Search Results (9,851)

Engineering education for sustainable development plays a critical role in preparing future engineers to address complex sustainability challenges aligned with the United Nations Sustainable Development Goals (SDGs). Higher education institutions contribute to this agenda through both teaching and research, yet the relationship between sustainability-oriented research and curriculum development remains insufficiently explored. This study addresses this gap by developing the Nakad model, a practical approach for examining the alignment between sustainability-oriented course Learning Outcomes (LOs) and faculty members’ research output using SDG mapping. The civil and environmental engineering department and the chemical engineering department were chosen as their research output increasingly contributes to sustainability-oriented knowledge. An exploratory document-based analysis was conducted, combining deductive qualitative content analysis of course syllabi with quantitative descriptive analysis of SDG frequencies of faculty members’ research output. Curriculum LOs were manually mapped to the SDGs and their targets, while faculty research output was mapped using the Scopus SDG classification. The results demonstrated differences in the correspondence between sustainability-oriented research and curriculum LOs across the two departments. In the chemical engineering department, the substantial engagement of faculty members in sustainability-related research was not reflected in the LOs. However, the civil and environmental engineering department demonstrated a more explicit and consistent alignment of sustainability in research and teaching, facilitated by the flexibility of elective courses to incorporate sustainability themes. The analysis also identified a shared gap in the representation of social sustainability across both departments. The Nakad model contributes to the research–teaching nexus by providing a diagnostic approach for identifying strengths, gaps and opportunities to strengthen sustainability integration in engineering education. Full article

Incorporating waste tyre rubber into cement composites could pose a sustainable strategy for reducing tyre waste, whilst simultaneously preserving natural resources. Accurate prediction of the compressive strength is essential for the efficient development and application of the new material. This study presents the experimental research and a new proposed machine learning modelling framework aimed at predicting the compressive strength of conventional and rubber-modified cement composites containing recycled rubber derived from end-of-life vehicle tyres. Experimental datasets from compressive strength tests on 48 concrete and 40 mortar specimens were used to develop predictive models, with varying rubber content. The empirical results show that increasing the percentage of waste rubber in the mixture designs negatively affects compressive strength, reducing it up to 96.06% for mortars, up to 64.4% for concrete made with ordinary Portland cement, and up to 60.7% for concrete made with sulfate-resistant cement. Three soft computing techniques were implemented using the DTREG predictive modelling software: the General Regression Neural Network (GRNN), Radial Basis Function Neural Network (RBFNN), and Support Vector Machine (SVM). The conventional linear regression (LR) model was also implemented and compared with these three machine learning models. They were trained using the experimentally measured results for the various mixtures. The model performance was evaluated using the standard estimators: the coefficient of determination (R²), correlation coefficient (r), and mean absolute percentage error (MAPE). The GRNN demonstrated the best predictive abilities for predicting the strength of mortars, with MAPE = 4.6% and R² = 99.27% on the original dataset; on the normalized dataset, MAPE was 3.9%, and R² was 99.65. For predicting the strength of concrete, GRNN also outperformed the other models on the original dataset with MAPE = 3.8% and R² = 98.4%. The rubber content was identified as the most influential parameter affecting the compressive strength, while density, admixture conditions, the water-cement ratio, and curing age also contributed to the model’s performance. The LR model presented the lowest predictive accuracy among all evaluated algorithms (models) because of the significant complex nonlinear interactions between predictors and target, which can not be adequately modelled with a linear framework. Within the investigated experimental domain, the developed models showed high predictive accuracy and can be used for preliminary strength estimation and interpolation for mixtures with similar characteristics. Full article

We present observations of the ionospheric response to the severe geomagnetic storm of 19–21 January 2026, obtained using a Digisonde DPS-4D and GPS-based vertical Total Electron Content (TEC) measurements over Almaty, Kazakhstan. Solar wind parameters, geomagnetic indices (AE and SYM-H), and Interplanetary Magnetic Field (IMF) components were analyzed to characterize the storm drivers. The ionospheric response to the geomagnetic storm of 12–13 November 2025 was additionally analyzed to provide an overall picture of the ionospheric effects within the specified latitude–longitude region. The evolution of the ionospheric disturbances revealed highly complex processes. The critical frequency and total electron content during the severe geomagnetic storms exhibited morphological similarities, reflecting a general response to geomagnetic storms, as well as notable differences in their storm-time responses and in the observed percentage deviations of foF2 and TEC relative to reference-day baselines. A combined observational approach provides a more complete picture of the ionospheric response during extreme space weather events. Full article

