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Climate change misinformation refers to inaccurate, incomplete, or misleading climate change-related information created and spread in the public domain. Despite substantial consensus among the scientific community on the reality of anthropogenic climate change, public opinion still remains divided. Combating the climate crisis requires immediate and meaningful actions; however, various actors generate and propagate climate change misinformation, with vested interests in sowing doubts in the public sphere about the reality and urgency of climate impacts. The United States of America, where public opinion holds a strong sway in many social and political spheres, acts as a pertinent case in point, where the prevalence of climate denial fueled by persistent climate change misinformation contributes to this divided public perspective. For this reason, it is imperative to enhance the understanding of the subtle ways climate change misinformation exists and functions. This article employs actor–network theory and the concept of black-boxing to explore a case of climate change misinformation in the United States, with the aim of comprehending the workings of climate change misinformation within its network.

In 6000-series Al-Mg-Si alloys, Zn is commonly added to enhance the aging response for improved properties. However, the segregation of Zn to interfaces and its interaction mechanisms with strengthening phases remain unclear. Combining experiments and theoretical calculations, we systematically investigated Zn segregation behavior at the β″/Al interfaces in Al-Mg-Si alloys. Using a modified β″-Mg5Al2Si4 model, we determined that the interface (100)β″//(130)Al has a slightly smaller formation energy of 1.06 kJ/mol, while (001)β″//(3¯20)Al has a relatively smaller interfacial energy of 116 mJ/m2. HAADF-STEM analysis revealed these interfacial morphologies and atomic distributions, showing that Zn atoms not only enter the β″ phase but are also inclined to segregate at the interfaces by occupying the Si3/Al sites. Furthermore, the stability of the β″/Al interfaces and the Zn segregation behavior are well explained at the atomic scale, with calculations showing that stronger hybridization between Zn-3d and Si-3s orbitals facilitates Zn segregation at the interfaces.

Traditional cement epoxy pavements suffer from inherent limitations such as terrible tribological properties, poor wear resistance, and weak impact resistance, presenting significant challenges to ensure the safety and continuous operation of urban roads. As a solution, high-performance cement epoxy composite grouting materials have emerged as the preferred option for engineering construction and road maintenance. In this study, CSP/epoxy cement (CSEC) composite materials were prepared by emulsion polymerization. The thermal properties of the materials were characterized, revealing that CSP enhances the thermal properties of epoxy cement (EC) to a certain extent. Furthermore, the frictional properties of CSEC composite materials and pure epoxy cement under different normal loads were investigated. The results indicated that the CSEC composite material exhibited a slight increase in friction coefficient and a notable decrease in wear rate compared to pure epoxy cement (EC). Specifically, the wear rate of CSEC decreased by 14.4% at a load of 20 N, highlighting the enhanced frictional performance facilitated by CSP. Mechanistic analysis attributed the improvement to the unique core-shell structure of CSP, which imparted higher impact resistance and eliminated alleviate residual stresses at the friction interface. This structural advantage further enhanced the wear resistance of materials, making it a promising choice for improving the durability and safety of urban road surfaces.

Strawberry red core root rot disease affects the growth and yield of strawberry (Fragaria ananassa Duch), which leads to economic losses in China. The study employed a tissue separation method to isolate and identify the causal agent responsible for strawberry red core root rot. This was achieved by the observation of its morphological characteristics, sequencing analyses, and pathogenicity tests. The sensitivity of five chemical fungicides against the two species of Fusarium was determined using the mycelial growth rate method, and the biological characteristics of the two species were examined. The pathogens were identified as Fusarium solani and Fusarium oxysporum. The optimal conditions for the mycelial growth of F. solani and F. oxysporum were determined to be potato sucrose agar at 25 °C and pH 6, and potato dextrose agar at 30 °C and pH 8, respectively, with a 24:24 light cycle. The most suitable carbon and nitrogen sources for the mycelial growth of F. solani were sucrose and sodium nitrate (NaNO3), while for F. oxysporum, they were glucose and peptone. A fungicide sensitivity test indicated that Prochloraz had a good inhibitory effect on the growth of F. solani and F. oxysporum with EC50 values of 0.088 mg L−1 and 0.162 mg. The growth inhibition effect of Azoxystrobin to F. solani and Carbendazim to F. oxysporum was not obvious. This study provides a theoretical basis for further research on strawberry red core root disease and its prevention.

Particle damping technology is applied in vibration and noise reduction because of its good broadband vibration reduction effect. The energy transfer and loss between particles are keys to the role of damping. This paper investigates the relationship between the thermal energy dissipation caused by the collision of particles and the input energy. The temperature rise characteristics under different vibration states are studied. The results show significant differences in the thermal dissipation characteristics of granular assemblies for different vibration states. Under equivalent excitation amplitudes, the frequency increases, and the thermal loss of the particles increases. At the same frequency, the excitation amplitudes increase, and the thermal loss of the particles decreases. Granular assemblies in strong vibrational states, such as a gas-like state, have intense vibrations and apparent temperature-increasing effects. However, in this vibration state, the input energy to the particles is considerable, and the thermal loss accounts for a small proportion of the total energy. In states such as solid-like states, micro-vibrational states, and intermediate vibrational states, the particles interact closely, and the input energy for the particles is small. Additionally, the movement of the particles is not intense, and the thermal loss accounts for a large proportion of the total energy. The thermal loss of the particles also shows a frequency variation characteristic. According to the different temperature rise characteristics of the particles, the proportion of thermal energy consumption is analyzed. The research shows that the proportion of thermal energy consumption is not more than 70%, so there are other forms of energy consumption in the vibration reduction and energy consumption of the particles.

The threshing rate is one of the important indexes to evaluate the effect of corn threshing. The weighing method is often used to calculate the depuration rate of maize at present. This method is time-consuming and laborious and can only calculate the overall threshing rate but does not give the threshing rate of individual corn ears. Different parameters of corn ears have complex effects on the threshing rate. By analyzing the threshing rate of each corn ear, we can choose the appropriate ear treatment method, optimize the processing equipment and process flow, and improve the threshing performance. This paper presents a method based on machine vision to detect the threshing rate of corn ears. In this method, machine vision was used to measure the parameters of the corncob and the area of the top of residual kernels. The area of the top of all kernels was restored based on the parameters of the corncob. The threshing rate of corn ears was calculated by the ratio of the area of the top of the missing kernel to the area of the top of all kernels after threshing. A bivariate linear regression area model was established to restore the area of the top of all corn kernels based on corncob parameters. The R2 was more significant than 0.98, and the goodness of fit was good. The machine vision inspection results showed that the maximum relative error of length and midsection radius was 7.46% and 5.55%, and the mean relative error was 2.58% and 2.23%. The maximum relative error of the corn ear threshing rate was 7.08%, and the mean relative error was 2.04%. When the residual kernels were concentrated in the midsection, the inspection result of the corn ear threshing rate was better. The maximum relative error was 3.98%, and the mean relative error was 1.07%. This paper provides a new idea and reference for measuring the threshing rate of corn ears.

