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The urban power system is a critical infrastructure that is essential for urban safety. The frequent occurrence of extreme natural disasters characterized by a low probability and high losses severely threatens the continuity of urban power supply and the overall safety of cities. Resilience is the capability of a system to withstand disturbances and rapidly return to normal operating conditions. Therefore, enhancing the resilience of urban power systems is crucial in the face of extreme natural disasters. This study provides an overview of the implications of enhancing resilience in urban power systems under extreme natural disasters, clarifying the processes by which urban power systems respond to extreme natural disasters. Subsequently, in conjunction with the challenges faced by the resilience construction of urban power systems under extreme natural disasters, key developmental elements for resilience improvement are analyzed from the information, physical, and application dimensions. Based on this, a framework for enhancing the resilience of urban power systems under extreme natural disasters is established. From the perspectives of data-driven sensing, scenario construction, response assessment, and emergency recovery, the framework dissects four critical technologies for improving the resilience of urban power systems. Furthermore, the following recommendations are proposed: (1) strengthening toplevel planning and coordinated design to upgrade power infrastructure through smart initiatives; (2) overcoming core issues and critical technologies and promoting the practical application of technological achievements; (3) enhancing standardized technical documents and establishing unified equipment configuration principles; and (4) advancing collaborative governance among diverse entities to establish an efficient emergency coordination mechanism.

The mountain base elevation, which refers to the initial altitude of a mountain or the mountain altitudinal belt, is of great significance for the study of mountain altitudinal zonality and the mass elevation effect. However, a fast and efficient algorithm for the automatic extraction of a mountain base elevation is still lacking; therefore, a new method based on mountain structure modeling to calculate the mountain base elevation is proposed for the Qinling-Daba Mountains (QDM). The result shows that 1) the mountain structures in different catchments of the Qinling-Daba Mountains can be divided into six types, namely, pyramid, low-diamond, diamond, inverse pyramid, hourglass, and ellipsoid, and each type has a specific distribution law; 2) based on the mountain structure, the calculation result of the mountain base elevation in the Qinling-Daba Mountains ranges from 99 m to 3,979 m, being high in the west, north, and south and low in the east and middle, which is consistent with the overall terrain distribution of the Qinling-Daba Mountains; and 3) there is a good linear correlation between the mountain base elevation and the average altitude in the Qinling-Daba Mountains with R2 = 0.96 (p < 0.01), which also indicates that the mountain base elevation quantification algorithm works well in the mountain areas with a complex terrain.

Background: The pattern recognition receptor Dectin-1 was initially discovered to play a pivotal role in mediating pulmonary antifungal immunity and promoting neutrophil-driven inflammation. Recent studies have revealed that Dectin-1 is overexpressed in asthma, but the specific mechanism remains elusive. Additionally, Dectin-1 has been implicated in promoting pyroptosis, a hallmark of severe asthma airway inflammation. Nevertheless, the involvement of the non-classical pyroptosis signal caspase-11/4 and its upstream regulatory mechanisms in asthma has not been completely explored. Methods: House dust mite (HDM)-induced mice was treated with Dectin-1 agonist Curdlan, Dectin-1 inhibitor Laminarin, and caspase-11 inhibitor wedelolactone separately. Subsequently, inflammatory cells in bronchoalveolar lavage fluid (BALF) were analyzed. Western blotting was performed to measure the protein expression of caspase-11 and gasdermin D (GSDMD). Cell pyroptosis and the expression of chemokine were detected in vitro. The correlation between Dectin-1 expression, pyroptosis factors and neutrophils in the induced sputum of asthma patients was analyzed. Results: Curdlan appeared to exacerbate neutrophil airway inflammation in asthmatic mice, whereas wedelolactone effectively alleviated airway inflammation aggravated by Curdlan. Moreover, Curdlan enhanced the release of caspase-11 activation fragments and N-terminal fragments of gasdermin D (GSDMD-N) stimulated by HDM both in vivo or in vitro. In mouse alveolar macrophages (MH-S cells), Curdlan/HDM stimulation resulted in vacuolar degeneration and elevated lactate dehydrogenase (LDH) release. In addition, there was an upregulation of neutrophil chemokines CXCL1, CXCL3, CXCL5 and their receptor CXCR2, which was suppressed by wedelolactone. In asthma patients, a positive correlation was observed between the expression of Dectin-1 on macrophages and caspase-4 (the human homology of caspase-11), and the proportion of neutrophils in induced sputum. Conclusion: Dectin-1 activation in asthma induced caspase-11/4 mediated macrophage pyroptosis, which subsequently stimulated the secretion of chemokines, leading to the exacerbation of airway neutrophil inflammation. [ABSTRACT FROM AUTHOR]

With the continuous prosperity of maritime transportation on a global scale and the resulting escalation in port trade volume, tugboats assume a pivotal role as essential auxiliary tools influencing the ingress and egress of vessels into and out of ports. As a result, the optimization of port tug scheduling becomes of paramount importance, as it contributes to the heightened efficiency of ship movements, cost savings in port operations, and the promotion of sustainable development within the realm of maritime transportation. However, a majority of current tugboat scheduling models tend to focus solely on the maximum operational time. Alternatively, the formulated objective functions often deviate from real-world scenarios. Furthermore, prevailing scheduling methods exhibit shortcomings, including inadequate solution accuracy and incompatibility with integer programming. Consequently, this paper introduces a novel multi-objective tugboat scheduling model to align more effectively with practical considerations. We propose a novel optimization algorithm, the Improved Grey Wolf Optimization (IGWO), for solving the tugboat scheduling model. The algorithm enhances convergence performance by optimizing convergence parameters and individual updates, making it particularly suited for solving integer programming problems. The experimental session designs several scale instances according to the reality of the port, carries out simulation experiments comparing several groups of intelligent algorithms, verifies the effectiveness of IGWO, and verifies it in the comprehensive port area of Huanghua Port to get the optimal scheduling scheme of this port area, and finally gives management suggestions to reduce the cost of tugboat operation through sensitivity analysis. [ABSTRACT FROM AUTHOR]

Indoor environmental parameters are closely related to the energy consumption and indoor thermal comfort of office buildings. Predicting these parameters, especially indoor temperature, can contribute to the management of energy consumption and thermal comfort levels in office buildings. An accurate indoor temperature prediction model is the basis for implementing this process. To this end, this paper first discusses the input and output parameters of the model, and then it compares the prediction effects of mainstream prediction model algorithms based on data mining under the same data conditions. The superiority of the XGBoost integrated learning algorithm is verified, and a further XGBoost-based indoor temperature online prediction method is designed. The effectiveness of the method is validated using actual data from a commercial office building in Haidian District, Beijing. Finally, optimization methods for the prediction method are discussed with regard to the scheduler mechanism proposed in this paper. Overall, this work can assist building operators in optimizing HVAC equipment running strategies, thus improving the indoor thermal comfort and energy efficiency of the building. [ABSTRACT FROM AUTHOR]