Characterizing methane (CH₄) adsorption behavior in marine shale reservoirs is of great significance for assessing geological natural gas reserves and elucidating adsorption mechanisms within complex pore systems. In this study, supercritical high-pressure CH₄ adsorption experiments were conducted on Lower Cambrian organic-rich marine Shuijingtuo shales under reservoir-relevant pressure and temperature conditions (30–90 °C and up to 32 MPa). The measured excess isotherms were analyzed using the Polanyi theory-derived Dubinin-Astakhov (D-A) model, which incorporates a pseudo-saturation pressure correction for supercritical conditions and accounts for the adsorbed phase density. The D-A model yielded excellent fits (R² = 0.976–0.990) and temperature-independent characteristic curves for all marine shale samples, confirming its suitability for supercritical CH₄ adsorption in heterogeneous pore systems. A strong positive correlation (R² = 0.87) was observed between total organic carbon (TOC) content and adsorption capacity. This relationship is attributable to the abundant nanoscale organic pores developed within the organic matter, which increase the micropore volume and BET surface area, thereby improving the gas uptake potential. Thermodynamic analysis incorporating real gas behavior and adsorbed phase volume reveals that simplified assumptions (assuming an ideal gas or negligible adsorbed phase volume) systematically overestimate the isosteric heat of adsorption, with this deviation being particularly pronounced at high surface coverages. The model-derived isosteric heat decreases with increasing surface coverage across all shale samples, an observation that is highly consistent with the preferential occupation of high-energy sites within a highly heterogeneous marine nanopore system. The theoretical framework of the isosteric heat of adsorption provided in this study is suitable for other gas–solid adsorption systems and establishes a foundation for future research on other thermodynamic analyses such as the adsorbed phase enthalpy and adsorbed phase specific heat capacity. Full article

Soil properties, fertilization, tillage, and straw management practices vary widely in the Mollisol region of Northeast China. Such multivariate complexity masks the individual and interactive effects of these factors on maize yield and limits the ability to define high -yield ranges or effective practice combinations. To address this issue, we applied four interpretable machine learning algorithms (XGBoost (XGB), random forest, extra trees, and multi-layer perceptron) to predict the effects of soil properties, fertilization types, tillage types, and cropping systems on maize yield and reveal the complex relationship between the factors affecting maize yield. Results showed that the XGB model provided the best prediction accuracy with R² (0.8562). N fertilizer, P fertilizer, soil organic carbon (SOC), pH, and available nitrogen (AN) were the important factors affecting maize yield. The high-predicted-yield ranges were identified as N fertilizer 180–220 kg/hm², P fertilizer 40–80 kg/hm², SOC content of 12–18 g/kg, pH of 6.5–7.5, and AN content of 120–150 mg/kg. Higher maize yields were associated with combined chemical fertilizer and straw return, deep tillage, and monocropping. Under these optimal conditions, maize yields reached 10,000–12,000 kg/hm². Key interactive effects included moderate SOC content in combination with chemical fertilizer and straw return or deep tillage, moderate N and P fertilization application, and AN exceeding 120 mg/kg together with available phosphorus above 40 mg/kg, collectively contributing to higher maize yield. These findings provide a practical, evidence-based reference for precision soil management aimed at stabilizing yield and improving soil fertility in the Mollisol region. Full article

Understanding how visual structure shapes attentional allocation is central to models of perceptual processing. Less is known about how formal properties like harmony and complexity shape exploration independent of salience or semantic content. This study examined how controlled structural variations relate to attention, facial engagement, and subjective judgment using eye-tracking and webcam-based facial coding. Participants ($N=40$) viewed stimuli derived from a common geometric base, manipulated into three conditions: high harmony (symmetrical, low complexity), high complexity (asymmetrical, disorganized), and structured complexity (high complexity with underlying order). Eye movements and facial expressions were recorded during free viewing. Metrics included time to first fixation, fixation duration, number of fixations, scanpath entropy, spatial dispersion, and facial-coding indices (neutral, happy, and surprise expression, and the ambient/focal coefficient K). None of the eye-tracking or facial-coding metrics differed significantly across conditions; given that the study was powered to detect only medium-to-large effects, this indicates no detectable difference under the present webcam-based, brief-exposure design rather than evidence that visual structure has no effect on attention. Subjective complexity ratings differed robustly across conditions, surviving correction for multiple comparisons: unexpectedly, the high-complexity condition was rated as less complex than the harmony and structured-complexity conditions, indicating the intended manipulation did not translate into perceived complexity as designed. A nominally significant difference in pleasantness ratings did not survive this correction. Using repeated-measures correlation to account for the non-independence of within-participant observations, scanpath entropy showed a nominal negative association with pleasantness that did not survive correction for multiple comparisons, and the coefficient K showed a weaker and partly inconsistent pattern of association with independent oculomotor indices than initial uncorrected analyses suggested. These findings indicate that, in the present study, formal visual structure shaped subjective complexity judgments more robustly than it shaped overt attentional or facial-affective engagement, a pattern consistent with—though not conclusive proof of—a broader dissociation between evaluative and attentional responses reported in face perception and developmental aesthetics research. Beyond this substantive finding, we report in detail how accounting for repeated-measures non-independence and applying an explicit multiplicity strategy changed our statistical conclusions, offering a worked methodological example for similarly structured webcam-based eye-tracking and facial-coding studies. Full article

