Search Results (655)

Cephalotaxus hainanensis, a valuable medicinal and endangered conifer, requires scientific conservation and precision management to ensure the sustainable utilization of its genetic and ecological resources. Nitrogen (N) is a key nutrient that regulates plant growth and metabolism; rapid and accurate nitrogen diagnosis is vital for optimizing fertilization, reducing nutrient losses, and promoting healthy plant development. This study employed a combined approach integrating stepwise regression, correlation analysis, and Least Absolute Shrinkage and Selection Operator (LASSO) regression to identify leaf color features strongly correlated with leaf nitrogen content (LNC). A support vector regression (SVR) model, suitable for small-sample datasets, was then employed to accurately estimate LNC across canopy layers. Nine color variables were found to be highly associated with LNC, among which the Green Minus Blue index (GMB) consistently appeared across all correlation methods, demonstrating strong robustness and generality. Color features effectively reflected LNC variations among nitrogen treatments—especially between N1 and N4—and across canopy layers, with the most pronounced contrasts observed between upper and lower leaves. The Spearman-based SVR model revealed that the middle canopy maintained the highest and most stable LNC. However, the lower leaves were most sensitive to nitrogen deficiency, while the upper leaves were more sensitive to nitrogen excess. Comprehensive analysis identified N2 as the optimal nitrogen treatment, representing a balanced nutrient state. Overall, this study confirms the reliability of color features for LNC estimation and highlights the importance of vertical canopy LNC distribution in nitrogen diagnostics, providing a theoretical and methodological foundation for color-based nitrogen diagnosis and precision nutrient management in evergreen conifers. Full article

This paper proposes a theoretical framework for studying rough intuitionistic fuzzy filters within the structure of BE-algebras. Building on rough set theory and intuitionistic fuzzy set theory, we introduce rough intuitionistic fuzzy filters via lower and upper approximation operators induced by congruence relations. To further generalize the framework, we define set-valued homomorphisms on BE-algebras and use them to formulate $\Gamma$-rough intuitionistic fuzzy filters. Several structural properties and characterization results are established, including stability under approximation operators, relationships with classical intuitionistic fuzzy filters, and preservation under homomorphic mappings. The proposed approach provides an algebraic mechanism for modeling uncertainty, hesitation, and imprecision in implication-based systems, with potential relevance to uncertainty-aware reasoning in artificial intelligence, decision-support systems, and medical diagnosis. Full article

This study aimed to investigate the biological characteristics of Corynebacterium hindlerae, a pathogen discovered in bovine hosts, analyze its genomic features, clarify genetic relationships and differences among strains, and provide a scientific basis for comprehensive clinical prevention and control. The environmental tolerance, biofilm formation capability, and motility of the isolated strain LSKT01 were analyzed. A total of 12 Corynebacterium strains were selected for phylogenetic analysis based on core genes, evaluation of Average Nucleotide Identity (ANI), and comparative genomic analysis covering gene families, synteny, SNPs, InDels, and structural variations (SVs). The isolate exhibited optimal growth at 37 °C, pH 5.5, and 1–2% NaCl concentration, demonstrated strong biofilm-forming ability, but showed weak or no motility. Phylogenetic analysis divided the five Corynebacterium hindlerae strains into two subgroups, with strain LSKT01 being most closely related to strain 1864 (ANI 95.80–98.70%). Comparative genomic analysis revealed highly conserved gene families among strains, while LSKT01 possessed 20 specific paralogs. Genome synteny analysis showed an average similarity >90%, and strains ISL_960a and MAL_1082b exhibited more complex structural variations. The successful isolation and purification of LSKT01 revealed its biological characteristics and genomic features, elucidating the genetic relationships and evolutionary divergence among C. hindlerae strains from diverse sources. This provides a theoretical basis for the rapid diagnosis and precise prevention and control of Corynebacterium hindlerae in Xinjiang. Full article

