Search Results (226)

Vacuum degassing (VD) is a critical refining step in electric arc furnace (EAF) steelmaking for producing clean steel with reduced nitrogen and hydrogen content. This study develops an Effective Equilibrium Reaction Zone (EERZ) model focused on denitrogenation (de-N) by simulating interfacial reactions at the bubble–steel interface (Z1). The model incorporates key process parameters such as argon flow rate, vacuum pressure, and initial nitrogen and sulfur concentrations. A robust empirical correlation was established between de-N efficiency and the mass of Z1, reducing prediction time from a day to under a minute. Additionally, the model was further improved by incorporating a dynamic surface exposure zone (Z_eye) to account for transient ladle eye effects on nitrogen removal under deep vacuum (<10 torr), validated using synchronized plant trials and Python-based video analysis. The integrated approach—combining thermodynamic-kinetic modeling, plant validation, and image-based diagnostics—provides a robust framework for optimizing VD control and enhancing nitrogen removal control in EAF-based steelmaking. Full article

Malaria continues to pose a significant global health burden, driving the search for novel antimalarial agents to address emerging drug resistance. This study evaluated the antiplasmodial potential of Ziziphus mauritiana Lam. (Rhamnaceae) roots through an integrated phytochemical and pharmacological approach. The ethanol extract, along with its derived fractions, demonstrated potent in vitro activity against the chloroquine-sensitive Plasmodium falciparum strain 3D7 (Pf3D7), with the ethyl acetate-soluble (IC₅₀ = 11.35 µg/mL) and alkaloid-rich (IC₅₀ = 4.75 µg/mL) fractions showing particularly strong inhibition. UHPLC-DAD-ESI-QTOF-MS/MS-based molecular networking enabled the identification of thirty-two secondary metabolites (1–32), comprising twenty-five cyclopeptide alkaloids (CPAs), five of which had not yet been described (11, 20, 22, 23, 25), and seven known triterpenoids. Bioactivity-guided isolation yielded thirteen purified compounds (5, 6, 14, 26–30, 32–36), with betulinic acid (30; IC₅₀ = 19.0 µM) and zizyberenalic acid (32; IC₅₀ = 20.45 µM) exhibiting the most potent antiplasmodial effects. Computational ADMET analysis identified mauritine F (4), hemisine A (10), and nummularine R (21) as particularly promising lead compounds, demonstrating favourable pharmacokinetic properties, low toxicity profiles, and predicted activity against both family A G protein-coupled receptors and evolutionarily distinct Plasmodium protein kinases. Quantitative analysis revealed exceptionally high concentrations of key bioactive constituents, notably zizyberenalic acid (24.3 mg/g) in the root extracts. These findings provide robust scientific validation for the traditional use of Z. mauritiana in malaria treatment while identifying specific cyclopeptide alkaloids and triterpenoids as valuable scaffolds for antimalarial drug development. The study highlights the effectiveness of combining advanced metabolomics, bioassay-guided fractionation, and computational pharmacology in natural product-based drug discovery against resistant malaria strains. Full article

The widespread integration of power electronic devices and renewable energy sources into power systems has significantly exacerbated voltage and current waveform distortion issues, where asymmetric loads—including single-phase nonlinear equipment and unbalanced three-phase power electronic installations—serve as critical harmonic sources whose inherent nonlinear and asymmetric characteristics increasingly compromise power quality. To enhance power quality management, this paper proposes a universal harmonic source modeling and operational state identification methodology integrating physical mechanisms with data-driven algorithms. The approach establishes an RL-series equivalent impedance model as its physical foundation, employing singular value decomposition and Z-score criteria to accurately characterize asymmetric load dynamics; subsequently applies Variational Mode Decomposition (VMD) to extract time-frequency features from equivalent impedance parameters while utilizing Density-Based Spatial Clustering (DBSCAN) for the high-precision identification of operational states in asymmetric loads; and ultimately constructs state-specific harmonic source models by partitioning historical datasets into subsets, substantially improving model generalizability. Simulation and experimental validations demonstrate that the synergistic integration of physical impedance modeling and machine learning methods precisely captures dynamic harmonic characteristics of asymmetric loads, significantly enhancing modeling accuracy, dynamic robustness, and engineering practicality to provide an effective assessment framework for power quality issues caused by harmonic source integration in distribution networks. Full article

