Search Results (2,512)

Buildings account for nearly 30% of global energy-related carbon emissions. In rapidly developing economies, the operational phase of buildings represents a major and growing source of emissions. However, emission pathways in hot-summer-cold-winter (HSCW) regions remain understudied. This study analyzes carbon emission peaks and influencing factors in the operational phase of existing civilian buildings in Anhui Province. It integrates energy balance tables, the LEAP model, carbon emission factors, and the STIRPAT model. The energy balance table method disaggregates building energy consumption into urban, rural residential and public sectors. It adjusts for transportation energy by deducting specific proportions of gasoline and diesel from industrial, commercial, and residential sectors. Heating energy calculations are simplified because the region has a HSCW climate with limited centralized heating. The LEAP model projects emissions under four scenarios from 2020 to 2060. The STIRPAT model with ridge regression reveals that the permanent population and energy structure negatively influence residential emissions with elasticities of −2.646 and −1.465, respectively. This finding is consistent with the province’s energy transition, where coal use dropped from 28.48% in 2005 to 0.45% in 2020 and electricity use rose from 39.86% to 59.01%. In contrast, per capita GDP, building area, and energy intensity show positive effects. For public buildings, tertiary industry added value and energy structure are key determinants. Scenario analysis identifies the blueprint scenario as optimal, with residential emissions peaking at 34.29 million tons in 2025 and declining to 9.19 million tons by 2060 through measures such as 10% building retrofits by 2025, 75% energy-saving standards for new constructions, 50% retrofits by 2060, and renewable energy integration with building electrification, outperforming the baseline scenario that peaks in 2036 at 49.46 million tons and other intermediate scenarios. The study underscores that energy structure optimization significantly decouples energy consumption from emissions, offering actionable pathways for dual carbon goals through policy synergies in building efficiency, population management, and clean energy adoption to foster sustainable development and the construction industry’s low-carbon transition. Full article

The industrial Internet of Things (IoT) allows traditional electromechanical systems to be connected to digital monitoring and control platforms, especially when field devices use industrial protocols that must be integrated into web services without modifying their main operation. This work implements an IoT architecture based on the Open Systems Interconnection (OSI) model to interconnect two Variable Frequency Drives (VFDs) through a LOGO! Programmable Logic Controller (LOGO! PLC), a Human–Machine Interface (HMI), a ZLAN5143D gateway, Node-RED, Message Queuing Telemetry Transport (MQTT), and Adafruit IO. The communication integrates RS485/Modbus RTU at the field level and Modbus TCP/IP over Ethernet at the upper network level using the gateway as the protocol conversion element. The validation was performed through Modbus Poll, variable acquisition, MQTT publication, and web visualization. The results show local communication response, acquisition of frequency, voltage, current, and revolutions per minute (RPM), together with remote control of start, stop, frequency setpoint, and rotation direction. The architecture is presented as a modular solution for electromechanical applications with IoT projection. Full article

To scientifically explore the vehicle capacity characteristics of circular earthwork transportation routes in water conservancy projects, this paper takes the second-phase project of the Huaihe River Sea Entrance Channel as the research background. Key influencing factors such as road conditions, vehicle performance parameters, safe car-following distance, and earthwork loading–unloading duration are comprehensively considered, and a cellular automaton simulation model is constructed. Horizontal comparative verification is carried out with the Intelligent Driver Model, System Dynamics model, and field measured data to verify model accuracy. The results reveal that the cellular automaton (CA) model yields a total vehicle transport trip count of 606, with a MAPE of 0.66% when compared against the field-measured average of 602 trips. The simulated average travel speed reaches 16.71 km/h, corresponding to a MAPE of 2.89% relative to the field measurement of 16.24 km/h. The error metrics of these two indicators are markedly lower than those derived from alternative models. Due to differences in modeling paradigms and applicable mechanisms, the three models exhibit distinct characteristics in simulation performance. Among them, the cellular automaton model is more suitable for the circular earthwork transportation scenario of this study, which can accurately reflect the coupling characteristics of microscopic traffic behaviors such as multi-route confluence and node queuing, and has high consistency with actual engineering operation. Sensitivity analysis indicates that improving earth loading efficiency and reasonably arranging excavator quantity can significantly enhance the overall transportation efficiency. The modeling ideas and simulation analysis method adopted in this paper are not only applicable to the specific engineering scenario, but also can be extended to similar water conservancy earthwork transportation and large-scale engineering logistics transportation fields. It can provide theoretical basis and engineering reference for earthwork scheduling optimization and quantitative calculation of traffic capacity in water conservancy projects. Full article

