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Phosphorus (P) is an essential nutrient for rice growth, and the presence of phosphate-solubilizing bacteria (PSB) is an effective means to increase soil P content. However, the direct application of PSB may have minimal significance due to their low survival in soil. Biochar serves as a carrier that enhances microbial survival, and its porous structure and surface characteristics ensure the adsorption of Bacillus megaterium. Inoculating rice husk biochar-immobilized with Bacillus megaterium (BMB) resulted in dissolved inorganic and organic P levels of 39.55 and 31.97 mL L−1, respectively. Subsequently, rice pot experiments were conducted to investigate the response of soil microbial P mobilization and P uptake in rice to fertilizer inputs. The organic fertilizer (OF) combined with BMB treatment (MOF) showed the highest soil available phosphorus (AP) at 38 days, with a value of 7.83 mg kg−1, as well as increased the pqqC abundance while decreasing the abundance of phoD bacterial communities compared with the control. Furthermore, the bioavailable P reservoir (H2O–Pi and NaHCO3–Pi) in soil was greatly increased through the fertilizer input and microbial turnover, with the highest H2O–Pi (3.66 mg kg−1) in OF treatment and the highest NaHCO3–Pi (52.65 mg kg−1) in MOF treatment. Additionally, carbon utilization analysis was applied using the commercial Biolog system, revealing that the MOF treatment significantly increased the utilization of carbohydrates, polymers, and amino acid carbon sources. Moreover, compared to the control, MOF treatment significantly increased the shoot (0.469%) and root P (0.516%) content while promoting root development and thereby supporting rice growth. Our study demonstrates that the MOF treatment displayed higher P levels in both soil and rice plants, providing a theoretical basis for further understanding the role of biochar-based bacterial agents in rice P management. [ABSTRACT FROM AUTHOR]

Stacking fermentation is critical in sauce-flavor Baijiu production, but winter production often sees abnormal fermentations, like Waistline and Sub-Temp fermentation, affecting yield and quality. This study used three machine learning models (Logistic Regression, KNN, and Random Forest) combined with multi-omics (metagenomics and flavoromics) to develop a classification model for abnormal fermentation. SHAP analysis identified 13 Sub-Temp Fermentation and 9 Waistline microbial biomarkers, along with 9 Sub-Temp Fermentation and 12 Waistline flavor biomarkers. Komagataeibacter and Gluconacetobacter are key for normal fermentation, while Ligilactobacillus and Lactobacillus are critical in abnormal cases. Excessive acid and ester markers caused unbalanced aromas in abnormal fermentations. Additionally, ecological models reveal the bacterial community assembly in abnormal fermentations was influenced by stochastic factors, while the fungal community assembly was influenced by deterministic factors. RDA analysis shows that moisture significantly drove Sub-Temp fermentation. Differential gene analysis and KEGG pathway enrichment identify metabolic pathways for flavor markers. This study provides a theoretical basis for regulating stacking fermentation and ensuring Baijiu quality. [ABSTRACT FROM AUTHOR]

In this article, a series of novel conducting copolymers P(FuPy-co-EDOT) are prepared via cyclic voltammetry electropolymerization method by using N-furfuryl pyrrole (FuPy) and 3,4-ethylenedioxythiophene (EDOT) as comonomers. The molecular structure, surface morphology, electrochemical, and optical properties of the resulting copolymers are characterized in detail upon varying the feed ratios of FuPy/EDOT in the range of 1/1 to 1/9. The results demonstrate that the prepared P(FuPy-co-EDOT) copolymers with a higher proportion of EDOT units (FuPy/EDOT: 2/8~1/9) possess good redox activity, tunable optical absorption performances, and low band gaps (1.75~1.86 eV). Spectroelectrochemistry studies indicate that the resulting copolymers with increased EDOT units show strengthened electrochromic characteristics, exhibiting a red-to-green-to-blue multicolor reversible transition, especially for the P(FuPy1-co-EDOT9) copolymer films. They also show increased optical contrast (9~34%), fast response time (0.8~2.4 s), and good coloring efficiency (110~362 cm2 C−1). Additionally, the complementary bilayer P(FuPy-co-EDOT)/PEDOT electrochromic devices (ECDs) are also assembled and evaluated to hold excellent electrochromic switching performances with relatively high optical contrast (25%), rapid response time (0.9 s), and satisfactory coloring efficiency (416 cm2 C−1). Together with the superior open circuit memory and cycling stability, they can be used as a new type of electrochromic material and have considerable prospects as promising candidates for electrochromic devices. [ABSTRACT FROM AUTHOR]

The development of the subway system in Shenyang City, China, plays a vital role in alleviating traffic congestion and promoting sustainable societal growth. However, the deformation of the surface caused by the tunneling of the shield presents a significant threat to the structural integrity of Shenyang Subway Line 2 and adjacent geotechnical structures. To tackle this challenge, a set of FEA (finite element analysis) simulations were carried out to examine surface deformation under various construction scenarios for Line 2. These simulations were compared with empirical formulas and numerical analyses conducted using Midas GTS NX 2019 software, in addition to actual site measurements. The outcomes of the finite element analysis (FEA) demonstrated a closer alignment with the empirical data than with traditional formulas. The maximum deformation was observed to be approximately twice as large as the equivalent diameter at the back of the excavation face. The analysis indicated that surface deformation is inversely correlated with overburden thickness (H), soil elasticity (E), and the grout filling rate of the shield tail (ψ), while it is directly proportional to the shield's outer diameter (D). This study provides important methods used in the shield tunneling process employed in the Shenyang subway and suggests that the developed methodologies may be applicable to similar subway projects. [ABSTRACT FROM AUTHOR]

The floating platform is a critical component of the floating offshore wind turbine (FOWT), and its internal structure design plays a key role in ensuring the safe operation of the FOWT. In this study, the local model of the floating platform was firstly parameterized, and a response surface model was obtained by conducting an orthogonal test. The response surface model was then optimized using a gradient descent algorithm. Finally, the internal structure arrangement was validated through a safety calibration. The optimization results indicate that the maximum stress of the optimized model is reduced by 22.12% compared to the original model, while maintaining the same mass, centroid, and other mass-related parameters. The optimization significantly improves the safety of the structure and provides valuable references for the design and construction of a FOWT platform. [ABSTRACT FROM AUTHOR]

In recent years, radar automatic target recognition (RATR) utilizing high-resolution range profiles (HRRPs) has received significant attention. Approaches based on deep learning have demonstrated remarkable efficacy in HRRP recognition tasks. However, the performance of neural networks is notably vulnerable to noise, leading to a detrimental effect on their recognition accuracy and overall robustness. To address this issue, a residual scattering attention network (RSAN) is proposed for HRRP target recognition, which comprises a residual scattering network, ResNet18, and a self-attention module. The residual scattering network is designed to suppress noise components and extract noise-robust features. It is derived from the improvement of a scattering network and does not need to learn parameters from the data. ResNet18 is employed for the purpose of extracting a deep representation of scattering features for HRRPs. Furthermore, a self-attention module is integrated into ResNet18, enabling the model to focus on target regions, thereby enhancing its feature-learning capability. The effectiveness and noise robustness of the proposed method are validated through experiments conducted on two measured datasets. [ABSTRACT FROM AUTHOR]