In response to challenges such as narrow visibility for ship navigators, limited field of view from a single camera, and complex maritime environments, this study proposes panoramic visual perception-assisted navigation technology. The approach includes introducing a region-of-interest search method based on SSIM and an elliptical weighted fusion method, culminating in the development of the ship panoramic visual stitching algorithm SSIM-EW. Additionally, the YOLOv8s model is improved by increasing the size of the detection head, introducing GhostNet, and replacing the regression loss function with the WIoU loss function, and a perception model yolov8-SGW for sea target detection is proposed. The experimental results demonstrate that the SSIM-EW algorithm achieves the highest PSNR indicator of 25.736, which can effectively reduce the stitching traces and significantly improve the stitching quality of panoramic images. Compared to the baseline model, the YOLOv8-SGW model shows improvements in the P, R, and mAP50 of 1.5%, 4.3%, and 2.3%, respectively, its mAP50 is significantly higher than that of other target detection models, and the detection ability of small targets at sea has been significantly improved. Implementing these algorithms in tugboat operations at ports enhances the fields of view of navigators, allowing for the identification of targets missed by AISs and radar systems, thus ensuring operational safety and advancing the level of vessel intelligence.

Investigations of the geochemical compositions of geothermal water, as well as their movements and geneses, are of great significance for the exploration and exploitation of hydrothermal resources. In Zhongshan City, a southern city in Guangdong Province, large amounts of geothermal heat have been discovered. The results of investigations show that the hydrochemical types of geothermal water in the study area are Cl-Na·Ca and Cl-Na. H-O isotopes are basically near the atmospheric precipitation line, and the calculated recharge elevation of geothermal water ranges from 716 to 822 m, which is close to the altitude of the North Peak Mountain in Taishan City. The deep thermal storage temperature ranges from 95.32 to 149.71 °C, and the depth of the thermal cycle ranges from 2638.57 m to 4581.07 m. The genetic model of the geothermal water in this area is that, at favorable structural positions with satisfied water storage conditions, the mixture of atmospheric precipitation and seawater that circulates deep in Earth is heated by terrestrial heat flow under actions such as deep heat exchange and water–rock reactions to leach the salt, finally forming the highly mineralized geothermal water that uplifts out of the surface along faults and crops. The formation of the genetic model of geothermal water will provide a geological basis and technical support for the efficient development and utilization of geothermal resources in Zhongshan City and the coastal area of Southeast China.

Rockfill dams are the most competitive type of dam in complex geological environments. Identifying the failure modes and causes in high dams over 100 m is critical for better guiding high dam designs and implementing safety prevention and control measures. To this end, this paper investigated numerous cases of earth–rock dam breaches and failure modes in rockfill dams globally, with a particular focus on dams over 100 m in height, encompassing all such dams in China. The study categorized dam failure modes based on whether the dams were built before or after 1980. It also examined the causes of dam failures in terms of dam height, foundation characteristics and thickness, and failure time. Additionally, the paper analyzed a rockfill dam in China, with a height of 136 m and over ten years of operation, as a case study. We analyzed the spatial and temporal characteristics and causes of failures, such as dam crest cracking, high-level seepage, and gallery cracking, using the design situation, monitoring data, and numerical simulation. The paper also addressed issues related to dam design and foundation treatment, providing recommendations for improvement. The study indicated that the overall risk of total failure for dams over 100 m is already low. However, longitudinal cracks on the dam crest, core wall seepage, hydraulic splitting, and seepage damage to the dam foundation are primary issues in the current high core wall rockfill dams. These issues are mainly caused by uneven structural deformation of the dam and its foundation. A reasonable design of rockfill materials and foundations can mitigate these failures.

Trueperella pyogenes is an important opportunistic pathogenic bacterium widely distributed in the environment. Pyolysin (PLO) is a primary virulence factor of T. pyogenes and capable of lysing many different cells. PLO is a member of the cholesterol-dependent cytolysin (CDC) family of which the primary structure only presents a low level of homology with other members from 31% to 45%. By deeply studying PLO, we can understand the overall pathogenic mechanism of CDC family proteins. This study established a mouse muscle tissue model infected with recombinant PLO (rPLO) and its single-point mutations, rPLO N139K and rPLO F240A, and explored its mechanism of causing inflammatory damage. The inflammatory injury abilities of rPLO N139K and rPLO F240A are significantly reduced compared to rPLO. This study elaborated on the inflammatory mechanism of PLO by examining its unit point mutations in detail. Our data also provide a theoretical basis and practical significance for future research on toxins and bacteria.

Path planning for an unmanned vehicle in an off-road uncertain environment is important for navigation safety and efficiency. Regarding this, a global improved A* algorithm is presented. Firstly, based on remote sensing images, the artificial potential field method is used to describe the distribution of risk in the uncertain environment, and all types of ground conditions are converted into travel time costs. Additionally, the improvements of the A* algorithm include a multi-directional node search algorithm, and a new line-of-sight algorithm is designed which can search sub-nodes more accurately, while the risk factor and the passing-time cost factor are added to the cost function. Finally, three kinds of paths can be calculated, including the shortest path, the path of less risk, and the path of less time-cost. The results of the simulation show that the improved A* algorithm is suitable for the path planning of unmanned vehicles in a complex and uncertain environment. The effectiveness of the algorithm is verified by the comparison between the simulation and the actual condition verification.

Acinetobacter baumannii has developed multiple drug resistances, posing a significant threat to antibiotic efficacy. LysECD7, an endolysin derived from phages, could be a promising therapeutic agent against multi-drug resistance A. baumannii. In this study, in order to further enhance the antibacterial efficiency of the engineered LysECD7, a few lipopolysaccharide-interacting peptides (Li5, MSI594 and Li5-MSI) were genetically fused with LysECD7. Based on in vitro antibacterial activity, the fusion protein Lys-Li5-MSI was selected for further modifications aimed at extending its half-life. A cysteine residue was introduced into Lys-Li5-MSI through mutation (Lys-Li5-MSIV12C), followed by conjugation with a C16 fatty acid chain via a protonation substitution reaction(V12C-C16). The pharmacokinetic profile of V12C-C16 exhibited a more favorable characteristic in comparison to Lys-Li5-MSI, thereby resulting in enhanced therapeutic efficacy against lethal A. baumannii infection in mice. The study provides valuable insights for the development of novel endolysin therapeutics and proposes an alternative therapeutic strategy for combating A. baumannii infections.

