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Author Correction: We apologize for unintentionally using the wrong figures (Figure 5b and Figure 6e) in the original article [...]

A reliable prediction model can greatly contribute to the research of car seating system vibration control. The novelty of this paper lies in the development of a hybrid method of an artificial neural network (ANN) and response surface methodology (RSM) to predict the peak seat-to-head transmissibility ratio of a seating suspension system and to evaluate its ride comfort for different seat design parameters. Additionally, this method can remove the experimental design of the RSM model. In this paper, four seat design parameters are selected as input parameters and arranged using the central composite design method. The peak transmissibility ratio from seat to head at 4 Hz is chosen as the response target output value. To illustrate this hybrid method, the response target output value of the peak transmissibility ratio is calculated from the frequency response of a five-degrees-of-freedom (5-DOF) lumped-parameter biodynamic seating suspension model. The input design parameters and the response target output values are used to train an ANN to establish the relationship between the seat design parameters and the peak transmissibility ratio. At the same time, the input design parameters and the response target output values predicted by the ANN are used to develop the relationship between the seat design parameters and the peak transmissibility ratio using the response surface method and linear regression models. The hybrid of the ANN and response surface methods makes the planning or design of experiments not essential. The hybrid model of the ANN and response surface method is more accurate and convenient than a linear regression model for the study of seating system vibration isolation.

Solid wood is renowned as a superior material for construction and furniture applications. However, characteristics such as dead knots, live knots, piths, and cracks are easily formed during timber’s growth and processing stages. These features and defects significantly undermine the mechanical characteristics of sawn timber, rendering it unsuitable for specific applications. This study introduces BDCS-YOLO (Bilateral Defect Cutting Strategy based on You Only Look Once), an artificial intelligence bilateral sawing strategy to advance the automation of timber processing. Grounded on a dual-sided image acquisition platform, BDCS-YOLO achieves a commendable mean average feature detection precision of 0.94 when evaluated on a meticulously curated dataset comprising 450 images. Furthermore, a dual-side processing optimization module is deployed to enhance the accuracy of defect detection bounding boxes and establish refined processing coordinates. This innovative approach yields a notable 12.3% increase in the volume yield of sawn timber compared to present production, signifying a substantial leap toward efficiently utilizing solid wood resources in the lumber processing industry.

Macrophages are an important component of the first-line defense against pathogens, including viruses [...]

The coupling effects of clamping pressure and catalyst distribution are comprehensively considered to improve proton exchange membrane fuel cell (PEMFC) performance. Numerical models were constructed to study the performance changes and the corresponding internal states of PEMFC under different clamping pressures. Since the increased clamping pressure reduces the uniformity of current density, non-uniform designs with decreased catalyst loading under channel and increased catalyst loading under rib are proposed for performance improvement. A weighted objective function considering current density magnitude and uniformity was constructed, and the performances of different catalyst loading distributions were analyzed. Compared to the uniform distribution, the optimized distribution with a variation of −15% and 15% under channel and rib had the maximum objective function value of 17.24%. The deformation analysis of the gas diffusion layer and optimization of catalyst loading distribution based on deformation analysis provided a reference for the assembly of PEMFC and the production of MEA.

Integrating electric vehicles (EVs) into the coupled power distribution network (PDN) and transportation network (TN) presents substantial challenges. This paper explores three key areas in EV integration: charging/discharging scheduling, charging navigation, and charging station planning. First, the paper discusses the features and importance of EV integrated traffic–power networks. Then, it examines key factors influencing EV strategy, such as user behavior, charging preferences, and battery performance. Next, the study establishes an EV charging and discharging model, with particular emphasis on the complexities introduced by factors such as pricing mechanisms and integration approaches. Furthermore, the charging navigation model and the role of real-time traffic information are discussed. Additionally, the paper highlights the importance of multi-type charging stations and the impact of uncertainty on charging station planning. The paper concludes by identifying significant challenges and potential opportunities for EV integration. Future research should focus on enhancing coupled network modeling, refining user behavior models, developing incentive pricing mechanisms, and advancing autonomous driving and automated charging technologies. Such efforts will be essential for achieving a sustainable and efficient EV ecosystem.

This study examined the agricultural water resource shortage and abundant ditch water resources in the Yinbei region of Ningxia. The effects of ditch water and Yellow River irrigation on the saline–alkali characteristics of soil and paddy were investigated using field monitoring and indoor detection methods in Pingluo County, Ningxia (106°31′ E, 38°51′ N). In addition to monitoring ditch water, four treatment groups were established: direct ditch water irrigation (T1), mixed ditch water and Yellow River water irrigation (T2), alternate ditch water and Yellow River water irrigation (T3), and irrigation solely with Yellow River water (CK). The results show the following: (1) The salinity of ditch water samples collected from the experimental field during the rice growth period was less than 1.60 g/L, and the pH of the samples was lower than 8.62; thus, they were classified as mildly brackish water. The application of ditch water irrigation did not result in soil saline–alkali aggravation and the accumulation of excessive amounts of heavy metals in soils and paddies in Pingluo County, Ningxia. (2) The rice yields for the CK, T1, T2, and T3 treatments were 10,437.5, 8318.4, 9182.1, and 9016.2 kg/hm2, respectively. Compared with Yellow River irrigation, the rice yields for the T1, T2, and T3 treatments were 20.3, 12.1, and 13.6% lower than that of CK, respectively, with minimal differences observed among them. Hence, under the condition of a water resource shortage in the Yellow River region, ditch water can be appropriately applied for mixed or alternate irrigation to ensure food security. This research has revealed the influences of ditch water irrigation on the saline–alkali properties of soil and the heavy metal contents of paddies.

Forest inspection is a crucial component of forest monitoring in China. The current methods for detecting changes in forest patches primarily rely on remote sensing imagery and manual visual interpretation, which are time-consuming and labor-intensive approaches. This study aims to automate the extraction of changed forest patches using UAVs and artificial intelligence technologies, thereby saving time while ensuring detection accuracy. The research first utilizes position and orientation system (POS) data to perform geometric correction on the acquired UAV imagery. Then, a convolutional neural network (CNN) is used to extract forest boundaries and compare them with the previous vector data of forest boundaries to initially detect patches of forest reduction. The average boundary distance algorithm (ABDA) is applied to eliminate misclassified patches, ultimately generating precise maps of reduced forest patches. The results indicate that using POS data with RTK positioning for correcting UAV imagery results in a central area correction error of approximately 4 m and an edge area error of approximately 12 m. The TernausNet model achieved a maximum accuracy of 0.98 in identifying forest areas, effectively eliminating the influence of shrubs and grasslands. When the UAV flying height is 380 m and the distance threshold is set to 8 m, the ABDA successfully filters out misclassified patches, achieving an identification accuracy of 0.95 for reduced forest patches, a precision of 0.91, and a kappa coefficient of 0.89, fully meeting the needs of forest inspection work in China. Select urban forests with complex scenarios in the research area can be used to better promote them to other regions. This study ultimately developed a fully automated forest change detection system.

In this study, nine concrete short columns and nine UHPC (ultra-high-performance concrete) short columns were produced, and an accelerated corrosion test method was used to compare and analyze the cracking phenomena and reinforcement corrosion after different durations of electrical current application. The analysis revealed that the corrosion rate of the UHPC specimens was approximately half of that of the concrete specimens, demonstrating excellent corrosion resistance. Although the corrosion rate of the UHPC specimen was lower, the length of the internal steel reinforcement decreased more significantly under prolonged electrification. For example, after 38 days of accelerated corrosion, the internal steel reinforcement in the ordinary concrete specimen measured 48 mm in length, while in the UHPC specimen, it measured only 43 mm. It was also found that the corrosion rates of both the concrete and UHPC specimens were significantly lower than the theoretical corrosion rate. This discrepancy is attributed to the fact that Faraday’s law, used to calculate the theoretical corrosion rate, does not fully account for factors such as the thickness of the protective layer and chloride ion concentration. The actual corrosion rate of the concrete specimens was generally only 70% of the theoretical value, while the UHPC specimens showed a corrosion rate which was only 40% of the theoretical value.