With the intensification of climate change and anthropogenic impacts, the ecological environment in drylands faces serious challenges, underscoring the necessity for regionally adapted ecological quality evaluation. This study evaluates the suitability of the original Remote Sensing Ecological Index (oRSEI), modified RSEI (mRSEI), and adapted RSEI (aRSEI) in a typical dryland region of northern China. Spatio-temporal changes in ecological quality from 2000 to 2022 were analyzed using Theil–Sen median trend analysis, the Mann–Kendall test, and the Hurst exponent. Multiple regression residual analysis quantified the relative contributions of climate change and human activities to ecological quality changes. Results showed that (1) the aRSEI was the most suitable index for the study area; (2) observed changes exhibited significant spatial heterogeneity, with improvements generally in the inner areas of the Yellow River and declines in the outer areas; and (3) changes in ecological quality were primarily driven by climate change and human activities, with human activities dominating from 2000 to 2011 and the influence of climate change increasing from 2012 to 2022. This study compares the efficacy of RSEIs in evaluating dryland ecological quality, identifies spatio-temporal change patterns, and elucidates driving mechanisms, offering scientific evidence and policy recommendations for targeted conservation and restoration measures to address future changes in dryland regions. [ABSTRACT FROM AUTHOR]

Effective tower crane layout planning is essential for the success of construction projects. Traditional optimization algorithms, which often provide a single optimal solution, may not always reveal the global optimum, leaving room for doubt. This paper introduces the competitor algorithm, a novel multi-optimal solution approach inspired by the competitive learning paradigm within classroom settings. This algorithm is designed to provide users with a diverse set of competitive solutions, while avoiding falling into local optima. This strategic diversification ensures that users are equipped with a comprehensive range of options, empowering them to make confident, informed decisions. Furthermore, we have streamlined the positioning range for tower cranes, transitioning from a two-dimensional plane to a one-dimensional segmented line, thus eliminating the need to explore extensive, non-competitive regions. The competitor algorithm's performance was validated through practical application, showcasing both its stability and optimization prowess, thereby confirming its reliable utility in real-world scenarios. [ABSTRACT FROM AUTHOR]

The heating energy consumption in public buildings in cold regions is notably significant, presenting substantial scope for energy savings and emission reductions. Flexible loads can actively participate in controlling the operation of the power grid, improving the energy utilization and the economy of the system. This study introduces flexible loads into the operation optimization of energy systems, establishing mathematical models for flexible thermal and electrical loads. A two-stage operation optimization method is proposed: the first stage simulates the starting and stopping control conditions of equipment at varying temperatures and times, selecting the optimal time period to regulate the thermal loads; the second stage employs a multi-objective particle swarm optimization algorithm to optimize the scheduling of the system's electrical load. Finally, an empirical analysis is carried out in a public building in Shenyang City as an example, and the results indicate that optimal scheduling of flexible thermal and electrical loads reduces the daily operating cost of the energy supply system by RMB 124.12 and decreases carbon emissions by 22.7%. [ABSTRACT FROM AUTHOR]

Video super-resolution (VSR), which takes advantage of multiple low-resolution (LR) video frames to reconstruct corresponding high-resolution (HR) frames in a video, has raised increasing interest. To upsample an LR frame (denoted by a reference frame), VSR methods usually align multiple neighboring frames (denoted by supporting frames) to the reference frame first in order to provide more relevant information. The existing VSR methods usually employ deformable convolution to conduct the frame alignment, where the whole supporting frame is aligned to the reference frame without a specific target and without supervision. Thus, the aligned features are not explicitly learned to provide the HR frame information and cannot fully explore the supporting frames. To address this problem, in this work, we propose a novel video super-resolution framework with Position-Guided Multi-Head Alignment, termed as PGMH-A, to explicitly align the supporting frames to different spatial positions of the HR frame (denoted by different heads). It injects explicit position information to obtain multi-head-aligned features of supporting frames to better formulate the HR frame. PGMH-A can be trained individually or end-to-end with the ground-truth HR frames. Moreover, a Position-Guided Multi-Head Fusion, termed as PGMH-F, is developed based on the attention mechanism to further fuse the spatial–temporal information across temporal supporting frames, across multiple heads corresponding to the different spatial positions of an HR frame, and across multiple channels. Together, the proposed Position-Guided Multi-Head Alignment and Fusion (PGMH-AF) can provide VSR with better local details and temporal coherence. The experimental results demonstrate that the proposed method outperforms the state-of-the-art VSR networks. Ablation studies have also been conducted to verify the effectiveness of the proposed modules. [ABSTRACT FROM AUTHOR]

The shrinkage method is one of the earliest and most widely used mining methods in China's underground mines, but shrinkage mining is often accompanied by a number of problems and safety hazards. With the continuous improvement of the filling process and filling material preparation and transportation technology, the application of trackless equipment such as drill jumbo and LHD (Load-Haul-Dump), and the continuous promulgation of mine safety and environmental protection policies, a large number of mines have transformed from the shrinkage method to the filling mining method. Suichang Gold Mine has improved its technical and economic indexes after transformation from the mine shrinkage method to the filling method. Its daily production capacity has increased from 30 t/d to 110 t/d, the dilution rate has decreased from 40% to 10%, the comprehensive recovery rate has increased from 95% to 98%, and the cost saving and revenue increase in the middle section of the test area of +180 m~+240 m has totaled 18,151,000 RMB. [ABSTRACT FROM AUTHOR]

As the world's largest resource production and consumption country, China is rich in mineral resources, but its per capita occupancy is lower than the world average. In this context, mining enterprises need to choose efficient, green, and safe mining methods. The backfilling method has been paid more and more attention in domestic mines because of its remarkable advantages in controlling ground pressure, reducing ore loss and dilution rate, and protecting the surface. Due to the high cost of land acquisition and the difficulty of tailing reservoir construction in Luohe Iron Mine, after careful consideration, the sublevel caving method is abandoned and the suitable filling mining method is explored. By using the finite element analysis software MIDAS GTS NX 2019 to simulate the stress and deformation of the stope roof and backfill under different stope structural parameters, the stope structural parameters that meet the mining technical conditions of Luohe Iron Mine can be determined, and combined with the actual mine conditions, the sublevel open stope subsequent filling method is finally adopted. The practical results show that after the transformation of the mining method, the economic and environmental benefits of the mine exceed expectations. This paper aims to show readers the latest progress in the transformation of mining methods in China and provide a useful reference for similar mining method transformation at home and abroad. [ABSTRACT FROM AUTHOR]

Simple Summary: This study aimed to conduct a comprehensive analysis of the factors involved in muscle growth and development, along with their associated biological functions and pathways, in cattle, pigs, and sheep. This was achieved by integrating an interpretable machine learning model with a comparative transcriptomic approach. The findings revealed that the elements closely linked to muscle growth and development across these three species exhibited high repeatability. Specifically, we identified 8 unique factors in cattle, 2 in pigs, and 1 in sheep, thereby providing novel insights into the mechanisms underlying muscle growth and development within these species. The growth and development of muscle tissue play a pivotal role in the economic value and quality of meat in agricultural animals, garnering close attention from breeders and researchers. The quality and palatability of muscle tissue directly determine the market competitiveness of meat products and the satisfaction of consumers. Therefore, a profound understanding and management of muscle growth is essential for enhancing the overall economic efficiency and product quality of the meat industry. Despite this, systematic research on muscle development-related genes across different species still needs to be improved. This study addresses this gap through extensive cross-species muscle transcriptome analysis, combined with interpretable machine learning models. Utilizing a comprehensive dataset of 275 publicly available transcriptomes derived from porcine, bovine, and ovine muscle tissues, encompassing samples from ten distinct muscle types such as the semimembranosus and longissimus dorsi, this study analyzes 113 porcine (n = 113), 94 bovine (n = 94), and 68 ovine (n = 68) specimens. We employed nine machine learning models, such as Support Vector Classifier (SVC) and Support Vector Machine (SVM). Applying the SHapley Additive exPlanations (SHAP) method, we analyzed the muscle transcriptome data of cattle, pigs, and sheep. The optimal model, adaptive boosting (AdaBoost), identified key genes potentially influencing muscle growth and development across the three species, termed SHAP genes. Among these, 41 genes (including NANOG, ADAMTS8, LHX3, and TLR9) were consistently expressed in all three species, designated as homologous genes. Specific candidate genes for cattle included SLC47A1, IGSF1, IRF4, EIF3F, CGAS, ZSWIM9, RROB1, and ABHD18; for pigs, DRP2 and COL12A1; and for sheep, only COL10A1. Through the analysis of SHAP genes utilizing Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, relevant pathways such as ether lipid metabolism, cortisol synthesis and secretion, and calcium signaling pathways have been identified, revealing their pivotal roles in muscle growth and development. [ABSTRACT FROM AUTHOR]