In this work, the evolution of mechanical properties of binary Al–Li alloys with four approximately equal gradient Li contents (0.91–3.98 wt.%) under aging conditions is thoroughly investigated. The alloys undergo aging treatments at 175 °C for x hours (x = 0–120 h), and the peak-aged times of the four alloys are 6 h, 12 h, 48 h and 48 h, respectively, as the Li concentration increases. Both in the solution-treated and peak-aged states, the elastic modulus of binary Al–Li alloys exhibits an approximately linear increase with increasing Li content, consistent with trends predicted by density functional theory (DFT) calculations. Due to the presence of Al3Li precipitates, the modulus of higher-Li-concentration alloys in the peak-aged state increases by approximately 1.4–2.5% compared with that of alloys in the solution-treated state. Additionally, the study finds that increasing Li content significantly enhances the tensile strength and yield strength of the alloy but decreases its ductility, leading to a transition in fracture mode from ductile to brittle, as evidenced by a microscopic analysis of fracture surfaces. Under peak-aged (175 °C/48 h), the alloy with the highest Li content exhibits the maximum tensile strength of 341 MPa and a yield strength of 296 MPa, while its elongation is the lowest at 2.1%. These findings contribute to a deeper understanding of the effects of aging precipitates on the mechanical properties of Al–Li alloys, providing fundamental guidance for the design of future generations of Al–Li alloys.

All-silicon terahertz absorbers have attracted considerable interest. We present a design and numerical study of an all-silicon polarization-insensitive terahertz metamaterial absorber. The meta-atoms of the metamaterial absorber are square silicon rings which can be viewed as gratings. By properly optimizing the structure of the meta-atom, we achieve a broadband absorptivity that is above 90% ranging from 0.77 THz to 2.53 THz, with a relative bandwidth of 106.7%. Impedance matching reduces the reflection of the terahertz waves and the (0, ±1)-order diffraction induce the strong absorption. The absorption of this absorber is insensitive to the polarization of the terahertz wave and has a large incident angle tolerance of up to 60 degrees. The all-silicon metamaterial absorber proposed here provides an effective way to obtain broadband absorption in the terahertz regime. Metamaterial absorbers have outstanding applications in terahertz communication and imaging.

In this paper, we construct an academic literature knowledge graph based on the relationship between documents to facilitate the storage and research of academic literature data. Keywords are an important type of node in the knowledge graph. To solve the problem that there are no keywords in some documents for several reasons in the process of knowledge graph construction, an improved keyword extraction algorithm called TP-CoGlo-TextRank is proposed by using word frequency, position, word co-occurrence frequency, and a word embedding model. By combining the word frequency and position in the document, the importance of words is distinguished. By introducing the GloVe word-embedding model, which brings the external knowledge of documents into the TextRank algorithm, and combining the internal word co-occurrence frequency in the documents, the word-adjacency relationship is transferred non-uniformly. Finally, the words with the highest scores are combined into phrases if they are adjacent in the original text. The validity of the TP-CoGlo-TextRank algorithm is verified by experiments. On this basis, the Neo4j graph database is used to store and display the academic literature knowledge graph, to provide data support for research tasks such as text clustering, automatic summarization, and question-answering systems.

To date, genomic and transcriptomic data on Coffea arabica L. in public databases are very limited, and there has been no comprehensive integrated investigation conducted on alternative splicing (AS). Previously, we have constructed and sequenced eighteen RNA-seq libraries of C. arabica at different ripening stages of fruit development. From this dataset, a total of 3824, 2445, 2564, 2990, and 3162 DSGs were identified in a comparison of different fruit ripening stages. The largest proportion of DSGs, approximately 65%, were of the skipped exon (SE) type. Biologically, 9 and 29 differentially expressed DSGs in the spliceosome pathway and carbon metabolism pathway, respectively, were identified. These DSGs exhibited significant variations, primarily in S1 vs. S2 and S5 vs. S6, and they involve many aspects of organ development, hormone transduction, and the synthesis of flavor components. Through the examination of research findings regarding the biological functions and biochemical pathways associated with DSGs and DEGs, it was observed that six DSGs significantly enriched in ABC transporters, namely, LOC113712394, LOC113726618, LOC113739972, LOC113725240, LOC113730214, and LOC113707447, were continually down-regulated at the fruit ripening stage. In contrast, a total of four genes, which were LOC113732777, LOC113727880, LOC113690566, and LOC113711936, including those enriched in the cysteine and methionine metabolism, were continually up-regulated. Collectively, our findings may contribute to the exploration of alternative splicing mechanisms for focused investigations of potential genes associated with the ripening of fruits in C. arabica.

Trueperella pyogenes can cause various infections in the organs and tissues of different livestock (including pigs, cows, goats, and sheep), including mastitis, endometritis, pneumonia, or abscesses. Moreover, diseases induced by T. pyogenes cause significant economic losses in animal husbandry. In recent large-scale investigations, T. pyogenes has been identified as one of the main pathogens causing endometritis in lactating cows. However, the main treatment for the above-mentioned diseases is still currently antibiotic therapy. Understanding the impact of endometritis associated with T. pyogenes on the fertility of cows can help optimize antibiotic treatment for uterine diseases, thereby strategically concentrating the use of antimicrobials on the most severe cases. Therefore, it is particularly important to continuously monitor the prevalence of T. pyogenes and test its drug resistance. This study compared the uterine microbiota of healthy cows and endometritis cows in different cattle farms, investigated the prevalence of T. pyogenes, evaluated the genetic characteristics and population structure of isolated strains, and determined the virulence genes and drug resistance characteristics of T. pyogenes. An amount of 186 dairy cows were involved in this study and 23 T. pyogenes strains were isolated and identified from the uterine lavage fluid of dairy cows with or without endometritis.

With the development of society, there is an increasingly urgent demand for light-weight, high-strength, and high-temperature-resistant structural materials. High-entropy alloys (HEAs) owe much of their unusual properties to the selection among three phases: solid solution (SS), intermetallic compound (IM), and mixed SS and IM (SS and IM). Therefore, accurate phase prediction is crucial for guiding the selection of element combinations to form HEAs with desired properties. Light high-entropy alloys (LHEAs), as a significant branch of HEAs, exhibit excellent performance in terms of specific strength. In this study, we employ a machine learning (ML) method to realize the design of light-weight high-entropy alloys based on solid solutions. We determined the Gradient Boosting Classifier model as the best machine learning model through a two-step feature and model selection, in which its accuracy and F1_Score achieve 0.9166 and 0.8923. According to the predicted results, we obtained Al28Li35Mg15Zn10Cu12 LHEAs, which are mainly composed of 90% solid solution. This alloy accords with the prediction results of machine learning. But it is made up of a two-phase solid solution. In order to obtain a light-weight high-entropy alloy dominated by a single solid solution, we designed Al24Li15Mg26Zn9Cu26 LHEAs on the basis of machine learning prediction results accompanied by expert experience. Its main structure includes a single-phase solid solution. Our work provides an alternative approach to the computational design of HEAs and provides a direction for future exploration of light-weight high-entropy alloys.

