Search Results (11,456)

In a large wind power base, it becomes unrealistic to rely only on synchronous generators to resist the uncertainty of wind power. A feasible way is to make wind turbines (WTs) and battery energy storage systems (BESSs) participate in frequency regulation. Taking into account the frequency regulation service of WTs and BESSs, the Coordinated Operation Strategy (COS) of the Wind–BESS–Thermal power model will become difficult to solve due to strong nonlinearity. To cope with this challenge, an improved Primary Frequency Regulation (PFR) model is first established considering the frequency regulation of WTs and BESSs. Based on the improved PFR model, the analytical expression of frequency stability constraints is deduced. Next, in view of the wind power uncertainty, the box-type ensemble robust optimization theory is introduced into the day-ahead optimal scheduling, and a robust COS model considering wind power uncertainty and frequency stability constraints is proposed. Then, a linear equivalent transformation method is designed, based on which the original COS model is transformed into a Mixed Integer Linear Programming (MILP) problem. Finally, a modified IEEE 39-bus system is adopted to test the effectiveness of the proposed method. Full article

Recycled water is commonly considered an environmentally friendly alternative water source for urban water systems, which can not only serve as a solution for water scarcity, but also reduce wastewater discharge from sewage systems. However, owing to the high degree of energy consumption during recycled water production, the utilization of recycled water may be detrimental to greenhouse gas emission reduction. In this work, we conduct a detailed investigation into greenhouse gas emissions from different sources in a typical multisource urban water system in China. Furthermore, we develop an optimization model for water resource allocation based on the rime optimization algorithm and regret theory. The results show that although greenhouse gas emissions from recycled water exceed those from other sources, their impact can be eliminated through rational water resource allocation. Specifically, compared with the original water resource allocation, the optimal results effectively reduce pollutant emissions by 7.6~11.1% without excessively increasing water resource shortages and greenhouse gas emissions. Additionally, both subjective preferences and recycled water utilization conditions have significant impacts on the optimization results, which should be carefully selected according to practical situations and technologies. Overall, the methods developed in this study provide a new general framework for the water resource allocation of multisource urban water systems in the context of greenhouse gas emission reduction and recycled water utilization, which can be employed in other areas. Full article

As new technologies shape education, helping students develop historical consciousness remains a challenge. Building on Nordic curricula that emphasize students as both “history-made” and “history-making” citizens, this paper proposes an approach that integrates artificial intelligence (AI) with implicit digital game-based learning (DGBL) to learn and develop historical consciousness in education. We outline how traditional, lecture-driven history teaching often fails to convey the abstract principles of historicity (the idea that individual identity, social institutions, values, and ways of thinking are historically conditioned) and the interpretation of the past, understanding of the present, and perspective on the future. Building on Jeismann’s definition of historical consciousness, we identify a gap between the theory-rich notions of historical consciousness and classroom practice, where many educators either do not recognize it or interpret it intuitively from the curriculum’s limited wording, leaving the concept generally absent from the classroom. We then examine three theory-based methods of enriching teaching and learning. Game-based learning provides an interactive environment in which students assume roles, make decisions, and observe consequences, experiencing historical consciousness instead of only reading about it. AI contributes personalized, adaptive content: branching narratives evolve based on individual choices, non-player characters respond dynamically, and analytics guide scaffolding. Implicit learning theory suggests that embedding core principles directly into gameplay allows students to internalize complex ideas without interrupting immersion; they learn by doing, not by explicit instruction. Finally, we propose a model in which these elements combine: (1) game mechanics and narrative embed principles of historical consciousness; (2) AI dynamically adjusts challenges, generates novel scenarios, and delivers feedback; (3) key concepts are embedded into the game narrative so that students absorb them implicitly; and (4) follow-up reflection activities transform tacit understanding into explicit knowledge. We conclude by outlining a research agenda that includes prototyping interactive environments, conducting longitudinal studies to assess students’ learning outcomes, and exploring transferability to other abstract concepts. By situating students within scenarios that explore historicity and temporal interplay, this approach seeks to transform history education into an immersive, reflective practice where students see themselves as history-made and history-making and view the world through a historical lens. Full article

