Search Results (64)

A group of sulfonium and selenonium salts bearing diverse benzylidene acetal substituents on their side chain moiety were designed and synthesized. Compared with our previous study, structural modifications in this study focused on multi-substitution of the phenyl ring and bioisosteric replacements at the sulfonium cation center. In vitro biological evaluation showed that selenonium replacement could significantly improve their α-glucosidase inhibitory activity. The most potent inhibitor 20c (10.0 mg/kg) reduced postprandial blood glucose by 48.6% (15 min), 52.8% (30 min), and 48.1% (60 min) in sucrose-loaded mice, outperforming acarbose (20.0 mg/kg). Docking studies of 20c with ntMGAM presented a new binding mode. In addition to conventional hydrogen bonding and electrostatic interaction, amino residue Ala-576 was first identified to contribute to binding affinity through π-alkyl and alkyl interactions with the chlorinated substituent and aromatic ring. The selected compounds exhibited a high degree of safety in cytotoxicity tests against normal cells. Kinetic characterization of α-glucosidase inhibition confirmed a fully competitive inhibitory mode of action for these sulfonium salts. Full article

Drought severely limits crop yield every year, making it critical to clarify the genetic basis of drought tolerance for breeding of improved varieties. As drought tolerance is a complex quantitative trait, we analyzed three phenotypic groups: (1) agronomic traits under well-watered (WW) and water-deficit (WD) conditions, (2) stress tolerance indices of these traits, and (3) phenotypic plasticity, using a multi-parent doubled haploid (DH) population assessed in multi-environment trials. Genome-wide association studies (GWAS) identified 130, 171, and 71 quantitative trait loci (QTL) for the three groups of phenotypes, respectively. Only one QTL was shared among all trait groups, 25 between stress indices and agronomic traits, while the majority of QTL were specific to their group. Functional annotation of candidate genes revealed distinct pathways of the three phenotypic groups. Candidate genes under WD conditions were enriched for stress response and epigenetic regulation, while under WW conditions for protein synthesis and transport, RNA metabolism, and developmental regulation. Stress tolerance indices were enriched for transport of amino/organic acids, epigenetic regulation, and stress response, whereas plasticity showed enrichment for environmental adaptability. Transcriptome analysis of 26 potential candidate genes showed tissue-specific drought responses in leaves, ears, and tassels. Collectively, these results indicated both shared and independent genetic mechanisms underlying drought tolerance, providing novel insights into the complex phenotypes related to drought tolerance and guiding further strategies for molecular breeding in maize. Full article

In the practical application of Real-Time Precise Point Positioning (RT-PPP), the outages in receiving spatial state representation (SSR) information due to communication anomalies can result in a decrease or even divergence of the positioning accuracy of RT-PPP. To mitigate the decline in positioning accuracy, we propose a method of INS aiding RT-PPP based on an optimized stochastic model. First, the correlation between SISRE and SSR age was analyzed by using a dataset of 1800 continuous time series. A new stochastic model called clock–orbit degradation (COD) stochastic model was established to match clock–orbit time-varying statistical characteristics. Second, we introduced Inertial Navigation System (INS) enhancement information to optimize the functional model, leveraging its autonomy and high-precision short-term position constraints. Finally, the real-world static and kinematic experiments were designed to verify the proposed method. The static results showed that the RT-PPP positioning accuracy with COD stochastic model is always higher than the traditional fixed equivalent-weight stochastic model at different level SSR outages. Even with SSR interruptions, the positioning accuracy can reach 0.131 m in the horizontal direction and 0.269 m in the 3D direction, representing improvements of 23.2% and 19.0%, respectively. Furthermore, the kinematic results showed that the positioning accuracy of PPP/INS with COD stochastic model had improved by 38.7% in the horizontal direction and 69.9% in the 3D direction at half an hour of SSR age. Full article