This research aims to address the limitations inherent in the traditional Extreme Learning Machine (ELM) algorithm, particularly the stochastic determination of input-layer weights and hidden-layer biases, which frequently leads to an excessive number of hidden-layer neurons and inconsistent performance. To augment the neural network's efficacy in pattern classification, Principal Component Analysis (PCA) is employed to reduce the dimensionality of the input matrix and alleviate multicollinearity issues during the computation of the input weight matrix. This paper introduces an enhanced ELM methodology, designated the PCA-DP-ELM algorithm, which integrates PCA with Double Pseudo-Inverse Weight Determination (DP). The PCA-DP-ELM algorithm proposed in this study consistently achieves superior average classification accuracy across various datasets, irrespective of whether assessed through longitudinal or cross-sectional experiments. The results from both experimental paradigms indicate that the optimized algorithm not only enhances accuracy but also improves stability. These findings substantiate that the proposed methodology exerts a positive influence on pattern classification. [ABSTRACT FROM AUTHOR]

Strong yet ductile alloys have long been desired for industrial applications to enhance structural reliability. This work produced two (CoCrNi)93.5Al3Ti3C0.5 medium-entropy alloys with exceptional strength–ductility combinations, via short/medium (3 min/30 min) annealing times after hot-rolling. Three types of intergranular precipitates including MC, M23C6 carbides, and L12 phase were detected in both samples. Noticeably, the high-density of intragranular L12 precipitates were only found in the medium-time annealed sample. Upon inspection of the deformed substructure, it was revealed that the plane slip is the dominant deformation mechanism of both alloys. This is related to the lower stacking fault energy, higher lattice friction induced by the C solute, and slip-plane softening caused by intragranular dense L12 precipitates. Additionally, we noted that the stacking fault and twinning act as the mediated mechanisms in deformation of the short-time annealed alloy, while only the former mechanism was apparent in the medium-time annealed alloy. The inhibited twinning tendency can be attributed to the higher energy stacking faults and the increased critical twinning stress caused by intragranular dense L12 precipitates. Our present findings provide not only guidance for optimizing the mechanical properties of high/medium-entropy alloys, but also a fundamental understanding of deformation mechanisms. [ABSTRACT FROM AUTHOR]

Compound 1 was previously identified by our team as a glycogen phosphorylase (GP) inhibitor with glucose-lowering activity and demonstrated to have protective effects against myocardial and cerebral ischemia. However, its impact on muscle function has not been clarified. This study is the first to evaluate the long-term effects of GP inhibitors on muscle function and metabolism. After a 28-day administration of Compound 1, we performed assays to assess muscle function and biochemical parameters in rats. We observed reductions in peak holding force, duration, tetanic contraction force, single-contraction force, and electromyographic signals under 20 s and 10 min contraction stimuli. The metabolic analysis showed no significant effects on muscle glycogen, ATP, lactic acid, and uric acid levels at low doses. In contrast, medium to high doses resulted in increased glycogen, decreased ATP, and reduced lactic acid (only at high doses), without affecting uric acid. These findings suggest that Compound 1 may adversely affect muscle function in rats, potentially due to the glycogen inhibition effects of GP inhibitors. This study provides crucial safety data and insights into the long-term effects of GP inhibitors on rat muscles, which will guide future developments and applications. [ABSTRACT FROM AUTHOR]

The sustainability of global agricultural produce supply chains is crucial for ensuring global food security, fostering environmental protection, and advancing socio-economic development. This study integrates bibliometric analysis, knowledge mapping, and the ABCDE framework to conduct a comprehensive qualitative and quantitative analysis of 742 relevant articles from the Web of Science core database spanning January 2009 to July 2023. Initially, bibliometric analysis and knowledge mapping reveal the annual progression of research on the sustainability of global agricultural produce supply chains, the collaborative networks among research institutions and authors, and the geographic distribution of research activities worldwide, successfully pinpointing the current research focal points. Subsequently, the ABCDE framework, constructed from the quantitative findings, helps us identify and comprehend the antecedents, barriers and challenges, impacts, and driving forces affecting the sustainability of these supply chains. The study identifies globalization and technological advancement as the primary forces shaping the sustainability of agricultural produce supply chains, despite them also posing challenges such as resource constraints and environmental pressures. Moreover, the application of innovative technologies, the optimization of organizational models, and active stakeholder engagement are key to propelling supply chains toward more sustainable development, exerting a profound impact on society, the environment, and the economy. In conclusion, this study suggests future research directions. The integrated methodology presented offers new perspectives and deep insights into the complexities of sustainable global agricultural produce supply chains, demonstrating its potential to foster knowledge innovation and practical applications, providing valuable insights for academic research and policy formulation in this domain. [ABSTRACT FROM AUTHOR]

In the process of promoting the commercialization of proton exchange membrane fuel cells, the long-term durability of the fuel cell has become a key consideration. While existing durability tests are critical for assessing cell performance, they are often time-consuming and do not quickly reflect the impact of actual operating conditions on the cell. In this study, improved testing protocols were utilized to solve this problem, which is designed to shorten the testing cycle and evaluate the degradation of the cell performance under real operating conditions more efficiently. Accelerated durability analysis for evaluating the MEA lifetime and performance decay process was carried out through two testing protocols—open circuit voltage (OCV)-based accelerated durability testing (ADT) and relative humidity (RH) cycling-based ADT. OCV-based ADT revealed that degradation owes to a combined mechanical and chemical process. RH cycling-based ADT shows that degradation comes from a mainly mechanical process. In situ fluoride release rate technology was employed to elucidate the degradation of the proton exchange membrane during the ADT. It was found that the proton exchange membrane suffered more serious damage under OCV-based ADT. The loss of F− after the durability test was up to 3.50 × 10−4 mol/L, which was 4.3 times that of the RH cycling-based ADT. In addition, the RH cycling-based ADT had a significant effect on the catalyst layer, and the electrochemically active surface area decreased by 48.6% at the end of the ADT. Moreover, it was observed that the agglomeration of the catalysts was more obvious than that of OCV-based ADT by transmission electron microscopy. It is worth noting that both testing protocols have no obvious influence on the gas diffusion layer, and the contact angle of gas diffusion layers does not change significantly. These findings contribute to understanding the degradation behavior of proton exchange membrane fuel cells under different working conditions, and also provide a scientific basis for developing more effective testing protocols. [ABSTRACT FROM AUTHOR]

Transcranial sonography (TCS) has been introduced to assess hyper-echogenicity in the substantia nigra of the midbrain for Parkinson's disease (PD); however, its subjective and resource-demanding nature has impeded its widespread application. An AI-empowered TCS-based PD classification tool is greatly demanding, yet relevant research is severely scarce. Therefore, we proposed a novel dual-channel CNXV2-DANet for TCS-based PD classification using a large cohort. A total of 1176 TCS images from 588 subjects were retrospectively enrolled from Beijing Tiantan Hospital, encompassing both the left and right side of the midbrain for each subject. The entire dataset was divided into a training/validation/testing set at a ratio of 70%/15%/15%. Development of the proposed CNXV2-DANet was performed on the training set with comparisons between the single-channel and dual-channel input settings; model evaluation was conducted on the independent testing set. The proposed dual-channel CNXV2-DANet was compared against three state-of-the-art networks (ConvNeXtV2, ConvNeXt, Swin Transformer). The results demonstrated that both CNXV2-DANet and ConvNeXt V2 performed more superiorly under dual-channel inputs than the single-channel input. The dual-channel CNXV2-DANet outperformed the single-channel, achieving superior average metrics for accuracy (0.839 ± 0.028), precision (0.849 ± 0.014), recall (0.845 ± 0.043), F1-score (0.820 ± 0.038), and AUC (0.906 ± 0.013) compared with the single channel metrics for accuracy (0.784 ± 0.037), precision (0.817 ± 0.090), recall (0.748 ± 0.093), F1-score (0.773 ± 0.037), and AUC (0.861 ± 0.047). Furthermore, the dual-channel CNXV2-DANet outperformed all other networks (all p-values < 0.001). These findings suggest that the proposed dual-channel CNXV2-DANet may provide the community with an AI-empowered TCS-based tool for PD assessment. [ABSTRACT FROM AUTHOR]

