Search Results (13,815)

The improvement of Lavandula angustifolia Mill. seed germination represents a crucial step towards the development of eco-biotechnological solutions for the sustainable propagation of aromatic plants. This study evaluated the effects of four biostimulant formulations, namely Amino 16 (amino acid-based), Razormin (humic–fulvic complex), Germinoseed (phytoextract-based), and Atonik (nitrophenolate), together with a non-treated control, on the germination efficiency and early growth of nine Lavandula genotypes under controlled laboratory conditions. A factorial design (9 × 5) with four replications was applied, and multiple germination indices were calculated. Data were analysed using a two-way ANOVA with genotype and treatment as main factors. Results indicated significant genotype-dependent responses. Amino 16 and Razormin markedly increased germination percentage, speed of emergence, and seedling vigour, achieving up to 100% germination in responsive genotypes such as ‘Ellagance Snow’ and ‘Blue Spear’. Correlation and clustering analyses revealed strong links between seed size, germination rate, and seedling development, suggesting a possible synergistic role of amino and humic substances in stimulating metabolic activation during germination. These findings demonstrate that eco-friendly biostimulants function as effective biotechnological activators of seed physiology, supporting low-input propagation systems and the transition toward a circular green bioeconomy. Full article

In the context of Industry 4.0 and smart manufacturing, predicting cutting tool remaining useful life (RUL) is crucial for enabling and enhancing the reliability and efficiency of CNC machining. This paper presents an innovative predictive model based on the data fusion architecture of Graph Neural Networks (GNNs) and Transformers to address the complexity of shallow multimodal data fusion, insufficient relational modeling, and single-task limitations simultaneously. The model harnesses time-series data, geometric information, operational parameters, and phase contexts through dedicated encoders, employs graph attention networks (GATs) to infer complex structural dependencies, and utilizes a cross-modal Transformer decoder to generate fused features. A dual-head output enables collaborative RUL regression and health state classification of cutting tools. Experiments are conducted on a multimodal dataset of 824 entries derived from multi-sensor data, constructing a systematic framework centered on tool flank wear width (VB), which includes correlation analysis, trend modeling, and risk assessment. Results demonstrate that the proposed model outperforms baseline models, with MSE reduced by 26–41%, MAE by 33–43%, R² improved by 6–12%, accuracy by 6–12%, and F1-Score by 7–14%. Full article

Accurate assessment of wetland hydraulic performance and solute treatment depends on the spatial resolution of bed topography and vegetation density. To evaluate this influence, synthetic shallow-water wetlands with spatially correlated random fields of bed elevation and vegetation density were used to examine how data resolution affects predictions of hydrodynamic residence time and treatment performance. Coarse-graining of input data produced modest median errors in nominal residence time, although variability across realizations increased with greater topographic heterogeneity. The variance of residence time was the most sensitive metric, showing a consistent tendency toward underestimation as grid size increased, with maximum median errors exceeding 10% and 35% for grid sizes equal to and twice the correlation length, respectively. In contrast, outlet concentration errors remained relatively small, typically below 5% even when grid size exceeded the correlation length of bed features, indicating a stronger dependence on nominal residence time than on variance. Within the range of vegetation stem density variability considered, heterogeneous vegetation patterns in a flat-bed wetland exerted comparatively little influence on residence time metrics and contaminant concentration at the outlet. The results provide insights into the reliability of wetland models under varying data resolutions and identify conditions under which coarse-graining is acceptable, offering guidance for field measurement strategies and numerical modeling. Full article

