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Fractional flow analysis is an efficient tool to evaluate the gas-trapping performance of foam in porous media. The pore-scale simulation study and the core-scale experimental work have been bridged via the fractional flow analysis to distinguish the characteristics of foam displacement inside the tight porous media with varying absolute permeability, injection rate, and foam quality. In this work, the combined investigation suggests that conventional foam-enhancing strategies, pursuing higher foam quality and stronger foam regime, are inefficient and restricted in tight reservoirs that the critical Sw corresponding to the limiting capillary pressure has increased around 37~43%, which indicates severely weakened gas-trapping capacity as permeability reduces one order of magnitude. The moderate mobility adjustment and corresponding optimized fluid injectivity exerting from the "weak foam" flow presents a staged decline feature of decreasing water fractional flow, which implies the existence of the delayed gas-trapping phenomenon when water saturation reduces to 0.5~0.6. The finding has supported the engineering ideal of promoting low-tension gas (LTG) drive processes as a potential solution to assist field gas injection applications suffering from gas channeling. Also, the validation with core-flooding experimental results has revealed several defects of the current pore network model of foam displacement in tight porous media, including exaggerated gas trapping and overestimated confining water saturation. This study has innovatively demonstrated the feasibility and potential of optimizing the foam performance of gas trapping and mobility control in tight reservoirs, which provides a clue that may eventually boost the efficiency of the gas injection process in enhanced oil recovery or CO2 sequestration projects. [ABSTRACT FROM AUTHOR]

Marek's disease (MD), an immunosuppressive disease induced by the Marek's disease virus (MDV), is regarded as an ideal model for lymphoma research to elucidate oncogenic and anti-oncogene genes. Using this model, we found that circRUNX2.2, derived from exon 6 of RUNX2, was significantly upregulated in MDV-infected tumorous spleens. In this study, we deeply analyzed the potential role of circRUNX2.2 in lymphoma cells. An open reading frame (ORF) in circRUNX2.2 with no stop codon was predicted, and small peptides (named circRUNX2.2-rt) presenting multiple ladder-like bands with different molecular weights encoded by circRUNX2.2 were detected via Western blotting assay. The polysome fraction assay reconfirmed the translation ability of circRUNX2.2, which could be detected in polysome fractions. Subsequent analysis verified that it translated in a rolling circle manner, rather than being assisted by the internal ribosome entry site (IRES) or m6A-mediated mechanism. Furthermore, we found that circRUNX2.2-rt was potently induced in MSB1 cells treated with sodium butyrate (NaB), which reactivated MDV and forced the MDV transition from the latent to reactivation phase. During this phase, MDV particles were clearly observed by electron microscopy, and the viral gene pp38 was also significantly upregulated. A biological function study showed that circRUNX2.2-rt promoted cell proliferation and cell cycle transition from the S to G2 phase and inhibited the apoptosis of MSB1. Further immunoprecipitation and mass spectrometry assays showed that 168 proteins potentially interacting with circRUNX2.2-rt were involved in multiple pathways related to cell cycle regulation, which proved that circRUNX2.2-rt could bind or recruit proteins to mediate the cell cycle. [ABSTRACT FROM AUTHOR]

Bromus tectorum, a winter annual plant, produces dimorphic diaspores: complex diaspores with multi-awns and simple diaspores with one awn. However, there is no information available about the role of awns and the germination characteristics of dimorphic diaspores. Dispersal germination and awns hygroscopicity of the dimorphic diaspores were assessed. The complex diaspore with multi-awns can easily be dispersed long distances from the mother plant by mammals. The simple diaspores with one awn are tightly attached to the mother plant. Caryopses from the two types of diaspores exhibited non-deep physiological dormancy at maturity, which can be released by dry storage and GA3 treatment. The awns have hygroscopic activity and can move in response to changes in moisture, moving the complex diaspore (the seed) into the soil. The seedling emergence from complex diaspores was significantly higher than those from simple diaspores at all burial depths. Germination of caryopses on the soil surface was poor. The optimal planting depth for both types of diaspores' emergence is 1–2 cm. The distinct characteristics of dimorphic diaspores and the beneficial influence of hygroscopic awns on dispersal, germination, and seedling establishment have significant ecological implications for B. tectorum's successful reproduction in unpredictable cold deserts. [ABSTRACT FROM AUTHOR]

The aim of this study was to examine the biological activity and probiotic properties of lactic acid bacteria (LAB) isolated from sweet potato stalk kimchi (SPK). Various LAB and Bacillus spp. are active in the early stages of the fermentation of kimchi made from sweet potato stalk. Four strains of LAB were identified, including SPK2 (Levilactobacillus brevis ATCC 14869), SPK3 (Latilactobacillus sakei NBRC 15893), SPK8 and SPK9 (Leuconostoc mesenteroides subsp. dextranicum NCFB 529). SPK2, SPK3, SPK8, and SPK9 showed 64.64–94.23% bile acid resistance and 78.66–82.61% pH resistance. We identified over 106 CFU/mL after heat treatment at 75 °C. Four strains showed high antimicrobial activity to Escherichia coli and Salmonella Typhimurium with a clear zone of >11 mm. SPK2 had the highest antioxidative potentials, higher than the other three bacteria, with 44.96 μg of gallic acid equivalent/mg and 63.57% DPPH scavenging activity. These results demonstrate that the four strains isolated from sweet potato kimchi stalk show potential as probiotics with excellent antibacterial effects and may be useful in developing health-promoting products. [ABSTRACT FROM AUTHOR]

Submerged plants and related disturbances can affect both the phosphorus (P) release and the microbial communities in sediments. In this study, a sediment resuspension system was constructed, and P variability characteristics influenced by Vallisneria natans (V. natans) and the response mechanism of the microbial community were studied. The results indicated that the total phosphorus (TP) content increased from 678.875 to 1019.133 mg/kg and from 1126.017 to 1280.679 mg/kg in sediments and suspended solids (SSs) during the sediment resuspension process, respectively. Organic P (OP) increased by 127.344 mg/kg and 302.448 mg/kg in sediments and SSs after the disturbance, respectively. The microbial communities in the sediments and the leaves of V. natans had higher Chao values after the disturbance, while Shannon values decreased after the disturbance compared to the control in SSs. Proteobacteria had the highest abundance with the value of 51.1% after the disturbance in the sediments and SSs, and the abundance values of Proteobacteria in rhizomes and leaves of V. natans could reach 73.2% on average. Chloroflexi, Acidobacteria, and Firmicutes were also the main phyla in the sediment resuspension system. Sodium hydroxide extractable P (NaOH-P) in sediments could reduce the bioavailability of this P fraction under disturbance conditions. The decrease in the abundance of Bacteroidetes and Nitrospirae indicated that they were more sensitive to the disturbance, and the rotational speed changed the survival conditions for the Bacteroidetes and Nitrospirae. The response mechanism of microbial community during the sediment resuspension process could reflect the influence of the microbial community on the changing characteristics of P and could provide a theoretical foundation for P control at the micro level. [ABSTRACT FROM AUTHOR]

Accurate traffic flow prediction in road networks is essential for intelligent transportation systems (ITS). Since traffic data are collected from the road network with spatial topological and time series sequences, the traffic flow prediction is regarded as a spatial–temporal prediction task. With the powerful ability to model the non-Euclidean data, the graph convolutional network (GCN)-based models have become the mainstream framework for traffic forecasting. However, existing GCN-based models either use the manually predefined graph structure to capture the spatial features, ignoring the heterogeneity of road networks, or simply perform 1-D convolution with fixed kernel to capture the temporal dependencies of traffic data, resulting in insufficient long-term temporal feature extraction. To solve those issues, a spatial–temporal correlation constrained dynamic graph convolutional network (STC-DGCN) is proposed for traffic flow forecasting. In STC-DGCN, a spatial–temporal embedding encoder module (STEM) is first constructed to encode the dynamic spatial relationships for road networks at different time steps. Then, a temporal feature encoder module with heterogeneous time series correlation modeling (TFE-HCM) and a spatial feature encoder module with dynamic multi-graph modeling (SFE-DCM) are designed to generate dynamic graph structures for effectively capturing the dynamic spatial and temporal correlations. Finally, a spatial–temporal feature fusion module based on a gating fusion mechanism (STM-GM) is proposed to effectively learn and leverage the inherent spatial–temporal relationships for traffic flow forecasting. Experimental results from three real-world traffic flow datasets demonstrate the superior performance of the proposed STC-DGCN compared with state-of-the-art traffic flow forecasting models. [ABSTRACT FROM AUTHOR]

