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Sail-assisted propulsion is an important energy-saving technology in the shipping industry, and the development of foldable wingsails has recently become a hot topic. This type of sail is usually composed of multiple elements, and its performance at different folding configurations is very sensitive to changes in incoming airflow, which result in practical operational challenges. Therefore, original and optimized three-element wingsails (bare and concave) are modeled and simulated using the unsteady RANS method with the k-ω SST turbulence model. Next, certain key design and structural parameters (such as angle of attack, apparent wind angle, and camber) are employed to characterize the auxiliary propulsion performance, and the differences are explained in combination with the flow field details. The results show that, in the unfolded state, the aerodynamic performance of the concave wingsail is better than that of the bare wingsail, exhibiting higher lift coefficients, lower drag coefficients, and a more stable surface flow. In the fully folded state, wherein both the nose and flap are rotated, the thrust performance of the concave wingsail remains superior. Specifically, at an angle of attack of 8 degrees, the thrust coefficient of the concave wingsail is approximately 23.5% higher than that of the bare wingsail, indicating improved wind energy utilization. The research results are of great significance for engineering applications and subsequent optimization design.

Short-term precipitation forecasting is essential for agriculture, transportation, urban management, and tourism. The radar echo extrapolation method is widely used in precipitation forecasting. To address issues like forecast degradation, insufficient capture of spatiotemporal dependencies, and low accuracy in radar echo extrapolation, we propose a new model: MS-DD3D-RSTN. This model employs spatiotemporal convolutional blocks (STCBs) as spatiotemporal feature extractors and uses the spatial-temporal loss (STLoss) function to learn intra-frame and inter-frame changes for end-to-end training, thereby capturing the spatiotemporal dependencies in radar echo signals. Experiments on the Sichuan dataset and the HKO-7 dataset show that the proposed model outperforms advanced models in terms of CSI and POD evaluation metrics. For 2 h forecasts with 20 dBZ and 30 dBZ reflectivity thresholds, the CSI metrics reached 0.538, 0.386, 0.485, and 0.198, respectively, representing the best levels among existing methods. The experiments demonstrate that the MS-DD3D-RSTN model enhances the ability to capture spatiotemporal dependencies, mitigates forecast degradation, and further improves radar echo prediction performance.

A talented endophytic Streptomyces sp. PH9030 is derived from the medicinal plant Kadsura coccinea (Lem.) A.C. Smith. The undescribed naphthoquinone naphthgeranine G (5) and seven previously identified compounds, 6–12, were obtained from Streptomyces sp. PH9030. The structure of 5 was identified by comprehensive examination of its HRESIMS, 1D NMR, 2D NMR and ECD data. The inhibitory activities of all the compounds toward α-glucosidase and their antibacterial properties were investigated. The α-glucosidase inhibitory activities of 5, 6, 7 and 9 were reported for the first time, with IC50 values ranging from 66.4 ± 6.7 to 185.9 ± 0.2 μM, as compared with acarbose (IC50 = 671.5 ± 0.2 μM). The molecular docking and molecular dynamics analysis of 5 with α-glucosidase further indicated that it may have a good binding ability with α-glucosidase. Both 9 and 12 exhibited moderate antibacterial activity against methicillin-resistant Staphylococcus aureus, with minimum inhibitory concentration (MIC) values of 16 μg/mL. These results indicate that 5, together with the naphthoquinone scaffold, has the potential to be further developed as a possible inhibitor of α-glucosidase.

Quantifying the effect of maize tassel on canopy reflectance is essential for creating a tasseling progress monitoring index, aiding precision agriculture monitoring, and understanding vegetation canopy radiative transfer. Traditional field measurements often struggle to detect the subtle reflectance differences caused by tassels due to complex environmental factors and challenges in controlling variables. The three-dimensional (3D) radiative transfer model offers a reliable method to study this relationship by accurately simulating interactions between solar radiation and canopy structure. This study used the LESS (large-scale remote sensing data and image simulation framework) model to analyze the impact of maize tassels on visible and near-infrared reflectance in heterogeneous 3D scenes by modifying the structural and optical properties of canopy components. We also examined the anisotropic characteristics of tassel effects on canopy reflectance and explored the mechanisms behind these effects based on the quantified contributions of the optical properties of canopy components. The results showed that (1) the effect of tassels under different planting densities mainly manifests in the near-infrared band of the canopy spectrum, with a variation magnitude of ±0.04. In contrast, the impact of tassels on different leaf area index (LAI) shows a smaller response difference, with a magnitude of ±0.01. As tassels change from green to gray during growth, their effect on reducing canopy reflectance increases. (2) The effect of maize tassel on canopy reflectance varied with spectral bands and showed an obvious directional effect. In the red band at the same sun position, the difference in tassel effect caused by the observed zenith angle on canopy reflectance reaches 200%, while in the near-infrared band, the difference is as high as 400%. The hotspot effect of the canopy has a significant weakening effect on the shadow effect of the tassel. (3) The non-transmittance optical properties of maize tassels reduce canopy reflectance, while their high reflectance increases it. Thus, the dual effects of tassels create a game in canopy reflectance, with the final outcome mainly depending on the sensitivity of the canopy spectrum to transmittance. This study demonstrates the potential of using 3D radiative transfer models to quantify the effects of crop fine structure on canopy reflectance and provides some insights for optimizing crop structure and implementing precision agriculture management (such as selective breeding of crop optimal plant type).

The emerging high-bandwidth services of 6G, such as high-definition video transmission and real-time interaction, have promoted the progress of the fiber optic access network industry, driving its development towards the next-generation PON with higher speed and larger system capacity. In response to the future requirements of 200 Gb/s/λ PON for both 20 km and 30 km downlink transmission scenarios, this paper proposes a Simplified Volterra Equalization (SVLE) scheme based on Nyquist PAM4 single-sideband modulation direct detection (SSBM-DD) scheme. In order to verify its advantages, the IQ-modulated Kramers–Kronig reception (KK) scheme is introduced for comparison. Simulation validation platforms for two schemes are conducted, and the performance comparison of the SVLE and KK schemes is carried out. In both, the impact of the carrier signal power ratio (CSPR) on receiver sensitivity, the influence of input optical power on power budget and receiver sensitivity, and the tolerance of receiver sensitivity to the linewidth of the DFB laser are investigated in the simulation. Finally, a comprehensive comparison of the two schemes is presented in terms of system performance, cost, and DSP complexity. In the 20 km downlink transmission scenario, the SVLE scheme outperforms the KK scheme by 4.9 dB in terms of power budget. The total number of multiplications of the SVLE scheme is 37, while that of the KK scheme is 4358. Therefore, the DSP complexity of the SVLE scheme is much lower than that of the KK scheme. The results of the comparison demonstrate that, in downlink transmission scenarios, the SVLE scheme is more suitable than the KK scheme as it exhibits a higher power budget, lower DSP complexity, and lower cost. Consequently, the proposed SVLE scheme could be a highly promising solution for future ultra-high-speed PON downlink transmission.

Phthalate acid esters (PAEs) are one of the most widely used plasticizers globally, extensively employed in various decoration materials. However, studies on the impact of these materials on indoor environmental PAE pollution and their effects on human health are limited. In this study, forty dust samples were collected from four types of stores specializing in decoration materials (flooring, furniture boards, wall coverings, and household articles). The levels, sources, exposure doses, and potential health risks of PAEs in dust from decoration material stores were assessed. The total concentrations of Σ9PAE (the sum of nine PAEs) in dust from all decoration-material stores ranged from 46,100 ng/g to 695,000 ng/g, with a median concentration of 146,000 ng/g. DMP, DEP, DBP, and DEHP were identified as the predominant components. Among all stores, furniture board stores exhibited the highest Σ9PAE (159,000 ng/g, median value), while flooring stores exhibited the lowest (95,300 ng/g). Principal component analysis (PCA) showed that decoration materials are important sources of PAEs in the indoor environment. The estimated daily intakes of PAEs through non-dietary dust ingestion and dermal-absorption pathways among staff in various decoration-material stores were 60.0 and 0.470 ng/kg-bw/day (flooring stores), 113 and 0.780 ng/kg-bw/day (furniture board stores), 102 and 0.510 ng/kg-bw/day (wall covering stores), and 114 and 0.710 ng/kg-bw/day (household article stores). Particularly, staff in wall-covering and furniture-board stores exhibited relatively higher exposure doses of DEHP. Risk assessment indicated that although certain PAEs posed potential health risks, the exposure levels for staff in decoration material stores were within acceptable limits. However, staff in wall covering stores exhibited relatively higher risks, necessitating targeted risk-management strategies. This study provides new insights into understanding the risk associated with PAEs in indoor environments.