To comprehensively understand the explosion risk in underground energy transportation tunnels, this study employed computational fluid dynamics technology and finite element simulation to numerically analyze the potential impact of an accidental explosion for a specific oil and gas pipeline in China and the potential damage risk to nearby buildings. Furthermore, the study investigated the effects of tunnel inner diameter (d = 4.25 m, 6.5 m), tunnel length (L = 4 km, 8 km, 16 km), and soil depth (primarily Lsoil = 20 m, 30 m, 40 m) on explosion dynamics and on structural response characteristics. The findings indicated that as the tunnel length and inner diameter increased, the maximum explosion overpressure gradually rose and the peak arrival time was delayed, especially when d = 4.25 m; with the increase in L, the maximum explosion overpressure rapidly increased from 1.03 MPa to 2.12 MPa. However, when d = 6.5 m, the maximum explosion overpressure increased significantly by 72.8% from 1.25 MPa. Evidently, compared to the change in tunnel inner diameter, tunnel length has a more significant effect on the increase in explosion risk. According to the principle of maximum explosion risk, based on the peak explosion overpressure of 2.16 MPa under various conditions and the TNT equivalent calculation formula, the TNT explosion equivalent of a single section of the tunnel was determined to be 1.52 kg. This theoretical result is further supported by the AUTODYN 15.0 software simulation result of 2.39 MPa (error < 10%). As the soil depth increased, the distance between the building and the explosion source also increased. Consequently, the vibration peak acceleration and velocity gradually decreased, and the peak arrival time was delayed. In comparison to a soil depth of 10 m, the vibration acceleration at soil depths of 20 m and 30 m decreased by 81.3% and 91.7%, respectively. When the soil depth was 10 m, the building was at critical risk of vibration damage.

While numerous studies have revealed the impact of different bullying behaviors, such as victimization and perpetration, on the psychological development of adolescents, the exploration of the correlates of positive/negative bystander behaviors and their potential underlying mechanisms remains scarce in China. The present study aims to compare the relationships between mental health and positive versus negative bystander behavior and to clarify whether self-efficacy and coping styles mediate the relationships between mental health and bullying dynamics. The current study was conducted on 11,734 students from 18 secondary schools in Suzhou, China (Meanage = 15.00, SDage = 1.47; 53.8% boys). The information on bullying victimization, perpetration, positive/negative bystander behaviors, as well as self-efficacy, coping styles and mental health variables (including depression, anxiety, sleep disturbance, suicide risk), were collected. Negative bystander behavior was positively associated with mental health problems, while positive bystander behavior was negatively associated with these factors. Also, further analysis showed that coping styles and self-efficacy mediated the relationship between different bullying behaviors and mental health outcomes. The results highlighted the comparison of the correlates of positive and negative bystander behaviors, which were comparably crucial to those of victims and perpetrators for prevention and intervention efforts. Promoting adaptive coping styles and self-efficacy to buffer the deleterious psychological consequences of bullying behavior in adolescents was also important.

In the Yangtze River Delta urban agglomeration, which is the region with the highest urbanization intensity in China, the development of cities leads to changes in land surface temperature (LST), while vegetation phenology varies with LST. To investigate the spatial and temporal changes in vegetation phenology and its response to LST in the study area, this study reconstructed the time series of the enhanced vegetation index (EVI) based on the MODIS EVI product and extracted the vegetation phenology indicators in the study area from 2002 to 2020, including the start of the growing season (SOS), the end of the growing season (EOS), and the growing season length (GSL), and analyzed the temporal–spatial patterns of vegetation phenology and LST in the study area, as well as the correlation between them. The results show that (1) SOS was advanced, EOS was postponed, and GSL was extended in the study area from 2002 to 2020, and there were obvious differences in the vegetation phenology indicators under different land covers and cities; (2) LST was higher in the southeast than in the northwest of the study area from 2002 to 2020, with an increasing trend; and (3) there are differences in the response of vegetation phenology to LST across land covers and cities, and SOS responds differently to LST at different times of the year. EOS shows a significant postponement trend with the annual mean LST increase. Overall, we found differences in vegetation phenology and its response to LST under different land covers and cities, which is important for scholars to understand the response of vegetation phenology to urbanization.

Microorganisms harbor catabolic plasmids to tackle refractory organic pollutants, which is crucial for bioremediation and ecosystem health. Understanding the impacts of plasmids on hosts provides insights into the behavior and adaptation of degrading bacteria in the environment. Here, we examined alterations in the physiological properties and gene expression profiles of Rhodococcus sp. strain p52 after losing two conjugative dioxin-catabolic megaplasmids (pDF01 and pDF02). The growth of strain p52 accelerated after pDF01 loss, while it decelerated after pDF02 loss. During dibenzofuran degradation, the expression levels of dibenzofuran catabolic genes on pDF01 were higher compared to those on pDF02; accordingly, pDF01 loss markedly slowed dibenzofuran degradation. It was suggested that pDF01 is more beneficial to strain p52 under dibenzofuran exposure. Moreover, plasmid loss decreased biofilm formation, especially after pDF02 loss. Transcriptome profiling revealed different pathways enriched in upregulated and downregulated genes after pDF01 and pDF02 loss, indicating different adaptation mechanisms. Based on the transcriptional activity variation, pDF01 played roles in transcription and anabolic processes, while pDF02 profoundly influenced energy production and cellular defense. This study enhances our knowledge of the impacts of degradative plasmids on native hosts and the adaptation mechanisms of hosts, contributing to the application of plasmid-mediated bioremediation in contaminated environments.

Identifying seismic CH4 anomalies via remote sensing has been verified as a legitimate method. However, there are still some problems, such as unknown reliability due to the complex characteristics of seismic anomalies. In this study, a multi-dimensional and multi-scale methane seismic anomaly extraction process for remote sensing was constructed with the Robust Satellite Technique (RST) based on the Atmospheric Infrared Sounder (AIRS) CH4 data and then applied to the 2023 Türkiye–Syria earthquake. This study obtained the two-dimensional temporal–spatial distribution of methane anomalies and temporal variation in the anomaly index. Based on this, the three-dimensional profile structure of the 8-day methane anomaly was extracted to determine the reliability of the anomaly. Finally, based on the daily methane anomaly, combined with atmospheric circulation and backward trajectory analysis as auxiliary tools, the influence of air mass migration was excluded to enhance the accuracy of CH4 anomaly determination. The results show that the three-dimensional anomalous structure is consistent with the geological characteristics of tectonic activities, and it appears as a “pyramid” or “inverted pyramid” type in a three-dimensional space. The anomalies caused by air mass migration can be eliminated by combining them with synoptic-scale circulation motion. The time series calculated at the epicenter or a certain point in a region may not accurately reflect the influence of regional or specific tectonic activity in the atmosphere. Thus, the optimal determination of the range and magnitude of atmospheric anomalies caused by tectonic activities is a difficult task for future research.

In optimization problems, the principle of symmetry provides important guidance. This article introduces an enhanced NSGA-II algorithm, termed NDE-NSGA-II, designed for addressing multi-objective optimization problems. The approach employs Tent mapping for population initialization, thereby augmenting its search capability. During the offspring generation process, a hybrid local search strategy is implemented to augment the population’s exploration capabilities. It is crucial to highlight that in elite selection, norm selection and average distance elimination strategies are adopted to strengthen the selection mechanism of the population. This not only enhances diversity but also ensures convergence, thereby improving overall performance. The effectiveness of the proposed NDE-NSGA-II is comprehensively evaluated across various benchmark functions with distinct true Pareto frontier shapes. The results consistently demonstrate that the NDE-NSGA-II method presented in this paper surpasses the performance metrics of the other five methods. Lastly, the algorithm is integrated with the DSSAT model to optimize maize irrigation and fertilization scheduling, confirming the effectiveness of the improved algorithm.