Despite remarkable progress in cancer immunotherapy, many agents that show efficacy in murine or in vitro models fail to translate clinically. Zebrafish (Danio rerio) have emerged as a powerful complementary model that addresses several limitations of traditional systems. Their optical transparency, genetic tractability, and conserved immune and oncogenic signaling pathways enable high-resolution, real-time imaging of tumor–immune interactions in vivo. Importantly, zebrafish offer a unique opportunity to study the core mechanisms of health and sickness, complementing other models and expanding our understanding of fundamental processes in vivo. This review provides an overview of zebrafish immune system development, highlighting tools for tracking innate and adaptive responses. We discuss their application in modeling immune evasion, checkpoint molecule expression, and tumor microenvironment dynamics using transgenic and xenograft approaches. Platforms for high-throughput drug screening and personalized therapy assessment using patient-derived xenografts (“zAvatars”) are evaluated, alongside limitations, such as temperature sensitivity, immature adaptive immunity in larvae, and interspecies differences in immune responses, tumor complexity, and pharmacokinetics. Emerging frontiers include humanized zebrafish, testing of next-generation immunotherapies, such as CAR T/CAR NK and novel checkpoint inhibitors (LAG-3, TIM-3, and TIGIT). We conclude by outlining the key challenges and future opportunities for integrating zebrafish into the immuno-oncology pipeline to accelerate clinical translation. Full article

In this study, we integrated HSQC-based DeepSAT with UPLC-MS/MS to guide the isolation of omega-3 polyunsaturated fatty acid derivatives (PUFAs) from marine resources. Through this approach, four new (1–4) and nine known (5–13) PUFA analogues were obtained from large-scale cultures of the marine dinoflagellate Prorocentrum lima, with lipidomic profiling identifying FA18:5 (5), FA18:4 (7), FA22:6 (8), and FA22:6 methyl ester (11) as major constituents of the algal oil extract. Structural elucidation was achieved through integrated spectroscopic analyses of IR, 1D and 2D NMR, and HR-ESI-MS data. Given the pivotal role of microglia in Alzheimer’s disease (AD) pathogenesis, we further evaluated the neuroprotective potential of these PUFAs by assessing their regulatory effects on critical microglial functions in human microglia clone 3 (HMC3) cells, including chemotactic migration and amyloid-β42 (Aβ42) phagocytic clearance. Pharmacological evaluation demonstrated that FA20:5 butanediol ester (1), FA18:5 (5), FA18:4 (7), FA22:6 (8), and (Z)-10-nonadecenoic acid (13) significantly enhanced HMC3 migration in a wound-healing assay. Notably, FA18:4 (7) also significantly promoted Aβ42 phagocytosis by HMC3 microglia while maintaining cellular viability and avoiding pro-inflammatory activation at 20 μM. Collectively, our study suggests that FA18:4 (7) modulates microglial function in vitro, indicating its potential to exert neuroprotective effects. Full article

Background: Hydroxyl and amino compounds play a significant role in defining the flavor and quality of sauce-flavor Baijiu, yet their comprehensive analysis remains challenging due to limitations in detection sensitivity. In this study, we developed a novel bromine isotope labeling approach combined with ultra-high performance liquid chromatography–high-resolution mass spectrometry (UHPLC-HRMS) to achieve high-coverage profiling of these compounds in sauce-flavor Baijiu. Methods: The method employs 5-bromonicotinoyl chloride (BrNC) for rapid (30 s) and mild (room temperature) labeling of hydroxyl and amino functional groups, utilizing bromine’s natural isotopic pattern (Δm/z = 1.998 Da) for efficient screening. Annotation was performed hierarchically at five confidence levels by integrating retention time, accurate mass, and MS/MS spectra. Results: A total of 309 hydroxyl and amino compounds, including flavor substances (e.g., tyrosol and phenethyl alcohol) and bioactive compounds (e.g., 3-phenyllactic acid), were identified in sauce-flavor Baijiu. The method exhibited excellent analytical performance, with wide linearity (1–4 orders of magnitude), precision (RSD < 18.3%), and stability (RSD < 15% over 48 h). When applied to sauce-flavor Baijiu samples of different grades, distinct compositional patterns were observed: premium-grade products showed greater metabolite diversity and higher contents of bioactive compounds, whereas lower-grade samples exhibited elevated concentrations of acidic flavor compounds. Conclusions: These results demonstrate that the established method is efficient for the comprehensive analysis of hydroxyl and amino compounds in complex food matrices. The findings provide valuable insights for quality control and flavor modulation in sauce-flavor Baijiu production. Full article

