Search

To ensure the operation safety and efficiency of an autonomous rice transplanter, a path planning method of obstacle avoidance based on the improved artificial potential field is proposed. Firstly, the obstacles are divided into circular or elliptic obstacles according to the difference between the length and width of an obstacle as well as the angle between the vehicle’s forward direction and the length direction of the obstacle. Secondly, improved repulsive fields for circular and elliptic models are developed. To escape the local minimum and goal inaccessibility of the traditional artificial potential field as well as meet the requirements of agronomy and vehicle kinematics constraints, the adaptive setting and adjusting strategy for virtual goal points is proposed according to relative azimuth between obstacle and vehicle. The path smoothing method based on the B-spline interpolation method is presented. Finally, the intelligent obstacle avoidance algorithm is designed, and the path evaluation rule is given to obtain the low-cost, non-collision, smooth and shortest obstacle avoidance path. To verify the effectiveness of the proposed obstacle avoidance algorithm, simulation and field experiments are conducted. Simulation and experimental results demonstrate that the proposed improved collision avoidance algorithm is highly effective and realizable.

A transonic hump with a Mach number (M∞) of 0.875 was utilized to compare the prediction capabilities of RANS, URANS, IDDES, and IDDES-SLA for shock-induced separation. The results matched the experimental data concerning pressure, friction coefficients, velocity distribution, and other parameters. The improved delayed detached eddy simulation with a modified sub-grid scale (IDDES-SLA) method performed the best amongst tested numerical methods, demonstrating differences of 1.3% and 4.1% from the experimental results for separation and reattachment locations, respectively. The primary difference between IDDES and IDDES-SLA is reflected in detailed data, such as velocity and high-frequency characteristics, although both methods exhibit similar simulation capabilities for the main structural features of the flow field. The POD and PSD analyses of the flow field results demonstrated that the IDDES-SLA method was more capable of resolving higher modes of flow field.

Slt2 is an important component of the Slt2-MAPK pathway and plays critical regulatory roles in growth, cell wall integrity, melanin biosynthesis, and pathogenicity of plant fungi. AaSlt2, an ortholog of the Saccharomyces cerevisiae Slt2 gene, was identified from A. alternata in this study, and its function was clarified by knockout of the gene. The ΔAaSlt2 strain of A. alternata was found to be defective in spore morphology, vegetative growth, and sporulation. Analysis of gene expression showed that expression of the AaSlt2 gene was significantly up-regulated during infection structure formation of A. alternata on hydrophobic and pear wax extract-coated surfaces. Further tests on onion epidermis confirmed that spore germination was reduced in the ΔAaSlt2 strain, together with decreased formation of appressorium and infection hyphae. Moreover, the ΔAaSlt2 strain was sensitive to cell wall inhibitors, and showed significantly reduced virulence on pear fruit. Furthermore, cell wall degradation enzyme (CWDE) activities, melanin accumulation, and toxin biosynthesis were significantly lower in the ΔAaSlt2 strain. Overall, the findings demonstrate the critical involvement of AaSlt2 in growth regulation, stress adaptation, infection structure formation, and virulence in A. alternata.

Background/Objectives: Physiologically based pharmacokinetic (PBPK) absorption models are instrumental for assessing drug absorption prior to clinical food effect studies, though discrepancies in predictive and actual outcomes are observed. This study focused on immediate release formulations of weakly basic water-insoluble compounds, namely rivaroxaban, ticagrelor, and PB-201, to investigate factors that could improve the predictive accuracy of PBPK models regarding food effects. Methods: Comprehensive in vitro experimental results provided the basis for the development of mechanistic absorption models, which were then combined with mechanistic disposition models to predict the systemic exposure of the model drugs in both fasted and fed states. Results: The developed PBPK models showed moderate to high predictive accuracy for food effects in Caucasian populations. For the Chinese population, the ticagrelor model’s initial overestimation of fed-state absorption was addressed by updating the permeability parameters from Caco-2 cell assays to those derived from parallel artificial membrane permeability assays in FaSSIF and FeSSIF media. This refinement was also applied to the rivaroxaban and ticagrelor models, leading to a more accurate representation of absorption in Caucasians. Conclusions: This study highlights the importance of apparent permeability in enhancing the predictive accuracy of PBPK absorption models for weakly basic water-insoluble compounds. Furthermore, the precipitation of PB-201 in the two-stage transfer experiments suggests that precipitation may not be a universal phenomenon for such compounds in vivo. Consequently, the precipitation rate constant, a theoretically essential parameter, should be determined based on experimental evidence to avoid overparameterization and ensure robust predictive accuracy of PBPK models.

Laser damage in films under long-term high-temperature conditions is a significant concern for advancing laser applications. This study focused on HfO2 films prepared using the sol–gel method with HfCl4 as a precursor. It examined the effects of temperature on various properties of the films, including their optical properties, microstructure, surface morphology, absorption, and laser-induced damage threshold (LIDT). The prepared film demonstrated desirable characteristics at the high temperature of 423 K, such as high transmittance, low absorption, and high LIDT. As the duration of its high-temperature exposure increased, the LIDT of the films gradually decreased. An intriguing finding was that the film’s LIDT exhibited an exponential decay pattern with prolonged heating time. This observation could be attributed to the power-law increase in defects on both the internal and surface areas of the film as the duration of high-temperature exposure lengthened. Moreover, even after a 15-day heating period at 423 K, the film maintained an LIDT of 12.9 J/cm2, indicating its potential applicability in practical high-temperature environments. This study provided a general pattern and a universal formula for understanding the laser damage of sol–gel films at high temperatures over time. Furthermore, it opened possibilities for future developments of laser films suitable for extreme environments.

Rice (Oryza sativa L.), a globally staple food crop, frequently encounters growth, developmental, and yield limitations due to phosphate deficiency. LOW PHOSPHATE ROOT1/2 (LPR1/2) are essential genes in plants that regulate primary root growth and respond to local phosphate deficiency signals under low phosphate stress. In rice, five LPR genes, designated OsLPR1–OsLPR5 based on their sequence identity with AtLPR1, have been identified. OsLPR3 and OsLPR5 are specifically expressed in roots and induced by phosphate deficiency, contributing to rice growth, development, and the maintenance of phosphorus homeostasis under low phosphate stress. In contrast, OsLPR2 is uniquely expressed in shoots, suggesting it may have distinct functions compared with other family members. This study employed Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-associated protein 9 (CRISPR/Cas9) gene editing technology to generate oslpr2 mutant transgenic lines and subsequently investigated the effect of OsLPR2 gene knockout on rice growth, phosphate utilization, and salt stress tolerance in the seedling stage, as well as the effect of OsLPR2 gene knockout on rice development and agronomic traits in the maturation stage. The results indicated that the knockout of OsLPR2 did not significantly impact rice seedling growth or phosphate utilization, which contrasts significantly with its homologous genes, OsLPR3 and OsLPR5. However, the mutation influenced various agronomic traits at maturity, including plant height, tiller number, and seed setting rate. Moreover, the OsLPR2 mutation conferred enhanced salt stress tolerance in rice. These findings underscore the distinct roles of OsLPR2 compared with other homologous genes, establishing a foundation for further investigation into the function of the OsLPR family and the functional differentiation among its members.