Maize, an important cereal crop and crucial industrial material, is widely used in various fields, including food, feed, and industry. Maize is also a highly adaptable crop, capable of thriving under various climatic and soil conditions. Against the backdrop of intensified climate change, studying the classification of maize growth stages can aid in adjusting planting strategies to enhance yield and quality. Accurate classification of the growth stages of maize breeding materials is important for enhancing yield and quality in breeding endeavors. Traditional remote sensing-based crop growth stage classifications mainly rely on time series vegetation index (VI) analyses; however, VIs are prone to saturation under high-coverage conditions. Maize phenotypic traits at different growth stages may improve the accuracy of crop growth stage classifications. Therefore, we developed a method for classifying maize growth stages during the vegetative growth phase by combining maize phenotypic traits with different classification algorithms. First, we tested various VIs, texture features (TFs), and combinations of VI and TF as input features to estimate the leaf chlorophyll content (LCC), leaf area index (LAI), and fractional vegetation cover (FVC). We determined the optimal feature inputs and estimation methods and completed crop height (CH) extraction. Then, we tested different combinations of maize phenotypic traits as input variables to determine their accuracy in classifying growth stages and to identify the optimal combination and classification method. Finally, we compared the proposed method with traditional growth stage classification methods based on remote sensing VIs and machine learning models. The results indicate that (1) when the VI+TFs are used as input features, random forest regression (RFR) shows a good estimation performance for the LCC (R2: 0.920, RMSE: 3.655 SPAD units, MAE: 2.698 SPAD units), Gaussian process regression (GPR) performs well for the LAI (R2: 0.621, RMSE: 0.494, MAE: 0.397), and linear regression (LR) exhibits a good estimation performance for the FVC (R2: 0.777, RMSE: 0.051, MAE: 0.040); (2) when using the maize LCC, LAI, FVC, and CH phenotypic traits to classify maize growth stages, the random forest (RF) classification method achieved the highest accuracy (accuracy: 0.951, precision: 0.951, recall: 0.951, F1: 0.951); and (3) the effectiveness of the growth stage classification based on maize phenotypic traits outperforms that of traditional remote sensing-based crop growth stage classifications.

In situ electroporation, a non-invasive technique for enhancing the permeability of cell membranes, has emerged as a powerful tool for intracellular delivery and manipulation. This method allows for the precise introduction of therapeutic agents, such as nucleic acids, drugs, and proteins, directly into target cells within their native tissue environment. Herein, we introduce an innovative electroporation strategy that employs a Janus particle (JP)-based microelectrode to generate a localized and controllable electric field within a microfluidic chip. The microfluidic device is engineered with an indium tin oxide (ITO)-sandwiched microchannel, where the electric field is applied, and suspended JP microelectrodes that induce a stronger localized electric field. The corresponding simulation model is developed to better understand the dynamic electroporation process. Numerical simulations for both single-cell and chain-assembled cell electroporation have been successfully conducted. The effects of various parameters, including pulse voltage, duration medium conductivity, and radius of Janus microelectrode, on cell membrane permeabilization are systematically investigated. Our findings indicate that the enhanced electric intensity near the poles of the JP microelectrode significantly contributes to the electroporation process. In addition, the distribution for both transmembrane voltage and the resultant nanopores can be altered by conveniently adjusting the relative position of the JP microelectrode, demonstrating a selective and in situ electroporation technique for spatial control over the delivery area. Moreover, the obtained differences in the distribution of electroporation between chain cells can offer insightful directives for the electroporation of tissues or cell populations, enabling the precise and targeted modulation of specific cell populations. As a proof of concept, this work can provide a robust alternative technique for the study of complex and personalized cellular processes.

Safe and eco-friendly preservatives are crucial to preventing food spoilage and illnesses, as foodborne diseases caused by pathogens result in approximately 600 million cases of illness and 420,000 deaths annually. ε-Poly-L-lysine (ε-PL) is a novel food preservative widely used in many countries. However, its commercial application has been hindered by high costs and low production. In this study, ε-PL’s biosynthetic capacity was enhanced in Streptomyces albulus WG608 through metabolic engineering guided by multi-omics techniques. Based on transcriptome and metabolome data, differentially expressed genes (fold change >2 or p < 0.05) and differentially expressed metabolites (fold change >1.2 or S. albulus WME10, achieved an ε-PL production rate of 77.16 g/L in a 5 L bioreactor, which is the highest reported ε-PL production to date. These results suggest that the integrative analysis of the transcriptome and metabolome can facilitate the identification of key pathways and genetic elements affecting ε-PL synthesis, guiding further metabolic engineering and thus significantly enhancing ε-PL production. The method presented in this study could be applicable to other valuable natural antibacterial agents.

Dopamine (DA) is an important catecholamine neurotransmitter in the mammalian central nervous system that affects many physiological functions. Hence, a highly sensitive and selective sensing platform is necessary for quantification of DA in the human body. In this study, ternary transition metal tellurides of CoNiTe2 were successfully synthesized using the hydrothermal method. The proposed CoNiTe2 nanomaterials were dispersed well in Nafion to form a well-dispersed suspension and, when dropped on a glassy carbon electrode (GCE) as the working electrode (CoNiTe2/Nafion/GCE) for electrochemical non-enzymatic DA sensing, displayed excellent electrocatalytic activity for dopamine electrooxidation. The morphology and physical/chemical properties of CoNiTe2 nanomaterials were characterized using field emission scanning electron microscopy (FESEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS). In order to obtain the best electrochemical response to DA from the fabricated CoNiTe2/Nafion/GCE, the experimental conditions of electrochemical sensing, including the CoNiTe2 loading amounts and pH values of the phosphate buffer solution (PBS), were explored to achieve the best electrochemical sensing performance. Under optimal conditions (2 mg of CoNiTe2 and pH 6.0 of PBS), the fabricated CoNiTe2/Nafion/GCE showed excellent electrocatalytic activity of DA electrooxidation. The CoNiTe2/Nafion/GCE sensing platform demonstrated excellent electrochemical performance owing to the optimal structural and electronic characteristics originating from the synergistic interactions of bimetallic Co and Ni, the low electronegativity of Te atoms, and the unique morphology of the CoNiTe2 nanorod. It exhibited a wide linear range from 0.05 to 100 μM, a high sensitivity of 1.2880 µA µM−1 cm−2, and a low limit of detection of 0.0380 µM, as well as acceptable selectivity for DA sensing. Therefore, the proposed CoNiTe2/Nafion/GCE could be considered a promising electrode material for electrochemical non-enzymatic DA sensing.

The instance segmentation model based on deep learning has addressed the challenges in intelligently detecting water leakage in shield tunneling. Due to the limited generalization ability of the baseline model, occurrences of missed detections, false detections, and repeated detections are encountered during the actual detection of tunnel water leakage. This paper adopts Mask R-CNN as the baseline model and introduces a mask cascade strategy to enhance the quality of positive samples. Additionally, the backbone network in the model is replaced with RegNetX to enlarge the model’s receptive field, and MDConv is introduced to enhance the model’s feature extraction capability in the edge receptive field region. Building upon these improvements, the proposed model is named Cascade-MRegNetX. The backbone network MRegNetX features a symmetrical block structure, which, when combined with deformable convolutions, greatly assists in extracting edge features from corresponding regions. During the dataset preprocessing stage, we augment the dataset through image rotation and classification, thereby improving both the quality and quantity of samples. Finally, by leveraging pre-trained models through transfer learning, we enhance the robustness of the target model. This model can effectively extract features from water leakage areas of different scales or deformations. Through instance segmentation experiments conducted on a dataset comprising 766 images of tunnel water leakage, the experimental results demonstrate that the improved model achieves higher precision in tunnel water leakage mask detection. Through these enhancements, the detection effectiveness, feature extraction capability, and generalization ability of the baseline model are improved. The improved Cascade-MRegNetX model achieves respective improvements of 7.7%, 2.8%, and 10.4% in terms of AP, AP0.5, and AP0.75 compared to the existing Cascade Mask R-CNN model.

