Search Results (1,309)

Middle school is a critical period for science education, yet the collective impact of socialization agents on students’ longitudinal science learning trajectories and subsequent STEM careers remains underexplored. This study investigates how seventh-grade (typically aged 12–13) socialization agents—parental educational encouragement, peer academic support, constructive school learning environment, and student self-esteem—collectively shape the developmental growth trajectories of science performance throughout middle school and predict the attainment of a college STEM degree and later engagement in STEM professions in adulthood. Using five-wave longitudinal data from the Longitudinal Study of American Youth (LSAY, N = 3116), we employed latent growth curve modeling (LGCM) to analyze these relationships. Results indicated that all four grade-7 socialization agents significantly predicted a higher initial level of science achievement. In addition, parental encouragement and a constructive school learning environment also predicted a positive growth rate of science achievement. Furthermore, both the initial level and growth of science performance significantly predicted successful graduation with a STEM degree. These middle school science trajectories, along with obtaining a STEM degree, sequentially mediated the relationships between the grade-7 socialization agents and adult STEM career engagement. The findings underscore the necessity of educational policies and interventions that foster a synergistic pro-learning socialization context in middle school to bolster students’ science education and pave the way for long-term STEM success. Full article

This study proposes a hybrid machine learning framework that integrates structured financial indicators and unstructured textual strategy disclosures to improve firm-level management performance prediction. Using corporate business reports from South Korean listed firms, strategic text was extracted and categorized under the Balanced Scorecard (BSC) framework into financial, customer, internal process, and learning and growth dimensions. Various machine learning and deep learning models—including k-nearest neighbors (KNNs), support vector machine (SVM), light gradient boosting machine (LightGBM), convolutional neural network (CNN), long short-term memory (LSTM), autoencoder, and transformer—were evaluated, with results showing that the inclusion of strategic textual data significantly enhanced prediction accuracy, precision, recall, area under the curve (AUC), and F1-score. Among individual models, the transformer architecture demonstrated superior performance in extracting context-rich semantic features. A soft-voting ensemble model combining autoencoder, LSTM, and transformer achieved the best overall performance, leading in accuracy and AUC, while the best single deep learning model (transformer) obtained a marginally higher F1 score, confirming the value of hybrid learning. Furthermore, analysis revealed that customer-oriented strategy disclosures were the most predictive among BSC dimensions. These findings highlight the value of integrating financial and narrative data using advanced NLP and artificial intelligence (AI) techniques to develop interpretable and robust corporate performance forecasting models. In addition, we operationalize information security narratives using a reproducible cybersecurity lexicon and derive security disclosure intensity and weight share features that are jointly evaluated with BSC-based strategic vectors. Full article

Measuring the fatigue crack growth rate via the crack growth experiment (a-N curve) is labor-intensive and time-consuming. A machine learning interpolation–extrapolation strategy (MLIES) aimed at enhancing the prediction accuracy of small-data models has been proposed to accelerate fatigue testing. Two specific approaches are designed by transforming a-N curve data from N to ΔN and from a to Δa (S1)/Δa/ΔN (S2) to enrich the data volume and leverage the incremental information. Thus, a simple and fast-responding single-layer neural network model can be trained based on the early-stage data points from fatigue testing and accurately predict the remaining part of an a-N curve, thereby enhancing the experimental efficiency. Through exponential data expansion and data augmentation, the trained neural network model is able to learn the underlying rules governing crack growth directly from the experimental data, requiring no explicit analytical crack growth laws. The proposed MLIES was validated on fatigue tests for aluminum alloy and titanium alloy samples under different experimental parameters. Results demonstrate its effectiveness in reducing testing time/cost by 15–32% while achieving over 30% higher prediction accuracy for the a-N curve compared to a traditional machine learning modeling approach. Our research offers a data-driven recipe for accurate crack growth prediction and accelerated fatigue testing. Full article

