Search Results (8,119)

Environmental protection has become one of the key challenges of our time. This has led to an increase in pro-environmental activities in the field of cosmetics and household chemicals, where manufacturers are increasingly trying to meet the expectations of consumers who are aware of the potential risks associated with the production of cosmetics and household chemistry products. This is one of the most important challenges of today’s industry, given that some of the raw materials still commonly used, such as surfactants, may be toxic to aquatic organisms. Many companies are choosing to use natural raw materials that have satisfactory performance properties but are also environmentally friendly. In addition, modern products are also characterized by reduced consumption of water, resources, and energy in production processes. These measures reduce the carbon footprint and reduce the amount of plastic packaging required. In the present study, seven formulations of environmentally friendly car shampoo concentrates were developed, based entirely on mixtures of bio-based surfactants. The developed formulations were tested for application on the car body surface, allowing the selection of the two best products. For these selected formulations, an in-depth physicochemical analysis was carried out, including pH, density, and viscosity measurements. Comparison of the results with commercial products available on the market was also performed. Additionally, using the multiple light scattering method, the foamability and foam stability were determined for the car shampoos developed. The results obtained indicate the very high application potential of the products under study, which combine high performance and environmental concerns. Full article

Monitoring sediment concentration in water bodies is crucial for assessing water quality, ecosystems, and environmental health. However, physical sampling and sensor-based approaches are labor-intensive and unsuitable for large-scale, continuous monitoring. This study employs machine learning models to estimate suspended sediment concentration using images captured in natural light, named RGB, and near-infrared (NIR) conditions. A controlled dataset of approximately 1300 images with SSC values ranging from 1000 mg/L to 150,000 mg/L was developed, incorporating temperature, time of image capture, and solar irradiance as additional features. Random forest regression and gradient boosting regression were trained on mean RGB values, red reflectance, time of captured, and temperature for natural light images, achieving up to 72.96% accuracy within a 30% relative error. In contrast, NIR images leveraged gray-level co-occurrence matrix texture features and temperature, reaching 83.08% accuracy. Comparative analysis showed that ensemble models outperformed deep learning models like Convolutional Neural Networks and Multi-Layer Perceptrons, which struggled with high-dimensional feature extraction. These findings suggest that using machine learning models and RGB and NIR imagery offers a scalable, non-invasive, and cost-effective way of sediment monitoring in support of water quality assessment and environmental management. Full article

(1) Background: Polyploid fish are highly important in increasing fish production, improving fish quality, and breeding new varieties. The loach (Misgurnus anguillicaudatus), as a naturally polyploid fish, serves as an ideal biological model for investigating the mechanisms of chromosome doubling; (2) Methods: In this study, tetraploidization in diploid loach was induced by heat shock treatment, and, for the first time, the role of the key cell cycle gene cdk1 (cyclin-dependent kinase 1) in chromosome doubling was investigated; (3) Results: The experimental results show that when eggs are fertilized for 20 min and then subjected to a 4 min heat shock treatment at 39–40 °C, this represents the optimal induction condition, resulting in a tetraploid rate of 44%. Meanwhile, the results of the cdk1 knockout model (2n cdk1^−/−) constructed using CRISPR/Cas9 showed that the absence of cdk1 significantly increased the chromosome doubling efficiency of the loach. The qPCR analysis revealed that knockout of cdk1 significantly upregulated cyclin genes (ccnb3,ccnc, and ccne1), while inhibiting expression of the separase gene espl1 (p < 0.05); (4) Conclusions: During chromosome doubling in diploid loaches induced by heat shock, knocking out the cdk1 gene can increase the tetraploid induction rate. This effect may occur through downregulation of the espl1 gene. This study offers novel insights into optimizing the induced breeding technology of polyploid fish and deciphering its molecular mechanism, while highlighting the potential application of integrating gene editing with physical induction. Full article

