Search Results (8,575)

Raman spectroscopy is a rapid, label-free, and non-destructive technique for probing molecular structures, making it a powerful tool for clinical pathogen identification. However, interpreting its complex spectral data remains challenging. In this study, we evaluate and compare a suite of machine learning models—including Support Vector Machines (SVM), XGBoost, LightGBM, Random Forests, k-nearest Neighbors (k-NN), Convolutional Neural Networks (CNNs), and fully connected Neural Networks—with and without Principal Component Analysis (PCA) for dimensionality reduction. Using Raman spectral data from 30 clinically important bacterial and fungal species that collectively account for over 90% of human infections in hospital settings, we conducted rigorous hyperparameter tuning and assessed model performance based on accuracy, precision, recall, and F1-score. The SVM with an RBF kernel combined with PCA emerged as the top-performing model, achieving the highest accuracy (0.9454) and F1-score (0.9454). Ensemble methods such as LightGBM and XGBoost also demonstrated strong performance, while CNNs provided competitive results among deep learning approaches. Importantly, interpretability was achieved via SHAP (Shapley Additive exPlanations), which identified class-specific Raman wavenumber regions critical to prediction. These interpretable insights, combined with strong classification performance, underscore the potential of explainable AI-driven Raman analysis to accelerate clinical microbiology diagnostics, optimize antimicrobial therapy, and improve patient outcomes. Full article

The combined effects of meteorological factors and aerosol chemical compositions on atmospheric visibility in Shanghai were investigated in this study based on the observed hourly dataset during 2022–2024. Correlation analysis and random forest modeling are employed to quantify the relative contributions of these factors. The results reveal significant negative correlations between visibility and both PM_2.5 concentration and relative humidity, with partial correlation coefficient of −0.62 and −0.61. Nitrate, ammonium, and other aerosol components substantially modulate these relationships. The random forest model explains 83% of the variance when only meteorological variables are considered, increasing to 93% with the inclusion of aerosol chemical composition. Under 30 km high-visibility conditions, PM_2.5 is the dominant predictor (39%) of atmospheric visibility variation, followed by relative humidity (35%). In contrast, during low-visibility conditions (lower than 7.5 km), relative humidity becomes the primary contributor (30%), the influence of PM_2.5 weakens (18%), and aerosol chemical components account for a larger share (30%). These findings provide important insights into the mechanisms governing visibility variability under different environmental conditions. Full article

Tea, derived from the leaves of Camellia sinensis is globally recognized for its cultural significance and potential health benefits. While extensively studied in adults, the effects of tea consumption in children remain underexplored. This review examines tea′s bioactive compounds, such as catechins, flavonoids, and L-theanine, and their cognitive, cardiovascular, metabolic, oral, and hepatoprotective benefits with a critical overview of its consumption in pediatric populations. Additionally, the review addresses potential risks, including caffeine-related effects, interference with iron absorption, and hepatotoxicity at high doses. Emerging evidence suggests that tea is a beneficial alternative to sugar-sweetened beverages for children when consumed in moderation. However, caution is warranted regarding caffeine content and the balance of bioactive components. This analysis underscores the importance of further research to establish safe and effective guidelines for tea consumption in children. Full article

This study analyses the factors influencing renewable energy infrastructure public–private partnership (PPP) financing, using data from 28 countries covering the period from 1996 to 2024. A composite institutional quality index was constructed using Principal Component Analysis (PCA). The analysis employs a panel econometric framework: the autoregressive distributed lag (ARDL) model to capture short- and long-term dynamics. The results highlight the significance of the time dimension on renewable energy PPP financing. In the short term, none of the predictor variables are significant, reflecting the inherently long-term character of renewable energy PPP investments. However, in the long term, gross domestic product per capita, inflation dynamics, efficiency in energy transmission, and institutional quality are identified as key determinants of renewable energy investment. The findings suggest that strengthening sector-specific regulatory frameworks and improving various aspects of institutional quality as defined by the World Governance Indicators can be important to attract private capital in energy PPPs. These institutional reforms, complemented by growth-oriented macroeconomic policies, would contribute to making renewable energy markets more attractive while reducing exposure to macroeconomic and institutional risks. Full article

