Search Results (818)

This study optimizes the ultrasound-assisted extraction (UAE) of bioactive compounds from Spirulina platensis and develops a rapid, non-destructive analytical method. A Box–Behnken design and desirability function were used to find the optimal extraction conditions to simultaneously maximize total polyphenols, proteins, C-phycocyanin, and antioxidant activity. The optimal conditions were a solid-to-liquid ratio of 35 g/L, a time of 20 min, a pH of 10, and a temperature of 45 °C. Independent validation confirmed the model’s reliability, with experimental results closely matching predicted values. Furthermore, Near-Infrared (NIR) spectroscopy, combined with Artificial Neural Networks (ANNs), was explored as a predictive tool. The models, particularly those based on a semi-process NIR spectrometer, showed excellent predictive capabilities for key physicochemical properties, with an RPD of 3.9064 for L* and 2.8351 for TDS. This research establishes a highly reproducible and scalable extraction protocol, complemented by a fast and accurate analytical method, providing a significant advancement for the industrial application and quality control of Spirulina platensis extracts. Full article

This article presents the results of a study on the possibility of using a single-speed multiphase model with free surface allowance for simulating boiling and condensation processes. The simulation is based on the VOF method, which allows the position of the interphase boundary to be tracked. To increase the stability of the iterative procedure for numerically solving volume fraction transfer equations using a finite volume discretization method on arbitrary unstructured grids, the basic VOF method is been modified by writing these equations in a semi-divergent form. The models of Tanasawa, Lee, and Rohsenow are considered models of interphase mass transfer, in which the evaporated or condensed mass linearly depends on the difference between the local temperature and the saturation temperature with accuracy in empirical parameters. This paper calibrates these empirical parameters for each mass transfer model. The results of our study of the influence of the values of the empirical parameters of models on the intensity of boiling and evaporation, as well as on the dynamics of the interphase boundary, are presented. This research is based on Stefan’s problem of the movement of the interphase boundary due to the evaporation of a liquid and the problem of condensation of vapor bubbles water columns. As a result of a series of numerical experiments, it is shown that the average error in the position of the interfacial boundary for the Tanasawa and Lee models does not exceed 3–6%. For the Rohsenow model, the result is somewhat worse, since the interfacial boundary moves faster than it should move according to calculations based on analytical formulas. To investigate the possibility of condensation modeling, the results of a numerical solution of the problem of an emerging condensing vapor bubble are considered. A numerical assessment of its position in space and the shape and dynamics of changes in its diameter over time is carried out using the VOF method, taking into account the free surface. It is shown herein that the Tanasawa model has the highest accuracy for modeling the condensation process using a VOF method taking into account the free surface, while the Rohsenow model is most unstable and prone to deformation of the bubble shape. At the same time, the dynamics of bubble ascent are modeled by all three models. The results obtained confirm the fundamental possibility of using a VOF method to simulate the processes of boiling and condensation and taking into account the dynamics of the free surface. At the same time, the problem of the studied models of phase transitions is revealed, which consists of the need for individual selection of optimal values of empirical parameters for each specific task. Full article

Most existing multispecies transport analytical models primarily focus on inlet boundary sources, limiting their applicability in real-world contaminated sites where contaminants often arise from multiple internal sources. This study presents a novel semi-analytical model for simulating multispecies contaminant transport driven by multiple time-dependent internal sources. The model incorporates key transport mechanisms, including advection, dispersion, rate-limited sorption, and first-order degradation. In particular, the inclusion of rate-limited sorption addresses limitations in traditional equilibrium-based models, which often underestimate pollutant concentrations for degradable species. The derivation of this semi-analytical model utilizes the Laplace transform, finite cosine Fourier transform, generalized integral transform, and a sequence of inverse transformations. Results indicate that the concentrations of contaminants and their degradation products are highly sensitive to the variations in time-dependent sources. The model’s most significant contribution lies in its capability to simulate the contaminant transport from multiple internal pollution sources at a contaminated site under the influence of rate-limited sorption. By enabling the representation of multiple time-varying sources, this model fills a critical gap in analytical approaches and provides a necessary tool for accurately assessing contaminant transport in complex, realistic pollution scenarios. Full article