A solar-assisted thermochemical cycle to store concentrated solar energy in solid elemental sulfur via the reversible interconversion of sulfuric acid and sulfur is being developed within the SULPHURREAL project. This process enables long-term, transportable energy storage through internal recycling of sulfur oxides. A central objective is to integrate SO₂ capture and conversion in separation-friendly steps that support closed-loop operation with minimal additives and limited downstream purification, while remaining compatible with industrial sulfuric acid and sulfur feedstocks. The method presented in this paper can also be feasible for SO₂ removal from fossil fuels and industrial emissions. With this purpose, indirect SO₂ conversion via bisulfite disproportionation was investigated using elemental sulfur as a heterogeneous auto-catalyst. Batch tests were performed in a pressurized, Teflon-lined autoclave with concentrated bisulfite solutions (3 M) at 140–180 °C for 3 h. Sodium bisulfite showed no conversions at 140–160 °C, whereas sulfur auto-catalysis was observed at T ≥ 170 °C. Ammonium bisulfite was also evaluated as a separable SO₂-capture intermediate; due to thermal instability, operation was limited to 170 °C, where sulfur formation remained detectable. For loop closure, NH₃ and H₂SO₄ must be recovered from the produced sulfate. This was addressed by reacting (NH₄)₂SO₄ with metal oxides in a tubular furnace at 500 °C. The evolved NH₃ was trapped in acid and quantified by ion chromatography. Near-quantitative NH₃ recovery (≈92–98%) was achieved with MgO and ZnO, and the corresponding metal sulfates were identified by XRD. These results support integrated process development and motivate kinetic and mass-balance studies toward continuous operation and scale-up. Full article

To explore the life-cycle carbon emission characteristics of zero-carbon buildings and the economic feasibility of carbon reduction strategies, this study takes the Life Cycle Assessment (LCA) method as the core and constructs a full life-cycle carbon emission accounting system for buildings covering building material production, transportation, construction, operation and demolition in accordance with the standards. Taking the Jinan Zero-Carbon Operation Center Project as a case, this paper systematically calculates its carbon emissions at all stages of the life cycle, identifies the key carbon emission stages and core influencing factors, and comparatively analyzes the economic efficiency of two carbon offset strategies, namely increasing photovoltaic power generation and purchasing green electricity, for the two goals of zero carbon in the operation stage and zero carbon in the full life cycle by using the equivalent annual cost (EAC) method. The results show that the total life-cycle carbon emissions of the case project reach 149,974.04 tCO₂e, with the operation stage and building material production stage being the core carbon emission stages, accounting for 75.50% and 24.19% respectively, while the carbon emissions in the transportation, construction and demolition stages account for a negligible proportion. The economic analysis indicates that although the increase in photovoltaic power generation systems involves a high initial investment, its equivalent annual cost is significantly lower than that of the green electricity purchase strategy. Comparative analysis using equivalent annual costs shows that adding a photovoltaic system achieves equivalent annual costs of $206,589.58 and $273,630.84 for operation stage and life-cycle zero-carbon targets, respectively. In contrast, purchasing green power certificates annually to achieve the same goals incurs equivalent annual costs of $316,223.13 and $317,096.45, representing annual savings of 34.67% and 13.71%. The carbon emission accounting method constructed in this study can provide a reference for the life-cycle carbon quantification of zero-carbon buildings, and the conclusions on the economic efficiency of carbon reduction strategies can serve as an economic decision-making basis for the planning, design and carbon reduction scheme selection of zero-carbon buildings. Full article

This paper presents an integrated assessment of liquid hydrogen as an aviation energy carrier, covering fuel production, aircraft performance, and fleet-level climate impacts. The results, based on the H2Avia research project, indicate substantial potential for reducing life-cycle global warming impacts compared to conventional kerosene. The analyses conducted for the interdisciplinary assessment are presented. The analysis shows that the use of liquid hydrogen eliminates CO₂ emissions during fuel burn, resulting in a significant reduction in global warming potential compared to conventional kerosene, despite remaining upstream emissions from production and transport. The aircraft application cases and the applied technologies assessment scenario are described. The modeled technologies essential for the hydrogen aircraft are discussed, and exemplary values are given. Integrated overall aircraft performance results are given and discussed. At the aircraft level, hydrogen-based aircraft require an 8–18% increase in design mission block energy compared to a 2040 kerosene baseline yet still achieve a reduction in effective global warming potential of 55–86% comparing a representative pair route between Europe and North America (6730 km). An overview of the fleet modeling approach and the applied scenarios is given. For a scenario with energy cost and climate impact as equally weighted minimization goals, the global fleet analysis yields a global warming potential reduction of 60% compared to the non-liquid hydrogen baseline scenario. Overall, the results suggest that liquid hydrogen-powered aircraft can deliver significant mission- and fleet-level reductions in global warming potential and thus represent a promising pathway for achieving long-term aviation climate targets. Full article

