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Maritime traffic network extraction and vessel flow prediction in complex inland port clusters
No full textPublisher Copyright: © 2025Integrated dispatching in inland port clusters is essential for enhancing regional maritime traffic efficiency and safety, hinging on the accurate identification of vessel traffic networks and the extraction of their underlying flow characteristics. This study proposes a comprehensive method for maritime traffic network construction and vessel flow prediction in the complex waterway systems of inland port clusters. First, an attention-enhanced Bidirectional Long Short-Term Memory (Bi-LSTM) network is employed to restore incomplete AIS trajectory data, ensuring the integrity of vessel movement records. Subsequently, a modified MD-DBSCAN clustering algorithm is introduced, which dynamically adjusts the ε and MinPts parameters based on local density variations to robustly extract traffic network structures. Building on this, a hybrid prediction model combining Graph Attention Networks (GAT) and Temporal Convolutional Networks (TCN), termed GAT-TCN, is developed to capture topological dependencies and temporal traffic dynamics across network nodes. Empirical validation using AIS data from the Pearl River Estuary results in the construction of a 60-node traffic network, with the proposed model achieving MAE, RMSE, PPTS and R2 values of 1.87, 2.52, 1.78 and 0.77, respectively. The GAT-TCN model outperforms baseline methods in capturing dynamic spatiotemporal vessel flow and improving prediction robustness in inland waterways.Peer reviewe
Catalyst driven optimization of cogasification and economic evaluation for enriched hydrogen syngas production from lignocellulosic waste toward biorefinery applications
No full textGrowing environmental concerns have driven the search for renewable energy sources, particularly H2 production. This study evaluated conversion of coconut shells and wood blends in downdraft gasifier to maximize H2 yield and minimize tar formation in syngas, using mineral catalysts of cement, dolomite, and limestone. Effects of key parameters temperature (700–900 °C), catalyst loading (0–30 wt%), and blending ratio (20–80 wt%) were investigated. Process optimization was performed in Design of Expert and economic analysis was carried out at optimal conditions. Results revealed that dolomite achieved highest H2 yield, with significant increased from 4.49 to 23.31 vol% as temperature varied from 700 to 900 °C at 15 wt% catalyst loading. In case of cement, H2 yield increased from 13.22 to 20.57 vol% followed by limestone. CO yield increased from 17.82 to 25.96 vol% at higher temperature. coconut shell proportion in blend marginally improved CO yield. However, higher catalyst loading reduced CO yield. Among all catalysts, limestone yielded highest CO (30.13 vol%) at 900 °C, 30 wt% catalyst loading, and CS50:W50 blend. Tar formation was reduced significantly from 8.02 to 1.17 g/Nm3 with increasing temperature and catalyst loading (dolomite case). Under optimal conditions (900 °C, 30 wt% catalyst loading, CS50:W50) process achieved maximum 23.31 vol% H2 yield and minimum 1.17 g/Nm3 tar formation. Economic analysis indicated 3.09 MYR/kg syngas production cost that could be further reduced by process scale-up and adopting autothermal gasification. Overall, this study aids in selecting an effective catalyst for biomass gasification and provides an economic analysis to assess its commercial viability.Peer reviewe
Towards intelligent cooperation of urban electric-traffic coupling network: A distributionally robust hierarchical optimization method with dependency of uncertainties
No full textPublisher Copyright: © 2025 Elsevier LtdWith a high proportion of renewable energy connected to the grid and large-scale traffic electrification, the impact of the uncertainty of renewable energy and electric vehicles on the power grid cannot be ignored. The distributionally robust optimization method considering Wasserstein metric has been attracted much attention because of its low conservatism and risk. In this paper, in order to more accurately generate an ambiguity set that takes into account the dependency of uncertainties between renewable energy generation and traffic flow, an extended ambiguity set is proposed, and the copula set of OD pairs on traffic flow, wind and solar output is constructed. Secondly, in the process of selecting fast charging stations, considering the waiting time in line and the uncertainty of wind and solar, the electricity price of fast charging stations is formulated to guide users to charge to assist thermal power units to stabilize the uncertainty of wind and solar. The enhanced user equilibrium is constructed to reflect the effective guidance of electricity price of fast charging stations to users. A bi-level distributionally robust optimization method is proposed to obtain the expected cost in the worst scenario of the extended Wasserstein ambiguity set. Finally, simulations are carried out in two simulation systems (IEEE 33-node distribution network and Nguyen–Dupuis traffic network) and (SCE-56 node distribution network and Sioux-Falls traffic network) respectively, which finally verifies the effectiveness of this method in dealing with uncertainty, saving energy and preventing node congestion.Peer reviewe
Recent advances in piezoelectric resonant infrared detectors