A comprehensive metabolic profiling of Catharanthus roseus (L.) G. Don was performed using tandem mass spectrometry, along with an evaluation of the biological activities of its various solvent extracts. Among these, the methanolic leaf extract exhibited mild radical scavenging activity, low to moderate antimicrobial activity, and limited cytotoxicity in both the brine shrimp lethality assay and MTT assay against HeLa and A549 cell lines. High-performance liquid chromatography–electrospray ionization–high-resolution tandem mass spectrometry (HPLC-ESI-HRMS/MS) analysis led to the annotation of 34 metabolites, primarily alkaloids. These included 23 indole alkaloids, two fatty acids, two pentacyclic triterpenoids, one amino acid, four porphyrin derivatives, one glyceride, and one chlorin derivative. Notably, two metabolites—2,3-dihydroxypropyl 9,12,15-octadecatrienoate and (10S)-hydroxypheophorbide A—were identified for the first time in C. roseus. Furthermore, Global Natural Products Social Molecular Networking (GNPS) analysis revealed 18 additional metabolites, including epoxypheophorbide A, 11,12-dehydroursolic acid lactone, and 20-isocatharanthine. These findings highlight the diverse secondary metabolite profile of C. roseus and support its potential as a source of bioactive compounds for therapeutic development. Full article

Relict fern species, confined to microhabitats with stable historical conditions, are especially vulnerable to climate change. The Alboran Arc hosts a unique relict fern flora, including Culcita macrocarpa, Diplazium caudatum, and Pteris incompleta, and functions as a major Pleistocene refuge. This study assesses the population trends and climate sensitivity of these species in Los Alcornocales Natural Park using annual abundance time series for a decade, empirical survival projections, and principal component analysis to identify key climatic drivers. Results reveal distinct climate response clusters among populations, though intra-specific variation highlights the importance of local conditions. Climate change is already impacting population viability, especially for P. incompleta, which shows high sensitivity to rising maximum temperatures and prolonged heatwaves. Climate-driven models forecast more severe declines than empirical ones, particularly for C. macrocarpa and P. incompleta, with the latter showing a projected collapse by the mid-century. In contrast, D. caudatum exhibits moderate vulnerability. Crucially, the divergence between models underscores the impact of conservation efforts: without reinforcement and reintroduction actions, projected declines would likely be more severe. These results project a decline in the populations of the studied ferns, highlighting the urgent need to continue implementing both in situ and ex situ conservation measures. Full article

Natural disasters like wildfires pose significant threats to communities, which necessitates timely and effective disaster response strategies. While Aspect-based Sentiment Analysis (ABSA) has been widely used to extract sentiment-related information at the sub-sentence level, the corresponding field of Aspect-based Emotion Analysis (ABEA) remains underexplored due to dataset limitations and the increased complexity of emotion classification. In this study, we used EmoGRACE, a fine-tuned BERT-based model for ABEA, which we applied to georeferenced tweets of the 2020 California wildfires. The results for this case study reveal distinct spatio-temporal emotion patterns for wildfire-related aspect terms, with fear and sadness increasing near wildfire perimeters. This study demonstrates the feasibility of tracking emotion dynamics across disaster-affected regions and highlights the potential of ABEA in real-time disaster monitoring. The results suggest that ABEA can provide a nuanced understanding of public sentiment during crises for policymakers. Full article

Vegetation dynamics are complexly influenced by multiple factors such as climate, human activities, and topography. In recent years, the frequency, intensity, and diversity of human activities have increased, placing substantial pressure on the growth of vegetation. Arid and semi-arid regions are particularly sensitive to climate change, and climate change and large-scale ecological restoration have led to significant changes in the dynamic of dryland vegetation. However, few studies have explored the nonlinear relationships between these factors and vegetation dynamic. In this study, we integrated trend analysis (using the Mann–Kendall test and Theil–Sen estimation) and machine learning algorithms (XGBoost-SHAP model) based on long time-series remote sensing data from 2001 to 2020 to quantify the nonlinear response patterns and threshold effects of bioclimatic variables, topographic features, soil attributes, and anthropogenic factors on vegetation dynamic. The results revealed the following key findings: (1) The kNDVI in the study area showed an overall significant increasing trend (p < 0.01) during the observation period, of which 26.7% of the area showed a significant increase. (2) The water content index (Bio 23, 19.6%), the change in land use (15.2%), multi-year average precipitation (pre, 15.0%), population density (13.2%), and rainfall seasonality (Bio 15, 10.9%) were the key factors driving the dynamic change of vegetation, with the combined contribution of natural factors amounting to 64.3%. (3) Among the topographic factors, altitude had a more significant effect on vegetation dynamics, with higher altitude regions less likely to experience vegetation greening. Both natural and anthropogenic factors exhibited nonlinear responses and interactive effects, contributing to the observed dynamic trends. This study provides valuable insights into the driving mechanisms behind the condition of vegetation in arid and semi-arid regions of China and, by extension, in other arid regions globally. Full article