The microparticle-enhanced microfiltration is a technique that combines the use of microparticulate adsorbent material dispersed in aqueous solution and microfiltration membranes for the removal of ions and emerging contaminants with low energy consumption. Thus, the objective of this work was to synthesize an organoclay, BAPTES, based on bentonite and (3-aminopropyl)triethoxysilane for use as a semi-synthetic adsorbent material in the microparticle-enhanced microfiltration process for the removal of AR27 in aqueous systems. For this purpose, the obtained organoclay was structurally characterized by FTIR-ATR-FEDS, SEM-EDS, DLS, and thermal analysis. In addition, equilibrium adsorption and kinetic studies of AR27 were performed. The results showed a significant increase in the adsorption capacity of AR27 by organoclay (86.06%) compared to natural bentonite (2.10%), due to the presence of ionizable amino groups in the organoclay structure that promote electrostatic interactions with the dye. Furthermore, kinetic studies showed that the adsorption process follows a pseudo-first-order model and that the equilibrium data better fits the Temkin model, indicating a heterogeneous adsorption surface with different binding energies. The evaluation of enhanced microfiltration with BAPTES microparticles showed that the adsorption capacity obtained in continuous flow experiments (14.25–33.63 mg g⁻¹) was lower than that determined experimentally under equilibrium conditions (~39.5 mg g⁻¹), suggesting that the residence time of the analyte and the adsorbent in the filtration cell is a determining factor in the retention values obtained. In addition, desorption studies revealed that basic pH had a greater effect than the presence of salts and the use of ethanol, favoring the weakening of the AR27-BAPTES interaction. Finally, the results highlight the potential use of BAPTES microparticle-enhanced microfiltration in applications involving the treatment of contaminated industrial effluents. Full article

This paper presents an analytical framework for the channel capacity evaluation of simultaneous wireless information and power transfer (SWIPT)-enabled opportunistic amplify-and-forward (OAF) relaying systems over Rayleigh fading channels. For the SWIPT, we employ a power splitter (PS) at the relay, which splits the received signal into the information transmission and the energy-harvesting parts. By modeling the partial channel state information (P-CSI)-based SWIPT OAF system as an equivalent non-SWIPT OAF configuration, a semi-lower bound and a new upper bound on the ergodic channel capacity are derived. A refined approximation is then obtained by averaging these bounds, yielding a simple yet accurate analytical estimate of the true capacity. Simulation results confirm that the proposed approximations closely track the actual performance across a wide range of signal-to-noise ratios (SNRs) and relay configurations. They further demonstrate that SR-based relay selection provides higher capacity than RD-based selection, primarily due to its direct influence on energy harvesting efficiency at the relay. In addition, diversity advantages manifest mainly as SNR improvements, rather than as gains in diversity order. The proposed framework thus serves as a practical and insightful tool for the capacity analysis and design of SWIPT-enabled cooperative networks, with direct relevance to energy-constrained Internet of Things (IoT) and wireless sensor applications. Full article

Illegal waste dumping poses a severe challenge to sustainable urban and regional development, undermining environmental integrity, public health, and the efficient use of resources. This study contributes to sustainability science by proposing a circular data feedback loop that enables dynamic, scalable, and cost-efficient monitoring and prevention of illegal dumping, aligned with the goals of sustainable waste governance. Historical data from the Slovenian illegal dumping register, UAV-based surveys and a newly developed application were used to update, monitor, and validate waste site locations. A comprehensive risk model, developed using machine learning methods, was created for the Municipality of Maribor (Slovenia). The modelling approach combined unsupervised and semi-supervised learning techniques, suitable for a positive-unlabeled (PU) dataset structure, where only confirmed illegal waste dumping sites were labeled. The approach demonstrates the feasibility of a circular data feedback loop integrating updated field data and predictive analytics to support waste management authorities and illegal waste dumping prevention. The fundamental characteristic of the stated approach is that each iteration of the loop improves the prediction of risk areas, providing a high-quality database for conducting targeted UAV overflights and consequently detecting locations of illegally dumped waste (LNOP) risk areas. At the same time, information on risk areas serves as the primary basis for each field detection of new LNOPs. The proposed model outperforms earlier approaches by addressing smaller and less conspicuous dumping events and by enabling systematic, technology-supported detection and prevention planning. Full article

