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Platycodon grandiflorum, a dual-purpose herb for food and medicine, is widely distributed in Asia. Although P. grandiflorum has relatively low requirements for its growing environment, temperature remains an important ecological factor affecting its growth, development, and quality formation. In order to explore the effect of different temperatures on P. grandiflorum during their growth period, the diversity in growth physiology, platycodin contents, and gene expression of key enzymes were investigated under constant (8 °C, 18 °C, and 28 °C) and variable (8–18 °C, 8–28 °C, and 18–28 °C) temperature conditions at each of the three levels. The results suggested that both constant and variable temperatures at high levels significantly increased the aboveground fresh weight of P. grandiflorum. However, the low–variable temperature was beneficial for the accumulation of dry and fresh weight in the roots. Regardless of whether temperatures were constant or variable at low levels, this increased the content of soluble sugars, proline, and peroxidase in P. grandiflorum, while upregulating the expression levels of key enzyme genes involved in platycodin synthesis. Meanwhile, a low–constant temperature inhibited the photosynthetic rate of P. grandiflorum. Furthermore, medium–constant and large-scale variable temperatures were conducive to the accumulation of platycodins in the roots. This research provides a theoretical basis and data support for the influence of temperature variations on P. grandiflorum quality formation.

Decision-making regarding the low-carbon supply chain, considering corporate social responsibility (CSR) and the heterogeneous preferences of consumers, has become an urgent topic to be explored. This paper explores the decision-making problem of a low-carbon supply chain considering the heterogeneous preferences of consumers under different CSR situations, analyzes the influence of important parameters on each equilibrium solution, compares the size relationship of each equilibrium solution under different CSR situations, and verifies the conclusions obtained through numerical simulation. Then, based on the obtained equilibrium solution, a CSR evolutionary game model of the low-carbon supply chain is constructed, and the evolutionary stability strategies of the two sides on the CSR game are solved. Finally, the evolutionary trajectory of the game system is intuitively presented using a simulation method, and the influence of the main parameters on the evolutionary trends of the two sides is analyzed. The findings are as follows: (1) When both manufacturers and retailers undertake CSR, the retail price and wholesale price are their lowest, while carbon emission reduction, total market demand, manufacturer utility, retailer utility, and supply chain total utility are the highest. (2) When a company undertakes CSR, carbon emission reduction, total market demand, manufacturer utility, retailer utility, and supply chain total utility all increase with the increase in the CSR degree of the company and the ratio of the potential scale of low-carbon consumers to the potential scale of ordinary consumers. (3) The evolutionary stability strategy for both manufacturers and retailers is to undertake CSR. In addition, the initial proportion of manufacturers and retailers that undertake CSR, the low-carbon preference of low-carbon consumers, and the increase in the ratio of the potential scale of low-carbon consumers to the potential scale of ordinary consumers can encourage both members of the supply chain to undertake CSR.

Severe fever with thrombocytopenia syndrome virus (SFTSV), also known as the Dabie Banda virus, is an emerging tick-borne Bunyavirus that causes severe fever with thrombocytopenia syndrome (SFTS). Currently, symptomatic treatment and antiviral therapy with ribavirin and favipiravir are used in clinical management. However, their therapeutical efficacy is hardly satisfactory in patients with high viral load. In this study, we explored the antiviral effects of selective estrogen receptor modulators (SERMs) on SFTSV infection and the antiviral mechanisms of a representative SERM, bazedoxifene acetate (BZA). Our data show that SERMs potently inhibited SFTSV-induced cytopathic effect (CPE), the proliferation of infectious viral particles, and viral RNA replication and that BZA effectively protected mice from lethal viral challenge. The mode of action analysis reveals that BZA exerts antiviral effects during the post-entry stage of SFTSV infection. The transcriptome analysis reveals that GRASLND and CYP1A1 were upregulated, while TMEM45B and TXNIP were downregulated. Our findings suggest that SERMs have the potential to be used in the treatment of SFTSV infection.

In air traffic control (ATC), speech communication with radio transmission is the primary way to exchange information between the controller and the pilot. As a result, the integration of automatic speech recognition (ASR) systems holds immense potential for reducing controllers’ workload and plays a crucial role in various ATC scenarios, which is particularly significant for ATC research. This article provides a comprehensive review of ASR technology’s applications in the ATC communication system. Firstly, it offers a comprehensive overview of current research, including ATC corpora, ASR models, evaluation measures and application scenarios. A more comprehensive and accurate evaluation methodology tailored for ATC is proposed, considering advancements in communication sensing systems and deep learning techniques. This methodology helps researchers in enhancing ASR systems and improving the overall performance of ATC systems. Finally, future research recommendations are identified based on the primary challenges and issues. The authors sincerely hope this work will serve as a clear technical roadmap for ASR endeavors within the ATC domain and make a valuable contribution to the research community.

In recent years, the civil aviation industry has actively promoted the automation and intelligence of control processes with the increasing use of various artificial intelligence technologies. Air–ground communication, as the primary means of interaction between controllers and pilots, typically involves one or more intents. Recognizing multiple intents within air–ground communication texts is a critical step in automating and advancing the control process intelligently. Therefore, this study proposes a hybrid detection method for multi-intent recognition in air–ground communication text. This method improves recognition accuracy by using different models for single-intent texts and multi-intent texts. First, the air–ground communication text is divided into two categories using multi-intent detection technology: single-intent text and multi-intent text. Next, for single-intent text, the Enhanced Representation through Knowledge Integration (ERNIE) 3.0 model is used for recognition; while the A Lite Bidirectional Encoder Representations from Transformers (ALBERT)_Sequence-to-Sequence_Attention (ASA) model is proposed for identifying multi-intent texts. Finally, combining the recognition results from the two models yields the final result. Experimental results demonstrate that using the ASA model for multi-intent text recognition achieved an accuracy rate of 97.84%, which is 0.34% higher than the baseline ALBERT model and 0.15% to 0.87% higher than other improved models based on ALBERT and ERNIE 3.0. The single-intent recognition model achieved an accuracy of 96.23% when recognizing single-intent texts, which is at least 2.18% higher than the multi-intent recognition model. The results indicate that employing different models for various types of texts can substantially enhance recognition accuracy.

