Search Results (9,115)

Reliable prediction of carbon allowance prices plays a crucial role in emissions trading systems, particularly for market participation, regulatory compliance, and long-term cost planning. In China, regional carbon markets differ markedly in trading activity, price formation mechanisms, and responsiveness to external signals, which limits the effectiveness of conventional single-model forecasting approaches. This study develops a unified machine learning framework designed to accommodate such cross-market heterogeneity. The framework incorporates a diverse set of explanatory variables, including historical price-based indicators, trading volume information, inter-market linkage signals, and macroeconomic factors. Three ensemble-based learning algorithms-XGBoost, LightGBM, and Random Forest—are implemented, and their outputs are further integrated using a weighted aggregation scheme to improve generalization across markets. The empirical evaluation across seven pilot markets shows that, while LightGBM consistently performs well as a standalone model, the proposed ensemble framework achieves superior stability and adaptability under varying market conditions. The forecasting accuracy is high across all cases, with coefficients of determination above 0.74 and reaching values greater than 0.92 in most markets. Further investigation through feature ablation highlights the heterogeneous role of external information, indicating that predictor importance varies significantly between markets and that no universal feature combination yields optimal performance. Leveraging the forecast outputs, the study also demonstrates practical applications in decision support, including timing strategies for allowance sales and dynamic cost assessment in offshore wind engineering scenarios. By systematically evaluating the marginal contribution of different information groups to predictive uncertainty, the framework offers a flexible tool for managing information-structure uncertainty in fragmented carbon markets. The proposed framework offers an integrated solution that connects predictive modeling with operational and engineering decision on processes, providing a flexible tool for managing uncertainty in fragmented carbon markets. Full article

Hybrid renewable energy systems (HRES) integrating photovoltaic arrays (PV), wind turbines (WT), and fuel cells (FC) require coordinated maximum power extraction to maintain stable operation under dynamic environmental and load conditions. Conventional MPPT approaches based on independent source-level control often suffer from adverse source interaction, increased steady-state oscillation, degraded DC-link stability, and reduced total extracted power when multiple renewable sources operate simultaneously. To address these limitations, this paper proposes an integrated perturb-and-observe control framework for coordinated power optimization in photovoltaic–wind–fuel-cell hybrid renewable energy systems connected through a shared DC-link structure. Unlike conventional independent MPPT controllers, the proposed strategy evaluates the aggregate power behavior of the integrated system and performs coordinated duty-cycle adaptation to improve renewable-energy utilization while suppressing source conflicts and dynamic coupling effects. The proposed controller is implemented and validated using a real-time digital simulator under a sequential disturbance profile consisting of an irradiance drop at 0.2 s, wind-speed increase at 0.4 s, hydrogen-pressure fluctuation at 0.6 s, and load variation at 0.8 s. Comparative evaluation against conventional perturb-and-observe, incremental conductance, and fuzzy-logic-based MPPT methods demonstrates that the proposed framework achieves a tracking efficiency of $97.8\%$, reduces steady-state tracking error to $2.2\%$, and improves settling time by $42.8\%$ under these dynamic operating conditions. In addition, the proposed controller exhibits lower oscillatory behavior, improved extracted renewable power, and enhanced DC-link stability during simultaneous multi-source disturbances. The results demonstrate that the proposed framework provides an effective real-time coordination strategy for hydrogen-enabled hybrid renewable energy systems operating under dynamically coupled renewable-source conditions. Full article