Johkasou systems have emerged as quintessential examples of decentralized wastewater treatment technologies due to their compact design, easy operation, and robust resistance to mechanical impact attributes that are particularly effective in mitigating and treating rural domestic wastewater. Although the efficiency of the Johkasou process in removing nitrogen and phosphorus has been well-documented, a comprehensive synthesis of the underlying mechanisms and influencing factors is still elusive. This review seeks to elucidate these aspects by detailing the biogeochemical pathways involved in nitrogen and phosphorus removal, characterizing the key microbial consortia, and addressing the potential accumulation of nitrous oxide (N2O). Furthermore, the review critically examines the impact of various media used in Johkasou systems on nutrient removal efficacy, with a particular emphasis on nitrogen. It also proposes a range of practical adjustments to design parameters, including dissolved oxygen (DO), pH, temperature, and hydraulic retention time (HRT), to enhance process performance. Finally, the practical implementation of Johkasou systems and their integration with ancillary processes in actual domestic sewage treatment scenarios are synthesized, providing a theoretical foundation for advancing Johkasou methodologies in rural areas.

The collapse of thick-hard roofs after coal has been extracted is not a consequential process in all cases. Rather, it happens due to the augmentation of high stress conducted at depth, followed by a wider range of damage as the floor cracks. The extent and spread of the cracks in the floor indicate the intensity of the collapse, and the mine will be submerged by the high-pressure water of the coal ash. Therefore, it is particularly important to study the mechanism of the combined effect of high stress on the roof and confined aquifer on the deformation and failure of the coal seam mining floor. This study analyzes and compares the impact of thick-hard magmatic rocks on the destruction of thin floor rock layers in coal seams. Plastic theory calculations are used to determine the plastic zone yield length of floor destruction under hard roof conditions, and the location and height of the maximum floor destruction depth are solved. An empirical formula and BP neural network are used to establish a prediction model for floor destruction. The results of the model’s prediction of the depth of floor failure were compared with the measured values, with an absolute error of 2.13 m and a residual of 10.3%, which was closer to the true values. The accuracy of the theoretical model and prediction model is verified using numerical simulation and on-site in situ measurements. Based on this, the deformation and destruction forms of the floor under pressure and the water inrush mechanism are summarized for mining under the condition of a thick-hard roof. Thus, the floor is subjected to high vertical stress, accompanied by significant disturbances generated during coal seam mining, resulting in intense working face pressures. The floor near the working face coal wall will experience severe compression and shear deformation and slide towards the goaf. The floor in the goaf is relieved of high vertical stress, and horizontal stress compression will result in shear failure, leading to floor heave and further increasing the height of the floor destruction zone. After the mining of the working face, the goaf will undergo two stages of re-supporting and post-mining compaction. During the re-supporting stage, the floor rock undergoes a transition from high-stress to low-stress conditions, and the instantaneous stress relief will cause plastic deformation and failure in the coal seam floor. The combined action of primary floor fractures and secondary fractures formed during mining can easily create effective water channels. These can connect to the aquifer or water-conducting structures, making them highly dangerous. The main modes of floor water inrush under the condition of a thick-hard roof are as follows: the high-stress mode, inducing a floor destruction zone connected to the water riser zone; the mining damage mode, connecting to water-conducting faults; the mining damage mode, connecting to water collapse columns; and the coupled water inrush mode, between the mining damage zone and the highly pressurized water floor.

The China–Russia crude oil pipeline (CRCOP) operates at a temperature that continuously thaws the surrounding permafrost, leading to secondary periglacial phenomena along the route. However, the evolution and formation mechanisms of these phenomena are still largely unknown. We used multi-temporal airborne light detection and ranging (LiDAR), geophysical, and field observation data to quantify the scale of ponding and icing, capture their dynamic development process, and reveal their development mechanisms. The results show that the average depth of ponding within 5 m on both sides of the pipeline was about 31 cm. The volumes of three icings (A–C) above the pipeline were 133 m3, 440 m3, and 186 m3, respectively. Icing development can be divided into six stages: pipe trench settlement, water accumulation in the pipe trench, ponding pressure caused by water surface freezing, the formation of ice cracks, water overflow, and icing. This study revealed the advantages of airborne LiDAR in monitoring the evolution of periglacial phenomena and provided a new insight on the development mechanisms of the phenomena by combining LiDAR with geophysics and field observation. The results of our study are of great significance for developing disaster countermeasures and ensuring the safe operation of buried pipelines.

This article presents a coordinated control method used for wireless power transfer (WPT) systems. This method can improve WPT system transmission efficiency while maintaining the constant output voltage. First, the topology of the DC–DC converter is selected and the equivalent circuit model of the WPT system is established. Then, the WPT system characteristics are discussed and the mutual inductance estimation process is presented. Furthermore, the coordinated control method is proposed, where the constant voltage output is achieved by connecting the Buck–Boost converter after the diode rectifier. Meanwhile, the optimal phase shift angle is calculated and sent to the controller to achieve maximum transmission efficiency tracking control, according to the measured load voltage and current. Finally, simulations and experiments are adopted to verify the proposed coordinated control method. The experimental results indicate that the average system transmission efficiency is increased by 1.80% and the efficiency fluctuation is decreased by 2.67% when the system load resistance varies, while the average system transmission efficiency is increased by 1.80%, and the efficiency fluctuation is decreased by 3.14% when the mutual inductance changes. This means the proposed coordinated control method is effective under the conditions of the WPT load and mutual inductance variations.

We aimed to investigate the species composition of a small mammal community and the prevalence of Echinococcus spp. in a typical endemic area of the Tibetan Plateau. One pika and five rodent species were identified based on the morphological characteristics of 1278 small mammal specimens collected during 2014–2019. Detection of Echinococcus DNA in tissue samples from small mammal specimens revealed that Ochotona curzoniae (pika, total prevalence: 6.02%, 26/432), Neodon fuscus (5.91%, 38/643), N. leucurus (2.50%, 3/120), and Alexandromys limnophilus (21.74%, 10/46) were infected by both E. multilocularis and E. shiquicus; Cricetulus longicaudatus (16.67%, 1/6) was infected by E. shiquicus; and no infection was detected in N. irene (0/15). Neodon fuscus and O. curzoniae were the two most abundant small mammal species. There was no significant difference in the prevalence of pika and the overall rodent species assemblage (6.26%, 53/846); however, the larger rodent populations suggested that more attention should be paid to their role in the transmission of echinococcosis in the wildlife reservoir, which has long been underestimated. Moreover, although DNA barcoding provides a more efficient method than traditional morphological methods for identifying large numbers of small mammal samples, commonly used barcodes failed to distinguish the three Neodon species in this study. The close genetic relationships between these species suggest the need to develop more powerful molecular taxonomic tools.

In the digital age, China’s economic development is transitioning from high speed to high quality. Through the application of digital technology, China’s manufacturing industry is moving toward more environmentally friendly and sustainable innovation, which makes it of great significance to study the effect of green investment and big data on innovation. Grounded in strategic management theory, this paper examines the interplay between CEO big data orientation, environmental investment, and their joint impact on technological innovation in manufacturing enterprises. Data are extracted from annual reports of listed Chinese manufacturing companies using computer-assisted text analysis methods and evaluated with negative binomial regression. The results indicate an inverted U-shaped relationship between CEO big data orientation and technological innovation. The results further explain that as CEO big data orientation increases, enterprises with higher levels of green investment will reach the peak of technological innovation performance earlier. According to China’s intelligent manufacturing in 2035, we have proposed some methods and suggestions for green investment and big data applications.