This study proposes an analytical model for the long-term prediction of boil-off gas (BOG) generation in cryogenic storage tanks. The model assumes a saturated liquid and a superheated vapor under open-vent conditions. Heat ingress is estimated using steady-state thermal conduction analysis, and evaporation is then computed from thermodynamic equilibrium. In the first stage, a thermal resistance network quantifies the heat flux transferred to the liquid and vapor regions inside the tank. The network represents external convection, insulation conduction, and internal convection as thermal resistances. In particular, natural convection on the external and internal tank walls, as well as heat transfer at the liquid–vapor interface, are incorporated through appropriate convective heat-transfer correlations. In the second stage, the temporal variations in temperature and phase change of the vapor and liquid are computed. Each phase is modeled as a lumped mass at equilibrium, and the heat ingress obtained from the thermal resistance network is used to simulate the temperature evolution and evaporation process. A numerical model is also developed to capture the time-dependent variations in liquid and vapor heights and the corresponding BOG generation. The proposed model is applied to a 1.0 m³ liquid nitrogen storage tank and validated through comparison with the BoilFAST and SINDA/FLUINT models. The results confirm the validity of the model in terms of heat ingress, vapor temperature evolution, and BOG history. This study provides a practical framework for predicting long-term evaporation phenomena in cryogenic storage tanks and is expected to contribute to the thermal design and performance evaluation of cryogenic storage systems. Full article

Background/Objectives: Psychosocial factors (e.g., adherence, substance use) contribute to increased morbidity and mortality after heart transplantation. We investigated alcohol consumption patterns and their associations with psychosocial factors in adults, who underwent heart transplantation surgery (HTX recipients). Methods: Our cross-sectional study was conducted at the Semmelweis University Heart and Vascular Centre between 2023 and 2025. In total, 201 HTX recipients (75.6% male, mean age: 56.33 ± 11.46 years) completed the Alcohol Use Disorders Identification Test (AUDIT), Brief Health Literacy Screening Tool (BRIEF), Medication Adherence Report Scale (MARS-5) modified to immunosuppressive medication, and 9-item Beck Depression Inventory (BDI-9). Statistical analysis included Pearson’s correlation tests and Multivariate Regression Analyses. Results: The AUDIT had a higher proportion of non-evaluable responses than other questionnaires (AUDIT 19.9% vs. 5.5–9%), with 41.0% of the participants abstinent, 54.7% low-risk, 4.3% medium-risk, and 6.5% at-risk drinkers. AUDIT correlated negatively with MARS-5 (r = −0.326; p = 0.000) and positively with BDI-9 (r = 0.208; p = 0.010). At-risk drinking was associated with a lower MARS-5 (r = −0.231; p = 0.002). Multivariate regression models significantly predicted the AUDIT (F = 5.106; p < 0.001, R² = 0.216) and AUDIT-C (F = 3.804; p = 0.002; R² = 0.146), with sex and adherence as independent predictors. Conclusions: The high proportion of non-evaluable AUDIT responses suggests limitations in multi-questionnaire use but does not diminish its clinical relevance. The presence of 6.5% at-risk and 4.3% medium-risk drinkers highlights the relevance of consumption pattern screening, beyond diagnosing alcohol use disorder. Associations between AUDIT, MARS-5, and BDI-9 emphasize the necessity for multidisciplinary care. Full article

The presented research delivers a comprehensive evaluation of anticorrosive cathodic electrodeposition (CED) coatings through an integrated performance and process capability analysis—an approach that remains extremely limited in the literature, particularly in the context of statistically designed experiments (DoEs) applied to CED systems. This study therefore addresses a notable gap by focusing on the role of polymerization variables in determining coating quality through DoE to quantify the influence on coating thickness uniformity, adhesion integrity and impact resistance, while all other deposition parameters were rigorously controlled. Prior to coating application, all specimens were prepared and conditioned in accordance with ISO 1513:2010. Coating thickness was determined in compliance with ISO 2808:2019, adhesion was characterized by cross-cut methodology according to ISO 2409:2020 and dynamic mechanical resistance was evaluated using a falling-weight apparatus in accordance with ISO 6272-1:2011. The obtained datasets were subjected to statistical capability analysis within the PalstatCAQ environment, providing Cp, Cpk, Pp and Ppk indices in line with ISO 22514-7:2021 and IATF 16949:2016 requirements. Results evidenced non-linear dependencies of thickness formation on curing parameters, with potential capability indices (Cp > 1.8; Pp ≈ 1.4) indicating favorable process dispersion, while performance indices (Cpk < 0.5; Ppk < 0.4) revealed systematic mean shifts and deviations from normality confirmed by Shapiro–Wilk and Anderson–Darling tests. Adhesion testing demonstrated a direct correlation between curing conditions and interfacial bonding, reaching ISO Grade 0 classification. Complementary impact resistance assessments corroborated these findings, showing that insufficient curing induced extensive cracking and delamination. Furthermore, SEM–EDX analysis performed on Sample n.3 of X₂ variable confirmed the chemical integrity and multilayered structure of the CED coating. Full article

