Search Results (925)

LNG serves as a pivotal element within integrated energy systems, especially in coastal regions where the implementation of a stable and reliable LNG cold energy power generation system significantly elevates energy efficiency. This system can effectively meet concurrent demands for cold energy utilization and electricity supply while contributing to the mitigation of carbon emissions. However, the inherent complexity of the system coupled with the scarcity of historical operational data for the novel dual-Rankine cycle process renders conventional reliability assessment methodologies inadequate. This study proposes an integrated framework utilizing fuzzy Bayesian methods to address data scarcity during the early stages of equipment deployment. A hierarchical risk factor model, incorporating process decomposition, expert evaluations, and triangular fuzzy numbers, is developed to quantify uncertainties in failure probabilities. The Bayesian network models the causal relationships among equipment failure factors, allowing for the inference of overall system reliability from individual equipment performance. Through a case study of a LNG terminal in Zhoushan, this approach integrates sensitivity analysis with forward-backward reasoning methodologies to rigorously evaluate and quantify system reliability under operational conditions. The results show that under high load conditions within the 1000 h prior to overhaul, following long-term accumulated operation, the probability of complete system shutdown in the power generation system is 3.30%, while the probability of the LNG cold energy power generation system failing to operate fully due to aging-related faults is 8.24%, demonstrating the system’s strong reliability under extreme conditions. Critical risks identified through backward inference include the seawater pump SWP1, with a posterior failure probability of 59.92% during complete shutdown, and the propane-side pump SWP3, with a posterior failure probability of 32.29% when the cold energy power generation system can only operate in a single-cycle mode. This study provides an advanced methodological framework for risk management in newly constructed LNG cold energy power generation systems, playing a crucial role in promoting sustainable, low-carbon technologies in the energy sector. Full article

The development of a robust and well-designed carbon emission governance framework is critical to achieving effective carbon reduction. To explore carbon reduction and regulation behaviour between governments and enterprises based on government rewards, penalties, regulations, and publicity and disclosure from the media, this study used evolutionary game theory to construct an evolutionary game model. In this model, strong and weak regulation are two strategies that can be selected by the government; truthful reports and underreporting of carbon emissions are two strategies that can be used by enterprises. Hence, we performed theoretical analysis and numerical simulations in this study. The results show that different strategy selections are influenced by an initial payoff matrix and initial parameter selection and construction. Under certain conditions, to impel the government to choose the strong regulation and the enterprises to choose the truthful report strategy, this study suggests decreasing the cost of government regulations and increasing the probability of publicity of the governments’ strong regulation behaviour and the enterprises’ truthful reporting of carbon emissions. Finally, increasing the penalty of the enterprises’ underreporting behaviour and the probability of disclosure on the behaviour of weak regulation and underreporting of carbon emissions. Full article

The global shipping fleet uses vast quantities of fossil fuels and releases significant levels of pollution. Supplying ships moored at quays in ports with onshore power allows them to shut down onboard engines, cutting fossil fuel use and reducing emissions. This is particularly significant when ports utilize green electricity. Equipping ports to connect serviced ships to onshore power grids involves substantial investments, which must be carefully optimized. The aim of this article is to develop a methodology, grounded in probability theory, for determining the electrical power required to connect ships to onshore power grids in ports. The proposed methodology was developed and validated through a case study of container terminal operations. By applying this methodology and considering the conditions of ship service in ports, it is possible to estimate both the number of ships and their berthing durations at quays, as well as the electrical power required from onshore networks to connect the vessels. The results of this research may be of interest to port managers, terminal operators, shipowners, and other stakeholders involved in the development of onshore power grids for ship connections in ports. Full article

