Search Results (6,536)

Polyploids play significant roles in tea production due to their strong tolerance to adverse environmental conditions and their high levels of certain chemical components. Tetraploid can be used to produce more polyploid tea plants, but there have been only a handful of tetraploids found in tea plants. In spite of the extremely low probabilities, bud mutant selection is an effective way to obtain polyploid tree crops. In the present study, a Dashu tea, cytochimera, derived from a bud mutation was identified by using flow cytometry and chromosome observation. The morphology and photosynthetic characteristics of leaves were investigated briefly. Some chemical components were determined. Finally, the pollen viability and ploidy of progeny were detected. The results show that tetraploid cells account for 71.48 ± 3.88%–72.19 ± 2.80% of the leaf tissue in this cytochimera. Compared with the original diploid, the cytochimera exhibited broader, longer, and thicker leaves. Its net photosynthetic rate (high to 41.77 ± 0.38 μmol CO₂‧m⁻²‧s⁻¹) was higher than that of the original diploid (peak value 28.00 ± 2.29 μmol CO₂‧m⁻²‧s⁻¹) for most of the day when measured in September. Notably, the total content of 19 free amino acids in the tender spring shoots of cytochimera was 22.96 ± 0.58 mg/g, approximately twice of that of the diploid materials analyzed. The contents of 10 free amino acids, including theanine, were significantly higher than those in diploids, with some free amino acid contents reaching up to seven times those observed in diploids. In addition, the cytochimera produced larger pollen grains than the original diploid, although the in vitro germination rate was lower (14.63 ± 1.11%). Three open-pollinated progenies of cytochimera were identified as triploids. To sum up, cytochimera has larger and thicker leaves, a higher photosynthetic rate, and higher content of total free amino acids and some free amino acids, especially theanine, than the original diploid. Moreover, cytochimera has a certain level of fertility and can produce triploids. These findings suggest the potential for selecting polyploid tea plants from bud mutants and for developing new tea germplasms with enhanced amino acid contents. Full article

Wire Arc Additive Manufacturing (WAAM) has been recognized as an efficient and cost-effective metal additive manufacturing technique due to its high deposition rate and scalability for large components. However, the quality and repeatability of WAAM parts are highly sensitive to process parameters such as arc voltage, current, wire feed rate, and torch travel speed, requiring advanced monitoring and adaptive control strategies. In this study, a vision-based monitoring system integrated with a reinforcement learning framework was developed to enable intelligent in situ control of WAAM. A custom optical assembly employing mirrors and a bandpass filter allowed simultaneous top and side views of the melt pool, enabling real-time measurement of layer height and width. These geometric features provide feedback to a tabular Q-learning algorithm, which adaptively adjusts voltage and wire feed rate through direct hardware-level control of stepper motors. Experimental validation across multiple builds with varying initial conditions demonstrated that the RL controller stabilized layer geometry, autonomously recovered from process disturbances, and maintained bounded oscillations around target values. While systematic offsets between digital measurements and physical dimensions highlight calibration challenges inherent to vision-based systems, the controller consistently prevented uncontrolled drift and corrected large deviations in deposition quality. The computational efficiency of tabular Q-learning enabled real-time operation on standard hardware without specialized equipment, demonstrating an accessible approach to intelligent process control. These results establish the feasibility of reinforcement learning as a robust, data-efficient control technique for WAAM, capable of real-time adaptation with minimal prior process knowledge. With improved calibration methods and expanded multi-physics sensing, this framework can advance toward precise geometric accuracy and support broader adoption of machine learning-based process monitoring and control in metal additive manufacturing. Full article

Given the significant impact of concrete’s compressive strength on structural service life, the development of accurate and efficient prediction methods is critically important. A hybrid machine learning modeling method based on the Whale Optimization Algorithm (WOA)-optimized XGBoost algorithm is proposed. Using 1030 sets of concrete mix proportion data covering eight key parameters—cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate, and curing age—the predictive performance of four models (linear regression, random forest, XGBoost, and WOA-XGBoost) was systematically compared. The results demonstrate that the WOA-XGBoost model achieved the highest goodness of fit (R² = 0.9208, MSE = 4.5546), significantly outperforming the other models, and exhibited excellent generalization capability and robustness. Feature importance and SHAP analysis further revealed that curing age, cement content, and water content are the key variables affecting compressive strength, with blast furnace slag showing a significant marginal diminishing effect. This study provides a high-precision data-driven tool for optimizing mix proportions and predicting the strength of complex-component concrete, offering significant application value in promoting the resource utilization of industrial waste and advancing the development of green concrete. Full article