This study aimed to investigate the phytochemical constituents of C. odorata leaves and stems and to evaluate their antioxidant, total phenol, α-glucosidase, and antibacterial activities. Furthermore, liquid chromatography-high-resolution mass spectrometry (LC–HRMS)-based metabolite profiling combined with principal component analysis (PCA) was applied to correlate metabolite composition with functional activities, providing comprehensive insights into the metabolomic diversity and bioactive differentiation between plant parts. The plant materials were extracted using 70% and 100% ethanol for 24 h. The leaf extract of ethanol 70% (EtOH 70) exhibited the highest antioxidant activity (IC₅₀ of 223.33 ± 9.20 µg/mL) and total phenolic content (113.15 mg GAE/g), while the stem EtOH 70% extract showed the strongest antidiabetic activity through α-glucosidase inhibitory activity (78.57%). Although appearing less potent, all extracts showed dose-dependent inhibitory activity, such as Staphylococcus aureus (highest value at 9.31 mm), Escherichia coli (highest value at 9.92 mm), and Salmonella typhimurium (highest value at 9.00 mm). Comparing the plant parts, leaf extracts generally showed more potent activity than stem extracts, particularly evident against E. coli (e.g., Leaf EtOH 70% at 5 mg/mL: 9.92 mm vs. Stem EtOH 70%: 7.97 mm). LC-HRMS analysis revealed the presence of phenolics, flavonoids, amino acids, organic acids, and alkaloids. Furthermore, the result indicates that C. odorata is a rich source of bioactive compounds with significant antioxidant, α-glucosidase inhibitory, and antibacterial potency. The findings advance existing knowledge beyond earlier phytochemical or single-activity studies, offering a more holistic understanding of C. odorata’s therapeutic potential and its relevance for natural product development. Full article

Cinema can be conceptualized as a socio-technical system in which scripts encode in-tended emotions, production processes transform them into multimodal experiences, and audiences generate emergent responses through reviews and ratings. This study investigates the emotional fidelity between designed affective trajectories in film scripts and perceived emotions expressed in audience reviews. A system-oriented computational framework was developed, integrating large-scale script and review data with transformer-based natural language processing models fine-tuned on the GoEmotions dataset. By applying a unified classification pipeline, we compare emotional distributions across scripts and reviews, analyze temporal and genre-specific patterns, and examine correlations with film success metrics such as profit and ratings. The results reveal both convergence and divergence between scripted intentions and audience responses, with genres functioning as semi-autonomous subsystems and historical trends reflecting context-dependent adaptation. Emotional fidelity—defined as the degree to which intended emotional expressions are preserved, transformed, or inverted in audience interpretation—is introduced as a system-level performance indicator. These findings advance theoretical perspectives on narrative communication as a feedback-driven socio-technical process and demonstrate how emotion mining can function as affective monitoring infrastructure for complex adaptive systems. The study contributes actionable insights for screenwriters, producers, and system designers seeking to enhance affective engagement. Full article

Injectable biomaterials for vocal fold disorders are being developed to provide not only mechanical reinforcement but also a regenerative microenvironment. Recent hydrogels based on hyaluronic acid (HA) derivatives, calcium hydroxylapatite and decellularized matrix scaffolds are designed to approximate the viscoelastic behavior of native tissue, allow controlled degradation, and modulate local immune responses. Rather than serving merely as space-filling agents, several of these materials deliver extracellular matrix (ECM)-like biochemical signals that help maintain pliability and overcome some limitations of conventional augmentation. Experimental and early clinical studies involving growth factor delivery, stem cell-based injections, and ECM-mimetic hydrogels have demonstrated improved mucosal wave vibration and reduced fibrosis in cases of scarring. In clinical series, benefits from basic fibroblast growth factor can persist for up to 12 months. Further progress will depend on correlating material properties with objective vibratory performance to achieve lasting restoration of phonation and advance true tissue-regenerative therapy. Full article

