Comparative leaf anatomy of some species of Abies and Picea (Pinaceae)

ABSTRACTA number of conifer species are still lacking anatomical data, which is significant because morphological and anatomical data are essential for systematic study. Leaf anatomy was studied in selected species of Abiesand Piceausing light and scanning electron microscopy. Both genera were found to have typical coniferous and highly xerophytic leaves with sunken stomata and an epidermis covered by a thick cuticle. In the genus Abies, species can be differentiated by the nature of the lignified hypodermis and the number and position of resin ducts. Abies firma and A. holophylla have a continuous hypodermis whereas in A. koreana and A. nephrolepis the hypodermis is discontinuous and represented by isolated cells or groups of four or five cells. On the other hand, in Picea leaf shape, stomata arrangement, and number, position, and nature of resin ducts are the key features for species differentiation. Picea jezoensis has a flattened leaf with stomata distributed on the adaxial surface whereas P. abies and P. koraiensis have a rectangular leaf with stomata found on surfaces

Indoor Thermal Environment Long-Term Data Analytics Using IoT Devices in Korean Apartments: A Case Study

IoT-based monitoring devices can transmit real-time and long-term thermal environment data, enabling innovative conversion for the evaluation and management of the indoor thermal environment. However, long-term indoor thermal measurements using IoT-based devices to investigate health effects have rarely been conducted. Using apartments in Seoul as a case study, we conducted long-term monitoring of thermal environmental using IoT-based real-time wireless sensors. We measured the temperature, relative humidity (RH), and CO2 in the kitchen, living room, and bedrooms of each household over one year. In addition, in one of the houses, velocity and globe temperatures were measured for multiple summer and autumn seasons. Results of our present study indicated that outdoor temperature is an important influencing factor of indoor thermal environment and indoor RH is a good indicator of residents’ lifestyle. Our findings highlighted the need for temperature management in summer, RH management in winter, and kitchen thermal environment management during summer and tropical nights. This study suggested that IoT devices are a potential approach for evaluating personal exposure to indoor thermal environmental risks. In addition, long-term monitoring and analysis is an efficient approach for analyzing complex indoor thermal environments and is a viable method for application in healthcare

Using convolutional neural network for predicting cyanobacteria concentrations in river water

Machine learning modeling techniques have emerged as a potential means for predicting algal blooms. In this study, synthetic spatio-temporal water quality data for a river section were generated with a 3D water quality model and used to investigate the capability of a convolutional neural network (CNN) for predicting harmful cyanobacterial blooms. The CNN model displayed a reasonable capacity for short-term predictions of cyanobacteria (Microcystis) biomass. In the nowcasting of Microcystis, the CNN performance had a Nash-Sutcliffe Efficiency (NSE) of 0.87. An increase in the forecast lead time resulted in a decrease in the prediction accuracy, reducing the NSE from 0.87 to 0.58. As the spatial observation density increased from 20% to 100% of the input image grids, the CNN prediction NSE had improved from 0.70 to 0.84. Adding noise to the data resulted in accuracy deterioration, but even at the noise amplitude of 10%, the accuracy was acceptable for some applications, with NSE = 0.76. Visualization of the CNN results characterized its performance variations across the studied river reach. Overall, this study successfully demonstrated the capability of the CNN model for cyanobacterial bloom prediction using high temporal frequency images. (c) 2020 Elsevier Ltd. All rights reserved