Search Results (259)

To address the challenges posed by weak early fault signal features, strong noise interference, low diagnostic accuracy, poor reliability when using single information sources, and the limited availability of high-quality samples in practical applications for permanent magnet synchronous motor (PMSM) bearings, this paper proposes an early bearing fault diagnosis method based on Hippopotamus Optimization Variational Mode Decomposition (HO-VMD) and weighted evidence fusion of current–vibration signals. The HO algorithm is employed to optimize the parameters of VMD for adaptive modal decomposition of current and vibration signals, resulting in the generation of intrinsic mode functions (IMFs). These IMFs are then selected and reconstructed based on their kurtosis to suppress noise and harmonic interference. Subsequently, the reconstructed signals are demodulated using the Teager–Kaiser Energy Operator (TKEO), and both time-domain and energy spectrum features are extracted. The reliability of these features is utilized to adaptively weight the basic probability assignment (BPA) functions. Finally, a weighted modified Dempster–Shafer evidence theory (WMDST) is applied to fuse multi-source feature information, enabling an accurate assessment of the PMSM bearing health status. The experimental results demonstrate that the proposed method significantly enhances the signal-to-noise ratio (SNR) and enables precise diagnosis of early bearing faults even in scenarios with limited sample sizes. Full article

Aloin, a kind of active phenolic component, is sourced from Aloe vera. Recently, the determination of aloin has received enormous attention, owing to its positive performance (including anti-tumor, antibacterial, detoxification, liver protection, anti-stomach damage, and skin protection activities) and painful side effects (increased carcinogenicity caused by excessive use of aloin) impacting human health. This investigation was inspired by the good fluorescence properties of carbon dots (CDs); CD-based sensors have aroused a great deal of interest due to their excellent sensitivity and selectivity. Thus, it is of great significance to develop novel CD-based sensors for aloin determination. Herein, N,F-CDs were designed and synthesized through a convenient hydrothermal strategy; the synthesized N,F-CDs possessed good fluorescence performance and a small particle size (near 4.3 nm), which demonstrated the successful preparation of N,F-CDs. The resulting N,F-CDs possessed a large Stokes shift and could emit a highly stable green fluorescence. The fluorescence of the N,F-CDs could be effectively quenched by aloin through the inner filter effect. Furthermore, the synthesis procedure was easy to operate. Finally, the N,F-CD-coated test strips were fabricated and combined with a miniaturized fluorimeter for the fluorescence detection of aloin via the inner filter effect for the first time. The N,F-CD-coated test strips were fabricated and used for the fluorescence sensing of aloin, and the results were compared with a typical ultraviolet (UV) method. The N,F-CD-coated test strips exhibited high recovery (96.9~106.1%) and sensitivity (31.8 nM, n = 3), good selectivity, low sample consumption (1 μL), high speed (5 min), good stability, and anti-interference properties. The results indicate that N,F-CD-coated test strips are applicable for the quantitative determination of aloin in bovine serum, orange juice, and urine samples. Full article

Rapid urbanization has exacerbated the urban heat island effect, posing a significant threat to human health and urban ecosystems. While numerous studies have demonstrated that urban morphology significantly influences land surface temperatures (LSTs), few have systematically explored the impact and contribution of urban morphology on LST across different functional zones. Therefore, this study takes Xi’an as a case and employs an interpretable CatBoost-SHAP machine learning model to evaluate the nonlinear influence of building landscape features on LST in different functional zones during summer. The results indicate the following: (1) The highest LST in the study area reached 52.68 °C, while the lowest was 21.68 °C. High-temperature areas were predominantly concentrated in the urban center and industrial zones with dense buildings, whereas areas around water bodies and green spaces exhibited relatively lower temperatures. (2) SHAP analysis revealed that landscape indicators exerted the most substantial impact across all functional zones, with green space zones contributing up to 62%. Among these, fractional vegetation coverage (FVC), as a core landscape factor, served as the primary cooling factor in all six functional zones and consistently demonstrated a negative effect. (3) Population density (POP) exhibited a generally high SHAP contribution across all functional zones, showing a positive correlation. Its effect was most pronounced in commercial zones, accounting for 16%. When POP ranged between 0 and 250 people, the warming effect was particularly prominent. (4) The mean building height (MBH) constituted a major influencing factor in most functional zones, especially in residential zones, where the SHAP value reached 0.7643. Within the range of 10–20 m, the SHAP value increased sharply, indicating a significant warming effect. (5) This study proposes targeted cooling strategies tailored to six functional zones, providing a scientific basis for formulating targeted mitigation strategies for different functional zones to alleviate the urban heat island effect. Full article