The backless cable-stayed bridge has the advantages of beautiful shape and reasonable force, but due to the low overall stiffness of the bridge pylon during cantilever construction, it is susceptible to the effect of solar temperature. To reveal the temperature deformation laws and achieve accurate alignment prediction during the installation process of steel–concrete composite pylons in complex environments, a refined numerical simulation model for the 3D bridge temperature field was established based on the proposed automatic sunshine-shadow recognition method. Subsequently, the optimal time periods for construction control are provided. The results of the study show that, during the cantilever construction of the bridge pylon, one pylon column will shade the other pylon column, resulting in asynchronous deformation that can reach 7.6 mm. The effect of solar temperature on the displacement of the bridge pylon is significant, where the maximum daily change in transverse displacement in the cantilevered state of the pylon can reach 33.6 mm, and the maximum change in cable force value can reach 52 kN. In order to mitigate the effect of solar radiation, the best construction time for the bridge pylon is 19:30~9:30, while the tensioning and measurement of the cable should be avoided from 6:00~18:00. This strategy ensures that the control of the pylon top displacement is maintained within 1/4000 of the pylon height, and the error in cable force is kept within 5%.

The algorithm proposed in this paper aims at solving the problem of star map matching in high-limiting-magnitude astronomical images, which is inspired by geometric voting star identification techniques. It is a two-step star map matching algorithm relying only on angular features, and adopts a reasonable matching strategy to overcome the problem of poor real-time performance of the geometric voting algorithm when the number of stars is large. The algorithm focuses on application scenarios where there are a large number of dense stars (limiting magnitude greater than 13, average number of stars per square degree greater than 185) in the image, which is different from the sparse star identification problem of the star tracker, which is more challenging for the robustness and real-time performance of the algorithm. The proposed algorithm can be adapted to application scenarios such as unreliable brightness information, centroid positioning error, visual axis pointing deviation, and a large number of false stars, with high accuracy, robustness, and good real-time performance.

Phosphogypsum (PG), a byproduct during the phosphoric acid production process, also known as the wet process, contains complex and diverse impurities, resulting in low utilization and considerable accumulation. This leads to a massive waste of land resources and a series of environmental pollution problems. Given the current urgent ecological and environmental situation, developing impurity removal processes with low energy consumption and high efficiency, exploring valuable resource recovery, preparing high value-added PG products, and broadening the comprehensive utilization ways of PG are significant strategies to promote the sustainable consumption of PG and sustainable development of the phosphorus chemical industry. This review comprehensively summarizes the advantages and disadvantages of existing PG impurity removal and utilization technologies and probes into the future development direction, which provides references and ideas for subsequent PG research.

Knowledge regarding the response of soil invertebrate communities to typhoon disturbance is limited, although it is known that soil invertebrates are sensitive to forest disturbances and that tropical cyclones (typhoons/hurricanes) are the most destructive natural disasters affecting the structure and function of forest ecosystems. To fill this knowledge gap, soil invertebrates in both litter and topsoil layers were investigated in four representative subtropical coastal forests of eastern China one week before the first typhoon (Hinnamnor) (T1), one day after the first typhoon (Hinnamnor) (T2), one day after the second typhoon (Muifa) (T3), and one week after the second typhoon (Muifa) (T4) in September 2022. Typhoon disturbances decreased the density and taxa abundance of soil invertebrate communities in litter layer, but the first typhoon disturbance increased these values in the topsoil layer. One week after the second typhoon disturbance, soil invertebrate communities in the litter layer showed a gradual recovery trend. Meanwhile, the soil invertebrate communities in the litter layer were more sensitive to typhoon disturbances than those in the topsoil layer. Furthermore, the responses of the soil invertebrate communities to the typhoon disturbances varied greatly with the forest types. The invertebrate densities in the litter layer decreased by 62.1%, 63.53%, 47.01%, and 46.92% in Chinese fir, second broad-leaved, mixed, and bamboo forests, respectively. Particularly, these two non-catastrophic typhoons significantly altered the functional group composition of detrital food webs in the short term, and the proportion of phytophages in detrital food webs in the litter layer increased after the typhoon disturbances. In conclusion, the effects of typhoon disturbances on soil invertebrate communities vary greatly with forest type and soil layer, and soil invertebrate communities can gradually recover after typhoon disturbances. The legacy effects of typhoon disturbances on the functional group composition of detrital food webs may influence carbon and nutrient cycling in forest ecosystems.

In this study, an electric oil and gas actuator based on fractional-order PID position feedback control is proposed, through which the damping coefficient of the suspension system is adjusted to realize the active control of the suspension. An FOPID algorithm is used to control the motor’s rotational angle to realize the damping adjustment of the suspension system. In this process, the road roughness is collected by the sensors as the criterion of damping adjustment, and the particle swarm algorithm is utilized to find the optimal objective function under different road surface slopes, to obtain the optimal cornering value. According to the mathematical and physical model of the suspension system, the simulation model and the corresponding test platform of this type of suspension system are built. The simulation and experimental results show that the simulation results of the fractional-order nonlinear suspension model are closer to the actual experimental values than those of the traditional linear suspension model, and the accuracy of each performance index is improved by more than 18.5%. The designed active suspension system optimizes the body acceleration, suspension dynamic deflection, and tire dynamic load to 89.8%, 56.7%, and 73.4% of the passive suspension, respectively. It is worth noting that, compared to traditional PID control circuits, the FOPID control circuit designed for motors has an improved control performance. This study provides an effective theoretical and empirical basis for the control and optimization of fractional-order nonlinear suspension systems.

To investigate the influence of external prestressing with carbon fiber-reinforced polymer (CFRP) tendons on the flexural and shear performance of reinforced concrete beams, a total of six simply supported beams with a length of 3.5 m and two two-span continuous beams with a length of 7 m were studied. The influences of the prestressing level of CFRP tendons, the pattern of straight-line and flexural reinforcement of externally prestressed CFRP tendons, and the existence of initial cracks in the beams on the flexural bearing capacity of the reinforced beams were explored through modeling tests. The calculating equation for anticipating the ultimate flexural bearing capacity of concrete beams reinforced with externally prestressed CFRP tendons was modified based on the test results. The findings indicate that the flexural bearing capacity of the test beams was significantly enhanced through both flexural and straight-line reinforcement. Specifically, the ultimate flexural bearing capacity of the simply supported beams exhibited improvements of approximately 160% and 170%, respectively, whereas the ultimate flexural bearing capacity of the continuous beams was enhanced by approximately 113% through the reinforcement of flexural arrangement. Additionally, the shear resistance capacity of the concrete beams was notably improved through the flexural arrangement reinforcement, and the shear resistance capacity of the beams exhibited a substantial increase as the prestressing level of the CFRP tendons increased. The flexural moments in the vicinity of the support point of the reinforced beam exhibited an increase ranging from 41.7% to 112.5% when the stirrup strains started to escalate rapidly. Additionally, the rigidity of the test beams showed a close correlation with both the prestressing level and the existence of initial cracks.