This paper proposes a deep learning-based intelligent detection device for insulation pull rod defects, addressing the issues of low detection accuracy, poor timeliness of intelligent analysis, and the difficulty in preserving detection results. Firstly, by constructing the pull rod defects dataset and training the YOLOv5s network, along with commonly used object detection algorithms in industrial defect detection, the feasibility of deep learning networks for insulation pull rod defects detection is explored. Secondly, the trained model is combined to build an intelligent detection device for pull rod defects, integrating insulation pull rod image acquisition and defect detection into a unified system. The research results demonstrate that the YOLOv5s network can quickly and accurately detect pull rod defects. On the test set constructed in this paper, the detection performance metric mAP@0.5:0.95 of the trained model reached 54.7%. Specifically, the mAP@0.5 score was 86.9% at a threshold of 0.5. The detection speed FPS reached 169.5, significantly improving the detection efficiency and accuracy compared to traditional object detection algorithms. By establishing an organic connection between the image hardware acquisition device and the deep learning network, the existing problems of inefficient detection and difficult storage of detection results in pull rod defects detection methods are effectively addressed. This research provides new insights for detecting insulation pull rod defects. [ABSTRACT FROM AUTHOR]

Longitudinal corrugated tubes (LCTs) exhibit stable platform force under axial compression but have low specific energy absorption. Conversely, circumferential corrugated tubes (CCTs) offer higher specific energy absorption but with unstable platform force. To overcome these limitations, this paper introduces a novel bi-directional corrugated tube (BCT) that amalgamates the strengths of both the CCT and LCT while mitigating their weaknesses. The BCT is formed by rolling a bi-directional corrugated structure into a circular tubular form. Numerical simulations of the BCT closely align with experimental results. The study further examines the influence of discrete parameters on the BCT's performance through simulations and identifies the tube's optimal design using the integral entropy TOPSIS method. A full factorial experimental approach is then employed to investigate the impact of radial amplitude, axial amplitude, and neutral surface diameter on the crushing behavior of the BCT, comparing it with the CCT and LCT. The results reveal that increasing A i enhances the axial resistance of the structure, while increasing A j reduces the buckling effect, resulting in a higher specific energy absorption and lower ultimate load capacity for the BCT compared to the CCT and LCT. A simultaneous multi-objective optimization of the CCT, LCT, and BCT confirms that the BCT offers superior specific energy absorption and ultimate load capacity. The optimal configuration parameters for the BCT have been determined, providing significant insights for practical applications in crashworthiness engineering. [ABSTRACT FROM AUTHOR]

Plants regulate the biosynthesis and emission of metabolic compounds to manage herbivorous stresses. In this study, as a destructive pest, the pre-infestation of rice striped stem borer (SSB, Chilo suppressalis) larvae on rice (Oryza sativa) reduced the subsequent SSB female adult oviposition preference. Widely targeted volatilomics and transcriptome sequencing were used to identify released volatile metabolic profiles and differentially expressed genes in SSB-infested and uninfested rice plants. SSB infestation significantly altered the accumulation of 71 volatile organic compounds (VOCs), including 13 terpenoids. A total of 7897 significantly differentially expressed genes were identified, and genes involved in the terpenoid and phenylpropanoid metabolic pathways were highly enriched. Correlation analysis revealed that DEGs in terpenoid metabolism-related pathways were likely involved in the regulation of VOC biosynthesis in SSB-infested rice plants. Furthermore, two terpenoids, (−)-carvone and cedrol, were selected to analyse the behaviour of SSB and predators. Y-tube olfactometer tests demonstrated that both (−)-carvone and cedrol could repel SSB adults at higher concentrations; (−)-carvone could simultaneously attract the natural enemies of SSB, Cotesia chilonis and Trichogramma japonicum, and cedrol could only attract T. japonicum at lower concentrations. These findings provide a better understanding of the response of rice plants to SSB and contribute to the development of new strategies to control herbivorous pests. [ABSTRACT FROM AUTHOR]

To synthesize an effective and versatile nano-platform serving as a promising carrier for controlled drug delivery, visible-light-induced diselenide-crosslinked polyurethane micelles were designed and prepared for ROS-triggered on-demand doxorubicin (DOX) release. A rationally designed amphiphilic block copolymer, poly(ethylene glycol)-b-poly(diselenolane diol-co-isophorone diisocyanate)-b-poly(ethylene glycol) (PEG-b-PUSe-b-PEG), which incorporates dangling diselenolane groups within the hydrophobic PU segments, was initially synthesized through the polycondensation reaction. In aqueous media, this type of amphiphilic block copolymer can self-assemble into micellar aggregates and encapsulate DOX within the micellar core, forming DOX-loaded micelles that are subsequently in situ core-crosslinked by diselenides via a visible-light-triggered metathesis reaction of Se-Se bonds. Compared with the non-crosslinked micelles (NCLMs), the as-prepared diselenide-crosslinked micelles (CLMs) exhibited a smaller particle size and improved colloidal stability. In vitro release studies have demonstrated suppressed drug release behavior for CLMs in physiological conditions, as compared to the NCLMs, whereas a burst release of DOX occurred upon exposure to an oxidation environment. Moreover, MTT assay results have revealed that the crosslinked polyurethane micelles displayed no significant cytotoxicity towards HeLa cells. Cellular uptake analyses have suggested the effective internalization of DOX-loaded crosslinked micelles and DOX release within cancer cells. These findings suggest that this kind of ROS-triggered reversibly crosslinked polyurethane micelles hold significant potential as a ROS-responsive drug delivery system. [ABSTRACT FROM AUTHOR]

Surface defect inspection based on deep learning has demonstrated outstanding performance in improving detection accuracy and model generalization. However, the small scale of defect datasets always limits the application of deep models in industry. Generative models can obtain realistic samples in a very cheap way, which can effectively solve this problem and thus has received widespread attention in recent years. This paper provides a comprehensive analysis and summary of the current studies of surface defect inspection methods proposed between 2022 and 2024. First, according to the use of generative models, these methods are classified into four categories: Variational Auto-Encoders (VAEs), Generative Adversarial Networks (GANs), Diffusion Models (DMs), and multi-models. Second, the research status of surface defect inspection based on generative models in recent years is discussed from four aspects: sample generation, detection objective, inspection task, and learning model. Then, the public datasets and evaluation metrics that are commonly used for surface defect inspection are discussed, and a comparative evaluation of defect inspection methods based on generative models is provided. Finally, this study discusses the existing challenges for the defect inspection methods based on generative models, providing insights for future research. [ABSTRACT FROM AUTHOR]

The question "How does it work" has motivated many scientists. Through the study of natural phenomena and behaviors, many intelligence algorithms have been proposed to solve various optimization problems. This paper aims to offer an informative guide for researchers who are interested in tackling optimization problems with intelligence algorithms. First, a special neural network was comprehensively discussed, and it was called a zeroing neural network (ZNN). It is especially intended for solving time-varying optimization problems, including origin, basic principles, operation mechanism, model variants, and applications. This paper presents a new classification method based on the performance index of ZNNs. Then, two classic bio-inspired algorithms, a genetic algorithm and a particle swarm algorithm, are outlined as representatives, including their origin, design process, basic principles, and applications. Finally, to emphasize the applicability of intelligence algorithms, three practical domains are introduced, including gene feature extraction, intelligence communication, and the image process. [ABSTRACT FROM AUTHOR]