Equipment fault diagnosis NER is to extract specific entities from Chinese equipment fault diagnosis text, which is the premise of constructing an equipment fault diagnosis knowledge graph. Named entity recognition for equipment fault diagnosis can also provide important data support for equipment maintenance support. Equipment fault diagnosis text has complex semantics, fuzzy entity boundaries, and limited data size. In order to extract entities from the equipment fault diagnosis text, this paper presents an NER model for equipment fault diagnosis based on RoBERTa-wwm-ext and Deep Learning network integration. Firstly, this model uses the RoBERTa-wwm-ext to extract context-sensitive embeddings of text sequences. Secondly, the context feature information is obtained through the BiLSTM network. Thirdly, the CRF is combined to output the label sequence with a constraint relationship, improve the accuracy of sequence labeling task, and complete the entity recognition task. Finally, experiments and predictions are carried out on the constructed dataset. The results show that the model can effectively identify five types of equipment fault diagnosis entities and has higher evaluation indexes than the traditional model. Its precision, recall, and F1 value are 94.57%, 95.39%, and 94.98%, respectively. The case study proves that the model can accurately recognize the entity of the input text. [ABSTRACT FROM AUTHOR]

The On-Load Tap Changer (OLTC) is the only movable mechanical component in a converter transformer. To ensure the reliable operation of the OLTC and to promptly detect mechanical faults in OLTCs to prevent them from developing into electrical faults, this paper proposes a fault diagnosis method for OLTCs based on a combination of Particle Swarm Optimization (PSO) algorithm and Least Squares Support Vector Machine (LSSVM) with multi-feature fusion. Firstly, a multi-feature extraction method based on time/frequency domain statistics, synchrosqueezed wavelet transform, singular value decomposition, and multi-scale modal decomposition is proposed. Meanwhile, the random forest algorithm is used to screen features to eliminate the influence of redundant features on the accuracy of fault diagnosis. Secondly, the PSO algorithm is introduced to optimize the hyperparameters of LSSVM to obtain optimal parameters, thereby constructing an optimal LSSVM fault diagnosis model. Finally, different types of feature combinations are utilized for fault diagnosis, and the impact of these feature combinations on the fault diagnosis results is compared. Experimental results indicate that features of different types can complement each other, making the OLTC state information carried by multi-dimensional features more comprehensive, which helps to improve the accuracy of fault diagnosis. Compared with four traditional fault diagnosis methods, the proposed method performs better in fault diagnosis accuracy, achieving the highest accuracy of 98.58%, which can help to detect mechanical faults in the OLTC early and reduce the system's downtime. [ABSTRACT FROM AUTHOR]

The genus Gymnopus plays a significant role in ecological systems, with certain species holding potential as food or medicinal resources. However, the species diversity of Gymnopus in China remains unclear. In recent years, more than one thousand Gymnopus specimens have been collected across China. Thus, through the integration of ecological evidence, detailed morphological studies, and phylogenetic analysis using a multiloci dataset of ITS + nLSU + tef1-ɑ, four new species—Gymnopus longistipes, Gymnopus striatipileatus, Gymnopus viridiscus, and Gymnopus spadiceus—have been differentiated from known species. Gymnopus similis has been newly documented from Jiangxi Province, China. Detailed descriptions and vivid illustrations have been provided based on the newly collected specimens, along with comparisons to closely related species. Additionally, a key to the reported species of Gymnopus s.l. from East China has been included. [ABSTRACT FROM AUTHOR]

In multi-source fusion deformation-monitoring methods that utilize fiber Bragg grating (FBG) data and other data types, the lack of FBG constraint points in edge regions often results in inaccuracies in fusion results, thereby impacting the overall deformation-monitoring accuracy. This study proposes a multi-source fusion deformation-monitoring calibration method and develops a calibration model that integrates vision and FBG multi-source fusion data. The core of this model is a normal distribution transform (NDT)–convolutional neural network (CNN)–self-attention (SA) calibration network. This network enhances continuity between points in point clouds using the NDT module, thereby reducing outliers at the edges of the fusion results. Experimental validation shows that this method reduces the absolute error to below 0.2 mm between multi-source fusion calibration results and high-precision measured point clouds, with a confidence interval of 99%. The NDT-CNN-SA network offers significant advantages, with a performance improvement of 36.57% over the CNN network, 14.39% over the CNN–gated recurrent unit (GRU)–convolutional block attention module (CBAM) network, and 9.54% over the CNN–long short term memory (LSTM)–SA network, thereby demonstrating its superior generalization, accuracy, and robustness. This calibration method provides smoother and accurate structural deformation data, supports real-time deformation monitoring, and reduces the impact of assembly deviation on product quality and performance. [ABSTRACT FROM AUTHOR]

The study of regional drought characteristics is vital for identifying drought patterns and offering scientifically informed guidance for drought warnings. This research focuses on the Sichuan Basin, where the Standardized Precipitation Evapotranspiration Index (SPEI) was calculated across various time scales using meteorological data from 1963 to 2022. Wavelet analysis was applied to examine the periodic fluctuations of the SPEI across different time scales. Drought events were identified using run-length theory and spatially visualized with ArcGIS 10.7 interpolation techniques to elucidate the temporal and spatial dynamics of drought occurrences. The findings are as follows: (1) Over nearly 60 years, the SPEI in the Sichuan Basin fluctuated between −1.5 and 1, with an insignificant annual downward trend but a significant downward trend in autumn (p < 0.05). (2) The SPEI displayed a primary cycle of 6 years in spring, autumn, and winter, while the summer cycle matched the annual SPEI cycle of 8 years. (3) Drought events were more frequent in the eastern part of the basin compared to the west. The area with high drought frequency shifted counterclockwise from east to north, northwest, west, and south with the changing seasons. (4) Drought duration was longer in the western and northern regions of the basin than in the east. Severe drought events were mainly concentrated in the Chengdu Plain and the Central Sichuan Hilly Region, although the drought intensity index was lowest in the Chengdu Plain and Chongqing in eastern Sichuan. The peak values of drought showed an insignificant decreasing trend, indicating a potential expansion in the extreme impacts of drought disasters in the study area. [ABSTRACT FROM AUTHOR]

Heilongjiang, located in the cold region, is China's largest rice-producing and commercializing province. The variety selection of rice in cold regions (RCR) is indispensable in promoting the rice industry's development, and technological innovation in breeding theory plays a significant role in breeding breakthrough. Based primarily on long-term research, contemplation, and breeding practices, the 4S (selected topic, selected breeding, selected progenies, and selected promotion) phenotypic-design breeding technical system for RCR, which has revolutionized the theoretical basis and deepened the conceptual model, has been developed through systematic analyses and generalization. The system covers several key aspects such as scientific question formulation, breeding objective optimization, parental taxa hybridization, progeny population selection, and innovation dissemination, aiming to improve the foresight, precision, and efficiency of breeding. Furthermore, the system has demonstrated successful cases of rice variety breeding over the past 20 years, such as for Suijing 3, Suijing 4, Suijing 18, and a series of other rice varieties, providing theoretical and technical supports for cold-region rice breeding. [ABSTRACT FROM AUTHOR]