Building envelopes and indoor environments exhibit thermal inertia, forming a virtual energy storage system in conjunction with the building air conditioner (AC) system. This system represents a current demand response resource for building electricity use. Thus, this study centers on the CatBoost algorithm within machine learning (ML) technology, utilizing the LASSO regression model for feature selection and applying the Optuna framework for hyperparameter optimization (HPO) to develop a cost-optimal control method for minimizing building AC loads. This method addresses the challenges associated with traditional load forecasting and control methods, which are often impacted by environmental temperature, building parameters, and user behavior uncertainties. These methods struggle to accurately capture the complex dynamics and nonlinear relationships of AC operations, making it difficult to devise AC operation and virtual energy storage scheduling strategies effectively. The proposed method was applied and validated using a case study of an office building in Nanjing, China. The prediction results showed coefficient of variation in root mean square error (CV-RMSE) values of 6.4% and 2.2%. Compared with the original operating conditions, the indoor temperature remained within a comfortable range, the AC load was reduced by 5.25%, and the operating energy costs were reduced by 24.94%. These results demonstrate that the proposed method offers improved computational efficiency, enhanced model performance, and economic benefits.

Low temperature combined with low light (LL) is an important factor limiting pepper quality and yield. ‘Hang Jiao No. 2′ were used as experimental materials, and different concentrations of MeJA (T1 (0 μM), T2 (100 μM), T3 (150 μM), T4 (200 μM), T5 (250 μM) and T6 (300 μM)) were sprayed under LL stress to explore the positive effect of exogenous methyl jasmonate (MeJA) on peppers under LL stress. The photosynthetic properties, osmoregulatory substance, reactive oxygen species, antioxidant enzyme activities, and related gene expressions of the peppers were measured. Our results demonstrated that 200 μM MeJA treatment significantly increased chlorophyll content, light quantum flux per active RC electron transfer (Eto/RC), maximum captured photonic flux per active RC (TRo/RC), energy flux for electron transfer in the excitation cross section (Eto/CSm), energy flux captured by absorption in the excitation cross section (TRo/CSm), soluble protein, and soluble sugar content. Moreover, it significantly improved the maximum photochemical efficiency of PSII (Fv/Fm) and performance index based on absorbed light energy (PI (abs)) by 56.77% and 67.00%, respectively, and significantly decreased malondialdehyde (MDA) content and relative conductivity by 30.55% and 28.17%, respectively. Additionally, antioxidant enzyme activities were elevated, and the expression of the related genes was activated in pepper seedlings under stress, leading to a significant reduction in reactive oxygen species content. In conclusion, our findings confirmed that 200 μM MeJA could reduce the injury of LL to pepper leaves to the photosynthetic organs of pepper leaves, protect the integrity of the cell membrane, and further improve the tolerance of pepper seedlings to LL.

Due to the limited supply of autologous bone grafts, there is a need to develop more bone matrix materials to repair bone defects. Xenograft bone is expected to be used for clinical treatment due to its exact structural similarity to natural bone and its high biocompatibility. In this study, decellularized antler cancellous bone matrix (DACB) was first prepared, and then the extent of decellularization of DACB was verified by histological staining, which demonstrated that it retained the extracellular matrix (ECM). The bioactivity of DACB was assessed using C3H10T1/2 cells, revealing that DACB enhanced cell proliferation and facilitated cell adhesion and osteogenic differentiation. When evaluated by implanting DACB into nude mice, there were no signs of necrosis or inflammation in the epidermal tissues. The bone repair effect of DACB was verified in vivo using sika deer during the antler growth period as an animal model, and the molecular mechanisms of bone repair were further evaluated by transcriptomic analysis of the regenerated tissues. Our findings suggest that the low immunogenicity of DACB enhances the production of bone extracellular matrix components, leading to effective osseointegration between bone and DACB. This study provides a new reference for solving bone defects.

The improvement in the utilization rate and nutritional value of soybean meal (SBM) represents a significant challenge in the feed industry. This study conducted a 50 kg SBM fermentation based on the 300 g small-scale fermentation of SBM in early laboratory research, to explore the combined effects of lactic acid bacteria (LAB) and acid protease on fermentation quality, chemical composition, microbial population, and macromolecular protein degradation during fermentation and aerobic exposure of SBM in simulated actual production. The results demonstrated that the increase in crude protein content and reduction in crude fiber content were considerably more pronounced after fermentation for 30 days (d) and subsequent aerobic exposure, compared to 3 d. It is also noteworthy that the treated group exhibited a greater degree of macromolecular protein degradation relative to the control and 30 d of fermentation relative to 3 d. Furthermore, after 30 d of fermentation, adding LAB and protease significantly inhibited the growth of undesired microbes including coliform bacteria and aerobic bacteria. In the mixed group, the microbial diversity decreased significantly, and Firmicutes replaced Cyanobacteria for bacteria in both groups’ fermentation.

Habitat quality is a comprehensive index reflecting ecological conditions, land use impact, and human survival. Susceptibility to geological disasters is influenced by factors such as ecology, the geological environment, and human activities. Analyzing the effects of habitat quality on geological disaster susceptibility and its spatial dynamics is crucial for ecological protection and assessing geological disaster risks. This research focused on Pingshan County, using the InVEST 3.7.0 model and ArcGIS to evaluate habitat quality and geological disaster susceptibility for 2020. The spatial relationships were examined with GeoDa to investigate the impact of habitat quality on geological disaster susceptibility. The findings are as follows: (1) Pingshan County generally exhibits high habitat quality, showing significant spatial clustering with geological disaster susceptibility—predominantly high–high in the west and low–low in the east. (2) The geological environment significantly influences the relationship between habitat quality and geological disaster susceptibility, with an overall positive correlation but negative correlations in certain areas. Geological disaster susceptibility is primarily governed by geological factors rather than habitat quality. (3) In mountainous regions with comparable ecological and geological conditions, variations in geological disaster susceptibility are chiefly driven by human activities. Including human activities as a metric significantly enhances the evaluation accuracy. This study provides a scientific foundation for ecological protection, the assessment of geological disaster susceptibility, and the development of mitigation policies.

During the overall design phase of solar-powered unmanned aerial vehicles (UAVs), a large amount of high-fidelity (HF) propeller aerodynamic performance data is required to enhance design performance, but the acquisition cost is prohibitively expensive. To improve model accuracy and reduce modeling costs, this paper constructs a multi-fidelity aerodynamic data fusion model by associating data with different fidelity. This model utilizes a low-fidelity computational method to quickly determine the design space. The constrained Latin hypercube sampling based on the successive local enumeration (SLE-CLHS) method and the expected improvement (EI) criterion were adopted to achieve the efficient initialization and fastest convergence of the Co-Kriging surrogate model within the design space. This modeling framework was applied to acquire the aerodynamic performance of high-altitude propellers, and the model was evaluated using various performance indicators. The results demonstrate that the proposed model has excellent predictive performance. Specifically, when the surrogate model was constructed using 350 high-fidelity samples, there were improvements of 13.727%, 12.241%, and 5.484% for thrust, torque, and efficiency compared with the surrogate model constructed from low-fidelity samples.

The density of the ocean is unevenly distributed along the depth direction, showing a stratified structure. When there is an external disturbance, large-scale internal solitary waves are easily generated. The internal solitary waves are bounded by the intermediate pycnocline, and the currents in the upper and lower layers will flow in opposite directions. This generates strong shear forces that threaten the safety of marine structures. In this paper, the flow field distribution characteristics of a cylinder under the action of internal solitary waves at different scales are analyzed as a research object. The whole cylinder is discretized into 40 regions, and the horizontal force applied to each section of the cylinder is extracted. The force characteristics of the cylinder are analyzed. It is concluded that the pressure is the main factor determining the magnitude of the total combined force. In addition, the paper extracts the main flow structures from the modal decomposition point of view and explains the reasons affecting the force behavior of the cylinder.

