Search Results (16,470)

Improving the nutritional quality of food through advanced and sustainable agricultural practices has become a key objective of modern vegetable crop production. Emphasis is placed on increasing the content of health-promoting bioactive compounds, such as vitamins and polyphenols, particularly flavonoids whose accumulation is strongly affected by various biotic and abiotic stress factors. To mitigate stress-induced limitations and enhance plant performance, biostimulants are increasingly applied. Among them, Trichoderma spp. are widely recognized for their ability to promote plant growth and resilience, primarily through enzymatic activity and the production of bioactive metabolites. The aim of this study was to evaluate the potential of the native Trichoderma sp. strain STP8 to enhance the production of bioactive compounds through seed and soil applications at planting and 26 days after planting (DAP), applied individually or in combination. A spore suspension (4 × 10⁶ spores mL⁻¹) was used. The experiment was arranged in a randomized complete block design with five replicates. At harvest (43 DAP), dry matter, ascorbic acid, chlorophyll, and carotenoid contents were determined. Additionally, flavonoids and non-flavonoids, total phenolics, individual phenolic compounds, and antioxidant capacity were analyzed. Achieved results demonstrate that the effects of the native Trichoderma sp. strain STP8 on lettuce secondary metabolism and antioxidant properties are strongly dependent on the developmental stage at which inoculation is performed, providing further insight into the stage-specific interactions between plans and Trichoderma. Practically, a single application at planting proved to be the most effective strategy for enhancing the accumulation of bioactive compounds, indicating that optimized application timing may improve the efficacy of Trichoderma-based biostimulants, while avoiding unnecessary repeated applications. These findings support the potential use of native Trichoderma strains as sustainable tools for improving the nutritional and functional quality of lettuce. Further research integrating physiological, biochemical, and molecular analyses is required to elucidate the mechanisms by which the native Trichoderma sp. strain STP8 regulates the biosynthesis of bioactive compounds in lettuce. Full article

The lipoxygenase (LOX) pathway plays a crucial role in the biosynthesis of characteristic aroma compounds in fruit. Despite the progress in understanding the LOX pathway in model plants like tomato, the distinct roles of individual members within the LOX gene family in flavor formation in the complex octoploid strawberry are still not fully understood. In this study, we identified 13 LOX genes in the strawberry genome and focused on a specific lipoxygenase gene, designated FaLOX11, which exhibited the highest expression during fruit ripening. Spatiotemporal expression analysis revealed that FaLOX11 expression was significantly correlated with 3-hexenal accumulation, quantified via headspace solid-phase microextraction coupled with gas chromatography–mass spectrometry (HS-SPME-GC-MS). Functional characterization via transient overexpression of FaLOX11 in strawberry fruits resulted in a significant increase in C6 volatile content, specifically a ~72% increase in 3-hexenal content. Conversely, RNA interference (RNAi)-mediated transient silencing of FaLOX11 significantly downregulated the expression of this gene and led to a marked reduction in the production of these key aroma compounds. Our findings demonstrate that FaLOX11 acts as a positive regulator of C6 aldehyde and alcohol biosynthesis, thereby playing a pivotal role in determining the characteristic aroma profile of strawberry fruit. This study provides new insights into the LOX pathway in strawberry and lays a solid molecular foundation for improving strawberry flavor quality. Full article