Water management is critical for wheat production under extreme drought conditions, and the mechanisms by which root dynamics and soil water utilization affect wheat yield are uncertain. This study was conducted in 2023–2024 under a mesophilic semi-arid climate with a two-factor partitioned experimental design, aiming to assess the response of different irrigation amounts in winter wheat crops on root growth and development, soil water utilization, and yields in different soil horizons. The results showed that variety and irrigation volume had significant effects on the spatial and temporal distribution of root and yield components, with irrigation volume having the greatest effect on yield. Compared with CK, deficit-regulated irrigation significantly promoted root penetration to deeper layers and delayed root senescence. DRWD, RLD, RSA, and RV decreased gradually with increasing soil depth, and the peaks of RLD, RSA, and RV appeared at the tassel to flowering stage, respectively; and under deficit-regulated irrigation, the contribution of the A2W4 treatment to stable yield was greater. Therefore, A2W4 is an effective water-saving irrigation method to improve grain yield and water-use efficiency under deficit-regulated irrigation. [ABSTRACT FROM AUTHOR]

Seed geometric parameters are important in yielding trait scorers, quantitative trait loci, and species recognition and classification. A novel method for automatic measurement of three-dimensional seed phenotypes is proposed. First, a handheld three-dimensional (3D) laser scanner is employed to obtain the seed point cloud data in batches. Second, a novel point cloud-based phenotyping method is proposed to obtain a single-seed 3D model and extract 33 phenotypes. It is connected by an automatic pipeline, including single-seed segmentation, pose normalization, point cloud completion by an ellipse fitting method, Poisson surface reconstruction, and automatic trait estimation. Finally, two statistical models (one using 11 size-related phenotypes and the other using 22 shape-related phenotypes) based on the principal component analysis method are built. A total of 3400 samples of eight kinds of seeds with different geometrical shapes are tested. Experiments show: (1) a single-seed 3D model can be automatically obtained with 0.017 mm point cloud completion error; (2) 33 phenotypes can be automatically extracted with high correlation compared with manual measurements (correlation coefficient (R2) above 0.9981 for size-related phenotypes and R2 above 0.8421 for shape-related phenotypes); and (3) two statistical models are successfully built to achieve seed shape description and quantification. [ABSTRACT FROM AUTHOR]

The accuracy and reliability of soil moisture retrieval based on Global Positioning System (GPS) single-star Signal-to-Noise Ratio (SNR) data is low due to the influence of spatial and temporal differences of different satellites. Therefore, this paper proposes a Random Forest (RF)-based multi-satellite data fusion Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) soil moisture retrieval method, which utilizes the RF Model's Mean Decrease Impurity (MDI) algorithm to adaptively assign arc weights to fuse all available satellite data to obtain accurate retrieval results. Subsequently, the effectiveness of the proposed method was validated using GPS data from the Plate Boundary Observatory (PBO) network sites P041 and P037, as well as data collected in Lamasquere, France. A Support Vector Machine model (SVM), Radial Basis Function (RBF) neural network model, and Convolutional Neural Network model (CNN) are introduced for the comparison of accuracy. The results indicated that the proposed method had the best retrieval performance, with Root Mean Square Error (RMSE) values of 0.032, 0.028, and 0.003 cm3/cm3, Mean Absolute Error (MAE) values of 0.025, 0.022, and 0.002 cm3/cm3, and correlation coefficients (R) of 0.94, 0.95, and 0.98, respectively, at the three sites. Therefore, the proposed soil moisture retrieval model demonstrates strong robustness and generalization capabilities, providing a reference for achieving high-precision, real-time monitoring of soil moisture. [ABSTRACT FROM AUTHOR]

In maritime transportation, a ship's draft survey serves as a primary method for weighing bulk cargo. The accuracy of the ship's draft reading determines the fairness of bulk cargo transactions. Human visual-based draft reading methods face issues such as safety concerns, high labor costs, and subjective interpretation. Therefore, some image processing methods are utilized to achieve automatic draft reading. However, due to the limitations in the spectral characteristics of RGB images, existing image processing methods are susceptible to water surface environmental interference, such as reflections. To solve this issue, we obtained and annotated 524 multispectral images of a ship's draft as the research dataset, marking the first application of integrating NIR information and RGB images for automatic draft reading tasks. Additionally, a dual-branch backbone named BIF is proposed to extract and combine spectral information from RGB and NIR images. The backbone network can be combined with the existing segmentation head and detection head to perform waterline segmentation and draft detection. By replacing the original ResNet-50 backbone of YOLOv8, we reached a mAP of 99.2% in the draft detection task. Similarly, combining UPerNet with our dual-branch backbone, the mIoU of the waterline segmentation task was improved from 98.9% to 99.3%. The inaccuracy of the draft reading is less than ±0.01 m, confirming the efficacy of our method for automatic draft reading tasks. [ABSTRACT FROM AUTHOR]

An accurate assessment of the spatial–temporal variations in regional net ecosystem productivity (NEP), water use efficiency (WUE), and carbon use efficiency (CUE) are vital for understanding the water–carbon cycle. We analyzed the spatial–temporal patterns of the NEP, WUE, and CUE in the middle reaches of the Yellow River (MRYR) from 2001 to 2022, and the factors that influenced them using remote sensing data, NEP estimation models, and various statistical methods. The results indicate that the recovery of the ecosystem in the MRYR is a result of the combined effects of climate change and human activities. Climate change in the MRYR led to warming and humidification from 2001 to 2022. The NEP, WUE, and CUE were characterized by increasing trends, with average growth rates of 7.75 gC m−2a−1, 0.012 gC m−2 mm−1a−1, and 0.009a−1, respectively. For four vegetation types, the interannual rates of change were, in descending order, grassland, cropland, shrubs, and forest. Spatially, the NEP, WUE, and CUE showed significant regional heterogeneity, increasing from the northwest to the southeast. Based on an analysis of the interannual anomalies, precipitation accumulation contributed to carbon sink accumulation. The correlation of the NEP, WUE, and CUE with the drought severity index (DSI) was high, and their correlation with precipitation showed latitudinal zonality, which suggests that precipitation (PRE) is the main climatic factor influencing the water–carbon cycle in the MRYR rather than temperature (TEM). There were 67,671.27 km2 of land that changed use during 2001–2022, and 15.07 Tg of NEP was added to these areas. [ABSTRACT FROM AUTHOR]

Automatic classification of buildings within port areas from synthetic aperture radar (SAR) images is crucial for effective port monitoring and planning. Yet, the unique challenges of SAR imaging, such as side-looking geometry, multi-bouncing scattering, and the compact arrangement of structures, often lead to incomplete building structures and blurred boundaries in classification results. To address these issues, this paper introduces SPformer, an efficient and lightweight pyramid transformer model tailored for semantic segmentation. The SPformer utilizes a pyramid transformer encoder with spatially separable self-attention (SSSA) to refine both local and global spatial information and to process multi-scale features, enhancing the accuracy of building structure delineation. It also integrates a lightweight all multi-layer perceptron (ALL-MLP) decoder to consolidate multi-scale information across various depths and attention scopes, refining detail processing. Experimental results on the Gaofen-3 (GF-3) 1 m port building classification dataset demonstrate the effectiveness of SPformer, achieving competitive performance compared to state-of-the-art models, with mean intersection over union (mIoU) and mean F1-score (mF1) reaching 77.14% and 87.04%, respectively, while maintaining a compact model size and lower computational requirements. Experiments conducted on the entire scene of SAR images covering port area also show the good capabilities of the proposed method. [ABSTRACT FROM AUTHOR]

