Influence of double-beam design on nuclear waste transmutation performance for CiADS-like reactor

Concrete seawall panels were tested under impact loading, and the effect of concrete compressive strength (50, 60, and 75 MPa) and reinforcement ratios of glass fiber-reinforced polymer (GFRP) bars (0.33%, 0.50%, and 0.83%) was investigated. The experimental analysis included the comparison of crack, force, deflection, acceleration, GFRP bar strains, and energy absorption behavior. Moreover, an analytical study was conducted to compare the experimental results to the predicted analytical results. The experimental data showed enhanced force and maximum deflection behavior of seawall panels with a higher reinforcement ratio, whereas concrete compressive strength adversely affected the impact behavior of panels because of brittleness becoming dominant, especially after 60 MPa. The strain and energy absorption of GFRP bars was maximum for the panel with 200 mm bar spacing and 60 MPa concrete compressive strength, whereas the maximum post-cracking energy absorption in concrete was the highest for the panel with 200 mm bar spacing and 50 MPa concrete compressive strength. The SDOF equation reliably estimated the impact behavior of the panels with a compressive strength of 60 MPa or lower and a bar spacing of not less than 200 mm, in the case of the panels with a reinforcement ratio of 0.50% or less. The results demonstrate the maximum design configuration of GFRP bars in seawall structures under a 2 m impact. • Influence of concrete compressive strength and reinforcement ratios on the impact response is studied. • Failure behaviour, deflection, force, acceleration, strain, and energy absorption are investigated. • Analytical verification (SDOF) and comparison with experimental results are conducted. • Optimal design configuration of GFRP-reinforced seawalls under 2 m impact is presented.

Lattice-matching-guided supramolecular assembly offers a promising strategy for developing high-performance electrocatalysts for overall water splitting. This study innovatively proposes a novel supramolecular configuration design between Ti3C2Tx MXene nanosheets and a covalent organic framework. Through the in situ growth strategy, a vertically oriented supramolecular network structure is constructed. Because of the high lattice matching, the O atoms in COF can periodically coordinate with the Ti atoms on the surface of Ti3C2Tx MXene, forming matrix Ti-O-C bonds. These arrayed bonds can serve as efficient charge transfer "bridges", accelerating the interface charge transfer kinetics process. This unique architecture not only provides a path for the diffusion of electrolytes but also fully exposes catalytically active sites, which directly contribute to the enhancement of electrocatalytic performance. Specifically, it shows that the 10-Ti3C2Tx@COF supramolecular nanohybrid prepared exhibits excellent catalytic activity in both hydrogen evolution reaction (HER) and oxygen evolution reaction (OER). At a current density of 10 mA cm-2, the HER overpotential is as low as 37 mV and only 284 mV for OER with impressive stability. Moreover, a low cell voltage of 1.558 V was achieved for the overall water splitting. This study provides a new material system and design inspiration for the development of high-performance overall water-splitting electrocatalysts through the supramolecular assembly strategy induced by lattice matching.

Safety evaluation of chevron markers and speed reduction markings for mountainous freeway combined alignments: a driving simulator study.

A decision-driven framework for the mass spectrometry analysis of previously uncharacterized protein modifications.

Prospective Payment Systems (PPS) have been widely adopted over the past two decades as instruments for cost containment and efficiency improvement in health systems. However, much of the existing literature focuses on individual payment arrangements or single-country experiences, with limited attention to broader temporal, structural, and regional patterns reported across PPS-related reforms. This study examines patterns reported in the PPS-related literature published between 2000 and 2024, with particular emphasis on temporal trends, payment architecture, and regional variation in reform approaches. This study constitutes a secondary narrative analysis of 168 studies included in a prior systematic review. No additional searches were conducted. Using an interpretive analytical framework, the included studies were synthesized across three dimensions: temporal trends reported in the literature, structural characteristics of payment architecture, and regional or country-level variation in implementation and reform patterns. From 8,615 identified records, 168 studies met the inclusion criteria. Diagnosis-Related Groups (DRG)-based systems (32.1%) and pay-for-performance arrangements (26.2%) were the most frequently examined configurations, followed by global budget and capitation approaches (17.9%). A substantial increase in publications was observed after 2016, with 63.7% of studies published between 2016 and 2024. Across the reviewed literature, three broad periods of policy and research emphasis were identified: an earlier period focused primarily on cost standardization and inpatient expenditure control (2000-2007); a subsequent period characterized by increasing attention to hybrid arrangements and quality-linked components (2008-2015); and a more recent period emphasizing bundled payments, context-adapted designs, and integration with budgetary constraints (2016-2024). Regional variation revealed heterogeneous implementation and reform patterns across North America, Europe, and Asia. The findings of this review suggest that PPS-related reforms are more appropriately understood as configurations of payment architecture embedded within broader institutional and governance contexts rather than as isolated payment models. Reported outcomes appear to depend less on model labels and more on design configuration and implementation capacity. Future research and policy analysis may benefit from more systematic reporting of payment architecture and its institutional prerequisites.

