Search Results (711)

The optimization model of intelligent identification for bridge cracks based on dual-parameter error indexes’ feedback mechanism is studied here. An interdisciplinary evaluation system of geometric morphology and fracture mechanics is proposed and established. The weighted average of two parameters is proposed as the index to evaluate the crack information model. The two parameters are as follows: (1) effective crack width index (ECWI), which reflects the geometric error of crack information vector graphics; (2) the tip curvature radius error (TCRE), which reflects the stress concentration degree of structural cracks. The aforementioned dual-parameter error evaluation indexes are processed by weighted averaging with reference to current specifications, and the recognition errors of cracks identified by the lightweight semantic segmentation model MobileNetV2-DeepLabv3+ are comprehensively evaluated. The above errors are fed back to the model training code, and parameters such as crack training hyperparameters and data augmentation parameters are adjusted for retraining. After iterative optimization from Version 1 to Version 5, the model’s prediction accuracy is improved: the Dice coefficient is increased by 3.5~32.4%, IoU by 5.3~56.5%, and PA by 0.42~1.33%, finally iterating to an optimized crack recognition model. This combined evaluation system of geometric morphology and fracture mechanics can optimize the information model through error feedback. Meanwhile, by virtue of this method, the disease photos from bridge inspections during the maintenance phase can be identified and converted into an information model of bridge diseases, which holds significant theoretical significance and engineering value for promoting digital maintenance. Full article

Cervical cancer is a major gynecological malignancy linked to hormonal dysregulation and genetic alterations. Chemotherapy is standard but limited by toxicity and chemoresistance, prompting interest in plant-derived adjuncts. This study examined the anticancer and immunomodulatory effects of Hesperidin (Hes), a citrus flavonoid, with Doxorubicin (DX) in HeLa cervical cancer cells. Cell viability was assessed by MTT assay, apoptotic markers (Bcl-2, Caspase-3) by RT-qPCR, and inflammatory cytokines (IL-1β, IL-6, TNF-α, IFN-γ) by ELISA. Cytokine levels were normalized to 10⁴ viable cells, and mRNA expression of all four cytokines was quantified by RT-qPCR, confirming protein-level changes and showing the strongest IL-6 suppression with Hes+DX. Chou–Talalay combination index (CI) analysis demonstrated synergistic interactions (CI < 1.0) between Hes and DX across all tested concentrations, with strong synergism (CI < 0.7) at medium and high doses, particularly at 48 and 72 h. Hes alone showed dose-dependent cytotoxicity, while the combination markedly increased Caspase-3, reduced Bcl-2, and decreased IL-1β, IL-6, and TNF-α, indicating enhanced intrinsic apoptosis and complementary immunomodulation. These results suggest that Hes augments DX’s pro-apoptotic and anti-inflammatory effects, potentially allowing lower chemotherapy doses and reduced systemic toxicity in cervical cancer treatment. Full article

Background/Objectives: Type 2 diabetes (T2D) is associated with elevated cardiovascular risk and mortality. While physical activity can reduce cardiovascular risk, sustaining behavioral change remains challenging. Wearable activity trackers offer a scalable approach to promote physical activity, but their effects on cardiovascular outcomes in adults with T2D have not been well studied. To evaluate the impact of a wrist-worn activity tracker on physical activity, cardiovascular markers, and metabolic outcomes in adults with T2D over four weeks. Methods: This pilot randomized controlled trial included eight adults with T2D (mean age 54.9 ± 12.6 years; intervention (FIT) group: n = 5; control (CON) group: n = 3). The intervention group received an activity tracker. Both groups used the Fitbit app to track daily activity. Physical activity metrics (steps, walking distance, energy expenditure) and cardiovascular markers (blood pressure, augmentation index, pulse wave velocity, subendocardial viability ratio [SEVR]) were assessed pre- and post-intervention. Non-parametric tests and Spearman correlations were used due to the small sample size. Results: The FIT group showed significant increases in walking distance and energy expenditure and reductions in systolic/diastolic blood pressure, pulse pressure, and mean arterial pressure (all p < 0.04). SEVR trended toward improvement (p = 0.07). No significant changes were seen in the CON group. Increased physical activity was strongly correlated with reductions in pulse pressure (ρ = −0.88) and fasting glucose (ρ = −0.82; both p < 0.05). Conclusions: A brief wearable-based intervention improved physical activity and cardiovascular markers in adults with T2D, supporting feasibility for diabetes care. Full article