Simple Summary: Methane (CH4) is a by-product of ruminant microbial fermentation, leading to a 2–12% loss of feed energy. The greenhouse gas effect of methane in the atmosphere is 28 times greater than that of carbon dioxide. Recent studies have shown that supplementing feed additives can effectively reduce ruminal methane emissions. This study aimed to evaluate the effectiveness of Sargassum mcclurei (S. mcclurei) in mitigating methane emissions using different treatment methods and supplementation levels through in vitro rumen fermentation. Three supplementation levels were tested for both dried and freeze-dried treatments over 48 h of in vitro rumen fermentation. Fermentation gas production was recorded, and after fermentation, methane production, dry matter degradation (DMD), and various fermentation parameters were measured. The addition of S. mcclurei affected crude protein degradation (CPD) and volatile fatty acid (VFA) production. The freeze-dried S. mcclurei at a 2% supplementation level reduced methane emissions by 18.85%. Methane emissions from ruminants significantly contribute to greenhouse gases. This study explores the methane mitigation effect and mechanism of S. mcclurei through in vitro rumen fermentation, aiming to establish its potential as a feed additive. We investigated the effects of freeze-dried and dried S. mcclurei at supplementation levels of 2%, 5%, and 10% of dry matter on nutrient degradation, ruminal fermentation, methane inhibition, and microbial community structure in in vitro rumen fermentation. The freeze-dried S. mcclurei at 2% supplementation significantly reduced CH4 emissions by 18.85% and enhanced crude protein degradability. However, total VFA and acetate concentrations were lower in both treatments compared to the control. The microbial shifts included a decrease in Lachnospiraceae_NK3A20_group and Ruminococcus and an increase in Selenomonas, Succinivibrio, and Saccharofermentans, promoting propionate production. Additionally, a significant reduction in Methanomicrobium was observed, indicating direct methane mitigation. Freeze-dried S. mcclurei at a 2% supplementation level shows potential as an effective methane mitigation strategy with minimal impact on rumen fermentation, supported by detailed insights into microbial community changes. [ABSTRACT FROM AUTHOR]

Pipeline maintenance technology based on pipeline intelligent plugging robot (PIPR) has become an effective method for failure accident prevention of high-pressure subsea oil and gas pipelines. However, during the plugging operation, the vortexes and pressure fluctuation are presented under turbulent flow field excitation, which may lead to vortex-induced vibration and failure of the plugging operation. Therefore, in order to ensure the reliability of pipeline plugging, the vibration characteristics are analyzed using numerical simulation, providing guidance on the structural optimization of PIPR's end face. Firstly, the flow field characteristics under different PIPR's end faces are investigated. Secondly, an experimental scheme is designed based on Latin Hypercube Sampling Design (LHSD) optimized by greedy strategy. A mathematical model of the end face's parameters and pressure gradient is established using a back propagation (BP) neural network. Then, an improved whale optimization algorithm (IWOA) is proposed to optimize the end face's parameters to minimize the pressure gradient of the flow field. Finally, the experimental study is performed to observe the turbulent flow field and pressure fluctuation to validate the optimization results. The results demonstrate that the PIPR's end face has a great influence on the vortex-induced vibration response. After structural optimization, the average pressure gradient of the optimal PIPR's end face has decreased by 84.69% and 54.55% before and after the plugging process, compared to the original end face. This study can provide a reference for pipeline plugging operations, which is significant for preventing pipeline failure accidents. [ABSTRACT FROM AUTHOR]

The reinforced part at the bottom of high-rise assembled monolithic concrete shear wall structures generally uses cast-in-place concrete due to high elastic–plastic deformation capacity requirements, which limits the advantages of prefabricated shear wall structures. This study investigates the feasibility of using cluster connections to assemble integral shear wall structures at the bottom by modifying the vertical reinforcement of the cluster connection. Numerical simulations using ABAQUS (Version 2019) were validated with laboratory test results. The seismic performance of prefabricated shear walls with cluster connections was examined under varying axial compression ratios. Results indicate that the prefabricated shear wall demonstrates higher bearing and deformation capacity compared to cast-in-place shear walls. The degradation of strength and equivalent stiffness in prefabricated walls is slower, showing better seismic performance under higher axial compression ratios. The cluster connection ensures effective force transmission, maintaining wall integrity. After optimization, the prefabricated shear wall with cluster connection meets the expected seismic performance, providing a basis for its application in reinforced bottom sections. [ABSTRACT FROM AUTHOR]

Accurately controlling the dynamic response and suppression of undesirable dynamics such as overshoots and vibrations is a vital requirement for soft robots operating in industrial environments. Pneumatically actuated soft robots usually undergo large overshoots and significant vibrations when deactuated because of their highly flexible bodies. These large vibrations not only decrease the reliability and accuracy of the soft robot but also introduce undesirable characteristics in the system. For example, it increases the settling time and damages the body of the soft robot, compromising its structural integrity. The dynamic behavior of the soft robots on deactuation needs to be accurately controlled to increase their utility in real-world applications. The literature on pneumatic soft robots still does not sufficiently address the issue of suppressing undesirable vibrations. To address this issue, we propose the use of impedance control to regulate the dynamic response of pneumatic soft robots since the superiority of impedance control is already established for rigid robots. The soft robots are highly nonlinear systems; therefore, we formulated a nonlinear discrete sliding mode impedance controller to control the pneumatic soft robots. The formulation of the controller in discrete-time allows efficient implementation for a high-order system model without the need for state-observers. The simplification and efficiency of the proposed controller enable fast implementation of an embedded system. Unlike other works on pneumatic soft robots, the proposed controller does not require manual tuning of the controller parameters and automatically calculates the parameters based on the impedance value. To demonstrate the efficacy of the proposed controller, we used a 6-chambered parallel soft robot as an experimental platform. We presented the comparative results with an existing state-of-the-art controller in SMC control of pneumatic soft robots. The experiment results indicate that the proposed controller can effectively limit the amplitude of the undesirable vibrations. [ABSTRACT FROM AUTHOR]

The reliability of GIS (gas-insulated switchgear) circuit breakers significantly depends on the condition of the insulated pull rods, with micro-defects on their surface posing a potential risk for micro-discharges and breakdown incidents. Experimentally investigating these micro-discharges is challenging due to their minute nature. This study introduces a framework to examine the linkage between micro-defects and micro-discharges, coupled with numerical simulations of the micro-discharge process in insulated pull rods afflicted by surface infiltration flaws under operational conditions. Initially, samples containing micro-defects were sectioned via water jet cutting for microstructural analysis through white light interferometry. Subsequently, a two-dimensional axisymmetric model simulating positive corona discharge from a needle to a plate electrode was employed to derive the relationship between charged particle density and the electric field in SF6 and air. Building on these observations, a micro-discharge model specific to micro-defects was developed. Comparative analysis of micro-discharge behaviors in SF6 and air for identical defect types was conducted. This research framework elucidates the discharge dynamics of charged particles in SF6 and air during micro-discharge events, shedding light on the mechanisms underpinning micro-discharges triggered by insulation rod defects. [ABSTRACT FROM AUTHOR]