No full textPublisher Copyright: © 2025 Elsevier B.V.Uncooled micro-electromechanical systems-based piezoelectric resonant infrared detectors exploit photothermal–piezoelectric coupling for highly sensitive, wavelength-selective detection. Their compact, low-power, and easily integrable design ensures stable performance in noisy environments, enabling advanced infrared sensing and processing in complex conditions. Here, we review two operating mechanisms of piezoelectric resonant infrared detectors and their state of the art results. It provides an overview of piezoelectric resonant infrared detectors fabricated using materials such as gallium nitride (GaN), zinc oxide (ZnO), and lithium niobate (LiNbO3). Subsequently, we discuss performance enhancements for aluminum nitride (AlN)-based detectors, focusing on infrared absorption, thermal resistance, detection sensitivity, and potential applications. Finally, we present potential challenges facing piezoelectric resonant infrared detectors and outline future research directions.Peer reviewe
A novel biomass-to-energy cogeneration system using zeotropic mixtures : Multi-objective optimization and environmental assessment
No full textPublisher Copyright: © 2025 The Institution of Chemical EngineersThe heavy dependence on fossil fuels over the many past decades has resulted to critical environmental and health challenges that must be urgently addressed. Adopting renewable energy sources, e.g. biomass, and maximum utilization of sustainable energy resources, e.g. waste heat recovery, are proven to be a viable and indeed inevitable measures for this. The present study proposes a novel waste-to-energy combined heat and power (CHP) system driven by municipal solid waste (MSW), integrating a biomass gasifier with supercritical CO2 (s-CO2), Kalina, and zeotropic organic Rankine cycle (ORC) subsystems. The system is designed to maximize energy efficiency and sustainability by effectively utilizing waste heat streams at varying temperature levels and employing zeotropic mixtures such as R1233zd(E) in the ORC cycle, to enhance thermodynamic performance and reduce environmental impact. Detailed sensitivity analyses are conducted to evaluate the influence of key parameters on the system performance, along with a comprehensive energy, exergy, exergo-economic, and environmental analysis. To achieve a balance between energy efficiency, cost-effectiveness, and emissions reduction, a multi-objective optimization via the genetic algorithm approach combined with TOPSIS method is used. The results indicate that in the base design, the system achieves energy efficiency of 76.65 %, exergy efficiency of 49.06 %, net power output of 3621 kW, a total cost rate of 265.6 $/h, and CO2 emissions of 489.8 kg/MWh. The optimization efforts enhance these key metrics by 13.93 %, 27.13 %, 28.8 %, −5.42 %, and −12.23 %, respectively. Based on these findings, the system has potential to serve as an efficient and sustainable waste-to-energy system.Peer reviewe
LUnar Geology Orbiter concept to study lunar Irregular Mare Patches and lava tubes from orbit
No full textPublisher Copyright: © 2025 IAAThe LUnar Geology Orbiter (LUGO) mission is proposed to study surface and subsurface geological features on the Moon that have not yet been the dedicated target of previous missions, with the goal of understanding its thermal evolution and supporting human exploration and future permanent settlements. The objectives of LUGO aim to clarify two important knowledge gaps: 1) to characterize Irregular Mare Patches (IMPs), enigmatic volcanic landforms located on the near side of the Moon, and to provide insights into their age and formational processes; and 2) to detect lava tubes and characterize their general shapes to evaluate their potential as future astronaut habitats. Such a mission would lead to discoveries regarding the improved dating, vertical structure, and shallow subsurface plumbing system of IMPs, as well as the existence, occurrence, and physical characteristics of several lava tubes. To achieve the scientific objectives, the mission is proposed to carry an instrument payload consisting of 1) a ground-penetrating radar with a frequency between 15 and 30 MHz, 2) a narrow-angle camera with a spatial scale better than 25 cm per pixel, 3) a hyperspectral camera with a spectral range from 500 to 1650 nm, and 4) a light detection and ranging sensor using single-photon detection at 1550 nm. LUGO may also characterize other geological targets, such as floor-fractured craters, thanks to its ability to cover long swaths of the lunar surface from its orbit. Furthermore, LUGO will serve as a technological blueprint for future missions that will help uncover the relationship between the geological and morphological characteristics at the lunar surface and the geological structure and stratigraphy of the shallow lunar crust.Peer reviewe
Built environment impacts on zonal shared e-scooter expenses: A Bayesian learning approach