Crop productivity is severely constrained by abiotic and biotic stresses, necessitating innovative strategies to enhance stress resilience. Glycerol-3-phosphate (G3P) is a central metabolite in carbohydrate and lipid metabolism, playing crucial roles in stress responses. In this study, we engineered a novel glycerol-3-phosphate dehydrogenase (GPDH) gene, designated OEGD, by fusing the N-terminal NAD-binding domain of rice OsGPDH1 with the feedback-resistant C-terminal catalytic domain of Escherichia coli gpsA. Overexpression of OEGD in rice enhanced tolerance to drought, phosphorus deficiency, high temperature, and cadmium (Cd²⁺) stresses, while also improving plant growth and yield under drought stress at the adult stage. Notably, the accumulation of glycerol-3-phosphate (G3P) and activities of antioxidant enzymes (SOD, POD, CAT) were significantly elevated in the transgenic plants following osmotic stimuli, and fatty acid profiles were altered, favoring stress adaptation. Transcriptomic analyses revealed that OEGD modulates cell wall biogenesis, reactive oxygen species (ROS) scavenging, and lipid metabolism pathways, with minimal disruption to core G3P metabolic genes. These findings highlight the potential of OEGD as a valuable genetic resource for improving stress resistance in rice. Full article

The research and related tests aimed to investigate the effect of different heat inputs on the microstructure and properties of the joint when using laser-backing welding for X80 steel, with the purpose of guiding a reasonable adjustment of heat inputs to obtain a sound and high-quality joint, and ultimately laying the foundation for the engineering application of laser-backing welding. The fiber-laser-backing welding is performed on a 22 mm thick X80 steel, before which a groove is prepared and assembled; joints were obtained under different heat inputs (162, 180, 210, 270 J/mm) with orthogonal combinations of laser power and welding speed. The microstructure and properties of the joints were characterized by using an optical microscope, scanning electron microscope, and microhardness tester. According to this investigation, the morphology of the joint is directly affected by the heat input, and insufficient heat input (<180 J/mm) will lead to an unacceptable weld profile. The width of the weld and heat-affected zone gets bigger as the heat input increases. The hardness nephograms of the joints under different heat inputs show that the weld has the highest hardness, followed by the coarse-grain heat-affected zone and the fine-grain heat-affected zone, sequentially. The less heat input, the lower the joint hardness; when the heat input increases to 270 J/mm, the coarse-grain zone near the fusion line shows obvious hardening. In addition, heat input also affects the impact toughness of the weld. The grain size of X80 steel with a lower content of niobium easily becomes coarse under excessive heat input (270 J/mm), resulting in the degradation of the grain-boundary slip ability; hence, the impact toughness of the joint deteriorates. The optimal heat input of 210 J/mm was identified, achieving a grain size of nearly 14 µm and providing a balanced combination of lower strength and higher impact toughness. Full article

Laser cladding (LC) is a promising technique for repairing aluminum alloy components, yet challenges like cracks and uneven surfaces persist due to unstable melt flow and thermal stress. This study employs both fluid flow and stress field models to investigate multi-track LC repair mechanisms. Using a finite volume method (FVM), the dynamic evolution of the molten pool was quantified, revealing a maximum flow velocity of 0.2 m/s, a depth of 0.7 mm, and a width of 4 mm under optimized parameters (1600 W laser power, 600 mm/min scan speed). The model also identified that surface flaws between 300 and 900 μm were fully melted and repaired by a current or adjacent track. Finite element analysis (FEA) showed that multi-layer cladding induced a cumulative thermal stress exceeding 1300 MPa at interlayer interfaces, necessitating ≥ 3 s cooling intervals to mitigate cracking risks. These findings provide critical insights into process optimization, demonstrating that adjusting laser power and scan speed can control molten pool stability and reduce residual stress, thus improving repair quality for aluminum alloys. Full article