Background: Drug-induced liver injury (DILI), a major type of adverse drug reaction, has become one of the leading causes of acute liver injury and liver failure worldwide. Its clinical significance lies not only in acute hepatocyte necrosis and functional failure but also in its role as a key initiating factor for liver cancer progression. Therefore, early diagnosis of DILI is of great importance. Methods: This study employed ultra-performance liquid chromatography-mass spectrometry (UPLC-MS/MS) to perform widely targeted metabolomics analysis on acetaminophen (APAP)-induced liver injury mice and healthy mice. Results: UPLC-QTRAP-MS/MS identified 41 differentially expressed metabolites primarily involved in glycerophospholipid metabolism, arginine and proline metabolism, primary bile acid biosynthesis, and glutathione metabolism pathways. The significant elevation of serum and hepatic alanine aminotransferase (ALT) and aspartate aminotransferase (AST) confirmed the successful establishment of the drug-induced liver injury (DILI) model. ROC curve analysis indicated 11 metabolites with AUC values exceeding 0.90 as potential biomarkers, including (R)-2-Hydroxybutyric acid, Glu-Gln, γ-Glu-Gln, 2-Methyllactic acid, L-Serine, Hyodeoxycholic acid, 3-Epideoxycholic acid, and Glycochenodeoxycholic acid 7-sulfate. Conclusions: We propose that these differential metabolites may serve as candidate biomarkers for DILI. Our findings provide a novel metabolomic signature derived directly from the injured tissue and offer a theoretical foundation for further research into early diagnosis of drug-induced liver injury. Full article

The management of supracondylar fractures in children remains a challenging area of orthopedic practice. Medial comminution is a recognized complication that can result in unstable fracture patterns, which can pose challenges in diagnosis and management. However, when anticipated surgical treatment with an additional medial K-wire is administered, stable fixation is typically ensured. However, an additional radial comminution poses several challenges for reduction, alignment assessment, and pin configuration for stable fixation, as presented in this case. This case report presents a fracture pattern of a Gartland type 3 fracture with medial and lateral comminution that has not been sufficiently described previously and illustrates an effective pin configuration that has yet to be theoretically described. Full article

Fault diagnosis is a core technology ensuring the safe and efficient operation of industrial systems. A paradigm shift has been observed wherein traditional signal analysis has been replaced by intelligent, algorithm-driven approaches. In recent years, large language models, digital twins, and knowledge graphs have been introduced. A new stage of intelligent integration has been reached that is characterized by data-driven methods, knowledge guidance, and physical–virtual fusion. In the present paper, the evolutionary context of fault diagnosis technologies was systematically reviewed, with a focus on the theoretical methods and application practices of traditional machine learning, digital twins, knowledge graphs, and large language models. First, the research background, core objectives, and development history of fault diagnosis were described. Second, the principles, industrial applications, and limitations of supervised and unsupervised learning were analyzed. Third, innovative uses were examined involving physical–virtual mapping in digital twins, knowledge modeling in knowledge graphs, and feature learning in large language models. Subsequently, a multi-dimensional comparison framework was constructed to analyze the performance indicators, applicable scenarios, and collaborative potential of different technologies. Finally, the key challenges faced in the current fault diagnosis field were summarized. These included data quality, model generalization, and knowledge reuse. Future directions driven by the fusion of large language models, digital twins, and knowledge graphs were also outlined. A comprehensive technical map was established for fault diagnosis researchers, as well as an up-to-date reference. Theoretical innovation and engineering deployment of intelligent fault diagnosis are intended to be supported. Full article

Social media is a valuable resource in many spheres of life in the 21st century; however, excessive, uncontrolled use is associated with various adverse health conditions. In this study, we used a quantitative approach, an observational design, and a comparative scope to compare levels of academic commitment and social media addiction, and their respective dimensions, grouping participants according to various sociodemographic and educational criteria. A total of participants was 1200 students (65.3% female) with an average age of 21.4 years, from the Faculty of Health Sciences at the National University of Chimborazo, Ecuador, and data were collected using the Ultrecht Academic Commitment Scale and Social Media Addiction Questionnaire. When grouped by major, statistically significant differences were found only for dedication (p = 0.038), lack of control over social media use (p = 0.016), and excessive social media use (p = 0.002). When grouped by social media use, there were statistically significant differences in all the dependent variables, with p-values ranging from 0.000 to 0.011. Regarding the frequency of social media use, no significant differences were found in academic engagement (p ≥ 0.05), while the opposite was observed for social media use. A comparative analysis identified categories with significant differences. The results enabling an accurate diagnosis and the adoption of the most appropriate educational strategies; also serves as a theoretical and methodological basis for further research on the subject. Full article

This paper is devoted to the study of the interrelationships among $\mathcal{LF}$-grills, $\mathcal{LF}$-ideals, $\mathcal{LF}$-neighborhoods, $\mathcal{LF}$-topologies, and $\mathcal{LF}$-co-topologies. We establish a categorical framework that demonstrates the interconnections among these concepts. In addition, we investigate categorical connections from $\mathcal{LF}$-ideal spaces to $\mathcal{LF}$-topological spaces and from $\mathcal{LF}$-grill spaces to $\mathcal{LF}$-topological spaces using concrete functors, confirming the existence of Galois correspondences between these spaces. Finally, the practical relevance of the theoretical framework is illustrated through applications in information systems and medical diagnosis. Full article