Hydraulic fracture diagnostics are essential for characterizing fracture geometry, connectivity, and effectiveness in unconventional reservoirs. However, the diversity of available techniques and fragmented understanding of their physical mechanisms hinder multidisciplinary communication and lead to inconsistent field decisions. This review provides a systematic assessment of diagnostic methods, focusing on their physical foundations, applicability, and limitations, and proposes a unified reference framework. Direct diagnostics, including microseismic monitoring, fiber-optic sensing (DTS and DAS), and tiltmeter measurements, are evaluated in terms of data characteristics, interpretation challenges, and field applicability. Indirect methods based on pressure, production, and tracer data—such as DFITs, pressure interference tests, and tracer analysis—are examined for their roles in fracture closure evaluation and interwell connectivity. The review further distinguishes between single-well and multi-well applications, providing a structured classification framework. It highlights that individual methods are constrained by non-uniqueness, modeling assumptions, and non-ideal field conditions, especially in complex fracture networks. Therefore, reliable characterization requires integrating multiple diagnostics with physics-based modeling and uncertainty-aware interpretation. Recent advances in AI and machine learning are also briefly discussed as tools to enhance automated analysis and support real-time, predictive diagnostics. Full article

Informal urban settlements grow through incremental and adaptive processes, yet the temporal logic through which their access networks emerge, endure, and consolidate has received relatively little systematic attention. This paper examines the configurational development of the access network in Likoni, Mombasa, where rapid informal urbanisation has transformed an area containing only sparse footpaths into a dense urban network over two decades. Using historical satellite imagery, the study mapped five temporal states of access network for 2006, 2011, 2016, 2021, and 2026. The study utilises Space Syntax angular segment analysis. The analysis combines measures of angular connectivity, segment length, global and local integration, global and local choice, intelligibility, and synergy. The study aims to address three main questions: whether early informal footpaths persisted as the structural basis of later development of access network, whether subsequent growth strengthened local or global accessibility, and whether densification improved the overall configurational accessibility and legibility of the system as a whole. The results indicate that a finer-grained and more locally integrated network was produced through subdivision, densification, and the multiplication of short connecting segments. However, the gains were uneven across scales. Global integration and choice remained concentrated along a limited set of inherited and edge-related corridors, while local integration and local choice spread more widely through the settlement. The paper argues that the development of Likoni is a process of selective consolidation. Early footpaths became a persistent movement skeleton, forming the subsequent major paths of the later stages of the settlement. Later growth intensified local accessibility—albeit, as demonstrated through Space Syntax analysis rather than direct observation of movement—without necessarily producing notable improvements in global integration or whole-system configurational intelligibility. This finding adds a temporal and syntactic dimension to the understanding of informal morphogenesis. Full article

Considering the production efficiency and performance limitations inherent in conventional wet process asphalt mixtures, this study investigates the synergistic potential of fast-melting styrene–butadiene–styrene (F-SBS) and crumb rubber (CR) in enhancing the performance of asphalt mixtures when applied through the dry process modification method. Firstly, high- and low-temperature rheological tests were conducted on modified asphalt containing different dosages of F-SBS (1–3%) and CR (1–10%) to determine the optimal dosage of the modifier for the asphalt mixture. Furthermore, a comprehensive comparative analysis was conducted to evaluate the performance of asphalt mixtures modified with conventional SBS/CR against the F-SBS/CR system across both wet and dry modification processes. Finally, microscopic tests were conducted on the modified asphalt and asphalt mixtures to further investigate the synergistic mechanisms and effects of F-SBS and CR. The results indicated that F-SBS (2.5%)/CR (8%)-modified asphalt exhibited superior rheological properties, enhanced compatibility, and improved storage stability. Additionally, the dry process F-SBS/CR asphalt mixture demonstrated a 12.9% improvement in high-temperature stability, a 19.1% improvement in split strength after freeze–thaw cycles, and a 14.4% improvement in fatigue resistance compared to wet process conventional SBS/CR asphalt mixtures. The microscopic test results indicate that F-SBS and CR modify the asphalt primarily through physical blending. Observations further confirm that the dry process enhances interfacial bonding among the modifiers, asphalt binder, and aggregates, promoting closer and more stable interactions and thus improving mixing efficiency and overall performance. This study confirms the advantages of applying F-SBS and CR in dry process asphalt mixtures, thereby providing guidance for establishing a connection between laboratory investigations and field construction practices in the future. Full article