Cultural heritage museums, as integral components of the urban built environment and public cultural space, not only preserve historical memory but also subtly shape visitors’ psychological experiences and well-being. Yet the mechanisms linking museum environmental quality with visitor mental experiences remain insufficiently explored. Drawing on 10,684 visitor reviews collected from Dianping, Weibo, and Ctrip, this study applies text mining and semantic analysis to construct an evaluation framework of visitor behavioral preferences and psychological experiences in heritage museums. The findings show that attention to spatial remains, historical artifacts, and cultural symbols is closely associated with positive emotions such as mystery, awe, and beauty, while adverse environmental conditions such as queuing and crowding often trigger negative feelings including fatigue, disappointment, and boredom. Further analysis reveals a clear pathway linking objects, behaviors, and experiences: spatial remains evoke psychological resonance through immersive perceptions of authenticity; artifacts are primarily linked to visual pleasure and emotional comfort; and cultural symbols are transformed into cognitive gains and spiritual meaning through interpretation and learning. Cross-regional comparison highlights significant differences among museums with distinct cultural backgrounds in terms of architectural aesthetics, educational value, and emotional resonance. This study not only offers a practical framework for the refined management and spatial optimization of heritage museums, but also demonstrates that high-quality cultural environments can promote mental health and emotional restoration. The results extend the interdisciplinary framework of museum research and provide empirical evidence for environmental improvement and public health promotion in cultural heritage spaces in the digital era. Full article

This study focuses on the susceptibility and surface degradation of low-alloy carbon steel 42CrMo4 to corrosion and cavitation erosion, as this steel is widely used in marine environments with aggressive chemical species and harsh conditions. Due to its high strength and fatigue resistance, 42CrMo4 steel is often employed in offshore mechanical components such as shafts and fasteners as well as crane parts in ports and harbors. Various experimental methods, including corrosion and cavitation tests, were used to assess the steel’s surface integrity under extreme conditions. Surface changes were monitored using modern analytical tools for precise assessments, including image and morphological analyses, to quantify degradation levels and specific parameters of defects induced by corrosion and cavitation. Non-destructive techniques such as optical microscopy (OM), scanning electron microscopy (SEM), energy-dispersive spectroscopy (EDS), and image analysis software were employed for the quantitative assessment of morphological parameters and elemental analysis. EDS analysis revealed changes in elemental composition, indicating corrosion products that caused significant mass loss and defect formation, with degradation increasing over time. The average corrosion rate of 42CrMo4 steel in a 3.5% NaCl solution reached a peak value of 0.846 mm/year after 120 days of exposure. Cavitation erosion behavior was measured based on mass loss, indicating the occurrence of different cavitation periods, with the steady-state period achieved after 60 min. The number of formed pits increased until 120 min, after which it decreased slightly. This indicates that a time frame of 120 min was identified as significant for changes in the mechanism of pit formation. Specifically, up to 120 min, pit formation was the dominant mechanism of cavitation erosion, while after that, as the number of pits slightly declined, the growth and merging of formed pits became the dominant mechanism. The cavitation erosion tests showed mass loss and mechanical damage, characterized by the formation of pits and cavities. The findings indicate that the levels of surface degradation were higher for corrosion than for cavitation. The presented approach also provides an assessment of the degradation mechanisms of 42CrMo4 steel exposed to corrosive and cavitation conditions. Full article