Functionally graded porous beam (FGPB) structures are widely used in engineering due to their light weight, high strength, and vibration-damping performance. However, their energy localization and vibration suppression characteristics remain largely unexplored. To address this gap, this study proposes an axially functionally graded porous beam (AFGPB) structure capable of achieving energy localization and suppressing vibration transmission. A semi-analytical model is first developed within the Rayleigh–Ritz framework, using Gaussian functions as basis functions to accurately represent the displacement field. The accuracy of the model is validated by comparing its vibration characteristics with those obtained using the finite element method (FEM). Subsequently, the vibration behavior of double-AFGPB with simply supported boundary constraints is investigated. A series of numerical results are presented in this study to analyze the influence of porosity parameters on the energy localization effect and vibration suppression performance. Results reveal that the porosity power-law index N and truncation coefficient δ play key roles in energy localization and vibration suppression performance. When N ≥ 4, the energy localization effect and the vibration attenuation of the double-AFGPB become more pronounced with increasing N and decreasing δ, particularly in the low-frequency range. Full article

Performance analysis in elite football still faces significant challenges: traditional descriptive statistics often fail to capture tactical adaptability, and African teams remain underrepresented in the scientific literature despite achieving historic breakthroughs. The FIFA World Cup Qatar 2022 marked a turning point, with Morocco becoming the first African nation to reach the semi-finals. This study systematically analyzed the tactical, physical, and structural performance of the Moroccan national team across seven matches using official FIFA post-match reports. A three-level methodological framework was adopted: (i) descriptive analysis of key performance indicators (KPIs); (ii) visual profiling through radar charts, heatmaps, and passing networks; and (iii) exploratory modelling using principal component analysis (PCA) and clustering. Results revealed consistent defensive organization, low ball possession (<40% in five matches), and effective counter-attacking transitions, with pressing peaks against Spain (288 actions) and France (299 actions). PCA explained 76% of the variance, identifying two principal axes (physical intensity vs. technical mastery; verticality vs. build-up play) and clustering distinguished three match types: low-block defensive games, transition-oriented games, and open matches. These findings highlight Morocco’s tactical adaptability and sustained physical commitment. The study demonstrates how AI-enhanced analytics and multidimensional data visualization can uncover latent performance patterns and support evidence-based decision-making. Practical implications include actionable insights for performance analysts and coaching staff, particularly as Morocco prepares for the 2025 Africa Cup of Nations and the FIFA World Cups in 2026 and 2030. This integrative approach can serve as a model for federations seeking data-driven performance optimization in elite football. Full article

The initial part of this study fills a notable research gap by investigating the substantial impact of numerical diffusion errors from different schemes on sloshing tank models. Multiple numerical models were developed: first- and higher-order upwind schemes equipped with precise wall treatment using ghost nodes, MacCormack and central methods that are explicit second-order finite difference methods, and Preissmann and staggered methods employed in full-implicit and semi-implicit modes. Furthermore, the separation of variables technique was proposed for simulating sloshing tanks and deriving an analytical equation for the tank’s natural period. An analytical solution to the perturbation was employed to examine the numerical diffusion of the schemes. Subsequently, two sloshing tests, resonant and near-resonant excitations, were employed to determine the numerical diffusion and calibrate the physical diffusion coefficients, respectively. Finally, an efficient and accurate numerical scheme was applied to a linear shallow water model including physical diffusion and coupled with a single degree of freedom (SDOF), to simulate tuned liquid dampers (TLDs). It shows that the efficiency of TLD is associated with a compact domain around resonance excitation. Contrary to SDOF alone, when SDOF interacts with TLD the impact of structural damping on reducing the response is minimal in resonance excitation. Full article

