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Retinal vessel segmentation is crucial for diagnosing and monitoring ophthalmic and systemic diseases. Optical Coherence Tomography Angiography (OCTA) enables detailed imaging of the retinal microvasculature, but existing methods for OCTA segmentation face significant limitations, such as susceptibility to noise, difficulty in handling class imbalance, and challenges in accurately segmenting complex vascular morphologies. In this study, we propose VDMNet, a novel segmentation network designed to overcome these challenges by integrating several advanced components. Firstly, we introduce the Fast Multi-Head Self-Attention (FastMHSA) module to effectively capture both global and local features, enhancing the network's robustness against complex backgrounds and pathological interference. Secondly, the Vessel Dynamic Convolution (VDConv) module is designed to dynamically adapt to curved and crossing vessels, thereby improving the segmentation of complex morphologies. Furthermore, we employ the Multi-Scale Fusion (MSF) mechanism to aggregate features across multiple scales, enhancing the detection of fine vessels while maintaining vascular continuity. Finally, we propose Weighted Asymmetric Focal Tversky Loss (WAFT Loss) to address class imbalance issues, focusing on the accurate segmentation of small and difficult-to-detect vessels. The proposed framework was evaluated on the publicly available ROSE-1 and OCTA-3M datasets. Experimental results demonstrated that our model effectively preserved the edge information of tiny vessels and achieved state-of-the-art performance in retinal vessel segmentation across several evaluation metrics. These improvements highlight VDMNet's superior ability to capture both fine vascular details and overall vessel connectivity, making it a robust solution for retinal vessel segmentation. [ABSTRACT FROM AUTHOR]

Although it is of great significance to master the height of the water-conducting fracture zone (WCFZ) to prevent coal mine disasters and ensure safe production, the most important thing is to predict the height and range of the WCFZ ahead of the working face design before coal mining. Therefore, the 150313 fully mechanized top-coal caving working face of the Yinying coal mine was taken as the engineering background. The development laws of WCFZ were studied using comprehensive research methods, including similar simulation experiments, key strata theory, the experience formula, the numerical simulation, etc. The results show that the WCFZ evolution stage is "goaf–caving zone–fracture zone" and the developing pattern is in a non-isosceles trapezoid gradually developing upward and forward. The height of the WCFZ in the 150313 working face is 89.36 m, and the fracture/mining ratio is 12.46, which is consistent with the actual production. Apparently, the set of indoor research methods in this paper is feasible to predict the height and scope of the WCFZ. The research results can provide a scientific reference for safe mining of the 15# coal seam in Shanxi Province and the prevention and control of roof water hazards. [ABSTRACT FROM AUTHOR]

To better construct an energy consumption model for the asphalt mixture production stage, this study divides the production process into four phases: aggregate dust removal, aggregate drying, asphalt heating, and mixture blending. Utilizing measured data from various regions in Anhui Province, the model considers factors such as season, mixture type, and energy consumption type. The study quantitatively compares and analyzes the energy consumption results obtained from three calculation methods: the theoretical method, the quota method, and the energy consumption model method. The results indicate that the total energy consumption shares of the aggregate dust removal, aggregate drying, asphalt heating, and mixture blending phases are 4.23%, 69.50%, 17.75%, and 8.52%, respectively. The primary energy consumption during the asphalt mixture production stage is concentrated in the aggregate drying and asphalt heating phases. The energy consumption model based on on-site measurements effectively reflects the actual energy consumption levels during the asphalt mixture production stage. Moreover, the total energy consumption calculated by the three methods follows the order quota method > energy consumption model method > theoretical method. By constructing an energy consumption model based on measured data, it is possible to more accurately assess the energy usage during the asphalt mixture production process. This helps optimize production techniques, reduce energy consumption and carbon emissions, and provide a scientific basis for sustainable road construction. [ABSTRACT FROM AUTHOR]

Wearable sensor-based human activity recognition (HAR) methods hold considerable promise for upper-level control in exoskeleton systems. However, such methods tend to overlook the critical role of data quality and still encounter challenges in cross-subject adaptation. To address this, we propose an active learning framework that integrates the relation network architecture with data sampling techniques. Initially, target data are used to fine tune two auxiliary classifiers of the pre-trained model, thereby establishing subject-specific classification boundaries. Subsequently, we assess the significance of the target data based on classifier discrepancy and partition the data into sample and template sets. Finally, the sampled data and a category clustering algorithm are employed to tune model parameters and optimize template data distribution, respectively. This approach facilitates the adaptation of the model to the target subject, enhancing both accuracy and generalizability. To evaluate the effectiveness of the proposed adaptation framework, we conducted evaluation experiments on a public dataset and a self-constructed electromyography (EMG) dataset. Experimental results demonstrate that our method outperforms the compared methods across all three statistical metrics. Furthermore, ablation experiments highlight the necessity of data screening. Our work underscores the practical feasibility of implementing user-independent HAR methods in exoskeleton control systems. [ABSTRACT FROM AUTHOR]

In order to further enhance the erosion resistance of cement concrete pavement materials, this study constructed an apparent rough hydrophobic structure layer by spraying a micro-nano substrate coating on the surface layer of the cement concrete pavement. This was followed by a secondary spray of a hydroxy-silicone oil-modified epoxy resin and a low surface energy-modified substance paste, which combine to form a superhydrophobic coating. The hydrophobic mechanism of the coating was then analysed. Firstly, the effects of different types and ratios of micro-nano substrates on the apparent morphology and hydrophobic performance of the rough structure layer were explored through contact angle testing and scanning electron microscopy (SEM). Subsequently, Fourier transform infrared spectroscopy and permeation gel chromatography were employed to ascertain the optimal modification ratio, temperature, and reaction mechanism of hydroxy-silicone oil with E51 type epoxy resin. Additionally, the mechanical properties of the modified epoxy resin-low surface energy-modified substance paste were evaluated through tensile tests. Finally, the erosion resistance of the superhydrophobic coating was tested under a range of conditions, including acidic, alkaline, de-icer, UV ageing, freeze-thaw cycles and wet wheel wear. The results demonstrate that relying solely on the rough structure of the concrete surface makes it challenging to achieve superhydrophobic performance. A rough structure layer constructed with diamond micropowder and hydrophobic nano-silica is less prone to cracking and can form more "air chamber" structures on the surface, with better wear resistance and hydrophobic performance. The ring-opening reaction products that occur during the preparation of modified epoxy resin will severely affect its mechanical strength after curing. Controlling the reaction temperature and reactant ratio can effectively push the modification reaction of epoxy resin through dehydration condensation, which produces more grafted polymer. It is noteworthy that the grafted polymer content is positively correlated with the hydrophobicity of the modified epoxy resin. The superhydrophobic coating exhibited enhanced erosion resistance (based on hydrochloric acid), UV ageing resistance, abrasion resistance, and freeze-thaw damage resistance to de-icers by 19.41%, 18.36%, 43.17% and 87.47%, respectively, in comparison to the conventional silane-based surface treatment. [ABSTRACT FROM AUTHOR]

Due to the dynamic complexities of the multi-unmanned vessel target assignment problem at sea, especially when addressing moving targets, traditional optimization algorithms often fail to quickly find an adequate solution. To overcome this, we have developed a multi-agent reinforcement learning algorithm. This approach involves defining a state space, employing preferential experience replay, and integrating self-attention mechanisms, which are applied to a novel offshore unmanned vessel model designed for dynamic target allocation. We have conducted a thorough analysis of strike positions and times, establishing robust mathematical models. Additionally, we designed several experiments to test the effectiveness of the algorithm. The proposed algorithm improves the quality of the solution by at least 30% in larger scale scenarios compared to the genetic algorithm (GA), and the average solution speed is less than 10% of the GA, demonstrating the feasibility of the algorithm in solving the problem. [ABSTRACT FROM AUTHOR]