Geological models form the basis for scientific investigations of both the surface and subsurface of urban environments. Urban cover, however, usually prohibits the collection of new subsurface data. Therefore, models depend on existing subsurface datasets that are often of poor quality and have an uneven spatial and temporal distribution, introducing significant uncertainty. This research proposes a novel method to mitigate uncertainty caused by clusters of uncertain data points in kriging-based geological modelling. This method estimates orientations from clusters of uncertain data and randomly selects points for geological interpolation. Unlike other approaches, it relies on the spatial distribution of the data and translating geological information from points to geological orientations. This research also compares the proposed approach to locally changing the accuracy of the interpolator through data-informed local smoothing. Using the Ouseburn catchment, Newcastle upon Tyne, UK, as a case study, results indicate good correlation between both approaches and known conditions, as well as improved performance of the proposed methodology in model validation. Findings highlight a trade-off between model uncertainty and model precision when using highly uncertain datasets. As urban planning, water resources, and energy analyses rely on a robust geological interpretation, the modelling objective ultimately guides the best modelling approach. Full article

While biodegradable plastics alleviate plastic pollution, their degradation-derived biodegradable microplastics (BMPs) pose new ecological risks, necessitating efficient quantification methods. This study explores a label-free approach by leveraging the intrinsic fluorescence of common biodegradable polyesters (PLA, PHB, PBS, PBAT, PCL). We find that biodegradable microplastics exhibit two types of characteristic fluorescence emission: one originating from molecular functional groups and the other originating from the chromophore formed by the aggregation of conjugated groups. Using PBAT as a model, we confirm that fluorescence intensity depends on the BMPs’ size and shape. Under 380 nm excitation, concentration-dependent signals are observed at 436 nm (indirectly from PBAT-enhanced water Raman scattering) and 465 nm (directly from PBAT intrinsic fluorescence), leading to successful linear models between BMPs’ mass concentration and fluorescence intensity over 100–500 mg/L, with correlation coefficients (R²) of 0.877 and 0.963, respectively. Compared with the fluorescence labeling method, the intrinsic fluorescence approach achieves comparable R² while exhibiting lower signal intensity (~10³). Nevertheless, its operational simplicity offers a distinct advantage for the rapid quantification of pre-isolated and purified microplastics. Full article

Accurate cooling load forecasting in chiller units is critical for building energy optimization, yet remains challenging due to non-stationary nonlinear dynamics driven by coupled external weather variability (solar radiation, ambient temperature) and internal thermal loads. Conventional models fail to capture the spatiotemporal coupling inherent in load time series, violating their stationarity assumptions. To address this, this research proposes OptiNet, a spatiotemporal forecasting framework integrating patch-specific dynamic filtering with graph neural networks. OptiNet partitions multi-sensor data into non-overlapping time patches to develop a dynamic spatiotemporal graph. A learnable routing mechanism then performs adaptive dependency filtering to capture time-varying temporal–spatial correlations, followed by graph convolution for load prediction. Validated on long-term industrial logs (52,075 multi-sensor samples at 20 min; district cooling plant in Zhangjiang, Shanghai, with multiple chillers, towers, pumps, building meters, and a weather station), OptiNet achieves consistently lower MAE and MSE than Graph WaveNet across 6–144-step horizons and sampling frequencies of 20–60 min; among 30 set-tings it leads in 26, with MSE reductions up to 27.8% (60 min, 72-step) and typical long-horizon (72–144 steps) gains of ≈2–18% MSE and ≈1–15% MAE. Crucially, the model provides interpretable spatial-temporal dependencies (e.g., “Zone B solar radiation influences Unit 2 load with 4-h lag”), enabling data-driven chiller sequencing strategies that reduce electricity consumption by 12.7% in real-world deployments—directly advancing energy-efficient building operations. Full article

