Search Results (1,134)

In this paper, a generic computational framework, based on the generalized-mode approach, is developed for the fully coupled time-domain aero-hydro-servo-elastic analysis of Hybrid Offshore Wind and Wave Energy Systems (HOWiWaESs), consisting of a Floating Offshore Wind Turbine (FOWT) and several wave energy converters (WECs) mechanically connected to it. The FOWT’s platform and the WECs of the HOWiWaES are modeled as a single floating body with conventional rigid-body modes, while the motions of the WECs relative to the FOWT are described as additional generalized modes of motion. A numerical tool is established by appropriately modifying/extending the OpenFAST source code. The frequency-dependent exciting forces and hydrodynamic coefficients, as well as hydrostatic stiffness terms, are obtained using the traditional boundary integral equation method, whilst the generalized-mode shapes are determined by developing appropriate 3D vector shape functions. The tool is applied for a 5 MW FOWT with a spar-type floating platform and a conic WEC buoy hinged on it via a mechanical arm, and results are compared with those of other investigators utilizing the multi-body approach. Two distinctive cases of a pitching and a heaving WEC are considered. A quite good agreement is established, indicating the potential of the developed tool to model floating HOWiWaESs efficiently. Full article

Additively manufactured (AM) aluminum (Al) alloys are very useful in sectors like automotive, manufacturing, and aerospace because they have unique mechanical properties, such as their light weight, etc. AlSi10Mg made by laser powder bed fusion (LPBF) is one of the most promising materials because it has a high strength-to-weight ratio, good thermal resistance, and good corrosion resistance. But machining AlSi10Mg parts is still hard because they have unique microstructural properties from the way they were produced. This research investigates the machining efficacy of the AM-AlSi10Mg alloy in distinct cutting conditions (dry, flood, chilled air, and minimal quantity lubrication with castor oil). The study assesses how different cooling conditions affect important performance metrics such as cutting temperature, surface roughness, and tool wear. Due to castor oil’s superior lubricating and film-forming properties, MQL (Minimal Quantity Lubrication) reduces heat generation between 80 °C and 98 °C for the distinct speed–feed combinations. The Multi-Objective Optimization by Ratio Analysis (MOORA) approach is used to determine the ideal cooling and machining conditions (MQL, Vc of 90 m/min, and fr of 0.05 mm/rev). The relative closeness values derived from the MOORA approach were used to predict machining results using machine learning (ML) models (MLP, GPR, and RF). The MLP showed the strongest relationship between the measured and predicted values, with R values of 0.9995 in training and 0.9993 in testing. Full article

Sustainable protein consumption is a key element in the transition toward more environmentally responsible food systems. Poultry, due to its relatively low carbon footprint and favorable health profile, holds significant potential to become an important component of the so-called “protein transition.” The aim of this article is to identify cognitive factors influencing consumer purchasing decisions regarding poultry and to formulate recommendations for marketing communication strategies that position poultry as a choice aligned with sustainability goals. This study is based on an exploratory factor analysis (EFA) conducted on a nationally representative sample of Polish consumers (AgriFood 2024). The results revealed three dominant decision-making determinants—taste, health, and convenience—collectively forming the original THC (Taste–Health–Convenience) model. This model provides a novel interpretive framework, showing how sustainability issues can be communicated through immediate, personally relevant consumer benefits, and subsequently expanded to include environmental and ethical aspects. The findings indicate that effective communication should emphasize tangible, everyday consumer benefits while also leveraging poultry’s lower climate impact compared to red meat. This article makes an original contribution to the debate on sustainable diets by presenting the THC model both as a tool for explaining the mechanisms of the attitude–behavior gap and as a practical instrument for designing campaigns that support the implementation of SDG 3 and SDG 12. Full article