The Northwest Arid Region faces the most serious resource-based water shortage in China, with challenges from engineering-, structural- and management-based water shortages. This water scarcity critically limits the socio-economic development of the region. Rational allocation of scarce water resources to achieve sustainable development of the ecological environment and economy has become a key issue in water resources research in the Northwest Arid Region. South-Central Ningxia, part of the Northwest Arid Region, exemplifies these challenges. This paper examines the urban and rural water supply projects in South-Central Ningxia. The current scheduling scheme focuses primarily on the distribution of water demand, with inadequate attention paid to water-quality requirements. Localized exceedances of water-quality standards indicate the existing scheduling scheme has failed to effectively control water-quality issues while ensuring water quantity. This study is the first to systematically evaluate the impact of the South-Central Ningxia Water Supply Project on water quality alongside field surveys and data analysis and propose an optimized scheduling scheme that addresses both water quantity and quality needs. The main findings are as follows: 1. Overall water quality is good, except for consistently high total nitrogen levels. 2. The optimized scheme significantly reduced total nitrogen levels, achieving a maximum reduction rate of 78.81%, and met all Class III standards. [ABSTRACT FROM AUTHOR]

Constructing offshore wind turbines on artificial islands is considered a viable option, but negative skin friction (NSF) is a significant adverse factor that cannot be ignored. The NSF adversely affects the bearing capacity of pile foundations. Currently, design methods for studying the impact of NSF group effects mainly rely on empirical approaches. Moreover, existing experimental studies do not simulate the NSF experienced by offshore wind turbine pile groups on artificial islands. In order to further explore the impact of pile group effects on NSF experienced by offshore wind turbine pile foundations on artificial islands, this study conducted indoor model tests on single piles and 3 × 3 rectangular pile groups in sandy soil under uniformly distributed loading on surrounding soil. The experiment measured the settlement of piles at various positions within single piles and rectangular pile groups, as well as the settlement of the soil surrounding the piles and the NSF. Through calculations, the experiment determined the neutral points and NSF group effect coefficients for each pile. The results indicate that densely spaced pile groups are advantageous in reducing settlement of the surrounding soil, thereby mitigating the adverse effects of NSF. Due to the influence of pile group effects, different positions within the group experience varying degrees of NSF. Consequently, in practical engineering applications, settlement of both the pile groups and the surrounding soil should be calculated separately. Furthermore, design considerations for the uplift forces and neutral points of piles at different positions within the pile group should adhere to distinct standards. [ABSTRACT FROM AUTHOR]

As micron-sized objects, mobile microrobots have shown significant potential for future biomedical applications, such as targeted drug delivery and minimally invasive surgery. However, to make these microrobots viable for clinical applications, several crucial aspects should be implemented, including customizability, motion-controllability, imageability, biodegradability, and biocompatibility. Developing materials to meet these requirements is of utmost importance. Here, a gelatin methacryloyl (GelMA) and (2-(4-vinylphenyl)ethene-1,1,2-triyl)tribenzene (TPEMA)-based multifunctional hydrogel with 3D printability, fluorescence imageability, biodegradability, and biocompatibility is demonstrated. By using 3D direct laser writing method, the hydrogel exhibits its versatility in the customization and fabrication of 3D microstructures. Spherical hydrogel microrobots were fabricated and decorated with magnetic nanoparticles on their surface to render them magnetically responsive, and have demonstrated excellent movement performance and motion controllability. The hydrogel microstructures also represented excellent drug loading/release capacity and degradability by using collagenase, along with stable fluorescence properties. Moreover, cytotoxicity assays showed that the hydrogel was non-toxic, as well as able to support cell attachment and growth, indicating excellent biocompatibility of the hydrogel. The developed multifunctional hydrogel exhibits great potential for biomedical microrobots that are integrated with customizability, 3D printability, motion controllability, drug delivery capacity, fluorescence imageability, degradability, and biocompatibility, thus being able to realize the real in vivo biomedical applications of microrobots. [ABSTRACT FROM AUTHOR]

Jingzhi Guanxin Oral Liquids (JZGX), a traditional Chinese medicine formulation prepared from the decoction of five herbs, has been utilized to relieve chest pain with coronary artery disease (CAD). However, the chemical composition and therapeutic mechanisms of JZGX remain obscured. In this research, the potential targets and pathways of JZGX against CAD were anticipated through network pharmacology based on analyzing its chemical constituents using UPLC-Q-TOF-MS/MS. One hundred seven ingredients in JZGX were identified. The 39 active chemicals and 37 key targets were screened, and CAD-related signaling pathways were clustered, mainly associated with lipid metabolism. Subsequently, the atherosclerotic CAD animal model employing 24 weeks of high-fat diet (HFD) ApoE−/− mice was constructed to investigate the JZGX efficacy and underlying mechanisms validating network forecasts. The histological staining examination and cardiovascular biomarker tests confirmed that JZGX reduced plaque formation in the aorta and decreased blood lipids in vivo. It featured anti-inflammatory, anti-thrombotic, and myocardial protective effects. JZGX prevented excessive lipid deposits and inflammation within the liver and exhibited hepatoprotective properties. Serum untargeted metabolomics analysis indicated that JZGX ameliorated metabolic abnormalities in atherosclerotic CAD mice and prompted lipid metabolism, especially linoleic acid. The PPARs and attached critical targets (SREBP1, FASN, PTGS2, and CYP3A), filtered from the networks and connected with lipid metabolism, were dramatically modulated through JZGX administration, as revealed by western blotting. The molecular docking outcomes showed that all 39 active ingredients in JZGX had good binding activity with PPARα and PPARγ. These findings illustrate that JZGX alleviates atherosclerotic CAD progression by remodeling the lipid metabolism and regulating PPAR-related proteins. [ABSTRACT FROM AUTHOR]

Fracture within the rock is one of the main factors leading to rock destabilization and has a significant effect on the stability of the project. In this study, sandstone is used as a research target, specimens with crack inclination angles of 0°, 30°, 45°, 60°, and 90° are prefabricated, and the split Hopkinson pressure bar (SHPB) impact test of sandstone with cracks is carried out based on digital image recognition technology to explore the dynamic damage characteristics of the specimens with five angles. The basic mechanical parameters of sandstone are tested to determine the RHT model intrinsic parameters, and the numerical computational RHT model of sandstone containing crack defects is established, which is verified in comparison with the test to analyze the validity of the model. Finally, the failure characteristics of the numerical model under initial stress were carried out. The study shows the following: with the increase in the fracture angle, the dynamic compressive strength and deformation modulus are distributed in a slanting V-shape, and the inclination angle of 45° is the smallest. The strain rate and energy dissipation rate are distributed in a slanting N-shape, and the inclination angle of 45° is the largest. The transmittance shows a decreasing trend, which is the opposite of the reflectivity pattern. The crack angle determines the location and direction of the initial crack, which affects the failure mode. In addition, the parameters of the RHT constitutive model suitable for sandstone are obtained, and the damage and strength patterns of the established RHT model are highly consistent with the laboratory test results. The damage range of numerical models for crack defects with different inclination angles is negatively correlated with confining pressure values and positively correlated with axial pressure values. The damage zones are symmetrically distributed approximately perpendicular to the direction of cracks, and the confining pressure has a contributing role in the peak of the element stresses; however, the axial compression has no contribution in the peak of the element stresses. [ABSTRACT FROM AUTHOR]

