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The effects of discharge parameters on the discharge process and plasma transport characteristics of a coaxial gun in the gas-prefilled mode are studied. The plasma optical intensity and ejection velocity are measured by photodiodes, the optical emission spectrum is taken by a spectroscopic system, and the plasma evolution in the transport process is captured by a high-speed camera. The plasma acceleration characteristics under different discharge parameters show that the velocity and electron density of the ejection plasma are mainly determined by the pre-filled pressure and discharge current, which is consistent with the snowplow model. The kinetic energy of ejection plasma can be significantly increased by reducing the outer loop inductance, which is conducive to increasing the energy utilization efficiency. The time-varying images of plasma radiation and the plasma density at different transport locations illuminate the transmission characteristics of coaxial gun discharge plasma. The results show that the snowplow effect continues to play a role in the plasma transport process, and the plasma accumulation is induced by the combination of shock wave compression. The current-driven magneto hydrodynamics instability occurs during the transport process, and the luminous signal of the plasma current sheet oscillates periodically. In addition, the plasma impact effect is obvious and the gas retarding effect is enhanced with the increase in the gas pressure. These results give us a more comprehensive view of the coaxial gun discharge process and plasma transport and provide a certain reference for optimizing the parameters selection and physical design of coaxial gun discharge plasma characteristics. [ABSTRACT FROM AUTHOR]

While newly built urban green spaces aim to address environmental concerns, the resulting green gentrification and social inequality caused by escalating property values have become critical topics of urban socio-spatial research. To prevent green initiatives from becoming unaffordable for their intended beneficiaries in rapidly urbanizing cities, it is essential to examine the spatial and temporal relationships between the construction of new green spaces and rising housing prices. This study employs a difference-in-differences methodology to analyze regional disparities, impact periodicity, and the influence of park size on housing prices, using Nanjing, China as a case study. This result reveals that the introduction of new-built parks in Nanjing significantly impacts housing prices within an 800 m radius. The premium effect of these parks is substantially higher in urban core areas compared to suburban locales, demonstrating spatial differentials. Suburban parks temporally exhibit a prolonged lag and a shorter premium impact duration. Moreover, among various park areas, medium-sized parks demonstrate the most pronounced leverage effect, approximately double that of large parks, while small parks do not significantly affect housing prices. To mitigate the exacerbation of premium effects and enhance social justice in green strategies, we advocate prioritizing the development of small parks, particularly in urban core areas, and leveraging the temporal delay in new-built park impacts for urban policy interventions. [ABSTRACT FROM AUTHOR]

The tracking of carbon and aerosol co-emissions is essential for environmental management. Satellite-based atmospheric synoptic observation networks provide large-scale and multifaceted data to help resolve emission behaviors. This study employs a comprehensive analysis of atmospheric dynamics, combustion byproducts, and aerosol characteristics around power plants. Strong correlations between Aerosol Optical Depth (AOD) at 500 nm and the column-averaged dry-air mole fraction of carbon dioxide (XCO2) were observed, revealing synchronous peaks in their emission patterns. The investigation into combustion completeness utilized metrics such as the ratio of carbon monoxide (CO)/XCO2 and Black Carbon Extinction (BCEXT)/Total Aerosol Extinction (TOTEXT). Discrepancies in these ratios across cases suggest variations in combustion efficiency and aerosol characteristics. Nitrogen dioxide (NO2) distributions closely mirrored XCO2, indicating consistent emission patterns, while variations in sulfur dioxide (SO2) distributions implied differences in sulfide content in the coal used. The influence of coal composition on AOD/XCO2 ratios was evident, with sulfide content contributing to variations besides combustion efficiency. This multifactorial analysis underscores the complex interplay of combustion completeness, aerosol composition, and coal components in shaping the air quality around power stations. The findings highlight the need for a nuanced understanding of these factors for effective air quality management. [ABSTRACT FROM AUTHOR]

Ensuring the security of transmitted content is an important task in network communication. Signcryption technology combines signature and encryption operations in a single step to achieve message authentication and confidentiality. In practical applications, users are usually in different cryptographic systems, the ordinary signcryption technology cannot realize communication between two different cryptographic systems. And the ciphertext structure of the existing signcryption schemes is relatively complex, signcryption is not efficient. Therefore, in order to solve the problem of efficient communication between different cryptosystems and ensure quantum security, this paper proposes a lattice-based efficient heterogeneous signcryption scheme for secure network communications. This signcryption scheme accomplishes signcryption through matrix operations and hash functions, which avoids complex signcryption structures, efficiently realizes bidirectional message communication between identity-based cryptosystems and certificateless cryptosystems. [ABSTRACT FROM AUTHOR]