Pavement condition assessment is a fundamental aspect of airport pavement management systems (APMS) for ensuring safe and efficient airport operations. However, conventional methods, which rely on extensive on-site inspections and complex calculations, are often time-consuming and resource-intensive. In response, Industry 4.0 has introduced machine learning (ML) as a powerful tool to streamline these processes. This study explores five ML algorithms (Linear Regression (LR), Decision Tree (DT), Random Forest (RF), Artificial Neural Network (ANN), and Support Vector Machine (SVM)) for predicting the Pavement Condition Index (PCI). Using basic alphanumeric distress data from three international airports, this study predicts both numerical PCI values (on a 0–100 scale) and categorical PCI values (3 and 7 condition classes). To address data imbalance, random oversampling (SMOTE—Synthetic Minority Oversampling Technique) and undersampling (RUS) were used. This study fills a critical knowledge gap by identifying the most effective algorithms for both numerical and categorical PCI determination, with a particular focus on validating class-based predictions using relatively small data samples. The results demonstrate that ML algorithms, particularly Random Forest, are highly effective at predicting both the numerical and the three-class PCI for the original database. However, accurate prediction of the seven-class PCI required the application of oversampling techniques, indicating that a larger, more balanced database is necessary for this detailed classification. Using 10-fold cross-validation, the successful models achieved excellent performance, yielding Kappa statistics between 0.88 and 0.93, an error rate of less than 7.17%, and an area under the ROC curve greater than 0.93. The approach not only significantly reduces the complexity and time required for PCI calculation, but it also makes the technology accessible, enabling resource-limited airports and smaller management entities to adopt advanced pavement management practices. Full article

Marine sediments enriched with rare earth elements (REEs) serve as a significant reservoir, particularly for heavy REEs. Conventional lab-based REE exploration restricts rapid and large-scale assessment, whereas hyperspectral imaging provides a promising approach for quantitative evaluation. This study evaluates the capacity of hyperspectral data for the quantitative determination of REEs in marine sediments. A total of 53 samples from various locations were analyzed to determine their chemical composition and spectral characteristics within the 380–1000 nm range under natural light. The influence of surface conditions on spectral integrity was evaluated, and multiple preprocessing and spectral feature extraction methods were applied to enhance data reliability. This study proposes a novel approach, termed Feature Importance within Pearson Correlation Coefficient-Based High-Correlation Spectral Range (PCCR-FI), designed for the identification of characteristic spectral bands associated with REEs. Machine learning models were subsequently constructed to estimate REE concentrations, and the following key findings were observed: (a) technical adjustments effectively addressed variations in sediment surface conditions, ensuring data reliability. (b) The PCCR-FI technique identified characteristic REEs spectral bands, enhancing processing efficiency and prediction accuracy. (c) The integration of the reciprocal logarithmic first derivative (TLOG-FD) technique with a multilayer perceptron (MLP) model, termed TLOG-FD-MLP, efficiently captured critical spectral features, resulting in improved prediction accuracy. For light REEs, the model achieved coefficient of determination (R²) values exceeding 0.60 and relative performance deviation (RPD) values exceeding 1.60, with some elements demonstrating R² values as high as 0.81 with RPD values surpassing 2.00. Furthermore, several heavy REEs exhibited moderate prediction performance, with R² values consistently exceeding 0.60. When considering the total REE content, an R² of 0.73 and an RPD of 1.97 were achieved. These findings demonstrate the use of hyperspectral imaging as a viable tool for quantitative evaluation of REE concentrations in marine sediments, providing valuable guidance for resource mapping and the exploration of seafloor polymetallic deposits. Full article

Background: Relative Energy Deficiency in Sport (REDs) results from an imbalance between energy intake and expenditure, leading to low energy availability (LEA) and impairments of physiological and/or psychological functions in female and male athletes. While physical determinants of REDs are well documented, psychological factors such as body image (BI) have received comparatively little attention. The aim of this scoping review was to synthesize the current scientific evidence on the relationship between BI and REDs. Methods: A scoping review examined the current literature, including quantitative and qualitative studies. The scoping review was conducted in April 2025 in PubMed, Web of Science, MEDLINE, SPORTDiscus, APA PsycArticles, APA PsycInfo, CINAHL and OpenDissertations. Studies were included if they examined BI aspects in relation to LEA or REDs in a sporting context, regardless of participants’ gender, age, level or sport. Inclusion criteria were based on the Population—Context—Concept (PPC) framework. Results: Seven studies met the inclusion criteria, covering athletes from various ages, genders, sports, and performance levels. Findings indicate that BI dissatisfaction—manifesting, for example, as a drive for thinness or muscularity, exercise dependence, and disordered eating—represents a relevant psychological factor associated with LEA in both female and male athletes. Conclusions: The relationship between BI and REDs is complex and insufficiently explored. Future research should address this link systematically across sports, performance levels, genders, and age groups. In sports medicine practice, screening tools should systematically incorporate psychological risk factors such as BI disturbances to enable early detection, targeted intervention, and prevention of long-term health consequences. Full article