Purpose: To investigate the associations between fluid accumulation at different levels in the retina and visual outcome in polypoidal choroidal vasculopathy (PCV). Design: A retrospective observational study. Institutional setting. Study Population: A total of 91 eyes from 91 patients of PCV were included, with 65 receiving intravitreal aflibercept monotherapy and 26 receiving combined intravitreal ranibizumab and photodynamic therapy (PDT). Observation Procedures: Best-corrected visual acuity (BCVA) and optical coherence tomography (OCT) examination results were recorded at baseline and 3, 6, and 12 months after treatment. Main Outcome Measures: The correlations between visual outcomes and fluid biomarkers including intraretinal fluid (IRF), subretinal fluid (SRF), serous pigment epithelium detachment (PED), and hemorrhage at fovea were analyzed. Results: No differences in treatment outcomes were noted between patients receiving aflibercept and those receiving combined ranibizumab and PDT. IRF and hemorrhage at baseline predicted poorer vision at 3, 6, and 12 months. The presence of IRF was associated with poorer vision at 6 months and 12 months (p < 0.05 for all). The presence of SRF or PED was not associated with better vision at any time point. No differences in the correlations between fluid markers and visual outcomes were noted between thin and thick subfoveal choroidal thickness groups. Conclusions: For PCV, IRF and hemorrhage at baseline served as surrogates for poor visual prognosis after treatment, and IRF was a biomarker for poor vision during the treatment course. No fluid markers predicted good visual prognosis or had a positive impact on vision at any time point.

In shield tunneling, the joint is one of the most vulnerable parts of the segmental lining. Opening of the joint reduces the overall stiffness of the ring, leading to structural damage and issues such as water leakage. Currently, the Winkler method is commonly used to calculate structural deformation, simplifying the interaction between segments and soil as radial and tangential Winkler springs. However, when introducing connection springs or reduction factors to simulate the joint stiffness of segments, the challenge lies in determining the reduction coefficient and the stiffness of the springs. Currently, the hyperstatic reflection method cannot simulate the discontinuity effect at the connection of the tunnel segments, while the state space method overlooks the nonlinear interaction between the tunnel and the soil. Therefore, this paper proposes a numerical simulation method considering the interaction between the tunnel and the soil, which is subjected to compression rather than tension, and the discontinuity of the joints between the segments. The model structure and external load are symmetrical, resulting in symmetrical calculation results. This method is based on the soft soil layers and shield tunnel structures of the Shanghai Metro, and the applicability of the model is verified through deformation calculations using three-dimensional laser scanning point clouds of sections from the Shanghai Metro Line 5. When the subgrade reaction coefficient is 5000 kN/m3, the model can effectively simulate the deformation of operational tunnels. By adjusting the bending stiffness of individual connection springs, we investigate the influence of bending stiffness reduction on the bending moment, radial displacement, and rotational displacement of the ring. The results indicate that a decrease in joint bending stiffness significantly affects the mechanical response of the ring, and the extent and degree of this influence are correlated with the joint position and the magnitude of joint bending stiffness.

Yellowfin tuna (Thunnus albacares) is a valuable pelagic migratory fish with potential for aquaculture. Despite this, there is limited understanding of the biological and physiological characteristics of this species, particularly regarding sex differences in growth performance. The liver, a crucial organ for digestion and metabolism, plays a significant role in regulating fish growth. This study aimed to compare liver enzyme activities and transcriptome profiles between female and male yellowfin tuna to uncover the molecular mechanisms underlying difference between the sexes. The results revealed that female yellowfin tuna exhibited higher amylase and lipid metabolism enzyme activities, while male yellowfin tuna showed higher glucose-6-phosphate dehydrogenase and antioxidant enzyme activities. Additionally, through Illumina sequencing technology, the study generated 37.74 Gb of clean data and identified 36,482 unique genes (UniGenes) in the liver transcriptome. A total of 2542 differentially expressed genes were found, with enriched Gene Ontology terms and pathways related to metabolic processes, particularly lipid metabolism and transport. These findings suggest that female yellowfin tuna have superior digestive enzyme activities and lipid metabolism, while male yellowfin tuna excel in sugar metabolism, ATP production, and antioxidant defense. This study provides valuable insights into sex differences in yellowfin tuna and could aid in advancing full-cycle aquaculture practices for this species.

(1) This paper, based on the characteristics of complex steel structures as well as difficult points in the process of their detailed design, introduces the product design concept of DfMA (Design for Manufacturing and Assembly) from the manufacturing industry and studies the detailed design method of BIM-FEM model conversion. The BIM software Revit (2020) is used as the basis for the BIM detailed design of the project, which achieves the purpose of rapid modeling and provides a detailed design model basis for finite element analysis. (2) Utilizing the Revit API and C# for secondary development as the technical means, this approach involves converting the geometric entity model described by CSG-Brep into an APDL stream. This creates an interface with the finite element analysis software ANSYS (12.0) to implement the detailed design of BIM-FEM model conversion, optimizing the algorithm for converting complex analysis models that require high precision for special-shaped steel structures. (3) This research addresses issues such as the disconnection between the design, manufacturing, and construction of special-shaped steel structures, providing support for design decisions. Moreover, it enhances the detailed design method by improving the standardization of special-shaped components under the condition of design diversity. (4) These studies provide sustainability for engineering design, manufacturing, and construction projects, enabling the maximization of benefits and product lifecycle management (PLM) through these projects. (5) Finally, a case study analysis was conducted on the Wuhan City New Generation Weather Radar Construction Project, designed by the Central South Architectural Design Institute (CSADI), to verify the detailed design of BIM-FEM model conversion. This proved the scientific validity, practicality, and necessity of this research.

With the increasing awareness of architectural heritage conservation and the development of digital technology, there is an urgent need in the field of architectural heritage restoration for a novel solution that can enhance data security, collaboration efficiency, and file management capabilities. This study proposes an Architectural Heritage Restoration Distributed Common Data Environment (AHR-DCDE) framework based on blockchain and IPFS technologies to address the above challenges. The AHR-DCDE framework significantly improves data security and collaborative efficiency in architectural heritage restoration projects by creating a decentralized collaborative design process that achieves data immutability, traceability, and efficient large-scale file processing capabilities. The AHR-DCDE framework significantly improves data security and collaborative efficiency in architectural heritage restoration projects by creating a decentralized collaborative design process that achieves data immutability, traceability, and efficient large-scale file processing capabilities. In this study, the practicality and effectiveness of the AHR-DCDE framework is verified by taking the heritage restoration design project of Pinghe Packing Factory in Wuhan, Hubei Province, as an example. Evaluation of the framework’s network latency, throughput, and storage costs indicates that AHR-DCDE can meet the requirements of architectural heritage restoration projects, possessing efficient capabilities for handling and sharing project data. Furthermore, the implementation of the AHR-DCDE framework also facilitates efficient collaboration among interdisciplinary teams, providing robust technical support for the protection and restoration of architectural heritage.

When the operating state of the power system changes, a modular multilevel converter power electronic transformer (MMC-PET) based on modular multilevel converters cannot perform efficient energy transfer and power conversion under conventional control strategies. To address the above problems, this paper proposes a passive, second-order super-helical sliding mode control strategy for MMC-PET by combining passive control and second-order super-helical sliding mode control with a stronger anti-interference capability. First, a Euler–Lagrange model based on positive and negative sequence separation is established according to the mathematical model of the MMC; second, the model of the system is passively analyzed, and a passive controller is designed according to its passivity, and the passive controller is further optimized by using the super-helical second-order sliding mode control, which improves the overall robustness and interference immunity; finally, the effectiveness and superiority of the super-twisting second-order sliding mode passive control strategy is demonstrated by verifying it through the construction of the MMC-PET simulation model and testing it under various non-ideal working conditions.

Transformer oil, crucial for transformer and power system safety, demands effective monitoring. Aiming to address the problems of expensive and bulky equipment, poor real-time performance, and single parameter detection of traditional measurement methods, this study proposes a quartz tuning fork-based simultaneous measurement system for online monitoring of the density, viscosity, and dielectric constant of transformer oil. Based on the Butterworth–Van Dyke quartz tuning fork equivalent circuit model, a working mechanism of transformer oil density, viscosity, and dielectric constant was analyzed, and a measurement model for oil samples was obtained. A miniaturized simultaneous measurement system was designed based on a dedicated chip for vector current-voltage impedance analysis for data acquisition and a Savitzky–Golay filter for data filtering. A transformer oil test platform was built to verify the simultaneous measurement system. The results showed that the system has good repeatability, and the measurement errors of density, viscosity, and dielectric constant are lower than 2.00%, 5.50%, and 3.20%, respectively. The online and offline results showed that the system meets the requirements of the condition maintenance system for online monitoring accuracy and real-time detection.