Star image registration is the most important step in the application of astronomical image differencing, stacking, and mosaicking, which requires high robustness, accuracy, and real-time capability on the part of the algorithm. At present, there are no high-performance registration algorithms available in this field. In the present paper, we propose a star image registration algorithm that relies only on radial module features (RMF) and rotation angle features (RAF) while providing excellent robustness, high accuracy, and good real-time performance. The test results on a large amount of simulated and real data show that the comprehensive performance of the proposed algorithm is significantly better than the four classical baseline algorithms as judged by the presence of rotation, insufficient overlapping area, false stars, position deviation, magnitude deviation, and complex sky background, making it a more ideal star image registration algorithm than current alternatives.

Approaching space target tracking is a typical and challenging mission in the space situational awareness (SSA) field. As the space-based radar is able to monitor the space targets of interest full-weather all-time, the space-based radar system is utilized in this paper. However, most multi-target tracking (MTT) filters in target tracking studies merely utilize the location or narrow measurements, and many potentially valuable electromagnetic scattering characteristics are missed, which leads to space target false tracking problems. The space-based radar transmits a wide-band signal, and the measured high-resolution range profile (HRRP) information is an effective characteristic for different target discrimination. Therefore, the HRRP characteristics of space targets are implemented into the update recursion of the MTT filter, which can be utilized to improve the tracking performance. Then, to predict the target HRRP sequence, the geometrical theory of diffraction (GTD) model is utilized. Additionally, a modified spatial spectrum method with a novel covariance matrix is designed to improve the scattering parameter estimation accuracy. Finally, an adapting threshold is devised for merging the Gaussian mixture (GM) components weights. The proposed threshold is on the basis of the proposed HRRP characteristic-aided probability hypothesis density (PHD) filter, and it can tackle the problem of space target discrimination. Simulation results validate the effectiveness and robustness of the proposed probability hypothesis density (HGI-PHD) filter aided by HRRP information and improved with GM weights.

The increased permeability of the lung microvascular endothelium is one critical initiation of acute lung injury (ALI). The disruption of vascular-endothelium integrity results in leakiness of the endothelial barrier and accumulation of protein-rich fluid in the alveoli. During ALI, increased endothelial-cell (EC) permeability is always companied by high frequency and amplitude of cytosolic Ca2+ oscillations. Mechanistically, cytosolic calcium oscillations include calcium release from internal stores and calcium entry via channels located in the cell membrane. Recently, numerous publications have shown substantial evidence that calcium-permeable channels play an important role in maintaining the integrity of the endothelium barrier function of the vessel wall in ALI. These novel endothelial signaling pathways are future targets for the treatment of lung injury. This short review focuses on the up-to-date research and provide insight into the contribution of calcium influx via ion channels to the disruption of lung microvascular endothelial-barrier function during ALI.

Reasonable allocation of space-based radar resources is a crucial aspect of improving the accuracy of space multi-target tracking and enhancing spatial awareness. The conventional resource allocation algorithm fails to exploit the high dynamic radar cross-section (RCS) characteristics, resulting in poor tracking robustness, tracking divergence, or even loss of tracking. However, the RCS of space targets fluctuates considerably in actual tracking scenarios, which cannot be disregarded for space target tracking tasks. To address this issue, we propose an adaptive allocation method that considers the dynamic RCS fluctuation characteristic for space-based radar tracking assignments. The proposed method exploits the predictable orbital information of space target to calculate the real-time observation angle of radar, and then obtains the multi-target dynamic RCS through the target RCS dataset. By combining the obtained RCS sequence, radar power, and bandwidth, an optimal model for radar tracking accuracy is established based on the multi-target posterior Cramér–Rao lower bound (PCRLB) to evaluate the tracking performance. By resolving the aforementioned multivariance optimization problem, we eventually acquire the results of power and bandwidth pre-allocation for tracking multiple space targets. Simulation results validate that, compared with the traditional methods, the proposed joint dynamic RCS power and bandwidth allocation (JRPBA) method can achieve superior tracking accuracy and minimize instances of missed tracking.

Interrupted-sampling repeater jamming (ISRJ) is a kind of intra-pulse coherent deception jamming that can generate false target peaks in the range profile and interfere with the detection and tracking of real targets. In this paper, an anti-ISRJ method based on the intra-pulse orthogonal waveform is proposed, which can recognize common interference signals by comparing sub-signal matched filtering results. For some special scenes where real targets cannot be directly differentiated from false targets, a new recognition method based on the energy discontinuity of the interference signal in the time domain is proposed in this paper. The method proposed in this paper can recognize real and false targets in all ISRJ modes without any prior information, such as jammer parameters, with a small amount of calculation, which is suitable for actual radar systems. Simulation experiments using different interference parameters show that although this method has a 3 dB loss of pulse compression gain, it can completely suppress different kinds of ISRJ interference when the SNR before pulse compression is higher than −20 dB, with 100% target detection probability.

In recent years, the emergence of large-scale pre-trained language models has made transfer learning possible in natural language processing, which overturns the traditional model architecture based on recurrent neural networks (RNN). In this study, we constructed a multi-intention recognition model, Ernie-Gram_Bidirectional Gate Recurrent Unit (BiGRU)_Attention (EBA), for air traffic control (ATC). Firstly, the Ernie-Gram pre-training model is used as the bottom layer of the overall architecture to implement the encoding of text information. The BiGRU module that follows is used for further feature extraction of the encoded information. Secondly, as keyword information is very important in Chinese radiotelephony communications, the attention layer after the BiGRU module is added to realize the extraction of keyword information. Finally, two fully connected layers (FC) are used for feature vector fusion and outputting intention classification vector, respectively. We experimentally compare the effects of two different tokenizer tools, the BERT tokenizer tool and Jieba tokenizer tool, on the final performance of the Bert model. The experimental results reveal that although the Jieba tokenizer tool has considered word information, the effect of the Jieba tokenizer tool is not as good as that of the BERT tokenizer tool. The final model’s accuracy is 98.2% in the intention recognition dataset of the ATC instructions, which is 2.7% higher than the Bert benchmark model and 0.7–3.1% higher than other improved models based on BERT.