To address the issues of signal waveform distortion and inter-symbol interference, both of which lead to performance degradation in airborne laser communication due to multipath transmission in airborne air-to-air channels, theoretical analysis and Monte Carlo ray tracing (MCRT) simulation methods were employed. Based on airborne application conditions, this research conducted numerical simulations of the Gaussian beam multipath transmission in an air-to-air channel, with a focus on analyzing the impact of meteorological conditions, communication distance, transmitter power, and initial pulse width on the pulse time spreading characteristics of received optical signals. In addition, an analysis of these parameters’ impact on the inter-symbol interference (ISI) and bit error rate (BER) was conducted. The research findings can serve as a reference for the design of anti-interference techniques in airborne laser communication links.

Defined benefit (DB) pension plans are a primary type of pension schemes with the sponsor assuming most of the risks. Longevity-indexed bonds have been used to hedge or transfer risks in pension plans. Our objective is to study an aggregated DB pension plan’s optimal risk management problem focusing on minimizing the solvency risk over a finite time horizon and to investigate the investment strategies in a market, comprising a longevity-indexed bond and a risk-free asset, under stochastic nominal interest rates. Using the dynamic programming technique in the stochastic control problem, we obtain the closed-form optimal investment strategy by solving the corresponding Hamilton–Jacobi–Bellman (HJB) equation. In addition, a comparative analysis implicates that longevity-indexed bonds significantly reduce solvency risk compared to zero-coupon bonds, offering a strategic advantage in pension fund management. Besides the closed-form solution and the comparative study, another novelty of this study is the extension of actuarial liability (AL) and normal cost (NC) definitions, and we introduce the risk neutral valuation of liabilities in DB pension scheme with the consideration of mortality rate.

The thermal-electric conversion efficiency is a crucial metric for evaluating the performance of a thermoelectric generator (TEG). However, accurate measurement of this efficiency remains a significant challenge due to various factors that impact heat flow measurements. We have observed that temperature fluctuations during temperature control are the primary factor contributing to measurement errors in heat flow under vacuum conditions. To address this issue, we have developed a time-dependent theoretical model for the thermal-electric coupling of a TEG measurement system based on Fourier’s theory of heat conduction. This model allows us to investigate the effects of both temperature fluctuation and structural parameters on the measurement error of TEG performance. Furthermore, we have proposed an error correction scheme for TEG performance based on our theoretical and experimental findings. These insights provide a theoretical framework and technical guidance for more precise measurements of TEG performance.

Alfalfa (Medicago sativa) is a perennial forage legume that is widely distributed all over the world; therefore, it has an extremely complex genetic background. Though population structure and phylogenetic studies have been conducted on a large group of alfalfa nuclear genomes, information about the chloroplast genomes is still lacking. Chloroplast genomes are generally considered to be conservative and play an important role in population diversity analysis and species adaptation in plants. Here, 231 complete alfalfa chloroplast genomes were successfully assembled from 359 alfalfa resequencing data, on the basis of which the alfalfa chloroplast pan-genome was constructed. We investigated the genetic variations of the alfalfa chloroplast genome through comparative genomic, genetic diversity, phylogenetic, population genetic structure, and haplotype analysis. Meanwhile, the expression of alfalfa chloroplast genes under cold stress was explored through transcriptome analysis. As a result, chloroplast genomes of 231 alfalfa lack an IR region, and the size of the chloroplast genome ranges from 125,192 bp to 126,105 bp. Using population structure, haplotypes, and construction of a phylogenetic tree, it was found that alfalfa populations could be divided into four groups, and multiple highly variable regions were found in the alfalfa chloroplast genome. Transcriptome analysis showed that tRNA genes were significantly up-regulated in the cold-sensitive varieties, while rps7, rpl32, and ndhB were down-regulated, and the editing efficiency of ycf1, ycf2, and ndhF was decreased in the cold-tolerant varieties, which may be due to the fact that chloroplasts store nutrients through photosynthesis to resist cold. The huge number of genetic variants in this study provide powerful resources for molecular markers.

Ophiopogonis Radix (OR) is a traditional Chinese medicine. In recent years, in order to achieve the purpose of drying, bleaching, sterilizing and being antiseptic, improving appearance, and easy storage, people often use sulfur fumigation for its processing. However, changes in the chemical composition of medicinal herbs caused by sulfur fumigation can lead to the transformation and loss of potent substances. Therefore, the development of methods to rapidly reveal the chemical transformation of medicinal herbs induced by sulfur fumigation can guarantee the safe clinical use of medicines. In this study, a combined full scan-parent ions list-dynamic exclusion acquisition-diagnostic product ions analysis strategy based on UHPLC-LTQ-Orbitrap MS was proposed for the analysis of steroidal saponins and their transformed components in sulfur-fumigated Ophiopogonis Radix (SF-OR). Based on precise mass measurements, chromatographic behavior, neutral loss ions, and diagnostic product ions, 286 constituents were screened and identified from SF-OR, including 191 steroidal saponins and 95 sulfur-containing derivatives (sulfates or sulfites). The results indicated that the established strategy was a valuable and effective analytical tool for comprehensively characterizing the material basis of SF-OR, and also provided a basis for potential chemical changes in other sulfur-fumigated herbs.

The newly prepared monolayer (ML) SiAs is expected to be a candidate channel material for next-generation nano-electronic devices in virtue of its proper bandgap, high carrier mobility, and anisotropic properties. The interfacial properties in ML SiAs field-effect transistors are comprehensively studied with electrodes (graphene, V2CO2, Au, Ag, and Cu) by using ab initio electronic structure calculations and quantum transport simulation. It is found that ML SiAs forms a weak van der Waals interaction with graphene and V2CO2, while it forms a strong interaction with bulk metals (Au, Ag, and Cu). Although ML SiAs has strong anisotropy, it is not reflected in the contact property. Based on the quantum transport simulation, ML SiAs forms n-type lateral Schottky contact with Au, Ag, and Cu electrodes with the Schottky barrier height (SBH) of 0.28 (0.27), 0.40 (0.47), and 0.45 (0.33) eV along the a (b) direction, respectively, while it forms p-type lateral Schottky contact with a graphene electrode with a SBH of 0.34 (0.28) eV. Fortunately, ML SiAs forms an ideal Ohmic contact with the V2CO2 electrode. This study not only gives a deep understanding of the interfacial properties of ML SiAs with electrodes but also provides a guide for the design of ML SiAs devices.

Soil improvement methods can result in changes in the microbial community in blueberry soil. Bacterial communities play an important role in soil fertilizer and plant nutrient acquisition. In this study, the response of microbial community composition, microbial function, and the nitrogen (N) cycle to different improvement methods was analyzed using high-throughput sequencing to investigate the best soil improvement method from a microbial perspective. The results showed that the highest microbial diversity was observed in the treatment involving peat combined with mushroom bran (T2), followed by the peat combined with acidified rice husk (T2) both in the rhizosphere and roots. The dominant phyla were Proteobacteria and Actinobacteria both in the blueberry rhizosphere soil and roots. Interestingly, Acidobacterium and Paludibaculum, belonging to the Acidobacteria phylum, exhibited the most significant influence and were most predominant in the T2 treatment rhizosphere soil. The T2 treatment promoted the growth of N fixation functional bacteria both in the rhizosphere soil and roots. At the module level, the T2 treatment enhanced N fixation and suppressed the assimilatory and dissimilatory nitrate reduction reactions, denitrification, and nitrification in the blueberry rhizosphere. Additionally, the T2 treatment increased the abundance of root endophytic microbes involved in N fixation. Overall, our findings suggest that the addition of peat combined with acidified rice husk is the optimal soil improvement method for blueberry cultivation.