Cinnamomum parthenoxylon (Jack) Meisner is an important spice tree species in southern China. In in vitro cultures of C. parthenoxylon, the young stem explants can differentiate into adventitious buds and roots under different exogenous growth regulator conditions. However, the underlying regulatory mechanisms governing this differentiation process remain unclear. In this study, physiological and biochemical characteristics were measured, and transcriptomic sequencing was performed in different differentiation processes. Significant changes in physiological and biochemical parameters were observed during the differentiation of the young stems. Soluble sugars, soluble proteins, malondialdehyde (MDA), zeatin riboside (ZR), abscisic acid (ABA), gibberellin (GA) content, the (IAA + GA + ZR)/ABA ratio, and polyphenol oxidase (PPO) activity displayed contrasting expression patterns during the formation of adventitious buds and roots. The RNA-seq result revealed that the differentiation direction of young stems is regulated by the synthesis of endogenous hormones and associated signaling pathways. At the same time, phenylpropanoid metabolism and glucose metabolism pathways acted as auxiliary pathways, facilitating the formation of adventitious buds and roots. Furthermore, quantitative real-time PCR (qRT-PCR) results were highly consistent with transcriptome sequencing results. This study lays the foundation for exploring the directional differentiation of young stems in C. parthenoxylon. Full article

The initiation condition of the shear band in gas hydrate-bearing sediment (GHBS) was analyzed in this study. First, the mathematical model considering the pore diffusion and stress conservation equations was constructed. The shear stress is assumed to be related to the porosity, shear strain, and shear strain ratio. The expansion of pores causes sediment softening, while the shear strain causes the stiffening of the sediment. The perturbation method was used to analyze the initiation condition of the shear band under porosity softening and strain stiffening based on the presented mathematical model. A numerical simulation was also performed. The development of the strain, stress, and porosity was analyzed. It is shown that the parameters of the sediment change with the strain and porosity. When the parameters are satisfied under certain conditions, the shear band will initiate and develop. The critical condition is when the porosity-softening effects overcome the strain-stiffening effects. In some special cases, the critical condition may be related to other factors, such as when strain softening induces other kinds of initiation of the shear band. Full article

L-type lectin receptor-like kinases (L-LecRLKs) play key roles in plant responses to environmental stresses and the regulation of growth and development. However, comprehensive studies of the L-LecRLK gene family in wheat (Triticum aestivum L.) are still limited. In this study, 248 L-LecRLK candidate genes were identified in wheat, which is the largest number reported in any species to date. Phylogenetic analysis grouped these genes into four clades (I–IV), with Group IV exhibiting significant monocot-specific expansion. Gene duplication analysis revealed that both whole-genome/segmental and tandem duplications contributed to family expansion, while Ka/Ks ratio analysis suggested that the genes have undergone strong purifying selection. The TaL-LecRLK genes displayed diverse exon-intron structures and conserved motif compositions. Promoter analysis revealed a cis-element associated with hormone signaling and abiotic stress responses. Transcriptome profiling showed that TaL-LecRLKs exhibit tissue- and stage-specific expression patterns. RNA-Seq data revealed that, under drought and heat stress conditions, TaL-LecRLK35-3D and TaL-LecRLK67-6B exhibited synergistic expression patterns, whereas TaL-LecRLK67-6A demonstrated antagonistic expression. A qRT-PCR further demonstrated that six TaL-LecRLKs may function through ABA-independent regulatory mechanisms. These findings provide valuable gene candidates for stress-resistant wheat breeding and shed light on the evolution and functional diversity of L-LecRLKs in plants. Full article