Autonomous driving, as a pivotal technology in modern transportation, is progressively transforming the modalities of human mobility. In this domain, vehicle detection is a significant research direction that involves the intersection of multiple disciplines, including sensor technology and computer vision. In recent years, many excellent vehicle detection methods have been reported, but few studies have focused on summarizing and analyzing these algorithms. This work provides a comprehensive review of existing vehicle detection algorithms and discusses their practical applications in the field of autonomous driving. First, we provide a brief description of the tasks, evaluation metrics, and datasets for vehicle detection. Second, more than 200 classical and latest vehicle detection algorithms are summarized in detail, including those based on machine vision, LiDAR, millimeter-wave radar, and sensor fusion. Finally, this article discusses the strengths and limitations of different algorithms and sensors, and proposes future trends.

Dendrobium officinale is an important edible and medicinal plant, with the Dendrobium officinale polysaccharide (DOP) being its primary active constituent, known for its diverse biological activities. In this study, DOP was extracted and characterized for its structural properties. The potential of DOP to ameliorate gastric ulcers (GUs) was investigated using an acetic-acid-induced GU model in rats. The results demonstrated that DOP exerted a multifaceted protective effect against GU, mitigating the deleterious impact on food intake and body weight in rats. DOP exhibited its protective action by attenuating cellular damage attributed to oxidative stress and inflammatory reactions mediated by enhanced activities of SOD, GSH, and GSH-PX, coupled with a downregulation in the expression of pro-inflammatory cytokines, including IL-1β, IL-6, and TNF-α. Furthermore, DOP effectively inhibited apoptosis in gastric mucosa cells of acetic-acid-induced GU rat models and facilitated the self-repair of damaged tissues. Remarkably, the DOP-200 and DOP-400 groups outperformed omeprazole in reducing the expression of IL-6 and malondialdehyde (MDA) in tissues, as well as IL-1β, IL-6, and TNF-α in serum. These groups also exhibited an improved expression of SOD in tissues and SOD, GSH, and GSH-PX in serum. A Western blot analysis of gastric mucosa demonstrated that the DOP-200 and DOP-400 groups significantly reduced the expression of NF-κBp65, phosphorylated NF-κBp65, FoxO3a, and Bim. The observed antagonism to GU appeared to be associated with the NF-κB cell pathway. Additionally, qRT-PCR results indicate that DOP reduced the mRNA transcription levels of IL-6, and TNF-α, which shows that the healing of GU is related to the reduction in the inflammatory reaction by DOP. However, the expression of EGF and VEGF decreased, suggesting that the mechanism of DOP inhibiting GU may not be directly related to EGF and VEGF, or there is an uncertain competitive relationship between them, so further research is needed.

Purebred Bamei piglets present problems, including slow growth, respiratory disease, and post-weaning stress. This study investigated the effects of Lactobacillus plantarum QP28-1- and Bacillus subtilis QB8-fermented feed supplementation on the growth performance, immunity, and intestinal microflora of Bamei piglets from Qinghai, China. A total of 48 purebred Bamei piglets (25 days; 6.8 ± 0.97 kg) were divided into the following four groups for a 28-day diet experiment: basal feed (CK); diet containing 10% Lactobacillus plantarum-fermented feed (L); diet containing 10% Bacillus subtilis-fermented feed (B); and diet containing a mixture of 5% Lactobacillus plantarum + 5% Bacillus subtilis-fermented feed (H). The daily weight gain and daily food intake of group H increased (p < 0.05), and the feed/weight gain ratios of the groups fed with fermented feed decreased more than that of the CK group. The levels of three immune factors, namely immunoglobulin (Ig)M, IgG, and interferon-γ, were higher (p < 0.05), whereas those of tumor necrosis factor-α, interleukin (IL)-1β, and IL-6 were lower (p < 0.05) in the fermented feed groups than in the CK group. Total protein was higher (p < 0.05), while urea nitrogen, total cholesterol and triglycerides were lower (p < 0.05) in the mixed-fermented feed group than in the CK group. Analysis of the gut microbiota showed that the addition of fermented feed increased the α-diversity of the gut microbiota, increasing the abundances of probiotics including Lactobacillus, Muribaculaceae, Ruminococcaceae, Prevotellaceae, and Rikenellaceae. Additionally, correlation analysis demonstrated that several of these probiotic bacteria were closely related to serum immunity. In conclusion, fermented feed supplementation rebuilt the intestinal microbiota of Bamei piglets, thereby reducing the feed/weight ratio, improving feed intake, and enhancing immunity.

Enhanced ozone (O3) pollution has emerged as a pressing environmental concern in China, particularly for densely populated megacities and major city clusters. However, volatile organic compounds (VOCs), the key precursors to O3 formation, have not been routinely measured. In this study, we characterize the spatial and temporal patterns of VOCs and examine the role of VOCs in O3 production in five cities (Dongying (DY), Rizhao (RZ), Yantai (YT), Weihai (WH), and Jinan (JN)) in the North China Plain (NCP) for two sampling periods (June and December) in 2021 through continuous field observations. Among various VOC categories, alkanes accounted for the largest proportion of VOCs in the cities. For VOCs, chemical reactivities, aromatic hydrocarbons, and alkenes were dominant contributors to O3 formation potential (OFP). Unlike inland regions, the contribution to OFP from OVOCs increased greatly at high O3 concentrations in coastal regions (especially YT). Model simulations during the O3 episode show that the net O3 production rates were 27.87, 10.24, and 10.37 ppbv/h in DY, RZ, and JN. The pathway of HO2 + NO contributed the most to O3 production in JN and RZ, while RO2 + NO was the largest contributor to O3 production in DY. The relative incremental reactivity (RIR) revealed that O3 formation in DY was the transitional regime, while it was markedly the VOC-limited regime in JN and RZ. The O3 production response is influenced by NOx concentration and has a clear daily variation pattern (the sensitivity is greater from 15:00 to 17:00). The most efficiencies in O3 reduction could be achieved by reducing NOx when the NOx concentration is low (less than 20 ppbv in this study). This study reveals the importance of ambient VOCs in O3 production over the NCP and demonstrates that a better grasp of VOC sources and profiles is critical for in-depth O3 regulation in the NCP.

In this paper, we propose a line planning and timetabling optimization model considering operation cost and passenger satisfaction based on passenger flow. By comprehensively considering the operation cost and passenger waiting cost, the comprehensive social benefits of the line network operation organization are evaluated from a unified perspective. The passenger flow model based on queuing theory is adopted, which can better describe the relationship between passenger flow change and passenger waiting time. The integrated optimization model of the line network is constructed, and mixed integer quadratic programming is adopted, which has the advantages of accurate results and fast convergence. Through the simulation case analysis, the correctness of the method proposed in this paper is verified.

In this research, we introduce a deep-learning-based framework designed for the prediction of transonic flow through a linear cascade utilizing large-scale point-cloud data. In our experimental cases, the predictions demonstrate a nearly four-fold speed improvement compared to traditional CFD calculations while maintaining a commendable level of accuracy. Taking advantage of a multilayer graph structure, the framework can extract both global and local information from the cascade flow field simultaneously and present prediction over unstructured data. In line with the results obtained from the test datasets, we conducted an in-depth analysis of the geometric attributes of the cascades reconstructed using our framework, considering adjustments made to the geometric information of the point cloud. We fine-tuned the input using 1603 data points and quantified the contribution of each point. The outcomes reveal that variations in the suction side of the cascade have a significantly more substantial influence on the field results compared to the pressure side and explain the way graph neural networks work for cascade flow-field prediction, enhancing the comprehension of graph-based flow-field prediction among developers and proves the potential of graph neural networks in flow-field prediction on large-scale point clouds and design.