There is limited evidence about the gender- and obesity-specific effects of personal care product and plasticizing chemicals (PCPPCs) on short sleep duration in adults. We evaluated the gender- and obesity-specific association of co-exposure to PCPPCs and short sleep duration among adults aged 20–60 years using the National Health and Nutrition Examination Survey (NHANES) 2011–2016, a secondary data source from the United States. Seventeen PCPPCs, including five phenols, two parabens, and ten phthalates, were detected, and sleep duration was assessed among 3012 adults. Logistic regression, weighted quantile sum (WQS) regression, and Bayesian kernel machine regression (BKMR) were employed. We found that bisphenol A (BPA), mono (caboxy-isooctyl) phthalate (MCOP), and mono (3-carboxypropyl) phthalate (MCPP) were consistently positively associated with short sleep duration in both females and males regardless of obesity status, except for BPA with general obesity. In particular, mono benzyl phthalate (MBzP) revealed a positive association in females, mono (2-ethyl-5-oxohexyl) phthalate (MEOHP) revealed a positive association in males, and MiBP revealed a positive association in abdominal obesity. Similar associations were observed in the mixture. Our study highlights that PCPPCs are independently associated with an increasing risk of short sleep duration in adults both individually and as a mixture; however, gender- and obesity-specific differences may have little effect on certain individual PCPPCs on short sleep duration.

Microbial flocculants (MBFs), a class of eco-friendly and biodegradable biopolymers produced by various microorganisms, have gained increasing attention as promising alternatives to conventional chemical flocculants in wastewater treatment and pollutant removal. This review presents a comprehensive overview of the current state of MBF research, encompassing their diverse sources (bacteria, fungi, and algae), major categories (polysaccharides, proteins, and glycoproteins), production processes, and flocculation performance and mechanisms. The wide-ranging applications of MBFs in removing suspended solids, heavy metals, dyes, and other pollutants from industrial and municipal wastewater are critically examined, highlighting their superior efficiency, selectivity, and environmental compatibility compared to traditional flocculants. Nonetheless, bioflocculants face significant challenges including high substrate costs, low production yields, and intricate purification methodologies, factors that impede their industrial scalability. Moreover, the risk of microbial contamination and the attendant health implications associated with the use of microbial flocculants (MBFs) necessitate thorough evaluation. To address the challenges of high production costs and variable product quality, strategies such as waste valorization, strain improvement, process optimization, and biosafety evaluation are discussed. Moreover, the development of multifunctional MBF-based flocculants and their synergistic use with other treatment technologies are identified as emerging trends for enhanced wastewater treatment and resource recovery. Future research directions are outlined, emphasizing the need for in-depth mechanistic studies, advanced characterization techniques, pilot-scale demonstrations to accelerate the industrial adoption of MBF, and moreover, integration with novel wastewater treatment processes, such as partial nitrification and the anammox process. This review is intended to inspire and guide further research and development efforts aimed at unlocking the full potential of MBFs as sustainable, high-performance, and cost-effective bioflocculants for addressing the escalating challenges in wastewater management and environmental conservation.

The groundwater of the Luohe Formation in Binchang mining area is the main source of water for industrial and agricultural use and for drinking water for residents in the area. In order to study the hydrochemical characteristics and water-quality status of Luohe Formation groundwater in the mining area, statistical analysis, Piper three-line diagram, ion ratio relationship, and other methods were used to study the hydrochemical characteristics and formation factors of the groundwater. The Nemerow index evaluation method and the fuzzy comprehensive evaluation method based on principal component analysis were used to evaluate the groundwater quality in the mining area. The results show that the groundwater is weakly acidic as a whole, and the content of SO42− and Cl− have strong variability in terms of spatial distribution. The groundwater chemical type gradually evolves from SO4 • HCO3 • Cl–Na, SO4–Na and SO4 • Cl–Na-type water in the north of the mining area to SO4 • HCO3 • Cl–Na • Ca, HCO3 • SO4–Na • Mg, and SO4 • Cl–Na • Ca • Mg-type water in the south. The formation of the hydrochemical composition of groundwater in the study area may be related to multiple factors such as cation-alternating adsorption, carbonate and sulfate dissolution, and hydraulic exchange with the groundwater of the upper Huachi Formation. Comparing the evaluation results of the Nemerow index method and the principal component analysis method, the latter’s evaluation results can take into account the contribution of each indicator to the overall groundwater quality, and to a certain extent can weaken the control effect of a certain pollution indicator, exceeding the limit on the entire evaluation result. Therefore, the evaluation results based on the principal component analysis method are more credible.

Aircraft airfoils are classified into two main categories: symmetrical and asymmetrical. Both types of airfoils have a significant impact on the flight performance and safety of the aircraft. The fast prediction of the aerodynamic coefficients and pressure distributions of airfoils is crucial for the design of aircraft. The traditional wind tunnel test and CFD methods have the disadvantages of high test cost and high time consumption. To solve these problems, a combined autoencoder (CAE) network is proposed in this paper, which can achieve the fast prediction of airfoil aerodynamic coefficients and pressure distributions. The network consists of an airfoil shape autoencoder (AE) network and a multilayer perceptron (MLP) network. Firstly, an autoencoder network reflecting the characteristics of the airfoil shape is established, and the effects of different latent variables on the performance of the autoencoder network are investigated. Then, the latent variables obtained from the autoencoder are concatenated with the inflow conditions such as the Reynolds number and the angle of attack to be used as inputs to the MLP network, and the aerodynamic coefficients of different airfoils in different inflow conditions are predicted. The effects of various latent variable inputs, as well as the direct input of the airfoil shape into the MLP network, on the prediction performance of aerodynamic coefficients are compared and analyzed. The optimal aerodynamic coefficient prediction network is then obtained. Finally, the CAE network is also applied to predict the pressure distributions of different airfoils in different inflow conditions and the effects of different latent variables and input conditions on the prediction performance of the pressure distributions are analyzed and compared with the advantages and disadvantages of the CAE network and the conditional variational autoencoder (CVAE) network. The results demonstrate that the proposed method is capable of accurately predicting aerodynamic characteristics in a shorter time, offering a valuable reference for the fast and efficient design of aircraft airfoils.

Both bacteria product flagellin and macrophages are implicated in HIV-1 infection/disease progression. However, the impact of their interaction on HIV-1 infection and the associated mechanisms remain to be determined. We thus examined the effect of the flagellins on HIV-1 infection of primary human macrophages. We observed that the pretreatment of macrophages with the flagellins from the different bacteria significantly inhibited HIV-1 infection. The mechanistic investigation showed that the flagellin treatment of macrophages downregulated the major HIV-1 entry receptors (CD4 and CCR5) and upregulated the CC chemokines (MIP-1α, MIP-1β and RANTES), the ligands of CCR5. These effects of the flagellin could be compromised by a toll-like receptor 5 (TLR5) antagonist. Given the important role of flagellin as a vaccine adjuvant in TLR5 activation-mediated immune regulation and in HIV-1 infection of macrophages, future investigations are necessary to determine the in vivo impact of flagellin–TLR5 interaction on macrophage-mediated innate immunity against HIV-1 infection and the effectiveness of flagellin adjuvant-based vaccines studies.

In this study, a numerical model of the cable cabin of a comprehensive pipe gallery was established to study the smoke flow diffusion behaviour of a comprehensive pipe gallery fire under a rectangular cross-section. The effects of fire source power (Q = 0.5, 1.0, 1.5, 2.0, 2.5, 3.0 MW) and fire source location (D = 10, 20, 40, 50, 60, 80, 100 m) on the smoke flow characteristics—such as smoke layer height and thickness, longitudinal airflow velocity, and ceiling temperature distribution—were analysed, and the corresponding prediction model was fitted. The results show the following: (1) The height of the smoke layer decreases with increasing fire power, and the predictive model of the smoke layer thickness obtained from the fitting is proportional to the smoke mass flow rate and inversely proportional to the aspect ratio of the pipe gallery. (2) Longitudinal air velocity prediction models of D < 50 m and D ≥ 50 m are fitted, and the average error between them and the numerical simulation values is 9.611%. (3) The temperature decay gradient of the smoke decreases gradually with increasing distance from the fire source, while there is a significant temperature difference between the two sides of the fire source. The average relative errors of the dimensionless temperature rise models fitted upstream and downstream of the fire source in the form of ΔTT0=AeBD−XH+C exponentials with respect to the numerical simulations were 11.688% and 7.296%, respectively. The results of the study can provide a reference for smoke flow and fire prevention and control in comprehensive pipe galleries.

