Experimental analysis of PM2.5 reduction characteristics between Korean red pine (Pinus densiflora) and sawtooth oak (Quercus acutissima) saplings under different densities and arrangement structures.

As global air pollution, particularly fine particulate matter (PM2.5), has become a major environmental problem, various PM2.5 mitigation technologies including green infrastructure have received significant attention. However, owing to spatial constraints on urban greening, there is a lack of management plans for urban forests to efficiently mitigate PM2.5. In this study, we assessed the PM2.5 reduction capabilities of Pinus densiflora (Korean red pine) and Quercus acutissima (sawtooth oak) by measuring the changes of PM2.5 concentrations using an experimental chamber system. In addition, the PM2.5 reduction efficiency in 90 min (PMRE90) and the amount of PM2.5 reduction per leaf area (PMRLA) were compared based on arrangement structures and density levels. The results showed that the PM2.5 reduction by plants was significantly greater than that of the control experiment without any plants, and an additional reduction effect of approximately 1.38 times was induced by a 1.5 m s-1 air flow. The PMRE90 of Korean red pine was the highest at medium density. In contrast, the PMRE90 of sawtooth oak was the highest at high density. The PMRLA of both species was highest at low densities. The different responses of the species to total reduction were well explained by total leaf area (TLA). The PMRE90 of both species was positively correlated with TLA. The PMRLA of sawtooth oak was approximately 2.3 times greater than that of Korean red pine. However, there were no significant differences in both PMRE90 and PMRLA between the arrangement structures. Our findings reveal the potential mechanisms of vegetation in reducing PM2.5 according to arrangement structure and density. This highlights the importance of efficiently using urban green spaces with spatial constraints on PM2.5 mitigation in the future.

Fork-shaped neural interface with multichannel high spatial selectivity in the peripheral nerve of a rat.

Objective.This study aims to develop and validate a sophisticated fork-shaped neural interface (FNI) designed for peripheral nerves, focusing on achieving high spatial resolution, functional selectivity, and improved charge storage capacities. The objective is to create a neurointerface capable of precise neuroanatomical analysis, neural signal recording, and stimulation.Approach.Our approach involves the design and implementation of the FNI, which integrates 32 multichannel working electrodes featuring enhanced charge storage capacities and low impedance. An insertion guide holder is incorporated to refine neuronal selectivity. The study employs meticulous electrode placement, bipolar electrical stimulation, and comprehensive analysis of induced neural responses to verify the FNI's capabilities. Stability over an eight-week period is a crucial aspect, ensuring the reliability and durability of the neural interface.Main results.The FNI demonstrated remarkable efficacy in neuroanatomical analysis, exhibiting accurate positioning of motor nerves and successfully inducing various movements. Stable impedance values were maintained over the eight-week period, affirming the durability of the FNI. Additionally, the neural interface proved effective in recording sensory signals from different hind limb areas. The advanced charge storage capacities and low impedance contribute to the FNI's robust performance, establishing its potential for prolonged use.Significance.This research represents a significant advancement in neural interface technology, offering a versatile tool with broad applications in neuroscience and neuroengineering. The FNI's ability to capture both motor and sensory neural activity positions it as a comprehensive solution for neuroanatomical studies. Moreover, the precise neuromodulation potential of the FNI holds promise for applications in advanced bionic prosthetic control and therapeutic interventions. The study's findings contribute to the evolving field of neuroengineering, paving the way for enhanced understanding and manipulation of peripheral neural functions.

Comparisons of PM2.5 mitigation with stand characteristics between evergreen Korean pine plantations and deciduous broad-leaved forests in the Republic of Korea.

Owing to industrialization and urbanization in recent decades, fine particulate matter (PM2.5) in the atmosphere has become a major environmental problem worldwide. This environmental issue pushed the use of forests as air filtering tools. However, there is a lack of continuous and long-term forest management to efficiently mitigate PM2.5. In this study, we assessed the potential of different forest types to control air pollution by measuring the seasonal PM2.5 concentrations inside and outside the forest for one year. In addition, the PM2.5 reduction efficiencies (PMREs) of two forest types were compared, and their relationship with stand characteristics was analyzed. The results showed that the average PMRE inside the forests was approximately 18.2%; the seasonal PMRE was highest in winter (approximately 28.1%) and lowest in summer (approximately 9.6%). The average PMRE of the Taehwa deciduous broad-leaved forest (TDF) (approximately 18.8%) was significantly higher than that of the Taehwa coniferous forest (TCF) (approximately 17.5%) (P < 0.001); differences were also observed seasonally. The PMRE in the TCF was higher in spring and summer (P < 0.001), while that in the TDF was higher in autumn and winter (P < 0.001). Furthermore, the PMRE in the TDF was negatively correlated with stand density (P = 0.003) and positively correlated with the average diameter at breast height (DBH) (P = 0.028). However, the PMRE in the TCF did not significantly correlate with stand characteristics. As such, the results of this study revealed the differences in PM2.5 mitigation according to stand characteristics, which should be considered in urban forest management.

Identifying and extracting bark key features of 42 tree species using convolutional neural networks and class activation mapping.

The significance of automatic plant identification has already been recognized by academia and industry. There were several attempts to utilize leaves and flowers for identification; however, bark also could be beneficial, especially for trees, due to its consistency throughout the seasons and its easy accessibility, even in high crown conditions. Previous studies regarding bark identification have mostly contributed quantitatively to increasing classification accuracy. However, ever since computer vision algorithms surpassed the identification ability of humans, an open question arises as to how machines successfully interpret and unravel the complicated patterns of barks. Here, we trained two convolutional neural networks (CNNs) with distinct architectures using a large-scale bark image dataset and applied class activation mapping (CAM) aggregation to investigate diagnostic keys for identifying each species. CNNs could identify the barks of 42 species with > 90% accuracy, and the overall accuracies showed a small difference between the two models. Diagnostic keys matched with salient shapes, which were also easily recognized by human eyes, and were typified as blisters, horizontal and vertical stripes, lenticels of various shapes, and vertical crevices and clefts. The two models exhibited disparate quality in the diagnostic features: the old and less complex model showed more general and well-matching patterns, while the better-performing model with much deeper layers indicated local patterns less relevant to barks. CNNs were also capable of predicting untrained species by 41.98% and 48.67% within the correct genus and family, respectively. Our methodologies and findings are potentially applicable to identify and visualize crucial traits of other plant organs.