This paper uses a statistical test based on entropy theory to propose a new way to distinguish between interdependence, contagion, and the decoupling hypotheses in the context of shock transmission and spillover. Applying the proposed approach, the three hypotheses are examined when measuring the extent of shock spillover between selected developed and emerging markets during idiosyncratic crisis and normal periods. The US and EU are identified as developed economies. However, emerging markets are classified by regions to determine whether their responses to shocks from developed economies are homogeneous or heterogeneous depending on the region to which they belong. The suggested entropy test is based on the conditional correlations obtained from an asymmetric dynamic conditional correlation generalized autoregressive conditional heteroscedasticity (A-DCC GARCH) model. In addition to economic methods, statistical methods based on the regime-switching technique are used to date the different phases of the global financial crisis (GFC) and the European sovereign debt crisis (ESDC). Our findings show that all emerging markets decoupled from developed economies in at least one of the phases of the two crises. These findings provide valuable insights for policymakers, investors, and asset managers for portfolio allocation and financial regulations. Full article

The rapid integration of generative AI, particularly ChatGPT, into academic settings has prompted urgent questions regarding its impact on students’ psychological and academic outcomes. Although generative AI holds considerable potential to transform educational practices, its effects on individual traits such as self-control and academic wellbeing remain insufficiently explored. This study addresses this gap through a sequential two-phase design. In the first phase, the ChatGPT Usage Scale was adapted and validated for a Turkish university student population (N = 413). Using confirmatory factor analysis and item response theory, the scale was confirmed as a psychometrically valid and reliable one-factor instrument. In the second phase, a separate sample (N = 449) was used to examine the relationships between ChatGPT usage, self-control, and academic wellbeing through a mediation model. The findings revealed that higher ChatGPT usage was significantly associated with lower levels of both self-control and academic wellbeing. Additionally, mediation analysis demonstrated that self-control partially mediates the negative relationship between ChatGPT usage and academic wellbeing. The study concludes that while generative AI tools are valuable, their integration into education presents a double-edged sword, highlighting the critical need to foster students’ self-regulatory skills to ensure they can harness these tools responsibly without compromising their academic and psychological health. Full article

Even though clinoptilolite mineral is the most important natural zeolite for technical applications, the molecular-level insights and detailed knowledge of their true local structures and adsorption behavior are largely lacking. An experimental determination of their surface structures, in particular, could be very challenging due to the sensitivity of some facets to temperature and impurities. In this study, we present a robust multiscale modeling framework to investigate the adsorption of 5-fluorouracil, an anticancer drug, on dispersion-corrected density functional theory (DFT-D3)-optimized Na-clinoptilolite surfaces. Using a combination of interface force field and polymer consistent force field-based molecular dynamics with simulated annealing and parallel replica sampling, followed by DFT-D3 optimizations, we explore a wide configurational space of surface–molecule interactions. Our results show that Na-clinoptilolite surfaces support very strong adsorption, with adsorption energies ranging from −430.0 to −174.4 kJ/mol. Surface models with exposed Na cations consistently exhibit stronger binding, in contrast to their known steric hindrance effects in bulk environments. Furthermore, cation-free surfaces displayed relatively weaker interactions, yet configurations exposing the 8-membered rings (8 MR) demonstrated more favorable adsorption than those exposing 10 MR channels due to enhanced hydrogen bonding and spatial and entropic confinement effects. These findings reveal the importance of surface composition, local geometry, and configurational sampling in determining adsorption performance and lay the groundwork for future studies on cation-specific and multicationic clinoptilolite systems. Full article

This study introduces a data obfuscation technique that leverages the exponential map of Lie-group generators. Originating from quantum machine learning frameworks, the method injects controlled noise into these generators, deliberately breaking symmetry and obscuring the source data while retaining predictive utility. Experiments on open medical datasets show that classifiers trained on obfuscated features match or slightly exceed the baseline accuracy obtained on raw data. This work demonstrates how Lie-group theory can advance privacy in sensitive domains by providing simultaneous data obfuscation and augmentation. Full article

In recent times, copper oxide nanosheets (CONSs) have shown a broad spectrum of industrial uses due to their unique properties, including high electrical conductivity, surface-enhanced catalytic activity, etc. Therefore, industrial processes involved in their manufacture can give rise to airborne particulates. Several in vivo studies have reported toxicity of these nanoparticles due to their interactions with biological molecules. Generally, literature-based assessment of their toxicity has centered on experimental findings. In this paper, we report for the first time, trend in CONSs interactions in intracellular and extracellular fluids, using the Nonlinear Mean Field Poisson–Boltzmann theory. Our theoretical prediction for zeta potential in the extracellular fluid environment align with published values in the literature. Based on this theoretical approach, we also demonstrate that double layer disjoining pressure due to interacting double layers of CONSs is generally higher in intracellular fluids. The findings of our theoretical approach highlight the importance of predicting the extent of cellular uptake potential of CONSs in organs that are prone to such airborne environmental particulates. Full article