Long non-coding (lnc) RNAs exhibit tissue-specific expression characteristics and have been shown to be involved in the regulation of various biological processes. The testis is one of the organs with the most abundant lncRNAs. However, the functions of many testis-specific or -enriched lncRNAs in male fertility remain undisclosed. In this study, we screened lncRNA 1700009J07Rik (07Rik) to investigate its roles in spermatogenesis and male fertility using knockout (KO) mice. We found that 07Rik mainly acted as an intact lncRNA rather than a small protein, being highly expressed in various spermatogenic cells, which suggests its potential involvement in spermatogenesis. Unexpectedly, the deletion of 07Rik did not impact spermatogenesis or sperm functions. Intriguingly, two-thirds of the male KO were infertile, which was ascribed to the lack of sexual behaviors rather than abnormalities in spermatogenesis or sperm functions. Further results reveal that, compared with wild-type mice, free testosterone content in serum was significantly reduced in the KO infertile (KO-I) mice, whereas it was remarkably elevated in the testes. Correspondingly, Hsd3b2, a key gene that promotes testosterone synthesis, was dramatically upregulated. Cyp19a1 and Cyp11b1, which are responsible for testosterone metabolism, were downregulated in the testes. In addition, the expression of sex hormone-binding globulin was observably elevated in the testes of 07Rik KO-I mice, which might partially explain the decrease in testosterone in the serum. These results suggest that disruptions in testosterone synthesis and metabolism might contribute to the loss of libido in 07Rik KO-I mice. Our findings expand the understanding of lncRNA function and provide novel insights into the role of lncRNAs in male fertility, particularly in relation to hormonal turnover disorders that mediate sexual behavior defects. Full article

This study investigates the combined effects of mesoscale eddies and rough sea surfaces on acoustic propagation in the eastern Arabian Sea and Gulf of Aden during summer monsoon conditions. Utilizing three-dimensional sound speed fields derived from CMEMS data, sea surface spectra from the SWAN wave model validated by Jason-3 altimetry, and the BELLHOP ray-tracing model, we quantify their synergistic impacts on underwater sound. A Monte Carlo-based dynamic sea surface roughness model is integrated with BELLHOP to analyze multiphysics interactions. The results reveal that sea surface roughness significantly influences surface duct propagation, increasing transmission loss by approximately 20 dB compared to a smooth sea surface, while mesoscale eddies deepen the surface duct and widen convergence zones by up to 5 km. In deeper waters, eddies shift convergence zones and reduce peak sound intensity in the deep sound channel. These findings enhance sonar performance and underwater communication in dynamic, monsoon-influenced marine environments. Full article

Lightning-generated whistlers (LW) play a crucial role in understanding magnetosphere–ionosphere coupling mechanisms and, perhaps, identifying precursor signals of natural disasters, such as volcanic eruptions and earthquakes. Traditional frequency–time image recognition techniques require approximately 40 years to analyze seven years of observational data from the China Seismo-Electromagnetic Satellite (CSES), which fails to meet the requirements for practical implementation. To address this issue, a novel and highly efficient model for LW recognition is proposed, integrating speech processing technology with a parallel bidirectional Simple Recurrent Unit (SRU) neural network. The proposed model significantly outperforms traditional methods in computational efficiency, reducing the parameter count by 99% to 0.1 M and enhancing processing speed by 99%, achieving 20 ms per sample. Despite these improvements, the model maintains excellent performance metrics, including 93% precision, 88.7% recall, and 90.7% F1-score, which is a measure of predictive performance. As a result, the model can process seven years of data in just 33 days, marking a 442-fold increase in processing speed compared to conventional approaches. Full article