In response to the stress commonly experienced by young people in high-pressure daily environments, a music-based stress-relief interactive system was developed by integrating music-assisted care with emotion-sensing technology. The design principles of the system were established through a literature review on stress, music listening, emotion detection, and interactive devices. A prototype was created accordingly and refined through interviews with four experts and eleven users participating in a preliminary experiment. The system is grounded in a four-stage guided imagery and music framework, along with a static activity model focused on relaxation-based stress management. Emotion detection was achieved using a wearable EEG device (NeuroSky’s MindWave Mobile device) and a two-dimensional emotion model, and the emotional states were translated into visual representations using seasonal and weather metaphors. A formal experiment involving 52 users was conducted. The system was evaluated, and its effectiveness confirmed, through user interviews and questionnaire surveys, with statistical analysis conducted using SPSS 26 and AMOS 23. The findings reveal that: (1) integrating emotion sensing with music listening creates a novel and engaging interactive experience; (2) emotional states can be effectively visualized using nature-inspired metaphors, enhancing user immersion and understanding; and (3) the combination of music listening, guided imagery, and real-time emotional feedback successfully promotes emotional relaxation and increases self-awareness. Full article

Allyl isothiocyanate (AITC) is a naturally occurring, pungent compound abundant in cruciferous vegetables and functions as a repellent for various organisms. The antibacterial effect of AITC against various bacteria has been reported, but there are no reports on the effect on Streptococcus mutans, a major bacterium contributing to dental caries. In this study, we investigated the inhibitory effect and mechanism of AITC on the survival and growth of S. mutans. AITC showed an antibacterial effect in a time- and concentration-dependent manner. In addition, bacterial growth was delayed in the presence of AITC, and there were almost no bacteria in the presence of 0.1% AITC. In a biofilm assay, the amount of biofilm formation with 0.1% AITC was significantly decreased compared to the control. RNA sequencing analysis showed that the expression of 39 genes (27 up-regulation and 12 down-regulation) and 38 genes (24 up-regulation and 14 down-regulation) of S. mutans was changed during the survival and the growth, respectively, in the presence of AITC compared with the absence of AITC. Protein–protein interaction analysis revealed that AITC mainly interacted with genes of unknown function in S. mutans. These results suggest that AITC may inhibit cariogenicity of S. mutans through a novel mechanism. Full article

Promoting sustainable development requires a clear understanding of how short-term fluctuations in anthropogenic emissions affect urban environmental quality. This is especially relevant for cities experiencing rapid industrial changes or emergency policy interventions. Among key environmental concerns, variations in ambient pollutants like ozone (O₃) are closely tied to both public health and long-term sustainability goals. However, traditional chemical transport models often face challenges in accurately estimating emission changes and providing timely assessments. In contrast, statistical approaches such as the difference-in-differences (DID) model utilize observational data to improve evaluation accuracy and efficiency. This study leverages the synthetic difference-in-differences (SDID) approach, which integrates the strengths of both DID and the synthetic control method (SCM), to provide a more reliable and accurate analysis of the impacts of interventions on city-level air quality. Using Shanghai’s 2022 lockdown as a case study, we compare the deweathered ozone (O₃) concentration in Shanghai to a counterfactual constructed from a weighted average of cities in the Yangtze River Delta (YRD) that did not undergo lockdown. The quasi-natural experiment reveals an average increase of 4.4 μg/m³ (95% CI: 0.24–8.56) in Shanghai’s maximum daily 8 h O₃ concentration attributable to the lockdown. The SDID method reduces reliance on the parallel trends assumption and improves the estimate stability through unit- and time-specific weights. Multiple robustness checks confirm the reliability of these findings, underscoring the efficacy of the SDID approach in quantitatively evaluating the causal impact of emission perturbations on air quality. This study provides credible causal evidence of the environmental impact of short-term policy interventions, highlighting the utility of SDID in informing adaptive air quality management. The findings support the development of timely, evidence-based strategies for sustainable urban governance and environmental policy design. Full article

