Search Results (1,628)

Carbon capture and storage (CCS) is widely regarded as a viable pathway for reducing greenhouse gas emissions; however, large-scale deployment remains constrained by project economics and policy uncertainty. This study presents a life cycle costing assessment of a proposed CCS retrofit at the Hunter Unit 3 coal-fired power plant in Emery County, Utah, encompassing carbon capture, transport, and subsurface storage. Results indicate that the project appears economically favorable under the assumptions of this screening-level analysis and under current policy conditions, with an estimated break-even time of approximately five years. The analysis identifies a large upfront capital investment exceeding $600,000,000, offset by planned revenue from federal tax credits totaling several billion dollars over the project lifetime. Sensitivity analyses show that project economics are dominated by capture costs and annual mass of CO₂ sequestration rates, while storage and transport costs play secondary roles. A synthetic policy-perturbation analysis of the $85/ton tax credit further demonstrates that policy volatility materially increases uncertainty in investment returns. These results highlight both the economic potential of CCS retrofits at existing power plants and the critical role of stable long-term policy in enabling investment. Full article

Accurate demand forecasting with uncertainty quantification is critical for materials management in power grid enterprises, yet existing methods struggle to capture multi-scale temporal dynamics across heterogeneous material categories while providing reliable confidence estimates. This paper proposes an Adaptive Decomposition–Ensemble Modeling (ADEM) method that integrates adaptive CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) with category-specific depth selection, a heterogeneous ensemble of a GBM (Gradient Boosting Machine), ELM (Extreme Learning Machine), and SVR (Support Vector Regression) with per-component optimized weights, and Bayesian uncertainty quantification with conformal calibration for distribution-free coverage guarantees. Experiments on real-world data spanning 18 material categories over 60 months demonstrate that ADEM consistently outperforms 14 baselines spanning statistical, machine learning, deep learning, and decomposition-based methods in both point prediction accuracy and prediction interval quality. Rolling-origin evaluation across six temporal windows further exhibits the robustness and statistical significance of these improvements. Full article

The semiconductor industry has long relied on Statistical Process Control (SPC) for yield and reliability management. In early technology nodes, classic univariate tools such as Shewhart charts, cumulative sums (CUSUM), exponentially weighted moving averages (EWMA), and the Cp/Cpk exponent could effectively monitor a finite set of key variables. However, sub-5nm and emerging 3 nm technologies have fundamentally changed the statistical environment. Advanced patterning, high-aspect-ratio etching, atomic layer deposition (ALD), chemical-mechanical polishing (CMP), and novel materials have drastically narrowed the process window. At these scales, nanometer-level deviations in critical dimensions (CD), overlay, or surface roughness can significantly impact yield. Simultaneously, modern wafer fabs generate massive amounts of high-frequency sensor data and high-dimensional metrology data. Traditional SPC assumptions—such as independence, normality, low dimensionality, and stationarity—often do not hold. Semiconductor data exhibits: (i) extremely high-dimensionality and strong intervariate correlations; (ii) a hierarchical structure encompassing fab → tooling → chamber → recipe → batch → wafer → field; and (iii) metrological delays and sampling limitations leading to incomplete and asynchronous observations. To address these challenges, this paper reviews advanced statistical methods applicable to wafer fabrication. These methods include multivariate statistical process control (MSPC) approaches such as Hotelling T² statistics, PCA/PLS combining T² and Q statistics, contribution diagnostics, time-series drift and change point detection, and Bayesian hierarchical modeling for uncertainty-aware monitoring in data-limited scenarios. Furthermore, we discuss how to integrate these methods with fault detection and classification (FDC), line-to-line monitoring (R2R), advanced process control (APC), and manufacturing execution systems (MES). This paper focuses on scalable, interpretable, and maintainable implementations that transform statistical analysis from a passive monitoring tool into an active component of data-driven fab control. Full article

Novel carbon-storing construction materials have the potential to reduce greenhouse gas emissions by removing carbon dioxide from the atmosphere and storing it in long-lived building products. In order to understand the full benefits and shortcomings of carbon-storing materials, life cycle assessments (LCAs) must be performed. However, material innovators who are looking to perform LCAs of their products during early-stage research and development (R&D) face many challenges. While these challenges have been reported in the literature, this information has been fragmented and required a more comprehensive investigation. We explored these LCA challenges by holding an in-person workshop with sixteen R&D teams who were developing carbon-storing materials and building designs. From the data collected in this workshop, we found that the R&D teams struggled with data availability, biogenic carbon, and uncertainty, which confirmed our findings from the literature. They also struggled with various other LCA topics. Since current LCA tools lack functions that would be useful for this user group, we also proposed a list of tool ideas that could address their LCA needs, which can inform future LCA tool development. Full article