To enhance the accuracy of agricultural area classification and enable remote sensing monitoring of agricultural regions, this paper investigates classification models and their application in change detection within rural areas, proposing the MC&N-PSPNet (CBAM into MobileNetV2 and NAM into PSPNet) network model. Initially, the HRSCD (High Resolution Semantic Change Detection) dataset labels undergo binary redrawing. Subsequently, to efficiently extract image features, the original PSPNet (Pyramid Scene Parsing Network) backbone network, ResNet50 (Residual Network-50), is substituted with the MobileNetV2 (Inverted Residuals and Linear Bottlenecks) model. Furthermore, to enhance the model’s training efficiency and classification accuracy, the NAM (Normalization-Based Attention Module) attention mechanism is introduced into the improved PSPNet model to obtain the categories of land cover changes in remote sensing images before and after the designated periods. Finally, the final change detection results are obtained by performing a different operation on the classification results for different periods. Through experimental analysis, this paper demonstrates the proposed method’s superior capability in segmenting agricultural areas, which is crucial for effective agricultural area change detection. The model achieves commendable performance metrics, including overall accuracy, Kappa value, MIoU, and MPA values of 95.03%, 88.15%, 93.55%, and 88.90%, respectively, surpassing other models. Moreover, the model exhibits robust performance in final change detection, achieving an overall accuracy and Kappa value of 93.24% and 92.29%, respectively. The results of this study show that the MC&N-PSPNet model has significant advantages in the detection of changes in agricultural zones, which provides a scientific basis and technical support for agricultural resource management and policy formulation.

The effective identification of reclaimed vegetation species is important for the subsequent management of ecological restoration projects in mining areas. Hyperspectral remote sensing has been used for identifying vegetation species. However, few studies have focused on mine-reclaimed vegetation. Even if there are studies in this field, the methods used by the researches are mainly traditional discriminant analyses. The environmental conditions of reclaimed mining areas lead to significant intraclass spectral differences in reclaimed vegetation, and there is uncertainty in the identification of reclaimed vegetation species using traditional classification models. In this study, in situ hyperspectral data were used to analyze the spectral variation in the reclaimed vegetation canopy in mine restoration areas and evaluate their potential in the identification of reclaimed vegetation species. We measured the canopy spectral reflectance of five vegetation species in the study area using the ASD FieldSpec 4. The spectral characteristics of vegetation canopy were analyzed by mathematically transforming the original spectra, including Savitzky–Golay smoothing, first derivative, reciprocal logarithm, and continuum removal. In addition, we calculated indicators for identifying vegetation species using mathematically transformed hyperspectral data. The metrics were submitted to a feature selection procedure (recursive feature elimination) to optimize model performance and reduce its complexity. Different classification algorithms (regularized logistic regression, back propagation neural network, support vector machines with radial basis function kernel, and random forest) were constructed to explore optimal procedures for identifying reclaimed vegetation species based on the best feature metrics. The results showed that the separability between the spectra of reclaimed vegetation can be improved by applying different mathematical transformations to the spectra. The most important spectral metrics extracted by the recursive feature elimination (RFE) algorithm were related to the visible and near-infrared spectral regions, mainly in the vegetation pigments and water absorption bands. Among the four identification models, the random forest had the best recognition ability for reclaimed vegetation species, with an overall accuracy of 0.871. Our results provide a quantitative reference for the future exploration of reclaimed vegetation mapping using hyperspectral data.

The accurate characterization of typhoon wind profile properties is of great importance in the field of wind engineering and wind design of high-rise structures. In this paper, the average typhoon wind profile characteristics are investigated using the 930 m height measurement data of Typhoon Lekima 2019 obtained from the observations of the mobile acoustic profiling radar deployed in the coastal area. Specifically, this paper adopts a cohesive hierarchical cluster analysis method to classify the mean wind profiles of Super Typhoon Lekima 2019, and the optimal number of clusters is obtained as two classes by the profile coefficient with the sum of squares of clustering errors, the Calinski–Harabasz index, and the Davies–Bouldin index, and the two classes of typical wind profiles are named as cluster 1 type and cluster 2 type. The model fitting analysis of the two types of typical wind profiles was carried out in the height range of 0~300 m after classification, and the effects of fitting the cluster 1-type mean wind profiles with the Vickery model and the Snaiki and Wu model and the cluster 2-type mean wind profiles with the Power-law model, the Log-law model, and the Deaves–Harris and Gryning models were discussed. The results show that the cohesive hierarchical cluster analysis method used in this paper can effectively categorize the mean typhoon profiles. In addition, this paper has some reference significance for future research on the characteristics of measured typhoon wind profiles and engineering applications such as the wind-resistant design of high-rise structures.

The development of childhood obesity is a complex process influenced by a combination of genetic predisposition and environmental factors, such as sleep, diet, physical activity, and socioeconomic status. Long-term solutions for decreasing the risk of childhood obesity remain elusive, despite significant advancements in promoting health and well-being in school and at home. Challenges persist in areas such as adherence to interventions, addressing underlying social determinants, and individual differences in response to treatment. Over the last decade, there has been significant progress in epigenetics, along with increased curiosity in gaining insights into how sleep and lifestyle decisions impact an individual’s health. Epigenetic modifications affect the expression of genes without causing changes to the fundamental DNA sequence. In recent years, numerous research studies have explored the correlation between sleep and the epigenome, giving a better understanding of DNA methylation, histone modification, and non-coding RNAs. Although significant findings have been made about the influence of sleep on epigenetics, a notable gap exists in the literature concerning sleep-related genes specifically associated with childhood obesity. Consequently, it is crucial to delve deeper into this area to enhance our understanding. Therefore, this review primarily focuses on the connection between sleep patterns and epigenetic modifications in genes related to childhood obesity. Exploring the interplay between sleep, epigenetics, and childhood obesity can potentially contribute to improved overall health outcomes. This comprehensive review encompasses studies focusing on sleep-related genes linked to obesity.

The reversible data hiding scheme in the encrypted domain is a potential solution to the concerns regarding user privacy in cloud applications. The 3D mesh model is an emerging file format and is widely used in engineering modeling, special effects, and video games. However, studies on reversible data hiding in encrypted 3D mesh models are still in the preliminary stage. In this paper, two novel techniques, multi-group partition (MGP) and closest pair prediction (CPP), are proposed to improve performance. The MGP technique adaptively classifies vertices into reference and embeddable vertices, while the CPP technique efficiently predicts embeddable vertices and generates shorter recovery information to vacate more redundancy for additional data embedding. Experimental results indicate that the proposed scheme significantly improves the embedding rate compared to state-of-the-art schemes and can be used in real-time applications.

As the demand for sensing and monitoring the marine environment increases, the Ocean Mobile Internet of Things (OM-IoT) has gradually attracted the interest of researchers. However, the unreliability of communication links represents a significant challenge to data transmission in the OM-IoT, given the complex and dynamic nature of the marine environment, the mobility of nodes, and other factors. Consequently, it is necessary to enhance the reliability of underwater data transmission. To address this issue, this paper proposes a reinforcement learning-based adaptive network coding (RL-ANC) approach. Firstly, the channel conditions are estimated based on the reception acknowledgment, and a feedback-independent decoding state estimation method is proposed. Secondly, the sliding coding window is dynamically adjusted based on the estimates of the channel erasure probability and decoding probability, and the sliding rule is adaptively determined using a reinforcement learning algorithm and an enhanced greedy strategy. Subsequently, an adaptive optimization method for coding coefficients based on reinforcement learning is proposed to enhance the reliability of the underwater data transmission and underwater network coding while reducing the redundancy in the coding. Finally, the sampling period and time slot table are updated using the enhanced simulated annealing algorithm to optimize the accuracy and timeliness of the channel estimation. Simulation experiments demonstrate that the proposed method effectively enhances the data transmission reliability in unreliable communication links, improves the performance of underwater network coding in terms of the packet delivery rate, retransmission, and redundancy transmission ratios, and accelerates the convergence speed of the decoding probability.

