The utilization of fruit-processing by-products is a practical approach to develop sustainable, value-added meat snacks. Sea buckthorn pomace, rich in colored and acidic compounds, is expected to influence dehydration behavior, appearance and storage stability of deshydrated meat products. The study was designed to investigate how different inclusion levels of sea buckthorn pomace powder (SBPP) - 0.75%, 1.25%, and 2.5% (w/w) - influence the quality and safety attributes of fermented and dehydrated meat snack products. The samples underwent fermentation at 25 ± 1 °C for a period of 12 hours, followed by dehydration at 50 ± 5 °C for 6 hours. Subsequent analyses comprised the assessment of key physicochemical parameters (moisture content, pH, and water activity), instrumental color measurement using the CIELab system, sensory profiling, determination of lipid oxidation through peroxide value, and evaluation of microbiological indicators. Increasing SBPP level decreased moisture (15.79% to 7.93%), water activity (0.455 c.u. to 0.358 c.u.) and pH (6.16 to 5.65), indicating enhanced shelf-stability. Color shifted toward a darker red-orange profile (L* 47.85 to 20.64; a* up to 27.09). Peroxide value remained low and unchanged (1.60 to 1.70 mEq O2/kg). Coliforms, moulds/yeasts and Salmonella were not detected. SBPP is therefore a clean-label ingredient enabling differentiated ripening-dehydrated meat snacks while supporting circular valorization of processing residues.
Aspect-Based Sentiment Analysis (ABSA) supports fine-grained understanding of user opinions by identifying sentiment toward specific aspects. Nevertheless, many existing approaches either rely on rigid feature representations or adopt deep learning models that introduce high computational cost and limited interpretability, reducing their suitability for scalable soft computing systems. This study proposes a hybrid intelligence framework for ABSA that combines TF-IDF n-gram representations with three-dimensional lexicon-based sentiment values and a threshold-adjusted Naïve Bayes ensemble. Contextual information is captured using unigram, bigram, and trigram features, while semantic polarity, objectivity, and subjectivity scores derived from SentiWordNet provide complementary sentiment knowledge. A weighted fusion of Multinomial and Gaussian Naïve Bayes classifiers is employed, alongside adaptive threshold calibration to improve minority-class detection. Experiments on a large restaurant review dataset demonstrate that the proposed approach achieves an overall accuracy of 0.92 with strong macro-averaged and weighted F1-scores, outperforming multiple baseline and hybrid methods. Statistical significance is confirmed using the Wilcoxon signed-rank test. Computational complexity analysis shows linear scalability with respect to corpus size and document length. The results indicate that the proposed hybrid framework delivers an effective balance between accuracy, interpretability, and computational efficiency, making it suitable for scalable soft computing systems and resource-constrained sentiment analysis applications.
The prediction and monitoring of pollutants in riverine ecosystems are essential for environmental protection and public health. This study proposes a methodology for the numerical modeling of hydrodynamics in aquatic systems using ANSYS Fluent. The results highlight the capability of ANSYS Fluent to determine critical parameters required for subsequent simulations, enabling accurate assessment of pollutant concentration distributions.
Electrochemical impedance spectroscopy (EIS) provides a powerful means of probing interfacial and charge-transport phenomena in semiconductor electrodes, yet interpretation becomes increasingly difficult when applied to nanostructured materials. Semiconductor nanowires, nanotubes, and porous films exhibit high surface areas, heterogeneous morphologies, and defect-rich interfaces, all of which modify their impedance response. Theoretical principles regarding EIS behavior in such systems are presented, beginning with semiconductor–electrolyte junction formation, space-charge layer development, band bending and the influence of surface states. Key elements of equivalentcircuit descriptions are examined, including double-layer capacitance, charge-transfer resistance and diffusion-related impedance contributions. Special emphasis is placed on constant-phase elements and distributed models required to capture non-ideal capacitive behavior characteristic of nanoscale electrodes. Common challenges in spectral interpretation, such as overlapping time constants, non-uniform current distribution and deviations from classical Randles-type responses, are summarized within a unified theoretical framework to support reliable modelling and analysis.
The integration of fluid-structure interaction (FSI) simulations into the design cycle of centrifugal pump impellers is an important part of this methodology, ensuring the safe conduct of bench tests. This study evaluates the behaviour of the CH 100/32-11-1 pump impeller using a combined numerical workflow involving ANSYS CFX and Mechanical. After a thorough analysis of mesh convergence, the hydraulic loads obtained from Computational Fluid Dynamics (CFD) data were transferred to the structural domain for quantitative assessment of equivalent stresses and strains. The analysis shows that the maximum equivalent stress for the optimised geometry is 24.45 MPa, which is within the tensile strength limit of PA12, confirming the feasibility of manufacturing a functional prototype using a Selective Laser Sintering (SLS). A comparative assessment with respect to the CH 6.3/20-1.1-2 and CH 100/125-75-5 models shows a non-linear scaling of structural loads, with the latter demonstrating critical failure stresses (51.7 MPa) exceeding the material's strength limits. The results confirm that the Fluid-Structure Interaction (FSI) numerical model is an important filter in the preparation stage for prototyping turbomachinery components using additive technologies.
