Search Results (724)

As climate change intensifies, transitioning the construction sector away from fossil fuels is vital to reducing global greenhouse gas emissions and localized urban pollution. This paper assesses the economic feasibility of electrifying construction machinery by developing an Annualized Cost of Ownership framework that incorporates mobile charging solutions, internalizes environmental and public health operational externalities (CO₂, PM_2.5, NO_x, and SO₂), and relies on Monte Carlo simulation with Cholesky decomposition to capture the interdependencies among cost drivers. We analyze twenty distinct models of excavators and wheel loaders—the two largest contributors to construction-machinery emissions—comprising functionally equivalent diesel and battery-electric variants. Our results show that several compact electric models are already cost-competitive even without internalizing environmental and public health operational externalities. When these are accounted for, the economic advantage of electric machinery increases, particularly in denser urban areas where local air pollution damages are severe. While projected battery cost reductions further lower electric ownership costs, the magnitude of this effect is modest. However, the weak penetration of electric construction equipment in the US underscores that targeted policy interventions—such as point-of-sale rebates, green procurement mandates, tax credits, charging infrastructure subsidies, or the creation of low-emission zones and noise ordinances that advantage electric construction machinery—are needed to accelerate market adoption. These measures are particularly critical in densely populated urban areas, where internalizing local air pollution and public health externalities significantly amplifies the economic value of zero-emission machinery. Full article

Resource allocation for cognitive Vehicle-to-Everything (V2X) networks is challenging due to dynamic spectrum sharing, strong interference coupling, and stringent latency constraints for safety-critical Vehicle-to-Vehicle (V2V) traffic. Although recent Multi-Agent Reinforcement Learning (MARL) approaches report promising gains, many evaluations are conducted at limited and fixed network scales, which restricts insights into scalability under dense spectrum reuse. This paper investigates cooperative multi-agent learning for interference-aware and deadline-constrained V2X resource management. We propose a Q-value Transformation (QTRAN)-based value decomposition framework under centralized training with decentralized execution (CTDE) for joint resource-block and power allocation among V2V agents. The proposed approach is implemented in a realistic V2V/V2I simulator incorporating Manhattan grid mobility, fast fading, explicit cross-tier and co-channel interference, and per-link payload/deadline dynamics. Beyond communication-level performance, improved timely delivery of V2V safety messages can support cooperative maneuvering, collision avoidance, platooning, and infrastructure-assisted traffic management. Extensive simulations across varying numbers of V2V agents benchmark QTRAN against independent learning baselines including MARL and centralized single-agent learning (SARL). Results show that QTRAN improves performance compared with the selected learning baselines and enhances the throughput–reliability trade-off under interference-coupled spectrum reuse. For instance, at $N_{\mathrm{V}2\mathrm{V}}=20$, QTRAN achieves a V2V rate of $0.194\pm 0.004$ and a V2I rate of $9.117\pm 0.213$, while reaching a V2V success rate of $0.812\pm 0.017$ with a low Deadline Miss Ratio of $0.001\pm 0.000$. At higher density ($N_{\mathrm{V}2\mathrm{V}}=50$), QTRAN sustains strong reliability (V2V success rate of $0.719\pm 0.006$ and Completion Ratio of $0.716\pm 0.006$) while maintaining competitive infrastructure throughput (V2I rate of $9.251\pm 0.114$). These results indicate that QTRAN effectively captures non-linear interference interactions, enabling coordinated decentralized spectrum and power decisions under the adopted density-based evaluation setting, thereby enhancing V2V reliability and throughput in cognitive Internet of Vehicles. Full article