The control of the injection speed in hydraulic injection molding machines is critical to product quality and production efficiency. This paper analyzes servomotor-driven constant pump hydraulic systems in injection molding machines to achieve optimal tracking control of the injection speed. We propose an efficient reinforcement learning (RL)-based approach to achieve fast tracking control of the injection speed within predefined time constraints. First, we construct a precise Markov decision process model that defines the state space, action space, and reward function. Then, we establish a tracking strategy using the Deep Deterministic Policy Gradient RL method, which allows the controller to learn optimal policies by interacting with the environment. Careful attention is also paid to the network architecture and the definition of states/actions to ensure the effectiveness of the proposed method. Extensive numerical results validate the proposed approach and demonstrate accurate and efficient tracking of the injection velocity. The controller's ability to learn and adapt in real time provides a significant advantage over the traditional Proportion Integration Differentiation controller. The proposed method provides a practical solution to the challenge of maintaining accurate control of the injection speed in the manufacturing process. [ABSTRACT FROM AUTHOR]

Truck cranes, which are crucial construction equipment, need to maintain good operational performance to ensure safe use. However, the complex and ever-changing working conditions they face often make it challenging to test their performance effectively. To address this issue, a multi-input and multi-output soft sensor technology model is suggested, utilizing a graph convolutional network and random forest to predict key performance indicators of crane operations such as luffing, telescoping, winching, and slewing under varying conditions. This method aims to streamline the process of testing and debugging truck cranes, ultimately reducing time and costs. Initially, the graph convolutional network model is employed to extract relevant feature information linked to the target variable. Subsequently, using this feature information and the RF model, multiple decision trees are constructed for regression prediction of the target variables. An operational dataset reflecting the crane's actual working conditions is then generated to assess the graph convolutional network and random forest model. The effectiveness of this approach is further confirmed through comparisons with other methods like gradient boosting trees, support vector regression, and multi-layer perceptron. [ABSTRACT FROM AUTHOR]

During the axial modification in helical gear form grinding, the contact line between the grinding wheel and the gear constantly changes, and the additional radial motion can cause a "tooth surface twist" phenomenon. An optimization method for tooth surface twist error was proposed to address this. Based on the gear meshing principles, a mathematical model for axial modification in form grinding was established to solve for the instantaneous contact lines at various positions on the actual modified tooth surface. By analyzing the influence of the grinding wheel installation angle on the axial modification contact line, an optimization model was constructed to reduce the twist of the transverse profile, reduce the twist of the flank profile, reduce the helix deviation, and improve the form grinding efficiency. The practical implications of this research are significant, as the Particle Swarm Optimization (PSO) algorithm was employed to optimize the form grinding parameters, leading to a method that effectively reduced tooth surface twist error and improved the form grinding accuracy of the modified tooth surface. [ABSTRACT FROM AUTHOR]

5-Hydroxytryptamine (5-HT) is an inhibitory neurotransmitter widely distributed in mammalian tissues, exerting its effects through binding to various receptors. It plays a crucial role in the proliferation of granulosa cells (GCs) and the development of follicles in female animals, however, its effect on porcine follicle development is not clear. The aim of this study is to investigate the expression of 5-HT and its receptors in various parts of the pig ovary, as well as the effect of 5-HT on porcine follicular development by using ELISA, quantitative real-time PCR (qPCR) and EdU assays. Firstly, we examined the levels of 5-HT and its receptors in porcine ovaries, follicles, and GCs. The findings revealed that the expression of different 5-HT receptors varied among follicles of different sizes. To investigate the relationship between 5-HT and its receptors, we exposed the GCs to 5-HT and found a decrease in 5-HT receptor expression compared to the control group. Subsequently, the treatment of GCs with 0.5 μM, 5 μM, and 50 μM 5-HT showed an increase in the expression of cell cycle-related genes, and EdU results indicated cell proliferation after the 0.5 μM 5-HT treatment. Additionally, the expression of genes involved in E2 synthesis was examined after the treatment of granulosa cells with 0.5 μM 5-HT. The results showed that CYP19A1 and HSP17β1 expression was decreased. These results suggest that 5-HT might affect the development of porcine follicle by promoting the proliferation of GCs and inhibiting the synthesis of estrogen. This provides a new finding for exploring the effect of 5-HT on follicular development, and lays a foundation for further research on the mechanism of 5-HT in follicles. [ABSTRACT FROM AUTHOR]

Mycorrhizal fungi, a category of fungi that form symbiotic relationships with plant roots, can participate in the induction of plant disease resistance by secreting phosphatase enzymes. While extensive research exists on the mechanisms by which mycorrhizal fungi induce resistance, the specific contributions of phosphatases to these processes require further elucidation. This article reviews the spectrum of mycorrhizal fungi-induced resistance mechanisms and synthesizes a current understanding of how phosphatases mediate these effects, such as the induction of defense structures in plants, the negative regulation of plant immune responses, and the limitation of pathogen invasion and spread. It explores the role of phosphatases in the resistance induced by mycorrhizal fungi and provides prospective future research directions in this field. [ABSTRACT FROM AUTHOR]

Polycyclic aromatic hydrocarbons (PAHs) are persistent environmental pollutants commonly found in water and sediments, posing significant health risks due to their toxicity, carcinogenicity, and mutagenicity. The stable and sustainable degradation of PAHs has garnered significant attention from researchers. Biological electrochemical systems (BESs) offer a promising approach with advantages in energy efficiency, safety, environmental protection, and long-term operation. This review examines the degradation performance and microbial community dynamics of BESs in the treatment of PAH-contaminated water and sediments. Additionally, the metabolites formed during the degradation process were also summarized. This review summarizes the degradation characteristics of PAH-contaminated water and sediments and aims to guide future research and optimize BESs for effective remediation of PAHs in various environmental settings. [ABSTRACT FROM AUTHOR]

(1) Background: In vitro diagnostic (IVD) tests are the main means of obtaining diagnostic information for clinical purposes. The electrochemiluminescence immunoassay (ECLIA) has become an important in vitro diagnostic technique. It has unique advantages and broad market prospects due to its sensitivity, detection limit, detection range and reagent stability. At present, there is a need to develop and optimize electrochemiluminescence immunoassay subsystems to achieve high-throughput outputs and accurate detection of instruments to promote their clinical application. (2) Methods: On the basis of the demand for clinical testing instruments with high detection accuracy and speed, this study constructed an electrochemiluminescence immunoassay system by designing magnetic separation modules, introducing PMT and optimizing the system timing regulation capability. (3) Results: The magnetic separation modules can increase the detection accuracy, the PMT system increases the detection sensitivity and optimized system timing can achieve maximum test output. Furthermore, this system performs well in terms of its linearity, detection limit, signal-to-noise ratio, precision and accuracy. (4) Conclusions: The electrochemiluminescence immunoassay system is capable of high throughput with good sensitivity and accuracy, meeting the basic requirements of clinical applications for detection capacity and output throughput. [ABSTRACT FROM AUTHOR]

The average daily throughput of large-scale passenger high-speed railway stations is large, and the design of the inbound space connecting with the underground and other modes of transport affects the passengers' wayfinding behaviour and time spent, which in turn affects the efficiency of the inbound station. How to optimise the design of station entry space and signage arrangement becomes the key to shortening the station entry time. In this paper, eye tracking, spatial syntax, and semantic difference methods are used to evaluate the passenger's wayfinding process in the underground hub of a large high-speed railway station and the spatial syntax is used to quantify and analyse the wayfinding path segments, to explore the influence of the spatial attributes of different nodes and the spatial arrangement of the guiding signs on the passenger's wayfinding behaviour data and the difference in attention, and to find out that the connectivity of the wayfinding nodes, the area of the field of view, and the passengers' The study concludes that the connectivity and visual field area of wayfinding nodes have a strong positive correlation with the passengers' route choice time, which has less influence on the correct rate of wayfinding and can be taken into less consideration in the subsequent design. While analysing the spatial density of signs and the correct rate of wayfinding in the sample, it is concluded that the density of guide signs is maintained in the interval of 5–11‰, and at the same time, the number is sufficient to point to the destination is a more appropriate interval, and ultimately, the impact of the correct rate of wayfinding of the weighting of the following: signage focus on the time > density of information > density of key information > diameter of the pupil. The study analyses the influencing factors affecting passengers' wayfinding behaviour from a human factors perspective and provides feedback on the design of underground entry spaces in large passenger high-speed rail stations. [ABSTRACT FROM AUTHOR]