Various forms of alumina have attracted considerable attention for their ability to remove anionic dyes from wastewater, attributed to their high specific surface area, and environmental safety. In this study, a series of modified alumina materials were synthesized for the first time using the reverse precipitation method with dual aluminum sources and without template agent to explore their applicability in various scenarios, including adsorption processes and regeneration cycles. The results revealed that non-modified alumina exhibited superior adsorption properties, while silicon-modified alumina demonstrated exceptional thermal stability during high temperature calcination. For silicon-modified alumina, the replacement of some Al–OH groups with silicon resulted in the formation of a protective silicon layer on the alumina surface, which delayed the sintering process. The pseudo-second-order kinetic model and Langmuir model were utilized to fit the experimental data. Furthermore, the adsorption and regeneration properties of silicon-modified alumina were investigated, revealing a maximum equilibrium adsorption capacity of 822.6 mg/g for Congo Red using non-modified alumina. Notably, the non-modified alumina demonstrated a 40.6% increase in its adsorption capacity compared to its initial capacity after six regeneration cycles at 1000 °C. Full article

The content of modified materials in multicomponent gel phase change materials directly affects their performance characteristics. To investigate the influence of different contents of modified materials on the performance features of Na₂HPO₄·12H₂O-based multicomponent Gel Phase Change Materials, four single factors (Na₂SiO₃·9H₂O, C₃₅H₄₉O₂₉, KCl, and nano-α-Fe₂O₃) and their interactions were selected as influencing factors. Using the Taguchi method with an L₂₇(3¹³) orthogonal array, multi-step melt–blending experiments were conducted to prepare a novel multi-component phase change material. The characteristics of the new multi-component phase change material, including supercooling degree (ΔT), phase change temperature (T_m), latent heat of phase change (ΔH_m), and cooling time (CT), were obtained. In addition, characterization techniques such as DSC, SEM, FT-IR, and XRD were employed to analyze its thermal properties, microscopic morphology, chemical stability, and crystal structure. Based on the experimental results, the signal-to-noise ratio (S/N) was used to rank the influence of each factor on the quality characteristics, and the p-value from analysis of variance (ANOVA) was employed to evaluate the significance of each factor on the performance characteristics. Then, the effects of each significant factor on the characteristics of the multiple gel phase change materials were analyzed in detail, and the optimal mixing ratio of the new multiple gel phase change materials was selected. The results showed that Na₂SiO₃·9H₂O, KCl, and α-Fe₂O₃ were the most critical process parameters. This research work enriches the selection of composite gel phase change materials for solar greenhouses and provides guidance for the selection of different modified material contents using Na₂HPO₄·12H₂O as the starting material. Full article

Limited surface energy and mass flux data hinder the understanding of glacier retreat mechanisms on the Qinghai–Tibet Plateau (QTP). Glaciers in the west Nyainqentanglha Range (WNR) supply meltwater to the densely populated Lhasa River basin (LRB) and Nam Co, the QTP’s second-largest endorheic lake. In this study, we used a glacier mass balance model based on the degree-day method (GMB-DDM) to understand the response of glacier changes to climate warming. The spatiotemporal variation in degree-day factors for ice (DDF_ice; plural form: DDFs_ice) was assessed to characterize the sensitivity of glacier melt to warming over 44 years in the WNR. Our results demonstrate that the GMB_DDM effectively captured the accelerated mass loss and regional heterogeneity of WNR glaciers from 2000 to 2020, particularly the intensified negative balance after 2014. Moreover, glacier ablation was more sensitive to warming in the WNR during 2000–2020 than 1976–2000, with DDF_ice increases of 21% ± 8% in the LRB and 31% ± 10% in the Nam Co basin (NCB). Increased precipitation during the ablation season and reduced glacier surface albedo can explain the increased sensitivity to warming during 2000–2020. These findings could support sustainable water resource management in the LRB, NCB, and the surrounding areas of the QTP. Full article