The global electric vehicle (EV) market has experienced sustained growth over the last decade; however, adoption within the commercial EV segment remains comparatively sluggish. This disparity is driven by three primary factors: the intrinsic limitations of lithium-ion battery chemistry, which imposes constraints on charge–discharge cycling, excessive charging durations for large battery packs used in long-haul semi-trucks, and diminished charging effectiveness under cold weather conditions, which further extends downtime and increases grid demand. To address these operational and infrastructural challenges, this article proposes a novel battery swapping station layout with ‘design-integrated safety’ features, enabling rapid battery replacement while ensuring compliance with safety codes and standards. Two complementary pricing strategies are developed for deployment under differing market structures. The first is a Cournot competition, applicable to deregulated environments, where firms strategically allocate battery inventory between EV swapping services and participation in a secondary energy market. As an extension of the Cournot competition model, the profit functions are analytically derived for a duopoly in which one firm engages in dual markets, enabling assessment of equilibrium outcomes under competitive conditions. The second strategy is a degradation-sensitive pricing framework, intended for regulated markets, which dynamically adjusts swap prices based on state-of-charge depletion, duty cycle intensity, environmental exposure, and nonlinear battery degradation effects. This formulation is evaluated for six representative operational cases, demonstrating its ability to incentivize shallow cycling, penalize deep discharges, and incorporate fair usage-based pricing. The proposed architectures and pricing models offer a viable pathway to accelerate commercial EV adoption while optimizing asset utilization and profitability for station operators. Full article

Bi-phasic (with a local minimum) response of fluorescence to scattering when probed by a single fiber (SF) was first observed in 2003. Subsequent experiments and Monte Carlo studies have shown the bi-phasic turning of SF fluorescence to occur at a dimensionless reduced scattering of ~1 and vary with absorption. The bi-phase of SF fluorescence received semi-empirical explanations; however, better understandings of the bi-phase and its dependence on absorption are necessary. This work demonstrates a quasi-analytical projection of a bi-phasic pattern comparable to that of SF fluorescence via photon-transport analyses of fluorescence in a center-illuminated-area-detection (CIAD) geometry. This model-approach is principled upon scaling of the diffuse fluorescence between CIAD and a SF of the same size of collection, which expands the scaling of diffuse reflectance between CIAD and a SF discovered for steady-state and time-domain cases. Analytical fluorescence for CIAD is then developed via radial-integration of radially resolved fluorescence. The radiance of excitation is decomposed to surface, collimated, and diffusive portions to account for the surface, near the point-of-entry, and diffuse portion of fluorescence associated with a centered illumination. Radiative or diffuse transport methods are then used to quasi-analytically deduce fluorescence excited by the three portions of radiance. The resulting model of fluorescence for CIAD, while limiting to iso-transport properties at the excitation and emission wavelengths, is compared against the semi-empirical model for SF, revealing bi-phasic turning [0.5~2.6] at various geometric sizes [0.2, 0.4, 0.6, 0.8, 1.0 mm] and a change of three orders of magnitude in the absorption of the background medium. This model projects a strong reduction in fluorescence versus strong absorption at high scattering, which differs from the semi-empirical SF model’s projection of a saturating pattern unresponsive to further increases in the absorption. This framework of modeling fluorescence may be useful to project frequency-domain and lifetime pattens of fluorescence in an SF and CIAD. Full article

This study focuses on Hohhot (the capital city of Inner Mongolia Autonomous Region, northern China), a representative arid-semi-arid town in northern China. Against the backdrop of concurrent rapid urbanization and ecological constraints, it undertakes a systematic investigation into the spatiotemporal evolution and driving mechanisms of ecological asset utilization efficiency, aiming to furnish scientific evidence for sustainable development in ecologically fragile urban areas. Employing a “technology-scale-structure” analytical framework and constructing an “input-output-benefit” evaluation system, this research integrates the super-efficiency slack-based measure (SBM) model with spatial analysis methodologies to conduct multidimensional assessments of ecological asset utilization efficiency across all administrative districts and counties from 2000 to 2020. Empirical results demonstrate an overall upward trajectory in Hohhot’s ecological asset utilization efficiency, with comprehensive efficiency increasing from 1.132 in 2000 to 1.397 in 2020. However, pure technical efficiency and scale efficiency exhibit significant asynchrony, reflecting inherent tensions between technological advancement and scale expansion. Spatially, efficiency distribution manifests substantial spatial clustering and heterogeneity, with identified hotspots demonstrating temporal migration patterns. Peripheral counties exhibit distinct “technological isolation” phenomena and diseconomies of scale. Mechanism analysis reveals that industrial structure optimization constitutes the primary driver of efficiency enhancement, while the catalytic effects of economic development and governmental investment exhibit diminishing marginal returns. Urbanization maintains a moderate influence, transitioning from extensive spatial expansion toward intensive functional upgrading. This study recommends a synergistic enhancement of ecological asset utilization efficiency through strategic pathways, including the following: First, advancing green industrial transformation. Second, establishing regional technology-sharing platforms. Third, implementing systematic ecological compensation mechanisms. Fourth, adopting spatially differentiated governance approaches. These measures are projected to foster coordinated environmental and economic development. This research provides theoretical underpinnings and policy implications for urban ecological asset management in arid and semi-arid regions globally. Full article