Background/Objectives: Multimodal large language models (LLMs) can interpret clinical text and images, but their performance in pediatric rash assessment remains uncertain. This study compared the clinical utility, safety, information quality, diagnostic correctness, and readability of ChatGPT, Gemini and Grok. Methods: Fifteen content-validated pediatric rash vignettes with brief histories and anonymized photographs were submitted once to each platform using a standardized zero-shot prompt. Three pediatricians blinded to platform identity independently rated the 45 responses using a five-point Clinical Utility and Safety (CUS) scale and a five-item modified DISCERN instrument. Diagnostic correctness was assessed descriptively; platform comparisons used Friedman tests with Bonferroni-adjusted Wilcoxon tests when appropriate. Results: Overall, 82.2% of CUS ratings were in categories 4–5 and 83.0% of modified DISCERN scores were ≥20/25; no rating was assigned to CUS category 1. Gemini and Grok had descriptively higher expert ratings than ChatGPT, but CUS did not differ significantly across platforms (p = 0.157), and although modified DISCERN differed globally (p = 0.038), no pairwise comparison remained significant after adjustment. In the single-query diagnostic assessment, at least one platform missed the reference diagnosis in 9/15 vignettes, and all three missed porphyria. Gemini generated the longest responses, whereas Grok produced the most linguistically complex text; neither response length nor readability was associated with expert ratings. Conclusions: The three multimodal LLMs produced predominantly clinically acceptable responses, but performance varied by vignette and platform. Because each vignette–platform combination was sampled once, diagnostic findings represent single-response observations rather than stable platform accuracy estimates. Clinical verification remains necessary. Full article

Accurate estimation of winter wheat canopy nitrogen concentration (CNC) supports crop diagnosis and precision nitrogen management, yet the relative value and redundancy of multispectral, structural, and chlorophyll-related variables remain unclear in small dat asets. Using 155 multi-temporal observations retained from the 2016–2017 and 2017–2018 growing seasons, seven prespecified feature combinations were evaluated with four traditional regression models under five-fold repeated season-stratified cross-validation. A stricter season-balanced, unit-level grouped five-fold cross-validation was added to prevent observations from the same field from occurring in both training and test partitions. Two fully connected neural networks were additionally assessed for the selected compact module. Spectral-only combinations yielded negative mean R² values, whereas LAI, vegetation cover, and chlorophyll content achieved a mean R² of 0.684. Combining these variables with four raw multispectral bands produced M5, which achieved mean RMSE, R², and RPD values of 0.328, 0.782, and 2.147, respectively, with 36.4% fewer variables than the full module. RF provided the best numerical performance under repeated sample-level cross-validation (R² = 0.796 ± 0.007), whereas M5 retained R² values of 0.736–0.778 under unit-grouped validation, with SVR performing best in that stricter setting. Parameter-removal analysis showed the largest incremental contribution for vegetation cover and limited additional value from LAI. Bidirectional cross-season validation remained direction- and model-dependent. Overall, controlled low-redundancy feature fusion was more beneficial than increased model complexity, while field-level and cross-season tests indicated that the strong within-dataset results should not be interpreted as broad generalization capability. Full article

Common carp (Cyprinus carpio) is an important freshwater aquaculture species, yet non-additive genetic effects remain poorly understood for economically important traits. Here, we dissected epistatic interactions affecting standard body length (SL) and fillet fat content (FC) by integrating pathway-level epistasis analysis, genomic selection (GS), and AlphaFold2 (AF2)-based structure prediction. BridGE analysis detected extensive epistatic signals for both traits. Incorporating selected interactions into GS models improved predictive ability within the sampled population, yielding correlations of 0.85 for SL and 0.87 for FC. An epistasis-informed reduction strategy downsized the initial 2.2 million SNPs to 12,902 candidate markers while retaining useful predictive information. AF2-based predictions provided complementary structural support for a subset of candidate driver interactions. Network and enrichment analyses further revealed distinct molecular features associated with SL and FC. Together, these findings suggest that incorporating epistatic information can improve genomic predictive ability of complex traits in common carp, and provide a basis for further evaluation of epistasis-informed selection in independent aquaculture populations. Full article