Bamei pigs, an eximious local breed reared on the Tibetan Plateau of China, are facing problems such as feed shortages, weaning stress, and antibiotic abuse. This study aimed to improve the quality of feed, growth performance, intestinal microbiota, and immunity of Bamei pigs through feeding with potentially probiotic-fermented feed. Different feeds were administered to weaned Bamei piglets for 60 days, creating the following five experimental treatment groups: basal feed group; Lactiplantibacillus plantarum-fermented-feed group; Bacillus subtilis-fermented-feed group; mixed-fermented-feed group; and antibiotic-supplemented-feed group. The results showed that the pH, neutral detergent fiber, and acid detergent fiber of the potentially probiotic-fermented feeds were significantly reduced; organic acids were produced; and Coliform bacteria, Clostridium, and aerobic bacteria were effectively inhibited. Feeding with potentially probiotic-fermented feed not only reduced the feed-conversion ratio but also improved immunity by increasing the production of anti-inflammatory cytokines, as well as decreasing pro-inflammatory cytokines and typical inflammatory pathways. The 16s rDNA high-throughput-sequencing results showed that probiotic-fermented feed improved the diversity of intestinal microbiota, inhibited the growth of the opportunistic pathogens Clostridium and Streptococcus, increased the relative abundance of Lactobacillus and Prevotella, and promoted gut health, demonstrating the promising application prospects of potentially probiotic-fermented feed.

This article proposes a direction-of-arrival (DOA) estimation algorithm for a random sparse linear array based on a novel graph neural network (GNN). Unlike convolutional layers and fully connected layers, which do not interact well with information between different antennas, the GNN model can adapt to the goniometry problem of non-uniform random sparse linear arrays without any prior information by applying neighbor nodes’ aggregation and update operations. This helps the model in learning signal features under complex environmental conditions. We train the model in an end-to-end way to reduce the complexity of the network. Experiments are conducted on the uniform and sparse linear arrays for various signal-to-noise ratio (SNR) and numbers of snapshots for comparison. We prove that the GNN model has superior angle estimation performance on arrays with large sparsity that cannot be used by traditional algorithms and surpasses existing deep learning models based on convolutional or fully connected structures. The proposed algorithm shows excellent DOA estimation performance under the complex conditions of limited snapshots, low signal-to-noise ratio, and large array sparsity as well. In addition, the algorithm has a low time calculation cost and is suitable for scenarios that require low latency.

Fe3C nanoparticles hold promise as catalysts and nanozymes, but their low activity and complex preparation have hindered their use. Herein, this study presents a synthetic alternative toward efficient, durable, and recyclable, Fe3C-nanoparticle-encapsulated nitrogen-doped hierarchically porous carbon membranes (Fe3C/N–C). By employing a simple one-step synthetic method, we utilized wood as a renewable and environmentally friendly carbon precursor, coupled with poly(ionic liquids) as a nitrogen and iron source. This innovative strategy offers sustainable, high-performance catalysts with improved stability and reusability. The Fe3C/N–C exhibits an outstanding peroxidase-like catalytic activity toward the oxidation of 3,3′,5,5′-tetramethylbenzidine in the presence of hydrogen peroxide, which stems from well-dispersed, small Fe3C nanoparticles jointly with the structurally unique micro-/macroporous N–C membrane. Owing to the remarkable catalytic activity for mimicking peroxidase, an efficient and sensitive colorimetric method for detecting ascorbic acid over a broad concentration range with a low limit of detection (~2.64 µM), as well as superior selectivity, and anti-interference capability has been developed. This study offers a widely adaptable and sustainable way to synthesize an Fe3C/N–C membrane as an easy-to-handle, convenient, and recoverable biomimetic enzyme with excellent catalytic performance, providing a convenient and sensitive colorimetric technique for potential applications in medicine, biosensing, and environmental fields.

The requirements for low latency and high stability in large-scale geo-distributed training simulations have made cloud-edge collaborative simulation an emerging trend. However, there is currently limited research on how to deploy simulation run-time resources (SRR), including edge servers, simulation services, and simulation members. On one hand, the deployment schemes of these resources are coupled and have mutual impacts. It is difficult to ensure overall optimum by deploying these resources separately. On the other hand, the pursuit of low latency and high system stability is often challenging to achieve simultaneously because high stability implies low server load, while a small number of simulation services implies high response latency. We formulate this problem as a multi-objective optimization problem for the joint deployment of SRR, considering the complex combinatorial relationship between simulation services. Our objective is to minimize the system time cost and resource usage rate of edge servers under constraints such as server resource capacity and the relationship between edge servers and base stations. To address this problem, we propose a learnable genetic algorithm for SRR deployment (LGASRD) where the population can learn from elites and adaptively select evolution operators performing well. Extensive experiments with different settings based on real-world data sets demonstrate that LGASRD outperforms the baseline policies in terms of optimality, feasibility, and convergence rate, verifying the effectiveness and excellence of LGASRD when deploying SRR.

Iron (Fe) is necessary for plant growth and development. The mechanism of uptake and translocation in Cadmium (Cd) is similar to iron, which shares iron transporters. Yellow stripe-like transporter (YSL) plays a pivotal role in transporting iron and other metal ions in plants. In this study, MsYSL6 and its promoter were cloned from leguminous forage alfalfa. The transient expression of MsYSL6-GFP indicated that MsYSL6 was localized to the plasma membrane and cytoplasm. The expression of MsYSL6 was induced in alfalfa by iron deficiency and Cd stress, which was further proved by GUS activity driven by the MsYSL6 promoter. To further identify the function of MsYSL6, it was heterologously overexpressed in tobacco. MsYSL6-overexpressed tobacco showed better growth and less oxidative damage than WT under Cd stress. MsYSL6 overexpression elevated Fe and Cd contents and induced a relatively high Fe translocation rate in tobacco under Cd stress. The results suggest that MsYSL6 might have a dual function in the absorption of Fe and Cd, playing a role in the competitive absorption between Fe and Cd. MsYSL6 might be a regulatory factor in plants to counter Cd stress. This study provides a novel gene for application in heavy metal enrichment or phytoremediation and new insights into plant tolerance to toxic metals.

Atmospheric aerosols have important effects on the environment and human health. In this study, we analyzed the atmospheric aerosol layers’ optical and physical properties over Africa utilizing CALIPSO level 2 products from 2007 to 2019. Interannual and seasonal variations in aerosol optical parameters were studied: the lowest aerosol layer (AODL), the base height of the lowest aerosol layer (BL), the top height of the lowest aerosol layer (HL), the top height of the highest aerosol layer (HH), the volumetric depolarization ratio of the lowest aerosol layer (DRL), the color ratio of the lowest aerosol layer (CRL), the total AOD of all aerosol layers (AODT), the number of aerosol feature layers (N), the thickness of the lowest aerosol layer (TL), and the AOD proportion of the lowest aerosol layer (PAODL). The annual mean AODT was slightly higher in southern Africa than in northern Africa. HL and HH had strongly positive correlations with landform elevations. However, HL and HH were greater in northern Africa than in southern Africa from March to August. The reason could be that northern Africa is dominated by deserts with high temperatures and intense atmospheric vertical convections leading to dust layers existing in the upper air. PAODL values were lower in northern Africa (daytime: 71%; nighttime: 61%) than in southern Africa (daytime: 78%; nighttime: 69%), revealing that aerosol stratifications were more frequent in northern Africa than in southern Africa. DRL values were higher in northern Africa (daytime: 0.16; nighttime: 0.11) than in southern Africa (daytime: 0.07; nighttime: 0.05) indicating the predominance of non-spherical dust particles in northern Africa. This work can provide an important understanding of regional aerosol layers’ optical and physical properties to scientists and local environmental protection agencies.