We present here an approach to the deployment of social robots in a science laboratory to monitor the behavior of students with respect to safety regulations to prevent accidents. Our vision is that the social robot should act as a friendly companion for students and encourage them to follow safe laboratory practices. Towards this goal, we developed a Laboratory Safety Assistant Framework (LSA) using a Misty II Plus robot and designed three dashboards within it as interventions. This LSA framework was evaluated using a participatory design (PD) study with twenty university students (eleven from Japan and nine from Egypt). For this study, we designed a questionnaire that contains 42 questions on the prior knowledge of students about socially assistive robots and their expectations about how socially assistive robots can create a secure environment in the scientific laboratory. The chi-square test revealed that there are no differences between groups in their perceptions of using Misty II to achieve safety inside science laboratories. In their perception of the capabilities of social robots and the sharing of feelings, students believe that using social robots like Misty II inside the science laboratory can make the lab safe and decrease risk inside the science laboratory without using the three dashboards of the LSA framework. However, the Wilcoxon signed-rank test revealed that there is a significant improvement in students’ perceptions ((Median = 106.5, Z = −2.39, p < 0.05, r = 0.53)) between students’ expectations of using social robots to achieve safety in scientific laboratories before and after they interacted with the social robot and knew about the feasibility of the three dashboards we designed. Furthermore, the t-test revealed participants’ experiences of sharing feelings with a social robot, and the intervention suggested by the LSA framework was to design a system integrating this into a social robot to enhance safety within the scientific laboratory (t (19) = 3.39, p = 0.003). Full article

This study introduces a Self-Attention (SA) Generative Adversarial Network (GAN) framework that applies artificial intelligence techniques to microwave sensing for electromagnetic imaging. The approach involves illuminating anisotropic objects using Transverse Magnetic (TM) and Transverse Electric (TE) electromagnetic waves, while sensing antennas collecting the scattered field data. To simplify the training process, a Back Propagation Scheme (BPS) is employed initially to calculate the preliminary permittivity distribution, which is then fed into the GAN with SA for image reconstruction. The proposed GAN with SA offers superior performance and higher resolution compared with GAN, along with enhanced generalization capability. The methodology consists of two main steps. First, TM waves are used to estimate the initial permittivity distribution along the z-direction using BPS. Second, TE waves estimate the x- and y-direction permittivity distribution. The estimated permittivity values are used as inputs to train the GAN with SA. In our study, we add 5% and 20% noise to compare the performance of the GAN with and without SA. Numerical results indicate that the GAN with SA demonstrates higher efficiency and resolution, as well as better generalization capability. Our innovation lies in the successful reconstruction of various uniaxial objects using a generator integrated with a self-attention mechanism, achieving reduced computational time and real-time imaging. Full article

This study analyzed the mechanical properties of epoxy adhesive materials used as functional coatings, focusing on how physical modifications impact their microstructure and strength. Compositions based on Epidian 5, 53 and 57 resins were cured using TFF, Z-1, or PAC curing agents and modified with various fillers: mineral (CaCO₃ calcium carbonate), active (activated carbon filler, CWZ-22), and nanostructured (montmorillonite, ZR-2) fillers. The best results were achieved with calcium carbonate (10–20 wt%) in Epidian 5 or 53 resins cured with TFF or Z-1, yielding tensile strength up to 64 MPa, compressive strength up to 145 MPa, and bending strength up to 123 MPa. Activated carbon and nanofillers showed moderate improvements, particularly in more flexible matrices. To support property prediction, machine learning algorithms were applied and successfully modeled the mechanical behavior based on composition data. The most accurate models reached R² values of 0.93–0.95 for compression and bending strength. While the models for compression and bending strength demonstrated high accuracy, the tensile strength model yielded lower predictive performance, indicating that further refinement and expanded input features are necessary. Shapley analysis further identified curing agents and fillers as key predictive features. This integrated experimental and data-driven approach offers an effective framework for optimizing epoxy-based coatings in industrial applications. Full article