Objective: The study investigates the dietary effects of Moringa oleifera leaf powder on obesity, blood biochemical parameters, and organ health in hyperlipidemic zebrafish (Danio rerio). Methodology: Adult hyperlipidemic zebrafish (n = 56/group) were fed for 12 weeks either with a high-cholesterol diet (HCD, 4% w/w) or HCD supplemented with 0.5% (w/w) M. oleifera leaf powder (0.5% MO) or HCD with 1.0% (w/w) M. oleifera leaf powder (1.0% MO). At different time points (0 to 12 weeks), the survivability and body weight (BW) of zebrafish were measured, while various biochemical and histological evaluations were performed after 12 weeks of feeding the respective diets. Additionally, an in silico approach was used to assess the binding interactions of MO phytoconstituents with 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase. Results: Following 12-week supplementation, higher zebrafish survivability was observed in the MO-supplemented groups compared to the survivability of the HCD group. Relative to the initial BW, only 4% BW enhancement was observed post 12 weeks of dietary intake of 1.0% MO, in contrast to 27% BW gain in the HCD group. MO supplementation at both (0.5% and 1.0%) effectively mitigates the HCD-induced dyslipidemia and significantly minimizes the atherogenic coefficient and atherogenic index. Similarly, MO reduces elevated blood glucose levels, the ALT/AST ratio, and augments ferric ion reduction (FRA) and paraoxonase (PON) activity in a dose-dependent manner. Likewise, MO (particularly at 1.0%) effectively restrained HCD-induced steatosis, hepatic interleukin (IL)-6 production, and protected the kidneys, testes, and ovaries from oxidative stress and cellular senescence. The in silico findings underscore that the six phytoconstituents (chlorogenic acid, isoquercetin, kaempferol 3-O-rutinoside, astragalin, apigetrin, and myricetin) of MO exhibited a strong interaction with HMG-CoA reductase active and binding site residues via hydrogen and hydrophobic interactions. Conclusions: The findings demonstrated an antioxidant, anti-inflammatory, and hypoglycemic effect of MO, guiding the events to prevent HCD-induced metabolic stress and safeguard vital organs. Full article

Spatial heterogeneity in soil deposition poses a significant challenge to accurate geotechnical characterization, which is essential for sustainable infrastructure development. This study presents an advanced geotechnical data-driven mapping framework, based on a monotonized and augmented formulation of Shepard’s inverse distance weighting (IDW) algorithm, implemented through the Google Earth Engine (GEE) platform. The approach is rigorously evaluated through a comparative analysis against the classical IDW and Kriging techniques using standard key performance indices (KPIs). Comprehensive field and laboratory data repositories were developed in accordance with international geotechnical standards (e.g., ASTM). Key geotechnical parameters, i.e., standard penetration test (SPT-N) values, shear wave velocity (Vs), soil classification, and plasticity index (PI), were used to generate high-resolution geospatial models for a previously unmapped region, thereby providing essential baseline data for building infrastructure design. The results indicate that the augmented IDW approach exhibits the best spatial gradient conservation and local anomaly detection performance, in alignment with Tobler’s First Law of Geography, and outperforms Kriging and classical IDW in terms of predictive accuracy and geologic plausibility. Compared to classical IDW and Kriging, the augmented IDW algorithm achieved up to a 44% average reduction in the RMSE and MAE, along with an approximately 30% improvement in NSE and PC. The difference in spatial areal coverage was found to be up to 20%, demonstrating an improved capacity to model spatial subsurface heterogeneity. Thematic design maps of the load intensity (LI), safe bearing capacity (SBC), and optimum foundation depth (OD) were constructed for ready application in practical design. This work not only establishes the inadequacy of conventional geostatistical methods in highly heterogeneous soil environments but also provides a scalable framework for geotechnical mapping with accuracy in data-poor environments. Full article