Relevant studies have shown that methane gas has a close relationship with seismic activity. The concentration of methane released within a tectonic zone can reflect the intensity status of tectonic activities, which is important for seismic monitoring. In this study, the January 2020 Xinjiang Jiashi earthquake was taken as the research object, and the mature Robust Satellite Technique (RST) algorithm was used to characterize the L3-level methane product data from the hyperspectral sensor, Atmospheric Infrared Sounder (AIRS), installed on the Earth Observing System (EOS) AQUA satellite at the monthly scale, 8-day scale and daily scale. An analysis of the spatial and temporal distribution of methane was carried out for before and after the earthquake based on the 3D structural condition of the gas, and the 3D structural conditions of the 8-day scale were introduced. An 8-day scale 3D structural condition was introduced and migration validation was performed, and the results showed that (1) the seismic methane anomaly-extraction process proposed in this study is feasible; (2) the 3D contour features indicated that the methane anomalies that occurred before the Jiashi earthquake were caused by geogenic emissions; (3) the anomaly-extraction algorithm from this study did not extract the corresponding anomalies in the non-seismic year, which indicated that the anomaly-extraction algorithm of this study has some degree of feasibility; and (4) the migrated validation of the Wenchuan earthquake of May 2008 further suggested that methane anomalies at the time of the Wenchuan earthquake were caused by the earthquake.

In general, remote sensing images depict intricate scenes. In cross-modal retrieval tasks involving remote sensing images, the accompanying text includes numerus information with an emphasis on mainly large objects due to higher attention, and the features from small targets are often omitted naturally. While the conventional vision transformer (ViT) method adeptly captures information regarding large global targets, its capability to extract features of small targets is limited. This limitation stems from the constrained receptive field in ViT’s self-attention layer, which hinders the extraction of information pertaining to small targets due to interference from large targets. To address this concern, this study introduces a patch classification framework based on feature similarity, which establishes distinct receptive fields in the feature space to mitigate interference from large targets on small ones, thereby enhancing the ability of traditional ViT to extract features from small targets. We conducted evaluation experiments on two popular datasets—the Remote Sensing Image–Text Match Dataset (RSITMD) and the Remote Sensing Image Captioning Dataset (RSICD)—resulting in mR indices of 35.6% and 19.47%, respectively. The proposed approach contributes to improving the detection accuracy of small targets and can be applied to more complex image–text retrieval tasks involving multi-scale ground objects.

In order to optimize the operating path of orchard mowers and improve their efficiency, we propose an MI-DBO (multi-strategy improved dung beetle optimization algorithm) to solve the problem of full-coverage path planning for mowers in standardized quadrilateral orchard environments. First, we analyzed the operation scenario of lawn mowers in standardized orchards, transformed the full-coverage path planning problem into a TSP (traveling salesman problem), and mathematically modeled the U-turn and T-turn strategies based on the characteristics of lawn mowers in orchards. Furthermore, in order to overcome the issue of uneven distribution of individual positions in the DBO (dung beetle optimization) algorithm and the tendency to fall into local optimal solutions, we incorporated Bernoulli mapping and the convex lens reverse-learning strategy in the initialization stage of DBO to ensure a uniform distribution of the initial population. During the algorithm iteration stage, we incorporated the Levy flight strategy into the position update formulas of breeding beetles, foraging beetles, and stealing beetles in the DBO algorithm, allowing them to escape from local optimal solutions. Simulation experiments show that for 18 types of orchards with different parameters, MI-DBO can find the mowing machine’s operation paths. Compared with other common swarm intelligence algorithms, MI-DBO has the shortest average path length of 456.36 m and can ensure faster optimization efficiency. Field experiments indicate that the algorithm-optimized paths do not effectively reduce the mowing machine’s missed mowing rate, but the overall missed mowing rate is controlled below 0.8%, allowing for the completion of mowing operations effectively. Compared with other algorithms, MI-DBO has the least time and fuel consumption for operations. Compared to the row-by-row operation method, using paths generated by MI-DBO reduces the operation time by an average of 1193.67 s and the fuel consumption rate by an average of 9.99%. Compared to paths generated by DBO, the operation time is reduced by an average of 314.33 s and the fuel consumption rate by an average of 2.79%.

Extra energy consumption, inefficient nitrogen removal, and excessive sludge production are major challenges faced by wastewater treatment plants (WWTPs) that rely on the traditional activated sludge process. Fermentation of wasted activated sludge (WAS) and novel nitrogen removal technologies based on partial nitrification (PN) have emerged as promising solutions to these issues. Recent studies have revealed an innovative strategy that integrates these two processes by supplementing fermentation liquid into activated sludge to induce PN. This review summarizes the research progress on PN establishment induced by the fermentation process. The microbiology and establishment methods of PN are briefly introduced, followed by a detailed discussion on the process, influencing factors, and product characteristics of WAS fermentation. The core section focuses on the side-stream and main-stream approaches of fermentation-induced PN, comparing their performance and application prospects. The potential mechanisms are explored, with an emphasis on the roles of free ammonia for the side-stream approach and the high tolerance of ammonium oxidizers to in-site fermentation stress for the main-stream approach. Finally, the limitations of the current research and future perspectives are discussed, highlighting the need for further investigation into microbial ecology, process optimization, and long-term stability. This review aims to provide insights into the synergistic integration of WAS fermentation and PN for sustainable and energy-efficient wastewater treatment.

Antibiotic residues have been found to have potentially harmful effects on ecological and human health. Carbon nitride-based photocatalysts have widely focused on antibiotic photocatalytic degradation. Herein, we prepared Fe-modified g-C3N4 nanorod bunches (FCNBs) using chemical vapor co-deposition. Specifically, through the process of calcination, a blend of urea and chlorophyllin sodium iron salt underwent an intriguing transformation, resulting in the integration of Fe into the framework of the g-C3N4 nanorod cluster. The resulting photocatalyst exhibited remarkable stability and superior dispersibility. The prepared FCNBs had a unique structure, which was beneficial for increasing light absorption. Furthermore, the Fe species formed a chemical coordination with the g-C3N4 matrix, thereby altering the electronic structure of the matrix. This modification facilitated charge transfer, prolonged the carrier lifetime, and enhanced light absorption, all of which significantly increased the photocatalytic activity. The oxytetracycline degradation efficiency of FCNBs was 82.5%, and they demonstrated outstanding stability in cycle trials. This work introduces a promising photocatalyst for the degradation of antibiotics.

Diabetic cardiomyopathy (DCM) represents a common pathological state brought about by diabetes mellitus (DM). Patchouli alcohol (PatA) is known for its diverse advantageous effects, notably its anti-inflammatory properties and protective role against metabolic disorders. Despite this, the influence of PatA on DCM remains relatively unexplored. To explore the effect of PatA on diabetes-induced cardiac injury and dysfunction in mice, streptozotocin (STZ) was used to mimic type 1 diabetes in mice. Serological markers and echocardiography show that PatA treatment protects the heart against cardiomyopathy by controlling myocardial fibrosis but not by reducing hyperglycemia in diabetic mice. Discovery Studio 2017 software was used to perform reverse target screening of PatA, and we found that JAK2 may be a potential target of PatA. RNA-seq analysis of heart tissues revealed that PatA activity in the myocardium was primarily associated with the inflammatory fibrosis through the Janus tyrosine kinase 2 (JAK2)/signal transducer and activator of the transcription 3 (STAT3) pathway. In vitro, we also found that PatA alleviates high glucose (HG) + palmitic acid (PA)-induced fibrotic and inflammatory responses via inhibiting the JAK2/STAT3 signaling pathway in H9C2 cells. Our findings illustrate that PatA mitigates the effects of HG + PA- or STZ-induced cardiomyopathy by acting on the JAK2/STAT3 signaling pathway. These insights indicate that PatA could potentially serve as a therapeutic agent for DCM treatment.