In complex mining environments, driverless mining trucks are required to cooperate with multiple intelligent systems. They must perform obstacle avoidance based on factors such as the site road width, obstacle type, vehicle body movement state, and ground concavity-convexity. Targeting the open-pit mining area, this paper proposes an intelligent mining road object detection (IMOD) model developed using a 5G-multi-UAV and a deep learning approach. The IMOD model employs data sensors to monitor surface data in real time within a multisystem collaborative 5G network. The model transmits data to various intelligent systems and edge devices in real time, and the unmanned mining card constructs the driving area on the fly. The IMOD model utilizes a convolutional neural network to identify obstacles in front of driverless mining trucks in real time, optimizing multisystem collaborative control and driverless mining truck scheduling based on obstacle data. Multiple systems cooperate to maneuver around obstacles, including avoiding static obstacles, such as standing and lying dummies, empty oil drums, and vehicles; continuously avoiding multiple obstacles; and avoiding dynamic obstacles such as walking people and moving vehicles. For this study, we independently collected and constructed an obstacle image dataset specific to the mining area, and experimental tests and analyses reveal that the IMOD model maintains a smooth route and stable vehicle movement attitude, ensuring the safety of driverless mining trucks as well as of personnel and equipment in the mining area. The ablation and robustness experiments demonstrate that the IMOD model outperforms the unmodified YOLOv5 model, with an average improvement of approximately 9.4% across multiple performance measures. Additionally, compared with other algorithms, this model shows significant performance improvements.

In order to accurately calculate the sunshine-induced temperature effect of an ultra-high bridge pylon, a refined numerical simulation model for the 3D bridge temperature field was established based on the proposed automatic sunshine shadow recognition method and pylon-height-related convection modification method. A suspension bridge H-shaped pylon with a height of 280 m was taken as an example, and the temperature field and corresponding thermal stress and deformation were calculated under typical meteorological conditions in spring, summer, autumn, and winter. The results show that the maximum temperature differences between the outer surfaces of the pylon can reach 19 °C and 16 °C for north–south walls and east–west walls, respectively, and exceed the recommended value of ±5 °C in the Chinese Specification. The maximum displacements can reach 370 mm and 110 mm at the top of the bridge pylon in the longitudinal and transverse directions of the bridge, respectively. After the modification of the convective coefficient of the outer surfaces with different wind speeds at different pylon heights, the temperature gradually decreased from the top to the bottom of the pylon, with a temperature difference of 8 °C. The significant influence of the sunshine shadow was shown on the temperature field and temperature effect of the bridge pylon. By considering the shadow effect, the maximum temperature difference can reach 12 °C between adjacent sunlit and shaded areas and can reach 14 °C between two pylon columns. A significant mutation of thermal stress existed in the shaded area, and the maximum stress could be reduced by 13 MPa compared with the adjacent sunlit area. Obvious asynchronous deformation was shown between two pylon columns, and the maximum asynchronous displacement at the top of the pylon can reach 18 mm and 45 mm in the longitudinal and transverse directions, respectively.

The assessment and prediction of the toxicity of engineered nanomaterials (NMs) present in mixtures is a challenging research issue. Herein, the toxicity of three advanced two-dimensional nanomaterials (TDNMs), in combination with an organic chemical (3,4-dichloroaniline, DCA) to two freshwater microalgae (Scenedesmus obliquus and Chlorella pyrenoidosa), was assessed and predicted not only from classical mixture theory but also from structure-activity relationships. The TDNMs included two layered double hydroxides (Mg-Al-LDH and Zn-Al-LDH) and a graphene nanoplatelet (GNP). The toxicity of DCA varied with the type and concentration of TDNMs, as well as the species. The combination of DCA and TDNMs exhibited additive, antagonistic, and synergistic effects. There is a linear relationship between the different levels (10, 50, and 90%) of effect concentrations and a Freundlich adsorption coefficient (KF) calculated by isotherm models and adsorption energy (Ea) obtained in molecular simulations, respectively. The prediction model incorporating both parameters KF and Ea had a higher predictive power for the combined toxicity than the classical mixture model. Our findings provide new insights for the development of strategies aimed at evaluating the ecotoxicological risk of NMs towards combined pollution situations.

The performance of classic radar geometry based on the line-of-sight (LOS) signal transmitted from radar to the target in the free space is affected by multipath echoes in urban areas, where non-line-of-sight (NLOS) signals reflected by obstacles are received by the radar. Based on prior information of the urban situation, this article proposes a novel two-stage localization algorithm with multipath exploitation in a dense clutter environment. In the offline stage, multipath propagation parameters of uniformly distributed samples in the radar field of view are predicted by the ray-tracing technique. In the online stage, a rough location of the target is estimated by the maximum similarity between measurements and the predicted parameters of reference samples at different locations. The similarity is described by the likelihood between measurements and the predicted multipath parameters with respect to all possible associated hypotheses. A gating threshold is derived to exclude less likely hypotheses and reduce the computational burden. The accurate target location is acquired by a non-linear least squares (NLS) optimization of the associated multipath components. Simulation results in various noise conditions show that the proposed method provides robust and accurate target localization results under dense clutter conditions, and the offline pre-calculation of ray-tracing ensures the real-time performance of the proposed localization algorithm. The root mean square error (RMSE) of simulation results shows the advantage of the proposed method over the existing method. The presented results suggest that the proposed method can be applied to NLOS target localization applications in complex environments.

Images captured in bad weather are not conducive to visual tasks. Rain streaks in rainy images will significantly affect the regular operation of imaging equipment; to solve this problem, using multiple neural networks is a trend. The ingenious integration of network structures allows for full use of the powerful representation and fitting abilities of deep learning to complete low-level visual tasks. In this study, we propose a generative adversarial network (GAN) with multiple attention mechanisms for image rain removal tasks. Firstly, to the best of our knowledge, we propose a pretrained vision transformer (ViT) as the discriminator in GAN for single-image rain removal for the first time. Secondly, we propose a neural network training method that can use a small amount of data for training while maintaining promising results and reliable visual quality. A large number of experiments prove the correctness and effectiveness of our method. Our proposed method achieves better results on synthetic and real image datasets than multiple state-of-the-art methods, even when using less training data.

Artificial intelligence for aircraft guidance is a hot research topic, and deep reinforcement learning is one of the promising methods. However, due to the different movement patterns of destinations in different guidance tasks, it is inefficient to train agents from scratch. In this article, a policy-reuse algorithm based on destination position prediction is proposed to solve this problem. First, the reward function is optimized to improve flight trajectory quality and training efficiency. Then, by predicting the possible termination position of the destinations in different moving patterns, the problem is transformed into a fixed-position destination aircraft guidance problem. Last, taking the agent in the fixed-position destination scenario as the baseline agent, a new guidance agent can be trained efficiently. Simulation results show that this method can significantly improve the training efficiency of agents in new tasks, and its performance is stable in tasks with different similarities. This research broadens the application scope of the policy-reuse approach and also enlightens the research in other fields.