Large-scale dense-phase carbon dioxide (CO₂) injection is an energy-intensive process in the carbon capture, utilization, and storage (CCUS) value chain. To address insufficient utilization of inlet pressure potential energy and sealing/friction losses of conventional reciprocating pumps under high-base-pressure dense-phase CO₂ transport conditions, this study develops a dense-phase CO₂-oriented structural optimization scheme for a hydraulically driven opposed-piston reciprocating pump based on force-coupling. A dynamic model was established to clarify the in situ recovery mechanism by which inlet pressure potential energy is converted into auxiliary thrust, enabling the drive load to shift from absolute pressure to net pressure difference. Simulation results show that under the rated 8 MPa inlet and 25 MPa discharge condition, the optimized opposed-piston configuration reduces peak driving oil pressure by 31.39% compared with the non-opposed reference configuration. Field reliability operation data show an average normalized specific energy consumption of 0.422 kWh/(MPa·m³) during the selected 24 h continuous operating period. The optimized configuration improves inlet-pressure utilization and reduces hydraulic power demand under high-base-pressure dense-phase CO₂ injection conditions, providing theoretical support and engineering reference for low-energy CCUS injection systems. Full article

Hierarchical Public Sector Organizations (PSOs), particularly military organizations, face persistent challenges in sustaining innovation due to structural rigidity, hierarchical control, and embedded resistance to change. While existing literature explains why innovation emerges and why it is resisted, significantly less attention has been devoted to understanding how innovation becomes institutionalized as a sustained organizational capability. This study addresses this gap by introducing the Bi-focal Innovation Contagion Model (BICM), an agent-based framework that conceptualizes innovation diffusion and resistance as a co-evolutionary “tug-of-war” between competing organizational forces. The model integrates top-down governance mechanisms and bottom-up innovation processes, capturing how heterogeneous actors interact within hierarchical systems to shape the diffusion, assimilation, and stabilization of innovation over time. Using the Israel Defense Forces (IDF) as an empirical source case, the model explores how Innovation Management Systems (IMS) may be designed to support the institutionalization of innovation as a self-sustaining organizational capability within hierarchical PSOs. Simulation results suggest that hybrid innovation architectures may better sustain innovation across varying leadership conditions. This occurs when centralized strategic coordination is combined with decentralized innovation activity and supported by mature innovation agents with sufficient centrality and hierarchical reinforcement. The findings highlight the critical role of IMS as an organizational architecture for achieving anti-fragility, enabling innovation dynamics to persist, adapt, and strengthen in the face of uncertainty, leadership turnover, and shifting strategic priorities. By integrating agent-based modeling with organizational theory, this study contributes a dynamic framework for understanding and designing sustainable innovation systems in hierarchical PSOs. Full article

Hydrogen oxy-combustion with high CO₂ dilution is a key component of supercritical CO₂ (sCO₂) power cycles, such as the Allam cycle, enabling high-efficiency, near-zero-emission power generation with integrated carbon capture. However, combustion behavior under high-CO₂ conditions remains insufficiently characterized, particularly with respect to mixing and flame stability. In this study, hydrogen combustion in an O₂–CO₂ environment was investigated experimentally and numerically using a custom-designed multi-hole burner. The experiments were conducted in a 1-bar combustion chamber, while the inlet pressures of the reactants were varied between 10 and 50 bar to isolate the effect of injection conditions. Numerical simulations were performed to analyze flow, mixing, and flame structure. The results show that increasing inlet pressure leads to a more compact and localized flame, despite reduced velocity levels in the combustor due to increased reactant density. Higher inlet pressures result in increased peak temperatures but lower mean combustor temperatures, indicating more intense but spatially confined heat release. The flow field remains structurally similar across cases, while reduced radial spreading and longer residence times influence combustion behavior. Stable flame operation was achieved over a wide range of conditions, demonstrating the feasibility of hydrogen oxy-combustion under high CO₂ dilution. The combined experimental and numerical analysis provides insight into the interplay between injection conditions, mixing, and reaction rates in highly CO₂-diluted hydrogen combustion. The obtained results support the development of compact and stable direct-fired combustors for next-generation supercritical CO₂ power cycles and hydrogen-based low-emission energy systems. Full article