Urban heat mitigation in megacities depends not only on cooling sources, but also on the connectivity through which cooling effects are transmitted across heterogeneous landscapes. However, existing studies have mainly focused on the static patterns of urban cold islands (UCIs), while the connectivity and disturbance response of urban cooling systems remain poorly understood. Taking Landsat-based summer thermal observations in Beijing, this study developed an integrated framework to assess the structure and resilience of the urban cold island network (CIN) by combining thermal source identification, resistance-surface construction, connectivity modeling, and disturbance simulations. Land surface temperature (LST) was extracted from Landsat 8 OLI/TIRS Collection 2 Level-2 surface temperature products acquired in July–August 2022, and cold island core sources (CICS) were subsequently identified by integrating thermal conditions with land-use characteristics. GeoDetector was used to quantify the explanatory power and interaction effects of natural, land-use, and socio-economic factors on LST spatial heterogeneity, serving as an attribution tool for interpreting thermal-environment drivers. These factors were then integrated into a resistance surface for circuit-theory-based connectivity analysis. Under the summer heat-stress scenario, 202 CICS covering 6416.95 km² were identified, mainly concentrated in peripheral mountainous areas. A total of 401 corridors were identified, including 70 primary corridors forming the structural backbone of the CIN. This spatial distribution reveals a mountain–plain cooling structure in Beijing, in which mountainous CICS constitute the regional cooling-supply base, while potential cooling transmission toward the urban core mainly depends on a limited number of backbone corridors. LULC was the dominant driver of LST, and its interactions with PD, NTL, and vegetation-related factors substantially enhanced explanatory power. Compared with random disturbance, targeted node removal led to an earlier and sharper decline in network resilience, with substantial deterioration already evident after approximately 20–30% of critical nodes were removed. These summer-based findings provide spatially explicit evidence for prioritizing cooling corridors, critical nodes, and restoration areas in connectivity-oriented urban heat mitigation and climate-responsive planning, thereby supporting hierarchical maintenance and restoration strategies based on their relative importance within the cooling network. Full article

The structural strength requirements for timber buildings have been significantly tightened in the second generation of Eurocodes (EN 1990:2023, EN 1991-1-7), which poses a particular challenge for solid timber frames with a beam-and-column structure, where the transfer of tensile forces via dowel connections is inherently limited. Existing multiscale frameworks for timber post-and-beam robustness lack operational detail at each scale, and no validated workflow currently bridges joint-level continuum damage mechanics and frame-level progressive failure analysis in compliance with the second-generation Eurocodes. This paper addresses this gap by proposing an effective two-scale finite element method (FEM) modelling framework for assessing the strength of such frames during column removal. Existing multiscale models describing the strength of timber structures with beam-and-column systems lack the operational details necessary to integrate failure mechanics at the joint level and progressive failure modelling at the frame level within a single, validated workflow. In this paper, this gap is addressed through three specific contributions: a physically modified quadratic Hashin-type failure criterion for timber, which eliminates the non-physical increase in shear strength under combined stress states perpendicular to the grain; a two-scale structure based on the finite element method (FEM), in which the results of continuous damage mechanics at the joint level directly parameterise non-linear joint elements with six degrees of freedom at the frame level, taking into account coupled directional wear and erosion of the elements; and quantitative validation of both scales against experimental data and the conversion factors for characteristic values of the second generation of Eurocode 5 (prEN 1995-1-1:2023). At the connection level, the simulated strength and stiffness values agree with the experiments to within an error of no more than 5%. At the frame level, the model correctly reproduces the non-linear ‘load–displacement’ relationship, the sequence of joint failure, and the axial forces in the chain line for vertical displacements up to 390 mm, which corresponds to experimental observations. Full article