Background: A novel series of pyrazolo[3,4-d]pyrimidinone derivatives were synthesized, characterized, and examined for their anti-inflammatory effects. Results: The findings indicated that compounds 5d, 5j, 5k, and 5m demonstrated significant anti-inflammatory effects through the selective inhibition of the COX-2 isozyme, with IC₅₀ values ranging from 0.27 to 2.34 μM, compared to celecoxib (IC₅₀ = 0.29 μM). Compound 5k emerged as the most potent, exhibiting a selectivity index (SI) of 95.8 for COX-2 relative to COX-1. In vivo tests additionally validated that compounds 5j and 5k demonstrated significant anti-inflammatory efficacy, exhibiting greater suppression percentages of generated paw edema than indomethacin, comparable to celecoxib, while preserving excellent safety profiles with intact gastric tissue. Mechanistic studies demonstrated that the anti-inflammatory efficacy of the target compounds was associated with a substantial decrease in serum levels of TNF-α and IL-6. Moreover, molecular modeling investigations corroborated the in vitro findings. Compound 5k displayed a binding free energy ΔG of −10.57 kcal/mol, comparable to that of celecoxib, which showed a ΔG of −10.19 kcal/mol. The intensified binding contacts in the COX-2 isozyme indicated the augmented inhibitory efficacy of 5k. Conclusions: Compound 5k exhibited dual activity by inhibiting the COX-2 isozyme and suppressing the pro-inflammatory cytokines TNF-α and IL-6, therefore providing a remarkable anti-inflammatory effect with increased therapeutic potential. Full article

Bioactive peptides have significant potential for applications in pharmaceuticals, the food industry, and cosmetics due to their wide spectrum of biological activities. However, their pronounced structural and functional heterogeneity complicates the classification and prediction of biological activity. This study uses data from the PepLab platform, comprising 2748 experimentally confirmed bioactive peptides distributed across 15 functional classes, including ACE inhibitors, antimicrobial, anticancer, antioxidant, toxins, and others. For each peptide, the amino acid sequence and key physicochemical descriptors are provided, calculated via the integrated DMPep module, such as GRAVY index, aliphatic index, isoelectric point, molecular weight, Boman index, and sequence length. The dataset exhibits class imbalance, with class sizes ranging from 14 to 524 peptides. An innovative methodology is proposed, combining descriptive statistical analysis, structural modeling via DEMATEL, and structural equation modeling with neural networks (SEM-NN), where SEM-NN is used to capture complex nonlinear causal relationships between descriptors and functional classes. The results of these dependencies are integrated into a multi-class machine learning model to improve interpretability and predictive performance. Targeted data augmentation was applied to mitigate class imbalance. The developed classifier achieved predictive accuracy of up to 66%, a relatively high value given the complexity of the problem and the limited dataset size. These results confirm that integrating structured dependency modeling with artificial intelligence is an effective approach for functional peptide classification and supports the rational design of novel bioactive molecules. Full article

The rise in energy prices due to global uncertainties and risks is accelerating the transition to renewable energy in countries. It is expected that embracing energy diversity instead of dependence on a single energy source, such as oil, will curb energy-related increases in inflation. In this study, the impact of the transition to renewable energy on inflation is investigated using the energy diversification index. For this purpose, the Chinese economy is analyzed with the Augmented ARDL method. According to the long-term results of the analysis covering the 1991–2023 period, the effect of energy diversity on inflation is negative. The study also examined the effect of composing an energy portfolio consisting of various renewable energy sources rather than a single renewable energy source on inflation. According to the results obtained, renewable energy diversity has a negative effect on inflation, too. As a result, inflation is expected to decrease as renewable energy diversification and overall energy diversification increase. In sum, inflation can be expected to fall when authorities increase both renewable energy diversity and overall energy diversity instead of solely depending on oil or any renewable energy source. Full article