In order to investigate the effect of thermochromic microcapsules on the surface coating performance of basswood board, two types of microcapsules prepared with syringaldehyde and sodium tripolyphosphate crosslinking agents were added to a UV primer and coated on the surface of basswood board. The color-change effect of the surface coating on basswood board with microcapsules added with syringaldehyde as the crosslinking agent was better than that with microcapsules added with sodium tripolyphosphate as the crosslinking agent, and the color difference varied more significantly with temperature. The effect of the two types of microcapsules on the glossiness of the surface coating on basswood board was relatively weak. The glossiness of the surface coating on basswood board with microcapsules containing syringaldehyde as the crosslinking agent showed an overall increasing trend with the increase in microcapsules, and the change trend was relatively gentle. The glossiness of the surface coating on basswood board with microcapsules containing sodium tripolyphosphate as the crosslinking agent increased first and then decreased as the amount of microcapsules added increased. The addition of microcapsules with syringaldehyde as the crosslinking agent had no significant effect on the reflectance in the visible light band of the surface coating on basswood board. Among the two groups of samples, the hardness increase in the surface coating on basswood board with syringaldehyde as the crosslinking agent was more significant. The adhesion level of the coating on the surface of the basswood board with the two microcapsules did not change. Neither of the microcapsules had a significant effect on the impact resistance of the surface on basswood board. In the comprehensive analysis, the surface coating on basswood board with microcapsules added with syringaldehyde as the crosslinking agent at a content of 4.0% had better comprehensive performance, better surface morphology, better color-change effect, and moderate mechanical properties. The color difference was found to be 21.0 at 25 °C, the reflectivity was found to be 57.06%, the hardness was found to be 3H, the adhesion was found to be five, and the impact resistance was found to be three. [ABSTRACT FROM AUTHOR]

To address the industry's demand for sustainable oilfield development and safe production, it is crucial to enhance the scientific rigor and accuracy of monitoring ground stability and reservoir parameter inversion. For the above purposes, this paper proposes a technical solution that employs two-dimensional time-series ground deformation monitoring based on ascending and descending Interferometric Synthetic Aperture Radar (InSAR) technique first, and the composite dislocation model (CDM) is utilized to achieve high-precision reservoir parameter inversion. To validate the feasibility of this method, the Liaohe Oilfield is selected as a typical study area, and the Sentinel-1 ascending and descending Synthetic Aperture Radar (SAR) images obtained from January 2020 to December 2023 are utilized to acquire the ground deformation in various line of sight (LOS) directions based on Multitemporal Interferometric Synthetic Aperture Radar (MT-InSAR). Subsequently, by integrating the ascending and descending MT-InSAR observations, we solved for two-dimensional ground deformation, deriving a time series of vertical and east-west deformations. Furthermore, reservoir parameter inversion and modeling in the subsidence trough area were conducted using the CDM and nonlinear Bayesian inversion method. The experimental results indicate the presence of uneven subsidence troughs in the Shuguang and Huanxiling oilfields within the study area, with a continuous subsidence trend observed in recent years. Among them, the subsidence of the Shuguang oilfield is more significant and shows prominent characteristics of single-source center subsidence accompanied by centripetal horizontal displacement, the maximum vertical subsidence rate reaches 221 mm/yr, and the maximum eastward and westward deformation is more than 90 mm/yr. Supported by the two-dimensional deformation field, we conducted a comparative analysis between the Mogi, Ellipsoidal, and Okada models in terms of reservoir parameter inversion, model fitting efficacy, and residual distribution. The results confirmed that the CDM offers the best adaptability and highest accuracy in reservoir parameter inversion. The proposed technical methods and experimental results can provide valuable references for scientific planning and production safety assurance in related oilfields. [ABSTRACT FROM AUTHOR]

Supercooled water in mixed-phase clouds plays a significant role in precipitation formation, atmospheric radiation, weather modification, and aircraft flight safety. Identifying supercooled water in mixed-phase clouds is a crucial-frontier scientific issue in atmospheric detection research. In this study, we propose a new algorithm for identifying supercooled water based on the multi-spectral peak characteristics in cloud radar power spectra, combined with radar reflectivity factor and mean Doppler velocity. Using microwave radiometer data, we conducted retrieval analyses on two stratocumulus cases in the spring over the northeastern Daxing'anling region, China. The retrieval results show that the supercooled water in the spring stratocumulus clouds over the region is widespread, with liquid water content (LWC) ranging around 0.1 ± 0.05 g/m3, and particle sizes not exceeding 10 μm. The influence of updrafts on supercooled water is evident, with both showing good consistency in spatiotemporal variation trends. Comparing the liquid water path (LWP) variations retrieved from cloud radar and microwave radiometer, both showed good consistency in variation trends and high LWC areas, indicating the reliability of the identification algorithm developed in this study. [ABSTRACT FROM AUTHOR]

Bitter melon vine (an agricultural waste product with high fiber content) is difficult to treat and has caused problems in the environment. This research aims to produce biochar through low-temperature pyrolysis assisted by non-polluting steam explosion. The physical and chemical properties of the biochar were characterized using scanning electron microscopy (SEM) images, specific surface area measurements (BET), X-ray diffraction patters (XRD), elemental analysis (EA), and Fourier transform infrared spectroscopy (FTIR). Next, the adsorption mechanism of methylene blue (MB) on the steam-exploded bitter melon vine biochar pyrolyzed at 200 °C (qBC200) and the effects of adsorption time, pH, initial concentration, adsorption temperature, and adsorbent dosage on the adsorption effect were investigated. Steam explosion destroyed the dense structure of the plant, increased the number of oxygen-containing surface functional groups, and improved the adsorption performance of the material. Therefore, qBC200 more effectively adsorbed MB than untreated biochar, reaching a saturated adsorption capacity of 267.72 mg/g. The MB adsorption kinetics and isothermal adsorption process of qBC200 align with the pseudo-second-order kinetic model and Langmuir isothermal equation (monolayer adsorption), respectively. The thermodynamic results show that MB adsorbs via a spontaneous, entropy-increasing exothermic reaction. The adsorption mechanism involves electrostatic attraction, hydrogen bonding, and π–π interactions. The prepared biomass with high fiber content is a promising new material for wastewater treatment. [ABSTRACT FROM AUTHOR]

The viable but non-culturable (VBNC) state is a survival strategy for many foodborne pathogens under adverse conditions. Yersinia enterocolitica (Y. enterocolitica) as a kind of primary foodborne pathogen, and it is crucial to investigate its survival strategies and potential risks in the food chain. In this study, the effectiveness of ultraviolet (UV) irradiation and chlorine treatment in disinfecting the foodborne pathogen Y. enterocolitica was investigated. The results indicated that both UV irradiation and chlorine treatment can induce the VBNC state in Y. enterocolitica. The bacteria completely lost culturability after being treated with 25 mg/L of NaClO for 30 min and a UV dose of 100 mJ/cm². The number of culturable and viable cells were detected using plate counting and a combination of fluorescein and propidium iodide (live/dead cells). Further research found that these VBNC cells exhibited reduced intracellular Adenosine Triphosphate (ATP) levels, and increased levels of reactive oxygen species (ROS) compared to non-induced cells. Morphologically, the cells changed from a rod shape to a shorter, coccobacillary shape with small vacuoles forming at the edges, indicating structural changes. Both condition-induced VBNC-state cells were able to resuscitate in tryptic soy broth (TSB) medium supplemented with Tween 80, sodium pyruvate, and glucose. These findings contribute to a better understanding of the survival mechanisms of Y. enterocolitica in the environment and are of significant importance for the development of effective disinfection strategies. [ABSTRACT FROM AUTHOR]