Optical solitons in ultrafast fiber lasers, as a result of dual balances between dispersion and nonlinearity as well as gain and loss, enable various soliton interactions. Soliton collisions are among the most intriguing soliton interactions, which fuel the understanding for particle-like properties of solitons. Here, we experimentally investigate the transient dynamics of collisions between a two-soliton molecule and a soliton singlet in a mode-locked fiber laser. By means of the dispersive Fourier transform technique, the evolving spectral interferograms of different collision scenarios are measured in real time. In particular, the “quasi-elastic” collision is observed, which shows that the soliton-molecule state remains unaltered after the collision and the group-velocity difference between the soliton molecule and the singlet is changed. It is directly demonstrated that a bond exchange occurs between the colliding solitons. By tuning the intra-cavity polarization controller, the dynamic processes of other collision outcomes, including the annihilation of a soliton in the soliton molecule as well as the formation of a stable unequally spaced soliton triplet, are also revealed. Our work facilitates a deeper understanding of soliton collision dynamics in ultrafast fiber lasers.

Flaviviruses, represented by Zika and dengue virus (ZIKV and DENV), are widely present around the world and cause various diseases with serious consequences. However, no antiviral drugs have been clinically approved for use against them. Azelnidipine (ALP) is a dihydropyridine calcium channel blocker and has been approved for use as an antihypertensive drug. In the present study, ALP was found to show potent anti-flavivirus activities in vitro and in vivo. ALP effectively prevented the cytopathic effect induced by ZIKV and DENV and inhibited the production of viral RNA and viral protein in a dose-dependent manner. Moreover, treatment with 0.3 mg/kg of ALP protected 88.89% of mice from lethal challenge. Furthermore, using the time-of-drug-addition assay, the enzymatic inhibition assay, the molecular docking, and the surface plasmon resonance assay, we revealed that ALP acted at the replication stage of the viral infection cycle by targeting the viral RNA-dependent RNA polymerase. These findings highlight the potential for the use of ALP as an antiviral agent to combat flavivirus infections.

Enterovirus infections can cause hand, foot, and mouth disease (HFDM), aseptic meningitis, encephalitis, myocarditis, and acute flaccid myelitis, leading to death of infants and young children. However, no specific antiviral drug is currently available for the treatment of this type of infection. The Unites States and United Kingdom health authorities recently approved a new antiviral drug, molnupiravir, for the treatment of COVID-19. In this study, we reported that molnupiravir (EIDD-2801) and its active form, EIDD-1931, have broad-spectrum anti-enterovirus potential. Our data showed that EIDD-1931 could significantly reduce the production of EV-A71 progeny virus and the expression of EV-A71 viral protein at non-cytotoxic concentrations. The results of the time-of-addition assay suggest that EIDD-1931 acts at the post-entry step, which is in accordance with its antiviral mechanism. The intraperitoneal administration of EIDD-1931 and EIDD-2801 protected 1-day-old ICR suckling mice from lethal EV-A71 challenge by reducing the viral load in various tissues of the infected mice. The pharmacokinetics analysis indicated that the plasma drug concentration overwhelmed the EC50 for enteroviruses, suggesting the clinical potential of molnupiravir against enteroviruses. Thus, molnupiravir along with its active form, EIDD-1931, may be a promising drug candidate against enterovirus infections.

Deep reinforcement learning (DRL) has been widely adopted recently for its ability to solve decision-making problems that were previously out of reach due to a combination of nonlinear and high dimensionality. In the last few years, it has spread in the field of air traffic control (ATC), particularly in conflict resolution. In this work, we conduct a detailed review of existing DRL applications for conflict resolution problems. This survey offered a comprehensive review based on segments as (1) fundamentals of conflict resolution, (2) development of DRL, and (3) various applications of DRL in conflict resolution classified according to environment, model, algorithm, and evaluating indicator. Finally, an open discussion is provided that potentially raises a range of future research directions in conflict resolution using DRL. The objective of this review is to present a guidance point for future research in a more meaningful direction.

The goal of this research is to offer an effective intelligent model for forecasting college students’ career decisions in order to give a useful reference for career decisions and policy formation by relevant departments. The suggested prediction model is mainly based on a support vector machine (SVM) that has been modified using an enhanced butterfly optimization approach with a communication mechanism and Gaussian bare-bones mechanism (CBBOA). To get a better set of parameters and feature subsets, first, we added a communication mechanism to BOA to improve its global search capability and balance exploration and exploitation trends. Then, Gaussian bare-bones was added to increase the population diversity of BOA and its ability to jump out of the local optimum. The optimal SVM model (CBBOA-SVM) was then developed to predict the career decisions of college students based on the obtained parameters and feature subsets that are already optimized by CBBOA. In order to verify the effectiveness of CBBOA, we compared it with some advanced algorithms on all benchmark functions of CEC2014. Simulation results demonstrated that the performance of CBBOA is indeed more comprehensive. Meanwhile, comparisons between CBBOA-SVM and other machine learning approaches for career decision prediction were carried out, and the findings demonstrate that the provided CBBOA-SVM has better classification and more stable performance. As a result, it is plausible to conclude that the CBBOA-SVM is capable of being an effective tool for predicting college student career decisions.

Mild traumatic brain injury (mTBI) without skull fracturing is the most common occurrence of all TBIs and is considered as a serious public health concern. Animal models of mTBI are essential to investigation of TBI and its effects. In the current study, we developed and characterized a reproducible mouse model of mild TBI, meanwhile, the effects of this mTBI model, as well as repetitive mTBIs (rmTBIs), on the endothelial function of mouse aortas were also studied. In variety of closed-head models of mTBI, impact velocity, weight, and dwell time are the main parameters that affect the severities of injury. Here, we used a device, converting parameters of velocity, tip weight, and dwell time into impact force, to develop a mouse model of close-head mTBI. Mice were subjected to a mild TBI induced by the impact forces of 500, 600, 700 and 800 kdyn, respectively. Later, brain injuries were assessed histologically and molecularly. Systemic and brain inflammation were measured by plasma cytokine assay and glial fibrillary acidic protein (GFAP) staining. The composite neurobehavioral test revealed significant acute functional deficits in mice after mTBI, corresponding to the degree of injury. Mice brain undergoing mTBI had significant elevated GFAP staining. Plasma cytokines interleukin-1β (IL-1β) and superoxide dismutase (SOD) were significantly increased within 2 h after mTBI. Taken together, these data suggest that the mTBI mouse model introduce within our study exhibits good repeatability and comparable pathological characters. Moreover, we used this mTBI mouse model to determine the effect of single or rmTBIs on systemic vasoconstriction and relaxation. The isometric-tension results indicate that rmTBIs induce a pronounced and long-lasting endothelial dysfunction in mouse aorta.