Promoting the clean energy transition is crucial for environmental sustainability and public health. Utilizing data from the China Health and Nutrition Survey (CHNS) spanning 2006–2015, this study employs a Difference-in-Differences (DID) model, treating China’s West–East Gas Pipeline Project (WEGT) as a quasi-natural experiment to evaluate the causal impact of natural gas infrastructure expansion on residents’ health. The empirical results indicate that the WEGT significantly improved public health, reducing the probability of self-reported recent illness by approximately 8.2 percentage points. Heterogeneity analysis shows more pronounced effects among urban residents and the elderly. Mechanism analysis reveals that the infrastructure improves health primarily by optimizing household energy structures and reducing industrial pollution emissions. Furthermore, the “Coal-to-Gas” policy synergistically enhances these health benefits. Economic co-benefits analysis estimates that the project reduced individual annual medical expenditures by approximately 540 RMB and increased the probability of employment by 6.9%. These findings provide empirical evidence for deepening supply-side structural reforms in energy and support the realization of the United Nations Sustainable Development Goals (SDGs), specifically by demonstrating how resilient infrastructure (SDG 9) enables affordable clean energy (SDG 7), which in turn delivers good health and well-being (SDG 3). Full article

In this study, a Metamaterial Incident Photon Reconstruction Theory (MIPRT) is developed to describe the modulation process of metamaterials on incident photons. The theory includes the Invariant Incident Photon Hypothesis and Resonant Phase Deconstruction and Quantification; it reveals the modulation characteristics of metamaterials on incident photons, not by first absorption and then re-emission but by inducing coherent destructive interference, which brings about redistribution of the spatial probability of photon occurrence. This theory is validated in a single-layer metamaterial, and a unique relationship between the resonant phase and amplitude is derived and confirmed by simulation. The proposed MIPRT brings a comprehensive understanding of the electromagnetic (EM) response characteristics of metamaterials and provides a new idea for metamaterial theory from another perspective. Full article

The aviation industry, responsible for approximately 2.5–3.5% of global greenhouse gas emissions, faces increasing pressure to adopt sustainable energy solutions. Hydrogen, with its high gravimetric energy density and zero carbon emissions during use, has emerged as a promising alternative fuel to support aviation decarbonization. However, its large-scale implementation remains hindered by cryogenic storage requirements, safety risks, infrastructure adaptation, and economic constraints. This study aims to identify and evaluate the primary technical and operational risks associated with hydrogen utilization in aviation through a comprehensive Monte Carlo Simulation-based risk assessment. The analysis specifically focuses on four key domains—hydrogen leakage, cryogenic storage, explosion hazards, and infrastructure challenges—while excluding economic and lifecycle aspects to maintain a technical scope only. A 10,000-iteration simulation was conducted to quantify the probability and impact of each risk factor. Results indicate that hydrogen leakage and explosion hazards represent the most critical risks, with mean risk scores exceeding 20 on a 25-point scale, whereas investment costs and technical expertise were ranked as comparatively low-level risks. Based on these findings, strategic mitigation measures—including real-time leak detection systems, composite cryotank technologies, and standardized safety protocols—are proposed to enhance system reliability and support the safe integration of hydrogen-powered aviation. This study contributes to a data-driven understanding of hydrogen-related risks and provides a technological roadmap for advancing carbon-neutral air transport. Full article

Rapid detection and follow-up of electromagnetic (EM) counterparts to gravitational wave (GW) signals from binary neutron star (BNS) mergers are essential for constraining source properties and probing the physics of relativistic transients. Observational strategies for these early EM searches are therefore critical, yet current practice remains suboptimal, motivating improved, coordination-aware approaches. We propose and evaluate the Two-Step Localization strategy, a coordinated observational protocol in which one wide-field auxiliary telescope and one narrow-field main telescope monitor the evolving GW sky localization in real time. The auxiliary telescope, by virtue of its large field of view, has a higher probability of detecting early EM emission. Upon registering a candidate signal, it triggers the main telescope to slew to the inferred location for prompt, high-resolution follow-up. We assess the performance of Two-Step Localization using large-scale simulations that incorporate dynamic sky-map updates, realistic telescope parameters, and signal-to-noise ratio (SNR)-weighted localization contours. For context, we compare Two-Step Localization to two benchmark strategies lacking coordination. Our results demonstrate that Two-Step Localization significantly reduces the median detection latency, highlighting the effectiveness of targeted cooperation in the early-time discovery of EM counterparts. Our results point to the most impactful next step: next-generation faster telescopes that deliver drastically higher slew rates and shorter scan times, reducing the number of required tiles; a deeper, truly wide-field auxiliary improves coverage more than simply adding more telescopes. Full article