Islamic banking is increasingly expected to align Sharia-based intermediation with sustainability objectives, yet empirical evidence remains limited on how sustainability disclosure links financing structures with bank performance. This study examines whether Islamic Sustainable Banking (ISB) functions as a mediating mechanism between profit-sharing financing, debt-based financing, and financial performance in Islamic banks in Indonesia and Malaysia. ISB is measured using an Islamic Sustainable Banking Disclosure Index that integrates Maqasid al-Shariah principles with SDG-oriented disclosure indicators. Using panel data from 23 Islamic banks over 2018–2023 and applying partial least squares structural equation modeling, mediation analysis, PLS-MGA, and permutation tests, the study finds that both profit-sharing and debt-based financing are negatively associated with ISB disclosure, while ISB is positively associated with net profit margin but not return on assets. The mediation results indicate statistically significant negative indirect associations through ISB, suggesting that sustainability disclosure operates as a conditional transmission mechanism rather than an automatic performance driver within the specified PLS-SEM model. Cross-country tests reveal significant differences between Indonesia and Malaysia, particularly in the associations between financing structures and profitability. The study contributes to Islamic sustainable finance by clarifying how Maqasid-oriented disclosure connects financing composition, governance capacity, and profitability, while offering practical implications for bank managers, regulators, and policymakers seeking to integrate sustainability into Islamic banking governance and financing decisions. Full article

Cascade hydropower alters river hydrological regimes and threatens aquatic ecosystems, calling for robust ecological risk assessment (ERA). Conventional assessments often rigidly apply the full five-layer Driving Force–Pressure–State–Impact–Response framework, leading to indicator redundancy and unbalanced weighting. Single weighting methods also fail to reconcile expert judgment with data variability. To address these issues, we developed a three-layer (target–element–indicator) evaluation system embedding DPSIR logic without its full structure, focusing on hydrological regime, water environmental quality, and aquatic ecology with ten indicators. We used an improved group AHP-CRITIC coupling method for weighting: AHP aggregates expert judgments via geometric mean, and CRITIC integrates data variability and inter-indicator conflict. Multi-attribute utility theory normalized indicators into a unified security index, applied to four cascade stations in the middle Jinsha River using 66-year (1953–2018) hydrological and seven-year (2013–2019) in situ monitoring data. The evaluation obtained a comprehensive index of 0.71 to 0.74, which is generally safe. River connectivity loss was the primary limiting factor. Hydrological alteration was mild overall with a value of 0.139, while extreme flow decline rate variation reached a high level of 0.83. Weekly regulated stations achieved over 97% ecological flow guarantee, which is much higher than daily regulated stations. This streamlined framework improves interpretability for cascade basins and supports sustainable watershed management. Full article

Electroencephalography (EEG) is a non-invasive technique used to monitor brain activity but is prone to physiological artifacts, especially eye movements (EOG) and muscle contractions (EMG). These artifacts are non-stationary and frequently overlap with neural oscillation bands, making them difficult to separate accurately from genuine EEG activity. Conventional single-domain filters often fail to eliminate such interference, resulting in either residual noise or the unintended suppression of authentic EEG data. To address these limitations, we propose a Frequency-Aware Residual U-Net (FARU-Net), a dual-domain, frequency-aware residual architecture for EEG artifact removal designed to improve restoration fidelity. Unlike models based solely on temporal features, FARU-Net explicitly modulates the spectral properties of the signal in the latent space through a Frequency-aware Bottleneck Module (FBM), while simultaneously refining temporal details. Additionally, Attention Gates (AGs) are integrated into the skip connections to refine feature fusion and reduce residual noise while preserving salient waveform structures. Comparative experiments on the EEGdenoiseNet benchmark demonstrate that FARU-Net achieves strong overall performance for single-channel EEG restoration. Across five independent test groups, the proposed model attains a mean Pearson correlation coefficient (CC) of 0.9681 and a mean signal-to-noise ratio improvement ($\Delta$SNR) of 26.66 dB. These results indicate that the proposed method effectively preserves both waveform morphology and spectral structure compared with conventional U-Net variants and CNN-based models. Full article