The gas equation of state (EOS) serves as a critical tool for analyzing the thermal effects within the hydrogen storage tank during refueling processes. It quantifies the dynamic relationships among pressure, temperature and volume, playing a vital role in numerical simulations of hydrogen refueling, the development of refueling protocols, and ensuring refueling safety. This study first establishes a lumped-parameter thermodynamic model for the hydrogen refueling process, which combines a zero-dimensional gas model with a one-dimensional tank wall model (0D1D). The model’s accuracy was validated against experimental data and will be used in combination with different EOSs to simulate hydrogen temperature and pressure. Subsequently, parameter values are derived for the van der Waals EOS and its modified forms—Redlich–Kwong, Soave, and Peng–Robinson. The accuracy of the modified forms is evaluated using the Joule–Thomson inversion curve. A polynomial EOS is formulated, and its parameters are numerically determined. Finally, the hydrogen temperatures and pressures calculated using the van der Waals EOS, Redlich–Kwong EOS, polynomial EOS, and the National Institute of Standards and Technology (NIST) database are compared. Within the initial and boundary conditions set in this study, the results indicate that among the modified forms for van der Waals EOS, the Redlich–Kwong EOS exhibits higher accuracy than the Soave and Peng–Robinson EOSs. Using the NIST-calculated hydrogen pressure as a benchmark, the relative error is 0.30% for the polynomial EOS, 1.83% for the Redlich–Kwong EOS, and 17.90% for the van der Waals EOS. Thus, the polynomial EOS exhibits higher accuracy, followed by the Redlich–Kwong EOS, while the van der Waals EOS demonstrates lower accuracy. This research provides a theoretical basis for selecting an appropriate EOS in numerical simulations of hydrogen refueling processes. Full article

Objectives: This systematic review aimed to examine hip rotator range of motion (ROM) and strength values across athletic, injured, and non-active populations, and to determine how these values differ when measured at different hip flexion angles. Methods: A systematic literature search was conducted in accordance with PRISMA guidelines across six electronic databases (PubMed, Scopus, Web of Science, SPORTDiscus, CINAHL, and Medline) from inception to June 2025. Eligible studies included observational, cross-sectional, case-control, or randomized controlled trial (RCT) studies that quantitatively assessed hip IR/ER ROM and/or strength in defined population groups (athletic, injured, or non-active). Two reviewers independently screened titles, abstracts, and full texts, extracted data on study design, population characteristics, measurement methods, and outcome variables, and assessed risk of bias using an established tool. Discrepancies were resolved by a third reviewer. Results: 11 studies met the inclusion criteria, including 1276 participants across athletic, injured, and non-active populations. Hip rotator ROM was measured in nine studies and strength in three, with varying testing angles (0° and/or 90° hip flexion). Overall, athletes showed greater ROM at 0° compared to injured and non-active groups, but had reduced ROM at 90° relative to non-active participants. Non-active individuals had the lowest ROM at 0°. Strength findings, though limited, indicated higher values at 90° than at 0°. Conclusions: Hip rotator ROM and strength vary across populations and testing angles, with ROM generally lower and strength higher at 90° of hip flexion. Due to methodological inconsistencies, findings should be interpreted as directional evidence, reinforcing the need for standardized assessment protocols in future research. Full article

Acoustic scattering is a highly effective tool for non-destructive control and structural analysis. In many real-world applications, understanding acoustic scattering is essential for accurately detecting and characterizing defects, assessing material properties, and evaluating structural integrity without causing damage. One of the most critical aspects of characterizing targets—such as plates, cylinders, and tubes immersed in water—is the analysis of the phase and group velocities of antisymmetric circumferential waves (A₁). Phase velocity helps identify and characterize wave modes, while group velocity allows for tracking energy, detecting, and locating anomalies. Together, they are essential for monitoring and diagnosing cylindrical shells. This research employs a Sugeno fuzzy inference system (SFIS) combined with a Fuzzy Subtractive Clustering (FSC) identification technique to predict the velocities of antisymmetric (A₁) circumferential signals propagating around an infinitely long cylindrical shell of different b/a radius ratios, where a is the outer radius, and b is the inner radius. These circumferential waves are generated when the shell is excited perpendicularly to its axis by a plane wave. Phase and group velocities are determined by using resonance eigenmode theory, and these results are used as training and testing data for the fuzzy model. The proposed approach demonstrates high accuracy in modeling and predicting the behavior of these circumferential waves. The fuzzy model’s predictions show excellent agreement with the theoretical results, as confirmed by multiple error metrics, including the Mean Absolute Error (MAE), Standard Error (SE), and Mean Relative Error (MRE). Full article