In this paper, we present a novel method to solve trivariate polynomial modular equations of the form $x(y^2+Ay+B)+z\equiv 0 (mod e).$ Our approach integrates Coppersmith’s method with lattice basis reduction to efficiently solve the former equation. Several variants of RSA are based on the cubic Pell equation $x^3+f{y}^3+f^2{z}^3-3fxyz\equiv 1 (mod N)$, where f is a cubic nonresidue modulus $N=pq$. In these variants, the public exponent e and the private exponent d satisfy $ed\equiv 1 (mod \psi (N\left)\right)$ with $\psi \left(N\right)=\left(p^2+p+1\right)\left(q^2+q+1\right)$. Moreover, d can be written in the form $d\equiv {\displaystyle \frac{v_0}{z_0}} (mod \psi \left(N\right))$ with any $z_0$ satisfying $gcd(z_0,\psi \left(N\right))=1$. In this paper, we apply our method to attack the variants when $d\equiv {\displaystyle \frac{v_0}{z_0}} (mod \psi \left(N\right))$ and when $|z_0|$ and $|v_0|$ are suitably small. We also show that our method significantly improves the bounds of the private exponents d of the previous attacks on the variants, particularly in the scenario of small private exponents and in the scenarios where partial information about the primes is available. Full article

This study examines how Generation Z’s values align with entrepreneurial orientation in the Fourth Sector (FS), which merges public, private, and non-profit dynamics to balance financial sustainability with socio-environmental impact. Using Structural Equation Modeling with Partial Least Squares (SEM-PLS), we analyze the influence of economic–financial, biospheric, and altruistic values of the university students’ inclination toward entrepreneurship in the FS. The study draws on a convenience sample of 139 undergraduate students from the School of Economics and Business Sciences at the University of Extremadura, located in the Autonomous Community of Extremadura, Spain. Our findings reveal that economic–financial values are the strongest predictor, underscoring the enduring importance of financial viability in shaping entrepreneurial intent. Biospheric values also play a significant role, highlighting sustainability and environmental awareness as key motivators. While altruistic values exhibit a positive relationship with FS entrepreneurship, this effect is not statistically significant, indicating that Generation Z prioritizes economic and environmental considerations over pure altruism when engaging in this sector. These insights contribute to the understanding of how a group of university students from Generation Z approaches sustainable business models and provide strategic guidance for fostering entrepreneurship that effectively integrates financial sustainability with environmental responsibility. Specifically, Generation Z is expected to be particularly receptive to entrepreneurship initiatives focused on biodiversity conservation. Full article

The relationship between structure and properties is fundamental in materials science, particularly for aliovalently doped perovskites, where structural changes significantly influence material performance. Accurate prediction of key structural parameters is essential for tailoring these materials for advanced applications. In this study, we developed an Artificial Neural Network (ANN) model to predict lattice constants with high accuracy, achieving near-perfect R² values and minimal prediction errors across training and testing datasets. To address the interpretability challenge commonly associated with black-box models, we integrated Partial Dependence Plots (PDPs), enabling a transparent analysis of how input features, including lattice parameters a, b, c, and the number of formula units per unit cell (Z), affect model predictions. Our findings show that parameters a, b, and c generally contribute to lattice expansion, while Z exhibits an inverse relationship due to its impact on packing density. The inclusion of PDPs offers novel insights into the underlying physical relationships and enhances the trustworthiness of machine learning (ML) predictions in the context of perovskite design. This approach demonstrates the utility of combining high-accuracy ML models with interpretability techniques to accelerate discovery in materials science. Full article