Green hydrogen is increasingly discussed as an energy carrier that can link electricity, gas, heat, and transport sectors. However, many existing reviews address this topic from separate viewpoints, such as hydrogen production technologies, Artificial Intelligence (AI) applications, or system integration, with less attention to how policy and market conditions affect deployment. This review brings these related aspects together in one structured discussion. The paper first reviews the hydrogen supply chain, including production, storage, transport, and utilization. It then discusses an integrated multi-energy architecture in which hydrogen interacts with electricity, natural gas, heat, and cooling networks. Policy instruments in five major economies, including the European Union, the United States, China, Japan, and India, are compared. The review also summarizes the main barriers to large-scale deployment, including high production costs, limited infrastructure, technological challenges, regulatory uncertainty, and supply-chain constraints. In addition, the current market structure and selected large-scale hydrogen projects planned in the United States are reviewed. The paper also examines the role of artificial intelligence in green hydrogen systems. AI applications are grouped into four main stages of the hydrogen value chain: forecasting renewable energy generation, improving electrolyzer design and operation, optimizing storage and distribution, and supporting system-level techno-economic assessment. Recent Machine Learning (ML) studies are compared based on their methods and their contributions to operation and planning. Overall, this review highlights the role of AI in enabling green hydrogen integration within multi-energy systems. Full article

Foreign-exchange decisions rest on hierarchically organized evidence whose latent structure is inadequately captured by Euclidean representations. Reinforcement-learning agents trained on flat embeddings inherit stability guarantees that do not transfer to the manifold supporting the latent state. We address both limitations through a hybrid architecture in which a schema-constrained structured chain-of-thought is embedded into a Poincaré ball, transported to a qubit register via angle encoding, and processed by an L-layer hardware-efficient variational ansatz on a state-vector backend. The circuit exposes two read-outs to the policy, namely, a scalar Pauli-Z observable and a projected quantum kernel inducing a fidelity-based similarity over magnet-price attractors, the latter identified via kernel-weighted recurrence density and finite-time Lyapunov statistics. The Lipschitz constraint on the action-value function is lifted from the hyperbolic geodesic distance to a joint metric on ${\mathbb{B}}_{\kappa }^n\times \mathbb{P}\left(\mathcal{H}\right)$. A stability theorem yields an explicit bound depending on the read-out operator norm, on the depth–width product of the ansatz, and on the curvature–Hilbert balance. The pipeline is evaluated on nine major FX crosses over a 2015–2025 out-of-sample window, with rolling-origin walk-forward retraining and broker-published transaction costs. The system attains $2.55\%$ pair-averaged non-compounded monthly P&L and $8.83\%$ maximum drawdown, with Sharpe $1.78$, Calmar $3.43$, and Probabilistic Sharpe Ratio exceeding $0.95$ on every cross. The gain remains significant under a deflated-Sharpe-ratio test with $N_{\mathrm{trials}}=42$ correction. Block-wise ablations exhibit strictly monotone degradation: removing the projected kernel costs $-4.15$ p.p. on annualized P&L, the joint Lipschitz penalty $-6.42$ p.p., the attractor module $-7.64$ p.p., and the hyperbolic embedding $-8.40$ p.p. The quantum block thereby instantiates a structurally non-classical, geometry-aware regularizer identifiable through ablation rather than asymptotically advantageous. Full article

