Rhaphidophoridae (Orthoptera: Ensifera), commonly known as cave crickets, are a wingless family and considered the most ancient lineage within Tettigoniidea. However, previous molecular phylogenetic studies and morphological hypotheses have shown inconsistencies. Although their fossils have been found in Baltic amber, their systematic placement remains unrevealed. This study reconstructed a comprehensive phylogeny integrating both extant and fossil lineages. Initially, we revealed relationships within extant lineages through molecular phylogenetics including all extant subfamilies for the first time. Subsequently, using a cladistic approach based on morphology, we confirmed the systematic position of fossil taxa †Protroglophilinae with a report of a new species. Integrating molecular and morphological phylogeney by total evidence tip-dating, we present the comprehensive phylogeny of Rhaphidophoridae considering both extant and fossil groups.

Despite having enabled insects to become the most abundant and successful group on Earth, wings have been lost in numerous insect lineages, including Orthoptera. Melanoplinae, a subfamily that includes over 100 genera and more than 800 species in Acrididae, exhibits various wing-types and dispersal abilities. Some species possess extensive flight capabilities with long wings, while many groups that inhabit alpine environments tend to reduce their wings and dispersal ability. In order to infer the evolutionary history of Melanoplinae and their wings, we conducted molecular phylogenetic research. We established the phylogeny using seven mitochondrial (Cox1, Cox2, CytB, Nad2, Nad5, 12S and 16S) and two nuclear genes (H3 and Wg) for 139 taxa. By investigating the wing types in Melanoplinae, we estimated the ancestral state of the wings and traced their evolutionary history. Our results present that loss and recovery of wings occurred multiple times within Melanoplinae, showing distinct histories across inner taxa within the subfamily.

A 1.8 μm thick polycrystalline diamond (PCD) thin film layer is prepared on a Si(100) substrate using hot-filament chemical vapor deposition. Thereafter, its thermal conductivity is measured using the conventional laser flash analysis (LFA) method, a LaserPIT-M2 instrument, and the newly proposed light source thermal analysis (LSTA) method. The LSTA method measures the thermal conductivity of the prepared PCD thin film layer using an ultraviolet (UV) lamp with a wavelength of 395 nm as the heat source and a thermocouple installed at a specific distance. In addition, the microstructure and quality of the prepared PCD thin films are evaluated using an optical microscope, a field emission scanning electron microscope, and a micro-Raman spectroscope. The LFA, LaserPIT-M2, and LSTA determine the thermal conductivities of the PCD thin films, which are 1.7, 1430, and 213.43 W/(m·K), respectively, indicating that the LFA method and LaserPIT-M2 are prone to errors. Considering the grain size of PCD, we conclude that the LSTA method is the most reliable one for determining the thermal conductivity of the fabricated PCD thin film layers. Therefore, the proposed LSTA method presents significant potential for the accurate and reliable measurement of the thermal conductivity of PCD thin films.

Two main sources of data, meteorological data and land surface characteristics, are essential to effectively run a distributed rainfall-runoff model. The specification and averaging of the land surface characteristics in a suitable way is crucial to obtaining accurate runoff output. Recent advances in remote sensing techniques are often being used to derive better representations of these land surface characteristics. Due to the mismatch in scale between digital land cover maps and numerical grid sizes, issues related to upscaling or downscaling occur regularly. A specific method is typically selected to average and represent the land surface characteristics. This paper examines the amount of flooding by applying the FLO-2D routing model, where vegetation heterogeneity is manipulated using the Manning’s roughness coefficient. Three different upscaling methods, arithmetic, dominant, and aggregation, were tested. To investigate further, the rainfall-runoff model with FLO-2D was facilitated in Yongdam catchment and heavy rainfall events during wet season were selected. The results show aggregation method provides better results, in terms of the amount of peak flow and the relative time taken to achieve it. These rwsults suggest that the aggregation method, which is a reasonably realistic description of area-averaged vegetation nature and characteristics, is more likely to occur in reality.