Mid-infrared nulling interferometry enables thermal characterization of warm exoplanets, but non-common path aberrations (NCPA) degrade starlight suppression by creating complex amplitude mismatch between interferometer arms. We investigate how architecture and NCPA spatial structure jointly determine null leakage and stability at $\mathsf{\lambda }=10.6 \mathsf{\mu }\mathrm{m}$. A unified statistical framework combines free-space propagation, a Houizot chalcogenide fiber, and a Labadie-type waveguide with extended Sauvage-type NCPA screens and adaptive optics (AO) residuals in nested Monte Carlo simulations. The mean raw null is governed mainly by the combined aberration amplitude of the two arms and depends only weakly on its allocation between them. AO residuals set leakage floors that depend on the reception configuration. Cases with similar mean raw nulls can still have different dispersions. Compared with free space, the single-mode spatial filters lower the mean leakage and reduce fluctuations, and they are less sensitive to the allocation of amplitude error between the arms. Their performance depends on modal selectivity: larger energy fractions in azimuthally symmetric radial modes are associated with poorer mean nulls and greater AO-driven fluctuations. These results show that NCPA tolerances cannot be specified by global wavefront error amplitude alone. They should also account for aberration spatial content and the modal projection imposed by the transmission architecture. Full article

Background/Objectives: Interprofessional collaboration (IPC) between physicians and registered nurses (RNs) is essential for managing increasingly complex patient care. Although IPC is associated with improved coordination and patient outcomes, hierarchical dynamics and organisational constraints may limit RNs’ meaningful participation. This study explored physicians’ perceptions of RNs’ professional roles and contributions within IPC in hospital settings. Methods: This qualitative descriptive study used nine semi-structured focus group interviews with 42 physicians from medical units across three university hospitals in the Capital Region of Denmark, recruited through convenience sampling. Interviews focused on everyday collaborative practices, including interprofessional meetings and ward rounds. Data were analysed using inductive qualitative content analysis. Results: Physicians’ accounts of IPC with RNs were captured in four themes: RNs’ contributions are central to IPC; Cultural conditions enabling IPC; Misaligned workflows and competing responsibilities shaping IPC; and Clinical partnership and expectations of support in IPC. Conclusions: From the participating physicians’ perspectives, IPC was shaped by RNs’ experience, relational culture, and organisational conditions. Physicians described relying particularly on RNs’ sharing of information and clinical assessments that they considered important for safe and coordinated care, which they perceived as reflective of RNs’ experience. However, they perceived organisational constraints as limiting the integration of nursing perspectives into decision-making. These findings indicate areas for further investigation and potential organisational development related to RNs’ participation in IPC. Full article

Injectable collagen biomaterials are used to modify the dermal extracellular matrix in aged skin; however, whether concentration-dependent rheological differences are associated with distinct fibroblast responses remain unclear. We compared 3% and 6% atelocollagen (AtCOL) and examined three mechanobiological programs: (i) integrin (ITG) α5β1–ERK–cyclin D1 signaling related to proliferation; (ii) ITGβ1–FAK–YAP signaling related to matrix synthesis; and (iii) ITGα11β1/Tensin-1–positive fibrillar adhesion related to collagen assembly. Under the tested oscillatory shear conditions, 6% AtCOL displayed higher storage modulus (G′), loss modulus (G″), and complex viscosity, together with a lower tan δ, than 3% AtCOL. In aged mouse skin, the 6% formulation was associated with greater increases in ITGα5, pERK1/2, cyclin D1, PCNA, pFAK, nuclear YAP, COL1A1, COL3A1, ITGα11β1/Tensin-1 co-expression signal, collagen type I/III ratio, collagen type I fiber bundle width, mature collagen content, dermal collagen density, and an instrument-derived skin elasticity index, while MMP1, MMP2, and MMP9 were reduced. In an H₂O₂-induced fibroblast senescence model, ITGβ1 knockdown attenuated AtCOL-associated proliferative, matrix-synthetic, and fibrillar-adhesion responses. Complementary ITGβ1 overexpression produced directionally concordant increases in ITGA11 expression, relative fibroblast proliferation, COL1A1, and COL3A1 and decreases in MMP1, MMP2, and MMP9. Collectively, these findings are consistent with a concentration-dependent, ITGβ1-centered mechanotransduction model linking fibroblast proliferation, matrix synthesis, and collagen assembly within a single AtCOL system. Full article