Returning crop straw to the soil is an effective measure to increase soil fertility and maintain crop yield in agroecosystems. In this study, we conducted a 34-year field experiment (1984–2017) to investigate the relationships among the impacts of straw return on soil nutrients, phosphorus (P) fractions, phosphorus transformation-related functional microbial communities, and key genes in black soil. The results of the soil chemical properties showed that straw return could slow the decline in soil pH and increase soil nutrients, i.e., soil organic carbon (SOC), available K (AK), and available P (AP) concentrations, with the straw addition (PKS) treatment resulting in the highest soil AP and AK contents. Compared with conventional fertilizer (NPK) and conventional fertilizer with straw (NPKS), straw return alone (S) significantly increased the soil AP and AK contents by 1.1–42.8 and 38.3–114.3 mg/kg (p < 0.05), respectively. The results of the phosphorus fraction experiment indicated that long-term straw return promoted labile and moderately stable phosphorus content by altering the composition and increasing the relative abundances of functional genes regulating soil P activation (ugpQ, ppk, phoD, and gcd). The compositional changes in the phosphorus-cycling-related functional genes of the soil were affected mostly by soil total P (TP), AK, and NO3−-N, whereas pH, SOC, and available N (AN) were the main environmental factors influencing the phosphorus-cycling-related microbial community composition. These direct measurements provide insight into the soil phosphorus fractions, phosphorus-cycling-related functional genes, and microbial communities associated with straw returning, which enhances our understanding of the chemical and biogeochemical behavior of soil phosphorus upon straw incorporation.

This paper describes the synthesis of NIPU by using cardanol as starting material. A cardanol formaldehyde oligomer was first prepared through the reaction of cardanol and formaldehyde, catalyzed by citric acid. The resulting oligomer was then subjected to epoxidation with m-chloroperbenzoic acid to obtain an epoxide compound, which was subsequently used to fix carbon dioxide (CO2) and form a cyclic carbonate. Using this cyclic carbonate, along with an amine, cardanol-based isocyanate polyurethane (NIPU) was prepared. Different characterization methods, such as Fourier transform infrared spectroscopy (FTIR), proton nuclear magnetic resonance (NMR), gel permeation chromatography (GPC), and thermogravimetric analysis (TGA), were used to confirm the synthesis of the four intermediate products and NIPU in the reaction process. This study highlights the promise of bio-based NIPU as a sustainable alternative in a number of applications while offering insightful information on the synthesis and characterization of the material.

With the objectives of achieving “peak carbon” and “carbon neutrality”, accurately quantifying the carbon emissions of road transportation becomes crucial. It is challenging to accurately describe the energy consumption of vehicles at both temporal and spatial scales from a macro perspective. Therefore, focusing on the quantitative model of vehicle micro energy consumption and road meso energy consumption, this paper reviewed and summarized the energy consumption model of road traffic in terms of data collection, quantification accuracy, and scope of application. Based on this analysis, this paper identifies the challenges of the current road traffic energy consumption model. Finally, we look forward to future research directions for studying quantitative models of energy consumption from road transportation.

In the aerospace sector, the development of lightweight aircraft heavily relies on the utilization of advanced aluminum–lithium alloys as primary structural materials. This study introduces an investigation aimed at optimizing the composition of an Al-2.32Li-1.44Cu-2.78Mg-0.3Ag-0.3Mn-0.1Zr alloy. The optimization process involves the selection of alloying elements through the application of machine learning techniques, with a focus on expected improvements in the specific modulus of these alloys. Expanding upon the optimization of the benchmark alloy’s components, a more generalized modulus prediction model for Al–Li alloys was formulated. This model was then employed to evaluate the anticipated specific modulus of alloys within a virtual search space, encompassing substitutional elements. The study proceeded to validate six Al–Li alloys with a notably high potential for achieving an improved specific modulus. The results revealed that an alloy incorporating 0.96 wt.% of Ga as a substitutional element exhibited the most favorable microstructure. This alloy demonstrated optimal tensile strength (523 MPa) and specific modulus (31.531 GPa/(g·cm−3)), closely resembling that of the benchmark alloy. This research offers valuable insights into the application of compositional optimization to enhance the mechanical properties of Al–Li alloys. It emphasizes the significance of selecting alloying elements based on considerations such as their solid solubility thresholds and the expected enhancement of the specific modulus in Al–Li alloys.

Coffee cherries contain a crucial flavor-precursor and chemical substances influencing roasted bean quality, yet limited knowledge exists on metabolite changes during cherry ripening. Our study identified 1078 metabolites, revealing 46 core differential metabolites using a KEGG pathway analysis. At the GF vs. ROF stage, amino acid synthesis dominated; ROF vs. BRF featured nucleotide catabolism; BRF vs. PRF exhibited glycoside and flavonoid synthesis; and PRF vs. PBF involved secondary metabolite synthesis and catabolism. The PRF stage emerged as the optimal cherry-harvesting period. A correlation analysis identified core differential metabolites strongly linked to taste indicators, suggesting their potential as taste markers. Notably, nucleotides and derivatives exhibited significant negative correlations with glycosides and flavonoids during ripening. This research systematically analyzed flavor and active substances in green coffee beans during cherry ripening, offering valuable insights into substance formation in Coffea arabica L.

With the development of continuous speech recognition technology, users have put forward higher requirements in terms of speech recognition accuracy. Low-resource speech recognition, as a typical speech recognition technology under restricted conditions, has become a research hotspot nowadays because of its low recognition rate and great application value. Under the premise of low-resource speech recognition technology, this paper reviews the research status of feature extraction and acoustic models, and conducts research on resource expansion. Especially in terms of the technical challenges faced by this technology, solutions are proposed, and future research directions are prospected.

In this study, the 1991 rock avalanche, in Touzhai, Zhaotong, Yunnan, China, was considered the study object. The investigation of the landslide accumulation body revealed that the Touzhai rock avalanche accumulation body has the characteristics of wide gradation and poor sorting. A combination of field investigations, indoor and outdoor experiments, and numerical simulations were used to invert the occurrence and spreading range of rock avalanche-debris flow hazards. To invert and analyze its dynamics and the crushing process, a three-dimensional discrete element modeling was performed on the real terrain data. Simulation results showed that the movement time of the numerically simulated Touzhai rock avalanche was approximately 200 s. After 50 s of movement, the peak velocity reached 32 m/s, and the velocity gradually decayed after the sliding mass rubbed violently against the valley floor and collided with the mountain. Due to the meandering nature of the gully, the sliding mass makes its way down the gully and constantly collides with the mountain, making particles appear to climb, with some particles being blocked by the valley. After 150 s of movement, the average velocity rate decreased substantially, and the landslide-avalanche debris reached the mouth of the trench. After 200 s of movement, the average sliding velocity tends to 0 m/s, where the avalanche debris tends to stop and accumulate. When the rock avalanche movement reaches the mouth of the gully, the avalanche debris spreads to the sides as it is no longer bounded by the hills on either side of the narrow gully, eventually forming a ‘trumpet-shaped’ accumulation, and the granular flow simulation matched the findings of the landslide site accumulation.