Agricultural land consolidation (ALC) is a critical instrument for protecting the environment and expanding cropland. However, implementing different consolidation methods, scales, and technologies may have adverse effects on ecological and environmental factors. The ecological effects of ALC are evaluated in this investigation, with the Manas River Basin in China as the research object. Initially, the research examined the changes in land use that occurred during various periods of ALC in the basin using land cover data (CLCD). Secondly, an enhanced water benefit-based ecological index (SWBEI) for arid regions was developed using the Google Earth Engine (GEE) platform. The spatiotemporal variations in ecological environment quality (EEQ) during various ALC periods were analysed. Ultimately, the effects of a variety of factors on EEQ were disclosed. The research results show that: (1) The principal land-use types in the Manas River Basin are barren land, grassland, and cropland, with substantial fluctuations in area. Cropland area is increasing, with the majority being converted from grassland and desolate land. During the initial phase of farmland consolidation, the most rapid growth was observed, with expansion occurring both inward and outward from existing cropland. (2) The SWBEI outperforms the water benefit-based ecological index (WBEI) in arid regions. (3) The EEQ of the basin and cropland typically exhibits an “increasing–decreasing–increasing trend”, with deterioration predominantly occurring during early-stage ALC and a gradual improvement in EEQ during late-stage ALC. The Gobi Desert belt at the foothills of mountains and high-altitude frigid regions exhibit a deteriorating trend in the EEQ, whereas the oasis areas in the middle reaches of the basin exhibit an improving trend. (4) The most significant explanatory power for the basin’s EEQ is attributed to climate factors, followed by topographic factors, hydrological factors, and human factors. The influence of human factors and hydrological factors on the basin’s EEQ is increasing. The primary factors that influence the EEQ of a basin are the actual evapotranspiration, temperature, and elevation. The explanatory power of these two factors for the basin’s EEQ is augmented by their interaction. In the long term, ALC helps improve the EEQ of the basin and cropland. This study provides a reference for improving ALC methods and approaches, enhancing the ecological environment of river basins, and balancing agricultural production efficiency. Full article

Recent advancements in generative AI have fostered the development of highly adept Large Language Models (LLMs) that integrate diverse data types to empower decision-making. Among these, multimodal retrieval-augmented generation (RAG) applications are promising because they combine the strengths of information retrieval and generative models, enhancing their utility across various domains, including clinical use cases. This paper introduces AlzheimerRAG, a multimodal RAG application for clinical use cases, primarily focusing on Alzheimer’s disease case studies from PubMed articles. This application incorporates cross-modal attention fusion techniques to integrate textual and visual data processing by efficiently indexing and accessing vast amounts of biomedical literature. Our experimental results, compared to benchmarks such as BioASQ and PubMedQA, yield improved performance in the retrieval and synthesis of domain-specific information. We also present a case study using our multimodal RAG in various Alzheimer’s clinical scenarios. We infer that AlzheimerRAG can generate responses with accuracy non-inferior to humans and with low rates of hallucination. Full article

The rapid advancement of artificial intelligence has fundamentally transformed how knowledge is created, disseminated, and applied in problem-solving, presenting new challenges for educational models. This study introduces Intelligent Problem-Solving Learning (IPSL)—a capability-based instructional design framework aimed at cultivating learners’ adaptability, creativity, and meta-learning in AI-enhanced environments. Grounded in connectivism, extended mind theory, and the concept of augmented intelligence, IPSL places human–AI collaboration at the core of instructional design. Using a design and development research (DDR) methodology, the study constructs a conceptual model comprising three main categories and eight subcategories, supported by eighteen instructional design principles. The model’s clarity, theoretical coherence, and educational relevance were validated through two rounds of expert review using the Content Validity Index (CVI) and Inter-Rater Agreement (IRA). IPSL emphasizes differentiated task roles—those exclusive to humans, suitable for human–AI collaboration, or fully delegable to AI—alongside meta-learning strategies that empower learners to navigate complex and unpredictable problems. This framework offers both theoretical and practical guidance for building future-oriented education systems, positioning AI as a learning partner while upholding essential human qualities such as ethical judgment, creativity, and agency. It equips educators with actionable principles to harmonize technological integration with human-centered learning in an age of rapid transformation. Full article