This contribution conceives the interaction between humans and artifacts as a relational threshold in which body, interfaces, and contexts co-produce themselves through affective dynamics. Through a genealogy of listening technologies – from domestic radio to voice interfaces – the article examines how design orients desire, pleasure, and behavior by crafting affordances that stabilize postures, attention, and daily rituals. The notion of affective openings is proposed as a critical-operational criterion to distinguish design configurations that produce capture from those that preserve practicable reversibility, contestability, and plurality of trajectories. Design emerges as an affectively and politically responsible practice when it organizes conditions of experience capable of translating human action into desirable action without transforming it into a closure of the field of possibilities.

Data-driven approaches of instructional design are more necessary than ever for higher education institutions in the era of digital transformation, especially when faced with noisy and heterogenous educational data. In the digital transformation age, the requirement for data-driven approaches to instructional design that are stable for educational data is growing. Past studies of smart education and problem-based learning (PBL) have been mostly descriptive models or single-dataset studies, with few studies providing evidence for robustness-aware instructional design under different data characteristics. To fill this gap, this study presents a robustness-aware multi-objective optimization method called Cross-Dataset Robust Multi-Objective Optimization (CD-RMO) for exploring PBL design configurations in intelligent learning environments. CD-RMO presents learning gain, development of digital literacy, and implementation cost/risk as three objectives to be optimized simultaneously, and adds robustness to data variability to the objective measurement. The framework is tested on three different heterogeneous datasets related to education: OULAD, EdNet, and KDD Cup 2010, by employing a common feature space and a common PBL design variable. Convergence, diversity, robustness and stability are used as indicators for performance assessment. Under the proposed modeling framework, the results show that CD-RMO yields higher quality Pareto-front, convergence accuracy and robustness consistency compared to the baselines based on experts, classical evolutionary algorithms and a state-of-the-art multi-objective optimizer. In addition, sensitivity and interaction analyses indicate that Scaffolding Strength, PBL Intensity, and Feedback Frequency are significant on robustness-aware optimization performance and other design variables have their effect mostly through interaction effects. Overall, the proposed framework shows that robustness-aware multi-objective optimization is a principled computational methodology to study various trade-offs in ID for intelligent higher education, offering a much more stable and interpretable framework for making decisions in the setting of realistic data uncertainty.

Configuration and parameter Design of electrified propulsion systems for three-dimensional Transportation: A comprehensive review

Co-generation of hydrogen and ammonia from waste tires valorization through chemical looping approach

Enhanced energy storage and temperature stability via configurational entropy design in (1-x)Na0.5Bi0.5TiO3-xSr(Hf0.29Zr0.29Zn0.14Nb0.28)O3 lead-free ceramics

Vaccine clinical trials face high costs, long timelines, and variable progression rates, yet systematic evidence linking trial design features to progression outcomes remains limited. This study aimed to identify trial design features associated with vaccine trial progression and to explore robust design configurations using machine learning approaches. We analyzed 1618 vaccine trials registered from 2012 to 2022. Progression was defined as phase advancement (phase I/II) or regulatory authorization (phase III). Logistic regression assessed associations with progression. Random forest classifiers with cross-validation were used to estimate predicted progression probabilities based on combinations of design features. Monte Carlo simulations compared model-identified robust configurations with randomly generated configurations. Among 1618 trials, 579 achieved phase progressions, corresponding to an overall observed progression rate of 35.8%. Larger sample size, preventive vaccine purpose, COVID-19 indication, and enrollment across all age groups were consistently associated with higher observed odds of progression in both univariable and multivariable logistic regression analyses. In machine learning analyses, the pooled mean predicted progression probability of model-identified robust configurations was 48.93%, compared with 39.44% for historically observed design configurations, corresponding to a relative increase of 24.1%. Simulations further showed a lower projected cumulative development duration (106.87 vs. 128.25 months; -16.7%) and reduced projected cost (USD 100.67M vs. USD 108.33M; -7.1%) for robust configurations compared with historical strategies. This study provides a data-driven framework for characterizing historical vaccine trial design patterns. By integrating machine learning with observational registry data, it supports hypothesis generation and descriptive benchmarking of design features that may inform the design of future prospective or causal investigations.

