Changsha University of Science and Technology

We developed a deep convolutional neural network‐based program code that automatically detects equatorial plasma bubbles (EPBs), segments EPB morphologies, and then extracts their features (including EPBs' northernmost foot latitudes and zonal drift velocities) from OI 630 nm airglow images. From 2012 to 2022, all‐sky airglow images from Qujing Station, China (geographic: 25°N, 104°E; geomagnetic: 15.1°N, 176.7°E), were manually labeled as EPBs or non‐EPBs. Some images showing typical EPB morphologies also had their contours annotated. This created two unique data sets which are both suitable for supervised learning models. Based on the above data sets, numerous experiments were conducted to train the EPB recognition model (EPB‐RM) and the EPB morphology segmentation model (EPB‐MSM). The results on the test set indicate that ResNet18+CBAM model can perform the best in recognizing EPB (precision: 99%, recall: 91%), and all models perform better during geomagnetically quiet periods and high solar activity periods (F10.7 value >140) than during geomagnetically disturbed periods (Dst index ≤ −30 nT or Kp index ≥3) and low solar activity periods. The EPB‐MSM based on Deeplabv3plus had the highest accuracy (88.2%) for extracting the EPB morphology. Statistical features extracted using the EPB feature extraction program (EPB‐FEP) were highly consistent with those extracted manually. The test results verified that machine learning is an excellent method for automatically detecting and extracting EPB characteristics. Our study provides a convenient tool for analyzing massive EPB images.

Real-time, efficient, and accurate air quality anomaly detection is of significant practical importance for traffic navigation and smart cities. Traditional unsupervised deep autoencoders (AE) often struggle to capture the spatiotemporal heterogeneity of air quality anomaly data. Anomaly reconstruction errors are frequently underestimated, and relying solely on one loss function can limit model accuracy and generalizability. It is challenging to perform interpretable spatiotemporal anomaly tracing based solely on model evaluation metrics. This paper proposes a novel air quality anomaly detection and interpretable spatiotemporal traceability analysis method. In the data preprocessing phase, Density Peak Clustering (DPC) combined with Anomaly Score (AS) calculation is utilized to accurately approach the spatiotemporal heterogeneous distribution of various air quality anomalies. Subsequently, we introduce a stochastic derivative-free trust-region algorithm that integrates multiple loss functions, creating the Stochastic derivative-free multi-error-optimized Performer Autoencoder (StoDEMO-PAE). This model replaces the traditional AE structure with a PAE adept at capturing outlier information and optimizes both the loss functions and the data training process. Finally, spatiotemporal traceability analysis is conducted using real-time air quality monitoring data and public complaint information from 95 monitoring stations in Haikou from May 2021 to March 2023. Experimental results demonstrate that StoDEMO-PAE achieves superior average performance on the experimental dataset (P value, R value, and F1 score of 0.788, 0.692, 0.743, respectively, representing an average improvement of 0.175, 0.245, 0.221 over 9 baseline models such as BeatGan, Anomaly Transformer, OmniAnomaly, and LSTM-AE). Furthermore, the integration of public complaint corpus information for air quality anomaly spatiotemporal traceability further validates the scientific robustness and practicality of the proposed method. The results of this study improve air quality anomaly detection, offering valuable insights for real-time urban air quality monitoring, early warnings, and interpretable spatiotemporal traceability.

The inadequate cooling capacity of the customary fluids forced the scientists to look for some alternatives that could fulfill the industry requirements. The inception of nanofluids has revolutionized the modern industry-oriented finished products. Nanofluids are the amalgamation of metallic nanoparticles and the usual fluids that possess a high heat transfer rate. Thus, meeting the cooling requirements of the engineering and industrial processes. Having such amazing traits of nanofluids in mind our aim here is to discuss the flow of nanofluid comprising Nickel-Zinc Ferrite and Ethylene glycol over a curved surface with heat transfer analysis. The heat equation contains nonlinear thermal radiation and heat generation/absorption effects. The envisioned mathematical model is supported by the slip and the thermal stratification boundary conditions. Apposite transformations are betrothed to obtain the system of ordinary differential equations from the governing system in curvilinear coordinates. A numerical solution is found by applying MATLAB build-in function bvp4c. The authentication of the proposed model is substantiated by comparing the results with published articles in limiting case. An excellent concurrence is seen in this case. The impacts of numerous physical parameters on Skin friction and Nusselt number and, on velocity and temperature are shown graphically. It is observed that heat generation/absorption has a significant impact on the heat transfer rate. It is also comprehended that velocity and temperature distributions have varied behaviors near and far away from the curve when the curvature is enhanced.