Peroxiredoxin 6 (Prdx6), a unique 1-Cys member of the peroxiredoxin family, exhibits peroxidase activity, phospholipase activity, and lysophosphatidylcholine acyltransferase (LPCAT) activity. Prdx6 has been known to be an important enzyme for the maintenance of lipid peroxidation repair, cellular metabolism, inflammatory signaling, and antioxidant damage. Growing research has demonstrated that the altered activity of this enzyme is linked with various pathological processes including central nervous system (CNS) disorders. This review discusses the distinctive structure, enzyme activity, and function of Prdx6 in different CNS disorders, as well as emphasizing the significance of Prdx6 in neurological disorders.

This article details a method for preparing cermet matrix composites via Fast hot pressing (FHP) sintering technology and emphasizes their potential use in extremely high-temperature settings. The material primarily consists of NiCr alloy, Cr3C2, and LaF3. An in-depth investigation was conducted on the tribological properties of the specimen by conducting sliding tests against a Si3N4 ball at varying temperatures, including room temperature (RT), 400 °C, 600 °C, and 800 °C. Advanced techniques such as scanning electron microscopy, micro-XRD, and micro-Raman spectroscopy were employed to examine the friction surfaces formed under different frictional temperatures. The findings reveal a uniform composition and high density within the composites. It is noteworthy that as the LaF3 content increases, the hardness of the ceramic phase diminishes. Conversely, the hardness of the alloy phase augments with the addition of LaF3, provided that its content remains below 15 wt%. The composite material containing 15 wt% LaF3 demonstrates superior hardness values, with the ceramic phase reaching HV1412 and the alloy phase achieving HV384. Furthermore, the coefficient of friction of the composite material was evaluated. The coefficient of friction of the composite is between 0.74 and 0.4 and the wear rate is 4.46 × 10−6–5.72 × 10−5 mm3N−1m−1 from room temperature to 800 °C. The lubrication behavior at low temperature is mainly attributed to the lubricating effect of LaF3, and at high temperature it is due to the tribochemical reaction to form LaCrO3 with good lubricating properties, which plays a synergistic lubricating role with Cr2O3.

Cardiovascular diseases pose a long-term risk to human health. This study focuses on the rich-spectrum mechanical vibrations generated during cardiac activity. By combining Fourier series theory, we propose a multi-frequency vibration model for the heart, decomposing cardiac vibration into frequency bands and establishing a systematic interpretation for detecting multi-frequency cardiac vibrations. Based on this, we develop a small multi-frequency vibration sensor module based on flexible polyvinylidene fluoride (PVDF) films, which is capable of synchronously collecting ultra-low-frequency seismocardiography (ULF-SCG), seismocardiography (SCG), and phonocardiography (PCG) signals with high sensitivity. Comparative experiments validate the sensor’s performance and we further develop an algorithm framework for feature extraction based on 1D-CNN models, achieving continuous recognition of multiple vibration features. Testing shows that the recognition coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE) of the 8 features are 0.95, 2.18 ms, and 4.89 ms, respectively, with an average prediction speed of 60.18 us/point, meeting the re-quirements for online monitoring while ensuring accuracy in extracting multiple feature points. Finally, integrating the vibration model, sensor, and feature extraction algorithm, we propose a dynamic monitoring system for multi-frequency cardiac vibration, which can be applied to portable monitoring devices for daily dynamic cardiac monitoring, providing a new approach for the early diagnosis and prevention of cardiovascular diseases.

Sheep horns are composed of bone and sheaths, and the BMPR1A gene is required for cartilage and osteogenic differentiation. Therefore, the BMPR1A gene may have a function related to the sheep horn, but its relationship with the sheep horn remains unclear. In this study, we first utilized RNA sequencing (RNA-seq) data to investigate the expression of the BMPR1A gene in different tissues and breeds of sheep. Second, whole-genome sequencing (WGS) data were used to explore the functional sites of the BMPR1A gene. Lastly, the allele-specific expression of the BMPR1A gene was explored. Our results indicate that BMPR1A gene expression is significantly higher in the normal horn groups than in the scurred groups. Importantly, this trend is consistent across several sheep breeds. Therefore, this finding suggests that the BMPR1A gene may be related to horn type. A total of 43 Single-Nucleotide Polymorphisms (SNPs) (F-statistics > 0.15) and 10 allele-specific expressions (ASEs) exhibited difference between the large and small horn populations. It is probable that these sites significantly impact the size of sheep horns. Compared to other polled species, we discovered ten amino acid sites that could influence horn presence. By combining RNA-seq and WGS functional loci results, we identified a functional site at position 40574836 on chromosome 25 that is both an SNP and exhibits allele-specific expression. In conclusion, we demonstrated that the BMPR1A gene is associated with horn type and identified some important functional sites which can be used as molecular markers in the breeding of sheep horns.

Diabetic retinopathy (DR) severely affects vision in individuals with diabetes. High glucose (HG) induces oxidative stress in retinal cells, a key contributor to DR development. Previous studies suggest that fibroblast growth factor-1 (FGF-1) can mitigate hyperglycemia and protect tissues from HG-induced damage. However, the specific effects and mechanisms of FGF-1 on DR remain unclear. In our study, FGF-1-pretreated adult retinal pigment epithelial (ARPE)-19 cells were employed to investigate. Results indicate that FGF-1 significantly attenuated HG-induced oxidative stress, including reactive oxygen species, DNA damage, protein carbonyl content, and lipid peroxidation. FGF-1 also modulated the expression of oxidative and antioxidative enzymes. Mechanistic investigations showed that HG induced high endoplasmic reticulum (ER) stress and upregulated specific proteins associated with apoptosis. FGF-1 effectively alleviated ER stress, reduced apoptosis, and restored autophagy through the adenosine monophosphate-activated protein kinase/mammalian target of the rapamycin signaling pathway. We observed that the changes induced by HG were dose-dependently reversed by FGF-1. Higher concentrations of FGF-1 (5 and 10 ng/mL) exhibited increased effectiveness in mitigating HG-induced damage, reaching statistical significance (p < 0.05). In conclusion, our study underscores the promising potential of FGF-1 as a safeguard against DR. FGF-1 emerges as a formidable intervention, attenuating oxidative stress, ER stress, and apoptosis, while concurrently promoting autophagy. This multifaceted impact positions FGF-1 as a compelling candidate for alleviating retinal cell damage in the complex pathogenesis of DR.

Functional neuroimaging studies of neuropsychiatric disorders and cognitive impairment are commonly conducted in the clinic setting but less so in the acutely medically ill while hospitalized. This is largely due to technical and logistical limitations, given the lack of portable devices with high spatial and temporal resolutions. This exploratory study reports on the development and implementation of a novel diffuse optical tomography (DOT) system that can be employed for bedside three-dimensional functional neuroimaging. To test this portable DOT system, our protocol included a task-based sequence involving the Months Backwards Test with imaging centered on the bilateral prefrontal cortex. Fifteen subjects were recruited from intensive care units and the general wards of a single tertiary academic hospital and included in our final analysis. Volumetric hemoglobin analyses of the dorsolateral prefrontal cortex (DLPFC) and dorsomedial prefrontal cortex (DMPFC) were reliably captured in all our subjects. The peak value was calculated to be 3.36 µM and 0.74 µM for oxygenated-hemoglobin (HbO) and total-hemoglobin (HbT) (p < 0.042, [HbT]), respectively. The standard error was calculated to be 4.58 uM and 3.68 uM for (HbO) and (HbT). We additionally developed a seed-based correlation analysis to demonstrate the capability of DOT in studying functional connectivity. The right DLPFC was found to be moderately associated with the left DLPFC in all our subjects (r = 0.656). The DMPFC was observed to be associated with the left DLPFC but less so (r = 0.273) at the group level. Overall, the contribution of left-to-right DLPFC connectivity was significantly higher than left DLPFC to DMPFC in our group (p = 0.012). Future studies should investigate the potential of such a DOT system in the research of neuropsychiatric and neurocognitive disorders within the hospital to study different types of mechanisms, pathophysiology, and interventions that occur acutely and can advance our knowledge of these disorders.