The widespread scrapping of retired mechanical parts has led to severe waste of resources and environmental burdens, posing a significant challenge to sustainable industrial development. To enable efficient recycling of retired mechanical parts and enhance the sustainability of their remanufacturing processes, the concept of biological genes is adopted to characterize the changes in the information of retired mechanical parts during the remanufacturing process as gene mutations of parts, aiming to maximize remanufacturing potential and devise an optimal generalized remanufacturing strategy for extending part life cycles. However, gene mutation of retired mechanical parts is not an isolated event. The modification of local genes may disrupt the original equilibrium of the part’s state, leading to conflicts such as material–performance, structure–function/performance, and function–performance. These conflicts constitute a major challenge and bottleneck in designing generalized remanufacturing schemes. Therefore, we propose a conflict identification and resolution method for gene mutation of retired mechanical parts. First, gene mutation graph of retired mechanical parts is established to express its all-potential remanufacturing pathways. Using discrimination rules and the element representation method from extenics, mutation conflicts are identified, and a conflict problem model is constructed. Then, the theory of inventive problem solving (TRIZ) engineering parameters are reconstructed and mapped to the mutation conflict parameters. The semantic mapping between the inventive principles and the transforming bridges is established by the Word2Vec algorithm, thereby improving the transforming bridge method to generate conflict resolution solutions. A coexistence degree function of transforming bridges is proposed to verify the feasibility of the resolution solutions. Finally, taking the generalized remanufacturing of a retired gear shaft as an example, we analyze and discuss the process and outcome of resolving gene mutation conflicts, thereby verifying the feasibility and effectiveness of the proposed concepts and methodology. Full article

The cornerstone of the global economy, oil and gas reservoir development, faces numerous challenges such as resource depletion, operational inefficiencies, safety concerns, and environmental impacts. In recent years, the integration of artificial intelligence (AI), particularly artificial general intelligence (AGI), has gained significant attention for its potential to address these challenges. This review explores the current state of AGI applications in the oil and gas sector, focusing on key areas such as data analysis, optimized decision and knowledge management, etc. AGIs, leveraging vast datasets and advanced retrieval-augmented generation (RAG) capabilities, have demonstrated remarkable success in automating data-driven decision-making processes, enhancing predictive analytics, and optimizing operational workflows. In exploration, AGIs assist in interpreting seismic data and geophysical surveys, providing insights into subsurface reservoirs with higher accuracy. During production, AGIs enable real-time analysis of operational data, predicting equipment failures, optimizing drilling parameters, and increasing production efficiency. Despite the promising applications, several challenges remain, including data quality, model interpretability, and the need for high-performance computing resources. This paper also discusses the future prospects of AGI in oil and gas reservoir development, highlighting the potential for multi-modal AI systems, which combine textual, numerical, and visual data to further enhance decision-making processes. In conclusion, AGIs have the potential to revolutionize oil and gas reservoir development by driving automation, enhancing operational efficiency, and improving safety. However, overcoming existing technical and organizational challenges will be essential for realizing the full potential of AI in this sector. Full article

The fusion of infrared and visible images provides complementary information from both modalities and has been widely used in surveillance, military, and other fields. However, most of the available fusion methods have only been evaluated with subjective metrics of visual quality of the fused images, which are often independent of the following relevant high-level visual tasks. Moreover, as a useful technique especially used in low-light scenarios, the effect of low-light conditions on the fusion result has not been well-addressed yet. To address these challenges, a decoupled and semantic segmentation-driven infrared and visible image fusion network is proposed in this paper, which connects both image fusion and the downstream task to drive the network to be optimized. Firstly, a cross-modality transformer fusion module is designed to learn rich hierarchical feature representations. Secondly, a semantic-driven fusion module is developed to enhance the key features of prominent targets. Thirdly, a weighted fusion strategy is adopted to automatically adjust the fusion weights of different modality features. This effectively merges the thermal characteristics from infrared images and detailed information from visible images. Additionally, we design a refined loss function that employs the decoupling network to constrain the pixel distributions in the fused images and produce more-natural fusion images. To evaluate the robustness and generalization of the proposed method in practical challenge applications, a Maritime Infrared and Visible (MIV) dataset is created and verified for maritime environmental perception, which will be made available soon. The experimental results from both widely used public datasets and the practically collected MIV dataset highlight the notable strengths of the proposed method with the best-ranking quality metrics among its counterparts. Of more importance, the fusion image achieved with the proposed method has over 96% target detection accuracy and a dominant high mAP@[50:95] value that far surpasses all the competitors. Full article