Pest infestations have always been a major factor affecting tea production. Real-time detection of tea pests using machine vision is a mainstream method in modern agricultural pest control. Currently, there is a notable absence of machine vision devices capable of real-time monitoring for small-sized tea pests in the market, and the scarcity of open-source datasets available for tea pest detection remains a critical limitation. This manuscript proposes a YOLOv8-FasterTea pest detection algorithm based on cross-domain transfer learning, which was successfully deployed in a novel tea pest monitoring device. The proposed method leverages transfer learning from the natural language character domain to the tea pest detection domain, termed cross-domain transfer learning, which is based on the complex and small characteristics shared by natural language characters and tea pests. With sufficient samples in the language character domain, transfer learning can effectively enhance the tiny and complex feature extraction capabilities of deep networks in the pest domain and mitigate the few-shot learning problem in tea pest detection. The information and texture features of small tea pests are more likely to be lost with the layers of a neural network becoming deep. Therefore, the proposed method, YOLOv8-FasterTea, removes the P5 layer and adds a P2 small target detection layer based on the YOLOv8 model. Additionally, the original C2f module is replaced with lighter convolutional modules to reduce the loss of information about small target pests. Finally, this manuscript successfully applies the algorithm to outdoor pest monitoring equipment. Experimental results demonstrate that, on a small sample yellow board pest dataset, the mAP@.5 value of the model increased by approximately 6%, on average, after transfer learning. The YOLOv8-FasterTea model improved the mAP@.5 value by 3.7%, while the model size was reduced by 46.6%. Full article

With the increasing popularity of dogs as pets, cases of hydrocephalus have risen significantly. Due to the ongoing challenges in the diagnosis and treatment of hydrocephalus, advancements in magnetic resonance imaging (MRI) technology have greatly enhanced the diagnostic capabilities in small animal clinical practice. Assessing ventricular size is crucial for the clinical management of hydrocephalus and other neurological disorders. However, methods for quantifying ventricular size and evaluating the severity of hydrocephalus requires further optimization. This study involved 137 hydrocephalus and 17 normal dogs. In hydrocephalus cases, the maximum percentage of the ventricle height to brain height (H-max%) was correlated with the area (A-max%) and volume (V-max%). Equations were calculated based on these findings, showing that the percentage of height can effectively represent the percentage of area and volume, which can indicate the diagnosis and monitoring of hydrocephalus prognosis. Full article

Fault sealing evaluation is an important part of the preliminary evaluation of gas storage reconstruction. This article takes the 1-29 fault block of the Nanpu No. 1 structural belt in the Bohai Bay Basin of China as an example, with the goal of constructing a reservoir-type gas storage facility in this fault block and conducts research on the evaluation of the fault sealing of this fault block. A method for evaluating the vertical and lateral sealing properties of faults has been established based on a large amount of well-logging data interpretation, well formation comparison, numerical simulation, and downhole tracer methods. Based on the data from the work area, qualitative evaluation indicators such as fault occurrence, fault properties, combination characteristics, and lithological configuration combinations of the upper and lower walls were selected for fault sealing evaluation. The study area developed two types of sealing: docking sealing and fault rock sealing. The mass fraction (SGR) of fault mudstone in the work area is generally high, reaching up to 78%. The normal pressure of the eight fault sections in the research area ranges from 14.98 MPa to 15.45 MPa, and the normal pressure of the fault plane is greater than 8 MPa. Combined with the SGR value of the fault mud ratio, which is generally greater than 50%, the corresponding sand-to-ground ratio is less than 50%, and the vertical sealing of the fault belongs to the moderate to good category. Based on the comprehensive evaluation above, it is believed that the fault sealing in the research area is good and suitable for the reconstruction of gas storage facilities. Full article