The Phytoseiulus persimilis specialized in preying on Tetranychus species, with particularly strong predation capability against Tetranychus urticae. To investigate the morphology of female reproductive structures and effects of different gravid times on structures of oocytes and embryos in Phytoseiulus persimilis, we employed paraffin sectioning, transmission electron microscopy (TEM), and scanning electron microscopy (SEM) on the model species of predatory mite Phytoseiulus persimilis. The female adult possessed several reproductive organs, including paired solenostomes, major ducts, embolus, calyces, and vesicles within the sperm-access system, as well as lyrate organ and the ovary. Furthermore, the reproductive system also encompassed the uterus, vagina, and genital pore, which were involved in egg development and expulsion. The solenostomes were situated between the third and fourth legs, and they were scarcely discernible in virgin, but they became apparent during mating. The occurrence of mating significantly influenced the nucleus of lyrate organ. In virgin, the nucleus exhibited underdeveloped morphology, whereas in mated individuals, it was well-formed. However, the duration of mating did not impact its development. The cellular structure of the ovary was solely associated with the stage of the surrounding oocyte and was not directly linked to mating occurrences. The uterus was barely visible outside of mating periods but became observable 12 h after mating when eggs were present within the body. At this point, it opened in preparation for egg laying when both the vagina and reproductive opening were open. Positioned in front of the vesicle but behind the ovary was the lyrate organ, with its lower part housing the uterus. The vagina was connected to the genital pore. No significant difference was observed in oocyte morphology between the virgin ovaries and the mated. Oocyte development occurred through four stages: during stage I (4–9 h after mating), yolk accumulation took place; stage II (10 h after mating) involved egg relocation; stage III (12–13 h after mating) was marked by eggshell formation; finally, at stage IV (14–16 h after mating), embryonic development commenced, leading to egg deposition. The fusion of sperm and egg occurred approximately 9–10 h after mating. These findings established a solid foundation for investigating the Phytoseiid reproductive mechanisms. [ABSTRACT FROM AUTHOR]

Simple Summary: Pigeons are one of the oldest domesticated birds in the world, and humans have been raising pigeons for thousands of years. Pigeon meat is edible and rich in protein, polyunsaturated fatty acids, minerals, and vitamins. As a late-maturing bird, pigeons need to be fed by their parents to obtain nutrients. Therefore, the dietary nutritional level of pigeon breeders is vital for production and reproduction. However, there is currently no explicit nutritional requirement for pigeon riboflavin internationally. This study aimed to investigate the effects of dietary riboflavin levels on the reproductive performance of pigeon breeders and the growth performance and carcass traits of offspring squabs to estimate the riboflavin requirement of pigeon breeders. The natural riboflavin content in the basic diet of corn–peas–soybean–wheat–sorghum–corn gluten is 1.20 mg/kg. Different doses of riboflavin (0, 2.5, 5, 10, and 15 mg/kg) were supplemented with the basal diet to produce five dietary treatments with total riboflavin levels of 1.20, 3.70, 6.20, 10.20, and 16.20 mg/kg. A total of 120 pairs of White King pigeons, aged 60 wks, were randomly allocated into five treatment groups, each consisting of 24 pairs. Each pair was individually raised for 8 wks. After the experiment, an assessment was conducted to evaluate the reproductive performance of the pigeon breeders, as well as the growth and carcass traits of offspring squabs at 28 days of age. The results showed that the dietary riboflavin levels had no significant effect on body weight, feed intake, egg weight, egg production, and egg fertility (p > 0.05). However, pigeons fed a diet without riboflavin had the lowest egg hatchability, egg yolk color, carcass trait, and riboflavin status, while exhibiting higher liver weight and liver index (p < 0.05). Moreover, the indices above showed increased or decreased linearly as the level of riboflavin was increased in the diet. Based on the broken-line regression model, pigeon breeders were determined to require a dietary riboflavin content of 11.4, 13.6, 13.4, 6.60, 4.28, 4.47, 4.67, 6.69, and 6.82 mg/kg to optimize hatchability, eviscerated weight, half-eviscerated weight, breast muscle weight, breast muscle percentage, liver weight, liver index, egg yolk riboflavin, and squab plasma riboflavin, respectively. In conclusion, the optimal supplemental dosage of riboflavin in the diets of pigeon breeders is 13.6 mg/kg. [ABSTRACT FROM AUTHOR]

In mineral, environmental, and engineering explorations, we frequently encounter geological bodies with varied sizes, depths, and conductivity contrasts with surround rocks and try to interpret them with single survey data. The conventional three-dimensional (3-D) inversions significantly rely on the size of the grids, which should be smaller than the smallest geological target to achieve a good recovery to anomalous electric conductivity. However, this will create a large amount of unknowns to be solved and cost significant time and memory. In this paper, we present a multi-scale (MS) stochastic inversion scheme based on shearlet transform for airborne electromagnetic (AEM) data. The shearlet possesses the features of multi-direction and multi-scale, allowing it to effectively characterize the underground conductivity distribution in the transformed domain. To address the practical implementation of the method, we use a compressed sensing method in the forward modeling and sensitivity calculation, and employ a preconditioner that accounts for both the sampling rate and gradient noise to achieve a fast stochastic 3-D inversion. By gradually updating the coefficients from the coarse to fine scales, we obtain the multi-scale information on the underground electric conductivity. The synthetic data inversion shows that the proposed MS method can better recover multiple geological bodies with different sizes and depths with less time consumption. Finally, we conduct 3-D inversions of a field dataset acquired from Byneset, Norway. The results show very good agreement with the geological information. [ABSTRACT FROM AUTHOR]

The extraction of navigation lines is a crucial aspect in the field autopilot system for intelligent agricultural equipment. Given that soybean seedlings are small, and straw can be found in certain Northeast China soybean fields, accurately obtaining feature points and extracting navigation lines during the soybean seedling stage poses numerous challenges. To solve the above problems, this paper proposes a method of extracting navigation lines based on the average coordinate feature points of pixel points in the bean seedling belt according to the calculation of the average coordinate. In this study, the soybean seedling was chosen as the research subject, and the Hue, Saturation, Value (HSV) colour model was employed in conjunction with the maximum interclass variance (OTSU) method for RGB image segmentation. To extract soybean seedling bands, a novel approach of framing binarised image contours by drawing external rectangles and calculating average coordinates of white pixel points as feature points was proposed. The feature points were normalised, and then the improved adaptive DBSCAN clustering method was used to cluster the feature points. The least squares method was used to fit the centre line of the crops and the navigation line, and the results showed that the average distance deviation and the average angle deviation of the proposed algorithm were 7.38 and 0.32. The fitted navigation line achieved an accuracy of 96.77%, meeting the requirements for extracting navigation lines in intelligent agricultural machinery equipment for soybean inter-row cultivation. This provides a theoretical foundation for realising automatic driving of intelligent agricultural machinery in the field. [ABSTRACT FROM AUTHOR]

Myxomycetes are an important component of terrestrial ecosystems, and in order to understand their diversity and phylogenetic relationships, taxonomic issues need to be addressed. In our 1985–2021 biodiversity investigations in Shaanxi Province, Jilin Province, the Inner Mongolia Autonomous Region, Hubei Province, and Henan Province, China, Diderma samples were observed on rotten leaves, rotten branches, and dead wood. The samples were studied, based on morphological features coupled with multigene phylogenetic analyses of nSSU, EF-1α, and COI sequence data, which revealed two new species (Diderma shaanxiense sp. nov. and D. clavatocolumellum sp. nov.) and two known species (D. radiatum and D. globosum). In addition, D. radiatum and D. globosum were newly recorded in Henan Province and the Inner Mongolia Autonomous Region, respectively. The paper includes comprehensive descriptions, detailed micrographs, and the outcomes of phylogenetic analyses for both the newly discovered and known species. Additionally, it offers morpho-logical comparisons between the new species and similar ones. [ABSTRACT FROM AUTHOR]