The polarization–Raman Lidar combined sun photometer is a powerful method for separating dust and urban haze backscatter, extinction, and mass concentrations. The observation was performed in Beijing during the 2019 National Day parade, the particle depolarization ratio at 532 nm and Lidar ratio at 355 nm are 0.13 ± 0.05 and 52 ± 9 sr, respectively. It is the typical value of a mixture of dust and urban haze. Here we quantify the contributions of cross-regional transported natural dust and urban haze mass concentrations to Beijing’s air quality. There is a significant correlation between urban haze mass concentrations and surface PM2.5 (R = 0.74, p < 0.01). The contributions of local emissions to air pollution during the 2019 National Day parade were insignificant, mainly affected by regional transport, including urban haze in North China plain and Guanzhong Plain (Hebei, Tianjin, Shandong, and Shanxi), and dust aerosol in Mongolia regions and Xinjiang. Moreover, the trans-regional transmission of natural dust dominated the air pollution during the 2019 National Day parade, with a relative contribution to particulate matter mass concentrations exceeding 74% below 4 km. Our results highlight that controlling anthropogenic emissions over regional scales and focusing on the effects of natural dust is crucial and effective to improve Beijing’s air quality.

Systemic lupus erythematosus (SLE) is a multifactorial autoimmune disease that afflicts multiple organs, especially kidneys and joints. In addition to genetic predisposition, it is now evident that DNA methylation and microRNAs (miRNAs), the two major epigenetic modifications, are critically involved in the pathogenesis of SLE. DNA methylation regulates promoter accessibility and gene expression at the transcriptional level by adding a methyl group to 5′ cytosine within a CpG dinucleotide. Extensive evidence now supports the importance of DNA hypomethylation in SLE etiology. miRNAs are small, non-protein coding RNAs that play a critical role in the regulation of genome expression. Various studies have identified the signature lupus-related miRNAs and their functional contribution to lupus incidence and progression. In this review, the mutual interaction between DNA methylation and miRNAs regulation in SLE is discussed. Some lupus-associated miRNAs regulate DNA methylation status by targeting the DNA methylation enzymes or methylation pathway-related proteins. On the other hand, DNA hyper- and hypo-methylation are linked with dysregulated miRNAs expression in lupus. Further, we specifically discuss the genetic imprinting Dlk1-Dio3 miRNAs that are subjected to DNA methylation regulation and are dysregulated in several autoimmune diseases, including SLE.

Persistent heavy haze episodes have repeatedly shrouded North China in recent years. Besides anthropogenic emissions, natural dust also contributes to the aerosols in this region. Through continuous observation by a dual-wavelength Raman lidar, the primary aerosol types and their contributions to air pollution in North China were determined. The following three aerosol types can be classified: natural dust, anthropogenic aerosols, and the mixture of anthropogenic aerosols and dust (polluted dust). The classification results are basically consistent with the classification results from the cloud–aerosol lidar and infrared pathfinder satellite observations (CALIPSO) satellite measurements. The relative bias of the lidar ratio between the Raman lidar and CALIPSO is less than 25% over 90% of the cases, indicating that the CALIPSO lidar ratio selection algorithm is reasonable. The classification results show that approximately 45% of aerosols below 1.8 km are contributed by polluted dust during our one year observations. The contribution of dust increased with height, from 6% at 500 m to 28% at 1,800 m, while the contribution of anthropogenic aerosols decreased from 49% to 25%. In addition, polluted dust is the major aerosol subtype below 1.0 km in spring (over 60%) and autumn (over 70%). Anthropogenic aerosols contribute more than 75% of air pollution in summer. In winter, anthropogenic aerosols prevailed (over 80%) in the lower layer, while polluted dust (around 60%) dominated the upper layer. Our results identified the primarily aerosol types to assess the contributions of anthropogenic and natural sources to air pollution in North China, and highlight that natural dust plays a crucial role in lower-layer air pollution in spring and autumn, while controlling anthropogenic aerosols will significantly improve air quality in winter.

The joint activity of multiple engineered nanoparticles (ENPs) has attracted much attention in recent years. Many previous studies have focused on the combined toxicity of different ENPs with nanostructures of the same dimension. However, the mixture toxicity of multiple ENPs with different dimensions is much less understood. Herein, we investigated the toxicity of the binary mixture of TiO2 nanospherical particles (NPs) and TiO2 nanotubes (NTs) to two freshwater algae with different morphology, namely, Scenedesmus obliquus and Chlorella pyrenoidosa. The physicochemical properties, dispersion stability, and the generation of reactive oxygen species (ROS) were determined in the single and binary systems. Classical approaches to assessing mixture toxicity were applied to evaluate and predict the toxicity of the binary mixtures. The results show that the combined toxicity of TiO2 NPs and NTs to S. obliquus was between the single toxicity of TiO2 NTs and NPs, while the combined toxicity to C. pyrenoidosa was higher than their single toxicity. Moreover, the toxicity of the binary mixtures to C. pyrenoidosa was higher than that to S. obliquus. A toxic unit assessment showed that the effects of TiO2 NPs and NTs were additive to the algae. The combined toxicity to S. obliquus and C. pyrenoidosa can be effectively predicted by the concentration addition model and the independent action model, respectively. The mechanism of the toxicity caused by the binary mixtures of TiO2 NPs and NTs may be associated with the dispersion stability of the nanoparticles in aquatic media and the ROS-induced oxidative stress effects. Our results may offer a new insight into evaluating and predicting the combined toxicological effects of ENPs with different dimensions and of probing the mechanisms involved in their joint toxicity.

Water vapor vertical profiles are important in numerical weather prediction, moisture transport, and vertical flux calculation. This study presents the Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) retrieval algorithm for water vapor vertical profiles and the retrieved results are validated with corresponding independent datasets under clear sky. The retrieved Vertical Column Densities (VCDs) and surface concentrations are validated with the Aerosol Robotic Network (AERONET) and National Climatic Data Centre (NCDC) datasets, achieving good correlation coefficients (R) of 0.922 and 0.876, respectively. The retrieved vertical profiles agree well with weekly balloon-borne radiosonde measurements. Furthermore, the retrieved water vapor concentrations at different altitudes (100–2000 m) are validated with the corresponding European Centre for Medium-range Weather Forecasts (ECMWF) ERA-interim datasets, achieving a correlation coefficient (R) varying from 0.695 to 0.857. The total error budgets for the surface concentrations and VCDs are 31% and 38%, respectively. Finally, the retrieval performance of the MAX-DOAS algorithm under different aerosol loads is evaluated. High aerosol loads obstruct the retrieval of surface concentrations and VCDs, with surface concentrations more liable to severe interference from such aerosol loads. To summarize, the feasibility of detecting water vapor profiles using MAX-DOAS under clear sky is confirmed in this work.