China frequently experiences natural disasters, making emergency language services a key link in information transmission, cross-lingual communication, and resource coordination during disaster relief. Traditional contingency plans rely on manual experience, which results in low efficiency, limited coverage, and insufficient dynamic adaptability. Large language models (LLMs), with their advantages in semantic understanding, multilingual adaptation, and scalability, provide new technical approaches for emergency language services. Our study establishes the country’s first generative evaluation index system for emergency language service contingency plans, covering eight major dimensions. Through an evaluation of 11 mainstream large language models, including Deepseek, we find that these models perform excellently in precise service stratification and resource network stereoscopic coordination but show significant shortcomings in legal/regulatory frameworks and mechanisms for dynamic evolution. It is recommended to construct a more comprehensive emergency language service system by means of targeted data augmentation, multi-model collaboration, and human–machine integration so as to improve cross-linguistic communication efficiency in emergencies and reduce secondary risks caused by information transmission barriers. Full article

Background: Systemic lupus erythematosus (SLE) is an autoimmune disorder associated with premature atherosclerosis and vascular impairment. However, the role of endocan, a biomarker of glycocalyx injury, is not completely clarified in the detection of vascular damage. Therefore, our aim was to investigate serum endocan in comparison with conventional inflammatory markers, arterial stiffness parameters, and carotid ultrasound findings in a cohort of young patients with SLE. Methods: We enrolled 47 clinically active young SLE patients (40 females and 7 males) in the study. Arterial stiffness indicated by augmentation index and pulse wave velocity (PWV) was measured by arteriography. Brachial artery flow-mediated dilatation and common carotid intima-media thickness were detected by ultrasonography. The serum concentrations of endocan, IL-6, MPO, MCP-1, MMP-3, -7, and -9, as well as TNFα, were measured by an enzyme-linked immunosorbent assay (ELISA). Results: We found significant negative correlations between serum endocan and both CH50 and C3. Serum endocan was higher in active SLE patients compared to inactive patients, however, the difference was not statistically significant (241.4 (183–295) vs. 200.3 (167–278) pg/mL; p = 0.313). Serum TNFα and hsCRP significantly correlated with PWV. However, we did not detect significant correlations between vascular diagnostic tests and serum endocan levels. Conclusions: Based on our results, serum endocan is associated with disease activity; however, further studies are needed to clarify the value of serum endocan in the cardiovascular risk estimation of SLE patients. Measurement of serum endocan, as well as the routine assessment of arterial stiffness parameters, should be integrated into the comprehensive management plans of young patients with SLE. Full article

With the accelerated development of economic globalization, it is of great significance to strengthen the ability to measure, evaluate, and warn of systemic financial risks for preventing and defusing financial risks. Thus, this research established the Time-Varying Parameter Factor-Augmented Vector Autoregression model (TVP-FAVAR), combined with the Markov Regime Switching Autoregressive Model, to dynamically measure China’s systemic financial risk. The network public opinion index is constructed and introduced into the financial risk early warning system to capture the dynamic impact of market sentiment on financial risks. After testing the nonlinear causal relationship between financial indicators based on the transfer entropy method, the Transformer deep learning model is applied to build a financial risk early warning system, and the performance is compared to traditional methods. The experimental results showed that (1) the trend of the systemic financial risk index based on the dynamic measurement of the TVP-FAVAR model fitted the actual situation well and that (2) the Transformer model public opinion index could fully and effectively mine the nonlinear relationship between data. Compared to traditional machine learning methods, the Transformer model has significant advantages in stronger prediction accuracy and generalization ability. This study provided a new technical path for financial risk early warning and has important reference value for improving the financial regulatory system. Full article

Accurate and early classification of retinal diseases such as diabetic retinopathy, cataract, and glaucoma is essential for preventing vision loss and improving clinical outcomes. Manual diagnosis from fundus images is often time-consuming and error-prone, motivating the development of automated solutions. This study proposes a deep learning-based classification model using a pretrained EfficientNetB3 architecture, fine-tuned on a publicly available Kaggle retinal image dataset. The model categorizes images into four classes: cataract, diabetic retinopathy, glaucoma, and healthy. Key enhancements include transfer learning, data augmentation, and optimization via the Adam optimizer with a cosine annealing scheduler. The proposed model achieved a classification accuracy of 95.12%, with a precision of 95.21%, recall of 94.88%, F1-score of 95.00%, Dice Score of 94.91%, Jaccard Index of 91.2%, and an MCC of 0.925. These results demonstrate the model’s robustness and potential to support automated retinal disease diagnosis in clinical settings. Full article