The occurrence of tree flash faults in hybrid overhead–underground lines presents a significant challenge to the smooth operation of power systems. However, research on localizing such faults is relatively scarce. This study conducts theoretical analyses on the formation of tree flash faults, constructs a tree flash fault discharge test platform, and simulates the discharge process. The tree flash fault discharge traveling wave signals were obtained through a high-frequency current acquisition system. Additionally, this paper establishes a model for the current traveling wave of tree flash faults and analyzes transmission attenuation. To enhance the bi-terminal traveling wave localization method, we introduce modal decomposition and the Hilbert–Huang transform. Modal decomposition is used to disentangle signals and derive the instantaneous frequencies of modal signal components through the Hilbert–Huang transform. This process helps determine the time at which the initial wavefront reaches the terminals of the mixed-line transmission. The simulation analysis carried out using PSCAD/EMTDC v4.6.3 demonstrates that this method effectively calibrates the wavefront timing of tree flash fault signals without requiring knowledge of their wave velocity along the mixed-line transmission. Therefore, this approach achieves precise localization of tree flash faults efficiently. [ABSTRACT FROM AUTHOR]

High levels of chromium (Cr) in soil pose a significant threat to both humans and the environment. Laboratory-based chemical analysis methods for Cr are time consuming and expensive; thus, there is an urgent need for a more efficient method for detecting Cr in soil. In this study, a deep neural network (DNN) approach was applied to the Land Use and Cover Area frame Survey (LUCAS) dataset to develop a hyperspectral soil Cr content prediction model with good generalizability and accuracy. The optimal DNN model was constructed by optimizing the spectral preprocessing methods and DNN hyperparameters, which achieved good predictive performance for Cr detection, with a correlation coefficient value of 0.79 on the testing set. Four important hyperspectral bands with strong Cr sensitivity (400–439, 1364–1422, 1862–1934, and 2158–2499 nm) were identified by permutation importance and local interpretable model-agnostic explanations. Soil iron oxide and clay mineral content were found to be important factors influencing soil Cr content. The findings of this study provide a feasible method for rapidly determining soil Cr content from hyperspectral data, which can be further refined and applied to large-scale Cr detection in the future. [ABSTRACT FROM AUTHOR]

Anthropogenic waste deposition in aquatic environments precipitates a decline in water quality, engendering pollution that adversely impacts human health, ecological integrity, and economic endeavors. The evolution of underwater robotic technologies heralds a new era in the timely identification and extraction of submerged litter, offering a proactive measure against the scourge of water pollution. This study introduces a refined YOLOv8-based algorithm tailored for the enhanced detection of small-scale underwater debris, aiming to mitigate the prevalent challenges of high miss and false detection rates in aquatic settings. The research presents the YOLOv8-C2f-Faster-EMA algorithm, which optimizes the backbone, neck layer, and C2f module for underwater characteristics and incorporates an effective attention mechanism. This algorithm improves the accuracy of underwater litter detection while simplifying the computational model. Empirical evidence underscores the superiority of this method over the conventional YOLOv8n framework, manifesting in a significant uplift in detection performance. Notably, the proposed method realized a 6.7% increase in precision (P), a 4.1% surge in recall (R), and a 5% enhancement in mean average precision (mAP). Transcending its foundational utility in marine conservation, this methodology harbors potential for subsequent integration into remote sensing ventures. Such an adaptation could substantially enhance the precision of detection models, particularly in the realm of localized surveillance, thereby broadening the scope of its applicability and impact. [ABSTRACT FROM AUTHOR]

With the development of the shipbuilding industry, it is necessary to improve tribological properties of polyether ether ketone (PEEK) as a water-lubricated bearing material. In this study, the sulfonated PEEK (SPEEK) with three distinct chemical structures was synthesized through direct sulfonated polymerization, and high fault tolerance and a controllable sulfonation degree ensured the batch stability. The tribological and mechanical properties of SPEEK with varying side groups (methyl and tert-butyl) and rigid segments (biphenyl) were compared after sintering in a vacuum furnace. Compared to the as-made PEEK, as the highly electronegative sulfonic acid group enhanced the hydration lubrication, the friction coefficient and wear rate of SPEEK were significantly reduced by 30% and 50% at least without affecting the mechanical properties. And lower steric hindrance and entanglement between molecular chains were proposed to be partially responsible for the lowest friction behavior of SPEEK with methyl side groups, making it a promising and competitive option for water-lubricated bearings. [ABSTRACT FROM AUTHOR]

Gardenia is both a food and medicine plant. It is widely used for cardiovascular protection, and its main bioactive ingredient is crocetin. This study aims to observe the therapeutic effects of crocetin on chronic heart failure in rats induced by various etiologies. It further compares the efficacy differences between preventative and treatment administration, varying dosages, and treatment durations, to provide improved guidance for medication in heart failure rats and determine which categories of chronic heart failure rats might benefit most from crocetin. Chronic heart failure models induced by abdominal aorta constriction, renal hypertension, and coronary artery ligation were constructed. By examining cardiac function, blood biochemistry, and histopathology, the study assessed the preventive and therapeutic effects of crocetin on load-induced and myocardial ischemia-induced heart failure. The results showed that in all three models, both treatment and preventative administration of crocetin significantly improved chronic heart failure in rats, especially in preventative administration. The results indicate crocetin may be beneficial for improving symptoms and functional capacity in rats with heart failure. Furthermore, long-term administration was more effective than short-term administration across all three rat models, with therapeutic onset observed over 6 weeks. [ABSTRACT FROM AUTHOR]

Foot-and-mouth disease (FMD) is a highly contagious and economically important disease of cloven-hoofed animals that hampers trade and production. To ensure effective infection, the foot-and-mouth disease virus (FMDV) evades host antiviral pathways in different ways. Although the effect of histone deacetylase 5 (HDAC5) on the innate immune response has previously been documented, the precise molecular mechanism underlying HDAC5-mediated FMDV infection is not yet clearly understood. In this study, we found that silencing or knockout of HDAC5 promoted FMDV replication, whereas HDAC5 overexpression significantly inhibited FMDV propagation. IFN-β and IFN-stimulated response element (ISRE) activity was strongly activated through the overexpression of HDAC5. The silencing and knockout of HDAC5 led to an increase in viral replication, which was evident by decreased IFN-β, ISG15, and ISG56 production, as well as a noticeable reduction in IRF3 phosphorylation. Moreover, the results showed that the FMDV capsid protein VP1 targets HDAC5 and facilitates its degradation via the proteasomal pathway. In conclusion, this study highlights that HDAC5 acts as a positive modulator of IFN-β production during viral infection, while FMDV capsid protein VP1 antagonizes the HDAC5-mediated antiviral immune response by degrading HDAC5 to facilitate viral replication. [ABSTRACT FROM AUTHOR]

Ardisia crenata Sims, an important ethnic medicine, is recorded in the Chinese Pharmacopoeia for treating laryngeal diseases and upper respiratory tract infections. This study aimed to evaluate the antimicrobial effect of extracts and potential antimicrobial compounds of A. crenata Sims. It was found that the roots of A. crenata Sims have a potential inhibitory effect on Candida albicans and Aspergillus flavus, with MICs of 1.56 mg/mL and 0.39 mg/mL, and the leaves of A. crenata Sims have a potential inhibitory effect on Pseudomonas aeruginosa and Staphylococcus aureus, with MICs of 3.12 mg/mL and 6.77 mg/mL, respectively. Meanwhile, five compounds including one catechin and four bergenins were obtained from roots. These components were identified on the fingerprint spectrum, representing chromatographic peaks 16, 21, 22, 23, and 25, respectively. Among these, 11-β-d-glucopyranosyl-bergenin and (−)-gallocatechin showed potential inhibition for Staphylococcus aureus and Pseudomonas aeruginosa with MIC of 0.26 and 0.33 mg/mL, respectively. The roots, stems, and leaves of A. crenata Sims are very similar in chemical composition, with large differences in content. Principal component analysis (PCA) and Hierarchical cluster analysis (HCA) showed that 16 batches of A. crenata Sims could be divided into four main production areas: Guizhou, Jiangsu, Guangxi, and Jiangxi. Furthermore, molecular docking results showed that 11-β-d-glucopyranosyl-bergenin had a better affinity for Casein lytic proteinase P (ClpP), and (−)-gallocatechin possessed a strong affinity for LasA hydrolysis protease and LasB elastase. These findings suggest catechin and bergenins from A. crenata Sims can be used as antimicrobial activity molecules. [ABSTRACT FROM AUTHOR]