Accurate quantification of riprap volume is critical for cost control, quality assurance, and navigation safety in inland waterway maintenance projects. Conventional methods, such as draft mark reading and RTK-based point surveying, are constrained by limited accuracy, low efficiency, and operational risk. To address these limitations, this study proposes a fully automated riprap volume quantification method based on portable LiDAR simultaneous localization and mapping. The proposed framework establishes a seamless, intervention-free workflow. This automated process sequentially integrates real-time scan monitoring, target vessel extraction, riprap segmentation, deck baseline reconstruction, and 3D volume estimation. Specifically, riprap-laden transport vessels are automatically identified using density-based clustering and trajectory information. Subsequently, deck-loaded riprap piles are extracted through point-cloud geometric analysis and quantified via deck fitting and mesh reconstruction. The method was validated through ten field experiments in the Jingjiang reach of the middle Yangtze River, China. Compared to benchmark volumes established via standard point-cloud processing software, the proposed method achieved an average relative error of 1.37% and a maximum error strictly below 5%. Furthermore, the system proved highly efficient, requiring an average processing time of only 392.1 s per dataset. The results demonstrate that the proposed method is accurate, efficient, and robust, and has strong potential for intelligent riprap quantification in inland waterway engineering. Full article

Cosmic large-scale structure formation is commonly described in terms of the evolution of density fluctuations and correlation statistics. However, such approaches primarily characterize amplitude variations and do not directly capture the spatial rearrangement of mass distributions. Recent developments based on optimal transport theory have introduced a complementary perspective, in which structure formation is understood as a transport process in the space of probability measures equipped with Wasserstein geometry. In this work, we extend this framework by introducing transport–information geometry, which unifies transport geometry with information geometry. Within this formulation, cosmological states are represented as elements of the product space of probability measures and statistical manifolds, allowing gravitational mass transport and generative deformations associated with galaxy formation to be treated in a unified manner. Using entropic optimal transport, we demonstrate that Wasserstein geometry and Kullback–Leibler-based information geometry are connected within a single mathematical structure, leading to a geometric interpretation of cosmological evolution as a coupled transport–information process endowed with a dual-affine structure. In this picture, gravitational evolution corresponds to generative deformation associated with e-geometry, while observational processes, including finite sampling and survey selection, are described as mixing and projection in m-geometry. This dual-affine cosmology provides a unified framework in which gravitational transport, galaxy bias, observational effects, and nonlinear multi-stream structures are consistently incorporated. The resulting formulation offers a systematic basis for cosmological inference, data analysis, and stochastic descriptions of structure formation. Full article

Despite overwhelming scientific consensus that human activity drives climate change, public opinion in the United States remains sharply polarized along political lines. This project tests whether a theory-driven narrative intervention can reduce divergence between individuals skeptical of climate change and those who accept the scientific consensus. Guided by narrative transportation theory, we hypothesize that an inclusive, character-driven video grounded in the authentic language of skeptical audiences will reduce polarization and increase civic engagement. The study proceeds in three phases. Phase 1 uses focus group discussions to identify words, phrases, and perspectives used by skeptical and accepting participants. Phase 2 integrates these findings into the production of a 2–3 min narrative short film, refined through iterative audience testing. Phase 3 employs a stratified online experiment assessing climate attitudes, policy support, and activism behaviors before exposure, immediately after, and one week later. Mediators include narrative transportation, perceived similarity, and character identification. We test whether pre-exposure divergence narrows over time and whether engagement mechanisms explain observed changes. Findings will inform climate communication policy, intervention design, and broader research on depolarization in polarized public issues. Full article

In the context of economic disparities, housing as a fundamental right highlights processes of social differentiation and stratification. From a complexity perspective, factors such as location, distance from development hubs, and designs that standardize needs exacerbate weaknesses in its conception. The new realities of living in housing prompt us to rethink design approaches that integrate housing and work. This research analyzes the Ciudad Alegría Social Housing Program, located in the city of Loja, Ecuador. The diagnostic method indicated that 24% of homes have commercial projections as a survival strategy. While these spatial patterns diminish the levels of habitability in the homes, they also provide benefits such as proximity between home and work, savings in transportation costs, interaction with neighbors, and mixed uses. These observations reveal gaps in the architectural design process, which fails to consider both service providers and users in decision-making related to the design of VIS programs, highlighting the need for this phenomenon to be elevated to public policy. Full article