We report a defect-selective luminescence response in calcium-deficient hydroxyapatite (HAp) induced by electron and ion irradiation. Compacted HAp pellets prepared from hydrothermally grown nanofibers were investigated to analyze defect-related luminescence using photoluminescence (PL) and cathodoluminescence (CL) techniques, both before and after compaction. Low-energy electron beam irradiation (15 keV) produced a two-stage luminescent response, an initial enhancement arising from field-assisted activation of OH-channel vacancies (V_OH and V_OH + H_i), followed by an exponential decay attributed to defect annealing. Monochromatic transient CL measurements show that this rise–decay behavior is selective to the OH-related bands at 2.57 and 2.95 eV, whereas the 3.32 and 3.67 eV emissions exhibit only a monotonic exponential decay. The corresponding decay constants further indicate that the activated OH-channel vacancies anneal more rapidly than the other centers, consistent with their higher electron-capture probability and lower structural stability. In contrast, ${\mathrm{Ga}}^{+}$ ion irradiation (30 keV, 1.4 × 10⁻¹³ A/µm²) induced progressive monotonic luminescence quenching, primarily driven by selective annealing of oxygen vacancies in $\mathrm{P}{\mathrm{O}}_4^{3-}$ groups. These complementary pathways, electron-induced activation and ion-driven suppression, demonstrate that irradiation serves as a versatile tool for defect engineering in hydroxyapatite. Beyond providing fundamental insights into vacancy stability, these results open new routes for tailoring the optical, sensing, and bioimaging functionalities of HAp through controlled irradiation. Full article

This study investigates the feasibility of absolute decoupling—where economies expand while CO₂ (Carbon Dioxide) emissions decline in absolute terms—by identifying its key macro–energy drivers across 79 countries (2000–2025). We construct a comprehensive panel of energy-system indicators and estimate the probability of decoupling using two complementary classifiers: a penalized logistic regression and a gradient-boosted decision tree model (GBM). The non-parametric GBM significantly outperforms the linear baseline (ROC–AUC ~0.80 vs. 0.67), revealing complex non-linearities in the transition process. Explainable AI analysis (SHAP) demonstrates that decoupling is not driven by GDP growth rates alone, but primarily by sharp reductions in energy intensity and the active displacement of fossil fuels. Crucially, our results indicate that increasing renewable capacity is insufficient for absolute decoupling if the fossil fuel share does not simultaneously decline. These findings challenge passive “green growth” narratives, suggesting that current policies are inadequate; achieving climate targets requires targeted mechanisms for active fossil fuel phase-out rather than merely relying on renewable additions or economic modernization. Full article