Previous research showed that a strain of Leuconostoc mesenteroides, isolated from goat’s raw milk cheese, was effective in slowing down the growth and reducing the maximum concentration of L. monocytogenes when evaluated in a milk model; furthermore, the extent of inhibition was dependent on the milk’s initial pH. The objectives of this study were as follows: (1) to determine whether the growth of L. monocytogenes in goat’s pasteurized milk cheese during maturation could be approximated from growth data obtained in the milk model medium, either in monoculture or in coculture with L. mesenteroides, and if so, (2) to model a milk-to-cheese conversion factor (Cf) for L. monocytogenes growth rate. Challenge tests were conducted by inoculating L. monocytogenes in monoculture and in coculture with L. mesenteroides in goat’s pasteurized milk adjusted at initial pH levels of 5.5, 6.0, and 6.5. The process of cheesemaking continued, and cheeses were ripened at 12 °C for 12 days. Each experimental growth curve was adjusted to a pH-driven dynamic model where the microbial maximum growth rate is a function of pH. As observed in the milk model medium, in coculture with L. mesenteroides, the optimum growth rate (μ_opt) of L. monocytogenes in maturing cheese was affected by the initial pH of milk: the lowest rate of 0.863 ± 0.042 day⁻¹ was obtained at the initial pH 5.5, in comparison to 1.239 ± 0.208 and 1.038 ± 0.308 day⁻¹ at pH 6.0 and 6.5, respectively. Regardless of the milk’s initial pH, L. mesenteroides did not reduce the maximum load of L. monocytogenes in maturing cheeses, as it did in the milk medium. On the contrary, at the milk’s initial pH of 5.5, 6.0, and 6.5, L. mesenteroides was able to decrease, on average, 2.2-fold, 1.5-fold, and 1.9-fold the μ_opt of L. monocytogenes in both milk medium and cheese, without significant differences between matrices. Following such validation in goat’s cheese, the square root of milk-to-cheese Cf for L. monocytogenes was estimated as 0.751 (SE = 0.0108), and the type of culture (monoculture and coculture) was not found to affect Cf (p = 0.320). In conclusion, this work validated the pre-acidification of milk as an efficient strategy that, when combined with the use of a protective culture, can synergically enhance the control of L. monocytogenes in cheese. Full article

Taiwan has set an ambitious target of net-zero carbon emissions by 2050, relying heavily on offshore wind capacity of 13.1 GW by 2030 and 40–55 GW by 2050. Floating offshore wind (FOW) is expected to play a central role in meeting these targets, particularly in deep-water areas where fixed-bottom technology is technically constrained. This study combined S-curve modeling for capacity projections, learning curves for cost estimation, and input–output analysis to quantify economic and environmental impacts under three deployment scenarios. Our findings indicate that FOW development provides substantial economic benefits, particularly under the high-growth scenario. During the construction phase through 2040, total output is projected to exceed NTD 1.97 trillion, generating more than NTD 1 trillion in gross value added (GVA) and over 470,000 full-time equivalent (FTE) jobs. By 2050, operations and maintenance (O&M) output is expected to reach approximately NTD 50 billion, supporting roughly 14,200 jobs and about NTD 13.8 billion in income. Annual CO₂ reduction could reach up to 10.4 Mt by 2050 under the high-growth scenario, or about 6.86 Mt under the low-growth case, demonstrating the potential of FOW to drive industrial development while advancing national decarbonization. Full article

