无锡学院

With the increasing demand for clean and efficient utilization of coal resources, the hydro-upgrading of anthracene oil (AO), a major coal tar fraction characterized by its high heteroatom content, remains technically challenging. In this work, citric acid (CA) is demonstrated as a multifunctional structural modulator for NiMoS/ gamma-Al2O3 catalysts, simultaneously affecting precursor dispersion, active phase morphology, acidity, and pore architecture. CA chelates metal ions, suppressing inert NiAl2O4 formation and promoting the transformation of Type I to stacked Type II Ni-Mo-S phases upon sulfidation. It also increases Br & oslash;nsted/Lewis acidity and introduces mesoporosity, thereby facilitating mass transport and reactant activation. These coordinated modifications lead to superior hydrodesulfurization (HDS) and hydrodenitrogenation (HDN) performance, as evidenced by the optimized 1.0CA catalyst (sulfur removal 89.3%, nitrogen removal 60.0%). This study offers a practical strategy for designing efficient hydrotreating catalysts through organic acid tailoring, with potential implications for clean fuel production from heavy aromatic feedstocks.

Modern industrial systems are typically characterized by closed-loop control, massive data volumes, and high dimensionality, posing significant challenges for root cause analysis. This paper presents a prior-informed differentiable graph learning approach designed for large-scale industrial processes. Specifically, a masked generalized structural equation model is developed for causal modeling. The framework leverages a differentiable learning schema to generate an adjacency matrix constrained by prior knowledge. This matrix acts as a mask on the input data, ensuring that the causal sub-model for each variable is exclusively conditioned on its parent nodes. To enhance robustness against process uncertainties, a probability-based objective function is employed during optimization. Furthermore, incorporating prior knowledge into the regularization term enhances the interpretability of the discovered causal structures. Finally, the paper introduces a root node identification technique specifically tailored for handling cyclic causal graphs. The effectiveness of the proposed method is validated through a numerical study and two industrial cases.

This paper studies decision-oriented proxy need prediction for urban electric vehicle (EV) charging under zone-hour aggregated observations, where the downstream objective is to support protection-oriented charging decisions rather than ordinary load estimation. Existing methods are still inadequate in this setting because aggregated charging data do not provide sufficient vehicle-level information to reconstruct a strict physical flexibility boundary, and conventional load forecasting models are not explicitly designed to serve asymmetric downstream decision requirements. To address this problem, we propose DREAM-Boost, a three-layer architecture that combines a residual extreme gradient boosting (XGBoost) backbone, a global asymmetric training objective, and a softly gated local expert correction. The backbone captures the dominant nonlinear residual structure, the asymmetric objective shifts learning pressure toward under-service reduction, and the softly gated local correction repairs structured residual underprediction while preserving decision-compatible tradeoffs. Experimental results under a shared held-out protocol show that DREAM-Boost improves the primary protection-oriented metric mean_under from 79.37 to 74.38 relative to the HARC-Boost reference baseline, while incurring moderate increases in mean_gap_abs and mean_cost. Additional ablation, robustness, and block-level statistical validation results further indicate that the method is particularly useful in high-risk and high-demand regimes and provides a controlled protection–cost tradeoff for aggregated urban EV charging support.

Global warming has intensified the risk of compound events,among which compound hot and dry events(CHDEs)have drawn considerable attention due to their high frequency and severe impacts.In this study,based on observa-tional data and simulations from the Coupled Model Intercomparison Project Phase 6(CMIP6),we compare the per-formance of indirect and direct emergent constraint approaches in projecting the frequency of global CHDEs.The results show that by the end of the 21st century,the frequency of CHDEs increases significantly over most global land areas,with the largest increases occurring in North America,South America,and Europe,while the increase is relatively smaller in eastern and southern Asia.Attribution analysis indicates that temperature rise is the primary driver of increased CHDEs frequency,whereas spatial variations in precipitation contribute to regional differences.The indirect constraint,based on global warming magnitude,effectively reduces projection uncertainty in most re-gions,with the largest reduction in uncertainty range reaching 37.4％(25.4％)for Shared Socioeconomic Pathways(SSP)5-8.5(SSP2-4.5)scenario.The direct constraint,established using the relationship between historical precipita-tion trends and future CHDEs frequency,can reduce the uncertainty range by up to 15.99％(14.87％)for SSP5-8.5(SSP2-4.5)scenario.Overall,the indirect constraint demonstrates broader applicability and achieves greater reduc-tions in projection uncertainty compared to the direct constraint.

The seasonal variation of the sea-land breeze (SLB) circulation is investigated in a typical coastal city Qingdao, China, using continuous 2-year observation data from Doppler LiDAR. The frequency of SLB varies seasonally, peaking in July and bottoming in January. The establishment time of the sea breeze is primarily concentrated between 08:00 LST and 12:00 LST, with its onset occurring approximately 1 hr earlier in spring and summer compared to autumn and winter. The top of the land-breeze circulation is lower than that of the sea-breeze circulation in all seasons and shows a decreasing trend over time. The top height of the sea-breeze circulation, apart from a gradual decline in spring, exhibits complex curvilinear changes in other seasons. It should be noted that a negative sea-land temperature difference (SLTD) is not a necessary condition for the formation of SLB in this study. Instead, the inversion layer structure is commonly found during the nocturnal hours of SLB days, resulting in a significant reduction in the atmospheric boundary layer height (ABLH) compared to the average, with a decrease of about 50% in winter and 10%-30% in other seasons. This stable ABL structure suppresses turbulent activity and enhances the wind speed difference between the lower and upper layers during the day, as well as the momentum transfer, which can be used to explain the development mechanism of the SLB circulation.