Large multiple datasets were simulated through sampling, and regression modeling results were compared with known parameters—an analysis undertaken here for the first time on such a scale. The study demonstrates that the impact of multicollinearity on the quality of parameter estimates is far stronger than commonly assumed, even at low or moderate correlations between predictors. The standard practice of assessing the significance of regression coefficients using t-statistics is compared with the actual precision of estimates relative to their true values, and the results are critically examined. It is shown that t-statistics for regression parameters can often be misleading. Two novel approaches for selecting the most effective variables are proposed: one based on the so-called reference matrix and the other on efficiency indicators. A combined use of these methods, together with the analysis of each variable’s contribution to determination, is recommended. The practical value of these approaches is confirmed through extensive testing on both simulated homogeneous and heterogeneous datasets, as well as on a real-world example. The results contribute to a more accurate understanding of regression properties, model quality characteristics, and effective strategies for identifying the most reliable predictors. They provide practitioners with better analytical tools. Full article

Objectives: The objective of this study was to enhance the solubility and bioavailability of canagliflozin (CFZ) using a spray drying technique with a Quality-by-Design (QbD) approach. Methods: The formulation of CFZ-loaded solid dispersions (CFZ-SDs) was optimized using a Box–Behnken design (BBD) with three factors at three levels, resulting in a total of fifteen experiments, including three central point replicates. The design space was determined using the BBD, and the optimized CFZ-SD was evaluated for reproducibility, morphology, and physical properties and subjected to in vitro and in vivo tests. Results: The optimal values for each X factor were identified using a response optimization tool, achieving a yield (Y1) of 62.8%, a solubility (Y2) of 9941 μg/mL, and a particle size (Y3) of 5.89 μm, all of which were within the 95% prediction interval (PI). Additionally, amorphization induced by spray drying was confirmed for the optimized CFZ-SD using scanning electron microscopy (SEM), differential scanning calorimetry (DSC), and powder X-ray diffraction (PXRD) analyses. In in vitro dissolution tests, the final dissolution rate of the CFZ-SD increased 3.58-fold at pH 1.2 and 3.84-fold at pH 6.8 compared to an Invokana^® tablet. In addition, relative to CFZ, it showed an 8.67-fold and 8.85-fold increase at pH 1.2 and pH 6.8, respectively. The in vivo pharmacokinetic behavior of CFZ and the CFZ-SD was evaluated in Sprague–Dawley rats following oral administration at a dose of 5 mg/kg. The AUC of the CFZ-SD increased 1.9-fold compared to that of CFZ. Conclusions: In this study, a solid dispersion (SD) formulation of CFZ, a BCS class IV SGLT2 inhibitor, was developed and optimized using a QbD approach to enhance solubility and oral bioavailability. Full article

Turn-milling is a relatively new process which combines turning and milling operations, offering a number of advantages such as chip breaking and interrupted cutting, which improves tool life. In addition to providing the capability of producing eccentric forms or shapes, it increases productivity for difficult-to-machine material at lower cost. This study investigates the influence of cutting speed and feed on surface roughness and tool wear in conventional turning and turn-milling of H13 tool steel. The tests were conducted for longitudinal and face machining strategies. It was found that the range of surface roughness in turning is lower than in turn-milling. In longitudinal turning, face-turning, and face turn-milling operations, surface roughness is elevated in the higher feeds. However, the surface roughness in longitudinal turn-milling operations can be reduced by increasing the feed. Although the simultaneous rotation of the tool and workpiece in turn-milling could negatively affect the surface quality, this operation provides the advantage of an interrupted cutting mechanism that produces discontinuous chips. Also, the wear of the endmill in longitudinal and face turn-milling operations is lower than the wear of the inserts used in conventional longitudinal and face turning. Using Response Surface Methodology (RSM), mathematical models were developed for surface roughness and tool wear in each operation. The RSM models developed in this study achieved coefficients of determination (R²) above 90%, with prediction errors below 7% for surface roughness and below 3% for tool wear. The analysis of variance (ANOVA) revealed that the feed and cutting speed are the most influential parameters on the surface roughness and tool wear, respectively, with p-value < 0.05. The experimental results demonstrated that tool wear in turn-milling was reduced by up to 50% compared to conventional turning. Full article