This paper simulates actual rainfall conditions and raindrops flowing to form a water flow path (WFP) on the pavement surface of the wide expressway. Then, the different linear combination conditions, including longitudinal slope (LS), transverse slope (superelevation, TS), gradual change rate of TS, and pavement width (PW), were simulated and analyzed. The results show that (1) the influence of each linear index on the maximum water film path length (WFPLmax) and maximum water flow depth (WFDmax) differs (according to the absolute values of Beta, LS has the greatest influence on WFPLmax, and PW has the greatest influence on the WFDmax for both straight-line and circular-curve sections); (2) when the design value of LS is between 1.1% and 4%, the WFDmax can be effectively reduced by lowering the value of LS; (3) in the case of a high design value of LS, it can be considered to increase the TS of the pavement arch from 2% to 2.5% to effectively reduce the WFPLmax, and the wider PW, the better the reducing effect; (4) while widening the expressway, adjusting the TS from 2% to 2.5% can effectively offset the increasing effect of PW on the WFDmax. This research aims to fill the research gap in the simulation of runoff characteristics of wide expressway asphalt pavements and to improve the alignment design of expressways from the drainage perspective for the improvement of driving safety. [ABSTRACT FROM AUTHOR]

Graphene and graphene-like two-dimensional layered nanomaterials-based photoelectrochemical (PEC) biosensors have recently grown rapidly in popularity thanks to their advantages of high sensitivity and low background signal, which have attracted tremendous attention in ultrahigh sensitive small molecule detection. This work proposes a non-enzymatic and visible-light-sensitive PEC biosensing platform based on ZIF-67@MoS2/rGO composite which is synthesized through a facile and one-step microwave-assisted hydrothermal method. The combination of MoS2 and rGO could construct van der Waals heterostructures, which not only act as visible-light-active nanomaterials, but facilitate charge carriers transfer between the photoelectrode and glassy carbon electrode (GCE). ZIF-67 anchored on MoS2/rGO heterostructures provides large specific surface areas and a high proportion of catalytic sites, which cooperate with MoS2 nanosheets, realizing rapid and efficient enzyme-free electrocatalytic oxidation of glucose. The ZIF-67@MoS2/rGO-modified GCE can realize the rapid and sensitive detection of glucose at low detection voltage, which exhibits a high sensitivity of 12.62 μAmM−1cm−2. Finally, the ZIF-67@MoS2/rGO PEC biosensor is developed by integrating the ZIF-67@MoS2/rGO with a screen-printed electrode (SPE), which exhibits a high sensitivity of 3.479 μAmM−1cm−2 and a low detection limit of 1.39 μM. The biosensor's selectivity, stability, and repeatability are systematically investigated, and its practicability is evaluated by detecting clinical serum samples. [ABSTRACT FROM AUTHOR]

BABY BOOM (BBM) is one of the members of the plant-specific APETALA2/ethylene-responsive factor (AP2/ERF) transcription factor superfamily. It acts as a key regulator of plant cell pluripotency, playing a significant role in promoting somatic embryogenesis. In this study, a BBM gene named PoBBM was screened, cloned, and identified from the third-generation full-length transcriptome data of Paeonia ostii. Its open reading frame was 2136 bp, encoding 711 amino acids. Sequence feature analysis revealed that it possessed two AP2 conserved domains and eight motifs, including bbm-1. The phylogenetic tree indicated that PoBBM clusters with AtBBM in the euANT group of the Arabidopsis AP2 family, which is most closely related to grape VvBBM and may have the same ancestry as grape. Subcellular localization demonstrated that the PoBBM protein was localized in the nucleus. Semi-quantitative reverse transcription polymerase chain reaction (qRT-PCR) was used to assess the PoBBM transcript levels during ten developmental stages of somatic embryos and in five tissue types of peonies. The results indicate that PoBBM was highly expressed in the early stages of peony somatic embryo development. The expression on 0–15 d was the highest and decreased gradually with somatic embryogenesis. The gene is almost not expressed after 40 d since somatic embryo formation. PoBBM was expressed in roots, stems, leaves, seeds, and calli, with the highest levels in seeds, followed by leaves and calli. The PoBBM protein displayed transcriptional self-activation activity, which may facilitate further research on its relationships with other proteins. The above results provide a key gene PoBBM for somatic embryogenesis in peonies, which is significant for advancing the establishment of a stable and efficient regeneration and genetic transformation system for peonies. [ABSTRACT FROM AUTHOR]

Enhancers are located upstream or downstream of key deoxyribonucleic acid (DNA) sequences in genes and can adjust the transcription activity of neighboring genes. Identifying enhancers and determining their functions are important for understanding gene regulatory networks and expression regulatory mechanisms. However, traditional enhancer recognition relies on manual feature engineering, which is time-consuming and labor-intensive, making it difficult to perform large-scale recognition analysis. In addition, if the original dataset is too small, there is a risk of overfitting. In recent years, emerging methods, such as deep learning, have provided new insights for enhancing identification. However, these methods also present certain challenges. Deep learning models typically require a large amount of high-quality data, and data acquisition demands considerable time and resources. To address these challenges, in this paper, we propose a data-augmentation method based on generative adversarial networks to solve the problem of small datasets. Moreover, we used regularization methods such as weight decay to improve the generalizability of the model and alleviate overfitting. The Transformer encoder was used as the main component to capture the complex relationships and dependencies in enhancer sequences. The encoding layer was designed based on the principle of k-mers to preserve more information from the original DNA sequence. Compared with existing methods, the proposed approach made significant progress in enhancing the accuracy and strength of enhancer identification and prediction, demonstrating the effectiveness of the proposed method. This paper provides valuable insights for enhancer analysis and is of great significance for understanding gene regulatory mechanisms and studying disease correlations. [ABSTRACT FROM AUTHOR]

Urban tourism imagery is an essential factor affecting the competitiveness of cities. However, most existing studies use small data methods such as interviews and questionnaires to explore tourists' perceptions of cities without combining big data for analysis. In addition, there is relatively little research on the tourism imagery of coastal cities. Therefore, this study analyzes the data from 523 travelogues from a three-dimensional perspective of identity, structure, and meaning, using methods such as BERTopic, aiming to explore the tourism imagery of coastal cities perceived by tourists. The results show the following: (1) In terms of identity, Sanya's urban tourism imagery has the attribute of "external explicit-internal implicit", which satisfies tourists' visual and spiritual experiences. (2) Regarding structure, Sanya has a clear regional division of imagery and a core–edge diffusion distribution among regions. (3) Regarding meaning, tourists' attitudes toward Sanya are polarized. Positive emotions predominate in the overall perception of meaning, but 25% of the negative emotions require urgent attention. This study provides a reference for the sustainable development of tourism cities and the marketing management of destinations. [ABSTRACT FROM AUTHOR]

