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In order to validate the applicability of pXRF for rapid in situ detection of heavy metals in urban soils and to accurately obtain an assessment of soil quality in Changchun, a city in northeast China, 164 soil samples from within the main urban area of Changchun were collected for pXRF analysis. The main stable elements Si and Ti were used to establish a matrix effect correction model, and the values of Cr (64.2 mg⋅kg−1), Cu (43.8 mg⋅kg−1), Zn (96.2 mg⋅kg−1), As (20.9 mg⋅kg−1), and Pb (57.4 mg⋅kg−1) were predicted. The empirical findings indicate that the quality of soil data from the pXRF was improved to different degrees under the correction model, and it became a relatively reliable dataset; the order of improvement was Cu > Pb > Cr > Zn > As. A comprehensive assessment indicated that Changchun City is primarily contaminated by the heavy metals As, Pb, and Cu, with the main sources being automobile manufacturing and pharmaceutical chemical production. These findings align with previous studies and have produced favorable outcomes in practical applications. This rapid, non-destructive and economical detection method is very applicable and economical for the sustainable monitoring and control of heavy metals in large cities. This study provides a basis for rapid large-scale prediction of urban soil safety and protection of local human health.

Many resting-state functional magnetic resonance imaging (rs-fMRI) studies have shown that the brain networks are disrupted in adolescent patients with juvenile myoclonic epilepsy (JME). However, previous studies have mainly focused on investigating brain connectivity disruptions from the perspective of static functional connections, overlooking the dynamic causal characteristics between brain network connections. In our study involving 37 JME patients and 35 Healthy Controls (HC), we utilized rs-fMRI to construct whole-brain functional connectivity network. By applying graph theory, we delved into the altered topological structures of the brain functional connectivity network in JME patients and identified abnormal regions as key regions of interest (ROIs). A novel aspect of our research was the application of a combined approach using the sliding window technique and Granger causality analysis (GCA). This method allowed us to delve into the dynamic causal relationships between these ROIs and uncover the intricate patterns of dynamic effective connectivity (DEC) that pervade various brain functional networks. Graph theory analysis revealed significant deviations in JME patients, characterized by abnormal increases or decreases in metrics such as nodal betweenness centrality, degree centrality, and efficiency. These findings underscore the presence of widespread disruptions in the topological features of the brain. Further, clustering analysis of the time series data from abnormal brain regions distinguished two distinct states indicative of DEC patterns: a state of strong connectivity at a lower frequency (State 1) and a state of weak connectivity at a higher frequency (State 2). Notably, both states were associated with connectivity abnormalities across different ROIs, suggesting the disruption of local properties within the brain functional connectivity network and the existence of widespread multi-functional brain functional networks damage in JME patients. Our findings elucidate significant disruptions in the local properties of whole-brain functional connectivity network in patients with JME, revealing causal impairments across multiple functional networks. These findings collectively suggest that JME is a generalized epilepsy with localized abnormalities. Such insights highlight the intricate network dysfunctions characteristic of JME, thereby enriching our understanding of its pathophysiological features.

Raspberry (Rubus corchorifolius L. f.) is easy to be infected by pathogens after harvesting. The species of pathogens are not only related to the fruit species, but also related to the cultivation environment. In this study, the main pathogens of post-harvest raspberry were isolated and identified from three different producing areas of raspberry in Taiyuan, Yangquan and Changzhi of Shanxi Province and their biological characteristics were studied. The pathogens were isolated and purified to determine their species by combining morphological and molecular biological characteristics, so as to determine the spore production of pathogens in the same period and their growth under different conditions. The results indicated that Trichoderma koningiopsis, Penicillium crustosum, Botrytis sp. and Rhizopus arrhizus were the main pathogens infecting post-harvest raspberry in the three regions. Penicillium crustosum had the highest spore production capacity, followed by Botrytis sp. and Rhizopus arrhizus. The optimum pH for the growth of four pathogens was 7, with Penicillium crustosum growing the fastest. Botrytis sp. and Penicillium crustosum presented faster growing during storage at 0, 4 and 10 ℃, while Trichoderma koningiopsis and Rhizopus arrhizus grew slower at 0 and 4 ℃.

Abstract In recent years, dynamic visual SLAM techniques have been widely used in autonomous navigation, augmented reality, and virtual reality. However, the increasing demand for computational resources by SLAM techniques limits its application on resource-constrained mobile devices. MEC technology combined with 5G ultra-dense networks enables complex computational tasks in visual SLAM systems to be offloaded to edge computing servers, thus breaking the resource constraints of terminals and meeting real-time computing requirements. This paper firstly introduces the research results in the field of visual SLAM in detail through three categories: static SLAM, dynamic SLAM, and SLAM techniques combined with deep learning. Secondly, the three major parts of the technology comparison between mobile edge computing and mobile cloud computing, 5G ultra-dense networking technology, and MEC and UDN integration technology are introduced to sort out the basic technologies related to the application of 5G ultra-dense network to offload complex computing tasks from visual SLAM systems to edge computing servers.