Personalized travel experiences have become a growing priority for tourists, while the built environment increasingly shapes tourists’ behavior. However, limited research has integrated behavioral drivers with algorithmic travel route optimization, particularly in the context of Generation Z tourists. To address this gap, this study proposes a hybrid framework that combines behavioral modeling with enhanced algorithmic techniques to generate customized travel itineraries for Generation Z. A behavioral influencing factors model is first constructed based on the Theory of Planned Behavior (TPB) and Social Influence Theory (SIT), identifying media influence (MI), subjective norms (SNs), and perceived built environment (PBE) as potential determinants of travel behavioral intention (BI). A Structural Equation Model (SEM) is then applied to empirically validate the hypothesized relationships. Results reveal that all three factors have a significant and positive impact on BI (p < 0.05). Building on this behavioral mechanism, an interest-based Ant Colony Optimization (ACO) algorithm is implemented by incorporating point-of-interest (POI) preferences and distance matrices to improve personalized route generation. Comparative analysis using social media keyword data demonstrates that the proposed method outperforms conventional travel route planning approaches in terms of route relevance and overall path satisfaction. This study offers a novel integration of psychological theory and computational optimization, providing both theoretical insights and practical implications for urban tourism planning and the development of smart tourism services. Full article

Geographical origins and varietal characteristics can significantly affect the quality of Citri Reticulatae Pericarpium (Chenpi), making rapid and accurate identification essential for consumer protection. To overcome the inefficiency and high cost of conventional detection methods, this study proposed a nondestructive approach that integrates hyperspectral imaging (HSI) with deep learning to classify Chenpi varieties and their geographical origins. Hyperspectral data were collected from 15 Chenpi varieties (citrus peel) across 13 major production regions in China using three dataset configurations: exocarp-facing-upward (Z), endocarp-facing-upward (F), and a fused dataset combining random orientations (ZF). Convolutional neural networks (CNNs) were developed and compared with conventional machine learning models, including partial least-squares discriminant analysis (PLS-DA), support vector machines (SVMs), and a multilayer perceptron (MLP). The CNN model achieved 96.39% accuracy for varietal classification with the ZF dataset, while the Z-PLSDA model optimized with second derivative (D2) preprocessing and competitive adaptive reweighted sampling (CARS) feature selection attained 91.67% accuracy in geographical origin discrimination. Feature wavelength selection strategies, such as CARS, simplified the model complexity while maintaining a classification performance comparable to that of the full-spectrum models. These findings demonstrated that HSI combined with deep learning could provide a rapid, nondestructive, and cost-effective solution for Chenpi quality assessment and origin traceability. Full article

Recently, interest in optimizing judging schemes for large-scale innovation competitions has grown as the complexities in evaluation processes continue to escalate. Although numerous methods have been developed to improve scoring fairness and precision, challenges such as evaluator subjectivity, workload imbalance, and the inherent uncertainty of scoring systems remain inadequately addressed. This study introduces a novel framework that integrates a genetic algorithm-based work cross-distribution model, advanced Z-score adjustment methods, and a BP neural network-enhanced score correction approach to tackle these issues. First, we propose a work crossover distribution model based on the concept of information entropy. The model employs a genetic algorithm to maximize the overlap between experts while ensuring a balanced distribution of evaluation tasks, thus reducing the entropy generated by imbalances in the process. By optimizing the distribution of submissions across experts, our model significantly mitigates inconsistencies arising from diverse scoring tendencies. Second, we developed modified Z-score and Z-score Pro scoring adjustment models aimed at eliminating the scoring discrepancies between judges, thereby enhancing the overall reliability of the normalization process and evaluation results. Additionally, evaluation metrics were proposed based on information theory. Finally, we incorporate a BP neural network-based score adjustment technique to further refine the assessment accuracy by capturing latent biases and uncertainties inherent in large-scale evaluations. Experimental results conducted on datasets from national-scale innovation competitions demonstrate that the proposed methods not only improve the fairness and robustness of the evaluation process but also contribute to a more scientific and objective assessment framework. This research advances the state of the art by providing a comprehensive and scalable solution for addressing the unique challenges of large-scale innovative competition judging. Full article