Against the backdrop of the accelerated integration of culture and tourism in China, red cultural tourism, as an important component of China’s cultural tourism system, urgently requires a systematic assessment of its development status and synergistic impact mechanisms. This study takes the Long March tourism resources in Yunnan as the research object and constructs a comprehensive evaluation system integrating social influence and ecological carrying capacity. By applying GIS spatial analysis, as well as K-means and XGBoost machine learning models, the development level of red cultural tourism in Yunnan is quantitatively assessed. Furthermore, the interpretable SHAP model is employed to identify the contribution of each evaluation indicator and to analyze the relationships among development levels under three different indicator models. The results reveal that (1) the development level of red cultural tourism in Yunnan generally exhibits a spatial pattern of being lower in the northwest and higher in the southeast; (2) transportation accessibility (TA), average annual precipitation (AAP), and average annual temperature (AAT) are the dominant indicators influencing the development level; (3) there are significant disparities in development levels among cities, indicating that future development needs to comprehensively consider both the social influence and ecological carrying capacity of red cultural tourism resources and adhere to a “social–ecological” synergistic development mechanism. This study not only uncovers the synergistic impacts of social and ecological dimensions on the development of red cultural tourism in Yunnan but also provides theoretical and data support for the optimization and sustainable development of Yunnan’s red cultural tourism resources. Full article

Understanding the mechanisms that maintain biodiversity is crucial for effective conservation in riverine ecosystems. However, the direct and indirect mechanisms by which land use patterns and water quality parameters influence plankton α- and β-diversity remain poorly elucidated. Here, we undertook a comprehensive survey of plankton communities across the Jialing River basin. Our results showed that Bacillariophyta and Chlorophyta were the dominant phytoplankton groups, whereas Protozoa and Copepoda predominated among zooplankton. Redundancy analysis identified dissolved oxygen and total phosphorus as key environmental factors shaping plankton community structure. Additionally, random forest models indicated that anthropogenic stressors exerted consistent effects on both α- and β-diversity of phytoplankton. Importantly, the decomposition of β-diversity revealed that species turnover constituted the major component, underscoring the importance of basin-scale management approaches. Structural equation modeling further demonstrated that land use practices predominantly affected phytoplankton β-diversity indirectly via water quality alterations, with a relatively weak direct effect. In contrast, neither the direct nor indirect effects of land use were significant for zooplankton communities. These findings suggest that phytoplankton may serve as more reliable bioindicators of anthropogenic disturbance than zooplankton in this freshwater system. Moreover, our findings highlight the central role of water quality in regulating phytoplankton diversity responses to environmental change. Consequently, we recommend that conservation strategies in the Jialing River basin focus on water quality monitoring and the mitigation of its ecological effects. Full article

To investigate the geographical and species differences regarding mineral element content of camel milk, this research used camel milk from the Tacheng, Altay, and Ili regions of Xinjiang and cow milk, goat milk, and horse milk from the Tacheng region as subjects. The contents of 22 mineral elements were measured using inductively coupled plasma mass spectrometry (ICP-MS) and inductively coupled plasma optical emission spectrometry (ICP-OES). The results showed that the contents of macro elements Ca, P, K, and Na in camel milk were significantly higher than those in other milk sources (p < 0.01). The contents of trace elements such as Se, Sr, and Ni were very significantly higher than those in other milk sources (p < 0.01). The content of 12 mineral elements in camel milk was very significantly higher than in other types of milk (p < 0.01). Principal component analysis (PCA) and factor analysis emphasized the relationship between element distribution and different milk sources, and the linear discriminant analysis (LDA) model could identify the species type of milk. Geographical analysis indicated that trace elements such as Sr, Ni, and Cr were highly significantly enriched in Tacheng camel milk (p < 0.01). The established LDA model achieved traceability of the geographical origin of Xinjiang camel milk. This research reveals the mineral nutritional advantages of camel milk and its geographical differentiation patterns, providing theoretical support for exploring the functional properties of camel milk and for identifying species and regions through minerals. It is important to promote the upgrading of the specialty dairy product industry. Full article

The interaction between groundwater and surface water can be significant in lakes or irrigation channels, as well as in large dam reservoirs or along portions of them. To evaluate this interaction at a sampling location directly controlled by a large dam equipped with reversible pump-turbines, data from Rn-222 and physicochemical parameters at specific depths and times were obtained and studied using Principal Component Analysis and Hierarchical Clustering. Dimension 1 explains 45.3% of the total variability in the original data, which can be interpreted as the result of external factors related to seasonal variability (e.g., temperature, turbulent flow, and precipitation), while Dimension 2 explains up to 31.2% and can be interpreted as the variability related to groundwater inputs. Five hierarchical clusters based on these dimensions were considered and were related to the temporal variability observed in the water column throughout the year, as well as the depth relationships observed between successive surveys. A hypothesis-driven conceptual piston-like effect model is proposed for groundwater–surface water interactions, considering the identified relationships between variables, including higher Rn-222 concentrations in surface water after heavy rain. According to this simplified conceptual model, water infiltrates in a weathered granitic recharging area; during heavy rain, it is forced through the fracture systems of a lesser-weathered granite. Thus, an overall increase in pressure over the hydrological system forces the older radon-enriched water to discharge into the Mondego River. This work highlights the importance of exploratory techniques such as PCA and Hierarchical Clustering, in addition to underlying knowledge of the geological setting, for the proposal of simplified conceptual models that help in the management of important reservoirs. This work also demonstrates the utility of Rn-222 as a simple tracer of groundwater discharge into surface water. Full article

