Search Results (84)

Higher education is increasingly expected to cultivate the sustainability competences described by the European GreenComp framework, yet empirical evidence linking specific pedagogies to competence-specific change remains limited. This study examined whether participation in “Sibiu Impact Makers” (SIM), an interdisciplinary, Design Thinking-based challenge module, was associated with changes in students’ self-reported GreenComp sustainability competences and LifeComp transversal competences. Using a one-group pre-test/post-test design, 113 undergraduates at Lucian Blaga University of Sibiu (Romania) completed a 45-item questionnaire operationalising GreenComp and the complementary LifeComp framework at the beginning and end of the semester; responses were matched through anonymous identifiers. Composite scales showed good-to-excellent reliability (Cronbach’s α = 0.70–0.95). Overall GreenComp scores increased with a small effect (Wilcoxon z = −2.47, p = 0.014, dz = 0.21), with the clearest change in “embracing complexity”; critical evaluation of sustainability information and problem framing remained significant after multiplicity correction. LifeComp competences, already high at baseline, remained stable. Exploratory subgroup analyses did not detect statistically significant differences in change across gender, faculty, study year and employment. Because the study lacked a control or comparison group, the observed pre–post changes cannot be attributed causally to the module. The study contributes a competence-specific GreenComp evaluation of an existing interdisciplinary Design Thinking module, highlighting that, within this module, short-term changes were more evident in analytical than in value-oriented sustainability competences. Full article

Background: The demand for natural gas and advancements in LNG technologies have made it easier and more cost-effective to transport large quantities of natural gas over long distances by cooling it and then delivering it to customers according to an annual delivery plan (ADP). This plan is crucial for the LNG supply chain to minimize operational costs and meet contractual obligations; however, there are random factors that deviate from the ADP’s initial plan, such as production rate interruptions, weather conditions, and a reduction in the number of berths. Methods: Available optimization methods for ADPs in the literature were reviewed using a systematic literature review (SLR) and validated with the PRISMA 2020 Statement checklist and flow diagram, which showed that many existing studies use simplified deterministic models to optimize ADPs within reasonable time and effort. This research suggests establishing a simulation-based optimization (sim-heuristics) framework to develop ADPs with an objective function that minimizes expected total costs while capturing real-life uncertainties. This approach will provide tactical insights to decision-makers and is adaptable for various operational policy evaluations. Conclusions: This simulation-based optimization approach could offer the LNG industry a powerful analytical engine for robust, real-world decision-making, bridging the gap between theoretical planning and operational complexity. Full article

Astrobiological observations usually return atmospheric, mineralogical, molecular, isotopic, or textural states rather than organisms. The interval between environmental habitability and confirmed life detection is therefore an evidential problem. This article develops planetary geochemical metabolism (PGM) as a scale-explicit description of abiotic water–rock–fluid reactions, including atmospheric or ice-shell boundary conditions where relevant, that sustain redox disequilibria, catalytic mineral interfaces, prebiotic molecular fluxes, and preservable mineral products. Proto-biosignatures are defined as contextual, non-diagnostic signatures of this interval: signals that do not demonstrate life, but increase the plausibility of prebiotic network organization relative to low-organization abiotic chemistry. A nondimensional Geochemical Metabolic Potential, ${\Phi }_{\mathrm{PGM}}$, is formalized as a target-specific screening index describing redox exergy, catalytic-interface density, reaction-network closure, environmental cycling efficacy, and preservation potential. The index is not calibrated as a probability of life. A reproducible Latin-hypercube experiment across $M_{\mathrm{sim}}=5000$ synthetic environments examines internal model behavior rather than ranking planets. Higher ${\Phi }_{\mathrm{PGM}}$ values mark hypotheses to be tested under declared scale, uncertainty, and observability constraints. Applications to early Earth, Noachian Mars, ocean worlds, and terrestrial exoplanets illustrate that habitability, prebiotic organization, biological inference, and preservation are distinct quantities. Full article

Despite the Chinese government’s efforts to promote social integration, migrants continue to face considerable obstacles in adapting to the urban life. These obstacles arise not only from the constraints of the hukou system, but also from the difficulties in sociocultural adaptation. While the literature on social integration is extensive, there remains a lack of a comprehensive and cohesive framework for assessing social integration of migrants in China. This study addresses this gap by developing the Social Integration of Migrants Scale (SIMS), an instrument designed to capture the multifaceted nature of migrant integration in urban China. Using a sample of 600 migrants from Shenzhen, China, the SIMS was tested for reliability, factorial validity, convergent validity, and discriminant validity. The results demonstrate the strong internal consistency of the scale. Factor analyses identified six key dimensions of integration: economic integration, acculturation, perceived discrimination, social relationships, civic engagement, and identity integration. The findings provided initial evidence of convergent and discriminant validity based on the scale’s internal structure. The SIMS thus offers a promising multidimensional instrument for assessing the integration of migrants within Chinese urban contexts, with external criterion validation identified as a priority for future research. Full article