The degradation of fluoroquinolones (FQs) via advanced oxidation processes (AOPs) is a promising avenue, yet the complete mineralization of certain FQ molecules remains elusive, raising concerns about the formation of toxic by-products. This study delineates five primary AOP degradation pathways for 16 commercially available FQ molecules, inferred from existing literature. Density functional theory (DFT) was employed to calculate the bond dissociation energies within these pathways to elucidate the correlation between bond strength and molecular architecture. Subsequently, Comparative Molecular Similarity Index Analysis (CoMSIA) models were constructed for various degradation reactions, including piperazine ring cleavage, defluorination, hydroxylation, and piperazine ring hydroxylation. Three-dimensional contour maps generated from these models provide a deeper understanding of the interplay between FQ molecular structure and bond dissociation energy. Furthermore, toxicity predictions for 16 FQ molecules and their advanced oxidation intermediates, conducted using VEGA 1.2.3 software, indicate that degradation products from pathways P2 and P5 pose a heightened health risk relative to their parent compounds. Furthermore, the application of the Multwfn program to compute the Fukui function for FQ molecules discerns the disparity in degradation propensities, highlighting that N atoms with higher f0 values can augment the likelihood of piperazine ring cleavage. HOMO-LUMO distribution diagrams further confirm that methoxy substitution at the 1-position leads to a dilution of HOMOs on the piperazine ring and an increased energy gap for free radical reactions, diminishing the reactivity with hydroxyl radicals. This study elucidates the pivotal role of structural characteristics in FQ antibiotics for their degradation efficiency within AOPs and unveils the underlying mechanisms of bond dissociation energy disparities. The toxicity parameter predictions for FQ molecules and their intermediates offer unique perspectives and theoretical underpinnings for mitigating the use of high-risk FQs and for devising targeted degradation strategies to circumvent the generation of toxic intermediates in AOPs through molecular structure optimization.

This study aimed to investigate the characteristic choroidal changes in patients with diabetic retinopathy and identify factors affecting choroidal thickness (CTh), choroidal vascular index (CVI), and choriocapillaris flow. We retrospectively analyzed 79 eyes of 48 patients with diabetes between August 2021 and February 2022. We collected laboratory data, including HbA1c, serum creatinine, blood urea nitrogen, triglyceride, total cholesterol, high-density lipoprotein, and low-density lipoprotein (LDL) levels. Optical coherence tomography images of the foveal avascular zone, retinal vascular density, choroidal flow, retinal thickness, CTh, and CVI were analyzed. Possible determining factors affecting CTh, CVI, and choriocapillaris flow were analyzed using nonparametric multivariate analysis. LDL (p < 0.001) positively correlated with CTh, whereas CVI (p = 0.007) was negatively correlated with CTh in diabetic patients with diabetes. We also identified a negative correlation between choriocapillaris flow and deep parafoveal retinal vessel density in patients with low-grade diabetic retinopathy (DR), which diminished in those with more advanced DR. Our study provides further information on the changes in choroidal structure and blood flow in patients with diabetes.

The BRI1 EMS suppressor 1(BES1) transcription factor is a crucial regulator in the signaling pathway of Brassinosteroid (BR) and plays an important role in plant growth and response to abiotic stress. Although the identification and functional validation of BES1 genes have been extensively explored in various plant species, the understanding of their role in woody plants—particularly the endangered species Phoebe bournei (Hemsl.) Yang—remains limited. In this study, we identified nine members of the BES1 gene family in the genome of P. bournei; these nine members were unevenly distributed across four chromosomes. In our further evolutionary analysis of PbBES1, we discovered that PbBES1 can be divided into three subfamilies (Class I, Class II, and Class IV) based on the evolutionary tree constructed with Arabidopsis thaliana, Oryza sativa, and Solanum lycopersicum. Each subfamily contains 2–5 PbBES1 genes. There were nine pairs of homologous BES1 genes in the synteny analysis of PbBES1 and AtBES1. Three segmental replication events and one pair of tandem duplication events were present among the PbBES1 family members. Additionally, we conducted promoter cis-acting element analysis and discovered that PbBES1 contains binding sites for plant growth and development, cell cycle regulation, and response to abiotic stress. PbBES1.2 is highly expressed in root bark, stem bark, root xylem, and stem xylem. PbBES1.3 was expressed in five tissues. Moreover, we examined the expression profiles of five representative PbBES1 genes under heat and drought stress. These experiments preliminarily verified their responsiveness and functional roles in mediating responses to abiotic stress. This study provides important clues to elucidate the functional characteristics of the BES1 gene family, and at the same time provides new insights and valuable information for the regulation of resistance in P. bournei.

The thermal-electric conversion efficiency is a crucial metric for evaluating the performance of a thermoelectric generator (TEG). However, accurate measurement of this efficiency remains a significant challenge due to various factors that impact heat flow measurements. We have observed that temperature fluctuations during temperature control are the primary factor contributing to measurement errors in heat flow under vacuum conditions. To address this issue, we have developed a time-dependent theoretical model for the thermal-electric coupling of a TEG measurement system based on Fourier’s theory of heat conduction. This model allows us to investigate the effects of both temperature fluctuation and structural parameters on the measurement error of TEG performance. Furthermore, we have proposed an error correction scheme for TEG performance based on our theoretical and experimental findings. These insights provide a theoretical framework and technical guidance for more precise measurements of TEG performance.

Crop leaf chlorophyll content (LCC) and fractional vegetation cover (FVC) are crucial indicators for assessing crop health, growth development, and maturity. In contrast to the traditional manual collection of crop trait parameters, unmanned aerial vehicle (UAV) technology rapidly generates LCC and FVC maps for breeding materials, facilitating prompt assessments of maturity information. This study addresses the following research questions: (1) Can image features based on pretrained deep learning networks and ensemble learning enhance the estimation of remote sensing LCC and FVC? (2) Can the proposed adaptive normal maturity detection (ANMD) algorithm effectively monitor maize maturity based on LCC and FVC maps? We conducted the following tasks: (1) Seven phases (tassel initiation to maturity) of maize canopy orthoimages and corresponding ground-truth data for LCC and six phases of FVC using UAVs were collected. (2) Three features, namely vegetation indices (VI), texture features (TF) based on Gray Level Co-occurrence Matrix, and deep features (DF), were evaluated for LCC and FVC estimation. Moreover, the potential of four single-machine learning models and three ensemble models for LCC and FVC estimation was evaluated. (3) The estimated LCC and FVC were combined with the proposed ANMD to monitor maize maturity. The research findings indicate that (1) image features extracted from pretrained deep learning networks more accurately describe crop canopy structure information, effectively eliminating saturation effects and enhancing LCC and FVC estimation accuracy. (2) Ensemble models outperform single-machine learning models in estimating LCC and FVC, providing greater precision. Remarkably, the stacking + DF strategy achieved optimal performance in estimating LCC (coefficient of determination (R2): 0.930; root mean square error (RMSE): 3.974; average absolute error (MAE): 3.096); and FVC (R2: 0.716; RMSE: 0.057; and MAE: 0.044). (3) The proposed ANMD algorithm combined with LCC and FVC maps can be used to effectively monitor maize maturity. Establishing the maturity threshold for LCC based on the wax ripening period (P5) and successfully applying it to the wax ripening-mature period (P5–P7) achieved high monitoring accuracy (overall accuracy (OA): 0.9625–0.9875; user’s accuracy: 0.9583–0.9933; and producer’s accuracy: 0.9634–1). Similarly, utilizing the ANMD algorithm with FVC also attained elevated monitoring accuracy during P5–P7 (OA: 0.9125–0.9750; UA: 0.878–0.9778; and PA: 0.9362–0.9934). This study offers robust insights for future agricultural production and breeding, offering valuable insights for the further exploration of crop monitoring technologies and methodologies.

Widespread exposure to organophosphorus flame retardants (OPFRs) has been observed in the general population. Emerging studies have revealed OPFRs possess endocrine-disturbing properties. The present study aims to assess the association between urinary metabolites of OPFRs, BMI, and serum lipid profiles. Data from the National Health and Nutrition Examination Survey (NHANES) 2017–2018 were obtained, with 1334 adults enrolled in the current study. Urinary concentrations of bis (1-chloro-2-propyl) phosphate (BCIPP), bis(2-chloroethyl) phosphate (BCEP), bis(1,3-dichloro-2-propyl) phosphate (BDCPP), dibutyl phosphate (DBUP), and diphenyl phosphate (DPHP) were quantified to assess OPFR exposure. Covariate-adjusted linear and logistic regression models were conducted to explore the associations between log2-transformed concentrations of OPFR metabolites, BMI, obesity, and serum lipid profiles. Stratified analyses were performed to assess the heterogeneity of associations by age, gender, race, etc. Positive associations were found between OPFR exposure and the risk of obesity. The multivariate linear analysis indicated that a one-unit increase in log2-transformed urinary concentrations of BCEP and BDCPP was associated with 0.27 (95% CI: 0.02–0.52, p = 0.0338) and 0.56 (95% CI: 0.25–0.87, p = 0.0004) higher BMI value, respectively. One log2-unit increase in urinary BCEP and BDCPP concentrations was associated with 1.1-fold (95% CI: 1.02–1.18, p = 0.0096) and 1.19-fold (95% CI: 1.09–1.30, p = 0.0001) risk for developing obesity. Furthermore, the non-linear relationship between exposure to OPFRs and obesity was identified. Additionally, multivariable linear regression showed that urinary DPHP concentrations were inversely correlated with serum triglyceride (TG) levels (β = −7.41, 95% CI: −12.13 to −2.68, p = 0.0022). However, no other OPFR metabolites were found to be significantly statistically associated with serum lipid levels after adjusting for potential confounders. In conclusion, environmental exposure to OPFRs might contribute to obesity and dysregulated TG concentrations in adults. Future prospective research is warranted to confirm the causal relationship between metabolites of OPFRs and obesity.