Early-life feeding strategies are known to affect growth, behavior, and stress physiology in dairy calves. This study examined the effects of different milk feeding regimes on heart rate (HR) and heart rate variability (HRV) during feeding and rehousing as indicators of autonomic activity. Dairy calves were fed either a restrictive milk allowance twice per day (6 L/d; RES; n = 21) or an unlimited amount of milk (ad libitum; ADL; n = 24) during the first three weeks of life. All calves were housed in individual straw bedded hutches from d 1 to 23 of life and were moved to a group pen on d 23 ± 2 of life. Starting at least one day before rehousing until one hour after the rehousing process HR, HRV, and variables in the time and frequency domain were measured continuously using a portable recording system. To study the cardiac response to the feeding process, six time windows of 5 min each were chosen as follows: resting time at 5.00 a.m., start of personnel activity in the barn, 15 min before feeding, during feeding, 15 min after feeding, and 1 h after feeding. For the evaluation of cardiac response to an unknown stressor such as rehousing, four time windows of 5 min each were selected as follows: resting time at 5.00 a.m., during rehousing, 30 min after rehousing, and 1 h after rehousing. During resting as well as before feeding and rehousing, HR was higher in ADL calves compared with RES calves. During feeding and rehousing, HR reached peak values which were comparable in both groups. HRV variables of the time and frequency domain indicated a shift towards a sympathetic dominance in the balance of the autonomic nervous system during feeding time, particularly in RES calves. Differences between resting and feeding values were demonstrated in RES calves at low-frequency and high-frequency power, whereas no differences were observed in ADL calves which did not react to the feeding process. The cardiac response of calves to rehousing was inconsistent in both groups. An increase in RMSSD and SD1 in ADL calves indicated that the vagal component in the vegetative neurological control was increased in these calves during rehousing. In conclusion, our findings indicate that restrictive milk feeding alters autonomic regulation and may increase physiological stress responses in calves. Full article

Porphyra umbilicalis is a red macroalga belonging to the genus Porphyra and the family Bangiaceae. Porphyra umbilicalis distinguishes itself among macroalgae due to its remarkable biochemical composition and nutritional value. It contains a broad spectrum of bioactive compounds, including macronutrients and micronutrients. Among the macronutrients, carbohydrates, proteins, and essential fatty acids are particularly abundant, with protein levels reaching up to 40% dw (dry weight). Its high protein content makes Porphyra umbilicalis a promising alternative and sustainable protein source, particularly for plant-based diets. Its micronutrients, including vitamins (C, E, and B-group), pigments, and mineral components, contribute to antioxidant protection, metabolic regulation, and maintenance of overall nutritional balance. What makes P. umbilicalis particularly distinctive is its content of unique bioactives such as porphyran, phycobiliproteins, and mycosporine-like amino acids. Preliminary evidence from animal and in vitro studies indicates that these unique bioactive compounds contribute to the anticancer, anti-inflammatory, and antioxidant effects of P. umbilicalis. However, more systematic research into its chemical composition is needed due to variability related to harvest location, environmental factors, and inconsistencies in the existing literature. Detailed data on the full chemical profile and bioavailability of specific compounds remain limited, underscoring the need for further investigation. Evidence on the health benefits of P. umbilicalis remains limited, as current studies are restricted to preclinical models and have not been validated through human trials, emphasizing the need for rigorous research to clarify its role in functional foods. Full article

This exploratory study assessed the influence of botanical origin and acquisition source on the phenolic profile and antioxidant properties of selected Romanian monofloral honeys. Eight samples were analyzed, representing five floral types: acacia, linden, rapeseed, lavender, and thyme. For acacia, linden, and rapeseed, both commercial and locally sourced honeys were included. Analytical techniques included total phenolic content (TPC, Folin–Ciocalteu), antioxidant assays (DPPH, ABTS, FRAP), color intensity (ABS₄₅₀), and phenolic compound profiling via HPLC-DAD-ESI⁺. TPC ranged from 179.26 ± 23.57 to 586.67 ± 18.33 mg GAE/100 g, with thyme and linden honeys presenting the highest values. Seventeen phenolic compounds were tentatively identified; gallic acid was predominant in thyme honey (127 mg/100 g), and linden honey contained high levels of rutin (70 mg/100 g) and galangin-glucoside. Antioxidant capacity varied notably by floral origin, with thyme and linden outperforming acacia samples. Significant correlations were found between total phenolics and ABTS (r = 0.86), and between ABS₄₅₀ and FRAP (r = 0.86). DPPH kinetics followed zero-order behavior (R² > 0.98). Principal component analysis (PC1 + PC2 = 88%) enabled preliminary separation by botanical origin. While based on a limited sample set, findings support the relevance of combining chromatographic, kinetic, and multivariate tools for exploratory honey characterization. Full article