Wide-bandgap (WBG) power converters impose stringent requirements, including high-frequency switching, strongly nonlinear dynamics, and limited computational time, which constrain conventional control and artificial intelligence (AI)-based approaches and hinder their practical deployment. Existing review studies have primarily focused on algorithmic structures or performance, while systematic analyses from a deployment feasibility perspective under hardware constraints remain limited. This paper examines AI applications in WBG power converters from a system-level deployment perspective and analyzes existing studies based on implementation feasibility. After outlining the physical characteristics and control requirements of WBG devices, it reviews AI-based modeling, AI-assisted model predictive control (MPC), and reinforcement learning (RL)-based direct control. These approaches are evaluated in terms of computational complexity, real-time feasibility, out-of-distribution (OOD) generalization, and integration with conventional control frameworks. Key deployment challenges, including safety-constrained RL, sim-to-real transfer, and field-programmable gate array (FPGA)/embedded implementation, are treated as core analytical dimensions. To support this assessment, this review introduces an AI Deployment Readiness framework organized around four analytical dimensions: (1) modeling accuracy, (2) safety assurance, (3) sim-to-real transfer capability, and (4) hardware implementability. Using this framework, prior studies are reassessed, and its applicability is further discussed for applications such as fault diagnosis and remaining useful life (RUL) prediction. The analysis identifies key bottlenecks and clarifies deployment-relevant considerations for high-frequency WBG systems. Full article

This study aimed to evaluate the effects of high-intensity ultrasound (40 W/5 min), applied with and without mild heating (59 °C and 23 °C), and of pasteurization (63 °C/30 min), on the physicochemical, rheological, and microbiological parameters, as well as on ascorbate oxidase activity, total carotenoid content, phenolic compound profile, and antioxidant capacity of passion fruit (Passiflora edulis Sims.) pulps. Ultrasound processing induced changes in color (L*, a*, and b*), resulting in high ∆E values. Following ultrasound treatment, an increase in apparent viscosity at 100 s⁻¹ was observed. Ultrasound also promoted partial inactivation of ascorbate oxidase and a significant reduction in mold and yeast counts. Moreover, the application of ultrasound without heating (US-20) promoted the retention of 55% of ascorbic acid after 63 days of storage. The condition with heating (US-60) led to an increase in catechin content in both bright red passion fruit pulp (173.96%) and yellow passion fruit pulp (5.89%), demonstrating a balance between the retention of bioactive compounds, microbial inactivation, and reduction in ascorbate oxidase activity. Therefore, these results highlight ultrasound as a non-thermal and sustainable technology capable of extending shelf life, maximizing the preservation of bioactive compounds, and enhancing the functional properties of fruit pulps. Full article

Electro-hydrostatic actuators (EHAs) are increasingly deployed in modern aircraft due to their compact size, fast response, and high power-to-weight ratio. However, existing airborne QAR and EICAS data are typically recorded as independent parameters without explicit correspondence to system health states, making degradation assessment and remaining useful life (RUL) prediction challenging. To address this issue, this paper proposes a spatiotemporal degradation modeling framework, termed PreDyn-ST, based on multivariate time series (MTS) data. The method integrates SimCLR-based contrastive pretraining and a dynamic feature fusion mechanism to capture evolving temporal dependencies and spatial sensor correlations. Specifically, graph convolutional networks (GCNs) incorporating physical connectivity priors are employed for spatial modeling, while a Transformer extracts long-range temporal patterns. A learnable dynamic weighting mechanism adaptively balances spatial and temporal features during training. The adaptive behavior is further analyzed using correlation statistical index (CSI) curves for interpretability. Experimental validation on a self-developed EHA degradation test bench and the C-MAPSS benchmark dataset demonstrates that PreDyn-ST achieves competitive and stable prediction performance. In particular, the method shows robust performance under complex operating conditions such as FD004. These results indicate the effectiveness of the proposed framework for accurate and interpretable degradation modeling in aerospace applications. Full article

The long-term creep performance of geosynthetics is crucial for the safe design of reinforced-soil structures. Previous studies have not sufficiently clarified the long-term creep behavior of high-density polyethylene (HDPE) geogrids or the influence of different failure criteria. Therefore, further research is needed to validate creep reduction factors’ (RF_CR) estimation and the applicability of the stepped isothermal method (SIM). In this study, the creep behavior of HDPE geogrids was examined using both conventional creep tests and SIM, conducted in accordance with ISO 13431 and ASTM D6992. Master curves were generated under load levels representing 40–60% of the ultimate tensile strength. The SIM results matched with the conventional tests in the early stage but exhibited higher creep strains beyond 1000 h, primarily due to the thermal sensitivity of HDPE. RF_CR values were determined using two design criteria, namely, 20% creep strain and creep rupture. For a 100-year design life, the RF_CR values based on a 20% creep strain were determined to be 3.04 and 2.43 based on the combined data and block-shift analysis, respectively, whereas the rupture criterion yielded a lower value of 2.30. These findings demonstrate that the 20% strain limit provides a more conservative and reliable criterion for estimating the long-term design strength. This study confirms the applicability of SIM for accelerated creep evaluation and provides practical guidance for the selection of RF_CR values in reinforced-soil design. Full article