This study evaluates whether an AI-built DeFi website case can match professionally developed DeFi platforms in digital marketing performance, innovation-related strategic behavior, and entrepreneurial impact. Using a multi-method design, we compare five established DeFi websites (Aave, Lido, Curve, MakerDAO, Uniswap) against one AI-built interface (Nexus Protocol). The analysis is designed as a five-platform benchmarking study of established professional DeFi websites, complemented by one AI-built case (Nexus Protocol) used as an illustrative comparison rather than a representative class of AI-built interface. The objectives are to (i) test differences in traffic composition and acquisition strategies, (ii) quantify how engagement signals predict authority and branded traffic, (iii) examine cognitive processing and trust-cue attention via eye tracking, and (iv) model emergent engagement and authority dynamics using agent-based simulation (ABM). Web analytics (March–October 2025) show significant variation in traffic composition across professional platforms (ANOVA F = 3.41, p = 0.0205), while regression models indicate that time on site and pages per visit positively predict Authority Score (R² = 0.61) and Branded Traffic (R² = 0.55), with bounce rate exerting an adverse effect. PCA and k-means clustering identify three strategic archetypes (innovation-driven, balanced-growth, efficiency-focused). Eye-tracking results show that professional interfaces generate tighter fixation clusters and shorter scan paths, indicating higher cognitive efficiency. In contrast, fixation on key UI elements and trust cues is comparable across interface types. ABM outputs further suggest that reduced engagement depth in the AI-built interface yields weaker long-run branded-traffic and authority trajectories. Overall, the study provides an integrated evaluation framework and evidence-based implications for AI-driven interface design in high-trust fintech environments. Full article

With the advancement in deep-sea resource development, the creep behavior of deep-sea remolded sediments under coupled temperature, confining pressure (σ₃), and stress effects has become a critical issue threatening engineering stability. The traditional Singh–Mitchell model, limited by its neglect of temperature effects and prediction of infinite strain, struggles to meet deep-sea environmental requirements. Based on low-temperature, high-pressure triaxial tests (with temperatures ranging from 4 to 40 °C and confining pressures ranging from 100 to 300 kPa), this study proposes a modified model incorporating temperature–stress–time coupling. The model introduces a hyperbolic creep strain rate decay function to achieve strain convergence, establishes a saturated strain–stress exponential relationship, and quantifies the effect of temperature on characteristic time via coupling through the Arrhenius equation. The modified model demonstrates R² values > 0.96 for full-condition creep curves. The results show several key findings: a 10 °C increase in temperature leads to a 30–50% growth in the steady-state creep rate; a 100 kPa increase in confining pressure enhances long-term strength by 20–30%. 20 °C serves as a critical temperature point. At this point, strain amplification reaches 2.1 times that of low-temperature ranges. These experimental findings provide crucial theoretical foundations and technical support for incorporating soil creep effects in deep-sea engineering design. Full article

Studies of swordfish growth provide essential biological parameters for stock assessment and fisheries management, informing both conventional population models and the evaluation of different management strategies. The present study aims to provide insight into the dynamics of the South Atlantic Ocean stock growth patterns. The sampling is the most complete to date in the literature, with a wide geographical distribution and in every month of the year. The analysis included 788 anal fins. Biometric relationships between different anal fin spine measurements and fish size were found. Some variation in the size of annulus one and vascularisation hiding some internal bands was found in larger specimens. Marginal increment ratio (MIR) and edge type analyses showed an annual band formation in the austral winter (July to September), thereby confirming the hypothesis of one annulus formation per year. Growth parameters were calculated using different growth models. The Gompertz model yielded the most reliable parameters (L_∞ = 341 cm LJFL, k = 0.13 yr⁻¹, T = 2.83 yr). The tagging and recapture data corroborated the selected model. Results were compared with other growth curves published. Full article

The parameter extraction of proton exchange membrane fuel cells (PEMFCs) has been an active area of study over the past few years, relying on metaheuristic optimizers and experimental datasets to achieve accurate current/voltage (I/V) curves. This work develops a mirage search optimizer (MSO) to precisely estimate the PEMFC model parameters. The MSO employs two search techniques based on the physical phenomena of light bending caused by atmospheric refractive index gradients: a superior mirage for global exploration and an inferior mirage for local exploitation. The MSO employs optical physics to direct search behavior, in contrast to conventional optimization approaches, allowing for a dynamic balance between exploration and exploitation. Convergence efficiency is increased by its iteration-dependent control and fitness-based influence. Using two common PEMFC modules, a comparison study with previously published methodologies and new, recently developed optimizers—the Educational Competition Optimizer (ECO), basketball team optimization (BTO), the fungal growth optimizer (FGO), and the naked mole rat optimizer (NMRO)—was conducted to evaluate the proposed MSO for parameter identification. Furthermore, the two models were tested under various temperatures and pressures. For the three examples studied, the MSO achieved the best sum of squared errors (SSE) values with an intriguing overall standard deviation (STD). It is undeniable that the STD and cropped SSE values, among other difficult techniques, are quite competitive and display the fastest convergence. According to the MSO, the BCS 500W, Ballard Mark V, and Modular SR-12 each have MSO values of 0.011697781, 0.852056, and 1.42098181379214 × 10⁻⁴, respectively. Additionally, the comparison results demonstrate that the proposed MSO can be successfully used to quickly and accurately define the PEMFC model. Full article