One of the limitations of the dung beetle optimization (DBO) is its susceptibility to local optima and its relatively low search accuracy. Several strategies have been utilized to improve the diversity, search precision, and outcomes of the DBO. However, the equilibrium between exploration and exploitation has not been achieved optimally. This paper presents a novel algorithm called the ODBO, which incorporates cat map and an opposition-based learning strategy, which is based on symmetry theory. In addition, in order to enhance the performance of the dung ball rolling phase, this paper combines the global search strategy of the osprey optimization algorithm with the position update strategy of the DBO. Additionally, we enhance the population’s diversity during the foraging phase of the DBO by incorporating vertical and horizontal crossover of individuals. This introduction of asymmetry in the crossover operation increases the exploration capability of the algorithm, allowing it to effectively escape local optima and facilitate global search.

Background: The escalating complexity of the COVID-19 epidemic underscores the need for heightened attention to booster vaccinations. This study aims to examine the changing trend in the public’s intention to receive the second COVID-19 booster vaccination over time and the associated factors following the COVID-19 policy optimization in China. Method: Eight cross-sectional surveys utilizing SMS questionnaire links were conducted in Guangzhou, China, from December 2022 to April 2023. The Mann–Kendall test was employed to analyze the trend in intentions to receive the second booster vaccination across the survey time. Adjusted and multivariate logistic analyses were used to analyze the factors associated with vaccination intention. Parallel analyses were performed for two subgroups with different COVID-19 infection statuses. Results: A total of 9860 respondents were surveyed in the eight rounds, of which 8048 completed the first booster vaccination and were included in the analysis. The overall COVID-19 infection rate was 60.0% (4832/8048), while the overall vaccination intention was 72.2% (5810/8048) among respondents. The vaccination intention exhibited a significant declining trend over time, decreasing from 81.5% in December 2022 to 52.2% in April 2023. An adjusted logistic regression analysis revealed that anxiety and depression were negatively associated with an intention to receive the second booster vaccination, while COVID-19-related preventive behaviors and a high engagement in COVID-19-related information were positively associated with an intention to receive the second booster vaccination. A subgroup analysis revealed that the association between psychological and behavioral characteristics and vaccination intention remained relatively stable among individuals with different histories of COVID-19 infections. Conclusion: There was a significant decline in the intention to receive the second booster vaccination following the optimization of the COVID policy in China. Our findings emphasize the urgency of the second booster vaccination and provide a foundation for the development of tailored interventions to enhance and sustain vaccination intention among the public.

Subsurface drainage pipes covered with filters and geotextiles are the key to preventing clogging and ensuring efficient drainage. To improve the salt discharge efficiency of these subsurface drainage pipes, different layers of geotextiles were set outside the pipes with the aid of uniform gravel filters. This paper reports our findings from laboratory simulation of subsurface drainage pipes and experiments. The study examined the influence of different layers of geotextiles on the drainage efficiency, salt discharge effects of subsurface drainage pipes, and the effect of superimposed geotextiles on the salt drainage efficiency as well as the anti-clogging effect of subsurface drainage pipes. The results are as follows: (1) The geotextile and filter material wrapped around the subsurface pipe facilitated the movement of water towards the subsurface pipe, which could promote the salt discharge of the subsurface pipe. However, in the single leaching experiment, the reduction in soil pH was not significant for different scenarios. (2) The salt removal rate of the geotextile-wrapped subsurface pipes was more than 95%. The salt removal rate of the double-layer geotextile scenario was the highest (96.7%), and the total salt content of soil profiles was 8.3% and 31.3% lower than those of the single-layer and triple-layer geotextile scenarios, respectively. The drainage efficiency of the double-layer geotextile scenario was the highest, and the salt distribution in the 0–60 cm profile was relatively uniform, ranging from 2.3 to 3.0 g∙kg−1. (3) The clogging in the triple-layer geotextile scenario was caused by the geotextile, i.e., a dense filter cake layer formed on the surface of the geotextile. The clogging in the single-layer and double-layer geotextile scenarios was the clogging of the geotextile itself, i.e., soil particles retained in the fiber structure of geotextiles. (4) In the case of the single-layer and double-layer geotextile scenarios, the soil particles failed to completely clog the selected geotextiles, and there were still a large number of pores retained. The double-layer geotextiles integrate filtration, clogging prevention, and drainage promotion to provide the best salt drainage with the subsurface pipe. This study reveals the influence of the filter on soil water salt and salt discharge and provides a theoretical explanation and technical justification for the application of the subsurface pipes salt discharge technology in saline soil ameliorate.

Subsurface pipes covered with geotextiles and filters are essential for preventing clogging and ensuring efficient drainage. To address low salt discharge efficiency due to subsurface drainage pipes (SDPs) clogging easily, sand gravel, straw, and combined sand gravel–straw were set above SDPs, respectively, within a setting of uniform geotextiles. The influences of different filter materials on the drainage efficiency and salt discharge effect of the SDPs, as well as the effects of different filter materials on the salt drainage efficiency and anti-siltation effect of the SDPs were studied by performing simulation experiments in a laboratory. The results confirmed the following: (1) The salt removal rates of the SDPs externally wrapped with materials exceeded 95%. The subsurface pipe treated with the sand gravel filter material had the highest desalting rate (93.69%) and soil profiles with total salt contents that were 17.7% and 20.5% lower than those treated with the straw and combined sand gravel–straw materials, respectively. (2) The soil salinity of the sand gravel filter material around the SDPs was between 1.57 and 3.6 g/kg, and the drainage rate (R) was 0.97, so its salt-leaching effect was the best. (3) The sand gravel filter material increased the characteristic particle size of the soil above the SDP by 8.4%. It could effectively intercept coarse particles, release fine particles, and facilitate the formation of a highly permeable soil skeleton consisting of coarse particles, such as sand particles surrounding the soil. (4) The use of the straw filter material produced dense filter cake layers on the upstream surfaces of the geotextiles. When the sand gravel and combined sand gravel–straw filter materials were used, soil particles remained in the geotextile fiber structure, and a large number of pores were still retained. Therefore, the sand gravel filter material was the most suitable for the treatment of Yinbei saline–alkali soil in Ningxia Hui Autonomous Region.

In Hymenoptera, the monophyly of Evaniomorpha has been the focus of debate among different scholars. In this study, we sequenced two mitochondrial genomes of Dendrocerus (Hymenoptera: Megaspilidae) to analyze the mitochondrial genomic features of Dendrocerus and provide new molecular data for phylogenetic studies of Evaniomorpha. The mitogenome sizes of D. bellus and D. anisodontus were 15,445 bp and 15,373 bp, respectively, with the trnG of D. bellus missing. The nucleotide composition was significantly biased toward adenine and thymine, with A + T contents of 81.2% (D. bellus) and 82.4% (D. anisodontus). Using Ceraphron sp. (Ceraphronidae) as reference, the Ka/Ks values of NAD4L and NAD6 in D. anisodontus were both greater than one, indicating that non-synonymous mutations are favored by Darwinian selection, which is rare in other hymenopteran species. Compared with Ceraphon sp. gene order, nine operations were identified in D. anisodontus, including four reversals, four TDRLs (tandem duplication random losses) and one transposition, or four reversals and five TDRLs. Phylogenetic analysis of 40 mitochondrial genomes showed that Evaniomorpha was not a monophyletic group, which was also supported by the PBD values. Ceraphronoidea is a monophyletic group and is a sister to Aulacidae + Gasteruptiidae. Based on the conserved region of the newly sequenced mitochondrial genomes, a pair of specific primers MegaF/MegaR was designed for sequencing the COX1 genes in Megaspilidae and a 60% rate of success was achieved in the genus Dendrocerus.