Ce-modified MnCo₂O₄ flower-like nanosheet electrodes were prepared on nickel foam by a hydrothermal-calcination route and sequentially optimized with respect to reaction time, nominal Ce content, and PVP addition. Comparative SEM, XRD, XPS, and N₂-sorption analyses identify MnCo₂O₄-9 h-3%Ce-PVP as the optimized electrode, with an open hierarchical nanosheet network and a BET surface area of 210.0 m² g⁻¹. The direct XRD/XPS control comparison distinguishes Ce-associated lattice and surface-state changes from PVP-associated synthesis effects without treating either trend as proof of substitutional Ce occupancy or quantitatively established oxygen vacancies. Likewise, PVP is treated as a morphology-directing additive whose transient adsorption or bridging role remains a synthesis hypothesis rather than a directly verified molecular mechanism. The optimized positive electrode delivers 2008 F g⁻¹ at 1 A g⁻¹, retains 1227 F g⁻¹ at 20 A g⁻¹, and shows 99.0% capacitance retention after 10,000 cycles at 5 A g⁻¹. A carbon-supported iron oxide negative electrode, designated C/Fe₂O₃ only as a sample label because its exact oxide phase was not independently resolved by XRD or Raman spectroscopy, provides 443 F g⁻¹ at 1 A g⁻¹. The resulting charge-balanced asymmetric device operates over 0–1.6 V and delivers 34.6 F g⁻¹ at 1 A g⁻¹, corresponding to 12.30 Wh kg⁻¹ at 0.8 kW kg⁻¹. At 10 A g⁻¹, it retains 29.8 F g⁻¹ and delivers 10.60 Wh kg⁻¹ at 8.0 kW kg⁻¹, equivalent to 86.1% capacitance retention over a tenfold increase in current density. All device-level gravimetric values are calculated using the total active mass of both electrodes. Full article

Cementitious sand and gravel (CSG) is commonly used in construction engineering; however, its mix proportion design is complex, and traditional physical experiments face limitations such as long cycles, high costs, and susceptibility to external factors when obtaining high-quality sample data. In this study, a foundational dataset was first acquired through physical experiments: 100 sets of CSG specimens with different mix proportions (cement content 40, 50, 60, 70 kg/m³; water-to-binder ratio 1.0, 1.2, 1.4; sand ratio 0.1, 0.2, 0.3, 0.4; fly ash content 20, 30, 40, 50 kg/m³) were prepared. After 28 days of standard curing, compressive strength and splitting tensile strength tests were conducted using a WAW-1000 electro-hydraulic servo universal testing machine, yielding 100 sets of real mechanical property data. The coefficients of variation for all test groups were below 10%, confirming the reliability and repeatability of the experimental data. On this basis, a data augmentation method based on Conditional Tabular Generative Adversarial Networks (CTGAN) is proposed. Through adversarial training between the generator and the discriminator, the model learns the multi-dimensional distribution characteristics of the original CSG data and generates 100 synthetic samples, which are then merged with the original data to expand the dataset to 200 samples. The quality of the synthetic data is evaluated using Wasserstein distance and correlation matrix heatmaps. Furthermore, a hybrid XGBoost–LSTM prediction model is proposed—XGBoost is used for feature construction to capture nonlinear interactions among mix proportion variables, and the constructed features are then fed into an LSTM network for sequential learning and regression prediction. The results show that the CTGAN-generated data are highly consistent with the original data in terms of kernel density distributions and variable correlations, with Wasserstein distance significantly superior to four comparative methods: Bootstrap, SMOTE, GaussianCopula, and TVAE. After augmentation, the XGBoost–LSTM model achieves a coefficient of determination (R²) of 0.9897 for compressive strength prediction (vs. 0.9793 before augmentation) and 0.9801 for splitting tensile strength (vs. 0.9882 before augmentation, a slight decrease). The mean absolute percentage errors (MAPE) are 4.49% and 4.11%, and the root mean square errors (RMSE) are 0.201 and 0.049, respectively; both error metrics are reduced compared with those before augmentation. Compared with baseline models including XGBoost, LSTM, Random Forest (RF), and Support Vector Regression (SVR), the XGBoost–LSTM model exhibits the best performance across all evaluation metrics, and Wilcoxon signed-rank tests confirm that the performance differences are statistically significant (p < 0.05). The proposed method of CTGAN-based data augmentation combined with the XGBoost-LSTM hybrid model provides an effective solution to the problem of insufficient CSG sample data and offers a reference for data enhancement and performance prediction of other small-sample materials. Full article

