoing evolution. © 2016 American Association of Avian Pathologists

Bubble dynamics in DNA

The formation of local denaturation zones (bubbles) in double-stranded DNA is an important example for conformational changes of biological macromolecules. We study the dynamics of bubble formation in terms of a Fokker-Planck equation for the probability density to find a bubble of size n base pairs at time t, on the basis of the free energy in the Poland-Scheraga model. Characteristic bubble closing and opening times can be determined from the corresponding first passage time problem, and are sensitive to the specific parameters entering the model. A multistate unzipping model with constant rates recently applied to DNA breathing dynamics [G. Altan-Bonnet et al, Phys. Rev. Lett. 90, 138101 (2003)] emerges as a limiting case.Comment: 9 pages, 2 figure

Towards unimolecular luminescent solar concentrators: Bodipy-based dendritic energy-transfer cascade with panchromatic absorption and monochromatized emission

A polymer-embedded dendritic, bodipy-based panchromatic absorber with a built-in energy gradient concentrates incident solar radiation at a terminal chromophore, resulting in a monochromatized emission directed to the sides of the polymer waveguide (see picture). This particular design minimizes self-absorption losses from the peripheral antenna units with an impressive S factor of 10 000. © 2011 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim

Observation of Gate-Tunable Coherent Perfect Absorption of Terahertz Radiation in Graphene

We report experimental observation of electrically tunable coherent perfect absorption (CPA) of terahertz (THz) radiation in graphene. We develop a reflection-type tunable THz cavity formed by a large-area graphene layer, a metallic reflective electrode, and an electrolytic medium in between. Ionic gating in the THz cavity allows us to tune the Fermi energy of graphene up to 1 eV and to achieve a critical coupling condition at 2.8 THz with absorption of 99%. With the enhanced THz absorption, we were able to measure the Fermi energy dependence of the transport scattering time of highly doped graphene. Furthermore, we demonstrate flexible active THz surfaces that yield large modulation in the THz reflectivity with low insertion losses. We anticipate that the gate-tunable CPA will lead to efficient active THz optoelectronics applications. © 2016 American Chemical Society

Silver nanoparticle-coated polyhydroxyalkanoate based electrospun fibers for wound dressing applications

Wound dressings are high performance and high value products which can improve the regeneration of damaged skin. In these products, bioresorption and biocompatibility play a key role. The aim of this study is to provide progress in this area via nanofabrication and antimicrobial natural materials. Polyhydroxyalkanoates (PHAs) are a bio-based family of polymers that possess high biocompatibility and skin regenerative properties. In this study, a blend of poly(3-hydroxybutyrate) (P(3HB)) and poly(3-hydroxyoctanoate-co-3-hydroxy decanoate) (P(3HO-co-3HD)) was electrospun into P(3HB))/P(3HO-co-3HD) nanofibers to obtain materials with a high surface area and good han-dling performance. The nanofibers were then modified with silver nanoparticles (AgNPs) via the dip-coating method. The silver-containing nanofiber meshes showed good cytocompatibility and interesting immunomodulatory properties in vitro, together with the capability of stimulating the human beta defensin 2 and cytokeratin expression in human keratinocytes (HaCaT cells), which makes them promising materials for wound dressing applications