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Aiming at the problems of the imprecise safety distance standards for low-altitude UAVs within complex low-altitude environments and the difficulty of managing heterogeneous vehicles, a UAV safety interval calibration method based on random heading is proposed. Firstly, a UAV clustering model based on K-Means++ was established for the performance characteristics of UAVs, using evaluation indexes such as contour coefficient, sum of squares of errors, Davidson–Bourdain index, etc. Then, combining this with the characteristics of UAVs with random heading, a UAV safety interval calibration model based on random heading was constructed, and the conflict probability and airspace utilization rate were determined and metered for UAV safety interval calibration. The experimental results showed that the profile coefficient, sum of squares of errors, and Davidson–Berding index of the iteratively optimized UAV clustering were optimized by 53.9%, 55.6%, and 46.6%, respectively, compared with the initial clustering results, and that the safe intervals calibrated in the experimental environment of a single category of UAVs were also applicable to the fusion airspace environment after validation. The research results can provide a theoretical basis and methodological support for the safety interval calibration of UAVs in low-altitude fusion operations.

To investigate the changes in the composition and structure of the dissolved organic matter (DOM) of the lysate solutions of different types of soil after green manure tilling treatment, we set up two types of soil materials (fluvo-aquic soil; coastal saline soil) and three green manure tilling treatments (T1: CK—without green manure, T2: tilling Dongmu70 rye, and T3: tilling rapeseed green manure); then, the soil leachate was obtained with a soil column simulation test and its DOM spectral properties were determined. The rapeseed green manure leachate demonstrated a significantly higher humic macromolecule content and aromaticity compared to Dongmu70 rye leachate. Fluorescence Index (FI) values (1.5–2.2) suggest a mixed origin of dissolved organic matter (DOM) from both terrestrial and microbial-derived sources. All Humification Index (HIX) values remained below 1, indicating low humification levels and limited stabilization of DOM within the leachate system, and Biological Index (BIX) values exceeding 1 across all soil layers highlight the predominance of a recent biological metabolism in shaping DOM autochthonous origins. The SUVA260 values in Dongmu70 rye–moist soils and rapeseed green manure–coastal saline soil exhibited reductions of 0.020–2.573 L·(mg·m)−1 relative to pre-drenching levels. After tilling rapeseed green manure, the SUVA254 value of coastal saline soil at the 60–90 cm layer decreased by 1.941 L·(mg·m)−1. This study shows that differences in green manure and soil type affect DOM sources and composition, reducing DOM leaching, with coastal saline soil + rapeseed green manure and fluvo-aquic soil + Dongmu70 rye being the advantageous combinations. The study results provide theoretical guidance for applying green manure coupled with freshwater leaching technology in the context of saline and alkaline land with multiple soil types.

As a cost-effective, environmentally friendly remediation technology, phytoremediation is defined as the use of green plants to remove pollutants from the environment or render them harmless and has been applied to a variety of contaminated sites throughout the world. There is a prominent phenomenon in which publications about phytoremediation increase each year and involve an increasing number of subject categories. This paper adopts the scientometric analysis method to assess the current state and explore the trends of phytoremediation research based on the bibliographic records retrieved from the Web of Science Core Collection (WoSCC). The results of this paper clearly answer the following questions. (1) What are the publishing characteristics of research on the topic of phytoremediation? What are the characteristics of academic collaboration in phytoremediation research? (2) What are the characteristics and development trends of phytoremediation research? (3) What are the hotspots and frontiers of phytoremediation research? Overall, the research method provides a new approach for the assessment of the performance of phytoremediation research. These results may help new researchers quickly integrate into the field of phytoremediation, as they can easily grasp the frontiers of phytoremediation research and obtain more valuable scientific information. This study also provides references for the follow-up research of relevant researchers.

This paper aims to evaluate the knowledge landscape of the phytoremediation of heavy metals (HMs) by constructing a series of scientific maps and exploring the research hotspots and trends of this field. This study presents a review of 6873 documents published about phytoremediation of HMs in the international context from the Web of Science Core Collection (WoSCC) (1989–2018). Two different processing software applications were used, CiteSpace and Bibliometrix. This research field is characterized by high interdisciplinarity and a rapid increase in the subject categories of engineering applications. The basic supporting categories mainly included “Environmental Sciences & Ecology”, “Plant Sciences”, and “Agriculture”. In addition, there has been a trend in recent years to focus on categories such as “Engineering, Multidisciplinary”, “Engineering, Chemical”, and “Green & Sustainable Science & Technology”. “Soil”, “hyperaccumulator”, “enrichment mechanism/process”, and “enhance technology” were found to be the main research hotspots. “Wastewater”, “field crops”, “genetically engineered microbes/plants”, and “agromining” may be the main research trends. Bibliometric and scientometric analysis are useful methods to qualitatively and quantitatively measure research hotspots and trends in phytoremediation of HM, and can be widely used to help new researchers to review the available research in a certain research field.