Offline handwritten Chinese character recognition involves the application of computer vision techniques to recognize individual handwritten Chinese characters. This technology has significantly advanced the research in online handwriting recognition. Despite its widespread application across various fields, offline recognition faces numerous challenges. These challenges include the diversity of glyphs resulting from different writers' styles and habits, the vast number of Chinese character labels, and the presence of morphological similarities among characters. To address these challenges, an optimization method based on a separated pre-training model was proposed. The method aims to enhance the accuracy and robustness of recognizing similar character images by exploring potential correlations among them. In experiments, the HWDB and Chinese Calligraphy Styles by Calligraphers datasets were employed, utilizing precision, recall, and the Macro-F1 value as evaluation metrics. We employ a convolutional self-encoder model characterized by high recognition accuracy and robust performance. The experimental results demonstrated that the separated pre-training models improved the performance of the convolutional auto-encoder model, particularly in handling error-prone characters, resulting in an approximate 6% increase in precision. [ABSTRACT FROM AUTHOR]

CrN and diamond-like carbon (DLC) coatings are deposited on the surface of 431 stainless steel by the direct current magnetron sputtering technique. The surface morphology, micro-structure, hardness, friction, and wear properties of CrN, CrN/DLC and multi-layer composite DLC coatings are investigated by scanning electron microscopy, X-ray diffraction, Raman spectroscopy, X-ray photoelectron spectroscopy, nanoindentation tester, scratch tester, and friction and wear tester. The results show that the surface of the single CrN coating is very rough for the columnar crystal structure with preferred orientation. When it serves as inner transition layers to form the composite DLC coatings, the surface gets much smoother, with reduced defects. The friction and wear results indicate that the composite DLC coatings exhibit lower coefficients of friction, and better wear and corrosion resistance in dry friction, deionized water, and seawater. In the dry wear and friction process, the single CrN coating is easily worn out, and severe friction oxidation and furrow wear both appear with a friction coefficient of 0.48. But the friction coefficient of a CrN coating in seawater is reduced to 0.16, and friction oxidation and wear loss are further reduced with water lubrication. The CrN/DLC coating has excellent tribological performance in three test concoctions and has the lowest friction coefficient of 0.08 in seawater, which is related to the higher sp3 bond content, density (1.907 g/cm3) and high degree of amorphization, contributing to high hardness and a self-lubrication effect. However, due to the limited thickness of CrN/DLC (1.14 µm), it easily peels off and fails during friction and wear in different testing conditions. In multi-layer composite DLC coatings, there are more sp2 bonds with decreased amorphization, high enough thickness (4.02 µm), and increased bonding strength for the formation of different carbides and nitrides of chromium as transition layers, which gives rise to the further decreased average friction coefficient and the lowest wear loss. Therefore, the CrN coating alone has good wear resistance, and, as with the inner transition layer with a DLC coating, it can effectively improve the overall thickness and the bonding strength of the multi-layer films by optimizing the chemical compounds of DLC coatings. These results provide experimental support and reference for the design and selection of surface coatings for 431 stainless steels in different working conditions. [ABSTRACT FROM AUTHOR]

In order to address the issues of excessive brittle intermetallic compounds (IMC) formation in the TC4 brazed joints, two types of novel Ti-Zr-Cu-Ni-Sn amorphous braze fillers were designed. The microstructure and shear strength of the TC4/Ti-Zr-Ni-Cu-Sn/TC4 brazed joints were studied by scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffractometer (XRD) and electronic universal materials testing machine. The results show that the optimized Ti35Zr25Ni15Cu20Sn5 braze filler whose chemical composition is closer to the eutectic point possesses a lower melting point compared with the equiatomic Ti23.75Zr23.75Ni23.75Cu23.75Sn5. This was beneficial to the sufficient diffusion of Cu and Ni elements with the base metal during brazing and reduces the residual (Ti,Zr)2(Ni,Cu) content in the joint, which helps to improve the joint performance. The room-temperature and high-temperature shear strength of the TC4 brazed joints using the near eutectic component Ti35Zr25Ni15Cu20Sn5 filler reached a maximum of 472 MPa and 389 MPa at 970 °C/10 min, which was 66% and 48% higher than that of the TC4 joints brazed with the equiatomic Ti23.75Zr23.75Ni23.75Cu23.75Sn5 braze filler. Microstructural evolution and the corresponding mechanical response were in-depth discussed. [ABSTRACT FROM AUTHOR]

Wheat is a vital global food crop, yet it faces challenges in saline–alkali soils where Fusarium crown rot significantly impacts growth. Variations in wheat growth across regions are often attributed to uneven terrain. To explore these disparities, we examined well-growing and poorly growing wheat samples and their rhizosphere soils. Measurements included wheat height, root length, fresh weight, and Fusarium crown rot severity. Well-growing wheat exhibited greater height, root length, and fresh weight, with a lower Fusarium crown rot disease index compared to poorly growing wheat. Analysis of rhizosphere soil revealed higher alkalinity; lower nutrient levels; and elevated Na, K, and Ca levels in poorly growing wheat compared to well-growing wheat. High-throughput sequencing identified a higher proportion of unique operational taxonomic units (OTUs) in poorly growing wheat, suggesting selection for distinct fungal species under stress. FUNGuild analysis indicated a higher prevalence of pathogenic microbial communities in poorly growing wheat rhizosphere soil. This study underscores how uneven terrains in saline–alkali soils affect pH, nutrient dynamics, mineral content, wheat health, and rhizosphere fungal community structure. [ABSTRACT FROM AUTHOR]

Despite the wealth of studies on benzothiadiazole (BTH) and its capacity to modulate grape polyphenol metabolism, comprehensive data detailing polyphenol accumulation at various grape growth stages remain scarce. Therefore, the present study utilized pre-harvest (at 21 days after flowering) canopy-applied BTH (50 mg/L) on Cabernet Gernischt and Cabernet Franc grapes to investigate the physicochemical and polyphenolic changes during grape maturation. The results showed that BTH treatment significantly affected the accumulation of total phenols, flavonoids, flavanols, anthocyanins, and tannins. Concurrently, it induced an increase in colorimetric parameters (CIRG, L*, b*, a*, and C*ab), resulting in the treated grapes exhibiting a greater color intensity and enhanced red-bluish colorimetric characteristics. Moreover, the BTH treatment amplified the anthocyanin content in the grapes, with the levels of petunidin-3-O-glucoside and peonidin-3-O-glucoside exhibiting remarkable increases, particularly in the Cabernet Franc grape. Notably, the content of peonidin-3-O-glucoside even surpassed that of malvidin-3-O-glucoside throughout the growth stages, indicating significant advantages in the treated samples. Additionally, the levels of flavanol, flavonols, phenolic acids, and stilbenes experienced a significant boost post-treatment. In conclusion, the application of BTH treatment can effectively enhance the accumulation of polyphenols and intensify the color of Cabernet Gernischt and Cabernet Franc grapes, thereby significantly improving the overall quality of wine grapes and ensuring the production of higher-quality wines. The research findings will serve as a theoretical foundation and provide scientific data for the appropriate utilization of BTH in wine grape cultivation. [ABSTRACT FROM AUTHOR]

Edamame (Glycine max (L.) Merr.) is a specialty soybean newly grown in the United States that has become the second most widely consumed soy food (25,000–30,000 tons annually). Poor crop establishment caused by soilborne diseases is a major problem limiting edamame production in the U.S. This study investigated 24 edamame cultivars/lines to determine their response to three soilborne pathogens causing seed rot and seedling damping off, including Rhizoctonia solani, Sclerotium rolfsii, Pythium irregulare, and Xanthomonas campestris pv. glycines, a seedborne pathogen that caused severe outbreaks of bacterial leaf pustules in mid-Atlantic regions in 2021. The hypothesis was that resistant variations existed among the genotypes, which could be used for production and future breeding efforts. The results reveal that all genotypes were affected, but partially resistant varieties could be clearly recognized by a significantly lower disease index (p < 0.05), and no genotype was resistant to all four diseases. Newly developed breeding lines showed overall higher disease resistance than commercial cultivars, particularly to R. solani and P. irregulare. This study found genetic variability in edamame, which can be helpful in breeding for resistance or tolerance to early-season diseases. The result will promote domestic edamame production and further strengthen and diversify agricultural economies in the U.S. [ABSTRACT FROM AUTHOR]