Under the influence of engineering disturbances, the loading rate of surrounding rock is in a state of continuous adjustment. This study conducts experimental investigations on the mechanical response characteristics under different strain rates (10⁻⁵ s⁻¹, 10⁻⁴ s⁻¹, and 10⁻³ s⁻¹). During the uniaxial loading process of coal–rock composite specimens, multi-parameter monitoring was implemented, and a systematic study was carried out on the ring-down count induced by microcracks, the energy values of acoustic emission (AE) events, the stage-dependent strain characteristics on the specimen surface, and the surface temperature variation characteristics. Additionally, the stress–strain curve characteristics under different strain rates were comparatively analyzed in stages. The loading process of the coal–rock composite specimens was reproduced using the Particle Flow Code (PFC3D 6.0) simulation software. The simulation results indicate that the stress–strain results obtained from the simulation are in good agreement with the laboratory test results; based on these simulation results, the energy accumulation and dissipation characteristics of the coal–rock composite specimens under the influence of strain rate were revealed. Furthermore, a microscopic damage model considering strain rate was constructed based on the Weibull probability statistics theory. The results show that strain rate has a significant impact on the strength, elastic modulus, and failure mode of the coal–rock composite specimens. At low strain rates, the specimens exhibit obvious progressive failure characteristics and strain localization phenomena, while at higher strain rates, they show brittle sudden failure characteristics. Meanwhile, the thermal imaging results reveal that at high strain rates, the overall temperature rise in the composite specimens is rapid, whereas at low strain rates, the overall temperature rise is slow—but the temperature rise in the coal portion is faster than that in the rock portion. The peak temperature at high strain rates is approximately 2 °C higher than that at low strain rates. The PFC simulation results demonstrate that the larger the strain rate, the faster the growth rate of plastic energy in the post-peak stage and the faster the release rate of elastic energy. Full article

Background and Objectives: Contrast-enhanced mammography (CEM) provides both morphological and functional information and may reflect breast cancer biology similarly to Magnetic Resonance Imaging (MRI). Materials and Methods: This single-center retrospective study included 399 women with Breast Imaging Reporting and Data System (BI-RADS) category 0 screening mammograms who subsequently underwent CEM. A total of 76 malignant lesions (68 invasive cancers, 8 ductal carcinoma in situ (DCIS)) with complete imaging and pathology data were analyzed. Invasive cancers were classified into luminal A, luminal B, luminal B/Human Epidermal Growth Factor Receptor 2 (HER2)-positive, HER2-enriched, and triple-negative, and grouped as luminal (Group 1) versus HER2-positive/triple-negative (Group 2). Results: Luminal subtypes predominated (47 of 68, 69%), while 21 of 68 (31%) were HER2-positive or triple-negative. Most cancers appeared as masses with spiculated margins and heterogeneous enhancement. Significant differences were observed in mass shape (p = 0.03) and internal enhancement (p = 0.01). Luminal tumors were more often irregular and spiculated with heterogeneous enhancement, whereas the HER2-positive/triple-negative tumors more frequently appeared round with rim or homogeneous enhancement. Deep learning-derived malignancy scores (iCAD ProFound AI^®) demonstrated good diagnostic performance (area under the curve (AUC) = 0.744, 95% confidence interval (CI) 0.654–0.821, p < 0.001). The median AI score was significantly higher in malignant compared with benign lesions (70% [interquartile range (IQR) 47–93] vs. 38% [IQR 25–61]; Mann–Whitney U test, p < 0.001). Among malignant lesions, iCAD scores varied across molecular subtypes, with higher median values observed in Group 1 versus Group 2 (87% vs. 55%), although the difference was not statistically significant (Mann–Whitney U test, p = 0.35). Conclusions: CEM features mirrored subtype-specific phenotypes previously described with MRI, supporting its role as a practical tool for enhanced tumor characterization. Although certain imaging and AI-derived parameters differed descriptively across subtypes, no statistically significant differences were observed. As deep-learning models continue to evolve, the integration of AI-enhanced CEM into clinical workflows holds strong potential to improve lesion characterization and risk stratification in personalized breast cancer diagnostics. Full article