Zeolites, both natural (e.g., clinoptilolite) and synthetic (e.g., FAU, ZSM-5), provide robust, tunable platforms for the removal of air pollutants and process-stream contaminants via adsorption and catalysis. This author-led article integrates experimental and theoretical insights on the adsorption of odorous compounds and ammonia (NH₃) and the catalytic abatement of nitrogen oxides (NOx) and nitrous oxide (N₂O), highlighting how topology, acidity, and metal speciation jointly control performance. Representative theoretical results show that adsorption on Brønsted acid sites is significantly more favorable (≈−1.1 eV for NH₃ and −0.37 eV for acetaldehyde) than on Na⁺ sites (≈0.02 eV and 1.22 eV, respectively), demonstrating the critical role of acid site distribution in adsorption selectivity. We dissect structure–function relationships encompassing pore size and connectivity, Si/Al ratio, Brønsted/Lewis site distribution, hydrophilicity/hydrophobicity, and the role of water, with emphasis on hierarchical porosity to alleviate transport limitations. Metal exchange and surface functionalization are discussed as levers to tailor adsorption strength and redox activity, supported by density functional theory (DFT) analyses and reaction pathways. We propose practical design descriptors (acid strength metrics, metal nuclearity, and confinement factors) that enable faster iteration of zeolite architecture for targeted separations and reactions. Sustainability considerations include the use of abundant natural zeolites, low-energy regeneration, stability under humid, mixed-stream conditions that minimize pressure drop and waste. The article closes with a forward look at data-guided optimization to accelerate “engineering zeolites” for durable, selective, and energy-efficient clean-air and process-intensification applications. Full article

Accurate prediction of slope displacement from high-frequency GNSS monitoring data is critical for early warning of landslides and tailings dam failures. However, existing deep learning approaches often neglect the spatial coordination imposed by geological structures and fail to decouple abrupt deformation signals from background noise, leading to non-physical oscillations and inconsistent long-term predictions. To address these limitations, this paper proposes a Symmetry-Aware Physics-Guided Spatio-Temporal Graph Network (PG-STGN). First, a geological hierarchy-aware graph is constructed by integrating geometric proximity with prior knowledge of exploration levels, where the resulting adjacency matrix is symmetric by design and reflects the physical symmetry of deformation interactions among monitoring points at the same elevation. A hierarchical masking mechanism restricts feature aggregation to physically connected neighborhoods while preserving this symmetry. Second, an improved dual-path temporal convolutional network (iTCN) decouples high-frequency abrupt variations from low-frequency evolutionary trends, enabling both sensitive detection of sudden deformation and stable tracking of long-term creep. Third, a physics-consistent loss function combining first-order temporal differencing and graph Laplacian regularization enforces kinematic smoothness and spatial coordination; the Laplacian itself is derived from the symmetric adjacency matrix, ensuring symmetric regularization across the monitoring network. Evaluated on a real-world slope GNSS dataset from a large-scale mining project, PG-STGN reduces mean squared error (MSE) by approximately 23.7% and achieves a global R² of 0.924, outperforming state-of-the-art spatio-temporal models. Ablation studies confirm that the symmetric physics-guided graph, dual-path decoupling, and consistency loss are each essential for suppressing spurious correlations and maintaining physically plausible predictions. The proposed framework provides a robust, interpretable, and symmetry-constrained solution for automated slope monitoring under complex geological conditions. Full article

In receiving-end modular multilevel converter (MMC)-based flexible high-voltage direct current (HVDC) grid-connected systems, high-frequency oscillation can significantly increase the peak values of the point of common coupling (PCC) voltage and grid current. To address this issue, this paper proposes a coordinated suppression strategy for high-frequency oscillation in receiving-end MMC grid-connected systems. First, an MMC impedance model is established based on harmonic linearization, and its frequency-domain interaction with the grid impedance is analyzed to clarify the formation mechanism of high-frequency oscillation and its main influencing factors. Then, considering the different roles of the voltage feedforward and current feedback channels in the target frequency band, a coordinated suppression strategy combining band-stop filtering in the voltage feedforward path with low-pass filtering and lead compensation in the current feedback path is designed. Hardware-in-the-loop experimental results show that the proposed method effectively identifies and suppresses high-frequency oscillation. Under the validated operating condition, the oscillation-induced peak increases in the PCC voltage and grid current are limited to within 20% and 12.5%, respectively, thereby suppressing further oscillation growth and reducing the risk of approaching the overvoltage and overcurrent protection thresholds. Full article