Solar energy, as a clean energy source, has tremendous potential for utilization. The advancement of solar energy utilization technology has led to an increasing demand for solar energy, resulting in a growing need for the accurate prediction of solar radiation. The main objective of this study is to develop a novel model for predicting solar radiation intervals, in order to obtain accurate and high-quality predictions. In this study, the daily sunshine duration (SD), average relative humidity (RHU), and daily average temperature (AT) were selected as the indicators affecting the daily global solar radiation (DGSR). The empirical study conducted in this research utilized daily solar radiation data and daily meteorological data collected at the Hami station in Xinjiang from January 2009 to December 2016. In this study, a novel solar radiation interval prediction model was developed based on the concept of "point prediction + interval prediction". The Conformer model was employed for the point prediction of solar radiation, while the Generalized Laplace (GLaplace) distribution was chosen as the prior distribution to account for the prediction error. Furthermore, the Solar DeepAR Forecasting (SDAR) model was utilized to estimate parameters of the fitted residual distribution and achieve the interval prediction of solar radiation. The results showed that both models performed well, with the Conformer model achieving a Mean Squared Error (MSE) of 0.8645, a Mean Absolute Error (MAE) of 0.7033 and the fitting coefficient R 2 of 0.7751, while the SDAR model demonstrated a Coverage Width-based Criterion (CWC) value of 0.068. Compared to other conventional interval prediction methods, our study's model exhibited superior accuracy and provided a more reliable solar radiation prediction interval, offering valuable information for ensuring power system safety and stability. [ABSTRACT FROM AUTHOR]

Shale oil and gas are currently the major fields of unconventional hydrocarbon exploration and development. The Fengcheng Formation (FF) shale in the Mahu Sag of the Junggar Basin is an alkaline lacustrine organic-rich shale with an extremely prospective shale oil potential. However, its strong heterogeneity and complex pore structure greatly influence the development of shale oil. It is significant to investigate the pore and geochemical characteristics of shale reservoirs for shale oil extraction. In this study, the pore structure and geochemical characteristics of FF have been investigated using core analysis, Rock-Eval pyrolysis, X-ray diffraction (XRD), field-emission scanning electron microscopy (FE-SEM), mercury injection capillary pressure (MICP), low-temperature gas adsorption (LTGA), and X-ray computed tomography (X-ray CT). The results show that the shale of FF has moderate organic matter abundance, and the kerogen is mainly of Type II, which is now at the peak of oil generation. Shale minerals are mainly composed of carbonate (dolomite and calcite) and siliceous (quartz and feldspar) minerals, with extremely low clay mineral content. The pore types are mainly intergranular pores (inter-P), intragranular pores (intra-P), and microfractures associated with mineral particles. The pore space is contributed predominantly by micropores of 0.5–1.2 nm and mesopores of 10–50 nm, whereas macropores are underdeveloped. The pores are mostly ink bottle- and slit-shaped, and the pore connectivity is relatively poor. The pore development of shale in the FF is influenced by organic matter abundance, thermal maturity, mineral composition, etc. Organic matter content (TOC), thermal maturity (Ro), and carbonate minerals have a positive effect on pore development, and the pore volume (PV) increases with TOC, Ro, and carbonate minerals. While clay minerals show a negative effect, the PV decreases with clay minerals. Additionally, the influence of the clay mineral content on the pore morphology of shale should not be ignored. This study investigates the pore structure and geochemical characteristics of the alkaline lacustrine shale of FF in Mahu Sag, which is significant to deepen the understanding of alkaline lacustrine shale and to improve the production of shale oil. [ABSTRACT FROM AUTHOR]

The extensive application of unmanned aerial vehicle (UAV) technology has increased academic interest in object detection algorithms for UAV images. Nevertheless, these algorithms present issues such as low accuracy, inadequate stability, and insufficient pre-training model utilization. Therefore, a high-quality object detection method based on a performance-improved object detection baseline and pretraining algorithm is proposed. To fully extract global and local feature information, a hybrid backbone based on the combination of convolutional neural network (CNN) and vision transformer (ViT) is constructed using an excellent object detection method as the baseline network for feature extraction. This backbone is then combined with a more stable and generalizable optimizer to obtain high-quality object detection results. Because the domain gap between natural and UAV aerial photography scenes hinders the application of mainstream pre-training models to downstream UAV image object detection tasks, this study applies the masked image modeling (MIM) method to aerospace remote sensing datasets with a lower volume than mainstream natural scene datasets to produce a pre-training model for the proposed method and further improve UAV image object detection accuracy. Experimental results for two UAV imagery datasets show that the proposed method achieves better object detection performance compared to state-of-the-art (SOTA) methods with fewer pre-training datasets and parameters. [ABSTRACT FROM AUTHOR]

Underwater target detection is widely used in various applications such as underwater search and rescue, underwater environment monitoring, and marine resource surveying. However, the complex underwater environment, including factors such as light changes and background noise, poses a significant challenge to target detection. We propose an improved underwater target detection algorithm based on YOLOv8n to overcome these problems. Our algorithm focuses on three aspects. Firstly, we replace the original C2f module with Deformable Convnets v2 to enhance the adaptive ability of the target region in the convolution check feature map and extract the target region's features more accurately. Secondly, we introduce SimAm, a non-parametric attention mechanism, which can deduce and assign three-dimensional attention weights without adding network parameters. Lastly, we optimize the loss function by replacing the CIoU loss function with the Wise-IoU loss function. We named our new algorithm DSW-YOLOv8n, which is an acronym of Deformable Convnets v2, SimAm, and Wise-IoU of the improved YOLOv8n(DSW-YOLOv8n). To conduct our experiments, we created our own dataset of underwater target detection for experimentation. Meanwhile, we also utilized the Pascal VOC dataset to evaluate our approach. The mAP@0.5 and mAP@0.5:0.95 of the original YOLOv8n algorithm on underwater target detection were 88.6% and 51.8%, respectively, and the DSW-YOLOv8n algorithm mAP@0.5 and mAP@0.5:0.95 can reach 91.8% and 55.9%. The original YOLOv8n algorithm was 62.2% and 45.9% mAP@0.5 and mAP@0.5:0.95 on the Pascal VOC dataset, respectively. The DSW-YOLOv8n algorithm mAP@0.5 and mAP@0.5:0.95 were 65.7% and 48.3%, respectively. The number of parameters of the model is reduced by about 6%. The above experimental results prove the effectiveness of our method. [ABSTRACT FROM AUTHOR]

Ledge formation presents a significant challenge in endodontic treatment. Yet, there is still a lack of educational tooth models for hands-on practice. This study aimed to create and evaluate a tooth model for ledge management practice. A natural tooth with curved roots was collected for scientific use under ethics committee approval. Following initial root canal preparation, the tooth was scanned using micro-computed tomography (μCT) and 3D reconstructed. A K-file, created via computer-aided design (CAD), was partly inserted into the root canal wall of the 3D reconstructed tooth. By subtracting the K-file from the tooth, a tooth model with a root canal ledge was produced. The model was then 3D printed for a hands-on workshop. An eight-item Likert-scale questionnaire was administered to 20 postgraduate students and 10 endodontists to assess the model's quality and training effectiveness. In addition, the success rate of bypassing and correcting the root canal ledge was documented. The feedback from both the students and experts was positive, and the results of the Mann–Whitney U test indicated no statistically significant differences found between the two groups (p > 0.05). The success rate of the students and the experts was 85% and 100%, respectively. In future applications, this novel tooth model is expected to address the existing gap in endodontic education and provide benefits for dental practitioners. [ABSTRACT FROM AUTHOR]

Graph Collaborative Filtering (GCF) methods have emerged as an effective recommendation approach, capturing users' preferences over items by modeling user–item interaction graphs. However, these methods suffer from data sparsity in real scenarios, and their performance can be improved using contrastive learning. In this paper, we propose an optimized method, named LoRA-NCL, for GCF based on Neighborhood-enriched Contrastive Learning (NCL) and low-rank dimensionality reduction. We incorporate low-rank features obtained through matrix factorization into the NCL framework and employ LightGCN to extract high-dimensional representations. Extensive experiments on five public datasets demonstrate that the proposed method outperforms a competitive graph collaborative filtering base model, achieving 4.6% performance gains on the MovieLens dataset, respectively. [ABSTRACT FROM AUTHOR]