Automotive manufacturing quality control faces persistent challenges such as limited defect samples, cross-domain variability, and the demand for interpretable decision-making. This work presents an explainable few-shot anomaly detection framework that integrates EfficientNet-based feature extraction, adaptive prototype learning, and component-specific attention mechanisms to address these requirements. The system is designed for rapid adaptation to novel defect types while maintaining interpretability through a multi-modal explainable AI module that combines visual, quantitative, and textual outputs. Evaluation on automotive datasets demonstrates promising performance on evaluated automotive components, achieving 99.4% accuracy for engine wiring inspection and 98.8% for gear inspection, with improvements of 5.2–7.6% over state-of-the-art baselines, including traditional unsupervised methods (PaDiM, PatchCore), advanced approaches (FastFlow, CFA, DRAEM), and few-shot supervised methods (ProtoNet, MatchingNet, RelationNet, FEAT), and with only 0.63% cross-domain degradation between wiring and gear inspection tasks. The architecture operates under real-time industrial constraints, with an average inference time of 18.2 ms, throughput of 60 components per minute, and memory usage below 2 GB on RTX 3080 hardware. Ablation studies confirm the importance of prototype learning (−4.52%), component analyzers (−2.79%), and attention mechanisms (−2.21%), with K = 5 few-shot configuration providing the best trade-off between accuracy and adaptability. Beyond performance, the framework produces interpretable defect localization, root-cause analysis, and severity-based recommendations designed for manufacturing integration with execution systems via standardized industrial protocols. These results demonstrate a practical and scalable approach for intelligent quality control, enabling robust, interpretable, and adaptive inspection within the evaluated automotive components. Full article

Mathematical creativity (MC) plays an important role in mathematics and education; however, its conceptualization and assessment remain inconsistent across empirical studies. This systematic review examined how MC has been defined, conceptualized, and assessed across 80 empirical studies involving K-12 populations. Through thematic analysis, the study identified three definition types: divergent thinking, problem-solving, and problem-posing, as well as affective–motivational emphasis. We organized theoretical frameworks into three categories: domain-general, domain-specific, and multidimensional frameworks. Results showed that the most common definitions emphasized divergent thinking components while fewer studies highlighted affective and dispositional factors. Domain-specific frameworks were the most frequently used, followed by multidimensional frameworks. Regarding assessment, studies predominantly relied on divergent-thinking scoring. Most assessments used criterion-referenced rubrics with norm-based comparisons. They were delivered mainly in paper-pencil format. Tasks were typically open-ended multiple-solution problems with fewer studies using self-reports or observational methods. Overall, the field prioritizes product-based scoring (e.g., fluency, flexibility, originality) over evidence about students’ solution processes (e.g., reasoning, metacognitive monitoring). To improve cross-context comparability, future work should standardize and transparently report age, grade, and country coding and scoring practices. Full article

Nowadays deodorants and antiperspirants play an important role in maintaining daily hygiene, exerting a substantial influence on both physical comfort and social functioning. Consequently, they can be regarded as a pivotal component of contemporary personal hygiene programs. The aim of this study was to undertake a comparative analysis of the elemental composition of diverse samples (72) of various roll-on deodorants and antiperspirants, sticks, and solid natural potassium–aluminium alums. These analyses were performed using ICP-OES and SEM-EDS techniques. The obtained results were then subjected to statistical and chemometric analysis. Studies demonstrated that Al and Zr were the most significant elements in the tested samples. Aluminium, a prevalent component in antiperspirants, was quantified in concentrations ranging from 0.9% to 4.4%, and in potassium–aluminium alums up to 4.7%. Aluminium and zirconium compounds were found to be the predominant elements in stick antiperspirants, with zirconium levels reaching up to 3%. The presence of lead was quantified in 35 of the 72 samples, with 19 samples exhibiting concentrations exceeding 1 mg/L. The highest lead level, reaching 15.90 mg/L, was found in potassium–aluminium alum. Furthermore, SEM-EDS analysis was conducted to verify the elemental composition, to provide data on additional ingredients, and to partially verify the information contained on the product labels. Full article