Drylands are characterized by unique ecosystem types, sparse vegetation, fragile environments, and vital ecosystem services. The accurate mapping of dryland ecosystems is essential for their protection and restoration, but previous approaches primarily relied on modifying land use data derived from remote sensing, lacking the direct utilization of latest remote sensing technologies and methods to map ecosystems, especially failing to effectively identify key ecosystems with sparse vegetation. This study attempts to integrate Google Earth Engine (GEE), random forest (RF) algorithm, multi-source remote sensing data (spectral, radar, terrain, texture), feature optimization, and image segmentation to develop a fine-scale mapping method for an ecologically critical area in northern China. The results showed the following: (1) Incorporating multi-source remote sensing data significantly improved the overall classification accuracy of dryland ecosystems, with radar features contributing the most, followed by terrain and texture features. (2) Optimizing the features set can enhance the classification accuracy, with overall accuracy reaching 91.34% and kappa coefficient 0.90. (3) User's accuracies exceeded 90% for forest, cropland, and water, and were slightly lower for steppe and shrub-steppe but were still above 85%, demonstrating the efficacy of the GEE and RF algorithm to map sparse vegetation and other dryland ecosystems. Accurate dryland ecosystems mapping requires accounting for regional heterogeneity and optimizing sample data and feature selection based on field surveys to precisely depict ecosystem patterns in complex regions. This study precisely mapped dryland ecosystems in a typical dryland region, and provides baseline data for ecological protection and restoration policies in this region, as well as a methodological reference for ecosystem mapping in similar regions. [ABSTRACT FROM AUTHOR]

The increasing prominence of climate change, geopolitical crises, and global economic slowdown highlights the challenges and structural deficiencies of traditional cross-border agro-food supply chains. As a result, there has been a growing consensus on the need to leverage digital technology to rebuild and innovate a safe, stable, and sustainable global food system. This study assessed the knowledge progress and development trends in the sustainable development of cross-border agro-food supply chains enabled by digital technology. A total of 352 authoritative papers from the core Web of Science database were selected for analysis. The Citespace tool was utilized to visually examine research elements. The findings reveal that research outcomes in this territory experienced a significant period of rapid growth, particularly after 2020. Sustainability and IEEE Access are the journals with the highest and second-highest number of publications. China and the France National Institute are the countries and research institutions with the largest number of publications in this field. The research hotspots are mainly the application of digital technologies, food safety, and supply chain system model innovation. In the past ten years, the research in this territory has gone through three stages: precise timeliness orientation, intelligent strategic decision-making orientation, and model predictability orientation. We further construct the 'antecedent–practice–performance' conceptual framework of the sustainability of the digital technology-enabled cross-border agro-food supply chain. Finally, this paper presents the potential research directions in this territory, focusing on four aspects: research method, research mechanism, research topic, and research frontier. [ABSTRACT FROM AUTHOR]

To explore the solution of the two-step method applied in the rapid construction of super large cross-section tunnels passing through IV-and V-grade surrounding rock sections in karst cave areas, based on an engineering example of the Lianhuashan Tunnel, we use the numerical calculation method to analyze the stability of surrounding rock and the design parameters of the control measures for super large cross-section tunnels during the construction of the step method. The calculated results show that the working face of IV-grade surrounding rock can be stabilized by an advanced small pipe, and the stability of the supporting structure should be controlled mainly by IV-grade surrounding rock. In order to control the stability of the tunnel face, it is necessary to use an advanced large pipe shed in the surrounding V-grade rock. The reinforcement range of the advanced large pipe shed is 120° and the length is 20 m. This is the most economical design parameter of the advanced large pipe shed, ensuring the deformation control effect. For control of the stability of the supporting structure, under the condition that the working space is suitable for large machinery, the settlement of the arch of the supporting structure can be obviously reduced by shortening the step cycle footage and reducing the step length, and the peripheral convergence of the supporting structure can be obviously reduced by reducing the step height. After comprehensive analysis and considering the development of karst caves, the advanced support measures, design parameters, bench excavation design parameters, initial support measures, karst cave treatment measures, and bench construction process of IV- and V-grade surrounding rock is determined. The application verification shows that the research results have a good control effect on the stability of the surrounding rock and cave and are suitable for large-scale mechanical operations, which can significantly improve the excavation speed of the super large cross-section tunnel passing through the IV- and V-grade surrounding rock sections in the karst cave area. [ABSTRACT FROM AUTHOR]

This article presents the distinctive butyrylcholinesterase (BChE) fluorescent probe P5, designed via a targeting-site method. This method was proposed to enhance the affinity of the probe for BChE by targeting the peripheral anionic site (PAS) of BChE. By mimicking the natural substrate butyrylcholine, the structure of the probe was optimized by introducing a positive charge. Fluorescent probe P5, selected from a series of designed fluorescent probes P1–P6, exhibited excellent affinity and specificity towards BChE, enabling rapid detection within 5 min with a low detection limit of 16.7 ng/mL. Furthermore, this probe can distinguish between normal cells and Alzheimer's disease (AD) model cells, and demonstrated good imaging results in a P12 cell AD model. The results of this study indicate that this novel fluorescent probe could serve as a promising tool for the rapid detection of BChE and accurate AD diagnosis. [ABSTRACT FROM AUTHOR]

Data augmentation is considered a promising technique to resolve the imbalance of large and small objects. Unfortunately, most existing methods augment all small objects indiscriminately, regardless of their learnability and proportion. This tends to result in wasteful enlargement for many weak, low-information objects but under-augmentation for rare and learnable objects. To this end, we propose a value-guided adaptive data augmentation for scale- and proportion-imbalanced small object detection (ValCopy-Paste). Specifically, we first develop a non-learning object value criteria to determine whether one object should be expanded. Both scale-based learnability and quantity-based necessity are involved in this criteria. Then, the value distribution of objects in the dataset can be further constructed on the basis of the relevant object values. This helps to ensure that those uncommon, learnable objects that deserve enhancement are more likely to be enhanced. Additionally, we propose to enhance the data by pasting the sampled objects into relatively smooth portions of fresh background images, rather than arbitrary areas of any background images. This helps to boost data diversity while reducing the interference from complicated backgrounds. Evidently, our method does not require sophisticated training and just depends on the size and distribution of the objects in the dataset. Extensive experiments on MS COCO 2017 and PASCAL VOC 2012 demonstrate that our method achieves better performance than state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Edge computing brings computation and storage resources to the edge of the mobile network to solve the problems of low latency and high real-time demand. However, edge computing is more vulnerable to malicious attacks due to its open and dynamic environments. In this article, we investigate security defense strategies in edge computing systems, focusing on scenarios with one attacker and multiple defenders to determine optimal defense strategies with minimal resource allocation. Firstly, we formulate the interactions between the defenders and the attackers as the mean-field Stackelberg game model, where the state and the objective functions of the defenders are coupled through the mean-field term, and are strongly influenced by the strategy of the attacker. Then, we analyze the local optimal strategies of the defenders given an arbitrary strategy of the attackers. We demonstrate the Nash equilibrium and the mean-field equilibrium for both the defenders and the attackers. Finally, simulation analysis will illustrate the dynamic evolution of the defense strategy of the defenders and the trajectory of the attackers based on the proposed Stackelberg game model. [ABSTRACT FROM AUTHOR]

Defect inspection is a critical task in ensuring the surface quality of steel plates. Deep neural networks have the potential to achieve excellent inspection accuracy if defect samples are sufficient. Nevertheless, it is very different to collect enough samples using cameras alone. To a certain extent, generative models can alleviate this problem but poor sample quality can greatly affect the final inspection performance. A sample generation method, which employs a generative adversarial network (GAN), is proposed to generate high-quality defect samples for training accurate inspection models. To improve generation quality, we propose a production-and-elimination, two-stage sample generation process by simulating the formation of defects on the surface of steel plates. The production stage learns to generate defects on defect-free background samples, and the elimination stage learns to erase defects on defective samples. By minimizing the differences between the samples at both stages, the proposed model can make generated background samples close to real ones while guiding the generated defect samples to be more realistic. Experimental results show that the proposed method has the ability to generate high-quality samples that can help train powerful inspection models and thereby improve inspection performance. [ABSTRACT FROM AUTHOR]