This study proposes a hybrid machine learning model based on full-spectrum pulsed neutron logging data to address the monitoring challenges of Carbon Capture, Utilization, and Storage (CCUS) under complex geological conditions. Traditional interpretation models for sequestered CO₂ saturation (e.g., macroscopic capture cross-section model, characteristic peak count model, and ratio model) heavily rely on prior parameters such as porosity, formation water salinity, and lithology. Acquiring these parameters in real time during practical engineering is often costly and difficult. To reduce the rigid dependence of accurate CO₂ saturation monitoring on complex prior parameters like porosity and salinity under heterogeneous geological settings, this research focuses on the Pearl River Mouth Basin, a core carbon sequestration target area in the Guangdong-Hong Kong-Macao Greater Bay Area, based on the evaluation results of offshore carbon sequestration macro-regions in China. Taking the primary reservoirs of the Enping and Wenchang Formations as typical geological prototypes, a high-fidelity, full-spectrum neutron–gamma response database was constructed using Monte Carlo simulations. Two machine learning strategies are proposed: a direct regression model (NMF+SVR) and a joint model (NMF+SVC/KMeans+SVR). Based on Monte Carlo simulated data, experimental results demonstrate that, compared with traditional petrophysical baseline models and simple machine learning models, the proposed joint learning method effectively reduces the dependence of CO₂ saturation monitoring on lithology and porosity. Furthermore, it is proven that even with a single-detector tool configuration, the method exhibits high prediction accuracy under complex lithological conditions. Notably, the two-step joint model achieves a Root Mean Square Error (RMSE) as low as 4.200%, significantly outperforming traditional physics-based models and single machine learning models such as MLP and RF. This study provides a physically interpretable and accurate technical reference for applying machine learning to pulsed neutron-logging-based CO₂ geological sequestration monitoring. Full article

As artificial intelligence (AI) becomes increasingly important in rural revitalization, building consensus among multiple stakeholders and developing participatory digital co-creation platforms has grown increasingly urgent. However, existing large language model (LLM) systems predominantly adopt a one-shot generation paradigm, making it challenging to accurately capture villagers’ cultural aspirations and frequently resulting in a significant disconnect between design outputs and community expectations. This situation reveals deficiencies in progressive deliberation mechanisms and cultural controllability. To address these issues, this study proposes a multimodal Participatory Landscape Demand Generation (PLDG) system to enhance AI-generated dialogue controllability, facilitate effective cultural translation in sensitive rural contexts, and promote sustainable development where landscape design both drives and reflects rural revitalization. The system leverages LLMs to simulate stakeholder participatory interactions in village landscape design scenarios. Using culturally distinctive Chinese villages as case studies, the research conducts multi-role simulated dialogues, multimodal semantic extraction, and iterative consensus-building, and evaluates the resultant data to generate landscape design proposals. The results indicate that the PLDG system significantly improves participation efficiency among diverse design stakeholders and enhances the sustainability of design decisions. Compared to conventional methods, metrics such as cultural compatibility, villager participation, and design innovation show substantial improvements. These findings demonstrate the considerable potential of human-AI collaboration in future rural planning. This study introduces the Culture Constraint-Driven Rural Landscape AI Collaborative Design Framework (PLDG), validating its practical efficacy in identifying culturally sensitive elements, ensuring cultural congruence, facilitating community participation, and fostering design innovation. Consequently, it provides a reusable, iterative operational tool for the digital renewal of sustainable rural landscapes. Full article