This paper presents the INFRARES tool, a fully parameterized, interactive, and freely available tool for the resilience assessment of bridges and tunnels within Greece’s transportation networks, under the impact of single or multiple hazards, including earthquakes and floods. The tool facilitates the application of a comprehensive methodology developed through the INFRARES project: Towards resilient transportation infrastructure in a multi-hazard environment research project. The resilience of each examined asset is quantified for the selected hazard scenario using a resilience index and a corresponding resilience grade. The INFRARES tool introduces two key innovations over previous approaches: first, it incorporates both structural and geotechnical components of bridges, overpasses, and tunnels in the vulnerability assessment step; second, it enables GIS-based visualization of the resilience index across selected single- or multi-hazard scenarios. In this context, INFRARES serves as a proactive decision-support tool, supporting authorities, infrastructure operators, and stakeholders to effectively assess, manage, and mitigate the impacts of diverse hazards on transportation systems, thereby enhancing the safety, reliability, resilience, and sustainability of transportation infrastructure under multi-hazard conditions. The proposed tool and the obtained results may support resilience-informed decision-making, prioritization of mitigation measures, and sustainable management of transportation infrastructure exposed to multiple natural hazards. Full article

With the rapid growth of transportation electrification and smart energy systems, the reliable operation of electric vehicle (EV) charging infrastructure has become an important issue for sustainable transport, since charging faults may interrupt service and shorten equipment lifetime. However, practical charging environments are often characterized by heterogeneous operating conditions and severely imbalanced fault distributions, which limit the effectiveness of conventional fault diagnosis methods. To address these challenges, this study proposes a lightweight Proto-Contrastive Discriminative Learning (PCDL) framework for intelligent fault diagnosis in EV charging systems. The proposed method combines supervised contrastive learning with a prototype-distance discrimination mechanism to improve the identification of rare abnormal states under long-tailed data conditions. Heterogeneous charging features, including discrete control signals and continuous total harmonic distortion (THD) indicators, are projected into a discriminative embedding space, while anomaly detection is performed according to the relative distances between samples and class prototypes. Experimental results on a publicly available EV charging-pile monitoring dataset, containing 122,144 samples with four discrete control/safety features and two THD-based power-quality features, demonstrate that the proposed framework maintains stable detection performance under imbalance ratios of 1:1, 1:10, and 1:100. Under the most challenging 1:100 condition, the proposed method achieves an F1-score of 84.21%, representing a 29.08% improvement over the strongest baseline method. In addition, the framework requires only approximately 11 KB of memory and maintains CPU inference latency below 6.3 ms, demonstrating strong potential for real-time deployment on resource-constrained edge devices. These results suggest that the proposed framework can provide a lightweight diagnostic tool for practical charging stations and support safer and more reliable EV charging operation. Full article

Hydrogen-rich shaft furnaces operated with coke oven gas (COG) represent an important low-carbon ironmaking route. Conventional porous-medium CFD models, however, do not explicitly resolve geometry-dependent burden descent or downward advection of solid sensible heat in variable-cross-section moving beds. To address this gap, a user-defined-scalar (UDS)-based pseudo-fluid moving-bed dual-temperature CFD framework is developed in this study. The framework couples geometry-dependent pseudo-solid kinematics, UDS-based transport of pseudo-solid species and sensible enthalpy, and a 12-step reduction-reforming-carbon reaction network on a fixed Eulerian mesh. It is applied to a 0.5 Mt·a⁻¹ industrial reactor through one reference case and three parametric groups covering solid descent velocity, cooling-side back pressure, and CH₄ content. Mesh-independence and mass-conservation checks indicate that the medium mesh is adequate for the intended trend-level assessment; the fine-to-medium deviations are 0.54% for DRI metallization, 0.23% for DRI outlet temperature, and 0.20% for top-gas temperature, with a net global mass residual of 1.53 × 10⁻⁶ kg·s⁻¹; the baseline DRI metallization (96.3%), carbon content (1.1%), and combined H₂ + CO utilization (29.45%) all fall within the reported ranges of the HBIS demonstration line and Energiron-ZR projects. As the descent velocity increases from 2.88 to 6.72 × 10⁻⁴ m·s⁻¹, DRI metallization drops from 98.0% to 79.4% and the outlet temperature rises from 313.3 to 719.4 K. Increasing the cooling-gas outlet back pressure from 60 to 100 kPa reduces the cooling-outlet excess flow from 1.49 to 0.11 kg·s⁻¹, indicating a dynamic gas-seal control between the two gas circuits, whereas raising the inlet CH₄ fraction from 10 to 23 vol% lowers the apparent CH₄ conversion from 29.5% to 18.5% and broadens the carbon-deposition zone. The framework offers a continuum basis for proof-of-concept and trend-level analysis of variable-cross-section hydrogen-rich moving-bed shaft furnaces. Full article