Under the influence of engineering disturbances, the loading rate of surrounding rock is in a state of continuous adjustment. This study conducts experimental investigations on the mechanical response characteristics under different strain rates (10⁻⁵ s⁻¹, 10⁻⁴ s⁻¹, and 10⁻³ s⁻¹). During the uniaxial loading process of coal–rock composite specimens, multi-parameter monitoring was implemented, and a systematic study was carried out on the ring-down count induced by microcracks, the energy values of acoustic emission (AE) events, the stage-dependent strain characteristics on the specimen surface, and the surface temperature variation characteristics. Additionally, the stress–strain curve characteristics under different strain rates were comparatively analyzed in stages. The loading process of the coal–rock composite specimens was reproduced using the Particle Flow Code (PFC3D 6.0) simulation software. The simulation results indicate that the stress–strain results obtained from the simulation are in good agreement with the laboratory test results; based on these simulation results, the energy accumulation and dissipation characteristics of the coal–rock composite specimens under the influence of strain rate were revealed. Furthermore, a microscopic damage model considering strain rate was constructed based on the Weibull probability statistics theory. The results show that strain rate has a significant impact on the strength, elastic modulus, and failure mode of the coal–rock composite specimens. At low strain rates, the specimens exhibit obvious progressive failure characteristics and strain localization phenomena, while at higher strain rates, they show brittle sudden failure characteristics. Meanwhile, the thermal imaging results reveal that at high strain rates, the overall temperature rise in the composite specimens is rapid, whereas at low strain rates, the overall temperature rise is slow—but the temperature rise in the coal portion is faster than that in the rock portion. The peak temperature at high strain rates is approximately 2 °C higher than that at low strain rates. The PFC simulation results demonstrate that the larger the strain rate, the faster the growth rate of plastic energy in the post-peak stage and the faster the release rate of elastic energy. Full article

This study investigates the tri-objective optimization of underground metro station structures, considering structural reliability, life-cycle economic cost, and annualized carbon emissions simultaneously. Using a representative metro station in Guangzhou as a case study, a multi-objective optimization framework is developed. The model defines structural failure probability, discounted life-cycle cost, and average annual carbon emissions as the primary objectives, with decision variables including concrete strength, cover thickness, the use of epoxy-coated reinforcement, and various maintenance/repair strategies. Material quantities are calculated through Building Information Modeling (BIM), while cost–carbon relationships are derived from industry price data and carbon emission factors. An improved multi-objective particle swarm optimization algorithm (OMOPSO) is used to derive the Pareto-optimal front. Case study results show that increasing cover thickness significantly improves durability and reduces carbon emissions with only moderate cost increases. In contrast, epoxy-coated reinforcement is excluded from the Pareto set due to its high cost under the given conditions. To facilitate practical decision-making, a weight-based solution selection method is introduced, and sensitivity analyses are performed to assess the model’s robustness. The study concludes by emphasizing the framework’s applicability and limitations: the findings are specific to the case context and require recalibration for use in other sites or construction practices. This research contributes by integrating durability, cost, and carbon considerations into an engineering-level optimization workflow, providing valuable decision support for sustainable metro station design. Full article

This study aims to fill the existing gap in laser detection research, particularly regarding how the waveform of outgoing laser pulses affects detection performance. Based on the mechanism of light cone beam expansion, this study emits three different laser pulse signals to detect short-range targets. A theoretical model for short-range ranging of these lasers is established, and the effects of emission power, divergence angle, and equivalent root mean square noise voltage on circumferential detection accuracy are simulated and experimentally measured. As emission power decreases, both echo amplitude and detection accuracy decline for all three pulsed lasers. Additionally, except for the inverted parabolic function, both echo amplitude and detection accuracy decrease with reduced divergence angle. An increase in equivalent root mean square noise voltage broadens the half-width of the probability density distribution for pulsed laser detection. The mean central position deviation between the ideal and measured detection probability density distributions of the heavy-tailed function laser pulses shows the best performance and the highest fidelity, which are +0.01 m, +0.05 m, and +0.02 m, respectively, which is of great significance for the development of laser detection technology. Full article