Lithium iron phosphate (LiFePO₄, LFP) batteries dominate grid-scale energy storage, yet their cycle life is capped by its capacity fade issues. Conventional failure workflows suffer from redundant tests, high cost, and long turnaround time because the underlying mechanisms remain unclear. Herein, multi-scale characterization coupled with electrochemical tests have been quantitatively established to reveal four synergistic fade modes of LFP: active-Li loss, FePO₄ secondary-phase formation, SEI rupture, and particle fracture. A two-tier “screen–validate” protocol is proposed to accurately and efficiently disclose its mechanism. In the screening tier, capacity, cyclic voltammetry, electrochemical impedance spectroscopy, low-magnification scanning electron microscopy, and snapshot X-ray diffraction (XRD) rapidly flag the most probable failure cause. The validation tier then deploys mechanism-matched in situ/ex situ tools (operando XRD, TEM, XPS, ToF-SIMS, etc.) to build a comprehensive evidence chain of dynamic structural evolution, materials loss tracking, and quantitative proof. The streamlined workflow preserves scientific rigor and reproducibility while cutting analysis time and cost, offering a closed-loop route for fast failure diagnosis and targeted optimization of next-generation LFP batteries. Full article

For humanoid robots to interact naturally with humans and seamlessly integrate into daily life, natural language serves as an essential communication medium. While recent advances in imitation learning have enabled robots to acquire complex motions through expert demonstration, traditional approaches often rely on rigid task specifications or single-modal inputs, limiting their ability to interpret high-level semantic instructions (e.g., natural language commands) or dynamically switch between actions. Directly translating natural language into executable control commands remains a significant challenge. To address this, we propose ToggleMimic, an end-to-end imitation learning framework that generates robotic motions from textual instructions, enabling language-driven multi-task control. In contrast to end-to-end methods that struggle with generalization or single-action models that lack flexibility, our ToggleMimic framework uniquely combines the following: (1) a two-stage policy distillation that efficiently bridges the sim-to-real gap, (2) a lightweight cross-attention mechanism for interpretable text-to-action mapping, and (3) a gating network that enhances robustness to linguistic variations. Extensive simulation and real-world experiments demonstrate the framework’s effectiveness, generalization capability, and robust text-guided control performance. This work establishes an efficient, interpretable, and scalable learning paradigm for cross-modal semantic-driven autonomous robot control. Full article

Accurate estimation of the Remaining Useful Life (RUL) of lithium-ion batteries is essential for enhancing the reliability and efficiency of energy storage systems. This study explores custom deep learning models to predict RUL using a dataset from the Hawaii Natural Energy Institute (HNEI). Three approaches are investigated: an Encoder-only Transformer model, its enhancement with SimSiam transfer learning, and a CNN–Encoder hybrid model. These models leverage advanced mechanisms such as multi-head attention, robust feedforward networks, and self-supervised learning to capture complex degradation patterns in the data. Rigorous preprocessing and optimisation ensure optimal performance, reducing key metrics such as mean squared error (MSE) and mean absolute error (MAE). Experimental results demonstrated that Transformer–CNN with Noise Augmentation outperforms other methods, highlighting its potential for battery health monitoring and predictive maintenance. Full article

This study introduces a novel methodology for estimating loading and fatigue damage in the blades of wind turbines, emphasizing non-Gaussian wind conditions’ impact. By calculating blade loading and fatigue using higher statistical moments of the irregular winds, the study demonstrates the significance of non-Gaussian effects on loading and fatigue predictions. A two-step methodology is developed to synthesize non-Gaussian wind processes, integrating the TurbSim (version 1.5) and Hermite moment model transformation methods. These wind time histories are then utilized in a fully coupled simulation of a floating wind turbine, integrating with a blade beam model. Preliminary analysis of wind thrust and the blade root bending moment indicates non-Gaussian effects on aerodynamic loading. Further analysis of fatigue reveals that fatigue hot spots vary along the blade surface, depending on short-term wind conditions and long-term wind distribution, with total fatigue life estimated by summing the fatigue damage at each potential hot spot. The probability density function of long-term wind process is estimated by fitting the Weibull distribution to measured buoy data. The results show that variations in long-term wind speed distributions lead to an average fatigue life difference of about 1.3 years (16%). The Gaussian wind model overestimates fatigue life by roughly 1.5 years (18%) compared to the non-Gaussian model. This highlights the importance of considering both long-term wind distributions and short-term wind characteristics for accurate fatigue assessment. The findings provide valuable insights for the design and operation of floating offshore wind turbines. Full article