In an intelligent driving environment, monitoring the physiological state of drivers is crucial for ensuring driving safety. This paper proposes a method for monitoring and analyzing driver physiological characteristics by combining electronic vehicle identification (EVI) with multimodal biometric recognition. The method aims to efficiently monitor the driver’s heart rate, breathing frequency, emotional state, and fatigue level, providing real-time feedback to intelligent driving systems to enhance driving safety. First, considering the precision, adaptability, and real-time capabilities of current physiological signal monitoring devices, an intelligent cushion integrating MEMSs (Micro-Electro-Mechanical Systems) and optical sensors is designed. This cushion collects heart rate and breathing frequency data in real time without disrupting the driver, while an electrodermal activity monitoring system captures electromyography data. The sensor layout is optimized to accommodate various driving postures, ensuring accurate data collection. The EVI system assigns a unique identifier to each vehicle, linking it to the physiological data of different drivers. By combining the driver physiological data with the vehicle’s operational environment data, a comprehensive multi-source data fusion system is established for a driving state evaluation. Secondly, a deep learning model is employed to analyze physiological signals, specifically combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. The CNN extracts spatial features from the input signals, while the LSTM processes time-series data to capture the temporal characteristics. This combined model effectively identifies and analyzes the driver’s physiological state, enabling timely anomaly detection. The method was validated through real-vehicle tests involving multiple drivers, where extensive physiological and driving behavior data were collected. Experimental results show that the proposed method significantly enhances the accuracy and real-time performance of physiological state monitoring. These findings highlight the effectiveness of combining EVI with multimodal biometric recognition, offering a reliable means for assessing driver states in intelligent driving systems. Furthermore, the results emphasize the importance of personalizing adjustments based on individual driver differences for more effective monitoring. Full article

The underground detection environment in coal mines is complex, with numerous interference sources. Traditional ground-penetrating radar (GPR) methods suffer from limited detection range, high noise levels, and weak deep signals, making it extremely difficult to accurately identify geological structures without stable feature feedback. During research, it was found that the detection energy of the same target significantly changes with the antenna direction. Based on this phenomenon, this paper proposes a geological radar advanced detection method using spatial scanning. This method overcomes constraints imposed by the underground coal mine environment on detection equipment, enhancing both detection range and accuracy compared to traditional approaches. Experiments using this method revealed pea-shaped response characteristics of planar geological structures in radar images, and the mechanisms behind their formation were analyzed. Additionally, this paper studied the changes in response characteristics under changes in target inclination, providing a basis for understanding the spatial distribution of geological structures. Finally, application experiments in underground coal mine environments explored the practical potential of this method. Results indicate that, compared to drilling data, this method achieves identification accuracies of 91.88%, 90.42%, and 78.72% for the depth and spatial extent of geological structures, providing effective technical support for coal mining operations. Full article

Forest fires can lead to a decline in ecosystem functions, such as biodiversity, soil quality, and carbon cycling, causing economic losses and health threats to human societies. Therefore, it is imperative to map forest-fire risk to mitigate the likelihood of forest-fire occurrence. In this study, we utilized the hierarchical analysis process (AHP), a comprehensive weighting method (CWM), and random forest to map the forest-fire risk in the Anning River Valley of Sichuan Province. We selected non-photosynthetic vegetation (NPV), photosynthetic vegetation (PV), normalized difference vegetation index (NDVI), plant species, land use, soil type, temperature, humidity, rainfall, wind speed, elevation, slope, aspect, distance to road, and distance to residential as forest-fire predisposing factors. We derived the following conclusions. (1) Overlaying historical fire points with mapped forest-fire risk revealed an accuracy that exceeded 86%, indicating the reliability of the results. (2) Forest fires in the Anning River Valley primarily occur in February, March, and April, typically months characterized by very low rainfall and dry conditions. (3) Areas with high and medium forest-fire risk were mainly distributed in Dechang and Xide counties, while low-risk areas were most prevalent in Xichang city and Mianning country. (4) Rainfall, temperature, elevation, and NPV emerged as the main influencing factors, exerting a dominant role in the occurrence of forest fires. Specifically, a higher NPV coverage correlates with an increased risk of forest fire. In conclusion, this study represents a novel approach by incorporating NPV and PV as key factors in triggering forest fires. By mapping forest-fire risk, we have provided a robust scientific foundation and decision-making support for effective fire management strategies. This research significantly contributes to advancing ecological civilization and fostering sustainable development. Full article