The mechanism of aerosol pollution transport remains highly elusive owing to the myriad of influential factors. In this study, ground station data, satellite data, ground-based LiDAR remote sensing data, sounding data, ERA5 reanalysis and a backward trajectory model were combined to investigate the formation process and optical properties of winter aerosol pollution in Beijing and surrounding areas. The analysis of ground station data shows that compared to 2019 and 2021, the pandemic lockdown policy resulted in a decrease in the total number of pollution days and a decrease in the average concentration of particulate matter in the Beijing area in 2020. The terrain characteristics of the Beijing-Tianjin-Hebei (BTH) made it prone to northeast and southwest winds. The highest incidence of aerosol pollution in Beijing occurs in February and March during the spring and winter seasons. Analysis of a typical heavy aerosol pollution process in the Beijing area from 28 February to 5 March 2019 shows that dust and fine particulate matter contributed to the primary pollution; surface air temperature inversion and an average wind speed of less than 3 m/s were conducive to the continuous accumulation of pollutants, which was accompanied by the oxidation reaction of NO2 and O3, forming photochemical pollution. The heavy aerosol pollution was transmitted and diffused towards the southeast, gradually eliminating the pollution. Our results provide relevant research support for the prevention and control of aerosol pollution.

Platelets assume a pivotal role in the pathogenesis of cardiovascular diseases (CVDs), emphasizing their significance in disease progression. Consequently, addressing CVDs necessitates a targeted approach focused on mitigating platelet activation. Eugenol, predominantly derived from clove oil, is recognized for its antibacterial, anticancer, and anti-inflammatory properties, rendering it a valuable medicinal agent. This investigation delves into the intricate mechanisms through which eugenol influences human platelets. At a low concentration of 2 μM, eugenol demonstrates inhibition of collagen and arachidonic acid (AA)-induced platelet aggregation. Notably, thrombin and U46619 remain unaffected by eugenol. Its modulatory effects extend to ATP release, P-selectin expression, and intracellular calcium levels ([Ca2+]i). Eugenol significantly inhibits various signaling cascades, including phospholipase Cγ2 (PLCγ2)/protein kinase C (PKC), phosphoinositide 3-kinase/Akt/glycogen synthase kinase-3β, mitogen-activated protein kinases, and cytosolic phospholipase A2 (cPLA2)/thromboxane A2 (TxA2) formation induced by collagen. Eugenol selectively inhibited cPLA2/TxA2 phosphorylation induced by AA, not affecting p38 MAPK. In ADP-treated mice, eugenol reduced occluded lung vessels by platelet thrombi without extending bleeding time. In conclusion, eugenol exerts a potent inhibitory effect on platelet activation, achieved through the inhibition of the PLCγ2–PKC and cPLA2-TxA2 cascade, consequently suppressing platelet aggregation. These findings underscore the potential therapeutic applications of eugenol in CVDs.

Cropland is a comprehensive system influenced by the natural environment and human activities. This article collects the data of cropland use, soil, and other geographic, social and economic factors in the study area and then uses the methods of system analysis, induction and deduction to propose a new research perspective for establishing a cognitive framework and analyzing cropland resources and their functions. The framework is used to assess the rapidly urbanizing region of Guangzhou and investigate the production, ecological, and living functions provided by cropland resources. Synergistic relationships between functions are analyzed using the hot and cold spot methods. The results indicate that the production function of cropland resources in Guangzhou is good, the ecological function is favorable, and the living function is relatively low. A synergistic relationship between the three functions is observed in 91% of areas of Guangzhou, whereas a balanced relationship occurs in some areas of the southern part of Zengcheng, the northwestern and northeastern parts of Conghua, and the western part of Nansha. This research provides guidance for managing cropland resources and ensuring their sustainable utilization.

In this paper, an ultracompact combined sensor for displacement and angle-synchronous measurement is proposed based on the self-imaging effect of optical microgratings. Using a two-grating structure, linear and angular displacement can be measured by detecting the change of phase and amplitude of the optical transmission, respectively, within one single structure in the meantime. The optically transmitted properties of the two-grating structure are investigated in both theory and simulation. Simulated results indicate that optical transmission changes in a sinusoidal relationship to the input linear displacement. Meanwhile, the amplitude of the curve decreases with an input pitch angle, indicating the ability for synchronous measurement within one single compact structure. The synchronous measurement of the linear displacement and the angle is also demonstrated experimentally. The results show a resolution down to 4 nm for linear displacement measurement and a maximum sensitivity of 0.26 mV/arcsec within a range of ±1° for angle measurement. Benefiting from a simple common-path structure without using optical components, including reflectors and polarizers, the sensor shows ultra-high compactness for multiple-degrees-of-freedom measuring, indicating the great potential for this sensor in fields such as integrated mechanical positioning and semiconductor fabrication.

ε-Poly-L-lysine (ε-PL) is a natural and safe food preservative mainly produced by the aerobic, filamentous bacterium Streptomyces albulus. Therefore, it is crucial to breed superior ε-PL-producing strains to enhance fermentation efficiency to reduce production costs. Metabolic engineering is an effective measure for strain modification, but there are few reports on key genes for ε-PL biosynthesis. In this study, metabolic flux analysis was employed to identify potential key genes in ε-PL biosynthesis in S. albulus WG-608. A total of six potential key genes were identified. Three effective key genes (ppc, pyc and pls) were identified for the first time in ε-PL biosynthesis through overexpression experiments. It also presents the first demonstration of the promoting effects of ppc and pyc on ε-PL biosynthesis. Three genes were then co-expressed in S. albulus WG-608 to obtain OE-ppc-pyc-pls, which exhibited an 11.4% increase in ε-PL production compared to S. albulus WG-608, with a 25.5% increase in specific ε-PL production. Finally, the metabolic flux analysis of OE-ppc-pyc-pls compared to S. albulus WG-608 demonstrated that OE-ppc-pyc-pls successfully altered the metabolic flux as expected. This study not only provides a theoretical basis for the metabolic engineering of ε-PL-producing strains but also provides an effective approach for the metabolic engineering of other metabolites.

Crack detection plays a critical role in ensuring road safety and maintenance. Traditional, manual, and semi-automatic detection methods have proven inefficient. Nowadays, the emergence of deep learning techniques has opened up new possibilities for automatic crack detection. However, there are few methods with both localization and segmentation abilities, and most perform poorly. The consistent nature of pavement over a small mileage range gives us the opportunity to make improvements. A novel data-augmentation strategy called CrackMover, specifically tailored for crack detection methods, is proposed. Experiments demonstrate the effectiveness of CrackMover for various methods. Moreover, this paper presents a new instance segmentation method for crack detection. It adopts a redesigned backbone network and incorporates a cascade structure for the region-based convolutional network (R-CNN) part. The experimental evaluation showcases significant performance improvements achieved by these approaches in crack detection. The proposed method achieves an average precision of 33.3%, surpassing Mask R-CNN with a Residual Network 50 backbone by 8.6%, proving its effectiveness in detecting crack distress.