Partial discharge (PD) is one of the major causes of insulation accidents in oil-immersed transformers, generating a large number of signals that represent the health status of the transformer. In particular, acoustic signals can be detected by sensors to locate the source of the partial discharge. However, the array, type, and quantity of sensors play a crucial role in the research on the localization of partial discharge sources within transformers. Hence, this paper proposes a novel sensor array for the specific localization of PD sources using COMSOL Multiphysics software 6.1 to establish a three-dimensional model of the oil-immersed transformer and the different defect types of two-dimensional models. "Electric-force-acoustic" multiphysics field simulations were conducted to model ultrasonic signals of different types of PD by setting up detection points to collect acoustic signals at different types and temperatures instead of physical sensors. Subsequently, simulated waveforms and acoustic spatial distribution maps were acquired in the software. These simulation results were then combined with the time difference of arrival (TDOA) algorithm to solve a system of equations, ultimately yielding the position of the discharge source. Calculated positions were compared with the actual positions using an error iterative algorithm method, with an average spatial error about 1.3 cm, which falls within an acceptable range for fault diagnosis in transformers, validating the accuracy of the proposed method. Therefore, the presented sensor array and computational localization method offer a reliable theoretical basis for fault diagnosis techniques in transformers. [ABSTRACT FROM AUTHOR]

Simple Summary: Zinc (Zn) is an essential microelement for normal poultry production. This study determined the microbiota, intestinal morphology and immune status after supplementation with two different doses of Zn in pigeons. The results revealed that Zn can increase the average daily gain and ileal gene expression and improve the serum immune indices and feces microbiota profiles. Our findings provides scientific basis for the application of Zn in pigeon production. We aimed to explore the effects of two different doses of Zn on the fecal microbiota in pigeons and the correlation between these effects and intestinal immune status. Zn doses affected pigeon growth performance, and pigeons in the T60 (60 mg/kg Zn) and T90 (90 mg/kg Zn) groups exhibited higher villus height and crypt depth in duodenum and ileum compared to the control group, respectively. Supplementation with Zn increased the expression of the IL8, CD798, TJP and NKTR genes (p < 0.05), while enhancing serum immunoglobulin (Ig) G, IgM, and IgA concentrations compared to the control pigeons (p < 0.05). T60 treatment reduced relative Actinobacteriota abundance, while Lactobacillus spp. abundance was highest in the T90 group compared to the two other groups. The core functional genera significantly associated with immune indices in these pigeons were Rhodococcus erythropolis and Lactobacillus ponti. Our findings will help facilitate the application of dietary Zn intake in pig production. [ABSTRACT FROM AUTHOR]

Pleurotus tuoliensis is a unique species discovered in Xinjiang, China, which is recognized for its significant edible, medicinal, and economic value. It has been successfully incorporated into industrial production. Controversy has emerged concerning the evolution and environmental adaptability of this species due to inadequate interspecific ecology and molecular data. This study examines the germplasm resources of P. tuoliensis in the Xinjiang region. A total of 225 wild and cultivated strains of P. tuoliensis were gathered from seven representative regions. Phylogenetic analysis revealed that seven populations were notably segregated into three distinct groups, primarily attributed to environmental factors as the underlying cause for this differentiation. Population historical size data indicate that P. tuoliensis underwent two expansion events, one between 2 and 0.9 Mya (Miocene) and the other between 15 and 4 Mya (Early Pleistocene). The ancient climate fluctuations in the Xinjiang region might have contributed to the comparatively modest population size during the Pliocene epoch. Moreover, through the integration of biogeography and ancestral state reconstruction, it was determined that group C of P. tuoliensis emerged initially and subsequently dispersed to groups D and B, in that order. Subsequently, group D underwent independent evolution, whereas group B continued to diversify into groups A and EFG. The primary factor influencing this mode of transmission route is related to the geographical conditions and prevailing wind direction of each group. Subsequent research endeavors focused on assessing the domestication adaptability of P. tuoliensis to different substrates. It was found that the metabolic processes adapted during the domestication process were mainly related to energy metabolism, DNA repair, and environmental adaptability. Processes adapted to the host adaptability include responses to the host (meiosis, cell cycle, etc.) and stress in the growth environment (cysteine and methionine metabolism, sulfur metabolism, etc.). This study analyzed the systematic evolution and genetic differentiation of P. tuoliensis in Xinjiang. The identified loci and genes provide a theoretical basis for the subsequent improvement of germplasm resources and conducting molecular breeding. [ABSTRACT FROM AUTHOR]

Sodium borohydride (NaBH4) is a nontoxic and ideal storage material for hydrogen due to its safety and high hydrogen storage capacity. In order to improve the practicality of the sodium borohydride hydrogen production system, we deposited non-precious metal catalytic materials on readily available polymer foams using a simple chemical plating method, developing a suitable 3D catalyst. Its high specific surface area enables it to produce hydrogen at a rate of up to 3.92 L min−1 g−1. Its unique structure gives the catalyst excellent durability. In addition, an efficient NaBH4-based H2 supply system was developed using this catalyst. Co-Cu-B can facilitate stable hydrogen production from NaBH4, yielding a consistent power output ranging from 0 to 100 W. This work provides a new pathway for developing high-efficiency monolithic self-supported catalysts for industrial applications. [ABSTRACT FROM AUTHOR]

Junctional adhesion molecule-A (JAM-A), also known as F11 receptor (F11R), is a transmembrane glycoprotein that is involved in various biological processes, including cancer initiation and progression. However, the functional characteristics and significance of JAM-A in pan-cancer remain unexplored. In this study, we used multiple databases to gain a comprehensive understanding of JAM-A in human cancers. JAM-A was widely expressed in various tissues, mainly located on the microtubules and cell junctions. Aberrant expression of JAM-A was detected in multiple cancers at both mRNA and protein levels, which can be correlated with poorer prognosis and may be attributed to genetic alterations and down-regulated DNA methylation. JAM-A expression was also associated with immune infiltration and may affect immunotherapy responses in several cancers. Functional enrichment analysis indicated that JAM-A participated in tight junction and cancer-related pathways. In vitro experiments verified that JAM-A knockdown suppressed the proliferation and migration abilities of breast cancer cells and liver cancer cells. Overall, our study suggests that JAM-A is a pan-cancer regulator and a potential biomarker for predicting prognosis and immune-therapeutic responses for different tumors. [ABSTRACT FROM AUTHOR]

Power quality is an important metric for the normal operation of a power system, and the accurate identification of transient signals is of great significance for the improvement of power quality. The diverse types of power system transient signals and strong characteristic coupling brings new challenges to the analysis and identification of power system transient signals. In order to enhance the identification accuracy of transient signals, one method of power system transient signal identification is proposed based on a dynamic large convolution kernel and multilevel feature fusion network. First, the more fine-grained and more informative features of the transient signals are extracted by the dynamic large convolution kernel feature extraction module. Then, the multi-scale local features are adaptively fused by the multilevel feature fusion module. Finally, the fused features are reduced in dimension by the fully connected layer in the classification module and fed into the SoftMax layer for transient signal type detection. The proposed method can effectively improve the small receptive field problem of convolutional neural networks and the lack of ability of Transformer network in extracting local context information. Compared with five other power quality transient disturbance identification models, the experimental results show that the proposed method has better diagnostic accuracy and anti-noise capability. [ABSTRACT FROM AUTHOR]