Background: Amid growing concerns about climate crisis and its psychological toll, understanding how people find meaning through their connection to nature is increasingly important. The first aim of this study is to examine the association between Nature Relatedness (NR) and Meaning in Life (MIL). The second aim is to investigate if gender moderates this association and to explore how Jewish traditions influence gender differences in this relationship. Methods: A multi-methods design was employed. Participants were recruited through purposive sampling of prominent Jewish community figures, followed by snowball sampling via informant referrals. Thirty-five participants completed the Meaning in Life Questionnaire (MLQ) and the NR Scale. Two questions provided qualitative insights into participants’ personal interpretations and culturally grounded meanings of NR and MIL in the context of climate change and Jewish traditions. Results: Hierarchical multiple regression analyses assessed the main effects of NR and gender, as well as their interaction, on MLQ subscales. NR positively correlated with the MLQ Search dimension, indicating that individuals with stronger NR actively seek meaning in life. Gender moderated this relationship: NR did not correlate with MLQ Presence overall, but higher NR was linked to greater MIL presence among female participants. Thematic analysis of qualitative responses revealed gender-based variations and emphasized the role of Jewish teachings in connecting NR to cultural and religious practices. Conclusions: The findings point to the importance of cultural, religious, and gender factors in shaping the relationship between NR and MIL in a time of climate change crisis, offering implications for positive mental health research and culturally sensitive interventions. Full article

Under the dual challenges of global energy crisis and climate change, the building sector, as a major carbon emitter consuming 33% of global primary energy, has seen its energy efficiency optimization become a critical pathway towards achieving carbon neutrality goals. The Window-to-Wall Ratio (WWR), serving as a core parameter in building envelope design, directly influences building energy consumption, with its optimized design playing a decisive role in balancing natural daylighting, ventilation efficiency, and thermal comfort. This study focuses on the traditional One-Seal dwellings (Yikeyin) in Kunming, China, establishing a dynamic wind field-thermal environment coupled analysis framework to investigate the impact mechanism of window dimensions (WWR and aspect ratio) on indoor thermal comfort under natural wind conditions in transitional climate zones. Utilizing the Grasshopper platform integrated with Ladybug, Honeybee, and Butterfly plugins, we developed parametric models incorporating Kunming’s Energy Plus Weather meteorological data. EnergyPlus and OpenFOAM were employed, respectively, for building heat-moisture balance calculations and Computational Fluid Dynamic (CFD) simulations, with particular emphasis on analyzing the effects of varying WWR (0.05–0.20) on temperature-humidity, air velocity, and ventilation efficiency during typical winter and summer weeks. Key findings include, (1) in summer, the baseline scenario with WWR = 0.1 achieves a dynamic thermal-humidity balance (20.89–24.27 °C, 65.35–74.22%) through a “air-permeable but non-ventilative” strategy, though wing rooms show humidity-heat accumulation risks; increasing WWR to 0.15–0.2 enhances ventilation efficiency (2–3 times higher air changes) but causes a 4.5% humidity surge; (2) winter conditions with WWR ≥ 0.15 reduce wing room temperatures to 17.32 °C, approaching cold thresholds, while WWR = 0.05 mitigates heat loss but exacerbates humidity accumulation; (3) a symmetrical layout structurally constrains central ventilation, maintaining main halls air changes below one Air Change per Hour (ACH). The study proposes an optimized WWR range of 0.1–0.15 combined with asymmetric window opening strategies, providing quantitative guidance for validating the scientific value of vernacular architectural wisdom in low-energy design. Full article