Flood hazards in arid oil-producing regions result from both natural hydrological processes and terrain changes due to land subsidence. In the Yibal field in northern Oman, long-term hydrocarbon extraction has caused measurable ground deformation, altering surface gradients and drainage patterns. This study presents a deformation-adjusted flood hazard assessment by integrating a 2013 photogrammetric DEM with a 2023 subsidence-corrected DEM derived from multi-temporal PS-InSAR observations (RADARSAT-2 and TerraSAR-X). Key hydrological indicators—including slope, drainage networks, Height Above Nearest Drainage (HAND), floodplain depth, Curve Number, and extreme precipitation from the wettest monthly rainfall in a 10-year archive—were recalculated for both years. Flood hazard maps for 2013 and 2023 were generated using an AHP-based multi-criteria framework across five hydrologically motivated scenarios. Results indicate that while the total area of high- and very-high-hazard zones changed only slightly in most scenarios (within ±6%), these zones shifted into subsidence-affected depressions, reflecting deformation-driven redistribution of flood-prone areas. Low-hazard zones grew most significantly, especially in Scenarios S2–S4, with increases of 160–320% compared to 2013, while moderate-hazard areas showed smaller but consistent growth. Floodplain-dominated conditions (S5) produced the most pronounced nonlinear response, with a substantial increase in very low hazard and localized concentration of very high hazard in areas of deepest subsidence. Geomorphic analysis using the Geomorphic Flood Index (GFI) shows deepening of flow pathways and expansion of geomorphic depressions between 2013 and 2023, supporting the modeled redistribution of hazards. These findings demonstrate that even moderate subsidence can significantly alter hydrological susceptibility and underscore the importance of incorporating deformation-adjusted terrain modeling into flood hazard assessments in petroleum fields and other subsidence-prone areas. Full article

Under the dual pressures of global climate change and anthropogenic activities, enhancing the ecological functions of hydraulic structures has become a critical direction for sustainable watershed management. While traditional spur dike designs primarily focus on bank protection and flood control, current demands require additional consideration of river ecosystem restoration. Numerical simulations were performed using the RNG k-ε turbulence model to solve the three-dimensional Reynolds-averaged Navier–Stokes equations, a formulation that enhances prediction accuracy for complex flows in curved channels, including separation and reattachment. Following a grid independence study and the application of standard wall functions for near-wall treatment, a comparative analysis was conducted to examine the flow characteristics and ecological effects within a 180° channel bend under three configurations: no spur dikes, a single-side arrangement, and a staggered arrangement of non-submerged, flow-aligned, rectangular thin-walled spur dikes. The results demonstrate that staggered spur dikes significantly reduce the lateral water surface gradient by concentrating the main flow, thereby balancing water levels along the concave and convex banks and suppressing lateral channel migration. Their synergistic flow-contracting effect enhances the kinetic energy of the main flow and generates multi-scale turbulent vortices, which not only increase sediment transport capacity in the main channel but also create diverse habitat conditions. Specifically, the bed shear stress in the central channel region reached 2.3 times the natural level. Flow separation near the dike heads generated a high-velocity zone, elevating velocity and turbulent kinetic energy by factors of 2.3 and 6.8, respectively. This shift promoted bed sediment coarsening and consequently increased scour resistance. In contrast, the low-shear wake zones behind the dikes, with weakened hydrodynamic forces, facilitated fine-sediment deposition and the growth of point bars. Furthermore, this study identifies a critical interface (observed at approximately 60% of the water depth) that serves as a key interface for vertical energy conversion. Below this height, turbulence intensity intermittently increases, whereas above it, energy dissipates markedly. This critical elevation, controlled by both the spur dike configuration and flow conditions, embodies the transition mechanism of kinetic energy from the mean flow to turbulent motions. These findings provide a theoretical basis and engineering reference for optimizing eco-friendly spur dike designs in meandering rivers. Full article