The rapid electrification of urban freight transport requires new optimization approaches that jointly consider logistics operations and energy system constraints. The problem is formulated as a mixed-integer linear programming (MILP) model that captures the interdependencies between vehicle operations, battery constraints, charging infrastructure availability and the temporal variability of photovoltaic energy. A multi-objective structure is adopted to minimize total energy costs and CO₂ emissions while maximizing the utilization of locally generated renewable energy. The model is evaluated using scenario-based simulations under three solar integration levels (0%, 30% and 60%). The results demonstrate that integrating solar energy into routing and charging decisions significantly reduces grid dependency, lowers emissions and improves overall system efficiency. Three types of charging stations are considered in the study (S1, S2, and S3), differing in photovoltaic (PV) energy penetration levels, ranging from conventional grid-based charging (S1) to high renewable integration stations (S3). The quantitative analysis reveals a clear resource and emission structure across the simulated scenarios. Incorporating charging stops grid-wide increases the total distance from theoretical routes to real tracks with stops to overcome the 120 kW battery limit. However, the integration of solar energy significantly alters the system’s environmental costs: total CO₂ emissions drop non-linearly by 33.4%, decreasing from 364.64 kg in the ‘Low Sun’ scenario to 243 kg in the ‘High Sun’ scenario. Furthermore, the localized impact shows that utilizing pure grid energy (S1) results in 405 kg of CO₂, while maximizing solar integration up to 60% (S3) reduces emissions to 162 kg. The sensitivity analysis showed how varying the share of solar energy at the two main stations (S2 and S3) affects the total CO₂ emissions, while maintaining the same routes. Three scenarios were examined: low (10% and 30%), base (30% and 60%) and high (50% and 90%) solar energy shares. As the share of solar energy in the system increases, a clear effect of emission reduction and energy cost optimization is observed. Full article

Carbon capture, utilization, and storage (CCUS) is critical for carbon neutrality, and deep saline aquifers are promising reservoirs for CO₂ sequestration. CO₂ diffusion in brine directly affects dissolution trapping efficiency and is strongly influenced by salt ions. Molecular dynamics simulations were employed to investigate CO₂ diffusion in NaCl brines under varying concentrations (0.1–5.0 mol/L), temperatures (298–353 K), and pressures (3–40 MPa). Diffusion coefficients were derived from mean square displacement, and radial distribution functions combined with hydrogen bond analysis were used to elucidate microscopic mechanisms. Results show that as NaCl concentration increases from 0.1 to 5.0 mol/L, the diffusion coefficient decreases by ~50%, reflecting the kinetic consequence of the salting-out effect. Raising temperature from 298 to 353 K enhances diffusion by ~149%, following Arrhenius behavior, while pressure shows negligible influence below 30 MPa but causes a 15% drop at 40 MPa. RDF analysis reveals that higher salinity densifies the CO₂ hydration shell without changing its coordination number, and ions do not accumulate near CO₂. Hydrogen bond analysis indicates that slower diffusion arises primarily from increased viscosity and steric hindrance from hydrated ions rather than disruption of hydrogen bonds. These molecular-level insights can guide site selection and injection strategy optimization for CO₂ geological storage in saline aquifers. Full article

This study investigates the sampling sufficiency required for accurately characterizing the morphological properties of rigid polyurethane foams across three distinct regions: core, crown, and lateral edge. A total of 200 individual cells were analyzed from 30 SEM micrographs, enabling the quantification of cell length, cell width, anisotropy index, linear cell density, and shape index. Average cell length ranged from 715 to 763 μm, while cell width varied between 386 and 531 μm depending on the region. The anisotropy index increased from 0.186 in the core to 0.289 in the lateral edge, indicating progressively more elongated cells. Linear cell density showed a marked decrease from 0.062 in the core to 0.001 in the crown, reflecting differences in cellular packing. Shape index values remained relatively stable, confirming its lower sensitivity to structural variations. Monte Carlo simulations were employed to evaluate sampling sufficiency for sample sizes ranging from 2 to 30. Results demonstrated that optimal sample sizes varied with foam region and parameter: 16 cells were sufficient for core and lateral regions, whereas up to 22 cells were required for the crown to capture higher structural heterogeneity. For anisotropy and shape indices, sufficient sampling ranged between 13 and 20 cells depending on the region. The results confirm that the core exhibits lower variability (CoV for cell length: 29.1%) compared to the crown (36.4%) and lateral edge (34.9%), supporting its more homogeneous structure. However, exclusive sampling from the core may lead to biased characterization, as crown and lateral regions display significantly higher variability in both geometry and orientation. These findings establish quantitative guidelines for sampling strategies in polyurethane foam morphology, contributing to improved reproducibility and reliability in structure–property investigations of cellular materials. Full article