Current status data are encountered in a wide range of applications, including tumorigenic experiments and demographic studies. In this case, each subject has one observation, and the only information obtained is whether the event of interest happened at the moment of observation. In addition to censoring, truncating is also very common in practice. This paper examines the regression analysis of current status data with informative censoring times, considering the presence of left truncation. In addition, we propose an inference approach based on sieve maximum likelihood estimation (SMLE). A copula-based approach is used to describe the relationship between the failure time of interest and the censoring time. The spline function is employed to approximate the unknown nonparametric function. We have established the asymptotic properties of the proposed estimator. Simulation studies suggest that the developed procedure works well in practice. We also applied the developed method to a real dataset derived from an AIDS cohort research. [ABSTRACT FROM AUTHOR]

The gene encoding 1-hydroxy-2-methyl-2-(E)-butenyl-4-pyrophosphate reductase, a key enzyme at the end of the methylerythritol 4-phosphate pathway (MEP), was cloned from the petals of Narcissus tazetta var. Chinensis 'Jinzhanyintai' and named NtHDR (accession number OQ739816). NtHDR exhibits a full-length coding sequence spanning 1380 base pairs, encoding a total of 460 amino acids. Through phylogenetic-tree analysis, it has been determined that NtHDR shared the closest evolutionary relationship with monocotyledons, specifically Asparagus officinalis and Zingiber officinale. Utilizing quantitative real-time PCR analysis, it was observed that NtHDR exhibited significant expression in both petals and corona, with expression levels varying throughout the flowering process of Narcissus. Specifically, in petals, NtHDR expression demonstrated a pattern of initial increase followed by subsequent decrease, while in corona, it consistently increased. Subsequent subcellular-localization experiments indicated that NtHDR was localized within the chloroplasts. To investigate the functional impact of NtHDR, a stable transformation was performed, where NtHDR was introduced into Nicotiana benthamiana. The resulting transgenic N. benthamiana flowers were analyzed using solid-phase microextraction coupled with gas chromatography–mass spectrometry (SPME-GC-MS) to characterize their volatile components. The analysis revealed the presence of the monoterpene compound linalool, as well as the phenylpropanoids benzyl alcohol and phenylethanol, within the floral fragrance components of the transgenic N. benthamiana plants. However, these compounds were absent in the floral fragrance components of wild-type tobacco plants, thus highlighting the impact of NtHDR transformation on the floral scent profile. [ABSTRACT FROM AUTHOR]

Taking COVID-19 as an example, we know that a pandemic can have a huge impact on normal human life and the economy. Meanwhile, the population flow between countries and regions is the main factor affecting the changes in a pandemic, which is determined by the airline network. Therefore, realizing the overall control of airports is an effective way to control a pandemic. However, this is restricted by the differences in prevention and control policies in different areas and privacy issues, such as how a patient's personal data from a medical center cannot be effectively combined with their passenger personal data. This prevents more precise airport control decisions from being made. To address this, this paper designed a novel data-sharing framework (i.e., PPChain) based on blockchain and federated learning. The experiment uses a CPU i7-12800HX and uses Docker to simulate multiple virtual nodes. The model is deployed to run on an NVIDIA GeForce GTX 3090Ti GPU. The experiment shows that the relationship between a pandemic and aircraft transport can be effectively explored by PPChain without sharing raw data. This approach does not require centralized trust and improves the security of the sharing process. The scheme can help formulate more scientific and rational prevention and control policies for the control of airports. Additionally, it can use aerial data to predict pandemics more accurately. [ABSTRACT FROM AUTHOR]

A Fe-N conjugated organic polymer (SMP-Fr-Py) was prepared from ferrocene and pyrrole using a Scholl coupling reaction, which significantly improved the performance of Cr(VI) removal compared to the polymer (HCP-Fr-Py) prepared by adding the cross-linker formaldehyde dimethyl acetal (FDA). The results showed that at a pH of 2 and at 25 °C, the removal of Cr(VI) reached 90% for SMP-Fr-Py and only 58% for HCP-Fr-Py after 20 min of reaction. Subsequently, 99% and 78% were achieved after 120 min of reaction, respectively. The test results showed that the removal reaction followed a pseudo-second-order kinetic model. The removal efficiency decreased with increasing solution pH and initial Cr(VI) concentration, but increased with increasing SMP-Fr-Py dosage, reaching three cycles. The characterization of the reaction complexes and measurements of Cr species conversion revealed the near absence of Cr(VI) species in the solution. Approximately 38% of Cr(VI) was found to be adsorbed on the material surface, with another fraction present in solution (24%) and on the material surface (38%) in the form of Cr(III). The overall study showed that the direct connection of ferrocene and pyrrole in SMP-Fr-Py through C-C bonding increased the conjugated structure of the polymer backbone, which facilitated electron transfer and transport. Furthermore, the Fe-N elements worked synergistically with each other more easily, which improved the removal performance of Cr(VI) and provided a reference for the subsequent work. [ABSTRACT FROM AUTHOR]

Traditional PDF document detection technology usually builds a rule or feature library for specific vulnerabilities and therefore is only fit for single detection targets and lacks anti-detection ability. To address these shortcomings, we build a double-layer detection model for malicious PDF documents based on an entropy method with multiple features. First, we address the single detection target problem with the fusion of 222 multiple features, including 130 basic features (such as objects, structure, content stream, metadata, etc.) and 82 dangerous features (such as suspicious and encoding function, etc.), which can effectively resist obfuscation and encryption. Second, we generate the best set of features (a total of 153) by creatively applying an entropy method based on RReliefF and MIC (EMBORAM) to PDF samples with 37 typical document vulnerabilities, which can effectively resist anti-detection methods, such as filling data and imitation attacks. Finally, we build a double-layer processing framework to detect samples efficiently through the AdaBoost-optimized random forest algorithm and the robustness-optimized support vector machine algorithm. Compared to the traditional static detection method, this model performs better for various evaluation criteria. The average time of document detection is 1.3 ms, while the accuracy rate reaches 95.9%. [ABSTRACT FROM AUTHOR]

The failure of an unstable slope has the characteristics of sudden disaster, rapid sliding speed, and a wide range of influence. This study selected an unstable slope in Chongqing as the evaluation object, divided it into the strong-deformation zone and weak-deformation zone, and analyzed its formation mechanism and deformation failure mode. Macroscopic qualitative analysis was processed by the geological exploration method, the slope stability and the sliding thrust were quantitatively analyzed by the limit equilibrium method, and the sensitivity analysis was calculated and conducted on its shear strength parameters by laboratory tests and a field large shear test. The historical formation process of the slope was deduced, and suggestions for the prevention and control of unstable slopes were proposed. Studies show that the deformation mechanism of an unstable slope is a push type, its deformation is mainly concentrated in the southern area of the rear edge, and the width of the slope ground tensile crack is 1~30 cm, with a visible depth of 3~30 mm and an extension of 3~12 m. The shear strength of potential sliding zone soil in the weak-deformation zone is: c p w = 35.70 kPa, φ p w = 14.80°, and in the strong-deformation zone it is: c p s = 32.54 kPa, φ p s = 13.54°. The results show that the formation of a steep cliff by the artificial mining of sandstone is the main cause of slope deformation, the action of groundwater is the main triggering factor for slope deformation, and the inclined rock mass at the front edge is the key block for the deformation. The strong-deformation zone of the slope is in an unstable state under rainstorm, and the weak-deformation zone of the slope is basically-stable under rainstorm. Unstable slopes are prone to slide during a rainstorm period, which threatens the lives and properties of the residents and road traffic safety around the slope. The research results provide an important theoretical basis and data support for the accurate judgment and stability analysis of instability failure modes and for the further effective prevention and control of unstable slopes. [ABSTRACT FROM AUTHOR]