Background: While most Escherichia coli strains are harmless members of the gastrointestinal microbiota, certain pathogenic variants can cause severe intestinal and extraintestinal diseases. A notable outbreak of E. coli O104:H4, involving both enteroaggregative (EAEC) and enterohemorrhagic (EHEC) strains, occurred in Europe, resulting in symptoms ranging from bloody diarrhea to life-threatening colitis and hemolytic uremic syndrome (HUS). Since treatment options remain limited and have changed little over the past 40 years, there is an urgent need for an effective vaccine. Such a vaccine would offer major public health and economic benefits by preventing severe infections and reducing outbreak-related costs. A multiepitope vaccine approach, enabled by advances in immunoinformatics, offers a promising strategy for targeting HUS-causing E. coli (O104:H4 and O157:H7 serotypes) with minimal disruption to normal microbiota. This study aimed to design an immunogenic multiepitope vaccine (MEV) construct using bioinformatics and immunoinformatic tools. Methods and Results: Comparative proteomic analysis identified 672 proteins unique to E. coli O104:H4, excluding proteins shared with the nonpathogenic E. coli K-12-MG1655 strain and those shorter than 100 amino acids. Subcellular localization (P-SORTb) identified 17 extracellular or outer membrane proteins. Four proteins were selected as vaccine candidates based on transmembrane domains (TMHMM), antigenicity (VaxiJen), and conservation among EHEC strains. Epitope prediction revealed ten B-cell, four cytotoxic T-cell, and three helper T-cell epitopes. Four MEVs with different adjuvants were designed and assessed for solubility, stability, and antigenicity. Structural refinement (GALAXY) and docking studies confirmed strong interaction with Toll-Like Receptor 4 (TLR4). In silico immune simulations (C-ImmSim) indicated robust humoral and cellular immune responses. In Conclusions, the proposed MEV construct demonstrated promising immunogenicity and warrants further validation in experimental models. Full article

This study aims to estimate the carbon footprint and relative uncertainties for design components of conventional and geopolymer concrete. All the design components of alkaline-activated geopolymer concrete, such as fly ash, ground granulated blast furnace slag, sodium hydroxide (NaOH), sodium silicate (Na₂SiO₃), superplasticizer, and others, are assessed to reflect the actual scenarios of the carbon footprint. The conjugate application of the life cycle assessment (LCA) tool SimPro 9.4 and @RISK Monte Carlo simulation justifies the variations in carbon emissions rather than a specific determined value for concrete binders, precursors, and filler materials. A reduction of 43% in carbon emissions has been observed by replacing cement with alkali-activated binders. However, the associative uncertainties of chemical admixtures reveal that even a slight increase may cause significant environmental damage rather than its benefit. Pearson correlations of carbon footprint with three admixtures, namely sodium silicate (r = 0.80), sodium hydroxide (r = 0.52), and superplasticizer (r = 0.19), indicate that the shift from cement to alkaline activation needs additional precaution for excessive use. Therefore, a suitable method of manufacturing chemical activators utilizing renewable energy sources may ensure long-term sustainability. Full article

SUMOylation plays a crucial role in regulating gene expression by promoting interactions between transcription factors and corepressors. Daxx, a multifunctional scaffold protein, specifically recognizes and binds SUMOylated transcription factors through its SUMO-interacting motifs (SIMs), acting as a transcriptional corepressor. In this review, we systematically elucidate the structural basis of the interaction between Daxx and SUMO, revealing the synergistic mechanism by which Daxx SIM phosphorylation and SUMO acetylation dynamically regulate Daxx function. In promyelocytic leukemia nuclear bodies (PML NBs), phosphorylation of Daxx’s SIM enhances its binding to SUMOylated PML, leading to the sequestration and inactivation of Daxx within PML NBs. Conversely, SUMO acetylation disrupts the electrostatic interactions between SUMO and SIMs, prompting the release of Daxx from PML NBs and its translocation to the nucleoplasm, where it inhibits the activity of transcription factors such as ETS1, GR, and SMAD4. Daxx SIMs are common binding sites for the interaction between SUMOylated transcription factors and Daxx, and different SUMOylated transcription factors may compete to bind to Daxx, which cross-regulates cellular life activities. This mechanism highlights the dynamic regulation of Daxx subcellular localization and transcriptional repression by SUMO and PML NBs, providing valuable insights into understanding Daxx-mediated transcriptional repression. Full article