Low carbon, low cost and sustainability are important development trends of ultra-high-performance concrete (UHPC). In this study, municipal solid waste incineration bottom ash (MSWIBA) was used to replace 5%, 10%, 20% and 30% of quartz sand (QS), respectively, and the effect of the MSWIBA substitution rate on the workability, wet packing density, mechanical properties, shrinkage, resistance to chloride ion corrosion, and resistance to sulfate corrosion of UHPC was studied. The mechanism analysis was carried out by combining X-ray diffraction (XRD), thermogravimetric analysis (TG), and scanning electron microscopy (SEM) tests, and UHPC heavy metal leaching tests, environmental impact assessment, and economic analysis were conducted. Results show that the active silicon and aluminum components in MSWIBA reacted with cement hydration products to optimize the matrix density. MSWIBA has an internal curing effect, which is beneficial for reducing the shrinkage of UHPC. When the MSWIBA replacement rate is 10%, the 28-day compressive strength of MSWIBA-UHPC is 128.7 MPa, which is equivalent to the benchmark group. The fluidity, corrosion resistance and heavy metal leaching all meet the requirements. The energy consumption, carbon emissions and costs are reduced by 0.22%, 2.30% and 6.67%, respectively. The research results can provide a reference for the development of ecological UHPC with economic, low-carbon and environmental benefits, as well as the harmless disposal and resource utilization of hazardous wastes such as MSWIBA. Full article

Intrusion Detection Systems (IDS) play a vital role in safeguarding networks, yet their effectiveness is often challenged, as cyberattacks evolve in new and unexpected ways. Machine learning models, although very powerful, usually perform well only on data that closely resembles what they were trained on. When faced with unfamiliar traffic, they often misclassify. In this work, we examine this generalization gap by training IDS models on one Denial-of-Service (DoS) variant, DoS Hulk, and testing them against other variants such as Goldeneye, Slowloris, and Slowhttptest. Our approach combines careful preprocessing, dimensionality reduction with Principal Component Analysis (PCA), and model training using Random Forests and Deep Neural Networks. To better understand model behavior, we tuned decision thresholds beyond the default 0.5 and found that small adjustments can significantly affect results. We also applied Shapley Additive Explanations (SHAP) to shed light on which features the models rely on, revealing a tendency to focus on fixed components that do not generalize well. Finally, using Uniform Manifold Approximation and Projection (UMAP), we visualized feature distributions and observed overlaps between training and testing datasets, but these did not translate into improved detection performance. Our findings highlight an important lesson: visual or apparent similarity between datasets does not guarantee generalization, and building robust IDS requires exposure to diverse attack patterns during training. Full article

While existing research highlights a positive association between computational thinking (CT) and self-regulation (SR) skills, limited attention has been given to the embodied and social processes within CT activities that support young children’s executive functions (EFs)—key components of SR. This study investigates how preschoolers develop basic and higher-order EFs, such as focused attention, inhibitory control, causal reasoning, and problem-solving, through their engagement with a tangible programming toy in teacher-guided small groups in a university-affiliated preschool. Informed by a we-syntonicity framework that integrates Papert’s concepts of body/ego syntonicity and Schutz’s “we-relationship”, we conducted a multimodal microanalysis of video-recorded group sessions. Our analysis focuses on two sessions, the “Obstacle Challenge” and “Conditionals”, featuring four excerpts. Findings reveal that children leverage bodily knowledge and empathy toward the toy—named Rapunzel—to sustain attention, manage impulses, reason about cause-effect, and collaborate on problem-solving. Three agents shape these processes: the toy, fostering collective engagement; the teacher, scaffolding learning and emotional regulation; and the children, coordinating actions and sharing affective responses. These findings challenge traditional views of SR as an individual cognitive activity, framing it instead as an embodied, social, and situated practice. This study underscores the importance of collaborative CT activities in fostering SR during early childhood. Full article

Lardizabala biternata is a culturally valued, endemic vine of the Chilean Winter Rainfall–Valdivian Forest biodiversity hotspot, traditionally harvested for its sweet, edible fruits. Despite its ecological singularity as the sole species in a monotypic genus, the species remains biologically and agronomically understudied, with no formal cultivation systems. There is currently no baseline information on its fruit morphology, which limits the design of conservation strategies and the development of its agronomic potential. This study provides the first phenotypic characterisation of L. biternata fruits, aimed at supporting germplasm evaluation, ex situ conservation, and sustainable domestication of this rare species. A total of 205 fruits were sampled across two seasons and two geographically distant populations. We measured 14 traits, including weight, length, diameter, pulp content, and seed metrics, and analysed morphological variation using t-tests, ANOVA, regression, and principal component analysis or PCA. Fruits averaged 21.0 g in weight, 54.2 mm in length, and 23.8 mm in diameter. Edible pulp constituted 44.4% of total fruit weight and showed strong positive correlations with fruit size, seed number, and seed weight. Significant differences were observed across seasons and populations, with cooler, wetter conditions associated with larger fruits and higher pulp yield. Our findings reveal substantial morphological variability and climate sensitivity, providing a crucial baseline for selecting desirable traits. This work informs ongoing efforts in plant domestication, sustainable agriculture, and the conservation of underutilised species of cultural and ecological importance. Full article