Detecting defects in grounded flat steel is essential for ensuring the safety and reliability of transmission tower grounding devices. However, traditional inspection methods, such as physical excavation and verification, are costly and time-consuming. This paper proposes a corrosion defect detection method for flat steel transmission tower grounding devices based on electromagnetic ultrasonic SH waves. In addition, using commercial software, a three-dimensional finite element simulation model of grounded flat steel with simulated pitting corrosion defects is constructed. The specified displacements applied to multiple surface sources mimic the horizontal shear vibrations generated by the electromagnetic ultrasonic transducer on the surface of the grounded flat steel during actual inspection. A simulation was used to investigate the propagation and attenuation characteristics of shear-horizontal ultrasonic SH0guided waves for simulated corrosion defects with various geometric configurations in grounded flat steel. The simulation investigated the propagation and attenuation characteristics of the SH0 wave in grounded flat steel and the detection of various defects for linear analysis of the results. The simulation results show that the attenuation of the electromagnetic ultrasonic guided wave is small, at only 0.0016 dB/mm, and the displacement amplitude of the echo signal decreases with the increase of the SH0 wave propagation distance. Increasing the depth and length of corrosion defects increases the echo signal amplitude. At the same time, the width of corrosion defects has little effect on the echo amplitude. Finally, a flat steel defect detection experiment was conducted, and the experimental results fit with the simulation to verify the accuracy of the simulation model. This detection method introduces a new idea for the on-site detection and quantitative identification of corrosion defects in grounded flat steel, which has significant reference value and can provide a more effective and economical method for ensuring the safety and dependability of transmission tower grounding devices. [ABSTRACT FROM AUTHOR]

Despite the widespread use of artificial intelligence-based methods in detecting electricity theft by smart grid customers, current methods suffer from two main flaws: a limited amount of data on electricity theft customers compared to that on normal customers and an imbalanced dataset that can significantly affect the accuracy of the detection method. Additionally, most existing methods for detecting electricity theft rely solely on one-dimensional electricity consumption data, which fails to capture the periodicity of consumption and overlooks the temporal correlation of customers' electricity consumption based on their weekly, monthly, or other time scales. To address the mentioned issues, this paper proposes a novel approach that first employed a time series generative adversarial network to balance the dataset by generating synthetic data for electricity theft customers. Then, a hybrid multi-time-scale neural network-based model was utilized to extract customers' features and a CatBoost classifier was applied to achieve classification. Experiments were conducted on a real-world smart meter dataset obtained from the State Grid Corporation of China. The results demonstrated that the proposed method could detect electricity theft by customers with a precision rate of 96.64%, a recall rate of 96.87%, and a significantly reduced false detection rate of 3.77%. [ABSTRACT FROM AUTHOR]

Heavy metal ions, one of the major pollutants in the environment, exhibit non-degradable and bio-chain accumulation characteristics, seriously damage the environment, and threaten human health. Traditional heavy metal ion detection methods often require complex and expensive instruments, professional operation, tedious sample preparation, high requirements for laboratory conditions, and operator professionalism, and they cannot be widely used in the field for real-time and rapid detection. Therefore, developing portable, highly sensitive, selective, and economical sensors is necessary for the detection of toxic metal ions in the field. This paper presents portable sensing based on optical and electrochemical methods for the in situ detection of trace heavy metal ions. Progress in research on portable sensor devices based on fluorescence, colorimetric, portable surface Raman enhancement, plasmon resonance, and various electrical parameter analysis principles is highlighted, and the characteristics of the detection limits, linear detection ranges, and stability of the various sensing methods are analyzed. Accordingly, this review provides a reference for the design of portable heavy metal ion sensing. [ABSTRACT FROM AUTHOR]

The advantages of cobalt-free, high specific capacity, high operating voltage, low cost, and environmental friendliness of spinel LiNi0.5Mn1.5O4 (LNMO) material make it one of the most promising cathode materials for next-generation lithium-ion batteries. The disproportionation reaction of Mn3+ leads to Jahn–Teller distortion, which is the key issue in reducing the crystal structure stability and limiting the electrochemical stability of the material. In this work, single-crystal LNMO was synthesized successfully by the sol-gel method. The morphology and the Mn3+ content of the as-prepared LNMO were tuned by altering the synthesis temperature. The results demonstrated that the LNMO_110 material exhibited the most uniform particle distribution as well as the presence of the lowest concentration of Mn3+, which was beneficial to ion diffusion and electronic conductivity. As a result, this LNMO cathode material had an optimized electrochemical rate performance of 105.6 mAh g−1 at 1 C and cycling stability of 116.8 mAh g−1 at 0.1 C after 100 cycles. [ABSTRACT FROM AUTHOR]

In recent years, Chinese offshore wind farms have experienced varying degrees of foundation scour since their completion. The maximum scour depth of pile foundations has far exceeded the design scour depth, which seriously threatens the safety of wind turbines. Among the current scour depth prediction formulas, the values calculated by the Chinese specification 65-1, 65-2 formula are small and the prediction results are on the dangerous side. The calculated value of the American Hec-18 formula is safe but conservative. The prediction formula of other specifications has a large deviation from the actual situation. Based on the available test data, the main factors influencing the local scour depth of pile foundations, the gauge analysis method, and the group method of data handling (GMDH), this paper proposes a prediction formula for the local scour depth of monopile foundation under the action of wave–current. In addition, monopile scour flume experiments were conducted. Combining the experimental data of the flume test and the scour monitoring data of the Rudong wind farm in Jiangsu, the calculated values of the depth prediction equation in this paper and the Chinese code equation, DNV code equation, HEC-18 equation, Rudolph equation, and Raaijmakers equation were compared and analyzed. The results show that the relative error, mean relative error, variance, and normalized variance between the predicted and measured values of this paper's formula are smaller than those of other prediction formulas. The formula in this paper has a high calculation accuracy and practical application value. [ABSTRACT FROM AUTHOR]

Plants of the genus Narcissus are well-known for their characteristic corona morphology, which structural origins have been a bone of contention among scholars. With "Jinzhanyintai" (JZ) and "Yulinglong" (YLL)—two major close-originated cultivars of Chinese narcissus (Narcissus tazetta L. var. chinensis Roem)—as materials, anatomic observation was made on floral organs during corona morphogenesis by dissection with hands under a stereomicroscope, paraffin section, scanning electron microscopy, and high-resolution X-ray tomography. It was uncovered that corona primordia of both cultivars appeared following the end of the differentiation of other floral organs, with differentiation sites located at the inner wall of the juncture of the base of tepals and the upper margin of the hypanthium. Affected by staminal filaments, the corona primordia of JZ experienced a three-stage differentiation process, namely blockage from the second whorl of stamens, blockage from the first whorl of stamens, and healing of corona primordia. However, the expanded spatial structure of the first whorl of petal-like stamens blocked the path of differentiation of YLL corona primordia, giving rise to slow differentiation of the corona primordia at the base of the first whorl of petal-like stamens and malformed differentiation of the corona primordia in the interval between the two whorls of petal-like stamens. Thus, a fragmented structure consisting of typical and fragmented coronas was formed. Furthermore, petal-like stamens of YLL in the lower part had a corona-like morphology. The spatio-temporal specificity of corona differentiation convincingly demonstrates that the corona is a structure independent of and different from the typical four whorls of floral organs, but also highly correlated with stamen. [ABSTRACT FROM AUTHOR]