Non-alcoholic fatty liver disease (NAFLD) is the primary chronic liver disease worldwide, mainly manifested by hepatic steatosis. Hepatic lipids may be derived from dietary intake, plasma free fatty acid (FFA) uptake, or hepatic de novo lipogenesis (DNL). Currently, cellular and animal models of hepatocellular steatosis are widely used to study the pathogenesis of NAFLD and to investigate therapeutic agents. However, whether there are differences between the in vivo and in vitro models of the mechanisms that cause lipid accumulation has not been reported. We used OA/PA-induced NCTC 1469 cells and high-fat-diet-fed C57BL/6J mice to simulate a hepatocyte steatosis model of NAFLD and to detect indicators related to FFA uptake and DNL. In addition, when serological indicators were analysed in the mouse model, it was found that serum FASN levels decreased. The results revealed that, in the cellular model, indicators related to DNL were decreased, FASN enzyme activity was unchanged, and indicators related to FFA uptake were increased, including the high expression of CD36; while, in the animal model, indicators related to both FFA uptake and de novo synthesis were increased, including the high expression of CD36 and the increased protein levels of FASN with enhanced enzyme activity. In addition, after an analysis of the serological indicators in the mouse model, it was found that the serum levels of FASN were reduced. In conclusion, the OA/PA-induced cellular model can be used to study the mechanism of FFA uptake, whereas the high-fat-diet-induced mouse model can be used to study the mechanism of FFA uptake and DNL. Combined treatment with CD36 and FASN may be more effective against NAFLD. FASN in the serum can be used as one of the indicators for the clinical diagnosis of NAFLD.

Integrating lexical information into Chinese character embedding is a valid method to figure out the Chinese named entity recognition (NER) issue. However, most existing methods focus only on the discovery of named entity boundaries, considering only the words matched by the Chinese characters. They ignore the association between Chinese characters and their left and right matching words. They ignore the local semantic information of the character’s neighborhood, which is crucial for Chinese NER. The Chinese language incorporates a significant number of polysemous words, meaning that a single word can possess multiple meanings. Consequently, in the absence of sufficient contextual information, individuals may encounter difficulties in comprehending the intended meaning of a text, leading to the emergence of ambiguity. We consider how to handle the issue of entity ambiguity because of polysemous words in Chinese texts in different contexts more simply and effectively. We propose in this paper the use of graph attention networks to construct relatives among matching words and neighboring characters as well as matching words and adding left- and right-matching words directly using semantic information provided by the local lexicon. Moreover, this paper proposes a short-sequence convolutional neural network (SSCNN). It utilizes the generated shorter subsequence encoded with the sliding window module to enhance the perception of local information about the character. Compared with the widely used Chinese NER models, our approach achieves 1.18%, 0.29%, 0.18%, and 1.1% improvement on the four benchmark datasets Weibo, Resume, OntoNotes, and E-commerce, respectively, and proves the effectiveness of the model.

Cadmium (Cd) is highly toxic and widely distributed in aquatic systems due to its high solubility and mobility in water, which can severely inhibit the survival of aquatic macrophytes. The phytotoxicity of Cd depends on environmental factors; however, it remains unclear whether and how light quality affects its toxicity on aquatic macrophytes. In this study, we investigated the effects of Cd on aquatic macrophytes Potamogeton crispus under different light qualities (white, blue, and red light). We evaluated morphological and photo-physiological traits, as well as the cellular antioxidant defense system. Our findings indicate that P. crispus under Cd stress showed notable damage in leaf morphology, decreased photosynthetic efficiency, inhibited HCO3− uptake, and reduced antioxidant enzyme activities, as well as oxidative damage indicated by MDA accumulation and superoxide (O2−) overproduction. However, compared with white or red light under Cd stress, blue light reduced structural damage and oxidative stress caused by Cd while increasing pigment synthesis and photosynthetic efficiency, as well as increasing ascorbate peroxidase (APX) activity. In conclusion, the changes induced by blue light in P. crispus’s photosynthesis and antioxidant system strengthen its tolerance to Cd. Further research on signal transmission in relation to light quality in Cd-exposed aquatic plants is still needed.

Atmospheric aerosols have important impacts on global radiative forcing, air pollution, and human health. This study investigated the optical and physical properties of aerosol layers over Australia from 2007 to 2019 using the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Level 2 aerosol products. Australia was divided into three sub-regions (western highlands, central plains, and eastern ranges). Interannual and seasonal optical property variations in aerosol layers in the three sub-regions were analyzed and compared. Results showed that annual mean values of AODL (lowest aerosol layer AOD) and AODT (total AOD of all aerosol layers) were always higher in the eastern ranges region than the other two regions from 2007 to 2019. The reason could be that Australian population was predominantly located in the eastern ranges region, where more human activities could bring significant aerosol loadings. BL (base height of the lowest aerosol layer), HL (top height of the lowest aerosol layer), and HH (top height of the highest aerosol layer) all showed trends of “western highlands > eastern mountains > central plains”, indicating that the higher the elevation, the higher the BL, HL, and HH. TL (thickness of the lowest aerosol layer) was higher during the day than at night, which might account for increased diurnal atmospheric convection and nocturnal aerosol deposition. DRL (depolarization ratio of the lowest aerosol layer) was higher in the western highlands and central plains than the eastern mountains, probably because these two regions have large deserts with more irregularly shaped dust aerosols. CRL (color ratio of the lowest aerosol layer) had slightly higher values in the eastern ranges than the other two regions, probably due to the wet climate of the eastern ranges, where aerosols were more hygroscopic and had larger particle sizes. This study can provide technical support for the control and management of regional air pollutants.

K+ channels are involved in many critical functions in lung physiology. Recently, the family of Ca2+-activated K+ channels (KCa) has received more attention, and a massive amount of effort has been devoted to developing selective medications targeting these channels. Within the family of KCa channels, three small-conductance Ca2+-activated K+ (KCa2) channel subtypes, together with the intermediate-conductance KCa3.1 channel, are voltage-independent K+ channels, and they mediate Ca2+-induced membrane hyperpolarization. Many KCa2 channel members are involved in crucial roles in physiological and pathological systems throughout the body. In this article, different subtypes of KCa2 and KCa3.1 channels and their functions in respiratory diseases are discussed. Additionally, the pharmacology of the KCa2 and KCa3.1 channels and the link between these channels and respiratory ciliary regulations will be explained in more detail. In the future, specific modulators for small or intermediate Ca2+-activated K+ channels may offer a unique therapeutic opportunity to treat muco-obstructive lung diseases.

The increasing incidence of diseases caused by highly carcinogenic aflatoxin M1 (AFM1) in food demands a simple, fast, and cost-effective detection technique capable of sensitively monitoring AFM1. Recent works predominantly focus on the electrochemical aptamer-based biosensor, which still faces challenges and high costs in experimentally identifying an efficient candidate aptamer. However, the direct electrochemical detection of AFM1 has been scarcely reported thus far. In this study, we observed a significant influence on the electrochemical signals of ferric ions at a gold nanoparticle-modified glassy carbon electrode (AuNPs/GCE) by adding varying amounts of AFM1. Utilizing ferricyanide as a sensitive indicator of AFM1, we have introduced a novel approach for detecting AFM1, achieving an unprecedentedly low detection limit of 1.6 × 10−21 g/L. Through monitoring the fluorescence quenching of AFM1 with Fe3+ addition, the interaction between them has been identified at a ratio of 1:936. Transient fluorescence analysis reveals that the fluorescence quenching process is predominantly static. It is interesting that the application of iron chelator diethylenetriaminepentaacetic acid (DTPA) cannot prevent the interaction between AFM1 and Fe3+. With a particle size distribution analysis, it is suggested that a combination of AFM1 and Fe3+ occurs and forms a polymer-like aggregate. Nonetheless, the mutual reaction mechanism between AFM1 and Fe3+ remains unexplained and urgently necessitates unveiling. Finally, the developed sensor is successfully applied for the AFM1 test in real samples, fully meeting the detection requirements for milk.