Objectives: The objective of this study is to develop a novel multi-task learning approach with attention encoders for classifying histologic subtypes and clinical stages of non-small cell lung cancer (NSCLC), with superior performance compared to currently popular deep-learning models., Material and Methods: Data were collected from six publicly available datasets in The Cancer Imaging Archive (TCIA). Following the inclusion and exclusion criteria, a total of 4548 CT slices from 758 cases were allocated. We evaluated multiple multi-task learning models that integrate attention mechanisms to resolve challenges in NSCLC subtype classification and clinical staging. These models utilized convolution-based modules in their shared layers for feature extraction, while the task layers were dedicated to histological subtype classification and staging. Each branch sequentially processed features through convolution-based and attention-based modules prior to classification., Results: Our study evaluated 758 NSCLC patients (mean age, 66.2 years ± 10.3; 473 men), spanning ADC and SCC cases. In the classification of histological subtypes and clinical staging of NSCLC, the MobileNet-based multi-task learning model enhanced with attention mechanisms (MN-MTL-A) demonstrated superior performance, achieving Area Under the Curve (AUC) scores of 0.963 (95 % CI: 0.943, 0.981) and 0.966 (95 % CI: 0.945, 0.982) for each task, respectively. The model significantly surpassed its counterparts lacking attention mechanisms and those configured for single-task learning, as evidenced by P-values of 0.01 or less for both tasks, according to DeLong's test., Conclusions: The integration of attention encoder blocks into our multi-task learning network significantly enhanced the accuracy of NSCLC histological subtyping and clinical staging. Given the reduced reliance on precise radiologist annotation, our proposed model shows promising potential for clinical application., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background and Purpose: Although a large collection of data has shown that ginsenosides, the major active ingredients from Ginseng, have neuroprotective and anti-depressant effect, the mechanism of action is incompletely understood. This study aims to elucidate the antidepressant mechanism of ginsenoside Rg1 (Rg1), a poorly absorbed ginsenoside, from the perspective of gut microbe to brain signaling., Methods: A mouse model of depression was induced by unpredictable mild stress (UMS). Behavioral and neurochemical tests were conducted to evaluate the effect and mechanism of Rg1 on depressive behavior. Non-target and target metabolomics were performed to identify the signaling metabolites underlying the antidepressant efficacy of Rg1. Gut microbial structure was analyzed by 16S rRNA sequencing and the potential functional strains associated with Rg1 action were investigated by in vitro bacterial culture. Chemical intervention was used to explore the mechanism of Rg1 and signaling metabolite., Results: Rg1 improved UMS-induced despair, anxiety-like and social avoidance behaviors in mice, which were accompanied by increased hypothalamic oxytocin secretion and restored neural proliferation in the hippocampus. Metabolomic analysis of the gut-brain axis revealed that Rg1 increased the concentration of serum and brain indole-3-acetic acid (IAA), a bacterial metabolite that was partially attributed to the enrichment of Lactobacillus murinus in the gut microbiome. Oral supplementation of IAA mimicked the anti-depressant action of Rg1, while oxytocin receptor antagonist abrogated the anti-depressant effects of both Rg1 and IAA., Conclusion: Our work provides a new gut-to-brain signaling mechanism for the antidepressant effects of Rg1. In particular, Rg1 enriches the abundance of Lactobacillus murinus, which in turn increases the level of brain IAA and potentiates hypothalamic oxytocin signal. These findings suggest a promising pathway for producing antidepressant effects through gut-brain crosstalk., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier GmbH. All rights reserved.)

Rationale and Objectives: To develop and validate a deep learning model for automated pathological grading and prognostic assessment of lung cancer using CT imaging, thereby providing surgeons with a non-invasive tool to guide surgical planning., Material and Methods: This study utilized 572 cases from the National Lung Screening Trial cohort, dividing them randomly into training (461 cases) and internal validation (111 cases) sets in an 8:2 ratio. Additionally, 224 cases from four cohorts obtained from the Cancer Imaging Archive, all diagnosed with non-small cell lung cancer, were included for external validation. The deep learning model, built on the MobileNetV3 architecture, was assessed in both internal and external validation sets using metrics such as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). The model's prognostic value was further analyzed using Cox proportional hazards models., Results: The model achieved high accuracy, sensitivity, specificity, and AUC in the internal validation set (accuracy: 0.888, macro AUC: 0.968, macro sensitivity: 0.798, macro specificity: 0.956). External validation demonstrated comparable performance (accuracy: 0.807, macro AUC: 0.920, macro sensitivity: 0.799, macro specificity: 0.896). The model's predicted signatures correlated significantly with patient mortality and provided valuable insights for prognostic assessment (adjusted HR 2.016 [95% CI: 1.010, 4.022])., Conclusions: This study successfully developed and validated a deep learning model for the preoperative grading of lung cancer pathology. The model's accurate predictions could serve as a useful adjunct in treatment planning for lung cancer patients, enabling more effective and customized interventions to improve patient outcomes., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Xiuhua Guo reports financial support was provided by National Natural Science Foundation of China. Xiuhua Guo reports financial support was provided by Beijing Municipal Health Commission. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)