The active peptide (APE) of Eupolyphaga sinensis Walker, which is prepared by bioenzymatic digestion, has significant antihyperlipidemic effects in vivo, but its mechanism of action on hyperlipidemia is not clear. Recent studies on amino acid metabolism suggested a possible link between it and hyperlipidemia. In this study, we first characterized the composition of APE using various methods. Then, the therapeutic effects of APE on hyperlipidemic rats were evaluated, including lipid levels, the inflammatory response, and oxidative stress. Finally, the metabolism-regulating mechanisms of APE on hyperlipidemic rats were analyzed using untargeted and targeted metabolomic approaches. The results showed that APE significantly reduced the accumulation of fat, oxidative stress levels, and serum pro-inflammatory cytokine levels. Untargeted metabolomic analysis showed that the mechanism of the hypolipidemic effect of APE was mainly related to tryptophan metabolism, phenylalanine metabolism, arginine biosynthesis, and purine metabolism. Amino-acid-targeted metabolomic analysis showed that significant differences in the levels of eight amino acids occurred after APE treatment. Among them, the expression of tryptophan, alanine, glutamate, threonine, valine, and phenylalanine was upregulated, and that of arginine and proline was downregulated in APE-treated rats. In addition, APE significantly downregulated the mRNA expression of SREBP-1, SREBP-2, and HMGCR. Taking these points together, we hypothesize that APE ameliorates hyperlipidemia by modulating amino acid metabolism in the metabolome of the serum and feces, mediating the SREBP/HMGCR signaling pathway, and reducing oxidative stress and inflammation levels.

The phenomenon of ordered self-assembly on solid substrates is a topic of interest in both fundamental surface science research and its applications in nanotechnology. The regulation and control of two-dimensional (2D) self-assembled supra-molecular structures on surfaces have been realized through applying external stimuli. By utilizing scanning tunneling microscopy (STM), researchers can investigate the detailed phase transition process of self-assembled monolayers (SAMs), providing insight into the interplay between intermolecular weak interactions and substrate–molecule interactions, which govern the formation of molecular self-assembly. This review will discuss the structural transition of self-assembly probed by STM in response to external stimuli and provide state-of-the-art methods such as tip-induced confinement for the alignment of SAM domains and selective chirality. Finally, we discuss the challenges and opportunities in the field of self-assembly and STM.

Crocodilians, which are a kind of animal secondary adaptation to an aquatic environment, their hindlimb can provide the power needed to engage in various life activities, even in low-oxygen water environments. The development of limbs is an important aspect of animal growth and development, as it is closely linked to body movement, support, heat production, and other critical functions. For the Chinese alligator, the hindlimb is one of the main sources of power, and its development and differentiation will directly influence the survival ability in the wild. Furthermore, a better understanding of the hindlimb developmental process will provide data support for the comparative evolutionary and functional genomics of crocodilians. In this study, the expression levels of genes related to hindlimb development in the Chinese alligator embryos during fetal development (on days 29, 35, 41, and 46) were investigated through transcriptome analysis. A total of 1675 differentially expressed genes (DEGs) at different stages were identified by using limma software. These DEGs were then analyzed using weighted correlation network analysis (WGCNA), and 4 gene expression modules and 20 hub genes were identified that were associated with the development of hindlimbs in the Chinese alligator at different periods. The results of GO enrichment and hub gene expression showed that the hindlimb development of the Chinese alligator embryos involves the development of the embryonic structure, nervous system, and hindlimb muscle in the early stage (H29) and the development of metabolic capacity occurs in the later stage (H46). Additionally, the enrichment results showed that the AMPK signaling pathway, calcium signaling pathway, HIF-1 signaling pathway, and neuroactive ligand–receptor interaction are involved in the development of the hindlimb of the Chinese alligator. Among these, the HIF-1 signaling pathway and neuroactive ligand–receptor interaction may be related to the adaptation of Chinese alligators to low-oxygen environments. Additionally, five DEGs (CAV1, IRS2, LDHA, LDB3, and MYL3) were randomly selected for qRT-PCR to verify the transcriptome results. It is expected that further research on these genes will help us to better understand the process of embryonic hindlimb development in the Chinese alligator.

In the current work, a fast θ scheme combined with the Legendre spectral method was developed for solving a fractional Klein–Gordon equation (FKGE). The numerical scheme was provided by the Legendre spectral method in the spatial direction, and for the temporal direction, a θ scheme of order O(τ2) with a fast algorithm was taken into account. The fast algorithm could decrease the computational cost from O(M2) to O(MlogM), where M denotes the number of time levels. In addition, correction terms could be employed to improve the convergence rate when the solutions have weak regularity. We proved theoretically that the scheme is unconditionally stable and obtained an error estimate. The numerical experiments demonstrated that our numerical scheme is accurate and efficient.

Long-term fertilization can result in changes in the nitrogen (N) cycle in maize rhizosphere soil. However, there have been few reports on the impacts of plant–soil–microbe regulatory mechanisms on the N cycle in soil. In this study, soil samples were collected from a long-term experimental site located at Jilin Agricultural University, Changchun City, Jilin Province, Northeast China. We then analyzed the changes in the functional genes related to the N cycle, soil enzyme activity, and maize root exudates under long-term fertilizer application using metagenomics and liquid chromatography analysis. We aimed to investigate the response of the N cycle to long-term fertilizers, the interaction among plant, soil, and microbes, and the effect of the plant–soil–microbe system on the N cycle. Long-term fertilization had a significant effect on soil N contents, N2O emissions, and enzyme activity related to the N cycle in maize rhizosphere soil. The functional genes of the N cycle were mainly enriched in the N degradation pathway in maize rhizosphere soil. N fertilizer application decreased the abundance of functional genes related to N fixation and degradation, denitrification, and assimilatory nitrate reduction (ANRA) and increased the abundance of functional genes participating in dissimilatory nitrate reduction (DNRA) and anaerobic ammonia oxidation/hydroxylamine oxidation. The soil environment was positively related to soil enzyme activity and negatively related to the microbial community composition and amino acids in root exudates. The contribution rate of microorganisms to the N cycle was the highest (r2 = 0.900), followed by amino acids (r2 = 0.836) and the soil environment (r2 = 0.832). Therefore, we concluded that N fertilizer is the main factor limiting the soil N cycle and that microorganisms are the main factor regulating the N cycle in the plant–soil–microorganism system.