Background and purpose: Clinically, the ability to identify individuals at risk of ischemic stroke remains limited. This study aimed to develop a nomogram model for predicting the risk of acute ischemic stroke. Methods: In this study, we conducted a retrospective analysis on patients who visited the Department of Neurology, collecting important information including clinical records, demographic characteristics, and complete hematological tests. Participants were randomly divided into training and internal validation sets in a 7:3 ratio. Based on their diagnosis, patients were categorized as having or not having ischemic stroke (ischemic and non-ischemic stroke groups). Subsequently, in the training set, key predictive variables were identified through multivariate logistic regression and least absolute shrinkage and selection operator (LASSO) regression methods, and a nomogram model was constructed accordingly. The model was then evaluated on the internal validation set and an independent external validation set through area under the receiver operating characteristic curve (AUC-ROC) analysis, a Hosmer-Lemeshow goodness-of-fit test, and decision curve analysis (DCA) to verify its predictive efficacy and clinical applicability. Results: Eight predictors were identified: age, smoking status, hypertension, diabetes, atrial fibrillation, stroke history, white blood cell count, and vitamin B12 levels. Based on these factors, a nomogram with high predictive accuracy was constructed. The model demonstrated good predictive performance, with an AUC-ROC of 0.760 (95% confidence interval [CI]: 0.736–0.784). The AUC-ROC values for internal and external validation were 0.768 (95% CI: 0.732–0.804) and 0.732 (95% CI: 0.688–0.777), respectively, proving the model’s capability to predict the risk of ischemic stroke effectively. Calibration and DCA confirmed its clinical value. Conclusions: We constructed a nomogram based on eight variables, effectively quantifying the risk of ischemic stroke. Full article

Laser-based direct energy deposition (DED-LB/M) has been a promising option for the surface repair of structural aluminum alloys due to the advantages it offers, including a small heat-affected zone, high forming accuracy, and adjustable deposition materials. However, the unequal powder particle size during powder-based DED-LB/M can cause unstable flow and an uneven material flow rate per unit of time, resulting in defects such as pores, uneven deposition layers, and cracks. This paper presents a multiscale, multiphysics numerical model to investigate the underlying mechanism during the powder-based DED-LB/M surface repair process. First, the worn surfaces of aluminum alloy components with different flaw shapes and sizes were characterized and modeled. The fluid flow of the molten pool during material deposition on the worn surfaces was then investigated using a model that coupled the mesoscale discrete element method (DEM) and the finite volume method (FVM). The effect of flaw size and powder supply quantity on the evolution of the molten pool temperature, morphology, and dynamics was evaluated. The rapid heat transfer and variation in thermal stress during the multilayer DED-LB/M process were further illustrated using a macroscale thermomechanical model. The maximum stress was observed and compared with the yield stress of the adopted material, and no relative sliding was observed between deposited layers and substrate components. Full article

Single-image super-resolution (SISR) techniques have become a vital tool for improving image quality and clarity in the rapidly evolving field of digital imaging. Convolutional neural network (CNN) and transformer-based SISR techniques are very popular. However, CNN-based techniques are not suitable when capturing long-range dependencies, and transformer-based techniques suffer from computational complexity. To tackle these problems, this paper proposes a novel method called dilated attention-based single-image super-resolution (DAIR). It comprises three components: low-level feature extraction, multi-scale dilated transformer block (MDTB), and high-quality image reconstruction. A convolutional layer is used to extract the base features from low-resolution images, which lays the foundation for subsequent processing. Dilated attention is introduced to MDTB to enhance its ability to capture image features at different scales and ensure superior image details and structure recovery. After that, MDTB refines these features to extract multi-scale global attributes and effectively grasps images’ long-distance relationships and features across multiple scales. Finally, low-level features obtained from feature extraction and multi-scale global features obtained from MDTB are aggregated to reconstruct high-resolution images. The comparison with existing methods validates the efficacy of the proposed method and demonstrates its advantage in improving image resolution and quality. Full article