The objectives of the current study were to isolate and identify the pathogen responsible for citrus canker and investigate the efficacy of sulfone derivatives containing 1,3,4-oxadiazole moiety on controlling citrus canker caused by Xanthomonas citri subsp. citri (Xcc) under in vitro and field conditions. In an in vitro study, we tested eight sulfone derivatives against Xcc and the results demonstrated that compound 3 exhibited the best antibacterial activity against Xcc, with a half-maximal effective concentration (EC50) value of 1.23 μg/mL, which was even better than those of commercial bactericides Kocide 3000 (58.21 μg/mL) and Thiodiazole copper (77.04 μg/mL), respectively. Meanwhile, under field experiments, compound 3 treatments demonstrated the highest ability to reduce the disease of citrus canker in leaves and fruits in two different places relative to an untreated control as well as the commercial bactericides Kocide 3000 and Thiodiazole copper. Meanwhile, compound 3 could stimulate the increase in peroxidase (POD), polyphenol oxidase (PPO), and phenylalanine ammonia lyase (PAL) activities in the navel orange leaves, causing marked enhancement of plant resistance against citrus canker. Moreover, compound 3 could damage the cell membranes, destruct the biofilm formation, inhibit the production of extracellular polysaccharide (EPS), and affect the cell membrane permeability to restrain the growth of the bacteria.

In citrus color mutants, the levels of carotenoid constituents and other secondary metabolites are different in their corresponding wild types. Terpenoids are closely related to coloration, bitterness, and flavor. In this study, terpenoid profiles and hormones in citrus fruits of two red-flesh mutants—Red Anliu orange and Red-flesh Guanxi pummelo—and their corresponding wild types were investigated using GC/MS, HPLC, and LC-MS/MS. Results showed that Red Anliu orange (high in carotenoids) and Anliu orange (low in carotenoids) accumulated low levels of limonoid aglycones but high levels of monoterpenoids; conversely, Red-flesh Guanxi pummelo (high in carotenoids) and Guanxi pummelo (deficient in carotenoids) accumulated high levels of limonoid aglycones but low levels of monoterpenoids. However, isopentenyl diphosphate was present at similar levels. A correlation analysis indicated that jasmonic and salicylic acids might play important roles in regulating terpenoid biosynthesis. Additionally, the similarities of carotenoid and volatile profiles between each mutant and its corresponding wild type were greater than those between the two mutants or the two wild types. The flux balance of terpenoid metabolism in citrus fruit tends toward stability among various citrus genera that have different terpenoid profiles. Bud mutations could influence metabolite profiles of citrus fruit to a limited extent.

Outbreaks of pine wilt disease (PWD, caused by the pinewood nematode Bursaphelenchus xylophilus), have caused mass mortality of the genus Pinus in Eurasia. Climate change may greatly influence the distribution and population dynamics of longhorn beetles of the genus Monochamus (the main vector of B. xylophilus), the survival and development of B. xylophilus, and the resistance of pines. The aim of this study was to investigate the effect of climatic variables associated with extensive PWD outbreaks in Masson pine (Pinus massoniana Lamb.) forest across the eastern part of the Three Gorges Reservoir region. Since its discovery in 2006, the most serious PWD outbreak occurred from 2014 to 2018; the most striking characteristic of this outbreak is the consistent increase in Masson pine mortality and extent of the affected areas. Moreover, 28 out of 46 PWD biological relevant climatic variables were selected and used for redundancy analysis. The ordination biplots reflect the complicated quantitative relationship between the PWD epidemic variables and the biologically relevant climatic variables of temperature, precipitation, relative humidity, and wind speed. The results will be useful for understanding the role climatic variables play in PWD outbreaks, for predicting the spread and pattern of PWD outbreaks, and for the advance preparation of management strategies with the purpose of preventing future PWD outbreaks.

Joint biomarker responses, oxidative stress and membrane systems, were determined for nano-metal-oxides (nMeO, i.e., nCeO2, nMgO, and nFe3O4) and sulfadiazine (SDZ) exposed at relevant low concentrations to two freshwater microalgae Scenedesmus obliquus and Chlorella pyrenoidosa. The impacts of dissolved organic matter (DOM) on the joint biomarker responses were also investigated. Results indicated that the presence of SDZ significantly decreased the level of intercellular reactive oxygen species (ROS) in the algal cells exposed to each nMeO. Reduction of cell membrane permeability (CMP) and mitochondrial membrane potential (MMP) in the algal cells was observed when the algae were exposed to the mixture of SDZ and the nMeO. The degree of reduction of the ROS level, CMP, and MMP significantly went down with the addition of DOM to a certain extent. Changes in cellular oxidative stress and membrane function depended on the types of both nMeO and algal species. This contribution provides an insight into the hazard assessment of a mixture consisting of emerging contaminants and DOM, as they can coexist in the aquatic environment.

Solar radiation intensity significantly influences the cooling loads of building, and the two are correlated and accorded to a certain extent. This study proposes a double effect LiBr–H2O absorption cooling system based on the parabolic trough collector (PTC) of solar heat energy. Thermodynamic models including PTC and absorption chiller are constructed, and their accuracy is verified by comparing the simulation results and the experimental data. Subsequently, the impact of variable design parameters on the thermodynamic performance is analyzed and discussed. The analysis of a solar cooling system in a hotel case study is related to its operation in a typical day, the average coefficient of performance of the absorption chiller is approximately 1.195, and the whole solar cooling system achieves 61.98% solar energy utilization efficiency. Furthermore, the performance comparison of a solar cooling system in different types of building indicates that higher matching and a higher correlation coefficient between the transient solar direct normal irradiance and cooling load is helpful in decreasing the heat loss and improving systemic performance. The solar cooling system in the office building exhibits a correlation coefficient of approximately 0.81 and achieves 69.47% systemic thermal efficiency.

To understand the role of the forest gaps and epiphytic mosses and lichens in the heavy metal cycles of forest ecosystems, the biomass, concentration, and storage of Cd, Pb, Cu, and Zn in epiphytic mosses and lichens on fallen logs and standing trees from the gap center to the closed canopy of an alpine forest ecosystem on the eastern Tibetan Plateau were investigated. Mosses were the dominant epiphytes on fallen logs and standing trees and contribute 82.1–95.1% of total epiphyte biomass in the alpine forest. A significantly higher biomass of epiphytic mosses and lichens was observed at the gap edge. The heavy metals concentration in mosses and lichens on fallen logs and standing trees varied widely with gap positions. Lower concentrations of Cd, Cu, and Pb were found in the mosses and lichens under the closed canopy, higher concentrations of Cd and Pb were detected in the mosses and lichens at the gap edge, and higher concentrations of Cu were found at the gap center. A significant difference in Zn concentration was observed between the mosses and lichens. No significant differences in Pb or Zn concentrations were observed in the mosses and lichens between the fallen log and standing tree substrates. Furthermore, the epiphytic mosses and lichens at the gap edge accumulated more Cd, Pb, and Cu, whereas the epiphytic lichens on the fallen logs and large shrubs at the gap center accumulated more Zn. In conclusion, gap regeneration accelerates the cycling of heavy metals in alpine forest ecosystems by promoting the growth of epiphytic mosses and lichens on fallen logs and standing trees at gap edges and increasing the concentration of heavy metals in these plants.