Typical lubricants behave in a non-Newtonian manner under conditions of high shear and high pressure, as is commonly observed in lubricated rolling/sliding contacts. To optimize and predict the friction therein, knowledge of the high-pressure rheological behaviors of lubricants and limiting shear stress (LSS) is essential. This study developed an approach for determining the LSS of lubricants based on friction mapping of rolling/sliding contacts, using a ball-on-disc traction machine. The main contribution lies in the introduction of a practical approach for the selection of a proper entrainment velocity for determining the LSS, with reduced thermal influences and near isothermal conditions. The proposed approach enables full film lubrication, while keeping the film as thin as possible to prevent excessive shear heating and, thus, thermal effects. The LSS of two lubricants, PAO40 and complex ester, has been measured at pressures ranging from 1.2 GPa to 1.7 GPa. A bilinear model has been used to describe the variation of LSS with pressure. The impact of entrainment velocity selection on the measurement of LSS is also discussed. [ABSTRACT FROM AUTHOR]

In this study, we established a feasible fertilization programming method for wheat production by exploring the effects of the combined application of chemical and organic fertilizers on wheat yield, nutrient uptake, soil nutrient content, and fertilizer utilization. Six treatments, no fertilizer (CK), conventional fertilizer (CF), optimized fertilizer (with reduced fertilizer amount) (RF), chemical fertilizer with organic fertilizer extract (RPAE), partial replacement of chemical fertilizer with raw amino acid powder (RAF), and partial replacement of chemical fertilizer with raw humic acid powder (RHF), were set up for a field experiment. The fertilizer application rates for the RF treatment were calculated based on fertilization-monitoring techniques (30.3% nitrogen and 24.8% phosphorus reductions in 2022 and 23.0% nitrogen and 1.5% phosphorus reductions in 2023). The effects of different fertilizer treatments on yield, dry matter accumulation, plant nutrient accumulation, soil nutrients, and nutrient utilization in wheat were investigated. The results showed that, on the basis of 23% nitrogen and 1.5% phosphorus reductions, there was no significant difference in wheat yield between the RF and CF treatments and that the utilization rate of nitrogen fertilizer was improved. The application of organic fertilizer promoted dry matter accumulation in different organs of wheat; increased plant nutrient accumulation; improved soil nutrient content, nutrient utilization rate, nutrient partial productivity, and nutrient agronomic use efficiency; and ensured stable and increased crop yield. Specifically, compared with CF, the RPAE, RAF, and RHF organic fertilizer treatments increased wheat yield by 3.85%, 1.97%, and 0.67%, respectively, and the utilization of nitrogen and phosphorus fertilizers induced by these treatments significantly increased by 40.46%, 39.28%, and 37.46% (nitrogen) and by 9.83%, 8.91%, and 7.46% (phosphorus), respectively. As a result of our experiment, we concluded that RPAE exerted the best effects among the three organic fertilizer treatments (RPAE, RAF, and RHF) and that its use can result in a higher wheat yield and fertilizer utilization rate in drip-irrigated wheat fields. The results of this study provide a theoretical basis for the combined application of chemical and organic fertilizers, which is conducive to sustainable agriculture development. [ABSTRACT FROM AUTHOR]

The farming–pastoral ecotone in northern China is an ecologically vulnerable area with low-quality arable land, and cash crops are an important economic source for local farmers. Although local governments have introduced supportive policies, there are still several factors that hinder the implementation of the policies: there is a lack of sufficient research on the distribution of specialty crops, and the driving factors for agricultural planting structure adjustment are not yet clear. In this study, the specialty cash crop of the daylily planting industry in Yunzhou District, in the Farming–Pastoral Ecotone in northern China, was selected as the research object. Field surveys were conducted to collect sample points and village-level survey data, which were further combined with Sentinel-1 and Sentinel-2 data, and vegetation indices. Support vector machine (SVM) and random forest (RF) classifiers were utilized to identify daylilies and compare the accuracy using different combinations of input data. Furthermore, the classification results were counted by village, and spatial autocorrelation was used to analyze the spatial distribution pattern of daylilies. Finally, in conjunction with the village-level survey data, Spearman correlation analysis, multiple regression trees (MRT), and random forests were employed to explore the driving factors of daylily cultivation. The results indicate that using an RF classification tree of 300 resulted in the optimal method, as it achieved the highest accuracy for crop classification. The overall accuracy and daylily classification accuracy were 94.6% and 94.75%, respectively. Daylily distributions were mainly concentrated near the Sanggan River, urban areas, and the tourism industry. The distribution area of daylilies in each village was concentrated in 13.4–38.8 hm2. Spatial clustering showed more aggregation of low–low and high–high types. Labor force and daylily yield were identified as the most significant influencing factors. Further analysis of the different regions revealed the importance of industry support policies and technical training. This study provides data to support the distribution of specialty crops in Yunzhou District and a technical basis for adjusting agricultural planting structures. [ABSTRACT FROM AUTHOR]

The solution of equilibrium positions is a critical component in the calculation of the dynamic characteristic coefficients of aerostatic bearings. The movement of the rotor in one direction leads to bidirectional variations in the air film force, resulting in low efficiency when using conventional calculation methods. It can even lead to iterative divergence if the initial value is improperly selected. This study concentrates on the orifice throttling aerostatic bearings and proposes a novel method called the bivariate interpolation method (BIM) to calculate the equilibrium position. The equilibrium equation for the rotor under the combined influence of air film forces, gravity, and external loads is established. A calculation program based on the finite difference method is developed to determine the equilibrium position. The process of solving the equilibrium position and the convergence is compared with the secant method and the search method. Furthermore, the variation trend of the equilibrium position and stiffness when the external loads changes are studied based on the BIM. Finally, the correctness of the BIM to solve the equilibrium position is proved by comparing it with the experiment results. The calculation results indicate that the BIM successfully resolves the problem of initial value selection and exhibits superior computational efficiency and accuracy. The equilibrium position initially moves away from the direction of the external load as the load increases, and then this gradually approaches the load direction. The main stiffness increases with increases in the external load, while the variation in cross stiffness depends on the direction of the external load. [ABSTRACT FROM AUTHOR]

Nature reserves play an important role in the protection of biological habitats and the maintenance of biodiversity, but the performance and mechanisms of desert steppe nature reserves in improving plant community productivity, biodiversity and soil nutrient content are still largely unknown. To investigate the conservation effects of desert steppe nature reserve management on plant productivity and biodiversity, we compared the plant biomass, diversity and soil nutrient content inside and outside the West Ordos National Nature Reserve through sample survey, biomass determination, diversity index calculation and soil nutrient content determination. We found the following: (1) The aboveground biomass and belowground biomass of plant communities in the nature reserve were significantly larger than those outside the reserve; and the aboveground biomass of plant communities in shrub–steppe was significantly larger than that of herb grassland in both the nature reserve and the outside of the reserve. (2) The Margalef richness index, Shannon–Wiener index and Simpson index were significantly greater in the reserve than outside the nature reserve. In the desert steppe, the establishment of the nature reserve increased the α-diversity of the plant community. (3) The soil organic carbon (SOC) and soil total nitrogen (STN) were greater in the nature reserve than outside the reserve, and for the 10–20 cm and 20–40 cm soil layers, the SOC and STN were significantly greater in the core protected zone of the reserve than outside the reserve. The reserve significantly increased the nutrient content of the deeper soil layers. (4) The aboveground biomass of the plant community had a significant positive linear relationship with the species richness index, the Shannon index, and the Simpson index. There was a positive correlation between the diversity of the plant community and the soil nutrients. In summary, the nature reserve improved local plant productivity, biodiversity and the soil nutrient content, and the soil nutrient content in deeper soil layers may be the driving factor for the increase in productivity and biodiversity, which deepens our understanding of the conservation effectiveness of the nature reserve and its mechanisms. [ABSTRACT FROM AUTHOR]