Time-varying wind conditions, on which wind turbines depend, significantly influence power generation performance. Accordingly, this study proposes an approach to improve the accuracy of wind power generation prediction by incorporating time-varying wind conditions into the OpenFAST wind turbine model. An OpenFAST wind turbine model was constructed using wind speed and wind direction data collected from the weather station near the wind farm. The numerical model was composed of cases where only wind speed was considered and cases where the time-varying wind direction was considered. And the hourly and daily power generation prediction results were compared and analyzed with the actual power generation through indicators such as correlation coefficient, RMSE (Root Mean Square Error) and NRMSE (Normalized Root Mean Square Error). As a result, in the model that reflects time-varying wind direction, errors were reduced and linear correlation was improved, both in comparison with actual power generation. Therefore, it can be concluded that this model enhances the accuracy of power generation prediction. Consequently, this study highlights the importance of considering time-varying wind direction in OpenFAST wind turbine simulation. Full article

This study analyzes how socio-demographic factors influence the sustainable consumption of organic vegetables in Romania. Following behavioral theories such as the Theory of Planned Behavior and Value-Belief-Norm models, the study explores how attitudes, trust, and socio-demographic characteristics shape individual purchasing decisions. The main objective was to analyze the frequency of consumption of organic vegetables, the reasons for action, the level of trust in organic certification and preferences regarding distribution channels in relation to socio-demographic variables. The research was based on a structured questionnaire applied to a sample of 533 respondents, selected from various regions of the country. The statistical analysis included descriptive and inferential methods, namely the Chi-square test for the association between variables, the Spearman coefficient for ordinal correlations and the Mann–Whitney U test for the comparison of independent groups. Results show that the frequency of organic vegetable consumption decreases as income and education rise, a pattern influenced by Romania’s market structure and cultural context, where higher-income consumers often prefer imported or internationally certified products, while middle-income groups sustain local purchases. Health is the main motivation for the purchase, while aspects related to environmental protection and support for local producers are mentioned secondarily. Price perception is significantly associated with age, income, and education, and the preference for purchasing channels especially depends on educational level. Trust in producers correlates with gender and income, while trust in certification labels shows no significant associations. These findings provide insight into how socio-demographic characteristics shape sustainable consumption behaviors and build a solid foundation for the development of the organic products market in Romania. Full article

Background: Obesity is a global epidemic and a complex chronic disease affecting more than one billion patients, leading to severe health issues like diabetes, heart disease, and cancer. While lifestyle changes are the first-line treatment, they are often insufficient. Current medications may cause severe side effects, including muscle loss and vision problems. Objectives: This systematic review aims to generalize and evaluate data from preclinical studies on the effect of flavonoid dihydroquercetin (DHQ) on weight loss in experimental animals compared with placebo-treated animals. Methods: This systematic review was conducted in accordance with the PRISMA guidelines. The protocol was registered in the PROSPERO database in August 2025 (CRD420251129793). Risk of bias (RoB) was assessed by using SYRCLE’s tool. Results: In total, eight studies included in the systematic review involved 175 animals (14 treatment groups and 9 control groups). Calculation of correlations between the reported effect on weight change and initial weight showed a strong association between these rates (R −0.9883). The intensity of DHQ effect depended on the condition: There were strong negative correlations between DHQ dose and the observed effect in diabetes mellitus (R −0.9056), hepatic lipid dysmetabolism (R −0.9339), and hepatic fibrosis (R −0.9025) in mice and rats’ data together. Conclusions: Intake of DHQ in the course of one month and three months resulted in a decrease in animals’ weight by 5.24% ± 1.95% and 18.29% ± 1.96% (p < 0.0001), respectively. Taken together, our results suggest the rationality for further research of DHQ as an anorexigenic agent, focusing on the stereochemistry of this flavonoid and its bioavailability optimization. Full article