Hyaluronic acid (HA) is a glycosaminoglycan that is found within the body and has both structural and signaling functions in the extracellular matrix. HA is biocompatible and biodegradable; it has a high water content and binds directly to certain cell-surface proteins. Due to these characteristics, it is considered a promising component for the design of biomaterials for regenerative wound healing. This review covers the most recent findings on the use of HA-based biomaterials in soft tissue repair, while also incorporating earlier, foundational studies relevant to the field, focusing on HA’s characteristics, cellular interactions, design, and preclinical and clinical results. The physicochemical characteristics of HA and their influence on cellular responses and tissue regeneration are discussed to show how material properties can be adjusted for specific therapeutic purposes. There have been great advances in chemically modified composite scaffolds and HA matrices, which offer better mechanical stability and controlled degradation. At the same time, new delivery systems have been built using HA, from nanoparticles to gene delivery platforms and growth factors, and these have given the material an active role as a therapeutic agent rather than just a passive one. This narrative review covers the clinical evidence for the effectiveness of commercial products for acute and diabetic wounds, as well as burns and chronic wounds, and discusses where their use is indicated. In the end, the current limitations of the research and future applications and directions are discussed. Full article

This study aims to accurately predict students’ academic performance trajectories for university entrance examinations by proposing a machine learning framework that explicitly accounts for missing data and uncertainty. Mock examination data are characterized by substantial missing values due to heterogeneous participation in exams, as well as inherent randomness caused by variations in test content and examinee conditions. Conventional single-value imputation methods cannot adequately reconstruct the missing values arising from such heterogeneous participation without introducing strong bias, and existing educational prediction models based on deterministic formulations do not account for the inherent randomness and uncertainty in examination scores, thereby limiting the reliability of their forecasts. To address these challenges, we employ GP-VAE and SAITS, state-of-the-art methods for time-series imputation, to reconstruct incomplete mock examination data. Furthermore, we develop a Bayesian Neural Network (BayesNN) to predict future academic performance while explicitly modeling uncertainty. By integrating temporally aware imputation with probabilistic prediction, the proposed framework aims to provide more accurate and reliable performance forecasts than existing approaches. We evaluate the effectiveness of the proposed method through comparative experiments involving various combinations of imputation techniques and prediction models. Experimental results demonstrate that the proposed framework achieves competitive predictive accuracy: the combination of deep imputation methods and BayesNN yields the lowest average estimation error of 15.98 points, compared with 16.75 points for the conventional combination of mean imputation and linear regression. The contribution of this study does not lie in proposing a new deep learning model itself, but rather in systematically comparing combinations of time-series imputation methods and uncertainty-aware prediction models using real-world mock examination sequence data with missing values, thereby providing effective design guidelines for educational data analysis. Full article

Enzyme immobilisation within Metal–organic Frameworks (MOFs) provides a promising strategy for improving enzyme dispersion and local environment control, although the resulting performance depends strongly on the host materials, enzyme type and immobilisation conditions. Conventional in situ biomimetic mineralisation typically produces enzyme–MOF composites (enzyme@MOFs) with irregular morphologies, broad particle size distributions and aggregation, which can compromise catalytic performance and reproducibility. This study presents a flow-based microfluidic biomimetic mineralisation strategy for preparing horseradish peroxidase-encapsulated ZnBDC-NH₂ MOF composites. A flow-focusing microfluidic chip containing multiple rectangular baffle structures was designed to enhance transverse mixing, extend the effective residence time, and mitigate clogging during particle formation. Under the selected conditions, homogeneous HRP@ZnBDC-NH₂ particles with an average hydrodynamic diameter of 868.5 nm and a polydispersity index of 0.266 were obtained. The homogeneous HRP@ZnBDC-NH₂ showed an encapsulation efficiency of 56.17% and a loading content of 1.49%. Michaelis–Menten analysis gave a Km value of 52.49 μM for HRP@ZnBDC-NH₂, suggesting improved apparent substrate affinity compared with the corresponding bulk-synthesised sample. The results support the use of baffle-structured microfluidics as a controllable platform for enzyme@MOF synthesis, while further studies on enzyme leaching, reusability, long-term stability and extended chip operation are required to evaluate its operational robustness. Full article