The electron spin state of transition metal ions represents a fundamental quantum property that is increasingly recognized as a pivotal design dimension for tuning the performance of cathode materials in Li/Na/K‑ion batteries. This review begins by consolidating the foundational principles through which spin states govern electrochemical properties, establishing a robust theoretical framework that bridges atomic-scale coordination environments with macroscopic electrode behavior. It further discusses advanced experimental and computational techniques for probing complex spin states and, critically, for establishing clear structure-spin-performance relationships. A central focus is placed on the rational design of spin configurations, whether via proactive engineering or suppression of unfavorable transitions, to optimize key electrochemical processes: modulating cationic vs. anionic redox competition, enhancing structural stability by mitigating Jahn-Teller distortions and magnetic frustration, and improving charge and ion transport. The review also highlights the emerging role of spin‑sensitive machine learning as an accelerated pathway for discovering superior cathode materials. By integrating theoretical insights, methodological advances, and application‑oriented studies, this work provides a comprehensive mechanistic framework and practical guidelines for the design of next‑generation high‑performance cathodes through deliberate spin‑state control.

The Nordic countries have well-established community energy traditions. However, the extent to which these traditions align with the European Union’s definition of energy communities varies from country to country.The EU’s Clean Energy Package introduced renewable and citizen energy communities (RECs/CECs) to strengthen citizen participation in electricity markets. Nordic countries, however, have long-standing traditions of community-based energy provision, including district heating cooperatives, small hydropower schemes, and wind cooperatives. This article examines how nationally embedded energy models in Denmark, Finland, Norway, and Sweden align with or diverge from the legal and governance criteria defined in the Renewable Energy Directive RED II and the Internal Electricity Market Directive IEMD. Using a comparative institutional framework, the study analyses regulatory design, ownership structures, and system configurations shaping implementation. The findings show that regulatory conditions, rather than cooperative traditions alone, determine alignment. Finland demonstrates high compatibility, Denmark and Sweden partial alignment, and Norway limited alignment, being outside the EU framework. The study highlights how institutional path dependencies condition the practical realisation of EU energy community provisions.

Policy design scholarship rests upon institutional conditions that do not universally obtain: fiscal sovereignty, pluralist accountability, and distributed design authority. How design logic operates when these conditions are absent remains insufficiently specified. This paper examines policy design under institutional constraint, concentrating upon cash transfer programs in Egypt, Tunisia, Morocco, Jordan, and Lebanon. Through structured comparative analysis across cases that vary in regime type and administrative capacity, the paper reconstructs design configurations along four dimensions: instrument choice, targeting architecture, legal form, and accountability arrangements. Despite considerable political heterogeneity, governments converge upon a common design logic. Cash instruments displace alternatives not for problem-solving fit but for fiscal adjustability and reversibility. Targeting operates as governance infrastructure permitting fiscal rationing, administrative centralization, and political insulation rather than as an allocation mechanism. Administrative legal forms avoid statutory entitlement and judicial review. Accountability orients toward external financiers rather than domestic populations. This convergence reflects institutional constraint (external fiscal dependence, concentrated executive discretion, continuous international engagement, and persistent crisis orientation) rather than capacity deficit or implementation failure. Within the convergent pattern, divergences in legal vehicle, targeting framing, and accountability intensity track variation in constraint intensity and in regime-specific modes of executive discretion. The paper advances three claims: design coherence requires analytical separation from normative desirability; discretion constitutes a design variable configured at the design stage; and targeting performs governance functions that account for its persistence despite allocation failures.

Alkaline zinc-iron flow batteries (AZIFBs) are considered promising candidates for grid-scale energy storage due to their inherent safety, cost-effectiveness, and high energy density. However, their long-term cycling stability is largely limited by chemical and mechanical membrane degradation in strongly alkaline environments, as well as the consequent damage caused by zinc dendrites. In this study, we develop a robust alkaline membrane by the configurational design of an all-carbon phenyl backbone and N-methylquinuclidinium groups, in which the alkaline-stable cross-linking polyphenylene oxide (PPO) enhances interchain cohesion. As a result, the membrane retained both chemical and mechanical stability without structural decomposition after immersion in 4 M NaOH for 1500 h at 80 °C. Moreover, it delivers exceptional mechanical resilience to avoid zinc dendrite piercing, even working in high alkaline electrolytes under high areal capacities. The AZIFB operates stably for over 1000 h at an ultrahigh areal capacity of 200 mAh cm-2, with an average Coulombic efficiency of 96.66% and an energy efficiency of 84.86%. This work guides the design of chemically robust membranes in alkaline zinc-based flow battery systems.