The cement industry is a major contributor to global carbon emissions. Therefore, reducing emissions while utilizing industrial wastes is critical for its sustainable development. Red mud, a solid waste byproduct of alumina smelting with main components like SiO₂, Al₂O₃, and CaO, can partially replace limestone as a raw material in cement production. TG-DSC thermal analysis clarified red mud’s three-stage weight loss characteristic during calcination (total weight loss rate of 22.11%), and orthogonal experiments identified calcination temperature as the core factor for its CaO content, with the optimal calcination pretreatment process confirmed (0.075–0.09 mm particle size, 1373 K, 1 h residence time, CaO content up to 21.1%). Based on the results, this study uses ANSYS Fluent 2021 R1 to simulate a TTF-type precalciner, establishing a validated multi-physical field model (all relative errors < 5%) to explore red mud blending ratios of 0%, 2.5%, 5%, 7.5% and 10%. Unlike previous experimental studies, this work uses a CFD model to quantify how red mud blending ratios affect the coupled thermo-chemical environment in a TTF precalciner, revealing a mechanism-driven trade-off among decomposition rate, CO₂, and NO_x that experiments alone cannot capture. Results show red mud slightly alters the internal temperature field and reduces the raw meal decomposition rate. The decomposition rate remains within the industrial acceptable range of 85–95% when the red mud blending ratio is no more than 5%, while further increasing the blending ratio to 7.5% and 10% causes the decomposition rate to drop below 85%. Therefore, a blending ratio of 5% is recommended, which balances waste utilization, decomposition rate, and emission reduction, providing solid technical support for red mud’s large-scale use in cement production. Full article

Laser Powder Bed Fusion (LPBF) is a complex manufacturing process that depends on precise control of printing parameters and melt pool geometry, which directly influence defect formation and final part quality. This study evaluated the reliability of a simplified thermal conduction-based melt pool model by combining post-process metallographic analysis with in situ dual-wavelength infrared thermal imaging. Experimental data were obtained through single-track printing on 316L, IN625, and CoCr alloys across a wide range of parameters. The simulated melt pool length showed strong agreement with thermal camera measurements (R²_adj > 0.78), while the width showed moderate but consistent correlation (R²_adj > 0.52). For melt pool depth, the model systematically deviated due to its inability to capture keyhole melting, although a strong linear correlation was still observed (R²_adj > 0.86). Cross-validation between metallographic measurements and thermal imaging revealed only a 6–9% discrepancy, confirming the reliability of both methods and the potential of dual-wavelength cameras for industrial process monitoring. Overall, the model proves to be a reliable tool for predicting melt pool surface geometry specifically within the conduction melting regime, while its predictive capability degrades significantly in the keyhole regime, where simulated peak temperatures reach up to 7000 °C and melt pool depth errors escalate due to the disregard of recoil pressure, liquid and vapor dynamics. Full article