In recent years, adaptive gamification and adaptive game-based learning (GBL) have attracted the interest of researchers and educators as a response to the “one-size-fits-all” approach of conventional gamified applications. However, their effectiveness has shown mixed results, and the literature concerning preschool and early primary education remains scattered. Therefore, we performed a systematic literature review of 19 empirical studies published between 2016 and 2026, following the PRISMA model, from a total of 5069 records identified across nine electronic databases. This review examines the methodological approaches and assessment tools employed, the content areas, educational levels, and educational contexts addressed, the theoretical frameworks and adaptive mechanisms utilised, and the learning and motivational outcomes reported for young learners. Our findings revealed a strong concentration on mathematics, a heavy reliance on researcher-developed platforms, and limited explicit theoretical grounding. Moreover, most studies adapted only the learning content, while the game elements themselves remained fixed. Although most studies reported positive learning and motivational outcomes, the results were not uniform, with prior knowledge being the most common moderating variable. Benefits are most visible when adaptive systems support children’s pacing, prior knowledge, or task difficulty, with some studies showing improvement in learning efficiency rather than learning gains. Overall, this review reveals the emerging trends and challenges in this field and provides a framework and insight for future researchers regarding the design of adaptive learning environments for young children. Full article

Sustaining learner motivation remains a persistent challenge in higher education Japanese-language e-learning, where learners often study with limited social presence and personalized encouragement. This paper proposes a preliminary Design Framework for Motivational Robots in E-Learning (DFMRE), derived through a retrospective, theory-guided synthesis of two previously published empirical studies on robot-assisted Japanese-language learning. The synthesis interprets the prior findings through Self-Determination Theory and self-efficacy theory and formulates five candidate design principles: human-affine compact hardware, multi-level learner-selectable gestures, calibrated vocal encouragement, learner-initiated interaction protocol, and content-independent system integration. To provide an initial external check, nine domain experts with diverse backgrounds in education, educational technology, human–robot interaction, and related fields provided a preliminary appraisal of the principles in terms of clarity, feasibility, transferability, and overall usefulness. The framework received broadly favorable ratings, including a mean overall usefulness score of 4.33 on a 5-point scale, while expert comments highlighted the need for clearer operational definitions and flexible interaction modes. Because the empirical base consists of two small-sample Japanese-language learning studies conducted at one institution using one robot platform, DFMRE should be read as an early, context-grounded, falsifiable proposal rather than as a confirmed model for higher education e-learning in general. Full article

Urban development increases impervious surfaces and stormwater runoff, while the construction sector generates large volumes of construction and demolition waste (CDW). Pervious concrete can mitigate runoff through infiltration and may also valorize CDW, yet its production and testing procedures remain heterogeneous, particularly with recycled aggregates. This study experimentally screens practical processing parameters for pervious concrete produced with basalt and CDW coarse aggregates. The influence of chemical admixture, consolidation method, and end-surface preparation was first assessed to define suitable production conditions: adequate cohesion without admixture required raising the water-to-cement ratio from 0.30 to 0.65; high-energy Proctor compaction crushed the CDW aggregates, favoring standard tamping-rod consolidation; and end-surface preparation had only a minor effect on compressive strength. The selected procedures were then applied to an ACI 522R-based basalt mixture designed for a target void content of 25%, which achieved a fresh density of 1985 kg/m³, a void ratio of 16.88%, and a mean permeability coefficient of 12.18 × 10⁻³ m/s, about twelve times the minimum required by ABNT NBR 16416. Its 28-day compressive strength (12.75 MPa) remained below the 20 MPa pavement-surfacing requirements, although within the typical range reported by ACI 522R for pervious concrete (2.8 to 28 MPa). Overall, aggregate gradation, compaction procedure, and admixture-enabled paste cohesion emerged as the dominant factors governing the strength–porosity–permeability trade-off, guiding subsequent mix optimization. Full article