Urban air quality degradation, driven by intensified urbanization and traffic, presents a critical public health challenge in most cities worldwide, such as Guelma, Algeria, where concentrations of fine particulate matter (PM2.5, PM10) and carbon dioxide (CO2) often exceed health standards. This study evaluates the efficacy of conventional greening by suggesting that the air purification potential of public gardens may be limited by suboptimal initial design. It is an exploratory modeling framework to propose a shift from qualitative landscaping to quantitative, performance-driven biophilic design. By simulating six parameterized intervention scenarios, this research indicates that simplistic canopy densification may be an insufficient strategy under high pollutant loads. Based on a specific summer field campaign and a limited temporal scope, the results identify a proposed design configuration protocol, where significant pollutant mitigation is achieved by a specific arrangement: a 60% urban forest index composed of high-efficiency species (Platanus × acerifolia, Acer saccharinum, Quercus spp.), combined with a 10% shrub layer and 40% grass cover to form a multilayered filter. This vegetative system is integrated with complementary engineered systems: linear water features covering 15% of the surface for particle wash-down, soil engineered to an aerodynamic roughness length (z0) of 0.15m across 65% of the site to enhance deposition, and pollutant-absorbing paving on 20% of the surface. This study suggests that enhancing the functional efficiency of urban gardens requires precise, multimechanism integration of biomass, water, and engineered surfaces, providing an exploratory framework for designing public spaces as potential infrastructure for sustainable air quality improvement.

Microreactors are very small, factory-fabricated nuclear fission reactors designed to be transportable and rapidly deployed to provide electricity and/or heat, often for niche markets, remote sites, microgrids, or off-grid applications. However, technoeconomic assessments for these systems have been limited by a lack of publicly available, fully scoped designs and cost estimates. This paper addresses that gap by presenting a detailed Class-3 cost estimate (a budget-level estimate prepared when the project scope is moderately defined, roughly 10% to 40%, that is used for budget authorization and early cost control, and typically carrying moderate uncertainty) for the Microreactor Applications Research, Validation, and EvaLuation (MARVEL) microreactor demonstration project. MARVEL is an 85-kW(thermal) reactor planned for installation and operation at Idaho National Laboratory’s Transient Reactor Test (TREAT) facility under a U.S. Department of Energy program. The estimate is based on the reactor system final design, which was approximately 90% complete as of September 2023, and incorporates detailed vendor quotations, labor requirements, inspection needs, and other project cost drivers. Although the MARVEL design itself is not optimized for economic performance, the resulting cost database provides a robust technical foundation for modeling the economics of microreactors. The tabulated MARVEL costs can be used by vendors, researchers, and other stakeholders as a starting point for more comprehensive technoeconomic analyses and for exploring alternative design configurations.

This paper presents a risk-informed system design optimization framework based on the American Society of Mechanical Engineers Plant Systems Design Standard. The approach integrates probabilistic risk assessment, availability allocation, and multi-objective optimization to support engineering design decisions that satisfy predefined safety, production, and regulatory targets while minimizing system cost. System-level unavailability targets are derived from plant-level risk and performance objectives. To efficiently address the resulting high-dimensional, nonlinear design space, a bi-objective optimization formulation is adopted, minimizing both total system cost and the deviation from the unavailability target. The optimization is implemented using the evolutionary non-dominated sorting genetic algorithm III (NSGA-III). The method is demonstrated through a case study of a pressurized water reactor service water system, evaluating multiple design configurations with different redundancy structures and component alternatives. Results show that starting from a highly redundant design, system configurations can be simplified to significantly reduce cost while driving system unavailability toward the specified target. The resulting Pareto fronts enable direct comparison of cost and availability across alternative architectures, supporting informed design decisions. This approach improves design efficiency, reduces the need for design changes, and supports robust and economically efficient solutions that satisfy safety and performance requirements.

This study is the first to numerically evaluate the thermal performance of MPCM-enhanced autoclaved aerated concrete (AAC) bricks combined with an auxiliary heater plate for passive space heating in cold and freezing climates. A two-dimensional finite-volume CFD model with time-dependent outdoor boundary conditions and an apparent-heat-capacity formulation for phase change is developed and implemented in a custom C++ solver. The effects of MPCM volume fraction (φ = 0, 4, 8, 12%) and heater-plate heat flux (q″hp = 0, 50, 100, 150 W/m2) are examined over a 24 h cycle with outdoor temperature varying between -15 and 15 °C. Indoor surface temperature (Tis), indoor heat flux (qis), MPCM melting fraction (MF), and daily power consumption (DPC) are employed as performance indicators. For q″ = 100-150 W/m2, φ = 8-12% reduces peak Tis by about 1-2 °C and peak qis by roughly 15-25%, while raising minimum values and delaying the peak by 1 h. At these heater powers, DPC drops from about 0.31-0.34 kWh/m2 for plain AAC to 0.24-0.28 kWh/m2 (0-30% saving) with MPCM, demonstrating the potential of the proposed AAC-MPCM-heater configuration for peak-load mitigation and energy-efficient envelope design in cold climates. Moreover, over a 40-year service life, the techno-economic analysis shows that 4 vol% MCPCM delivers the highest returns, with NPV = $11,689-$15,970 and IRR = 13.55-17.45% across q''=0-150 W/m2.