We propose a sequential design method aiming at the estimation of an extreme quantile based on a sample of binary data corresponding to peaks over a given threshold. This study is motivated by an industrial challenge in material reliability and consists of estimating a failure quantile from trials whose outcomes are reduced to indicators of whether the specimen has failed at the tested stress levels. The proposed approach relies on a splitting strategy that decomposes the target extreme probability into a product of higher-order conditional probabilities, enabling a progressive exploration of the tail of the distribution through sampling under truncated laws. We consider GEV and Weibull models for the underlying distribution, and the sequential estimation of their parameters is carried out using an enhanced maximum likelihood procedure specifically adapted to binary data, addressing the substantial uncertainty inherent to such limited information. Full article

The initiation of the caving process in block/panel caving is critical to the success of mines. However, there is no widely adopted methodology for modeling the onset of caving. This study proposes a methodology to model the initial stages of caving using the Discrete Element Method, in which the rock mass is represented using the Bonded Particle Model, and the undercut material is modeled with non-cohesive discrete particles. The collapse of the rock mass was replicated following a parameter calibration process, and the results were compared with actual mining data of the observed initial fragmentation. Key parameters were identified, such as allowable normal and shear stresses, which are essential to accurately represent the collapse of the rock mass and the evolution of the early stage of rock fragmentation. Low allowable stress values led to premature collapse and finer fragmentation, whereas higher values delayed cave back failure and resulted in coarser initial fragmentation. The results showed the formation of large rock fragments between 14 and 15 m during the initial cave back failures. Subsequently, larger fragments ranging from 2 to 9 m were observed detaching from the cave back as draw progressed, with sizes comparable to those reported in some block/panel caving operations. The main contribution of this work is a methodology that demonstrates the feasibility of modeling caving initiation, which is crucial in a context where increasing rock mass strength and deposit depth require design changes at the production level and pose significant uncertainty in the rock mass response. Full article

The construction industry is one of the primary global contributors to carbon emissions, with both construction materials and operational energy recognized as critical factors in achieving net-zero goals. Given that structural systems are embodied carbon-intensive, significant early-stage carbon reductions are possible. This paper introduces the dual-layer sustainable optimization framework (DLSOF), a methodology that integrates system-level substitution with span-level optimization and a single life-cycle assessment (LCA) approach focused on embodied carbon (EC) that is applicable to various construction types and climate regions. To validate DLSOF, two representative models of reinforced concrete buildings were selected for analysis: one focused on alternate structural systems and the other on span optimization for a standard slab configuration. The results indicate that, in most cases, span optimization achieves a reduction in embodied carbon of 33%, whilst system-level substitution, in most cases, achieves a reduction of approximately 30%. The dual-layer approach, in comparison to conventional baseline designs, achieves approximately a 52% reduction in embodied carbon. Uncertainty analysis indicates variability in design and data inputs, but the overall trend of embodied carbon reduction remains consistent. The results highlight the critical nature of the early structural design stage. For engineers, the DLSOF provides a practical design pathway, and it offers flexibility to accommodate diverse sustainability goals across varying geographical contexts. This study establishes a replicable and transferable model for low-carbon structural design by systematically integrating design optimization with embodied carbon assessment. Full article

Inverse scattering problems seek anomalies in a medium given data measured after the interaction with emitted waves. Due to noise, predictions about the nature of these inclusions should be complemented with uncertainty estimates. To this end, we propose a progressive framework for inverse scattering from low- to high-dimensional Bayesian formulations depending on the prior information and the problem complexity. We aim to reduce computational costs by exploiting educated prior information. When we look for a few well-separated inclusions in a known medium with information about their number, we resort to low-dimensional parameterizations in terms of a few random variables representing their shape and material constants. We test this approach detecting anomalies in tissues and deposits in stratified subsoils. In more complex situations where the anomalies may overlap, we propose high-dimensional parameterizations obtained from Karhunen–Loève (KL) or Fourier expansions of the density and velocity fields. We employ these methods to characterize oil and gas reservoirs in a salt dome configuration, where the screening effect of the dome cap prevents the obtention of adequate prior information. We characterize the posterior probability by means of affine invariant ensemble and functional ensemble MCMC samplers depending on dimensionality. This provides information on configurations with the highest a posteriori probability and the uncertainty around them, identifying factors that could reduce the uncertainty. In high-dimensional setups, techniques based on KL developments are more effective and stable. A recurring issue is the choice of the a priori covariance (which strongly affects the results) and the choice of its hyperparameters. Here, we use educated choices. Formulations that include them as additional parameters could be a next step at a higher cost. Full article