Patent intelligence is hard to automate: data is heterogeneous and multilingual, and monitoring runs on daily cycles. This paper reports an engineering case study of a production low-code workflow on the n8n platform that integrates twelve RSS feeds from five patent offices (EPO, WIPO, USPTO, TIPO, and MOIP). Over a four-month deployment (October 2025 to January 2026; an approximately 120-day window), the system processed 340 items, normalized six timestamp formats, handled English, Chinese, and Korean, and generated three stakeholder-specific output formats with no manual intervention in formatting or delivery. From this deployment, we identify five design lessons: P1 (Hybrid Intelligence Architecture), P2 (Format Normalization at Boundaries), P3 (Separation of Content and Presentation), P4 (Graceful Degradation), and P5 (Configuration Externalization), each supported by differentiated within-case evidence. Output quality was assessed exploratorily with two LLM-based evaluators, whose inter-rater agreement was low for semantic dimensions; a small expert pilot (four English-language items, four raters) provided only a preliminary reference, on which the system output did not exhibit any obvious serious errors. Applying the lessons in other domains is future work, not a contribution. The findings are documented engineering experience from a single production case. Full article

Background/Objectives: Commercial collagen peptide products may differ in their structural characteristics and in vitro biological properties depending on manufacturing processes and hydrolysis conditions. However, comparative studies integrating simulated gastrointestinal digestion with structural, physicochemical, and cell-based analyses remain limited. This study comparatively evaluated commercially available bovine collagen peptide products marketed in Türkiye before and after simulated gastrointestinal digestion. Methods: Collagen peptide samples were characterized before and after simulated gastrointestinal digestion by determining hydroxyproline content, degree of hydrolysis (DH), molecular weight (MW) distribution, antioxidant activity, and fibroblast responses to investigate digestion-induced structural and biological changes. Results: Significant differences were observed among the evaluated products in hydroxyproline content, degree of hydrolysis, molecular weight distribution, antioxidant activity, and fibroblast responses. Simulated gastrointestinal digestion further hydrolyzed all collagen peptide samples, although the extent of structural modification varied among products. Antioxidant responses also differed following digestion, with some products maintaining relatively stable radical scavenging activity, whereas others exhibited increased or decreased antioxidant responses. Cell viability assays demonstrated that all collagen peptide samples were non-cytotoxic toward L929 fibroblasts under the experimental conditions. Conclusions: The findings indicate that simulated gastrointestinal digestion influences the structural characteristics and in vitro biological responses of commercial collagen peptide products to different extents. The observed differences among products suggest differential in vitro bioactivity associated with their structural characteristics and digestion behavior. Nevertheless, these findings are limited to in vitro observations and require confirmation through peptide characterization, bioavailability studies, animal models, and well-designed clinical investigations before conclusions regarding physiological efficacy or potential health benefits can be drawn. Full article

Diffusion solidification is an effective method to produce non-dendritic microstructures and reduce casting defects in magnesium alloys. However, the influence of precursor alloy superheat on the solidification behavior and the resulting microstructure remains insufficiently understood. In this study, pure magnesium was used as the high thermal mass (HTM) alloy and three Mg-Al-Zn alloys with different aluminum and zinc contents were used as the low thermal mass (LTM) alloys. The effects of superheat on grain morphology, solute diffusion, and constitutional supercooling were investigated through a combination of experimental casting and numerical simulation using Ansys Fluent and Matlab. The results show that the solidified interface consists of five distinct regions, including two base metals, two transition zones, and a central controlled diffusion solidification zone. A higher superheat of the HTM alloy relative to the LTM alloy promotes a wider transition zone and finer globular grains, whereas equal or lower superheat leads to columnar or rosette structures. The Mg-30 wt.%Al-3.5 wt.%Zn alloy with moderate aluminum content produces fine globular grains due to a thinner constitutional supercooling layer and a higher degree of supercooling, which suppresses grain growth and increases nucleation rate. In contrast, the Mg-55 wt.%Al-6.5 wt.%Zn alloy with high aluminum content forms coarse rosette and columnar grains. Among the conditions investigated, the combination of 10 °C HTM and 5 °C LTM superheats tends to promote the formation of fine equiaxed grains. Increasing superheat above this range reduces supercooling and coarsens grains, while decreasing superheat inhibits interface diffusion and promotes solute segregation. The findings provide a theoretical basis for designing precursor alloy compositions and superheat parameters in controlled diffusion solidification of magnesium alloys. Full article