Medical image segmentation can effectively identify lesions in medicine, but some small and rare lesions cannot be well identified. Existing studies do not take into account the uncertainty of the occurrence of diseased tissue, and the problem of long-tailed distribution of medical data. Meanwhile, the grayscale image obtained from Magnetic Resonance Imaging (MRI) detection has problems, such as the features being difficult to extract and invalid features being difficult to distinguish. In order to solve these problems, we propose a new weighted attention ResUNet (WA-ResUNet) and a class weight formula based on the number of images contained in the class, which improves the performance of the model in the low-frequency class and the overall effect of the model by improving the degree of attention paid to the valid features and invalid ones and rebalancing the learning efficiency among the classes. We evaluated our method on an uterine MRI dataset and compared it with the ResUNet. WA-ResUNet increased Intersection over Union (IoU) in the low-frequency class (Nabothian cysts) by 21.87%, and the overall mIoU increased by more than 6.5%.

The quality of oocytes determines the development potential of an embryo and is dependent on their timely fertilization after ovulation. Postovulatory oocyte aging is an inevitable factor during some assisted reproduction technology procedures, which results in poor fertilization rates and impairs embryo development. We found that fisetin, a bioactive flavonol contained in fruits and vegetables, delayed postovulatory oocyte aging in mice. Fisetin improved the development of aged oocytes after fertilization and inhibited the Sirt1 reduction in aged oocytes. Fisetin increased the GSH level and Sod2 transcription level to inhibit ROS accumulation in aged oocytes. Meanwhile, fisetin attenuated aging-induced spindle abnormalities, mitochondrial dysfunction, and apoptosis. At the molecular level, fisetin decreased aging-induced aberrant expression of H3K9me3. In addition, fisetin increased the expression levels of the mitochondrial transcription factor Tfam and the mitochondrial genes Co2 and Atp8 by upregulating Sirt1 in aged oocytes. Finally, inhibition of Sirt1 reversed the anti-aging effects of fisetin. Taken together, fisetin delayed postovulatory oocyte aging by upregulating Sirt1.

Based on ship navigational requirements and safety in foggy conditions and with a particular emphasis on avoiding ship collisions and improving navigational abilities, we constructed a fog navigation dataset along with a new method for enhancing foggy images and perceived visibility using a discriminant deep learning architecture and the EfficientNet neural network by replacing the SE module and incorporating a convolution block attention module and focal loss function. The accuracy of our model exceeded 95%, which meets the needs of an intelligent ship navigation environment in foggy conditions. As part of our research, we also determined the best enhancement algorithm for each type of fog according to its classification.

Although the use of detergents in thermal proteome profiling (TPP) has become a common practice to identify membrane protein targets in complex biological samples, surprisingly, there is no proteome-wide investigation into the impacts of detergent introduction on the target identification performance of TPP. In this study, we assessed the target identification performance of TPP in the presence of a commonly used non-ionic detergent or a zwitterionic detergent using a pan-kinase inhibitor staurosporine, our results showed that the addition of either of these detergents significantly impaired the identification performance of TPP at the optimal temperature for soluble target protein identification. Further investigation showed that detergents destabilized the proteome and increased protein precipitation. By lowering the applied temperature point, the target identification performance of TPP with detergents is significantly improved and is comparable to that in the absence of detergents. Our findings provide valuable insight into how to select the appropriate temperature range when detergents are used in TPP. In addition, our results also suggest that the combination of detergent and heat may serve as a novel precipitation-inducing force that can be applied for target protein identification.

At present, deep-sea enzymes are a research hotspot. In this study, a novel copper–zinc superoxide dismutase (CuZnSOD) was successfully cloned and characterized from a new species of sea cucumber Psychropotes verruciaudatus (PVCuZnSOD). The relative molecular weight of the PVCuZnSOD monomer is 15 kDa. The optimum temperature of PVCuZnSOD is 20 °C, and it maintains high activity in the range of 0–60 °C. It also has high thermal stability when incubated at 37 °C. PVCuZnSOD has a maximum activity of more than 50% in the pH range of 4–11 and a high activity at pH 11. In addition, PVCuZnSOD has strong tolerance to Ni2+, Mg2+, Ba2+, and Ca2+, and it can withstand chemical reagents, such as Tween20, TritonX-100, ethanol, glycerol, isopropanol, DMSO, urea, and GuHCl. PVCuZnSOD also shows great stability to gastrointestinal fluid compared with bovine SOD. These characteristics show that PVCuZnSOD has great application potential in medicine, food, and other products.

Coffee (Coffea arabica L.) is one of the most consumed non-alcoholic beverages. To expand the coffee market and attract consumers to a diversity of coffee types, data on the nutritional potential of different coffee varieties are needed. The present study assessed the phytochemical constituents, i.e., total flavonoid content (TFC), total anthocyanin content (TAC), and total phenolic content (TPC), and the antioxidant activities of extracts from Bourbon coffee berries differing in color. Furthermore, the metabolome profiles of the four Bourbon berries, i.e., purple (PF), red (RF), orange (ORF), and yellow (YF), were explored. The results indicated that the TFC and TPC were significantly higher in RF (7.12 ± 0.96 and 9.01 ± 1.36 mg/g), followed by YF (6.54 ± 0.38 and 7.80 ± 0.10 mg/g), ORF (6.31 ± 0.15 and 7.20 ± 0.40 mg/g), and PF (4.66 ± 0.53 and 6.60 ± 1.10 mg/g). A UPLC-MS/MS analysis resulted in the identification of 579 metabolites belonging to 11 different compound classes. Of these, 317 metabolites were differentially accumulated in the four varieties. These differentially accumulated metabolites were majorly classified as flavonoids, organic acids, phenolics, and amino acids and derivatives. Based on the metabolome profiles, we discuss the major compounds in each Bourbon coffee variety and highlight their nutritional and health potentials. These findings will serve as a foundation for the promotion of each variety and provide useful information for coffee improvement programs.

Underwater video object detection is a challenging task due to the poor quality of underwater videos, including blurriness and low contrast. In recent years, Yolo series models have been widely applied to underwater video object detection. However, these models perform poorly for blurry and low-contrast underwater videos. Additionally, they fail to account for the contextual relationships between the frame-level results. To address these challenges, we propose a video object detection model named UWV-Yolox. First, the Contrast Limited Adaptive Histogram Equalization method is used to augment the underwater videos. Then, a new CSP_CA module is proposed by adding Coordinate Attention to the backbone of the model to augment the representations of objects of interest. Next, a new loss function is proposed, including regression and jitter loss. Finally, a frame-level optimization module is proposed to optimize the detection results by utilizing the relationship between neighboring frames in videos, improving the video detection performance. To evaluate the performance of our model, We construct experiments on the UVODD dataset built in the paper, and select mAP@0.5 as the evaluation metric. The mAP@0.5 of the UWV-Yolox model reaches 89.0%, which is 3.2% better than the original Yolox model. Furthermore, compared with other object detection models, the UWV-Yolox model has more stable predictions for objects, and our improvements can be flexibly applied to other models.

