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Due to the severe damage and huge loss caused by natural disasters to road networks, the protection of the road network is essential. Edge importance identification can help preserve the road network by protecting key edges. This paper proposes a new network performance measure method and introduces a new edge load redistribution method in cascading failure model. To identify different edge importance in the network, this paper proposes three edge importance evaluation metrics, including information entropy of degree values, information entropy of iterative factors and two-dimensional evaluation metric based on the Pareto non-dominated set which combines two single metrics. A case study of highway road in Sichuan province with 204 nodes and 322 edges which was affected by Luding Earthquake is conducted to demonstrate the best one of the three metrics, including data from the Department of Transport of Sichuan Province. The final results of the chi-square test and Kendall’s correlation coefficient comparing the importance ranking of the three metrics with the ranking derived from the network performance assessment model indicate that the two-dimensional evaluation metrics have the best performance and that the road network tends to collapse at the same time when attacked against the road network under different edge rankings, suggesting that the effect of cascading failures should be limited early.

Public transportation infrastructure is a typical, complex, coupled network that is usually composed of connected bus lines and subway networks. This study proposes an entropy-based node importance identification method for this type of coupled network that is helpful for the integrated planning of urban public transport and traffic flows, as well as enhancing network information dissemination and maintaining network resilience. The proposed method develops a systematic entropy-based metric based on five centrality metrics, namely the degree centrality (DC), betweenness centrality (BC), closeness centrality (CC), eigenvector centrality (EC), and clustering coefficient (CCO). It then identifies the most important nodes in the coupled networks by considering the information entropy of the nodes and their neighboring ones. To evaluate the performance of the proposed method, a bus–subway coupled network in Chengdu, containing 10,652 nodes and 15,476 edges, is employed as a case study. Four network resilience assessment metrics, namely the maximum connectivity coefficient (MCC), network efficiency (NE), susceptibility (S), and natural connectivity (NC), were used to conduct group experiments. The experimental results demonstrate the following: (1) the multi-functional fitting analysis improves the analytical accuracy by 30% as compared to fitting with power law functions only; (2) for both CC and CCO, the improved metric's performance in important node identification is greatly improved, and it demonstrates good network resilience. [ABSTRACT FROM AUTHOR]

The immune status of tumor‐infiltrating lymphocytes (TILs) is essential for the effectiveness of cancer immunotherapies. However, due to the diversity of immune status in TILs, cellular heterogeneity, and the applicability to the clinic, it is still lacking effective strategies to meet clinical needs. We developed a novel immuno‐recognition‐induced method based on rolling circle amplification (RCA), namely immunoRCA, to in situ visualize the immune status of TILs in actual clinical samples. This developed immunoRCA method, in which, feature mRNAs were used as the biomarkers for the immune status of TILs, has a low fluorescence background, high sensitivity, and specificity. The immunoRCA was able to efficiently evaluate the immune status of CD8+ T cells regulated by activating or inhibiting factors, track the T cell type and immune status during in vitro expansion, and in situ visualize the number, location, and immune status of TILs in clinical specimens. [ABSTRACT FROM AUTHOR]

Distributed generators (DGs) have a high penetration rate in distribution networks (DNs). Understanding their impact on a DN is essential for achieving optimal power flow (OPF). Various DG models, such as stochastic and forecasting models, have been established and are used for OPF. While conventional OPF aims to minimize operational costs or power loss, the "Dual-Carbon" target has led to the inclusion of carbon emission reduction objectives. Additionally, state-of-the-art optimization techniques such as machine learning (ML) are being employed for OPF. However, most current research focuses on optimization methods rather than the problem formulation of the OPF. The purpose of this paper is to provide a comprehensive understanding of the OPF problem and to propose potential solutions. By delving into the problem formulation and different optimization techniques, selecting appropriate solutions for real-world OPF problems becomes easier. Furthermore, this paper provides a comprehensive overview of prospective advancements and conducts a comparative analysis of the diverse methodologies employed in the field of optimal power flow (OPF). While mathematical methods provide accurate solutions, their complexity may pose challenges. On the other hand, heuristic algorithms exhibit robustness but may not ensure global optimality. Additionally, machine learning techniques exhibit proficiency in processing extensive datasets, yet they necessitate substantial data and may have limited interpretability. Finally, this paper concludes by presenting prospects for future research directions in OPF, including expanding upon the uncertain nature of DGs, the integration of power markets, and distributed optimization. The main objective of this review is to provide a comprehensive understanding of the impact of DGs in DN on OPF. The article aims to explore the problem formulation of OPF and to propose potential solutions. By gaining in-depth insight into the problem formulation and different optimization techniques, optimal and sustainable power flow in a distribution network can be achieved, leading to a more efficient, reliable, and cost-effective power system. This offers tremendous benefits to both researchers and practitioners seeking to optimize power system operations. [ABSTRACT FROM AUTHOR]