Prostate cancer remains the most frequently diagnosed malignancy in men and a leading cause of cancer-related mortality. Multiparametric MRI (mpMRI) has become the gold standard for non-invasive diagnosis, staging, and follow-up. Yet, its widespread adoption is hampered by long acquisition times, inter-reader variability, and interpretative complexity. Though most papers focus on specific applications without offering a cohesive therapeutic perspective, artificial intelligence (AI) has recently attracted attention as a potential solution to these shortcomings. For instance, deep learning models can help optimize imaging protocols for biparametric and multiparametric MRI, and AI-based reconstruction techniques have shown promise for reducing acquisition times without sacrificing diagnostic performance. Several systems have produced outcomes in the diagnostic phase that are comparable to those of skilled radiologists, as demonstrated in multicenter settings such as PI-CAI. Radiomics and radiogenomics provide more detailed insights into the biology of the disease by extracting quantitative features associated with tumor aggressiveness, extracapsular expansion, and treatment response, in addition to detection. Despite these developments, methodological variability, a lack of multicenter validation, proprietary algorithms, and unresolved standardization and governance difficulties continue to restrict clinical translation. Our work emphasizes the maturity of existing technologies, ongoing gaps, and the progressive integration necessary for successful clinical adoption by presenting AI applications aligned with the patient pathway. In this context, this review aims to outline how AI can support the entire patient journey—from acquisition and protocol selection to detection, quantitative analysis, treatment assessment, and follow-up—while maintaining a clinically centered perspective that emphasizes practical relevance over theoretical discussion, potentially enabling more reliable, effective, and customized patient care in the field of prostate cancer. Full article

Transitioning from theoretical learning to practical application remains a significant challenge for students in medical and veterinary science education, particularly in the context of medical imaging and ultrasound interpretation. Traditional lecture-based methods offer limited support for developing the spatial reasoning and technical skills required for ultrasound pregnancy diagnosis. This study evaluates the effectiveness of an interactive mixed reality (MR) training tool, Ewe Scan, delivered through the Apple Vision Pro, compared to traditional lecture-based instruction. Forty-two undergraduate students were randomly assigned to either a lecture-trained or MR-trained group and assessed immediately after training and again after six weeks. Results showed that MR-trained students significantly outperformed their lecture-trained peers in both immediate comprehension and retention over time, particularly in ultrasound interpretation skills. The MR-trained group also reported higher levels of engagement, confidence, and satisfaction with their training experience. These findings suggest that MR-based learning enhances educational outcomes by improving spatial understanding, increasing active engagement, and supporting knowledge retention. Integrating MR simulations into ultrasound education offers a scalable, ethical, and effective alternative to traditional training methods, contributing to advancements in medical imagery education. Full article

Advances in plant nutrition have driven substantial progress in modern fertilization technologies. Nevertheless, excessive chemical fertilizer application, low nutrient-use efficiency, and the resulting environmental pollution remain widespread. We have reviewed the research progress and existing limitations in the field of plant nutrition and fertilization technology. Based on the traditional plant nutrition diagnosis and integrating visual diagnosis methods, this study explores the intrinsic relationship between plant growth status, nutrient supply conditions, and crop yield and proposed the concept of “status nutrition”. Variations in environmental nutrient conditions lead plants to exhibit distinct growth status in terms of vigor and phenotype. We define the plant nutritional status reflected by this growth status as “status nutrition”. Based on growth characteristics, plant growth status can be classified as weak, normal, or vigorous, corresponding to deficient, appropriate, and excessive environmental nutrient supply, respectively. Guided by this concept, an innovative rice “extremely dense planting” technology is integrated by increasing planting density, eliminating tiller-stage fertilization, and optimizing nitrogen management. The technology adapts to growth status with low nutrient demand, coordinates population growth and main-stem panicle formation, and achieves high yield with reduced fertilizer inputs. Further research is needed on the nutrient metabolism mechanisms of plants under different growth statuses and the growth status grading system. The promotion of “extremely dense planting” is constrained by crop variety traits and soil fertility, and its parameters urgently need to be optimized. Overall, the framework of “status nutrition” provides important theoretical support for the development and application of crop high-yield cultivation technologies. Full article