No full textShared e-scooters are reshaping urban mobility, yet trip expense patterns, a key to operator viability, remain unexplored. This study examines how built environment factors affect zonal-level shared e-scooter trip expenses in Chicago, using a novel lognormal regression model enhanced by Bayesian Additive Regression Trees (LN + BART). The model outperforms traditional methods by accommodating the right-skewed distribution and capturing the nonlinear effects on the trip expenses. Results reveal threshold effects: areas with higher median income level, higher POI (Point of Interest) density, and closer distance to CBD (Central Business District) yield disproportionately higher revenues. However, zones with higher percentages of car-free households show lower e-scooter usage, highlighting affordability barriers despite clear mobility needs. This research advances transport economics by combining distribution-aware modeling with Bayesian machine learning, enhancing prediction and interpretability. It also offers important insights for operators to optimize deployment.Peer reviewe
Investigation of building load prediction models based on integration of mechanism methods and data-driven models
No full textPublisher Copyright: © 2025 Elsevier LtdIn the district energy systems, the quality of data often proves inferior, resulting in that the historical building data may be partially or entirely absent. The traditional data-driven models may generate poor fitting results in such scenarios, while mechanism models typically involve a time-consuming simulation process, especially for large-space building load calculation. This paper proposes building load prediction models based on the integration of mechanism methods and data-driven models to deal with the problem for different building types and in different degrees of data quantity. The mechanism methods are performed based on the specific building in the case, and the base structure of data-driven models is not limited by this method. Two cases with different building types and load types are selected for the experiment. This paper investigates the building load prediction capabilities of different models, including different base structures and whether mechanism methods are integrated, in different training data sampling scenarios. Based on the experiment results, the proposed models achieve generalization and robustness in different cases and scenarios.Peer reviewe
Correlation between macroscopic necking and microscopic kink bands during quasi-static tension for a selective laser melted β-Ti alloy
No full text| openaire: EC/HE/101078829/EU//HIGMAM Publisher Copyright: © 2024 Elsevier B.V.Macroscopic necking is of significant importance as it limits the ductility of materials. At the microscopic level, deformation bands serve as indicators of strain localization. This study investigates the correlation between the two length-scale behaviors for a hardening-free β-Ti alloy prepared by selective laser melting, where necking is dominant under tension. The macro/micro features and the tensile flow stresses can be reproduced by a crystal plasticity finite element model (CPFEM) with explicit grain structures. Through the mechanism-based simulation, the identified correlation between microstructure and mechanical properties can be attributed to (1) limited strain rate sensitivity (SRS), (2) low strain hardening, and (3) the presence of hard particles. The first two favor strong necking, while the third is essential for the formation of deformation bands. Two kinds of deformation bands were identified: lenticular kink bands which only appeared in the necking region, and slim slip bands. The former can only be reproduced with a compact set of active slip systems, in conjunction with the abovementioned three conditions. Increasing SRS resulted in the suppression of necking, leading to the disappearance of the typical kink band morphology. The presence of hard particles induced an unloading yield effect, which can enhance ductility by slowing the progress of necking. The present results strengthen the fundamental understanding of (1) the effect of both SRS and hardening capability on necking, and (2) the necessity of necking, hard particles, and the depletion of active slip systems for the kink band formation. These findings are peculiarly relevant to the studied β-Ti alloy.Peer reviewe
A data-driven framework for risk and resilience analysis in maritime transportation systems : A case study of domino effect accidents in arctic waters
No full textPublisher Copyright: © 2025Resilience is a complex concept that extends beyond risk, including the ability to absorb risks from external disturbances to maintain an acceptable level of safety. In the context of maritime transportation systems (MTS), resilience can be understood as a ship's ability to withstand disasters and ensure safe navigation in the face of unexpected incidents. This study proposes a data-driven framework for the quantitative analysis of risk and resilience in MTS, considering the temporal trends and domino effects of maritime accidents. The first step involves data preparation, which includes the collection, processing, and storage of global maritime accident data from the Lloyd's List Intelligence database spanning from 2014 to 2023. Next, an analysis of evolution trends is conducted to explore temporal trends and domino effects, focusing on the severity and pollution of maritime accidents. Arctic waters, known for their typical domino effects in maritime accidents, are chosen as a case study to illustrate the proposed risk and resilience analysis approach by considering the absorptive capacity in the evolution of maritime accidents. Furthermore, proactive and reactive risk control options are suggested for critical domino accident scenarios in Arctic waters to provide targeted recommendations for managing risks in Arctic shipping.Peer reviewe