Leaf traits can reflect plant photosynthetic capacity, resource utilization strategy and adaptability to the environment. However, whether species’ leaf traits are tightly related to the functioning of their community and how that relationship varies with environmental gradients remain largely unexplored. We measured 6 leaf traits, including petiole fineness (PF), specific leaf area (SLA), leaf area (LA), leaf length–width ratio (LLWR), leaf nitrogen content (LN) and leaf phosphorus content (LP), of 733 populations (415 species) of 19 woody angiosperm communities in the eastern Qinghai–Tibetan Plateau across multiple climatic zones or vegetation types. Through meta-analysis and relative importance analysis, the relationship between leaf traits of species and their community dominance and its change with environments were analyzed. The results showed that species dominance was correlated positively with their LA and LP, suggesting that species with high light interception and resource utilization capacity can easily become dominant species in woody angiosperm communities. Along the altitudinal gradient, the effect of PF and SLA on species dominance increased and changed significantly in their pattern, from positive or nonsignificant in temperate forests to negative in alpine and subalpine shrubs, suggesting that increasing petiole mechanical support and lamina protection cost is a dominant leaf growth strategy in stressful high-altitude environments. Our findings demonstrate that the demand for efficient light acquisition and/or utilization and species adaptability or tolerance to specific environmental stress are key mechanisms by which leaf traits govern community composition and functioning.

Uterine myomas affect 70% of women of reproductive age, potentially impacting their fertility and health. Manual film reading is commonly used to identify uterine myomas, but it is time-consuming, laborious, and subjective. Clinical treatment requires the consideration of the positional relationship among the uterine wall, uterine cavity, and uterine myomas. However, due to their complex and variable shapes, the low contrast of adjacent tissues or organs, and indistinguishable edges, accurately identifying them in MRI is difficult. Our work addresses these challenges by proposing an instance segmentation network capable of automatically outputting the location, category, and masks of each organ and lesion. Specifically, we designed a new backbone that facilitates learning the shape features of object diversity, and filters out background noise interference. We optimized the anchor box generation strategy to provide better priors in order to enhance the process of bounding box prediction and regression. An adaptive iterative subdivision strategy ensures that the mask boundary details of objects are more realistic and accurate. We conducted extensive experiments to validate our network, which achieved better average precision (AP) results than those of state-of-the-art instance segmentation models. Compared to the baseline network, our model improved AP on the uterine wall, uterine cavity, and myomas by 8.8%, 8.4%, and 3.2%, respectively. Our work is the first to realize multiclass instance segmentation in uterine MRI, providing a convenient and objective reference for the clinical development of appropriate surgical plans, and has significant value in improving diagnostic efficiency and realizing the automatic auxiliary diagnosis of uterine myomas.

The trade-offs between key functional traits in plants have a decisive impact on biomass production. However, how precipitation and nutrient deposition affect the trade-offs in traits and, ultimately, productivity is still unclear. In the present study, a mesocosm experiment was conducted to explore the relationships between biomass production and the aboveground and belowground key functional traits and their trade-offs under changes in precipitation and nutrient depositions in Leymus chinensis, a monodominant perennial rhizome grass widespread in the eastern Eurasian steppe. Our results showed that moisture is the key factor regulating the effect of nitrogen (N) and phosphorus (P) deposition on increased biomass production. Under conditions of average precipitation, water use efficiency (WUE) was the key trait determining the biomass of L. chinensis. There were obvious trade-offs between WUE and leaf area, specific leaf area, leaf thickness, and leaf dry matter. Conversely, under increasing precipitation, the effect of restricted soil water on leaf traits was relieved; the key limiting trait changed from WUE to plant height. These findings indicate that the shift of fundamental traits of photosynthetic carbon gain induced by precipitation under N and P deposition is the key ecological driving mechanism for the biomass production of typical dominant species in semi-arid grassland.

The space cable-driven continuous manipulator (SCCM) has a slender structure, ultra-high degrees of freedom, and a low mass, which make it suitable for equipment inspection and maintenance operations in an unstructured and limited space environment. In this paper, the SCCM including the cable network and plenty of joint links was deeply modeled. Firstly, the mapping relationship between the cable-driving space, joint space, and task space of the SCCM was studied, and the complete kinematic relationship of the SCCM was established. Secondly, the stiffness components of the SCCM are discussed, and the stiffness modeling method of each part is given. Finally, the Cartesian space equivalent stiffness model of the end was established. Then, a dynamic co-modeling method of Matlab + Adams is proposed, which greatly improved the modeling efficiency while ensuring the modeling accuracy. Finally, based on the stiffness model, the end stiffness characteristics of a specific configuration were analyzed, and the influence of the cable tension on the stiffness and frequency of the manipulator was analyzed. Based on the dynamic co-modeling, the task trajectory dynamics’ simulation analysis and space slit crossing experiment were carried out, which verified that the designed SCCM can meet the needs of slit crossing.

The 2195 aluminum alloy is widely used in cryogenic storage tanks for space vehicles, where it can reach a temperature of 20 K. In order to explore the reasons for the increased strength of 2195 aluminum alloys at cryogenic temperatures, uniaxial tensile tests were conducted in the range of 20 K–298 K. Tensile fracture was observed. In addition, the microstructures under different temperatures were observed using EBSD (electron back-scattered diffraction) and TEM (transmission electron microscopy) techniques, and the dislocation density of the material was quantitatively characterized using the modified Williamsone–Hall method based on XRD (X-ray diffraction) analysis. The results indicated that the ultimate strength increased at an increasing rate with the temperature decrease, while the elongation increase was insignificant. The fracture’s surface exhibited that dimple characteristics seemed to be unapparent while the quantity of tearing ridges was enhanced by the temperature decrease. Meanwhile, the fracture mode changed from ductile to brittle fracture. The microdeformation degree revealed by KAM images showed an aggravating trend, and the deformation tended to be more uniform. The increasingly enhanced dislocation density quantitatively revealed by the modified Williamsone–Hall method also proved this and that the increase in dislocations had a similar trend to that of tensile strength, which was furtherly revealed by TEM images. This indicated that the more regions are involved in deformation, the more dislocations are generated in the material during deformation, resulting in an increase in strength at cryogenic temperatures.

Phenols are important readily available synthetic building blocks and starting materials for organic synthetic transformations, which are widely found in agrochemicals, pharmaceuticals, and functional materials. The C–H functionalization of free phenols has proven to be an extremely useful tool in organic synthesis, which provides efficient increases in phenol molecular complexity. Therefore, approaches to functionalizing existing C–H bonds of free phenols have continuously attracted the attention of organic chemists. In this review, we summarize the current knowledge and recent advances in ortho-, meta-, and para-selective C–H functionalization of free phenols in the last five years.