Understanding root decomposition dynamics is essential to address declining carbon sequestration and nutrient imbalances in monoculture plantations. This study elucidates how forest gaps regulate Pinus massoniana root decomposition through comparative methodological analysis, providing theoretical foundations for near-natural forest management and carbon–nitrogen cycle optimization in plantations. The results showed the following: (1) Root decomposition was significantly accelerated by the in situ soil litterbag method (ISLM) versus the traditional litterbag method (LM) (decomposition rate (k) = 0.459 vs. 0.188), reducing the 95% decomposition time (T_0.95) by nearly nine years (6.53 years vs. 15.95 years). ISLM concurrently elevated the root potassium concentration and reconfigured the relationships between root decomposition and soil nutrients. (2) Lower-order roots (orders 1–3) decomposed significantly faster than higher-order roots (orders 4–5) (k = 0.455 vs. 0.193). This disparity was amplified under ISLM (lower-/higher-order root k ratio = 4.1) but diminished or reversed under LM (lower-/higher-order root k ratio = 0.8). (3) Forest gaps regulated decomposition through temporal phase interactions, accelerating decomposition initially (0–360 days) while inhibiting it later (360–720 days), particularly for higher-order roots. Notably, forest gap effects fundamentally reversed between methodologies (slight promotion under LM vs. significant inhibition under ISLM). Our study reveals that conventional LM may obscure genuine ecological interactions during root decomposition, confirms lower-order roots as rapid nutrient-cycling pathways, provides crucial methodological corrections for plantation nutrient models, and advances theoretical foundations for precision management of P. massoniana plantations. Full article

The pharmaceutical industry faces significant challenges when promising drug candidates fail during development due to suboptimal ADME (absorption, distribution, metabolism, excretion) properties or toxicity concerns. Natural compounds are subject to the same pharmacokinetic considerations. In silico approaches offer a compelling advantage—they eliminate the need for physical samples and laboratory facilities, while providing rapid and cost-effective alternatives to expensive and time-consuming experimental testing. Computational methods can often effectively address common challenges associated with natural compounds, such as chemical instability and poor solubility. Through a review of the relevant scientific literature, we present a comprehensive analysis of in silico methods and tools used for ADME prediction, specifically examining their application to natural compounds. Whereas we focus on identifying the predominant computational approaches applicable to natural compounds, these tools were developed for conventional drug discovery and are of general use. We examine an array of computational approaches for evaluating natural compounds, including fundamental methods like quantum mechanics calculations, molecular docking, and pharmacophore modeling, as well as more complex techniques such as QSAR analysis, molecular dynamics simulations, and PBPK modeling. Full article

The exponential increase in textual data generated across sectors such as healthcare, finance, and smart manufacturing has intensified the need for effective Big Data analytics. Large language models (LLMs) have become critical tools because of their advanced language processing capabilities. However, their static nature limits their ability to incorporate real-time and domain-specific knowledge. Retrieval-augmented generation (RAG) addresses these limitations by enriching LLM outputs through external content retrieval. Nevertheless, traditional RAG systems remain inefficient, often exhibiting high retrieval latency, redundancy, and diminished response quality when scaled to large datasets. This paper proposes an innovative structured RAG framework specifically designed for large-scale Big Data analytics. The framework transforms unstructured partial prompts into structured semantically coherent partial prompts, leveraging element-specific embedding models and dimensionality reduction techniques, such as principal component analysis. To further improve the retrieval accuracy and computational efficiency, we introduce a multi-level filtering approach integrating semantic constraints and redundancy elimination. In the experiments, the proposed method was compared with structured-format RAG. After generating prompts utilizing two methods, silhouette scores were computed to assess the quality of embedding clusters. The proposed method outperformed the baseline by improving the clustering quality by 32.3%. These results demonstrate the effectiveness of the framework in enhancing LLMs for accurate, diverse, and efficient decision-making in complex Big Data environments. Full article