To study the influence of the laser power, scanning speed, and cleaning water content on the laser cleaning effect and obtain the best cleaning parameters, this paper conducted a simulation analysis of the laser cleaning process and carried out a pulse laser cleaning of porcelain insulators experiment to verify. The results show that the cleaning rate gradually increases as the laser power increases from 20 W to 25 W. As the scanning speed increases from 1000 mm/s to 2500 mm/s, the laser overlapping rate gradually decreases, and the cleaning takes the lead in increasing and then decreasing. The appropriate cleaning water content is conducive to laser cleaning; when the water content is 0.115 g, the cleaning efficiency reaches the highest value of 98.20%. When the laser power is 25 W, and the scanning speed is 2000 mm/s, the cleaning efficiency can reach the highest value of 96.87%. This paper shows that the reasonable choice of cleaning parameters can effectively clean the insulator surface filth and obtain a better surface morphology. [ABSTRACT FROM AUTHOR]

Hydrocarbon potential evaluation is a high priority in oil/gas exploration which is significantly influenced by evaluation methods and subjective cognition. With the aim of quantitatively establishing a hydrocarbon generation and expulsion (HGE) model of source rocks, a data-driven approach is proposed based on abundant and readily available Rock-Eval/total organic carbon content (TOC)/vitrinite reflectance (VRo) datasets. This approach takes into account the original hydrocarbon generation potential (GPIo) and the loss of TOC. Hydrocarbon generation simulation was also adopted in this study. This data-driven method was applied to the Lower Paleogene Shahejie Formation (Es4), which has three sets of superior source rocks (average thickness > 250 m, TOC > 3%). The GPIo of the Es4 low-maturity source rock was 600 mg HC/g TOC. The initial maturity values of hydrocarbon generation and expulsion were 0.36% and 0.46%, respectively. The Gaosheng sub-member of the Es4 source rock had the largest amount of hydrocarbon generation but a limited amount of hydrocarbon expulsion, implying a favorable exploration of shale oil resources. By contrast, the values of the conventional, unconventional tight, and shale oil resource potentials of the Niuxintuo sub-member were 3.20 × 107, 7.70 × 107, and 5.93 × 108 t, respectively, indicating good prospects for tight oil and shale oil exploration. A comparison between the previous method and the data-driven method showed considerable differences in the restoration of GPIo and TOC, and both had their own limitations. This data-driven approach provides a quick and valid source rock evaluation method and can greatly enhance the accuracy of resource assessment. [ABSTRACT FROM AUTHOR]

Many repeated manual feature adjustments and much heuristic parameter tuning are required during the debugging of machine learning (ML)-based transient stability assessment (TSA) of power systems. Furthermore, the results produced by ML-based TSA are often not explainable. This paper handles both the automation and interpretability issues of ML-based TSA. An automated machine learning (AutoML) scheme is proposed which consists of auto-feature selection, CatBoost, Bayesian optimization, and performance evaluation. CatBoost, as a new ensemble ML method, is implemented to achieve fast, scalable, and high performance for online TSA. To enable faster deployment and reduce the heavy dependence on human expertise, auto-feature selection and Bayesian optimization, respectively, are introduced to automatically determine the best input features and optimal hyperparameters. Furthermore, to help operators understand the prediction of stable/unstable TSA, an interpretability analysis based on the Shapley additive explanation (SHAP), is embedded into both offline and online phases of the AutoML framework. Test results on IEEE 39-bus system, IEEE 118-bus system, and a practical large-scale power system, demonstrate that the proposed approach achieves more accurate and certain appropriate trust solutions while saving a substantial amount of time in comparison to other methods. [ABSTRACT FROM AUTHOR]

Lithium-rich layered oxide (LLO) are considered high-capacity cathode materials for next-generation lithium-ion batteries. In this study, LLO cathode materials were synthesized via the hydroxide coprecipitation method followed by a two-step lithiation process using different lithium contents and lithium sources. The effects of lithium content and lithium source on structure and electrochemical performance were investigated. This study demonstrated the clear impact of Li/TM ratio on electrochemical performance. Lower Li/TM ratio reduced the irreversible capacity loss in the first cycle and provided better cycling stability among all samples. The best results exhibited an initial discharge capacity of 279.65 mAh g−1 and reached a discharge capacity of 231.9 mAh g−1 (82.9% capacity retention) after 30 cycles. The sample using Li2CO3 as lithium source exhibits better electrochemical performance than the sample using LiOH as lithium source. Therefore, it is important to choose the appropriate lithium source and optimal lithium content for improving structural properties and electrochemical performance of LLO. [ABSTRACT FROM AUTHOR]

Five alkali-activated analcime (ANA) sorbents (ANA-MK 1, ANA 2, ANA 3, ANA-MK 4, and ANA-MK 5) were developed for ammonium (NH4+) ion removal. Acid treatment and calcination were used as pre-treatments for analcime, and metakaolin (MK) was used as a blending agent in three sorbents. Sorption experiments were performed to evaluate the effects of sorbent dosage (1–20 g L−1), initial NH4+ ion concentration (5–1000 g L−1), and contact time (1 min–24 h). ANA-MK 1, ANA 2, and ANA-MK 4 were the most efficient sorbents for NH4+ ion removal, with a maximum experimental sorption uptake of 29.79, 26.00, and 22.24 mg g−1, respectively. ANA 3 and ANA-MK 5 demonstrated lower sorption capacities at 7.18 and 12.65 mg g−1, respectively. The results for the sorption of NH4+ ions onto the alkali-activated analcime surfaces were modeled using several isotherms. The Langmuir, Freundlich, Sips, and Bi-Langmuir isotherms were the best isotherm models to represent the studied systems. The results of the kinetic studies showed the maximum NH4+ ion removal percentage of the sorbents was ~80%, except for ANA-MK 5, which had a ~70% removal. Moreover, the pseudo-first-order, pseudo-second-order, and Elovich models were applied to the experimental data. The results showed that the sorption process for ANA-MK 1, ANA 2, ANA 3, and ANA-MK 4 followed the Elovich model, whereas the pseudo-second-order model provided the best correlation for ANA-MK 5. [ABSTRACT FROM AUTHOR]

In this paper, we discuss regression analysis of bivariate interval-censored failure time data that often occur in biomedical and epidemiological studies. To solve this problem, we propose a kind of general and flexible copula-based semiparametric partly linear additive hazards models that can allow for both time-dependent covariates and possible nonlinear effects. For inference, a sieve maximum likelihood estimation approach based on Bernstein polynomials is proposed to estimate the baseline hazard functions and nonlinear covariate effects. The resulting estimators of regression parameters are shown to be consistent, asymptotically efficient and normal. A simulation study is conducted to assess the finite-sample performance of this method and the results show that it is effective in practice. Moreover, an illustration is provided. [ABSTRACT FROM AUTHOR]

In recent years, bamboo has been well exploited in the pulp and paper industry. Moso bamboo (Phyllostachys edulis (Carriere) J. Houzeau; hereafter M), originated from China with a long history of cultivation as the most abundant resource with the widest distribution area among bamboo plants in China. In this study, Moso bamboo (M) and nine bamboo varieties were selected in the bamboo germplasm resources storage bank of the Anhui Taiping Experimental Station of ICBR. The characteristics of their cell structures were compared, and the differences were analyzed from a genetic perspective. The results showed that M had the highest fiber cell length, fiber cell width, length, and parenchyma width, while GJ showed the lowest of these measurements [P. edulis 'Kikko-chiku',G.H.Lai]. The fiber wall thickness of Q [P. edulis f. obliquinoda (Z.P.Wang et N.X.Ma) Ohrnberger] was the smallest, while its fiber lumen diameter was the highest in the group. The parenchyma wall thickness and parenchyma lumen diameter of Q were the smallest in the group. The fiber cells of M and Q had better flexibility, which is conducive to improving the tensile strength, break resistance, and folding resistance of paper made from these materials. SY and GJ may be more suitable for ornamental items because of their special appearances. The purpose of this study was to explore the genetic variation patterns of various cell structure indicators among Moso bamboo and its varieties, as well as to develop a strategy of bamboo growing and lumbering based on the local conditions, providing reference data for the utilization of non-woody forest resources. [ABSTRACT FROM AUTHOR]