Soil erosion and the consequent loss of nutrients have consistently been significant factors contributing to land degradation and nonpoint source pollution. While runoff serves as the primary carrier for nutrient loss, the hydraulic processes governing the mechanisms of nutrient loss remain not entirely clear. This paper aims to investigate the impacts of rainfall intensity and the slope gradient on hydraulic parameters, soil loss rates, and ammonia nitrogen loss rates, with the objective of determining the optimal hydraulic parameters for more accurate predictions of soil erosion and nutrient loss rates. A series of simulated rainfall experiments with three rainfall intensities (25, 50, and 75 mm min−1) and four slope gradients (8.7%, 17.6%, 26.8%, and 36.4%) were conducted on a 5 m × 10 m slope. The results indicated that the flow velocity, shear stress, stream power, unit stream power, and unit energy all increased with the increase in slope gradient or rainfall intensity. The water depth decreased with an increase in the slope gradient but increased with an increase in the rainfall intensity. Laminar flow occurred in all experiments (Reynolds number < 500). Only the overland flow under a 25 mm h−1 rainfall intensity and 8.7% slope gradient was subcritical flow (Froude number < 1). Hydraulic parameters, the soil loss rate, and ammonia nitrogen loss rate could be all expressed as the product of rainfall intensity and slope power function, with R2 ranging from 0.949 to 0.997. The average soil loss rate and process soil erosion rate could both be fitted using the power function of hydraulic parameters, with the optimal fitting parameter being stream power (R2 = 0.980 and 0.909). The average ammonia nitrogen loss rate exhibited a linear relationship with the hydraulic parameters, and the optimal fitting parameter was also stream power (R2 = 0.933). However, there were relatively low correlations between hydrodynamic parameters and the ammonia nitrogen loss rate (R2 = 0.450–0587). Our results contribute to a deeper understanding of the hydraulic processes involved in nutrient loss.

The recalcitrant nature of emerging contaminants (ECs) in aquatic environments necessitates the development of effective strategies for their remediation, given the considerable impacts they pose on both human health and the delicate balance of the ecosystem. Semiconductor-based photocatalytic technology is recognized for its dual benefits in effectively addressing both ECs and energy-related challenges simultaneously. Among the plethora of photocatalysts, black phosphorus (BP) stands as a promising nonmetallic candidate, offering a host of advantages including its tunable direct band gap, broad-spectrum light absorption capabilities, and exceptional charge mobility. Nevertheless, pristine BP frequently underperforms, primarily due to issues related to its limited ambient stability and the rapid recombination of photogenerated electron–hole pairs. To overcome these challenges, substantial research efforts have been devoted to the creation of BP-based photocatalysts in recent years. However, there is a noticeable absence of reviews regarding the advancement of BP-based materials for the degradation of ECs in aqueous solutions. Therefore, to fill this gap, a comprehensive review is undertaken. In this review, we first present an in-depth examination of the fabrication processes for bulk BP and BP nanosheets (BPNS). The review conducts a thorough analysis and comparison of the merits and limitations inherent in each method, thereby delineating the most auspicious avenues for future research. Then, in line with the pathways followed by photogenerated electron–hole pairs at the interface, BP-based photocatalysts are systematically categorized into heterojunctions (Type I, Type II, Z-scheme, and S-scheme) and hybrids, and their photocatalytic performances against various ECs and the corresponding degradation mechanisms are comprehensively summarized. Finally, this review presents personal insights into the prospective avenues for advancing the field of BP-based photocatalysts for ECs remediation.

Polygalacturonase (PG) is one of the largest families of hydrolytic enzymes in plants. It is involved in the breakdown of pectin in the plant cell wall and even contributes to peel cracks. Here, we characterize PGs and outline their expression profiles using the available reference genome and transcriptome of Akebia trifoliata. The average length and exon number of the 47 identified AktPGs, unevenly assigned on 14 chromosomes and two unassembled contigs, were 5399 bp and 7, respectively. The phylogenetic tree of 191 PGs, including 47, 57, 51, and 36 from A. trifoliata, Durio zibethinus, Actinidia chinensis, and Vitis vinifera, respectively, showed that AktPGs were distributed in all groups except group G and that 10 AktPGs in group E were older, while the remaining 37 AktPGs were younger. Evolutionarily, all AktPGs generally experienced whole-genome duplication (WGD)/segmental repeats and purifying selection. Additionally, the origin of conserved domain III was possibly associated with a histidine residue (H) substitute in motif 8. The results of both the phylogenetic tree and expression profiling indicated that five AktPGs, especially AktPG25, could be associated with the cracking process. Detailed information and data on the PG family are beneficial for further study of the postharvest biology of A. trifoliata.

As a common interface circuit for renewable energy integrated into the power grid, the inverter is prone to work under a three-phase unbalanced weak grid. In this paper, the instability of grid-connected inverters under the unbalanced grid condition is investigated. First, a dual second-order generalized integrator phase-locked loop (DSOGI-PLL)-based inverter under balanced and unbalanced conditions is modeled. A fourth-order impedance model is established to describe its impedance characteristics under the unbalanced grid condition. To analyze this multi-input multi-output system, a simplified stability analysis method based on the generalized Nyquist stability criterion and matrix theory is proposed. Then, the influences of circuit and control parameters on the stability of the grid-connected inverter system under the unbalanced grid condition are investigated. Finally, the accuracy of the derived frequency-coupled impedance model is verified via simulations, and the effectiveness of the proposed simplified stability analysis method on the system stability analysis is verified via both simulations and hardware experiments.

Accurate wind speed prediction is significantly important for the full utilization of wind energy resources and the improvement in the economic benefits of wind farms. Because the ensemble forecast takes into account the uncertainty of information about the atmospheric motion, domestic and foreign weather service forecast centers often choose to use the ensemble numerical forecast to achieve the fine forecast of wind speed. However, due to the unavoidable systematic errors of the ensemble numerical forecast model, it is necessary to correct the deviation in the ensemble numerical forecast wind speed. Considering the typical spatio-temporal characteristics of the grid prediction data of the wind field, based on Convolutional Long–Short Term Memory (ConvLSTM) units and attention mechanism, this paper takes the complex and representative North China region as the research area, aiming to reveal the shortcomings of existing deep learning integrated prediction correction models in extracting temporal features of grid prediction data. We propose a new ensemble prediction wind field correction model integrating multi-factor and spatio-temporal characteristics. This model uses reanalyzed land data provided by the European Center for Medium-Range Weather Forecasts as the real data to correct the deviation in the near-surface 10 m wind field data predicted by the regional ensemble numerical prediction model of the China Meteorological Administration. We used the reanalyzed land data provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) as the live data to correct the deviation in the near-surface 10 m wind field data predicted by the regional ensemble numerical forecast model of the China Meteorological Administration (CMA). At the same time, Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) were used as the scoring indicators, and the results of the China Meteorological Administration–Regional Ensemble Prediction System (CMA–REPS) ensemble average, multiple linear regression method correction, Long–Short Term Memory (LSTM) method correction, and U-net (UNET) method correction were compared. Compared with the UNET model method, the experimental results show that when processing the 10 m zonal wind data, 10 m meridional wind data, and 10 m average wind speed data of CMA–REPS 24 h forecasts, the correction results of our model can reduce the RMSE score index by 9.15%, 4.83%, and 7.79%. At the same time, when processing the 48 h and 72 h near-surface 10 m wind field data of the CMA–REPS forecast, our model can improve the prediction accuracy of CMA–REPS near-surface wind forecast data. Therefore, the correction effect of the proposed model in a complex terrain area is evidently better compared to other methods.

Changes in intestinal mucosal barrier permeability lead to antigen sensitization and mast cell-mediated allergic reactions, which are considered to play important roles in the occurrence and development of food allergies. It has been suggested that protein causes increased intestinal permeability via mast cell degranulation, and we investigated the effect of camellia Moringa oleifera leaves protein on intestinal permeability and explored its role in the development of food allergies. The current study investigated the effect of M. oleifera leaves protein on intestinal permeability through assessments of transepithelial electrical resistance (TEER) and transmembrane transport of FITC-dextran by Caco-2 cells. The expression levels of Toll-like receptor 4 (TLR4), IL-8, Occludin, Claudin-1, and perimembrane protein family (ZO-1) were detected by real-time PCR and Western blotting. The effect of M. oleifera leaves protein on intestinal permeability was verified in mice in vivo. The serum fluorescence intensity was measured using the FITC-dextran tracer method, and the expression of tight junction proteins was detected using Western blotting. The results showed that M. oleifera leaves protein widened the gaps between Caco-2 cells, reduced transmembrane resistance, and increased permeability. This protein also reduced the mRNA and protein levels of Occludin, Claudin-1, and ZO-1. Animal experiments showed that intestinal permeability was increased, and that the expression of the tight junction proteins Occludin and Claudin-1 were downregulated in mice. This study shows that M. oleifera leaves protein has components that increase intestinal permeability, decrease tight junction protein expression, promote transmembrane transport in Caco-2 cells, and increase intestinal permeability in experimental animals. The finding that M. oleifera leaves active protein increases intestinal permeability suggests that this protein may be valuable for the prevention, diagnosis, and treatment of M. oleifera leaves allergy.