In the current geopolitical context, sustainable energy development has become one of the pillars of global economic growth. This issue is well recognized in the European Union, which has undertaken a number of measures to achieve sustainable development goals. For these measures to be effective, it is essential to conduct a reliable, multi-variant diagnosis of the state of energy development in the EU-27 countries. This paper addresses this highly topical and important issue. It presents a new proprietary method—the Entropy–Evolutionary Evaluation of Sustainability (E³)—based on a multidimensional approach to researching and evaluating the state of sustainable energy development in the EU-27 countries between 2014 and 2023. Through the integration of 19 indicators representing the adopted dimensions of the study (energy, economic, environmental, and social), the method enabled both a static assessment and a dynamic analysis of energy transition processes across space and time. To determine the weights of the indicators for each dimension of sustainable energy development, the CRITIC, Entropy, and equal weight methods, along with the Laplace criterion, were applied. The Analytic Hierarchy Process method was used to establish the weights of the dimensions themselves. An important component of the approach was the inclusion of scenario studies, which made it possible to assess sustainable energy development under five variants: baseline, level, equilibrium, transformational, and neutral. These scenarios were based on different weight values assigned to three factors: the level of energy development (L), its stability (S), and the trajectory of change ($\tilde{T}$). The results, expressed in the form of a total index value and dimensional indices, reveal significant diversity among the EU-27 countries in terms of sustainable energy development. Sweden, Finland, Denmark, Latvia, and Austria achieved the best results, while Cyprus, Malta, Ireland, and Luxembourg—countries heavily dependent on energy imports, with limited diversification of their energy mix and high energy costs—performed the worst. The developed method and the results obtained should serve as a valuable source of knowledge to support decision-making and the formulation of strategies concerning the pace and direction of actions related to the energy transition. Full article

This study provides the first comprehensive, field-inventory-based assessment of urban ecosystem services within a Russian university campus, focusing on the woody vegetation of the Lobachevsky State University of Nizhny Novgorod. Utilizing a detailed field tree inventory combined with the i-Tree framework (including i-Tree Eco, i-Tree Canopy, UFORE, and i-Tree Hydro models), we quantified the campus’s capacity for carbon storage and sequestration, air pollutant removal, and stormwater runoff mitigation. The campus green infrastructure, comprising 1887 trees across 32 species with a density of 145.5 stems per hectare, demonstrated significant ecological value. Results show a carbon storage density of 26.61 t C ha⁻¹ and an annual gross carbon sequestration of 11.43 tons. Furthermore, the campus trees removed 1213.7 kg of air pollutants annually (a deposition rate of 9.35 g m⁻²), with ozone, particulate matter, and sulfur dioxide showing the highest deposition. The campus also retained 956.1 m³ of stormwater annually. These findings, particularly the high carbon sequestration rates, are attributed to the dominance of relatively young, fast-growing tree species. This research establishes a critical baseline for understanding urban ecosystem services in a previously under-researched geographical context. The detailed, empirical data offers crucial insights for urban planners and policymakers in Nizhny Novgorod and beyond, advocating for the strategic integration of ecosystem services assessments into campus planning and broader urban green infrastructure development across Russian cities. The study underscores the significant role of university campuses as vital components of urban green infrastructure, contributing substantially to environmental sustainability and human well-being. Full article