Nucleic acids, as essential biomacromolecules, have garnered considerable scientific interest in diagnosis and therapy owing to their unique molecular characteristics, including sequence‐specific programmability, precise molecular recognition capabilities, and diverse biological functionalities. In recent years, with the deepening of the understanding of the physicochemical properties of nucleic acids and the development of nanotechnology, nucleic acid‐based nanoprobes with sophisticated structures and functions have been gradually developed, opening up a new direction for efficient diagnosis, followed by customized therapy for diseases. Despite the progress, the affinity and stability of nucleic acids to target sites still suffer from severe challenges, especially in complex physiological environments. Polyvalent nucleic acids, integrated with different functional elements, have demonstrated enormous advantages in effectively solving the above limitations by the elaborate design and construction of multifunctional DNA nanostructures, making them promising candidates for smart‐responsive nanoprobes. In this review, we commence with an exploration of nucleic acids, subsequently delineating state‐of‐the‐art strategies for the fabrication of polyvalent nucleic acid nanostructures. We then predominantly underscore the latest advancements in these nanostructures as innovative platforms for cancer theranostics. Further, we critically examine the biomedical applications of polyvalent nucleic acids, alongside addressing the prevailing challenges and future prospects in the realm of nucleic acid‐facilitated diagnostics and therapeutics.

Recognizing the unique roles of attapulgite (ATP)-loaded and FeSx in enhancing the dispersion and osxidation resistance of nanoscale zero-valent iron (nZVI) as well as facilitating a high electron transfer rate for removal of target pollutants is important but challenging, especially in hexavalent chromium (Cr(VI))-containing wastewater systems. Herein, S-nZVI@ATP, a composite material consisting of sulfidized nZVI loaded onto ATP, was utilized to remove Cr(VI), and the corresponding reaction mechanisms was explored. The findings revealed that the removal efficiency of Cr(VI) (RCr) for S-nZVI@ATP was 97.93% at S/Fe molar ratio (S/FeMRR) of 0.12, S-nZVI/ATP mass ratio (S-nZVI/ATPMSR) of 4:1, pH of 3, and an initial Cr(VI) concentration of 20 mg/L. In the pH range of 3 to 7, S-nZVI@ATP exhibited excellent removal performance for Cr (VI), with the highest RCr 99.71% at pH 3. Coexisting ions such as SO4²⁻, CO3²⁻, PO4³⁻, and HCO3⁻ showed varying degrees of inhibition on the removal of Cr(VI). HCO3⁻ displayed positive effects at concentrations of 10 and 15 mmol/L (RCr = 99.99%). The removal process followed the Pseudo-second-order kinetic model and Freundlich adsorption isothermal model, with an adsorption amount reaching 19.25 mg/g at equilibrium. Thermodynamic calculations revealed that the material adsorbed Cr(VI) onto the S-nZVI@ATP by spontaneous heat absorption. By studying the kinetics, thermodynamics, and adsorption isothermal model, analyzing the morphology of Fe and Cr, and characterizing the materials before and after the reaction, the removal mechanism of Cr(VI) was determined as adsorption-redox-co-precipitation.

The traditional phase‐segregated arc suppression (AS) methods often neglect the influence of line voltage drop (LVD), resulting in the problem of large residual current when single‐line‐to‐ground (SLG) faults occur at the end of heavily loaded lines, making reliable AS difficult. To effectively suppress fault currents during medium and low impedance SLG faults at the end of heavily loaded lines, the phase‐segregated AS method considering LVD is proposed in this paper. Additionally, the phase‐segregated AS method, which is based on sliding mode control using an exponential convergence rate, is utilized to reduce the total harmonics distortion of fault current and neutral point voltage after current injection. The simulation results validate the effectiveness of the proposed method under the case of the medium or low impedance ground fault occurring at the end of heavily loaded lines.