The power conversion efficiencies (PCEs) of organic photovoltaics (OPVs) have reached more than 19%, along with the prosperous development of materials and device engineering. It is meaningful to make a comprehensive review of the research of OPVs for further performance improvement. In this review, some typical materials of high-performance OPVs are summarized, including representative polymer donor materials, non-fullerene acceptor materials, and interfacial modification materials, as well as their design rules for molecular engineering. From the point of view of device engineering, active layer treatment and deposition technology are introduced, which can play a critical role in adjusting the degree of molecular aggregation and vertical distribution. Meanwhile, a ternary strategy has been confirmed as an efficient method for improving the performance of OPVs, and the multiple roles of the appropriate third component in the photo-electronic conversion process are emphasized and analyzed. The challenges and perspectives concerning this region are also put forward for further developing high-performance OPVs.

The electrocardiogram (ECG) is a highly effective non-invasive tool for monitoring heart activity and diagnosing cardiovascular diseases (CVDs). Automatic detection of arrhythmia based on ECG plays a critical role in the early prevention and diagnosis of CVDs. In recent years, numerous studies have focused on using deep learning methods to address arrhythmia classification problems. However, the transformer-based neural network in current research still has a limited performance in detecting arrhythmias for the multi-lead ECG. In this study, we propose an end-to-end multi-label arrhythmia classification model for the 12-lead ECG with varied-length recordings. Our model, called CNN-DVIT, is based on a combination of convolutional neural networks (CNNs) with depthwise separable convolution, and a vision transformer structure with deformable attention. Specifically, we introduce the spatial pyramid pooling layer to accept varied-length ECG signals. Experimental results show that our model achieved an F1 score of 82.9% in CPSC-2018. Notably, our CNN-DVIT outperforms the latest transformer-based ECG classification algorithms. Furthermore, ablation experiments reveal that the deformable multi-head attention and depthwise separable convolution are both efficient in extracting features from multi-lead ECG signals for diagnosis. The CNN-DVIT achieved good performance for the automatic arrhythmia detection of ECG signals. This indicates that our research can assist doctors in clinical ECG analysis, providing important support for the diagnosis of arrhythmia and contributing to the development of computer-aided diagnosis technology.

Biochar is a potential amendment for the remediation of Cd-contaminated soils. Although the immobilization effect of biochar on soil Cd has been studied under indoor laboratory conditions, the effect of biochar on rice Cd uptake and soil Cd fractions under field conditions is still poorly understood. Here, the Cd content of the different organs of rice and the Cd fractions in soil were characterized for three years after the application of different amounts of biochar (0, 7.5, 15, 30 t ha−1, and 3 t ha−1 year−1). The Cd content of brown rice, husk, leaf, stem and sheath, and root under biochar treatment could be maximally reduced by up to 26.25%, 20.16%, 20.74%, 33.2%, and 26.89%, respectively. Biochar altered the Cd fractions in soil, including the decrease in exchangeable Cd content and the increase in Fe-Mn oxide bound Cd and organic bound Cd. The concentration factor of Cd uptake by rice was reduced by 32% under biochar application, while biochar had little influence on the transfer factor and distribution factor. The immobilization effect of biochar on soil Cd lasted for at least three years, but the trend of Cd immobilization efficiency over time for different amounts of biochar treatment was different. The Risk Assessment Code (RAC) of Cd in soil with biochar amendment could be reduced to a medium risk level from a high risk level. Redundancy analysis (RDA) revealed that changes in soil pH and Fe-Mn oxide bound Cd content caused by biochar application contributed most to the reduction in the Cd content of rice organs. These findings would enhance our understanding of the immobilization effect of biochar on Cd in paddy soil under field conditions.

To investigate the vertical distribution of aerosol optical characteristics in Nanyang City, a ground-based dual-wavelength (532 nm and 355 nm) lidar system was developed for aerosol observation at the Nanyang Normal University Station (NYNU) from November 2021 to December 2022. Spatio-temporal dynamics information on vertical distributions of aerosol optical properties during polluted and non-polluted days was obtained. Aerosols were characterized by low altitudes (up to 2 km), thinner layers, and high-altitude (up to 4 km) thick layers during non-polluted and polluted days, with extinction coefficient values of ~0.03 km−1 and ~0.2 km−1, respectively. The mean values of the extinction coefficient at different altitudes (0~5 km) were all about ten-times higher on polluted days (0.04~0.19 km−1) than on non-polluted days (0.004~0.02 km−1). These results indicate that aerosol loadings and variations at different altitudes (0~5 km) were much higher and more prominent on polluted days than non-polluted days. The results show ten-times larger aerosol optical depth (AOD) values (0.4~0.6) on polluted days than on non-polluted days (0.05~0.08). At the same time, AOD values on both polluted and non-polluted days slightly decreased from 19:00 to 05:00, possibly due to dry depositions at nighttime. For the first time, this study established a ground-based lidar remote sensing system to investigate the vertical distribution of atmospheric aerosol optical characteristics in Henan Province. The experimental results can provide scientific dataset support for the local government to prevent and control air pollution.

Cloud contamination is a common issue that severely reduces the quality of optical satellite images in remote sensing fields. With the rapid development of deep learning technology, cloud contamination is expected to be addressed. In this paper, we propose Denoising Diffusion Probabilistic Model-Cloud Removal (DDPM-CR), a novel cloud removal network that can effectively remove both thin and thick clouds in optical image scenes. Our network leverages the denoising diffusion probabilistic model (DDPM) architecture to integrate both clouded optical and auxiliary SAR images as input to extract DDPM features, providing significant information for missing information retrieval. Additionally, we propose a cloud removal head adopting the DDPM features with an attention mechanism at multiple scales to remove clouds. To achieve better network performance, we propose a cloud-oriented loss that considers both high- and low-frequency image information as well as cloud regions in the training procedure. Our ablation and comparative experiments demonstrate that the DDPM-CR network outperforms other methods under various cloud conditions, achieving better visual effects and accuracy metrics (MAE = 0.0229, RMSE = 0.0268, PSNR = 31.7712, and SSIM = 0.9033). These results suggest that the DDPM-CR network is a promising solution for retrieving missing information in either thin or thick cloud-covered regions, especially when using auxiliary information such as SAR data.

Composting is an effective way to dispose of agricultural waste; however, its application is limited in the winter and in areas with low average annual temperatures. This study screened out a composite microbial agent (CMA) including Bacillus (B.) cereus QS7 and B. pumilus QM6 that could grow at 10–15 °C and investigated the effects of the CMA as an inoculant on the physicochemical parameters and microbial communities of compost made from mushroom residue mixed with sheep manure. The results showed that CMA inoculation prolonged the days of high temperature above 60 °C. When the ratio of mushroom residue to sheep manure was at 9:1, compost increased the accumulation of nitrogen, and reduced the electrical conductivity (EC). Under this ratio, the inoculation of CMA accelerated the degradation of organic matter (OM) (decreased by 12.22%) and total organic carbon (TOC) (decreased by 8.13%) and increased the germination rate and seed germination index (GI) to 100% and 106.86, respectively. Microbial community structure analysis showed that the relative abundance of Flavobacterium was 30.62% on day 15 and was higher after CMA inoculation in the high mushroom residue condition, which was higher than that of other groups, and the relative abundance of thermophilic fungi increased. This study demonstrated that CMA improves the quality and efficiency of mushroom residue and sheep manure composting, and it provides evidence to improve the efficiency of low-temperature composting.