Thai medicinal flowers, namely Mesua ferrea L. (Bunnak), Mammea siamensis T. Anderson (Saraphi), and Clitoria ternatea (Anchan) have long been valued for their traditional medicinal. This study investigated their phytochemical composition and bioactivities, with a particular focus on antioxidant and antibacterial properties. Methods: Ethanolic flower extracts were analyzed by high-performance liquid chromatography (HPLC) and liquid chromatography–mass spectrometry (LC–MS). Antioxidant activities were determined by DPPH, ABTS, and FRAP assays. Antibacterial activity against Escherichia coli, E. coli O157:H7, Salmonella Typhi, Shigella dysenteriae, and Vibrio cholerae were assessed by agar well diffusion, broth dilution methods, and time–kill assays. Biofilm formation, biofilm disruption, and bacterial adhesion to Caco-2 cells were evaluated. Morphological changes in E. coli O157:H7 were examined using scanning electron microscopy (SEM), and leakage of intracellular contents (DNA, RNA, proteins) were quantified. Results: HPLC analysis revealed the highest level of gallic acid in M. ferrea and quercetin in M. siamensis. LC–MS analysis identified fifteen putative metabolites across the flower extracts, including quercetin, kaempferol, catechin, and luteolin derivatives, with species-specific profiles. C. ternatea extract exhibited the greatest total flavonoid content and antioxidant activity. Among the extracts, M. ferrea exhibited the strongest inhibitory effect, with inhibition zone of 13.00–15.00 mm and MIC/MBC values of 31.25–62.5 mg/mL. All extracts exhibited time-dependent bactericidal activity, significantly inhibited biofilm formation, disrupted established biofilms, and reduced bacterial adhesion to intestinal epithelial cells. SEM revealed membrane disruption in E. coli O157:H7 and leakage of intracellular components. Conclusions: Thai medicinal flower extracts, particularly M. ferrea, possess strong antioxidant and antibacterial activities. Their ability to inhibit biofilm formation, interfere with bacterial adhesion, and disrupt bacterial membranes highlights their potential as natural alternatives for preventing or controlling enteric bacterial infections. Full article

The quality of groundwater, a crucial freshwater resource in cold regions, directly affects human health. This study used groundwater quality monitoring data collected in the central Songnen Plain in 2014 and 2022 as a case study. The improved DRASTICL model was used to assess the vulnerability index, while water quality indicators were selected using a random forest algorithm and combined with the entropy-weighted groundwater quality index (E-GQI) approach to realize water quality assessment. Furthermore, self-organizing maps (SOM) were used for pollutant source analysis. Finally, the study identified the synergistic migration mechanism of NH₄⁺ and Cl⁻, as well as the activation trend of As in reducing environments. The uncertainty inherent to health risk assessment was considered by developing a kernel density estimation–trapezoidal fuzzy number–Monte Carlo simulation (KDE-TFN-MCSS) model that reduced the distribution mis-specification risks and high-risk misjudgment rates associated with conventional assessment methods. The results indicated that: (1) The water chemistry type in the study area was predominantly HCO₃⁻–Ca²⁺ with moderately to weakly alkaline water, and the primary and nitrogen pollution indicators were elevated, with the average NH₄⁺ concentration significantly increasing from 0.06 mg/L in 2014 to 1.26 mg/L in 2022, exceeding the Class III limit of 1.0 mg/L. (2) The groundwater quality in the central Songnen Plain was poor in 2014, comprising predominantly Classes IV and V; by 2022, it comprised mostly Classes I–IV following a banded distribution, but declined in some central and northern areas. (3) The results of the SOM analysis revealed that the principal hardness component shifted from Ca²⁺ in 2014 to Ca²⁺–Mg²⁺ synergy in 2022. Local high values of As and NH₄⁺ were determined to reflect geogenic origin and diffuse agricultural pollution, whereas the Cl⁻ distribution reflected the influence of de-icing agents and urbanization. (4) Through drinking water exposure, a deterministic evaluation conducted using the conventional four-step method indicated that the non-carcinogenic risk (HI) in the central and eastern areas significantly exceeded the threshold (HI > 1) in 2014, with the high-HI area expanding westward to the central and western regions in 2022; local areas in the north also exhibited carcinogenic risk (CR) values exceeding the threshold (CR > 0.0001). The results of a probabilistic evaluation conducted using the proposed simulation model indicated that, except for children’s CR in 2022, both HI and CR exceeded acceptable thresholds with 95% probability. Therefore, the proposed assessment method can provide a basis for improved groundwater pollution zoning and control decisions in cold regions. Full article