Metal-based nanoparticles (NPs) are the most widely used engineered nanomaterials. The individual toxicities of metal-based NPs have been plentifully studied. However, the mixture toxicity of multiple NP systems (n ≥ 3) remains much less understood. Herein, the toxicity of titanium dioxide (TiO2) nanoparticles (NPs), silicon dioxide (SiO2) NPs and zirconium dioxide (ZrO2) NPs to unicellular freshwater algae Scenedesmus obliquus was investigated individually and in binary and ternary combination. Results show that the ternary combination systems of TiO2, SiO2 and ZrO2 NPs at a mixture concentration of 1 mg/L significantly enhanced mitochondrial membrane potential and intracellular reactive oxygen species level in the algae. Moreover, the ternary NP systems remarkably increased the activity of the antioxidant defense enzymes superoxide dismutase and catalase, together with an increase in lipid peroxidation products and small molecule metabolites. Furthermore, the observation of superficial structures of S. obliquus revealed obvious oxidative damage induced by the ternary mixtures. Taken together, the ternary NP systems exerted more severe oxidative stress in the algae than the individual and the binary NP systems. Thus, our findings highlight the importance of the assessment of the synergistic toxicity of multi-nanomaterial systems.

The study of pollution due to combined antibiotics and metals is urgently needed. Photochemical processes are an important transformation pathway for antibiotics in the environment. The mechanisms underlying the effects of metal-ion complexation on the aquatic photochemical transformation of antibiotics in different dissociation forms are crucial problems in science, and beg solutions. Herein, we investigated the mechanisms of direct photolysis of norfloxacin (NOR) in different dissociation forms in water and metal ion Mg2+ effects using quantum chemical calculations. Results show that different dissociation forms of NOR had different maximum electronic absorbance wavelengths (NOR2+ < NOR0 < NOR+) and showed different photolysis reactivity. Analysis of transition states (TS) and reaction activation energies (Ea) indicated NOR+ generally underwent loss of the piperazine ring (C10–N13 bond cleavage) and damage to piperazine ring (N13–C14 bond cleavage). For NOR2+, the main direct photolysis pathways were de-ethylation (N7–C8 bond cleavage) and decarboxylation (C2–C5 bond cleavage). Furthermore, the presence of Mg2+ changed the order of the wavelength at maximum electronic absorbance (NOR+-Mg2+ < NOR0-Mg2+ < NOR2+-Mg2+) and increased the intensities of absorbance peaks of all three dissociation species of NOR, implying that Mg2+ played an important role in the direct photolysis of NOR0, NOR+, and NOR2+. The calculated TS results indicated that the presence of Mg2+ increased Ea for most direct photolysis pathways of NOR, while it decreased Ea for some direct photolysis pathways such as the loss of the piperazine ring and the damage of the piperazine ring of NOR0 and the defluorination of NOR+.

The detection of existence of fullerenes (C60 and C70) makes it necessary to explore whether soot nanoparticles can participate in new nanometer-sized particle formation and growth in the atmosphere. This study describes a theoretical investigation at multiple levels on the role of the fullerenes (as model compounds to represent nanoparticles of soot) in the formation of complexes with a common atmospheric nucleating precursor (sulfuric acid, SA) and a biogenic organic acid (cis-pinonic acid, CPA), as well as initial growth of nano-sized biogenic aerosols. Quantum chemical density-functional theory calculations identify the formation of stable fullerene-[CPA-SA] ternary complexes, which likely leads to an enhanced nucleation of SA with CPA. Relevant thermochemical parameters including the changes of Gibbs free energy, enthalpy, and entropy for the complex formation also support that fullerene-[CPA-SA] is most likely to be a newly formed nuclei. The sizes of the critical nucleus of the fullerene-[CPA-SA-H2O] systems were found to be approximately 1.3 nm by large-scale molecular dynamics simulations. This study may provide a new insight into the mechanisms underlying the formation of new particle in the atmospheric environment.

The gut microbiota plays a pivotal role in upholding intestinal health, fostering intestinal development, fortifying organisms against pathogen intrusion, regulating nutrient absorption, and managing the body’s lipid metabolism. However, the influence of different cultivation modes on the growth indices and intestinal microbes of Salmo trutta fario remains underexplored. In this study, we employed high-throughput sequencing and bioinformatics techniques to scrutinize the intestinal microbiota in three farming modes: traditional pond aquaculture (TPA), recirculating aquaculture (RA), and flow-through aquaculture (FTA). We aimed to assess the impact of different farming methods on the water environment and Salmo trutta fario’s growth performance. Our findings revealed that the final weight and weight gain rate in the FTA model surpassed those in the other two. Substantial disparities were observed in the composition, relative abundance, and diversity of Salmo trutta fario gut microbiota under different aquaculture modes. Notably, the dominant genera of Salmo trutta fario gut microbiota varied across farming modes: for instance, in the FTA model, the most prevalent genera were SC-I-84 (7.34%), Subgroup_6 (9.93%), and UTCFX1 (6.71%), while, under RA farming, they were Bacteroidetes_vadinHA17 (10.61%), MBNT15 (7.09%), and Anaeromyxoactor (6.62%). In the TPA model, dominant genera in the gut microbiota included Anaeromyxobacter (8.72%), Bacteroidetes_vadinHA17 (8.30%), and Geobacter (12.54%). From a comparative standpoint, the genus-level composition of the gut microbiota in the RA and TPA models exhibited relative similarity. The gut microbiota in the FTA model showcased the most intricate functional diversity, while TPA farming displayed a more intricate interaction pattern with the gut microbiota. Transparency, pH, dissolved oxygen, conductivity, total dissolved solids, and temperature emerged as pivotal factors influencing Salmo trutta fario gut microbiota under diverse farming conditions. These research findings offer valuable scientific insights for fostering healthy aquaculture practices and disease prevention and control measures for Salmo trutta fario, holding substantial significance for the sustainable development of the cold-water fish industry in the Qinghai–Tibet Plateau.