SHapley Additive exPlanations (SHAP) analysis has been applied in disease diagnosis and treatment effect evaluation. However, its application in the prediction and diagnosis of dairy cow diseases remains limited. We investigated whether the variance and autocorrelation of deviations in daily activity, rumination time, and milk electrical conductivity, along with daily milk yield, could be used to predict clinical mastitis in dairy cows using popular machine learning (ML) algorithms and identifying key predictive features using SHAP analysis. Quantile regression (QR) with second- or third-order polynomial models with the median or upper quantiles was used to process raw data from mastitic and healthy cows. Nine variables from the 14-day period preceding mastitis onset were identified as significantly associated with mastitis through logistic regression. These variables were used to train and validate prediction models using eleven classical ML algorithms. Among them, the partial least squares model demonstrated superior performance, achieving an AUC of 0.789, sensitivity of 0.500, specificity of 0.947, accuracy of 0.793, precision of 0.833, and F1-score of 0.625. SHAP analysis results revealed positive contributions of three features to mastitis prediction, whereas two features had negative contributions. These findings provide a theoretical basis for developing clinical decision-support tools in commercial farming settings. Full article

Metamaterials (MMs)-based terahertz (THz) biosensors hold promise for clinical diagnosis, featuring label-free operation, simple, rapid detection, low cost, and multi-cell-type discrimination. However, liquid around cells causes severe interference to sensitive detection. Most existing MMs-based cell biosensors detect dead cells without culture medium (losing original morphology), hindering stable, sensitive multi-cell discrimination. Here, a terahertz biosensor composed of a microcavity and MMs can be used to detect and discriminate multiple cell types within suspension. Its detection mechanism relies on cellular size (radius)/density in suspension, which induces effective permittivity (ε_eff) differences. By designing MMs’ split rings with luxuriant gaps, the biosensor achieves a theoretical sensitivity of ~328 GHz/RIU, enabling sensitive responses to suspended cells. It shows a robust, increasing frequency shift (610–660 GHz) over 72 h of cell apoptosis. Moreover, it discriminates nerve cells, glioblastoma (GBM) cells, and their 1:1 mixture with obviously distinct frequency responses (~650, ~630, ~620 GHz), which suggests effective and reliable multi-cell-type recognition. Overall, this study and its measurement method should pave the way for metamaterial-based terahertz biosensors for living cell detection and discrimination, and this technology may inspire further innovations in tumor investigation and treatment. Full article

This study presents a method for enriching oil-suspended particles within a rectangular microfluidic channel using acoustic standing waves. A modified Helmholtz equation is solved to establish the acoustic field model, and the equilibrium between acoustic radiation forces and viscous drag is described by combining Gor’kov potential theory with the Stokes drag model. Based on this force balance, the particle motion equation is derived, enabling the determination of the critical particle size necessary for efficient enrichment in oil-filled microchannels. A two-dimensional standing-wave microchannel model is subsequently developed, and the influences of acoustic, fluidic, and particle parameters on particle migration and aggregation are systematically investigated through theoretical analysis and numerical simulations. The results indicate that when the channel dimension and acoustic wavelength satisfy the half-wavelength resonance condition, a stable standing-wave field forms, effectively focusing suspended particles at the acoustic pressure nodes. Enrichment efficiency is found to be strongly dependent on inlet flow velocity, particle diameter, acoustic frequency, temperature, and particle density. Lower flow velocities and larger particle sizes result in higher enrichment efficiencies, with the most uniform and stable pressure distribution achieved when the acoustic frequency matches the resonant channel width. Increases in temperature and particle density enhance the acoustic radiation force, thereby accelerating the aggregation of particles. These findings offer theoretical foundations and practical insights for acoustically assisted online monitoring of wear particles in lubricating oils, contributing to advanced condition assessment and fault diagnosis in mechanical systems. Full article

The reliability of the Medium-Voltage Direct-Current (MVDC) power supply system is crucial for train operation, as it powers control, communication, and other critical onboard systems. Accurately locating insulation faults within this system can significantly reduce troubleshooting difficulty and prevent major operational losses. This study addresses a key challenge in applying Time-Domain Reflectometry (TDR) for fault location in single-core cables of IT systems: the incident-end impedance mismatch caused by the variable characteristic impedance of such cables, which fluctuates with installation distance from a ground plane. First, the mechanism through which this mismatch attenuates the primary fault reflection and generates secondary reflections is theoretically modeled. A resistive-capacitive (RC) coupling network is then designed to achieve bidirectional impedance matching between the test equipment and the cable under test while maintaining essential DC isolation. Simulation and experimental results demonstrate that the proposed network effectively mitigates the mismatch issue. In experiments, it increased the proportion of the primary reflected wave entering the receiver by over 30 percentage points and suppressed the secondary reflection by approximately 80%. These improvements enhance waveform clarity and signal strength, directly leading to more accurate fault location. The proposed solution, validated in a railway context, also holds significant potential for improving insulation fault diagnosis in analogous high-voltage cable applications, such as electric vehicle powertrains. Full article