We present a novel framework for hyperspectral satellite image classification that explicitly balances spatial nearness with spectral similarity. The proposed method is trained on closed-set datasets, and it generalizes well to open-set agricultural scenarios that include both class distribution shifts and presence of novel and absence of known classes. This scenario is reflective of real-world agricultural conditions, where geographic regions, crop types, and seasonal dynamics vary widely and labeled data are scarce and expensive. The input data are projected onto a lower-dimensional spectral manifold, and a pixel-wise classifier generates an initial class probability saliency map. A kernel-based spectral-spatial weighting strategy fuses the spatial-spectral features. The proposed approach improves the classification accuracy by $7.22$–$15\%$ over spectral-only models on benchmark datasets. Incorporating an additional unsupervised learning refinement step further improves accuracy, surpassing several recent state-of-the-art methods. Requiring only 1–$10\%$ labeled training data and at most two tuneable parameters, the framework operates with minimal computational overhead, qualifying it as a data-efficient and scalable few-shot learning solution. Recent deep architectures although exhibit high accuracy under data rich conditions, often show limited transferability under low-label, open-set agricultural conditions. We demonstrate transferability to new domains—including unseen crop classes (e.g., paddy), seasons, and regions (e.g., Piedmont, Italy)—without re-training. Rice paddy fields play a pivotal role in global food security but are also a significant contributor to greenhouse gas emissions, especially methane, and extent mapping is very critical. This work presents a novel perspective on hyperspectral classification and open-set adaptation, suited for sustainable agriculture with limited labels and low-resource domain generalization. Full article

Recently, the environmental impact of halocarbons has become increasingly concerning, particularly due to the growing influence of non-regulated halocarbons on stratospheric ozone depletion and their adverse health effects in the troposphere. Previous model studies have highlighted the importance of halocarbon emissions from the YRD. However, only several reports have discussed the long-term pollution characteristics and health risks of halocarbons in the YRD based on observational data. The continuous observation of halocarbons was conducted in the central part of the YRD (Shanxi site) from 2018 to 2023. The result showed that rise in halocarbon levels was primarily driven by alkyl halides, including dichloromethane (1.194 ppb to 1.831 ppb), chloromethane (0.205 ppb to 1.121 ppb), 1,2-dichloroethane (0.399 ppb to 0.772 ppb), and chloroform (0.082 ppb to 0.300 ppb). The PMF and CBPF analysis revealed that pharmaceutical manufacturing (37.0% to 60.2%), chemical raw material manufacturing (8.0% to 19.9%), solvent use in machinery manufacturing (12.4% to 24.7%), solvent use in electronic industry, and background sources were the main sources of halocarbons at the Shanxi site. Among them, the contributions of chemical raw material manufacturing, as well as of solvent use in machinery manufacturing and electronic industry, are increasing. These aspects are all dominated by local emissions. Furthermore, the carcinogenic risks of chloroform and 1,2-dichloroethane, which rank first in this regard, are increasing. Also, attention should be paid to solvent use in the electronic industry and the background. The probabilities of these activities coming with health risks that exceed the acceptable levels are 94.8% and 94.9%. This study enriches the regional observation data in the YRD region, offering valuable insights into halocarbon pollution control measures for policy development. Full article

The energy–water–carbon (EWC) nexus has become a critical concern for industrial systems seeking sustainable development, yet existing assessment approaches often require intensive computation and lack practical adaptability. This study proposes a probability-pinch analysis (P-PA) framework that enhances conventional pinch analysis (PA) by integrating allocation-based correction factors to account for system inefficiencies across all time intervals explicitly. The framework incorporates PA tools, specifically the Power Cascade Table (PCT), Water Cascade Table (WCT), and Energy Planning Pinch Diagram (EPPD), to design ideal energy–water systems that do not consider losses. Correction factors based on probable energy and water flows are then incorporated to capture system inefficiencies, with design modifications proposed to meet annual carbon reduction targets. Results from an industrial plant case study validate the effectiveness of P-PA in establishing minimum resource targets while achieving a 46% reduction in carbon emissions through system modifications. Deviations from conventional PA were within 10%, confirming the framework’s accuracy and reliability in designing integrated energy–water systems within the EWC nexus. It could serve as a handy tool for designing large-scale energy–water systems that require substantial computational effort, but it may be less accurate for small-scale applications. Nevertheless, compared with conventional PA-based approaches, P-PA offers a balanced combination of rigor, simplicity, and adaptability, making it well-suited for industrial EWC nexus analysis and decision support in sustainable process design. Full article