To improve the low-carbon economic operation of integrated energy systems under wind power uncertainty, this paper develops an optimal scheduling model for an integrated energy system coupling oxygen-enriched combustion with hydrogen–ammonia–carbon utilization pathways. The proposed framework integrates oxygen-enriched combustion, electrolysis-based hydrogen production, methanation, hydrogen fuel cells, ammonia synthesis, urea synthesis, captured CO₂ utilization, reward–penalty ladder-type carbon trading, and IGDT-based wind power uncertainty scheduling. A deterministic scheduling model is first established to minimize the total operating cost, and Information Gap Decision Theory is then introduced to formulate risk-averse and opportunity-seeking scheduling strategies under wind power uncertainty. Simulation results show that, compared with the post-combustion carbon capture scenario and the conventional coal-fired scenario, the proposed system reduces the total operating cost by 3.37% and 8.03%, respectively, and reduces the wind curtailment cost by 40.2% and 57.0%, respectively. Compared with the post-combustion carbon capture scenario, carbon emissions are reduced by 17.7%. The hydrogen–ammonia–urea chain generates approximately 15.68 × 10⁴ CNY of urea revenue and improves carbon resource utilization. Under an IGDT deviation factor of 0.03, the risk-averse strategy increases the total operating cost by approximately 10.30 × 10⁴ CNY to enhance operational robustness, while the opportunity-seeking strategy reduces the total operating cost by approximately 10.30 × 10⁴ CNY and decreases carbon emissions by 19.6 t. These simulation results verify the effectiveness of the proposed scheduling framework under the designed case study. The proposed framework can improve the low-carbon economy, renewable energy accommodation, carbon resource utilization, and adaptability to wind power uncertainty of the studied integrated energy system. Full article

Direct air capture (DAC) based on adsorption is a promising negative-emission technology owing to its operational flexibility, modular deployment potential, and comparatively low regeneration temperature. In this study, a dynamic three-dimensional mathematical model was developed to investigate a structured adsorption-based DAC reactor operating under a temperature–vacuum swing adsorption cycle. The model couples heat and mass transfer among the gas, adsorbent, metal structure, and heat-transfer fluid and was used to evaluate the temporal and spatial evolution of temperature and CO₂ adsorption capacity during adsorption and regeneration. The effects of internal cooling, heat-source temperature, and vacuum pressure on cyclic performance were systematically analyzed. The results show that introducing an internal cooling source significantly accelerates adsorbent-bed cooling and increases the cyclic working capacity by approximately 10%. Parametric simulations indicate that higher regeneration temperature and lower vacuum pressure enhance CO₂ desorption, with optimal performance achieved at a heat-source temperature of 90 °C and a vacuum pressure of 1 kPa. Under these conditions, the DAC system reaches an annual CO₂ productivity of 125 tCO₂·year⁻¹, with mechanical and thermal energy consumptions of 4.72 and 11.91 GJ·tCO₂⁻¹, respectively. This work provides a useful modeling framework for reactor design and operating-parameter optimization in adsorption-based DAC systems. Full article

Traditional European craft practices face dual pressures: the erosion of tacit knowledge held by aging practitioners, and the risk of cultural homogenization through uninformed digital adoption. This paper presents a comparative analysis of a structured design pilot conducted across five Representative Craft Instances (RCIs): glassblowing, tapestry, marble/silversmithing, porcelain, and woodcarving within the Horizon Europe CRAEFT project. Drawing on co-creative workshops, motion capture pipelines, physically based rendering (PBR), interactive simulation, and additive manufacturing, we analyze how context-specific digital tools performed as mediators rather than modernizers across heterogeneous craft domains. Cross-domain analysis reveals that digital tools achieve cultural legitimacy only when introduced through co-creative, practitioner-led cycles; that gesture and tacit knowledge are transferable via structured computational pipelines; and that methodological portability, not workflow replication, is the appropriate model for cross-context scaling. Implications are discussed for sustainable heritage policy, design education, and the development of craft-sensitive digital infrastructure in Europe. A cross-RCI comparative assessment matrix evaluates all five domains across seven analytical dimensions: practitioner adoption, perceived usefulness, cultural legitimacy, technical maturity, sustainability impact, transferability potential, and educational effectiveness. Finally, practitioner reflective accounts from participating designers and craftspeople are presented to ground the analytical findings empirically. Full article