Metaheuristic algorithms are widely utilized as effective tools for solving complex optimization problems. Among them, the Harmony Search (HS) algorithm has garnered significant attention for its simple structure and excellent performance. The efficacy of the HS algorithm is heavily dependent on the configuration of its internal parameters, with the Harmony Memory Considering Rate (HMCR) and Pitch Adjusting Rate (PAR) playing pivotal roles. These parameters determine the probabilities of using the Random Generation (RG), Harmony Memory Consideration (HMC), and Pitch Adjustment (PA) operators, thereby controlling the balance between exploration and exploitation. However, a systematic empirical analysis of the interaction between these parameters and the characteristics of the problem at hand remains insufficient. Thus, this study conducts a comprehensive empirical analysis of the performance sensitivity of the HS algorithm to variations in HMCR and PAR values. The analysis is performed on a suite of 23 benchmark functions, encompassing diverse characteristics such as unimodality/multimodality and separability/non-separability, along with 5 real-world optimization problems. Through extensive experimentation, the performance for each parameter combination was evaluated on a rank-based system and visualized using heatmaps. The results experimentally demonstrate that the algorithm’s performance is most sensitive to the HMCR value across all function types, establishing that setting a high HMCR value (≥0.9) is a prerequisite for securing stable performance. Conversely, the optimal PAR value showed a direct correlation with the topographical features of the problem landscape. For unimodal problems, a low PAR value between 0.1 and 0.3 was more effective, whereas for complex multimodal problems with numerous local optima, a relatively higher PAR value between 0.3 and 0.5 proved more efficient in searching for the global optimum. This research provides a guideline into the parameter settings of the HS algorithm and contributes to enhancing its practical applicability by proposing a systematic parameter tuning strategy based on problem characteristics. Full article

We refine a bottom-up, quantity-clearing framework of Bitcoin price formation that couples its fixed 21-million-coin cap with plausible demand growth and execution behavior. This approach relies on first-principles economic supply-and-demand dynamics rather than assumptions about anticipated Bitcoin price appreciation, its price history, or its potential effectiveness in demonetizing other asset classes. We considered five key high-level factors that may affect price determination: level of market demand; intertemporal investment preferences; fiat-denominated withdrawal sensitivity; initial liquid supply; and daily withdrawal levels from liquid supply. With a goal of both increasing understanding of the impacts of price drivers and developing probabilistic forecasts, we show two models: (1) a baseline to assess the impacts of parameter changes, alone and in combination, on Bitcoin price trajectories and liquid supply over time and (2) a Monte Carlo simulation that incorporates uncertainty across a range of uncertain parameterizations and presents probabilistic price and liquid supply forecasts to 2036. Our baseline model highlighted the importance of liquid supply and withdrawal sensitivity in price impacts. The Monte Carlo simulation results suggest a 50% likelihood that Bitcoin price will exceed USD 5.17 M by April 2036. Generally, prices from the low single millions to the low tens of millions per Bitcoin by 2036 emerge under broad parameter sets; hyperbolic paths to higher price levels are relatively rare and concentrate when liquid supply falls near or below BTC 2 M and withdrawal sensitivity is low. Our results help locate where right-tail risk and disorderly market outcomes concentrate and suggest that policy tools are available to help guide trajectories. Full article

Nucleosides are of significant interest to biomedical and pharmaceutical research and have been successfully separated in hydrophilic interaction liquid chromatography (HILIC). However, there have been few studies focusing on the retention mechanisms, and detailed retention mechanisms are not clearly understood. The quantitative assessment methodology based on the linear relationship between the observed retention factors and the phase ratio has been shown to be a new tool to investigate the retention mechanisms of polar compounds in HILIC. This study evaluated the retention mechanisms of 16 nucleosides on a bare silica column. The retention contributions by partitioning, adsorption, and electrostatic attractions are quantitatively determined, and the main retention mechanism can be unambiguously identified for each nucleoside. The study results indicate that the main retention mechanism can shift with the salt concentration in the mobile phase, but partitioning seems to dominate at higher salt concentrations. In addition, the partitioning coefficients are measured using the quantitative assessment methodology and have a relatively strong correlation with the log P values of the nucleosides. Considering large errors in the log P values for these very polar compounds, the partitioning coefficients measured experimentally in the HILIC system may provide a more accurate measure for polarity assessment. Full article