Reducing carbon emissions in concrete production while improving structural performance has become a priority in the transition to carbon neutrality. High-volume fly ash and slag systems significantly reduce carbon emissions. However, their complex hydration interactions alter strength development, rendering conventional empirical strength models inadequate for reliable low-carbon binder design. To overcome this limitation, this study proposes an integrated hydration-mechanism-based framework that links strength prediction with carbon-oriented binder optimization. A unified hydration model is developed based on the coupled evolution of capillary water and calcium hydroxide, enabling a consistent description of cement hydration, slag latent hydraulic reaction, and fly ash pozzolanic reaction in ternary binder systems. A single set of material-specific kinetic parameters is applied across the investigated mixture proportions and curing ages, without recalibration for each mixture. Building on the predicted degree of reaction, a compressive strength model centered on effective reaction contributions is calibrated using 1030 experimental data points encompassing 3–365 days and 2.33–82.60 MPa. Within the calibration database, the model reproduces early-age strength reduction, later-age compensation, and strength crossover behavior induced by mineral admixtures. The strength model is subsequently embedded into a genetic algorithm framework to perform theoretical binder-level low-carbon inverse design under 28-day strength and composition-domain constraints. The optimization results reveal boundary-dominated solutions, with mineral admixture replacement ratios approaching upper limits and a reduced water content contributing to further calculated emission reduction. Because aggregates, paste volume, superplasticizer dosage, and workability are not included, the resulting binder compositions are theoretical candidates rather than complete concrete mixture designs. This work presents a physically interpretable and optimization-ready framework for binder-level low-carbon design within the adopted model domain. Full article

Yttria-stabilized zirconia (YSZ) is a mechanically robust ceramic of interest for high-temperature microwave components and other applications requiring stable dielectric properties. However, reliable microwave permittivity data are difficult to establish due to strong dependence on composition, sample geometry, and the inherently limited sensitivity of broadband techniques for low-loss, high-permittivity dielectrics. This work presents the first application of the spherical dielectric resonator method, based on a recently formulated simplified electrodynamic model, to a non-magnetic dielectric material, extending the characterization across a broad temperature range. Spherical YSZ samples with 3.08 mol% Y₂O₃ content were characterized over the 5–17 GHz frequency range at room temperature and as a function of temperature from 20 °C to over 330 °C using dielectric resonator measurements in five different cylindrical cavities with a high electric energy filling factor in the sample ($p_e\approx 0.96$). Complex permittivity was extracted with a simplified equivalent spherical-enclosure model and cross-checked for a representative case using a radial mode-matching approach. At room temperature, the extracted real part of permittivity was consistent across the investigated band, while the dielectric loss tangent was on the order of ${10}^{-3}$ and showed a slight increase with frequency. Temperature measurements indicated that both real part of permittivity and dielectric loss tangent increase monotonically with temperature. The obtained results provide a consistent microwave dataset for polycrystalline YSZ. Full article

Arsenic contamination of drinking water threatens more than 100 million people worldwide, particularly in regions where decentralized, low-cost treatment solutions are urgently needed. Although biochar-based adsorbents have shown promise, the performance of seaweed-derived biochars—especially under environmentally relevant, low arsenic concentrations—remains poorly characterized. Here, we developed and evaluated a Fe-activated biochar derived from the kelp Macrocystis pyrifera for As(V) removal under drinking-water-relevant conditions. Fe activation with FeCl₂—rather than textural modification via KOH—was critical for introducing Fe–O surface complexes that governed chemisorption and drove high removal efficiency at low concentrations. The Fe-activated biochar exhibited a BET surface area of 22.23 m² g⁻¹, a point of zero charge near neutral pH (pH_PZC ≈ 6.36), and low ash content (<20%), all favoring arsenate adsorption. Adsorption kinetics were best described by the pseudo-second-order model (R² > 0.99), consistent with a strong contribution of surface chemical interactions under the tested conditions. Fe-activated biochar removed 53–67% of As(V) across initial concentrations of 0.01–0.20 mg L⁻¹, approaching an adsorption plateau within 12–20 h, positioning this sustainable, kelp-derived material as a promising candidate for point-of-use arsenic treatment in affected regions. Full article