Compared to single-modal methods, multimodal semantic segmentation methods leverage the rich complementary information between modalities to improve segmentation accuracy, attracting increasing attention. However, differences in imaging principles between modalities lead to incompatibilities that increase the difficulty of fusion. Efficiently fusing multiscale features across modalities and effectively exploiting their complementary information remains a challenging task. In this article, we propose a multiscale gated fusion network (MGFNet) for effectively preserving the discriminative features of different modalities at different scales and utilizing complementary information. Specifically, to preserve the discriminative features of different modalities, we design a multiscale gated fusion module to selectively fuse useful features from different modalities by extracting their complementary features at different scales. In addition, we propose a cross-modal interaction module to adaptively capture long-range dependencies and facilitate the exchange of complementary features between modalities. Finally, the cross-modal multiscale extraction module effectively extracts multiscale features from the fused features and integrates complementary information across modalities. Extensive experiments on the Vaihingen and Potsdam datasets demonstrate that our proposed MGFNet achieves superior performance compared to currently popular methods. The code of MGFNet is available at https://github.com/DrWuHonglin/MGFNet.

Processing hard and brittle materials typically involves extensive rough grinding, often resulting in material damage. In this paper, ultrasonic vibration-assisted machining (UVAM) is applied to the rough grinding of zirconia ceramics, achieving both high efficiency and minimal damage. We systematically studied the axial ultrasonic vibration-assisted end grinding (AUEG) of zirconia ceramics, and smooth surfaces with little damage were obtained across a wide range of cutting depths (2–20 µm). This paper presents a novel theoretical model for the grinding process of AUEG, dividing the grinding process into Stage I (initial machined surface creation) and Stage II (machined surface refinement). In Stage I, ultrasonic vibration facilitates the nucleation of microcracks and reduces crack size. In Stage II, the cutting depth is varied between 0 and amplitude, and the abrasive grains dynamically remove the residual damage generated in Stage I in the form of a plastic flow. The model is supported by analyses of the machined surface and subsurface quality, as well as grinding forces. Results indicate that, with appropriate ultrasonic amplitude, the surface morphology is dominated by plastic flow and brittle damage is significantly reduced. The surface roughness Sa was reduced from a maximum of 663.2 to 146.78 nm. In addition, AUEG significantly reduced the grinding force fluctuation with the variation coefficients of grinding force reduced from a maximum of 8.46 to 3.2% (Fx), 7.81 to 2.14% (Fy), and 6 to 3.92% (Fz), respectively. This study provides support for high-quality and high-efficiency grinding of hard and brittle materials.

The quantitative detection of lactic acid is crucial in medical diagnosis, sports medicine, and the food industry. Currently, non-enzymatic electrochemical lactate sensors have attracted significant attention for lactate detection owing to their high sensitivity, excellent chemical stability, low cost, and ease of miniaturization. Transition metal-based materials are extensively utilized in non-enzymatic electrochemical lactate sensors due to their superior electron transport properties, straightforward preparation processes, and cost-effectiveness. In this study, we present an enzyme-free electrochemical sensor platform based on indium tin oxide (ITO) and nickel hydroxide(Ni(OH)2) nanostructures, fabricated on an ITO substrate using a multiple potential step method. The resulting Ni(OH)2-ITO electrodes serve as an effective enzyme mimic, enhancing the electrocatalytic oxidation and detection of LA. The Ni(OH)2 nanostructures synthesized from nickel chloride exhibit a well-defined redox peak, indicating high catalytic activity. The Ni(OH)2-ITO electrodes demonstrates superior electrocatalytic activity and sensing performance, characterized by a low positive potential (-0.47 V vs Ag/AgCl), a limit of detection (LOD) of 0.4038 μM (S/N = 3), a wide linear range from 0.0067 to 79.3966 mM (R² = 0.9923), and a sensitivity of 0.1325 mA·mM⁻¹·cm⁻². Additionally, the sensor exhibits excellent selectivity against common interference such as uric acid (UA), ascorbic acid (AA), Mg²⁺, Na⁺, and Ca²⁺. This work highlights the potential application of a robust alternative analytical tool for LA determination.