In 2013, during Typhoon Soulik, wind data were collected at various heights above the ground (15, 27, 53, 67, and 82 m) on the 550 kV 52# pole transmission tower in Ningde City, Fujian Province. The wind speed profile, turbulence intensity, gust factor, crest factor, and power spectrum were analyzed using 10 min interval wind speed records. The results show the following: (1) the average wind velocity of Typhoon Soulik varies in accordance with both the power law and the logarithmic law, but the Deaves–Harris model exhibits significant discrepancies; (2) the turbulence intensity in u, v, and w orientations decreases with the average wind velocity at each height. Exponential fitting is conducted on the strength of turbulence and gust factor profiles in each direction based on the standards of different countries, resulting in the derivation of empirical expressions; (3) the integral scale components of turbulence in u, v, and w orientations exhibit a positive correlation with both average wind velocity and height. The turbulence integral scale ratios in the longitudinal, transverse, and vertical directions at heights of 15, 53, and 82 m are 1:0.68:0.11, 1:0.67:0.27, and 1:0.67:0.30, respectively; (4) the Von Karman empirical spectrum and the modified Kaimal cross-spectrum model closely match the observed wind power spectrum of Typhoon Soulik. The presented results contribute to furthering references for wind-resistant design of structures in typhoon-prone areas and prevention of typhoon-related disasters.

Deep learning has emerged as a powerful tool for investigating intricate biological processes in plants by harnessing the potential of large-scale data. Gene regulation is a complex process that transcription factors (TFs), cooperating with their target genes, participate in through various aspects of biological processes. Despite its significance, the study of gene regulation has primarily focused on a limited number of notable instances, leaving numerous aspects and interactions yet to be explored comprehensively. Here, we developed DEGRN (Deep learning on Expression for Gene Regulatory Network), an innovative deep learning model designed to decipher gene interactions by leveraging high-dimensional expression data obtained from bulk RNA-Seq and scRNA-Seq data in the model plant Arabidopsis. DEGRN exhibited a compared level of predictive power when applied to various datasets. Through the utilization of DEGRN, we successfully identified an extensive set of 3,053,363 high-quality interactions, encompassing 1430 TFs and 13,739 non-TF genes. Notably, DEGRN’s predictive capabilities allowed us to uncover novel regulators involved in a range of complex biological processes, including development, metabolism, and stress responses. Using leaf senescence as an example, we revealed a complex network underpinning this process composed of diverse TF families, including bHLH, ERF, and MYB. We also identified a novel TF, named MAF5, whose expression showed a strong linear regression relation during the progression of senescence. The mutant maf5 showed early leaf decay compared to the wild type, indicating a potential role in the regulation of leaf senescence. This hypothesis was further supported by the expression patterns observed across four stages of leaf development, as well as transcriptomics analysis. Overall, the comprehensive coverage provided by DEGRN expands our understanding of gene regulatory networks and paves the way for further investigations into their functional implications.

Soil microorganisms play crucial roles in the stability of the global carbon pool, particularly in permafrost peatlands that are highly sensitive to climate change. Microtopography is a unique characteristic of peatland ecosystems, but how microtopography affects the microbial community structures and their functions in the soil is only partially known. We characterized the bacterial and fungal community compositions by amplicon sequencing and their abundances via quantitative PCR at different soil depths in three microtopographical positions (hummocks, flats, and hollows) in permafrost peatland of the Greater Xing’an Mountains in China. The results showed that the soil of hummocks displayed a higher microbial diversity compared to hollows. Microtopography exerted a strong influence on bacterial community structure, while both microtopography and soil depth greatly impacted the fungal community structure with variable effects on fungal functional guilds. Soil water content, dissolved organic carbon, total phosphorus, and total nitrogen levels of the soil mostly affected the bacterial and fungal communities. Microtopography generated variations in the soil water content, which was the main driver of the spatial distribution of microbial abundances. This information stressed that the hummock–flat–hollow microtopography of permafrost peatlands creates heterogeneity in soil physicochemical properties and hydrological conditions, thereby influencing soil microbial communities at a microhabitat scale. Our results imply that changes to the water table induced by climate warming inducing permafrost degradation will impact the composition of soil microbes in peatlands and their related biogeochemical functions, eventually providing feedback loops into the global climate system.

Many cities in coastal areas are prone to typhoon disasters due to their location on the Pacific storm path, and the direct effect of catastrophic winds can lead to the destruction of low-rise buildings and severe damage to high-rise structures. The purpose of this study was to enhance the understanding of boundary layer wind profiles of strong typhoons in coastal areas and reduce property losses and casualties caused by wind disasters. Based on the field measurements of wind profile acoustic radar in coastal areas, the variation characteristics of the boundary layer wind profile during the passage of super typhoon Lekima were first studied in depth, and the evolution law of the typhoon boundary layer profile was summarized. Then, the effects of typhoon horizontal structure, topography, wind speed, and time distance on the characteristics of the typhoon profile were discussed, respectively. Finally, the evolution characteristics of wind profile parameters were obtained by fitting three wind profile theoretical models. Due to the strong variability of typhoon profile morphology, the theoretical model of wind profile is only applicable to the wind profile from the bottom to the low-level jet height of typhoons, while wind parameters are closely related to the spatial location of the typhoon wind field.

Anaerobic ammonium oxidation (ANAMMOX) has emerged as a promising sustainable nitrogen removal technology that offers significant advantages over conventional nitrification–denitrification processes, such as reduced energy consumption, a 60% reduction in oxygen demand, and a 90% reduction in sludge production. However, the practical application of ANAMMOX is hindered by several challenges, including the slow growth of ANAMMOX bacteria, long start-up periods, and high sensitivity to environmental disturbances. Recent studies have highlighted the crucial role of extracellular polymeric substances (EPSs) in the formation, activity, and stability of ANAMMOX biofilms and granules. An EPS is a complex mixture of high-molecular-weight polymers secreted by microorganisms, mainly composed of polysaccharides, proteins, nucleic acids, and lipids. The diverse physicochemical properties and functional groups of EPSs enable them to serve as a structural scaffold, protective barrier, sorption site, electron shuttle, and nutrient source for ANAMMOX bacteria. This review aims to provide an overview of the latest research progress on harnessing the potential of EPSs to enhance the ANAMMOX process. The characteristics, compositions, and extraction methods of ANAMMOX-derived EPSs are summarized. The mechanisms of how EPSs facilitate the enrichment, immobilization, aggregation, and adaptation of ANAMMOX bacteria are elucidated. The strategies and effects of EPS supplementation on improving the performance and robustness of ANAMMOX reactors under various stresses are critically reviewed. The challenges and future perspectives of the EPS-mediated optimization of the ANAMMOX process are also discussed. This review sheds new light on exploiting EPSs as a renewable bioresource to develop more efficient and stable ANAMMOX applications for sustainable wastewater treatment.

The microclimate environment can be conveniently controlled with accuracy by plant incubators, in which the cuttings propagation method can efficiently enhance seedling production. To ensure air flow evenly throughout the incubator, the scientific design of the air inlet is crucial. This study utilized a computational fluid dynamics (CFD) model to simulate the airflow patterns in a culture layer under different air inlet conditions. Furthermore, the optimal design parameters were determined by way of response surface methodology (RSM) and the Non-dominated Sorting Genetic Algorithm-II (NSGA-II). Adopting the optimal parameters, a prototype was manufactured, and a cuttings experiment was carried out with apple cuttings in the incubator. The results showed that the optimal air inlet radius is 90 mm, the optimal air inlet height is 188 mm, and the optimal uniform flow plate hole diameter is 13 mm. Meanwhile, the apple cuttings were able to root. Therefore, this incubator with optimal parameters can be used for cuttings. The study provides a methodological and theoretical basis for the future optimizing of air inlet parameters and promoting cuttings rooting.