Stacked plate heat exchangers are widely used in thermal energy storage systems and a comprehensive and accurate analysis is necessary for their application and optimization. The fluid flow distribution between the plates is important to ensure even and full usage of the thermal energy storage potential. However, due to the complex topography of the plate surface, it would be computationally expensive to simulate the flow distribution in the multiple channels using a full three-dimensional model, so this work applies a reduced-dimensional model to significantly reduce the computational cost of the simulation and provides a comprehensive analysis of the effect of the internal structure on the internal flow distribution. The work extends a previously presented model to consider transient flow and a multichannel height distribution strategy to allow for simulating multiple channels between stacks of plates. Based on fully-developed flow assumptions, the three-dimensional model is reduced to a planar model, thus obtaining simulation results with satisfactory accuracy at a significantly lower computational cost. The model is verified by a three-dimensional simulation of a sliced two-channel model representing the considered system. The reduced-dimensional model gives similar results to the three-dimensional model for different geometrical and physical parameters. Lastly, the extended reduced-dimensional model is used to simulate the flow of a full two-channel model and the influence of the plate topography on the internal flow distribution is investigated through a comprehensive parametric analysis. The analysis shows that the complex topography of the plate surface eliminates the variation in inlet velocity and significantly changes the internal fluid flow, eventually resulting in a consistent velocity distribution. [ABSTRACT FROM AUTHOR]

Wheel loaders play an important role during emergency rescues under geologic hazard conditions, such as debris flow and landslide. The current literature shows a relative lack of research on the failure mechanism and safety threshold of emergency supplies and vehicles under debris flow. This work presents a method to quantitatively study the interaction between debris flow intensity and wheel loader damage index. The proposed method employs force analysis and finite element modeling to obtain a series of stability thresholds of wheel loaders following the change of debris flow intensity. The results show that the maximum stress of the vehicle frame under the overall impact of over 0.15 MPa exceeds 250 MPa on the side. For the engine compartment shell, the stress of a large area on the shell exceeds the tensile yield strength, and the displacement exceeds 150 mm when impacted by stones at a speed of over 10 m/s. The intake and exhaust system, together with the cooling system in the engine compartment, are likely to be damaged. For the fuel tank, the deformation is more than 100 mm when the impact speed of stone exceeds 12.5 m/s. The critical impact conditions triggering skidding, tipping, and suspending for the wheel loader are 14 KN, 16.2−29.2 KN , and 2.4 m3 , respectively. The research results can improve the accuracy of vulnerability assessment of vehicles and emergency equipment under debris flow. [ABSTRACT FROM AUTHOR]

Currently, malaria is still one of the major public health problems commonly caused by the four Plasmodium species. The similar symptoms of malaria and the COVID‐19 epidemic of fever or fatigue lead to frequent misdiagnosis. The disadvantages of existing detection methods, such as time‐consuming, costly, complicated operation, need for experienced technicians, and indistinguishable typing, lead to difficulties in meeting the clinical requirements of rapid, easy, and accurate typing of common Plasmodium species. In this study, we developed and optimized a universal two‐dimensional labelled probe‐mediated melting curve analysis (UP‐MCA) assay based on multiplex and asymmetric PCR for rapid and accurate typing of five Plasmodium species, including novel human Plasmodium, Plasmodium knowlesi (Pk), in a single closed tube following genome extraction. The assay showed a limit of detection (LOD) of 10 copies per reaction and could accurately distinguish Plasmodium species from intra‐plasmodium and other pathogens. Additionally, we proposed and validated different methods of fluorescence quenching and tag design for probes that are suitable for UP‐MCA assays. Moreover, the clinical performance of the Plasmodium UP‐MCA assay using a base‐quenched universal probe was evaluated using 226 samples and showed a sensitivity of 100% (164/164) and specificity of 100% (62/62) at a 99% confidence interval, with the microscopy method as the gold standard. In summary, the UP‐MCA assay showed excellent sensitivity, specificity, and accuracy for genotyping Plasmodium species spp. Additionally, it facilitates convenient and rapid Plasmodium detection in routine clinical practice and has great potential for clinical translation. [ABSTRACT FROM AUTHOR]