Problematic smartphone use (PSU) has emerged as a global concern. Previous research has suggested a link between parental burnout and adolescents’ PSU; however, the dynamic nature and underlying mechanisms of this relationship have been underexplored. This study employed a two-wave longitudinal design with an eight-month interval to investigate the association between parental burnout and adolescents’ PSU. Given the established roles of harsh parenting and adolescents’ depression as predictors of adolescents’ PSU, these variables were examined as potential mediators in this relationship. The study sample consisted of 315 Chinese adolescents and their parents (97 boys and 218 girls, mean age = 14.37 years; 52 fathers and 263 mothers, mean age = 42.76 years). The results revealed that: (a) parental burnout positively predicted adolescents’ PSU eight months later; (b) adolescents’ depression mediated the longitudinal relationship between parental burnout and adolescents’ PSU, whereas harsh parenting (parent-reported and adolescent-reported) did not; and (c) harsh parenting (parent-reported) and adolescents’ depression sequentially mediated the longitudinal relationship between parental burnout and adolescents’ PSU, whereas harsh parenting (adolescent-reported) and adolescents’ depression did not. These findings not only enhance our understanding of the underlying connection between parental burnout and adolescents’ PSU but also provide valuable theoretical insights for developing future prevention and intervention measures targeting PSU among adolescents.

Leukemia is a malignant disease of progressive accumulation characterized by high morbidity and mortality rates, and investigating its disease genes is crucial for understanding its etiology and pathogenesis. Network propagation methods have emerged and been widely employed in disease gene prediction, but most of them focus on static biological networks, which hinders their applicability and effectiveness in the study of progressive diseases. Moreover, there is currently a lack of special algorithms for the identification of leukemia disease genes. Here, we proposed DyNDG, a novel dynamic network-based model, which integrates differentially expressed genes to identify leukemia-related genes. Initially, we constructed a time-series dynamic network to model the development trajectory of leukemia. Then, we built a background–temporal multilayer network by integrating both the dynamic network and the static background network, which was initialized with differentially expressed genes at each stage. To quantify the associations between genes and leukemia, we extended a random walk process to the background–temporal multilayer network. The experimental results demonstrate that DyNDG achieves superior accuracy compared to several state-of-the-art methods. Moreover, after excluding housekeeping genes, DyNDG yields a set of promising candidate genes associated with leukemia progression or potential biomarkers, indicating the value of dynamic network information in identifying leukemia-related genes. The implementation of DyNDG is available at both https://ngdc.cncb.ac.cn/biocode/tool/BT7617 and https://github.com/CSUBioGroup/D yNDG.

In this paper, we investigate the existence of mild solutions of the nonlinear fractional Rayleigh-Stokes problem for a generalized second grade fluid on an infinite interval. We firstly show the boundedness and continuity of solution operator. And then, by using a generalized Arzelà-Ascoli theorem and some new techniques, we get the compactness on the infinite interval. Moreover, we prove the existence of global mild solutions of nonlinear fractional Rayleigh-Stokes problem.

Epoxy grout is an effective underwater grouting material. Permeability performance and cementation mechanism of the epoxy grout have important influences on the effectiveness of the grouting of the underwater media. To investigate the permeation characteristics and cementation performance of the epoxy grout in different underwater media, by carrying out permeation grouting experiments with different underwater media, the variation law of permeation and diffusion distance of grout with different mass ratios in underwater media with time is studied. The microscopic reinforcement characteristics of the interface between epoxy grout and different underwater media were analyzed by SEM, and the cementation mechanism of grout in different underwater media was revealed from the microscopic point of view. The results indicate that the mass ratio of the grout is a crucial factor influencing the permeation depth in underwater media. The permeation depth of the grout first increases and then tends to stabilize, while it increases with the mass ratio of the grout. The grout exhibited the highest filling ratio in the sand and gravel media, with a maximum of 68.88%. The effect of grout reinforcement in sand and gravel media was significantly more pronounced than that in other medias, while the effect of grout-ground-level cementation was the least pronounced in cohesive soil layers. Finally, the reaction mechanism of grout in underwater media is discussed. The research results can provide some reference for the selection of grouting materials and the optimization of proportion in underwater grouting engineering.