Alzheimer’s disease (AD) is a typical progressive neurodegenerative disorder, and with multiple possible pathogenesis. Among them, coumarin derivatives could be used as potential drugs as monoamine oxidase-B (MAO-B) inhibitors. Our lab has designed and synthesized coumarin derivatives based on MAO-B. In this study, we used nuclear magnetic resonance (NMR)-based metabolomics to accelerate the pharmacodynamic evaluation of candidate drugs for coumarin derivative research and development. We detailed alterations in the metabolic profiles of nerve cells with various coumarin derivatives. In total, we identified 58 metabolites and calculated their relative concentrations in U251 cells. In the meantime, the outcomes of multivariate statistical analysis showed that when twelve coumarin compounds were treated with U251cells, the metabolic phenotypes were distinct. In the treatment of different coumarin derivatives, there several metabolic pathways changed, including aminoacyl-tRNA biosynthesis, D-glutamine and D-glutamate metabolism, glycine, serine and threonine metabolism, taurine and hypotaurine metabolism, arginine biosynthesis, alanine, aspartate and glutamate metabolism, phenylalanine, tyrosine and tryptophan biosynthesis, glutathione metabolism and valine, leucine and isoleucine biosynthesis. Our work documented how our coumarin derivatives affected the metabolic phenotype of nerve cells in vitro. We believe that these NMR-based metabolomics might accelerate the process of drug research in vitro and in vivo.

Uncarboxylated osteocalcin (GluOC), a small-molecule protein specifically synthesized and secreted by osteoblasts, is important in the regulation of energy metabolism. In our previous study, GluOC was shown to be effective in ameliorating dyslipidemia and hepatic steatosis in KKAy mice. However, the underlying mechanism of GluOC action on hepatocytes has not been well validated. In this study, oleic acid/palmitic acid (OA/PA)-induced HepG2 and NCTC 1469 cells were used as non-alcoholic fatty liver disease (NAFLD) cell models, and triacylglycerol (TG) levels were measured by oil red O staining, Nile Red staining, and ELISA. The fatty acid synthesis-related protein expression was detected by real-time quantitative polymerase chain reaction, Western blotting, and immunofluorescence. The results show that GluOC reduced triglyceride levels, and decreased the expression of sterol regulatory element-binding protein-1c (SREBP-1c) and stearyl-coenzyme A desaturase 1 (SCD1). si-SCD1 mimicked the lipid accumulation-reducing effect of GluOC, while overexpression of SCD1 attenuated the effect of GluOC. In addition, GluOC activated AMP-activated protein kinase (AMPK) phosphorylation to affect lipid metabolism in hepatocytes. Overall, the results of this study suggest that GluOC decreases SCD1 by activating AMPK to alleviate hepatocyte lipid accumulation, which provides a new target for improving NAFLD in further research.

Heat treatment affects the structural properties of meat proteins, which in turn leads to changes in their sensitivity to digestive enzymes, further affecting the nutritional value of meat and meat products. The mechanism of changes in the structure and digestive properties of myosin under different heating conditions were studied. An increase in heating temperature led to the exposure of internal groups to a polar environment, but to a decrease in the sturdy α-helix structure of myosin (p < 0.05). The results of tryptophan fluorescence verified that the tertiary structure of the protein seemed to be unfolded at 70 °C. Higher protein denaturation after overheating, as proven by the sulfhydryl contents and turbidity, caused irregular aggregate generation. The excessive heating mode of treatment at 100 °C for 30 min caused myosin to exhibit a lower degree of pepsin digestion, which increased the Michaelis constant (Km value) of pepsin during the digestion, but induced the production of new peptides with longer peptide sequences. This study elucidates the effects of cooking temperature on the conformation of myosin and the change in digestibility of pepsin treatment during heating.

The contamination of fermented feeds and foods with fungi and mycotoxins is a major food safety issue worldwide. Certain lactic acid bacteria (LAB), generally recognized as safe (GRAS) fermentation probiotics, are able to reduce microbial and mycotoxins contamination. In this study, Lactiplantibacillus (L.) plantarum Q1-2 and L. salivarius Q27-2 with antifungal properties were screened as inoculants for mixed fermenting feed, and the fermentation and nutritional qualities, microbial community, and mycotoxins of mixed fermented feed were analyzed at different fermentation periods (1, 3, 7, 15, and 30 days, respectively). The findings indicated that the utilization of Q1-2 and Q27-2 strains in fermenting feed led to a decrease in pH and an increase in lactic acid concentration and the proportion of Lactiplantibacillus, while effectively restraining the proliferation of undesirable microorganisms. In particular, Q1-2 reduced the relative abundance of fungi including Fusarium and Aspergillus. Compared to the control group, the Q1-2 and Q27-2 groups reduced aflatoxin B1 by 34.17% and 16.57%, and deoxynivalenol by up to 90.61% and 51.03%. In short, these two LAB inoculants could reduce the contents of aflatoxin B1 and deoxynivalenol to the limited content levels stipulated by the Chinese National Standard GB 13078-2017. These findings suggest that the LAB strains of Q1-2 and Q27-2 have potential applications in the feed industry for the mitigation of mycotoxin pollution, thereby enhancing the quality of animal feed.

Cardiac and respiratory diseases are the primary causes of health problems. If we can automate anomalous heart and lung sound diagnosis, we can improve the early detection of disease and enable the screening of a wider population than possible with manual screening. We propose a lightweight yet powerful model for simultaneous lung and heart sound diagnosis, which is deployable in an embedded low-cost device and is valuable in remote areas or developing countries where Internet access may not be available. We trained and tested the proposed model with the ICBHI and the Yaseen datasets. The experimental results showed that our 11-class prediction model could achieve 99.94% accuracy, 99.84% precision, 99.89% specificity, 99.66% sensitivity, and 99.72% F1 score. We designed a digital stethoscope (around USD 5) and connected it to a low-cost, single-board-computer Raspberry Pi Zero 2W (around USD 20), on which our pretrained model can be smoothly run. This AI-empowered digital stethoscope is beneficial for anyone in the medical field, as it can automatically provide diagnostic results and produce digital audio records for further analysis.

Conventional mat-type seedlings are still widely used in autonomous rice transplanters and automatically supplying seedling devices suited to conventional mat-type seedlings is difficult to develop. Thus, an autonomous rice transplanter carries at least one person to load the seedling pieces into the seedling box, which has led to an increase in the labor force and low operational efficiency. To solve this problem from another point of view, a machine vision-based system for the real-time monitoring of the seedling amount in a seedling box is developed. This system aims to achieve the monitoring of the fault of seedlings and seedling amount in the seedling box. According to the real-time and accuracy requirements of the image, the image acquisition platform is designed based on a previously developed autonomous rice transplanter. A camera model was developed and camera parameters for correcting the image distortion is obtained. The image processing method and segment method of seedling rows are presented. The algorithms for fault diagnosis and the calculation of the number of remaining seedlings are proposed by image analysis. The software is developed for seedling box fault diagnosis and monitoring the remaining number of seedlings. Field experiments are carried out to test the effectiveness of the developed monitoring system. The experimental results show that the image processing time is less than 1.5 s and the relative error of the seedling amount is below 3%, which indicates that the designed monitoring system can accurately realize the fault diagnosis of the seedling pieces and monitor for the remaining amount of each row. By combining the navigation information, the developed monitoring system can predict the distance from which the remaining seedlings in the seedling box can be planted, which can guarantee remaining seedlings in a seedling box are enough for transplanting until the rice transplanter returns to the supplying seedling site. This implies that one person can provide seedlings for multiple autonomous rice transplanters. This study was limited to supplying the seedling when the rice transplanter passed through the place of the seedling storage situated at the headland. In the future, we decide to conduct a study on path planning of breakpoint endurance so that the rice transplanter can automatically return to the supplying seedling place when the seedling amount in the seedling box is not enough.

