Development of underground detection system using a metal detector and aluminum tag for, Copris ochus (Coleoptera: Scarabaeidae).

Tracking of soil-dwelling insects poses greater challenges compared to aboveground-dwelling animals in terrestrial systems. A metal detector system consisting of a commercially available detector and aluminum tags was developed for detecting dung beetle, Copris ochus Motschulsky (Coleoptera: Scarabaeidae). First, detection efficacy of the system was evaluated by varying volumes of aluminum tags attached on a plastic model of the insect and also by varying angles. Then, detection efficacy was evaluated by varying depths of aluminum-tagged models under soil in 2 vegetation types. Finally, the effects of tag attachment on C. ochus adults were assessed for survivorship, burrowing depth, and horizontal movement. Generally, an increase in tag volume resulted in greater detection distance in semi-field conditions. Maximum detection distance of aluminum tag increased up to 17 cm below soil surface as the tag size (0.5 × 1.0 cm [width × length]) and thickness (16 layers) were maximized, resulting in a tag weight of 31.4 mg, comprising ca. 9% of average weight of C. ochus adult. Furthermore, the detection efficacy did not vary among angles except for 90°. In the field, metal detectors successfully detected 5 aluminum-tagged models in 20 × 10 m (W × L) arena within 10 min with detection rates ≥85% for up to depth of 10 cm and 45%-60% at depth of 20 cm. Finally, aluminum tagging did not significantly affect survivorship and behaviors of C. ochus. Our study indicates the potential of metal detector system for tracking C. ochus under soil.

Modification of Anaerobic Digestion Model No. 1 for modeling anaerobic digestion of cattle manure with changing solids retention time.

In this study, Anaerobic Digestion Model No.1 (ADM1) was modified to incorporate changes in biochemical parameters due to solids retention time (SRT) variations. Cattle manure (CM) and thermally hydrolyzed CM were selected for testing. Continuous anaerobic digestion reactors were operated under different SRT conditions ranging from 6.6 to 36.0 days for both samples. The biochemical parameters (kch, kli, kpr, um,ac, um,bu, um,pro, um,va, Kac, Kbu, Kpro, and Kva) for each SRT condition were determined. To modify ADM1, the equations obtained through linear regression were substituted into biochemical parameters as a function of SRT. The modified ADM1 demonstrated superior accuracy compared with conventional ADM1. This study implies the feasibility of optimizing biochemical parameters for modeling in response to changes in environmental variables.

Identification of a Ternary Nitride Ti10Cu3N4 with a Unique Structure Type.

This study presents the synthesis and detailed structural analysis of the ternary nitride Ti10Cu3N4. Initially identified as Ti3CuN within the Ti-Cu-N ternary phase diagram, its crystal structure remained unresolved and was characterized solely as belonging to the tetragonal crystal system. Through a comprehensive structural analysis, this study proposes a revised stoichiometry as Ti10Cu3N4; its crystal structure represents a previously unreported structure type within the P4 2 /mnm space group. Its atomic arrangement was elucidated through a combination of X-ray powder diffraction profile analysis and density functional theory calculations, corroborated by neutron diffraction studies. Furthermore, the hydrogen storage properties of Ti10Cu3N4 were characterized, demonstrating a hydrogen absorption capacity of approximately 0.6 wt % with desorption occurring in the temperature range of 200-550 °C.

Ethyl Formate Fumigation for Controlling Two Major Aphid Pests, Aphis spiraecola and Aphis gossypii, on Passion Fruit, from Cultivation to Post-Harvest Storage.

Tropical and subtropical crops are being increasingly cultivated in South Korea, leading to an increase in damage by exotic insect pests. Consequently, ethyl formate (EF) is currently being considered for quarantine and pre-shipment fumigation. In this study, we evaluated the effectiveness of EF fumigation for controlling Aphis spiraecola Patch and Aphis gossypii Glover, two representative quarantine pests on passion fruit ("Pink Bourbon") during greenhouse cultivation and post-harvest storage. The efficacy of EF against both aphids in terms of the lethal concentration causing 50% mortality (LCt50%) and LCt99% was 1.36-2.61 g h/m3 and 3.73-7.55 g h/m3 under greenhouse conditions (23 °C), and 1.37-2.02 g h/m3 and 3.80-14.59 g h/m3 post-harvest (5 °C), respectively. EF at 4 g/m3 for 4 h resulted in 100% mortality of A. spiraecola, which was more resistant to EF, without causing phytotoxic damage to the trees in a 340 m3 greenhouse. Post-harvest fruit fumigation at 10 g/m3 for 4 h in a mid-size (0.8 m3) fumigation chamber resulted in complete disinfection. Moreover, the EF level decreased below the EF threshold within 10 min after natural ventilation in the greenhouse. Therefore, our results suggest EF fumigation as an effective method for controlling A. spiraecola and A. gossypii.

Is replacing missing values of PM2.5 constituents with estimates using machine learning better for source apportionment than exclusion or median replacement?

East Asian countries have been conducting source apportionment of fine particulate matter (PM2.5) by applying positive matrix factorization (PMF) to hourly constituent concentrations. However, some of the constituent data from the supersites in South Korea was missing due to instrument maintenance and calibration. Conventional preprocessing of missing values, such as exclusion or median replacement, causes biases in the estimated source contributions by changing the PMF input. Machine learning (ML) can estimate the missing values by training on constituent data, meteorological data, and gaseous pollutants. Complete data from the Seoul Supersite in 2018 was taken, and a random 20% was set as missing. PMF was performed by replacing missing values with estimates. Percent errors of the source contributions were calculated compared to those estimated from complete data. Missing values were estimated using a random forest analysis. Estimation accuracy (r2) was as high as 0.874 for missing carbon species and low at 0.631 when ionic species and trace elements were missing. For the seven highest contributing sources, replacing the missing values of carbon species with estimates minimized the percent errors to 2.0% on average. However, replacing the missing values of the other chemical species with estimates increased the percent errors to more than 9.7% on average. Percent errors were maximal at 37% on average when missing values of ionic species and trace elements were replaced with estimates. Missing values, except for carbon species, need to be excluded. This approach reduced the percent errors to 7.4% on average, which was lower than those due to median replacement. Our results show that reducing the biases in source apportionment is possible by replacing the missing values of carbon species with estimates. To improve the biases due to missing values of the other chemical species, the estimation accuracy of the ML needs to be improved.