The application of three-dimensional laser scanning technology in the field of tunnel deformation monitoring has changed the traditional measurement method. It provides an automated and intelligent solution for monitoring the geometric deformation of tunnel sections due to its high efficiency and independence from environmental influences. In this paper, based on B-spline fitting and iterative nearest point (ICP) alignment, the calculation of the difference between the radial distance and the design radius of a tunnel is transformed into a curve transformation that iterates over the nearest-neighbor points and calculates the difference in the distance between the corresponding points. The innovation of this paper is that the high-precision tunnel deformation monitoring method integrating B-spline fitting and ICP alignment can automatically compensate for the missing point clouds, is not affected by the point clouds of the tunnel inner and outer liner appendages, is more sensitive in the local deformation feedback and can be applied to a variety of tunnel shapes. The results indicate that our method maximally improves the accuracy of the horizontal convergence calculation by 28.6 mm and the accuracy of the vault settlement by 27.8 mm in comparison with the least squares circle fitting algorithm.

The nervous system plays a leading role in the regulation of physiological functions and activities in the body. However, a variety of diseases related to the nervous system have a serious impact on human health. It is increasingly clear that neurological diseases are multifactorial pathological processes involving multiple cellular systems, and the onset of these diseases usually involves a diverse array of molecular mechanisms. Unfortunately, no effective therapy exists to slow down the progression or prevent the development of diseases only through the regulation of a single factor. To this end, it is pivotal to seek an ideal therapeutic approach for challenging the complicated pathological process to achieve effective treatment. In recent years, fisetin, a kind of flavonoid widely existing in fruits, vegetables and other plants, has shown numerous interesting biological activities with clinical potentials including anti-inflammatory, antioxidant and neurotrophic effects. In addition, fisetin has been reported to have diverse pharmacological properties and neuroprotective potentials against various neurological diseases. The neuroprotective effects were ascribed to its unique biological properties and multiple clinical pharmacological activities associated with the treatment of different neurological disorders. In this review, we summarize recent research progress regarding the neuroprotective potential of fisetin and the underlying signaling pathways of the treatment of several neurological diseases.

Ensuring road safety, structural stability and durability is of paramount importance, and detecting road cracks plays a critical role in achieving these goals. We propose a GM-ResNet-based method to enhance the precision and efficacy of crack detection. Leveraging ResNet-34 as the foundational network for crack image feature extraction, we consider the challenge of insufficient global and local information assimilation within the model. To overcome this, we incorporate the global attention mechanism into the architecture, facilitating comprehensive feature extraction across the channel and the spatial width and height dimensions. This dynamic interaction across these dimensions optimizes feature representation and generalization, resulting in a more precise crack detection outcome. Recognizing the limitations of ResNet-34 in managing intricate data relationships, we replace its fully connected layer with a multilayer fully connected neural network. We fashion a deep network structure by integrating multiple linear, batch normalization and activation function layers. This construction amplifies feature expression, stabilizes training convergence and elevates the performance of the model in complex detection tasks. Moreover, tackling class imbalance is imperative in road crack detection. Introducing the focal loss function as the training loss addresses this challenge head-on, effectively mitigating the adverse impact of class imbalance on model performance. The experimental outcomes on a publicly available crack dataset emphasize the advantages of the GM-ResNet in crack detection accuracy compared to other methods. It is worth noting that the proposed method has better evaluation indicators in the detection results compared with alternative methodologies, highlighting its effectiveness. This validates the potency of our method in achieving optimal crack detection outcomes.

This work outlines a design for a wire rope maintenance device that is based on commonly used, low-dropping point lubricating grease for wire rope lubrication and operates along the strand’s twist direction. Unlike similar existing devices, this device scrapes abrasives from the wire rope’s surface along the strand’s twist direction and applies lubricating grease in the same direction. It addresses issues related to the accumulation of old lubricating grease between strands, as well as the problems of a heavy weight, high traction force requirements, complex operation, unstable motion, potential surface damage to the wire rope, and the strong pollution found in existing products. The wheel system of this device was kinematically modeled and subjected to force analysis, and its accuracy was verified through simulations and experiments. Test results show that when this device is used for cleaning and lubricating wire ropes, it requires less than 150 N of traction force, maintains a stable speed of 0.6 m/s, and ensures coaxiality within ±0.1 mm, thus meeting the maintenance requirements of ropeway wire ropes. In future work, the effects of different factors, such as changes in scraper shape and size, lubricating grease application speed, and temperature and pressure inside the grease storage chamber can be studied to understand their influence on the application of grease to wire ropes.

Impacts of Mn alloying on lattice stabilities, magnetic properties, electronic structures of the bcc and fcc phases and the fcc→bcc phase transition in Fe16−xMnx (x = 0, 1 and 2) alloys are studied by first-principles calculations. Results show that the doped Mn atom prefers ferromagnetic and antiferromagnetic interaction with the host Fe atoms in the bcc and fcc phases, respectively. In these two phases, the magnetic moment of Mn is smaller and larger than Fe, respectively. The local moment of Fe is decided by the Fe-Mn distance in the bcc phase, whereas in the fcc phase, it is determined by spatial orientation with Mn. In the different phases, Mn prefers different site occupations, which can be understood from the electronic density of states near Fermi energy, implying a possibility of element redistribution during phase transition. The driving force of phase transition decreases with Mn alloying. Both destabilized bcc phase and stabilized fcc phase contribute to the inhibited phase transition, but the latter plays a dominant role. Antiferromagnetism is recognized as the key reason for the enhanced stability of the fcc phase by Mn alloying.

Sea fog poses a considerable challenge to port operations, impacting maritime safety and efficiency. During the past five years, the average annual downtime of the navigation dispatch department in Ningbo Zhoushan Port due to weather was 800–1000 h, of which approximately 300 h can be attributed to sea fog. This study addresses the issue by developing a comprehensive sea fog monitoring system for Ningbo Zhoushan Port. The system utilizes automatic weather stations (AWS) and visibility laser imaging, detection, and ranging (LIDAR) to assess sea fog severity and improve monitoring accuracy. By increasing monitoring frequency and adopting corresponding warning measures, the system aims to enhance maritime safety and efficiency in Ningbo Zhoushan Port. The results showed that the implemented system successfully determines sea fog severity, enables real-time monitoring, and provides precise visibility assessments. Joint assessments revealed a substantial increase in the annual operating time and revenue of the port. These findings underscore the importance of advanced monitoring techniques in optimizing port operations, reducing collision risks, and mitigating economic losses caused by sea fog.

Modern cities require urban compact development to be sustainable. The evaluation of urban compact development may help create more accurate and realistic policies. The spatio-temporal dynamic evolution of urban compact development efficiency and its regional differences in China are examined in this study. This paper analysis uses 282 cities from 2005 to 2021. The unexpected output super-efficiency SBM model measures urban compact development efficiency. In this study, the urban compact development efficiency’s spatial and temporal patterns are also examined using kernel density estimation (KDE) and the Theil index (TI). The average efficiency of urban compact development in China has decreased slightly. However, compact efficiency disparities are decreasing. Eastern cities have a relatively stable compact efficiency, while central and western cities vary more. The compact efficiency polarisation has not changed fundamentally. The compact city growth model’s spatial agglomeration is poor, limiting its spatial spillover impact. Thus, compact urban development is necessary to speed up planning, facilitate inter-city production factor movement by creating a comprehensive transport network, and maximise co-location benefits with the regional integration strategy. This method will gradually reduce regional urban development disparities and push Chinese cities towards more refined and sustainable compact development.

Aiming at the low contrast of skin lesion image and inaccurate segmentation of lesion boundary, a skin lesion segmentation method based on multi-level split receptive field and attention is proposed. Firstly, the depth feature extraction module and multi-level splitting receptive field module are used to extract image feature information; secondly, the hybrid pooling module is used to build long-term and short-term dependencies and integrate global information and local information. Finally, the reverse residual external attention module is introduced to construct the decoding part, which can mine the potential relationship between data sets and improve the network segmentation ability. Experiments on ISBI2017 and ISIC2018 data sets show that the Dice similarity coefficient and Jaccard index reach 88.67% and 91.84%, 79.25% and 81.48%, respectively, and the accuracy reaches 93.89% and 96.16%. The segmentation method is superior to the existing algorithms as a whole. Simulation experiments show that the network has a good effect on skin lesion image segmentation and provides a new method for skin disease diagnosis.