Fillers (<0.075 mm) in hot mix asphalt (HMA) play a pivotal role in optimizing bitumen content, filling voids, and improving mechanical performance. In many agricultural countries, large quantities of rice and wheat husk waste are produced, while the road construction industry faces material shortages of conventional filler materials and related performance challenges. This study evaluates the feasibility of using rice husk (RH) and wheat husk (WH) fillers on HMA performance. Unlike previous studies that primarily focused on ash-derived agricultural residues, this work investigates the direct utilization of raw husk materials, eliminating the need for energy-intensive processing. Few studies directly examine the aggregate gradation and binder concentration with respect to rice and wheat husk ash. As a result, the relative effectiveness of these two agricultural waste fillers in improving the volumetric and Marshall properties of asphalt mixtures is yet unknown. Fifteen mixtures with varying bitumen contents (3.0–5.0%) were tested to determine the optimum bitumen content (OBC). Subsequently, modified mixtures were prepared at the OBC using RH and WH fillers at five replacement levels (5.0–15.0%). The Marshall Mix design method was employed to assess stability, flow, density, and air voids content. The control mixture showed a Marshall stability of 14.86 kN, flow of 3.52 mm, density of 2.342 g/cm³, and air voids of 2.9%. At their optimum filler contents (i.e., 10.33% for RH and 10.43% for WH), the modified mixtures achieved higher Marshall stability (14.96 kN and 15.06 kN, respectively), with flow values of 3.51 mm and 2.83 mm, and densities of 2.335 g/cm³ and 2.330 g/cm³. Statistical analysis using ANOVA at the OBC confirmed that RH and WH fillers can be used as alternative fillers in HMA without adversely affecting Marshall performance, while contributing to agricultural waste valorization and resource conservation. Full article

The increasing demand for sustainable building materials has motivated the search for alternative supplementary cementitious materials to reduce the use of Portland cement while maintaining concrete performance. The present study aims to investigate the synergistic effects of bagasse ash (BA) and rice husk ash (RHA) as partial cement replacements in ternary blended concrete. Previous studies used agricultural ashes individually or in binary form only, whereas in the present work, the synergistic effect of BA and RHA is systematically studied, and Response Surface Methodology (RSM) is used to develop predictive models and optimize the performance of concrete. The slump, compressive strength and splitting tensile strength were evaluated using a Central Composite Design (CCD) to determine the effect of the levels of replacement of BA and RHA. Quadratic regression models were built and evaluated using analysis of variance (ANOVA). All models were statistically significant (p < 0.05) and had high predictive accuracy (R² > 0.92). The results revealed that increases in BA and RHA contents reduced the workability because of their high specific surface areas and porous structures, while moderate combinations improved the compressive and splitting tensile strengths due to the synergistic filler effects, secondary pozzolanic reactions, and matrix densification. The multi-objective optimization based on the desirability function provided an optimal mixture of 5% BA and 15% RHA with an overall desirability of 92.8%, which provided the best compromise between workability and mechanical performance. Experimental validation of the optimized mixture showed good agreement of the model predictions with prediction errors of less than 5%, confirming the reliability and robustness of the developed RSM models. The results show that synergistic use of BA and RHA is a feasible and sustainable solution for producing high-performance ternary blended concrete and provides a reliable framework for the optimization of the mixture. Full article