This study investigates curves in a 7-dimensional space, represented by spatial generalized octonion-valued functions of a single variable, where the general octonions include real, split, semi, split semi, quasi, split quasi, and para octonions. We begin by constructing a new frame, referred to as the $G_2$-frame, for spatial generalized octonionic curves, and subsequently derive the corresponding derivative formulas. We also present the connection between the $G_2$-frame and the standard orthonormal basis of spatial generalized octonions. Moreover, we verify that Frenet–Serret formulas hold for spatial generalized octonionic curves. We establish the $G_2$-congruence of two spatial generalized octonionic curves and present the correspondence between the Frenet–Serret frame and the $G_2$-frame. A key advantage of the $G_2$-frame is that the associated frame equations involve lower-order derivatives. This method is both time-efficient and computationally efficient. To demonstrate the theory, we present an example of a unit-speed spatial generalized octonionic curve and compute its $G_2$-frame and invariants using MATLAB. Full article

With the increasing integration of renewable energy, virtual power plants (VPPs) have emerged as key market participants by aggregating distributed energy resources. However, their involvement in electricity markets is increasingly challenged by two major uncertainties: price volatility and the intermittency of renewable generation. This study presents the first application of Information Gap Decision Theory (IGDT) within a two-stage cooperative scheduling framework for VPPs. A novel bidding strategy model is proposed, incorporating both robust and opportunistic optimization methods to explicitly account for decision-making behaviors under different risk preferences. In the day-ahead stage, a risk-responsive bidding mechanism is designed to address price uncertainty. In the real-time stage, the coordinated dispatch of micro gas turbines, energy storage systems, and flexible loads is employed to minimize adjustment costs arising from wind and solar forecast deviations. A case study using spot market data from Shandong Province, China, shows that the proposed model not only achieves an effective balance between risk and return but also significantly improves renewable energy integration and system flexibility. This work introduces a new modeling paradigm and a practical optimization tool for precision trading under uncertainty, offering both theoretical and methodological contributions to the coordinated operation of flexible resources and the design of electricity market mechanisms. Full article

This study addresses engineering challenges associated with sandy residual deposits in the coastal zone of Quanzhou, China, characterized by high void ratios (e > 0.8), low cohesion (c < 10 kPa), and strong liquefaction tendencies induced by marine dynamic forces. Focusing on the beach sands of Shenhu Bay and Qingshan Bay, 123 in situ dynamic penetration tests and 12 laboratory physical–mechanical tests (including water content, particle gradation, relative density, and triaxial shear strength) were conducted. The correlations between the physical and mechanical properties of these coastal sandy soils and their foundation bearing capacity were systematically analyzed. Results reveal that the sands, predominantly medium-to-fine grains with 8–15% biogenic debris, are generally in a loose-to-medium dense state (relative density ~34%), with negligible cohesion. Shear strength depends primarily on the internal friction angle (28.89–37.43°). Correlation analyses show that water content (17.8–31.92%) and particle gradation parameters (uniformity coefficient C_u and curvature coefficient C_c) significantly influence bearing capacity, with bearing capacity increasing by 12.15% per 14.12% rise in water content and 35% per 0.518 increase in C_c. An improved foundation bearing capacity model based on the Prandtl–Reissner theory is proposed by integrating particle gradation and water content, tailored for beach foundations in Quanzhou. Model validation demonstrates an average error of approximately 15%, outperforming traditional models. These findings provide valuable theoretical support for assessing foundation stability in building construction projects in Quanzhou and similar coastal regions. Full article

Within biogeographic regions, local communities are structured mainly by abiotic (environmental) filtering, external resource supply, and biotic interactions. In recent years, we investigated abiotic filtering and external resource supply as drivers of the latitudinal distribution of rocky intertidal species along the Atlantic Canadian coast in Nova Scotia. Here, we evaluate biotic interactions between the main sessile species groups. Specifically, we studied abundance relationships between seaweeds and filter-feeding invertebrates and between barnacles and mussels using data collected at mid-to-high intertidal elevations at eight wave-exposed locations every summer from 2014 to 2017. We assessed such relationships for each location and year through generalized additive modeling (GAM). Of the 32 relationships evaluated for seaweeds vs. filter-feeders, 31% were significant and consistently negative, suggesting competitive interactions. For barnacles vs. mussels, 25% of the relationships were significant and mostly positive, consistent with facilitation of mussel colonization by barnacles in harsh environments. The variability explained by these models was moderate, however, between around 10% and 50%. Overall, these results suggest that interactions between the studied sessile species groups are infrequent and, when present, relatively weak in these highly stressful habitats, which supports current ecological theory on community organization. Full article