Objective: To investigate the impact of proteinuria severity on obstetric and neonatal outcomes and to assess the predictive value of 24 h urinary protein excretion, both alone and within a multivariable model, for adverse pregnancy outcomes. Methods: This retrospective cohort study included 203 pregnant women with proteinuria who were classified into mild (≥0.3 g/day and <3.0 g/day, n = 50), severe (≥3.0 g/day and <5.0 g/day, n = 67), and massive (≥5.0 g/day; n = 86) groups based on 24 h urine protein levels. Maternal and neonatal outcomes were compared between these groups. Correlation analysis, receiver operating characteristic (ROC) curve analysis, and multivariable logistic regression were used to evaluate the predictive value of proteinuria for obstetric complications and identification of increased risk of early delivery. The AUC values of the proteinuria-only model and the multivariable model were compared using the DeLong test, as both models were derived from the same dataset and therefore represented correlated ROC curves. Results: The incidence of obstetric complications was significantly higher in the severe (68.7%) and massive (81.4%) proteinuria groups compared with the mild group (32.0%; p < 0.001). Increasing proteinuria severity was associated with earlier gestational age at delivery, lower birth weight, and higher rates of fetal growth restriction (all p < 0.001). The 24 h proteinuria level demonstrated moderate predictive ability for obstetric complications (AUC 0.73; 95% CI 0.66–0.80). A multivariable model including nephrotic-range proteinuria (≥3 g/day) and gestational age at diagnosis showed improved discriminatory performance compared with proteinuria alone (AUC 0.81; 95% CI 0.75–0.88). The model based on continuous 24 h proteinuria yielded an AUC of 0.73 (95% CI, 0.66–0.80) for identifying pregnancies at increased risk of obstetric complications. The multivariable model showed a numerically higher AUC of 0.81 (95% CI, 0.73–0.86); however, the difference between the two AUCs was not statistically significant according to the DeLong test (z = 0.82, p = 0.41). Conclusions: The severity of maternal proteinuria is associated with a higher likelihood of adverse maternal and neonatal outcomes, and higher proteinuria levels appear to show a graded association with increasing risk. A multivariable model integrating proteinuria with key clinical parameters demonstrated moderate discriminatory ability for obstetric complications, may support a more holistic approach to risk stratification in clinical practice. Full article

As cities continue to expand, the role of forests in mitigating carbon emissions during urban growth has become a critical concern for both researchers and policymakers. This study constructs a comprehensive framework to assess new-type urbanization and forest health, calculates relevant metrics, and applies the Environmental Kuznets Curve model to examine how contemporary urbanization affects carbon emissions while accounting for the moderating role of forest quality. The results indicate that the impact of urbanization on carbon emissions generally follows an inverted U-shaped pattern, although significant regional variations exist. Forest quality has not yet fully realized its potential in reducing carbon footprints, largely due to the need for overall improvement in the forestry sector. In terms of how urbanization affects forest quality, traditional factors such as population migration and industrial restructuring remain the primary drivers. There is a discernible tension between conventional urban expansion and sustainable forestry development. Although modern urbanization and forest quality show promising synergies, both are constrained by their current developmental stages, which limits their effectiveness in substantially curbing carbon emissions. Full article