Drawing on conservation of resources theory, this study investigates how and when charismatic leadership influences employees’ unethical pro-supervisor behavior (i.e., behaviors committed by subordinates that contribute to the leader’s interests but are unethical). We identify relational energy as the mediating mechanism that links charismatic leadership to unethical pro-supervisor behavior and introduce moral identity as a boundary condition in this relationship. Utilizing multi-time and multi-source data from 422 supervisor-subordinate dyads across three large enterprises in China, we developed and tested a moderated mediation model. Our findings indicate that relational energy mediates the relationship between charismatic leadership and subordinates’ unethical pro-supervisor behavior, with this indirect relationship being weaker among employees with high levels of moral identity. Based on these findings, this study sheds light on the conditions under which charismatic leadership triggers a negative outcome and inspires managers on how to better inhibit subordinates’ unethical pro-supervisor behavior while displaying charisma.

The safety performance of horizontal and crest vertical curve combinations (also named as crest combinations or crest combined curves) is substantially associated with their geometric design. To evaluate their safety performance accurately, three Bayesian hierarchical negative binomial (NB) models with various structures of temporal correlation (including linear time trend, quadratic time trend, and autoregressive-1) are proposed for building a relationship between crash frequency and the separated and combined geometric design attributes of crest combination on freeways. An 8 year (2011–2018) crash dataset of 124 crest combination sections on four freeways in Washington state is collected and used for the model development and comparison. The results of model assessment indicate that the hierarchical NB model with autoregressive-1 is clearly superior to other alternatives. The parameter estimation results in the model reveal that in addition to the crash exposure variables (i.e., section length and annual average daily traffic), four geometric design attributes (vertical curvature, horizontal curvature, approach grade, and overlapping proportion) and two roadway configuration characteristics (lane width and left shoulder width) have significant effects on the safety performance. Considerable over-dispersion, cross-group heterogeneity, and temporal correlation are also found in the best-performing model. According to the results, some strategies for highway design are proposed to improve the safety performance of freeway crest combinations.

In recent years, the advantage of convolutional neural networks in capturing local spatial information has achieved significant performance in single-image super-resolution. However, most methods typically use small convolution kernels to aggregate local and global features, thereby ignoring the interactions between channels and spatial dimensions within larger receptive fields. To address this issue, we propose a hybrid feature enhancement network (HFEN) for image super-resolution reconstruction. Specifically, we have designed a large-kernel-transposed attention module that applies self-attention to the channel dimension while expanding the receptive field, effectively capturing channel information and long-range dependencies. In addition, to utilize the rich feature representations in feedforward networks, we designed a multi-scale aggregation feedforward network to extract multi-scale information and aggregate features. Experiments have shown that HFEN is significantly superior to state-of-the-art lightweight SR methods. Code is available at https://github.com/SJHunag/HFEN.

Immunosuppressant drugs (ISDs) are widely used in the treatment of organ rejection following human transplantation and in autoimmune diseases. Herein, this study demonstrates that carbonylated covalent organic frameworks (COFs) with pore-matching capabilities can serve as promising interference-resistant adsorbents for the rapid and efficient capture of ISDs (cyclosporin A (CsA), tacrolimus (FK-506), and rapamycin (RPM)) from complex whole blood matrices. Under optimized conditions, MCOF-2-COOH, with a pore size 1.5 times the diameter of the drug molecule, demonstrated superior ISDs adsorption performance, achieving an adsorption capacity of up to 84.95 mg g−1 in 10 min. Instrumental characterization and theoretical calculations elucidated the potential adsorption matrix, revealing that the COF provides multiple forces, including hydrogen bonding, electrostatics, and π-π interactions, with the carboxyl site playing a crucial role. This study provides both a theoretical basis and experimental evidence for the use of COF materials in the selective adsorption of drugs from complex matrices, as well as a strategy for designing functionally customized COFs for drug therapy monitoring applications.

The continuous industrial developments mean global economies are under heightened pressure to enact pro-environmental policies and solve climate change and global warming. In order to examine the sustainable development transition, the current study articulates natural resources, digital infrastructure, renewable energy investments, institutional governance, and economic growth to study climate change in top-10 resource-consuming economies. The empirical analysis analyzes the empirical dataset from 1996 to 2022 to report that renewable energy investments, digital infrastructure, and institutional governance lower climate change challenges. In contrast, dependency on natural resources and economic growth endanger ecological sustainability. Our robust theoretical and econometric analysis helps us suggest sustainable development policy frameworks to help achieve sustainable development goals.