This qualitative study investigates the challenges teachers face when teaching English as a foreign language (EFL) to deaf (in this article, deaf (lower case) refers to the audiological condition of hearing loss, whereas Deaf (capitalized) is used to denote individuals who identify as members of the Deaf community and share a common sign language and distinct cultural values) and hard-of-hearing (DHH) students in German schools for the Deaf. The study is situated within a structural–theoretical professionalization framework, which focuses on the relationship between institutional conditions, teacher education structures, and professional action. Semi-structured interviews were conducted with 16 teachers of DHH students and the data were examined using qualitative content analysis. The findings reveal five central areas of challenge: (1) heterogeneity of the student body; (2) limited time (for preparing and adapting materials); (3) restricted subject-matter and sign-language competence, including missing links between EFL didactics and Deaf education in teacher training; (4) uncertainties surrounding the language design of EFL instruction, particularly the role of American Sign Language (ASL), German Sign Language (DGS), and written English; and (5) the lack of consistent, accessible exam formats and standards. Teachers report substantial insecurity due to the absence of coherent concepts, policy frameworks, and specialized training pathways, which fosters divergent classroom practices and tensions within teaching staff. The results highlight an urgent need for systematic integration of Deaf education, sign language training, and EFL pedagogy in teacher education, as well as for evidence-based guidelines on language classroom practice and assessment for DHH learners. Full article

To investigate the aging behavior of high-temperature-vulcanized silicone rubber (HTV-SiR) used in composite insulator sheds under coupled electrical, thermal, humidity, and mechanical stresses, accelerated aging tests were conducted to emulate the service conditions of ±800 kV ultra-high-voltage direct current (UHVDC) systems in Guangzhou, China. The physicochemical, mechanical, and electrical properties of the specimens were systematically characterized. The results show simultaneous degradation of both electrical and mechanical performance. In particular, the tensile strength exhibits a significant monotonic decrease and drops to 49.52% of its initial value under the most severe condition (0.5 kV·mm⁻¹ and 5% tensile strain) after 75 days. In contrast, the DC breakdown strength shows a non-monotonic “rise-then-fall” trend and decreases more markedly with increasing tensile strain. To address the one-shot and destructive nature of tensile testing and the associated statistical uncertainties, a lifetime prediction framework was developed by integrating a generalized Eyring acceleration relation with a stochastic degradation process. Under representative service conditions of 0.09 kV·mm⁻¹ and 0.2% tensile strain, the predicted lifetimes corresponding to failure probabilities of 10%, 75%, and 90% are 1.77, 9.08, and 17.90 years, respectively. The applicability of the model is supported by field-aged specimens. These findings provide a mechanistically grounded and reliability-oriented basis for condition assessment, lifetime-margin evaluation, material screening, and maintenance planning of UHVDC composite insulators operating in hot–humid environments. Full article

Offshore wind energy has developed rapidly in recent years as a crucial component of renewable energy. However, offshore wind turbines (OWTs) face significant challenges in operations under complex marine environmental conditions, such as multimodal nonlinear vibrations, reliable structural monitoring, efficient maintenance, and sustainable long-term operations. The model-updating-based parameter identification takes advantages of structural vibration measurements, assisting in structural health monitoring. However, the traditional methods have not fully accounted for the parameter uncertainties and the need for real-time state updating, making them insufficient to meet the long-term online monitoring requirements for OWTs. This study introduces an innovative structural parameter identification framework that integrates modal parameter identification with Bayesian recursive updating. The proposed framework enables more efficient updates and uncertainty quantification of critical physical parameters for OWTs. It combines the covariance-driven stochastic subspace identification (COV-SSI) method for automatic modal parameter identification with the unscented Kalman filter (UKF) for parameter estimation. A 10 MW jacket-type offshore wind turbine was used as a case study. First, the numerical simulations were conducted to generate synthetic measurements for method validation and demonstration, enabling stepwise updating of the tower material’s elastic modulus across different sea conditions. A comparison of update speed and the convergence rate with the traditional time-step-based UKF method demonstrated the superiority of the proposed sea-condition-based approach in terms of computational efficiency and stability. Finally, the proposed framework was systematically validated using scaled model experimental data of a jacket-type OWT with a 4.2% identification error, confirming its engineering applicability. This research provides reliable technical support for the safety assessment of offshore wind turbine structures. Full article

The European Union aims to achieve climate neutrality by 2050, with biogas and biomethane expected to play an increasingly important role in the decarbonisation of the energy system. This study investigates the economic and social determinants shaping the development of biogas production in European countries and identifies an optimal investment strategy for new biogas plants under varying environmental conditions. An expert–mathematical method was applied to assess and hierarchise twenty economic and social factors influencing biogas production, based on evaluations provided by 71 experts from eleven European countries. Subsequently, individual choice criteria derived from game theory were used to determine the optimal strategy for biogas plant construction under conditions of uncertainty. The results indicate that six determinants—EU-level production support mechanisms, investment costs, national support instruments, process efficiency improvements, community involvement, and agricultural raw material prices—account for 52.9% of the total impact on biogas development potential. Among the analysed investment options, large-scale biogas plants with an installed capacity of 3 MW were identified as the optimal strategy, offering the lowest unit production costs and the lowest risk of cost overruns across diverse economic and social environments. These findings provide policy-relevant insights for supporting efficient and socially acceptable biogas deployment in Europe. Full article