This study presents an optimized methodology for the placement of water film thickness sensors, integrating information theory with experimental validation. Initially, the two-dimensional shallow-water equations are employed to simulate the spatiotemporal evolution of water film thickness across the entire runway, providing a comprehensive foundational dataset. By applying information entropy theory, the total information content at each runway grid point is quantified. Analysis indicates that grid points with higher total information content generally correspond to regions of greater water film thickness. The optimal placement for a single sensor is determined by identifying the location that maximizes total information content, and its effectiveness is validated through controlled rain–fog experiments. The results demonstrate that positioning a single sensor at a site with higher water film thickness reduces the overall mean estimation error by 57%, thereby enhancing prediction accuracy. By extending the single-sensor placement framework, the total information content across all runway points is recalculated, and additional rain–fog experiments are conducted to verify the optimal locations. By incorporating a correlation coefficient–distance (C–D) model to define each sensor’s influence radius, a collaborative multi-sensor placement strategy is developed and implemented at Seletar Airport, Singapore. The findings show that sensor locations with higher water film thickness correspond to increased total information content, and that expanding the number of deployed sensors further improves estimation accuracy. Compared with conventional placement approaches, which rely on subjective judgment and long-term operational experience, the proposed method enhances estimation accuracy by over 23% when deploying two sensors. These results provide a robust basis for the strategic placement of runway water film thickness sensors and contribute to more precise assessments of pavement surface conditions. Full article

Core mathematics courses are fundamental to the academic success of engineering students in higher education. These courses equip students with skills and knowledge applicable to their specialized fields. However, first-year engineering students often face significant challenges in mathematics due to a range of factors, including insufficient preparation, mathematics anxiety, and difficulty connecting theoretical concepts to real-life applications. The transition from secondary to tertiary mathematics remains a key area of educational research, with ongoing discussions about effective pedagogical approaches for teaching engineering mathematics. This study utilized a belief survey to gain general insights into the attitudes of first-year mathematics students towards the subject. In addition, it employed the activity theory framework to conduct a deeper exploration of the experiences of first-year engineering students, aiming to identify contradictions, or “tensions,” encountered within a flipped-classroom learning environment. Quantitative data were collected using surveys that assessed students’ self-reported confidence, competence, and knowledge development. Results from Friedman’s and Wilcoxon’s Signed-Rank Tests, conducted with a sample of 20 participants in 10 flipped-classroom sessions, statistically showed significant improvements in all three areas. All of Friedman’s test statistics were above 50, with p-values below 0.05, indicating meaningful progress. Similarly, Wilcoxon’s Signed-Rank Test results supported these findings, with p values under 0.05, leading to the rejection of the null hypothesis. The qualitative data, derived from student questionnaire comments and one-to-one interviews, elucidated critical aspects of flipped-classroom delivery. The analysis revealed emerging contradictions (“tensions”) that trigger “expansive learning”. These tensions encompassed the following: student expectation–curriculum structure; traditional versus novel delivery systems; self-regulation and accountability; group learning pace versus interactive learning; and the interplay between motivation and anxiety. These tensions are vital for academic staff and stakeholders to consider when designing and delivering a first-year mathematics course. Understanding these dynamics can lead to more effective, responsive teaching practices and support student success during this crucial transition phase. Full article

Background: The human visual system is capable of processing large quantities of visual objects with varying levels of abstraction. The brain also exhibits hierarchical integration and learning capabilities that combine various attributes of visual objects (e.g., color, shape, local features, and categories) into coherent representations. However, prevailing theories in visual neuroscience employ simple stimuli or natural images with uncontrolled feature correlations, which constrains the systematic investigation of multidimensional representation dynamics. Methods: In this study, we aimed to bridge this methodological gap by developing a novel large tangram paradigm in visual cognition research and proposing cognitive-associative encoding as a mathematical basis. Critical representation dimensions—including animacy, abstraction level, and local feature density—were computed across a public dataset of over 900 tangrams, enabling the construction of a hierarchical model of visual representation. Results: Neural responses to 85 representative images were recorded using Electroencephalography (n = 24), and subsequent behavioral analyses and neural decoding revealed that distinct representational dimensions are independently encoded and dynamically expressed at different stages of cognitive processing. Furthermore, representational similarity analysis and temporal generalization analysis indicated that higher-order cognitive processes, such as “change of mind,” reflect the selective activation or suppression of local feature processing. Conclusions: These findings demonstrate that tangram stimuli, structured through cognitive-associative encoding, provide a generalizable computational framework for investigating the dynamic stages of human visual object cognition. Full article