Genetic improvement of milk fatty acid content traits in dairy cattle is of great significance. However, chromatography-based methods to measure milk fatty acid content have several disadvantages. Thus, quick and accurate predictions of various milk fatty acid contents based on the mid-infrared spectrum (MIRS) from dairy herd improvement (DHI) data are essential and meaningful to expand the amount of phenotypic data available. In this study, 24 kinds of milk fatty acid concentrations were measured from the milk samples of 336 Holstein cows in Shandong Province, China, using the gas chromatography (GC) technique, which simultaneously produced MIRS values for the prediction of fatty acids. After quantification by the GC technique, milk fatty acid contents expressed as g/100 g of milk (milk-basis) and g/100 g of fat (fat-basis) were processed by five spectral pre-processing algorithms: first-order derivative (DER1), second-order derivative (DER2), multiple scattering correction (MSC), standard normal transform (SNV), and Savitzky–Golsy convolution smoothing (SG), and four regression models: random forest regression (RFR), partial least square regression (PLSR), least absolute shrinkage and selection operator regression (LassoR), and ridge regression (RidgeR). Two ranges of wavebands (4000~400 cm−1 and 3017~2823 cm−1/1805~1734 cm−1) were also used in the above analysis. The prediction accuracy was evaluated using a 10-fold cross validation procedure, with the ratio of the training set and the test set as 3:1, where the determination coefficient (R2) and residual predictive deviation (RPD) were used for evaluations. The results showed that 17 out of 31 milk fatty acids were accurately predicted using MIRS, with RPD values higher than 2 and R2 values higher than 0.75. In addition, 16 out of 31 fatty acids were accurately predicted by RFR, indicating that the ensemble learning model potentially resulted in a higher prediction accuracy. Meanwhile, DER1, DER2 and SG pre-processing algorithms led to high prediction accuracy for most fatty acids. In summary, these results imply that the application of MIRS to predict the fatty acid contents of milk is feasible.

The purpose of this study is to investigate the air quality levels of Nanyang city according to Chinese air quality standards. Therefore, in this study, fine particulate matter (PM2.5), coarse particulate matter (PM10), and total suspended particulate (TSP) were analyzed from 19 November 2021 to 19 March 2022 in Nanyang city. The results show that the average concentrations of PM2.5, PM10, and TSP were 106.47 µg/m3, 137.32 µg/m3, and 283.40 µg/m3, respectively. The numbers of days that meet the national secondary air quality standard of 24-h average concentrations were 29.75% for PM2.5, 63.64% for PM10, and 63.64% for TSP, indicating that most of the time, the air quality of Nanyang city remains polluted in winter, especially with more contributions of PM2.5 compared to PM10 and TSP. The higher concentrations were observed between 07:00 and 08:00, suggesting that vehicular emissions can be a major cause of air pollution in Nanyang city. The results also show a significant positive correlation between particulate matter and relative humidity, and a weak correlation with temperature and wind speed, which suggests that higher relative humidity increases the formation of particulate matter. This study can provide theoretical support for the local government to formulate air pollution prevention and control policies for Nanyang city.

This paper describes a benchmark dataset for the detection of fuel particles in 2D and 3D image data in a rotary kiln combustion chamber. The specific challenges of detecting the small particles under demanding environmental conditions allows for the performance of existing and new particle detection techniques to be evaluated. The data set includes a classification of burning and non-burning particles, which can be in the air but also on the rotary kiln wall. The light-field camera used for data generation offers the potential to develop and objectively evaluate new advanced particle detection methods due to the additional 3D information. Besides explanations of the data set and the contained ground truth, an evaluation procedure of the particle detection based on the ground truth and results for an own particle detection procedure for the data set are presented.

The quadrotor aircraft has the characteristics of simple structure, high attitude maintenance performance and strong maneuverability, and is widely used in air surveillance, post−disaster search and rescue, target tracking and military industry. In this paper, a robust control scheme based on linear active disturbance rejection is proposed to solve the problem that the quadrotor is susceptible to various disturbances during the take−off process of non−horizontal planes and strong disturbances. Linear Active Disturbance Rejection Control (LADRC) is a product of a tracking differentiator (TD), a linear extended state observer (LESO) and an error feedback control law (PD) and is a control technique for estimating compensation for uncertainty. Radial Basis Function Neural Networks (RBFNN) is a well−performing forward network with best approximation, simple training, fast learning convergence and the ability to overcome local minima problems. Combined with the advantages and disadvantages of LADRC, Adaptive Control and Neural Network, the coupling force between each channel, gust crosswind disturbance and additional resistance of offshore platform jitter in the flight state of the quadrotor are optimized. In the control, the RBF neural network is designed, the nonlinear control signal is wirelessly approximated and the uncertain disturbance to the quadrotor is identified online. Finally, the real−time estimation and compensation are performed by LESO to realize the full−attitude take−off of the quadrotor. In addition, this paper uses adaptive control to optimize the parameters of LADRC to reduce the problem of many LADRC parameters and difficulty to integrate. Finally, the robust control system mentioned in this paper is simulated and verified, and the simulation results show that the control scheme has the advantages of simple parameter adjustment and stronger robustness.

Aiming at the problem of maximum power point tracking (MPPT) of photovoltaic arrays in photovoltaic power generation systems, a particle swarm optimization (PSO) MPPT method combined with adaptive linear active disturbance rejection control (A-LADRC) algorithm was proposed and designed. In this method, PSO is used to track the maximum power point (MPP), and then the A-LADRC controller was used to track the reference voltage. The controller enhances the anti-interference ability against various external disturbances in the MPPT process and accelerates the response speed of the system. Compared with the perturbation observation method (P&O), traditional PSO and LADRC, the proposed method has good tracking performance and an anti-interference ability under various external disturbances.

Laser damage resistance of films is the key to the performance and durability of high-power laser systems. High temperature, however, often leads to a certain degree of decline in film properties. Here, aiming to explore the mechanism of laser damage under high temperatures, Nb2O5 films were prepared using sol–gel method with NbCl5 as precursor and citric acid as additive. The effect of annealing temperatures in optical performance, microstructure, surface morphologies, and chemical composition of films were studied. Further investigation was carried out of the laser-induced damage threshold (LIDT) under different in situ high temperatures of the as-deposited films. The results showed that the films had prominent optical transmittance and high LIDT. Under 293 K, the LIDT of the film was the highest of 24.8 J/cm2. The increase of temperature brought down the LIDT of the films. It was attributed to the occurrence of oxygen vacancies, the sharp increase of temperature, and rise of defects and destruction of network structure. In this study, even though the LIDT of the film decreased a lot at 523 K, it was still high up to 14.1 J/cm2, representing the great potential for applications in authentic high-temperature environments.

This paper proposes a control scheme combining improved particle swarm optimization (IPSO) and adaptive linear active disturbance rejection control (ALADRC) to solve the high-speed train (HST) speed tracking control problem. Firstly, in order to meet the actual operation of a HST, a multi-mass point dynamic model with time-varying coefficients was established. Secondly, linear active disturbance rejection control (LADRC) was proposed to control the speed of the HST, and the anti-disturbance ability of the system was improved by estimating and compensating for the total disturbance suffered by the carriage during the operation of the HST. Meanwhile, to solve the problem of difficult parameter tuning of the LADRC, IPSO was introduced to optimize the parameters. Thirdly, the adaptive control (APC) was introduced to compensate for the observation error caused by the bandwidth limitation of the linear state expansion observer in LADRC and the tracking error caused by an unknown disturbance during the train’s operation. Additionally, the Lyapunov theory was used to prove the stability of the system. Finally, the simulation results showed that the designed control scheme is more effective in solving the problem of HST speed tracking.