Contemporary European societies face overlapping societal challenges—ecological degradation, immigration pressures, and widening economic inequality—which generate a pervasive climate of uncertainty affecting citizens’ perceptions of their own life conditions. This study investigates how social pessimism, conceptualised as a multidimensional orientation reflecting perceived threats across environmental, migratory, and distributive domains, relates to subjective financial insecurity at the individual level. Drawing on harmonised cross-national data from the CRONOS-II panel (N = 8993), covering eleven European countries, we construct a composite pessimism index and analyse its association with perceived financial strain using multivariate and multilevel regression models. Results demonstrate that individuals who express greater societal pessimism report significantly higher levels of financial insecurity, even after controlling for income, education, employment status, and country-level heterogeneity. This relationship is moderated by socioeconomic position; specifically, the pessimism–insecurity link is strongest among lower-income and less-educated groups, suggesting that material precarity and anticipatory anxiety compound one another. Cross-national analysis reveals substantial variation in effect magnitude, with the strongest associations observed in Hungary, Portugal, and the Czech Republic, and the weakest in Slovenia and Iceland. These findings contribute to the interdisciplinary understanding of how macro-level societal concerns permeate individual wellbeing, demonstrating that subjective economic vulnerability is shaped not only by objective circumstances but also by the broader socio-political climate in which citizens interpret their life situations. The results underscore the need for policies that address both material conditions and the affective dimensions of societal uncertainty in order to strengthen social cohesion and reduce perceived economic risk. Theoretically, we frame social pessimism as a formative composite capturing perceived threat to societal stability, offering an integrative perspective on how structurally distinct societal concerns converge to shape economic subjectivities. Full article

Gaussian processes (GPs) are a popular method in machine learning (ML) to model complex systems. One advantage of GPs over other ML models is their ability to quantify uncertainty in predictions. In the past, many advanced methods for GPs have been developed and published for various applications. Adaptive learning (ADL) is one of these applications, in which the consideration of uncertainty prediction plays a major role. The goal of ADL is to replace costly and time-consuming experiments and simulations of complex systems with surrogate models. This is achieved by strategically minimizing queries to maximize efficiency. In the ML literature, various reviews cover either GP methods or ADL strategies. Their focus is more on specific aspects. A comprehensive overview of different GP methods in various ADL applications was missing. This review categorizes GPs and related advanced methods for the first time in the context of ADL applications. A classification is provided for advanced GP methods, ADL methodologies, and practical application areas of GPs with ADL. This review distinguishes between ADL strategies with single-point and batch-query methods for Bayesian optimization and active learning, and highlights real-world applications such as material and product design, as well as efficient modeling for costly simulations and experiments. By combining these aspects, it offers a comprehensive guide for researchers and practitioners applying ADL with GPs to their specific use cases. Full article

Increasingly shaped by heterogeneous on-chain activity rather than a single shared market process, this study investigates 7-day-ahead forecasting using 147 market and on-chain indicators across eight major blockchain ecosystems from October 2023 to April 2025. We benchmark statistical, deep-learning, and foundation-model baselines under multiple feature-selection pipelines using both error metrics and Diebold–Mariano tests. TiRex achieves the best average MAPE ($0.0428$) in a univariate setting without additional optimized covariates, while TFT remains slightly weaker even under its best feature-input configuration (MAPE: $0.0435$; $p=0.9359$ versus TiRex), suggesting a persistent practical advantage for TiRex. Importantly, TiRex’s zero-shot nature confers a substantial efficiency edge: by bypassing feature selection, it delivers comparable accuracy at a fraction of the computational cost. At the same time, feature selection materially affects many model families, with Boruta chosen in roughly 71.7% of best configurations. Taken together, the evidence supports a selective-feature principle: robust forecasting depends on validated, chain-specific features rather than larger feature sets. Feature-importance and overlap analyses further indicate a mixed structure of transferability, where broad market proxies provide baseline context while chain-specific variables drive marginal gains. Overall, this study highlights that effective multi-chain forecasting is primarily a feature selection problem under statistical uncertainty, while also showing that zero-shot designs like TiRex can achieve state-of-the-art accuracy with unmatched efficiency, offering practical implications for building leaner, more robust trading systems. Full article