This qualitative action research case study explored how a blended literacy learning intervention combining the flipped classroom model with youth-selected multimodal texts influenced sixth-grade Academic Intervention Services (AIS) students’ comprehension of figurative language. The study was conducted over four months in a New York State middle school and involved seven students identified as at-risk readers. Initially, students engaged with teacher-created instructional videos outside of class and completed analytical activities during class time. However, due to low engagement and limited comprehension gains, the intervention was revised to incorporate student autonomy through the selection of multimodal texts such as graphic novels, song lyrics, and YouTube videos. Data was collected through semi-structured interviews, journal entries, surveys, and classroom artifacts, and then analyzed using inductive coding and member checking. Findings indicate that students demonstrated increased the comprehension of figurative language when given choice in both texts and instructional videos. Participants reported increased motivation, deeper engagement, and enhanced meaning-making, particularly when reading texts that reflected their personal interests and experiences. The study concludes that a blended literacy model emphasizing autonomy and multimodality can support comprehension and bridge the gap between in-school and out-of-school literacy practices. Full article

The traditional research methods of the notch frequency phenomenon are mainly discussed by experimental observation or the semi-analytical finite element method. In this paper, the notch frequency characteristics of ultrasonic guided waves are simulated by the general finite element method. Firstly, the theoretical dispersion curve of the longitudinal mode in the axially loaded rod is derived by the acoustic elasticity theory, and the finite element simulation is carried out by ABAQUS/Explicit 6.14 to simulate the wave propagation in the seven-wire steel strand. In order to verify the model, laboratory experiments are carried out on three types of prestressed steel strands with diameters of 12.7 mm, 15.2 mm, and 17.8 mm, respectively. Each specimen is gradually loaded from 50 kN to 110 kN in increments of 30 kN. At each loading level, the ultrasonic signal is obtained, and the corresponding notch frequency is extracted from the spectrum. The experimental results confirm the accuracy of the model, and the maximum deviation between the predicted notch frequency and the measured value is 3%. The results show that the proposed method provides a robust and non-destructive means for structural health monitoring in civil engineering applications, and has the potential to be more widely used in complex waveguide structures. Full article

Ultra-high-performance concrete (UHPC) is increasingly employed in long-span and heavily loaded structural applications; however, the accurate prediction of its flexural capacity remains a significant challenge because of the complex interactions among geometric parameters, reinforcement details, and advanced material properties. Existing design codes and single-architecture machine learning models often struggle to capture these nonlinear relationships, particularly when experimental datasets are limited in size and diversity. This study proposes a compact hybrid CNN–Transformer model that combines convolutional layers for local feature extraction with self-attention mechanisms for modeling long-range dependencies, enabling robust learning from a database of 120 UHPC beam tests drawn from 13 laboratories worldwide. The model’s predictive performance is benchmarked against conventional design codes, analytical and semi-empirical formulations, and alternative machine learning approaches including Convolutional Neural Networks (CNN), eXtreme Gradient Boosting (XGBoost), and K-Nearest Neighbors (KNN). Results show that the proposed architecture achieves the highest accuracy with an R² of 0.943, an RMSE of 41.310, and a 25% reduction in RMSE compared with the best-performing baseline, while maintaining strong generalization across varying fiber dosages, reinforcement ratios, and shear-span ratios. Model interpretation via SHapley Additive exPlanations (SHAP) analysis identifies key parameters influencing capacity, providing actionable design insights. The findings demonstrate the potential of hybrid deep-learning frameworks to improve structural performance prediction for UHPC beams and lay the groundwork for future integration into reliability-based design codes. Full article