The Sc(OTf)3-catalyzed dearomative [5+1] annulations between readily available 3-aminophenols and O-alkyl ortho-oxybenzaldehydes were developed for synthesis of spiro[chromane-3,1′-cyclohexane]-2′,4′-dien-6′-ones. The "two-birds-with-one-stone" strategy was disclosed by the dearomatization of phenols and direct α-C(sp3)–H bond functionalization of oxygen through cascade condensation/[1,5]-hydride transfer/dearomative-cyclization process. In addition, the antifungal activity assay and derivatizations of products were conducted to further enrich the utility of the structure. [ABSTRACT FROM AUTHOR]

As vital equipment in modern industry, the health state of rotating machinery influences the production process and equipment safety. However, rotating machinery generally operates in a normal state most of the time, which results in limited fault data, thus greatly constraining the performance of intelligent fault diagnosis methods. To solve this problem, this paper proposes a novel fault diagnosis method for rotating machinery with limited multisensor fusion samples based on the fused attention-guided Wasserstein generative adversarial network (WGAN). Firstly, the dimensionality of collected multisensor data is reduced to three channels by principal component analysis, and then the one-dimensional data of each channel are converted into a two-dimensional pixel matrix, of which the RGB images are obtained by fusing the three-channel two-dimensional images. Subsequently, the limited RGB samples are augmented to obtain sufficient samples utilizing the fused attention-guided WGAN combined with the gradient penalty (FAWGAN-GP) method. Lastly, the augmented samples are applied to train a residual convolutional neural network for fault diagnosis. The effectiveness of the proposed method is demonstrated by two case studies. When training samples per class are 50, 35, 25, and 15 on the KAT-bearing dataset, the average classification accuracy is 99.9%, 99.65%, 99.6%, and 98.7%, respectively. Meanwhile, the methods of multisensor fusion and the fused attention mechanism have an average improvement of 1.51% and 1.09%, respectively, by ablation experiments on the WT gearbox dataset. [ABSTRACT FROM AUTHOR]

Passive location based on TDOA (time difference of arrival) and FDOA (frequency difference of arrival) is the mainstream method for target localization. This paper proposes a fast time–frequency difference positioning method to address issues such as low accuracy, large computational resource utilization, and limited suitability for real-time signal processing in the conventional CAF (cross-ambiguity function)-based approach, aiming to complete the processing of the target radiation source to obtain the target parameters within a short timeframe. In the mixing product operation step of the CAF, a frequency-domain approach replaces the time-domain convolution operation in PW-ZFFT (pre-weighted Zoom-FFT) to reduce the computational load of the CAF. Additionally, a quadratic surface fitting method is used to enhance the accuracy of TDOA and FDOA. The localization solution is obtained using Newton's method, which can provide more accurate results compared to analytical methods. Next, a signal processing platform is designed with FPGA (field-programmable gate array) and multi-core DSP (digital signal processor), and works by dividing and mapping the algorithm functional modules according to the hardware's characteristics. We analyze the architectural advantages of multi-core DSP and design methods to improve program performance, such as EDMA transfer optimization, inline function optimization, and cache optimization. Finally, this paper constructs simulation tests in typical positioning scenarios and compares them to hardware measurement results, thus confirming the correctness and real-time capability of the program. [ABSTRACT FROM AUTHOR]

Morchella esculenta is a precious edible and medicinal fungus rich in protein, polysaccharides, polyphenols, amino acids, triterpenes, and other active components. In this study, MS-1 was isolated from the fruiting body of M. esculenta. Through conducting single-factor experiments and the response surface analysis of the culture conditions, the optimal culture components of an M. esculenta fermentation broth for extracellular polysaccharide production were determined, namely, 3.7% glucose, 2% yeast extract, and 0.15% sodium chloride. The polysaccharides MSF and MSL were extracted from the fruiting body of M. esculenta and the fermentation broth, respectively, and analyzed with gel permeation chromatography (GPC), monosaccharide composition, scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), and in vivo and in vitro antioxidant and anti-inflammatory activity analyses. The research results show that the calculated MW of MSF is 2.34 × 105 Da, and the calculated MW of MSL is 1.40 × 105 Da. MSF is composed of three monosaccharides: D-galactose, D-glucose, and D-mannose (molar ratio of 4.34:90.22:5.45). MSL consists of five monosaccharides: D-arabinose, D-galactose, D-glucose, D-mannose, and glucuronic acid (molar ratio of 0.31:14.71:13.03:71.43:0.53). The in vitro antioxidant test results show that MSF and MSL both have significant antioxidant activities. Activity experiments on MSF and MSL in zebrafish showed that MSF and MSL have significant repair effects on the oxidative damage caused by metronidazole in zebrafish embryos, and there were significant changes in the transcriptional activity levels of the oxidative stress-related genes SOD, Keap1, and Nrf2. Therefore, the polysaccharides MSF and MSL from MS-1 can be used as important raw materials for functional foods and drugs. [ABSTRACT FROM AUTHOR]

Field Programmable Gate Arrays (FPGAs), renowned for their reconfigurable nature, offer unmatched flexibility and cost-effectiveness in engineering experimentation. They stand as the quintessential platform for hardware acceleration and prototype validation. With the increasing ubiquity of FPGA chips and the escalating scale of system designs, the significance of their accompanying Electronic Design Automation (EDA) tools has never been more pronounced. The placement process, serving as the linchpin in FPGA EDA, directly influences FPGA development and operational efficiency. This paper introduces an FPGA placement methodology hinging on the Verilog-to-Routing (VTR) framework. We introduce a novel packing approach grounded in the weighted Edmonds' Blossom algorithm, ensuring that the CLB generation strategy aligns more closely with load-balanced distribution. Furthermore, we enhanced the electric field-driven resolver placement process for CLB locations and leverage GPU-accelerated design. Experimental results demonstrate substantial improvements over the traditional VTR algorithm, with an average optimization of 28.42% in the packing process runtime, an average acceleration ratio of 2.85 times in the placement phase, and a 39.97% reduction in total packing and placement runtime consumption. [ABSTRACT FROM AUTHOR]

The reliable operation of industrial equipment is imperative for ensuring both safety and enhanced production efficiency. Machine learning technology, particularly the Light Gradient Boosting Machine (LightGBM), has emerged as a valuable tool for achieving effective fault warning in industrial settings. Despite its success, the practical application of LightGBM encounters challenges in diverse scenarios, primarily stemming from the multitude of parameters that are intricate and challenging to ascertain, thus constraining computational efficiency and accuracy. In response to these challenges, we propose a novel innovative hybrid algorithm that integrates an Arithmetic Optimization Algorithm (AOA), Simulated Annealing (SA), and new search strategies. This amalgamation is designed to optimize LightGBM hyperparameters more effectively. Subsequently, we seamlessly integrate this hybrid algorithm with LightGBM to formulate a sophisticated fault warning system. Validation through industrial case studies demonstrates that our proposed algorithm consistently outperforms advanced methods in both prediction accuracy and generalization ability. In a real-world water pump application, the algorithm we proposed achieved a fault warning accuracy rate of 90%. Compared to three advanced algorithms, namely, Improved Social Engineering Optimizer-Backpropagation Network (ISEO-BP), Long Short-Term Memory-Convolutional Neural Network (LSTM-CNN), and Grey Wolf Optimizer-Light Gradient Boosting Machine (GWO-LightGBM), its Root Mean Square Error (RMSE) decreased by 7.14%, 17.84%, and 13.16%, respectively. At the same time, its R-Squared value increased by 2.15%, 7.02%, and 3.73%, respectively. Lastly, the method we proposed also holds a leading position in the success rate of a water pump fault warning. This accomplishment provides robust support for the timely detection of issues, thereby mitigating the risk of production interruptions. [ABSTRACT FROM AUTHOR]