The rot caused by fungal infection is the main factor affecting the postharvest storage of Yuluxiang pear. In order to clarify the species and biological characteristics of pathogenic bacteria that cause postharvest rot of Yuluxiang pear, the postharvest pathogenic bacteria were isolated and purified, combining morphological and molecular biological characteristics to determine its species, and to clarify the growth of pathogenic bacteria under different temperature, pH, carbon and nitrogen source conditions. The results showed that: Alternaria alternata, Aspergillus, Penicillium polonicum and Fusarium were the main pathogens causing postharvest rot of Yuluxiang pear. The optimum growth temperature of Alternaria alternata and Fusarium was 25~30 ℃, while the optimum growth temperature of Aspergillus and Penicillium polonicum were 25~35 ℃ and 20~25 ℃, respectively. The optimum growth pH of Alternaria alternata and Aspergillus were 6~10 and 7, respectively, while the optimum growth pH of Penicillium polonicum and Fusarium were both 5~7. The optimum growth carbon sources of Alternaria alternata and Penicillium polonicum are glucose and mannitol, respectively, the optimum growth carbon source of Aspergillus and Fusarium was maltose. The optimum growth nitrogen source of Alternaria alternata, Aspergillus and Fusarium was beef extract, and the optimum growth nitrogen source of Penicillium polonicum was peptone.

Postharvest decay is an urgent problem that affects the storage of pears. Low temperature storage is one of the most important methods to reduce the prevalence of fruit diseases during storage. In this study, the microbial diversity of postharvest Yuluxiang pear (Pyrus × michauxii “Yu Lu Xiang”) fruits stored at low temperature for different lengths of times was analyzed. Illumina MiSeq high-throughput sequencing was used to analyze the composition and diversity of fungal communities. The results showed that the fungi within fruit were classified into 6 phyla, 18 classes, 40 orders, 72 families, and 92 genera based on the 97% sequence similarity level. They belonged to 6 phyla, 18 classes, 40 orders, 72 families, and 92 genera. The highest richness of fungi was obtained after 30 d of treatment. The β-diversity index showed that the fungal community composition of these fruit was significantly different at the beginning of storage compared with the different timepoints of samples at low temperature during storage. The comparison of fungal composition at the phylum level indicated that Ascomycota was dominant in the different timepoints of samples at low temperature, while Alternaria was the primary fungus at the genus level. A correlation analysis was used to further explore the correlation between fungi and fruit firmness, titratable acid, and solid soluble contents at low temperatures during storage. Aureobasidium and Didymella positively correlated with the soluble solids and hardness. Phoma positively correlated with the titratable acid, and Aspergillus positively correlated with titratable acid and hardness. This study can guide the industrial production of Yulu pear and also provide a theoretical basis to prevent and control diseases during the storage period of Yulu pear.

Portable X-ray fluorescence spectrometry (pXRF) is an analytical technique that can be used for rapid and non-destructive analysis in the field. However, the testing accuracy and precision for trace elements are significantly affected by the matrix effect, which comes mainly from major elements that constitute most of the matrix of a sample. To solve this problem, many methods based on linear regression models have been proposed, but when extreme values or outliers occur, the application of these methods will be greatly affected. In this study, 16 certified reference materials were collected for pXRF analysis, and the major elements most closely related to the elements to be measured were employed as correction indicators to calibrate the analysis results through the application of multiple linear regression analysis. Some statistical parameters were calculated to evaluate the correction results. Compared with the calibration data obtained from simple linear regression analysis without taking major elements into account, those corrected by the new method were of higher quality, especially for elements of Co, Zn, Mo, Ta, Tl, Pb, Cd and Sn. The results show that the new method can effectively suppress the influence of the matrix effect.

In recent years, with the continuous deepening of smart city construction, there have been significant changes and improvements in the field of intelligent transportation. The semantic segmentation of road scenes has important practical significance in the fields of automatic driving, transportation planning, and intelligent transportation systems. However, the current mainstream lightweight semantic segmentation models in road scene segmentation face problems such as poor segmentation performance of small targets and insufficient refinement of segmentation edges. Therefore, this article proposes a lightweight semantic segmentation model based on the LiteSeg model improvement to address these issues. The model uses the lightweight backbone network MobileNet instead of the LiteSeg backbone network to reduce the network parameters and computation, and combines the Coordinate Attention (CA) mechanism to help the network capture long-distance dependencies. At the same time, by combining the dependencies of spatial information and channel information, the Spatial and Channel Network (SCNet) attention mechanism is proposed to improve the feature extraction ability of the model. Finally, a multi-scale transposed attention encoding (MTAE) module was proposed to obtain features of different resolutions and perform feature fusion. In this paper, the proposed model is verified on the Cityscapes dataset. The experimental results show that the addition of SCNet and MTAE modules increases the mean Intersection over Union (mIoU) of the original LiteSeg model by 4.69%. On this basis, the backbone network is replaced with MobileNet, and the CA model is added at the same time. At the cost of increasing the minimum model parameters and computing costs, the mIoU of the original LiteSeg model is increased by 2.46%. This article also compares the proposed model with some current lightweight semantic segmentation models, and experiments show that the comprehensive performance of the proposed model is the best, especially in achieving excellent results in small object segmentation. Finally, this article will conduct generalization testing on the KITTI dataset for the proposed model, and the experimental results show that the proposed algorithm has a certain degree of generalization. [ABSTRACT FROM AUTHOR]