Purpose To determine the utility of short gamete coincubation in in vitro fertilization (IVF‐S) combined with early rescue intracytoplasmic sperm injection (R‐ICSI) and split IVF‐ICSI in preventing low fertilization based on a retrospective cohort study. Methods Couples with a high risk of low IVF fertilization during the first ART cycle underwent IVF‐S with R‐ICSI or split IVF‐ICSI. Fertilization rate, embryo quality, and clinical outcomes were measured. Results After propensity score matching, we included 188 couples in the IVF‐S with R‐ICSI group as Group 1 and 720 in the split IVF‐ICSI group as Group 2. Normal fertilization rates were similar; however, Group 1 had a higher multiple pronuclei rate (10.42% vs. 4.50%, p < 0.001) but a higher embryo utilization rate (59.84% vs. 53.60%, p < 0.001). The groups were similar in the rates of high‐quality embryos, embryo implantation, clinical pregnancy, and live birth. Low IVF fertilization rate was 4.79% and 9.03% in Group 1 and Group 2, respectively, with similar fertilization rate and embryo development. Conclusion IVF‐S with early R‐ICSI and split IVF‐ICSI were effective strategies in preventing low fertilization rate. IVF‐S with early R‐ICSI could become the preferred approach because of its advantages—higher embryo utilization rate, fewer ICSI procedures, similar clinical pregnancy rate, and live birth rate.

Background Obesity has been confirmed to be associated with infertility. However, the association between metabolically healthy obesity (MHO), a subset of obesity with no metabolic abnormalities, and female infertility has not yet been investigated. This study aimed to examine the association between MHO and the risk of female infertility among United States. Methods This study utilized a cross-sectional design and included 3542 women aged 20–45 years who were selected from the National Health and Nutrition Examination Survey (NHANES) 2013–2020 database. The association between MHO and the risk of infertility was evaluated using risk factor–adjusted logistic regression models. Results Higher BMI and WC were associated with increased infertility risk after adjusting for potential confounding factors (OR (95% CI): 1.04(1.02, 1.06), P  = 0.001; OR (95% CI): 1.02 (1.01, 1.03), P  < 0.001; respectively). After cross-classifying by metabolic health and obesity according to BMI and WC categories, individuals with MHO had a higher risk of infertility than those with MHN (OR (95% CI): 1.75(0.88, 3.50) for BMI criteria; OR (95% CI): 2.01(1.03, 3.95) for WC criteria). A positive linear relationship was observed between BMI/WC and infertility risk among metabolically healthy women ( P non−linearity =0.306, 0.170; respectively). Conclusions MHO was associated with an increased risk of infertility among reproductive-aged women in the US. Obesity itself, regardless of metabolic health status, was associated with a higher infertility risk. Our results support implementing lifestyle changes aimed at achieving and maintaining a healthy body weight in all individuals, even those who are metabolically healthy.

Big data thinking and artificial intelligence are rapidly reshaping how geoscientists analyze, model, and interpret the Earth [...]

We study a reduced electrostatic fluid model for the tokamak scrape-off layer, which incorporates temperature gradient and vorticity gradient as two free energy fields. Two scenarios of field coupling are addressed: (1) sheath condition; (2) vortex wave coupling. For the sheath condition induced field coupling, the poloidal E×B flow shear is coupled with the temperature gradient. Combining an eigenmode analysis and the nonlinear phase dynamics approach, our findings indicate that in the absence of a vorticity gradient, the overall effect of the sheath condition induced flow shear can either stabilize or destabilize the interchange mode, depending on the competition between the flow shear suppression and the temperature gradient driving. This is different from the case where the gradient drive and shear damping are decoupled. When the field coupling is mediated by wave interactions, by setting an idealized step-like temperature and vorticity profiles, a joint mode forms through resonant interaction between the interfacial waves driven by the temperature and vorticity gradients, respectively. Near the phase locking condition, the joint mode can be more unstable than pure temperature gradient driven mode.

Helicobacter pylori ( H. pylori ) is an infectious bacterium that colonizes the stomach of approximately half of the global population. It has been classified as a Group I carcinogen by the World Health Organization due to its strong association with an increased incidence of gastric cancer and exacerbation of stomach diseases. The primary treatment for H. pylori infection currently involves triple or quadruple therapy, primarily consisting of antibiotics and proton pump inhibitors. However, the increasing prevalence of antibiotic resistance poses significant challenges to this approach, underscoring the urgent need for an effective vaccine. In this study, a novel multi-epitope H. pylori vaccine was designed using immunoinformatics. The vaccine contains epitopes derived from nine essential proteins. Software tools and online servers were utilized to predict, evaluate, and analyze the physiochemical properties, secondary and tertiary structures, and immunogenicity of the candidate vaccine. These comprehensive assessments ultimately led to the formulation of an optimal design scheme for the vaccine. Through constructing a novel multi-epitope vaccine based on immunoinformatics, this study offers promising prospects and great potential for the prevention of H. pylori infection. This study also provides a reference strategy to develop multi-epitope vaccines for other pathogens.

T-cell exhaustion (Tex) is considered to be a reason for immunotherapy resistance and poor prognosis in lung adenocarcinoma. Therefore, we used weighted correlation network analysis to identify Tex-related genes in the cancer genome atlas (TCGA). Unsupervised clustering approach based on Tex-related genes divided patients into cluster 1 and cluster 2. Then, we utilized random forest and the least absolute shrinkage and selection operator to identify nine key genes to construct a riskscore. Patients were classified as low or high-risk groups. The multivariate cox analysis showed the riskscore was an independent prognostic factor in TCGA and GSE72094 cohorts. Moreover, patients in cluster 2 with high riskscore had the worst prognosis. The immune response prediction analysis showed the low-risk group had higher immune, stromal, estimate scores, higher immunophenscore (IPS), and lower tumor immune dysfunction and exclusion score which suggested a better response to immune checkpoint inhibitors (ICIs) therapy in the low-risk group. In the meantime, we included two independent immunotherapy cohorts that also confirmed a better response to ICIs treatment in the low-risk group. Besides, we discovered differences in chemotherapy and targeted drug sensitivity between two groups. Finally, a nomogram was built to facilitate clinical decision making.