Textile equipment monitoring and scheduling are constrained by device heterogeneity, stringent real-time requirements, and complex dynamic resource scheduling. To address these challenges, this study proposes a cloud-edge-end collaborative remote monitoring and scheduling system for textile equipment. The proposed system aims to overcome the limitations of traditional solutions in compatibility, real-time performance, and resource utilization. This work is positioned as an applied systems study, in which the scheduling modules are used as monitoring-driven service extensions rather than as standalone algorithmic contributions. We develop (i) an adaptive multi-protocol parsing mechanism, (ii) a collaborative hierarchical alerting framework, and (iii) monitoring-driven computing-resource and production-scheduling services. The system is implemented across the terminal device layer, edge computing layer, and central cloud layer. Embedded acquisition terminals were designed to support multiple industrial protocols, including Modbus RTU, OPC UA, and EtherCAT. Dynamic protocol adaptation was used to identify, parse, and map heterogeneous protocol frames into a unified information model at runtime. In the workshop deployment reported in this study, field validation was conducted on 120 air-jet looms connected through RS485-based Modbus RTU. Other interfaces were evaluated as prototype-supported communication options rather than as quantitatively validated workshop interfaces. A cloud-edge-end collaborative alerting framework is designed by combining an improved OPTICS algorithm with a graph neural network (GNN) model. It improves the redundant-alarm filtering rate by 42.1%, achieves 96.8% root-cause diagnosis accuracy, and keeps the end-to-end alert latency at or below 200 ms at the 99th percentile. A cross-layer resource scheduling strategy incorporating a fuzzy PID controller is proposed, accompanied by a weighted multi-criteria resource-optimization model. This strategy increases the average CPU utilization of edge nodes to 84.3 ± 3.6% and reduces burst-task response latency to 236 ± 48 ms. In addition, an adaptive particle-swarm optimization module based on a scalarized composite scheduling objective reduces the equipment idle rate to 6.5% and shortens the average order completion time by 28.4%. Overall, the proposed framework demonstrates the feasibility of cloud-edge-end collaborative monitoring and scheduling in the validated RS485/Modbus-RTU-based weaving-workshop scenario, while its application to other textile processes, machine types, and communication configurations requires further protocol-specific adaptation and field validation. Full article

Developing ecological networks is essential for preserving regional ecosystem stability and for reducing risks associated with degraded landscape security and ecological health. However, regional ecological network studies in lake environments that combine long-term temporal comparison with location-specific optimization remain relatively limited. Taking the Chagan Lake region as the study area, this research examined landscape pattern changes in 2000, 2010, and 2020 by integrating Morphological Spatial Pattern Analysis (MSPA) with connectivity metrics to determine major ecological source areas. General and key ecological corridors were then identified and evaluated through the joint use of the Minimum Cumulative Resistance (MCR) model and the gravity model. Based on these results, the evolution of the regional ecological network during the past two decades was analyzed, and an optimization strategy was proposed. The results show that important ecological source areas became increasingly fragmented over time. Although corridor numbers continued to rise, connectivity between the eastern and western sectors and across parts of the central zone remained weak. In response, three supplementary ecological source areas, together with restoration nodes and stepping-stone patches, are proposed to reinforce structural linkage and improve the overall network configuration. The study offers a practical basis for ecological network refinement in the Chagan Lake region and provides methodological support for enhancing landscape connectivity and ecosystem resilience. Full article