Interest in research on FDM systems using inexpensive materials like PLA and ABS is constantly increasing. In this regard, the scope of this study is narrowed to exclusively focus on PLA. To improve the surface finish of PLA printed products, it is important to have optimal values of the most important process parameters, notably layer height, temperature, and printing speed. The surface roughness is a critical aspect of additive manufacturing that directly impacts the functionality, aesthetics, and overall performance of printed parts. To accomplish the improvement of surface quality, the statistical method ANOVA (Analysis of Variance) is used to analyze data and identify the most relevant process parameters that impact roughness and dimensional precision. The response variables are identified during this study in order to define the optimal printing parameters for improving part quality and ensuring the best surface finishes. Additionally, the dimensional accuracy of the parts is analyzed in order to check the reliability and effectiveness of the optimum parameters. The results are validated through this additional assessment, which also provides insight into the capabilities and limitations of inexpensive FDM machines when the optimized parameters are used. In conclusion, this study emphasizes the significance of enhancing parameters to improve the performance of 3D printed components, providing insightful information about the potential of PLA as an inexpensive material for applications that need both high surface quality and precise dimensional control. According to the analysis, the thickness of the layers and printing speed have a significant role in the roughness for a better desired surface quality. Full article

Popcorn maize (Zea mays everta) exhibits complex morphologies that challenge structural analysis. This study assessed the fidelity of the three-dimensional (3D) reconstruction and printing of four popcorn morphologies, unilateral, bilateral, multilateral, and mushroom, by integrating structured-light 3D scanning and (DIA), which can support the construction of food replicas. Morphometric parameters (projected area, perimeter, Feret diameter, circularity, and roundness) and fractal descriptors (fractal dimension, lacunarity, and entropy) were quantified as the relative ratios of printed/real parameters (P/R) to compare real flakes with their 3D-printed counterparts. Results revealed the lowest mean errors for Feret diameter (6%) and projected area (10%), while deviations in circularity and roundness were more pronounced in mushroom flakes. With respect to the actual mean values of the morphological parameters, real flakes showed slightly larger perimeter values (86 mm for real and 82 mm for printed objects) and a higher fractal dimension (1.36 for real and 1.33 for printed), indicating greater texture irregularity, whereas the projected area remained highly comparable (225 mm² in real/229 mm² in printed). These parameters reinforced that the overall morphological fidelity remained high (P/R = 0.9–1.0), despite localized deviations in circularity and fractal descriptors. Less complex morphologies (unilateral and bilateral) demonstrated higher structural fidelity (P/R = 0.95), whereas multilateral and mushroom types showed greater variability due to surface irregularity. Fractal dimension and lacunarity effectively described textural complexity, highlighting the role of flake geometry and moisture in determining expansion patterns and printing accuracy. Principal Component Analysis confirmed that circularity and fractal indicators are critical descriptors for distinguishing morphological fidelity. Overall, the findings demonstrated that 3D scanning and printing provided reliable physical replicas of irregular food structures as popcorn flakes supporting their application in food engineering. Full article

Symmetry plays a critical role in preserving the structural balance and statistical integrity of datasets, particularly in privacy-preserving machine learning. Differential privacy introduces random noise to individual data points to ensure privacy while maintaining the overall symmetry of statistical distributions. However, excessive noise can reduce the utility of data, model accuracy, and computational efficiency. This study proposes a symmetry-preserving optimization framework for differentially private machine learning by integrating feature importance and t-SNE (t-distributed Stochastic Neighbor Embedding), UMAP (Uniform Manifold Approximation and Projection), and PCA (Principal Component Analysis), respectively. Feature importance derived from a random forest selects high-value features to improve data relevance. At the same time, t-SNE preserves geometric symmetry by retaining local and global structures more effectively than PCA or UMAP. Therefore, t-SNE is the best feature extraction method for dimensionality reduction in the proposed scheme. Experimental results demonstrate that the t-SNE method significantly enhances model performance under differential privacy, showing improved accuracy and reduced computational time compared to PCA and UMAP while preserving the underlying symmetry of the data distributions. Full article